PT AU BA BE GP AF BF CA TI SO SE BS LA DT CT CY CL SP HO DE ID AB C1 RP EM RI OI FU FX CR NR TC Z9 PU PI PA SN EI BN J9 JI PD PY VL IS PN SU SI MA BP EP AR DI D2 PG WC SC GA UT J Beydoun, G; Low, G; Garcia-Sanchez, F; Valencia-Garcia, R; Martinez-Bejar, R Beydoun, Ghassan; Low, Graham; Garcia-Sanchez, Francisco; Valencia-Garcia, Rafael; Martinez-Bejar, Rodrigo Identification of ontologies to support information systems development INFORMATION SYSTEMS English Article Information systems development; Ontologies; Early requirements; Ontology retrieval; i* Models METHODOLOGIES; MODELS; MAS Ontologies can provide many benefits during information systems development. They can provide domain knowledge to requirement engineers, are reusable software components for web applications or intelligent agent developers, and can facilitate semi-automatic model verification and validation. They also assist in software extensibility, interoperability and reuse. All these benefits critically depend on the provision of a suitable ontology (ies). This paper introduces a semantically-based three stage-approach to assist developers in checking the consistency of the requirements models and choose the most suitable and relevant ontology (ies) for their development project from a given repository. The early requirements models, documented using the i* language, are converted to a retrieval ontology. The consistency of this retrieval ontology is then checked before being used to identify a set of reusable ontologies that are relevant for the development project. The paper also provides an initial validation of each of the stages. (C) 2014 Elsevier Ltd. All rights reserved. [Beydoun, Ghassan] Univ Wollongong, Sch Informat Syst & Technol, Wollongong, NSW 2522, Australia; [Low, Graham] Univ New S Wales, Sch Informat Syst Technol & Management, Sydney, NSW 2052, Australia; [Garcia-Sanchez, Francisco; Valencia-Garcia, Rafael; Martinez-Bejar, Rodrigo] Univ Murcia, Fac Comp Sci, E-30001 Murcia, Spain Beydoun, G (reprint author), Univ Wollongong, Sch Informat Syst & Technol, Wollongong, NSW 2522, Australia. beydoun@uow.edu.au [Anonymous], INFORM SOFTW TECHNOL; Ashamalla A., 2009, P 18 INT C INF SYST, P345; Ashamalla A., 2012, MODELLING REAL TIME, P158; Benevides A.B., 2009, MOD BAS TOOL CONC MO; Beydoun G, 2011, J SYST SOFTWARE, V84, P2363, DOI 10.1016/j.jss.2011.07.010; Beydoun G, 2006, LECT NOTES COMPUT SC, V3529, P111; Beydoun G., 2008, INTERNETWORKED WORLD; Brandao A.A.F., 2004, OBJ OR PROGR SYST LA; Brandao AAF, 2007, LECT NOTES COMPUT SC, V4405, P122; Broekstra J., 2002, P 1 INT SEM WEB C IT; Calero C., 2006, ONTOLOGIES SOFTWARE; Cordi V., 2004, AOIS2004 CAISE04; Grau BC, 2008, J WEB SEMANT, V6, P309, DOI 10.1016/j.websem.2008.05.001; Djuric D., 2005, J OBJECT TECHNOLOGY, V4, P109; Eschenbach C, 1995, INT J HUM-COMPUT ST, V43, P723, DOI 10.1006/ijhc.1995.1071; Grau G, 2008, INFORM SOFTWARE TECH, V50, P76, DOI 10.1016/j.infsof.2007.10.006; Guarino N., 1998, P INT C FORM ONT INF; Henderson-Sellers B, 2011, J SYST SOFTWARE, V84, P301, DOI 10.1016/j.jss.2010.10.025; Lamsweerde A., 1991, P AAAI SPRING S SERI; LIXI LIXI, 2005, LANG LEND; Lopez-Lorca A., 2010, P 19 INT C INF SYST; Lopez-Lorca A., 2011, INFORM SYSTEMS DEV, P455; McBride B, 2002, IEEE INTERNET COMPUT, V6, P55, DOI 10.1109/MIC.2002.1067737; Okouya D., 2008, P 1 INT WORKSH TRANS, P55; Pinto H.S., 2001, P INT JOINT C ART IN; Ruiz F., 2006, ONTOLOGIES SOFTWARE, P49, DOI 10.1007/3-540-34518-3_2; Sadrei E., 2007, J REQUIREME IN PRESS; Schreiber G., 2001, KNOWLEDGE ENG MANAGE; Shanks G, 2003, COMMUN ACM, V46, P85, DOI 10.1145/944217.944244; Shu G, 2007, ADV ENG SOFTW, V38, P59, DOI 10.1016/j.advengsoft.2006.05.004; Sirin E., 2004, DESCR LOG WORKSH DL, P212; Stumme G., 2001, WORKSH ONT ING SHAR; Tran QNN, 2005, LECT NOTES ARTIF INT, V3508, P157; Tran QNN, 2006, COMPUT SYST SCI ENG, V21, P117; Wagner G., 2000, P 2 INT S AG THEORY; Yu E, 2001, WIRTSCHAFTSINF, V43, P123; Yu E. S. K., 1997, Proceedings of the Third IEEE International Symposium on Requirements Engineering (Cat. No.97TB100086), DOI 10.1109/ISRE.1997.566873 37 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0306-4379 1873-6076 INFORM SYST Inf. Syst. DEC 2014 46 45 60 10.1016/j.is.2014.05.002 16 Computer Science, Information Systems Computer Science AN1BX WOS:000340318800003 J Yang, L; Cao, YG; Zhu, XH; Zeng, SH; Yang, GJ; He, JY; Yang, XC Yang, Lei; Cao, YunGang; Zhu, XiaoHua; Zeng, ShengHe; Yang, GuoJiang; He, JiangYong; Yang, XiuChun Land surface temperature retrieval for arid regions based on Landsat-8 TIRS data: a case study in Shihezi, Northwest China JOURNAL OF ARID LAND English Article thermal infrared remote sensing; land surface temperature retrieval; split-window algorithm; Landsat-8 TIRS MONO-WINDOW ALGORITHM; EMISSIVITY; SATELLITE; VALIDATION; GREECE; TM Scientific interest in geophysical information about land surface temperature (LST) is ever increasing, as such information provides a base for a large number of applications, including environmental and agricultural monitoring. Therefore, the research of LST retrieval has become a hot topic. Recent availability of Landsat-8 satellite imagery provides a new data source for LST retrieval. Hence, exploring an adaptive method with reliable accuracy seems to be essential. In this study, basing on features of Landsat-8 TIRS thermal infrared channels, we re-calculated parameters in the atmospheric transmittance empirical models of the existing split-window algorithm, and estimated the ground emissivity with the help of the land cover classification map of the study area. Furthermore, a split-window algorithm was rebuilt by virtual of the estimation model of the updated atmospheric transmittance and the ground emissivity, and then a remote sensing retrieval for the LST of Shihezi city in Xinjiang Uygur autonomous region of Northwest China was conducted on the basis of this modified algorithm. Finally, precision validation of the new model was implemented by using the MODIS LST products. The results showed that the LST retrieval from Landsat-8 TIRS data based on our algorithm has a higher credibility, and the retrieved LST is more consistent with the MODIS LST products. This indicated that the modified algorithm is suitable for retrieving LST with competitive accuracy. With higher resolutions, Landsat-8 TIRS data may provide more accurate observation for LST retrieval. [Yang, Lei; Cao, YunGang] Southwest Jiaotong Univ, Fac Geosci & Environm Engn, Dept Remote Sensing & Geog Informat Engn, Chengdu 611756, Peoples R China; [Zhu, XiaoHua] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China; [Zeng, ShengHe; Yang, GuoJiang; He, JiangYong] Xinjiang Acad Agr & Reclamat Sci, Inst Farmland Water Conservancy & Soil Fertilizer, Shihezi 832000, Peoples R China; [Yang, XiuChun] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China; [Yang, XiuChun] Shangqiu Normal Univ, Coll Environm & Planning, Shangqiu 476000, Peoples R China Yang, XC (reprint author), Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China. yangxiuchun@caas.cn Xinjiang Production and Construction Corps [2013AB017]; Doctorial Fund Project of Xinjiang Production and Construction Corps [2012BB001]; National Natural Science Foundation of China [31372354]; International Science & Technology Cooperation Program of China [2013DFR30760] This study was funded by the Project of Scientific and Technological Support to Xinjiang from Xinjiang Production and Construction Corps (2013AB017), the Doctorial Fund Project of Xinjiang Production and Construction Corps (2012BB001), the National Natural Science Foundation of China (31372354) and the International Science & Technology Cooperation Program of China (2013DFR30760). Becker F., 1995, REMOTE SENS REV, V12, P225, DOI DOI 10.1080/02757259509532286; Friedel MJ, 2012, ENVIRON MODELL SOFTW, V37, P217, DOI 10.1016/j.envsoft.2012.04.016; [高磊 GAO Lei], 2007, [地理与地理信息科学, Geography and Geo-information Science], V23, P9; Hall MR, 2012, J MATER CIVIL ENG, V24, P32, DOI 10.1061/(ASCE)MT.1943-5533.0000357; Irons JR, 2012, REMOTE SENS ENVIRON, V122, P11, DOI 10.1016/j.rse.2011.08.026; Jiang LP, 2006, REMOTE SENSING LAND, P5; Jiang LP, 2006, REMOTE SENSING LAND, P88; Jimenez-Munoz JC, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2003JD003480; Jimenez-Munoz JC, 2010, IEEE GEOSCI REMOTE S, V7, P176, DOI 10.1109/LGRS.2009.2029534; Katsiabani K, 2009, ADV SPACE RES, V43, P930, DOI 10.1016/j.asr.2008.09.002; KAUFMAN YJ, 1992, IEEE T GEOSCI REMOTE, V30, P871, DOI 10.1109/36.175321; Li H, 2010, INT GEOSCI REMOTE SE, P2448, DOI 10.1109/IGARSS.2010.5649801; Li ZL, 2013, REMOTE SENS ENVIRON, V131, P14, DOI 10.1016/j.rse.2012.12.008; Ma Y, 2010, INT J APPL EARTH OBS, V12, P110, DOI 10.1016/j.jag.2009.12.002; Maimaitiyiming M, 2014, ISPRS J PHOTOGRAMM, V89, P59, DOI 10.1016/j.isprsjprs.2013.12.010; MCMILLIN LM, 1975, J GEOPHYS RES-OC ATM, V80, P5113, DOI 10.1029/JC080i036p05113; Peng SS, 2014, P NATL ACAD SCI USA, V111, P2915, DOI 10.1073/pnas.1315126111; Price J C, 1984, J GEOPHYS RES-ATMOS, V89, P231; Qin Z, 2001, INT J REMOTE SENS, V22, P3719, DOI 10.1080/01431160010006971; Qin Z H, 2001, REMOTE SENSING LAND, V56, P33; Qin ZH, 2001, J GEOPHYS RES-ATMOS, V106, P22655, DOI 10.1029/2000JD900452; Sobrino JA, 2001, REMOTE SENS ENVIRON, V75, P256, DOI 10.1016/S0034-4257(00)00171-1; Son NT, 2012, INT J APPL EARTH OBS, V18, P417, DOI 10.1016/j.jag.2012.03.014; Srivastava PK, 2009, ADV SPACE RES, V43, P1563, DOI 10.1016/j.asr.2009.01.023; Stathopoulou M, 2007, INT J REMOTE SENS, V28, P3291; VANDEGRIEND AA, 1993, INT J REMOTE SENS, V14, P1119; Vinnikov K Y, 2011, J GEOPHYS RES-ATMOS, V116, P2156; Wan Z, 2004, INT J REMOTE SENS, V25, P261, DOI 10.1080/0143116031000116417; Wan ZM, 2008, REMOTE SENS ENVIRON, V112, P59, DOI 10.1016/j.rse.2006.06.026; Yao J Q, 2012, THESIS XINJIANG NORM; Zheng G Q, 2010, J SHANDONG JIANZHU U, V25, P519; Zhou J, 2010, CHINESE GEOGR SCI, V20, P123, DOI 10.1007/s11769-010-0123-z 32 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1674-6767 2194-7783 J ARID LAND J. Arid Land DEC 2014 6 6 704 716 10.1007/s40333-014-0071-z 13 Environmental Sciences Environmental Sciences & Ecology AM3DT WOS:000339733200006 J Rayward, WB Rayward, W. Boyd Information Revolutions, the Information Society, and the Future of the History of Information Science LIBRARY TRENDS English Article SCIENTIFIC COMMUNICATION; PATTERNS; DOCUMENTATION; RETRIEVAL; LIBRARIES; SYSTEMS; TECHNOLOGY; EXCHANGE; MEMEX; WAR This paper aims to discuss the future of information history by interrogating its past. It presents in outline an account of the conditions and the trajectory of events that have culminated in today's "information revolution" and "information society." It suggests that we have already passed through at least two information orders or revolutions as we transition, first, from the long era of print that began over five hundred years ago with Gutenberg and the printing press. We have then moved through a predigital era after World War II, finally to a new era characterized by the advent of the ubiquitous technologies that are considered to herald a new "digital revolution" and the creation of new kind of "information society." It argues that it is possible to see that the past is now opening itself to new kinds of scrutiny as a result of the apparently transformative changes that are currently taking place. It suggests that the future of the history of information science is best thought of as part of a still unrealized convergence of diverse historical approaches to understanding how societies are constituted, sustained, reproduced, and changed in part by information and the infrastructures that emerge to manage information access and use. In conclusion it suggests that different bodies of historical knowledge and historical research methodologies have emerged as we move into the digital world that might be usefully brought together in the future to broaden and deepen explorations of important historical information phenomena from Gutenberg to Google. [Rayward, W. Boyd] Univ Illinois, Grad Sch Lib & Informat Sci, Chicago, IL 60680 USA; [Rayward, W. Boyd] Univ New S Wales, Sch Informat Syst Technol & Management, Sydney, NSW 2052, Australia; [Rayward, W. Boyd] Univ Chicago, Chicago, IL 60637 USA; [Rayward, W. Boyd] Univ New S Wales, Sydney, NSW 2052, Australia Rayward, WB (reprint author), Univ Illinois, Grad Sch Lib & Informat Sci, Chicago, IL 60680 USA. Abbate Janet, 1999, INVENTING INTERNET; Adkinson B. W., 1978, 2 CENTURIES FEDERAL; Alter P, 1980, NOTES RECORDS ROYAL, V34, P241, DOI 10.1098/rsnr.1980.0010; [Anonymous], 1963, SCI GOV INF RESP TEC; [Anonymous], 1959, P INT C SCI INF NOV, V1-2; Aubin D, 2003, OSIRIS, V18, P79; Beall J, 2005, COLL RES LIBR, V66, P431; BECKER J, 1984, J AM SOC INFORM SCI, V35, P164, DOI 10.1002/asi.4630350309; Binkley Robert C., 1936, MANUAL METHODS REPRO; Black A., 2007, EARLY INFORM SOC INF, P3; Borgman C. L., 2007, SCHOLARSHIP DIGITAL; Bourne CP, 2003, HISTORY OF ONLINE INFORMATION SERVICES, 1963-1976, P1; Bowker GC, 1996, INFORM PROCESS MANAG, V32, P49, DOI 10.1016/0306-4573(95)00049-M; Bowker G C, 1999, SORTING THINGS OUT C; BUCKLAND MK, 1992, J AM SOC INFORM SCI, V43, P284, DOI 10.1002/(SICI)1097-4571(199205)43:4<284::AID-ASI3>3.0.CO;2-0; BURKE C, 1992, J AM SOC INFORM SCI, V43, P648, DOI 10.1002/(SICI)1097-4571(199212)43:10<648::AID-ASI2>3.0.CO;2-L; Burke P., 2010, SOCIAL HIST MEDIA GU; Burke P., 2012, SOCIAL HIST KNOWLEDG; Bush V., 1991, MEMEX HYPERTEXT V BU, P85; Case D. O., 2007, LOOKING INFORM SURVE; Casey R. S., 1951, PUNCHED CARDS THEIR; Chandler Alfred D., 1977, VISIBLE HAND MANAGER; Chandler Jr Alfred D., 2000, NATION TRANSFORMED I; CLEVERDON CW, 1960, ASLIB PROC, V12, P421, DOI 10.1108/eb049778; COCK AG, 1983, NOTES REC ROY SOC, V37, P249, DOI 10.1098/rsnr.1983.0013; Concilium Bibliographicum, 1910, ANNOTATIONS CONCILI, V6, P23; Cortada J., 1993, COMPUTER IBM NCR BUR; Cortada J. W., 1996, INFORM TECHNOLOGY BU; Crestadoro A., 1856, ART MAKING CATALOGUE; Davis W., 1937, SCI NEWS LETT, V32, P229, DOI 10.2307/3913336; Dee CR, 2007, J MED LIBR ASSOC, V95, P416, DOI 10.3163/1536-5050.95.4.416; Dewhirst D. W., 1964, BIBLIO GEN ASTRONOMI; Downs RB, 1949, LIBR QUART, V19, P157; Eisentstein E., 1979, PRINTING PRESS AGENT, V1-2; European Commission, 1994, EUROPE GLOBAL INFORM; Farkas-Conn I., 1990, DOCUMENTATION INFORM; GARDEY D., 2008, ECRIRE CALCULER CLAS; Garfield E., 1955, NEEDED NATL SCI INTE; Garfield E., 1977, ESSAYS INFORM SCI, V3, P504; GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108; GARVEY WD, 1972, INFORM STORAGE RET, V8, P159, DOI 10.1016/0020-0271(72)90001-0; GARVEY WD, 1972, INFORM STORAGE RET, V8, P111, DOI 10.1016/0020-0271(72)90040-X; GARVEY WD, 1972, INFORM STORAGE RET, V8, P207, DOI 10.1016/0020-0271(72)90031-9; GARVEY WD, 1964, AM DOC, V15, P258, DOI 10.1002/asi.5090150404; Geddes P., 1915, CITIES EVOLUTION INT; George L. A., 2002, CELEBRATING 70 YEARS; Gill D., 2008, COMPLETE DICT SCI BI, V5, P403; Greenway F., 1996, SCI INT HIST INT COU; Griffiths JM, 2002, IEEE ANN HIST COMPUT, V24, P35, DOI 10.1109/MAHC.2002.1024761; Growoll A., 1903, 3 CENTURIES ENGLISH; Haigh T, 2011, ANNU REV INFORM SCI, V45, P431; Headrick D. R., 1981, TOOLS EMPIRE TECHNOL; Headrick Daniel R., 2000, INFORM CAME AGE TECH; Henderson MM, 1999, ASIS MONOGR, P169; HERNER S, 1984, J AM SOC INFORM SCI, V35, P157, DOI 10.1002/asi.4630350308; Heuvel C. van den, 2011, PLACES SPACES MAPPIN; Hirde P., 1989, J AM SOC INFORM SCI, V40, P424; Hobart M. E., 2000, INFORM AGES LITERACY; Hurd JM, 2000, J AM SOC INFORM SCI, V51, P1279, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1044>3.0.CO;2-1; Johns Adrian, 1998, NATURE BOOK PRINT KN; Keen M., 1966, FACTORS DETERMINING, V1; Keen M., 1966, FACTORS DETERMINING, V2; Kumar K., 1995, POSTINDUSTRIAL POSTM; LIEVROUW LA, 1990, TECHNOL SOC, V12, P457, DOI 10.1016/0160-791X(90)90015-5; Lindberg D. A., 2000, EFFECTIVE CLIN PRACT, V4, P256; Linder L. H., 1959, RISE CURRENT COMPLET; Machlup F., 1983, STUDY INFORM INTERDI; Mak B., 2011, PAGE MATTERS; Malcles L.-N., 1989, BIBLIOGRAPHIE; Manzer Bruce M., 1977, ABSTRACT J 1790 1920; Masure L., 1913, RAPPORT SITUATION TR, V123; McCallum SH, 2002, IEEE ANN HIST COMPUT, V24, P34, DOI 10.1109/MAHC.2002.1010068; McCormick E. M., 1963, P CLIN LIB APPL DAT, P157; McCrimmon B., 1981, POWER POLITICS PRINT; McKitterick D., 2003, PRINT MANUSCRIPT SEA; McNeeley I. F., 2008, REINVENTING KNOWLEDG; Menzel H., 1960, REV STUDIES FLOW INF, V1-2; Miles W. D., 1982, HIST NATL LIB MED NA; Otlet P., 1907, IIB B, V12, P3; Otlet P., 2006, INT ORG DISSEMINATIO, P87; Otlet P., 1906, ASPECTS LIVRE C INAU, VVIII; Otlet P., 1934, TRIATE DOCUMENTATION; Otlet P., 1903, INT ORG DISSEMINATIO, P71; Peiss K, 2007, LIBR TRENDS, V55, P370, DOI 10.1353/lib.2007.0018; Povey M., 1998, HIST MODERN FACT PRO; Price D. J. de Solla, 1963, LITTLE SCI BIG SCI; Price D. J. de Solla, 1961, SCI BABYLON; Rayward W. B., 1983, STUDY INFORM INTERDI, P343; Rayward WB, 1996, INFORM PROCESS MANAG, V32, P3, DOI 10.1016/0306-4573(95)00046-J; Rayward W. B., 1975, FID PUBLICATION, V520; Rayward W. B., 2004, HIST HERITAGE SCI TE; Rayward W. B., 2008, EUROPEAN MODERNISM A, P1; Rayward WB, 1997, J AM SOC INFORM SCI, V48, P289, DOI 10.1002/(SICI)1097-4571(199704)48:4<289::AID-ASI2>3.0.CO;2-S; Rayward WB, 2002, IEEE ANN HIST COMPUT, V24, P4, DOI 10.1109/MAHC.2002.1010066; Reagle Jr Joseph M., 2010, GOOD FAITH COLLABORA; Renear AH, 2009, SCIENCE, V325, P828, DOI 10.1126/science.1157784; Renoliet J.-J., 1999, UNESCO OUBLIEE SOC N; Richards P. S., 1994, SCI INFORM WARTIME; Robins Kevin, 1999, TIMES TECHNOCULTURE; Rodriguez MA, 2006, J INF SCI, V32, P149, DOI 10.1177/0165551506062327; ROGERS FB, 1964, B MED LIBR ASSOC, V52, P150; Royal Society, 1948, ROYAL SOC SCI INF C; SCHROEDE.B, 1973, SCI STUD, V3, P93, DOI 10.1177/030631277300300201; Scott E. W., 1953, AM DOC, V4, P90, DOI 10.1002/asi.5090040303; Segesta J, 2002, IEEE ANN HIST COMPUT, V24, P23, DOI 10.1109/MAHC.2002.1024760; Shaw RR, 1944, LIBR QUART, V14, P229; Standage Tom, 1998, VICTORIAN INTERNET R; Star S. L., 1999, SORTING THINGS OUT C; STAR SL, 1989, SOC STUD SCI, V19, P387, DOI 10.1177/030631289019003001; Swanson D., 1964, INTELLECTUAL FDN LIB; TAGUE J, 1981, LIBR TRENDS, V30, P125; Tate V. D., 1950, AM DOC, V1, P3, DOI 10.1002/asi.5090010102; U. S. Office of Scientific and Technical Information, 2000, WORKSH FUT INF INFR; Vaden W. M., 1992, OAK RIDGE TECHN INFO; Varlejs J., 2004, HIST HERITAGE SCI TE, P89; Ward H B, 1921, Science, V54, P424, DOI 10.1126/science.54.1401.424; Webster Frank, 2002, THEORIES INFORM SOC; Weigand W., 1996, IRRESPRESSIBLE REFOR; Weinberger D., 2008, EVERYTHING IS MISCEL; WELLISCH H, 1972, J LIBR, V4, P157, DOI 10.1177/096100067200400302; Wells H. G., 1918, 4 YEAR ANTICIPATIONS; Williams E., 1953, FARMINGTON PLAN HDB; Williams R. V., 2002, CHRONOLOGY INFORM SC; Williams R. V., 1999, HIST HER SCI INF SYS; Williams RV, 2002, IEEE ANN HIST COMPUT, V24, P16, DOI 10.1109/MAHC.2002.1010067; Wilson TD, 2006, J DOC, V62, P658, DOI 10.1108/00220410610714895; Woolston J. E., 2004, HIST HERITAGE SCI TE, P373; WOOSTER H, 1987, J AM SOC INFORM SCI, V38, P321, DOI 10.1002/(SICI)1097-4571(198709)38:5<321::AID-ASI2>3.0.CO;2-T; Wouters P., 1999, THESIS U AMSTERDAM; Yancey R., 2005, 50 YEARS CITATION IN; ZIMAN JM, 1969, NATURE, V224, P318, DOI 10.1038/224318a0 131 0 0 JOHNS HOPKINS UNIV PRESS BALTIMORE JOURNALS PUBLISHING DIVISION, 2715 NORTH CHARLES ST, BALTIMORE, MD 21218-4363 USA 0024-2594 1559-0682 LIBR TRENDS Libr. Trends WIN 2014 62 3 681 713 33 Information Science & Library Science Information Science & Library Science AF2TL WOS:000334565300012 J Clarizia, MP; Ruf, CS; Jales, P; Gommenginger, C Clarizia, Maria Paola; Ruf, Christopher S.; Jales, Philip; Gommenginger, Christine Spaceborne GNSS-R Minimum Variance Wind Speed Estimator IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Delay-Doppler map; global navigation satellite systems (GNSS)-reflectometry; minimum variance (MV) estimator; ocean surface wind speed REFLECTED GPS SIGNALS; DELAY-DOPPLER MAPS; OCEAN SURFACE; SEA-SURFACE; SCATTEROMETER MEASUREMENTS; RETRIEVAL; MODEL; ALGORITHM; SCATTERING; ASCAT A Minimum Variance (MV) wind speed estimator for Global Navigation Satellite System-Reflectometry (GNSS-R) is presented. The MV estimator is a composite of wind estimates obtained from five different observables derived from GNSS-R Delay-Doppler Maps (DDMs). Regression-based wind retrievals are developed for each individual observable using empirical geophysical model functions that are derived from NDBC buoy wind matchups with collocated overpass measurements made by the GNSS-R sensor on the United Kingdom-Disaster Monitoring Constellation (UK-DMC) satellite. The MV estimator exploits the partial decorrelation that is present between residual errors in the five individual wind retrievals. In particular, the RMS error in the MV estimator, at 1.65 m/s, is lower than that of each of the individual retrievals. Although they are derived from the same DDM, the partial decorrelation between their retrieval errors demonstrates that there is some unique information contained in them. The MV estimator is applied here to UK-DMC data, but it can be easily adapted to retrieve wind speed for forthcoming GNSS-R missions, including the UK's TechDemoSat-1 (TDS-1) and NASA's Cyclone Global Navigation Satellite System (CYGNSS). [Clarizia, Maria Paola; Ruf, Christopher S.] Univ Michigan, Ann Arbor, MI 48109 USA; [Clarizia, Maria Paola] Natl Oceanog Ctr, Southampton SO14 3ZH, Hants, England; [Jales, Philip] Surrey Satellite Technol Ltd, Guildford GU2 7YE, Surrey, England; [Gommenginger, Christine] Natl Oceanog Ctr, Southampton SO14 3ZH, Hants, England Clarizia, MP (reprint author), Univ Michigan, Ann Arbor, MI 48109 USA. ALLAN DW, 1966, PR INST ELECTR ELECT, V54, P221, DOI 10.1109/PROC.1966.4634; BROWN GS, 1977, IEEE T ANTENN PROPAG, V25, P67, DOI 10.1109/TAP.1977.1141536; Brown ST, 2006, IEEE T GEOSCI REMOTE, V44, P611, DOI 10.1109/TGRS.2005.859351; Chelton DB, 2006, MON WEATHER REV, V134, P2055, DOI 10.1175/MWR3179.1; CHELTON DB, 1985, J GEOPHYS RES-OCEANS, V90, P4707, DOI 10.1029/JC090iC03p04707; Chen G., 2002, J GEOPHYS RES, V107; Clarizia M., 2009, GEOPHYS RES LETT, V36; Clarizia M. P., 2012, THESIS U SOUTHAMPTON; Clarizia MP, 2012, IEEE T GEOSCI REMOTE, V50, P960, DOI 10.1109/TGRS.2011.2162245; Draper D. W., 2002, J GEOPHYS RES, V107; Elfouhaily T, 2002, IEEE T GEOSCI REMOTE, V40, P560, DOI 10.1109/TGRS.2002.1000316; Figa-Saldana J, 2002, CAN J REMOTE SENS, V28, P404; Garrison JL, 2002, IEEE T GEOSCI REMOTE, V40, P50, DOI 10.1109/36.981349; Garrison JL, 1998, GEOPHYS RES LETT, V25, P2257, DOI 10.1029/98GL51615; Germain O, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020991; Gleason S, 2013, IEEE T GEOSCI REMOTE, V51, P4853, DOI 10.1109/TGRS.2012.2230401; Gleason S, 2005, IEEE T GEOSCI REMOTE, V43, P1229, DOI 10.1109/TGRS.2005.845643; Gleason S. T., 2006, THESIS U SURREY SURR; Gohil BS, 2006, PROC SPIE, V6410, DOI 10.1117/12.693563; Gohil BS, 2013, IEEE GEOSCI REMOTE S, V10, P377, DOI 10.1109/LGRS.2012.2207369; Gommenginger CP, 2002, IEEE T GEOSCI REMOTE, V40, P251, DOI 10.1109/36.992782; Hersbach H., 2007, J GEOPHYS RES, V112; Hersbach H, 2010, J ATMOS OCEAN TECH, V27, P721, DOI 10.1175/2009JTECHO698.1; Horstmann J, 2003, IEEE T GEOSCI REMOTE, V41, P2277, DOI 10.1109/TGRS.2003.814658; Katzberg S. J., 2006, GEOPHYS RES LETT, V33; Komjathy A, 2004, J ATMOS OCEAN TECH, V21, P515, DOI 10.1175/1520-0426(2004)021<0515:ROOSWS>2.0.CO;2; Li X.-M., 2014, IEEE T GEOSCI REMOTE, V52, P2930; Lowe S. T., 2002, RADIO SCI, V37; Marchan-Hernandez JF, 2008, IEEE T GEOSCI REMOTE, V46, P2914, DOI 10.1109/TGRS.2008.922144; Marchan-Hernandez JF, 2010, IEEE GEOSCI REMOTE S, V7, P621, DOI 10.1109/LGRS.2010.2043213; Martin-Neira M, 2001, IEEE T GEOSCI REMOTE, V39, P142, DOI 10.1109/36.898676; Monaldo FM, 2001, IEEE T GEOSCI REMOTE, V39, P2587, DOI 10.1109/36.974994; Portabella M, 2006, IEEE T GEOSCI REMOTE, V44, P3356, DOI 10.1109/TGRS.2006.877952; Rodriguez-Alvarez N, 2013, IEEE T GEOSCI REMOTE, V51, P626, DOI 10.1109/TGRS.2012.2196437; Ruf C., 2013, IEEE Geoscience and Remote Sensing Magazine, V1, DOI 10.1109/MGRS.2013.2260911; Ruf C., 2013, P IEEE AER C, P1; SAMUELCAHN E, 1994, AM STAT, V48, P34, DOI 10.2307/2685083; Soisuvarn S, 2013, IEEE T GEOSCI REMOTE, V51, P3744, DOI 10.1109/TGRS.2012.2219871; Soulat F., 2003, THESIS U POLITECNICA; STOFFELEN A, 1993, ADV SPACE RES, V13, P53, DOI 10.1016/0273-1177(93)90527-I; Thompson DR, 2005, IEEE T GEOSCI REMOTE, V43, P2810, DOI 10.1109/TGRS.2005.857895; Unwin M., 2013, INT J SPACE SCI ENG, V1, P20; Unwin M., 2013, P IEEE AER C, P1; Unwin M, 2011, PROCEEDINGS OF THE 24TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS 2011), P2970; Unwin M. J., 2003, P ION GNSS, P1; Valencia E, 2011, IEEE GEOSCI REMOTE S, V8, P750, DOI 10.1109/LGRS.2011.2107500; Verspeek J, 2012, IEEE T GEOSCI REMOTE, V50, P2488, DOI 10.1109/TGRS.2011.2180730; Verspeek J, 2010, IEEE T GEOSCI REMOTE, V48, P386, DOI 10.1109/TGRS.2009.2027896; Wackerman CC, 1996, IEEE T GEOSCI REMOTE, V34, P1343, DOI 10.1109/36.544558; Wentz FJ, 1997, J GEOPHYS RES-OCEANS, V102, P8703, DOI 10.1029/96JC01751; Zavorotny VU, 2000, IEEE T GEOSCI REMOTE, V38, P951, DOI 10.1109/36.841977 51 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing NOV 2014 52 11 6829 6843 10.1109/TGRS.2014.2303831 15 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AN0MN WOS:000340278800005 J Gomez-Lopez, MT; Gasca, RM Teresa Gomez-Lopez, Maria; Gasca, Rafael M. Using Constraint Programming in Selection Operators for Constraint Databases EXPERT SYSTEMS WITH APPLICATIONS English Article Complex data; Optimal query evaluation; Selection Operator; Constraint Databases; Constraint Programming FORMAL CONCEPT ANALYSIS; MONOTONICITY; CONSISTENCY; QUERIES; SYSTEMS; OBJECTS; INDEX Constraint Databases represent complex data by means of formulas described by constraints (equations, inequations or Boolean combinations of both). Commercial database management systems allow the storage and efficient retrieval of classic data, but for complex data a made-to-measure solution combined with expert systems for each type of problem are necessary. Therefore, in the same way as commercial solutions of relational databases permit storing and querying classic data, we propose an extension of the Selection Operator for complex data stored, and an extension of SQL language for the case where both classic and constraint data need to be managed. This extension shields the user from unnecessary details on how the information is stored and how the queries are evaluated, thereby enlarging the capacity of expressiveness for any commercial database management system. In order to minimize the selection time, a set of strategies have been proposed, which combine the advantages of relational algebra and constraint data representation. (C) 2014 Elsevier Ltd. All rights reserved. [Teresa Gomez-Lopez, Maria; Gasca, Rafael M.] Univ Seville, Dept Languages & Comp Syst, Seville, Spain Gomez-Lopez, MT (reprint author), Univ Seville, Dept Languages & Comp Syst, Seville, Spain. maytegomez@us.es; gasca@us.es Junta de Andalucia by means of la Consejeria de Innovacion; Ciencia y Empresa [P08-TIC-04095]; Ministry of Science and Technology of Spain [TIN2009-13714]; European Regional Development Fund (ERDF/FEDER) This work has been partially funded by the Junta de Andalucia by means of la Consejeria de Innovacion, Ciencia y Empresa (P08-TIC-04095) and by the Ministry of Science and Technology of Spain (TIN2009-13714) and the European Regional Development Fund (ERDF/FEDER). Araya I, 2010, LECT NOTES COMPUT SC, V6308, P61, DOI 10.1007/978-3-642-15396-9_8; Bixby R. E., 1998, LECT NOTES COMPUTER, V42; Brisaboa NR, 2013, INFORM SYST, V38, P635, DOI 10.1016/j.is.2013.01.005; Brisaboa NR, 2010, LECT NOTES COMPUT SC, V6413, P33, DOI 10.1007/978-3-642-16385-2_5; Brodsky A, 1999, SIGMOD RECORD, VOL 28, NO 2 - JUNE 1999, P577; Cai M., 2004, CDB, P180; Chabert G, 2009, LECT NOTES COMPUT SC, V5732, P188; Chabert G., 2005, SAC, P1439; Cheeseman P., 1991, P INT JOINT C ART IN, V1, P331; Dechter R., 2003, MORGAN KAUFMANN SERI; Deo A.D., 2002, THESIS U ERLANGUNG A; Goldin D., 2004, LECT NOTES COMPUTER, V74, P168; Goldin D., 2003, PCK50, P42, DOI 10.1145/778348.778356; Gomez-Lopez MT, 2005, LECT NOTES COMPUT SC, V3588, P848; Gomez-Lopez MT, 2009, DATA KNOWL ENG, V68, P146, DOI 10.1016/j.datak.2008.09.002; Gomez-Lopez M.T., 2011, WORKSH MOD DRIV ENG; Granvilliers L., 1999, ICTAI 99 P 11 IEEE I, P373; Grumbach S, 2000, CONSTRAINT DATABASES, P365; Gting R. H., 1993, LNCS, V692, P14; Kanellakis G. M. K. P. C., 1990, S PRINC DAT SYST, P299; Krzysztof A., 2003, PRINCIPLES CONSTRAIN; Lee KCK, 2011, DATA KNOWL ENG, V70, P842, DOI 10.1016/j.datak.2011.06.001; Lin PL, 2003, INFORM PROCESS MANAG, V39, P543, DOI 10.1016/S0306-4573(02)00034-1; Malpica JA, 2007, EXPERT SYST APPL, V32, P47, DOI 10.1016/j.eswa.2005.11.011; Marriott K., 1998, PROGRAMMING CONSTRAI; Mayol E., 2003, CONSISTENCY PRESERVI, V47; Park K, 2014, EXPERT SYST APPL, V41, P1294, DOI 10.1016/j.eswa.2013.08.027; Poelmans J, 2013, EXPERT SYST APPL, V40, P6538, DOI 10.1016/j.eswa.2013.05.009; Poelmans J, 2013, EXPERT SYST APPL, V40, P6601, DOI 10.1016/j.eswa.2013.05.007; REVESZ P, 2010, TEXTS COMPUT SCI, P1; Revesz P., 2001, INTRO CONSTRAINT DAT; Revesz P, 2000, CONSTRAINT DATABASES, P383; Revesz P. Z., 2008, ENCY GIS, P661, DOI 10.1007/978-0-387-35973-1_790; Revesz PZ, 1998, ACM T DATABASE SYST, V23, P58, DOI 10.1145/288086.288088; Revesz PZ, 1995, LECT NOTES COMPUT SC, V893, P425; Rochart X.L.G., REFERENCE MANUAL; Talebi ZA, 2013, DATA KNOWL ENG, V83, P111, DOI 10.1016/j.datak.2012.11.001; Toman D, 2000, CONSTRAINT DATABASES, P391; Tossebro E, 2006, LECT NOTES COMPUT SC, V4197, P383; Tossebro E, 2011, GEOINFORMATICA, V15, P633, DOI 10.1007/S10707-010-0120-5; Trombettoni G, 2010, LECT NOTES COMPUT SC, V6308, P491, DOI 10.1007/978-3-642-15396-9_39; Veltri P, 2001, INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY: CODING AND COMPUTING, PROCEEDINGS, P634 42 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 1 2014 41 15 6773 6785 10.1016/j.eswa.2014.04.047 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AM2PX WOS:000339694400020 J Glavas, G; Snajder, J Glavas, Goran; Snajder, Jan Event graphs for information retrieval and multi-document summarization EXPERT SYSTEMS WITH APPLICATIONS English Article Event extraction; Information extraction; Information retrieval; Multi-document summarization; Natural language processing VECTOR-SPACE MODEL; SEMANTICS; ONTOLOGY; TRACKING With the number of documents describing real-world events and event-oriented information needs rapidly growing on a daily basis, the need for efficient retrieval and concise presentation of event-related information is becoming apparent. Nonetheless, the majority of information retrieval and text summarization methods rely on shallow document representations that do not account for the semantics of events. In this article, we present event graphs, a novel event-based document representation model that filters and structures the information about events described in text. To construct the event graphs, we combine machine learning and rule-based models to extract sentence-level event mentions and determine the temporal relations between them. Building on event graphs, we present novel models for information retrieval and multi-document summarization. The information retrieval model measures the similarity between queries and documents by computing graph kernels over event graphs. The extractive multi-document summarization model selects sentences based on the relevance of the individual event mentions and the temporal structure of events. Experimental evaluation shows that our retrieval model significantly outperforms well-established retrieval models on event-oriented test collections, while the summarization model outperforms competitive models from shared multi-document summarization tasks. (C) 2014 Elsevier Ltd. All rights reserved. [Glavas, Goran; Snajder, Jan] Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Zagreb 10000, Croatia Snajder, J (reprint author), Univ Zagreb, Fac Elect Engn & Comp, Text Anal & Knowledge Engn Lab, Unska 3, Zagreb 10000, Croatia. goran.glavas@fer.hr; jan.snajder@fer.hr ACE, 2005, EV DET REC ACE ENT V; Ahn D., 2006, P WORKSH ANN REAS TI, P1, DOI 10.3115/1629235.1629236; Allan J., 2002, TOPIC DETECTION TRAC, V12; Allen J.F., 1983, ACM COMMUN, V26, P832; Amati G., 2003, THESIS U GLASGOW; Aone C., 2000, P 6 APPL NAT LANG PR, P76, DOI 10.3115/974147.974158; Atkinson J, 2013, EXPERT SYST APPL, V40, P4346, DOI 10.1016/j.eswa.2013.01.017; Atkinson M., 2009, P 18 INT WORLD WID W, P1153, DOI 10.1145/1526709.1526903; Baralis E, 2013, EXPERT SYST APPL, V40, P6976, DOI 10.1016/j.eswa.2013.06.047; Barzilay R., 1999, P 37 ANN M ASS COMP, P550, DOI 10.3115/1034678.1034760; Bejan C., 2008, P 6 INT C LANG RES E; Bethard S., 2013, 2 JOINT C LEX COMP S, V2; Borgwardt K. M., 2007, THESIS LUDWIG MAXIMI; Bramsen P., 2006, P 2006 C EMP METH NA, P189, DOI 10.3115/1610075.1610105; Buttcher S., 2007, P SIGIR 2007, P63, DOI 10.1145/1277741.1277755; Canhasi E, 2014, EXPERT SYST APPL, V41, P535, DOI 10.1016/j.eswa.2013.07.079; Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22; Chang A. X., 2012, P 8 INT C LANG RES E; Christensen J., 2013, P NAACL HLT, P1163; Conroy J. M., 2004, P DOC UND C DUC 2004; Dang H. T., 2008, P TEXT AN C, P1; Daniel N., 2003, P HLT NAACL 03 WORKS, V5, P9, DOI 10.3115/1119467.1119469; Du P., 2010, P ACM INT C INF KNOW, P1757, DOI 10.1145/1871437.1871722; Filatova E., 2004, P ACL WORKSH SUMM, V111).; Finkel J.R., 2005, P 43 ANN M ASS COMP, P363, DOI 10.3115/1219840.1219885; Gartner T, 2003, LECT NOTES ARTIF INT, V2777, P129, DOI 10.1007/978-3-540-45167-9_11; Ge J., 2003, P 2003 DOC UND WORKS; Gennari SP, 2002, COGNITION, V83, P49, DOI 10.1016/S0010-0277(01)00166-4; Glavas G., 2013, P CICLING 2013, V408-422; Glavas G., 2013, P 51 ANN M ASS COMP, P797; Gower J. C., 1969, APPL STATIST, P54, DOI 10.2307/2346439; Grishman R., 1996, P COLING, V96, P466; Grover C., 2010, P 5 INT WORKSH SEM E, P333; Hammack R., 2011, HDB PRODUCT GRAPHS D; Hatzivassiloglou V., 2000, P 23 ANN INT ACM SIG, P224, DOI 10.1145/345508.345582; He RF, 2012, EXPERT SYST APPL, V39, P2375, DOI 10.1016/j.eswa.2011.08.084; Humphreys K., 1998, P 7 MESS UND C MUC 7; Kawahara D., 2013, P INT JOINT C NAT LA, P37; Kingsbury P., 2002, P 3 INT C LANG RES E, P1989; Kolomiyets O., 2012, P 50 ANN M ASS COMP; Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044; Li WJ, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P369; Lin C.-H., 2007, IADIS INT C WWW INT; Lin C-Y., 2000, P 18 INT C COMP LING, V1, P495, DOI 10.3115/990820.990892; Lin C.-Y., 2004, P WORKSH TEXT SUMM B, P74; Llorens H., 2010, P 5 INT WORKSH SEM E, P284; MacCartney B., 2006, P LREC 06, V319, P449; Makkonen J., 2003, P HLT NAACL STUD WOR, P43; Makkonen J, 2004, INFORM RETRIEVAL, V7, P347, DOI 10.1023/B:INRT.0000011210.12953.86; Mayo B., 1950, ANALYSIS, V10, P109, DOI 10.2307/3326687; Menchetti S., 2005, P 22 INT C MACH LEAR, P585, DOI 10.1145/1102351.1102425; Michel JB, 2011, SCIENCE, V331, P176, DOI 10.1126/science.1199644; Nallapati R., 2004, P 13 ACM INT C INF K, P446, DOI 10.1145/1031171.1031258; Nenkova A., 2005, MSRTR2005101; Noh TG, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P492, DOI 10.1145/1571941.1572026; Ouyang Y, 2011, INFORM PROCESS MANAG, V47, P227, DOI 10.1016/j.ipm.2010.03.005; Over P., 2004, P WORKSH AUT SUMM DU; Over P., 2002, P WORKSH AUT SUMM DU; Page L., 1998, PAGERANK CITATION RA; Pai MY, 2013, EXPERT SYST APPL, V40, P2447, DOI 10.1016/j.eswa.2012.10.056; Pan Z., 1993, POLIT COMMUN, V10, P55; Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008; Pustejovsky J., 2003, CORPUS LINGUISTICS, V2003, P40; PUSTEJOVSKY J, 1991, COGNITION, V41, P47, DOI 10.1016/0010-0277(91)90032-Y; Pustejovsky J., 2003, NEW DIRECTIONS QUEST, V2003, P28; ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302; Saggion H., 2004, P DOC UND C, P6; SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220; Saric F., 2012, P 6 INT WORKSH SEM E; Sarkar IN, 2012, J AM MED INFORM ASSN, V19, P249, DOI 10.1136/amiajnl-2011-000480; Sbattella L, 2013, J SYST SOFTWARE, V86, P1426, DOI 10.1016/j.jss.2013.01.029; Suzuki J., 2005, THESIS NARA I SCI TE; Suzuki J., 2004, P 42 ANN M ASS COMP, P119, DOI 10.3115/1218955.1218971; Turney PD, 2010, J ARTIF INTELL RES, V37, P141; UzZaman N., 2010, P 5 INT WORKSH SEM E, P276; UzZaman N., 2013, P 7 INT WORKSH SEM E; Van Dijk T. A., 1985, DISCOURSE COMMUN, V10, P69; Verhagen M., 2007, P 4 INT WORKSH SEM E, P75, DOI 10.3115/1621474.1621488; Verhagen M., 2010, P 5 INT WORKSH SEM E, P57; Wei CP, 2007, IEEE T SYST MAN CY A, V37, P273, DOI 10.1109/TSMCA.2006.886377; Wu Z, 1994, P 32 ANN M ASS COMP, P133, DOI 10.3115/981732.981751; Yan R., 2011, P C EMP METH NAT LAN, P433; Yang CC, 2009, IEEE T SYST MAN CY A, V39, P850, DOI 10.1109/TSMCA.2009.2015885; Yang YM, 1999, IEEE INTELL SYST APP, V14, P32, DOI 10.1109/5254.784083 84 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. NOV 1 2014 41 15 6904 6916 10.1016/j.eswa.2014.04.004 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AM2PX WOS:000339694400032 J Zhang, YH; Feng, Z; Fei, Y Zhang Yong-Heng; Feng, Zhang; Fei, You A New Replacement Algorithm of Web Search Engine Cache based on User Behavior APPLIED MATHEMATICS & INFORMATION SCIENCES English Article behaviour characteristics; information retrieval; search engine; search algorithms; cache replacement The efficiency of retrieval system is crucial for large-scale information retrieval systems. By analyzing the documents and the users query logs of a real search engine based on the Web caching, through a large number of statistical analyzed of user behavior and found that the search engine query terms entered by the user in the process of clicking and queries to the URL of the page showed a clear temporal locality, and the distribution of user queries characteristics meet power function and has a good self-similarity. In this paper, analyzed of the search engine ranking based on the user behavior investigated mass distribution of information on the website and use the URL into the mirror degree, directory depth parameters and other web related degree feedback, then a replacement algorithm for Web caching is proposed. Through the establishment of retrieval performance model for analysis and simulation results show that this approach under the search algorithm can effectively reduce the execution time of retrieval, and the optimal parameter selection for this blocking organization is discussed. [Zhang Yong-Heng; Feng, Zhang; Fei, You] Yulin Univ, Sch Informat, Yulin 719000, Peoples R China; [Feng, Zhang] Northwestern Polytech Univ, Sch Automat, Xian 710072, Peoples R China Zhang, YH (reprint author), Yulin Univ, Sch Informat, Yulin 719000, Peoples R China. 709863637@qq.com Natural Science Basic Research Plan in Shaanxi Province of China [2012K12-03-08, 2013JM8005]; Funding Project for Department of Education of Shaanxi Province [2013JK1151, 2013JK1167]; Research and Cooperation Project for Department of Yulin city [2011SKJ07, 2012cxy3-26, 2013ky0271] This work is partially supported by Natural Science Basic Research Plan in Shaanxi Province of China(Program No. 2012K12-03-08, 2013JM8005), Funding Project for Department of Education of Shaanxi Province (No. 2013JK1151, No. 2013JK1167). Research and Cooperation Project for Department of Yulin city (No. 2011SKJ07, No. 2012cxy3-26, No. 2013ky0271). Thanks for the help. Aggour A. I., 2013, APPL MATH INFORM SCI, V1, P1; Ammar MA, 2001, J COMPUTING CIVIL EN, V15; Chidlovskii B, 1999, COMPUT NETW, V31, P1347, DOI 10.1016/S1389-1286(99)00035-3; De PK, 2011, APPL MATH INFORM SCI, V5, P253; HASEGAWA A, 1973, APPL PHYS LETT, V23, P171, DOI 10.1063/1.1654847; Hawking D, 1999, COMPUT NETW, V31, P1321, DOI 10.1016/S1389-1286(99)00024-9; JEONG BS, 1995, IEEE T PARALL DISTR, V6, P142; Lei M, 2001, J COMPUT SCI TECHNOL, V16, P410, DOI 10.1007/BF02948958; Mastorocostas P, 2012, ENG APPL ARTIF INTEL, V25, P200, DOI 10.1016/j.engappai.2011.04.004; Mastorocostas P. C., 2012, P 21 IEEE INT C FUZZ; PEDERSEN TP, 1992, LECT NOTES COMPUT SC, V576, P129; Silverstein Craig, 1998, SIGIR FORUM, V33, P6; Soboroff I, 2001, P 24 ANN INT ACM SIG, P6673 13 0 0 NATURAL SCIENCES PUBLISHING CORP-NSP NEW YORK 19 W 34 ST, SUITE 1018, NEW YORK, NY 10001 USA 2325-0399 APPL MATH INFORM SCI Appl. Math. Inf. Sci. NOV 2014 8 6 3049 3054 6 Mathematics, Applied; Physics, Mathematical Mathematics; Physics AK0SA WOS:000338123900045 J Xu, JC; Ren, JY; Sun, L; Xu, TH Xu, Jiucheng; Ren, Jinyu; Sun, Lin; Xu, Tianhe Cloud Model and Tolerance Granular Space-based Image Retrieval Methods APPLIED MATHEMATICS & INFORMATION SCIENCES English Article Cloud model; Tolerance granular space; Gray histogram; Grid point INCOMPLETE DECISION SYSTEMS; FEATURE-SELECTION; UNCERTAINTY MEASURES; TEXTURE; COLOR; RECOGNITION; INFORMATION; ENTROPY In the existing tolerance granular space models, grid points in each layer are established only taking space position into account, which ignore uncertainties of image texture, such as randomness, fuzziness and relevance. As a matter of fact, it is very important to extract grid points for constructing tolerance granular space, which are tolerance granules' position or center. Therefore, it is very meaningful for the accurate texture feature-description of images to extract grid points well. To address this issue, we firstly apply cloud model to extracting grid points, and establish two new tolerance granular space models. Then, similarity measures based on cloud model and tolerance granule space are presented and two novel image retrieval methods are introduced, including an image texture recognition and a color image retrieval method. Finally, simulation experiments are done on images of image test set chosen from Corel Database, to compare our proposed methods with the conventional color histogram-based image retrieval method, the salient regions and nonsubsampled contourlet transform-based image retrieval method, and tolerance granular-based multi-level texture image retrieval method. The experimental results demonstrate that the proposed methods are indeed efficient and of practical value to many real-world problems. [Xu, Jiucheng; Ren, Jinyu; Sun, Lin; Xu, Tianhe] Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453007, Peoples R China; [Xu, Jiucheng; Sun, Lin] Engn Technol Res Ctr Comp Intelligence & Data Min, Beijing, Henan Province, Peoples R China Ren, JY (reprint author), Henan Normal Univ, Coll Comp & Informat Engn, Xinxiang 453007, Peoples R China. jy_ren1985@163.com National Natural Science Foundation of China [61370169, 60873104]; Key Project of Science and Technology Department of Henan Province [112102210194, 142102210056]; Science and Technology Research Key Project of Educational Department of Henan Province [12A520027, 13A520529]; Key Project of Science and Technology of Xinxiang Government [ZG13004]; Education Fund for Youth Key Teachers of Henan Normal University This work was supported by the National Natural Science Foundation of China (Nos. 61370169, 60873104), the Key Project of Science and Technology Department of Henan Province (Nos. 112102210194, 142102210056), the Science and Technology Research Key Project of Educational Department of Henan Province (Nos. 12A520027, 13A520529), the Key Project of Science and Technology of Xinxiang Government (No. ZG13004), and the Education Fund for Youth Key Teachers of Henan Normal University. Bu Y.D., 2012, RES EXTRACTING TEXTU; Chen Z, 2012, PATTERN RECOGN LETT, V33, P1257, DOI 10.1016/j.patrec.2012.03.008; Cui YY, 2009, INTERNATIONAL JOINT CONFERENCE ON COMPUTATIONAL SCIENCES AND OPTIMIZATION, VOL 1, PROCEEDINGS, P910, DOI 10.1109/CSO.2009.478; Duygulu P, 2002, LECT NOTES COMPUT SC, V2353, P97; He ZY, 2009, SIGNAL PROCESS, V89, P1501, DOI 10.1016/j.sigpro.2009.01.021; Jiang R., 2000, CHINESE J COMPUTERS, V23, P470; Li Hesong, 2008, 2008 International Conference on Computer Science and Software Engineering (CSSE 2008), DOI 10.1109/CSSE.2008.821; Li S.Q., 2011, J GUANGXI NORMAL U N, V29, P173; Mallik J, 2010, COMPUT VIS IMAGE UND, V114, P745, DOI 10.1016/j.cviu.2010.01.006; Ning J.J., 2012, RES IMPLY TECHNOLOGY; [秦昆 QIN Kun], 2008, [计算机工程与应用, Computer Engineering and Application], V44, P56; Qin K, 2011, COMPUT MATH APPL, V62, P2824, DOI 10.1016/j.camwa.2011.07.048; Qin K., 2010, FSKD, V2010, P524; Rui Y, 1999, J VIS COMMUN IMAGE R, V10, P39, DOI 10.1006/jvci.1999.0413; Shi YQ, 2008, International Conference on Intelligent Computation Technology and Automation, Vol 2, Proceedings, P136, DOI 10.1109/ICICTA.2008.80; Sun L, 2014, BIO-MED MATER ENG, V24, P763, DOI 10.3233/BME-130865; Sun L, 2014, BIO-MED MATER ENG, V24, P1307, DOI 10.3233/BME-130933; Sun L, 2012, KNOWL-BASED SYST, V36, P206, DOI 10.1016/j.knosys.2012.06.010; SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487; Wang G.Y., 2012, CLOUD MODEL GRANULAR, P1; Wang G.Y., 2012, LNCS LNAI, V7413, P313; Wang XY, 2011, COMPUT STAND INTER, V33, P59, DOI 10.1016/j.csi.2010.03.004; Wang XY, 2013, COMPUT ELECTR ENG, V39, P746, DOI 10.1016/j.compeleceng.2013.01.005; Wang XY, 2012, COMPUT STAND INTER, V34, P31, DOI 10.1016/j.csi.2011.05.001; Wu T., 2007, COMPUTER ENG DESIGN, V6, P2905; Xu J.C., 2011, J GUANGXI NORMAL U N, V29, P186; Xu JC, 2013, APPL MATH INFORM SCI, V7, P829; Xu JC, 2014, BIO-MED MATER ENG, V24, P1001, DOI 10.3233/BME-130897; [徐久成 Xu Jiucheng], 2012, [模式识别与人工智能, Pattern Recognition and Artificial Intelligence], V25, P225; Xu K., 2010, J IMAGE GRAPHICS, V15, P575; Yao X.K., 2005, J HEBEI NORMAL U NAT, V34, P21; Yue J, 2011, MATH COMPUT MODEL, V54, P1121, DOI 10.1016/j.mcm.2010.11.044; Zeng Z.G., 2010, 2010 3 INT C IMAG SI, P1327, DOI 10.1109/CISP.2010.5648001; Zhang HM, 2011, WOODHEAD PUBL MATER, P46; Zhang K., 2008, P 27 CHIN CONTR C JU, P149; Zheng Z., 2009, J CHONGQING U POSTS, V21, P484 36 0 0 NATURAL SCIENCES PUBLISHING CORP-NSP NEW YORK 19 W 34 ST, SUITE 1018, NEW YORK, NY 10001 USA 2325-0399 APPL MATH INFORM SCI Appl. Math. Inf. Sci. NOV 2014 8 6 3145 3157 13 Mathematics, Applied; Physics, Mathematical Mathematics; Physics AK0SA WOS:000338123900056 J Poslad, S; Kesorn, K Poslad, Stefan; Kesorn, Kraisak A Multi-Modal Incompleteness Ontology model (MMIO) to enhance information fusion for image retrieval INFORMATION FUSION English Article Multi-Modal Ontology; Knowledge base; Incomplete Ontology; Visual and textual information fusion SUPPORT VECTOR MACHINES; WEB; ANNOTATION; METHODOLOGY; FEATURES; DATABASE A significant effort by researchers has advanced the ability of computers to understand, index and annotate images. This entails automatic domain specific knowledge-base (KB) construction and metadata extraction from visual information and any associated textual information. However, it is challenging to fuse visual and textual information and build a complete domain-specific KB for image annotation due to several factors such as: the ambiguity of natural language to describe image features; the semantic gap when using image features to represent visual content and the incompleteness of the metadata in the KB. Typically the KB is based upon a domain specific Ontology. However, it is not an easy task to extract the data needed from annotations and images, and then to automatically process these and transform them into an integrated Ontology model, because of the ambiguity of terms and because of image processing algorithm errors. As such, it is difficult to construct a complete KB covering a specific domain of knowledge. This paper presents a Multi-Modal Incompleteness Ontology-based (MMIO) system for image retrieval based upon fusing two derived indices. The first index exploits low-level features extracted from images. A novel technique is proposed to represent the semantics of visual content, by restructuring visual word vectors into an Ontology model by computing the distance between the visual word features and concept features, the so called concept range. The second index relies on a textual description which is processed to extract and recognise the concepts, properties, or instances that are defined in an Ontology. The two indexes are fused into a single indexing model, which is used to enhance the image retrieval efficiency. Nonetheless, this rich index may not be sufficient to find the desired images. Therefore, a Latent Semantic Indexing (LSI) algorithm is exploited to search for similar words to those used in a query. As a result, it is possible to retrieve images with a query using (similar) words that do not appear in the caption. The efficiency of the KB is validated experimentally with respect to three criteria, correctness, multimodality, and robustness. The results show that the multi-modal metadata in the proposed KB could be utilised efficiently. An additional experiment demonstrates that LSI can handle an incomplete KB effectively. Using LSI, the system can still retrieve relevant images when information in the Ontology is missing, leading to an enhanced retrieval performance. (C) 2014 Elsevier B.V. All rights reserved. [Kesorn, Kraisak] Naresuan Univ, Fac Sci, Comp Sci & Informat Technol Dept, Phitsanulok 65000, Thailand; [Poslad, Stefan] Queen Mary Univ London, Sch Elect & Elect Engn & Comp Sci, London E1 4N5, England Kesorn, K (reprint author), Naresuan Univ, Fac Sci, Comp Sci & Informat Technol Dept, Phitsanulok 65000, Thailand. stefan@eecs.qmul.ac.uk; kraisakk@nu.ac.th National Science and Technology Development (NSTDA), Thailand [SCH-NR2011-851] This research has been supported in part by National Science and Technology Development (NSTDA), Thailand. Project No: SCH-NR2011-851. We also thank Ms. Jenny Williams for her proof-reading. AlSumait L, 2008, SURV TEXT MIN 2 CLUS; Arndt R., 2007, P 6 INT SEM WEB C, P11; Bai L., 2008, ADAPT MULTIMED RETR; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Brank J., 2005, P C DAT MIN DAT WAR, P166; Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902; Chen YX, 2013, IEEE T KNOWL DATA EN, V25, P2257, DOI 10.1109/TKDE.2012.192; Chisholm E., 1999, NEW TERM WEIGHTING F; Csurka G., 2004, INT WORKSH STAT LEAR, P1; Dasiopoulou S, 2010, MULTIMED TOOLS APPL, V46, P331, DOI 10.1007/s11042-009-0387-4; Dasiopoulou S., 2007, SEMANTIC BASED VIS I, P269; Dasiopoulou S., 2009, METADATA SEMANT; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; Gasevic D., 2009, MODEL DRIVEN ENG ONT; GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008; Guarino E.N., 1995, INT J HUM-COMPUT ST, V43, P625; GUIDA G, 1993, IEEE T KNOWL DATA EN, V5, P204, DOI 10.1109/69.219731; Haubold A, 2006, 2006 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO - ICME 2006, VOLS 1-5, PROCEEDINGS, P1761, DOI 10.1109/ICME.2006.262892; Hollink L., 2003, WORKSH KNOWL MARK SE, P1; Hu JY, 2004, IEEE MULTIMEDIA, V11, P22, DOI 10.1109/MMUL.2004.1261103; Ji Y, 2013, PATTERN RECOGN, V46, P914, DOI 10.1016/j.patcog.2012.08.010; Jiang YG, 2009, COMPUT VIS IMAGE UND, V113, P405, DOI 10.1016/j.cviu.2008.10.002; Kesorn K., 2012, IEEE T MULTIMED, V14, P1520; Kesorn K, 2011, EXPERT SYST APPL, V38, P11472, DOI 10.1016/j.eswa.2011.03.021; Khalid YIAM, 2011, LECT NOTES COMPUT SC, V7067, P382, DOI 10.1007/978-3-642-25200-6_36; Li TJ, 2011, INFORM FUSION, V12, P85, DOI 10.1016/j.inffus.2010.03.007; Llorente A, 2009, LECT NOTES COMPUT SC, V5478, P570, DOI 10.1007/978-3-642-00958-7_52; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; McGuinness D., 2003, SEMANTIC WEB WHY WHA; Meersman R. A., 1999, Foundations of Intelligent Systems. 11th International Symposium, ISMIS'99. Proceedings; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; MPEG Requirements Group, 1999, 1SC29WG11 ISOIEC JTC, V12, P1; Nack F, 2005, IEEE MULTIMEDIA, V12, P54, DOI 10.1109/MMUL.2005.12; Nagypal G., 2007, POSSIBLY IMPERFECT O; Natsev A., 2007, P ACM INT C MULT, P991, DOI 10.1145/1291233.1291448; POSLAD S., 2009, UBIQUITOUS COMPUTING; Praks P., 2003, P SIAM C APPL LIN AL, P1; Rahman MA, 2006, ICECE 2006: Proceedings of the 4th International Conference on Electrical and Computer Engineering, P291; Russell BC, 2008, INT J COMPUT VISION, V77, P157, DOI 10.1007/s11263-007-0090-8; Schreiber AT, 2001, IEEE INTELL SYST APP, V16, P66, DOI 10.1109/5254.940028; SCHREIBER G, 1994, IEEE EXPERT, V9, P28, DOI 10.1109/64.363263; Shawe-Taylor J, 2011, NEUROCOMPUTING, V74, P3609, DOI 10.1016/j.neucom.2011.06.026; Sinclair PAS. S., 2005, 2 ANN EUR SEM WEB C, P28; Sivic J., 2008, IEEE C COMP VIS PATT, P1; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Smith JR, 1997, IEEE MULTIMEDIA, V4, P12, DOI 10.1109/93.621578; Song X., 2004, P 6 ACM SIGMM INT WO, P315, DOI 10.1145/1026711.1026762; Swain M.J., 1997, TECH REP; Tansley R., 2000, MULTIMEDIA THESAURUS; Troncy R., 2004, P 2004 ACM S DOC ENG, P87, DOI 10.1145/1030397.1030415; WALTER B, 2008, IEEE S INT RAY TRAC, P81; Wang H, 2008, MULTIMED TOOLS APPL, V39, P189, DOI 10.1007/s11042-008-0202-7; WANG LM, 2009, XIBEI SHIFAN DAXUE X, V45, P13; Wang ZB, 2008, INFORM FUSION, V9, P176, DOI 10.1016/j.inffus.2007.04.003; Wu L., 2009, P 1 ACM WORKSH LARG, P19, DOI 10.1145/1631058.1631064; Yang L, 2008, NEUROCOMPUTING, V72, P203, DOI 10.1016/j.neucom.2008.02.025; Zhao R, 2002, IEEE T MULTIMEDIA, V4, P189; Zhu JH, 2005, LECT NOTES ARTIF INT, V3782, P518, DOI 10.1109/ISEIM.2005.193603 58 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion NOV 2014 20 225 241 10.1016/j.inffus.2014.02.003 17 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Computer Science AJ7FV WOS:000337863500021 J Salcedo-Sanz, S; Casanova-Mateo, C; Munoz-Mari, J; Camps-Valls, G Salcedo-Sanz, Sancho; Casanova-Mateo, Carlos; Munoz-Mari, Jordi; Camps-Valls, Gustau Prediction of Daily Global Solar Irradiation Using Temporal Gaussian Processes IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Covariance; Gaussian process regression (GPR); physical parameter retrieval; solar irradiation BIOPHYSICAL PARAMETERS; POWER PREDICTION; RADIATION; RETRIEVAL; FORECAST; MACHINE; MODEL Solar irradiation prediction is an important problem in geosciences with direct applications in renewable energy. Recently, a high number of machine learning techniques have been introduced to tackle this problem, mostly based on neural networks and support vector machines. Gaussian process regression (GPR) is an alternative nonparametric method that provided excellent results in other biogeophysical parameter estimation. In this letter, we evaluate GPR for the estimation of solar irradiation. Noting the nonstationary temporal behavior of the signal, we develop a particular time-based composite covariance to account for the relevant seasonal signal variations. We use a unique meteorological data set acquired at a radiometric station that includes both measurements and radiosondes, as well as numerical weather prediction models. We show that the so-called temporal GPR outperforms ten state-of-the-art statistical regression algorithms (even when including time information) in terms of accuracy and bias, and it is more robust to the number of predictions used. [Salcedo-Sanz, Sancho] Univ Alcala De Henares, Dept Signal Proc & Commun, Madrid 28871, Spain; [Casanova-Mateo, Carlos] Univ Valladolid, Dept Appl Phys, E-47011 Valladolid, Spain; [Munoz-Mari, Jordi; Camps-Valls, Gustau] Univ Valencia, Image Proc Lab, Valencia 46980, Spain Salcedo-Sanz, S (reprint author), Univ Alcala De Henares, Dept Signal Proc & Commun, Madrid 28871, Spain. sancho.salcedo@uah.es; jordi.munoz@uv.es; gustavo.camps@uv.es Spanish Ministry of Economy and Competitiveness (MINECO) [ECO2010-22065-C03-02, TIN2012-38102-C03-01] This work was supported in part by the Spanish Ministry of Economy and Competitiveness (MINECO) under Project ECO2010-22065-C03-02 and Project TIN2012-38102-C03-01. Anton M, 2008, ANN GEOPHYS-GERMANY, V26, P401; Bezzi M., 2005, GEOMATICS WORKBOOKS; Bhardwaj S, 2013, SOL ENERGY, V93, P43, DOI 10.1016/j.solener.2013.03.020; Camps-Valls G, 2006, IEEE GEOSCI REMOTE S, V3, P93, DOI 10.1109/LGRS.2005.857031; Chen JL, 2011, RENEW ENERG, V36, P413, DOI 10.1016/j.renene.2010.06.024; Dorvlo ASS, 2002, APPL ENERG, V71, P307, DOI 10.1016/S0306-2619(02)00016-8; Geraldi E, 2012, IEEE T GEOSCI REMOTE, V50, P2934, DOI 10.1109/TGRS.2011.2178855; Holben BN, 1998, REMOTE SENS ENVIRON, V66, P1, DOI 10.1016/S0034-4257(98)00031-5; Kalogirou S. A., 2014, SOLAR ENERGY ENG, P583; KANAMITSU M, 1991, WEATHER FORECAST, V6, P425, DOI 10.1175/1520-0434(1991)006<0425:RCIITG>2.0.CO;2; Khatib T, 2012, RENEW SUST ENERG REV, V16, P2864, DOI 10.1016/j.rser.2012.01.064; Lazaro-Gredilla M, 2014, IEEE GEOSCI REMOTE S, V11, P838, DOI 10.1109/LGRS.2013.2279695; Lorenz E, 2009, IEEE J-STARS, V2, P2, DOI 10.1109/JSTARS.2009.2020300; Pasolli L, 2010, IEEE GEOSCI REMOTE S, V7, P464, DOI 10.1109/LGRS.2009.2039191; Rasmussen CE, 2005, ADAPT COMPUT MACH LE, P1; Rehman S, 2000, RENEW ENERG, V21, P583, DOI 10.1016/S0960-1481(00)00078-1; Sahin M, 2014, INT J ENERG RES, V38, P205, DOI 10.1002/er.3030; Salcedo-Sanz S, 2013, LECT NOTES COMPUT SC, V8206, P318, DOI 10.1007/978-3-642-41278-3_39; SAMPSON PD, 1992, J AM STAT ASSOC, V87, P108, DOI 10.2307/2290458; Verrelst J, 2012, IEEE T GEOSCI REMOTE, V50, P1832, DOI 10.1109/TGRS.2011.2168962; Verrelst J, 2013, IEEE J-STARS, V6, P867, DOI 10.1109/JSTARS.2012.2222356; Wittmann M, 2008, IEEE J-STARS, V1, P18, DOI 10.1109/JSTARS.2008.2001152; Zeng JW, 2013, RENEW ENERG, V52, P118, DOI 10.1016/j.renene.2012.10.009 23 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. NOV 2014 11 11 1936 1940 10.1109/LGRS.2014.2314315 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI9SV WOS:000337277500017 J Ozkan, S; Ates, T; Tola, E; Soysal, M; Esen, E Ozkan, Savas; Ates, Tayfun; Tola, Engin; Soysal, Medeni; Esen, Ersin Performance Analysis of State-of-the-Art Representation Methods for Geographical Image Retrieval and Categorization IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Geographic information systems; image representation; image retrieval FEATURES This letter studies the performance of various image representation schemes used for image search problems for the purpose of geographic image retrieval from satellite imagery. We compare the most widely adopted method of the bag-of-words (BoW) approach with the more recently introduced vector of locally aggregated descriptors (VLAD) and its more compact binary version product quantized VLAD (VLAD-PQ). We show with the experiments on a publicly available 21-class land-use/land-cover data set that the VLAD-based representation outperforms BoW at the cost of increased query time, but the more compact VLAD-PQ representation achieves very similar performance as VLAD without the increased time requirement. [Ozkan, Savas; Soysal, Medeni; Esen, Ersin] Sci & Technol Res Council Turkey TUBITAK, Space Technol Res Inst, TR-06531 Ankara, Turkey; [Ates, Tayfun] Middle E Tech Univ, TR-06800 Ankara, Turkey; [Tola, Engin] Aurvis Res & Dev, TR-06530 Ankara, Turkey Ozkan, S (reprint author), Sci & Technol Res Council Turkey TUBITAK, Space Technol Res Inst, TR-06531 Ankara, Turkey. savas.ozkan@tubitak.gov.tr Aptoula E, 2014, IEEE T GEOSCI REMOTE, V52, P3023, DOI 10.1109/TGRS.2013.2268736; Arandjelovic R, 2012, PROC CVPR IEEE, P2911, DOI 10.1109/CVPR.2012.6248018; Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027; Bordes J., 2008, P BMVC, P1; Chen L., 2011, P URB REM SENS JOINT, P385; Gemert J.C., 2010, IEEE T PATTERN ANAL, V32, P1271; Jaakkola T., 1998, P ADV NEUR INF SYST, P487; Jegou H, 2012, IEEE T PATTERN ANAL, V34, P1704, DOI 10.1109/TPAMI.2011.235; Jegou H, 2011, IEEE T PATTERN ANAL, V33, P117, DOI 10.1109/TPAMI.2010.57; Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Li CS, 1997, INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL I, P576; Li YK, 2007, IEEE T GEOSCI REMOTE, V45, P853, DOI 10.1109/TGRS.2007.892008; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Ozdemir B, 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), DOI 10.1109/ICPR.2010.278; Sivic J., 2003, Proceedings Ninth IEEE International Conference on Computer Vision; Yang Y, 2013, IEEE T GEOSCI REMOTE, V51, P818, DOI 10.1109/TGRS.2012.2205158; Yatesand R., 1999, MODEL INFORM RETRIEV 17 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. NOV 2014 11 11 1996 2000 10.1109/LGRS.2014.2316143 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI9SV WOS:000337277500029 J Ruan, ZW; Wang, GJ; Xue, JH; Lin, XG; Jiang, Y Ruan, Zhiwei; Wang, Guijin; Xue, Jing-Hao; Lin, Xinggang; Jiang, Yong Detection of user-registered dog faces NEUROCOMPUTING English Article Deformable part-based model; Dog faces detection; Object detection; User-registered detection Dog face detection is an important object detection task, widely applied in many fields such as auto-focus and image retrieval. In many applications, users only care about specific target species, which are unknown to a detection system until the users register some relevant information like a limited number of target samples. We call this scenario the detection of user-registered dog faces. Due to the great variation between different dog species, no single model can describe all the species well. Meanwhile, it is also impractical to learn individual models for every potential target species that the users may care about, given the large number of dog species. Furthermore, the- registered samples are usually too few to train a robust detector directly. In this context, we propose a novel user-registered object detection framework. This framework can generate an adaptive detector, from only a limited number of user-registered target samples and a couple of off-line trained auxiliary models. In addition, we build an annotated dog face dataset, which contains 10,712 images of 32 species. Experimental results on the dataset demonstrate that the proposed framework can achieve superior detection performance to the state-of-the-art approaches. (C) 2014 Elsevier B.V. All rights reserved. [Ruan, Zhiwei; Wang, Guijin; Lin, Xinggang] Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China; [Xue, Jing-Hao] UCL, Dept Stat Sci, London WC1E 6BT, England; [Jiang, Yong] Canon Informat Technol Beijing Co LTD, Beijing, Peoples R China Wang, GJ (reprint author), Tsinghua Univ, Dept Elect Engn, Beijing 100084, Peoples R China. rzw09@mails.tsinghua.edu.cn; wangguijin@tsinghua.edu.cn; jinghao.xue@ucl.ac.uk; xglin@tsinghua.edu.cn; jiangyong@canon-ib.com.cn National Natural Science Foundation of China [61271390] We are grateful to the reviewers for their constructive comments and suggestions. The work was partially sponsored by National Natural Science Foundation of China (No. 61271390). Aytar Y., 2012, BMVC, P1; Azizpour H., 2012, ECCV, P836; Dai W., 2007, ICML, P193; Dalai N., 2005, CVPR, P886; Everingham M., PASCAL VISUAL OBJECT; Felzenszwalb PF, 2010, IEEE T PATTERN ANAL, V32, P1627, DOI 10.1109/TPAMI.2009.167; Grabner H., 2006, CVPR, V1, P260; Kozakaya T., 2009, IM PROC ICIP 2009 16, P1213; Liu C, 2010, IEICE T INF SYST, VE93D, P1321, DOI 10.1587/transinf.E93.D.1321; Parkhi OM, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P1427, DOI 10.1109/ICCV.2011.6126398; Qi ZQ, 2011, NEUROCOMPUTING, V74, P1769, DOI 10.1016/j.neucom.2011.02.011; Ruan Z., 2014, IEICE T INF SYST, V97-D, P1394; Viola P, 2004, INT J COMPUT VISION, V57, P137, DOI 10.1023/B:VISI.0000013087.49260.fb; Wang M, 2012, PROC CVPR IEEE, P3274; Yang KY, 2011, IEEE T IMAGE PROCESS, V20, P3341, DOI 10.1109/TIP.2011.2158231; Zeng BB, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P1397; Zhang WW, 2011, IEEE T IMAGE PROCESS, V20, P1696, DOI 10.1109/TIP.2010.2099126 17 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing OCT 22 2014 142 SI 256 266 10.1016/j.neucom.2014.03.058 11 Computer Science, Artificial Intelligence Computer Science AN1KO WOS:000340341400027 J Cobos, C; Munoz-Collazos, H; Urbano-Munoz, R; Mendoza, M; Leon, E; Herrera-Viedma, E Cobos, Carlos; Munoz-Collazos, Henry; Urbano-Munoz, Richar; Mendoza, Martha; Leon, Elizabeth; Herrera-Viedma, Enrique Clustering of web search results based on the cuckoo search algorithm and Balanced Bayesian Information Criterion INFORMATION SCIENCES English Article Cuckoo search algorithm; Clustering of web result; Web document clustering; Balanced Bayesian Information Criterion; k-Mean K-MEANS; EVOLUTIONARY ALGORITHMS; MODEL SELECTION The clustering of web search results - or web document clustering - has become a very interesting research area among academic and scientific communities involved in information retrieval. Web search result clustering systems, also called Web Clustering Engines, seek to increase the coverage of documents presented for the user to review, while reducing the time spent reviewing them. Several algorithms for clustering web results already exist, but results show room for more to be done. This paper introduces a new description-centric algorithm for the clustering of web results, called WDC-CSK, which is based on the cuckoo search meta-heuristic algorithm, k-means algorithm, Balanced Bayesian Information Criterion, split and merge methods on clusters, and frequent phrases approach for cluster labeling. The cuckoo search meta-heuristic provides a combined global and local search strategy in the solution space. Split and merge methods replace the original Levy flights operation and try to improve existing solutions (nests), so they can be considered as local search methods. WDC-CSK includes an abandon operation that provides diversity and prevents the population nests from converging too quickly. Balanced Bayesian Information Criterion is used as a fitness function and allows defining the number of clusters automatically. WDC-CSK was tested with four data sets (DMOZ-50, AMBIENT, MORESQUE and ODP-239) over 447 queries. The algorithm was also compared against other established web document clustering algorithms, including Suffix Tree Clustering (STC), Lingo, and Bisecting k-means. The results show a considerable improvement upon the other algorithms as measured by recall, F-measure, fall-out, accuracy and SSLk. (C) 2014 Elsevier Inc. All rights reserved. [Cobos, Carlos; Munoz-Collazos, Henry; Urbano-Munoz, Richar; Mendoza, Martha] Univ Cauca, Informat Technol Res Grp GTI, Popayan, Colombia; [Cobos, Carlos; Mendoza, Martha] Univ Cauca, Dept Comp Sci, Elect & Telecommun Engn Fac, Popayan, Colombia; [Leon, Elizabeth] Univ Nacl Colombia, Syst & Ind Engn, Fac Engn, Popayan, Colombia; [Herrera-Viedma, Enrique] Univ Granada, Dept Comp Sci & Artificial Intelligence, Granada, Spain; [Herrera-Viedma, Enrique] King Abdulaziz Univ, Dept Elect & Comp Engn, Fac Engn, Jeddah 21589, Saudi Arabia Cobos, C (reprint author), Univ Cauca, Informat Technol Res Grp GTI, Sect Tulcan Off 422 FIET, Popayan, Colombia. ccobos@unicauca.edu.co University of Cauca (Colombia) [VRI-2560]; National University of Colombia; Excellence Andalusian Projects [TIC-5299, TIC-5991]; National Project (Spain) [TIN2010-17876] The work in this paper was supported by a Research Grant from the University of Cauca (Colombia) under Project VRI-2560, the National University of Colombia, the Excellence Andalusian Projects TIC-5299 and TIC-5991, and the National Project TIN2010-17876 (Spain). We are especially grateful to Colin McLachlan for suggestions relating to the English text. Ahmadi-Abkenari F, 2012, INFORM SCIENCES, V184, P266, DOI 10.1016/j.ins.2011.08.022; Alba E, 2002, IEEE T EVOLUT COMPUT, V6, P443, DOI 10.1109/TEVC.2002.800880; Alba E, 2005, WILEY SER PARA DIST, P1, DOI 10.1002/0471739383; [Anonymous], 2014, DATA CLUSTERING ALGO; Bacanin N., 2011, P EUR COMP C ECC 11, P245; Beil F., 2002, KDD 02, P436, DOI DOI 10.1145/775047.775110; Berkhin P, 2006, GROUPING MULTIDIMENSIONAL DATA: RECENT ADVANCES IN CLUSTERING, P25, DOI 10.1007/3-540-28349-8_2; Berkhin P., 2002, SURVEY CLUSTERING DA; Bernardini A, 2009, 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P206; Cantu-Paz E., 2000, EFFICIENT ACCURATE P; Carpineto C, 2012, INFORM PROCESS MANAG, V48, P358, DOI 10.1016/j.ipm.2011.08.004; Carpineto C, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P170; Carpineto C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541884; Carullo M, 2009, PATTERN RECOGN LETT, V30, P870, DOI 10.1016/j.patrec.2009.04.001; Chehreghani MH, 2009, DECIS SUPPORT SYST, V47, P374, DOI 10.1016/j.dss.2009.04.002; Cobos C., 2010, 2010 IEEE C EV COMP, P4637; Cobos C, 2011, APPL MATH COMPUT, V218, P2558, DOI 10.1016/j.amc.2011.07.073; Cobos C, 2011, IEEE C EVOL COMPUTAT, P1350; Cobos C, 2012, LECT NOTES ARTIF INT, V7637, P179, DOI 10.1007/978-3-642-34654-5_19; Cobos C, 2013, INFORM PROCESS MANAG, V49, P607, DOI 10.1016/j.ipm.2012.12.002; Cobos C., 2010, 2010 IEEE C EV COMP, P4629; Domingo-Ferrer J, 2012, INFORM SCIENCES, V200, P123, DOI 10.1016/j.ins.2012.02.067; Eiben A.E., 2012, AUTONOMOUS SEARCH, P15; Fersini E, 2010, INFORM PROCESS MANAG, V46, P117, DOI 10.1016/j.ipm.2009.08.003; FORGY EW, 1965, BIOMETRICS, V21, P768; Fraley C, 1998, COMPUT J, V41, P578, DOI 10.1093/comjnl/41.8.578; Fung BCM, 2003, SIAM PROC S, P59; Geem ZW, 2001, SIMULATION, V76, P60; Goel S., 2011, 2011 WORLD C INF COM, P916; Hammouda K., 2001, WEB MINING CLUSTERIN, P1; Han J., 2001, GEOGRAPHIC DATA MINI, P1; Hansen MH, 2001, J AM STAT ASSOC, V96, P746, DOI 10.1198/016214501753168398; He X., 2011, 2011 INT C MACH LEAR, P1466; Hemalatha M, 2009, 2009 SECOND INTERNATIONAL CONFERENCE ON THE APPLICATIONS OF DIGITAL INFORMATION AND WEB TECHNOLOGIES (ICADIWT 2009), P531, DOI 10.1109/ICADIWT.2009.5273918; Jain A, 1988, ALGORITHMS CLUSTERIN; Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504; Jing L., 2008, SURVEY TEXT CLUSTERI; Lee I, 2011, ARTIF INTELL REV, V36, P69, DOI 10.1007/s10462-011-9203-4; Lee S., 2012, INT J SECURITY APPL, V6, P449; Li YJ, 2008, DATA KNOWL ENG, V64, P381, DOI 10.1016/j.datak.2007.08.001; Loia V, 2007, IEEE T FUZZY SYST, V15, P1294, DOI 10.1109/TFUZZ.2006.889970; Luque G, 2011, STUD COMPUT INTELL, V367, P3, DOI 10.1007/978-3-642-22084-5; Mahamed G.H.O., 2007, INTELL DATA ANAL, V11, P583; Mahdavi M., 2008, APPL MATH COMPUT, V201, P441; Mahdavi M., 2009, DATA MIN KNOWL DISC, V18, P370; Manning C, 2008, INTRO INFORM RETRIEV; Matsuo Tetsuji, 2010, 2010 14th Biennial IEEE Conference on Electromagnetic Field Computation (CEFC 2010), DOI [10.1109/CEFC.2010.5481696, 10.1109/CEFC.2010.5481443]; Mecca G, 2007, DATA KNOWL ENG, V62, P504, DOI 10.1016/j.datak.2006.10.006; Muhr M, 2009, PROCEEDINGS OF THE 20TH INTERNATIONAL WORKSHOP ON DATABASE AND EXPERT SYSTEMS APPLICATION, P363, DOI 10.1109/DEXA.2009.39; Naldi MC, 2014, NEUROCOMPUTING, V127, P30, DOI 10.1016/j.neucom.2013.05.046; Navigli R., 2010, P 2010 C EMP METH NA, P116; Nguyen QH, 2007, IEEE C EVOL COMPUTAT, P2390, DOI 10.1109/CEC.2007.4424770; Oren Z., 1998, P 21 ANN INT ACM SIG, P46; OSINSKI S, 2005, ADV WEB INTELLIGENCE, V3528, P439, DOI 10.1007/11495772_68; OSINSKI S, 2006, 28 EUR C IR RES ECIR, V3936, P167; Osinski S, 2005, IEEE INTELL SYST, V20, P48, DOI 10.1109/MIS.2005.38; Porcel C, 2012, INFORM SCIENCES, V184, P1, DOI 10.1016/j.ins.2011.08.026; Reddy D, 2012, PROC TECH, V4, P395, DOI 10.1016/j.protcy.2012.05.061; Redmond SJ, 2007, PATTERN RECOGN LETT, V28, P965, DOI 10.1016/j.patrec.2007.01.001; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Scaiella U., 2012, P 5 ACM INT C WEB SE, P223; Senthilnath J, 2013, ADV INTELL SYST, V202, P65, DOI 10.1007/978-81-322-1041-2_6; Senthilnath J, 2013, IEEE J-STARS, V6, P861, DOI 10.1109/JSTARS.2012.2217941; Senthilnath J, 2012, IEEE J-STARS, V5, P762, DOI 10.1109/JSTARS.2012.2187432; Serrano-Guerrero J, 2011, INFORM SCIENCES, V181, P1503, DOI 10.1016/j.ins.2011.01.012; Smit SK, 2009, IEEE C EVOL COMPUTAT, P399, DOI 10.1109/CEC.2009.4982974; Song W, 2009, EXPERT SYST APPL, V36, P9095, DOI 10.1016/j.eswa.2008.12.046; Steinbach M., 2000, KDD WORKSH TEXT MIN, P1; Sugiyama M, 2001, NEURAL COMPUT, V13, P1863, DOI 10.1162/08997660152469387; Valian E., 2011, INT J ARTIF INTELL A, V2, P8; Webb A., 2002, STAT PATTERN RECOGNI; Wei X., 2003, P SIGIR 03 TOR CA, P267; Wolpert D. H., 1997, IEEE Transactions on Evolutionary Computation, V1, DOI 10.1109/4235.585893; Wu XD, 2008, KNOWL INF SYST, V14, P1, DOI 10.1007/s10115-007-0114-2; Xiang-Wei L., 2005, P 2005 INT C MACH LE, V2354, P2352; Xin-She Y., 2009, WORLD C NAT BIOL INS, P210; Xu R, 2012, IEEE T SYST MAN CY B, V42, P1243, DOI 10.1109/TSMCB.2012.2188509; Yang X., 2010, INT J MATH MODELLING, V1, P330, DOI DOI 10.1504/IJMMNO.2010.035430; Yang X.-S., 2008, NATURE INSPIRED META, P128; Yang X.-S., 2008, NATURE INSPIRED META; Zeng H.-J., 2004, P 27 ANN INT ACM SIG, P210, DOI 10.1145/1008992.1009030; Zhang D, 2004, LECT NOTES COMPUT SC, V3007, P69; Zheng SZ, 2009, PROCEEDINGS OF THE SECOND INTERNATIONAL SYMPOSIUM ON ELECTRONIC COMMERCE AND SECURITY, VOL II, P446, DOI 10.1109/ISECS.2009.16; Zhong-Yuan Z., 2010, ISORA 10, P317 84 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. OCT 10 2014 281 248 264 10.1016/j.ins.2014.05.047 17 Computer Science, Information Systems Computer Science AN1AR WOS:000340315600016 J Wang, ZY; Cui, P; Li, FT; Chang, E; Yang, SQ Wang, Zhiyu; Cui, Peng; Li, Fangtao; Chang, Edward; Yang, Shiqiang A data-driven study of image feature extraction and fusion INFORMATION SCIENCES English Article Image annotation; Image feature; Web-scale; Fusion OBJECT RECOGNITION; CORTEX; CLASSIFICATION; RETRIEVAL; STIMULI; SCALE Feature analysis is the extraction and comparison of signals from multimedia data, which can subsequently be semantically analyzed. Feature analysis is the foundation of many multimedia computing tasks such as object recognition, image annotation, and multimedia information retrieval. In recent decades, considerable work has been devoted to the research of feature analysis. In this work, we use large-scale datasets to conduct a comparative study of four state-of-the-art, representative feature extraction algorithms: color-texture codebook (CT), SIFT codebook, HMAX, and convolutional networks (ConvNet). Our comparative evaluation demonstrates that different feature extraction algorithms enjoy their own advantages, and excel in different image categories. We provide key observations to explain where these algorithms excel and why. Based on these observations, we recommend feature extraction principles and identify several pitfalls for researchers and practitioners to avoid. Furthermore, we determine that in a large training dataset with more than 10,000 instances per image category, the four evaluated algorithms can converge to the same high level of category-prediction accuracy. This result supports the effectiveness of the data-driven approach. Finally, based on learned clues from each algorithm's confusion matrix, we devise a fusion algorithm to harvest synergies between these four algorithms and further improve class-prediction accuracy. (C) 2014 Elsevier Inc. All rights reserved. [Wang, Zhiyu; Cui, Peng; Yang, Shiqiang] Tsinghua Univ, Dept Comp Sci, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China; [Wang, Zhiyu; Cui, Peng; Yang, Shiqiang] Tsinghua Univ, Beijing Key Lab Networked Multimedia, Beijing 100084, Peoples R China; [Li, Fangtao; Chang, Edward] Google Beijing, Beijing, Peoples R China Cui, P (reprint author), Tsinghua Univ, Dept Comp Sci, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China. cuip@mail.tsinghua.edu.cn National Natural Science Foundation of China [61370022, 61003097, 60933013, 61210008]; International Science and Technology Cooperation Program of China [2013DFG12870]; National Program on Key Basic Research Project [2011CB302206] This work is supported by the National Natural Science Foundation of China (Nos. 61370022, 61003097, 60933013, and 61210008), International Science and Technology Cooperation Program of China (No. 2013DFG12870), and the National Program on Key Basic Research Project (No. 2011CB302206). Alajlan Naif, 2012, INF SCI; Bengio Y., 2009, LEARNING DEEP ARCHIT; Boureau YL, 2010, PROC CVPR IEEE, P2559, DOI 10.1109/CVPR.2010.5539963; Cao Y., 2010, IEEE C COMP VIS PATT; Chang E.Y., 2007, ADV NEURAL INFORM PR, V20, P16; Chang E.Y., 2011, FDN LARGE SCALE MULT; Chang E. Y., 2000, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, DOI 10.1109/IVL.2000.853848; Chang T, 1993, IEEE T IMAGE PROCESS, V2, P429, DOI 10.1109/83.242353; Cui P, 2011, DATA MIN KNOWL DISC, V22, P467, DOI 10.1007/s10618-010-0195-5; Cui P, 2012, IEEE T MULTIMEDIA, V14, P102, DOI 10.1109/TMM.2011.2176110; Cui Peng, 2007, CVPR, P1; Deng J., 2009, IEEE C COMP VIS PATT; Deselaers T, 2008, INFORM RETRIEVAL, V11, P77, DOI 10.1007/s10791-007-9039-3; Dor O, 2012, INFORM SCIENCES, V189, P176, DOI 10.1016/j.ins.2011.11.039; FUKUSHIMA K, 1980, BIOL CYBERN, V36, P193, DOI 10.1007/BF00344251; Gawne TJ, 2002, J NEUROPHYSIOL, V88, P1128, DOI 10.1152/jn.00131.2002; Geusebroek JM, 2001, IEEE T PATTERN ANAL, V23, P1338, DOI 10.1109/34.977559; Gevers T, 1999, PATTERN RECOGN, V32, P453, DOI 10.1016/S0031-3203(98)00036-3; Gevers T, 2004, IEEE T PATTERN ANAL, V26, P113, DOI 10.1109/TPAMI.2004.1261083; Goodrum A. A., 2000, Informing Science, V3; Hinton GE, 2006, NEURAL COMPUT, V18, P1527, DOI 10.1162/neco.2006.18.7.1527; HUBEL DH, 1968, J PHYSIOL-LONDON, V195, P215; Jegou H., 2009, IEEE INT C COMP VIS; Jiang Y.G., 2007, ACM INT C IM VID RET; Kohavi R., 1998, MACH LEARN, V30, P271, DOI DOI 10.1023/A:1017181826899; Lampl I, 2004, J NEUROPHYSIOL, V92, P2704, DOI 10.1152/jn.00060.2004; Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791; LEU JG, 1991, PATTERN RECOGN, V24, P949, DOI 10.1016/0031-3203(91)90092-J; Liu Shaowei, 2013, COMPUT VIS IMAGE UND; Lowe D.G., 1999, IEEE INT C COMP VIS; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; lyengar Giridharan, 2003, P 11 ACM INT C MULT, P255; Miller EK, 2000, NAT REV NEUROSCI, V1, P59, DOI 10.1038/35036228; Ojala T, 2002, IEEE T PATTERN ANAL, V24, P971, DOI 10.1109/TPAMI.2002.1017623; Oliva A, 1997, COGNITIVE PSYCHOL, V34, P72, DOI 10.1006/cogp.1997.0667; Pedronette Daniel Carlos Guimardes, 2012, INF SCI, V207, P19; Pichler O, 1996, PATTERN RECOGN, V29, P733, DOI 10.1016/0031-3203(95)00127-1; Prasad S, 2008, IEEE T GEOSCI REMOTE, V46, P1448, DOI 10.1109/TGRS.2008.916207; Ranzato M.A., 2007, IEEE C COMP VIS PATT; Riesenhuber M, 1999, NEURON, V24, P87, DOI 10.1016/S0896-6273(00)80824-7; Rodriguez JJ, 2006, IEEE T PATTERN ANAL, V28, P1619, DOI 10.1109/TPAMI.2006.211; Roubos JA, 2003, INFORM SCIENCES, V150, P77, DOI 10.1016/S0020-0255(02)00369-9; Serre T., 2006, THESIS MIT; Serre T, 2007, IEEE T PATTERN ANAL, V29, P411, DOI 10.1109/TPAMI.2007.56; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Smith J., 1996, IEEE T PATTERN ANAL; Snoek Cees G.M., 2005, P 13 ANN ACM INT C M, P399, DOI 10.1145/1101149.1101236; Sun QS, 2005, PATTERN RECOGN, V38, P2437, DOI 10.1016/j.patcog.2004.12.013; Tong S., 2001, ACM MULTIMEDIA, P107; van de Weijer J, 2006, IEEE T PATTERN ANAL, V28, P150, DOI 10.1109/TPAMI.2006.3; Vedaldi A, 2009, IEEE I CONF COMP VIS, P606, DOI 10.1109/ICCV.2009.5459183; Wang M, 2009, IEEE T MULTIMEDIA, V11, P465, DOI 10.1109/TMM.2009.2012919; Wang M, 2012, IEEE T IMAGE PROCESS, V21, P4649, DOI 10.1109/TIP.2012.2207397; Xu C., 2012, PRACT APPL INTELL SY, P129; Yager RR, 2004, INFORM SCIENCES, V163, P175, DOI 10.1016/j.ins.2003.03.018; Yang J, 2003, PATTERN RECOGN, V36, P1369, DOI 10.1016/S0031-3203(02)00262-5; Yasuda M., 2009, CEREBRAL CORTEX; Zha Zheng-Jun, 2008, IEEE C COMP VIS PATT, P1, DOI DOI 10.1109/CVPR.2008.4587384; Zha Zheng-Jun, 2009, P 17 ACM INT C MULT, P15, DOI 10.1145/1631272.1631278; Zha ZJ, 2009, J VIS COMMUN IMAGE R, V20, P97, DOI 10.1016/j.jvcir.2008.11.009; Zha ZJ, 2012, IEEE T MULTIMEDIA, V14, P17, DOI 10.1109/TMM.2011.2174782; Zhang X., 2011, IEEE INT C COMM ICC, P1 62 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. OCT 10 2014 281 536 558 10.1016/j.ins.2014.02.030 23 Computer Science, Information Systems Computer Science AN1AR WOS:000340315600037 J Lu, K; Wang, Q; Xue, J; Pan, WG Lu, Ke; Wang, Qian; Xue, Jian; Pan, Weiguo 3D model retrieval and classification by semi-supervised learning with content-based similarity INFORMATION SCIENCES English Article 3D model retrieval; 3D model recognition; Distance measure; Disjoint information; Semi-supervised learning 3-D OBJECT RETRIEVAL; IMAGE SEARCH; RECOGNITION; INFORMATION; REGULARIZATION; DISTANCE; RANKING The rapid development of 3D digital technology has led to an increasing volume of 3D model data. In addressing the management of such large scale data, effective content-based 3D model retrieval and recognition methods are highly desirable. In 3D model retrieval and recognition tasks, the distance measure between two 3D models plays an important role. In this paper, we propose a novel 3D model retrieval and recognition method that employs both a distance histogram and 3D moment invariants as features that are invariant to 3D object scaling, translation, and rotation. Disjoint information is used to measure the distance between the feature histograms, and the Euclidean distance is applied in calculating the distance between two moment features. These measures are then combined as the 3D model distance. Using this distance measure, the relationships between all 3D models in the dataset are formulated as a graph structure. A semi-supervised learning process is then conducted to estimate the relevance among the 3D models, and this is employed for 3D model retrieval and classification. To evaluate the effectiveness of the proposed method, we conduct experiments on two datasets. Experimental results and a comparison with state-of-the-art methods demonstrate that the proposed method achieves improved performance for 3D model retrieval and recognition tasks. (C) 2014 Elsevier Inc. All rights reserved. [Lu, Ke; Wang, Qian; Xue, Jian; Pan, Weiguo] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Lu, K (reprint author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. luk@ucas.ac.cn National Natural Science Foundation of China [U1301251, 61370138, 61103130, 61271435, 61202245]; National Program on Key Basic Research Projects (973 programs) [2010CB731804-1, 2011CB706901-4]; Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality [IDHT20130513]; Beijing Municipal Natural Science Foundation [4141003, 4131004] This work was supported by the National Natural Science Foundation of China (Grant Nos. U1301251, 61370138, 61103130, 61271435, and 61202245); National Program on Key Basic Research Projects (973 programs) (Grant Nos. 2010CB731804-1 and 2011CB706901-4); The Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (No. IDHT20130513); and Beijing Municipal Natural Science Foundation under Grant (Nos. 4141003, 4131004). [Anonymous], 1999, MPEG98M2819; Ansary TF, 2007, IEEE T MULTIMEDIA, V9, P78, DOI 10.1109/TMM.2006.886359; Aspert N., 2002, P IEEE INT C MULT EX, V1, P705, DOI DOI 10.1007/978-3-642-04268-3_92; Auli-Llinas F, 2013, INFORM SCIENCES, V239, P266, DOI 10.1016/j.ins.2013.03.027; Bimbo A., 2006, ACM T MULTIM COMPUT, V2, P20, DOI 10.1145/1126004.1126006; Bustos B, 2005, ACM COMPUT SURV, V37, P345, DOI 10.1145/1118890.1118893; Chen DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669; Cheng J, 2013, INFORM SCIENCES, V221, P274, DOI 10.1016/j.ins.2012.09.002; Cover T, 2006, ELEMENTS INFORM THEO; Daras P, 2010, INT J COMPUT VISION, V89, P229, DOI 10.1007/s11263-009-0277-2; Galvez A, 2012, INFORM SCIENCES, V192, P174, DOI 10.1016/j.ins.2010.11.007; Gao Y, 2010, PATTERN RECOGN, V43, P1142, DOI 10.1016/j.patcog.2009.07.012; Gao Y, 2011, IEEE T MULTIMEDIA, V13, P1007, DOI 10.1109/TMM.2011.2160619; Gao Y, 2012, IEEE T IMAGE PROCESS, V21, P2269, DOI 10.1109/TIP.2011.2170081; Gao Y, 2014, IEEE T IND ELECTRON, V61, P2088, DOI 10.1109/TIE.2013.2262760; Gao Y, 2013, IEEE T IMAGE PROCESS, V22, P363, DOI 10.1109/TIP.2012.2202676; Gao Y., 2014, IEEE MULTIMEDIA MAG; Gao Y, 2012, INFORM SCIENCES, V194, P224, DOI 10.1016/j.ins.2012.01.003; Gao Y, 2012, IEEE T IMAGE PROCESS, V21, P4290, DOI 10.1109/TIP.2012.2199502; Hou CQ, 2013, INFORM SCIENCES, V221, P262, DOI 10.1016/j.ins.2012.09.006; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Khan A, 2012, INFORM SCIENCES, V216, P155, DOI 10.1016/j.ins.2012.06.014; Kim S, 2008, PATTERN RECOGN, V41, P754, DOI 10.1016/j.patcog.2007.03.018; Li B, 2013, MULTIMED TOOLS APPL, V62, P821, DOI 10.1007/s11042-011-0873-3; Li F, 2009, IEEE SIGNAL PROC LET, V16, P227, DOI 10.1109/LSP.2008.2010819; Li F, 2010, SIGNAL PROCESS-IMAGE, V25, P18, DOI 10.1016/j.image.2009.11.001; Li W., 2004, INT C COMP VIS PATT, P102; Li X., 2007, P IEEE 11 INT C COMP; lp C., 2002, P 7 ACM S SOL MOD AP, P273; Lu K, 2009, PROG NAT SCI, V19, P495, DOI 10.1016/j.pnsc.2008.06.025; Mahmoudi S., 2002, P INT C PATT REC IEE, P11; Noguera JM, 2012, INFORM SCIENCES, V215, P37, DOI 10.1016/j.ins.2012.05.010; Ohbuchi R., 2008, P IEEE ICCV 2009 WOR; Ohbuchi R., 2008, P IEEE C SHAP MOD AP, P1; Ohkita Y, 2012, IEEE INT CONF MULTI, P593, DOI 10.1109/ICMEW.2012.109; Osada R, 2002, ACM T GRAPHIC, V21, P807, DOI 10.1145/571647.571648; Pan ZB, 2013, INFORM SCIENCES, V221, P284, DOI 10.1016/j.ins.2012.09.003; Papadakis P, 2010, INT J COMPUT VISION, V89, P177, DOI 10.1007/s11263-009-0281-6; Paquet E, 2000, SIGNAL PROCESS-IMAGE, V16, P103, DOI 10.1016/S0923-5965(00)00020-5; Salgian A., 2007, P IEEE COMP SOC C CO; Shih JL, 2007, PATTERN RECOGN, V40, P283, DOI 10.1016/j.patcog.2006.04.034; Shilane P., 2004, P SHAP MOD INT; Vranic D., 2003, P IEEE INT C IM PROC, P757; Vranic D., 2004, THESIS U LEIPZIG; Wang M, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2333112.2333120; Wang M, 2012, IEEE T MULTIMEDIA, V14, P858, DOI 10.1109/TMM.2012.2187181; Wang M, 2012, IEEE T IMAGE PROCESS, V21, P4649, DOI 10.1109/TIP.2012.2207397; Wang M, 2013, IEEE T IMAGE PROCESS, V22, P1395, DOI 10.1109/TIP.2012.2231088; Wang W, 2013, INFORM SCIENCES, V237, P271, DOI 10.1016/j.ins.2013.03.012; Zhou D., 2004, P ADV NEUR INF PROC; Zhuang FZ, 2012, INFORM SCIENCES, V199, P20, DOI 10.1016/j.ins.2012.02.058 51 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. OCT 10 2014 281 703 713 10.1016/j.ins.2014.03.079 11 Computer Science, Information Systems Computer Science AN1AR WOS:000340315600049 J Patterson, BD Patterson, Bruce D. Crystallography using an X-ray free-electron laser CRYSTALLOGRAPHY REVIEWS English Review crystallography; XFEL; X-ray diffraction; protein crystallography; time-resolved X-ray diffraction; resonant diffraction SERIAL FEMTOSECOND CRYSTALLOGRAPHY; PROTEIN NANOCRYSTALLOGRAPHY; STRUCTURAL DYNAMICS; LATTICE-DYNAMICS; 3-DIMENSIONAL NANOCRYSTALS; ANOMALOUS DISPERSION; MATERIALS SCIENCE; RADIATION-DAMAGE; ROOM-TEMPERATURE; PHASE RETRIEVAL An X-ray free-electron laser (XFEL) produces short pulses (10-50fs) of intense (mJm(-2) at 120Hz) X-rays, with high transverse coherence. Such pulses open novel spectroscopic and scattering methods for static and time-resolved studies of matter, and many are based on X-ray crystallography. With serial femtosecond crystallography, the XFEL allows high-resolution structural determination on sub-micron protein crystals. Although the XFEL pulse is destructive, its short duration ensures that effectively undamaged material is probed. Coherent scattering features provide information on the physical crystal form and may assist in determining the crystallographic phase. By introducing synchronized optical laser pulses, one can perform pump-probe' measurements of dynamic properties, on the sub-picosecond timescale. These include photo-initiated structural modifications in biomolecules, photo-excited lattice vibrations and photo-driven structural phase transitions. As with synchrotron radiation, the XFEL wavelength can be tuned to atomic resonances, allowing time-resolved resonant-diffraction measurements, which are particularly sensitive to selected order parameters (lattice, charge, spin, and orbital) in magnetic or correlated electron materials. Finally, it is anticipated that the special properties of XFEL pulses will allow entirely new types of X-ray scattering measurements, such as ptychographic crystallography on 2D bio-crystals, correlation-function determination of nanoparticle geometry and nonlinear crystallographic mixing of optical and X-ray pulses. Paul Scherrer Inst, SwissFEL Project, CH-5232 Villigen, Switzerland Patterson, BD (reprint author), Paul Scherrer Inst, SwissFEL Project, CH-5232 Villigen, Switzerland. bruce.patterson@psi.ch Alberts B, 2012, SCIENCE, V338, P1511, DOI 10.1126/science.1234108; Allaria E, 2012, NAT PHOTONICS, V6, P699, DOI [10.1038/nphoton.2012.233, 10.1038/NPHOTON.2012.233]; Altarelli M, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0311; Altarelli M, 2006, 2006097 DESY, P2006; Altarelli M, 2006, LECT NOTES PHYS, V697, P201; Amann J, 2012, NAT PHOTONICS, V6, P693, DOI [10.1038/nphoton.2012.180, 10.1038/NPHOTON.2012.180]; Aquila A, 2012, OPT EXPRESS, V20, P2706, DOI 10.1364/OE.20.002706; Armbruster A, 2004, FEBS LETT, V570, P119, DOI 10.1016/j.febslet.2004.06.029; Authier A, 2005, DYNAMICAL THEORY XRA; Ayvazyan V, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.104802; Barends TRM, 2013, ACTA CRYSTALLOGR D, V69, P838, DOI 10.1107/S0907444913002448; Barty A, 2012, NAT PHOTONICS, V6, P35, DOI [10.1038/nphoton.2011.297, 10.1038/NPHOTON.2011.297]; Beaud P, 2011, CHIMIA, V65, P308, DOI 10.2533/chimia.2011.308; Beaud P, NATURE MAT IN PRESS; Beaud P, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.155702; Becker J, 2012, NUCL INSTRUM METH A, V694, P82, DOI 10.1016/j.nima.2012.08.008; Beye M, 2013, NATURE, V501, P191, DOI 10.1038/nature12449; Bodenthin Y, 2011, J PHYS-CONDENS MAT, V23, DOI 10.1088/0953-8984/23/3/036002; Bogan MJ, 2013, ANAL CHEM, V85, P3464, DOI 10.1021/ac303716r; Brehm W, 2014, ACTA CRYSTALLOGR D, V70, P101, DOI 10.1107/S1399004713025431; Bunk O, 2008, ULTRAMICROSCOPY, V108, P481, DOI 10.1016/j.ultramic.2007.08.003; Caffrey M, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0621; Caleman C, 2011, J MOD OPTIC, V58, P1486, DOI 10.1080/09500340.2011.597519; Cammarata M, 2008, NAT METHODS, V5, P881, DOI [10.1038/nmeth.1255, 10.1038/NMETH.1255]; Castleton CWM, 2000, PHYS REV B, V62, P1033, DOI 10.1103/PhysRevB.62.1033; Cavalleri A, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.237401; Caviezel A, 2012, PHYS REV B, V87; Caviezel A, 2012, PHYS REV B, V86, DOI 10.1103/PhysRevB.86.174105; Cha W, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/035022; Chapman HN, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0313; Chapman HN, 2011, NATURE, V470, P73, DOI 10.1038/nature09750; Chen JPJ, 2014, ACTA CRYSTALLOGR A, V70, P143, DOI 10.1107/S2053273313032038; Chen JPJ, 2014, ACTA CRYSTALLOGR A, V70, P154, DOI 10.1107/S2053273313032725; Choi J, 2007, J KOREAN PHYS SOC, V50, P1372, DOI 10.3938/jkps.50.1372; Clark JN, 2013, SCIENCE, V341, P56, DOI 10.1126/science.1236034; Cocco D, 2013, PROC SPIE, V8849, DOI 10.1117/12.2024402; Daranciang D, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.087601; Decker FJ, 2013, P FEL 2013 NEW YORK, P514; Dejoie C, 2013, J APPL CRYSTALLOGR, V46, P791, DOI 10.1107/S0021889813005888; de Jong S, 2013, NAT MATER, V12, P882, DOI [10.1038/nmat3718, 10.1038/NMAT3718]; EISENBER.P, 1971, PHYS REV LETT, V26, P684, DOI 10.1103/PhysRevLett.26.684; Elsaesser T, 2014, J CHEM PHYS, V140, DOI 10.1063/1.4855115; Elsaesser T, 2010, ACTA CRYSTALLOGR A, V66, P168, DOI 10.1107/S0108767309048181; Elser V, 2013, ACTA CRYSTALLOGR A, V69, P559, DOI 10.1107/S0108767313023362; Emma P, 2010, NAT PHOTONICS, V4, P641, DOI [10.1038/nphoton.2010.176, 10.1038/NPHOTON.2010.176]; FIENUP JR, 1982, APPL OPTICS, V21, P2758, DOI 10.1364/AO.21.002758; Fink J, 2013, REP PROG PHYS, V76, DOI 10.1088/0034-4885/76/5/056502; Forst M, 2011, PHYS REV B, V84, DOI 10.1103/PhysRevB.84.241104; Frank Matthias, 2014, IUCrJ, V1, P95, DOI 10.1107/S2052252514001444; Fraser JS, 2011, P NATL ACAD SCI USA, V108, P16247, DOI 10.1073/pnas.1111325108; Fritz DM, 2007, SCIENCE, V315, P633, DOI 10.1126/science.1135009; Fromme P, 2011, CURR OPIN STRUC BIOL, V21, P509, DOI 10.1016/j.sbi.2011.06.001; Fujiyoshi Y, 2011, J ELECTRON MICROSC, V60, pS149, DOI 10.1093/jmicro/dfr033; Fung R, 2009, NAT PHYS, V5, P64, DOI [10.1038/nphys1129, 10.1038/NPHYS1129]; Geloni G, 2011, J MOD OPTIC, V58, P1391, DOI 10.1080/09500340.2011.586473; Glover TE, 2012, NATURE, V488, P603, DOI 10.1038/nature11340; Goodman J., 1985, STAT OPTICS; Gorfman S, 2014, CRYSTALLOGR REV, V20, P210, DOI 10.1080/0889311X.2014.908353; Grenier S, 2007, PHYS REV LETT, V99, DOI 10.1103/PhysRevLett.99.206403; Grunwald M, 2006, PHYS REV LETT, V96, DOI 10.1103/PhysRevLett.96.255701; Hattne J, 2014, NAT METHODS, V11, P545, DOI [10.1038/nmeth.2887, 10.1038/NMETH.2887]; HAUPTMAN H, 1986, ANGEW CHEM INT EDIT, V25, P603, DOI 10.1002/anie.198606031; Hau-Riege SP, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.217402; Helliwell JR, 2014, CRYSTALLOGR REV, V20, P207, DOI 10.1080/0889311X.2014.895896; HENDRICKSON WA, 1988, PROTEINS, V4, P77, DOI 10.1002/prot.340040202; Overington JP, 2006, NAT REV DRUG DISCOV, V5, P993, DOI 10.1038/nrd2199; Hoppe VW, 1969, ACTA CRYSTALLOGR A, V25, P502; Hoppe VW, 1969, ACTA CRYSTALLOGR A, V25, P508; Hoppe VW, 1969, ACTA CRYSTALLOGR A, V25, P495, DOI DOI 10.1107/S0567739469000088; Huber T, 2014, PHYS REV LETT; Hunter MS, 2011, METHODS, V55, P387, DOI 10.1016/j.ymeth.2011.12.006; Ishikawa T, 2012, NAT PHOTONICS, V6, P540, DOI 10.1038/nphoton.2012.141; Jiang LH, 2011, MICROSC MICROANAL, V17, P879, DOI 10.1017/S1431927611012244; Johnson SL, 2010, ACTA CRYSTALLOGR A, V66, P157, DOI 10.1107/S0108767309053859; Johnson SL, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.037203; Johnson SL, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.175503; Johnson SL, 2013, PHYS REV B, V87, DOI 10.1103/PhysRevB.87.054301; Juve V, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.217401; KAM Z, 1977, MACROMOLECULES, V10, P927, DOI 10.1021/ma60059a009; KARLE J, 1986, SCIENCE, V232, P837, DOI 10.1126/science.232.4752.837; KARLE J, 1989, ACTA CRYSTALLOGR A, V45, P303, DOI 10.1107/S0108767388013042; Kern J, 2013, SCIENCE, V340, P491, DOI 10.1126/science.1234273; Kern J, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0590; Kewish CM, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/035005; Kirian RA, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0331; Kirian RA, 2010, OPT EXPRESS, V18, P5713, DOI 10.1364/OE.18.005713; Kubacka T, 2014, SCIENCE, V343, P1333, DOI 10.1126/science.1242862; Larson BC, 2009, DIFFUSE SCATTERING F, P139; Lee WS, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1837; Liu HG, 2013, ACTA CRYSTALLOGR A, V69, P365, DOI 10.1107/S0108767313006016; Liu W, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0314; Liu W, 2013, SCIENCE, V342, P1521, DOI 10.1126/science.1244142; Liu W, 2005, J STRUCT BIOL, V150, P23, DOI 10.1016/j.jsb.2004.12.007; Lovesey SW, 2005, PHYS REP, V411, P233, DOI 10.1016/j.physrep.2005.01.003; MADEY JMJ, 1971, J APPL PHYS, V42, P1906, DOI 10.1063/1.1660466; Marmiroli B, 2014, J SYNCHROTRON RADIAT, V21, P193, DOI 10.1107/S1600577513027951; McNeil BWJ, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.134802; Milathianaki D, 2013, SCIENCE, V342, P220, DOI 10.1126/science.1239566; Millane RP, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0498; Miller RJD, 2014, SCIENCE, V343, P1108, DOI 10.1126/science.1248488; Moffat K, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0568; Mohr-Vorobeva E, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.036403; Nave C, 2005, J SYNCHROTRON RADIAT, V12, P299, DOI 10.1107/S0909049505003274; Nederlof I, 2013, ACTA CRYSTALLOGR D, V69, P852, DOI 10.1107/S0907444913002734; Neutze R, 2000, NATURE, V406, P752, DOI 10.1038/35021099; Newton MA, 2012, J AM CHEM SOC, V134, P5036, DOI 10.1021/ja2114163; Patterson BD, 2014, CHIMIA, V68, P73, DOI 10.2533/chimia.2014.73; Patterson BD, 2010, SLACTN10026; Pedrini B, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms2622; Pedrini B, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0500; Pfeifer T, 2010, OPT LETT, V35, P3441, DOI 10.1364/OL.35.003441; Ping Y, 2013, PHYS REV LETT, V111, DOI 10.1103/PhysRevLett.111.065501; Qiu H, 2000, BIOMATERIALS, V21, P223, DOI 10.1016/S0142-9612(99)00126-X; Reiche S, 1998, 20 INT FEL C WILL; Rohringer N, 2012, NATURE, V481, P488, DOI 10.1038/nature10721; Saldin DK, 2011, OPT EXPRESS, V19, P17318, DOI 10.1364/OE.19.017318; Saldin EL, 2001, NUCL INSTRUM METH A, V475, P357, DOI 10.1016/S0168-9002(01)01539-X; Samaras M, 2003, J NUCL MATER, V323, P213, DOI 10.1016/j.jnucmat.2003.08.020; Schlichting I, 2012, CURR OPIN STRUC BIOL, V22, P1; Schotte F, 2003, SCIENCE, V300, P1944, DOI 10.1126/science.1078797; Shi D, 2013, ELIFE, V2, DOI 10.7554/eLife.01345; Shvyd'ko Y, 2012, PHYS REV SPEC TOP-AC, V15, DOI 10.1103/PhysRevSTAB.15.100702; Shwartz S, 2014, PHYS REV LETT, V112, DOI 10.1103/PhysRevLett.112.163901; Sokolowski-Tinten K, 2001, PHYS REV LETT, V87, part. no., DOI 10.1103/PhysRevLett.87.225701; Sokolowski-Tinten K, 2004, J PHYS-CONDENS MAT, V16, pR1517, DOI 10.1088/0953-8984/16/49/R04; Sokolowski-Tinten K, 2003, NATURE, V422, P287, DOI 10.1038/nature01490; Son SK, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.218102; Spence JCH, 2012, REP PROG PHYS, V75, DOI 10.1088/0034-4885/75/10/102601; Spence JCH, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0309; Spence JCH, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0325; Spence JCH, 2011, OPT EXPRESS, V19, P2866, DOI 10.1364/OE.19.002866; Starodub D, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2288; Staub U, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.126402; Staub U, 2008, J SYNCHROTRON RADIAT, V15, P469, DOI 10.1107/S0909049508019614; Stellato F, 2014, IUCRJ PHYS FELS, DOI [10.1107/S2052252514010070, DOI 10.1107/S2052252514010070]; Svergun DI, 2003, REP PROG PHYS, V66, P1735, DOI 10.1088/0034-4885/66/10/R05; Szlachetko J, 2012, CHEM COMMUN, V48, P10898, DOI 10.1039/c2cc35086f; Tamasaku K, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.254801; Thibault P, 2008, SCIENCE, V321, P379, DOI 10.1126/science.1158573; Trigo M, 2013, NAT PHYS, V9, P790, DOI [10.1038/nphys2788, 10.1038/NPHYS2788]; Von Dreele RB, 2007, J APPL CRYSTALLOGR, V40, P133, DOI 10.1107/S0021889806045493; Weierstall U, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms4309; White TA, 2012, J APPL CRYSTALLOGR, V45, P335, DOI 10.1107/S0021889812002312; White TA, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0330; Wilkins SB, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.167205; Wittenberg JS, 2014, NANO LETT, V14, P1995, DOI 10.1021/nl500043c; Woerner M, 2010, J CHEM PHYS, V133, DOI 10.1063/1.3469779; Yang X, 2013, PHYS REV SPEC TOP-AC, V16, DOI 10.1103/PhysRevSTAB.16.120701; Yano J, 2008, INORG CHEM, V47, P1711, DOI 10.1021/ic7016837; Yefanov O, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0333; Zachariasen WH, 1944, THEORY XRAY DIFFRACT 151 0 0 TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0889-311X 1476-3508 CRYSTALLOGR REV Crystallogr. Rev OCT 2014 20 4 242 294 10.1080/0889311X.2014.939649 53 Crystallography Crystallography AN2DE WOS:000340393000002 J Bannour, H; Hudelot, C Bannour, Hichem; Hudelot, Celine Building and using fuzzy multimedia ontologies for semantic image annotation MULTIMEDIA TOOLS AND APPLICATIONS English Article Image annotation; Multimedia ontology; Ontology building; Ontological reasoning; Fuzzy DL; Spatial information; Contextual information DESCRIPTION LOGICS; RETRIEVAL This paper proposes a methodology for building fuzzy multimedia ontologies dedicated to image annotation. The built ontology incorporates visual, conceptual, contextual and spatial knowledge about image concepts in order to model image semantics in an effective way. Indeed, our approach uses visual and conceptual information to build a semantic hierarchy that will serve as a backbone of our ontology. Contextual and spatial information about image concepts are then computed and incorporated in the ontology in order to model richer semantic relationships between these concepts. Fuzzy description logics are used as a formalism to represent our ontology and the inherent uncertainty and imprecision of this kind of information. Subsequently, we propose a new approach for image annotation based on hierarchical image classification and a multi-stage reasoning framework for reasoning about the consistency of the produced annotation. In this approach, fuzzy ontological reasoning is used in order to achieve a semantically relevant decision on the belonging of a given image to the set of concepts from the annotation vocabulary. An empirical evaluation of our approach on Pascal VOC'2009 and Pascal VOC'2010 datasets has shown a significant improvement on the average precision results. [Bannour, Hichem; Hudelot, Celine] Ecole Cent Paris, MAS Lab, F-92295 Chatenay Malabry, France Bannour, H (reprint author), Ecole Cent Paris, MAS Lab, F-92295 Chatenay Malabry, France. hichem.bannour@ecp.fr; celine.hudelot@ecp.fr Baader F, 2003, DESCRIPTION LOGIC HD; Bannour H, 2012, P 21 ACM INT C INF K, P2431; Bannour H, 2011, CONT BAS MULT IND CB; Bannour H, 2012, LECT NOTES COMPUT SC, V7131, P4; Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214; Bart E, 2008, COMPUTER VISION PATT; Bloch I, 2005, IMAGE VISION COMPUT, V23, P89, DOI 10.1016/j.imavis.2004.06.013; Bobillo F, 2011, INFORM SCIENCES, V181, P758, DOI 10.1016/j.ins.2010.10.020; Carneiro G, 2007, IEEE T PATTERN ANAL, V29, P394, DOI 10.1109/TPAMI.2007.61; Choi MJ, 2010, PROC CVPR IEEE, P129, DOI 10.1109/CVPR.2010.5540221; CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411; Dasiopoulou S, 2010, MULTIMED TOOLS APPL, V46, P331, DOI 10.1007/s11042-009-0387-4; Dasiopoulou S, 2009, LECT NOTES COMPUT SC, V5850, P105, DOI 10.1007/978-3-642-10562-3_4; Deng J, 2009, COMPUTER VISION PATT; Everingham M, 2010, PASCAL VISUAL OBJECT; Everingham M, 2009, PASCAL VISUAL OBJECT; Fan JP, 2008, IEEE T IMAGE PROCESS, V17, P407, DOI 10.1109/TIP.2008.916999; Griffin G, 2008, COMPUTER VISION PATT; Gruber TR, 1995, INT J HUM-COMPUT ST, V43, P907, DOI 10.1006/ijhc.1995.1081; Gupta A, 2012, NEURAL INF PROCESS, V7667, P196; Hauptmann A, 2007, INT C IM VID RETR CI; Hollink L, 2004, INT WORKSH KNOWL MAR; Horridge M., 2011, SEMANTIC WEB, V2, P11; Hudelot C, 2010, FRONT ARTIF INTEL AP, V215, P497, DOI 10.3233/978-1-60750-606-5-497; Hudelot C, 2008, FUZZY SET SYST, V159, P1929, DOI 10.1016/j.fss.2008.02.011; Kompatsiaris Y, 2008, SEMANTIC MULTIMEDIA; Lavrenko V, 2003, NEURAL INFORM PROCES; Li Fei-Fei, 2005, P IEEE COMP SOC C CO, V2, P524; Li LJ, 2010, COMPUTER VISION PATT; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Lowe DG, 1999, INT C COMP VIS ICCV; Marszalek M, 2007, COMPUTER VISION PATT; Simou N, 2008, SIGNAL IMAGE VIDEO P, V2, P321, DOI 10.1007/s11760-008-0084-1; Simou N, 2005, WIAMIS; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Spaccapietra S, 2004, BRAZ S GEOINF; Stoilos G, 2007, WORKSH OWL EXP DIR O; Straccia U, 2001, J ARTIF INTELL RES, V14, P137; Straccia U, 2006, CAPTURING INTELLIGEN, V1, P73, DOI 10.1016/S1574-9576(06)80006-7; Straccia U, 2010, INT SYM MVL, P319, DOI 10.1109/ISMVL.2010.65; Straccia U, 2012, ARXIVABS12071410 COR; Tousch AM, 2012, PATTERN RECOGN, V45, P333, DOI 10.1016/j.patcog.2011.05.017; Wu L, 2012, IEEE T PATTERN ANAL, V34, P863, DOI 10.1109/TPAMI.2011.195; Xiao JX, 2010, PROC CVPR IEEE, P3485, DOI 10.1109/CVPR.2010.5539970; Yang JC, 2010, LECT NOTES COMPUT SC, V6315, P113; Yao BZ, 2010, P IEEE, V98, P1485, DOI 10.1109/JPROC.2010.2050411; Zhou X, 2010, EUR C COMP VIS ECCV 47 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. OCT 2014 72 3 2107 2141 10.1007/s11042-013-1491-z 35 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AN4IE WOS:000340550300003 J Zghal, HB; Moreno, A Zghal, Hajer Baazaoui; Moreno, Antonio A system for information retrieval in a medical digital library based on modular ontologies and query reformulation MULTIMEDIA TOOLS AND APPLICATIONS English Article Ontology; Semantic information retrieval; Medical digital library; Query reformulation WEB Ontologies have proven to be useful in the area of Information Retrieval and the biomedical informatics community has acknowledged, in recent years, their utility. However, building and updating manually ontologies is a long and tedious task. This paper proposes a system that allows any search engine to develop its semantic layer by applying ontology learning techniques on Web snippets and applies it to a well-known medical digital library, PubMed. The new system (SemPubMed) automatically builds new ontology fragments related to the user's query and then it reformulates queries using the new concepts in order to improve information retrieval. Our system has endured a twofold evaluations. On the one hand, we have evaluated the quality of the modular ontologies built by the system. On the other hand, we have studied how the semantic reformulation of the queries has led to an improvement of the quality of the results given by PubMed, both in terms of precision and recall. Obtained results show that adding semantic layer to PubMed enables an improvement of query reformulation and predicted ranking score. [Zghal, Hajer Baazaoui] Manouba Univ, Riadi GDL Lab, Tunis, Tunisia; [Moreno, Antonio] Univ Rovira & Virgili, ITAKA Res Grp, E-43007 Tarragona, Spain Zghal, HB (reprint author), Manouba Univ, Riadi GDL Lab, Tunis, Tunisia. hajer.baazaouizghal@riadi.rnu.tn; antonio.moreno@urv.cat Spanish-Tunisian AECID [A/030058/10] This research work has been supported by the Spanish-Tunisian AECID project number A/030058/10, "A framework for the integration of Ontology Learning and Semantic Search". Baruzzo A, 2009, J DIGIT INF, V10; Ben Mustapha Nesrine, 2012, Model and Data Engineering. Proceedings of the 2nd International Conference, MEDI 2012, DOI 10.1007/978-3-642-33609-6_9; Ben Mustapha N, 2011, LECT NOTES COMPUT SC, V6882, P538, DOI 10.1007/978-3-642-23863-5_55; Berland M, 1999, P 37 ANN M ASS COMP, P57, DOI 10.3115/1034678.1034697; Bettembourg Charles, 2012, J Biomed Semantics, V3, P7, DOI 10.1186/2041-1480-3-7; Boldi P, 2011, INFORM RETRIEVAL, V14, P257, DOI 10.1007/s10791-010-9155-3; Christopher D, 1999, FDN STAT NATURAL LAN; Corby O, 2004, P 16 EUR C ART INT E, P705; Elloumi-Chaabene Manel, 2011, Proceedings of the 6th International Conference on Software and Database Technologies. ICSOFT 2011; Ferran N, 2005, LIB MANAGEMENT, V26, P206, DOI 10.1108/01435120510596062; Kafsi S, 2012, SEM PUBMED SEMANTIC, P1932; Kiefer S, 2011, LECT NOTES COMPUT SC, V7046, P382; Mastora A, 2008, LNCS, V5173, P427; Mayr P, 2007, REDUCING SEMANTIC CO, P213; Perez-Carballo J, 2011, DESIGN PRINCIPLES HE; Price Colin, 2010, International Journal of Biomedical Engineering and Technology, V3, DOI 10.1504/IJBET.2010.032701; Sanchez D, 2008, DATA KNOWL ENG, V64, P600, DOI 10.1016/j.datak.2007.10.001; Sanchez D, 2008, AI COMMUN, V21, P27; Sanchez D, 2012, EXPERT SYST APPL, V39, P5792, DOI 10.1016/j.eswa.2011.11.088; Sanchez David, 2007, International Journal of Metadata, Semantics and Ontologies, V2, DOI 10.1504/IJMSO.2007.016805; Suomela S, 2005, LECT NOTES COMPUT SC, V3408, P315; Swe Thinn Mya Mya, 2011, COMPUTER SCI INFORM, V1-2; Tan P-N, 2005, INTRO DATA MINING; Turney P.D., 2001, LNCS, V2167, P491; Vallet D, 2005, LECT NOTES COMPUT SC, V3532, P455; Yu H, 2009, CIKM, P2099; Zhao PX, 2005, LECT NOTES COMPUT SC, V3453, P699 27 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. OCT 2014 72 3 2393 2412 10.1007/s11042-013-1527-4 20 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AN4IE WOS:000340550300016 J Soysal, M; Logoglu, KB; Tekin, M; Esen, E; Saracoglu, A; Acar, BO; Ozan, EC; Ates, TK; Sevimli, H; Sevinc, M; Atil, I; Ozkan, S; Arabaci, MA; Tankiz, S; Karadeniz, T; Onur, D; Selcuk, S; Alatan, AA; Ciloglu, T Soysal, Medeni; Logoglu, K. Berker; Tekin, Mashar; Esen, Ersin; Saracoglu, Ahmet; Acar, Banu Oskay; Ozan, Ezgi Can; Ates, Tugrul K.; Sevimli, Hakan; Sevinc, Muge; Atil, Ilkay; Ozkan, Savas; Arabaci, Mehmet Ali; Tankiz, Seda; Karadeniz, Talha; Onur, Duygu; Selcuk, Sezin; Alatan, A. Aydin; Ciloglu, Tolga Multimodal concept detection in broadcast media: KavTan MULTIMEDIA TOOLS AND APPLICATIONS English Article Intelligent multimedia systems; Concept detection; Broadcast video indexing; Multimodal semantic indexing RELEVANCE FEEDBACK; IMAGE RETRIEVAL; AUDIO CLASSIFICATION; FEATURES; RECOGNITION; ALGORITHMS; VIDEO; NEWS Concept detection stands as an important problem for efficient indexing and retrieval in large video archives. In this work, the KavTan System, which performs high-level semantic classification in one of the largest TV archives of Turkey, is presented. In this system, concept detection is performed using generalized visual and audio concept detection modules that are supported by video text detection, audio keyword spotting and specialized audio-visual semantic detection components. The performance of the presented framework was assessed objectively over a wide range of semantic concepts (5 high-level, 14 visual, 9 audio, 2 supplementary) by using a significant amount of precisely labeled ground truth data. KavTan System achieves successful high-level concept detection performance in unconstrained TV broadcast by efficiently utilizing multimodal information that is systematically extracted from both spatial and temporal extent of multimedia data. [Soysal, Medeni; Logoglu, K. Berker; Tekin, Mashar; Esen, Ersin; Saracoglu, Ahmet; Acar, Banu Oskay; Ozan, Ezgi Can; Ates, Tugrul K.; Sevimli, Hakan; Sevinc, Muge; Atil, Ilkay; Ozkan, Savas; Arabaci, Mehmet Ali; Tankiz, Seda; Karadeniz, Talha; Onur, Duygu; Selcuk, Sezin; Alatan, A. Aydin; Ciloglu, Tolga] TUBITAK UZAY, Ankara, Turkey Soysal, M (reprint author), TUBITAK UZAY, METU Campus, Ankara, Turkey. medenis@gmail.com Akbani R, 2004, LECT NOTES COMPUT SC, V3201, P39; Ates T. K., 2011, 2011 IEEE 19th Signal Processing and Communications Applications Conference (SIU 2011), DOI 10.1109/SIU.2011.5929823; Barrington L, 2007, INT CONF ACOUST SPEE, P725; Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014; Biatov K, 2008, P ICSPCS, P1; Chang S, 2008, P TRECVID; Chang YC, 2006, IEEE T EVOLUT COMPUT, V10, P617, DOI 10.1109/TEVC.2005.863130; Chang P., 2010, Proceedings of the 2010 DoD High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC 2010), DOI [10.1109/HPCMP-UGC.2010.59, 10.1109/ICIIC.2010.25]; Cheng J, 2000, INT C PATT RECOG, P668; Chu S, 2009, IEEE T AUDIO SPEECH, V17, P1142, DOI 10.1109/TASL.2009.2017438; Clarin C, 2006, P PCSC CIT, V6, P150; Clavel C, 2005, P IEEE INT C MULT EX, P1306; Crandall D, 2004, PROC CVPR IEEE, P379; Dalal N, 2005, PROC CVPR IEEE, P886; Deselaers T, 2008, P 19 INT C PATT REC, P1; Fergus R, 2003, PROC CVPR IEEE, P264; Ghimire D, 2010, P PSIVT, P422; GOTLIEB CC, 1990, COMPUT VISION GRAPH, V51, P70, DOI 10.1016/S0734-189X(05)80063-5; Huang J, 1997, PROC CVPR IEEE, P762; Huang RQ, 2006, IEEE T AUDIO SPEECH, V14, P907, DOI 10.1109/TSA.2005.858057; Huttenlocher D P, 1993, PATTERN ANAL MACHINE, V15, P850; Jansohn C, 2009, P 17 ACM INT C MULT, P601, DOI 10.1145/1631272.1631366; Jia W, 2006, P SMC, V3, P2413; Jiang Y, 2007, P ACM INT C IM VID R, P494; Jones M, 1999, P CVPR, V1; Jones M, 2003, P ICCV; Jones MJ, 2002, INT J COMPUT VISION, V46, P81, DOI 10.1023/A:1013200319198; Lin CC, 2005, IEEE T SPEECH AUDI P, V13, P644, DOI 10.1109/TSA.2005.851880; Liu Y, 2009, P 12 INT C COMP INF, P404; Lopes A, 2009, P ESPC CIT; Lopes APB, 2009, SIBGRAPI, P224, DOI 10.1109/SIBGRAPI.2009.32; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Mamou J, 2007, P SIGIR, P615, DOI 10.1145/1277741.1277847; Manjunath B, 2002, INTRO MPEG 7 MULTIME, V1; Mesaros A, 2010, P 1010 EUR SIGN PROC, P1267; Mikolajczyk K, 2004, LECT NOTES COMPUT SC, V3021, P69; MPEG, 2001, 15938 ISOIEC; Muller H, 2000, INT C PATT RECOG, P1043; Nam J, 1998, P ICIP 98, V1, P353, DOI 10.1109/ICIP.1998.723496; Over P, 2011, P TRECVID; Ozan E, 2011, P SIU, P391; Peng Y, 2008, P TRECVID, V3; Petridis S, 2010, LECT NOTES ARTIF INT, V6040, P399; Phan R, 2010, COMPUT VIS IMAGE UND, V114, P66, DOI 10.1016/j.cviu.2009.07.004; Phan R, 2008, P CCECE 2008; Phillips PJ, 2000, IEEE T PATTERN ANAL, V22, P1090, DOI 10.1109/34.879790; Portelo J, 2009, INT CONF ACOUST SPEE, P1973, DOI 10.1109/ICASSP.2009.4959998; Rocchio JJ, 1971, PRENTICE HALL SERIES, P313; Saracoglu A, 2010, P SIU, P621; Saracoglu A, 2006, P SIU, P1; Scholkopf B, 2000, NEURAL COMPUT, V12, P1207, DOI 10.1162/089976600300015565; Smeaton AF, 2009, SIGNALS COMMUN TECHN, P151, DOI 10.1007/978-0-387-76569-3_6; Snoek CGM, 2007, IEEE T MULTIMEDIA, V9, P280, DOI 10.1109/TMM.2006.886275; Snoek CGM, 2006, IEEE T PATTERN ANAL, V28, P1678, DOI 10.1109/TPAMI.2006.212; Snoek CGM, 2010, P TRECVID; Stricker M., 1995, Proceedings of the SPIE - The International Society for Optical Engineering, V2420, DOI 10.1117/12.205308; Sundaram S, 2008, INT CONF ACOUST SPEE, P49, DOI 10.1109/ICASSP.2008.4517543; Tao L., 2004, P IEEE COMP SOC INT, V2, P138; van de Sande KEA, 2010, IEEE T PATTERN ANAL, V32, P1582, DOI 10.1109/TPAMI.2009.154; Viola M, 2003, P CVPR; Viola P, 2001, PROC CVPR IEEE, P511; Wang Y, 2000, IEEE SIGNAL PROC MAG, V17, P12; Wu P., 1999, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries (CBAIVL'99), DOI 10.1109/IVL.1999.781114; Yilmaz E, 2006, P 15 ACM INT C INF K, P102, DOI 10.1145/1183614.1183633; Yoon JH, 2001, IEEE IMAGE PROC, P42; You JY, 2010, SIGNAL PROCESS-IMAGE, V25, P287, DOI 10.1016/j.image.2010.02.001; Zhou XS, 2003, MULTIMEDIA SYST, V8, P536, DOI 10.1007/s00530-002-0070-3; Zubari U, 2010, EUSIPCO; Zuo HQ, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4, P37 69 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. OCT 2014 72 3 2787 2832 10.1007/s11042-013-1564-z 46 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AN4IE WOS:000340550300032 J Hou, SJ; Zhou, SB; Siddique, MA Hou, Sujuan; Zhou, Shangbo; Siddique, Muhammad Abubakar A compressed sensing approach for query by example video retrieval MULTIMEDIA TOOLS AND APPLICATIONS English Article Video retrieval; Video signal processing; Compressed sensing; Similarity measure IMAGE RETRIEVAL; REPRESENTATION; INFORMATION; DOMAIN Recently, compressed Sensing (CS) has theoretically been proposed for more efficient signal compression and recovery. In this paper, the CS based algorithms are investigated for Query by Example Video Retrieval (QEVR) and a novel similarity measure approach is proposed. Combining CS theory with the traditional discrete cosine transform (DCT), better compression efficiency for spatially sparse is achieved. The similarity measure from three levels (frame level, shot level and video level, respectively) is also discussed. For several different kinds of natural videos, the experimental results demonstrate the effectiveness of system by the proposed method. [Hou, Sujuan; Zhou, Shangbo; Siddique, Muhammad Abubakar] Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China; [Hou, Sujuan; Zhou, Shangbo] Chongqing Univ, Key Lab Dependable Serv Comp Cyber Phys Soc, Minist Educ, Chongqing 400030, Peoples R China Zhou, SB (reprint author), Chongqing Univ, Coll Comp Sci, Chongqing 400030, Peoples R China. shbzhou@cqu.edu.cn National Natural Science Foundation of China [61103114, 61004112] This work was supported by National Natural Science Foundation of China (Grant No. 61103114 and 61004112). Borgnat P, 2008, INT CONF ACOUST SPEE, P3785, DOI 10.1109/ICASSP.2008.4518477; Browne P, 2005, P INT C IM PROC 2005, P1208; Candes EJ, 2008, IEEE SIGNAL PROC MAG, V25, P21, DOI 10.1109/MSP.2007.914731; Candes EJ, 2006, IEEE T INFORM THEORY, V52, P489, DOI 10.1109/TIT.2005.862083; Chan WL, 2008, OPT LETT, V33, P974, DOI 10.1364/OL.33.000974; Chen X, 2012, IEEE T MULTIMEDIA, V14, P3, DOI 10.1109/TMM.2011.2167223; Donoho DL, 2006, IEEE T INFORM THEORY, V52, P1289, DOI 10.1109/TIT.2006.871582; Feng GC, 2003, J ELECTRON IMAGING, V12, P390, DOI 10.1117/1.1579699; Gan L, 2008, P EUSIPCO; Gao SH, 2010, PROC CVPR IEEE, P3555, DOI 10.1109/CVPR.2010.5539943; Haupt J, 2008, IEEE SIGNAL PROC MAG, V25, P92, DOI 10.1109/MSP.2007.914732; Hou SJ, 2012, OPT ENG, V51, DOI 10.1117/1.OE.51.4.047405; Karpenko A, 2011, IEEE T PATTERN ANAL, V33, P618, DOI 10.1109/TPAMI.2010.118; Kekre H. B., 2009, ICGST INT J GRAPHICS, V9, P1; Kekre HB, 2010, INT J ENG SCI TECHNO, V2, P362; Kompatsiaris I, 2011, MULTIMED TOOLS APPL, V55, P1, DOI 10.1007/s11042-010-0618-8; Kong J, 2010, P 3 IEEE INT C COMP, P701; Lazebnik S, 2003, CVPR, V2, P319; Lienhart R, 1997, P SOC PHOTO-OPT INS, V3312, P271, DOI 10.1117/12.298460; Liu Z, 2010, P 17 INT C IM PROC; Lu J, 2011, IEEE MULTIMEDIA, V18, P8, DOI 10.1109/MMUL.2011.52; Mandal MK, 1999, IMAGE VISION COMPUT, V17, P513, DOI 10.1016/S0262-8856(98)00143-7; Miao J, 2013, MAGN RESON IMAGING, V31, P75, DOI 10.1016/j.mri.2012.06.028; Obdrzalek S, 2003, LECT NOTES COMPUT SC, V2781, P490; Pang L, 2011, MULTIMED TOOLS APPL, V55, P151, DOI 10.1007/s11042-010-0605-0; PRAKS P, 2008, IEEE IMAGE PROC, P25; Willett RM, 2007, ELECT IMAGING 2007; Wu J, 2012, IEEE T MULTIMEDIA, V14, P291, DOI 10.1109/TMM.2011.2174969; Yeh MC, 2011, IEEE T MULTIMEDIA, V13, P320, DOI 10.1109/TMM.2010.2094999; Yuso Y, 2000, P BRIT MACH VID C BR; Zhang Y, 2008, IEEE INT C AC SPEECH; Zhang Y, 2008, IEEE INT S CIRC SYST; Zhao SJ, 2011, MULTIMED TOOLS APPL, V55, P105, DOI 10.1007/s11042-010-0602-3; Zhao X, 2011, IEEE T IMAGE PROCESS, V20, P790, DOI 10.1109/TIP.2010.2068553 34 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. OCT 2014 72 3 3031 3044 10.1007/s11042-013-1573-y 14 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AN4IE WOS:000340550300040 J Vaizman, Y; McFee, B; Lanckriet, G Vaizman, Yonatan; McFee, Brian; Lanckriet, Gert Codebook-Based Audio Feature Representation for Music Information Retrieval IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Audio content representations; music information retrieval; music recommendation; sparse coding; vector quantization CLASSIFICATION; SIMILARITY; MODEL Digital music has become prolific in the web in recent decades. Automated recommendation systems are essential for users to discover music they love and for artists to reach appropriate audience. When manual annotations and user preference data is lacking (e. g. for new artists) these systems must rely on content based methods. Besides powerful machine learning tools for classification and retrieval, a key component for successful recommendation is the audio content representation. Good representations should capture informative musical patterns in the audio signal of songs. These representations should be concise, to enable efficient (low storage, easy indexing, fast search) management of huge music repositories, and should also be easy and fast to compute, to enable real-time interaction with a user supplying new songs to the system. Before designing new audio features, we explore the usage of traditional local features, while adding a stage of encoding with a pre-computed codebook and a stage of pooling to get compact vectorial representations. We experiment with different encoding methods, namely the LASSO, vector quantization (VQ) and cosine similarity (CS). We evaluate the representations' quality in two music information retrieval applications: query-by-tag and query-by-example. Our results show that concise representations can be used for successful performance in both applications. We recommend using top-VQ encoding, which consistently performs well in both applications, and requires much less computation time than the LASSO. [Vaizman, Yonatan; Lanckriet, Gert] Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA; [McFee, Brian] Columbia Univ, Ctr Jazz Studies, New York, NY 10027 USA; [McFee, Brian] Columbia Univ, LabROSA, New York, NY 10027 USA Vaizman, Y (reprint author), Univ Calif San Diego, Dept Elect & Comp Engn, La Jolla, CA 92093 USA. yvaizman@eng.ucsd.edu; brm2132@columbia.edu; gert@ece.ucsd.edu AUCOUTURIER J.-J., 2002, P 3 INT C MUS INF RE, P157; Barrington L., 2008, P INT SOC MUS INF RE, P723; Berenzweig A, 2004, COMPUT MUSIC J, V28, P63, DOI 10.1162/014892604323112257; Bertin-Mahieux T, 2008, J NEW MUSIC RES, V37, P115, DOI 10.1080/09298210802479250; Bertin-Mahieux T., 2012, P 13 INT C MUS INF R; Boyd Stephen, 2010, Foundations and Trends in Machine Learning, V3, DOI 10.1561/2200000016; Celma O, 2010, MUSIC RECOMMENDATION AND DISCOVERY, P1, DOI 10.1007/978-3-642-13287-2; Coates A., 2011, P INT C MACH LEARN I; Coates A., 2010, J MACH LEARN JMLR, V15, P48109; Coviello E., 2012, NEUR INF PROC SYST N; Coviello E., 2012, P 13 INT SOC MUS INF; Coviello E, 2011, IEEE T AUDIO SPEECH, V19, P1343, DOI 10.1109/TASL.2010.2090148; Eck D., 2007, ADV NEURAL INF PROCE; Ellis D. P., 2007, P 8 INT C MUS INF RE, P339; Ellis K, 2013, IEEE T AUDIO SPEECH, V21, P2554, DOI 10.1109/TASL.2013.2279318; Joder C, 2009, IEEE T AUDIO SPEECH, V17, P174, DOI 10.1109/TASL.2008.2007613; Flexer A., 2006, P 7 INT C MUS INF RE, P111; Foote J. T., 1997, INT SOC OPT PHOTON V, P138; Grosse R., 2007, P C UNC AI; Hamel P., 2011, P INT SOC MUS INF RE; Hamel P., 2010, P INT SOC MUS INF RE; Henaff M., 2011, P INT S MUS INF RETR, P681; Hoffman M., 2008, P INT C MUS INF RETR, P349; Hong M., 2012, ARXIV12083922; Jaccard P., 1901, B SOC VAUD SCI NAT, V37, P547; Jebara T, 2004, J MACH LEARN RES, V5, P819; Logan B., 2001, P IEEE INT C MULT EX, P745; Logan B., 2000, P INT SOC MUS INF RE, V28; Lyon RF, 2010, NEURAL COMPUT, V22, P2390, DOI 10.1162/NECO_a_00011; Mairal J, 2010, J MACH LEARN RES, V11, P19; Mandel M., 2008, P 9 INT S MUS INF RE, P577; Mandel MI, 2006, MULTIMEDIA SYST, V12, P3, DOI 10.1007/s00530-006-0032-2; Manzagol A., 2008, P INT SOC MUS INF RE; McFee B., 2010, P 27 INT C MACH LEAR; McFee B, 2012, IEEE T AUDIO SPEECH, V20, P2207, DOI 10.1109/TASL.2012.2199109; McKinney M.F., 2003, P ISMIR, V3, P151; Meng A., 2005, P INT C MUS INF RETR, P604; Nam J., 2012, P INT SOC MUS INF RE, P565; Reed J., 2006, P ISMIR, P89; Slaney M., 2008, P INT C MUS INF RETR, P313; Smith EC, 2006, NATURE, V439, P978, DOI 10.1038/nature04485; Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267; Tingle D., 2010, P MIR NEW YORK NY US; Tomasik B., 2009, P INT SOC MUS INF RE, P405; Turnbull D, 2008, IEEE T AUDIO SPEECH, V16, P467, DOI 10.1109/TASL.2007.913750; Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560; Wulfing J., 2012, P INT SOC MUS INF RE, P139; Yang Y., 2012, P ECCV, P722; Yeh C., 2012, P ICMR; Yeh C.-C. M., 2013, P ICASSP, P246; Yoshii K, 2008, IEEE T AUDIO SPEECH, V16, P435, DOI 10.1109/TASL.2007.911503 51 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. OCT 2014 22 10 1483 1493 10.1109/TASLP.2014.2337842 11 Acoustics; Engineering, Electrical & Electronic Acoustics; Engineering AM7HS WOS:000340037200003 J Wang, J; Xu, XG; Ding, SG; Zeng, J; Spurr, R; Liu, X; Chance, K; Mishchenko, M Wang, Jun; Xu, Xiaoguang; Ding, Shouguo; Zeng, Jing; Spurr, Robert; Liu, Xiong; Chance, Kelly; Mishchenko, Michael A numerical testbed for remote sensing of aerosols, and its demonstration for evaluating retrieval synergy from a geostationary satellite constellation of GEO-CAPE and GOES-R JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER English Article Testbed of remote sensing of aerosols; GEO-CAPE; TEMPO; GOES-R; Optimization; Linearized codes for radiative transfer and scattering ABSORPTION CROSS-SECTIONS; OZONE UV SPECTROSCOPY; RADIATIVE-TRANSFER; NEXT-GENERATION; MISSION; ALGORITHM; CAPABILITIES; TEMPERATURE; SENSITIVITY; MODELS We present a numerical testbed for remote sensing of aerosols, together with a demonstration for evaluating retrieval synergy from a geostationary satellite constellation. The testbed combines inverse (optimal-estimation) software with a forward model containing linearized code for computing particle scattering (for both spherical and non-spherical particles), a kernel-based (land and ocean) surface bi-directional reflectance facility, and a linearized radiative transfer model for polarized radiance. Calculation of gas absorption spectra uses the HITRAN (High-resolution TRANsmission molecular absorption) database of spectroscopic line parameters and other trace species cross-sections. The outputs of the testbed include not only the Stokes 4-vector elements and their sensitivities (Jacobians) with respect to the aerosol single scattering and physical parameters (such as size and shape parameters, refractive index, and plume height), but also DFS (Degree of Freedom for Signal) values for retrieval of these parameters. This testbed can be used as a tool to provide an objective assessment of aerosol information content that can be retrieved for any constellation of (planned or real) satellite sensors and for any combination of algorithm design factors (in terms of wavelengths, viewing angles, radiance and/or polarization to be measured or used). We summarize the components of the testbed, including the derivation and validation of analytical formulae for Jacobian calculations. Benchmark calculations from the forward model are documented. In the context of NASA's Decadal Survey Mission GEO-CAPE (GEOstationary Coastal and Air Pollution Events), we demonstrate the use of the testbed to conduct a feasibility study of using polarization measurements in and around the O-2 A band for the retrieval of aerosol height information from space, as well as an to assess potential improvement in the retrieval of aerosol fine and coarse mode aerosol optical depth (AOD) through the synergic use of two future geostationary satellites, GOES-R (Geostationary Operational Environmental Satellite R-series) and TEMPO (Tropospheric Emissions: Monitoring of Pollution). Strong synergy between GEOS-R and TEMPO are found especially in their characterization of surface bi-directional reflectance, and thereby, can potentially improve the AOD retrieval to the accuracy required by GEO-CAPE. (C) 2014 Elsevier Ltd. All rights reserved. [Wang, Jun; Xu, Xiaoguang; Ding, Shouguo; Zeng, Jing] Univ Nebraska, Dept Earth & Atmospher Sci, Lincoln, NE 68588 USA; [Spurr, Robert] RT Solut Inc, Cambridge, MA 02138 USA; [Liu, Xiong; Chance, Kelly] Harvard Smithsonian Ctr Astrophys, Cambridge, MA 02138 USA; [Mishchenko, Michael] NASA, Goddard Inst Space Studies, New York, NY 10025 USA Wang, J (reprint author), Univ Nebraska, Dept Earth & Atmospher Sci, 303 Bessey Hall, Lincoln, NE 68588 USA. jwang7@unl.edu NASA Earth Science Division, GEO-CAPE mission study and Glory mission science activities Funding for this study was provided by the NASA Earth Science Division as part of GEO-CAPE mission study and Glory mission science activities. J. Wang is grateful to Jassim (Jay) A. Al-Saadi and Hal H. Maring for their support, and thanks the GEO-CAPE aerosol working group and science working group for their constructive suggestions and fruitful discussions. The Holland Computing Center of University of Nebraska - Lincoln and NASA High End Computing program are acknowledged for their help in computing. Bak J, 2013, ATMOS MEAS TECH, V6, P239, DOI 10.5194/amt-6-239-2013; Bodhaine BA, 1999, J ATMOS OCEAN TECH, V16, P1854, DOI 10.1175/1520-0426(1999)016<1854:ORODC>2.0.CO;2; BRION J, 1993, CHEM PHYS LETT, V213, P610, DOI 10.1016/0009-2614(93)89169-I; Butz A, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047888; Chance K, 2013, PROC SPIE, V8866, DOI 10.1117/12.2024479; Clarisse L, 2013, ATMOS CHEM PHYS, V13, P2195, DOI 10.5194/acp-13-2195-2013; Coulson ADP, POLARIZATION INTENSI, P291; Coulson KL, 1960, TABLES RELATED RAD E; COX C, 1954, J OPT SOC AM, V44, P838, DOI 10.1364/JOSA.44.000838; Crisp D, 2012, ATMOS MEAS TECH, V5, P687, DOI 10.5194/amt-5-687-2012; DAUMONT D, 1992, J ATMOS CHEM, V15, P145, DOI 10.1007/BF00053756; Donlon C, 2012, REMOTE SENS ENVIRON, V120, P37, DOI 10.1016/j.rse.2011.07.024; Dubovik O, 2011, ATMOS MEAS TECH, V4, P975, DOI 10.5194/amt-4-975-2011; Dubuisson P, 2009, REMOTE SENS ENVIRON, V113, P1899, DOI 10.1016/j.rse.2009.04.018; EVANS KF, 1991, J QUANT SPECTROSC RA, V46, P413, DOI 10.1016/0022-4073(91)90043-P; Fishman J, 2012, B AM METEOROL SOC, V93, P1547, DOI 10.1175/BAMS-D-11-00201.1; GARCIA RDM, 1989, J QUANT SPECTROSC RA, V41, P117, DOI 10.1016/0022-4073(89)90133-7; HANSEN JE, 1974, SPACE SCI REV, V16, P527, DOI 10.1007/BF00168069; Hasekamp OP, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005260; Hasekamp OP, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015469; Hess M, 1998, B AM METEOROL SOC, V79, P831, DOI 10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2; Kahn R, 1997, J GEOPHYS RES-ATMOS, V102, P16861, DOI 10.1029/96JD01934; Kahn RA, 2009, IEEE T GEOSCI REMOTE, V47, P4095, DOI 10.1109/TGRS.2009.2023115; Kalashnikova OV, 2011, J QUANT SPECTROSC RA, V112, P2149, DOI 10.1016/j.jqsrt.2011.05.010; Kalashnikova OV, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006756; Kaufman YJ, 2002, NATURE, V419, P215, DOI 10.1038/nature01091; Knobelspiesse K, 2012, OPT EXPRESS, V20, P21457, DOI 10.1364/OE.20.021457; Knobelspiesse K, 2011, ATMOS CHEM PHYS, V11, P6245, DOI 10.5194/acp-11-6245-2011; Kuze A, 2009, APPL OPTICS, V48, P6716, DOI 10.1364/AO.48.006716; Lee TF, 2006, WEATHER FORECAST, V21, P649, DOI 10.1175/WAF935.1; Lenoble J, 2013, AEROSOL REMOTE SENSI; Levy RC, 2007, J GEOPHYS RES-ATMOS, V112, DOI [10.1029/2006JD007811, 10.1029/2006JD007815]; Lucht W, 2000, IEEE T GEOSCI REMOTE, V38, P977, DOI 10.1109/36.841980; Maignan F, 2009, REMOTE SENS ENVIRON, V113, P2642, DOI 10.1016/j.rse.2009.07.022; MALICET J, 1995, J ATMOS CHEM, V21, P263, DOI 10.1007/BF00696758; Martin RV, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD001027; Martonchik JV, 2009, SATELLITE AEROSOL RE; Martonchik JV, 2002, IEEE T GEOSCI REMOTE, V40, P1520, DOI 10.1109/TGRS.2002.801142; McClatchey RA, 1972, AFCRL720497; Mishchenko MI, 1996, APPL OPTICS, V35, P4927, DOI 10.1364/AO.35.004927; Mishchenko MI, 2004, J QUANT SPECTROSC RA, V88, P149, DOI 10.1016/j.jqsrt.2004.03.030; Mishchenko MI, 2007, B AM METEOROL SOC, V88, P677, DOI 10.1175/BAMS-88-5-677; Mishchenko MI, 1998, J QUANT SPECTROSC RA, V60, P309, DOI 10.1016/S0022-4073(98)00008-9; NRC, 2007, EARTH SCI APPL SPAC; Orphal J, 2003, J QUANT SPECTROSC RA, V82, P491, DOI 10.1016/S0022-4073(03)00173-0; Remer LA, 2005, J ATMOS SCI, V62, P947, DOI 10.1175/JAS3385.1; Ricchiazzi P, 1998, B AM METEOROL SOC, V79, P2101, DOI 10.1175/1520-0477(1998)079<2101:SARATS>2.0.CO;2; Rodgers C. D., 2000, INVERSE METHODS ATMO; Rodgers CD, 1996, P SOC PHOTO-OPT INS, V2830, P136, DOI 10.1117/12.256110; Rothman LS, 2013, J QUANT SPECTROSC RA, V130, P4, DOI 10.1016/j.jqsrt.2013.07.002; Schmit TJ, 2005, B AM METEOROL SOC, V86, P1079, DOI 10.1175/BAMS-86-8-1079; Spurr R, 2008, S-P B ENVIRON SCI, P229, DOI 10.1007/978-3-540-48546-9_7; Spurr R, 2012, J QUANT SPECTROSC RA, V113, P425, DOI 10.1016/j.jqsrt.2011.11.014; Stam DL, 1999, J GEOPHYS RES-ATMOS, V104, P16843, DOI 10.1029/1999JD900159; Tanre D, 2011, ATMOS MEAS TECH, V4, P1383, DOI 10.5194/amt-4-1383-2011; Torres O, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008809; van Donkelaar A, 2013, J GEOPHYS RES-ATMOS, V118, P5621, DOI 10.1002/jgrd.50479; Vijay N, 2012, ASTROPHYS J, V748, P28; Wang J, 2004, P 13 C SAT MET OC 20; Wang J, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051136; WANNER W, 1995, J GEOPHYS RES-ATMOS, V100, P21077, DOI 10.1029/95JD02371; Waquet F, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010619; Winker DM, 2010, B AM METEOROL SOC, V91, P1211, DOI 10.1175/2010BAMS3009.1; Xu XG, 2013, J GEOPHYS RES-ATMOS, V118, P6396, DOI 10.1002/jgrd.50515; Zeng J, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035645 65 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0022-4073 1879-1352 J QUANT SPECTROSC RA J. Quant. Spectrosc. Radiat. Transf. OCT 2014 146 SI 510 528 10.1016/j.jqsrt.2014.03.020 19 Spectroscopy Spectroscopy AM2RA WOS:000339697300049 J Calvanese, D; Eiter, T; Ortiz, M Calvanese, Diego; Eiter, Thomas; Ortiz, Magdalena Answering regular path queries in expressive Description Logics via alternating tree-automata INFORMATION AND COMPUTATION English Article Expressive Description Logics; Query answering; Computational complexity; Automata on infinite trees CONJUNCTIVE QUERIES; DECISION PROCEDURE; MODAL-LOGICS; PROGRAMS; DATALOG; SHIQ Expressive Description Logics (DLs) have been advocated as formalisms for modeling the domain of interest in various application areas, including the Semantic Web, data and information integration, peer-to-peer data management, and ontology-based data access. An important requirement there is the ability to answer complex queries beyond instance retrieval, taking into account constraints expressed in a knowledge base. We consider this task for positive 2-way regular path queries (P2RPQs) over knowledge bases in the expressive DL ZIQ. P2RPQs are more general than conjunctive queries, union of conjunctive queries, and regular path queries from the literature. They allow regular expressions over roles and data joins that require inverse paths. The DL ZIQ extends the core DL ALC with qualified number restrictions, inverse roles, safe Boolean role expressions, regular expressions over roles, and concepts of the form there exists S.Self in the style of the DL SRIQ. Using techniques based on two-way tree-automata, we first provide as a stepping stone an elegant characterization of TBox and ABox satisfiability testing which gives us a tight ExPTIME bound for this problem (under unary number encoding). We then establish a double exponential upper bound for answering P2RPQs over ZIQ knowledge bases; this bound is tight. Our result significantly pushes the frontier of 2EXPTIME decidability of query answering in expressive DLs, both with respect to the query language and the considered DL Furthermore, by reducing the well known DL SRIQ to ZIQ (with an exponential blow-up in the size of the knowledge base), we also provide a tight 2EXPTIME upper bound for knowledge base satisfiability in SRIQ and establish the decidability of query answering for this significant fragment of the new OWL 2 standard. (C) 2014 Elsevier Inc. All rights reserved. [Eiter, Thomas; Ortiz, Magdalena] Vienna Univ Technol, Inst Informat Syst, A-1040 Vienna, Austria; [Calvanese, Diego] Free Univ Bozen Bolzano, KRDB Res Ctr, I-39100 Bolzano, Italy Ortiz, M (reprint author), Vienna Univ Technol, Inst Informat Syst, Favoritenstr 9-11, A-1040 Vienna, Austria. calvanese@inf.unibz.it; eiter@kr.tuwien.ac.at; ortiz@kr.tuwien.ac.at FWF projects [T515, P20480]; EU [FP7-318338] This work has been partially supported by the FWF projects T515 Recursive Queries over Semantically Enriched Data Repositories and P20480 Reasoning in Hybrid Knowledge Bases, and the EU large-scale Integrating Project Optique Scalable End-user Access to Big Data, grant agreement No. FP7-318338. Abiteboul S., 1995, FDN DATABASES; Abiteboul S, 1999, J COMPUT SYST SCI, V58, P428, DOI 10.1006/jcss.1999.1627; Abiteboul S., 2000, DATA WEB RELATIONS S; Baader F, 2003, DESCRIPTION LOGIC HD; Barcelo P, 2012, ACM T DATABASE SYST, V37, DOI 10.1145/2389241.2389250; Bechhofer S, 2004, OWL WEB ONTOLOGY LAN; Berglund A., 2010, XML PATH LANGUAGE XP; Bienvenu M., 2014, P 14 INT C PRINC KNO; Bonatti P., 2008, LMCS, V4, P1; Buneman P., 1997, Proceedings of the Sixteenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, PODS 1997, DOI 10.1145/263661.263675; Calvanese D., 2007, P 22 NAT C ART INT A, P391; Calvanese D., 2008, ACM T COMPUT LOG, V9; Calvanese D., 2003, P 10 INT WORKSH KNOW, V79; Calvanese D., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275504; Calvanese D, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P714; Calvanese D., 2002, P 2002 DESCR LOG WOR, P107; Calvanese D., 2000, P 7 INT C PRINC KNOW, P176; Calvanese D, 2003, SIGMOD RECORD, V32, P83; Calvanese D, 2007, J AUTOM REASONING, V39, P385, DOI 10.1007/s10817-007-9078-x; Calvanese D., 2011, P 22 INT JOINT C ART, P798; Calvanese D, 1999, IJCAI-99: PROCEEDINGS OF THE SIXTEENTH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, VOLS 1 & 2, P84; Calvanese D, 2002, J COMPUT SYST SCI, V64, P443, DOI 10.1006/jcss.2001.1805; Chandra A.K., 1977, P 9 ANN ACM S THEOR, P77, DOI 10.1145/800105.803397; Grau BC, 2008, J WEB SEMANT, V6, P309, DOI 10.1016/j.websem.2008.05.001; Deutsch A., 2001, LECT NOTES COMPUT SC, V2397, P21; Eiter T, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P759; Eiter T, 2009, LECT NOTES ARTIF INT, V5514, P26, DOI 10.1007/978-3-642-02261-6_3; EMERSON EA, 1991, PROCEEDINGS - 32ND ANNUAL SYMPOSIUM ON FOUNDATIONS OF COMPUTER SCIENCE, P368, DOI 10.1109/SFCS.1991.185392; FISCHER MJ, 1979, J COMPUT SYST SCI, V18, P194, DOI 10.1016/0022-0000(79)90046-1; Florescu D., 1998, Proceedings of the Seventeenth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems. PODS 1998, DOI 10.1145/275487.275503; Rudolph S, 2010, J ARTIF INTELL RES, V39, P429; Glimm B., 2008, J ARTIFICIAL INTELLI, V31, P151; Grahne G., 2003, P PODS 2003, P111, DOI 10.1145/773153.773165; Hagedorn S., 2013, WORKSH P JOINT 2013, P38; Harris S., 2013, SPARQL 1 1 QUERY LAN; Horrocks I, 2004, ARTIF INTELL, V160, P79, DOI 10.1016/j.artint.2004.06.002; Horrocks I, 2000, LECT NOTES ARTIF INT, V1831, P482; Horrocks I., 2006, P 10 INT C PRINC KNO, P57; Horrocks I., 2000, Proceedings Seventeenth National Conference on Artificial Intelligence (AAAI-2000). Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000); Horrocks I., 2005, P 1 INT WORKSH OWL E; Horrocks I, 2007, J AUTOM REASONING, V39, P249, DOI 10.1007/s10817-007-9079-9; Hustadt U., 2004, P 11 INT C LOG PROGR, P21; Hustadt U, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P466; Hustadt U, 2007, J AUTOM REASONING, V39, P351, DOI 10.1007/s10817-007-9080-3; Kazakov Y., 2008, P 11 INT C PRINC KNO, P274; Krisnadhi A., 2007, P 20 WORKSH DESCR LO, V250, P88; Krotzsch M, 2007, LECT NOTES COMPUT SC, V4825, P310; Kupferman O., 2002, LNCS, V2392, P423; Kupferman O., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276748; Levy AY, 1998, ARTIF INTELL, V104, P165, DOI 10.1016/S0004-3702(98)00048-4; Lutz C., 2007, P 2007 DESCR LOG WOR, V250, P100; MULLER DE, 1987, THEOR COMPUT SCI, V54, P267, DOI 10.1016/0304-3975(87)90133-2; MULLER DE, 1995, THEOR COMPUT SCI, V141, P69, DOI 10.1016/0304-3975(94)00214-4; Ortiz M., 2011, P 22 INT JOINT C ART, P1039; Ortiz M, 2008, J AUTOM REASONING, V41, P61, DOI 10.1007/s10817-008-9102-9; Ortiz M., 2008, P 4 LAT AM WORKSH NO, V408, P1; Rosati R., 2007, P 20 INT WORKSH DESC, V250, P451; Rudolph S, 2008, LECT NOTES ARTIF INT, V5293, P362, DOI 10.1007/978-3-540-87803-2_30; San Martin M., 2009, LECT NOTES COMPUTER, V5554, P293; Schild K., 1991, P 12 INT JOINT C ART, P466; SHMUELI O, 1993, J LOGIC PROGRAM, V15, P231, DOI 10.1016/0743-1066(93)90040-N; STREETT RS, 1989, INFORM COMPUT, V81, P249, DOI 10.1016/0890-5401(89)90031-X; Thomas W., 1990, HDB THEORETICAL COMP, P133; Tobies S, 2001, J LOGIC COMPUT, V11, P85, DOI 10.1093/logcom/11.1.85; Tobies S., 2001, THESIS LUFG THEORETI; VARDI MY, 1986, J COMPUT SYST SCI, V32, P183, DOI 10.1016/0022-0000(86)90026-7; Vardi M.Y., 1985, LECTURE NOTES COMPUT, V193, P413; Vardi MY, 1998, LECT NOTES COMPUT SC, V1443, P628 68 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0890-5401 1090-2651 INFORM COMPUT Inf. Comput. OCT 2014 237 12 55 10.1016/j.ic.2014.04.002 44 Computer Science, Theory & Methods; Mathematics, Applied Computer Science; Mathematics AL9NP WOS:000339468300002 J Guo, GD; Lai, A Guo, Guodong; Lai, Alice A survey on still image based human action recognition PATTERN RECOGNITION English Article Action recognition; Still image based; Various cues; Databases; Survey; Evaluation CONTEXT; OBJECTS; POSE Recently still image-based human action recognition has become an active research topic in computer vision and pattern recognition. It focuses on identifying a person's action or behavior from a single image. Unlike the traditional action recognition approaches where videos or image sequences are used, a still image contains no temporal information for action characterization. Thus the prevailing spatio-temporal features for video-based action analysis are not appropriate for still image-based action recognition. It is more challenging to perform still image-based action recognition than the video-based one, given the limited source of information as well as the cluttered background for images collected from the Internet. On the other hand, a large number of still images exist over the Internet. Therefore it is demanding to develop robust and efficient methods for still image-based action recognition to understand the web images better for image retrieval or search. Based on the emerging research in recent years, it is time to review the existing approaches to still image-based action recognition and inspire more efforts to advance the field of research. We present a detailed overview of the state-of-the-art methods for still image-based action recognition, and categorize and describe various high-level cues and low-level features for action analysis in still images. All related databases are introduced with details. Finally, we give our views and thoughts for future research. (C) 2014 Elsevier Ltd. All rights reserved. [Guo, Guodong; Lai, Alice] W Virginia Univ, Lane Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA Guo, GD (reprint author), W Virginia Univ, Lane Dept Comp Sci & Elect Engn, Morgantown, WV 26506 USA. guodong.guo@mail.wvu.edu Aggarwal JK, 2011, ACM COMPUT SURV, V43, DOI 10.1145/1922649.1922653; Aggarwal JK, 1999, COMPUT VIS IMAGE UND, V73, P428, DOI 10.1006/cviu.1998.0744; Alexe B, 2010, PROC CVPR IEEE, P73, DOI 10.1109/CVPR.2010.5540226; Bay H, 2006, LECT NOTES COMPUT SC, V3951, P404; Belongie S, 2000, IEEE WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO LIBRARIES, PROCEEDINGS, P20; Blank M, 2005, IEEE I CONF COMP VIS, P1395; Bourdev L, 2009, IEEE I CONF COMP VIS, P1365, DOI 10.1109/ICCV.2009.5459303; Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324; CANNY J, 1986, IEEE T PATTERN ANAL, V8, P679; CEDRAS C, 1995, IMAGE VISION COMPUT, V13, P129, DOI 10.1016/0262-8856(95)93154-K; Chen Y., 2007, ADV NEURAL INFORM PR, P289; Chen YX, 2006, IEEE T PATTERN ANAL, V28, P1931; Dalai N., 2005, INT C COMP VIS PATT, V1, P886, DOI DOI 10.1109/CVPR.2005.177; Delaitre V., 2011, ADV NEURAL INFORM PR; Delaitre V., 2010, P BRIT MACH VIS C BM, P7; DENG J, 2009, IEEE C COMP VIS PATT, P248; Desai C, 2009, IEEE I CONF COMP VIS, P229, DOI 10.1109/ICCV.2009.5459256; Desai C., 2012, ECCV, P158; Desai C., 2010, IEEE COMP SOC C COMP, P9; Easel B., 2003, PATTERN RECOGN, V36, P259; Everingham M, 2010, INT J COMPUT VISION, V88, P303, DOI 10.1007/s11263-009-0275-4; Farhadi A, 2009, PROC CVPR IEEE, P1778; Felzenszwalb PF, 2010, IEEE T PATTERN ANAL, V32, P1627, DOI 10.1109/TPAMI.2009.167; FRIEDMAN A, 1979, J EXP PSYCHOL GEN, V108, P316, DOI 10.1037//0096-3445.108.3.316; Gavrila DM, 1999, COMPUT VIS IMAGE UND, V73, P82, DOI 10.1006/cviu.1998.0716; Grubinger M., 2006, INT WORKSH ONTOIMAGE, P13; Gupta A, 2009, IEEE T PATTERN ANAL, V31, P1775, DOI 10.1109/TPAMI.2009.83; Hamid R, 2005, PROC CVPR IEEE, P1031; Hedetniemi S.T., 1991, DISCRETE MATH, V86, P257; Ikizler N., 2008, INT C PATT REC, P1; IKIZLER N, 2007, WORKSH HUM MOT, V4814, P271; Ikizler-Cinbis N, 2012, IEEE T MULTIMEDIA, V14, P1031, DOI 10.1109/TMM.2012.2187180; Ikizler-Cinbis N, 2009, IEEE I CONF COMP VIS, P995, DOI 10.1109/ICCV.2009.5459368; Ji XF, 2010, IEEE T SYST MAN CY C, V40, P13, DOI 10.1109/TSMCC.2009.2027608; Khan F.S., 2013, INT J COMPUT VISION, P1; Koniusz P., 2011, IEEE INT C IM PROC, P2413; Koniusz P, 2011, IEEE IMAGE PROC, P661; Kruger V, 2007, ADV ROBOTICS, V21, P1473; Kumar MP, 2009, IEEE I CONF COMP VIS, P552, DOI 10.1109/ICCV.2009.5459192; Kumar M.P., 2012, P INT C MACH LEARN, P465; Laptev I, 2005, INT J COMPUT VISION, V64, P107, DOI 10.1007/s11263-005-1838-7; Lazebnik S, 2006, IEEE C COMP VIS PATT, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Le D.T., 2013, P 3 ACM C INT C MULT, P231; Lee D. D., 2000, ADV NEURAL INFORM PR, P556; LEE HJ, 1985, COMPUT VISION GRAPH, V30, P148, DOI 10.1016/0734-189X(85)90094-5; Li L.-J., 2007, ICCV, P1; Li PJ, 2011, 2011 FIRST ASIAN CONFERENCE ON PATTERN RECOGNITION (ACPR), P32; Li PJ, 2011, LECT NOTES COMPUT SC, V6897, P302; Lowe D., 1999, ICCV, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410; Maji S., 2011, IEEE C COMP VIS PATT, P3177; Oliva A, 2001, INT J COMPUT VISION, V42, P145, DOI 10.1023/A:1011139631724; Poppe R, 2010, IMAGE VISION COMPUT, V28, P976, DOI 10.1016/j.imavis.2009.11.014; Prest A, 2012, IEEE T PATTERN ANAL, V34, P601, DOI 10.1109/TPAMI.2011.158; Raja K, 2011, IEEE IMAGE PROC, P25, DOI 10.1109/ICIP.2011.6116197; Ramanan D., 2006, ADV NEURAL INFORM PR, P1129; Sapp B, 2010, LECT NOTES COMPUT SC, V6312, P406, DOI 10.1007/978-3-642-15552-9_30; Scholkopf B., 2001, LEARNING KERNELS SUP; Schuldt C, 2004, INT C PATT RECOG, P32, DOI 10.1109/ICPR.2004.1334462; Sener F., 2012, EUR C COMP VIS WORKS, P263; Shapovalova N, 2011, LECT NOTES COMPUT SC, V6669, P58; Sharma G., 2013, IEEE C COMP VIS PATT; Sharma G, 2012, PROC CVPR IEEE, P3506, DOI 10.1109/CVPR.2012.6248093; Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888; Tahir M., 2009, IEEE INT C COMP VIS, P178; Taylor CJ, 2000, COMPUT VIS IMAGE UND, V80, P349, DOI 10.1006/cviu.2000.0878; Thurau C., 2008, IEEE C COMP VIS PATT, P1, DOI DOI 10.1109/CVPR.2008.4587721; Turaga P, 2008, IEEE T CIRC SYST VID, V18, P1473, DOI 10.1109/TCSVT.2008.2005594; Wang Y., 2006, IEEE C COMP VIS PATT, P1654; WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967; Yang WL, 2010, PROC CVPR IEEE, P2030, DOI 10.1109/CVPR.2010.5539879; Yang Y, 2011, PROC CVPR IEEE, P1385; Yao B., 2011, INT C MACH LEARN, pD3; Yao B., 2012, ECCV, P173; Yao BP, 2010, PROC CVPR IEEE, P9, DOI 10.1109/CVPR.2010.5540234; Yao BP, 2011, PROC CVPR IEEE, P1577; YAO BP, 2008, EUR C COMP VIS, V5302, P697; Yao BP, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P1331; Yao BP, 2012, IEEE T PATTERN ANAL, V34, P1691, DOI 10.1109/TPAMI.2012.67; Yao BP, 2010, PROC CVPR IEEE, P17, DOI 10.1109/CVPR.2010.5540235; Zeng ZH, 2009, IEEE T PATTERN ANAL, V31, P39, DOI 10.1109/TPAMI.2008.52; Zheng Y, 2012, IEEE IMAGE PROC, P785 81 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. OCT 2014 47 10 3343 3361 10.1016/j.patcog.2014.04.018 19 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AK4KA WOS:000338392400012 J Kuttschreuter, M; Rutsaert, P; Hilverda, F; Regan, A; Barnett, J; Verbeke, W Kuttschreuter, Margot; Rutsaert, Pieter; Hilverda, Femke; Regan, Aine; Barnett, Julie; Verbeke, Wim Seeking information about food-related risks: The contribution of social media FOOD QUALITY AND PREFERENCE English Article Channel use; Segmentation; Food risk; Online resources; Traditional media; Risk Information Seeking and Processing model BENEFIT COMMUNICATION; HEALTH INFORMATION; CONSUMER; MANAGEMENT; COMPLEMENTARITY; PERCEPTIONS; CONSUMPTION; RETRIEVAL; MESSAGES; EUROPE In the current information landscape, there are numerous channels for consumers to find information on issues pertaining to food safety. The rise in popularity of social media makes communicators question the extent to which resources should be allocated to these channels in order to reach new segments or audiences which are hard to reach through more traditional dissemination channels. A segmentation approach was used to identify groups of consumers based on their inclination to use different channels to seek information about food-related risks, including traditional media, online media and social media. In the wake of the 2011 Escherichia coli contamination crisis, the study focused on a bacterial contamination of fresh vegetables. Results were obtained through an online survey among 1264 participants from eight European countries in September 2012. Four segments were identified: 'a high cross-channel inclination' (24%), 'an established channel inclination' (31%), 'a moderate cross-channel inclination' (26%) and 'a low cross-channel inclination' (19%). Results show that social media can act as a complementary information channel for a particular segment, but that it is not a substitute for traditional or online media. Individuals who showed an inclination to use social media in conjunction with other channels considered it more important to be well informed, were more motivated to find additional information, were more sensitive to risks in general and perceived the likelihood of a food incident in the future to be larger. The 'high cross-channel inclination' segment contained relatively younger and more Southern European participants. (C) 2014 Elsevier Ltd. All rights reserved. [Kuttschreuter, Margot; Hilverda, Femke] Univ Twente, Dept Psychol Conflict Risk & Safety, NL-7500 AE Enschede, Netherlands; [Rutsaert, Pieter; Verbeke, Wim] Univ Ghent, Dept Agr Econ, B-9000 Ghent, Belgium; [Regan, Aine] Univ Coll Dublin, Sch Publ Hlth Physiotherapy & Populat Sci, Dublin, Ireland; [Barnett, Julie] Brunel Univ, Dept Informat Syst & Comp, Uxbridge UB8 3PH, Middx, England Kuttschreuter, M (reprint author), Univ Twente, Dept Psychol Conflict Risk & Safety, POB 217, NL-7500 AE Enschede, Netherlands. margot.kuttschreuter@utwente.nl FoodRisC project - Seventh Framework Programme (CORDIS FP7) of the European Commission [245124] This study is part of the FoodRisC project, which is funded under the Seventh Framework Programme (CORDIS FP7) of the European Commission; Grant Agreement No. 245124. Barnett J., 2011, BMC Public Health, V11; Brossard D, 2013, SCIENCE, V339, P40, DOI 10.1126/science.1232329; Bunce S, 2012, AUST LIBR J, V61, P34; Clarke CE, 2012, HEALTH COMMUN, V27, P244, DOI 10.1080/10410236.2011.578332; deAlmeida MDV, 1997, EUR J CLIN NUTR, V51, pS16; Dean M, 2007, FOOD QUAL PREFER, V18, P460, DOI 10.1016/j.foodqual.2006.05.004; de Waal E, 2005, COMMUNICATIONS-GER, V30, P55, DOI 10.1515/comm.2005.30.1.55; Dutta-Bergman MJ, 2004, J BROADCAST ELECTRON, V48, P41, DOI 10.1207/s15506878jobem4801_3; EFSA, 2012, FOOD IS COOK STORM P; European Commission, 2010, SPEC EUR 354 FOOD RE; European Commission, 2012, SPEC EUR 390 CYB SEC; FIELD A., 2009, DISCOVERING STAT USI; Frewer LJ, 1996, RISK ANAL, V16, P473, DOI 10.1111/j.1539-6924.1996.tb01094.x; Frewer L.J., CRITICAL RE IN PRESS; Griffin RJ, 2004, MEDIA PSYCHOL, V6, P23, DOI 10.1207/s1532785xmep0601_2; Griffin RJ, 1999, ENVIRON RES, V80, pS230, DOI 10.1006/enrs.1998.3940; Hesse BW, 2005, ARCH INTERN MED, V165, P2618, DOI 10.1001/archinte.165.22.2618; Hochstotter N, 2009, INFORM SCIENCES, V179, P1796, DOI 10.1016/j.ins.2009.01.028; Houghton JR, 2008, FOOD POLICY, V33, P13, DOI 10.1016/j.foodpol.2007.05.001; Jacob C, 2010, FOOD CONTROL, V21, P1, DOI 10.1016/j.foodcont.2009.04.011; Kahlor L, 2003, RISK ANAL, V23, P355, DOI 10.1111/1539-6924.00314; Kamakura W., 1998, MARKET SEGMENTATION; Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003; Kobayashi M, 2000, ACM COMPUT SURV, V32, P144, DOI 10.1145/358923.358934; Kornelis M, 2007, RISK ANAL, V27, P327, DOI 10.1111/j.1539-6924.2007.00885.x; Krystallis A, 2007, HEALTH RISK SOC, V9, P407, DOI 10.1080/13698570701612683; Kuttschreuter M, 2006, RISK ANAL, V26, P1045, DOI 10.1111/j.1539-6924.2006.00799.x; Laksanalamai P, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0042448; Loewenstein GF, 2001, PSYCHOL BULL, V127, P267, DOI 10.1037//0033-2909.127.2.267; McCombs M., 1972, JOURNALISM MONOGRAPH, V11, P371; Mellmann A, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0022751; Mintel, 2011, SOC MED NETW UK MAY; Noar SM, 2006, J HEALTH COMMUN, V11, P21, DOI 10.1080/10810730500461059; Petts J., 2001, SOCIAL AMPLIFICATION; Pieniak Z, 2010, FOOD POLICY, V35, P448, DOI 10.1016/j.foodpol.2010.05.002; Redmond EC, 2006, BRIT FOOD J, V108, P732, DOI 10.1108/00070700610688377; Rogers E.M., 1995, DIFFUSION INNOVATION; Rutsaert P, 2013, FOOD CONTROL, V34, P386, DOI 10.1016/j.foodcont.2013.04.030; Rutsaert P, 2013, TRENDS FOOD SCI TECH, V30, P84, DOI 10.1016/j.tifs.2012.10.006; Rutsaert P, 2014, FOOD POLICY, V46, P84, DOI 10.1016/j.foodpol.2014.02.003; STAYMAN DM, 1991, J MARKETING RES, V28, P232, DOI 10.2307/3172812; Tatham R. L., 2006, MULTIVARIATE DATA AN; Ter Huurne E, 2008, J RISK RES, V11, P847, DOI 10.1080/13669870701875750; Tian Y, 2008, HEALTH COMMUN, V23, P184, DOI 10.1080/10410230801968260; Tilley A., 2008, PACIFIC JOURNALISM R, V14, P94; van Kleef E, 2006, APPETITE, V47, P46, DOI 10.1016/j.appet.2006.02.002; Verbeke W, 2005, EUR REV AGRIC ECON, V32, P347, DOI 10.1093/eurrag/jbi017; Visschers VHM, 2013, FOOD POLICY, V42, P71, DOI 10.1016/j.foodpol.2013.07.003; Voordouw J, 2011, FOOD QUAL PREFER, V22, P384, DOI 10.1016/j.foodqual.2011.01.009; Zhang L., 2013, INFORM SCI INT J EME, V16, P1 50 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0950-3293 1873-6343 FOOD QUAL PREFER Food. Qual. Prefer. OCT 2014 37 10 18 10.1016/j.foodqual.2014.04.006 9 Food Science & Technology Food Science & Technology AJ7GG WOS:000337864600002 J Wu, GF; Liu, LJ; Chen, FY; Fei, T Wu, Guofeng; Liu, Liangjie; Chen, Fangyuan; Fei, Teng Developing MODIS-based retrieval models of suspended particulate matter concentration in Dongting Lake, China INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Lake management; Suspended particulate matter; Remote sensing; Empirical model RADIATIVE-TRANSFER CODE; WATER-QUALITY; COASTAL WATERS; SATELLITE DATA; SEDIMENT CONCENTRATION; ATMOSPHERIC CORRECTION; BIOOPTICAL MODEL; APALACHICOLA BAY; VECTOR VERSION; POYANG LAKE To case-II waters, suspended particulate matter (SPM) is one of the dominant water constituents, SPM concentration (C-SPM) is a key parameter describing water quality, and developing remote sensing-based C-SPM retrieval models is foundation for obtaining its spatiotemporal distributions. This study aimed to develop moderate resolution imaging spectroradiometer (MODIS)-based C-SPM empirical retrieval models in Dongting Lake, China. The 95 C-SPM measurements on 31 August 2012 and 14 June 2013 and their corresponding MODIS Terra images were used to calibrate models, and the model calibration results showed that the 250 m MODIS red band obtained better fitting accuracies than the near infrared band; the quadratic and exponential models of single red band explained 75% (estimated standard errors (SE) = 6.19 mg/l) and 71% (SE = 6.54 mg/l) of the variation of C-SPM; and the quadratic and exponential models of red minus shortwave infrared (SWIR) band at 1240 and 1640 nm explained 72-73% (SE= 6.43-6.48 mg/l) and 68-69% (SE = 6.83-6.96 mg/l) of the variations of C-SPM, respectively. The quadratic and exponential models of red band and red minus SWIR band were applied to the MODIS Terra image on 16 September 2013 to estimate C-SPM values. By comparing the estimated C-SPM values on 16 September 2013 and the measured ones on 17 September 2013 at 40 sampling points for model validations, the results indicated that there exited significantly strong correlations between the measured and estimated C-SPM values at a significance level of 0.05 for all models, and the exponential model of red minus SWIR band at 1240 nm achieved the best estimation result within all models. Such result provided foundation for obtaining the spatiotemporal distribution information of C-SPM from MODIS images in Dongting Lake, which will be helpful for understanding, managing and protecting this ecosystem. (C) 2014 Elsevier B.V. All rights reserved. [Wu, Guofeng] Shenzhen Univ, Natl Adm Surveying Mapping & GeoInformat, Key Lab GeoEnvironm Monitoring Coastal Zone, Shenzhen 518060, Peoples R China; [Wu, Guofeng] Shenzhen Univ, Shenzhen Key Lab Spatial Smart Sensing & Serv, Shenzhen 518060, Peoples R China; [Wu, Guofeng] Shenzhen Univ, Coll Life Sci, Shenzhen 518060, Peoples R China; [Wu, Guofeng; Liu, Liangjie; Chen, Fangyuan; Fei, Teng] Wuhan Univ, Sch Resource & Environm Sci, Wuhan 430079, Peoples R China; [Wu, Guofeng; Liu, Liangjie; Chen, Fangyuan; Fei, Teng] Wuhan Univ, Minist Educ, Key Lab Geog Informat Syst, Wuhan 430079, Peoples R China Wu, GF (reprint author), Shenzhen Univ, Natl Adm Surveying Mapping & GeoInformat, Key Lab GeoEnvironm Monitoring Coastal Zone, Shenzhen 518060, Peoples R China. guofeng.wu@szu.edu.cn National Natural Science Foundation of China [41171290, 40971191] This study was supported by the National Natural Science Foundation of China (Grant Nos. 41171290 and 40971191). Binding CE, 2010, INT J REMOTE SENS, V31, P5239, DOI 10.1080/01431160903302973; Chen SS, 2011, ECOL INFORM, V6, P147, DOI 10.1016/j.ecoinf.2010.12.001; Chen SS, 2009, REMOTE SENS ENVIRON, V113, P2670, DOI 10.1016/j.rse.2009.08.005; Chen SS, 2011, MAR ENVIRON RES, V72, P265, DOI 10.1016/j.marenvres.2011.09.014; Cigizoglu HK, 2006, J HYDROL, V317, P221, DOI 10.1016/j.jhydrol.2005.05.019; Cui LJ, 2013, LAKE RESERV MANAGE, V29, P47, DOI 10.1080/10402381.2013.768733; Davies-Colley RJ, 2001, J AM WATER RESOUR AS, V37, P1085, DOI 10.1111/j.1752-1688.2001.tb03624.x; Ding XW, 2011, INT J APPL EARTH OBS, V13, P894, DOI 10.1016/j.jag.2011.06.009; Doxaran D, 2006, APPL OPTICS, V45, P2310, DOI 10.1364/AO.45.002310; Doxaran D, 2009, ESTUAR COAST SHELF S, V81, P321, DOI 10.1016/j.ecss.2008.11.013; Feng L, 2012, J GEOPHYS RES-OCEANS, V117, DOI 10.1029/2011JC007864; G Wu, 2008, J LAKE SCI, V28, P6113; Giardino C, 2007, REMOTE SENS ENVIRON, V109, P183, DOI 10.1016/j.rse.2006.12.017; Giardino C, 2010, MT RES DEV, V30, P157, DOI 10.1659/MRD-JOURNAL-D-09-00042.1; He XQ, 2013, REMOTE SENS ENVIRON, V133, P225, DOI 10.1016/j.rse.2013.01.023; Hu CM, 2004, REMOTE SENS ENVIRON, V93, P423, DOI 10.1016/j.rse.2004.08.007; Huang SF, 2012, NAT HAZARDS, V62, P93, DOI 10.1007/s11069-011-9921-6; Huang X., 1999, ECOINVESTIGATION OBS; Jiang H., 2011, YANGTZE RIVER, V42, P87; Jiang XW, 2009, CHIN J OCEANOL LIMN, V27, P614, DOI 10.1007/s00343-009-9160-9; Kirk J. T. O, 1994, LIGHT PHOTOSYNTHESIS; Kotchenova SY, 2007, APPL OPTICS, V46, P4455, DOI 10.1364/AO.46.004455; Kotchenova SY, 2006, APPL OPTICS, V45, P6762, DOI 10.1364/AO.45.006762; Kutser T, 2007, J COASTAL RES, P180; Li R., 2004, ENV INFORM ARCH, V2, P893; Liu Cande, 2006, Chinese Geographical Science, V16, P79, DOI 10.1007/s11769-006-0026-1; [刘茜 LIU Qian], 2008, [遥感技术与应用, Remote Sensing Technology and Application], V23, P7; Liu YS, 2003, PROG PHYS GEOG, V27, P24, DOI 10.1191/0309133303pp357ra; Long CM, 2013, REMOTE SENS ENVIRON, V129, P197, DOI 10.1016/j.rse.2012.10.019; Ma R.H., 2010, REMOTE SENSING LAKE; Miller RL, 2004, REMOTE SENS ENVIRON, V93, P259, DOI 10.1016/j.rse.2004.07.012; [莫登奎 Mo Dengkui], 2013, [中国农学通报, Chinese Agricultural Science Bulletin], V29, P192; Morozov E, 2010, INT J REMOTE SENS, V31, P6541, DOI 10.1080/01431161.2010.508802; Ondrusek M, 2012, REMOTE SENS ENVIRON, V119, P243, DOI 10.1016/j.rse.2011.12.018; Pozdnyakov D, 2005, REMOTE SENS ENVIRON, V97, P352, DOI 10.1016/j.rse.2005.04.018; Sipelgas L, 2009, BOREAL ENVIRON RES, V14, P415; Sipelgas L, 2006, ADV SPACE RES, V38, P2182, DOI 10.1016/j.asr.2006.03.011; Tarrant PE, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR008709; Uddin S, 2012, AQUAT ECOSYST HEALTH, V15, P41, DOI 10.1080/14634988.2012.668114; Wang HQ, 2010, INT J REMOTE SENS, V31, P439, DOI 10.1080/01431160902893485; Wang JJ, 2010, SCI TOTAL ENVIRON, V408, P1131, DOI 10.1016/j.scitotenv.2009.11.057; Wang JJ, 2010, INT J REMOTE SENS, V31, P1103, DOI 10.1080/01431160903330339; Wang MH, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028599; Wang YX, 2011, AFR J AGR RES, V6, P6167, DOI 10.5897/AJAR11.1268; Wu GF, 2013, INT J APPL EARTH OBS, V24, P63, DOI 10.1016/j.jag.2013.03.001; Xing QG, 2013, IEEE J-STARS, V6, P731, DOI 10.1109/JSTARS.2013.2238659; Zhang B, 2008, ENVIRON MONIT ASSESS, V145, P339, DOI 10.1007/s10661-007-0043-2; Zhang YC, 2010, INT J ENV RES PUB HE, V7, P3545, DOI 10.3390/ijerph7093545; Zhang YZ, 2003, IEEE T GEOSCI REMOTE, V41, P622, DOI 10.1109/TGRS.2003.808906; Zhao HH, 2011, INT J REMOTE SENS, V32, P6653, DOI 10.1080/01431161.2010.512938 50 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0303-2434 INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. OCT 2014 32 46 53 10.1016/j.jag.2014.03.025 8 Remote Sensing Remote Sensing AJ7NT WOS:000337884100004 J Planinsic, P; Singh, J; Gleich, D Planinsic, Peter; Singh, Jagmal; Gleich, Dusan SAR Image Categorization Using Parametric and Nonparametric Approaches Within a Dual Tree CWT IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Data mining; feature extraction; image texture analysis; support vector machines (SVMs); wavelet transforms INFORMATION-RETRIEVAL; TRANSFORM; MODEL This letter presents synthetic aperture radar (SAR) image classification based on feature descriptors within the discrete wavelet transform (DWT) domain using parametric and nonparametric features. The DWT enables an efficient multiresolution description of SAR images due to its geometric and stochastic features. A 2-D DWT, a real 2-D oriented dual tree wavelet transform (2-D RODTWT) and an oriented dual tree complex wavelet transform (2-D ODTCWT) were used for the estimation of subband features. First and second moments, entropy, coding gain, and fractal dimension were used for the nonparametric approach. A parametric approach considers a Gauss Markov Random Field model for feature extraction. A database with 2000 images representing 20 different classes with 100 images per class was used for classification efficiency assessment. Several SAR scenes were divided into small patches with dimension of 200 x 200 pixels. 10% and 20% of the test images per class were used during the learning stage. Supervised learning using a support vector machine was used for all experiments. The experimental results showed that the proposed methods had superior performances compared with (GLCM) and log comulants of Fourier transform. Amongst the proposed methods, the nonparametric features within oriented dual tree complex wavelet transform gave the best results for classes when categorizing SAR images. [Planinsic, Peter; Gleich, Dusan] Univ Maribor, Fac Elect Engn & Comp Sci, Lab Signal Proc & Remote Control, SLO-2000 Maribor, Slovenia; [Singh, Jagmal] DLR, Earth Observat Ctr, D-82234 Wessling, Germany Planinsic, P (reprint author), Univ Maribor, Fac Elect Engn & Comp Sci, Lab Signal Proc & Remote Control, SLO-2000 Maribor, Slovenia. Banjanin B., 2001, IMAGE VISION COMPUT, V19, P447; Bouman C, 1993, IEEE Trans Image Process, V2, P296, DOI 10.1109/83.236536; CHELLAPPA R, 1985, IEEE T SYST MAN CYB, V15, P298; COIFMAN RR, 1992, IEEE T INFORM THEORY, V38, P713, DOI 10.1109/18.119732; Daubechies I., 1992, 10 LECT WAVELETS; He C, 2012, IEEE J-STARS, V5, P1272, DOI 10.1109/JSTARS.2012.2189555; Hearst MA, 1998, IEEE INTELL SYST APP, V13, P18, DOI 10.1109/5254.708428; Hebar M, 2009, IEEE T GEOSCI REMOTE, V47, P2818, DOI 10.1109/TGRS.2009.2013697; Li T, 2006, IEEE T MULTIMEDIA, V8, P564, DOI 10.1109/TMM.2006.870730; Lina J.-M., 1995, P SOC PHOTO-OPT INS, V2569, P169; NORROS I, 1994, QUEUEING SYST, V16, P387, DOI 10.1007/BF01158964; Parra C., 2003, Conference Proceedings. 1st International IEEE EMBS Conference on Neural Engineering 2003 (Cat. No.03EX606); Popescu AA, 2012, IEEE GEOSCI REMOTE S, V9, P80, DOI 10.1109/LGRS.2011.2160838; Selesnick IW, 2005, IEEE SIGNAL PROC MAG, V22, P123, DOI 10.1109/MSP.2005.1550194; Shyu CR, 2007, IEEE T GEOSCI REMOTE, V45, P839, DOI 10.1109/TGRS.2006.890579; Singh J, 2013, IEEE T GEOSCI REMOTE, V51, P5273, DOI 10.1109/TGRS.2012.2230892; Sivia DS, 1996, DATA ANAL BAYESIAN T; Tison C, 2004, IEEE T GEOSCI REMOTE, V42, P2046, DOI 10.1109/TGRS.2004.834630; Vestergaard J. S., 2012, P IMAGE SIGNAL PROCE, V8537; Wornell G., 1996, SIGNAL PROCESSING FR; Zhan X, 2013, IEEE GEOSCI REMOTE S, V10, P1090, DOI 10.1109/LGRS.2012.2230394 21 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. OCT 2014 11 10 1757 1761 10.1109/LGRS.2014.2308328 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8NH WOS:000337174100019 J Liu, DW; Lv, CC; Liu, K; Xie, Y; Miao, JG Liu, Dawei; Lv, Changchun; Liu, Kai; Xie, Yan; Miao, Jungang Retrieval Analysis of Atmospheric Water Vapor for K-Band Ground-Based Hyperspectral Microwave Radiometer IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Channel analysis; hyperspectral microwave radiometer; retrieval of water vapor profile REMOTE MEASUREMENTS; TEMPERATURE; PROFILES; INFORMATION In this letter, we study the performance of a K-band ground-based hyperspectral microwave radiometer for the observation of atmospheric water vapor. First, a prototype of a K-band ground-based hyperspectral microwave radiometer for atmospheric sounding is proposed. This microwave radiometer is able to split the 18-26-GHz signal into 80 hyperspectral channels with identical bandwidth. Simulation studies, including the retrieval performance of water vapor and the vertical resolution of observation compared with the five-humidity-channel radiometer TP/WVP-3000 under the same conditions, are presented to assess the capability of the prototype. Simulation results show that the vertical resolution of this prototype is better than that of TP/WVP-3000 at a higher altitude, and the RMS water vapor error improves by about 10% at an altitude of 0-6 km. Moreover, by simulation, we analyze the impact of the radiometer channel number on the Shannon information gain and the RMS water vapor error of the hyperspectral microwave radiometer. At an altitude of 1.5-6 km, more information can be obtained by increasing the number of microwave spectrum channels. For water vapor profiling, the improvement of the retrieval RMS error from 10 to 800 channels at a higher altitude exceeds about 5%-10%. [Liu, Dawei; Lv, Changchun; Liu, Kai; Xie, Yan; Miao, Jungang] Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China Liu, DW (reprint author), Beihang Univ, Sch Elect & Informat Engn, Beijing 100191, Peoples R China. dawliu@gmail.com National Natural Science Foundation of China [41105007] This work was supported by the National Natural Science Foundation of China under Research Grant 41105007. Blackwell WJ, 2011, IEEE T GEOSCI REMOTE, V49, P128, DOI 10.1109/TGRS.2010.2052260; Blackwell WJ, 2005, IEEE T GEOSCI REMOTE, V43, P2535, DOI 10.1109/TGRS.2005.855071; Blackwell WJ, 2007, INT GEOSCI REMOTE SE, P2814; Boukabara S.-A., 2011, P IEEE SENSORS, P1881; Carpenter R., 2003, RADIO SCI, V38; Chalon G., 2001, P 52 C IAF OCT, P1; Cimini D, 2011, IEEE T GEOSCI REMOTE, V49, P4959, DOI 10.1109/TGRS.2011.2154337; Durre I, 2006, J CLIMATE, V19, P53, DOI 10.1175/JCLI3594.1; Hewison T. J., 2006, THESIS U READING REA; Hewison TJ, 2007, IEEE T GEOSCI REMOTE, V45, P2163, DOI 10.1109/TGRS.2007.898091; Levenberg K., 1944, Quarterly of Applied Mathematics, V2; Liebe H. J., 1993, P AGARD ATM PROP EFF, V1; Lohnert U, 2012, ATMOS MEAS TECH, V5, P1121, DOI 10.5194/amt-5-1121-2012; Marquardt D. W., 1963, J SIAM, V11, P164; Marzano FS, 2006, J HYDROL, V328, P121, DOI 10.1016/j.jhydrol.2005.11.042; PECKHAM G, 1974, Q J ROY METEOR SOC, V100, P406, DOI 10.1002/qj.49710042512; Peckham GE, 2000, Q J ROY METEOR SOC, V126, P2933, DOI 10.1256/smsqj.56915; Rodgers C. D., 2000, INVERSE METHODS ATMO; RODGERS CD, 1976, REV GEOPHYS, V14, P609, DOI 10.1029/RG014i004p00609; Westwater E. R., 1999, P 9 ARM SCI TEAM M S, P1 20 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. OCT 2014 11 10 1835 1839 10.1109/LGRS.2014.2311833 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8NH WOS:000337174100035 J Penalver-Martinez, I; Garcia-Sanchez, F; Valencia-Garcia, R; Rodriguez-Garcia, MA; Moreno, V; Fraga, A; Sanchez-Cervantes, JL Penalver-Martinez, Isidro; Garcia-Sanchez, Francisco; Valencia-Garcia, Rafael; Angel Rodriguez-Garcia, Miguel; Moreno, Valentin; Fraga, Anabel; Luis Sanchez-Cervantes, Jose Feature-based opinion mining through ontologies EXPERT SYSTEMS WITH APPLICATIONS English Article Opinion mining; Ontology; Sentiment analysis; Feature extraction; Part of speech tagging; Polarity identification SENTIMENT ANALYSIS; REVIEWS; RETRIEVAL; DOMAIN The idiosyncrasy of the Web has, in the last few years, been altered by Web 2.0 technologies and applications and the advent of the so-called Social Web. While users were merely information consumers in the traditional Web, they play a much more active role in the Social Web since they are now also data providers. The mass involved in the process of creating Web content has led many public and private organizations to focus their attention on analyzing this content in order to ascertain the general public's opinions as regards a number of topics. Given the current Web size and growth rate, automated techniques are essential if practical and scalable solutions are to be obtained. Opinion mining is a highly active research field that comprises natural language processing, computational linguistics and text analysis techniques with the aim of extracting various kinds of added-value and informational elements from users' opinions. However, current opinion mining approaches are hampered by a number of drawbacks such as the absence of semantic relations between concepts in feature search processes or the lack of advanced mathematical methods in sentiment analysis processes. In this paper we propose an innovative opinion mining methodology that takes advantage of new Semantic Web-guided solutions to enhance the results obtained with traditional natural language processing techniques and sentiment analysis processes. The main goals of the proposed methodology are: (1) to improve feature-based opinion mining by using ontologies at the feature selection stage, and (2) to provide a new vector analysis-based method for sentiment analysis. The methodology has been implemented and thoroughly tested in a real-world movie review-themed scenario, yielding very promising results when compared with other conventional approaches. (C) 2014 Elsevier Ltd. All rights reserved. [Penalver-Martinez, Isidro; Garcia-Sanchez, Francisco; Valencia-Garcia, Rafael; Angel Rodriguez-Garcia, Miguel] Univ Murcia, Dept Informat & Sistemas, Fac Informat, E-30100 Murcia, Spain; [Moreno, Valentin; Fraga, Anabel; Luis Sanchez-Cervantes, Jose] Univ Carlos III Madrid, Dept Comp Sci, Madrid 28911, Spain Rodriguez-Garcia, MA (reprint author), Univ Murcia, Dept Informat & Sistemas, Fac Informat, Campus Espinardo, E-30100 Murcia, Spain. ipmartinez1@gmail.com; frgarcia@um.es; valencia@um.es; miguelangel.rodriguez@um.es; vmpelayo@inf.uc3m.es; afraga@inf.uc3m.es; joseluis.s.cervantes@alumnos.uc3m.es Spanish Ministry of Economy and Competitiveness; European Commission (FEDER/ ERDF) project SeCloud [TIN2010-18650] This work has been supported by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through project SeCloud (TIN2010-18650). Ahmad T., 2012, INT J COMPUTER SCI E, V4; Apostol T. M., 2006, MATH ANAL; Baccianella S., 2010, 7 C INT LANG RES EV, V25, P2010; Baccianella S, 2009, LECT NOTES COMPUT SC, V5478, P461, DOI 10.1007/978-3-642-00958-7_41; Balahur A, 2010, LECT NOTES COMPUT SC, V5723, P142; Barforush AA, 2012, J WEB ENG, V11, P269; Berners-Lee T., 2001, SCI AM; Cambria E, 2013, IEEE INTELL SYST, V28, P12; Chen L, 2012, EXPERT SYST APPL, V39, P9588, DOI 10.1016/j.eswa.2012.02.158; Cruz FL, 2013, EXPERT SYST APPL, V40, P3174, DOI 10.1016/j.eswa.2012.12.031; Deng ZH, 2014, EXPERT SYST APPL, V41, P3506, DOI 10.1016/j.eswa.2013.10.056; Eirinaki M, 2012, J COMPUT SYST SCI, V78, P1175, DOI 10.1016/j.jcss.2011.10.007; Esuli A., 2005, CIKM 2005, P617; Feldman R, 2013, COMMUN ACM, V56, P82, DOI 10.1145/2436256.2436274; Gamon M., 2005, COLING 2005, P841; Ghiassi M, 2013, EXPERT SYST APPL, V40, P6266, DOI 10.1016/j.eswa.2013.05.057; Gladun A, 2013, INFORM HEALTH SOC CA, V38, P150, DOI 10.3109/17538157.2012.735730; Kontopoulos E, 2013, EXPERT SYST APPL, V40, P4065, DOI 10.1016/j.eswa.2013.01.001; Lee L., 2004, ACL 04 P 42 ANN M AS, P271; Manning C. D., 2000, P 2000 JOINT SIGDAT, V13, P63, DOI DOI 10.3115/1117794.1117802; Martin-Valdivia MT, 2013, EXPERT SYST APPL, V40, P3934, DOI 10.1016/j.eswa.2012.12.084; Min HJ, 2012, EXPERT SYST APPL, V39, P11830, DOI 10.1016/j.eswa.2012.01.116; Moreno A., 2010, PROCESAMIENTO LENGUA, V45, P31; Ochoa JL, 2013, EXPERT SYST APPL, V40, P2058, DOI 10.1016/j.eswa.2012.10.017; Rodriguez-Garcia MA, 2014, KNOWL-BASED SYST, V56, P15, DOI 10.1016/j.knosys.2013.10.006; Saleh MR, 2011, EXPERT SYST APPL, V38, P14799, DOI 10.1016/j.eswa.2011.05.070; Shadbolt N, 2006, IEEE INTELL SYST, V21, P96, DOI 10.1109/MIS.2006.62; Singh VK, 2013, 2013 IEEE INTERNATIONAL MULTI CONFERENCE ON AUTOMATION, COMPUTING, COMMUNICATION, CONTROL AND COMPRESSED SENSING (IMAC4S), P712; Stojanovic L, 2002, LECT NOTES ARTIF INT, V2473, P285; Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6; Toutanova K., 2003, P HLT NAACL, P252; Zhai ZW, 2011, EXPERT SYST APPL, V38, P9139, DOI 10.1016/j.eswa.2011.01.047; Zhao LL, 2009, LECT NOTES ARTIF INT, V5914, P204; Zhou L, 2008, J AM SOC INF SCI TEC, V59, P98, DOI 10.1002/asi.20735 34 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. OCT 1 2014 41 13 5995 6008 10.1016/j.eswa.2014.03.022 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AI4YJ WOS:000336872300028 J Krishnasamy, G; Kulkarni, AJ; Paramesran, R Krishnasamy, Ganesh; Kulkarni, Anand J.; Paramesran, Raveendran A hybrid approach for data clustering based on modified cohort intelligence and K-means EXPERT SYSTEMS WITH APPLICATIONS English Article Clustering; Cohort intelligence; Meta-heuristic algorithm PARTICLE SWARM OPTIMIZATION; IMAGE SEGMENTATION; INFORMATION-RETRIEVAL; GENETIC ALGORITHM; COLONY APPROACH; MUTATION; SYSTEM; MODEL; RECOGNITION; IMPROVE Clustering is an important and popular technique in data mining. It partitions a set of objects in such a manner that objects in the same clusters are more similar to each another than objects in the different cluster according to certain predefined criteria. K-means is simple yet an efficient method used in data clustering. However, K-means has a tendency to converge to local optima and depends on initial value of cluster centers. In the past, many heuristic algorithms have been introduced to overcome this local optima problem. Nevertheless, these algorithms too suffer several short-comings. In this paper, we present an efficient hybrid evolutionary data clustering algorithm referred to as K-MCI, whereby, we combine K-means with modified cohort intelligence. Our proposed algorithm is tested on several standard data sets from UCI Machine Learning Repository and its performance is compared with other well-known algorithms such as K-means, K-means++, cohort intelligence (CI), modified cohort intelligence (MCI), genetic algorithm (GA), simulated annealing (SA), tabu search (TS), ant colony optimization (ACO), honey bee mating optimization (HBMO) and particle swarm optimization (PSO). The simulation results are very promising in the terms of quality of solution and convergence speed of algorithm. (C) 2014 Elsevier Ltd. All rights reserved. [Krishnasamy, Ganesh; Paramesran, Raveendran] Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, Malaysia; [Kulkarni, Anand J.] Univ Windsor, Odette Sch Business, Windsor, ON N9B 3P4, Canada Krishnasamy, G (reprint author), Univ Malaya, Fac Engn, Dept Elect Engn, Kuala Lumpur 50603, Malaysia. krishnasamy.ganesh@gmail.com; kulk0003@uwindsor.ca; ravee@um.edu.my HIR-MOHE [UM.C/HIR/MOHE/ENG/42] This work was supported by the HIR-MOHE Grant No. UM.C/HIR/MOHE/ENG/42. Albayrak M, 2011, EXPERT SYST APPL, V38, P1313, DOI 10.1016/j.eswa.2010.07.006; Anaya AR, 2011, EXPERT SYST APPL, V38, P1171, DOI 10.1016/j.eswa.2010.05.010; Arthur D., 2007, P 18 ANN ACM SIAM S, V1, P1027, DOI DOI 10.1145/1283383.1283494; Bache K., 2013, UCI MACHINE LEARNING; Bassiou N, 2011, PATTERN RECOGN, V44, P145, DOI 10.1016/j.patcog.2010.07.006; Bhandari AK, 2014, EXPERT SYST APPL, V41, P3538, DOI 10.1016/j.eswa.2013.10.059; Bhattacharya A, 2010, J BIOMED INFORM, V43, P560, DOI 10.1016/j.jbi.2010.02.001; Carmona CJ, 2012, EXPERT SYST APPL, V39, P11243, DOI 10.1016/j.eswa.2012.03.046; Chan CCH, 2008, EXPERT SYST APPL, V34, P347, DOI 10.1016/j.eswa.2006.09.031; Chen C.-Y., 2004, SENSING CONTROL, V2, P789; Cheng YM, 2009, EXPERT SYST APPL, V36, P5761, DOI 10.1016/j.eswa.2008.06.100; Ci S, 2007, COMPUT COMMUN, V30, P2968, DOI 10.1016/j.comcom.2007.05.027; Cura T, 2012, EXPERT SYST APPL, V39, P1582, DOI 10.1016/j.eswa.2011.07.123; Das S, 2009, APPL SOFT COMPUT, V9, P226, DOI 10.1016/j.asoc.2007.12.008; Dhanapal R, 2008, KNOWL-BASED SYST, V21, P466, DOI 10.1016/j.knosys.2008.03.010; Fan S, 2008, ENERG CONVERS MANAGE, V49, P1331, DOI 10.1016/j.enconman.2008.01.008; Fathian M, 2007, APPL MATH COMPUT, V190, P1502, DOI 10.1016/j.amc.2007.02.029; Fathian M, 2008, INT J ADV MANUF TECH, V38, P809, DOI 10.1007/s00170-007-1132-7; Gunes S, 2010, EXPERT SYST APPL, V37, P7922, DOI 10.1016/j.eswa.2010.04.043; Han J., 2005, DATA MINING CONCEPTS; Hatamlou A, 2013, INFORM SCIENCES, V222, P175, DOI 10.1016/j.ins.2012.08.023; Hung YS, 2013, EXPERT SYST APPL, V40, P775, DOI 10.1016/j.eswa.2012.08.037; Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504; Jun S, 2014, EXPERT SYST APPL, V41, P3204, DOI 10.1016/j.eswa.2013.11.018; Kao YT, 2008, EXPERT SYST APPL, V34, P1754, DOI 10.1016/j.eswa.2007.01.028; Kaufman L, 2005, WILEY SERIES PROBABI; Kim KJ, 2008, EXPERT SYST APPL, V34, P1200, DOI 10.1016/j.eswa.2006.12.025; Kulkarni AJ, 2013, IEEE SYS MAN CYBERN, P1396, DOI 10.1109/SMC.2013.241; Kuo RJ, 2006, EXPERT SYST APPL, V30, P313, DOI 10.1016/j.eswa.2005.07.036; Lee ZJ, 2008, APPL SOFT COMPUT, V8, P55, DOI 10.1016/j.asoc.2006.10.012; Macintyre G, 2010, PATTERN RECOGN LETT, V31, P2138, DOI 10.1016/j.patrec.2010.01.006; Maulik U, 2000, PATTERN RECOGN, V33, P1455, DOI 10.1016/S0031-3203(99)00137-5; Niknam T, 2010, APPL SOFT COMPUT, V10, P183, DOI 10.1016/j.asoc.2009.07.001; Portela NM, 2014, EXPERT SYST APPL, V41, P1492, DOI [10.1016/j.eswa.2013.08.046, 10.1016/j.eswa2013.08.046]; Seker A, 2013, EXPERT SYST APPL, V40, P5341, DOI 10.1016/j.eswa.2013.03.043; SELIM SZ, 1991, PATTERN RECOGN, V24, P1003, DOI 10.1016/0031-3203(91)90097-O; SELIM SZ, 1984, IEEE T PATTERN ANAL, V6, P81; Shelokar PS, 2004, ANAL CHIM ACTA, V509, P187, DOI 10.1016/j.aca.2003.12.032; SiangTan K., 2011, PATTERN RECOGN, V44, P1; Stacey A, 2003, IEEE C EVOL COMPUTAT, P1425; Sung CS, 2000, PATTERN RECOGN, V33, P849, DOI 10.1016/S0031-3203(99)00090-4; Yuan T, 2008, EUR J OPER RES, V190, P228, DOI 10.1016/j.ejor.2007.06.007; Zhang CS, 2010, EXPERT SYST APPL, V37, P4761, DOI 10.1016/j.eswa.2009.11.003; Zhao F, 2014, EXPERT SYST APPL, V41, P4083, DOI 10.1016/j.eswa.2014.01.003; Zhao N, 2010, EXPERT SYST APPL, V37, P4805, DOI 10.1016/j.eswa.2009.12.035; Zheng BC, 2014, EXPERT SYST APPL, V41, P1476, DOI 10.1016/j.eswa.2013.08.044; Zhisheng Z., 2010, EXPERT SYSTEMS APPL, V37, P1800 47 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. OCT 1 2014 41 13 6009 6016 10.1016/j.eswa.2014.03.021 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AI4YJ WOS:000336872300029 J Yang, T; Fu, DM Yang, Tao; Fu, Dongmei Semi-supervised classification with Laplacian multiple kernel learning NEUROCOMPUTING English Article Semi-supervised classification; Graph-based regularizer; Multiple kernel learning SUPPORT VECTOR MACHINES; 3-D OBJECT RETRIEVAL; MANIFOLD REGULARIZATION; REPRESENTER THEOREM; MULTIVIEW FEATURES; RECOGNITION; CONSTRAINTS; ANNOTATION Laplacian Support Vector Machine (lapSVM) is a SVM with an additional graph-based regularization for semi-supervised learning (SSL). As its base classifier is a single kernel SVM, it may be inefficient to deal with multi-source or multi-attribute complex datasets. Instead of one single kernel, we know that multiple kernels could correspond to different notions of similarity or information from multiple sources and represent differences between features. Therefore, we extend lapSVM to multiple kernel occasion, namely Laplacian Multiple Kernel Learning (lapMKL), improving the ability of processing more complex data in semi-supervised classification task. The proposed lapMKL is solved by Level Method, which was used in multiple kernel learning (MKL) and showed relatively high efficiency. Experiments on several data sets and comparisons with state of the art methods show that the proposed lapMKL is competitive and even better. (C) 2014 Elsevier B.V. All rights reserved. [Yang, Tao; Fu, Dongmei] Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China Yang, T (reprint author), Univ Sci & Technol Beijing, Sch Automat & Elect Engn, Beijing 100083, Peoples R China. yang_taoustb@126.com National Natural Science Foundation of China [61272358]; key construction disciplines project of Beijing [00012007] This work is supported by the National Natural Science Foundation of China under Grant no. 61272358 and The key construction disciplines project of Beijing under Grant no. 00012007. ARONSZAJN N, 1950, T AM MATH SOC, V68, P337, DOI DOI 10.2307/1990404; Bach F.R., 2004, P 21 INT C MACH LEAR, P6; Bache K., 2013, UCI MACHINE LEARNING; Belkin M, 2006, J MACH LEARN RES, V7, P2399; Bishop M., 2006, PATTERN RECOGNITION; Burges CJC, 1998, DATA MIN KNOWL DISC, V2, P121, DOI 10.1023/A:1009715923555; Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199; Chapelle O., 2005, P 10 INT WORKSH ART, P57; Chapelle O., 2006, SEMISUPERVISED LEARN; Chapelle O, 2008, J MACH LEARN RES, V9, P203; Chen L, 2012, IEEE T NEUR NET LEAR, V23, P902, DOI 10.1109/TNNLS.2012.2190420; Cortes C., 2012, ARXIV12023712; Cortes C., 2010, P 27 INT C MACH LEAR, P247; Dinuzzo F, 2007, J MACH LEARN RES, V8, P2467; Gao Y, 2014, IEEE T IND ELECTRON, V61, P2088, DOI 10.1109/TIE.2013.2262760; Gao Y, 2013, IEEE T IMAGE PROCESS, V22, P363, DOI 10.1109/TIP.2012.2202676; Gao Y, 2012, IEEE T IMAGE PROCESS, V21, P4290, DOI 10.1109/TIP.2012.2199502; Gonen M, 2011, J MACH LEARN RES, V12, P2211; Griffin G., CALTECH 256 OBJECT C; Joachims T., 1999, P INT C MACH LEARN, V99, P200; Kloft M., UCBEECS201021 U CAL; Kloft M, 2011, J MACH LEARN RES, V12, P953; Kloft M., 2009, ADV NEURAL INFORM PR, V22, P997; Lanckriet GRG, 2004, J MACH LEARN RES, V5, P27; Li Y., 2009, P 26 INT C MACH LEAR, P633; Liu WF, 2013, IEEE T IMAGE PROCESS, V22, P2676, DOI 10.1109/TIP.2013.2255302; Luo Y, 2013, IEEE T NEUR NET LEAR, V24, P709, DOI 10.1109/TNNLS.2013.2238682; Orabona F., 2011, P 28 INT C MACH LEAR, P249; Rakotomamonjy A, 2008, J MACH LEARN RES, V9, P2491; Scholkopf B, 2001, LECT NOTES ARTIF INT, V2111, P416; Sindhwani V., 2008, RKHS MULTIVIEW LEARN, P976; Sonnenburg S, 2006, J MACH LEARN RES, V7, P1531; Suzuki T, 2011, MACH LEARN, V85, P77, DOI 10.1007/s10994-011-5252-9; Tomioka R., 2010, ARXIV10012615; Wang M, 2009, IEEE T MULTIMEDIA, V11, P465, DOI 10.1109/TMM.2009.2012919; Wang M, 2013, IEEE T IMAGE PROCESS, V22, P1395, DOI 10.1109/TIP.2012.2231088; Xu Z., 2009, ADV NEURAL INFORM PR, P1825; Xu ZL, 2010, IEEE T NEURAL NETWOR, V21, P1033, DOI 10.1109/TNN.2010.2047114; Yu J, 2011, IEEE T IMAGE PROCESS, V20, P3257, DOI 10.1109/TIP.2011.2158225; Yu J, 2012, IEEE T IMAGE PROCESS, V21, P4636, DOI 10.1109/TIP.2012.2207395; Yu J, 2012, IEEE T SYST MAN CY B, V42, P1413, DOI 10.1109/TSMCB.2012.2192108; Yu J, 2013, PATTERN RECOGN, V46, P483, DOI 10.1016/j.patcog.2012.08.006; Yu J, 2014, IEEE T MULTIMEDIA, V16, P159, DOI 10.1109/TMM.2013.2284755; Yu J, 2012, IEEE T IMAGE PROCESS, V21, P3262, DOI 10.1109/TIP.2012.2190083; Zhou D., 2006, ADV NEURAL INFORM PR, P1601; Zhou D., 2005, P 22 INT C MACH LEAR, P1041; Zhu X., 2003, P 20 INT C MACH LEAR, P912, DOI DOI 10.1109/18.850663; Zhu X., SEMISUPERVISED LEARN 48 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing SEP 22 2014 140 19 26 10.1016/j.neucom.2014.03.039 8 AJ6BT WOS:000337775400003 J Jasiewicz, J; Netzel, P; Stepinski, TF Jasiewicz, Jaroslaw; Netzel, Pawel; Stepinski, Tomasz F. Landscape similarity, retrieval, and machine mapping of physiographic units GEOMORPHOLOGY English Article Landscape similarity; Landscape search; Physiographic mapping; Pattern recognition; Supervised classification; Web application MULTIMEDIA INFORMATION-RETRIEVAL; LAND-USE; GEOMETRIC SIGNATURE; SPATIAL-PATTERNS; CLASSIFICATION; LANDFORMS; REGIONALIZATION; TOPOGRAPHY; EXAMPLE; METRICS We introduce landscape similarity - a numerical measure that assesses affinity between two landscapes on the basis of similarity between the patterns of their constituent landform elements. Such a similarity function provides core technology for a landscape search engine - an algorithm that parses the topography of a study area and finds all places with landscapes broadly similar to a landscape template. A landscape search can yield answers to a query in real time, enabling a highly effective means to explore large topographic datasets. In turn, a landscape search facilitates auto-mapping of physiographic units within a study area. The country of Poland serves as a test bed for these novel concepts. The topography of Poland is given by a 30 m resolution DEM. The geomorphons method is applied to this DEM to classify the topography into ten common types of landform elements. A local landscape is represented by a square tile cut out of a map of landform elements. A histogram of cell-pair features is used to succinctly encode the composition and texture of a pattern within a local landscape. The affinity between two local landscapes is assessed using the Wave-Hedges similarity function applied to the two corresponding histograms. For a landscape search the study area is organized into a lattice of local landscapes. During the search the algorithm calculates the similarity between each local landscape and a given query. Our landscape search for Poland is implemented as a GeoWeb application called TerraEx-Pl and is available at http://sil.uc.edu/. Given a sample, or a number of samples, from a target physiographic unit the landscape search delineates this unit using the principles of supervised machine learning. Repeating this procedure for all units yields a complete physiographic map. The application of this methodology to topographic data of Poland results in the delineation of nine physiographic units. The resultant map bears a close resemblance to a conventional physiographic map of Poland; differences can be attributed to geological and paleogeographical input used in drawing the conventional map but not utilized by the mapping algorithm. (C) 2014 Elsevier B.V. All rights reserved. [Jasiewicz, Jaroslaw; Netzel, Pawel; Stepinski, Tomasz F.] Univ Cincinnati, Dept Geog, Space Informat Lab, Cincinnati, OH 45221 USA; [Jasiewicz, Jaroslaw] Adam Mickiewicz Univ, Inst Geoecol & Geoinformat, PL-60680 Poznan, Poland; [Netzel, Pawel] Univ Wroclaw, Dept Climatol & Atmosphere Protect, PL-51621 Wroclaw, Poland Stepinski, TF (reprint author), Space Informat Lab, 215 Braunstein Hall, Cincinnati, OH 45221 USA. stepintz@uc.edu National Science Foundation [BCS-1147702]; Polish National Science Centre [DEC-2012/07/B/ST6/01206]; University of Cincinnati Space Exploration Institute This work was supported in part by the National Science Foundation under Grant BCS-1147702, the Polish National Science Centre under grant DEC-2012/07/B/ST6/01206, and by the University of Cincinnati Space Exploration Institute. Allen TR, 1996, PHOTOGRAMM ENG REM S, V62, P1261; Barnsley MJ, 1996, PHOTOGRAMM ENG REM S, V62, P949; Bue BD, 2006, COMPUT GEOSCI-UK, V32, P604, DOI 10.1016/j.cageo.2005.09.004; Cain D.H., 1997, LANDSCAPE ECOL, V12; CHA S, 2007, INT J MATH MODELS ME, V1, P300; Daly C, 2008, INT J CLIMATOL, V28, P2031, DOI 10.1002/joc.1688; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; Dikau R, 1995, Z GEOMORPHOLOGIE S, V101, P109; Dikau R., 1991, LANDFORM CLASSIFICAT; Dragut L, 2012, GEOMORPHOLOGY, V141, P21, DOI 10.1016/j.geomorph.2011.12.001; Duda R.O., 2001, PATTERN CLASSIFICATI, P571; Dylik J, 1952, B SOC SCI LETT, V3, P5; Dylik J, 1956, B PERYGLACJALNY, V4, P195; Evans I, 1972, SPATIAL ANAL GEOMORP, P17; Fearer TM, 2008, J BIOGEOGR, V35, P2012, DOI 10.1111/j.1365-2699.2008.01960.x; Gallant AL, 2005, IEEE GEOSCI REMOTE S, V2, P384, DOI 10.1109/LGRS.2005.848529; Galon R, 1972, GEOMORFOLOGIA POLSKI, V2; Gawde AJ, 2009, IND CROP PROD, V30, P59, DOI 10.1016/j.indcrop.2009.01.010; Gevers T., 2004, EMERGING TOPICS COMP, P333; GOOD JEG, 1993, FORESTRY, V66, P261, DOI 10.1093/forestry/66.3.261; Hammond EH, 1954, ANN ASSOC AM GEOGR, V44, P33; Hanjalic A, 2008, P IEEE, V96, P541, DOI 10.1109/JPROC.2008.916338; Haralick R.M., 1986, HDB PATTERN RECOGNIT, P247; Herzog F, 2001, ENVIRON MONIT ASSESS, V72, P37, DOI 10.1023/A:1011949704308; Iwahashi J, 2007, GEOMORPHOLOGY, V86, P409, DOI 10.1016/j.geomorph.2006.09.012; Jasiewicz J, 2013, IEEE GEOSCI REMOTE S, V10, P155, DOI 10.1109/LGRS.2012.2196019; Jasiewicz J, 2013, GEOMORPHOLOGY, V182, P147, DOI 10.1016/j.geomorph.2012.11.005; Johnson PA, 2008, RIVER RES APPL, V24, P823, DOI 10.1002/rra.1080; Kondracki J., 2002, GEOGRAFIA REGIONALNA; Kupfer JA, 2012, ECOL INFORM, V9, P11, DOI 10.1016/j.ecoinf.2012.02.001; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Long J, 2010, ENVIRON MANAGE, V46, P134, DOI 10.1007/s00267-010-9510-6; MacMillan R.A., 2004, COMPUTERS ENV URBAN, V28, P175, DOI DOI 10.1016/S0198-9715(03)00019-X; Marks L., 2005, PRZEGLAD GEOLOGICZNY, V53, P988; Martin-Duque JF, 2003, ENVIRON MANAGE, V32, P488, DOI 10.1007/s00267-003-2848-2; Mehryar M., 2012, FDN MACHINE LEARNING; Meybeck M, 2001, MT RES DEV, V21, P34, DOI 10.1659/0276-4741(2001)021[0034:ANTFMA]2.0.CO;2; Minar J, 2008, GEOMORPHOLOGY, V95, P236, DOI 10.1016/j.geomorph.2007.06.003; Niesterowicz J, 2013, APPL GEOGR, V45, P250, DOI 10.1016/j.apgeog.2013.09.023; Olaya V, 2009, DEV SOIL SCI, V33, P141, DOI 10.1016/S0166-2481(08)00006-8; O'Neill RV, 1988, LANDSCAPE ECOL, V1, P153, DOI 10.1007/BF00162741; PIKE RJ, 1988, MATH GEOL, V20, P491, DOI 10.1007/BF00890333; Stepinski TF, 2014, IEEE J-STARS, V7, P257, DOI 10.1109/JSTARS.2013.2260727; WICKHAM JD, 1994, LANDSCAPE ECOL, V9, P7, DOI 10.1007/BF00135075; Wood J, 1996, THESIS U LANCASTER U 45 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0169-555X 1872-695X GEOMORPHOLOGY Geomorphology SEP 15 2014 221 104 112 10.1016/j.geomorph.2014.06.011 9 Geography, Physical; Geosciences, Multidisciplinary Physical Geography; Geology AN1JG WOS:000340338000008 J Tobler, K; Zhao, YL; Weissman, A; Majumdar, A; Leong, M; Shoham, Z Tobler, Kyle J.; Zhao, Yulian; Weissman, Ariel; Majumdar, Abha; Leong, Milton; Shoham, Zeev Worldwide survey of IVF practices: trigger, retrieval and embryo transfer techniques ARCHIVES OF GYNECOLOGY AND OBSTETRICS English Article In vitro fertilization; Survey; Oocyte retrieval; Embryo transfer IN-VITRO FERTILIZATION; BED REST; UTERINE CAVITY; INVITRO FERTILIZATION; OOCYTE ASPIRATION; COMPLICATIONS; PREGNANCY; METAANALYSIS; TRIAL; SITE To identify common and varying practice patterns used by in vitro fertilization (IVF) providers from a broadly distributed, worldwide survey. Specific information regarding clinical IVF practices involving the oocyte maturation triggering, oocyte retrieval and embryo transfer was elicited. This is an internet-based questionnaire study of IVF practices throughout the world. We used 26 multiple choice questions regarding common clinical practices. The data reported are weighted based on the number of IVF cycles performed at the specific IVF center, represented by a single respondent. Surveys were completed from 359 centers in 71 countries throughout the world. The most common practice patterns (defined as a parts per thousand yen75 % of IVF cycles) identified included: use of human chorionic gonadotropin (hCG) for trigger with an antagonist protocol, no routine patient monitoring from hCG trigger to oocyte retrieval, timing oocyte retrieval 34-37 h following oocyte maturing trigger, use of a single lumen retrieval needle, no routine tests following oocyte retrieval prior to patient discharge and use of ultrasound assistance with embryo transfer. This is the largest and most diversely represented survey of specific IVF practices addressing oocyte maturation triggers, oocyte retrieval and embryo transfers. Several uniform practice patterns were identified that can be correlated with evidence-based medicine; however, we identified multiple variable practice patterns which is likely the result of the absence of definitive evidence to guide IVF practitioners. The results of this survey allow IVF providers to compare their specific practice patterns with those of a global diverse population of IVF providers. [Tobler, Kyle J.; Zhao, Yulian] Johns Hopkins Univ, Sch Med, Div Reproduct Endocrinol & Infertil, Dept Gynecol & Obstet,Falls Concourse, Lutherville Timonium, MD 21093 USA; [Weissman, Ariel] Tel Aviv Univ, Edith Wolfson Med Ctr, Holon Sackler Fac Med, Dept Obstet & Gynecol,IVF Unit, IL-69978 Tel Aviv, Israel; [Majumdar, Abha] Sir Ganga Ram Hosp, Ctr IVF & Human Reprod, New Delhi, India; [Leong, Milton] Hong Kong Sanat & Hosp, IVF Ctr, Hong Kong, Hong Kong, Peoples R China; [Shoham, Zeev] Kaplan Med Ctr, Dept Obstet & Gynecol, Rehovot, Israel; [Shoham, Zeev] Hebrew Univ Jerusalem, Jerusalem, Israel; [Shoham, Zeev] Hebrew Univ Jerusalem, Hadassah Med Sch, IL-91010 Jerusalem, Israel Tobler, K (reprint author), Johns Hopkins Univ, Sch Med, Div Reproduct Endocrinol & Infertil, Dept Gynecol & Obstet,Falls Concourse, 10751 Falls Rd,Suite 280, Lutherville Timonium, MD 21093 USA. ktobler1@jhmi.edu Abou-Setta AM, 2007, REPROD BIOMED ONLINE, V14, P611; Abou-Setta AM, 2007, FERTIL STERIL, V88, P333, DOI 10.1016/j.fertnstert.2006.11.161; Amarin ZO, 2004, BJOG-INT J OBSTET GY, V111, P1273, DOI 10.1111/j.1471-0528.2004.00346.x; AMSTEY MS, 1981, JAMA-J AM MED ASSOC, V245, P839, DOI 10.1001/jama.245.8.839; BENNETT S, 1993, J ASSIST REPROD GEN, V10, P100, DOI 10.1007/BF01204450; BENNETT SJ, 1993, J ASSIST REPROD GEN, V10, P72, DOI 10.1007/BF01204444; Bjercke S, 2000, J ASSIST REPROD GEN, V17, P319, DOI 10.1023/A:1009401027251; Botta G, 1997, HUM REPROD, V12, P2489, DOI 10.1093/humrep/12.11.2489; Brown J, 2010, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD006107.pub3; Coroleu B, 2002, HUM REPROD, V17, P341, DOI 10.1093/humrep/17.2.341; Derks RS, 2009, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD007682.pub2; DICKER D, 1993, FERTIL STERIL, V59, P1313; Franco JG, 2004, HUM REPROD, V19, P1785, DOI 10.1093/humrep/deh308; Fritz MA, 2011, CLIN GYNECOLOGIC END; Gaikwad S, 2013, FERTIL STERIL, V100, P729, DOI 10.1016/j.fertnstert.2013.05.011; Hannoun A, 2008, GYNECOL OBSTET INVES, V66, P274, DOI 10.1159/000156378; Haydardedeoglu B, 2011, FERTIL STERIL, V95, P812, DOI 10.1016/j.fertnstert.2010.09.013; Kwan I, 2013, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD004829.pub3; MANSOUR RT, 1994, J ASSIST REPROD GEN, V11, P478, DOI 10.1007/BF02215712; Min JK, 2004, HUM REPROD, V19, P3, DOI 10.1093/humrep/deh028; Purcell KJ, 2007, FERTIL STERIL, V87, P1322, DOI 10.1016/j.fertnstert.2006.11.060; Rezábek K, 2001, Ceska Gynekol, V66, P175; Roest J, 1996, HUM REPROD UPDATE, V2, P345, DOI 10.1093/humupd/2.4.345; SCOTT RT, 1989, J IN VITRO FERTIL EM, V6, P98, DOI 10.1007/BF01130734; Sharif K, 1998, FERTIL STERIL, V69, P478, DOI 10.1016/S0015-0282(97)00534-7; Vaisbuch E, 2012, REPROD BIOMED ONLINE, V25, P139, DOI 10.1016/j.rbmo.2012.04.005; VANOS HC, 1992, HUM REPROD, V7, P349; Van Voorhis BJ, 2010, FERTIL STERIL, V94, P1346, DOI 10.1016/j.fertnstert.2010.06.048; WATERSTONE J, 1991, LANCET, V337, P1413, DOI 10.1016/0140-6736(91)93094-P; Youssef MAFM, 2011, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD008046.pub3; Youssef MAFM, 2011, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD003719.pub3 31 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0932-0067 1432-0711 ARCH GYNECOL OBSTET Arch. Gynecol. Obstet. SEP 2014 290 3 561 568 10.1007/s00404-014-3232-6 8 Obstetrics & Gynecology Obstetrics & Gynecology AN3WB WOS:000340518300026 J Scott, RJ; Fox, SB; Desai, J; Grieu, F; Amanuel, B; Garrett, K; Harraway, J; Cheetham, G; Pattle, N; Haddad, A; Byron, K; Rudzki, B; Waring, P; Iacopetta, B Scott, Rodney J.; Fox, Stephen B.; Desai, Jayesh; Grieu, Fabienne; Amanuel, Benhur; Garrett, Kerryn; Harraway, James; Cheetham, Glenice; Pattle, Neville; Haddad, Afaf; Byron, Keith; Rudzki, Barney; Waring, Paul; Iacopetta, Barry KRAS mutation testing of metastatic colorectal cancer in Australia: Where are we at? ASIA-PACIFIC JOURNAL OF CLINICAL ONCOLOGY English Article Group 1: major specialty; pathology; Group 2: tumor type; gastrointestinal cancer; colorectal; Group 3: other specific research area; biomarker EXTERNAL QUALITY ASSESSMENT; RAS MUTATIONS; ASSOCIATION; MULTICENTER; CETUXIMAB Aim: To carry out a nationwide study of KRAS testing in metastatic colorectal cancer as reported by nine major molecular pathology service providers in Australia, including mutation frequencies and turnaround times that might impact on patient care. Methods: Participating laboratories contributed information on KRAS mutation frequencies, including the G13D mutation type, as well as turnaround times for tumor block retrieval and testing. Results: The KRAS mutation frequency observed by nine different test sites for a total of 3688 metastatic colorectal cancers ranged from 34.4% to 40.7%, with an average across all sites of 38.8%. The average frequency of the G13D mutation type among all cases was 8.0%. The median turnaround time was 17 days (range 0-191), with 20% of cases requiring more than 4 weeks for a KRAS test result. The major contributor to long turnaround times was the time taken to retrieve archived blocks of primary tumor, particularly from sources external to the test site. Conclusion: The frequency of KRAS mutations in metastatic colorectal cancer reported by the major Australian test sites is very similar to that reported by other large overseas studies. More widespread introduction of routine testing at the time of initial diagnosis should eliminate the long turnaround times currently being experienced in a significant proportion of cases. Future expansion of testing to include other KRAS and NRAS mutation hotspots may spur the introduction of next-generation sequencing platforms. [Scott, Rodney J.] Hunter Area Pathol Serv, Newcastle, NSW, Australia; [Fox, Stephen B.; Desai, Jayesh] Univ Melbourne, Peter MacCallum Canc Ctr, Melbourne, Vic, Australia; [Pattle, Neville; Haddad, Afaf] Univ Melbourne, Dorevitch Pathol, Melbourne, Vic, Australia; [Rudzki, Barney; Waring, Paul] Univ Melbourne, Dept Pathol, Melbourne, Vic, Australia; [Grieu, Fabienne; Amanuel, Benhur] PathWest QEII, Dept Mol Anat Pathol, Nedlands, WA, Australia; [Iacopetta, Barry] Univ Western Australia, Sch Surg, Nedlands, WA 6009, Australia; [Garrett, Kerryn] St John God Pathol, Subiaco, WA, Australia; [Harraway, James] Sullivan Nicolaides Pathol, Brisbane, Qld, Australia; [Cheetham, Glenice] SA Pathol, Adelaide, SA, Australia; [Byron, Keith] Healthscope Pathol, Clayton, Vic, Australia Iacopetta, B (reprint author), Univ Western Australia, Sch Surg M507, Nedlands, WA 6009, Australia. barry.iacopetta@uwa.edu.au Andreyev HJN, 1998, J NATL CANCER I, V90, P675, DOI 10.1093/jnci/90.9.675; Bellon E, 2011, ONCOLOGIST, V16, P467, DOI 10.1634/theoncologist.2010-0429; Blons H, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0068945; Chen J, 2013, CANCER CHEMOTH PHARM, V71, P265, DOI 10.1007/s00280-012-2005-9; Chretien Anne-Sophie, 2013, Cancer Med, V2, P11, DOI 10.1002/cam4.47; Ciardiello F, 2011, TARGET ONCOL, V6, P133, DOI 10.1007/s11523-011-0181-x; De Roock W, 2010, LANCET ONCOL, V11, P753, DOI 10.1016/S1470-2045(10)70130-3; Dijkstra JR, 2013, VIRCHOWS ARCH, V462, P39, DOI 10.1007/s00428-012-1356-2; Douillard JY, 2013, NEW ENGL J MED, V369, P1023, DOI 10.1056/NEJMoa1305275; Hadd AG, 2013, J MOL DIAGN, V15, P234, DOI 10.1016/j.jmoldx.2012.11.006; Heinemann V, 2013, CANCER TREAT REV, V39, P592, DOI 10.1016/j.ctrv.2012.12.011; Karapetis CS, 2008, NEW ENGL J MED, V359, P1757, DOI 10.1056/NEJMoa0804385; Lievre A, 2013, EUR J CANCER, V49, P2126, DOI 10.1016/j.ejca.2013.02.016; Malapelle U, 2014, J CLIN PATHOL, V67, P1, DOI 10.1136/jclinpath-2013-201835; Netzel BC, 2013, CLIN CHIM ACTA, V425, P1, DOI 10.1016/j.cca.2013.06.025; Peeters M, 2013, CLIN CANCER RES, V19, P1902, DOI 10.1158/1078-0432.CCR-12-1913; Tejpar S, 2012, J CLIN ONCOL, V30, P3570, DOI 10.1200/JCO.2012.42.2592; van Krieken JH, 2013, VIRCHOWS ARCH, V462, P27, DOI 10.1007/s00428-012-1354-4; Vaughn CP, 2011, GENE CHROMOSOME CANC, V50, P307, DOI 10.1002/gcc.20854; Watanabe T, 2013, JPN J CLIN ONCOL, V43, P706, DOI 10.1093/jjco/hyt062; Whitehall V, 2009, J MOL DIAGN, V11, P543, DOI 10.2353/jmoldx.2009.090057 21 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1743-7555 1743-7563 ASIA-PAC J CLIN ONCO Asia-Pac. J. Clin. Oncol. SEP 2014 10 3 261 265 10.1111/ajco.12201 5 Oncology Oncology AN4FW WOS:000340544000009 J Truppa, V; De Simone, DA; Mortari, EP; De Lillo, C Truppa, Valentina; De Simone, Diego Antonio; Mortari, Eva Piano; De Lillo, Carlo Effects of brief time delays on matching-to-sample abilities in capuchin monkeys (Sapajus spp.) BEHAVIOURAL BRAIN RESEARCH English Article Visual perception; Sensory memory; Short-term memory; Matching-to-sample; Capuchin monkey SHORT-TERM-MEMORY; CEBUS-APELLA; RHESUS-MONKEYS; WORKING-MEMORY; PERIRHINAL CORTEX; VISUAL MEMORY; ICONIC MEMORY; AREA TE; PERFORMANCE; BABOONS Traditionally, studies of delayed matching-to-sample (DMTS) tasks in nonhuman species have focused on the assessment of the limits of the retrieval of information stored in short- and long-term memory systems. However, it is still unclear if visual recognition in these tasks is affected by very brief delay intervals, which are typically used to study rapidly decaying types of visual memory. This study aimed at evaluating if tufted capuchin monkeys' ability to recognise visual stimuli in a DMTS task is affected by (i) the disappearance of the sample stimulus and (ii) the introduction of delay intervals (0.5, 1.0, 2.0 and 3.0 s) between the disappearance of the sample and the presentation of the comparison stimuli. The results demonstrated that the simple disappearance of the sample and the introduction of a delay of 0.5 s did not affect capuchins' performance either in terms of accuracy or response time. A delay interval of 1.0 s produced a significant increase in response time but still did not affect recognition accuracy. By contrast, delays of 2.0 and 3.0 s determined a significant increase in response time and a reduction in recognition accuracy. These findings indicate the existence in capuchin monkeys of processes enabling a very accurate retention of stimulus features within time frames comparable to those reported for humans' sensory memory (0.5-1.0 s). The extent to which such processes can be considered analogous to the sensory memory processes observed in human visual cognition is discussed. (C) 2014 Elsevier B.V. All rights reserved. [Truppa, Valentina; De Simone, Diego Antonio; Mortari, Eva Piano] CNR, Inst Cognit Sci & Technol, I-00197 Rome, Italy; [De Simone, Diego Antonio] Univ Roma La Sapienza, Dept Philosophy, I-00161 Rome, Italy; [Mortari, Eva Piano] Univ Roma La Sapienza, Dept Neurobiol, I-00185 Rome, Italy; [De Lillo, Carlo] Univ Leicester, Sch Psychol, Leicester LE1 9HN, Leics, England Truppa, V (reprint author), CNR, Inst Cognit Sci & Technol, Via Ulisse Aldrovandi 16-8, I-00197 Rome, Italy. valentina.truppa@istc.cnr.it; diegoantonio.desimone@gmail.com; eva.pianomortari@gmail.com; cdl2@leicester.ac.uk PNR-CNR Aging Program We wish to thank Massimiliano Bianchi and Simone Cartarinacci for help with animal management. We acknowledge Sara Privitera, Luca Santini and Leonardo Ancilotto for help with data collection and Raffaele Sardella for help with video scoring. We thank the anonymous reviewer for her/his thoughtful and constructive comments. We also thank the Comune di Roma-Museo Civico Zoologia and the Fondazione Bioparco for hosting the Unit of Cognitive Primatology and Primate Centre. This research was supported by the PNR-CNR Aging Program 2012-2014. Alfaro JWL, 2012, J BIOGEOGR, V39, P272, DOI 10.1111/j.1365-2699.2011.02609.x; Atkinson RC, 1968, PSYCHOLOGY LEARNING, V2, P90; AVERBACH E, 1961, AT&T TECH J, V40, P309; Baddeley A, 1990, HUMAN MEMORY THEORY; BARTUS RT, 1978, J GERONTOL, V33, P858; Basile BM, 2011, CURR BIOL, V21, P774, DOI 10.1016/j.cub.2011.03.044; Buffalo EA, 2000, LEARN MEMORY, V7, P375, DOI 10.1101/lm.32100; Buffalo EA, 1999, LEARN MEMORY, V6, P572, DOI 10.1101/lm.6.6.572; Chelonis JJ, 2014, BEHAV PROCESS, V103, P261, DOI 10.1016/j.beproc.2014.01.002; COLOMBO M, 1986, Q J EXP PSYCHOL-B, V38, P425; COLTHEAR.M, 1974, Q J EXP PSYCHOL, V26, P633, DOI 10.1080/14640747408400456; COLTHEART M, 1980, PHILOS T ROY SOC B, V290, P57, DOI 10.1098/rstb.1980.0082; DAMATO MR, 1985, J EXP PSYCHOL ANIM B, V11, P35, DOI 10.1037/0097-7403.11.1.35; DAMATO MR, 1971, J EXP ANAL BEHAV, V15, P327, DOI 10.1901/jeab.1971.15-327; D'AMATO M R, 1972, Learning and Motivation, V3, P304, DOI 10.1016/0023-9690(72)90026-4; De Lillo C, 2011, J EXP PSYCHOL ANIM B, V37, P341, DOI 10.1037/a0022989; De Lillo C, 2007, BEHAV BRAIN RES, V181, P96, DOI 10.1016/j.bbr.2007.03.030; Elmore LC, 2011, CURR BIOL, V21, P975, DOI 10.1016/j.cub.2011.04.031; ETKIN M, 1969, J COMP PHYSIOL PSYCH, V69, P544, DOI 10.1037/h0028209; Fagot J, 2011, NEUROPSYCHOLOGIA, V49, P3870, DOI 10.1016/j.neuropsychologia.2011.10.003; Flicker C, 1984, NEUROBIOL AGING, V5, P75; Fragaszy DM, 2004, BIOL GENUS CEBUS; Fujita K, 2009, ANIM COGN, V12, P575, DOI 10.1007/s10071-009-0217-0; Galvao OD, 2008, REV NEUROSCIENCE, V19, P149; Galvao ODF, 2005, PSYCHOL REC, V55, P219; GIBBS ME, 1979, NEUROSCI LETT, V13, P279, DOI 10.1016/0304-3940(79)91507-6; GOLDMANRAKIC PS, 1992, SCI AM, V267, P111; Goto K, 2009, JAPANESE PSYCHOL RES, V3, P122; Katz JS, 2002, J EXP PSYCHOL ANIM B, V28, P358, DOI 10.1037//0097-7403.28.4.358; King JE, 1966, PSYCHON SCI, V6, P429; KING JE, 1968, ANIM BEHAV, V16, P271, DOI 10.1016/0003-3472(68)90008-0; Lynch Alfaro JW, 2012, AM J PRIMATOL, V74, P273; MARRIOTT JG, 1980, AGE, V3, P7, DOI 10.1007/BF02434999; McDowell AA, 1960, J GENET PSYCHOL, V97, P59; MISHKIN M, 1975, J EXP PSYCHOL ANIM B, V1, P326, DOI 10.1037/0097-7403.1.4.326; NELSON KR, 1978, J EXP ANAL BEHAV, V30, P153, DOI 10.1901/jeab.1978.30-153; ODEN DL, 1988, J EXP PSYCHOL ANIM B, V14, P140, DOI 10.1037//0097-7403.14.2.140; OVERMAN WH, 1980, NEUROSCIENCE, V5, P1825, DOI 10.1016/0306-4522(80)90032-9; Roberts WA, 1976, PROCESSES ANIMAL MEM, P79; Rodriguez JS, 2011, J NEUROSCI METH, V196, P258, DOI 10.1016/j.jneumeth.2011.01.012; Ruff CC, 2007, PSYCHOL SCI, V18, P901, DOI 10.1111/j.1467-9280.2007.01998.x; SAHAKIAN BJ, 1988, BRAIN, V111, P695, DOI 10.1093/brain/111.3.695; Shettleworth S, 1998, EVOLUTION BEHAV; Sligte IG, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0001699; SPERLING G, 1960, PSYCHOL MONOGR, V74, P1; Spinozzi G, 2003, J COMP PSYCHOL, V117, P15, DOI 10.1037/0735-7036.117.1.15; Steingrimsdottir HS, 2011, AM J ALZHEIMERS DIS, V26, P247, DOI 10.1177/1533317511402816; Tavares MCH, 2002, BEHAV BRAIN RES, V131, P131, DOI 10.1016/S0166-4328(01)00368-0; Tomasello M, 1997, PRIMATE COGNITION; Truppa V, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0023809; Truppa V, 2010, ANIM COGN, V13, P1; VAUCLAIR J, 1993, PSYCHOL SCI, V4, P99, DOI 10.1111/j.1467-9280.1993.tb00468.x; Vonk J, 2003, ANIM COGN, V6, P77, DOI 10.1007/s10071-003-0159-x; WASHBURN DA, 1989, J EXP PSYCHOL ANIM B, V15, P393, DOI 10.1037//0097-7403.15.4.393; Weinstein B, 1941, J COMP PSYCHOL, V31, P195, DOI 10.1037/h0063449; Wong YJ, 2009, J NEUROPHYSIOL, V102, P3461, DOI 10.1152/jn.00243.2009; Wright AA, 2005, J EXP PSYCHOL ANIM B, V31, P425, DOI 10.1037/0097-7403.31.4.425; WRIGHT AA, 1985, SCIENCE, V229, P287, DOI 10.1126/science.9304205; Wright AA, 2012, OXFORD HDB COMP COGN, P239 59 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0166-4328 1872-7549 BEHAV BRAIN RES Behav. Brain Res. SEP 1 2014 271 240 248 10.1016/j.bbr.2014.05.023 9 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AN1DN WOS:000340323000033 J Dettmeyer, RB Dettmeyer, R. B. The role of histopathology in forensic practice: an overview FORENSIC SCIENCE MEDICINE AND PATHOLOGY English Review Forensic histopathology; Histopathological time estimation; Drug-induced histopathology; Postmortem histopathology INFANT-DEATH-SYNDROME; HUMAN SKIN WOUNDS; BRAIN-INJURY; ANTIGEN RETRIEVAL; UNEXPECTED DEATH; IMMUNOHISTOCHEMICAL LOCALIZATION; HISTOLOGICAL EXAMINATION; POSTMORTEM; SUDDEN; MYOCARDITIS The role of forensic histopathology in routine practice is to establish the cause of death in particular cases. This is achieved on the basis of microscopic analysis of representative cell and tissue samples taken from the major internal organs and from abnormal findings made at autopsy. A prerequisite of this is adherence to the quality standards set out for conventional histological/cytological staining and enzyme histochemical and immunohistochemical methods. The interpretation of histological findings is performed by taking into account macroscopic autopsy findings and information on previous history. Histological analysis may prompt postmortem biochemical and chemical-toxicological investigations. The results of histological analysis need to be classified by experts in the context of the available information and the need to withstand the scrutiny of other experts. Univ Giessen, Inst Forens Med, D-35392 Giessen, Germany Dettmeyer, RB (reprint author), Univ Giessen, Inst Forens Med, Frankfurter Str 58, D-35392 Giessen, Germany. Reinhard.Dettmeyer@forens.med.uni-giessen.de Amberg R, 1995, IMMUNHISTOCHEMIE REC, P101; Armstrong EJ, 2006, AM J FOREN MED PATH, V27, P151, DOI 10.1097/01.paf.0000203221.17854.38; Bernardi FDC, 2005, J CLIN PATHOL, V58, P1261, DOI 10.1136/jcp.2005.027953; BETZ P, 1993, INT J LEGAL MED, V106, P31, DOI 10.1007/BF01225021; BETZ P, 1993, INT J LEGAL MED, V105, P329, DOI 10.1007/BF01222117; Brunt EM, 1999, AM J GASTROENTEROL, V94, P2467, DOI 10.1111/j.1572-0241.1999.01377.x; Buttner A, 2006, ADDICTION, V101, P1339, DOI 10.1111/j.1360-0443.2006.01505.x; Byard RW, 2006, FOREN PATHOL REV, V4, P93, DOI 10.1007/978-1-59259-921-9_4; Byard RW, 2008, J FORENSIC SCI, V53, P460, DOI 10.1111/j.1556-4029.2008.00662.x; Byard RW, 2007, FORENSIC SCI INT, V171, P118, DOI 10.1016/j.forsciint.2006.10.008; Byard RW, 2002, J FORENSIC SCI, V47, P202; Campobasso CP, 2008, AM J FOREN MED PATH, V29, P154, DOI 10.1097/PAF.0b013e318177eab7; Casali MB, 2012, FORENSIC SCI INT, V223, P78, DOI 10.1016/j.forsciint.2012.08.004; Cecchi R, 2010, INT J LEGAL MED, V124, P523, DOI 10.1007/s00414-010-0505-x; CLEVENGER CV, 1991, J ANAL TOXICOL, V15, P151; Cummings PM, 2011, ATLAS FORENSIC HISTO; de la Grandmaison GL, 2010, J FORENSIC SCI, V55, P85, DOI 10.1111/j.1556-4029.2009.01240.x; Dettmeyer R, 2004, PEDIATR RES, V55, P947, DOI 10.1203/01.pdr.0000127022.45831.54; Dettmeyer R, 2008, FORENSIC SCI INT, V174, P229, DOI 10.1016/j.forsciint.2007.05.009; Dettmeyer RB, 2011, FORENSIC HISTOPATHOL, P213; Dettmeyer RB, 2011, FORENSIC HISTOPATHOL, P150; Dettmeyer RB, 2011, FORENSIC HISTOPATHOL, P219; Dettmeyer RB, 2011, FORENSIC HISTOPATHOL, P403; DiMaio VJM, 2007, HDB FORENSIC PATHOLO; Dokhan DB, 1993, APPL IMMUNOHISTO M M, V1, P149; Dressler J, 2007, INT J LEGAL MED, V121, P365, DOI 10.1007/s00414-006-0126-6; Durwald W, 1987, GERICHTLICHE MED, P87; Evers H, 2009, FORENSIC SCI MED PAT, V5, P182, DOI 10.1007/s12024-009-9092-x; FALZI G, 1964, Munch Med Wochenschr, V106, P978; Ferrara SD, 2010, INT J LEGAL MED, V120, P293; Ferries JAJ, 1981, CAN SOC FORENSIC SCI, V14, P113; Fineschi V, 2009, FORENSIC SCI INT, V186, P22, DOI 10.1016/j.forsciint.2009.01.006; Fineschi V, 2006, PATHOLOGY HEART SUDD; Hausmann R, 2004, FOREN PATHOL REV, P53; HAUSMANN R, 1994, INT J LEGAL MED, V106, P298, DOI 10.1007/BF01224775; Iacobuzio-Donahue CA, 2001, AM J SURG PATHOL, V25, P1067, DOI 10.1097/00000478-200108000-00012; Janssen W, 1977, FORENSISCHE HISTOLOG; Kakar Puneet, 2006, Int J Cardiol, V112, pe5, DOI 10.1016/j.ijcard.2006.01.033; Karch SB, 2009, KARCHS PATHOLOGY DRU, P523; Kilian JG, 1999, LANCET, V354, P1841, DOI 10.1016/S0140-6736(99)10385-4; Kondo Toshikazu, 2007, Legal Medicine, V9, P109, DOI 10.1016/j.legalmed.2006.11.009; Langlois NEI, 2006, MED SCI LAW, V46, P310; LAU G, 2008, FOREN PATHOL REV, V5, P239; Leestma JE, 2009, FORENSIC NEUROPATHOL; Lunetta P, 2005, FOREN PATHOL REV, V3, P3, DOI 10.1007/978-1-59259-910-3_1; Magdalan J, 2009, ARCH TOXICOL, V83, P55, DOI 10.1007/s00204-008-0376-9; Matschke J, 2009, INT J LEGAL MED, V123, P189, DOI 10.1007/s00414-008-0293-8; Molina DK, 2007, AM J FOREN MED PATH, V28, P1, DOI 10.1097/01.paf.0000257388.83605.0a; Oehmichen M, 2009, FORENSIC NEUROPATHOL; Oehmichen M., 2009, Legal Medicine, V11, P118, DOI 10.1016/j.legalmed.2008.11.003; Ogata M, 1999, INT J LEGAL MED, V113, P19, DOI 10.1007/s004140050273; Osawa Motoki, 2008, Legal Medicine, V10, P143, DOI 10.1016/j.legalmed.2007.10.002; Pampin JB, 2012, PRACTICAL MANUAL FOR; Perper JA, 1980, MICROSCOPIC DIAGNOSI; Pileri SA, 1997, J PATHOL, V183, P116; Pomara C, 2008, CLIN TOXICOL, V46, P322, DOI 10.1080/15563650701419011; Puschel K., 1998, DTSCH ARZTEBL, V95, P2697; RAEKALLIO J, 1960, NATURE, V188, P234, DOI 10.1038/188234a0; Rasten-Almqvist P, 2002, APMIS, V110, P469, DOI 10.1034/j.1600-0463.2002.100605.x; RITCHIE S, 1957, AM J PATHOL, V33, P1035; Roulson J, 2005, HISTOPATHOLOGY, V47, P551, DOI 10.1111/j.1365-2559.2005.02243.x; Sandritter W, 1977, HISTOPATHOLOGIE; Sannohe S, 2002, J FORENSIC SCI, V47, P1391; Sinicina I, 2010, INT J LEGAL MED, V124, P55, DOI 10.1007/s00414-009-0351-x; Takahashi Shirushi, 2008, Leg Med (Tokyo), V10, P43, DOI 10.1016/j.legalmed.2007.05.010; THOMSEN H, 1995, FORENSIC SCI INT, V71, P87, DOI 10.1016/0379-0738(94)01640-Q; Tsokos M, 2001, INT J LEGAL MED, V114, P291, DOI 10.1007/s004140000172; Tsokos Michael, 2003, Leg Med (Tokyo), V5, P73, DOI 10.1016/S1344-6223(03)00010-5; Tsokos M, 2005, FORENSIC SCI INT, V149, P25, DOI 10.1016/j.forsciint.2004.05.010; Uzin I, 2008, FORENSIC SCI INT, V178, P157; Vandewoestyne M, 2009, INT J LEGAL MED, V123, P169, DOI 10.1007/s00414-008-0271-1; VONWASIELEWSKI R, 1994, HISTOCHEMISTRY, V102, P165; Welte T, 2004, FORENSIC SCI INT, V139, P21, DOI 10.1016/j.forsciint.2003.09.018; Werner M, 1996, HISTOCHEM CELL BIOL, V105, P253, DOI 10.1007/BF01463928; Wollersen H, 2009, LEG MED TOKYO S1, V11, pS488; Wollersen H, 2009, LEG MED, V11, pS498; Xiaohong Zhao, 2002, Leg Med (Tokyo), V4, P47, DOI 10.1016/S1344-6223(01)00054-2; YUNGINGER JW, 1991, J FORENSIC SCI, V36, P857; Zaitoun AM, 1998, PATHOLOGY, V30, P100 79 0 0 HUMANA PRESS INC TOTOWA 999 RIVERVIEW DRIVE SUITE 208, TOTOWA, NJ 07512 USA 1547-769X 1556-2891 FORENSIC SCI MED PAT Forensic Sci. Med. Pathol. SEP 2014 10 3 401 412 10.1007/s12024-014-9536-9 12 Medicine, Legal; Pathology Legal Medicine; Pathology AN3CK WOS:000340462300016 J Johnson, JD Johnson, J. David Health-related information seeking: Is it worth it? INFORMATION PROCESSING & MANAGEMENT English Article Human information behavior; Information seeking; Health-related information seeking; Avoidance; Ignorance DECISION-MAKING; CLINICAL QUESTIONS; RETRIEVAL SYSTEMS; CANCER-PATIENTS; BEHAVIOR; COMMUNICATION; KNOWLEDGE; RISK; LIFE; PHYSICIANS In spite of often compelling reasons for why people should seek information, they persistently engage in lower levels of it than might be expected, at times seeking no information at all. The idealized model reflecting dogged persistence, pursuing rational search strategies, until a high-quality answer is found is often assumed in the design of information systems. However, many people confronted with health problems engage in avoidance and denial, making a health care system dependent on proactivity problematic. This essay explores six conditions, the idealized model, avoidance, bewilderment, serendipity, ignorance is bliss, and indolence, that arise from low and high effort strategies when typed by good, contingent, and bad health outcomes. Since a substantial proportion of the population does not act in accordance with our assumptions, it may be time for policy makers, system designers and researchers to revisit their approaches to facilitating health-related information seeking. (C) 2014 Elsevier Ltd. All rights reserved. Univ Kentucky, Dept Commun, Lexington, KY 40506 USA Johnson, JD (reprint author), Univ Kentucky, Dept Commun, 242 Grehan Bldg, Lexington, KY 40506 USA. jdj@uky.edu Adelman M. B., 1987, COMMUNICATING SOCIAL, P212; Allen T. J., 1977, MANAGING FLOW TECHNO; Altman D G, 1985, J Community Health, V10, P156, DOI 10.1007/BF01323958; ANDERSON D M, 1989, Health Education Research, V4, P419, DOI 10.1093/her/4.4.419; Andrews J. E., 2005, J MED LIBR ASSOC, V93, P48; Andrykowski MA, 1997, J CLIN ONCOL, V15, P2139; Armstrong K, 2002, PREV MED, V34, P590, DOI 10.1006/pmed.2002.1022; Armstrong K, 2002, MED DECIS MAKING, V22, P76, DOI 10.1177/02729890222062946; Arora N. K., 2007, J GEN INTERNAL MED, V23, P223, DOI [10.1007/s11606-007-0406-y, DOI 10.1007/S11606-007-0406]; Barbour J. B., 2011, J HLTH COMMUNICATION; Barzilai-Nahon K., 2009, GATEKEEPING CRITICAL, V43, P1; Berry D., 2007, HLTH COMMUNICATION T; BETTINGHAUS EP, 1986, PREV MED, V15, P475, DOI 10.1016/0091-7435(86)90025-3; Bhavnani SK, 2010, J AM SOC INF SCI TEC, V61, P659, DOI 10.1002/asi.21217; Bilodeau B A, 1996, Oncol Nurs Forum, V23, P691; Blau PM, 1954, HUM RELAT, V7, P337, DOI 10.1177/001872675400700305; Brashers DE, 2001, J COMMUN, V51, P477, DOI 10.1111/j.1460-2466.2001.tb02892.x; Brashers DE, 2000, COMMUN MONOGR, V67, P63; Broadway M. D., 1993, PUBLIC LIB SEP, P253; Cameron KA, 2011, ROUTL COMMUN SER, P306; Campbell T. F., 1994, AHCPR PUB, V95 0015; Case D. O., 2012, LOOKING INFORM; Case D. O., 2012, HLTH INFORM SEEKING; Case DO, 2006, ANNU REV INFORM SCI, V40, P293, DOI 10.1002/aris.1440400114; Cialdini R. B., 2001, INFLUENCE SCI PRACTI; Clarke P., 1973, NEW MODELS MASS COMM, P205; Lau Annie Y S, 2008, J Med Internet Res, V10, pe2, DOI 10.2196/jmir.963; Cook P. L., 1987, INT COMM ASS MONTR; Craigie M., 2002, J MED INTERNET RES; Culnan M. J., 1983, DECISION SCI, V14, P194, DOI 10.1111/j.1540-5915.1983.tb00180.x; Culver JD, 1997, J GEN INTERN MED, V12, P466, DOI 10.1046/j.1525-1497.1997.00084.x; Dawes M, 2003, INT J MED INFORM, V71, P9, DOI 10.1016/S1386-5056(03)00023-6; DEGNER LF, 1992, J CLIN EPIDEMIOL, V45, P941, DOI 10.1016/0895-4356(92)90110-9; Dervin B., 1998, Journal of Knowledge Management, V2, DOI 10.1108/13673279810249369; Eheman CR, 2009, J HEALTH COMMUN, V14, P487, DOI 10.1080/10810730903032945; Ely JW, 2005, J AM MED INFORM ASSN, V12, P217, DOI 10.1197/jamia.M1608; Ferguson T., 2007, E PATIENTS THEY CAN; Fisher K. E., 2005, THEORIES INFORM BEHA, P1; Grant A. M., 2009, J APPL PSYCHOL, V94, P1261; Health and Human Services, 2010, NAT ACT PLAN IMPR HL; Helmes AW, 2000, CANCER EPIDEM BIOMAR, V9, P1377; Henwood F, 2003, SOCIOL HEALTH ILL, V25, P589, DOI 10.1111/1467-9566.00360; Hersh WR, 2005, MED DECIS MAKING, V25, P147, DOI 10.1177/0272989X05275557; Hersh WR, 2002, J AM MED INFORM ASSN, V9, P283, DOI 10.1197/jamia.M0996; Hersh WR, 1998, JAMA-J AM MED ASSOC, V280, P1347, DOI 10.1001/jama.280.15.1347; Herskovic JR, 2007, J AM MED INFORM ASSN, V14, P212, DOI 10.1197/jamia.M2191; Hines SC, 2001, J COMMUN, V51, P498, DOI 10.1111/j.1460-2466.2001.tb02893.x; HOROWITZ GL, 1983, JAMA-J AM MED ASSOC, V250, P2494, DOI 10.1001/jama.250.18.2494; Horvitz E., 2010, AMIA ANN S; Hoyer W. D., 1987, COMPREHENSION MISCOM; HUDSON J, 1980, PERS GUID J, V59, P164; Johnson J. D., 1996, INFORM SEEKING ORG D; Johnson J. D., 1997, CANC RELATED INFORM; Johnson JD, 2005, J HEALTH COMMUN, V10, P323, DOI 10.1080/10810730590950048; Johnson JDE, 2006, INFORM PROCESS MANAG, V42, P569, DOI 10.1016/j.ipm.2004.12.001; Jones Josette, 2011, Open Nurs J, V5, P24, DOI 10.2174/1874434601105010024; Kortum P, 2008, J MED INTERNET RES, V10, DOI 10.2196/jmir.986; Lerman C, 1996, JAMA-J AM MED ASSOC, V275, P1885, DOI 10.1001/jama.275.24.1885; Lerman C, 1999, JAMA-J AM MED ASSOC, V281, P1618, DOI 10.1001/jama.281.17.1618; LERMAN C, 1995, AM J MED GENET, V57, P385, DOI 10.1002/ajmg.1320570304; Lewis T, 2006, MEDIA CULT SOC, V28, P521, DOI 10.1171/0163443706065027; Lobb EA, 2004, BRIT J CANCER, V90, P321, DOI 10.1038/sj.bjc.6601502; Lowery W., 2002, ASS ED JOURN MASS CO; Lynch B. P., 1989, HUMANISTS WORK DISCI, P29; March J. G., 1994, PRIMER DECISION MAKI; Markey K, 2007, J AM SOC INF SCI TEC, V58, P1123, DOI 10.1002/asi.20601; Marteau TM, 1998, BRIT MED J, V316, P693; McKechnie L, 2005, THEORIES INFORM BEHA, P289; Menon T, 2006, MANAGE SCI, V52, P1129, DOI 10.1287/mnsc.1060.0525; Merriam-Webster, 2004, MERR WEBST COLL DICT; MILLER SM, 1987, J PERS SOC PSYCHOL, V52, P345, DOI 10.1037//0022-3514.52.2.345; O'Hair H. D., 2008, HLTH COMMUNICATION 2; Sundar SS, 2011, ROUTL COMMUN SER, P181; Pelikan J., 1992, IDEA U REEXAMINATION; Pescosolido BA, 2002, ADV MED SOC, V8, P3; Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643; Rainie L, 2012, NETWORKED: THE NEW SOCIAL OPERATING SYSTEM, P1; Rees A. M., 1991, MANAGING CONSUMER HL, P15; Rimal RN, 2001, J COMMUN, V51, P633, DOI 10.1111/j.1460-2466.2001.tb02900.x; Schwartz B., 2004, PARADOX CHOICE WHY M; Seidman J. J., 2006, INTERNET HLTH CARE T, P195; Shim M, 2006, J HEALTH COMMUN, V11, P157, DOI 10.1080/10810730600637475; Shook D. E., 1990, J MANAGE STUD, V27, P196; Smithson M., 1989, IGNORANCE UNCERTAINT; Spink Amanda, 2004, Health Info Libr J, V21, P44, DOI 10.1111/j.1471-1842.2004.00481.x; Stein J. A., 1989, SEARCHING HLTH INFOR; Steinke C., 1991, INFORMATION SEEKING, P5; Street Jr R. L., 2007, NIH PUBLICATION, V07-6225; SWINEHAR.JW, 1968, AM J PUBLIC HEALTH N, V58, P1265, DOI 10.2105/AJPH.58.7.1265; Turner MM, 2006, HUM COMMUN RES, V32, P130, DOI 10.1111/j.1468-2958.2006.00006.x; Wahlin TBR, 2007, PATIENT EDUC COUNS, V65, P279, DOI 10.1016/j.pec.2006.08.009; Welkenhuysen M, 2001, J MED GENET, V38, P540, DOI 10.1136/jmg.38.8.540; Westbrook JI, 2005, MED DECIS MAKING, V25, P178, DOI 10.1177/0272989X05275155; White RW, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1629096.1629101; White R. W., 2009, AMIA ANN S; White R. W., 2013, SIGIR 13 C RES DEV I; White RW, 2014, J AM MED INFORM ASSN, V21, P49, DOI 10.1136/amiajnl-2012-001473; WITTE K, 1992, COMMUN MONOGR, V59, P329; ZEMORE R, 1987, PSYCHOL REP, V60, P874; Zeng QT, 2006, J AM MED INFORM ASSN, V13, P80, DOI 10.1197/jamia.M1820 100 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. SEP 2014 50 5 708 717 10.1016/j.ipm.2014.06.001 10 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN0XJ WOS:000340307000007 J Kimura, M Kimura, Maiko Differences in representations of Japanese name authority data among CJK countries and the Library of Congress INFORMATION PROCESSING & MANAGEMENT English Article Authority files; Information retrieval; Name authority records This study aims to compare representations of Japanese personal and corporate name authority data in Japan, South Korea, China (including Hong Kong and Taiwan), and the Library of Congress (LC) in order to identify differences and to bring to light issues affecting name authority data sharing projects, such as the Virtual International Authority File (VIAF). For this purpose, actual data, manuals, formats, and case reports of organizations as research objects were collected. Supplemental e-mail and face-to-face interviews were also conducted. Subsequently, five check points considered to be important in creating Japanese name authority data were set, and the data of each organization were compared from these five perspectives. Before the comparison, an overview of authority control in Chinese, Japanese, Korean-speaking (CJK) countries was also provided. The findings of the study are as follows: (1) the databases of China and South Korea have mixed headings in Kanji and other Chinese characters; (2) few organizations display the correspondence between Kanji and their yomi; (3) romanization is not mandatory in some organizations and is different among organizations; (4) some organizations adopt representations in their local language; and (5) some names in hiragana are not linked with their local forms and might elude a search. (C) 2014 Elsevier Ltd. All rights reserved. Keio Univ, Grad Sch Lib & Informat Sci, Tokyo 108, Japan Kimura, M (reprint author), Keio Univ, Grad Sch Lib & Informat Sci, Tokyo 108, Japan. mayizi@a8.keio.jp Mita Society for Library and Information Science; Fuji Xerox Setsutaro Kobayashi Memorial Fund; Keio University The research described in this paper was funded by a grant to Maiko Kimura from the Mita Society for Library and Information Science in 2012, from the Fuji Xerox Setsutaro Kobayashi Memorial Fund in 2013, and from the doctoral course research support program of Keio University in 2013. I would like to thank my supervisor, Prof. Shunsaku Tamura for the patient guidance. I would also like to thank Dr. Barbara B. Tillett for precious advice and encouragement. [Anonymous], 2000, HAKSUL CHONGBO CHONG; CALIS, CALIS ONL CAT; CALIS Union Catalog Center, CALIS UN CAT AUTH; CALIS Union Catalog Center, GUAN YU LIAN HE MU L; Chan K., 2000, JOURNAL OF EAST ASIA, V120, P1; China Academic Library & Information System (CALIS) Union Catalog Center, 2010, CALIS CHENG YUAN GUA; Cooperative Committee for Chinese Name Authority (CCCNA), 2003, ZHONG WEN MING CHENG; Harrison S. E., 1992, CATALOGING CLASSIFIC, V15, P3; Hong Kong Chinese Authority Name Workgroup (HKCAN), HKCAN DAT OPAC; ILCAA (Tokyo Gaikokugo Daigaku Ajia Afurika Gengo Bunks Kenkyujo), 2005, ZUS AJ MOJ NYUM; Joint University Librarians Advisory Committee (JULAC), 2010, HKCAN PROJ PROGR ACH; JULAC, HKCAN; Keio University Media Center (Keio), KOSMOS; Kim M., 2009, B NATL COLL U LIB, V27, P41; Kim S., 2012, NANOTECHNOLOGY, V23, P1; Kimura M., AUTHORITY DATA UNPUB; Kimura M, 2013, LIBR INFORM SC, P19; Koga R, 2010, MEDIANET, V17, P20; Korea Statistics Korea, 2000, RES 200 POP HOUS CEN; Kudo Y., 2011, CATALOGING CLASSIFIC, V49, P97, DOI 10.1080/01639374.2011.536751; LC, 2008, APP C MULT REC; Li Y., 2012, GUO JIA TU SHU GUAN; Library of Congress (LC), 2008, NONL SCRIPT DAT NAM; Lin Q., 2011, DANG AN JI KAN, V10, P52; Liu C., 2010, ZHONG WEN MING CHENG; Liu L., 2004, JOURNAL OF EAST ASIA, V133, P23; Lo P., 2004, CATALOGING CLASSIFIC, V39, P465, DOI 10.1300/J104v39n01_14; Luo C., 2011, RES LIB SCI, V16, P30; Maeda Y., 2002, GEKKAN SHINIKA, V13, P26; McEwan A., 2013, CATALOGING CLASSIFIC, V51, P55; Ministry of Justice (MOJ), SHOG NI ROM MOCH KOT; Miyamoto T., 2009, AJIA KANJI BUNKA; Miyazawa A., 2002, WORKSH AUTH CONTR CH; Naito E., 2004, Cataloging & Classification Quarterly, V38, DOI 10.1300/J104v38n03_19; National Central Library (NCL), 2011, GUO JIA TU SHU GUAN; National Diet Library (NDL) Kokuritsu Kokkai Toshokan Shoshibu, 2004, DAI 4 KAI SHOSH CHOS; National Institute of Informatics (NII), 1987, ONR SHIS NYUS, P8; National Library of Korea (NLK), 2006, KUKK CHONG PAIL UI H; NCL, NAT CENTR LIB ONL CA; NCL, 2012, GUO JIA TU SHU GUAN; NDL, WEB NDL AUTH; NDL, 2012, BACH KOK TENK FAIR V; NDL, 2013, SHOSH DET SAK TSUR; NDL, 2013, TOK KAR SHOSH DET HE; NDL, 2010, NDL SHOSH JOH NYUS 1, V12, P1; NDL, 2012, JAPAN MARC MARC21 FO; NII, CATP FOM SOG MOK DET; NII, 2013, NACSIS CAT DET BES R; Niwa M., 1996, NIHON MYOJI DAIJITEN; NLK, 2009, KUNGN CHUNG TOS CHON; Onnagawa M., 2009, THESIS; Palmer H. E., 1931, PRINCIPLES ROMANIZAT; Park H., 2001, WORKSH AUTH CONTR CH; Patton G., 2013, HONG KONG CHINESE AU; Qiu J., 2010, ZHONGGUO XING SHI DA; Seoul National University Library (SNUL), S SEARCH; Shim K., 2006, J KOREAN LIB INFORM, V40, P221; Takebe Y., 1979, NIHONGO NO HYOKI; The Committee of Cataloging of the Japan Library Association (CCJLA), 1965, NIH MOK KIS; Tillett B. B., 2002, WORKSH AUTH CONTR CH; Wan A., 2010, LIB INFORM SERVICE S, V1, P171; Xu J., 2012, C10003747; Yi M., 2012, CHONGBO HAKHOE 2012, P69; Yu A. J., 2002, WORKING PAPER; Yunoki T., 2002, GEKKAN SHINIKA, V13, P14; Zhao Y., 2008, RES LIB SCI, V7, P29 66 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. SEP 2014 50 5 733 751 10.1016/j.ipm.2014.03.006 19 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN0XJ WOS:000340307000009 J Soulier, L; Tamine, L; Bahsoun, W Soulier, Laure; Tamine, Lynda; Bahsoun, Wahiba On domain expertise-based roles in collaborative information retrieval INFORMATION PROCESSING & MANAGEMENT English Article Collaborative information retrieval; Domain expertise; Ranking model; Learning-method SEARCH PROCESS; INTERNET USE; HEALTH-CARE; FEEDBACK; STRATEGIES; SEEKING; TERMS; WEB Collaborative information retrieval involves retrieval settings in which a group of users collaborates to satisfy the same underlying need. One core issue of collaborative IR models involves either supporting collaboration with adapted tools or developing IR models for a multiple-user context and providing a ranked list of documents adapted for each collaborator. In this paper, we introduce the first document-ranking model supporting collaboration between two users characterized by roles relying on different domain expertise levels. Specifically, we propose a two-step ranking model: we first compute a document-relevance score, taking into consideration domain expertise-based roles. We introduce specificity and novelty factors into language-model smoothing, and then we assign, via an Expectation-Maximization algorithm, documents to the best-suited collaborator. Our experiments employ a simulation-based framework of collaborative information retrieval and show the significant effectiveness of our model at different search levels. (C) 2014 Elsevier Ltd. All rights reserved. [Soulier, Laure; Tamine, Lynda; Bahsoun, Wahiba] Univ Toulouse 3, IRIT, F-31062 Toulouse 9, France Soulier, L (reprint author), Univ Toulouse 3, IRIT, 118 Route Narbonne, F-31062 Toulouse 9, France. soulier@irit.fr; tamine@irit.fr; wbahsoun@irit.fr Allen B., 1991, THE LIBRARY QUARTERL, P188; Attfield Simon J, 2006, Health Informatics J, V12, P165, DOI 10.1177/1460458206063811; Attfield S, 2010, INFORM PROCESS MANAG, V46, P632, DOI 10.1016/j.ipm.2009.10.003; Callan J., 2004, HLT NAACL CONFERENCE; Castells P., 2011, ECIR CONFERENCE PROC; Catledge L. D., 1995, WWW 95, P1065; Collins-Thompson K., 2011, CIKM CONFERENCE PROC, P403; Conrad J. G., 2007, ICAIL CONFERENCE PRO, P321; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; DENNING PJ, 2008, COMMUN ACM, V51, P19; ECDPC, 2011, TECHNICAL REPORT; Erickson T., 2010, CSCW CONFERENCE PROC; Filho F. F., 2010, ICDC CONFERENCE PROC, P89; Foley C, 2010, INFORM PROCESS MANAG, V46, P762, DOI 10.1016/j.ipm.2009.10.010; Foley C., 2008, THESIS; Foley C, 2009, LECT NOTES COMPUT SC, V5478, P42, DOI 10.1007/978-3-642-00958-7_7; Fox S., 2008, TECHNICAL REPORT; Gianoutsos S., 1996, CSCW CONFERENCE PROC, P14; Goggins S., 2010, INTERNATIONAL WORKSH; Golovchinsky G., 2011, HCIR CONFERENCE PROC; Golovchinsky G, 2009, COMPUTER, V42, P47, DOI 10.1109/MC.2009.73; Hansen P, 2005, INFORM PROCESS MANAG, V41, P1101, DOI 10.1016/j.ipm.2004.04.016; Harman D., 2002, TEXT RETR C TREC 200, P46; Harrison-Walker L., 2006, BUS HORIZONS, V49, P7; Hembrooke HA, 2005, J AM SOC INF SCI TEC, V56, P861, DOI 10.1002/asi.20180; Holscher C, 2000, COMPUT NETW, V33, P337, DOI 10.1016/S1389-1286(00)00031-1; Horowitz D., 2010, WWW 10, P431; Hsieh-yee L., 1993, JASIS, V44, P161; ISAACS EA, 1987, J EXP PSYCHOL GEN, V116, P26, DOI 10.1037/0096-3445.116.1.26; Jelinek F., 1980, Pattern Recognition in Practice. Proceedings of an International Workshop; Joho H, 2009, LECT NOTES COMPUT SC, V5478, P66, DOI 10.1007/978-3-642-00958-7_9; Kang RG, 2010, IUI 2010, P329; Kashyap A., 2010, CIKM 2010, P719; Keskustalo H, 2006, LECT NOTES COMPUT SC, V3936, P191; Kim G, 2006, INFORM PROCESS MANAG, V42, P1218, DOI 10.1016/j.ipm.2005.12.004; Kim J. Y., 2012, WSDM CONFERENCE PROC, P213; KUHLTHAU C, 1992, P ASIS ANNU MEET, V29, P67; Marsland S, 2009, CH CRC MACH LEARN PA, P1; McMullan M, 2006, PATIENT EDUC COUNS, V63, P24, DOI 10.1016/j.pec.2005.10.006; Moraveji N., 2011, CHI CONFERENCE PROCE, P1797; Morris M. R., 2006, INTERNATIONAL WORKSH, P97; Morris M. R., 2011, INTERNATIONAL WORKSH, P11; Morris M. R., 2009, COLLABORATIVE WEB SE; Morris MR, 2007, UIST 2007: PROCEEDINGS OF THE 20TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P3; Morris MR, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P481; Over P, 2001, INFORM PROCESS MANAG, V37, P369, DOI 10.1016/S0306-4573(00)00053-4; Pickens J., 2008, SIGIR 2008, P315; Podichetty VK, 2006, POSTGRAD MED J, V82, P274, DOI 10.1136/pgmj.2005.040675; Privault Caroline, 2010, Artificial Intelligence and Law, V18, DOI 10.1007/s10506-010-9090-z; ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302; Rodriguez Perez J. A., 2011, INTERNATIONAL WORKSH, P29; RUDD J, 1986, COLL RES LIBR, V47, P315; Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283; Shah C., 2012, COAGMENTOA CASE STUD; Shah C., 2012, COLLABORATIVE INFORM; Shah C, 2010, INFORM PROCESS MANAG, V46, P773, DOI 10.1016/j.ipm.2009.10.002; Shah C., 2012, REPORT ON THE SECOND; Smeaton A. F., 2006, TABLETOP 06 1 IEEE I, P151; Smirnova E, 2011, LECT NOTES COMPUT SC, V6611, P580, DOI 10.1007/978-3-642-20161-5_58; Soboroff I., 2005, HLT EMNLP, P105; Soulier L., 2013, AIRS CONFERENCE PROC, P109; TAYLOR RS, 1968, COLL RES LIBR, V29, P178; Toomela A, 2007, INTEGR PSYCHOL BEHAV, V41, P198, DOI 10.1007/s12124-007-9015-x; Twidale MB, 1997, INFORM PROCESS MANAG, V33, P761, DOI 10.1016/S0306-4573(97)00040-X; Twidale MB, 1996, ASLIB PROC, V48, P177; Vakkari P, 2003, INFORM PROCESS MANAG, V39, P445, DOI 10.1016/S03064573(02)0003l-6; Wald HS, 2007, PATIENT EDUC COUNS, V68, P218, DOI 10.1016/j.pec.2007.05.016; WANG JG, 2010, MAGN FRING FIELDS, P1; White R. W., 2009, WSDM CONFERENCE PROC, P132; White R. W., 2011, CHI CONFERENCE PROCE, P2837; White R. W., 2012, SIGIR CONFERENCE PRO, P1055; White RW, 2005, ACM T INFORM SYST, V23, P325, DOI 10.1145/1080343.1080347; Wielhorski K., 1994, THE PUBLIC ACCESS CO, V5, P5 73 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. SEP 2014 50 5 752 774 10.1016/j.ipm.2014.04.002 23 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN0XJ WOS:000340307000010 J Badre, D; Lebrecht, S; Pagliaccio, D; Long, NM; Scimeca, JM Badre, David; Lebrecht, Sophie; Pagliaccio, David; Long, Nicole M.; Scimeca, Jason M. Ventral Striatum and the Evaluation of Memory Retrieval Strategies JOURNAL OF COGNITIVE NEUROSCIENCE English Article VENTROLATERAL PREFRONTAL CORTEX; PREDICTION ERROR SIGNALS; LEXICAL-DECISION TASK; WORKING-MEMORY; PARIETAL CORTEX; PARKINSONS-DISEASE; EPISODIC MEMORY; BASAL GANGLIA; COMPUTATIONAL MODEL; INTEGRATIVE THEORY Adaptive memory retrieval requires mechanisms of cognitive control that facilitate the recovery of goal-relevant information. Frontoparietal systems are known to support control of memory retrieval. However, the mechanisms by which the brain acquires, evaluates, and adapts retrieval strategies remain unknown. Here, we provide evidence that ventral striatal activation tracks the success of a retrieval strategy and correlates with subsequent reliance on that strategy. Human participants were scanned with fMRI while performing a lexical decision task. A rule was provided that indicated the likely semantic category of a target word given the category of a preceding prime. Reliance on the rule improved decision-making, as estimated within a drift diffusion framework. Ventral striatal activation tracked the benefit that relying on the rule had on decision-making. Moreover, activation in ventral striatum correlated with a participant's subsequent reliance on the rule. Taken together, these results support a role for ventral striatum in learning and evaluating declarative retrieval strategies. [Badre, David; Scimeca, Jason M.] Brown Univ, Providence, RI 02912 USA; [Lebrecht, Sophie] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA; [Pagliaccio, David] Washington Univ, St Louis, MO 63130 USA; [Long, Nicole M.] Univ Penn, Philadelphia, PA 19104 USA Badre, D (reprint author), Brown Univ, Box 1821, Providence, RI 02912 USA. David_Badre@brown.edu National Institute of Neurological Disease and Stroke [NS065046]; Alfred P. Sloan Foundation; James S. McDonnell Foundation This work was supported by the National Institute of Neurological Disease and Stroke (NS065046), the Alfred P. Sloan Foundation, and the James S. McDonnell Foundation. ANDERSON JR, 1989, PSYCHOL REV, V96, P703, DOI 10.1037/0033-295X.96.4.703; Anderson MC, 2004, SCIENCE, V303, P232, DOI 10.1126/science.1089504; ATKINSON RC, 1971, SCI AM, V225, P82; Badre D, 2012, NEURON, V73, P595, DOI 10.1016/j.neuron.2011.12.025; Badre D, 2012, CEREB CORTEX, V22, P527, DOI 10.1093/cercor/bhr117; Badre D., CEREBRAL CO IN PRESS; Badre D, 2007, NEUROPSYCHOLOGIA, V45, P2883, DOI 10.1016/j.neuropsychologia.2007.06.015; Badre D, 2012, P NATL ACAD SCI USA, V109, P19878, DOI 10.1073/pnas.1216902109; Badre D, 2005, NEURON, V47, P907, DOI 10.1016/j.neuron.2005.07.023; Balota DA, 1999, J EXP PSYCHOL GEN, V128, P32, DOI 10.1037//0096-3445.128.1.32; Becker S, 2003, J COGNITIVE NEUROSCI, V15, P821, DOI 10.1162/089892903322370744; Benjamin AS, 2007, PSYCHOL LEARN MOTIV, V48, P175, DOI 10.1016/S0079-7421(07)48005-7; Besner D., 1991, BASIC PROCESSES READ, P264; Binder JR, 2011, TRENDS COGN SCI, V15, P527, DOI 10.1016/j.tics.2011.10.001; Bornstein AM, 2011, CURR OPIN NEUROBIOL, V21, P374, DOI 10.1016/j.conb.2011.02.009; Brainard DH, 1997, SPATIAL VISION, V10, P433, DOI 10.1163/156856897X00357; Braver T. S., 2000, ATTENTION PERFORM, VXVIII, P713, DOI DOI 10.1016/S0165-0173(03)00143-7; Bray S, 2007, J NEUROPHYSIOL, V97, P3036, DOI 10.1152/jn.01211.2006; Cabeza R, 2008, NAT REV NEUROSCI, V9, P613, DOI 10.1038/nrn2459; Cavanagh JF, 2011, NAT NEUROSCI, V14, P1462, DOI 10.1038/nn.2925; Chatham CH, 2014, NEURON, V81, P930, DOI 10.1016/j.neuron.2014.01.002; Chatham CH, 2013, FRONT BEHAV NEUROSCI, V7, DOI 10.3389/fnbeh.2013.00083; Cohen NJ, 1997, MEMORY, V5, P131, DOI 10.1080/741941149; Cools R, 2011, CURR OPIN NEUROBIOL, V21, P402, DOI 10.1016/j.conb.2011.04.002; Cools R, 2001, BRAIN, V124, P2503, DOI 10.1093/brain/124.12.2503; Crescentini C, 2008, NEUROPSYCHOLOGIA, V46, P434, DOI 10.1016/j.neuropsychologia.2007.08.021; Crescentini C, 2011, NEUROPSYCHOLOGY, V25, P720, DOI 10.1037/a0024674; D'Ardenne K, 2012, P NATL ACAD SCI USA, V109, P19900, DOI 10.1073/pnas.1116727109; Daw ND, 2011, NEURON, V69, P1204, DOI 10.1016/j.neuron.2011.02.027; Dayan P., 2004, SCIENCE, V304, P452; Dobbins IG, 2002, NEURON, V35, P989, DOI 10.1016/S0896-6273(02)00858-9; Donkin C., 2009, P 31 ANN C COGN SCI; FAVREAU M, 1983, MEM COGNITION, V11, P565, DOI 10.3758/BF03198281; Gabrieli JDE, 1998, P NATL ACAD SCI USA, V95, P906, DOI 10.1073/pnas.95.3.906; Gelman A, 2004, BAYESIAN DATA ANAL; GERSHBERG FB, 1995, NEUROPSYCHOLOGIA, V33, P1305, DOI 10.1016/0028-3932(95)00103-A; Glascher J, 2010, NEURON, V66, P585, DOI 10.1016/j.neuron.2010.04.016; Gold BT, 2006, J NEUROSCI, V26, P6523, DOI 10.1523/JNEUROSCI.0808-06.2006; Han S, 2009, PSYCHON B REV, V16, P469, DOI 10.3758/PBR.16.3.469; Han S, 2010, J NEUROSCI, V30, P4767, DOI 10.1523/JNEUROSCI.3077-09.2010; Heekeren HR, 2006, P NATL ACAD SCI USA, V103, P10023, DOI 10.1073/pnas.0603949103; Hutchinson J Benjamin, 2014, Cereb Cortex, V24, P49, DOI 10.1093/cercor/bhs278; Hutchinson JB, 2009, LEARN MEMORY, V16, P343, DOI 10.1101/lm.919109; Kucera H., 1967, COMPUTATIONAL ANAL P; Lauwereyns J, 2002, NATURE, V418, P413, DOI 10.1038/nature00892; Li JA, 2011, J NEUROSCI, V31, P5504, DOI 10.1523/JNEUROSCI.6316-10.2011; Maddox WT, 2005, MEM COGNITION, V33, P303, DOI 10.3758/BF03195319; Mcnab F, 2008, NAT NEUROSCI, V11, P103, DOI 10.1038/nn2024; MEYER DE, 1971, J EXP PSYCHOL, V90, P227, DOI 10.1037/h0031564; Miller EK, 2001, ANNU REV NEUROSCI, V24, P167, DOI 10.1146/annurev.neuro.24.1.167; MOSCOVITCH M, 1992, J COGNITIVE NEUROSCI, V4, P257, DOI 10.1162/jocn.1992.4.3.257; NEELY JH, 1977, J EXP PSYCHOL GEN, V106, P226, DOI 10.1037/0096-3445.106.3.226; Nelson SM, 2010, NEURON, V67, P156, DOI 10.1016/j.neuron.2010.05.025; O'Connor AR, 2010, J NEUROSCI, V30, P2924, DOI 10.1523/JNEUROSCI.4225-09.2010; O'Reilly RC, 2006, NEURAL COMPUT, V18, P283, DOI 10.1162/089976606775093909; Pessiglione M, 2006, NATURE, V442, P1042, DOI 10.1038/nature05051; Rastle K, 2002, Q J EXP PSYCHOL-A, V55, P1339, DOI 10.1080/02724980244000099; Ratcliff R, 2008, NEURAL COMPUT, V20, P873, DOI 10.1162/neco.2008.12-06-420; RATCLIFF R, 1978, PSYCHOL REV, V85, P59, DOI 10.1037//0033-295X.85.2.59; Ratcliff R, 2004, PSYCHOL AGING, V19, P278, DOI 10.1037/0882-7974.19.2.278; Ratcliff R, 2012, NEURAL COMPUT, V24, P1186, DOI 10.1162/NECO_a_00270; Ratcliff R, 2004, PSYCHOL REV, V111, P159, DOI 10.1037/0033-295X.111.1.159; Rugg MD, 2000, TRENDS COGN SCI, V4, P108, DOI 10.1016/S1364-6613(00)01445-5; Schultz W, 1997, SCIENCE, V275, P1593, DOI 10.1126/science.275.5306.1593; Scimeca JM, 2012, NEURON, V75, P380, DOI 10.1016/j.neuron.2012.07.014; Shenhav A, 2013, NEURON, V79, P217, DOI 10.1016/j.neuron.2013.07.007; SIMPSON EH, 1951, J ROY STAT SOC B, V13, P238; Schwarze U, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0054324; Spaniol J, 2009, NEUROPSYCHOLOGIA, V47, P1765, DOI 10.1016/j.neuropsychologia.2009.02.028; SQUIRE LR, 1992, PSYCHOL REV, V99, P195, DOI 10.1037//0033-295X.99.2.195; Stuss D.T., 1994, NEUROPSYCHOLOGY, V8, P355, DOI 10.1037/0894-4105.8.3.355; Sutton R. S., 1998, REINFORCEMENT LEARNI; Vilberg KL, 2008, NEUROPSYCHOLOGIA, V46, P1787, DOI 10.1016/j.neuropsychologia.2008.01.004; Wagner AD, 2005, TRENDS COGN SCI, V9, P445, DOI 10.1016/j.tics.2005.07.001; Wiecki Thomas V, 2013, Front Neuroinform, V7, P14, DOI 10.3389/fninf.2013.00014; Yarkoni T, 2011, NAT METHODS, V8, P665, DOI [10.1038/nmeth.1635, 10.1038/NMETH.1635] 76 0 0 MIT PRESS CAMBRIDGE 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA 0898-929X 1530-8898 J COGNITIVE NEUROSCI J. Cogn. Neurosci. SEP 2014 26 9 1928 1948 10.1162/jocn_a_00596 21 Neurosciences; Psychology, Experimental Neurosciences & Neurology; Psychology AN4GI WOS:000340545300005 J Zhang, PY; Soergel, D Zhang, Pengyi; Soergel, Dagobert Towards a comprehensive model of the cognitive process and mechanisms of individual sensemaking JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Review information use; information seeking; cognition INFORMATION-SEEKING; TASK COMPLEXITY; SENSE-MAKING; KNOWLEDGE; PERSPECTIVE; SEARCH; REPRESENTATIONS; RETRIEVAL; FRAMEWORK This review introduces a comprehensive model of the cognitive process and mechanisms of individual sensemaking to provide a theoretical basis for: empirical studies that improve our understanding of the cognitive process and mechanisms of sensemaking and integration of results of such studies; education in critical thinking and sensemaking skills; the design of sensemaking assistant tools that support and guide users. The paper reviews and extends existing sensemaking models with ideas from learning and cognition. It reviews literature on sensemaking models in human-computer interaction (HCI), cognitive system engineering, organizational communication, and library and information sciences (LIS), learning theories, cognitive psychology, and task-based information seeking. The model resulting from this synthesis moves to a stronger basis for explaining sensemaking behaviors and conceptual changes. The model illustrates the iterative processes of sensemaking, extends existing models that focus on activities by integrating cognitive mechanisms and the creation of instantiated structure elements of knowledge, and different types of conceptual change to show a complete picture of the cognitive processes of sensemaking. The processes and cognitive mechanisms identified provide better foundations for knowledge creation, organization, and sharing practices and a stronger basis for design of sensemaking assistant systems and tools. [Zhang, Pengyi] Peking Univ, Dept Informat Management, Beijing 100871, Peoples R China; [Soergel, Dagobert] SUNY Buffalo, Grad Sch Educ, Dept Lib & Informat Studies, Buffalo, NY 14260 USA Zhang, PY (reprint author), Peking Univ, Dept Informat Management, 5 Yiheyuan Rd, Beijing 100871, Peoples R China. pengyi.zhang@pku.edu.cn; dsoergel@buffalo.edu Eugene Garfield Dissertation Fellowship from the Beta Phi Mu Library & Information Studies Honor Society; Beijing Key Discipline Construction Fund We were formerly at the College of Information Studies, University of Maryland. This work was partially supported by the Eugene Garfield Dissertation Fellowship from the Beta Phi Mu Library & Information Studies Honor Society. The first author also received support from the Beijing Key Discipline Construction Fund. We thank the editor and two anonymous reviewers for their constructive comments, which helped us to significantly improve the article. Alberta Learning. Learning and Teaching Resources Branch, 2004, FOCUS INQUIRY TEACHE; Anderson J. R., 1976, LANGUAGE MEMORY THOU; Anderson J. R., 1983, ARCHITECTURE COGNITI; Anderson J. R., 1996, EDUC RES, V25, P5, DOI 10.3102/0013189X025004005; Anderson R. C., 1984, EDUC RES, V13, P5, DOI 10.3102/0013189X013009005; Arnheim R, 1969, VISUAL THINKING; Arthur W. B., 1994, AM EC REV, V84; Attfield S, 2011, HUM-COMPUT INTERACT, V26, P38, DOI 10.1080/07370024.2011.556548; Ausubel D. P., 1978, ED PSYCHOL; Baldonado M. Q. W., 1997, P SIGCHI C HUM FACT, P11, DOI 10.1145/258549.258563; Barrett E. C., 2009, THESIS U BIRMINGHAM; BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320; Bystrom K, 2002, J AM SOC INF SCI TEC, V53, P581, DOI 10.1002/asi.10064; Bystrom K, 2005, J AM SOC INF SCI TEC, V56, P1050, DOI 10.1002/asi.20197; BYSTROM K, 1995, INFORM PROCESS MANAG, V31, P191, DOI 10.1016/0306-4573(94)00041-Z; Cacioppo J. T., 1981, COGNITIVE ASSESSMENT, P309; CARLEY K, 1992, SOC FORCES, V70, P601, DOI 10.2307/2579746; Chi M. T. H, 1992, COGNITIVE MODELS SCI, P129; Chi M. T. H, 2007, HDB RES CONCEPTUAL C; Chi MTH, 2006, CAMBRIDGE HANDBOOK OF EXPERTISE AND EXPERT PERFORMANCE, P167; Chipman S. F., 2000, COGNITIVE TASK ANAL; Choo C. W, 1998, KNOWING ORG ORG USE; Choo C. W., 2006, KNOWING ORG; Cooper J., 2001, INT ENCY SOCIAL BEHA; Creswell J. W., 2003, RES DESIGN QUALITATI; Das J. P., 1994, ASSESSMENT COGNITIVE; Denzin N. K., 2003, COLLECTING INTERPRET; Dervin B., 1998, Journal of Knowledge Management, V2, DOI 10.1108/13673279810249369; Dervin B, 1992, QUALITATIVE RES INFO, P64; Dervin B., 2012, PUBLIC COMMUNICATION, P147; Dervin B., 2010, ENCY LIB INFORM SCI, P4696; Ericsson K., 1993, PROTOCOL ANAL VERBAL; Faisal S., 2009, CHI 2009 WORKSH SENS; Festinger L, 1957, THEORY COGNITIVE DIS; FLAVELL JH, 1979, AM PSYCHOL, V34, P906, DOI 10.1037/0003-066X.34.10.906; Gaines B. R., 2010, VISUALIZING LOGICAL; Gersh J, 2006, COMMUN ACM, V49, P63, DOI 10.1145/1121949.1121984; Gibbs RW, 2008, CAMB HANDB PSYCHOL, P1; Grabowski B. L., 1996, GENERATIVE LEARNING, P897; Gredler M. E., 2008, LEARNING INSTRUCTION; Hearst M. A., 2009, SEARCH USER INTERFAC; Hoffman R. R., 1992, PSYCHOL EXPERTISE CO; HOFFMAN RR, 1995, ORGAN BEHAV HUM DEC, V62, P129, DOI 10.1006/obhd.1995.1039; Hsieh H., 2002, 15 ANN ACM S US INT, P217; Huang XL, 2006, INFORM RES, V12; Hyerle D., 2008, VISUAL TOOLS TRANSFR; Hyerle N., 2011, STUDENT SUCCESSES TH; Ingwersen P., 1992, INFORM RETRIEVAL INT; Ingwersen P., 2005, TURN INTEGRATION INF; Johnson-Laird PN, 1999, ANNU REV PSYCHOL, V50, P109, DOI 10.1146/annurev.psych.50.1.109; Jonassen D. H., 1996, 1996 INT C LEARN SCI, P433; JONASSEN DH, 1993, J COMPUT-BASE INSTR, V20, P1; Kaufman J. C., 2010, CAMBRIDGE HDB CREATI; KAVALE KA, 1980, LEARN DISABILITY Q, V3, P34, DOI 10.2307/1510673; Kim S., 2005, 68 ANN M AM SOC INF; Kirk R. E., 1995, EXPT DESIGN PROCEDUR; Kirsh David, 2010, AI & Society, V25, DOI 10.1007/s00146-010-0272-8; Kirsh D., 2009, CHI 2009 SENS WORKSH; Klein G, 2006, IEEE INTELL SYST, V21, P88, DOI 10.1109/MIS.2006.100; Klein G, 2006, IEEE INTELL SYST, V21, P70, DOI 10.1109/MIS.2006.75; Krizan L., 1999, INTELLIGENCE ESSENTI; Kuhlthau C. C., 2004, SEEKING MEANING PROC; Kuhlthau C. C., 1993, SCH LIB MEDIA Q, V22, P11; KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-#; Kurtz CF, 2003, IBM SYST J, V42, P462; Lave J., 1991, SITUATED LEARNING LE; LOUIS MR, 1980, ADMIN SCI QUART, V25, P226, DOI 10.2307/2392453; Lowrance J. D., 2001, P 1 INT C KNOWL CAPT; Maxwell J. A., 2005, QUALITATIVE RES DESI; Miles M. B., 2013, QUALITATIVE DATA ANA; Minsky M, 1977, THINKING READINGS CO, P355; Morgan G., 1983, ORG SYMBOLISM, P3; Nesset V, 2013, LIBR INFORM SCI RES, V35, P97, DOI 10.1016/j.lisr.2012.11.007; Neuman D., 2011, CONSTRUCTING KNOWLED, P14; Neuman D, 2011, LEARNING IN INFORMATION-RICH ENVIRONMENTS: I-LEARN AND THE CONSTRUCTION OF KNOWLEDGE IN THE 21ST CENTURY, P1, DOI 10.1007/978-1-4419-0579-6; NISBETT RE, 1977, PSYCHOL REV, V84, P231, DOI 10.1037/0033-295X.84.3.231; Norman D. A., 1976, STRUCTURE HUMAN MEMO; Novak J. D., 1998, LEARNING CREATING US; Patwardhan S., 2006, ACL 2006 WORKSH INF; Pennington N., 1991, CARDOZO LAW REV, V13, P51; Piaget J., 1976, CARMICHAELS MANUAL C, V1, P703; Piaget J., 1936, ORIGINS INTELLIGENCE; Pirolli P, 2005, P INT C INT AN, V2005, P2; Pirolli P, 2011, HUM-COMPUT INTERACT, V26, P1, DOI 10.1080/07370024.2011.556557; Polya G., 2004, SOLVE IT NEW ASPECT; Potter W. J., 1996, ANAL THINKING RES QU; Qu Y, 2008, INFORM PROCESS MANAG, V44, P534, DOI 10.1016/j.ipm.2007.09.006; Qu Y., 2003, CHI03 EXT HUM FACT C; Richardson M., 2009, THEORETICAL ISSUES E, V10, P335, DOI 10.1080/14639220802368872; Riloff E, 1996, PROCEEDINGS OF THE THIRTEENTH NATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND THE EIGHTH INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE, VOLS 1 AND 2, P1044; Rumelhart D. E., 1981, COGNITIVE SKILLS THE, P335; Rumelhart D. E., 1981, SEMANTIC FACTORS COG, P37; Rumelhart D. E., 1988, STEVENS HDB EXPT PSY; Rumelhart D. E., 1977, SCH AQUISITION KNOWL; Russell D. M., 1993, P INTERACT 93 CHI 93; Savolainen R, 2006, J AM SOC INF SCI TEC, V57, P1116, DOI 10.1002/asi.20400; Savolainen R, 2009, J AM SOC INF SCI TEC, V60, P2244, DOI 10.1002/asi.21167; Seligman L., 2006, European Journal of Innovation Management, V9, DOI 10.1108/14601060610640050; Snowden David J, 2005, Inform Prim Care, V13, P45; Soergel D, 1985, ORG INFORM PRINCIPLE; Stefik M. J., 1999, KNOWLEDGE SHARING CH; ten Berge T, 1999, THEOR PSYCHOL, V9, P605, DOI 10.1177/0959354399095002; Toulmin S., 1979, INTRO REASONING; Treffinger D. J., 2005, CREATIVE PROBLEM SOL; Tufte E. R., 2006, BEAUTIFUL EVIDENCE; Vakkari P, 1999, INFORM PROCESS MANAG, V35, P819, DOI 10.1016/S0306-4573(99)00028-X; Vakkari P, 2000, J DOC, V56, P540, DOI 10.1108/EUM0000000007127; Vivacqua A. S., 2009, CHI 2009 SENS WORKSH; Vosniadou S., 1989, SIMILARITY ANALOGICA; VOSNIADOU S, 1987, REV EDUC RES, V57, P51, DOI 10.3102/00346543057001051; Wang W., 1997, P ACM HYP 97 APR, P112, DOI 10.1145/267437.267450; Weick K. E., 1995, SENSEMAKING ORG; Weick KE, 2005, ORGAN SCI, V16, P409, DOI 10.1287/orsc.1050.0133; Wertheimer M., 1938, SOURCE BOOK GESTALT, P71, DOI 10.1037/11496-005; WHITE MD, 1975, LIBR QUART, V45, P337; Winston P. H., 1975, PSYCHOL COMPUTER VIS, P211; Wittrock M. C., 1990, EDUC PSYCHOL, V24, P345, DOI DOI 10.1207/S15326985EP2404_2; Wright W., 2006, SIGCHI C HUM FACT CO, P801; Wu A., 2010, P 3 INT S VIS INF CO; Zhang J., 2000, ENCY LIB INFORM SCI, V68, P164; Zhang JJ, 1997, COGNITIVE SCI, V21, P179; Zhang P., 2008, P ANN M AM SOC INF S; Zhang P., 2010, THESIS U MWRYLAND CO 123 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH SEP 2014 65 9 1733 1756 10.1002/asi.23125 24 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN3IC WOS:000340479500001 J Lopatovska, I Lopatovska, Irene Toward a model of emotions and mood in the online information search process JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article online searching; information models; psychological aspects HUMAN-COMPUTER INTERACTION; STUDENTS PERCEPTIONS; USER FRUSTRATION; RESEARCH ANXIETY; REAL-LIFE; WEB; SEEKING; BEHAVIOR; RETRIEVAL; LIBRARY This article reports the results of a study that examined relationships between primary emotions, secondary emotions, and mood in the online information search context. During the experiment, participants were asked to search Google to obtain information on the two given search tasks. Participants' primary emotions were inferred from analysis of their facial expressions, data on secondary emotions were obtained through participant interviews, and mood was measured using the Positive Affect Negative Affect Scale (PANAS; Watson, Clark, & Tellegen, 1988) prior, during, and after the search. The search process was represented by the collection of search actions, search performance, and search outcome quality variables. The findings suggest existence of direct relationships between primary emotions and search actions, which in turn imply the possibility of inferring emotions from search actions and vice versa. The link between secondary emotions and searchers' evaluative judgments, and lack of evidence of any relationships between secondary emotions and other search process variables, point to the strengths and weaknesses of self-reported emotion measures in understanding searchers' affective experiences. Our study did not find strong relationships between mood and search process and outcomes, indicating that while mood can have a limited effect on search activities, it is a relatively stable and long-lasting state that cannot be easily altered by the search experience and, in turn, cannot significantly affect the search. The article proposes a model of relationships between emotions, mood, and several facets of the search process. Directions for future work are also discussed. Pratt Inst, Sch Lib & Informat Sci, New York, NY 10011 USA Lopatovska, I (reprint author), Pratt Inst, Sch Lib & Informat Sci, 144 W 14th St, New York, NY 10011 USA. ilopatov@pratt.edu Arapakis I., 2008, P 31 ANN INT ACM SIG, P395, DOI 10.1145/1390334.1390403; Barrett LF, 1999, CURR DIR PSYCHOL SCI, V8, P10; Bell DJ, 2004, LECT NOTES COMPUT SC, V2997, P57; Bilal D, 2002, INFORM PROCESS MANAG, V38, P649, DOI 10.1016/S0306-4573(01)00057-7; Bilal D, 2007, INFORM PROCESS MANAG, V43, P65, DOI 10.1016/j.ipm.2006.05.008; Bilal D, 2000, J AM SOC INFORM SCI, V51, P646, DOI 10.1002/(SICI)1097-4571(2000)51:7<646::AID-ASI7>3.0.CO;2-A; Bower G. H., 1992, HDB EMOTION MEMORY R, P3; Brave S, 2005, INT J HUM-COMPUT ST, V62, P161, DOI 10.1016/j.ijhcs.2004.11.002; Chen HM, 2001, J AM SOC INF SCI TEC, V52, P888, DOI 10.1002/asi.1159; CLARK LA, 1988, J PERS SOC PSYCHOL, V54, P296, DOI 10.1037//0022-3514.54.2.296; Cohn J. F., 2007, OXFORD U PRESS SERIE, P222; Damasio A., 2005, DESCARTES ERROR EMOT; Darwin C., 2005, EXPRESSION EMOTIONS; Dervin B., 2007, INFORM EMOTION EMERG, P51; Du S., 2011, J VIS, V11, P1; Dumais S. T., 2010, P 3 S INF INT CONT, P185, DOI 10.1145/1840784.1840812; Ekman P, 1984, APPROACHES EMOTION, P319; Ekman P, 2003, EMOTIONS REVEALED RE; EKMAN P, 1992, COGNITION EMOTION, V6, P169, DOI 10.1080/02699939208411068; Ekman P., 2003, UNMASKING FACE; Flavian-Blanco C, 2011, COMPUT HUM BEHAV, V27, P540, DOI 10.1016/j.chb.2010.10.002; Fulton C, 2009, ASLIB PROC, V61, P245, DOI 10.1108/00012530910959808; Gunes H, 2007, J NETW COMPUT APPL, V30, P1334, DOI 10.1016/j.jnca.2006.09.007; Gwizdka J., 2008, P WORKSH COGN WEB IN, P83; Gwizdka J, 2009, J AM SOC INF SCI TEC, V60, P2452, DOI 10.1002/asi.21183; Henson RK, 2006, COUNS PSYCHOL, V34, P601, DOI 10.1177/0011000005283558; Isen A. M., 1993, HDB EMOTIONS, P261, DOI New York, NY, US; Jaimes A, 2007, COMPUT VIS IMAGE UND, V108, P116, DOI 10.1016/j.cviu.2006.10.019; Jansen B. J., 2003, P 4 INT C INT COMP L, P65; Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4; Joachims T., 2005, P 28 ANN INT ACM SIG, P154, DOI DOI 10.1145/1076034.1076063; Kalbach J, 2006, J AM SOC INF SCI TEC, V57, P813, DOI 10.1002/asi.20299; Klein J, 2002, INTERACT COMPUT, V14, P119; Kracker J, 2002, J AM SOC INF SCI TEC, V53, P282, DOI 10.1002/asi.10040; Kracker J, 2002, J AM SOC INF SCI TEC, V53, P295, DOI 10.1002/asi.10041; KUHLTHAU CC, 1993, J DOC, V49, P339, DOI 10.1108/eb026918; KUHLTHAU CC, 1991, J AM SOC INFORM SCI, V42, P361, DOI 10.1002/(SICI)1097-4571(199106)42:5<361::AID-ASI6>3.0.CO;2-#; Lazar J, 2006, INTERACT COMPUT, V18, P187, DOI 10.1016/j.intcom.2005.06.001; Lazarus R. S., 1984, APPROACHES EMOTION, P247; Levenson R. W., 1994, NATURE EMOTION FUNDA, P123; Lindgaard G, 2006, BEHAV INFORM TECHNOL, V25, P115, DOI 10.1080/01449290500330448; Lopatovska I., 2007, INFORM PROCESSING MA, V44, P92; Lopatovska I, 2011, INFORM PROCESS MANAG, V47, P575, DOI 10.1016/j.ipm.2010.09.001; Lopatovska I., 2011, P SSCI 2011 WACI 201; Lopatovska I., 2009, P 72 ANN M AM SOC IN; Lopatovska I., 2008, ASS LIB INF SCI ED A; MacDonald C. M., 2012, P AM SOC INFORM SCI, V49, P1, DOI [10.1002/meet.14504901303, DOI 10.1002/MEET.14504901303]; MANO H, 1994, ORGAN BEHAV HUM DEC, V57, P38, DOI 10.1006/obhd.1994.1003; Mooney C, 2006, LECT NOTES COMPUT SC, V3936, P570; Morris W. N., 1999, WELL BEING FDN HEDON, P169; MURRAY IR, 1993, J ACOUST SOC AM, V93, P1097, DOI 10.1121/1.405558; Nahl D, 2004, P ASIST ANNU, V41, P191, DOI 10.1002/meet.1450410122; Nahl D, 1998, J AM SOC INFORM SCI, V49, P1017, DOI 10.1002/(SICI)1097-4571(1998)49:11<1017::AID-ASI8>3.0.CO;2-Z; Nahl D., 1997, P 60 ASIS ANN M NOV, P89; Nahl D, 1996, J AM SOC INFORM SCI, V47, P276; O'Brien HL, 2008, J AM SOC INF SCI TEC, V59, P938, DOI 10.1002/asi.20801; Onwuegbuzie AJ, 2004, J AM SOC INF SCI TEC, V55, P41, DOI 10.1002/asi.10342; Payne R., 2001, EMOTIONS WORK THEORY; Peter C, 2006, INTERACT COMPUT, V18, P139, DOI 10.1016/j.intcom.2005.10.006; Picard RW, 2001, IEEE T PATTERN ANAL, V23, P1175, DOI 10.1109/34.954607; Plutchik R., 1980, EMOTION THEORY RES E, P3; Reisenzein R, 2006, J PERS SOC PSYCHOL, V91, P295, DOI 10.1037/0022-3514.91.2.295; Sherry A, 2005, J PERS ASSESS, V84, P37, DOI 10.1207/s15327752jpa8401_09; Spielberger C. D., 1983, MANUAL STATE TRAIT A; Spink A, 1997, J AM SOC INFORM SCI, V48, P382; Stenmark D, 2008, J AM SOC INF SCI TEC, V59, P2232, DOI 10.1002/asi.20931; Tenopir C, 2008, INFORM PROCESS MANAG, V44, P105, DOI 10.1016/j.ipm.2006.10.007; Thayer R. E., 1996, ORIGIN EVERYDAY MOOD; Tomkins S. S., 1984, APPROACHES EMOTION, P163; Toms EG, 2009, LECT NOTES COMPUT SC, V5714, P192; Wang PL, 2000, INFORM PROCESS MANAG, V36, P229, DOI 10.1016/S0306-4573(99)00059-X; Wang PL, 1998, J AM SOC INFORM SCI, V49, P115, DOI 10.1002/(SICI)1097-4571(1998)49:2<115::AID-ASI3>3.0.CO;2-1; WATSON D, 1988, J PERS SOC PSYCHOL, V54, P1063, DOI 10.1037/0022-3514.54.6.1063; White R. W., 2005, P 28 ANN INT ACM SIG, P35, DOI 10.1145/1076034.1076044; White R. W., 2004, THESIS U GLASGOW GLA; Wilhem FH, 2006, INTERACT COMPUT, V18, P171, DOI 10.1016/j.intcom.2005.07.001; Wright A, 2012, COMMUN ACM, V55, P12, DOI 10.1145/2160718.2160724 77 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH SEP 2014 65 9 1775 1793 10.1002/asi.23078 19 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN3IC WOS:000340479500003 J Ding, Y; Zhang, G; Chambers, T; Song, M; Wang, XL; Zhai, CX Ding, Ying; Zhang, Guo; Chambers, Tamy; Song, Min; Wang, Xiaolong; Zhai, Chengxiang Content-based citation analysis: The next generation of citation analysis JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article bibliographic citations; citation analysis CONCEPT SYMBOLS; CLASSIFICATION; INFORMATION; SCIENCE; RETRIEVAL; STATEMENTS; REFERENCES; SOCIOLOGY; RELEVANCE; NETWORKS Traditional citation analysis has been widely applied to detect patterns of scientific collaboration, map the landscapes of scholarly disciplines, assess the impact of research outputs, and observe knowledge transfer across domains. It is, however, limited, as it assumes all citations are of similar value and weights each equally. Content-based citation analysis (CCA) addresses a citation's value by interpreting each one based on its context at both the syntactic and semantic levels. This paper provides a comprehensive overview of CAA research in terms of its theoretical foundations, methodical approaches, and example applications. In addition, we highlight how increased computational capabilities and publicly available full-text resources have opened this area of research to vast possibilities, which enable deeper citation analysis, more accurate citation prediction, and increased knowledge discovery. [Ding, Ying; Zhang, Guo; Chambers, Tamy] Indiana Univ, Dept Informat & Lib Sci, Sch Informat & Comp, Bloomington, IN 47405 USA; [Song, Min] Yonsei Univ, Dept Lib & Informat Sci, Coll Liberal Arts, Seoul 120749, South Korea; [Wang, Xiaolong; Zhai, Chengxiang] Univ Illinois, Dept Comp Sci, Coll Engn, Urbana, IL 61801 USA Ding, Y (reprint author), Indiana Univ, Dept Informat & Lib Sci, Sch Informat & Comp, 1320 E 10th St,LI 011, Bloomington, IN 47405 USA. dingying@indiana.edu; guozhang@indiana.edu; tisch@indiana.edu; min.song@yonsei.ac.kr; xwang95@illinois.edu; czhai@illinois.edu National Research Foundation of Korea - Korean Government [NRF-2009-361-A00027] Min Song was supported by the National Research Foundation of Korea Grant funded by the Korean Government (NRF-2009-361-A00027). Abu-Jbara A., 2011, 49 ANN M ASS COMP LI, P500; Angrosh M. A., 2010, P 2010 JOINT C DIG L, P293, DOI DOI 10.1145/1816123.1816168.1816168; Arnold A, 2009, LECT NOTES COMPUT SC, V5682, P541, DOI 10.1007/978-3-642-03417-6_53; Athar A., 2011, P ACL 2011 STUD SESS, P81; Becher T., 1989, ACAD TRIBES TERRITOR; BONZI S, 1982, J AM SOC INFORM SCI, V33, P208; Borgman C. L., 1990, SCHOLARLY COMMUNICAT; Bradshaw S, 2003, LECT NOTES COMPUT SC, V2769, P499; BROOKS TA, 1985, J AM SOC INFORM SCI, V36, P223, DOI 10.1002/asi.4630360402; Byrnes J. E., 2013, PEERJPREPRINTS; CANO V, 1989, J AM SOC INFORM SCI, V40, P284, DOI 10.1002/(SICI)1097-4571(198907)40:4<284::AID-ASI10>3.0.CO;2-Z; Chang J., 2009, P 12 INT C ART INT S, V5, P81; CHUBIN DE, 1975, SOC STUD SCI, V5, P423, DOI 10.1177/030631277500500403; CRONIN B, 1981, J DOC, V37, P16, DOI 10.1108/eb026703; Cronin B., 2005, HAND SCI ACAD WRITIN; Cronin B., 1984, CITATION PROCESS ROL; Dietz L., 2007, P 24 INT C MACH LEAR, P233, DOI 10.1145/1273496.1273526; Ding Y., 2013, PLOS ONE, V8, P1; Ding Y, 2013, J INFORMETR, V7, P583, DOI 10.1016/j.joi.2013.03.003; Dong C., 2011, 5 INT JOINT C NAT LA, P623; Elkiss A, 2008, J AM SOC INF SCI TEC, V59, P51, DOI 10.1002/asi.20707; FROST CO, 1979, LIBR QUART, V49, P399; Fujii A., 2007, P 30 ANN INT ACM SIG, P793, DOI DOI 10.1145/1277741.1277912; Garfield E., 1974, ESSAYS INFORMATION S, V2, p[1974, 62]; Garfield E., 1964, STAT ASS METH MECH D, P189; Garzone M. A., 1997, AUTOMATED CLASSIFICA; GILBERT GN, 1977, SOC STUD SCI, V7, P113; Glaser BG, 1967, DISCOVERY GROUNDED T; Glenisson P, 2005, INFORM PROCESS MANAG, V41, P1548, DOI 10.1016/j.ipm.2005.03.021; He Q., 2010, P 19 INT C WORLD WID, P421, DOI 10.1145/1772690.1772734; He Q., 2011, P WSDM 11 FEBR 9 12, P755, DOI 10.1145/1935826.1935926; HERLACH G, 1978, J AM SOC INFORM SCI, V29, P308, DOI 10.1002/asi.4630290608; Hjorland B, 2001, ANNU REV INFORM SCI, V35, P249; Hodges T. L., 1972, CITATION INDEXING IT; Huang W., 2012, P 21 ACM INT C INF K, P1910; Jenssen TK, 2001, NAT GENET, V28, P21, DOI 10.1038/ng0501-21; Kataria S., 2011, P 22 INT JOINT C ART, P2274; Kupiec J., 1995, P 18 ANN INT ACM SIG, P68, DOI 10.1145/215206.215333; Lipetz B.-A., 1965, J AM SOC INFORM SCI, V16, P81; Liu Y., 2009, P 26 ANN INT C MACH, P665; Maricic S, 1998, J AM SOC INFORM SCI, V49, P530, DOI 10.1002/(SICI)1097-4571(19980501)49:6<530::AID-ASI5>3.0.CO;2-8; Marsh EE, 2003, J DOC, V59, P647, DOI 10.1108/00220410310506303; MCCAIN KW, 1989, SCIENTOMETRICS, V17, P127, DOI 10.1007/BF02017729; Mei Q., 2008, P ASS COMP LING ACL, P816; Meij E., 2007, RIAO 07 LARG SCAL SE, P665; MERTON RK, 1957, AM SOCIOL REV, V22, P635, DOI 10.2307/2089193; Mohammad S., 2009, HUMAN LANGUAGE TECHN, P584; Mons B, 2011, NAT GENET, V43, P281, DOI 10.1038/ng0411-281; MORAVCSIK MJ, 1975, SOC STUD SCI, V5, P86, DOI 10.1177/030631277500500106; Nallapati R. M., 2008, 14TH ACM SIGKDD INTE, P542; Nanba H., 1999, P 16 INT JOINT C ART, P926; Nanba H., 2004, LREC; Nanba H., 2000, ADV CLASSIFICATION R, V11, P117; Nicolaisen J, 2007, ANNU REV INFORM SCI, V41, P609, DOI 10.1002/aris.2007.1440410120; OCONNOR J, 1982, INFORM PROCESS MANAG, V18, P125, DOI 10.1016/0306-4573(82)90036-X; OPPENHEIM C, 1978, J AM SOC INFORM SCI, V29, P225, DOI 10.1002/asi.4630290504; PERITZ BC, 1983, SCIENTOMETRICS, V5, P303, DOI 10.1007/BF02147226; Pettigrew KE, 2001, J AM SOC INF SCI TEC, V52, P62, DOI 10.1002/1532-2890(2000)52:1<62::AID-ASI1061>3.3.CO;2-A; Pham SB, 2003, LECT NOTES ARTIF INT, V2903, P759; Qazvinian Vahed, 2008, International Journal of Knowledge Management Studies, V2, DOI 10.1504/IJKMS.2008.019750; Qazvinian V., 2008, COLING 08 P 22 INT C, V1, P689; Qazvinian V., 2010, P 48 ANN M ASS COMP, P555; Ramakrishnan C., 2012, SOURCE CODE BIOL MED, V7, P1; REESPOTTER LK, 1989, INFORM PROCESS MANAG, V25, P677, DOI 10.1016/0306-4573(89)90101-5; Ritchie A., 2008, 17 ACM C INF KNOWL M, P213; Ritchie A., 2006, WORKSH CAN COMP LING, P25; Schlitt T, 2003, GENOME RES, V13, P2568, DOI 10.1101/gr.1111403; Schneider JW, 2006, SCIENTOMETRICS, V68, P573, DOI 10.1007/s11192-006-0131-z; Schneider JW, 2004, J DOC, V60, P524, DOI 10.1108/00220410410560609; SHADISH WR, 1995, SOC STUD SCI, V25, P477, DOI 10.1177/030631295025003003; Siddharthan A., 2007, NAACL HLT 2007 P, P316; Small H., 1982, PROGR COMMUNICATION, V3, P287; Small H, 2011, SCIENTOMETRICS, V87, P373, DOI 10.1007/s11192-011-0349-2; SMALL HG, 1978, SOC STUD SCI, V8, P327, DOI 10.1177/030631277800800305; SNIZEK WE, 1991, SCIENTOMETRICS, V20, P25, DOI 10.1007/BF02018141; SPIEGELROSING I, 1977, SOC STUD SCI, V7, P97, DOI 10.1177/030631277700700111; Stansbury MC, 2002, LIBR INFORM SCI RES, V24, P157, DOI 10.1016/S0740-8188(02)00110-X; Suppe F, 1998, PHILOS SCI, V65, P381, DOI 10.1086/392651; Tang J, 2009, LECT NOTES ARTIF INT, V5476, P572; Teufel S., 2000, ARGUMENTATIVE ZONING; Teufel S., 2006, 7 SIGDIAL WORKSH DIS, P80; Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936; VINKLER P, 1987, SCIENTOMETRICS, V12, P47, DOI 10.1007/BF02016689; Voos H., 1976, J ACAD LIB, V1, P19; White MD, 2001, INFORM PROCESS MANAG, V37, P721, DOI 10.1016/S0306-4573(00)00043-1; White MD, 2006, LIBR TRENDS, V55, P22, DOI 10.1353/lib.2006.0053; Zhang G, 2013, J AM SOC INF SCI TEC, V64, P1490, DOI 10.1002/asi.22850; Ziman J. M., 1968, PUBLIC KNOWLEDGE ESS 88 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH SEP 2014 65 9 1820 1833 10.1002/asi.23256 14 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN3IC WOS:000340479500006 J Du, JT Du, Jia Tina The information journey of marketing professionals: Incorporating work task-driven information seeking, information judgments, information use, and information sharing JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article information seeking; information dissemination; information use COGNITIVE AUTHORITY; RELEVANCE CRITERIA; ENGINEERS; BEHAVIOR; QUALITY; DESIGN; WEB; ENVIRONMENT; RETRIEVAL; FRAMEWORK Marketing professionals' work activities are heavily reliant on access to and the use of large amounts of quality information. This study aims to examine the information journey experienced by marketing professionals, including task-driven information seeking, information judgments, information use, and information sharing, from a more contextualized and holistic viewpoint. The information journey presents a more comprehensive picture of user-information interaction than is usually offered in the literature. Using a diary method and post-diary in-depth interviews, data consisting of 1,198 diary entries relating to 101 real work tasks were collected over a period of 5 work days. The data were used to ascertain characteristics of the stages of marketing professionals' information journeys as well as the relationships between them. Five stages of the information journey, including determining the need for work task-generated information, seeking such information, judging and evaluating the information found, making sense of and using the obtained information, and sharing the obtained or assembled information, were identified. The information journey also encompassed types of gaps and gap-bridge techniques that occurred during information seeking and use. Based on the empirical findings, an information journey model was developed. The implications for information systems design solutions that enable different stages of the information journey to be linked together are also discussed. Univ S Australia, Lib & Informat Management Program, Sch Informat Technol & Math Sci, Adelaide, SA 5001, Australia Du, JT (reprint author), Univ S Australia, Lib & Informat Management Program, Sch Informat Technol & Math Sci, Mawson Lakes Campus,Internal Post Code MLK-06, Adelaide, SA 5001, Australia. tina.du@unisa.edu.au Division of Information Technology, Engineering and Environment Early Career Research Development Grant at the University of South Australia I thank Denise Wood and Gus Geursen for comments on the article draft and the anonymous reviewers for insightful comments and suggestions. This research project was funded by the Division of Information Technology, Engineering and Environment Early Career Research Development Grant at the University of South Australia. Adams A, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P160, DOI 10.1145/1065385.1065424; Allard S, 2009, J AM SOC INF SCI TEC, V60, P443, DOI 10.1002/asi.21004; Allen B. L., 2001, NEW REV INFORMATION, V2, P1; ALLEN TJ, 1966, IEEE T ENG MANAGE, VEM13, P72; Ashill N. J., 2001, QUALITATIVE MARKET R, V4, P52, DOI 10.1108/13522750110364578; Attfield S, 2003, J DOC, V59, P187, DOI 10.1108/00220410310463860; BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J; Bennett R, 2007, J DOC, V63, P702, DOI 10.1108/00220410710827763; Blandford A., 2010, INTERACTING INFORM; Bolger N, 2003, ANNU REV PSYCHOL, V54, P579, DOI 10.1146/annurev.psych.54.101601.145030; Bystrom K, 2005, J AM SOC INF SCI TEC, V56, P1050, DOI 10.1002/asi.20197; Case D. O., 2007, LOOKING INFORM SURVE; Choo C., 2000, 1 MONDAY, V5; Dervin B., 1992, QUALITATIVE RES INFO, P61; DESHPANDE R, 1987, J MARKETING RES, V24, P114, DOI 10.2307/3151759; Du J. T., 2012, P IEEE VTC 2012 FALL, P1, DOI DOI 10.1109/VTCFALL.2012.6399309; Du J. T., 2013, INFORM RES, V18; Du JTN, 2011, J AM SOC INF SCI TEC, V62, P1446, DOI 10.1002/asi.21551; Edwards K., 1994, P C COMP HUM FACT CO, P89; Ellis D, 1997, J DOC, V53, P384, DOI 10.1108/EUM0000000007204; Fagin R., 2003, P 12 INT C WORLD WID, P366; Fidel R, 2004, J AM SOC INF SCI TEC, V55, P939, DOI 10.1002/asi.20041; Ford N, 2004, J AM SOC INF SCI TEC, V55, P769, DOI 10.1002/asi.20021; Freund L., 2005, P 68 AM SOC INF SCI, P1; Hansen P, 2005, INFORM PROCESS MANAG, V41, P1101, DOI 10.1016/j.ipm.2004.04.016; Hansen P., 2000, ACMSIGIR 2000 WORKSH; Hertzum M, 2000, INFORM PROCESS MANAG, V36, P761, DOI 10.1016/S0306-4573(00)00011-X; Hertzum M., 2002, Information and Organization, V12, DOI 10.1016/S1471-7727(01)00007-0; Hughes B, 2010, J AM SOC INF SCI TEC, V61, P433, DOI 10.1002/asi.21245; Johnson JD, 2003, INFORM PROCESS MANAG, V39, P735, DOI 10.1016/S0306-4573(02)00030-4; Kari J., 2010, INFORM RES, V15; Katerattanakul P., 1999, P 20 INT C INF SYST, P279; Kim S., 2010, INFORM RES, V15; Knight S. A., 2005, J INF SCI, V8, P159; Krippendorff K, 2004, CONTENT ANAL INTRO I; Kuhlthau CC, 2001, J DOC, V57, P25, DOI 10.1108/EUM0000000007076; Kwasitsu L, 2003, LIBR INFORM SCI RES, V25, P459, DOI 10.1016/S0740-8188(03)00054-9; Landry CF, 2006, J AM SOC INF SCI TEC, V57, P1896, DOI 10.1002/asi.20385; Leckie GJ, 1996, LIBR QUART, V66, P161; Li YL, 2010, J AM SOC INF SCI TEC, V61, P1771, DOI 10.1002/asi.21359; Narayanan S, 1999, HUM FACTOR ERGON MAN, V9, P203, DOI 10.1002/(SICI)1520-6564(199921)9:2<203::AID-HFM5>3.0.CO;2-3; ODAY VL, 1993, HUMAN FACTORS IN COMPUTING SYSTEMS, P438; Ottesen G., 2004, MARKETING INTELLIGEN, V22, P520, DOI DOI 10.1108/02634500410551905; Pettigrew KE, 2001, ANNU REV INFORM SCI, V35, P43; Pilerot O, 2011, J DOC, V67, P312, DOI 10.1108/00220411111109494; Rieh SY, 2002, J AM SOC INF SCI TEC, V53, P145, DOI 10.1002/asi.10017.abs; ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J; Savolainen R, 2006, J AM SOC INF SCI TEC, V57, P1116, DOI 10.1002/asi.20400; Savolainen R, 2006, INFORM PROCESS MANAG, V42, P519, DOI 10.1016/j.ipm.2005.01.009; Savolainen R, 2011, J AM SOC INF SCI TEC, V62, P1243, DOI 10.1002/asi.21546; SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3; SHUCHMAN HL, 1982, INT FORUM INFORM DOC, V7, P3; Sonnenwald D. H., 2006, INFORM RES, VII, P11; Sonnenwald DH, 2000, INFORM PROCESS MANAG, V36, P461, DOI 10.1016/S0306-4573(99)00039-4; Strauss A., 1990, BASICS QUALITATIVE R; Stvilia B, 2007, J AM SOC INF SCI TEC, V58, P1720, DOI 10.1002/asi.20652; Talja S., 2002, New Review of Information Behaviour Research, V3; Talja S, 2006, INFORM SCI KNOWL MAN, V8, P113; Taylor R. S., 1991, PROGR COMMUNICATION, P217; Tenopir C., 2004, COMMUNICATION PATTER; Thivant E., 2008, INFORM RES, V13; Todd RJ, 1999, INFORM PROCESS MANAG, V35, P851, DOI 10.1016/S0306-4573(99)00030-8; Toms EG, 2002, J AM SOC INF SCI TEC, V53, P1232, DOI 10.1002/asi.10165; Vakkari P, 2003, ANNU REV INFORM SCI, V37, P413, DOI 10.1002/aris.1440370110; Vakkari P, 1997, P INT C RES INF NEED, P451; Vakkari P, 2000, J DOC, V56, P540, DOI 10.1108/EUM0000000007127; Wang R, 1996, J MANAGE INFORM SYST, V12, P4; Widen G., 2012, INFORM RES, V17; Wilson T. D., 2010, INFORM RES, V15; Wilson T. D., 2000, Informing Science, V3 70 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH SEP 2014 65 9 1850 1869 10.1002/asi.23085 20 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN3IC WOS:000340479500008 J Brandao, WC; Santos, RLT; Ziviani, N; de Moura, ES; da Silva, AS Brandao, Wladmir C.; Santos, Rodrygo L. T.; Ziviani, Nivio; de Moura, Edleno S.; da Silva, Altigran S. Learning to expand queries using entities JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article query expansion; information retrieval; machine learning SET; WEB A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. Recently, methods that exploit named entities have been shown to be more effective for query expansion than traditional pseudorelevance feedback methods. In this article, we introduce a supervised learning approach that exploits named entities for query expansion using Wikipedia as a repository of high-quality feedback documents. In contrast with existing entity-oriented pseudorelevance feedback approaches, we tackle query expansion as a learning-to-rank problem. As a result, not only do we select effective expansion terms but we also weigh these terms according to their predicted effectiveness. To this end, we exploit the rich structure of Wikipedia articles to devise discriminative term features, including each candidate term's proximity to the original query terms, as well as its frequency across multiple article fields and in category and infobox descriptors. Experiments on three Text REtrieval Conference web test collections attest the effectiveness of our approach, with gains of up to 23.32% in terms of mean average precision, 19.49% in terms of precision at 10, and 7.86% in terms of normalized discounted cumulative gain compared with a state-of-the-art approach for entity-oriented query expansion. [Brandao, Wladmir C.; Santos, Rodrygo L. T.; Ziviani, Nivio] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG, Brazil; [de Moura, Edleno S.; da Silva, Altigran S.] Univ Fed Amazonas, Dept Comp Sci, BR-69077000 Manaus, Amazonas, Brazil Brandao, WC (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, Av Antonio Carlos 6627-4010, BR-31270010 Belo Horizonte, MG, Brazil. Wladmir@dcc.ufmg.br; rodrygo@dcc.ufmg.br; nivio@dcc.ufmg.br; edleno@dcc.ufam.edu.br; alti@dcc.ufam.edu.br Brazilian National Institute of Science and Technology [MCT-CNPq 573871/2008-6]; Project MinGroup [CNPq-CT-Amazonia 575553/2008-1]; CNPq The authors are thankful for the partial support given by the Brazilian National Institute of Science and Technology for the web (Grant MCT-CNPq 573871/2008-6), Project MinGroup (Grant CNPq-CT-Amazonia 575553/2008-1), and the authors' individual grants and scholarships from CNPq. Amati G, 2004, LECT NOTES COMPUT SC, V2997, P127; Baeza-Yates R., 2011, MODERN INFORM RETRIE; Beitzel S. M., 2005, P 28 ANN INT ACM SIG, P581, DOI 10.1145/1076034.1076138; Bendersky M., 2012, P 5 ACM INT C WEB SE, P443; BrandAo W. C., 2011, P IADIS INT C WWW IN, P365; Buttcher S., 2006, P 15 TEXT RETR C GAI; Cao G., 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377; Clarke C. L. A., 2009, P 18 TEXT RETR C GAI; Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Cui H., 2002, P 11 INT C WORLD WID, P325, DOI DOI 10.1145/511446.511489; Cutts M., 2012, P SES SAN FRANSC CA; Dalip DH, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P295; Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200; Gabrilovich E, 2009, ACM T WEB, V3, DOI 10.1145/1513876.1513877; Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989; Hawking D., 2001, P 10 TEXT RETR C GAI; He B., 2009, P 18 ACM C INF KNOWL, P2011, DOI 10.1145/1645953.1646289; He B, 2007, INFORM PROCESS MANAG, V43, P1294, DOI 10.1016/j.ipm.2006.11.002; Hu M., 2007, P 16 ACM C C INF KNO, P243, DOI 10.1145/1321440.1321476; Jain A., 2007, P IEEE INT C INF REU, P209; Jain R, 1991, ART COMPUTER SYSTEMS; Jansen BJ, 2000, INFORM PROCESS MANAG, V36, P207, DOI 10.1016/S0306-4573(99)00056-4; Joachims T., 2006, P 12 ACM SIGKDD INT, P217, DOI DOI 10.1145/1150402.1150429; Kotov A., 2012, P 5 ACM INT C WEB SE, P403; Kumaran G., 2008, P 31 ANN INT ACM SIG, P11, DOI 10.1145/1390334.1390339; Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI 10.1145/383952.383972; Lee CJ, 2009, LECT NOTES COMPUT SC, V5839, P168; Li H., 2010, Proceedings of the 19th World Congress of Soil Science: Soil solutions for a changing world, Brisbane, Australia, 1-6 August 2010. Symposium 3.3.2 Molecular biology and optimizing crop nutrition, P1; Li M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P1025; Li Y., 2007, P 30 ANN INT ACM SIG, P797, DOI 10.1145/1277741.1277914; Lin Y., 2011, P 34 INT ACM SIGIR C, P405; Liu T., 2009, FDN TRENDS INFORM RE, V3, P225, DOI DOI 10.1561/1500000016; Milne D. N., 2007, P 16 ACM C INF KNOWL, P445, DOI 10.1145/1321440.1321504; Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995; Peng F., 2007, P 30 ANN INT ACM SIG, P639, DOI 10.1145/1277741.1277851; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; Possas B, 2005, ACM T INFORM SYST, V23, P397, DOI 10.1145/1095872.1095874; Radlinski F, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P667; Risvik K. M., 2003, P 12 INT C WORLD WID; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Silverstein C, 1999, ACM SIGIR FORUM, V33, P6, DOI [10.1145/331403.331405, DOI 10.1145/331403.331405]; Strohman T., 2005, P INT C INT AN MCLEA; Udupa R, 2009, LECT NOTES COMPUT SC, V5766, P104; Vapnik V, 1995, NATURE STAT LEARNING; Weerkamp W., 2012, ACM T WEB, V6; Xu Y, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P59, DOI 10.1145/1571941.1571954; Xu Y., 2008, P 17 ACM C INF KNOWL, P1441, DOI 10.1145/1458082.1458322; Zesch T., 2007, P BIANN C SOC COMP L, P213; Zhai C., 2008, FDN TRENDS INF RETR, V2, P137, DOI DOI 10.1561/1500000008; Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019; Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI 10.1145/502585.502654 51 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH SEP 2014 65 9 1870 1883 10.1002/asi.23084 14 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AN3IC WOS:000340479500009 J Vredeveldt, A; Baddeley, AD; Hitch, GJ Vredeveldt, Annelies; Baddeley, Alan D.; Hitch, Graham J. The effectiveness of eye-closure in repeated interviews LEGAL AND CRIMINOLOGICAL PSYCHOLOGY English Article COGNITIVE INTERVIEW; EYEWITNESS TESTIMONY; FOCUSED MEDITATION; GAZE-AVERSION; MEMORY; RECALL; EFFICACY; RETRIEVAL; CHILDRENS; EVENTS Purpose. Closing the eyes during recall can help witnesses remember more about a witnessed event. This study examined the effectiveness of eye-closure in a repeated recall paradigm with immediate free recall followed 1 week later by both free and cued recall. We examined whether eye-closure was more or less effective during the second free-recall attempt compared with the first, whether eye-closure during the first recall attempt had an impact on subsequent free-and cued-recall performance, and whether eye-closure during the second free recall could facilitate the recall of new, previously unreported, information (reminiscence). Method. Participants witnessed a videotaped event and participated in a first free-recall attempt (with eyes open or closed) a few minutes later. After a week, they provided another free recall, followed by a cued-recall interview (with eyes open or closed). Results. Eye-closure during the first free-recall attempt had no significant effect on performance during any of the recall attempts. However, eye-closure during the second session increased the amount of correct visual information reported in that session by 36.7% in free recall and by 35.3% in cued recall, without harming testimonial accuracy. Crucially, eye-closure also facilitated the recall of new, previously unreported visual information. Conclusions. The findings extend previous research in showing that the eye-closure instruction can still be effective when witnesses are interviewed repeatedly, and that it can facilitate the elicitation of new information. Thus, the eye-closure instruction constitutes a simple and time-efficient interview tool for police interviewers. [Vredeveldt, Annelies; Baddeley, Alan D.; Hitch, Graham J.] Univ York, Dept Psychol, York YO10 5DD, N Yorkshire, England; [Vredeveldt, Annelies] Univ Cape Town, Dept Psychol, ZA-7701 Cape Town, South Africa Vredeveldt, A (reprint author), Univ Cape Town, Dept Psychol, Private Bag X3, ZA-7701 Cape Town, South Africa. anneliesvredeveldt@gmail.com Bornstein BH, 1998, APPL COGNITIVE PSYCH, V12, P119, DOI 10.1002/(SICI)1099-0720(199804)12:2<119::AID-ACP500>3.0.CO;2-4; Brock P, 1999, PSYCHOL CRIME LAW, V5, P29, DOI 10.1080/10683169908414992; BURKE A, 1992, MEM COGNITION, V20, P277, DOI 10.3758/BF03199665; Campos L, 2006, MEMORY, V14, P27, DOI 10.1080/0965821044000476; Campos L, 2008, APPL COGNITIVE PSYCH, V22, P1211, DOI 10.1002/acp.1430; CARLSON RF, 1976, J PSYCHOL, V92, P117; CARRIER M, 1992, MEM COGNITION, V20, P633, DOI 10.3758/BF03202713; Caruso EM, 2011, COGNITION, V118, P280, DOI 10.1016/j.cognition.2010.11.008; Dando C, 2009, APPL COGNITIVE PSYCH, V23, P138, DOI 10.1002/acp.1451; Dando C, 2009, PSYCHOL CRIME LAW, V15, P679, DOI 10.1080/10683160802203963; Davis MR, 2005, APPL COGNITIVE PSYCH, V19, P75, DOI 10.1002/acp.1048; Field A. P., 2004, DISCOVERING STAT USI; Fisher R. P., 1992, MEMORY ENHANCING TEC; FLIN R, 1992, BRIT J PSYCHOL, V83, P323; Gabbert F, 2009, LAW HUMAN BEHAV, V33, P298, DOI 10.1007/s10979-008-9146-8; Glenberg AM, 1998, MEM COGNITION, V26, P651, DOI 10.3758/BF03211385; Goldsmith M, 2002, J EXP PSYCHOL GEN, V131, P73, DOI 10.1037//0096-3445.131.173; Kebbell MR, 1999, PSYCHOL CRIME LAW, V5, P101, DOI 10.1080/10683169908414996; Kohnken G, 1999, PSYCHOL CRIME LAW, V5, P3, DOI 10.1080/10683169908414991; La Rooy D, 2009, EVALUATION CHILD SEX, P327; Larsson AS, 2003, APPL COGNITIVE PSYCH, V17, P203, DOI 10.1002/acp.863; LIPTON JP, 1977, J APPL PSYCHOL, V62, P90, DOI DOI 10.1037//0021-9010.62.1.90; Markson L, 2009, BRIT J PSYCHOL, V100, P553, DOI 10.1348/000712608X371762; Marsh EJ, 2005, APPL COGNITIVE PSYCH, V19, P531, DOI 10.1002/acp.1095; Mastroberardino S, 2012, PSYCHOL CRIME LAW, V18, P245, DOI 10.1080/10683161003801100; MCCAULEY MR, 1995, J APPL PSYCHOL, V80, P510, DOI 10.1037//0021-9010.80.4.510; Memon A, 2010, PSYCHOL PUBLIC POL L, V16, P340, DOI 10.1037/a0020518; Memon A, 1997, BRIT J PSYCHOL, V88, P179; Milne R., 2001, NATL EVALUATION PEAC; Odinot G, 2009, APPL COGNITIVE PSYCH, V23, P90, DOI 10.1002/acp.1443; Payne D. G, 1987, PSYCHOL BULL, V10, P5; Perfect TJ, 2008, LAW HUMAN BEHAV, V32, P314, DOI 10.1007/s10979-007-9109-5; PEZDEK K, 1993, APPL COGNITIVE PSYCH, V7, P299, DOI 10.1002/acp.2350070404; Phelps FG, 2006, BRIT J DEV PSYCHOL, V24, P577, DOI 10.1348/026151005X49872; Roediger HL, 2006, PERSPECT PSYCHOL SCI, V1, P181, DOI 10.1111/j.1745-6916.2006.00012.x; SCRIVNER E, 1988, J APPL PSYCHOL, V73, P371, DOI 10.1037//0021-9010.73.3.371; TURTLE JW, 1994, J APPL PSYCHOL, V79, P260, DOI 10.1037/0021-9010.79.2.260; Tversky B, 2000, COGNITIVE PSYCHOL, V40, P1, DOI 10.1006/cogp.1999.0720; Vredeveldt A, 2013, PSYCHOL CRIME LAW, V19, P893, DOI 10.1080/1068316X.2012.700313; Vredeveldt A, 2011, MEM COGNITION, V39, P1253, DOI 10.3758/s13421-011-0098-8; Vredeveldt A., 2012, EUR J PSYCHOL, V8, P284, DOI [10.5964/ejop.v8i2.472, DOI 10.5964/EJOP.V8I2.472]; Wagstaff G. F., 2007, CONT HYPNOSIS, V24, P97, DOI [10.1002/ch.334, DOI 10.1002/CH.334]; Wagstaff G. F., 2011, J POLICE CRIM PSYCHO, V26, P152, DOI [10.1007/s11896-010-9082-7, DOI 10.1007/S11896-010-9082-7]; Wagstaff GF, 2011, INT J CLIN EXP HYP, V59, P146, DOI 10.1080/00207144.2011.546180; Wagstaff GF, 2004, INT J CLIN EXP HYP, V52, P434, DOI 10.1080/00207140490889062; Wais PE, 2010, J NEUROSCI, V30, P8541, DOI 10.1523/JNEUROSCI.1478-10.2010; Wang AY, 2000, AM J PSYCHOL, V113, P331, DOI 10.2307/1423362 47 4 4 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1355-3259 2044-8333 LEGAL CRIMINOL PSYCH Legal Criminol. Psychol. SEP 2014 19 2 282 295 10.1111/lcrp.12013 14 Criminology & Penology; Law; Psychology, Multidisciplinary Criminology & Penology; Government & Law; Psychology AN4ZL WOS:000340598500011 J Horvath, JC Horvath, Jared Cooney The Neuroscience of PowerPoint (TM) MIND BRAIN AND EDUCATION English Article DIVIDED-ATTENTION; OBJECT CATEGORIZATION; SENSORY MODALITIES; OLDER-ADULTS; MEMORY; TASKS; FMRI; INFORMATION; RESOURCES; RETRIEVAL Many concepts have been published relevant to improving the design of PowerPoint (TM) (PP) presentations for didactic purposes, including the redundancy, modality, and signaling principles of multimedia learning. In this article, we review the recent neuroimaging findings that have emerged elucidating the neural structures involved in many of these concepts. First, we explore the research suggesting that the brain utilizes similar structures to process written text and oral speech leading to neural competition and impaired performance during dual linguistic text/audition tasks (redundancy principle). Next, we examine research that demonstrates that the brain processes visual images in a manner different from and parallel to oral speech leading to improved performance during dual nonlinguistic visual/audition tasks (modality principle). Finally, we look at how the brain responds to contextual and direct attention cues (signaling principle). We link this research to PP design and suggest a number of concrete ways to implement these findings to improve the didactic strength of slide-show presentations. Univ Melbourne, Sch Psychol Sci, Melbourne, Vic 3010, Australia Horvath, JC (reprint author), Univ Melbourne, Sch Psychol Sci, Redmond Barry Bldg Room 613, Melbourne, Vic 3010, Australia. jhorvath@student.unimelb.edu.au Alais D, 2006, P R SOC B, V273, P1339, DOI 10.1098/rspb.2005.3420; Amadieu F, 2011, COMPUT HUM BEHAV, V27, P36, DOI 10.1016/j.chb.2010.05.009; Arrighi R, 2011, FRONT PSYCHOL, V2, DOI 10.3389/fpsyg.2011.00056; Bemis DK, 2013, CEREB CORTEX, V23, P1859, DOI 10.1093/cercor/bhs170; Boucheix JM, 2013, LEARN INSTR, V25, P71, DOI 10.1016/j.learninstruc.2012.11.005; Buchweitz A, 2012, HUM BRAIN MAPP, V33, P1868, DOI 10.1002/hbm.21327; Chun MM, 1998, COGNITIVE PSYCHOL, V36, P28, DOI 10.1006/cogp.1998.0681; Costafreda SG, 2006, HUM BRAIN MAPP, V27, P799, DOI 10.1002/hbm.20221; de Koning BB, 2007, APPL COGNITIVE PSYCH, V21, P731, DOI 10.1002/acp.1346; Dennis N. A., 2011, NEUROBIOL AGING, V32, P2318; Fernandes MA, 2006, NEUROPSYCHOLOGIA, V44, P2452, DOI 10.1016/j.neuropsychologia.2006.04.020; Fernandes MA, 2007, CAN J EXP PSYCHOL, V61, P128, DOI 10.1037/cjep2007014; Fernandes MA, 2003, PSYCHOL AGING, V18, P219, DOI 10.1037/0882-7974.18.2.219; Giesbrecht B., 2012, VISION RES, V85, P80; Hallett T. L., 2006, Journal of Technology in Human Services, V24, DOI 10.1300/J017v24n02_10; Hannula DE, 2009, NEURON, V63, P592, DOI 10.1016/j.neuron.2009.08.025; Holle H, 2010, NEUROIMAGE, V49, P875, DOI 10.1016/j.neuroimage.2009.08.058; Huang TR, 2010, PSYCHOL REV, V117, P1080, DOI 10.1037/a0020664; Hugdahl K, 2009, SCAND J PSYCHOL, V50, P11, DOI 10.1111/j.1467-9450.2008.00676.x; Hugdahl K, 2011, BRAIN COGNITION, V76, P211, DOI 10.1016/j.bandc.2011.03.006; Jamet E, 2007, CONTEMP EDUC PSYCHOL, V32, P588, DOI 10.1016/j.cedpsych.2006.07.001; Johnson JA, 2006, NEUROIMAGE, V31, P1673, DOI 10.1016/j.neuroimage.2006.02.026; Kalyuga S, 2004, HUM FACTORS, V46, P567, DOI 10.1518/hfes.46.3.567.3809; Kensinger EA, 2003, J NEUROSCI, V23, P2407; Kim H, 2011, NEUROIMAGE, V54, P2446, DOI 10.1016/j.neuroimage.2010.09.045; Koelewijn T, 2010, ACTA PSYCHOL, V134, P372, DOI 10.1016/j.actpsy.2010.03.010; Kristjansson A, 2010, ATTEN PERCEPT PSYCHO, V72, P5, DOI 10.3758/APP.72.1.5; Kuhl T, 2011, COMPUT HUM BEHAV, V27, P29, DOI 10.1016/j.chb.2010.05.008; Lin Lin, 2009, Computers in the Schools, V26, DOI 10.1080/07380560903095162; Lin L, 2011, J EDUC COMPUT RES, V45, P183, DOI 10.2190/EC.45.2.d; Liu HC, 2011, COMPUT HUM BEHAV, V27, P2410, DOI 10.1016/j.chb.2011.06.012; Mayer RE, 2009, MULTIMEDIA LEARNING, 2ND EDITION, P1, DOI 10.1017/CBO9780511811678; Mayer R. E., 2001, MULTIMEDIA LEARNING; Mishra J, 2012, J NEUROSCI, V32, P12294, DOI 10.1523/JNEUROSCI.0867-12.2012; Naveh-Benjamin M, 2006, MEM COGNITION, V34, P90, DOI 10.3758/BF03193389; Ozcelik E, 2010, COMPUT HUM BEHAV, V26, P110, DOI 10.1016/j.chb.2009.09.001; PASHLER H, 1994, PSYCHOL BULL, V116, P220, DOI 10.1037/0033-2909.116.2.220; Perrone-Bertolotti M, 2012, J NEUROSCI, V32, P17554, DOI 10.1523/JNEUROSCI.2982-12.2012; Petkov C. I., 2013, CURR BIOL, V23, pR156; POSNER MI, 1980, Q J EXP PSYCHOL, V32, P3, DOI 10.1080/00335558008248231; Pros R. C., 2013, INTANGIBLE CAPITAL, V9, P184; Ragan ED, 2012, PROCEEDINGS OF THE INTERNATIONAL WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES, P91, DOI 10.1145/2254556.2254576; Rai U., 2009, INDIAN J SOCIAL SCI, V6, P71; Savoy A, 2009, COMPUT EDUC, V52, P858, DOI 10.1016/j.compedu.2008.12.005; Schumacher EH, 2003, J COGNITIVE NEUROSCI, V15, P1111, DOI 10.1162/089892903322598085; Sigman M, 2008, J NEUROSCI, V28, P7585, DOI 10.1523/JNEUROSCI.0948-08.2008; Summerfield C, 2009, TRENDS COGN SCI, V13, P403, DOI 10.1016/j.tics.2009.06.003; Sweller J., 2011, COGNITIVE LOAD THEOR, V1, P141; Talsma D, 2006, PSYCHOPHYSIOLOGY, V43, P541, DOI 10.1111/j.1469-8986.2006.00452.x; Toh S. C., 2010, P ASC SYDN, P988; Uncapher MR, 2008, J COGNITIVE NEUROSCI, V20, P240, DOI 10.1162/jocn.2008.20026; Uncapher MR, 2009, NEUROBIOL LEARN MEM, V91, P139, DOI 10.1016/j.nlm.2008.10.011; Uncapher MR, 2005, J COGNITIVE NEUROSCI, V17, P1923, DOI 10.1162/089892905775008616; Vohn R, 2007, HUM BRAIN MAPP, V28, P1267, DOI 10.1002/hbm.20350; Wecker C, 2012, COMPUT EDUC, V59, P260, DOI 10.1016/j.compedu.2012.01.013; Werner JS, 2014, NEW VISUAL NEUROSCIENCES, P1; Werner S, 2010, CEREB CORTEX, V20, P1829, DOI 10.1093/cercor/bhp248; Werner S, 2010, J NEUROSCI, V30, P2662, DOI 10.1523/JNEUROSCI.5091-09.2010; Westerberg CE, 2011, NEUROPSYCHOLOGIA, V49, P3439, DOI 10.1016/j.neuropsychologia.2011.08.019; Yang F. Y., 2012, COMPUT EDUC, V62, P208; Yue CL, 2013, J EDUC PSYCHOL, V105, P266, DOI 10.1037/a0031971 61 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1751-2271 1751-228X MIND BRAIN EDUC Mind Brain Educ. SEP 2014 8 3 137 143 10.1111/mbe.12052 7 Education & Educational Research; Psychology, Developmental Education & Educational Research; Psychology AN4DF WOS:000340537100004 J Shokralla, S; Gibson, JF; Nikbakht, H; Janzen, DH; Hallwachs, W; Hajibabaei, M Shokralla, Shadi; Gibson, Joel F.; Nikbakht, Hamid; Janzen, Daniel H.; Hallwachs, Winnie; Hajibabaei, Mehrdad Next-generation DNA barcoding: using next-generation sequencing to enhance and accelerate DNA barcode capture from single specimens MOLECULAR ECOLOGY RESOURCES English Article biodiversity; COI; DNA; genomics; heteroplasmy; Lepidoptera; taxonomy; Wolbachia PRESERVATIVE ETHANOL; ASTRAPTES-FULGERATOR; MITOCHONDRIAL-DNA; HOST-SPECIFICITY; FLIES DIPTERA; PCR; AMPLIFICATION; BIODIVERSITY; INTEGRATION; DROSOPHILA DNA barcoding is an efficient method to identify specimens and to detect undescribed/cryptic species. Sanger sequencing of individual specimens is the standard approach in generating large-scale DNA barcode libraries and identifying unknowns. However, the Sanger sequencing technology is, in some respects, inferior to next-generation sequencers, which are capable of producing millions of sequence reads simultaneously. Additionally, direct Sanger sequencing of DNA barcode amplicons, as practiced in most DNA barcoding procedures, is hampered by the need for relatively high-target amplicon yield, coamplification of nuclear mitochondrial pseudogenes, confusion with sequences from intracellular endosymbiotic bacteria (e.g. Wolbachia) and instances of intraindividual variability (i.e. heteroplasmy). Any of these situations can lead to failed Sanger sequencing attempts or ambiguity of the generated DNA barcodes. Here, we demonstrate the potential application of next-generation sequencing platforms for parallel acquisition of DNA barcode sequences from hundreds of specimens simultaneously. To facilitate retrieval of sequences obtained from individual specimens, we tag individual specimens during PCR amplification using unique 10-mer oligonucleotides attached to DNA barcoding PCR primers. We employ 454 pyrosequencing to recover full-length DNA barcodes of 190 specimens using 12.5% capacity of a 454 sequencing run (i.e. two lanes of a 16 lane run). We obtained an average of 143 sequence reads for each individual specimen. The sequences produced are full-length DNA barcodes for all but one of the included specimens. In a subset of samples, we also detected Wolbachia, nontarget species, and heteroplasmic sequences. Next-generation sequencing is of great value because of its protocol simplicity, greatly reduced cost per barcode read, faster throughout and added information content. [Shokralla, Shadi; Gibson, Joel F.; Nikbakht, Hamid; Hajibabaei, Mehrdad] Univ Guelph, Biodivers Inst Ontario, Dept Integrat Biol, Guelph, ON N1G 2W1, Canada; [Shokralla, Shadi] Mansoura Univ, Dept Microbiol, Mansoura 35516, Egypt; [Janzen, Daniel H.; Hallwachs, Winnie] Univ Penn, Dept Biol, Philadelphia, PA 19104 USA Hajibabaei, M (reprint author), Univ Guelph, Biodivers Inst Ontario, Dept Integrat Biol, 50 Stone Rd East, Guelph, ON N1G 2W1, Canada. mhajibab@uoguelph.ca Government of Canada through Genome Canada; Ontario Genomics Institute through the Biomonitoring 2.0 project [OGI-050]; NSF [DEB 0515699]; JRS Biodiversity Foundation; Wege Foundation of Grand Rapids, Michigan; NSERC PDF This project was funded by the Government of Canada through Genome Canada and the Ontario Genomics Institute through the Biomonitoring 2.0 project (OGI-050) to M. H., an NSF grant DEB 0515699 to D.H.J. and the JRS Biodiversity Foundation and the Wege Foundation of Grand Rapids, Michigan, to the Guanacaste Dry Forest Conservation Fund. J.F.G. is also funded by an NSERC PDF. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. We are grateful to Area de Conservacion Guanacaste for protecting the forest habitat that we sampled. Berthier K, 2011, SYST ENTOMOL, V36, P285, DOI 10.1111/j.1365-3113.2010.00561.x; Binladen J, 2007, PLOS ONE, V2, DOI 10.1371/journal.pone.0000197; Boessenkool S, 2012, MOL ECOL, V21, P1806, DOI 10.1111/j.1365-294X.2011.05306.x; Brower AVZ, 2006, SYST BIODIVERS, V4, P127, DOI 10.1017/S147720000500191X; Chacon IA, 2013, ZOOKEYS, P11, DOI 10.3897/zookeys.264.4440; Dyer KA, 2004, GENETICS, V168, P1443, DOI 10.1534/genetics.104.027854; Edgar RC, 2010, BIOINFORMATICS, V26, P2460, DOI 10.1093/bioinformatics/btq461; Engelstadter J, 2009, ANNU REV ECOL EVOL S, V40, P127, DOI 10.1146/annurev.ecolsys.110308.120206; Gilles A, 2011, BMC GENOMICS, V12, DOI 10.1186/1471-2164-12-245; Hajibabaei M, 2005, PHILOS T ROY SOC B, V360, P1959, DOI 10.1098/rstb.2005.1727; Hajibabaei M, 2012, TRENDS GENET, V28, P535, DOI 10.1016/j.tig.2012.08.001; Hajibabaei M, 2007, TRENDS GENET, V23, P167, DOI 10.1016/j.tig.2007.02.001; Hajibabaei M, 2012, BMC ECOL, V12, DOI 10.1186/1472-6785-12-28; Hebert PDN, 2004, P NATL ACAD SCI USA, V101, P14812, DOI 10.1073/pnas.0406166101; Hebert PDN, 2003, P ROY SOC B-BIOL SCI, V270, P313, DOI 10.1098/rspb.2002.2218; Hoffmann AA, 2013, P ROY SOC B-BIOL SCI, V280, DOI 10.1098/rspb.2013.0371; Hollingsworth PM, 2009, P NATL ACAD SCI USA, V106, P12794, DOI 10.1073/pnas.0905845106; Janzen DH, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019874; Janzen DH, 2009, MOL ECOL RESOUR, V9, P1, DOI 10.1111/j.1755-0998.2009.02628.x; Janzen DH, 2012, INVERTEBR SYST, V26, P478, DOI 10.1071/IS12038; KIMURA M, 1980, J MOL EVOL, V16, P111, DOI 10.1007/BF01731581; Knee W, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0047243; KONDO R, 1990, GENETICS, V126, P657; Kvist S, 2013, MOL PHYLOGENET EVOL, V69, P39, DOI 10.1016/j.ympev.2013.05.012; Kwong S, 2012, CLADISTICS, V28, P639, DOI 10.1111/j.1096-0031.2012.00408.x; Magnacca KN, 2010, BMC EVOL BIOL, V10, DOI 10.1186/1471-2148-10-174; Margulies M, 2005, NATURE, V437, P376, DOI 10.1038/nature03959; Matsumoto M, 2003, MOL PHYLOGENET EVOL, V27, P429, DOI 10.1016/S1055-7903(03)00013-7; Pan XL, 2012, P NATL ACAD SCI USA, V109, pE23, DOI 10.1073/pnas.1116932108; Park JJ, 2012, ENTOMOL RES, V42, P104, DOI 10.1111/j.1748-5967.2012.00445.x; Polz MF, 1998, APPL ENVIRON MICROB, V64, P3724; SANGER F, 1977, P NATL ACAD SCI USA, V74, P5463, DOI 10.1073/pnas.74.12.5463; Savolainen V, 2005, PHILOS T ROY SOC B, V360, P1805, DOI 10.1098/rstb.2005.1730; Schmieder R, 2011, BIOINFORMATICS, V27, P863, DOI 10.1093/bioinformatics/btr026; Schoch CL, 2012, P NATL ACAD SCI USA, V109, P6241, DOI 10.1073/pnas.1117018109; Shokralla S, 2010, BIOTECHNIQUES, V48, P233, DOI 10.2144/000113362; Shokralla S, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0021252; Shokralla S, 2012, MOL ECOL, V21, P1794, DOI 10.1111/j.1365-294X.2012.05538.x; Smith MA, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0036514; Smith MA, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0014424; Smith MA, 2006, P NATL ACAD SCI USA, V103, P3657, DOI 10.1073/pnas.0511318103; Song H, 2008, P NATL ACAD SCI USA, V105, P13486, DOI 10.1073/pnas.0803076105; Taberlet P, 2012, MOL ECOL, V21, P2045, DOI 10.1111/j.1365-294X.2012.05470.x; Tamura K, 2011, MOL BIOL EVOL, V28, P2731, DOI 10.1093/molbev/msr121; Taylor RW, 2005, NAT REV GENET, V6, P389, DOI 10.1038/nrg1606; Tyc J, 2013, MOL PHYLOGENET EVOL, V69, P255, DOI 10.1016/j.ympev.2013.05.024; Vollmer NL, 2011, CURR GENET, V57, P115, DOI 10.1007/s00294-010-0331-1; Wallace LJ, 2012, FOOD RES INT, V49, P446, DOI 10.1016/j.foodres.2012.07.048; Zhang Z, 2000, J COMPUT BIOL, V7, P203, DOI 10.1089/10665270050081478 49 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1755-098X 1755-0998 MOL ECOL RESOUR Mol. Ecol. Resour. SEP 2014 14 5 892 901 10.1111/1755-0998.12236 10 Biochemistry & Molecular Biology; Ecology; Evolutionary Biology Biochemistry & Molecular Biology; Environmental Sciences & Ecology; Evolutionary Biology AN2OL WOS:000340425100002 J Mahmoud, A; Niu, N Mahmoud, Anas; Niu, Nan Supporting requirements to code traceability through refactoring REQUIREMENTS ENGINEERING English Article Information retrieval; Traceability; Refactoring LATENT SEMANTIC ANALYSIS; IN-SOURCE CODE; CLONE DETECTION; IDENTIFIER NAMES; SOFTWARE; DOCUMENTATION; RECOVERY; SYSTEM; TREES; STATE In this paper, we hypothesize that the distorted traceability tracks of a software system can be systematically re-established through refactoring, a set of behavior-preserving transformations for keeping the system quality under control during evolution. To test our hypothesis, we conduct an experimental analysis using three requirements-to-code datasets from various application domains. Our objective is to assess the impact of various refactoring methods on the performance of automated tracing tools based on information retrieval. Results show that renaming inconsistently named code identifiers, using Rename Identifier refactoring, often leads to improvements in traceability. In contrast, removing code clones, using eXtract Method (XM) refactoring, is found to be detrimental. In addition, results show that moving misplaced code fragments, using Move Method refactoring, has no significant impact on trace link retrieval. We further evaluate Rename Identifier refactoring by comparing its performance with other strategies often used to overcome the vocabulary mismatch problem in software artifacts. In addition, we propose and evaluate various techniques to mitigate the negative impact of XM refactoring. An effective traceability sign analysis is also conducted to quantify the effect of these refactoring methods on the vocabulary structure of software systems. [Mahmoud, Anas; Niu, Nan] Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA Mahmoud, A (reprint author), Mississippi State Univ, Dept Comp Sci & Engn, Mississippi State, MS 39762 USA. amm560@msstate.edu; niu@cse.msstate.edu US NSF (National Science Foundation) [CCF1238336] We would like to thank the partner company for the generous support of our research. This work is supported in part by the US NSF (National Science Foundation) Grant No. CCF1238336. Advani D, 2005, REFACTORING TRENDS A; Anquetil N, 1998, CASCON 98, P4; Anquetil N, 1999, WORK C REV ENG, P235; Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053; Antoniol G, 2004, 7TH INTERNATIONAL WORKSHOP ON PRINCIPLES OF SOFTWARE EVOLUTION, P31; Aslam J, 2005, ANN INT ACM SIGIR C, P573; Asuncion H, 2010, ICSE 10, P95; Aversano L, 2010, EUR C SOFTW MAINT RE, P81; Baker BS, 1995, WCRE 95, P86; Baxter ID, 1998, PROC IEEE INT CONF S, P368, DOI 10.1109/ICSM.1998.738528; Ben Charrada E, 2012, INT REQ ENG C, P61; Binkley D, 2009, SCI COMPUT PROGRAM, V74, P430, DOI 10.1016/j.scico.2009.02.006; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Bourquin F, 2007, EUR C SOFTW MAINT RE, P149; Bruntink M, 2005, IEEE T SOFTWARE ENG, V31, P804, DOI 10.1109/TSE.2005.114; Caprile B, 2000, PROC IEEE INT CONF S, P97, DOI 10.1109/ICSM.2000.883022; Cleland-Huang J, 2005, 13TH IEEE INTERNATIONAL CONFERENCE ON REQUIREMENTS ENGINEERING, PROCEEDINGS, P135, DOI 10.1109/RE.2005.78; Cleland-Huang J, 2007, COMPUTER, V40, P27, DOI 10.1109/MC.2007.195; Cleland-Huang J, 2003, IEEE T SOFTWARE ENG, V29, P796, DOI 10.1109/TSE.2003.1232285; Cleland-Huang J, 2012, INT C REQ ENG FDN SO, P179; David K, 2003, CSLI LECT NOTES, V139; De Lucia A, 2012, ICPC, P193; Dean A, 1999, DESIGN ANAL EXPT; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; Deissenbock F, 2005, PROG COMPREHEN, P97, DOI 10.1109/WPC.2005.14; De Lucia A, 2009, EMPIR SOFTW ENG, V14, P57, DOI 10.1007/s10664-008-9090-8; De Lucia A, 2006, PROC IEEE INT CONF S, P299; Demsar J, 2006, J MACH LEARN RES, V7, P1; Dig D, 2005, PROC IEEE INT CONF S, P389; Duala-Ekoko E, 2010, ACM T SOFTW ENG METH, V20, DOI 10.1145/1767751.1767754; Egyed A, 2003, IEEE T SOFTWARE ENG, V29, P116, DOI 10.1109/TSE.2003.1178051; Eick S, 1998, IEEE T SOFTWARE ENG, V27, P1; Feilkas M, 2009, INT C PROGRAM COMPRE, P188; Fokaefs M, 2012, J SYST SOFTWARE, V85, P2241, DOI 10.1016/j.jss.2012.04.013; Fontanaa F, 2011, J OBJECT TECHNOL, V11, P1; Fowler M, 1999, REFACTORING IMPROVIN; Furnas G, 1988, ACM SIGIR 88, P465; Gabrilovich E, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P1606; Gamma E, 1995, DESIGN PATTERNS ELEM; Gibiec M, 2010, INT C AUT SOFTW ENG, P245; Giulio A, 2000, ANN SOFTW ENG, V9, P35; Gotel O, 2012, INT C REQ ENG, P71; Gotel OCZ, 2011, INT REQUIR ENG CONF, P121, DOI 10.1109/RE.2011.6051655; Haiduc S, 2010, WCRE 10, P35; Han EH, 2000, LECT NOTES COMPUT, V1910, P424; Huffman-Hayes J, 2006, IEEE T SOFTWARE ENG, V32, P4; Huffman-Hayes J, 2003, INT C REQ ENG, P138; Jones KS, 2007, INFORM PROCESS MANAG, V43, P1449, DOI 10.1016/j.ipm.2007.03.009; Kamiya T, 2002, IEEE T SOFTWARE ENG, V28, P654, DOI 10.1109/TSE.2002.1019480; Katic M, 2009, WSEAS INT C APPL COM, P140; Kim M, 2004, INT S EMP SOFTW ENG, P83, DOI DOI 10.1109/ISESE.2004.1334896; Kolb R, 2006, J SOFTW MAINT EVOL-R, V18, P109, DOI 10.1002/smr.329; Koschke R, 2006, WORK CONF REVERSE EN, P253; Laitinen K., 1996, Software Engineering Notes, V21; Lawrie D, 2010, WORK C REV ENG, P3; Lawrie D, 2007, IEEE INT WORK C SOUR, P213; Lehman M, 1984, J SYST SOFTW, V1, P213; Lethbridge TC, 2003, IEEE SOFTWARE, V20, P35, DOI 10.1109/MS.2003.1241364; Luo J, 2012, INT C INT MULT COMP, P123; Mader P, 2008, INT REQUIR ENG CONF, P23, DOI 10.1109/RE.2008.24; Mahmoud A, 2012, INT C PROGR COMPR IC, P183; Mahmoud A, 2013, INT REQ ENG C, P32; Mahmoud A, 2011, INT WORKSH TRAC EM F, P3; Maletic JI, 2000, PROC INT C TOOLS ART, P46, DOI 10.1109/TAI.2000.889845; Manning C, 2008, INTRO INFORM RETRIEV; Mantyla M, 2006, INT S EMP SOFTW ENG, P297; Mayrand J, 1996, INTERNATIONAL CONFERENCE ON SOFTWARE MAINTENANCE, PROCEEDINGS, P244; Mealy E, 2007, AUSTR SOFTW ENG C AS, P307; Meneely A, 2012, ITRUST ELECT HLTH CA; Mens T, 2004, IEEE T SOFTWARE ENG, V30, P126, DOI 10.1109/TSE.2004.1265817; Moser R, 2006, LECT NOTES COMPUT SC, V4039, P287; Murphy GC, 2006, IEEE SOFTWARE, V23, P76, DOI 10.1109/MS.2006.105; Murphy-Hill E, 2008, ICSE'08 PROCEEDINGS OF THE THIRTIETH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING, P421; Murphy-Hill E, 2009, PROC INT CONF SOFTW, P287, DOI 10.1109/ICSE.2009.5070529; Niu N, 2013, PROCEEDINGS OF THE 35TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2013), P572; Niu N, 2012, IEEE INT REQ ENG C, P81; Opdyke W, 1990, S OBJ OR PROGR EMPH; Opdyke W, 1992, THESIS U ILLINOIS UR; Porter M, 1997, READINGS INFORM RETR, P313; Roy C, 2007, 541 QUEENS U SCH COM; Roy CK, 2008, WORK CONF REVERSE EN, P81, DOI 10.1109/WCRE.2008.54; Spanoudakis G, 2004, HDB SOFTW ENG KNOWL, V3, P395; Sridhara G, 2010, INT C AUT SOFTW ENG, P43; Sultanov H, 2011, REQUIR ENG, V16, P209, DOI 10.1007/s00766-011-0121-4; Sundaram SK, 2010, REQUIR ENG, V15, P313, DOI 10.1007/s00766-009-0096-6; Takang AA, 1996, J PROGRAM LANG, V4, P143; Teufel S, 2007, TEXT SPEECH LANG TEC, V37, P163; Thies A, 2010, INT WORKSH REC SYST, P1; Tourwe T, 2003, EUR C SOFTW MAINT RE, P91; Tsantalis N, 2009, IEEE T SOFTWARE ENG, V35, P347, DOI 10.1109/TSE.2009.1; Wilking D, 2007, E INF SOFTW ENG J, V1, P44 91 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0947-3602 1432-010X REQUIR ENG Requir. Eng. SEP 2014 19 3 SI 309 329 10.1007/s00766-013-0197-0 21 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AN4LT WOS:000340560000005 J Duncan, BN; Prados, AI; Lamsal, LN; Liu, Y; Streets, DG; Gupta, P; Hilsenrath, E; Kahn, RA; Nielsen, JE; Beyersdorf, AJ; Burton, SP; Fiore, AM; Fishman, J; Henze, DK; Hostetler, CA; Krotkov, NA; Lee, P; Lin, MY; Pawson, S; Pfister, G; Pickering, KE; Pierce, RB; Yoshida, Y; Ziemba, LD Duncan, Bryan N.; Prados, Ana I.; Lamsal, Lok N.; Liu, Yang; Streets, David G.; Gupta, Pawan; Hilsenrath, Ernest; Kahn, Ralph A.; Nielsen, J. Eric; Beyersdorf, Andreas J.; Burton, Sharon P.; Fiore, Arlene M.; Fishman, Jack; Henze, Daven K.; Hostetler, Chris A.; Krotkov, Nickolay A.; Lee, Pius; Lin, Meiyun; Pawson, Steven; Pfister, Gabriele; Pickering, Kenneth E.; Pierce, R. Bradley; Yoshida, Yasuko; Ziemba, Luke D. Satellite data of atmospheric pollution for US air quality applications: Examples of applications, summary of data end-user resources, answers to FAQs, and common mistakes to avoid ATMOSPHERIC ENVIRONMENT English Review Satellite data; Air quality; End-user resources; Remote sensing AEROSOL OPTICAL DEPTH; OZONE MONITORING EXPERIMENT; RETRIEVAL ALGORITHM; TROPOSPHERIC OZONE; ECONOMIC RECESSION; ISOPRENE EMISSIONS; NITROGEN-OXIDES; UNITED-STATES; NOX EMISSIONS; MEXICO-CITY Satellite data of atmospheric pollutants are becoming more widely used in the decision-making and environmental management activities of public, private sector and non-profit organizations. They are employed for estimating emissions, tracking pollutant plumes, supporting air quality forecasting activities, providing evidence for "exceptional event" declarations, monitoring regional long-term trends, and evaluating air quality model output. However, many air quality managers are not taking full advantage of the data for these applications nor has the full potential of satellite data for air quality applications been realized. A key barrier is the inherent difficulties associated with accessing, processing, and properly interpreting observational data. A degree of technical skill is required on the part of the data end-user, which is often problematic for air quality agencies with limited resources. Therefore, we 1) review the primary uses of satellite data for air quality applications, 2) provide some background information on satellite capabilities for measuring pollutants, 3) discuss the-many resources available to the end-user for accessing, processing, and visualizing the data, and 4) provide answers to common questions in plain language. Published by Elsevier Ltd. [Duncan, Bryan N.; Prados, Ana I.; Lamsal, Lok N.; Gupta, Pawan; Kahn, Ralph A.; Krotkov, Nickolay A.; Pawson, Steven; Pickering, Kenneth E.; Yoshida, Yasuko] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Prados, Ana I.; Hilsenrath, Ernest] Univ Maryland, Joint Ctr Earth Syst Technol, Baltimore, MD 21201 USA; [Lamsal, Lok N.; Gupta, Pawan] Univ Space Res Assoc, Goddard Earth Sci Technol & Res, Columbia, MD USA; [Liu, Yang] Emory Univ, Rollins Sch Publ Hlth, Atlanta, GA 30322 USA; [Streets, David G.] Argonne Natl Lab, Argonne, IL 60439 USA; [Hilsenrath, Ernest] Sigma Space Corp, Lanham, MD USA; [Nielsen, J. Eric; Yoshida, Yasuko] Sci Syst & Applicat Inc, Lanham, MD USA; [Beyersdorf, Andreas J.; Burton, Sharon P.; Hostetler, Chris A.; Ziemba, Luke D.] NASA, Langley Res Ctr, Hampton, VA 23665 USA; [Fiore, Arlene M.] Columbia Univ, Dept Earth & Environm Sci, Palisades, NY USA; [Fiore, Arlene M.] Columbia Univ, Lamont Doherty Earth Observ, Palisades, NY USA; [Fishman, Jack] St Louis Univ, St Louis, MO 63103 USA; [Henze, Daven K.] Univ Colorado, Boulder, CO 80309 USA; [Lee, Pius] NOAA, College Pk, MD USA; [Lin, Meiyun] Princeton Univ, Princeton, NJ 08544 USA; [Lin, Meiyun] NOAA, Geophys Fluid Dynam Lab, Princeton, NJ USA; [Pfister, Gabriele] Natl Ctr Atmospher Res, Boulder, CO 80307 USA; [Pierce, R. Bradley] NOAA, Madison, WI USA Duncan, BN (reprint author), NASA, Goddard Space Flight Ctr, Code 614, Greenbelt, MD 20771 USA. Bryan.N.Duncan@nasa.gov NASA Air Quality Applied Sciences Team (AQAST); Applied Remote SEnsing Training (ARSET) program, within NASA's Applied Sciences Program This work was funded by the NASA Air Quality Applied Sciences Team (AQAST) and the Applied Remote SEnsing Training (ARSET) program, within NASA's Applied Sciences Program. We thank Ginger Butcher, the NASA Aura Mission's education and public outreach lead, for her comments which greatly improved the readability of the article. Abbot DS, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017336; Beirle S, 2011, SCIENCE, V333, P1737, DOI 10.1126/science.1207824; Bloom AA, 2010, SCIENCE, V327, P322, DOI 10.1126/science.1175176; Boersma KF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008816; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; Bowman KW, 2013, ATMOS ENVIRON, V80, P571, DOI 10.1016/j.atmosenv.2013.07.007; Bucsela EJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008838; Bucsela EJ, 2013, ATMOS MEAS TECH, V6, P2607, DOI 10.5194/amt-6-2607-2013; Bucsela EJ, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013118; Castellanos P, 2012, SCI REP-UK, V2, DOI 10.1038/srep00265; CHAMEIDES WL, 1992, J GEOPHYS RES-ATMOS, V97, P6037; CHAMEIDES WL, 1988, SCIENCE, V241, P1473, DOI 10.1126/science.3420404; Chatfield RB, 2012, ATMOS ENVIRON, V61, P103, DOI 10.1016/j.atmosenv.2012.06.033; Chudnovsky A, 2013, ATMOS CHEM PHYS, V13, P10907, DOI 10.5194/acp-13-10907-2013; Chudnovsky AA, 2013, ENVIRON POLLUT, V172, P131, DOI 10.1016/j.envpol.2012.08.016; Clarisse L, 2009, NAT GEOSCI, V2, P479, DOI 10.1038/ngeo551; Crumeyrolle S., 2013, ATMOS CHEM PHYS DISC, V13, P23421; de Wildt MD, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2011GL049541; Deeter MN, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017553; de Foy B, 2009, ATMOS CHEM PHYS, V9, P9599; de Laat ATJ, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005264; Duncan B., 2013, ATMOS ENV, V81; Duncan BN, 2010, ATMOS ENVIRON, V44, P2213, DOI 10.1016/j.atmosenv.2010.03.010; Engel-Cox JA, 2004, ATMOS ENVIRON, V38, P2495, DOI 10.1016/j.atmosenv.2004.01.039; EPA, 2012, EPA454R12001; Fioletov VE, 2013, J GEOPHYS RES-ATMOS, V118, P11399, DOI 10.1002/jgrd.50826; Fioletov VE, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL049402; Fiore A., 2014, ENVIRON MANAGE, V64, P22; Fishman J, 2003, ATMOS CHEM PHYS, V3, P893; Fishman J, 2008, B AM METEOROL SOC, V89, P805, DOI 10.1175/2008BAMS2526.1; Flynn C., 2014, ATMOS ENV; He H, 2013, ATMOS CHEM PHYS, V13, P7859, DOI 10.5194/acp-13-7859-2013; Hilsenrath E., 2013, NASA EARTH OBSERVER, V25, P10; Hoff RM, 2009, J AIR WASTE MANAGE, V59, P645, DOI 10.3155/1047-3289.59.6.645; Holben BN, 1998, REMOTE SENS ENVIRON, V66, P1, DOI 10.1016/S0034-4257(98)00031-5; Holben BN, 2001, J GEOPHYS RES-ATMOS, V106, P12067, DOI 10.1029/2001JD900014; Hooghiemstra PB, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD017043; Hudman RC, 2012, ATMOS CHEM PHYS, V12, P7779, DOI 10.5194/acp-12-7779-2012; Ichoku C., 2012, ATMOS RES; Jiang X, 2007, REMOTE SENS ENVIRON, V107, P45, DOI 10.1016/j.rse.2006.06.022; Kahn RA, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014601; Kahn RA, 2012, SURV GEOPHYS, V33, P701, DOI 10.1007/s10712-011-9153-z; Kar J, 2010, ATMOS CHEM PHYS, V10, P5213, DOI 10.5194/acp-10-5213-2010; Kasibhatla P., 1997, GEOPHYS RES LETT, V24; Kaynak B, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010714; Kim SW, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011343; Kim SW, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL027749; Knapp KR, 2005, INT J REMOTE SENS, V26, P4097, DOI 10.1080/0143116050500099329; Knepp T., 2013, J ATMOS CHEM; Kondragunta S, 2008, J APPL METEOROL CLIM, V47, P425, DOI 10.1175/2007JAMC1392.1; Lamsal LN, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009235; Lamsal LN, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013351; Lamsal L.N., 2011, GEOPHYS RES LETT, V28; Leue C, 2001, J GEOPHYS RES-ATMOS, V106, P5493, DOI 10.1029/2000JD900572; Levelt P.F., 2006, IEEE T GEOSCI REMOTE, V44, P1092; Li C, 2013, GEOPHYS RES LETT, V40, P6314, DOI 10.1002/2013GL058134; Lin MY, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD018151; Lu ZF, 2012, ENVIRON SCI TECHNOL, V46, P7463, DOI 10.1021/es300831w; Lyapustin A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014985; Lyapustin A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014986; MARTIN RV, 2007, J GEOPHYS RES, V112, P9309; Martin RV, 2008, ATMOS ENVIRON, V42, P7823, DOI 10.1016/j.atmosenv.2008.07.018; Martin RV, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004869; McLinden CA, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2011GL050273; Millet DB, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008950; Munchak LA, 2013, ATMOS MEAS TECH, V6, P1747, DOI 10.5194/amt-6-1747-2013; NSTC, 2013, AIR QUAL OBS SYST US; Ordonez C, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006305; Palmer PI, 2001, J GEOPHYS RES-ATMOS, V106, P14539, DOI 10.1029/2000JD900772; Palmer PI, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002153; Palmer PI, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006689; Patadia F, 2013, ATMOS CHEM PHYS, V13, P9525, DOI 10.5194/acp-13-9525-2013; Prados AI, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007968; Prados A.I., 2012, EARTHZINE MAGAZI OCT; Prados AI, 2010, IEEE J-STARS, V3, P359, DOI 10.1109/JSTARS.2010.2047940; Russell AR, 2012, ATMOS CHEM PHYS, V12, P12197, DOI 10.5194/acp-12-12197-2012; Scheffe R.D., 2012, J AIR WASTE MANAGE, V59, P579; SILLMAN S, 1995, J GEOPHYS RES-ATMOS, V100, P14175, DOI 10.1029/94JD02953; Streets DG, 2013, ATMOS ENVIRON, V77, P1011, DOI 10.1016/j.atmosenv.2013.05.051; Van Damme M, 2014, ATMOS CHEM PHYS, V14, P2905, DOI 10.5194/acp-14-2905-2014; Velders GJM, 2001, J GEOPHYS RES-ATMOS, V106, P12643, DOI 10.1029/2000JD900762; Wang J, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018174; Witte JC, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039236; Worden HM, 2013, ATMOS CHEM PHYS, V13, P837, DOI 10.5194/acp-13-837-2013; Worden J, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL027806; Zhang H, 2009, J AIR WASTE MANAGE, V59, P1358, DOI 10.3155/1047-3289.59.11.1358; Zhu L, 2013, J GEOPHYS RES-ATMOS, V118, P3355, DOI 10.1002/jgrd.50166; Ziemba LD, 2013, GEOPHYS RES LETT, V40, P417, DOI 10.1029/2012GL054428; Zoogman P, 2011, ATMOS ENVIRON, V45, P7143, DOI 10.1016/j.atmosenv.2011.05.058 89 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1352-2310 1873-2844 ATMOS ENVIRON Atmos. Environ. SEP 2014 94 647 662 10.1016/j.atmosenv.2014.05.061 16 Environmental Sciences; Meteorology & Atmospheric Sciences Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences AN1AY WOS:000340316300069 J Pearson, DG; Ball, K; Smith, DT Pearson, David G.; Ball, Keira; Smith, Daniel T. Oculomotor preparation as a rehearsal mechanism in spatial working memory COGNITION English Article Visual; Spatial; Working memory; Eye movement; Attention; Saccade SHORT-TERM-MEMORY; FRONTAL EYE FIELD; INDIVIDUAL-DIFFERENCES; VISUAL-ATTENTION; PREMOTOR THEORY; SERIAL MEMORY; WORD-LENGTH; MOVEMENTS; SPAN; INHIBITION There is little consensus regarding the specific processes responsible for encoding, maintenance, and retrieval of information in visuo-spatial working memory (VSWM). One influential theory is that VSWM may involve activation of the eye-movement (oculomotor) system. In this study we experimentally prevented healthy participants from planning or executing saccadic eye-movements during the encoding, maintenance, and retrieval stages of visual and spatial working memory tasks. Participants experienced a significant reduction in spatial memory span only when oculomotor preparation was prevented during encoding or maintenance. In contrast there was no reduction when oculomotor preparation was prevented only during retrieval. These results show that (a) involvement of the oculomotor system is necessary for optimal maintenance of directly-indicated locations in spatial working memory and (b) oculomotor preparation is not necessary during retrieval from spatial working memory. We propose that this study is the first to unambiguously demonstrate that the oculomotor system contributes to the maintenance of spatial locations in working memory independently from the involvement of covert attention. (C) 2014 The Authors. Published by Elsevier B.V. [Pearson, David G.] Univ Aberdeen, Sch Psychol, Aberdeen AB9 1FX, Scotland; [Ball, Keira; Smith, Daniel T.] Univ Durham, CNRU, Durham DH1 3HP, England Smith, DT (reprint author), Univ Durham, CNRU, Durham DH1 3HP, England. daniel.smith2@durham.ac.uk Allen R, 2006, Q J EXP PSYCHOL, V59, P1101, DOI 10.1080/02724980543000097; Altmann GTM, 2004, COGNITION, V93, pB79, DOI 10.1016/j.cognition.2004.02.005; Awh E, 1998, J EXP PSYCHOL HUMAN, V24, P780, DOI 10.1037//0096-1523.24.3.780; Awh E, 2006, NEUROSCIENCE, V139, P201, DOI 10.1016/j.neuroscience.2005.08.023; Awh E, 2001, TRENDS COGN SCI, V5, P119, DOI 10.1016/S1364-6613(00)01593-X; Baddeley A, 2003, NAT REV NEUROSCI, V4, P829, DOI 10.1038/nrn1201; Baddeley A. D., 1986, WORKING MEMORY; BADDELEY AD, 1975, J VERB LEARN VERB BE, V14, P575, DOI 10.1016/S0022-5371(75)80045-4; Baddeley AD, 2000, J EXP PSYCHOL GEN, V129, P126, DOI 10.1037//0096-3445.129.1.126; Ball K, 2013, COGNITION, V129, P439, DOI 10.1016/j.cognition.2013.08.006; Belopolsky AV, 2012, J EXP PSYCHOL HUMAN, V38, P902, DOI 10.1037/a0028662; Belopolsky AV, 2009, ATTEN PERCEPT PSYCHO, V71, P620, DOI 10.3758/APP.71.3.620; Belopolsky AV, 2009, ACTA PSYCHOL, V132, P124, DOI 10.1016/j.actpsy.2009.01.002; Berch DB, 1998, BRAIN COGNITION, V38, P317, DOI 10.1006/brcg.1998.1039; Bernardis P, 2011, Q J EXP PSYCHOL, V64, P2438, DOI 10.1080/17470218.2011.604202; Brandt SA, 1997, J COGNITIVE NEUROSCI, V9, P27, DOI 10.1162/jocn.1997.9.1.27; Gaunt JT, 2012, J EYE MOVEMENT RES, V5; BRUCE CJ, 1985, J NEUROPHYSIOL, V53, P603; Cabeza Roberto, 2000, Current Opinion in Neurology, V13, P415, DOI 10.1097/00019052-200008000-00008; Campana G, 2007, NEUROPSYCHOLOGIA, V45, P2340, DOI 10.1016/j.neuropsychologia.2007.02.009; Craighero L, 2004, CURR BIOL, V14, P331, DOI 10.1016/j.cub.2004.01.054; Della Sala S, 1999, NEUROPSYCHOLOGIA, V37, P1189, DOI 10.1016/S0028-3932(98)00159-6; DEMPSTER FN, 1982, INTELLIGENCE, V6, P201, DOI 10.1016/0160-2896(82)90014-9; DERENZI E, 1977, CORTEX, V13, P424; Ferreira F, 2008, TRENDS COGN SCI, V12, P405, DOI 10.1016/j.tics.2008.07.007; Foulsham T, 2013, J EXP PSYCHOL GEN, V142, P41, DOI 10.1037/a0028227; Gabay S, 2010, NEUROPSYCHOLOGIA, V48, P3102, DOI 10.1016/j.neuropsychologia.2010.06.022; Gaunt J. T., 2014, J EYE MOVEMENT RES, V7, P1; Gaymard B, 1999, EXP BRAIN RES, V129, P288, DOI 10.1007/s002210050899; Godijn R, 2012, MEM COGNITION, V40, P52, DOI 10.3758/s13421-011-0132-x; Guerard K, 2009, ACTA PSYCHOL, V132, P136, DOI 10.1016/j.actpsy.2009.01.003; Gyselinck V, 2008, APPL COGNITIVE PSYCH, V22, P353, DOI 10.1002/acp.1411; Helstrup T, 1999, EUR J COGN PSYCHOL, V11, P357, DOI 10.1080/713752325; Henderson M., 2004, INTERFACE LANGUAGE V, P161; Hermens F, 2010, J EYE MOVEMENT RES, V3; Hoover MA, 2008, COGNITION, V108, P533, DOI 10.1016/j.cognition.2008.02.011; Hunt AR, 2003, J EXP PSYCHOL HUMAN, V29, P1068, DOI 10.1037/0096-1523.29.5.1068; Johansson R, 2012, J EXP PSYCHOL HUMAN, V38, P1289, DOI 10.1037/a0026585; Kemps E, 2001, MEMORY, V9, P13, DOI 10.1080/09658210042000012; Klauer KC, 2004, J EXP PSYCHOL GEN, V133, P355, DOI 10.1037/0096-3445.133.3.355; Klein R., 1980, ATTENTION PERFORM, VVIII, P259; KLEIN RM, 1994, ATTENTION PERFORM, V15, P333; Land MF, 2004, EXP BRAIN RES, V159, P151, DOI 10.1007/s00221-004-1951-9; Land MF, 2002, NEUROCASE, V8, P80, DOI 10.1093/neucas/8.1.80; Le Bigot N, 2009, PSYCHOL RES-PSYCH FO, V73, P89, DOI 10.1007/s00426-008-0135-9; Logie R. H., 1991, MENTAL IMAGES HUMAN, P105; Logie RH, 2011, CURR DIR PSYCHOL SCI, V20, P240, DOI 10.1177/0963721411415340; Martarelli CS, 2013, PSYCHOL RES-PSYCH FO, V77, P303, DOI 10.1007/s00426-012-0439-7; McAfoose J, 2009, NEUROPSYCHOL REV, V19, P130, DOI 10.1007/s11065-008-9063-0; MORRIS N, 1989, BRIT J PSYCHOL, V80, P333; MURRAY DJ, 1967, CAN J PSYCHOLOGY, V21, P263, DOI 10.1037/h0082978; Norton D., 1971, VISION RES, V11, P929; Parmentier FBR, 2005, J EXP PSYCHOL LEARN, V31, P412, DOI 10.1037/0278-7393.31.3.412; Pearson D. G., 2007, IMAGINATIVE MINDS, P187; Pearson DG, 2011, INT J COGN THER, V4, P122; Pearson DG, 2003, Q J EXP PSYCHOL-A, V56, P1089, DOI 10.1080/02724980343000044; Postle BR, 2006, Q J EXP PSYCHOL, V59, P100, DOI 10.1080/17470210500151410; RAFAL RD, 1988, BRAIN, V111, P267, DOI 10.1093/brain/111.2.267; Repovs G, 2006, NEUROSCIENCE, V139, P5, DOI 10.1016/j.neuroscience.2005.12.061; Richardson DC, 2009, TRENDS COGN SCI, V13, P235, DOI 10.1016/j.tics.2009.02.006; Richardson DC, 2000, COGNITION, V76, P269, DOI 10.1016/S0010-0277(00)00084-6; Rudkin SJ, 2007, Q J EXP PSYCHOL, V60, P79, DOI 10.1080/17470210600587976; Shah P, 1996, J EXP PSYCHOL GEN, V125, P4, DOI 10.1037/0096-3445.125.1.4; SHEPHERD M, 1986, Q J EXP PSYCHOL-A, V38, P475; Smith DT, 2004, CURR BIOL, V14, P792, DOI 10.1016/j.cub.2004.04.035; Smith DT, 2010, NEUROPSYCHOLOGIA, V48, P1269, DOI 10.1016/j.neuropsychologia.2009.12.028; Smith DT, 2012, NEUROPSYCHOLOGIA, V50, P1104, DOI 10.1016/j.neuropsychologia.2012.01.025; Smith DT, 2012, J EXP PSYCHOL HUMAN, V38, P1438, DOI 10.1037/a0027794; Smith DT, 2014, VISION RES, V95, P11, DOI 10.1016/j.visres.2013.12.003; SMYTH MM, 1994, Q J EXP PSYCHOL-A, V47, P91; Sommer MA, 2001, J NEUROPHYSIOL, V85, P1673; Spivey MJ, 2001, PSYCHOL RES-PSYCH FO, V65, P235, DOI 10.1007/s004260100059; Thompson JM, 2006, MEMORY, V14, P437, DOI 10.1080/09658210500464293; Tremblay S, 2006, PSYCHON B REV, V13, P452, DOI 10.3758/BF03193869; TRESCH MC, 1993, NEUROPSYCHOLOGIA, V31, P211, DOI 10.1016/0028-3932(93)90085-E 75 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0010-0277 1873-7838 COGNITION Cognition SEP 2014 132 3 416 428 10.1016/j.cognition.2014.05.006 13 Psychology, Experimental Psychology AM6ZG WOS:000340013900013 J Spasic, I; Livsey, J; Keane, JA; Nenadic, G Spasic, Irena; Livsey, Jacqueline; Keane, John A.; Nenadic, Goran Text mining of cancer-related information: Review of current status and future directions INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS English Review Cancer; Natural language processing; Data mining; Electronic medical records OF-THE-ART; PATHOLOGY REPORTS; CLINICAL INFORMATION; GENE METHYLATION; BIOMEDICAL TEXT; MEDICAL-RECORD; DATABASE; SYSTEM; EXTRACTION; RETRIEVAL Purpose: This paper reviews the research literature on text mining (TM) with the aim to find out (1) which cancer domains have been the subject of TM efforts, (2) which knowledge resources can support TM of cancer-related information and (3) to what extent systems that rely on knowledge and computational methods can convert text data into useful clinical information. These questions were used to determine the current state of the art in this particular strand of TM and suggest future directions in TM development to support cancer research. Methods: A review of the research on TM of cancer-related information was carried out. A literature search was conducted on the Medline database as well as IEEE Xplore and ACM digital libraries to address the interdisciplinary nature of such research. The search results were supplemented with the literature identified through Google Scholar. Results: A range of studies have proven the feasibility of TM for extracting structured information from clinical narratives such as those found in pathology or radiology reports. In this article, we provide a critical overview of the current state of the art for TM related to cancer. The review highlighted a strong bias towards symbolic methods, e.g. named entity recognition (NER) based on dictionary lookup and information extraction (IE) relying on pattern matching. The F-measure of NER ranges between 80% and 90%, while that of IE for simple tasks is in the high 90s. To further improve the performance, TM approaches need to deal effectively with idiosyncrasies of the clinical sublanguage such as non-standard abbreviations as well as a high degree of spelling and grammatical errors. This requires a shift from rule-based methods to machine learning following the success of similar trends in biological applications of TM. Machine learning approaches require large training datasets, but clinical narratives are not readily available for TM research due to privacy and confidentiality concerns. This issue remains the main bottleneck for progress in this area. In addition, there is a need for a comprehensive cancer ontology that would enable semantic representation of textual information found in narrative reports. (C) 2014 The Authors. Published by Elsevier Ireland Ltd. [Spasic, Irena] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, S Glam, Wales; [Livsey, Jacqueline] Christie NHS Fdn Trust, Clin Outcomes Unit, Manchester M20 4BX, Lancs, England; [Keane, John A.; Nenadic, Goran] Univ Manchester, Sch Comp Sci, Manchester M13 9PL, Lancs, England; [Keane, John A.; Nenadic, Goran] Hlth E Res Ctr, Manchester M13 9PL, Lancs, England; [Keane, John A.; Nenadic, Goran] Manchester Inst Biotecnol, Manchester M1 7DN, Lancs, England Spasic, I (reprint author), Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF24 3AA, S Glam, Wales. i.spasic@cs.cardiff.ac.uk Christie NHS Foundation Trust; Health e-Research Centre (HeRC); Serbian Ministry of Education and Science [III44006, III47003] This work was partly funded by The Christie NHS Foundation Trust. GN acknowledges support from the Health e-Research Centre (HeRC) and Serbian Ministry of Education and Science (projects III44006; III47003). The authors wish to acknowledge the following members of the Clinical Outcomes Unit at The Christie NHS Foundation Trust: Matt Barker-Hewitt, Tom Liptrot, Catherine O'Hara and Ben Wilson. Ahmed J, 2011, NUCLEIC ACIDS RES, V39, pD960, DOI 10.1093/nar/gkq910; [Anonymous], 2013, SNOMED CT; Aronson A. R., 2001, EFFECTIVE MAPPING BI, P17; Ashburner M, 2000, NAT GENET, V25, P25; Baasiri RA, 1999, ONCOGENE, V18, P7958; Bader JL, 2003, J MED INTERNET RES, V5, DOI 10.2196/jmir.5.4.e31; Berman JJ, 2002, ARTIF INTELL MED, V26, P25, DOI 10.1016/S0933-3657(02)00050-7; Blake C., 2001, IEEE C DAT MIN SAN J, P59; Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061; Buckley Julliette M, 2012, J Pathol Inform, V3, P23, DOI 10.4103/2153-3539.97788; Burnside B., 2000, 14 INT C EXH COMP AS, P449; Burnside Elizabeth S, 2009, J Am Coll Radiol, V6, P851, DOI 10.1016/j.jacr.2009.07.023; Burnside ES, 2009, RADIOLOGY, V251, P663, DOI 10.1148/radiol.2513081346; Butt Luke, 2013, Australas Med J, V6, P292, DOI 10.4066/AMJ.2013.1654; Cancer Research UK, 2013, UK CANC INC 2010 COU; Centers for Disease Control and Prevention, 2013, CANC DAT STAT TOOLS; Chan Kitty S, 2010, Med Care Res Rev, V67, P503, DOI 10.1177/1077558709359007; Chen H, 2010, INTEGR SER INFORM SY, V21, P1, DOI 10.1007/978-1-4419-1278-7; Cios KJ, 2002, ARTIF INTELL MED, V26, P1, DOI 10.1016/S0933-3657(02)00049-0; Coden A, 2009, J BIOMED INFORM, V42, P937, DOI 10.1016/j.jbi.2008.12.005; Cohen KB, 2008, PLOS COMPUT BIOL, V4, DOI 10.1371/journal.pcbi.0040020; Cote R, 2010, NUCLEIC ACIDS RES, V38, pW155, DOI 10.1093/nar/gkq331; Crowley RS, 2010, J AM MED INFORM ASSN, V17, P253, DOI 10.1136/jamia.2009.002295; Dang PA, 2008, AM J ROENTGENOL, V191, P313, DOI 10.2214/AJR.07.3508; Datta MW, 2006, MOL CANCER, V5, DOI 10.1186/1476-4598-5-9; D'Avolio LW, 2010, J AM MED INFORM ASSN, V17, P375, DOI 10.1136/jamia.2009.001412; Denny JC, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002823; Denny JC, 2012, MED DECIS MAKING, V32, P188, DOI 10.1177/0272989X11400418; Denny JC, 2010, J AM MED INFORM ASSN, V17, P383, DOI 10.1136/jamia.2010.004804; Fang YC, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-471; Fang YC, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-22; FRIEDMAN C, 1994, J AM MED INFORM ASSN, V1, P161; Friedman C, 2013, J BIOMED INFORM, V46, P765, DOI 10.1016/j.jbi.2013.06.004; FRIEDMAN C, 2000, AMIA S, P270; Gerner M., 2010, BMC BIOINFORMATICS, P11; Harkema H., 2011, J AM MED INFORM A S1, V18, pi150; Heintzelman NH, 2013, J AM MED INFORM ASSN, V20, P898, DOI 10.1136/amiajnl-2012-001076; Heinze D.T., 2000, 17 NAT C ART INT 12; Hirschman L, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-S1-S11; Hripcsak G, 2002, RADIOLOGY, V224, P157, DOI 10.1148/radiol.2241011118; Jacquemin C., 2001, SPOTTING DISCOVERING; Jain N. L., 1997, AMIA ANN FALL S, P829; Jin Y., 2005, IDENTIFYING EXTRACTI; Kadoyama K, 2011, J EXP CLIN CANC RES, V30, DOI 10.1186/1756-9966-30-93; Kang N, 2013, J AM MED INFORM ASSN, V20, P876, DOI 10.1136/amiajnl-2012-001173; Korhonen A, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0033427; Bajdik CD, 2005, BMC BIOINFORMATICS, V6, DOI 10.1186/1471-2105-6-78; Leaman R., 2009, 3 INT S LANG BIOL ME; Lee C.-H., 2007, 2 INT C INN COMP INF, P172, DOI 10.1109/ICICIC.2007.556; Mamlin Burke W, 2003, AMIA Annu Symp Proc, P420; Martinez D., 2013, 4 INT WORKSH HLTH DO; Martinez D., 2011, 20 ACM INT C INF KNO, P1877; McCowan IA, 2007, J AM MED INFORM ASSN, V14, P736, DOI 10.1197/jamia.M2130; Mohanty SK, 2007, BMC CANCER, V7, DOI 10.1186/1471-2407-7-144; Napolitano G, 2010, CANCER CAUSE CONTROL, V21, P1887, DOI 10.1007/s10552-010-9616-4; Nassif H., 2010, 1 ACM INT HLTH INF S, P76; Nassif H., 2009, ICDMW 09, P37; National Cancer Institute, 2013, NCI THES; Nelson SJ, 2011, J AM MED INFORM ASSN, V18, P441, DOI 10.1136/amiajnl-2011-000116; Nguyen AN, 2010, J AM MED INFORM ASSN, V17, P440, DOI 10.1136/jamia.2010.003707; Noy NF, 2009, NUCLEIC ACIDS RES, V37, pW170, DOI 10.1093/nar/gkp440; Office for National Statistics, 2013, DEATHS REG ENGL WAL; Office for National Statistics, 2012, DEATHS REG ENGL WAL; Park SB, 2003, LECT NOTES COMPUT SC, V2736, P403; Polpinij J., 2010, ICDM WORKSH ADV DAT; Rokach L, 2004, LECT NOTES ARTIF INT, V3055, P217; Rosse C, 2003, J BIOMED INFORM, V36, P478, DOI 10.1016/j.jbi.2003.11.007; Savova GK, 2010, J AM MED INFORM ASSN, V17, P507, DOI 10.1136/jamia.2009.001560; Schadow Gunther, 2003, AMIA Annu Symp Proc, P584; Settles B, 2005, BIOINFORMATICS, V21, P3191, DOI 10.1093/bioinformatics/bti475; Sobin LH, 2009, TNM CLASSIFICATION M; Spasic I, 2010, J AM MED INFORM ASSN, V17, P532, DOI 10.1136/jamia.2010.003657; Spasic I, 2005, BRIEF BIOINFORM, V6, P239, DOI 10.1093/bib/6.3.239; Spasić Irena, 2013, J Biomed Semantics, V4, P27, DOI 10.1186/2041-1480-4-27; Srinivasan A., 2013, ALEPH MANUAL; Strauss JA, 2013, J AM MED INFORM ASSN, V20, P349, DOI 10.1136/amiajnl-2012-000928; Tanenblatt M., 2010, LANG RESOUR EVAL, P546; Tate AR, 2011, BMJ OPEN, V1, DOI 10.1136/bmjopen-2010-000025; The Royal College of Radiologists, 2006, STAND REP INT IM INV; Uzuner O, 2007, J AM MED INFORM ASSN, V14, P550, DOI 10.1197/jamia.M2444; Wagholikar K., 2012, IEEE 2 INT C HEALTHC, P12; Warner Jeremy L, 2011, J Oncol Pract, V7, pe15, DOI 10.1200/JOP.2011.000240; Whetzel Patricia L, 2013, J Biomed Semantics, V4 Suppl 1, pS8, DOI 10.1186/2041-1480-4-S1-S8; Xie BY, 2013, BIOINFORMATICS, V29, P638, DOI 10.1093/bioinformatics/btt014; Xu H, 2004, ST HEAL T, V107, P565; Yang Y., 1992, ANN S COMP APPL MED, P460; Zhao D, 2011, J BIOMED INFORM, V44, P859, DOI 10.1016/j.jbi.2011.05.004; Zhu F, 2013, J BIOMED INFORM, V46, P200, DOI 10.1016/j.jbi.2012.10.007 88 0 0 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 1386-5056 1872-8243 INT J MED INFORM Int. J. Med. Inform. SEP 2014 83 9 605 623 10.1016/j.ijmedinf.2014.06.009 19 Computer Science, Information Systems; Health Care Sciences & Services; Medical Informatics Computer Science; Health Care Sciences & Services; Medical Informatics AN0GO WOS:000340262600001 J Font, F; Serra, J; Serra, X Font, Frederic; Serra, Joan; Serra, Xavier Class-based tag recommendation and user-based evaluation in online audio clip sharing KNOWLEDGE-BASED SYSTEMS English Article Collaborative tagging; Tag recommendation; User study; Folksonomy; Freesound Online sharing platforms often rely on collaborative tagging systems for annotating content. In this way, users themselves annotate and describe the shared contents using textual labels, commonly called tags. These annotations typically suffer from a number of issues such as tag scarcity or ambiguous labelling. Hence, to minimise some of these issues, tag recommendation systems can be employed to suggest potentially relevant tags during the annotation process. In this work, we present a tag recommendation system and evaluate it in the context of an online platform for audio clip sharing. By exploiting domain-specific knowledge, the system we present is able to classify an audio clip among a number of predefined audio classes and to produce specific tag recommendations for the different classes. We perform an in-depth user-based evaluation of the recommendation method along with two baselines and a former version that we described in previous work. This user-based evaluation is further complemented with a prediction-based evaluation following standard information retrieval methodologies. Results show that the proposed tag recommendation method brings a statistically significant improvement over the previous method and the baselines. In addition, we report a number of findings based on the detailed analysis of user feedback provided during the evaluation process. The considered methods, when applied to real-world collaborative tagging systems, should serve the purpose of consolidating the tagging vocabulary and improving the quality of content annotations. (C) 2014 Elsevier B.V. All rights reserved. [Font, Frederic; Serra, Xavier] Univ Pompeu Fabra, Mus Technol Grp, Barcelona, Spain; [Serra, Joan] Spanish Natl Res Council, Artificial Intelligence Res Inst IIIA, CSIC, Bellaterra, Spain Font, F (reprint author), Univ Pompeu Fabra, Mus Technol Grp, Barcelona, Spain. frederic.font@upf.edu Spanish Ministry of Science and Innovation [BES-2010-037309 FPI]; Generalitat de Catalunya [2009-SGR-1434, TIN2009-14247-C02-01]; CSIC [JAEDOC069/2010]; European Commission [ICT-2011-8-318770]; FP7-2007-2013/ERC [267583] We would like to thank Perfecto Herrera for his help in designing the online experiment and also all Freesound users that participated. This work has been supported by BES-2010-037309 FPI from the Spanish Ministry of Science and Innovation (TIN2009-14247-C02-01; F.F.), 2009-SGR-1434 from Generalitat de Catalunya (J.S.), JAEDOC069/2010 from CSIC (J.S.), ICT-2011-8-318770 from the European Commission (J.S.), and FP7-2007-2013/ERC Grant Agreement 267583 (CompMusic; F.F., X.S.). Anderson A., 2008, P 23 C ART INT AAAI; Cao H., 2009, P C MACH LEARN PRINC, P35; Chen Z., 2010, P ACM WWW 2010, P1079, DOI 10.1145/1772690.1772813; De Meo P, 2009, INFORM SYST, V34, P511, DOI 10.1016/j.is.2009.02.004; Font F., 2013, P 21 ACM C MULT ACM, P411; Font F., 2013, P 53 AES C SEM AUD; Font F, 2013, INT J SEMANT WEB INF, V9, P1, DOI 10.4018/jswis.2013040101; Garg N, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P67; Halpin H., 2006, P 1 SEM AUTH ANN WOR, P1; Hogg R. V., 1995, INTRO MATH STAT; HOLM S, 1979, SCAND J STAT, V6, P65; Ivanov I., 2010, P INT C MULT INF RET, P497, DOI 10.1145/1743384.1743471; Jaschke R., 2009, P 3 ACM C REC SYST, P369, DOI 10.1145/1639714.1639790; Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506; Li J., 2006, P 14 ANN ACM INT C M, P911, DOI DOI 10.1145/1180639.1180841; Lipczak M., 2008, P ECML PKDD DISC CHA, P84; MANN HB, 1947, ANN MATH STAT, V18, P50, DOI 10.1214/aoms/1177730491; Marinho L. B., 2009, P C MACH LEARN PRINC, P7; Naaman M, 2008, IEEE MULTIMEDIA, V15, P34, DOI 10.1109/MMUL.2008.69; Rendle S., 2009, P C MACH LEARN PRINC, P235; Salzberg SL, 1997, DATA MIN KNOWL DISC, V1, P317, DOI 10.1023/A:1009752403260; Sigurbjornsson B., 2008, P 17 INT C WORLD WID, P327, DOI 10.1145/1367497.1367542; Sood S. C., 2007, P 1 INT C WEBL SOC M, P1; Sordo M., 2012, THESIS U POMPEU FABR; Toderici G, 2010, PROC CVPR IEEE, P3447, DOI 10.1109/CVPR.2010.5539985; Turnbull D, 2008, IEEE T AUDIO SPEECH, V16, P467, DOI 10.1109/TASL.2007.913750 26 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. SEP 2014 67 131 142 10.1016/j.knosys.2014.06.003 12 Computer Science, Artificial Intelligence Computer Science AM9SP WOS:000340221600011 J Tardon, LJ; Barbancho, I; Barbancho, AM; Roig, C Tardon, Lorenzo J.; Barbancho, Isabel; Barbancho, Ana M.; Roig, Carles A probability model for key analysis in music KNOWLEDGE-BASED SYSTEMS English Article Music information retrieval (MIR); Key probability model; Key profile model; Key analysis; Tonal behaviour; Pitch Class Profiles ALGORITHM This paper presents a novel method to analyse the tonal behaviour of a music piece. The method is based on the development of a novel probability model of the predominant key using the well known Pitch Class Profile (PCP or chroma) descriptor. This feature represents the importance of each note of the chromatic scale within the spectral content of the audio signal. Making use of PCP feature, a main novel contribution presented is the development of new profile models for the characterization of major and minor keys. Most of the key profile models found in the literature assign a single value to each pitch class for a particular key. This value represents the salience of this pitch class in a specific key. The new key profiles, that will be described in this paper, are based on the identification of specific probability density functions (PDFs), defined after the analysis of the presence of the pitch classes of the chromatic scale in the different keys. (C) 2014 Elsevier B.V. All rights reserved. [Tardon, Lorenzo J.; Barbancho, Isabel; Barbancho, Ana M.; Roig, Carles] Univ Malaga, ATIC Res Grp, ETSI Telecomunicac, Andalucia Tech, E-29071 Malaga, Spain Tardon, LJ (reprint author), Univ Malaga, ATIC Res Grp, ETSI Telecomunicac, Andalucia Tech, Campus Teatinos S-N, E-29071 Malaga, Spain. lorenzo@ic.uma.es; ibp@ic.uma.es; abp@ic.uma.es; carles@ic.uma.es Ministerio de Economia y Competitividad of the Spanish Government [IPT-2011-0885-430000]; Junta de Andalucia [P11-TIC-7154]; Ministerio de Educacion, Cultura y Deporte This work has been funded by the Ministerio de Economia y Competitividad of the Spanish Government under Project No. IPT-2011-0885-430000, by the Junta de Andalucia under Project No. P11-TIC-7154 and by the Ministerio de Educacion, Cultura y Deporte through the 'Programa Nacional de Movilidad de Recursos Humanos del Plan Nacional de I-D+i 2008-2011, prorrogado por Acuerdo de Consejo de Ministros de 7 de octubre de 2011'. Cristina de la Bandera developed parts of the software used. This work has been done in the context of Campus de Excelencia Internacional Andalucia Tech, Universidad de Malaga. Abramowitz M., 1965, HDB MATH FUNCTIONS F; Apel W., 2000, HARVARD DICT MUSIC; Barbancho A.M., 2013, DATABASE PIANO CHORD; Benward B., 1993, MUSIC THEORY PRACTIC; Cannam C., 2013, MIREX 2013 ENTRY VAM; Chai W, 2006, IEEE SIGNAL PROC MAG, V23, P124; Chai W., 2005, THESIS MIT; de la Benders C., 2010, P 7 INT S COMP MUS M, P221; Downie J.S., 2013, MIREX CONTEST WEBSIT; Downie J.S., 2012, MIREX CONTEST WEBSIT; Duan ZY, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4, P1361; Fletcher N. H., 1991, PHYS MUSICAL INSTRUM; Frankland BW, 1996, MUSIC PERCEPT, V14, P57; Fujishima T., P INT COMP MUS C ICM, P464; Gomez E., 2004, P 5 INT SOC MUS INF; Gomez E., 2005, P INT COMP MUS C ICM; Gomez E., 2006, THESIS; Hu N., 2003, P IEEE WORKSH APPL S, P185; Izmirli O., 2006, P MUS INF RETR C ISM, P127; Izmirli O., 2005, P INT COMP MUS C ICM, P211; Krumhansl Carol K., 1990, COGNITIVE FDN MUSICA; KRUMHANSL CL, 1982, PSYCHOL REV, V89, P334, DOI 10.1037//0033-295X.89.4.334; Law A., 1999, SIMULATION MODELING; Pashler H., 2002, STEVENS HDB EXPT PSY, V1; Paulus J, 2009, IEEE T AUDIO SPEECH, V17, P1159, DOI 10.1109/TASL.2009.2020533; Pearson E., 1976, STUDIES HIST STAT PR; Peeters G., 2011, MIREX 2011 AUDIO KEY; Piston G., 1978, HARMONY; Rubinstein RY, 1981, SIMULATION MONTE CAR; Ryynanen MP, 2008, COMPUT MUSIC J, V32, P72, DOI 10.1162/comj.2008.32.3.72; Schoenberg A., 1969, STRUCTURAL FUNCTIONS; Schoenberg A., 1978, THEORY HARMONY; Tardon-Garcia LJ, 1998, ELECTRON LETT, V34, P2347, DOI 10.1049/el:19981593; Temperley D., 2005, MIREX 2005 SIMBOLIC; Temperley D., 2001, COGNITION BASIC MUSI; Temperley D., 2002, LECT NOTES COMPUTER, V2445, P195; Werts D., 1983, THESIS; Wyatt K., 1998, POCKET MUSIC THEORY 38 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. SEP 2014 67 169 179 10.1016/j.knosys.2014.05.015 11 Computer Science, Artificial Intelligence Computer Science AM9SP WOS:000340221600014 J Chiu, DY; Pan, YC Chiu, Deng-Yiv; Pan, Ya-Chen Topic knowledge map and knowledge structure constructions with genetic algorithm, information retrieval, and multi-dimension scaling method KNOWLEDGE-BASED SYSTEMS English Article Knowledge structure; Topic knowledge map; Information retrieval; Genetic algorithm; Independent chi-square; Multi-dimension scaling FUZZY COGNITIVE MAPS; SELF-ORGANIZING MAP; NETWORKS; NEWS This work presents a novel automated approach to construct topic knowledge maps with knowledge structures, followed by its application to an internationally renowned journal. Knowledge structures are diagrams showing the important components of knowledge in study. Knowledge maps identify the locations of objects and illustrate the relationship among objects. In our study, the important components derived from knowledge structures are used as objects to be spotted in a topic knowledge map. The purpose of our knowledge structures is to find out the major topics serving as subjects of article collections as well as related methods employed in the published papers. The purpose of topic knowledge maps is to transform high-dimensional objects (topic, paper, and cited frequency) into a 2-dimensional space to help understand complicated relatedness among high-dimensional objects, such as the related degree between an article and a topic. First, we adopt independent chi-square test to examine the independence of topics and apply genetic algorithm to choose topics selection with best fitness value to construct knowledge structures. Additionally, high-dimensional relationships among objects are transformed into a 2-dimensional space using the multi-dimension scaling method. The optimal transformation coordinate matrix is also determined by using a genetic algorithm to preserve the original relations among objects and construct appropriate topic knowledge maps. (C) 2014 Elsevier B.V. All rights reserved. [Chiu, Deng-Yiv; Pan, Ya-Chen] ChungHua Univ, Dept Informat Management, Hsinchu 300, Taiwan Chiu, DY (reprint author), ChungHua Univ, Dept Informat Management, 707,Sec 2,Wu Fu Rd, Hsinchu 300, Taiwan. chiuden@chu.edu.tw; d09403019@cc.chu.edu.tw National Science Council of the Republic of China, Taiwan (NSC) [98-2221-E-216-038] The authors would like to thank the National Science Council of the Republic of China, Taiwan, for financially supporting this research under Contract No. NSC 98-2221-E-216-038. Carroll J.D., 1997, J MARKETING RES, V34, P385; Chen TT, 2010, ELECTRON LIBR, V28, P477, DOI 10.1108/02640471011033602; Chen T.T., 2005, P INF VIS 9 INT C IV, P135; Chiu DY, 2009, EXPERT SYST APPL, V36, P9438, DOI 10.1016/j.eswa.2008.12.032; Chou HC, 2007, EXPERT SYST APPL, V33, P499, DOI 10.1016/j.eswa.2006.05.020; Ding Y, 2012, PROCEDIA ENGINEER, V29, P537, DOI 10.1016/j.proeng.2011.12.757; Dittenbach M, 2002, NEUROCOMPUTING, V48, P199, DOI 10.1016/S0925-2312(01)00655-5; Dwivedi YK, 2011, INFORM SYST MANAGE, V28, P43, DOI 10.1080/10580530.2011.536112; Fujita S, 2009, INFORM PROCESS MANAG, V45, P664, DOI 10.1016/j.ipm.2009.04.008; Goode S, 2011, DECIS SUPPORT SYST, V50, P702, DOI 10.1016/j.dss.2010.08.018; Greenberg Y, 2009, SPEECH COMMUN, V51, P585, DOI 10.1016/j.specom.2007.10.006; Hong TH, 2002, EXPERT SYST APPL, V23, P1, DOI 10.1016/S0957-4174(02)00022-2; Kohonen T., 2001, SELF ORGANIZING MAPS; Kuo RJ, 2010, DECIS SUPPORT SYST, V49, P451, DOI 10.1016/j.dss.2010.05.006; Lee JH, 2012, COMPUT EDUC, V59, P353, DOI 10.1016/j.compedu.2012.01.017; Lin F.R., 2006, INFORM PROCESS MANAG, V42, P551; Lin FR, 2009, DECIS SUPPORT SYST, V46, P774, DOI 10.1016/j.dss.2008.11.020; Nie K, 2009, SYST RES BEHAV SCI, V26, P629, DOI 10.1002/sres.926; Ong TH, 2005, DECIS SUPPORT SYST, V39, P583, DOI 10.1016/j.dss.2004.03.008; PARK KS, 1995, INT J HUM-COMPUT ST, V42, P157, DOI 10.1006/ijhc.1995.1007; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; Rahman N., 2012, EXPERT SYST APPL, V39, P4729; SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220; Schvaneveldt R.W., 1985, MCCS859 NEW MEX STAT; Shamsinejadbabki P, 2012, J INTELL INF SYST, V38, P669, DOI 10.1007/s10844-011-0172-5; Shih JY, 2008, EXPERT SYST APPL, V34, P850, DOI 10.1016/j.eswa.2006.10.031; TABER R, 1991, EXPERT SYST APPL, V2, P83, DOI 10.1016/0957-4174(91)90136-3; Tseng YH, 2007, INFORM PROCESS MANAG, V43, P1216, DOI 10.1016/j.ipm.2006.11.011; Wan M, 2012, J INTELL INF SYST, V38, P321, DOI 10.1007/s10844-011-0158-3; Webster J., 2002, MIS Q, V26, P8; ZHANG WR, 1992, IEEE T SYST MAN CYB, V22, P103, DOI 10.1109/21.141315 31 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. SEP 2014 67 412 428 10.1016/j.knosys.2014.03.008 17 Computer Science, Artificial Intelligence Computer Science AM9SP WOS:000340221600032 J Rotter, P Rotter, Pawel Relevance feedback based on n-tuplewise comparison and the ELECTRE methodology and an application in content-based image retrieval MULTIMEDIA TOOLS AND APPLICATIONS English Article Multiple criteria analysis; Relational MCDM; ELECTRE III; Preference elicitation; Content-based Image Retrieval REPRESENTATION In this article we propose a method for information retrieval based on relational Multi-Criteria Decision Making. We assume that a user cannot define precise search criteria so that these criteria must be found based on the user's assessment of several sample alternatives ('alternatives' here are database records, e.g. images). This situation is common in Content-based Image Retrieval, where it is easier for a user to indicate relevant images than to describe a proper query, especially in formal language. The proposed algorithm for the elicitation of criteria is based on ELECTRE III-a method originally designed for ranking a set of alternatives according to defined criteria. In our algorithm, however, the direction of reasoning is reversed: we start with several sample alternatives that have been assigned a rank by the user and then we select criteria that are compatible (in the sense of ELECTRE methodology) with the user's preferences expressed on a sample set. Then, having determined the user's criteria, we apply classical ELECTRE III to retrieve the relevant solutions from the database. We implemented the method in Matlab and tested it on the Microsoft Cambridge Image Database. AGH Univ Sci & Technol, PL-30059 Krakow, Poland Rotter, P (reprint author), AGH Univ Sci & Technol, Al Mickiewicza 30, PL-30059 Krakow, Poland. rotter@agh.edu.pl Polish Ministry of Science and Higher Education under SIMPOZ project [0128/R/t00/2010/12] This work was supported by the Polish Ministry of Science and Higher Education under SIMPOZ project, no. 0128/R/t00/2010/12. We thank to our colleagues who participated in experiments and to anonymous reviewers for many valuable comments and suggestions. El Sayad I, 2012, MULTIMED TOOLS APPL, V60, P455, DOI 10.1007/s11042-010-0596-x; Everingham M, 2010, INT J COMPUT VISION, V88, P303, DOI 10.1007/s11263-009-0275-4; Figueira J, 2005, INT SER OPER RES MAN, V78, P133, DOI 10.1007/0-387-23081-5_4; Heesch D, 2007, SEMANTIC BASED VISUA, P160; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Lew M.S., 2001, PRINCIPLES VISUAL IN; Li HF, 2007, I C WIREL COMM NETW, P6659; Lian ZH, 2013, PATTERN RECOGN, V46, P449, DOI 10.1016/j.patcog.2012.07.014; Mousseau V, 1998, J GLOBAL OPTIM, V12, P157, DOI 10.1023/A:1008210427517; Mousseau V, 2000, COMPUT OPER RES, V27, P757, DOI 10.1016/S0305-0548(99)00117-3; Muneesawang P, 2006, SPRINGER SERIES SIGN; Rotter P, 2008, LECT NOTES ARTIF INT, V5097, P861, DOI 10.1007/978-3-540-69731-2_82; Rotter P, 2009, ARTIF INTELL, P235; Rotter P, 2012, MULTIMED TOOLS APPL, V60, P573, DOI 10.1007/s11042-011-0828-8; Roy B, 1997, EUR J OPER RES, V99, P26, DOI 10.1016/S0377-2217(96)00379-7; Schroff F, 2011, IEEE T PATTERN ANAL, V33, P754, DOI 10.1109/TPAMI.2010.133; Shen XJ, 2008, J VIS COMMUN IMAGE R, V19, P145, DOI 10.1016/j.jvcir.2007.04.009; Skulimowski AMJ, 2011, LECT NOTES ARTIF INT, V6746, P190; Skulimowski AMJ, 1996, DECISION SUPPORT SYS; Smeets D, 2010, LECT NOTES COMPUT SC, V6169, P162, DOI 10.1007/978-3-642-14061-7_16; Tao D, 2009, SEMANTIC MINING TECH; Tian Y, 2009, SEMANTIC MINING TECH, P350; Vasconcelos N, 2007, COMPUTER, V40, P20, DOI 10.1109/MC.2007.239; Vogel J, 2006, PATTERN RECOGN, V39, P897, DOI 10.1016/j.patcog.2005.10.024; Wong WT, 2006, EUR J OPER RES, V173, P938, DOI 10.1016/j.ejor.2005.08.002 25 1 1 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. SEP 2014 72 1 667 685 10.1007/s11042-013-1384-1 19 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AM5IF WOS:000339889800030 J Xiao, QK; Luo, YC; Wang, HY Xiao, Qinkun; Luo, Yichuang; Wang, Haiyun Motion retrieval based on Switching Kalman Filters Model MULTIMEDIA TOOLS AND APPLICATIONS English Article Motion retrieval; Multi-view; Graph model; S-KFM A novel content-based motion descriptor is proposed. Firstly, the multi-view image information is captured to represent motion, and then the Switching Kalman Filters Model (S-KFM), which is a kind of the Dynamic Bayesian Network (DBN), is built based on the images fusion and the optical stream technology. Secondly, through the S-KFM inferring and sequence signal coding, a graph-based motion descriptor can be obtained. Lastly, motion matching results based on the graph model descriptor show our method is effective. [Xiao, Qinkun; Luo, Yichuang] Xian Technol Univ, Dept Elect Informat Engn, Xian 710032, Peoples R China; [Wang, Haiyun] STMicroelect R&D Asia Pacific, Singapore 554574, Singapore Xiao, QK (reprint author), Xian Technol Univ, Dept Elect Informat Engn, Xian 710032, Peoples R China. xiaoqinkun10000@163.com; 623634511@qq.com; haiyun_w@gmail.com National Basic Research Project of China [2010CB731800]; China National Foundation [60972095, 61271362] This work is partly supported by the National Basic Research Project of China (No. 2010CB731800) and the China National Foundation (No. 60972095, 61271362). Bashir FI, 2007, IEEE T MULTIMEDIA, V9, P58, DOI 10.1109/TMM.2006.886346; Chakrabarti K, 2002, ACM T DATABASE SYST, V27, P188, DOI 10.1145/568518.568520; Chao MW, 2012, IEEE T VIS COMPUT GR, V18, P729, DOI 10.1109/TVCG.2011.53; Feng Liu, 2003, Computer Vision and Image Understanding, V92, DOI 10.1016/j.cviu.2003.06.001; Gao Y, 2011, IEEE T MULTIMEDIA, V13, P1007, DOI 10.1109/TMM.2011.2160619; Gao Y, 2012, IEEE T IMAGE PROCESS, V21, P2269, DOI 10.1109/TIP.2011.2170081; Keogh E, 2004, P 30 INT C VER LARG, P780, DOI 10.1016/B978-012088469-8/50069-3; Kovar L, 2002, ACM T GRAPHIC, V21, P473; Lin Y, 2006, P ACM GRAPHITE, P31, DOI 10.1145/1174429.1174434; Muller M, 2006, P ACM SCA; Nixon MS, 2008, FEATURE EXTRACTION I, P135; Pavlovic V., 1999, ICCV, P94; Qian Huang, 2010, IEEE Transactions on Circuits and Systems for Video Technology, V20, DOI 10.1109/TCSVT.2010.2045807; Qinkun X, 2011, NEUROCOMPUTING, V74, P2340; Russell S, 2004, ARTIF INTELL, P430; Shah VP, 2008, IEEE T GEOSCI REMOTE, V46, P1323, DOI 10.1109/TGRS.2008.916211; Tam GKL, 2007, IEEE T VIS COMPUT GR, V13, P470, DOI 10.1109/TVCG.2007.1011; Tang JKT, 2012, PATTERN RECOGN LETT, V33, P420, DOI 10.1016/j.patrec.2011.06.005; Tian JW, 2011, INT J SOFTW ENG KNOW, V21, P523, DOI 10.1142/S0218194011005396; Xiao QK, 2008, ELECTRON LETT, V44, P847, DOI 10.1049/el:20080314; Xiaobai Liu, 2011, IEEE Transactions on Circuits and Systems for Video Technology, V21, DOI 10.1109/TCSVT.2011.2129410; Xiaohua Duan, 2013, IEEE Transactions on Multimedia, V15, DOI 10.1109/TMM.2012.2225029; Yang Y, 2012, IEEE T PATTERN ANAL, V34, P723, DOI 10.1109/TPAMI.2011.170; Zhang Z, 2012, IEEE T PATTERN ANAL, V34, P436, DOI 10.1109/TPAMI.2011.157 24 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. SEP 2014 72 1 951 966 10.1007/s11042-013-1416-x 16 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AM5IF WOS:000339889800043 J Altadmri, A; Ahmed, A Altadmri, Amjad; Ahmed, Amr A framework for automatic semantic video annotation MULTIMEDIA TOOLS AND APPLICATIONS English Article Semantic video annotation; Video search engine; Video information retrieval; Commonsense knowledgebases; Semantic gap VISUAL-SEARCH; RETRIEVAL; CONCEPTNET The rapidly increasing quantity of publicly available videos has driven research into developing automatic tools for indexing, rating, searching and retrieval. Textual semantic representations, such as tagging, labelling and annotation, are often important factors in the process of indexing any video, because of their user-friendly way of representing the semantics appropriate for search and retrieval. Ideally, this annotation should be inspired by the human cognitive way of perceiving and of describing videos. The difference between the low-level visual contents and the corresponding human perception is referred to as the 'semantic gap'. Tackling this gap is even harder in the case of unconstrained videos, mainly due to the lack of any previous information about the analyzed video on the one hand, and the huge amount of generic knowledge required on the other. This paper introduces a framework for the Automatic Semantic Annotation of unconstrained videos. The proposed framework utilizes two non-domain-specific layers: low-level visual similarity matching, and an annotation analysis that employs commonsense knowledgebases. Commonsense ontology is created by incorporating multiple-structured semantic relationships. Experiments and black-box tests are carried out on standard video databases for action recognition and video information retrieval. White-box tests examine the performance of the individual intermediate layers of the framework, and the evaluation of the results and the statistical analysis show that integrating visual similarity matching with commonsense semantic relationships provides an effective approach to automated video annotation. [Altadmri, Amjad; Ahmed, Amr] Lincoln Univ, Sch Comp Sci, Lincoln, England Altadmri, A (reprint author), Lincoln Univ, Sch Comp Sci, Lincoln, England. atadmri@lincoln.ac.uk; aahmed@lincoln.ac.uk Ahmed A, 2009, SEMANTIC MINING TECH, P1; Altadmri A, 2009, IEEE INT C INT COMP, V3, P636; Altadmri A, 2009, IEEE INT C SIGN IM P, P74; Altadmri A, 2009, IASTED INT C ART INT, V683, P34; Amir A, 2004, COMPUT VIS IMAGE UND, V96, P216, DOI 10.1016/j.cviu.2004.02.006; Bagdanov AD, 2007, ICSC 2007: International Conference on Semantic Computing, Proceedings, P713, DOI 10.1109/ICSC.2007.30; Basharat A, 2008, COMPUT VIS IMAGE UND, V110, P360, DOI 10.1016/j.cviu.2007.09.016; Bay H, 2006, LECT NOTES COMPUT SC, V3951, P404; Blank M, 2005, IEEE I CONF COMP VIS, P1395; Brox T, 2011, IEEE T PATTERN ANAL, V33, P500, DOI 10.1109/TPAMI.2010.143; Chandrasekaran B, 1999, IEEE INTELL SYST APP, V14, P20, DOI 10.1109/5254.747902; Deng J, 2009, PROC CVPR IEEE, P248; Deng Y, 1997, INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL II, P534; Farhadi A, 2010, LECT NOTES COMPUT SC, V6314, P15, DOI 10.1007/978-3-642-15561-1_2; Fellbaum C, 1998, WORDNET ELECT LEXICA; Fergus R, 2010, P IEEE, V98, P1453, DOI 10.1109/JPROC.2010.2048990; Guillaumin M, 2009, IEEE I CONF COMP VIS, P309, DOI 10.1109/ICCV.2009.5459266; Gupta A, 2009, IEEE T PATTERN ANAL, V31, P1775, DOI 10.1109/TPAMI.2009.83; Haering N, 2000, IEEE T CIRC SYST VID, V10, P857, DOI 10.1109/76.867923; Hauptmann AG, 2007, ICSC 2007: International Conference on Semantic Computing, Proceedings, P79, DOI 10.1109/ICSC.2007.68; Hsu MH, 2008, LECT NOTES COMPUT SC, V4993, P213; Ikizler N, 2007, LECT NOTES COMPUT SC, V4814, P271; Jiang YG, 2010, IEEE T MULTIMEDIA, V12, P42, DOI 10.1109/TMM.2009.2036235; Kapoor A, 2010, INT J COMPUT VISION, V88, P169, DOI 10.1007/s11263-009-0268-3; LENAT DB, 1995, COMMUN ACM, V38, P33, DOI 10.1145/219717.219745; Liu H, 2004, BT TECHNOL J, V22, P211, DOI 10.1023/B:BTTJ.0000047600.45421.6d; Liu JG, 2009, PROC CVPR IEEE, P1996; Lowe D., 1999, P 7 IEEE INT C COMP, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410; Motulsky H., 1999, ANAL DATA GRAPHPAD P; Ngo CW, 2009, TREC VID RETR EV WOR; Niebles J, 2007, IEEE C COMP VIS PATT, P1; Over P, 2011, TRECVID 2010, P1; Shyu ML, 2008, IEEE T MULTIMEDIA, V10, P252, DOI 10.1109/TMM.2007.911830; Siersdorfer S, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P395, DOI 10.1145/1571941.1572010; Sivic J, 2009, IEEE T PATTERN ANAL, V31, P591, DOI 10.1109/TPAMI.2008.111; Smeaton AF, 2006, INFORM PROCESS MANAG, V42, P1330, DOI 10.1016/j.ipm.2005.11.003; Stanford_NLP_Group, 2008, STANF NLP LOG LIN PA; TrecVid, 2011, TREE VID RETR TRACK; UCF_Computer_Vision_lab, 2011, UCF ACT DAT 11 11 20; Ulges A, 2010, COMPUT VIS IMAGE UND, V114, P429, DOI 10.1016/j.cviu.2009.08.002; Ventura C, 2012, LECT NOTES COMPUT SC, V7131, P652; Wei XY, 2011, IEEE T CIRC SYST VID, V21, P62, DOI 10.1109/TCSVT.2011.2105597; Yuan P, 2008, IEEE INT C DAT MIN W, P847; Zhao WL, 2010, IEEE T MULTIMEDIA, V12, P448, DOI 10.1109/TMM.2010.2050651 44 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. SEP 2014 72 2 1167 1191 10.1007/s11042-013-1363-6 25 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AM5IR WOS:000339891300008 J He, YC; Lu, HT; Xie, SN He, Yangcheng; Lu, Hongtao; Xie, Saining Semi-supervised non-negative matrix factorization for image clustering with graph Laplacian MULTIMEDIA TOOLS AND APPLICATIONS English Article Non-negative matrix factorization; Clustering; Semi-supervised learning; Image clustering Non-negative matrix factorization (NMF) plays an important role in multivariate data analysis, and has been widely applied in information retrieval, computer vision, and pattern recognition. NMF is an effective method to capture the underlying structure of the data in the parts-based low dimensional representation space. However, NMF is actually an unsupervised method without making use of supervisory information of data. In recent years, semi-supervised learning has received a lot of attentions, because partial label information can significantly improve learning quality of the algorithms. In this paper, we propose a novel semi-supervised non-negative matrix factorization (SEMINMF) algorithm, which not only utilizes the local structure of the data characterized by the graph Laplacian, but also incorporates the label information as the fitting constraints to learn. Hence, it can learn from labeled and unlabeled data. By this means our SEMINMF can obtain a more discriminative powerful representation space. Experimental results show the effectiveness of our proposed novel method in comparison to the state-of-the-art algorithms on several real world applications. [He, Yangcheng; Lu, Hongtao; Xie, Saining] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, MOE Microsoft Lab Intelligent Comp & Intelligent, Shanghai 200240, Peoples R China He, YC (reprint author), Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, MOE Microsoft Lab Intelligent Comp & Intelligent, Shanghai 200240, Peoples R China. h331076268@126.com National Basic Research Program of China (973 program) [2009CB320901]; NSFC [61272247]; National High Technology Research and Development Program of China (863 program) [2008AA02Z310]; National Natural Science Foundation of China [60873133]; Innovation Ability Special Fund of Shanghai Jiao Tong University [Z030026] This work was supported in part by the National Basic Research Program of China (973 program) under Grant 2009CB320901, NSFC (no. 61272247), the National High Technology Research and Development Program of China (863 program) under Grant 2008AA02Z310, the National Natural Science Foundation of China under Grant 60873133, and the Innovation Ability Special Fund of Shanghai Jiao Tong University under Grant Z030026. Bach F, 2003, LEARNING SPECTRAL CL; Basu S, 2004, P 10 ACM SIGKDD INT, P22; Cai D, 2011, IEEE T PATTERN ANAL, V33, P1548, DOI 10.1109/TPAMI.2010.231; Chapelle O., 2006, SEMISUPERVISED LEARN, V2; Chen YH, 2008, KNOWL INF SYST, V17, P355, DOI 10.1007/s10115-008-0134-6; Chung F.R.K., 1997, SPECTRAL GRAPH THEOR; Cormen T. H., 2001, INTRO ALGORITHMS; Das Gupta M, 2011, 2011 IEEE C COMP VIS, P2841; De la Torre F, 2006, P 23 INT C MACH LEAR, P241, DOI 10.1145/1143844.1143875; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Ding C, 2008, IEEE DATA MINING, P183, DOI 10.1109/ICDM.2008.130; Ding C, 2007, ACM INT C P SERIES, V227, P521; Guillamet D, 2002, P 5 CAT C ART INT; Hoyer PO, 2004, J MACH LEARN RES, V5, P1457; Kim J, 2008, SPARSE NONNEGATIVE M; Lee DD, 2001, ADV NEUR IN, V13, P556; Lee H, 2010, IEEE SIGNAL PROC LET, V17, P4, DOI 10.1109/LSP.2009.2027163; Li T, 2006, IEEE DATA MINING, P362; Lin TC, 2007, INT J COMPUTER SCI E, V1, P253; Liu H, 2010, 24 AAAI C ART INT; Liu HF, 2012, IEEE T PATTERN ANAL, V34, P1299, DOI 10.1109/TPAMI.2011.217; Lovasz L., 1986, MATCHING THEORY; Ma Z., 2011, P 19 ACM INT C MULT, P283; Perona P, 2004, ADV NEURAL INF PROCE, V17, P1601; Philbin J, 1986, IEEE C COMP VIS PATT, P1; Saul L, 1997, P 2 C EMP METH NAT L, P81; Shashua A, 2005, P 22 INT C MACH LEAR, P792, DOI 10.1145/1102351.1102451; von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z; Xu W, 2003, P 26 ANN INT ACM SIG, P267, DOI DOI 10.1145/860435.860485; Xu W, 2004, P INT C RES DEV INF, P202, DOI 10.1145/1008992.1009029; Yang Y, 2011, 25 AAAI C ART INT, P555; Yang Y, 2010, IEEE T IMAGE PROCESS, V19, P2761, DOI 10.1109/TIP.2010.2049235; Ye J, 2007, ADV NEURAL INF PROCE, V20, P1649; Yu S. X., 2003, Proceedings Ninth IEEE International Conference on Computer Vision; [张营 ZHANG Ying], 2008, [仪表技术与传感器, Intstrument Technique and Sensor], P1; Zhang Z, 2005, IEEE T PATTERN ANAL, V34, P253; Zhang ZS, 2008, I C MECH MACH VIS PR, P1; Zhou DY, 2004, ADV NEUR IN, V16, P321 38 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. SEP 2014 72 2 1441 1463 10.1007/s11042-013-1465-1 23 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AM5IR WOS:000339891300019 J Li, B; Godil, A; Johan, H Li, Bo; Godil, Afzal; Johan, Henry Hybrid shape descriptor and meta similarity generation for non-rigid and partial 3D model retrieval MULTIMEDIA TOOLS AND APPLICATIONS English Article 3D model retrieval; Non-rigid models; Partial similarity retrieval; Hybrid shape descriptor; Meta similarity OBJECT RECOGNITION; SURFACES; FEATURES; INFORMATION Non-rigid and partial 3D model retrieval are two significant and challenging research directions in the field of 3D model retrieval. Little work has been done in proposing a hybrid shape descriptor that works for both retrieval scenarios, let alone the integration of the component features of the hybrid shape descriptor in an automatic way. In this paper, we propose a hybrid shape descriptor that integrates both geodesic distance-based global features and curvature-based local features. We also develop an automatic algorithm to generate meta similarity resulting from different component features of the hybrid shape descriptor based on Particle Swarm Optimization. Experimental results demonstrate the effectiveness and advantages of our framework, as well as the significant improvements in retrieval performances. The framework is general and can be applied to similar approaches that integrate more features for the development of a single algorithm for both non-rigid and partial 3D model retrieval. [Li, Bo; Godil, Afzal] NIST, Gaithersburg, MD 20899 USA; [Johan, Henry] Fraunhofer IDM NTU, Singapore, Singapore Li, B (reprint author), NIST, Gaithersburg, MD 20899 USA. li.bo.ntu0@gmail.com; afzal.godil@nist.gov; henryjohan@ntu.edu.sg Akbar S, 2006, 2006 INT C COMP INF, P1; Arthur D, 2007, SODA 07, P1027; Attene M, 2010, EUR WORKSH 3D OBJ RE, P23; Ben-Chen M, 2008, 3DOR, P1; Biasotti S, 2006, EUR IT CHAPT C EUR, P23; Bober M, 2001, IEEE T CIRC SYST VID, V11, P716, DOI 10.1109/76.927426; BORG I., 2005, MODERN MULTIDIMENSIO; Bronstein AM, 2011, ACM T GRAPHIC, V30; Bronstein MM, 2010, PROC CVPR IEEE, P1704, DOI 10.1109/CVPR.2010.5539838; Bustos B, 2012, MULTIMED TOOLS APPL, V58, P81, DOI 10.1007/s11042-010-0689-6; Cohen SD, 1999, ICCV, P1076; Cornea ND, 2005, INT C SHAP MOD APPL, P368, DOI DOI 10.1109/SMI.2005.1; Daras P, 2012, IEEE T MULTIMEDIA, V14, P374, DOI 10.1109/TMM.2011.2176111; Eberhart R. C., 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406), DOI 10.1109/CEC.1999.785508; Elbaz AE, 2003, IEEE T PATTERN ANAL, V25, P1285; Faloutsos C., 1995, SIGMOD Record, V24; Furuya T, 2009, CIVR ACM, P1; Gal R, 2006, ACM T GRAPHIC, V25, P130, DOI 10.1145/1122501.1122507; Garland M, 1997, SIGGRAPH 97 C P, P209, DOI 10.1145/258734.258849; Gatzke T, 2005, INT C SHAP MOD APPL, P246; Groenen P, 2004, MULTIDIMENSIONAL SCA; Hamza AB, 2003, DGCI LECT NOTES COMP, V2886, P378, DOI 10.1007/978-3-540-39966-7_36; Heider P, 2011, EG 3DOR 11, P49; HORN B.K.P., 1984, IEEE, V72, P1671; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Johnson AE, 1999, IEEE T PATTERN ANAL, V21, P433, DOI 10.1109/34.765655; Kalogerakis E, 2010, ACM T GRAPHIC, V29, DOI 10.1145/1778765.1778839; KOENDERINK JJ, 1992, IMAGE VISION COMPUT, V10, P557, DOI 10.1016/0262-8856(92)90076-F; KRUSKAL JB, 1964, PSYCHOMETRIKA, V29, P1, DOI 10.1007/BF02289565; Laga H, 2008, LECT NOTES ARTIF INT, V4938, P210, DOI 10.1007/978-3-540-78159-2_20; Lavoue G, 2011, EUR WORKSH SHOP 3D O, P41; Lee CH, 2005, ACM T GRAPHIC, V24, P659, DOI 10.1145/1073204.1073244; Levy B., 2006, IEEE INT C SHAP MOD, P13; Li B, 2013, MULTIMED TOOLS APPL, V62, P821, DOI 10.1007/s11042-011-0873-3; Li B, 2012, LNCS; Li Fei-Fei, 2005, P IEEE COMP SOC C CO, V2, P524; Li XL, 2009, 2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, P437, DOI 10.1109/ICIP.2009.5414415; Lian Z, 2011, 3DIMPVT IEEE, P116; Lian Z, 2011, EUR WORKSH 3D OBJ RE, P79; Lian Z, 2012, INT J COMPUT VISION, V102, P221; Lian Z, 2010, SHAPE MODELING INT, P25; Lian Z, 2010, EUR WORKSH 3D OBJ RE, P101; Lian ZH, 2013, PATTERN RECOGN, V46, P449, DOI 10.1016/j.patcog.2012.07.014; Lian ZH, 2010, IEEE IMAGE PROC, P3181, DOI 10.1109/ICIP.2010.5654226; Liu Y, 2010, INT J COMPUT VISION, V89, P408, DOI 10.1007/s11263-009-0298-x; Liu Y, 2006, CVPR 2006, P2025; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Maes C, 2010, P 4 IEEE INT C BTAS, P1; Nguyen HV, 2011, ICIP, P2893; Ohbuchi R, 2008, IEEE INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS 2008, PROCEEDINGS, P93, DOI 10.1109/SMI.2008.4547955; Osada R., 2001, Proceedings International Conference on Shape Modeling and Applications, DOI 10.1109/SMA.2001.923386; Rabin J, 2010, LECT NOTES COMPUT SC, V6315, P771, DOI 10.1007/978-3-642-15555-0_56; Raviv D, 2010, P ACM WORKSH 3D OBJ, P39, DOI DOI 10.1145/1877808.1877817; Reuter M, 2006, COMPUT AIDED DESIGN, V38, P342, DOI 10.1016/j.cad.2005.10.011; Rusinkiewicz S, 2004, 3DPVT, P486; SAMMON JW, 1969, IEEE T COMPUT, VC 18, P401, DOI 10.1109/T-C.1969.222678; SCHWARTZ EL, 1989, IEEE T PATTERN ANAL, V11, P1005, DOI 10.1109/34.35506; Sfikas K, 2012, VISUAL COMPUT, V28, P943, DOI 10.1007/s00371-012-0714-z; Shi Y., 1998, P IEEE INT C EV COMP, P69, DOI DOI 10.1109/ICEC.1998.699146; Shilane P, 2004, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS, P167, DOI 10.1109/SMI.2004.1314504; Siddiqi K, 2008, MACH VISION APPL, V19, P261, DOI 10.1007/s00138-007-0097-8; Smeets D, 2009, LECT NOTES COMPUT SC, V5702, P757; Smeets D, 2010, LECT NOTES COMPUT SC, V6169, P162, DOI 10.1007/978-3-642-14061-7_16; Sun JA, 2009, COMPUT GRAPH FORUM, V28, P1383; Sundar H, 2003, SMI 2003: SHAPE MODELING INTERNATIONAL 2003, PROCEEDINGS, P130; Tang S, 2012, EVALUATION LOCAL SHA; Tierny J, 2009, COMPUT GRAPH FORUM, V28, P41, DOI 10.1111/j.1467-8659.2008.01190.x; Toldo R, 2010, VISUAL COMPUT, V26, P1257, DOI 10.1007/s00371-010-0519-x; Vedaldi A., 2008, VLFEAT OPEN PORTABLE; Veltkamp RC, 2007, UUCS2007015 UTR U DE; Villani C, 2003, TOPICS OPTIMAL TRANS; Wu HY, 2010, PROC CVPR IEEE, P438, DOI 10.1109/CVPR.2010.5540180; Wuhrer S, 2007, INT J SHAPE MODELING, V13, P139, DOI 10.1142/S0218654307000981; Zhang H, 2007, P EUR STAT OF THE AR, P1 74 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. SEP 2014 72 2 1531 1560 10.1007/s11042-013-1464-2 30 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AM5IR WOS:000339891300023 J Shamsi, A; Nezamabadi-pour, H; Saryazdi, S Shamsi, Asma; Nezamabadi-pour, Hossein; Saryazdi, Saeid A short-term learning approach based on similarity refinement in content-based image retrieval MULTIMEDIA TOOLS AND APPLICATIONS English Article Content-based image retrieval; Relevance feedback; Short-term learning; Similarity refinement; Query refinement RELEVANCE FEEDBACK; INFORMATION-RETRIEVAL; CLASSIFICATION; COLOR; TEXTURE This paper presents a new relevance feedback approach based on similarity refinement. In the proposed approach weight correction of feature's components is done by a proposed rule set using mean and standard deviation of feature vectors of relevant (positive) and irrelevant (negative) images. Also, the weight of each type of features is adjusted according to the relevant images' rank in the retrieval based on only the same type of feature. To evaluate the performance of the proposed method, a set of comparative experiments on a general database containing 20,000 images of various semantic groups are performed. The results confirm the effectiveness of the proposed method comparing with two well-known methods. [Shamsi, Asma; Nezamabadi-pour, Hossein; Saryazdi, Saeid] Shahid Bahonar Univ Kerman, Dept Elect Engn, Kerman, Iran Nezamabadi-pour, H (reprint author), Shahid Bahonar Univ Kerman, Dept Elect Engn, POB 76169-133, Kerman, Iran. nezam@uk.ac.ir Iran Telecommunication Research Center, ITRC The authors would like to thank the MTAP Editorial Board and the anonymous reviewers for their very helpful suggestions. This work was supported in part by the Iran Telecommunication Research Center, ITRC. Albanesi MG, 2001, IEEE INT C IM AN PRO, P410; [Anonymous], 2000, JTC1SC29WG11 ISOIEC; Arthur SM, 2000, P IEEE WORKS CONT BA, P68; Barrett S, 2009, IEEE INT CON MULTI, P838; Bertini M, 2007, 14TH INTERNATIONAL CONFERENCE ON IMAGE ANALYSIS AND PROCESSING WORKSHOPS, PROCEEDINGS, P160, DOI 10.1109/ICIAPW.2007.43; Chen YX, 2005, IEEE T IMAGE PROCESS, V14, P1187, DOI 10.1109/TIP.2005.849770; Cheng PC, 2008, EXPERT SYST APPL, V34, P2193, DOI 10.1016/j.eswa.2007.02.030; Clough P, 2008, LNCS, V5152, P473; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; Deselaers T, 2008, IEEE C COMP VIS PATT, P1, DOI DOI 10.1109/ICPR.2008.4761366; He XF, 2003, IEEE T CIRC SYST VID, V13, P39, DOI 10.1109/TCSVT.2002.808087; Huang TS, 2002, 2ND INTERNATIONAL CONFERENCE ON DEVELOPMENT AND LEARNING, PROCEEDINGS, P155; KIM DH, 2003, ACM INT C MAN DAT SI, P599; Laaksonen J, 2004, PATTERN ANAL APPL, V2-3, P140; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Manjunath BS, 2002, INTRO MPEG 7; Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803; Modaghegh H, 2010, AUST J BASIC APPL SC, V4, P171; Muller H, 2001, PATTERN RECOGN LETT, V22, P593, DOI 10.1016/S0167-8655(00)00118-5; Nezambadi-pour H, 2009, EXPERT SYST APPL, V36, P5948, DOI 10.1016/j.eswa.2008.07.008; Nezamabadi-Pour H, 2004, PATTERN RECOGN LETT, V25, P1547, DOI 10.1016/j.patrec.2004.05.019; Nezamabadi-pour H, 2005, J MODARRES, V22, P89; Nezamabadi-pour H, 2004, J COMPUT SCI ENG, V2, P37; Papa JP, 2009, INT J IMAG SYST TECH, V19, P120, DOI 10.1002/ima.20188; Park D.K., 2000, P ACM WORKSH MULT, P51, DOI 10.1145/357744.357758; Park SJ, 2000, M5984 MPEG; Plantaniotis KN, 2000, COLOR IMAGE PROCESSI; Qian F, 2003, MULTIMED TOOLS APPL, V21, P35, DOI 10.1023/A:1025030131788; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Rubner Y, 2001, COMPUT VIS IMAGE UND, V84, P25, DOI 10.1006/cviu.2001.0934; Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644; Rui Y, 1997, INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL II, P815; Schettini R, 1999, INT C IM PROC, V3, P75; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Smith JR, 1999, COMPUT VIS IMAGE UND, V75, P165, DOI 10.1006/cviu.1999.0771; Wan X, 1998, IEEE T CIRC SYST VID, V8, P628; Wei LY, 2009, PATTERN RECOGN, V42, P1126, DOI 10.1016/j.patcog.2008.08.028; Wood M. E. J., 1998, Proceedings ACM Multimedia 98, DOI 10.1145/290747.290750; Wu J, 2010, LECT NOTES COMPUT SC, V5916, P650; Xu XQ, 2009, NEUROCOMPUTING, V72, P2259, DOI 10.1016/j.neucom.2008.12.029; Yoo HW, 2002, PATTERN RECOGN, V35, P749, DOI 10.1016/S0031-3203(01)00072-3; Zhou XS, 2003, MULTIMEDIA SYST, V8, P536, DOI 10.1007/s00530-002-0070-3; Zhuang Y, 2001, IEEE P COMPUT GRAPH, P62 44 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. SEP 2014 72 2 2025 2039 10.1007/s11042-013-1503-z 15 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AM5IR WOS:000339891300044 J Lucke, S; Lachnit, H; Stuttgen, MC; Uengoer, M Lucke, Sara; Lachnit, Harald; Stuettgen, Maik C.; Uengoer, Metin The impact of context relevance during extinction learning LEARNING & BEHAVIOR English Article Predictive learning; Extinction; Attention; Context; Renewal CONNECTIONIST MODEL; LATENT INHIBITION; CONDITIONED FEAR; PAST EXPERIENCE; STIMULUS; DISCRIMINATION; RETRIEVAL; INFORMATION; SIMILARITY; ATTENTION In two predictive-learning experiments, we investigated the role of the informational value of contexts for the formation of context-specific extinction learning. The contexts were each composed of two elements from two dimensions, A and B. In Phase 1 of each experiment, participants received acquisition training with a target cue Z in context A1B1 (the numbers assign particular values on the context dimensions). In Phase 2, participants were trained with conditional discriminations between two other cues, X and Y, for which only one of the two context dimensions was relevant. In a third phase, participants received extinction trials with cue Z in context A2B2. During a final test phase, we observed that a partial change of the extinction context disrupted extinction performance when the extinction context was changed on the dimension that had been trained as being relevant for the conditional discrimination. However, when the extinction context was changed on the irrelevant context dimension, extinction performance was not affected. Our results are consistent with the idea that relevant contexts receive more attention than do irrelevant contexts, leading to stronger context-specific processing of information learned in the former than in the latter type of contexts. [Lucke, Sara; Lachnit, Harald; Uengoer, Metin] Univ Marburg, Dept Psychol, D-35032 Marburg, Germany; [Stuettgen, Maik C.] Ruhr Univ Bochum, Fac Psychol, Dept Biopsychol, Bochum, Germany Lucke, S (reprint author), Univ Marburg, Dept Psychol, Gutenbergstr 18, D-35032 Marburg, Germany. sara.lucke@uni-marburg.de DFG [LA 564/22-1] This research was supported by DFG Grant No. LA 564/22-1, awarded to H. L. and M. U. We thank Katrin Bahlinger, Friederike Barth, Daniel Elport, Constanze Fink, Franziska Heinz, Barnd Hengstebeck, Esther Rebsamen, Simon Samstag, Farsin Siegmund, Jan-Matthis Wasserfuhr, Francisco Wilhelm, and Rui Zhang for their help with the data collection. The last author of this article has published previously with "Ungor" as his surname. Black A. H., 1972, CLASSICAL CONDITION, P64, DOI DOI 10.1016/J.COGPSYCH.2004.11.001; BOUTON ME, 1994, J EXP PSYCHOL ANIM B, V20, P219, DOI 10.1037//0097-7403.20.3.219; Bouton M. E., 1997, LEARN MOTIV, P358; Bouton ME, 2004, LEARN MEMORY, V11, P485, DOI 10.1101/lm.78804; BOUTON ME, 1993, PSYCHOL BULL, V114, P80, DOI 10.1037//0033-2909.114.1.80; BOUTON ME, 1979, LEARN MOTIV, V10, P445, DOI 10.1016/0023-9690(79)90057-2; BOUTON ME, 1994, ANIM LEARN BEHAV, V22, P317, DOI 10.3758/BF03209840; BOUTON ME, 1983, J EXP PSYCHOL ANIM B, V9, P248, DOI 10.1037/0097-7403.9.3.248; BOX GEP, 1954, ANN MATH STAT, V25, P290, DOI 10.1214/aoms/1177728786; CHANNELL S, 1983, ANIM LEARN BEHAV, V11, P67, DOI 10.3758/BF03212309; DARBY RJ, 1995, J EXP PSYCHOL ANIM B, V21, P143, DOI 10.1037//0097-7403.21.2.143; HALL G, 1990, J EXP PSYCHOL ANIM B, V16, P271, DOI 10.1037//0097-7403.16.3.271; HALL G, 1989, J EXP PSYCHOL ANIM B, V15, P232, DOI 10.1037/0097-7403.15.3.232; Kinder A, 2003, PSYCHOPHYSIOLOGY, V40, P226, DOI 10.1111/1469-8986.00024; Kruschke JK, 2001, J MATH PSYCHOL, V45, P812, DOI 10.1006/jmps.2000.1354; KRUSCHKE JK, 1992, PSYCHOL REV, V99, P22, DOI 10.1037/0033-295X.99.1.22; Kruschke JK, 2006, PSYCHOL REV, V113, P677, DOI 10.1037/0033-295X.113.4.677; Lachnit H, 2001, BIOL PSYCHOL, V56, P151, DOI 10.1016/S0301-0511(01)00067-9; Lachnit H, 2000, BIOL PSYCHOL, V53, P105, DOI 10.1016/S0301-0511(00)00043-0; León Samuel P., 2008, Escritos de Psicología, V2, P65; Leon SP, 2010, EXP PSYCHOL, V57, P46, DOI 10.1027/1618-3169/a000006; Leon SP, 2012, SPAN J PSYCHOL, V15, P10, DOI 10.5209/rev_SJOP.2012.v15.n1.37279; Lober K, 2002, BIOL PSYCHOL, V59, P163, DOI 10.1016/S0301-0511(02)00004-2; Lucke S, 2013, LEARN BEHAV, V41, P285, DOI 10.3758/s13420-013-0104-z; MACKINTOSH NJ, 1975, PSYCHOL REV, V82, P276, DOI 10.1037/h0076778; Melchers KG, 2004, LEARN MOTIV, V35, P167, DOI 10.1016/S0023-9690(03)00044-4; Melchers KG, 2008, BEHAV PROCESS, V77, P413, DOI 10.1016/j.beproc.2007.09.013; Melchers KG, 2005, LEARN MOTIV, V36, P20, DOI 10.1016/j.lmot.2004.06.002; Melchers KG, 2005, J EXP PSYCHOL ANIM B, V31, P477, DOI 10.1037/0097-7403.31.4.477; Paredes- Olay M. C., 1999, PSICOLOGICA, V20, P195; PEARCE JM, 1994, PSYCHOL REV, V101, P587, DOI 10.1037//0033-295X.101.4.587; Pearce JM, 1998, OCCASION SETTING ASS, P249, DOI DOI 10.1037/10298-009; PEARCE JM, 1987, PSYCHOL REV, V94, P61, DOI 10.1037//0033-295X.94.1.61; PRESTON GC, 1986, Q J EXP PSYCHOL-B, V38, P217; RESCORLA RA, 1973, J COMP PHYSIOL PSYCH, V85, P331, DOI 10.1037/h0035046; Rosas J. M., 2006, INT J PSYCHOL PSYCHO, V6, P147; Rosas JM, 2013, WIRES COGN SCI, V4, P237, DOI 10.1002/wcs.1225; Rosas JM, 2006, PSICOLOGICA, V27, P35; Shanks DR, 1998, J EXP PSYCHOL ANIM B, V24, P405, DOI 10.1037/0097-7403.24.4.405; Ungör Metin, 2006, J Exp Psychol Anim Behav Process, V32, P441, DOI 10.1037/0097-7403.32.4.441; Ungor M, 2008, LEARN MOTIV, V39, P181, DOI 10.1016/j.lmot.2007.08.001; Williams DA, 1999, J EXP PSYCHOL ANIM B, V25, P461, DOI 10.1037/0097-7403.25.4.461 42 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1543-4494 1543-4508 LEARN BEHAV Learn Behav. SEP 2014 42 3 256 269 10.3758/s13420-014-0143-0 14 Psychology, Biological; Behavioral Sciences; Psychology, Experimental; Zoology Psychology; Behavioral Sciences; Zoology AL7NO WOS:000339321100006 J Saraclar, M; Chelba, C; Ramabhadran, B Saraclar, M.; Chelba, Ciprian; Ramabhadran, Bhuvana Editorial for the special issue on spoken content retrieval COMPUTER SPEECH AND LANGUAGE English Editorial Material Speech retrieval; Spoken document retrieval; Spoken term detection A typical spoken content retrieval solution integrates multiple technologies that belong to the areas of automatic speech recognition and information retrieval. Due to the rich set of challenges many of them language specific as well as widespread impact, numerous research sites in the world are actively engaged in this research area. This special issue highlights some of the recent advances in spoken content retrieval. (C) 2014 Published by Elsevier Ltd. [Saraclar, M.] Bogazici Univ, TR-34342 Istanbul, Turkey; [Chelba, Ciprian] Google Inc, Mountain View, CA 94043 USA; [Ramabhadran, Bhuvana] IBM Corp, TJ Watson Res Ctr, Yorktown Hts, NY 10568 USA Saraclar, M (reprint author), Bogazici Univ, TR-34342 Istanbul, Turkey. Saraclar, Murat/E-8640-2010 Chelba C., 2011, SPOKEN LANGUAGE UNDE, P417; Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992; Eskevich M, 2014, COMPUT SPEECH LANG, V28, P1021, DOI 10.1016/j.csl.2013.12.005; Lee HY, 2014, COMPUT SPEECH LANG, V28, P1045, DOI 10.1016/j.csl.2013.12.003; Metze F, 2014, COMPUT SPEECH LANG, V28, P1066, DOI 10.1016/j.csl.2013.12.004; Tejedor J, 2014, COMPUT SPEECH LANG, V28, P1083, DOI 10.1016/j.csl.2013.09.008 6 0 0 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0885-2308 1095-8363 COMPUT SPEECH LANG Comput. Speech Lang. SEP 2014 28 5 SI 1019 1020 10.1016/j.csl.2014.05.002 2 Computer Science, Artificial Intelligence Computer Science AK7LF WOS:000338609100001 J Eskevich, M; Jones, GJF Eskevich, Maria; Jones, Gareth J. F. Exploring speech retrieval from meetings using the AMI corpus COMPUTER SPEECH AND LANGUAGE English Article Speech retrieval; Recall-focused information retrieval; Informal spoken content search; Retrieval unit segmentation RECOGNITION; DOCUMENTS; ALGORITHM Increasing amounts of informal spoken content are being collected, e.g. recordings of meetings, lectures and personal data sources. The amount of this content being captured and the difficulties of manually searching audio data mean that efficient automated search tools are of increasing importance if its full potential is to be realized. Much existing work on speech search has focused on retrieval of clearly defined document units in ad hoc search tasks. We investigate search of informal speech content using an extended version of the AMI meeting collection. A retrieval collection was constructed by augmenting the AMI corpus with a set of ad hoc search requests and manually identified relevant regions of the recorded meetings. Unlike standard ad hoc information retrieval focussing primarily on precision, we assume a recall-focused search scenario of a user seeking to retrieve a particular incident occurring within meetings relevant to the query. We explore the relationship between automatic speech recognition (ASR) accuracy, automated segmentation of the meeting into retrieval units and retrieval behaviour with respect to both precision and recall. Experimental retrieval results show that while averaged retrieval effectiveness is generally comparable in terms of precision for automatically extracted segments for manual content transcripts and ASR transcripts with high recognition accuracy, segments with poor recognition quality become very hard to retrieve and may fall below the retrieval rank position to which a user is willing search. These changes impact on system effectiveness for recall-focused search tasks. Varied ASR quality across the relevant and non-relevant data means that the rank of some well-recognized relevant segments is actually promoted for ASR transcripts compared to manual ones. This effect is not revealed by the averaged precision based retrieval evaluation metrics typically used for evaluation of speech retrieval. However such variations in the ranks of relevant segments can impact considerably on the experience of the user in terms of the order in which retrieved content is presented. Analysis of our results reveals that while relevant longer segments are generally more robust to ASR errors, and consequentially retrieved at higher ranks, this is often at the expense of the user needing to engage in longer content playback to locate the relevant content in the audio recording. Our overall conclusion being that it is desirable to minimize the length of retrieval units containing relevant content while seeking to maintain high ranking of these items. (C) 2014 Elsevier Ltd. All rights reserved. [Eskevich, Maria; Jones, Gareth J. F.] Dublin City Univ, Sch Comp, CNGL Ctr Global Intelligent Content, Dublin 9, Ireland Eskevich, M (reprint author), Dublin City Univ, Sch Comp, CNGL Ctr Global Intelligent Content, Dublin 9, Ireland. meskevich@computing.dcu.ie; gjones@computing.dcu.ie Science Foundation Ireland [08/RFP/CMS1677]; Science Foundation Ireland as part of Centre for Next Generation Localisation (CNGL) project at DCU [07/CE/I1142] This work is supported by Science Foundation Ireland, under the Research Frontiers Programme 2008 (Grant 08/RFP/CMS1677), and Grant 07/CE/I1142 as part of the Centre for Next Generation Localisation (CNGL) project at DCU. Akiba T., 2013, P NTCIR 10 WORKSH M; Akiba T., 2011, P NTCIR 9 WORKSH M T; Buttcher S., 2010, INFORM RETRIEVAL IMP; Byrne W, 2004, IEEE T SPEECH AUDI P, V12, P420, DOI 10.1109/TSA.2004.828702; Carletta J, 2007, LANG RESOUR EVAL, V41, P181, DOI 10.1007/s10579-007-9040-x; Chia T. K., 2010, ACM T INFORM SYST, V28, p[2, 1, 30]; Chibelushi C, 2009, LECT NOTES ENG COMP, P710; Choi F. Y. Y., 2000, P 1 N AM CHAPT ASS C, P26; Eskevich M., 2012, P 34 EUR C INF RETR, P170; Galley M., 2003, P 41 ANN M ASS COMP, P562; Garofolo J.S., 2000, P RIAO 2000 CONT BAS, P1; Garofolo J.S., 1999, P ESCA WORKSH ACC IN, P1; Glass J., 2007, INTERSPEECH 2007, P2553; Hearst M., 1993, 9324 U CAL COMP SCI; Hiemstra D., 2001, THESIS U TWENTE; Hsueh P.-Y., 2006, P IEEE ACL WORKSH SP, P98; James D.A., 1995, THESIS U CAMBRIDGE, P3; Jones G.J.F., 1996, P 19 ANN INT ACM SIG, P30, DOI 10.1145/243199.243208; Jones G.J.F., 2002, P ECDL2002 SEP, P276; Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137; Larson M, 2009, LECT NOTES COMPUT SC, V5478, P755, DOI 10.1007/978-3-642-00958-7_80; LOVINS JB, 1968, MECH TRANSL, V11, P22; Malioutov I, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P25; Mamou J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148183; Metze F, 2005, LECT NOTES COMPUT SC, V3869, P126; Pecina P, 2008, LECT NOTES COMPUT SC, V5152, P674, DOI 10.1007/978-3-540-85760-0_86; Popescu-Belis A, 2012, IEEE MULTIMEDIA, V19, P48, DOI 10.1109/MMUL.2011.21; Popescu-Belis A., 2009, P ICMI MLMI 2009 11; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; Renals S., 2007, P IEEE WORKSH AUT SP, P238; Renals S., 2008, IEEE WORKSH HANDS FR, P115; Repp S, 2008, IEEE T LEARN TECHNOL, V1, P145, DOI 10.1109/TLT.2008.22; Rigamonti M, 2007, LECT NOTES COMPUT SC, V4662, P102; Sanderson M, 2007, LECT NOTES COMPUT SC, V4425, P505; Saraclar M., 2004, P HLT NAACL, P129; Sharp Bernadette, 2008, International Journal of Speech Technology, V11, DOI 10.1007/s10772-009-9048-2; Shou X.M., 2003, P AAAI SPRING S INT, P28; Utiyama M., 2001, P 9 C EUR CHAPT ASS, P491; Voorhees E.M., 2000, P 8 TEXT RETR C TREC, P1 39 1 1 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0885-2308 1095-8363 COMPUT SPEECH LANG Comput. Speech Lang. SEP 2014 28 5 SI 1021 1044 10.1016/j.csl.2013.12.005 24 Computer Science, Artificial Intelligence Computer Science AK7LF WOS:000338609100002 J Lee, HY; Chou, PW; Lee, LS Lee, Hung-yi; Chou, Po-wei; Lee, Lin-shan Improved open-vocabulary spoken content retrieval with word and subword lattices using acoustic feature similarity COMPUTER SPEECH AND LANGUAGE English Article Spoken content retrieval; Spoken term detection; Pseudo-relevance feedback; Random walk INFORMATION-RETRIEVAL; SEARCH Spoken content retrieval will be very important for retrieving and browsing multimedia content over the Internet, and spoken term detection (STD) is one of the key technologies for spoken content retrieval. In this paper, we show acoustic feature similarity between spoken segments used with pseudo-relevance feedback and graph-based re-ranking can improve the performance of STD. This is based on the concept that spoken segments similar in acoustic feature vector sequences to those with higher/lower relevance scores should have higher/lower scores, while graph-based re-ranking further uses a graph to consider the similarity structure among all the segments retrieved in the first pass. These approaches are formulated on both word and subword lattices, and a complete framework of using them in open vocabulary retrieval of spoken content is presented. Significant improvements for these approaches with both in-vocabulary and out-of-vocabulary queries were observed in preliminary experiments. (C) 2014 Elsevier Ltd. All rights reserved. [Lee, Hung-yi; Lee, Lin-shan] Natl Taiwan Univ, Grad Inst Commun Engn, Taipei 10617, Taiwan; [Chou, Po-wei] Natl Taiwan Univ, Dept Elect Engn, Taipei 10617, Taiwan Lee, HY (reprint author), Natl Taiwan Univ, Grad Inst Commun Engn, 1,Sec 4,Roosevelt Rd, Taipei 10617, Taiwan. tlkagkb93901106@yahoo.com.tw Akbacak M., 2008, ICASSP; Akbacak M., 2009, THESIS U COLORADO; Akiba T., 2011, P NTCIR 9 WORKSH; Alberti C., 2009, ICASSP; Allauzen C., 2004, P WORKSH INT APPR SP; Aradilla G., 2006, ICSLP; Bisani M, 2008, SPEECH COMMUN, V50, P434, DOI 10.1016/j.specom.2008.01.002; Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X; Cao G., 2008, P 31 ANN INT ACM SIG; Chan C.-A., 2011, INTERSPEECH; Chelba C, 2007, COMPUT SPEECH LANG, V21, P458, DOI 10.1016/j.csl.2006.09.001; Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992; Chelba C., 2005, ACL; Chen C.-P., 2011, THESIS NATL TAIWAN U; Chen C.-P., 2010, INTERSPEECH; Chen Y.-N., 2011, ICASSP; Garcia A., 2006, ICASSP; Garofolo J.S., 2000, TEXT RETR C TREC, V8; Glass J., 2007, INTERSPEECH; Goto M., 2007, INTERSPEECH; Hansen J.H., 2004, SPEECHFIND SPOKEN DO; Hazen T.J., 2009, ASRU; Hori T., 2007, ICASSP; Hsu W.H., 2007, P ACM INT C MULT, P971, DOI 10.1145/1291233.1291446; Itoh Y., 2007, INTERSPEECH; Jansen A., 2012, INTERSPEECH; Kamvar S. D., 2003, P 12 INT WORLD WID W, P261; Kong S.-Y., 2009, ICASSP; Kurland O., 2005, P 28 ANN INT ACM SIG; Langville AN, 2005, SIAM REV, V47, P135, DOI 10.1137/S0036144503424786; Lee H.-Y, 2009, ASRU; Lee H.-Y, 2011, APSIPA; Lee HY, 2013, IEEE T AUDIO SPEECH, V21, P1272, DOI 10.1109/TASL.2013.2248721; Lee H.-Y, 2012, INTERSPEECH; Lee H.-Y, 2012, SLT; Lee LS, 2005, IEEE SIGNAL PROC MAG, V22, P42; Logan B, 2005, IEEE T MULTIMEDIA, V7, P899, DOI 10.1109/TMM.2005.854429; Logan B., 2000, ICSLP; Lv Y., 2009, P 18 ACM C INF KNOWL; Lv Y., 2010, P 33 INT ACM SIGR C; Manaskasemsak B., 2005, P 11 INT C PAR DISTR, V1, P257; Mangu L, 2000, COMPUT SPEECH LANG, V14, P373, DOI 10.1006/csla.2000.0152; Meng C.-H., 2009, ICASSP; Meye C.D., 2000, MATRIX ANAL APPL LIN, P661; Montgomery J., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009078; Ng K., 2000, THESIS MIT; Oard D. W., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009002; Otterbacher J, 2009, INFORM PROCESS MANAG, V45, P42, DOI 10.1016/j.ipm.2008.06.004; Pan H.-L.C.Y.-C., 2007, ASRU; Pan YC, 2012, IEEE T AUDIO SPEECH, V20, P632, DOI 10.1109/TASL.2011.2163512; Pan Y.-C., 2007, INTERSPEECH; Parada C., 2009, ASRU; Rastrow A., 2009, INTERSPEECH; Saraclar M., 2004, P HLT NAACL, P129; Szoke I., 2008, P 31 ANN INT ACM SIG; Tao T., 2006, P 29 ANN INT ACM SIG; Tian X., 2008, P ACM INT C MULT, P131, DOI 10.1145/1459359.1459378; Tu T.-W., 2011, ASRU; Turunen V. T., 2007, P ACM SIGIR C RES DE, P631, DOI 10.1145/1277741.1277849; Turunen V.T., 2008, INTERSPEECH; Wallace R., 2007, INTERSPEECH; Wang D., 2008, ICASSP; Wang H., 2012, ICASSP; Yeh C.-F., 2011, ICASSP; Zhang Y., 2009, ASRU; Zhang Y., 2010, ICASSP; Zhou B., 2003, THESIS U COLORADO 67 1 1 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 0885-2308 1095-8363 COMPUT SPEECH LANG Comput. Speech Lang. SEP 2014 28 5 SI 1045 1065 10.1016/j.csl.2013.12.003 21 Computer Science, Artificial Intelligence Computer Science AK7LF WOS:000338609100003 J Barrios, JM; Bustos, B; Skopal, T Barrios, Juan Manuel; Bustos, Benjamin; Skopal, Tomas Analyzing and dynamically indexing the query set INFORMATION SYSTEMS English Article; Proceedings Paper 5th International Workshop on Similarity Search and Applications (SISAP) AUG 09-10, 2012 Toronto, CANADA Fields Inst Res Math Sci Similarity search; Metric indexing; Multimedia information retrieval; Content-based multimedia retrieval SEARCH ALGORITHM AESA; VIDEO COPY DETECTION; METRIC-SPACES; DESCRIPTORS Most of the current metric indexes focus on indexing the collection of reference. In this work we study the problem of indexing the query set by exploiting some property that query objects may have. Thereafter, we present the Snake Table, which is an index structure designed for supporting streams of k-NN searches within a content-based similarity search framework. The index is created and updated in the online phase while resolving the queries, thus it does not need a preprocessing step. This index is intended to be used when the stream of query objects fits a snake distribution, that is, when the distance between two consecutive query objects is small. In particular, this kind of distribution is present in content-based video retrieval systems, image classification based on local descriptors, rotation-invariant shape matching, and others. We show that the Snake Table improves the efficiency of k-NN searches in these systems, avoiding the building of a static index in the offline phase. (C) 2013 Elsevier Ltd. All rights reserved. [Barrios, Juan Manuel] ORAND SA, Santiago, Chile; [Barrios, Juan Manuel; Bustos, Benjamin] Univ Chile, Dept Comp Sci, PRISMA, Santiago, Chile; [Skopal, Tomas] Charles Univ Prague, Fac Math & Phys, SIRET Res Grp, CR-11636 Prague 1, Czech Republic Barrios, JM (reprint author), ORAND SA, Santiago, Chile. juan.barrios@orand.cl; bebustos@dcc.uchile.cl; skopal@ksi.mff.cuni.cz Amato G., 2011, P INT WORKSH SIM SEA, P81; Barrios JM, 2013, MULTIMED TOOLS APPL, V62, P75, DOI 10.1007/s11042-011-0915-x; Barrios JM, 2009, SISAP 2009: 2009 SECOND INTERNATIONAL WORKSHOP ON SIMILARITY SEARCH AND APPLICATIONS, PROCEEDINGS, P156, DOI 10.1109/SISAP.2009.30; Barrios J.M., 2012, TRECVID; Batko M, 2010, MULTIMED TOOLS APPL, V47, P599, DOI 10.1007/s11042-009-0339-z; Batko M, 2009, SISAP 2009: 2009 SECOND INTERNATIONAL WORKSHOP ON SIMILARITY SEARCH AND APPLICATIONS, PROCEEDINGS, P158, DOI 10.1109/SISAP.2009.24; Braunmuller B, 2001, IEEE T KNOWL DATA EN, V13, P79, DOI 10.1109/69.908982; Bustos B., 2008, P 1 INT WORKSH SIM S, P105; Chavez E, 2001, ACM COMPUT SURV, V33, P273, DOI 10.1145/502807.502808; Ciaccia P, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P426; Falchi F., 2008, LSDS IR 2008, P43; Falchi F., 2009, EDBT 2009, P780; Keogh E.J., 2006, VLDB, P882; Kim C, 2005, IEEE T CIRC SYST VID, V15, P127; Law-To J., 2007, MUSCLE VCD 2007 LIVE; Manjunath BS, 2001, IEEE T CIRC SYST VID, V11, P703, DOI 10.1109/76.927424; Mico Luisa, 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), DOI 10.1109/ICPR.2010.951; MICO ML, 1994, PATTERN RECOGN LETT, V15, P9, DOI 10.1016/0167-8655(94)90095-7; Muhammad G, 2012, DIGIT INVEST, V9, P49, DOI 10.1016/j.diin.2012.04.004; Paredes R, 2006, LECT NOTES COMPUT SC, V4007, P85; Skopal T, 2012, IEEE T KNOWL DATA EN, V24, P868, DOI 10.1109/TKDE.2011.19; VIDAL E, 1994, PATTERN RECOGN LETT, V15, P1, DOI 10.1016/0167-8655(94)90094-9; Zezula P., 2005, ADV DATABASE SYSTEMS 23 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0306-4379 1873-6076 INFORM SYST Inf. Syst. SEP 2014 45 37 47 10.1016/j.is.2013.05.010 11 Computer Science, Information Systems Computer Science AK7NB WOS:000338613900004 J Rinaldi, AM Rinaldi, Antonio M. A multimedia ontology model based on linguistic properties and audio-visual features INFORMATION SCIENCES English Article Multimedia ontology; OWL; WordNet; Semantic network; P2P IMAGE RETRIEVAL; STANDARD; WORDNET The exponential growth of informative contents needs intelligent information systems able to use data to create information. To aim this goal, these systems should have formal models to represent knowledge. In this way complex data can be managed and used to perform new tasks and implement innovative functionalities. This article describes a general and formal ontology model to represent knowledge using multimedia data and linguistic properties to bridge the gap between the target semantic classes and the available low-level multimedia descriptors. This model has been implemented in a system to edit, manage and share ontology in the WEB. The system provides a graphical interface to add multimedia objects by means of user interaction. The multimedia features are automatically extracted using algorithms based on MPEG-7 descriptors. (C) 2014 Elsevier Inc. All rights reserved. [Rinaldi, Antonio M.] Univ Naples Federico II, DIETI, I-80125 Naples, Italy; [Rinaldi, Antonio M.] Univ Naples Federico II, IKNOS LAB Intelligent & Knowledge Syst LUPT, I-80134 Naples, Italy Rinaldi, AM (reprint author), Univ Naples Federico II, DIETI, Via Claudio 21, I-80125 Naples, Italy. antoniomaria.rinaldi@unina.it Albanese M., 2005, P 11 INT C DISTR MUL; Albanese M., 2004, ACM SIGIR SEM WEB IN; Arguedas I.E., 2013, P 18 INT C DIG SIGN; Arias JJP, 2011, INFORM SCIENCES, V181, P855, DOI 10.1016/j.ins.2010.10.017; Arndt R., 2009, HDB ONTOLOGIES; Assem M. van, 2006, RDF OWL REP IN PRESS; Aygul F.A., 2012, P INT C KNOWL ENG ON; Bannour H., 2013, MULTIMED TOOLS APPL, P1; Bobrow D.G., 1976, OVERVIEW KRL KNOWLED; Brachman R., 1979, ASS NETWORKS REPRESE, P3; BRACHMAN RJ, 1977, INT J MAN MACH STUD, V9, P127, DOI 10.1016/S0020-7373(77)80017-5; BRACHMAN RJ, 1985, COGNITIVE SCI, V9, P171, DOI 10.1207/s15516709cog0902_1; Cataldo A, 2010, COMPUT ENVIRON URBAN, V34, P117, DOI 10.1016/j.compenvurbsys.2009.09.004; Chang SF, 2001, IEEE T CIRC SYST VID, V11, P688; Chen Y., 2003, P 7 INT S SIGN PROC; CLANCEY WJ, 1993, INT J INTELL SYST, V8, P33, DOI 10.1002/int.4550080104; Cristani M., 2005, International Journal on Semantic Web and Information Systems, V1, DOI 10.4018/jswis.2005040103; Danesi M., 1999, ANAL CULTURES; Dean M., 2004, OWL WEB ONTOLOGY LAN; Denny M, 2004, ONTOLOGY TOOLS SURVE; Euzenat J., 2007, ONTOLOGY MATCHING; Fox M.S., 1986, P 1 INT WORKSH EXP D; GAINES BR, 1993, INT J INTELL SYST, V8, P155; Genesereth M. R., 1987, LOGICAL FDN ARTIFICI; GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008; Guarino N., 1994, PHILOS COGNITIVE SCI; Hare J.S., 2006, LNCS, V4011; Huang XX, 2007, J ZHEJIANG UNIV-SC A, V8, P864, DOI 10.1631/jzus.2007.A0864; Hunter J., 2001, P INT SEM WEB WORK S; Hunter J, 2003, IEEE T CIRC SYST VID, V13, P49, DOI 10.1109/TCSVT.2002.808088; Kang S.-K., 2013, MULTIMED TOOLS APPL, P1; Kannan P., 2012, P INT C ADV ENG SCI; Kim M.E., 2012, P 14 INT C ADV COMM; Kompatsiaris I., 2004, INTEGRATING KNOWLEDG, P21; Lee M., 2012, P 14 INT C ADV COMM; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Li Q., 2011, P 3 INT C ADV COMP C; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Mallik A., 2012, INT J MULTIMEDIA INF, V1, P249; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; Minsky M., 1974, FRAMEWORK REPRESENTI; Mojsilovic A., 2001, P 2001 INT C IM PROC; NECHES R, 1991, AI MAG, V12, P36; Nutter J.T., 1998, ENCY ARTIFICIAL INTE; Pino C., 2011, P 13 WSEAS INT C MAT; Rasiwasia N., 2010, P INT C MULT MM 10; Rui Y, 1999, J VIS COMMUN IMAGE R, V10, P39, DOI 10.1006/jvci.1999.0413; Russell SJ, 2003, ARTIFICIAL INTELLIGE; Schreiber AT, 2001, IEEE INTELL SYST APP, V16, P66, DOI 10.1109/5254.940028; Schreiber G., 1993, KADS PRINCIPLED APPR; Sikora T, 2001, IEEE T CIRC SYST VID, V11, P696, DOI 10.1109/76.927422; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Sokhn M., 2011, P 9 IFIP TC 6 INT C; Straccia U., 2010, 40 IEEE INT S MULT V; Tousch AM, 2012, PATTERN RECOGN, V45, P333, DOI 10.1016/j.patcog.2011.05.017; Tsinaraki C., 2004, P RIAO 04; Tsinaraki C., 2003, P 15 INT C ADV INF S; Wang P, 2013, INFORM SCIENCES, V230, P147, DOI 10.1016/j.ins.2012.12.028; Wang P., 2012, INT J MULTIMEDIA INF, V1, P87, DOI DOI 10.1007/S13735-012-0010; Woods William A, 1975, REPRESENTATION UNDER, P35 60 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 1 2014 277 234 246 10.1016/j.ins.2014.02.017 13 Computer Science, Information Systems Computer Science AK4JE WOS:000338390200015 J Chen, YC; Horng, GB; Huang, CC Chen, Yu-Chi; Horng, Gwoboa; Huang, Chang-Chin Privacy protection in on-line shopping for electronic documents INFORMATION SCIENCES English Article Cryptography; Privacy protection; Blind decoding; RSA; Oracle attack INFORMATION-RETRIEVAL; SIGNATURE SCHEME; SYSTEMS Blind decoding schemes are proposed as tools for protecting customers' privacy in on-line shopping for electronic documents in such a way that the documents' owner has no way of knowing which documents the customers have purchased. However, most of the blind decoding schemes suffer from the oracle problem where an adversary may obtain useful information, such as the signature of a message, by interacting with the documents' owner (the oracle) using tricky requests. In this paper, a secure blind decoding scheme based on RSA scheme is proposed. Its security against one more decoding key and oracle attacks is formally proved. (C) 2014 Elsevier Inc. All rights reserved. [Chen, Yu-Chi; Horng, Gwoboa; Huang, Chang-Chin] Natl Chung Hsing Univ, Dept Comp Sci & Engn, Taichung, Taiwan Horng, GB (reprint author), Natl Chung Hsing Univ, Dept Comp Sci & Engn, Taichung, Taiwan. wycchen@ieee.org; gbhorng@cs.nchu.edu.tw; phd9308@cs.nchu.edu.tw National Science Council of the Republic of China [NSC96-2628-E-005-076-MY3, NSC102-2917-I-005-007] The authors thank the reviewers for their comments that helped improve the paper. We are extremely grateful to an anonymous referee who pointed out a flaw in an earlier version. This research was partially supported by the National Science Council of the Republic of China under contracts NSC96-2628-E-005-076-MY3 and NSC102-2917-I-005-007. Advanced Encryption Standard (AES), 2001, FIPS197 NIST; Al-Fayoumi M., 2005, American Journal of Applied Sciences, V2; Anderson R, 1995, LECT NOTES COMPUT SC, V963, P236; [Anonymous], 1995, FIPS1801 NIST; Bellare M, 2003, J CRYPTOL, V16, P185, DOI 10.1007/s00145-002-0120-1; Boneh D, 2007, LECT NOTES COMPUT SC, V4622, P50; Chaum D., 1990, LECT NOTES COMPUTER, V403, P236; Chaum D., 1983, ADV CRYPTOLOGY CRYPT, V82, P199; Chen CH, 2009, INT J INNOV COMPUT I, V5, P801; Chen YC, 2009, FIFTH INTERNATIONAL CONFERENCE ON INFORMATION ASSURANCE AND SECURITY, VOL 2, PROCEEDINGS, P105, DOI 10.1109/IAS.2009.338; Chor B, 1998, J ACM, V45, P965, DOI 10.1145/293347.293350; Damgard I., 1997, S CRYPT INF SEC SCIS; Data Encryption Standard (DES), 1977, FIPS46 NBS; ELGAMAL T, 1985, IEEE T INFORM THEORY, V31, P469, DOI 10.1109/TIT.1985.1057074; Mambo M., 1996, LECT NOTES COMPUTER, V1163, P321; National Institute of Standards and Technology, 2009, NIST FIPS PUBL, V186-3; Ohta K., 1997, LNCS, V1396, P273; Phong L., 2008, 2008 CRYPT; RIVEST RL, 1978, COMMUN ACM, V21, P120, DOI 10.1145/359340.359342; Sakurai K., 1996, LECT NOTES COMPUTER, V1174, P257; SHANNON CE, 1949, AT&T TECH J, V28, P656; Sion R., 2007, P NETW DISTR SYST SE; Wright D, 2002, INFOR, V40, P71 23 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 1 2014 277 321 326 10.1016/j.ins.2014.02.065 6 Computer Science, Information Systems Computer Science AK4JE WOS:000338390200021 J Borrego, C; Castillo, S; Robles, S Borrego, Carlos; Castillo, Sergio; Robles, Sergi Striving for sensing: Taming your mobile code to share a robot sensor network INFORMATION SCIENCES English Article Intelligent system; Mobile code; Robot network; DIN network; Wireless sensor network RELAY This article presents a general purpose, multi-application mobile node sensor network based on mobile code. This intelligent system can work in delay and disruption tolerant (DIN) scenarios. Mobile nodes host software mobile code with task missions and act as DTN routers following the store-carry-and-forward paradigm. Most similar proposals are unable to simultaneously run different applications, with different routing algorithms, movement models, and information retrieval strategies. The keystone of the approach in this paper is using mobile code at two levels: for the application, and for the definition of the behavior in terms of routing algorithms, movement policies and sensor retrieval preferences. The intelligence of the system lies in its ability to adapt to the environment, dynamically optimizing routing algorithms using local and global information and influencing node movement. Simulations and an implementation of a real scenario have been undertaken to prove the feasibility and usability of the system, and to study its performance. A proposal for a real-world application in the context of refugee camp management is presented. (C) 2014 Elsevier Inc. All rights reserved. [Borrego, Carlos; Castillo, Sergio; Robles, Sergi] UAB, dEIC, Barcelona 08193, Spain Borrego, C (reprint author), UAB, dEIC, Barcelona 08193, Spain. cborrego@deic.uab.cat; sergio.castillo@uab.cat; sergi.robles@uab.cat Ministerio de Ciencia e Innovacion of Spain [FPA2007-66708-C03-02] This work was partly supported by Grant FPA2007-66708-C03-02 from the Ministerio de Ciencia e Innovacion of Spain. Almasaeid H.M., 2007, P 10 ACM S MOD AN SI; Boneh D, 2003, SIAM J COMPUT, V32, P586, DOI 10.1137/S0097539701398521; Borrego C., 2012, J INFORM SCI; Bulusu N., 2001, INT S COMM THEOR APP; Burns B., 2006, INT C ROB AUT; Chen B., 2010, COMPUT-AIDED CIV INF, V25; Chen B., 2004, INT DES ENG TECHN C; Chen B., 2006, SOFTW PRACT EXPT ARC, V36; Dimokas N., 2011, J WIREL NETWORK, V17; Errol L.L., 2007, IEEE T COMPUT, V56, P134; Farrell S., 2006, DELAY DISRUPTION TOL; Giuditta N., 2011, P 1 INT C APPL THEOR, P94; Gupta H., 2011, IEEE COMP SOC C COMP; Hess F., 2002, 9 ANN INT WORKSH SEL; Hoblos G., 2000, IEEE INT C CONT APPS, P467; Hossein Z.P.D., 2012, COMP NETWORK, V56, P34; Jiang J.-R., 2011, INT J AD HOC UBIQUIT, V7; Johnson R.A.I., 2009, J REF STUD, V24, P23; Keranen A., 2009, P SIM 2009 P 2 INT C; Kremer S., 2002, COMP COMMUN, V25; Krishnamachari L., 2002, 22 INT C DISTR COMP; Lee S, 2010, IEEE J SEL AREA COMM, V28, P742, DOI 10.1109/JSAC.2010.100611; Li Q, 2003, J PARALLEL DISTR COM, V63, P75, DOI 10.1016/S0743-7315(02)00033-3; Madria SK, 2002, INFORM SCIENCES, V141, P279; Martinez-de Diosa J.R., 2008, IMAGE VISION COMPUT, V26; Ott J., 2011, IEEE INT C PERV COMP; Pignaton de Freitas E., 2011, THESIS HALMSTAD U; Polastre J., 2004, P SENSYS 2004 P 2 IN; Ranganathan A., 2010, INT S COLL TECHN SYS; Rojas J., 2011, AFRICON, P1; Saleem M, 2011, INFORM SCIENCES, V181, P4597, DOI 10.1016/j.ins.2010.07.005; Sausen PS, 2010, INFORM SCIENCES, V180, P653, DOI 10.1016/j.ins.2009.11.016; Scott J., 2006, 3 IFIP WIR DEM NETW; Scott K., 2007, 5050 RFC; Shahaya Sheela M., 2010, INT J COMP APPL, V9; Shih T.K., 2001, INFORM SCI, V137, P5373; Sugihara R, 2010, IEEE T MOBILE COMPUT, V9, P127, DOI 10.1109/TMC.2009.113; Valenteemail J., 2011, SENS AGR FORE SENS, V11; Venkataraman M, 2011, AD HOC NETW, V9, P1270, DOI 10.1016/j.adhoc.2011.01.007; Vieira M.A.M., 2011, AD HOC NETW; Zhang YC, 2006, IEEE J SEL AREA COMM, V24, P829, DOI 10.1109/JSAC.2005.863855 41 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 1 2014 277 338 357 10.1016/j.ins.2014.02.072 20 Computer Science, Information Systems Computer Science AK4JE WOS:000338390200023 J Zhang, J; Wei, Q; Chen, GQ Zhang, Jin; Wei, Qiang; Chen, Guoqing A heuristic approach for lambda-representative information retrieval from large-scale data INFORMATION SCIENCES English Article Information retrieval; Information representativeness; Web search; Heuristic algorithm QUERY EVALUATION; DATABASES; RANKING; OVERLOAD; SCHEME Retrieving representative information from large-scale data becomes an important research issue nowadays, especially in the context of mobile business/search where the screen size and navigability are limited. This paper focuses on certain aspects of representativeness in database queries and web search, and proposes an approach to extracting a subset of results from original search results in light of high coverage and low redundancy. In the paper, the notion of lambda-represent is introduced, which enables us to describe the lambda-represent relationship between the sets of data objects. Then, the lambda-representative problem is formulated as an extension of the typical set covering problem, which leads to developing a heuristic approach (namely, LamRep) to coping with the problem effectively and efficiently. Notably, LamRep is incorporated with a "vote" mechanism, enhanced with an algorithmic acceleration strategy. Data experiments on benchmark data and a real-world example show that LamRep outperforms the other approaches. (C) 2014 Elsevier Inc. All rights reserved. [Zhang, Jin] Renmin Univ China, Sch Business, Dept Management Sci & Engn, Beijing 100872, Peoples R China; [Zhang, Jin; Wei, Qiang; Chen, Guoqing] Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing 100084, Peoples R China Wei, Q (reprint author), Tsinghua Univ, Sch Econ & Management, Dept Management Sci & Engn, Beijing 100084, Peoples R China. zhangjin@rbs.org.cn; weiq@sem.tsinghua.edu.cn; chengq@sem.tsinghua.edu.cn National Natural Science Foundation of China [71110107027/71372044/71302158]; Tsinghua University Initiative Scientific Research Program [20101081741]; MOE Project of Key Research Institute of Humanities and Social Sciences at Universities of China [12JJD630001] The work was partly supported by the National Natural Science Foundation of China (71110107027/71372044/71302158), Tsinghua University Initiative Scientific Research Program (20101081741), and the MOE Project of Key Research Institute of Humanities and Social Sciences at Universities of China (Grant Number 12JJD630001). Agrawal R., 2009, P 2 ACM INT C WEB SE, P5, DOI 10.1145/1498759.1498766; Allan J., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Antiqueira L, 2009, INFORM SCIENCES, V179, P584, DOI 10.1016/j.ins.2008.10.032; BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861; Boley D, 1999, DECIS SUPPORT SYST, V27, P329, DOI 10.1016/S0167-9236(99)00055-X; Bollegala D, 2012, INFORM SCIENCES, V217, P78, DOI 10.1016/j.ins.2012.06.015; Cai XY, 2011, INFORM SCIENCES, V181, P3816, DOI 10.1016/j.ins.2011.04.052; Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025; Carterette B., 2009, P 18 ACM C INF KNOWL, P1287, DOI 10.1145/1645953.1646116; Chaudhuri S, 2006, ACM T DATABASE SYST, V31, P1134, DOI 10.1145/1166074.1166085; CHEN GQ, 1992, J AM SOC INFORM SCI, V43, P304, DOI 10.1002/(SICI)1097-4571(199205)43:4<304::AID-ASI6>3.0.CO;2-X; Chen H., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148245; Chen YF, 2008, COMPUT HUM BEHAV, V24, P1977, DOI 10.1016/j.chb.2007.08.004; Chuang C.Y., 2012, INF SCI, V230, P56; Chuang W.T., 2000, P 23 ANN INT ACM SIG, P152, DOI 10.1145/345508.345566; Edmunds A, 2000, INT J INFORM MANAGE, V20, P17, DOI 10.1016/S0268-4012(99)00051-1; Farhoomand AF, 2002, COMMUN ACM, V45, P127, DOI 10.1145/570907.570909; Feather J., 2000, INFORM SOC STUDY CON; Feyereisl J, 2012, INFORM SCIENCES, V194, P4, DOI 10.1016/j.ins.2011.04.025; Forsati R., 2012, INF SCI, V220, P269; Gollapudi S., 2009, P 18 INT C WORLD WID, P381, DOI 10.1145/1526709.1526761; Gupta Vishal, 2010, Journal of Emerging Technologies in Web Intelligence, V2, DOI 10.4304/jetwi.2.3.258-268; He JY, 2011, J AM SOC INF SCI TEC, V62, P550, DOI 10.1002/asi.21468; Hochbaum DS, 1998, NAV RES LOG, V45, P615; Homem N, 2010, INFORM SCIENCES, V180, P4958, DOI 10.1016/j.ins.2010.08.024; Hsu WC, 2013, INFORM SCIENCES, V241, P195, DOI 10.1016/j.ins.2013.03.055; Ilyas IF, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1391729.1391730; Jones S, 2013, INFORM SCIENCES, V236, P56, DOI 10.1016/j.ins.2013.02.018; Lewis D., 1996, DYING INFORM; Li Y., 2005, SIGKDD EXPLORATIONS, V7, P91, DOI DOI 10.1145/1117454.1117466; Lian X, 2013, INFORM SCIENCES, V226, P23, DOI 10.1016/j.ins.2012.10.020; Liang WF, 2011, INFORM SCIENCES, V181, P869, DOI 10.1016/j.ins.2010.10.006; Lietard L, 2012, INFORM SCIENCES, V188, P1, DOI 10.1016/j.ins.2011.11.017; Liu B., 1998, WEB DATA MINING EXPL; Ma B.J., 2011, J ENTERPRISE INFORM, V24, P310; Mamoulis N., 2007, ACM T DATABASE SYST, V32; Marian A, 2004, ACM T DATABASE SYST, V29, P319, DOI 10.1145/1005566.1005569; Meyer J.A., 1998, MARKETING INTELLIGEN, V16, P200, DOI DOI 10.1108/02634509810217318; Nomoto T., 2001, P 24 ANN INT ACM SIG, P26, DOI 10.1145/383952.383956; Pan F, 2005, Fifth IEEE International Conference on Data Mining, Proceedings, P338; Pan Y., 2011, J RETAIL; Papadimitriou CH, 1982, COMBINATORIAL OPTIMI; Radev D., 2004, LREC 2004 LISB; Radlinski F., 2008, P 25 INT C MACH LEAR, P784, DOI 10.1145/1390156.1390255; Salton G., 1971, SMART RETRIEVAL SYST; Spink A., 2004, WEB SEARCH PUBLIC SE; Straccia U, 2012, INFORM SCIENCES, V198, P1, DOI 10.1016/j.ins.2012.02.026; TREC, 1999, TEXT RETR C; Vee E, 2008, PROC INT CONF DATA, P228, DOI 10.1109/ICDE.2008.4497431; Yeh JY, 2005, INFORM PROCESS MANAG, V41, P75, DOI 10.1016/j.ipm.2004.04.003; Yu ZW, 2012, INFORM SCIENCES, V203, P83, DOI 10.1016/j.ins.2012.03.012; ZADEH LA, 1971, INFORM SCIENCES, V3, P177, DOI 10.1016/S0020-0255(71)80005-1; Zhai CX, 2006, INFORM PROCESS MANAG, V42, P31, DOI 10.1016/j.ipm.2004.11.003; Zhai C.X., 2003, P 26 ANN INT ACM SIG, P10; Zhang J, 2012, IEEE T NEUR NET LEAR, V23, P928, DOI 10.1109/TNNLS.2012.2193415; Zhang J, 2012, KNOWL-BASED SYST, V32, P91, DOI 10.1016/j.knosys.2011.08.013 56 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. SEP 1 2014 277 825 841 10.1016/j.ins.2014.03.017 17 Computer Science, Information Systems Computer Science AK4JE WOS:000338390200050 J Coscia, JLO; Mateos, C; Crasso, M; Zunino, A Ordiales Coscia, Jose Luis; Mateos, Cristian; Crasso, Marco; Zunino, Alejandro Refactoring code-first Web Services for early avoiding WSDL anti-patterns: Approach and comprehensive assessment SCIENCE OF COMPUTER PROGRAMMING English Article Web services; Code-first; WSDL anti-patterns; Service understandability; Service retrievability METRICS SUITE; DOCUMENTS; DESIGN; ART Previous research of our own [34] has shown that by avoiding certain bad specification practices, or WSDL anti-patterns, contract-first Web Service descriptions expressed in WSDL can be greatly improved in terms of understandability and retrievability. The former means the capability of a human discoverer to effectively reason about a Web Service functionality just by inspecting its associated WSDL description. The latter means correctly retrieving a relevant Web Service by a syntactic service registry upon a meaningful user's query. However, code-first service construction dominates in the industry due to its simplicity. This paper proposes an approach to avoid WSDL anti-patterns in code-first Web Services. We also evaluate the approach in terms of services understandability and retrievability, deeply discuss the experimental results, and delineate some guidelines to help code-first Web Service developers in dealing with the trade-offs that arise between these two dimensions. Certainly, our approach allows services to be more understandable, due to anti-pattern remotion, and retrievable as measured by classical Information Retrieval metrics. (C) 2014 Elsevier B.V. All rights reserved. [Ordiales Coscia, Jose Luis; Mateos, Cristian; Crasso, Marco; Zunino, Alejandro] UNICEN Univ, ISISTAN Res Inst, Tandil, Argentina Mateos, C (reprint author), UNICEN Univ, ISISTAN Res Inst, Campus Univ,B7001BBO, Tandil, Argentina. jlordiales@gmail.com; cmateos@conicet.gov.ar; mcrasso@gmail.com; azunino@conicet.gov.ar ANPCyT [PAE-PICT 2007-02311, PICT-2012-0045] We thank the anonymous reviewers for their comments and suggestions to improve the paper. We also acknowledge the financial support provided by ANPCyT through grants PAE-PICT 2007-02311 and PICT-2012-0045. Agichtein E., 2006, 29 ANN INT ACM SIGIR, P3; Al Dallal J, 2011, IEEE T SOFTWARE ENG, V37, P788, DOI 10.1109/TSE.2010.97; Al-Masri E., 2007, P 16 INT C WORLD WID, P1257, DOI DOI 10.1145/1242572.1242795; Baski D, 2011, IET SOFTW, V5, P320, DOI 10.1049/iet-sen.2010.0089; Beaton J, 2008, 2008 IEEE SYMPOSIUM ON VISUAL LANGUAGES AND HUMAN-CENTRIC COMPUTING, PROCEEDINGS, P193, DOI 10.1109/VLHCC.2008.4639084; Blake MB, 2008, IEEE INTERNET COMPUT, V12, P62, DOI 10.1109/MIC.2008.112; Bloch J., 2006, 21 ACM SIGPLAN S OBJ, P506; CHIDAMBER SR, 1994, IEEE T SOFTWARE ENG, V20, P476, DOI 10.1109/32.295895; Coscia JLO, 2013, INT J WEB GRID SERV, V9, P107; Crasso M., 2011, J DATABASE MANAGE, V22, P103; Crasso M, 2010, IEEE INTERNET COMPUT, V14, P48, DOI 10.1109/MIC.2010.81; Crasso M, 2008, SCI COMPUT PROGRAM, V71, P144, DOI 10.1016/j.scico.2008.02.002; Crasso M, 2011, INFORM SYST FRONT, V13, P407, DOI 10.1007/s10796-009-9221-9; Erl T., 2007, SOA PRINCIPLES SERVI; Feldman J.S.R., 2006, TEXT MINING HDB ADV; Fowler M, 1999, REFACTORING IMPROVIN; Huston B, 2001, J SYST SOFTWARE, V58, P261, DOI 10.1016/S0164-1212(01)00043-7; Korfhage R.R., 1997, INFORM STORAGE RETRI; Mateos C, 2013, IEEE INTERNET COMPUT, V17, P46, DOI 10.1109/MIC.2013.4; Mateos C., 2012, SADIO ELECT J INFORM, V11, P31; McCandless M., 2010, LUCENE ACTION 2 EDIT; Milne D, 2013, ARTIF INTELL, V194, P222, DOI 10.1016/j.artint.2012.06.007; Ordiales J.L. Coscia, 2012, CLEI ELECT J, V16; OW2 Consortium, 2010, EASYWSDL TOOLB; Papazoglou MP, 2008, INT J COOP INF SYST, V17, P223, DOI 10.1142/S0218843008001816; Pasley J, 2006, IEEE INTERNET COMPUT, V10, P72, DOI 10.1109/MIC.2006.45; Ramey J., 2006, CHI 2006 EXT ABSTR H, P45, DOI 10.1145/1125451.1125464; Rodriguez JM, 2010, IFIP ADV INF COMM TE, V341, P139; Rodriguez J.M., 2013, MIGRATING LEGACY APP, P126; Rodriguez JM, 2010, SCI COMPUT PROGRAM, V75, P1001, DOI 10.1016/j.scico.2010.01.002; Rodriguez JM, 2013, J WEB ENG, V12, P131; Rodriguez JM, 2013, IEEE INTERNET COMPUT, V17, P44, DOI 10.1109/MIC.2011.162; SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220; Sneed H.M., 2010, 12 IEEE INT S WEB SY, P111; Spinellis D, 2005, IEEE SOFTWARE, V22, P9, DOI 10.1109/MS.2005.111; The Apache Software Foundation, 2005, JAVA2WSDL BUILD WSDL; Tsui F.F., 2006, ESSENTIALS SOFTWARE 37 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-6423 1872-7964 SCI COMPUT PROGRAM Sci. Comput. Program. SEP 1 2014 89 C 374 407 10.1016/j.scico.2014.03.015 34 Computer Science, Software Engineering Computer Science AJ8CB WOS:000337929200008 J Zhang, Y; Guindon, B; Li, XW Zhang, Ying; Guindon, Bert; Li, Xinwu A Robust Approach for Object-Based Detection and Radiometric Characterization of Cloud Shadow Using Haze Optimized Transformation IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Image processing; information retrieval; terrain mapping SPATIAL-RESOLUTION; IMAGES; REMOVAL; LAND; COMPOSITES Cloud shadows in satellite imagery hinder understanding of ground surface conditions due to reduced illumination and the potential for confusion with illuminated low-reflectance objects such as water bodies. This paper extends the application of the haze optimized transform (HOT) from haze mapping to include object-oriented detection of clouds and cloud shadows. An integrated processing chain encompassing these tasks has been implemented and successfully applied to Landsat Enhanced Thematic Mapper Plus and Multispectral Scanner imagery covering a variety of land covers and landscapes. The results confirm that the HOT-based method for cloud shadow detection is robust and effective. Cloud shadows have been identified and extracted with overall accuracy of about 95.3%. Clear-sky dark pixels (e. g., small lakes) are well separated from cumulus cloud shadow pixels. The spatial distribution of HOT response in a given cloud patch can be used to estimate the extent and variation of incoming visible radiation reduction in its corresponding shadow patch. This information, in turn, has been used to apply a radiometric gain to compensate for the shadowing effect on the land. The HOT response has been tested for radiometric characterization of cloud shadows and subsequent shadow illumination compensation. [Zhang, Ying; Guindon, Bert] Nat Resources Canada, Canada Ctr Remote Sensing, Earth Sci Sect, Ottawa, ON K1A 0Y7, Canada; [Li, Xinwu] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100094, Peoples R China Zhang, Y (reprint author), Nat Resources Canada, Canada Ctr Remote Sensing, Earth Sci Sect, Ottawa, ON K1A 0Y7, Canada. ying.zhang@nrcan.gc.ca; bert.guindon@nrcan.gc.ca; xwli@ceode.ac.cn Government Related Initiatives Program of the Canadian Space Agency; National Natural Science Foundation of China [41120114001] This work was supported in part by the Government Related Initiatives Program of the Canadian Space Agency and in part by the "Major International Cooperation Exchange Project" of the National Natural Science Foundation of China under Grant 41120114001. Arellano P., 2003, THESIS INT I GEOINF, P62; BERENDES T, 1992, IEEE T GEOSCI REMOTE, V30, P430, DOI 10.1109/36.142921; Berk A., 1989, GLTR890122; [陈奋 CHEN Fen], 2006, [计算机工程, Computer Engineering], V32, P185; Guo L. J., 1993, P 9 THEM C GEOL REM, P287; Guo L. J., 1990, INT J REMOTE SENS, V11, P1521; Leblon B, 1996, REMOTE SENS ENVIRON, V58, P322, DOI 10.1016/S0034-4257(96)00079-X; Le Hegarat-Mascle S, 2009, ISPRS J PHOTOGRAMM, V64, P351, DOI 10.1016/j.isprsjprs.2008.12.007; LISSENS G, 2000, P IGARSS, P834; Liu W, 2012, IEEE J-STARS, V5, P1296, DOI 10.1109/JSTARS.2012.2189558; Lu D, 2007, INT J REMOTE SENS, V28, P4027; Luo Y, 2008, REMOTE SENS ENVIRON, V112, P4167, DOI 10.1016/j.rse.2008.06.010; Melgani F, 2006, IEEE T GEOSCI REMOTE, V44, P442, DOI 10.1109/TGRS.2005.861929; Park S, 2013, INT J REMOTE SENS, V34, P1234, DOI 10.1080/01431161.2012.720043; PEARCE WA, 1985, IEEE T GEOSCI REMOTE, V23, P634, DOI 10.1109/TGRS.1985.289381; Pech R. P., 1986, J REMOTE SENS, V7, P389; SHU JSP, 1990, PATTERN RECOGN, V23, P647, DOI 10.1016/0031-3203(90)90040-R; Simpson JJ, 2000, IEEE T GEOSCI REMOTE, V38, P972, DOI 10.1109/36.841979; Simpson JJ, 1998, IEEE T GEOSCI REMOTE, V36, P880, DOI 10.1109/36.673680; Song M., 2002, P ASPRS ACSM ANN C F; Thanh H. N., 2006, P C REM SENS SOC JPN, VX0715A, P73; Tseng DC, 2008, APPL MATH COMPUT, V205, P584, DOI 10.1016/j.amc.2008.05.050; Wang B, 1999, IEICE T INF SYST, VE82D, P453; Zhang Y, 2002, REMOTE SENS ENVIRON, V82, P173, DOI 10.1016/S0034-4257(02)00034-2; Zhang Y, 2003, IEEE T GEOSCI REMOTE, V41, P1082, DOI 10.1109/TGRS.2003.811817; Zhu Z, 2012, REMOTE SENS ENVIRON, V118, P83, DOI 10.1016/j.rse.2011.10.028 26 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2014 52 9 5540 5547 10.1109/TGRS.2013.2290237 8 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8MU WOS:000337171900023 J Izquierdo-Verdiguier, E; Gomez-Chova, L; Bruzzone, L; Camps-Valls, G Izquierdo-Verdiguier, Emma; Gomez-Chova, Luis; Bruzzone, Lorenzo; Camps-Valls, Gustavo Semisupervised Kernel Feature Extraction for Remote Sensing Image Analysis IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Biophysical parameter estimation; classification; clustering; feature extraction; generative kernels; kernel methods; partial least squares (PLS); principal component analysis (PCA); semisupervised learning PARTIAL LEAST-SQUARES; COMPONENT ANALYSIS; COMPOSITE KERNELS; CLASSIFICATION; SPACES This paper presents a novel semisupervised kernel partial least squares (KPLS) algorithm for nonlinear feature extraction to tackle both land-cover classification and biophysical parameter retrieval problems. The proposed method finds projections of the original input data that align with the target variable (labels) and incorporates the wealth of unlabeled information to deal with low-sized or underrepresented data sets. The method relies on combining two kernel functions: the standard radial-basis-function kernel based on labeled information and a generative, i.e., probabilistic, kernel directly learned by clustering the data many times and at different scales across the data manifold. The construction of the kernel is very simple and intuitive: Two samples should belong to the same class if they consistently belong to the same clusters at different scales. The effectiveness of the proposed method is successfully illustrated in multi-and hyperspectral remote sensing image classification and biophysical parameter estimation problems. Accuracy improvements in the range between +5% and 15% over standard principal component analysis (PCA), +4% and 15% over kernel PCA, and +3% and 10% over KPLS are obtained on several images. The average gain in the root-mean-square error of +5% and reductions in bias estimates of +3% are obtained for biophysical parameter retrieval compared to standard PCA feature extraction. [Izquierdo-Verdiguier, Emma; Gomez-Chova, Luis; Camps-Valls, Gustavo] Univ Valencia, IPL, Valencia 46980, Spain; [Bruzzone, Lorenzo] Univ Trento, Dipartimento Ingn & Sci Informaz, I-38100 Trento, Italy Izquierdo-Verdiguier, E (reprint author), Univ Valencia, IPL, Valencia 46980, Spain. emma.izquierdo@uv.es; luis.gomez-chova@uv.es; lorenzo.bruzzone@ing.unitn.it; gustavo.camps@uv.es LIFE-VISION [TIN2012-38102-C03-01]; Generalitat Valenciana [GV/2013/079] This work was supported in part by Project TIN2012-38102-C03-01 (LIFE-VISION) and in part by the Generalitat Valenciana under Project GV/2013/079. Alonso L., 2009, P IGARSS, VII, P202; Arenas-Garcia J, 2013, IEEE SIGNAL PROC MAG, V30, P16, DOI 10.1109/MSP.2013.2250591; Arenas-Garcia J, 2008, IEEE T GEOSCI REMOTE, V46, P2872, DOI 10.1109/TGRS.2008.918765; Bishop C. M., 2006, PATTERN RECOGNITION; Blackwell WJ, 2005, IEEE T GEOSCI REMOTE, V43, P2535, DOI 10.1109/TGRS.2005.855071; Braun ML, 2008, J MACH LEARN RES, V9, P1875; Camps-Valls G, 2010, IEEE GEOSCI REMOTE S, V7, P587, DOI 10.1109/LGRS.2010.2041896; Camps-Valls G, 2006, IEEE GEOSCI REMOTE S, V3, P93, DOI 10.1109/LGRS.2005.857031; Camps-Valls G., 2009, KERNEL METHODS REMOT; Chapelle O., 2002, P ADV NEUR INF PROC, P585; Chapelle O., 2006, SEMISUPERVISED LEARN; Chapelle O., 2003, CLUSTER KERNELS SEMI; Duda RO, 1973, PATTERN CLASSIFICATI; Fasbender D, 2008, IEEE T GEOSCI REMOTE, V46, P1847, DOI 10.1109/TGRS.2008.917131; Gomez-Chova L, 2011, INT GEOSCI REMOTE SE, P3570; Gomez-Chova L, 2012, IEEE GEOSCI REMOTE S, V9, P312, DOI 10.1109/LGRS.2011.2167212; GREEN AA, 1988, IEEE T GEOSCI REMOTE, V26, P65, DOI 10.1109/36.3001; Gu YF, 2008, IEEE GEOSCI REMOTE S, V5, P43, DOI 10.1109/LGRS.2007.907304; Jaakkola T., 1998, P ADV NEUR INF SYST, P487; Jolliffe I., 2010, PRINCIPAL COMPONENT; KRAMER MA, 1991, AICHE J, V37, P233, DOI 10.1002/aic.690370209; Kuo BC, 2009, IEEE T GEOSCI REMOTE, V47, P1139, DOI 10.1109/TGRS.2008.2008308; Nielsen AA, 2011, IEEE T IMAGE PROCESS, V20, P612, DOI 10.1109/TIP.2010.2076296; Rosipal R, 2002, J MACH LEARN RES, V2, P97, DOI 10.1162/15324430260185556; Rosipal R, 2006, LECT NOTES COMPUT SC, V3940, P34; Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323; SAMPSON PD, 1989, NEUROTOXICOL TERATOL, V11, P477, DOI 10.1016/0892-0362(89)90025-1; Serra J., 1988, IMAGE ANAL MATH MORP, V2; Shawe-Taylor J., 2004, KERNEL METHODS PATTE; Tuia D, 2009, IEEE GEOSCI REMOTE S, V6, P224, DOI 10.1109/LGRS.2008.2010275; Tuia D, 2011, IEEE J-STARS, V4, P65, DOI 10.1109/JSTARS.2010.2069085; Tuia D, 2010, IEEE GEOSCI REMOTE S, V7, P88, DOI 10.1109/LGRS.2009.2015341; Wold H, 1966, MULTIVARIATE ANAL, P391 33 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2014 52 9 5567 5578 10.1109/TGRS.2013.2290372 12 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8MU WOS:000337171900026 J Peres, LF; Libonati, R; DaCamara, CC Peres, Leonardo F.; Libonati, Renata; DaCamara, Carlos C. Land-Surface Emissivity Retrieval in MSG-SEVIRI TIR Channels Using MODIS Data IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Emissivity; land-surface temperature (LST); linear regression; MSG/SEVIRI; TERRA/MODerate resolution Imaging Spectroradiometer (MODIS) TEMPERATURE/EMISSIVITY PRODUCTS; BIDIRECTIONAL REFLECTIVITY; DIRECTIONAL EMISSIVITY; TERRESTRIAL MATERIALS; ATMOSPHERIC WINDOW; NATURAL SURFACES; AVHRR CHANNEL-4; VALIDATION; ALGORITHM; ASTER A procedure is presented that allows using information from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor to improve the quality of emissivity maps for the Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (SEVIRI) currently in use as input to a generalized split window (SW) algorithm for land-surface temperature (LST) retrievals in the operational chain of the Satellite Application Facility on Land Surface Analysis (LSA SAF). Information from MODIS is incorporated by means of linear regression models expressing emissivity in SEVIRI thermal-infrared channels as a linear combination of emissivities in MODIS bands. The linear models are applied to the MODIS emissivity product MOD11C3, and a comparison is performed with the operational LSA-SAF product. Special attention is devoted to the semiarid and arid regions of North Africa where emissivity is highly variable. When compared with the new emissivity maps, the LSA-SAF product displays more uniform emissivity values over these regions, leading to higher retrievals for all channels (bias around 0.03) except for IR3.9 (bias from -0.05 to -0.08). The root-mean-square error (RMSE) varies from 0.06 to 0.09 (0.02 to 0.03) for IR3.9 (IR10.8 and IR12.0) and is about 0.06 for IR8.7. The impact on LST is assessed by comparing the retrievals from a SW algorithm using as input the following: 1) the SEVIRI emissivity LSA-SAF product and 2) SEVIRI emissivity maps from MOD11C3. The uncertainty in the LSA-SAF emissivity product results into LST values with bias ranging from -0.4 to -1.0 K and RMSE around 1.6 K. The new emissivity maps based on MODIS data may be an alternative to the standard LSA-SAF emissivity product over semiarid and arid areas, which cover 26% of the land surfaces within the SEVIRI full disk. [Peres, Leonardo F.] Univ Fed Rio de Janeiro, Dept Meteorol, BR-21949900 Rio De Janeiro, RJ, Brazil; [Libonati, Renata] Ctr Previsao Tempo & Estudos Climat, Inst Nacl Pesquisas Espaciais, BR-12630000 Cachoeira Paulista, SP, Brazil; [DaCamara, Carlos C.] Univ Lisbon, Inst Dom Luiz, Ctr Geofis, P-1749016 Lisbon, Portugal Peres, LF (reprint author), Univ Fed Rio de Janeiro, Dept Meteorol, Campus Ilha Fundao Cidade Univ, BR-21949900 Rio De Janeiro, RJ, Brazil. leonardo.peres@igeo.ufrj.br Satellite Application Facility on Land Surface Analysis (LSA SAF) Research performed was partly funded by the Satellite Application Facility on Land Surface Analysis (LSA SAF). The Moderate Resolution Imaging Spectroradiometer products were available through the Land Processes Distributed Active Archive Center, which was established as part of NASA's EOS Data and Information System initiative to process, archive, and distribute land-related data collected by EOS sensors. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectral library was available courtesy of the Jet Propulsion Laboratory, California Institute of Technology, Pasadena. The authors are indebted to W. J. W. Salisbury for answering a number of questions and requests concerning the Johns Hopkins University Spectral Library. The operational LSA-SAF emissivity product was available courtesy of the LSA SAF. [Anonymous], 2003, SAFLANDURD62 LSASAF; [Anonymous], 1999, LRIT HRIT GLOB SPEC; Baldridge AM, 2009, REMOTE SENS ENVIRON, V113, P711, DOI 10.1016/j.rse.2008.11.007; BECKER F, 1990, INT J REMOTE SENS, V11, P369; Belward A. S., 1995, P 21 ANN C REM SENS, P1099; CASELLES V, 1989, REMOTE SENS ENVIRON, V29, P135, DOI 10.1016/0034-4257(89)90022-9; Dash P, 2002, INT J REMOTE SENS, V23, P4511, DOI 10.1080/01431160210146659; Friedl MA, 2002, REMOTE SENS ENVIRON, V83, P287, DOI 10.1016/S0034-4257(02)00078-0; Gillespie A, 1998, IEEE T GEOSCI REMOTE, V36, P1113, DOI 10.1109/36.700995; Hulley GC, 2011, IEEE T GEOSCI REMOTE, V49, P1304, DOI 10.1109/TGRS.2010.2063034; Jacob F, 2004, REMOTE SENS ENVIRON, V90, P137, DOI 10.1016/j.rse.2003.11.015; Jiang GM, 2006, REMOTE SENS ENVIRON, V105, P326, DOI 10.1016/j.rse.2006.07.015; Loveland TR, 2000, INT J REMOTE SENS, V21, P1303, DOI 10.1080/014311600210191; Minnaert M, 1941, ASTROPHYS J, V93, P403, DOI 10.1086/144279; Nerry F, 1998, REMOTE SENS ENVIRON, V66, P298, DOI 10.1016/S0034-4257(98)00066-2; Ogawa K., 2003, GEOPHYS RES LETT, V30, P39; Peres LF, 2008, INT J REMOTE SENS, V29, P7251, DOI 10.1080/01431160802036532; Peres LF, 2005, IEEE T GEOSCI REMOTE, V43, P1834, DOI 10.1109/TGRS.2005.851172; Peres LF, 2004, REMOTE SENS ENVIRON, V91, P377, DOI 10.1016/j.rse.2004.03.011; Petitcolin F, 2002, INT J REMOTE SENS, V23, P3473, DOI 10.1080/01431160110075578; Petitcolin F, 2002, INT J REMOTE SENS, V23, P3443, DOI 10.1080/01431160110075569; PRICE JC, 1984, J GEOPHYS RES-ATMOS, V89, P7231, DOI 10.1029/JD089iD05p07231; SALISBURY JW, 1994, J GEOPHYS RES-SOL EA, V99, P11897, DOI 10.1029/93JB03600; SALISBURY JW, 1994, REMOTE SENS ENVIRON, V47, P345, DOI 10.1016/0034-4257(94)90102-3; SALISBURY JW, 1992, REMOTE SENS ENVIRON, V42, P83, DOI 10.1016/0034-4257(92)90092-X; Snyder WC, 1997, REMOTE SENS ENVIRON, V60, P101, DOI 10.1016/S0034-4257(96)00166-6; Snyder WC, 1998, INT J REMOTE SENS, V19, P2753, DOI 10.1080/014311698214497; SOBRINO JA, 1991, REMOTE SENS ENVIRON, V38, P19, DOI 10.1016/0034-4257(91)90069-I; Sobrino JA, 2000, INT J REMOTE SENS, V21, P353, DOI 10.1080/014311600210876; Sobrino JA, 1999, APPL OPTICS, V38, P3931, DOI 10.1364/AO.38.003931; Takashima Y., 1987, REMOTE SENS ENVIRON, V23, P51; Trigo I. F., 2008, J GEOPHYS RES, V113, DOI D17108-1-D17108-12; Trigo IF, 2011, INT J REMOTE SENS, V32, P2725, DOI 10.1080/01431161003743199; Trigo IF, 2008, IEEE T GEOSCI REMOTE, V46, P307, DOI 10.1109/TGRS.2007.905197; VANDEGRIEND AA, 1993, INT J REMOTE SENS, V14, P1119; Wan ZM, 1997, IEEE T GEOSCI REMOTE, V35, P980; Wan ZM, 2002, REMOTE SENS ENVIRON, V83, P163, DOI 10.1016/S0034-4257(02)00093-7; Wan ZM, 1996, IEEE T GEOSCI REMOTE, V34, P892; Wan ZM, 2008, REMOTE SENS ENVIRON, V112, P59, DOI 10.1016/j.rse.2006.06.026; Wang K, 2007, INT J REMOTE SENS, V28, P2549, DOI 10.1080/01431160600702665; Wilber A. C., 1999, NASATP1999209362 41 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2014 52 9 5587 5600 10.1109/TGRS.2013.2290778 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8MU WOS:000337171900028 J Hou, JL; Huang, CL Hou, Jinliang; Huang, Chunlin Improving Mountainous Snow Cover Fraction Mapping via Artificial Neural Networks Combined With MODIS and Ancillary Topographic Data IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Artificial neural network (ANN); fractional snow cover (FSC); Moderate Resolution Imaging Spectroradiometer (MODIS); mountainous area; remote sensing; snow REFLECTANCE MODEL; GRAIN-SIZE; RETRIEVAL; FORESTS; PRODUCTS; AREA A multilayer feedforward artificial neural network (ANN) is developed for mountainous fractional snow cover (FSC) mapping. This is trained with back propagation to learn the relationship between FSC and Moderate Resolution Imaging Spectroradiometer (MODIS) products (reflectance at seven bands, normalized difference snow index, land surface temperature (LST), and FSC) and elevation. In this paper, images from Landsat Enhanced Thematic Mapper Plus (ETM+) and MODIS products from three periods are chosen to test and validate the proposed method at the Heihe River Basin. Three binary snow cover maps derived from Landsat ETM+ images are used to calculate FSC. Two of these maps are first used to train, calibrate, and test the ANN. The other independent image is used to test the generalization ability of network. Results show that the ANN can easily incorporate auxiliary information to improve the accuracy of snow cover mapping effectively. It is also capable of mapping snow cover fraction in a complicated mountainous area with considerable generalization. For the nonindependent test set, the performance evaluation results show that the improvements of ANN-based methods are apparent compared with MODIS FSC products (higher correlation coefficient, lower root-mean-square error, and more accurate total snow cover area). For the temporal/temporal-spatial independent test set, ANN-based methods perform slightly worse than the nonindependent test set, but the accuracy of the ANN methods still shows some improvement. Elevation, LST, and FSC play more important roles in the training process of the ANN. Overall, experiment 8, which integrated all input information, is approved the best in all test sets. [Hou, Jinliang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China; [Hou, Jinliang; Huang, Chunlin] Chinese Acad Sci, Cold & Arid Reg Environm & Engn Res Inst, Lanzhou 730000, Peoples R China Hou, JL (reprint author), Univ Chinese Acad Sci, Beijing 100049, Peoples R China. hjl_0503@163.com; huangcl@lzb.ac.cn westgis.CAREERI, SCI paper/O-2255-2013 westgis.CAREERI, SCI paper/0000-0001-5298-1494 National Science Foundation of China [41271358]; One Hundred Person Project of the Chinese Academy of Sciences [29Y127D01]; Chinese Academy of Sciences [KZCX2-XB3-15] This work was supported in part by the National Science Foundation of China under Grant 41271358, by the One Hundred Person Project of the Chinese Academy of Sciences under Grant 29Y127D01, and by the Chinese Academy of Sciences Project under Grant KZCX2-XB3-15. [Anonymous], 2009, P 66 E SNOW C NIAG L, P13; [Anonymous], THESIS; Barrett T. P., 2005, NATURE, P303; Barton J., 2000, P 57 E SNOW C SYR NY, P171; Basheer IA, 2000, J MICROBIOL METH, V43, P3, DOI 10.1016/S0167-7012(00)00201-3; Bioucas-Dias JM, 2012, IEEE J-STARS, V5, P354, DOI 10.1109/JSTARS.2012.2194696; Cao M. S., REMOTE SENSING CRYOS, V3, P55; [曹云刚 CAO Yungang], 2006, [冰川冻土, Journal of Glaciology and Geocryology], V28, P562; Chander G, 2009, REMOTE SENS ENVIRON, V113, P893, DOI 10.1016/j.rse.2009.01.007; [陈晓娜 Chen Xiaona], 2010, [资源科学, Resources Science], V32, P1761; Dobreva ID, 2011, REMOTE SENS ENVIRON, V115, P3355, DOI 10.1016/j.rse.2011.07.018; Foppa N, 2004, ANN GLACIOL, V38, P245, DOI 10.3189/172756404781814735; Hall DK, 2002, REMOTE SENS ENVIRON, V83, P181, DOI 10.1016/S0034-4257(02)00095-0; HALL DK, 1995, REMOTE SENS ENVIRON, V54, P127, DOI 10.1016/0034-4257(95)00137-P; Hall DK, 2007, HYDROL PROCESS, V21, P1534, DOI 10.1002/hyp.6715; [郝晓华 HAO Xiaohua], 2008, [冰川冻土, Journal of Glaciology and Geocryology], V30, P132; Hao XH, 2012, SPECTROSC SPECT ANAL, V32, P2753, DOI 10.3964/j.issn.1000-0593(2012)10-2753-06; Kaufman YJ, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014399; Klein AG, 1998, HYDROL PROCESS, V12, P1723, DOI 10.1002/(SICI)1099-1085(199808/09)12:10/11<1723::AID-HYP691>3.0.CO;2-2; Liu Liangming, 2012, Geomatics and Information Science of Wuhan University, V37; Metsamaki S, 2012, REMOTE SENS ENVIRON, V123, P508, DOI 10.1016/j.rse.2012.04.010; Metsamaki SJ, 2005, REMOTE SENS ENVIRON, V95, P77, DOI 10.1016/j.rse.2004.11.013; Painter TH, 2009, REMOTE SENS ENVIRON, V113, P868, DOI 10.1016/j.rse.2009.01.001; Painter TH, 2003, REMOTE SENS ENVIRON, V85, P64, DOI 10.1016/S0034-4257(02)00187-6; Riggs GA, 2006, MODIS SNOW PRODUCTS; Rittger K., 2012, ADV WATER RESOUR, V51, P367; Robinson DA, 2000, PROF GEOGR, V52, P307, DOI 10.1111/0033-0124.00226; Romanov P., 2003, J GEOPHYS RES-ATMOS, V108; Salomonson VV, 2004, REMOTE SENS ENVIRON, V89, P351, DOI 10.1016/j.rse.2003.10.016; Salomonson VV, 2006, IEEE T GEOSCI REMOTE, V44, P1747, DOI 10.1109/TGRS.2006.876029; Tedesco M, 2004, REMOTE SENS ENVIRON, V90, P76, DOI 10.1016/j.rse.2003.12.002; Vermote E. F., 2011, MODIS SURFACE REFLEC; Vikhamar D, 2003, REMOTE SENS ENVIRON, V88, P309, DOI 10.1016/j.rse.2003.06.004; Vikhamar D, 2003, REMOTE SENS ENVIRON, V84, P69, DOI 10.1016/S0034-4257(02)00098-6; Wang J., 1999, REMOTE SENSING TECHN, V14, P29; [延昊 Yan Hao], 2004, [应用气象学报, Journal of Applied Meteorolgical Science], V15, P665; Zhu J, 2012, INT J APPL EARTH OBS, V18, P251, DOI 10.1016/j.jag.2012.02.001 37 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2014 52 9 5601 5611 10.1109/TGRS.2013.2290996 11 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8MU WOS:000337171900029 J Munoz-Sabater, J; de Rosnay, P; Jimenez, C; Isaksen, L; Albergel, C Munoz-Sabater, Joaquin; de Rosnay, Patricia; Jimenez, Carlos; Isaksen, Lars; Albergel, Clement SMOS Brightness Temperature Angular Noise: Characterization, Filtering, and Validation IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Noise filtering; numerical weather predictions (NWPs); Soil Moisture and Ocean Salinity (SMOS) APERTURE SYNTHESIS RADIOMETERS; RECONSTRUCTION; IMPACT; SPACE; MODEL The 2-D interferometric radiometer on board the Soil Moisture and Ocean Salinity (SMOS) satellite has been providing a continuous data set of brightness temperatures, at different viewing geometries, containing information of the Earth's surface microwave emission. This data set is affected by several sources of noise, which are a combination of the noise associated with the radiometer itself and the different views under which a heterogeneous target, such as continental surfaces, is observed. As a result, the SMOS data set is affected by a significant amount of noise. For many applications, such as soil moisture retrieval, reducing noise from the observations while keeping the signal is necessary, and the accuracy of the retrievals depends on the quality of the observed data set. This paper investigates the averaging of SMOS brightness temperatures in angular bins of different sizes as a simple method to reduce noise. All the observations belonging to a single pixel and satellite overpass were fitted to a polynomial regression model, with the objective of characterizing and evaluating the associated noise. Then, the observations were averaged in angular bins of different sizes, and the potential benefit of this process to reduce noise from the data was quantified. It was found that, if a 2 degrees angular bin is used to average the data, the noise is reduced by up to 3 K. Furthermore, this method complements necessary data thinning approaches when a large volume of data is used in data assimilation systems. [Munoz-Sabater, Joaquin; de Rosnay, Patricia; Isaksen, Lars; Albergel, Clement] European Ctr Medium Range Weather Forecasts, Reading RG2 9AX, Berks, England; [Jimenez, Carlos] Observ Paris, Lab Etud Rayonnement & Matien Astrophys, F-75014 Paris, France Munoz-Sabater, J (reprint author), European Ctr Medium Range Weather Forecasts, Shinfield Pk, Reading RG2 9AX, Berks, England. joaquin.munoz@ecmwf.int European Space Agency [4000101703/10/NL/FF/fk] This work was supported by the European Space Agency under Contract 4000101703/10/NL/FF/fk. Anterrieu E., 2008, P MICRORAD, P1; Anterrieu E, 2008, IEEE T GEOSCI REMOTE, V46, P606, DOI 10.1109/TGRS.2007.914799; Balsamo G, 2009, J HYDROMETEOROL, V10, P623, DOI 10.1175/2008JHM1068.1; Camps A, 1997, RADIO SCI, V32, P657, DOI 10.1029/96RS03198; Camps A, 2008, IEEE T GEOSCI REMOTE, V46, P146, DOI 10.1109/TGRS.2007.907603; Camps A., 2005, Radio Science, V40; de Rosnay P, 2013, Q J ROY METEOR SOC, V139, P1199, DOI 10.1002/qj.2023; Drusch M., 2009, GEOPHYS RES LETT, V36; Font J., 2008, REMOTE SENSING EUROP, P223, DOI DOI 10.1080/01431160210163119; Haseler J., 2004, 454 EUR CTR MED RANG; Kerr Y., 2000, P AER C, V8, P119; Kerr YH, 2001, IEEE T GEOSCI REMOTE, V39, P1729, DOI 10.1109/36.942551; Kerr YH, 2010, P IEEE, V98, P666, DOI 10.1109/JPROC.2010.2043032; KERR YH, 1990, IEEE T GEOSCI REMOTE, V28, P384, DOI 10.1109/36.54364; Loveland TR, 2000, INT J REMOTE SENS, V21, P1303, DOI 10.1080/014311600210191; Munoz-Sabater J., 2012, GEOSCI REMOTE SENS L, V9, P252; Munoz-Sabater J., 2011, WP1200 EUR CTR MED 2; Munoz-Sabater J., 2013, ESA LIV PLAN S ED UK; Pinori S., 2008, P MICR RAD REM SENS, P1; Randa J., 2008, 1551 US DEP COMM NAT; Sahr K., 2003, CARTOGR GEOGR INF SC, V30, P121, DOI 10.1559/152304003100011090; Schervish MJ, 1996, AM STAT, V50, P203, DOI 10.2307/2684655; SUESS M, 2004, PRAC IGARSS, P1914; Torres F., 2005, ERROR BUDGET MAP SRD 24 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2014 52 9 5827 5839 10.1109/TGRS.2013.2293200 13 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8MU WOS:000337171900050 J Ouellette, JD; Johnson, JT; Kim, S; van Zyl, JJ; Moghaddam, M; Spencer, MW; Tsang, L; Entekhabi, D Ouellette, Jeffrey D.; Johnson, Joel T.; Kim, Seungbum; van Zyl, Jakob J.; Moghaddam, Mahta; Spencer, Michael W.; Tsang, Leung; Entekhabi, Dara A Simulation Study of Compact Polarimetry for Radar Retrieval of Soil Moisture IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Compact polarimetry; data cube; retrieval algorithm; soil moisture; time series A compact polarimetric (CP) radar system requires fewer measurements than a fully polarimetric (FP) system, thus allowing added flexibility in radar system design. Previous studies have shown the potential of using compact polarimetry for radar remote sensing of soil moisture. This paper extends previous studies by considering a time series data cube retrieval algorithm and measurements in the presence of vegetation. Vegetation information is assumed to be provided by an ancillary data source in the retrieval process. The performance of an algorithm for reconstructing FP information from CP measurements of vegetated soil surfaces is also examined. The results of the study show that only a modest degradation in soil moisture retrieval performance occurs when compact-pol measurements are used in place of full-pol data. [Ouellette, Jeffrey D.; Johnson, Joel T.] Ohio State Univ, Columbus, OH 43210 USA; [Kim, Seungbum; van Zyl, Jakob J.; Spencer, Michael W.] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA; [Moghaddam, Mahta] Univ So Calif, Los Angeles, CA 90089 USA; [Tsang, Leung] Univ Washington, Seattle, WA 98195 USA; [Entekhabi, Dara] MIT, Cambridge, MA 02139 USA Ouellette, JD (reprint author), Ohio State Univ, Columbus, OH 43210 USA. ouellette.18@osu.edu; johnson@ece.osu.edu Ainsworth TL, 2008, REMOTE SENS ENVIRON, V112, P2876, DOI 10.1016/j.rse.2008.02.005; Arii M., 2009, THESIS CALTECH PASAD; Borgeaud M., 1987, J ELECTROMAGNET WAVE, V1, P67; Colliander A., 2010, SMOS 2010 CAL VAL WO; Dubois P. C., 1995, IEEE T GEOSCI REMOTE, V33, P195; Jackson TJ, 2004, REMOTE SENS ENVIRON, V92, P475, DOI 10.1016/j.rse.2003.10.021; Kim SB, 2012, IEEE T GEOSCI REMOTE, V50, P1853, DOI 10.1109/TGRS.2011.2169454; Lee JS, 2009, OPT SCI ENG-CRC, P1; LEE JS, 1994, INT J REMOTE SENS, V15, P2299; Nord ME, 2009, IEEE T GEOSCI REMOTE, V47, P174, DOI 10.1109/TGRS.2008.2000925; PEPLINSKI NR, 1995, IEEE T GEOSCI REMOTE, V33, P803, DOI 10.1109/36.387598; Souyris JC, 2005, IEEE T GEOSCI REMOTE, V43, P634, DOI 10.1109/TGRS.2004.842486; Truong-Loi ML, 2012, CAN J REMOTE SENS, V38, P452; Truong-Loi ML, 2009, IEEE T GEOSCI REMOTE, V47, P3608, DOI 10.1109/TGRS.2009.2031428; van Zyl J. J., 2011, JPL SPACE SCI TECHNO, P312 15 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing SEP 2014 52 9 5966 5973 10.1109/TGRS.2013.2294133 8 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AI8MU WOS:000337171900060 J Wang, HX; Kemao, Q; Liang, RH; Wang, HY; Zhao, M; He, XF Wang, Haixia; Kemao, Qian; Liang, Ronghua; Wang, Huayin; Zhao, Ming; He, Xiaofei Oriented boundary padding for iterative and oriented fringe pattern denoising techniques SIGNAL PROCESSING English Article Optical interferometry; Image denoising; Oriented padding IMAGE SEGMENTATION; INTERPOLATION; DEMODULATION; REDUCTION Optical interferometric techniques offer non-contact, high accuracy and full filed measurement, which are very attractive in various research and application fields. Fringe patterns are the recorded results of these techniques and often require denoising at the pre-processing step to increase the accuracy and robustness of information retrieval. Among various fringe pattern denoising techniques, iterative and oriented denoising techniques based on partial differential equations in the spatial domain are effective and widely used. However, these techniques introduce errors near boundary areas if traditional image padding methods such as zero padding and symmetric padding are used. Due to a large number of iterations needed in these techniques, the error will flood from the boundary into the inner part of the fringe pattern. Since fringe patterns have a flow-like structure represented by fringe orientation, padding along the fringe orientation helps to reduce the error. An oriented padding method is thus proposed for iterative and oriented fringe pattern denoising techniques that require partial derivative estimations. Simulated fringe patterns are tested and quantitative results are given to demonstrate the performance of the proposed method. Experimental results are also given for verification. (C) 2014 Elsevier B.V. All rights reserved. [Wang, Haixia; Liang, Ronghua] Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310014, Zhejiang, Peoples R China; [Wang, Haixia] Nanyang Technol Univ, Multiplatform Game Innovat Ctr, Singapore 639798, Singapore; [Kemao, Qian; Zhao, Ming] Nanyang Technol Univ, Sch Comp Engn, Singapore 639798, Singapore; [Wang, Huayin] Zhejiang Sci Tech Univ, Hangzhou 310018, Zhejiang, Peoples R China; [He, Xiaofei] Zhejiang Univ, Hangzhou 310027, Zhejiang, Peoples R China Liang, RH (reprint author), Zhejiang Univ Technol, Coll Informat Engn, Hangzhou 310014, Zhejiang, Peoples R China. hxwang@zjut.edu.cn; mkmqian@ntu.edu.sg; rhliang@zjut.edu.cn; wanghuayin@gmail.com; mzhao2@e.ntu.edu.sg; xiaofeihe@cad.zju.edu.cn Multi-plAtform Game Innovation Centre (MAGIC) - Singapore National Research Foundation under IDM Futures Funding Initiative; National Natural Science Foundation of China [61379076]; Science and Technology Plan of Zhejiang province [2012C23122, 2013C33055]; Zhejiang Provincial Natural Science Foundation [LY12F02037] This research is partially supported by Multi-plAtform Game Innovation Centre (MAGIC), funded by the Singapore National Research Foundation under its IDM Futures Funding Initiative and administered by the Interactive & Digital Media Programme Office, Media Development Authority, National Natural Science Foundation of China (61379076), Science and Technology Plan of Zhejiang province (2012C23122,2013C33055), and Zhejiang Provincial Natural Science Foundation (LY12F02037). Aghdasi F, 1996, IEEE T IMAGE PROCESS, V5, P611, DOI 10.1109/83.491337; Bertalmio M, 2003, IEEE T IMAGE PROCESS, V12, P882, DOI 10.1109/TIP.2003.815261; BERTALMIO M, 2000, P 27 ANN C COMP GRAP, P417, DOI DOI 10.1145/344779.344972; Efros A. A., 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision, DOI 10.1109/ICCV.1999.790383; Fu M.F., 2002, IEEE INT S CIRC SYST, V3; Gao XB, 2011, IEEE T SYST MAN CY B, V41, P518, DOI 10.1109/TSMCB.2010.2065800; Golub G.H., 1996, MATRIX COMPUTATIONS, P486; Hwang JW, 2004, IEEE SIGNAL PROC LET, V11, P359, DOI 10.1109/LSP.2003.821718; Kaufmann GH, 1996, OPT ENG, V35, P9, DOI 10.1117/1.600874; Kemao Q, 2006, FRINGE, V2005, P217; Li K, 2010, OPT LETT, V35, P3718; Li X, 2001, IEEE T IMAGE PROCESS, V10, P1521, DOI 10.1109/83.951537; Robinson D. W., 1993, INTERFEROGRAM ANAL D; RODDIER C, 1987, APPL OPTICS, V26, P1668, DOI 10.1364/AO.26.001668; Tang C, 2008, OPT LETT, V33, P2179, DOI 10.1364/OL.33.002179; Villa J, 2009, OPT LETT, V34, P1741, DOI 10.1364/OL.34.001741; Wang HX, 2009, OPT LETT, V34, P1141; Wang HX, 2012, APPL OPTICS, V51, P413, DOI 10.1364/AO.51.000413; Wang HX, 2011, APPL OPTICS, V50, P1687, DOI 10.1364/AO.50.001687; Wang HX, 2009, OPT EXPRESS, V17, P15118; Yu QF, 2002, APPL OPTICS, V41, P2650, DOI 10.1364/AO.41.002650; Zhou HY, 2013, COMPUT VIS IMAGE UND, V117, P1004, DOI 10.1016/j.cviu.2012.11.015; Zhou X, 1999, APPL OPTICS, V38, P795, DOI 10.1364/AO.38.000795 23 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0165-1684 1879-2677 SIGNAL PROCESS Signal Process. SEP 2014 102 112 121 10.1016/j.sigpro.2014.03.006 10 Engineering, Electrical & Electronic Engineering AI8WK WOS:000337207500011 J Chakareski, J Chakareski, Jacob Vertex selection via a multi-graph analysis SIGNAL PROCESSING English Article Multi-graph vertex selection; Mean hitting time; Generalised Lagrange Multiplier method; Branch and bound optimisation; Supermodularity and monotonicity; Greedy approximation; Viral marketing; Information retrieval SOCIAL NETWORKS; CENTRALITY We study vertex selection in the presence of multiple graphs associated with a vertex set V representing an online community. First, we formulate a collection of Markov chains on the graph ensemble and describe the characteristics of the associated mean hitting times on V. Then, we design a branch and bound optimisation technique for computing the subset of vertices A that exhibits the smallest hitting time cost across the multi-graph, given a constraint on the volume of A. The complexity of the branch and bound technique limits its application to medium-size graphs. Thus, we formulate a greedy optimisation method for computing a close approximation to the optimal subset at lower complexity, which can be implemented in a decentralised way, for further complexity reduction. We prove that the objective function under consideration is supermodular and monotonic, which guarantees near-optimal solutions for the greedy method. This is verified in our numerical experiments that compare its performance to that of the branch and bound technique, on smaller size graphs. The experiments also examine the hitting-time trade-off across the multi-graph that our optimisation exhibits, governed by the sampling cost factors lambda(j) associated with each graph layer G(j). Relative to conventional community graph centrality methods for vertex selection, we demonstrate a substantially lower sampling (network) cost and higher data dissemination rate, on actual Facebook and Internet topology data. The generality of our framework allows for its application to information retrieval, from a collection of items represented by a multi-graph. Here, we demonstrate higher semantic consistency over state-of-the-art single-graph methods, on the popular DBLP data set. (C) 2014 Elsevier B.V. All rights reserved. Univ Alabama, Dept Elect & Comp Engn, Tuscaloosa, AL 35487 USA Chakareski, J (reprint author), Univ Alabama, Dept Elect & Comp Engn, Tuscaloosa, AL 35487 USA. jakov@jakov.org Anagnostopoulos A., 2008, P 14 ACM SIGKDD INT, P7, DOI 10.1145/1401890.1401897; Atsan E., 2007, P S COMP INF SCI NOV; Bagherjeiran A., 2008, P INT C DAT MIN WORK, P837; Barabasi AL, 1999, SCIENCE, V286, P509, DOI 10.1126/science.286.5439.509; Bhatt R., 2010, P 19 ACM INT C INF K, P1039, DOI 10.1145/1871437.1871569; Bollobas B., 2001, RANDOM GRAPHS; Bonachich P., 1987, SOC NETWORKS, V92, P1170; Borgatti SP, 2005, SOC NETWORKS, V27, P55, DOI 10.1016/j.socnet.2004.11.008; Campbell AT, 2008, IEEE INTERNET COMPUT, V12, P12, DOI 10.1109/MIC.2008.90; Casella G., 2001, DUXBURY ADV SERIES; Cosley D.J.C.D., 2008, P 14 SIGKDD INT C KN, P160; EVERETT H, 1963, OPER RES, V11, P399, DOI 10.1287/opre.11.3.399; FREEMAN LC, 1979, SOC NETWORKS, V1, P215, DOI 10.1016/0378-8733(78)90021-7; Gao W., 2010, ACM COMPUT SURV, V42; Gibs J., 2009, NIELSENWIRE BLOG OCT; Horn R.A., 1993, MATRIX ANAL; Jeh G., 2003, P 12 INT C WORLD WID, P271, DOI DOI 10.1145/775152.775191; Krause A, 2008, J WATER RES PL-ASCE, V134, P516, DOI 10.1061/(ASCE)0733-9496(2008)134:6(516); Krause A, 2011, IEEE T AUTOMAT CONTR, V56, P2390, DOI 10.1109/TAC.2011.2164010; Lauw HW, 2010, IEEE INTERNET COMPUT, V14, P15, DOI 10.1109/MIC.2010.25; Leenders RTAJ, 2002, SOC NETWORKS, V24, P21, DOI 10.1016/S0378-8733(01)00049-1; Lin CY, 2008, IEEE MULTIMEDIA, V15, P78; Loecher M., 2009, P JOINT STAT M AUG; McPherson M, 2001, ANNU REV SOCIOL, V27, P415, DOI 10.1146/annurev.soc.27.1.415; Minoux M., 1977, P 8 IFIP C OPT TECHN, P234; Mislove A., 2010, P 3 INT C WEB SEARCH; Mitra P, 2009, NDT: 2009 FIRST INTERNATIONAL CONFERENCE ON NETWORKED DIGITAL TECHNOLOGIES, P366; NEMHAUSER GL, 1978, MATH PROGRAM, V14, P265, DOI 10.1007/BF01588971; Newman M. E. J., 2010, NETWORKS INTRO; Shepitsen A, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P259; Shi X., 2008, P 19 C HYP HYP JUN, P61, DOI 10.1145/1379092.1379108; Singla P., 2008, P 17 INT C WORLD WID, P655, DOI 10.1145/1367497.1367586; Teevan J., 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111 33 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0165-1684 1879-2677 SIGNAL PROCESS Signal Process. SEP 2014 102 139 150 10.1016/j.sigpro.2014.03.018 12 Engineering, Electrical & Electronic Engineering AI8WK WOS:000337207500014 J Kitsos, I; Magoutis, K; Tzitzikas, Y Kitsos, Ioannis; Magoutis, Kostas; Tzitzikas, Yannis Scalable entity-based summarization of web search results using MapReduce DISTRIBUTED AND PARALLEL DATABASES English Article Text data analytics through summaries and synopses; Interactive data analysis through queryable summaries and indices; Information retrieval and named entity mining; MapReduce; Cloud computing Although Web Search Engines index and provide access to huge amounts of documents, user queries typically return only a linear list of hits. While this is often satisfactory for focalized search, it does not provide an exploration or deeper analysis of the results. One way to achieve advanced exploration facilities exploiting the availability of structured (and semantic) data in Web search, is to enrich it with entity mining over the full contents of the search results. Such services provide the users with an initial overview of the information space, allowing them to gradually restrict it until locating the desired hits, even if they are low ranked. This is especially important in areas of professional search such as medical search, patent search, etc. In this paper we consider a general scenario of providing such services as meta-services (that is, layered over systems that support keywords search) without a-priori indexing of the underlying document collection(s). To make such services feasible for large amounts of data we use the MapReduce distributed computation model on a Cloud infrastructure (Amazon EC2). Specifically, we show how the required computational tasks can be factorized and expressed as MapReduce functions. A key contribution of our work is a thorough evaluation of platform configuration and tuning, an aspect that is often disregarded and inadequately addressed in prior work, but crucial for the efficient utilization of resources. Finally we report experimental results about the achieved speedup in various settings. [Kitsos, Ioannis; Magoutis, Kostas; Tzitzikas, Yannis] FORTH ICS, Inst Comp Sci, Iraklion, Greece; [Kitsos, Ioannis; Magoutis, Kostas; Tzitzikas, Yannis] Univ Crete, Dept Comp Sci, Iraklion, Greece Tzitzikas, Y (reprint author), FORTH ICS, Inst Comp Sci, Iraklion, Greece. kitsos@ics.forth.gr; magoutis@ics.forth.gr; tzitzik@ics.forth.gr EU [317715, 2012-2016]; Amazon Web Services Many thanks to Carlo Allocca and to Pavlos Fafalios for their contributions. We thankfully acknowledge the support of the iMarine (FP7 Research Infrastructures, 2011-2014) and PaaSage (FP7 Integrated Project 317715, 2012-2016) EU projects and of Amazon Web Services through an Education Grant. We also acknowledge the interesting discussions we had in the context of the MUMIA COST action (IC1002, 2010-2014). Allocca C., 2012, LECT NOTES COMPUTER, V7295, P453; Amdahl G.M., 1967, VALIDITY SINGLE PROC, P483; Apache Software Foundation, AP HAD PROJ DEV OP S; Armbrust M, 2010, COMMUN ACM, V53, P50, DOI 10.1145/1721654.1721672; Assel M., 2011, P INT C INT MIN SEM; Bonino Dario, 2010, World Patent Information, V32, DOI 10.1016/j.wpi.2009.05.008; Broder A., 2002, SIGIR Forum, V36; Callaghan G., GEN ARCHITECTURE TEX; Callan J., 2002, INFORM RETRIEVAL SER, V7, P127; Caputo A, 2009, LECT NOTES COMPUT SC, V5722, P241; Carpineto C, 2012, INFORM PROCESS MANAG, V48, P358, DOI 10.1016/j.ipm.2011.08.004; Chen S., 2008, IRPTR0805; Cheng T., 2007, P ACM SIGMOD INT C M, P1144, DOI 10.1145/1247480.1247636; Clinton D., OPENSEARCH COLLECTIO; Cunningham H, 2002, P 40 ANN M ASS COMP; Das D., 2007, ENG TECHNOLOGY, V4, P192; Dean J, 2008, COMMUN ACM, V51, P107, DOI 10.1145/1327452.1327492; Ernde B., 2010, P 18 TEXT RETR C TRE; Fafalios P., 2012, P 5 INF RET FAC C IR; Fafalios P., 2013, P 1 INT WORKSH INT T; Grossman R.L., 2008, CORROSION, P920; Halevy AY, 2001, VLDB J, V10, P270, DOI 10.1007/s007780100054; Herzig D.M., 2012, P WWW NEW YORK, P141; Husain Mohammad Farhan, 2010, 2010 IEEE 3rd International Conference on Cloud Computing (CLOUD 2010), DOI 10.1109/CLOUD.2010.36; Hwang J., IBM PATTERN MODELING; Jaccard P., 1912, NEW PHYTOL, V11, P37, DOI DOI 10.1111/J.1469-8137.1912.TB05611.X; Jestes J., 2011, PVLDB, V5, P109; Jimenez-Ruiz E, 2009, LECT NOTES COMPUT SC, V5554, P173, DOI 10.1007/978-3-642-02121-3_16; Joho H., 2010, P 3 INF INT CONT S I, P13, DOI 10.1145/1840784.1840789; Kaki M, 2005, INTERACT COMPUT, V17, P187, DOI 10.1016/j.intcom.2005.01.001; Kaki M., 2005, P SIGCHI C HUM FACT, P131, DOI 10.1145/1054972.1054991; Kitsos I, 2012, IEEE IFIP NETW OPER, P25; Kohn A., 2008, PROFESSIONAL SEARCH, P195; Kules B, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P313; Kulkarni P., THESIS; Li B., 2011, P ACM SIGMOD INT C M, P985; Marketakis Y., 2009, P INT C MAN EM DIG E; Massie M., 2012, MONITORING GANGLIA; Massie ML, 2004, PARALLEL COMPUT, V30, P817, DOI 10.1016/j.parco.2004.04.001; McCreadie R., 2009, P LSDS IR, P41; McCreadie R, 2012, INFORM PROCESS MANAG, V48, P873, DOI 10.1016/j.ipm.2010.12.003; Mika P, 2008, IEEE INTELL SYST, V23, P82, DOI 10.1109/MIS.2008.94; Nenkova A., 2012, MINING TEXT DATA, P43; Papadimitriou S, 2008, IEEE DATA MINING, P512, DOI 10.1109/ICDM.2008.142; Pavlo A, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P165; Phaal P., SFLOW IS IND STANDAR; Poosala V., 1996, ACM SIGMOD RECORD, V25, P294, DOI 10.1145/235968.233342; Pratt W, 2000, J AM MED INFORM ASSN, V7, P605; Sacco GM, 2009, INFORM RETRIEVAL SER, V25, P1, DOI 10.1007/978-3-642-02359-0; Thakker D., JAVA ANNOTATION PATT; Tom W., 2009, HADOOP DEFINITIVE GU; Tzitzikas Y, 2003, LECT NOTES ARTIF INT, V2782, P78; Tzitzikas Y, 2005, VLDB J, V14, P112, DOI 10.1007/s00778-003-0119-8; Urbani J, 2009, LECT NOTES COMPUT SC, V5823, P634, DOI 10.1007/978-3-642-04930-9_40; van Zwol R, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P879; Venner J., 2009, P HADOOP; White Ryen W., 2006, COMMUN ACM, V49, P36, DOI 10.1145/1121949.1121978; Wilson M.L., 2008, P ACM IEEE CS JOINT, P52, DOI 10.1145/1378889.1378899; Zhai K., 2012, P 21 INT C WORLD WID, P879; Zhang C., 2012, P 15 INT C EXT DAT T, P38 60 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0926-8782 1573-7578 DISTRIB PARALLEL DAT Distrib. Parallel Databases SEP 2014 32 3 SI 405 446 10.1007/s10619-013-7133-7 42 Computer Science, Information Systems; Computer Science, Theory & Methods Computer Science AI6II WOS:000336975900005 J Singh, R; Khare, A Singh, Rajiv; Khare, Ashish Fusion of multimodal medical images using Daubechies complex wavelet transform - A multiresolution approach INFORMATION FUSION English Article Wavelet transform; Multimodal medical image fusion; Medical imaging; Daubechies complex wavelet transform; Fusion metrics; Phase information DECOMPOSITION; SHRINKAGE; MRI; PET Multimodal medical image fusion is an important task for the retrieval of complementary information from medical images. Shift sensitivity, lack of phase information and poor directionality of real valued wavelet transforms motivated us to use complex wavelet transform for fusion. We have used Daubechies complex wavelet transform (DCxWT) for image fusion which is approximately shift invariant and provides phase information. In the present work, we have proposed a new multimodal medical image fusion using DCxWT at multiple levels which is based on multiresolution principle. The proposed method fuses the complex wavelet coefficients of source images using maximum selection rule. Experiments have been performed over three different sets of multimodal medical images. The proposed fusion method is visually and quantitatively compared with wavelet domain (Dual tree complex wavelet transform (DTCWT), Lifting wavelet transform (LWT), Multiwavelet transform (MWT), Stationary wavelet transform (SWT)) and spatial domain (Principal component analysis (PCA), linear and sharp) image fusion methods. The proposed method is further compared with Contourlet transform (CT) and Nonsubsampled contourlet transform (NSCT) based image fusion methods. For comparison of the proposed method, we have used five fusion metrics, namely entropy, edge strength, standard deviation, fusion factor and fusion symmetry. Comparison results prove that performance of the proposed fusion method is better than any of the above existing fusion methods. Robustness of the proposed method is tested against Gaussian, salt & pepper and speckle noise and the plots of fusion metrics for different noise cases established the superiority of the proposed fusion method. (C) 2012 Elsevier B.V. All rights reserved. [Singh, Rajiv; Khare, Ashish] Univ Allahabad, Dept Elect & Commun, Allahabad 211002, Uttar Pradesh, India Khare, A (reprint author), Univ Allahabad, Dept Elect & Commun, Allahabad 211002, Uttar Pradesh, India. jkrajivsingh@gmail.com; ashishkhare@hotmail.com Singh, Rajiv/H-2377-2014 Singh, Rajiv/0000-0003-4022-9945 Department of Science and Technology, New Delhi, India [SR/FTP/ETA-023/2009]; University Grants Commission, New Delhi, India [36-246/2008(SR)] This work was supported in part by the Department of Science and Technology, New Delhi, India, under Grant No. SR/FTP/ETA-023/2009 and the University Grants Commission, New Delhi, India, under Grant No. 36-246/2008(SR). The authors are thankful to Dr. J. Tian for his fruitful discussions during revision of the manuscript. Goshtasby AA, 2007, INFORM FUSION, V8, P114, DOI 10.1016/j.inffus.2006.04.001; Baradarani A, 2012, PATTERN RECOGN, V45, P657, DOI 10.1016/j.patcog.2011.06.013; Burt P., 1983, IEEE T COMMUN, VCOM-9, P532; Burt P. J., 1993, P 4 INT C COMP VIS B, P173, DOI DOI 10.1109/ICCV.1993.378222; Chibani Y, 2006, ISPRS J PHOTOGRAMM, V60, P306, DOI 10.1016/j.isprsjprs.2006.05.001; Clevers JGPW, 2008, IMAGE FUSION: ALGORITHMS AND APPLICATIONS, P67, DOI 10.1016/B978-0-12-372529-5.00004-4; Clonda D, 2004, SIGNAL PROCESS, V84, P1, DOI 10.1016/j.sigpro.2003.06.001; Constantinos S., 2001, 35 AS C SIGN SYST CO, V2, P1263; Daneshvar S, 2010, INFORM FUSION, V11, P114, DOI 10.1016/j.inffus.2009.05.003; Dasarathy BV, 2012, INFORM FUSION, V13, P1, DOI 10.1016/j.inffus.2011.06.003; Daubechies I., 1992, 10 LECT WAVELETS; Giesel F. L., 2009, Experimental Oncology, V31, P106; Granlund G, 1995, SIGNAL PROCESSING CO; Hernandez AI, 1999, IEEE T BIO-MED ENG, V46, P1186, DOI 10.1109/10.790494; Hill P., 2002, P 13 BRIT MACH VIS C; Kannathal N., COMPUTER METHODS PRO, V82; Kenneth E., 2005, 27 ANN INT C ENG MED, P4689; Khare A., 2007, IEEE S COMP INT IM S, P36; Khare A, 2009, INT J WAVELETS MULTI, V7, P587, DOI 10.1142/S0219691309003100; Khare A, 2010, SIGNAL PROCESS, V90, P428, DOI 10.1016/j.sigpro.2009.07.008; Khare A., 2006, WSEAS Transactions on Signal Processing, V2; Khare A, 2005, INT J WAVELETS MULTI, V3, P477, DOI 10.1142/S021969130500097X; Khare A, 2010, IMAGING SCI J, V58, P340, DOI 10.1179/136821910X12750339175826; Kor S, 2004, P ANN INT IEEE EMBS, V26, P1479; Kotwal K, 2013, INFORM FUSION, V14, P5, DOI 10.1016/j.inffus.2011.03.008; LAWTON W, 1993, IEEE T SIGNAL PROCES, V41, P3566, DOI 10.1109/78.258098; Lewis JJ, 2007, INFORM FUSION, V8, P119, DOI 10.1016/j.inffus.2005.09.006; Lewis J.J., 2004, P 7 INT C INF FUS ST, P555; Li H., 1995, IEEE T GRAPHICAL MOD, V57, P235; Li ST, 2011, INFORM FUSION, V12, P74, DOI 10.1016/j.inffus.2010.03.002; LINA JM, 1995, APPL COMPUT HARMON A, V2, P219, DOI 10.1006/acha.1995.1015; Lindseth F., 2001, INT C SERIES, V1230, P254, DOI 10.1016/S0531-5131(01)00052-8; Liu YH, 2010, 2ND IEEE INTERNATIONAL CONFERENCE ON ADVANCED COMPUTER CONTROL (ICACC 2010), VOL. 2, P264; Maintz J B, 1998, Med Image Anal, V2, P1, DOI 10.1016/S1361-8415(01)80026-8; Mallat S., 1989, IEEE PAMI, V11, P674; Naidu VPS, 2008, DEFENCE SCI J, V58, P338; Nakamoto Y, 2009, MOL IMAGING BIOL, V11, P46, DOI 10.1007/s11307-008-0168-x; Nikolov S. G., 2001, COMPUTATIONAL IMAGIN, V19, P213; Petrovic V., 2001, THESIS U MANCHESTER; Piella G., 2002, P ADV CONC INT VIS S, P175; Ramesh C., 2002, P 5 INT C INF FUS, V1, P317, DOI 10.1109/ICIF.2002.1021168; Rockinger O., 1998, SIGNAL PROCESSING SE, V3374, P378; Rojas GM, 2007, COMPUT MED IMAG GRAP, V31, P17, DOI 10.1016/j.compmedimag.2006.10.002; Schoder H., RADIOLOGY, V231; Selesnick IW, 2005, IEEE SIGNAL PROC MAG, V22, P123, DOI 10.1109/MSP.2005.1550194; Shangli C., 2008, 2 INT C BIOINF BIOM, P2523; Singh R., 2009, 7 INT C ADV PATT REC, P232; Skarbnik N., 2010, IMPORTANCE PHASE IMA, P1; Tian J, 2011, OPT COMMUN, V284, P80, DOI 10.1016/j.optcom.2010.08.085; TOET A, 1989, OPT ENG, V28, P789; TOET A, 1989, PATTERN RECOGN LETT, V9, P245, DOI 10.1016/0167-8655(89)90003-2; Xydeas CS, 2000, ELECTRON LETT, V36, P308, DOI 10.1049/el:20000267; Yunfeng Z., 2008, CONTR DEC C CCDC, P2411 53 3 3 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1566-2535 1872-6305 INFORM FUSION Inf. Fusion SEP 2014 19 SI 49 60 10.1016/j.inffus.2012.09.005 12 Computer Science, Artificial Intelligence; Computer Science, Theory & Methods Computer Science AE6UJ WOS:000334133100007 J Qeli, E; Omasits, U; Goetze, S; Stekhoven, DJ; Frey, JE; Basler, K; Wollscheid, B; Brunner, E; Ahrens, CH Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J.; Frey, Juerg E.; Basler, Konrad; Wollscheid, Bernd; Brunner, Erich; Ahrens, Christian H. Improved prediction of peptide detectability for targeted proteomics using a rank-based algorithm and organism-specific data JOURNAL OF PROTEOMICS English Article Targeted proteomics; Peptide detectability; Machine learning; Rank prediction algorithms; Proteotypic peptides; SRM PROTEIN INFERENCE PROBLEM; TANDEM MASS-SPECTROMETRY; ACID INDEX DATABASE; QUANTITATIVE PROTEOMICS; SHOTGUN PROTEOMICS; GLOBAL ANALYSIS; RNA-SEQ; QUANTIFICATION; REPRODUCIBILITY; IDENTIFICATION The in silico prediction of the best-observable "proteotypic" peptides in mass spectrometry-based workflows is a challenging problem. Being able to accurately predict such peptides would enable the informed selection of proteotypic peptides for targeted quantification of previously observed and non-observed proteins for any organism, with a significant impact for clinical proteomics and systems biology studies. Current prediction algorithms rely on physicochemical parameters in combination with positive and negative Mining sets to identify those peptide properties that most profoundly affect their general detectabllity. Here we present PeptideRank, an approach that uses learning to rank algorithm for peptide detectability prediction from shotgun proteomics data, and that eliminates the need to select a negative dataset for the training step. A large number of different peptide properties are used to train ranking models in order to predict a ranking of the best-observable peptides within a protein. Empirical evaluation with rank accuracy metrics showed that PeptideRank complements existing prediction algorithms. Our results indicate that the best performance is achieved when it is trained on organism-specific shotgun proteomics data, and that PeptideRank is most accurate for short to medium-sized and abundant proteins, without any loss in prediction accuracy for the important class of membrane proteins. Biological significance Targeted proteomics approaches have been gaining a lot of momentum and hold immense potential for systems biology studies and clinical proteomics. However, since only very few complete proteomes have been reported to date, for a considerable fraction of a proteome there is no experimental proteomics evidence that would allow to guide the selection of the best-suited proteotypic peptides (PTPs), i.e. peptides that are specific to a given proteoform and that are repeatedly observed in a mass spectrometer. We describe a novel, rank-based approach for the prediction of the best-suited PTPs for targeted proteomics applications. By building on methods developed in the field of information retrieval (e.g. web search engines like Google's PageRank), we circumvent the delicate step of selecting positive and negative training sets and at the same time also more closely reflect the experimentalist's need for selecting e.g. the 5 most promising peptides for targeting a protein of interest. This approach allows to predict PTPs for not yet observed proteins or for organisms without prior experimental proteomics data such as many non-model organisms. (C) 2014 Elsevier B.V. All rights reserved. [Qeli, Ermir; Omasits, Ulrich; Goetze, Sandra; Stekhoven, Daniel J.; Basler, Konrad; Brunner, Erich; Ahrens, Christian H.] Univ Zurich, Inst Mol Life Sci, CH-8057 Zurich, Switzerland; [Omasits, Ulrich; Goetze, Sandra; Wollscheid, Bernd] ETH, Inst Mol Syst Biol, CH-8093 Zurich, Switzerland; [Frey, Juerg E.; Ahrens, Christian H.] Inst Plant Prod Sci, Res Grp Mol Diagnost Genom & Bioinformat, Agroscope, CH-8820 Wadenswil, Switzerland Omasits, U (reprint author), ETH, Inst Mol Syst Biol, Auguste Piccard Hof 1, CH-8093 Zurich, Switzerland. omasitsu@ethz.ch; christian.ahrens@agroscope.admin.ch University of Zurich; Swiss National Science Foundation SNSF [31003A_130723]; Swiss Initiative for Systems Biology SystemsX.ch [IPP 2011/121]; Marie-Heim Vogtlin fellowship [PMPDP3_122836]; European Union [262067-PRIME-XS]; Functional Genomics Center Zurich Ermir Qeli was partially funded by a grant from the University of Zurich's Research Priority Project in Systems Biology/Functional Genomics. CHA acknowledges support from the Swiss National Science Foundation SNSF under grant 31003A_130723, and the Swiss Initiative for Systems Biology SystemsX.ch under grant IPP 2011/121. SG was supported by a Marie-Heim Vogtlin fellowship (PMPDP3_122836) and EB acknowledges support by the 7th Framework Programme of the European Union (Contract no. 262067-PRIME-XS). The Functional Genomics Center Zurich and its managing director Ralph Schlapbach are gratefully acknowledged for access to the mass spectrometry facilities and continued support. Abbatiello SE, 2010, CLIN CHEM, V56, P291, DOI 10.1373/clinchem.2009.138420; Addona TA, 2009, NAT BIOTECHNOL, V27, P633, DOI 10.1038/nbt.1546; Ahrens CH, 2010, NAT REV MOL CELL BIO, V11, P789, DOI 10.1038/nrm2973; Alves P, 2007, PAC S BIOCOMPUT, P409; Anderson L, 2006, MOL CELL PROTEOMICS, V5, P573, DOI 10.1074/mcp.M500331-MCP200; Anderson NL, 2009, MOL CELL PROTEOMICS, V8, P883, DOI 10.1074/mcp.R800015-MCP200; Balgley BM, 2008, ELECTROPHORESIS, V29, P3047, DOI 10.1002/elps.200800050; Barnidge DR, 2003, ANAL CHEM, V75, P445, DOI 10.1021/ac026154+; Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324; Burges CJC, 2005, P INT C MACH LEARN, P86; Cao Z, 2007, ICML2007; Chapelle O, 2010, INFORM RETRIEVAL, V13, P201, DOI 10.1007/s10791-009-9109-9; de Godoy LMF, 2008, NATURE, V455, P1251, DOI 10.1038/nature07341; Deutsch EW, 2008, EMBO REP, V9, P429, DOI 10.1038/embor.2008.56; Domon B, 2010, NAT BIOTECHNOL, V28, P710, DOI 10.1038/nbt.1661; Dost B, 2009, LECT NOTES COMPUT SC, V5541, P356, DOI 10.1007/978-3-642-02008-7_26; Eyers CE, 2011, MOL CELL PROTEOMICS, V10; Freund Y, 2003, J MACHINE LEARNING R, V4, P933, DOI 10.1162/jmlr.2003.4.6.933; Friedman JH, 1999, GREEDY FUNCTION APPR; Fusaro VA, 2009, NAT BIOTECHNOL, V27, P190, DOI 10.1038/nbt.1524; Geng X, 2007, FEATURE SELECTION RA, P407; Gerster S, 2010, P NATL ACAD SCI USA, V107, P12101, DOI 10.1073/pnas.0907654107; Ghaemmaghami S, 2003, NATURE, V425, P737, DOI 10.1038/nature02046; Grobei MA, 2009, GENOME RES, V19, P1786, DOI 10.1101/gr.089060.108; Hens K, 2011, NAT METHODS, V8, P1065, DOI [10.1038/nmeth.1763, 10.1038/NMETH.1763]; Herbrich R, 2000, ADV NEUR IN, P115; Huang T, 2013, COMPUT BIOL CHEM, V43, P46, DOI 10.1016/j.compbiolchem.2012.12.008; Huttenhain R, 2009, CURR OPIN CHEM BIOL, V13, P518, DOI 10.1016/j.cbpa.2009.09.014; Huttenhain R, 2013, MOL CELL PROTEOMICS, V12, P1005, DOI 10.1074/mcp.O112.026617; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Joachims T, 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067; Kawashima S, 2008, NUCLEIC ACIDS RES, V36, pD202, DOI 10.1093/nar/gkm998; Kawashima S, 2000, NUCLEIC ACIDS RES, V28, P374, DOI 10.1093/nar/28.1.374; Keller A, 2002, ANAL CHEM, V74, P5383, DOI 10.1021/ac025747h; Kendall MG, 1938, BIOMETRIKA, V30, P81, DOI 10.2307/2332226; Kristensen DB, 2004, MOL CELL PROTEOMICS, V3, P1023, DOI 10.1074/mcp.T400004-MCP200; Kuster B, 2005, NAT REV MOL CELL BIO, V6, P577, DOI 10.1038/nrm1683; Lange V, 2008, MOL CELL PROTEOMICS, V7, P1489, DOI 10.1074/mcp.M800032-MCP200; Lange V, 2008, MOL SYST BIOL, V4, DOI 10.1038/msb.2008.61; Li YF, 2010, J PROTEOME RES, V9, P6288, DOI 10.1021/pr1005586; Lipton MS, 2002, P NATL ACAD SCI USA, V99, P11049, DOI 10.1073/pnas.172170199; Loevenich SN, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-59; Lu P, 2007, NAT BIOTECHNOL, V25, P117, DOI 10.1038/nbt1270; Mallick P, 2007, NAT BIOTECHNOL, V25, P125, DOI 10.1038/nbt1275; Malmstrom J, 2009, NATURE, V460, P762, DOI 10.1038/nature08184; Metzler D, 2007, INFORM RETRIEVAL, V10, P257, DOI 10.1007/s10791-006-9019-z; Mortazavi A, 2008, NAT METHODS, V5, P621, DOI 10.1038/nmeth.1226; Nesvizhskii AI, 2005, MOL CELL PROTEOMICS, V4, P1419, DOI 10.1074/mcp.R500012-MCP200; Omasits U, 2014, BIOINFORMATICS, V30, P884, DOI 10.1093/bioinformatics/btt607; Omasits U, 2013, GENOME RES, V23, P1916, DOI 10.1101/gr.151035.112; Peng M, 2012, NAT METHODS, V9, P524, DOI 10.1038/nmeth.2031; Picotti P, 2013, NATURE, V494, P266, DOI 10.1038/nature11835; Picotti P, 2010, NAT METHODS, V7, P43, DOI [10.1038/nmeth.1408, 10.1038/NMETH.1408]; Picotti P, 2012, NAT METHODS, V9, P555, DOI [10.1038/nmeth.2015, 10.1038/NMETH.2015]; Qeli E, 2010, NAT BIOTECHNOL, V28, P647, DOI 10.1038/nbt0710-647; Rost H, 2012, MOL CELL PROTEOMICS, V11, P540, DOI 10.1074/mcp.M111.013045; Schmidt A, 2008, MOL CELL PROTEOMICS, V7, P2138, DOI 10.1074/mcp.M700498-MCP200; Spearman C, 1904, AM J PSYCHOL, V15, P72, DOI 10.2307/1412159; Stekhoven DJ, 2014, J PROTEOMICS, V99, P123, DOI 10.1016/j.jprot.2014.01.015; Surinova S, 2013, NAT PROTOC, V8, P1602, DOI 10.1038/nprot.2013.091; Swaney DL, 2010, J PROTEOME RES, V9, P1323, DOI 10.1021/pr900863u; Tabb DL, 2010, J PROTEOME RES, V9, P761, DOI 10.1021/pr9006365; Tang HX, 2006, BIOINFORMATICS, V22, pE481, DOI 10.1093/bioinformatics/btl237; Vizcaino JA, 2010, CURR PROTOC PROTEIN, V60, P2541; Webb-Robertson BJM, 2010, BIOINFORMATICS, V26, P1677, DOI 10.1093/bioinformatics/btq251; Wedge CW, 2007, P 9 ANN C GEN EV COM; Wolf-Yadlin A, 2007, P NATL ACAD SCI USA, V104, P5860, DOI 10.1073/pnas.0608638104; Wu Q, 2007, J INF RETR, V13, P254; Xu J, 2007, P 30 ANN INT ACM SIG, P391, DOI 10.1145/1277741.1277809; Yu H, 2012, HDB NATURAL COMPUTIN, P479 70 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1874-3919 1876-7737 J PROTEOMICS J. Proteomics AUG 28 2014 108 269 283 10.1016/j.jprot.2014.05.011 15 Biochemical Research Methods Biochemistry & Molecular Biology AN1AP WOS:000340315400021 J Yu, Q; Tang, HJ; Tan, KC; Yu, HY Yu, Qiang; Tang, Huajin; Tan, Kay Chen; Yu, Haoyong A brain-inspired spiking neural network model with temporal encoding and learning NEUROCOMPUTING English Article Spiking neural networks (SNNs); Pattern recognition; Cognitive memory; Temporal encoding; Temporal learning AUTOASSOCIATIVE MEMORIES; SYNAPTIC MODIFICATION; PATTERN-RECOGNITION; NEURONS; CLASSIFICATION; INFORMATION; PLASTICITY; PRECISION; CORTEX; TRAINS Neural coding and learning are important components in cognitive memory system, by processing the sensory inputs and distinguishing different patterns to allow for higher level brain functions such as memory storage and retrieval. Benefitting from biological relevance, this paper presents a spiking neural network of leaky integrate-and-fire (LIE) neurons for pattern recognition. A biologically plausible supervised synaptic learning rule is used so that neurons can efficiently make a decision. The whole system contains encoding, learning and readout. Utilizing the temporal coding and learning, networks of spiking neurons can effectively and efficiently perform various classification tasks. It can classify complex patterns of activities stored in a vector, as well as the real-world stimuli. Our approach is also benchmarked on the nonlinearly separable Iris dataset. The proposed approach achieves a good generalization, with a classification accuracy of 99.63% for training and 92.55% for testing. In addition, the trained networks demonstrate that the temporal coding is a viable means for fast neural information processing. (C) 2014 Elsevier B.V. All rights reserved. [Yu, Qiang; Tan, Kay Chen] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore; [Tang, Huajin] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore; [Tang, Huajin] Sichuan Univ, Coll Comp Sci, Chengdu 610065, Peoples R China; [Yu, Haoyong] Natl Univ Singapore, Dept Bioengn, Singapore 117576, Singapore Tang, HJ (reprint author), ASTAR, Inst Infocomm Res, Singapore 138632, Singapore. Agency for Science, Technology, and Research (A*STAR), Singapore under SERC [092 157 0130] This work was supported by Agency for Science, Technology, and Research (A*STAR), Singapore under SERC Grant 092 157 0130. Adeli H., 1995, MACHINE LEARNING NEU; Bair W, 1996, NEURAL COMPUT, V8, P1185, DOI 10.1162/neco.1996.8.6.1185; Bi GQ, 2001, ANNU REV NEUROSCI, V24, P139, DOI 10.1146/annurev.neuro.24.1.139; Bohte SM, 2002, IEEE T NEURAL NETWOR, V13, P426, DOI 10.1109/72.991428; Bohte SM, 2002, NEUROCOMPUTING, V48, P17, DOI 10.1016/S0925-2312(01)00658-0; Borst A, 1999, NAT NEUROSCI, V2, P947, DOI 10.1038/14731; Brader JM, 2007, NEURAL COMPUT, V19, P2881, DOI 10.1162/neco.2007.19.11.2881; Butts DA, 2007, NATURE, V449, P92, DOI [10.1038/nature06105, 10.1038/natureO6105]; Carey MR, 2005, NAT NEUROSCI, V8, P813, DOI 10.1038/nn1470; Delorme A., 1999, NEUROCOMPUTING, V24, P26; Eurich CW, 2000, NEURAL COMPUT, V12, P1519, DOI 10.1162/089976600300015240; Fallahnezhad M, 2011, EXPERT SYST APPL, V38, P386, DOI 10.1016/j.eswa.2010.06.077; Foehring RC, 1999, CAN J EXP PSYCHOL, V53, P45, DOI 10.1037/h0087299; Froemke RC, 2002, NATURE, V416, P433, DOI 10.1038/416433a; Gerstner W, 2002, SPIKING NEURON MODEL; Ghosh-Dastidar S, 2007, INTEGR COMPUT-AID E, V14, P187; Gollisch T, 2008, SCIENCE, V319, P1108, DOI 10.1126/science.1149639; Gutig R, 2006, NAT NEUROSCI, V9, P420, DOI 10.1038/nn1643; Hansel C, 1997, EUR J NEUROSCI, V9, P2309, DOI 10.1111/j.1460-9568.1997.tb01648.x; Hebb Donald O., 2002, ORG BEHAV NEUROPSYCH; HODGKIN AL, 1952, J PHYSIOL-LONDON, V117, P500; HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554; HOPFIELD JJ, 1995, NATURE, V376, P33, DOI 10.1038/376033a0; Ito M, 2000, BRAIN RES, V886, P237, DOI 10.1016/S0006-8993(00)03142-5; Izhikevich EM, 2001, NEURAL NETWORKS, V14, P883, DOI 10.1016/S0893-6080(01)00078-8; Izhikevich EM, 2004, IEEE T NEURAL NETWOR, V15, P1063, DOI 10.1109/TNN.2004.832719; Izhikevich EM, 2003, IEEE T NEURAL NETWOR, V14, P1569, DOI 10.1109/TNN.2003.820440; Jaeger H, 2001, 148 GMD; Johnson C., 2010, IJCNN, P1; Kempter R, 1999, PHYS REV E, V59, P4498, DOI 10.1103/PhysRevE.59.4498; Kempter R, 1999, ADV NEUR IN, V11, P125; KNUDSEN EI, 1994, J NEUROSCI, V14, P3985; Legenstein R, 2005, NEURAL COMPUT, V17, P2337, DOI 10.1162/0899766054796888; Lukosevicius M, 2009, COMPUTER SCI REV, V3, P127, DOI DOI 10.1016/J.COSREV.2009.03.005; Maass W, 1996, NEURAL COMPUT, V8, P1, DOI 10.1162/neco.1996.8.1.1; Maass W, 2002, NEURAL COMPUT, V14, P2531, DOI 10.1162/089976602760407955; Masquelier T, 2009, NEURAL COMPUT, V21, P1259, DOI 10.1162/neco.2008.06-08-804; Mitra S., 2008, VLSI IEEE T BIOMEDIC, V3, P32; Olshausen BA, 1997, VISION RES, V37, P3311, DOI 10.1016/S0042-6989(97)00169-7; Tsukada M, 2005, BIOL CYBERN, V92, P139, DOI 10.1007/s00422-004-0523-1; Panzeri S, 2010, TRENDS NEUROSCI, V33, P111, DOI 10.1016/j.tins.2009.12.001; Ponulak F, 2010, NEURAL COMPUT, V22, P467, DOI 10.1162/neco.2009.11-08-901; RANDIC M, 1993, J NEUROSCI, V13, P5228; Reinagel P, 2000, J NEUROSCI, V20, P5392; Rumelhart DE, 1986, LEARNING INTERNAL RE, P318; Seamans JK, 2004, PROG NEUROBIOL, V74, P1, DOI 10.1016/j.pneurobio.2004.05.006; Song S, 2000, NAT NEUROSCI, V3, P919; Tan KC, 2009, EXPERT SYST APPL, V36, P8616, DOI 10.1016/j.eswa.2008.10.013; Tang H., 2012, IJCNN12, P1; Tang HJ, 2010, NEURAL COMPUT, V22, P1899, DOI 10.1162/neco.2010.07-09-1050; Thach W. T., 1996, Behavioral and Brain Sciences, V19, P411; TREVES A, 1991, NETWORK-COMP NEURAL, V2, P371, DOI 10.1088/0954-898X/2/4/004; TREVES A, 1990, PHYS REV A, V42, P2418, DOI 10.1103/PhysRevA.42.2418; Uzzell VJ, 2004, J NEUROPHYSIOL, V92, P780, DOI 10.1152/jn.01171.2003; Wade JJ, 2010, IEEE T NEURAL NETWOR, V21, P1817, DOI 10.1109/TNN.2010.2074212; Wysoski SG, 2008, NEUROCOMPUTING, V71, P2563, DOI 10.1016/j.neucom.2007.12.038; Yu Q., 2012, 2012 INT JOINT C NEU, P1 57 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing AUG 22 2014 138 3 13 10.1016/j.neucom.2013.06.052 11 Computer Science, Artificial Intelligence Computer Science AI9OK WOS:000337261700002 J Unsworth, N; Brewer, GA; Spillers, GJ Unsworth, Nash; Brewer, Gene A.; Spillers, Gregory J. Strategic search from long-term memory: An examination of semantic and autobiographical recall MEMORY English Article Strategic search WORKING-MEMORY; RETRIEVAL STRATEGIES; NATURAL CATEGORIES; CONTEXT; CAPACITY; STORAGE Searching long-term memory is theoretically driven by both directed (search strategies) and random components. In the current study we conducted four experiments evaluating strategic search in semantic and autobiographical memory. Participants were required to generate either exemplars from the category of animals or the names of their friends for several minutes. Self-reported strategies suggested that participants typically relied on visualization strategies for both tasks and were less likely to rely on ordered strategies (e.g., alphabetic search). When participants were instructed to use particular strategies, the visualization strategy resulted in the highest levels of performance and the most efficient search, whereas ordered strategies resulted in the lowest levels of performance and fairly inefficient search. These results are consistent with the notion that retrieval from long-term memory is driven, in part, by search strategies employed by the individual, and that one particularly efficient strategy is to visualize various situational contexts that one has experienced in the past in order to constrain the search and generate the desired information. [Unsworth, Nash; Spillers, Gregory J.] Univ Oregon, Dept Psychol, Eugene, OR 97403 USA; [Brewer, Gene A.] Arizona State Univ, Dept Psychol, Tempe, AZ 85287 USA Unsworth, N (reprint author), Univ Oregon, Dept Psychol, Eugene, OR 97403 USA. nashu@uoregon.edu BAHRICK HP, 1975, J EXP PSYCHOL GEN, V104, P54, DOI 10.1037//0096-3445.104.1.54; BOND CF, 1987, J PERS SOC PSYCHOL, V52, P1110, DOI 10.1037//0022-3514.52.6.1110; Bousfield WA, 1944, J GEN PSYCHOL, V30, P149; Brewer D. D., 2005, J SOCIAL STRUCTURE, V6; Burgess PW, 1996, MEMORY, V4, P359, DOI 10.1080/096582196388906; Conway MA, 2000, PSYCHOL REV, V107, P261, DOI 10.1037//0033-295X.107.2.261; GRAESSER A, 1978, J EXP PSYCHOL-HUM L, V4, P86, DOI 10.1037//0278-7393.4.1.86; Greenberg DL, 2009, J NEUROSCI, V29, P10900, DOI 10.1523/JNEUROSCI.1202-09.2009; GRONLUND SD, 1986, J EXP PSYCHOL LEARN, V12, P550, DOI 10.1037/0278-7393.12.4.550; GRUENEWALD PJ, 1980, J EXP PSYCHOL-HUM L, V6, P225, DOI 10.1037//0278-7393.6.3.225; HERRMANN DJ, 1981, J MATH PSYCHOL, V24, P139, DOI 10.1016/0022-2496(81)90040-7; Hills TT, 2012, PSYCHOL REV, V119, P431, DOI 10.1037/a0027373; Hills TT, 2012, J EXP PSYCHOL LEARN, V38, P218, DOI 10.1037/a0025161; Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388; Mandler G., 1967, PSYCHOL LEARN MOTIV, V1, P328; Mandler G., 1975, ATTENTION PERFORM, P499; Nickerson R., 1981, NEBR SYM MOTIV, V28, P73; NORMAN DA, 1979, COGNITIVE PSYCHOL, V11, P107, DOI 10.1016/0010-0285(79)90006-9; Polyn SM, 2009, PSYCHOL REV, V116, P129, DOI 10.1037/a0014420; Raaijmakers J. G. W., 1980, PSYCHOL LEARN MOTIV, V14, P207, DOI 10.1016/S0079-7421(08)60162-0; REISER BJ, 1985, COGNITIVE PSYCHOL, V17, P89, DOI 10.1016/0010-0285(85)90005-2; Rosen VM, 1997, J EXP PSYCHOL GEN, V126, P211, DOI 10.1037//0096-3445.126.3.211; Ryan L, 2008, NEUROPSYCHOLOGIA, V46, P2109, DOI 10.1016/j.neuropsychologia.2008.02.030; Schelble JL, 2012, MEM COGNITION, V40, P218, DOI 10.3758/s13421-011-0149-1; Shiffrin R. M., 1970, MODELS HUM MEMORY, P375; SHIFFRIN RM, 1969, PSYCHOL REV, V76, P179, DOI 10.1037/h0027277; Unsworth N, 2013, MEM COGNITION, V41, P242, DOI 10.3758/s13421-012-0261-x; WALKER WH, 1985, COGNITIVE SCI, V9, P261, DOI 10.1207/s15516709cog0902_3; WHITTEN WB, 1981, MEM COGNITION, V9, P566, DOI 10.3758/BF03202351; Williams M. D., 1980, ATTENTION PERFORM, P671; WILLIAMS MD, 1981, COGNITIVE SCI, V5, P87, DOI 10.1207/s15516709cog0502_1; WIXTED JT, 1994, PSYCHON B REV, V1, P89, DOI 10.3758/BF03200763 32 0 0 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND 0965-8211 1464-0686 MEMORY Memory AUG 18 2014 22 6 687 699 10.1080/09658211.2013.812736 13 Psychology, Experimental Psychology AE4KJ WOS:000333951000009 J Ai, QH; Zhang, W; Xie, YM; Huang, WH; Liang, H; Cao, H Ai, Qinghua; Zhang, Wen; Xie, Yanming; Huang, Wenhua; Liang, Hong; Cao, Hui Post-marketing safety monitoring of Shenqifuzheng injection: a solution made of Dangshen (Radix Codonopsis) and Huangqi (Radix Astragali Mongolici) JOURNAL OF TRADITIONAL CHINESE MEDICINE English Review Post-marketing re-evaluation; Safety research; Shenqifuzheng injection OBJECTIVE: To identify the potential risk factors associated with Shenqifuzheng injection (SFI), a solution made of Dangshen (Radix Codonopsis) and Huangqi (Radix Astragali Mongolici), for the timely provision of information to regulatory authorities. METHODS: A comprehensive analysis of the production process, quality standards, pharmacology, post-marketing clinical studies, and safety evaluation using the primary literature of adverse reactions (ADR), case analyses, and systematic reviews, intensive hospital safety monitoring of post-marketing drugs, and data provided by the hospital information system (HIS). RESULTS: Sub-acute toxicity tests suggesting that a dose of 15 mL/kg (concentrated solution) had specific biological effects, whereas a smaller dose engendered no observable effects. Long-term toxicity testing in domestic rabbits showed that after SFI was administered for 90 days, the animals in each dosing group showed.no chronic toxic reactions. Among 20 100 cases observed, the incidence of an ADR was 1.85 parts per thousand. From March to November 2013, of the leading institutions and 22 sub-centers involved in the post-marketing clinical safety intensive hospital monitoring, 21 units completed 8484 cases of monitoring, and reported 23 cases of adverse reactions. No damage to renal function was found using SFI at a dosage and a treatment course larger and longer than that recommended for the adjuvant treatment of tumors. This could reduce the mortality rate of admitted patients based on the analysis of the data provided by the HIS. A total of 16 clinical case reports of adverse reactions related to SFI in 1999-2012 were obtained through literature retrieval. These reports contained information concerning 17 cases, with adverse reaction symptoms including thrombocytopenia, rash, chills, feeling cold, palpitation, dyspnea, edema of a lower extremity, palpebral edema, and superficial vein inflammation, among others. CONCLUSION: This study introduces "get full access" to the flow of information on medicines regarding their ADR incidence rate and characteristics and factors. It supports the safety of SFI for clinical, research, and production uses based on objective, reliable, and scientific information to provide safe medication. (C) 2014 JTCM. All rights reserved. [Ai, Qinghua; Zhang, Wen; Xie, Yanming] China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing 100700, Peoples R China; [Huang, Wenhua; Liang, Hong] Livzon Grp Limin Pharmaceut Factory, Natl Engn Res Ctr Modernizat TCM, Shaoguan 512028, Guangdong, Peoples R China; [Cao, Hui] Natl Engn Res Ctr Modernizat TCM, Zhuhai 519020, Peoples R China Xie, YM (reprint author), China Acad Chinese Med Sci, Inst Basic Res Clin Med, Beijing 100700, Peoples R China. dataming5288@163.com; kovhui-cao@aliyun.com National Science and Technology [2009ZX09502-030] Supported by National Science and Technology Major Projects for "Major New Drugs Innovation and Development": Study on Key Technologies of Post-marketing Evaluation for Chinese Medicine (No. 2009ZX09502-030) Ai Qing-Hua, 2013, Zhongguo Zhong Yao Za Zhi, V38, P3129; Li Yuan-Yuan, 2013, Zhongguo Zhong Yao Za Zhi, V38, P3031; Shen Hao, 2013, Zhongguo Zhong Yao Za Zhi, V38, P3200; Song CS, 1987, STUDY GEN PHARM EXPT; Xie Yanming, 2010, Zhongguo Zhong Yao Za Zhi, V35, P1494; Xie Yanming, 2011, Zhongguo Zhong Yao Za Zhi, V36, P2768; Xu HB, 2010, ZHONG GUO YAO XUE ZA, V45, P1767; Zhang S, 2012, ZHONG GUO ZHI YE YAO, V19, P17 8 0 0 JOURNAL TRADITIONAL CHINESE MED BEIJING 16 NANXIAOJIE, DONGZHIMEN NEI, BEIJING, 100700, PEOPLES R CHINA 0255-2922 1577-7014 J TRADIT CHIN MED J. Tradit. Chin. Med. AUG 15 2014 34 4 498 503 6 Integrative & Complementary Medicine Integrative & Complementary Medicine AN4SH WOS:000340578300017 J Mahesh, C; Prakash, S; Sathiyamoorthy, V; Gairola, RM Mahesh, C.; Prakash, Satya; Sathiyamoorthy, V.; Gairola, R. M. An improved approach for rainfall estimation over Indian summer monsoon region using Kalpana-1 data ADVANCES IN SPACE RESEARCH English Article Infrared; Rainfall estimation; Orography; Cooling index; Elliptical weighting function PRECIPITATION; INDEX In this paper, an improved Kalpana-1 infrared (IR) based rainfall estimation algorithm, specific to Indian summer monsoon region is presented. This algorithm comprises of two parts: (i) development of Kalpana-1 IR based rainfall estimation algorithm with improvement for orographic warm rain underestimation generally suffered by IR based rainfall estimation methods and (ii) cooling index to take care of the growth and decay of clouds and thereby improving the precipitation estimation. In the first part, a power-law based regression relationship between cloud top temperature from Kalpana-1 IR channel and rainfall from Tropical Rainfall Measuring Mission (TRMM) - precipitation radar specific to the Indian region is developed. This algorithm tries to overcome the inherent orographic issues of the IR based rainfall estimation techniques. Over the windward sides of the Western Ghats, Himalayas and Arakan Yoma mountain chains, separate regression coefficients are generated to take care of the orographically produced warm rainfall. Generally global rainfall retrieval methods fail to detect the warm rainfall over these regions. Rain estimated over the orographic region is suitably blended with the rain retrieved over the entire domain comprising of the Indian monsoon region and parts of the Indian Ocean using another regression relationship. While blending, a smoothening function is applied to avoid rainfall artefacts and an elliptical weighting function is introduced for the purpose. In the second part, a cooling index to distinguish rain/no-rain conditions is developed using Kalpana-1 IR data. The cooling index identifies the cloud growing/decaying regions using two consecutive half-hourly IR images of Kalpana-1 by assigning appropriate weights to growing and non-growing clouds. Intercomparison of estimated rainfall from the present algorithm with TRMM-3B42/3B43 precipitation products and Indian Meteorological Department (IMD) gridded rain gauge data are found to be encouraging. The advantages of the present algorithm are that it requires only two IR images as input without depending on other sources of information and simple to implement. The present algorithm performs better than the existing Kalpana-1 IR based rainfall estimation algorithm. Comparison with IMD rainfall data suggests that the underestimation of average rainfall has decreased by 30% for the present algorithm. (C) 2014 COSPAR. Published by Elsevier Ltd. All rights reserved. [Mahesh, C.; Prakash, Satya; Sathiyamoorthy, V.; Gairola, R. M.] Indian Space Res Org, Ctr Space Applicat, EPSA, Atmospher & Ocean Sci Grp, Ahmadabad 380015, Gujarat, India Mahesh, C (reprint author), Indian Space Res Org, Ctr Space Applicat, EPSA, Atmospher & Ocean Sci Grp, Ahmadabad 380015, Gujarat, India. mahinair@gmail.com Nirma University, Ahmedabad The authors would like to thank the Director, Space Applications Centre, Deputy Director, EPSA, SAC and Group Director, AOSG, SAC for the encouragement and support received during the study. The authors also would like to thank Nirma University, Ahmedabad for the support extended during the study. Adler R. F., 1994, REMOTE SENS REV, V11, P125, DOI 10.1080/02757259409532262; ARKIN PA, 1979, MON WEATHER REV, V107, P1382, DOI 10.1175/1520-0493(1979)107<1382:TRBFCO>2.0.CO;2; Ba MB, 2001, J APPL METEOROL, V40, P1500, DOI 10.1175/1520-0450(2001)040<1500:GMRAG>2.0.CO;2; Cressman GP, 1959, MON WEATHER REV, V87, P367, DOI DOI 10.1175/1520-0493(1959)087<0367:A00AS>2.0.C0;2; Houze RA, 2012, REV GEOPHYS, V50, DOI 10.1029/2011RG000365; Huffman GJ, 2007, J HYDROMETEOROL, V8, P38, DOI 10.1175/JHM560.1; Iguchi T., 2000, J APPL METEOROL, V39, P147; Mahesh C, 2011, ATMOS RES, V102, P358, DOI 10.1016/j.atmosres.2011.09.003; Prakash S, 2010, METEOROL ATMOS PHYS, V110, P45, DOI 10.1007/s00703-010-0106-8; Prakash S., 2009, ISPRS ARCH 38 W3 WOR, P227; Rajeevan M, 2006, CURR SCI INDIA, V91, P296; Roe GH, 2005, ANNU REV EARTH PL SC, V33, P645, DOI 10.1146/annurev.earth.33.092203.122541; SCOFIELD RA, 1987, MON WEATHER REV, V115, P1773, DOI 10.1175/1520-0493(1987)115<1773:TNOCPE>2.0.CO;2; Suprit K, 2008, INT J CLIMATOL, V28, P643, DOI 10.1002/joc.1566; Vicente GA, 1998, B AM METEOROL SOC, V79, P1883, DOI 10.1175/1520-0477(1998)079<1883:TOGIRE>2.0.CO;2; Woodley W.L., 1971, WEATHER, V26, P279; Woodley W.L., 1972, NOAA TECH MEMO ERL; Xu LM, 1999, J APPL METEOROL, V38, P569, DOI 10.1175/1520-0450(1999)038<0569:AMITTT>2.0.CO;2 18 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0273-1177 1879-1948 ADV SPACE RES Adv. Space Res. AUG 15 2014 54 4 685 693 10.1016/j.asr.2014.04.019 9 Astronomy & Astrophysics; Geosciences, Multidisciplinary; Meteorology & Atmospheric Sciences Astronomy & Astrophysics; Geology; Meteorology & Atmospheric Sciences AM2PH WOS:000339692800011 J Subramaniyan, S; Heo, S; Patil, S; Li, L; Hoger, H; Pollak, A; Lubec, G Subramaniyan, Saraswathi; Heo, Seok; Patil, Sudarshan; Li, Lin; Hoger, Harald; Pollak, Arnold; Lubec, Gert A hippocampal nicotinic acetylcholine alpha 7-containing receptor complex is linked to memory retrieval in the multiple-T-maze in C57BL/6j mice BEHAVIOURAL BRAIN RESEARCH English Article Spatial memory; Multiple T-Maze; 5-HT1A receptor; 5-HT7 receptor; nACh(alpha 4) receptor; nACh(alpha 7) receptor MASS-SPECTROMETRIC ANALYSIS; WORKING-MEMORY; SPATIAL MEMORY; BARNES MAZE; WATER-MAZE; CD1 MICE; SUBUNITS; 5-HT1A; RAT; PERFORMANCE The link between the cholinergic and serotonergic system in cognitive function is well-documented. There is, however, limited information on spatial memory and this formed the rationale to carry out a study with the aim to show a specific link between nicotinic and serotonergic receptor complexes rather than the corresponding subunits, to spatial memory retrieval in a land maze. A total of 46 mice were used and divided into two groups, trained and untrained (yoked) in the multiple-T-Maze (MTM) and following training during the first four days, probe trials for memory retrieval were performed on days 8, 16 and 30. Six hours following scarification, hippocampi were taken for the analysis of native receptor complex levels using blue-native gels followed by immunoblotting with specific antibodies. 5-HT1A-, 5-HT7-, nAChe(alpha 4)- and nACh-(alpha 7)-containing receptor complexes were observed and were paralleling memory retrievals and receptor complex levels were shown to be significantly different between trained and yoked animals. Only levels of a nicotinic acetylcholine alpha 7 receptor-containing complex at an apparent molecular weight of approximately 480 kDa were shown to be linked to memory retrieval on day 8 but not to retrievals on days 16 and 30 when memory extinction has taken place. Correlation between nACh(alpha 4)-, 5-HT1A- and 5-HT7-containing receptors and latencies on day 16 may point to a probable link in extinction mechanisms. A series of the abovementioned receptor complexes were correlating among each other probably indicating a serotonergic/cholinergic network paralleling spatial memory formation. (C) 2014 Elsevier B.V. All rights reserved. [Subramaniyan, Saraswathi; Heo, Seok; Patil, Sudarshan; Li, Lin; Pollak, Arnold; Lubec, Gert] Univ Vienna, Dept Pediat, A-1090 Vienna, Austria; [Hoger, Harald] Med Univ Vienna, Abt Lab & genetik, A-2325 Himberg, Austria Lubec, G (reprint author), Univ Vienna, Dept Pediat, Austria Wahringer Gurtel 18, A-1090 Vienna, Austria. gert.lubec@meduniwien.ac.a Verein "Unser Kind" This is to gratefully acknowledge the partial financial assistance by the Verein "Unser Kind". Bancroft A, 2000, NEUROPHARMACOLOGY, V39, P2770, DOI 10.1016/S0028-3908(00)00099-X; Bert B, 2005, NEUROBIOL LEARN MEM, V84, P57, DOI 10.1016/j.nlm.2005.03.005; Bettany JH, 2001, PHARMACOL BIOCHEM BE, V70, P467, DOI 10.1016/S0091-3057(01)00643-8; Carli M, 1997, BRAIN RES, V774, P167, DOI 10.1016/S0006-8993(97)81700-3; Castner SA, 2011, BIOL PSYCHIAT, V69, P12, DOI 10.1016/j.biopsych.2010.08.006; Curzon P, 2006, NEUROSCI LETT, V410, P15, DOI 10.1016/j.neulet.2006.09.061; Dringenberg HC, 1999, BRAIN RES, V837, P242, DOI 10.1016/S0006-8993(99)01669-8; Gasbarri A, 2008, BEHAV BRAIN RES, V195, P164, DOI 10.1016/j.bbr.2007.12.020; Guan ZZ, 2000, J NEUROCHEM, V74, P237, DOI 10.1046/j.1471-4159.2000.0740237.x; Heo S, 2010, ELECTROPHORESIS, V31, P1813, DOI 10.1002/elps.200900727; Heo S, 2011, BEHAV BRAIN RES, V216, P389, DOI 10.1016/j.bbr.2010.08.018; Heo S, 2010, ELECTROPHORESIS, V31, P3789, DOI 10.1002/elps.201000374; Jones S, 1999, TRENDS NEUROSCI, V22, P555, DOI 10.1016/S0166-2236(99)01471-X; Kang SU, 2009, NAT PROTOC, V4, P1093, DOI 10.1038/nprot.2009.92; Khiroug SS, 2002, J PHYSIOL-LONDON, V540, P425, DOI 10.1113/jphysiol.2001.013847; King MV, 2008, TRENDS PHARMACOL SCI, V29, P482, DOI 10.1016/j.tips.2008.07.001; Koseki H, 2009, SYNAPSE, V63, P805, DOI 10.1002/syn.20657; Levin ED, 2002, J NEUROBIOL, V53, P633, DOI 10.1002/neu.10151; Levin ED, 2002, NEUROSCIENCE, V109, P757, DOI 10.1016/S0306-4522(01)00538-3; Liu Q, 2012, BMC NEUROSCI, V13, DOI 10.1186/1471-2202-13-155; Maruki K, 2003, NEUROSCI LETT, V351, P95, DOI 10.1016/S0304-3940(03)00950-9; Maviel T, 2003, NEUROSCIENCE, V120, P1049, DOI 10.1016/S0306-4522(03)00403-2; Nott A, 2006, BRAIN RES, V1081, P72, DOI 10.1016/j.brainres.2006.01.052; Ogren SO, 2008, BEHAV BRAIN RES, V195, P54, DOI 10.1016/j.bbr.2008.02.023; OrrUrtreger A, 1997, J NEUROSCI, V17, P9165; Palma E, 1999, J BIOL CHEM, V274, P18335, DOI 10.1074/jbc.274.26.18335; Patil SS, 2012, HIPPOCAMPUS, V22, P1075, DOI 10.1002/hipo.20956; Patil SS, 2009, BEHAV BRAIN RES, V198, P58, DOI 10.1016/j.bbr.2008.10.029; Paylor R, 1998, LEARN MEMORY, V5, P302; Pocivavsek A, 2006, PSYCHOPHARMACOLOGY, V188, P597, DOI 10.1007/s00213-006-0416-1; Renner U, 2012, J CELL SCI, V125, P2486, DOI 10.1242/jcs.101337; Roberts AJ, 2004, EUR J NEUROSCI, V19, P1913, DOI 10.1111/j.1460.9568.2004.03288.x; Roberts AJ, 2012, HIPPOCAMPUS, V22, P762, DOI 10.1002/hipo.20938; Sarnyai Z, 2000, P NATL ACAD SCI USA, V97, P14731, DOI 10.1073/pnas.97.26.14731; Schwenk J, 2009, SCIENCE, V323, P1313, DOI 10.1126/science.1167852; Schwenk J, 2012, NEURON, V74, P621, DOI 10.1016/j.neuron.2012.03.034; Welinder C, 2011, J PROTEOME RES, V10, P1416, DOI 10.1021/pr1011476 37 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0166-4328 1872-7549 BEHAV BRAIN RES Behav. Brain Res. AUG 15 2014 270 137 145 10.1016/j.bbr.2014.05.012 9 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AM2PV WOS:000339694200017 J Torii, M; Li, G; Li, ZW; Oughtred, R; Diella, F; Celen, I; Arighi, CN; Huang, HZ; Vijay-Shanker, K; Wu, CH Torii, Manabu; Li, Gang; Li, Zhiwen; Oughtred, Rose; Diella, Francesca; Celen, Irem; Arighi, Cecilia N.; Huang, Hongzhan; Vijay-Shanker, K.; Wu, Cathy H. RLIMS-P: an online text-mining tool for literature-based extraction of protein phosphorylation information DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION English Article RESOURCE Protein phosphorylation is central to the regulation of most aspects of cell function. Given its importance, it has been the subject of active research as well as the focus of curation in several biological databases. We have developed Rule-based Literature Mining System for protein Phosphorylation (RLIMS-P), an online text-mining tool to help curators identify biomedical research articles relevant to protein phosphorylation. The tool presents information on protein kinases, substrates and phosphorylation sites automatically extracted from the biomedical literature. The utility of the RLIMS-P Web site has been evaluated by curators from Phospho. ELM, PhosphoGRID/BioGrid and Protein Ontology as part of the BioCreative IV user interactive task (IAT). The system achieved F-scores of 0.76, 0.88 and 0.92 for the extraction of kinase, substrate and phosphorylation sites, respectively, and a precision of 0.88 in the retrieval of relevant phosphorylation literature. The system also received highly favorable feedback from the curators in a user survey. Based on the curators' suggestions, the Web site has been enhanced to improve its usability. In the RLIMS-P Web site, phosphorylation information can be retrieved by PubMed IDs or keywords, with an option for selecting targeted species. The result page displays a sortable table with phosphorylation information. The text evidence page displays the abstract with color-coded entity mentions and includes links to UniProtKB entries via normalization, i.e. the linking of entity mentions to database identifiers, facilitated by the GenNorm tool and by the links to the bibliography in UniProt. Log in and editing capabilities are offered to any user interested in contributing to the validation of RLIMS-P results. Retrieved phosphorylation information can also be downloaded in CSV format and the text evidence in the BioC format. RLIMS-P is freely available. [Torii, Manabu; Li, Gang; Li, Zhiwen; Celen, Irem; Arighi, Cecilia N.; Huang, Hongzhan; Wu, Cathy H.] Univ Delaware, Ctr Bioinformat & Computat Biol, Newark, DE 19711 USA; [Torii, Manabu; Li, Gang; Li, Zhiwen; Arighi, Cecilia N.; Huang, Hongzhan; Vijay-Shanker, K.; Wu, Cathy H.] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19711 USA; [Oughtred, Rose] Princeton Univ, Lewis Sigler Inst Integrat Genom, Princeton, NJ 08544 USA; [Diella, Francesca] European Mol Biol Lab, Struct & Computat Biol Unit, D-69117 Heidelberg, Germany; [Arighi, Cecilia N.; Huang, Hongzhan; Wu, Cathy H.] Georgetown Univ, Med Ctr, Dept Biochem Mol & Cellular Biol, Washington, DC 20007 USA Arighi, CN (reprint author), Univ Delaware, Ctr Bioinformat & Computat Biol, Newark, DE 19711 USA. arighi@dbi.udel.edu National Science Foundation [ABI-1062520]; National Library of Medicine of the National Institutes of Health [G08LM010720]; Office of the Director, National Institutes of Health [R01RR024031] National Science Foundation [ABI-1062520 to M. T., G. L., Z.L., C. A., H. H., K. V. and C. W.], the National Library of Medicine of the National Institutes of Health [G08LM010720 to C. A., H. H., K. V. and C. W.] and the Office of the Director, National Institutes of Health [R01RR024031 (PI: Dr Mike Tyers) to R.O.]. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Arighi CN, 2011, METHODS MOL BIOL, V694, P63, DOI 10.1007/978-1-60761-977-2_5; Brembeck FH, 2006, CURR OPIN GENET DEV, V16, P51, DOI 10.1016/j.gde.2005.12.007; Comeau DC, 2013, DATABASE-OXFORD, DOI 10.1093/database/bat064; Dinkel H, 2011, NUCLEIC ACIDS RES, V39, pD261, DOI 10.1093/nar/gkq1104; Hirschman L, 2012, DATABASE-OXFORD, DOI 10.1093/database/bas020; Hornbeck PV, 2012, NUCLEIC ACIDS RES, V40, pD261, DOI 10.1093/nar/gkr1122; Hu ZZ, 2004, COMPUT BIOL CHEM, V28, P409, DOI 10.1016/j.compbiolchem.2004.09.010; Hu ZZ, 2005, BIOINFORMATICS, V21, P2759, DOI 10.1093/bioinformatics/bti390; Kim J.D., 2012, BMC BIOINFORMATICS, V26, P1471; Narayanaswamy M, 2005, BIOINFORMATICS, V21, pI319, DOI 10.1093/bioinformatics/bti1011; Natale DA, 2014, NUCLEIC ACIDS RES, V42, pD415, DOI 10.1093/nar/gkt1173; National Library of Medicine, 2013, FACT SHEET MED SUBJ; Pawson T, 2009, CURR OPIN CELL BIOL, V21, P147, DOI 10.1016/j.ceb.2009.02.005; Peng Y, 2012, COMM COM INF SC, V312, P1; Ross Karen E, 2013, Front Genet, V4, P62, DOI 10.3389/fgene.2013.00062; Ross KE, 2013, DATABASE-OXFORD, DOI 10.1093/database/bat038; Sadowski I, 2013, DATABASE-OXFORD, DOI 10.1093/database/bat026; Schmidt C.J., 2012, IEEE INT C BIOINF BI, P523; Stark C, 2010, DATABASE-OXFORD, DOI 10.1093/database/bap026; Torii M, 2013, P 4 BIOCREATIVE CHAL, V1, P247; Torii M., 2013, ACM C BIOINF COMP BI, P201; Tudor CO, 2012, DATABASE-OXFORD, DOI 10.1093/database/bas044; UniProt Consortium, 2014, Nucleic Acids Res, V42, pD191, DOI 10.1093/nar/gkt1140; Wei C.H., 2011, BMC BIOINFORMATICS, V3, P1471; Wu CH, 2003, NUCLEIC ACIDS RES, V31, P345, DOI 10.1093/nar/gkg040; Zhang Luxi, 2012, Critical Reviews in Oncogenesis, V17, P233 26 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1758-0463 DATABASE-OXFORD Database AUG 13 2014 bau081 10.1093/database/bau081 10 Mathematical & Computational Biology Mathematical & Computational Biology AM9DH WOS:000340179700001 J Wu, ME; Chang, SY; Lu, CJ; Sun, HM Wu, Mu-En; Chang, Shih-Ying; Lu, Chi-Jen; Sun, Hung-Min A communication-efficient private matching scheme in Client-Server model INFORMATION SCIENCES English Article Private matching; Oblivious transfer; Universal hash function; Private information retrieval; Communication complexity SECRET HANDSHAKES; SET INTERSECTION; INFORMATION-RETRIEVAL; SOCIAL NETWORKS; DISJOINTNESS; OPERATIONS; PROTOCOL; DATASETS In a Private Matching (PM) scheme, the client C has a dataset X of m elements, and the server S has a dataset Y of n elements. The client C can learn the set intersection X boolean AND Y without leaking any information to the server S. Previously, the most efficient PM scheme requires communication of complexity (O) over tilde (m + n), which increases linearly with n. This may not be efficient enough in Client-Server models because the server's dataset Y is usually large. In this paper, we propose a PM scheme based on Oblivious Transfer (OT) and universal hash function. Our scheme requires communication of complexity (O) over tilde (m . log(2)n). Thus, our scheme is especially suitable for Client-Server models. We show that our scheme becomes more efficient when log(2)(mn)(1+Delta) = (O) over tilde (n/m) for security parameter Delta > 0. However, utilizing the universal hash function would cause a mismatch issue which affects the accuracy of the PM scheme. In addition, it leaks the server's information. Therefore, we define approximate PM by relaxing the definition of PM; it is proved to be almost as secure as a PM scheme in a Client-Server model with proper configurations. (C) 2014 Elsevier Inc. All rights reserved. [Chang, Shih-Ying; Sun, Hung-Min] Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu 30043, Taiwan; [Lu, Chi-Jen] Acad Sinica, Inst Informat Sci, Taipei, Taiwan; [Wu, Mu-En] Soochow Univ, Dept Math, Taipei, Taiwan Sun, HM (reprint author), Natl Tsing Hua Univ, Dept Comp Sci, Hsinchu 30043, Taiwan. mnasia1@gmail.com; sychang@is.cs.nthu.edu.tw; cjlu@iis.sinica.edu.tw; hmsun@cs.nthu.edu.tw National Science Council, Taiwan [NSC-101-2221-E-007-026-MY3] This work was supported in part by the National Science Council, Taiwan, under Contract NSC-101-2221-E-007-026-MY3. Agrawal R., 2003, P 2003 ACM SIGMOD IN, P86, DOI DOI 10.1145/872757.872771; Ateniese G., 2007, P NETW DISTR SYST SE, P159; Balfanz D, 2003, P IEEE S SECUR PRIV, P180, DOI 10.1109/SECPRI.2003.1199336; Bilogrevic I, 2011, J SYST SOFTWARE, V84, P1910, DOI 10.1016/j.jss.2011.04.027; Bradshaw R. W., 2004, P 11 ACM C COMP COMM, P146, DOI DOI 10.1145/1030083.1030104; Bursztein E, 2011, P IEEE S SECUR PRIV, P506, DOI 10.1109/SP.2011.28; Camenisch J, 2009, LECT NOTES COMPUT SC, V5628, P108, DOI 10.1007/978-3-642-03549-4_7; Castelluccia C, 2004, LECT NOTES COMPUT SC, V3329, P293; Chang YC, 2004, LECT NOTES COMPUT SC, V3108, P50; Chmielewski L., 2008, 3 INT C AV SEC REL, P327; Chu CK, 2008, J UNIVERS COMPUT SCI, V14, P397; Chun JY, 2013, INFORM SCIENCES, V231, P113, DOI 10.1016/j.ins.2011.07.003; Dachman-Soled D, 2009, LECT NOTES COMPUT SC, V5536, P125, DOI 10.1007/978-3-642-01957-9_8; Damgard I, 2001, LECT NOTES COMPUT SC, V1992, P119; De Cristofaro E, 2010, LECT NOTES COMPUT SC, V6052, P143, DOI 10.1007/978-3-642-14577-3_13; Freedman M., 2007, P IPTPS; Freedman MJ, 2004, LECT NOTES COMPUT SC, V3027, P1; Goldreich O., 2000, SECURE MULT IN PRESS; Guha S., 2012, P 9 S NETW SYST DES; Hazay C, 2008, LECT NOTES COMPUT SC, V4948, P155, DOI 10.1007/978-3-540-78524-8_10; Hoepman JH, 2007, LECT NOTES COMPUT SC, V4572, P31, DOI 10.1007/978-3-540-73275-4_3; Hohenberger S, 2006, LECT NOTES COMPUT SC, V4258, P277; Holt J., 2006, RECONCILING CA OBLIV; Holt J., 2003, P 2 ACM WORKSH PRIV, P1, DOI 10.1145/1005140.1005142; Indyk P, 2006, LECT NOTES COMPUT SC, V3876, P245; Jarecki S, 2009, LECT NOTES COMPUT SC, V5444, P577; Jeckmans A., 2011, P 18 ACM C COMP COMM, P793; Kantarcioglu M, 2008, IEEE T INF TECHNOL B, V12, P606, DOI 10.1109/TITB.2007.908465; Kencl L., 2010, COMPUTER SCI REV, V4, P251; Kerschbaum F., 2012, P 27 ANN ACM S APPL, P1451; Kerschbaum F., 2012, P 7 ACM S INF COMP C, P85; Kiayias A, 2005, LECT NOTES COMPUT SC, V3570, P109; Kissner L, 2005, LECT NOTES COMPUT SC, V3621, P241; Li M, 2011, IEEE INFOCOM SER, P2435; Li YP, 2005, COMPUTER SECURITY IN THE 21ST CENTURY, P25, DOI 10.1007/0-387-24006-3_3; Lipmaa H, 2005, LECT NOTES COMPUT SC, V3650, P314; Liu L, 2011, P IEEE INT C COMP SC, V4, P363; Marconi L., 2011, INT J INF SECUR, P1; Mezzour G, 2009, LECT NOTES COMPUT SC, V5888, P189, DOI 10.1007/978-3-642-10433-6_13; Mitzenmacher M., 2005, PROBABILITY COMPUTIN; Narayanan A., 2011, P NDSS; Ostrovsky R, 2007, LECT NOTES COMPUT SC, V4450, P393; Paillier P, 1999, LECT NOTES COMPUT SC, V1592, P223; Pervez Z, 2013, J SUPERCOMPUT, V63, P538, DOI 10.1007/s11227-012-0829-z; Shah D, 2007, INFORM SCIENCES, V177, P5468, DOI 10.1016/j.ins.2007.07.013; Shih DH, 2006, INFORM SCIENCES, V176, P550, DOI 10.1016/j.ins.2004.12.008; Shundong L, 2008, INFORM SCIENCES, V178, P244, DOI 10.1016/j.ins.2007.07.015; Stern JP, 1998, LECT NOTES COMPUT SC, V1514, P357; Vergnaud D, 2006, LECT NOTES COMPUT SC, V3969, P252; Wang Y, 2013, PROCEDIA COMPUT SCI, V17, P781, DOI 10.1016/j.procs.2013.05.100; Wang Y., 2012, P IEEE 11 INT C TRUS, P609; Wu Z., 2007, P 3 INT WORKSH SEC P, P85; Xu S., 2004, P 11 ACM C COMP COMM, P158, DOI 10.1145/1030083.1030105; Yang Y., 2006, P 15 ACM INT C INF K, P852, DOI 10.1145/1183614.1183763; Yao A. C., 1986, P 27 IEEE S FDN COMP, P162, DOI DOI 10.1109/SFCS.1986.25; Ye Q., 2009, LNCS, V5984, P211; Ye Q, 2009, INT J APPL CRYPTOGR, V1, P225; Ye QS, 2008, LECT NOTES COMPUT SC, V5107, P155; Zhan J., 2011, IEEE 3 INT C PRIV SE, P1163; Zheng Y., 2012, COMPUTER SECURITY ES, P361; Zhong S, 2007, INFORM SCIENCES, V177, P490, DOI 10.1016/j.ins.2006.08.010; Zhou L, 2006, LECT NOTES COMPUT SC, V3903, P332 62 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. AUG 10 2014 275 348 359 10.1016/j.ins.2014.01.016 12 Computer Science, Information Systems Computer Science AI8TF WOS:000337199200022 J Manousaki, T; Tsakogiannis, A; Lagnel, J; Sarropoulou, E; Xiang, JZ; Papandroulakis, N; Mylonas, CC; Tsigenopoulos, CS Manousaki, Tereza; Tsakogiannis, Alexandros; Lagnel, Jacques; Sarropoulou, Elena; Xiang, Jenny Z.; Papandroulakis, Nikos; Mylonas, Constantinos C.; Tsigenopoulos, Costas S. The sex-specific transcriptome of the hermaphrodite sparid sharpsnout seabream (Diplodus puntazzo) BMC GENOMICS English Article Sparidae; Sharpsnout seabream; Diplodus puntazzo; RNA-Seq; Transcriptome; Gonads; Brain; Sex differentiation; Hermaphroditism PROTANDROUS BLACK PORGY; DIFFERENTIAL EXPRESSION ANALYSIS; SEXUALLY DIMORPHIC EXPRESSION; HIGH WATER TEMPERATURE; DM-DOMAIN GENE; ACANTHOPAGRUS-SCHLEGELI; RNA-SEQ; MOLECULAR-CLONING; DESERT-HEDGEHOG; OVARIAN DEVELOPMENT Background: Teleosts are characterized by a remarkable breadth of sexual mechanisms including various forms of hermaphroditism. Sparidae is a fish family exhibiting gonochorism or hermaphroditism even in closely related species. The sparid Diplodus puntazzo (sharpsnout seabream), exhibits rudimentary hermaphroditism characterized by intersexual immature gonads but single-sex mature ones. Apart from the intriguing reproductive biology, it is economically important with a continuously growing aquaculture in the Mediterranean Sea, but limited available genetic resources. Our aim was to characterize the expressed transcriptome of gonads and brains through RNA-Sequencing and explore the properties of genes that exhibit sex-biased expression profiles. Results: Through RNA-Sequencing we obtained an assembled transcriptome of 82,331 loci. The expression analysis uncovered remarkable differences between male and female gonads, while male and female brains were almost identical. Focused search for known targets of sex determination and differentiation in vertebrates built the sex-specific expression profile of sharpsnout seabream. Finally, a thorough genetic marker discovery pipeline led to the retrieval of 85,189 SNPs and 29,076 microsatellites enriching the available genetic markers for this species. Conclusions: We obtained a nearly complete source of transcriptomic sequence as well as marker information for sharpsnout seabream, laying the ground for understanding the complex process of sex differentiation of this economically valuable species. The genes involved include known candidates from other vertebrate species, suggesting a conservation of the toolkit between gonochorists and hermaphrodites. [Manousaki, Tereza; Tsakogiannis, Alexandros; Lagnel, Jacques; Sarropoulou, Elena; Papandroulakis, Nikos; Mylonas, Constantinos C.; Tsigenopoulos, Costas S.] Hellen Ctr Marine Res, Inst Marine Biol Biotechnol & Aquaculture IMBBC, Iraklion, Greece; [Xiang, Jenny Z.] Weill Cornell Med Coll, Genom Resources Core Facil, New York, NY USA Tsigenopoulos, CS (reprint author), Hellen Ctr Marine Res, Inst Marine Biol Biotechnol & Aquaculture IMBBC, Iraklion, Greece. tsigeno@hcmr.gr Ministry of Education and Religious Affairs [36]; EU; Hellenic Republic Financial support for this study has been provided by the Ministry of Education and Religious Affairs, under the Call "ARISTEIA I" of the National Strategic Reference Framework 2007-2013 (SPARCOMP, #36), co-funded by the EU and the Hellenic Republic through the European Social Fund. We would like to thank V. Terzoglou and E. Kaitetzidou for help in RNA extractions, Irini Sigelaki for help in sampling and Dr. Hooman Moghadam, Dr. Gianpaolo Zampicinini, Dr. Jon B. Kristoffersen and Ji Hyoun Kang for valuable discussions. Finally, we would like to thank three anonymous reviewers for their valuable comments on the manuscript. ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999; Anders S, 2010, GENOME BIOL, V11, DOI 10.1186/gb-2010-11-10-r106; [Anonymous], 2001, ANIM BEHAV, V61, P271; Atz JW, 1964, INTERSEXUALITY VERTE, P145; Babraham Bioinformatics, FASTQC QUAL CONTR TO; Barnett DW, 2011, BIOINFORMATICS, V27, P1691, DOI 10.1093/bioinformatics/btr174; Benayoun BA, 2009, ADV EXP MED BIOL, V665, P207; Berbejillo J, 2012, MOL REPROD DEV, V79, P504, DOI 10.1002/mrd.22053; Bitgood MJ, 1996, CURR BIOL, V6, P298, DOI 10.1016/S0960-9822(02)00480-3; Bohne A, 2013, MOL BIOL EVOL, V30, P2268, DOI 10.1093/molbev/mst124; Bradley KM, 2011, G3-GENES GENOM GENET, V1, P3, DOI 10.1534/g3.111.000190; Bull JJ, 1983, EVOLUTION SEX DETERM; BUXTON CD, 1990, ENVIRON BIOL FISH, V28, P113, DOI 10.1007/BF00751031; Chassot AA, 2012, DEVELOPMENT, V139, P4461, DOI 10.1242/dev.078972; Clark AM, 2000, BIOL REPROD, V63, P1825, DOI 10.1095/biolreprod63.6.1825; Conesa A, 2005, BIOINFORMATICS, V21, P3674, DOI 10.1093/bioinformatics/bti610; Coveney D, 2007, GENE EXPR PATTERNS, V7, P82, DOI 10.1016/j.modgep.2006.05.012; Danecek P, 2011, BIOINFORMATICS, V27, P2156, DOI 10.1093/bioinformatics/btr330; Diaz Cerio O, 2012, ENVIRON SCI TECHNOL, V46, P7763; Erisman BE, 2013, INTEGR COMP BIOL, V53, P736, DOI 10.1093/icb/ict077; Ewen K, 2009, MOL CELL PROTEOMICS, V8, P2624, DOI 10.1074/mcp.M900108-MCP200; Flicek P, 2013, NUCLEIC ACIDS RES, V41, pD48, DOI 10.1093/nar/gks1236; Forconi M, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0056006; Friedrich U, 2011, HUM MOL GENET, V20, P1132, DOI 10.1093/hmg/ddq557; Garcia-Reyero N, 2009, BMC GENOMICS, V10, DOI 10.1186/1471-2164-10-308; Wu GC, 2012, BIOL REPROD, V86, DOI 10.1095/biolreprod.111.095695; Gholami K, 2013, INT J MED SCI, V10, P1121, DOI 10.7150/ijms.5918; Godwin J, 2010, FRONT NEUROENDOCRIN, V31, P203, DOI 10.1016/j.yfrne.2010.02.002; Grabherr MG, 2011, NAT BIOTECHNOL, V29, P644, DOI 10.1038/nbt.1883; Graham P, 2003, BIOESSAYS, V25, P1, DOI 10.1002/bies.10207; Guiguen Y, 2010, GEN COMP ENDOCR, V165, P352, DOI 10.1016/j.ygcen.2009.03.002; Haas BJ, 2013, NAT PROTOC, V8, P1494, DOI 10.1038/nprot.2013.084; Hattori RS, 2012, P NATL ACAD SCI USA, V109, P2955, DOI 10.1073/pnas.1018392109; He CL, 2003, CYTOGENET GENOME RES, V101, P309, DOI 10.1159/000074354; Herpin A, 2013, MOL BIOL EVOL, V30, P2328, DOI 10.1093/molbev/mst130; Herpin A, 2011, FEBS J, V278, P1010, DOI 10.1111/j.1742-4658.2011.08030.x; Hiraki T, 2012, P ROY SOC B-BIOL SCI, V279, P5014, DOI 10.1098/rspb.2012.2011; Ikeda Y, 2012, PLOS ONE, V7, p115f; Jiang WB, 2011, COMP BIOCHEM PHYS B, V160, P187, DOI 10.1016/j.cbpb.2011.08.005; Kamiya T, 2012, PLOS GENET, V8, DOI 10.1371/journal.pgen.1002798; Karube M, 2007, J EXP ZOOL PART A, V307A, P625, DOI 10.1002/jez.416; Kitano T, 1999, J MOL ENDOCRINOL, V23, P167, DOI 10.1677/jme.0.0230167; Klimogianni A., 2011, Journal of Fisheries and Aquatic Science, V6, P62, DOI 10.3923/jfas.2011.62.73; Kobayshi Yasuhisa, 2010, Biology of Sex Differences, V1, DOI 10.1186/2042-6410-1-3; Kong YW, 2014, CHEM BIOL, V21, P488, DOI 10.1016/j.chembiol.2014.02.013; KOOPMAN P, 1991, NATURE, V351, P117, DOI 10.1038/351117a0; Lagnel J, 2009, BIOINFORMATICS, V25, P824, DOI 10.1093/bioinformatics/btp067; Langmead B, 2009, GENOME BIOL, V10, DOI 10.1186/gb-2009-10-3-r25; Le Page Y, 2010, EUR J NEUROSCI, V32, P2105, DOI 10.1111/j.1460-9568.2010.07519.x; Li B, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-323; Li H, 2009, BIOINFORMATICS, V25, P2078, DOI 10.1093/bioinformatics/btp352; Li L, 2013, MOL CELL ENDOCRINOL, V382, P915; Liu ZH, 2007, J ENDOCRINOL, V194, P223, DOI 10.1677/JOE-07-0135; Loukovitis D, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0016599; Loukovitis D, 2012, ANIM GENET, V43, P753, DOI 10.1111/j.1365-2052.2012.02346.x; Luo Ruibang, 2012, Gigascience, V1, P18, DOI 10.1186/2047-217X-1-18; Marin I, 1998, SCIENCE, V281, P1990, DOI 10.1126/science.281.5385.1990; Martinez P, 2009, GENETICS, V183, P1443, DOI 10.1534/genetics.109.107979; Martins RS, 2007, REPROD BIOL ENDOCRIN, V5, P19; Matson CK, 2012, NAT REV GENET, V13, P163, DOI 10.1038/nrg3161; Matsuda M, 2002, NATURE, V417, P559, DOI 10.1038/nature751; Mayer C, PHOBOS TANDEM REPEAT; Menke Douglas B., 2002, Gene Expression Patterns, V2, P359, DOI 10.1016/S1567-133X(02)00022-4; Micale V, 1996, AQUACULTURE, V140, P281, DOI 10.1016/0044-8486(95)01179-X; Mylonas C. C., 2011, Sparidae: Biology and aquaculture of gilthead sea bream and other species, P95; Nakamoto M, 2009, GENESIS, V47, P289, DOI 10.1002/dvg.20498; Nakamoto M, 2007, MOL REPROD DEV, V74, P1239, DOI 10.1002/mrd.20689; Nakamoto M, 2006, BIOCHEM BIOPH RES CO, V344, P353, DOI 10.1016/j.bbrc.2006.03.137; Nanda I, 2002, P NATL ACAD SCI USA, V99, P11778, DOI 10.1073/pnas.182314699; Nef S, 2005, DEV BIOL, V287, P361, DOI 10.1016/j.ydbio.2005.09.008; Nicol B, 2012, MOL REPROD DEV, V79, P51, DOI 10.1002/mrd.21404; O'Hara WA, 2011, BMC DEV BIOL, V11, DOI 10.1186/1471-213X-11-72; Ospina-Alvarez N, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0002837; Pajuelo JG, 2008, J APPL ICHTHYOL, V24, P68, DOI 10.1111/j.1439-0426.2007.01010.x; Palaiokostas C, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0068389; Palaiokostas C, 2013, BMC GENOMICS, V14, DOI 10.1186/1471-2164-14-566; Papadaki M, 2008, AQUACULTURE, V276, P187, DOI 10.1016/j.aquaculture.2009.01.033; Peichel CL, 2004, CURR BIOL, V14, P1416, DOI 10.1016/j.cub.2004.08.030; Piferrer F, 2013, DEV DYNAM, V242, P360, DOI 10.1002/dvdy.23924; Piferrer F, 2012, MAR BIOTECHNOL, V14, P591, DOI 10.1007/s10126-012-9445-4; Quinn EM, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0058815; Rapaport F, 2013, GENOME BIOL, V14, DOI 10.1186/gb-2013-14-9-r95; Schulz MH, 2012, BIOINFORMATICS, V28, P1086, DOI 10.1093/bioinformatics/bts094; Sekido R, 2008, NATURE, V453, P930, DOI 10.1038/nature06944; SINCLAIR AH, 1990, NATURE, V346, P240, DOI 10.1038/346240a0; Skowronski MT, 2009, J HISTOCHEM CYTOCHEM, V57, P915, DOI 10.1369/jhc.2009.954057; Small CM, 2009, BMC GENOMICS, V10, DOI 10.1186/1471-2164-10-579; Smith CA, 2009, NATURE, V461, P267, DOI 10.1038/nature08298; Smith EK, 2013, COMP BIOCHEM PHYS B, V165, P125, DOI 10.1016/j.cbpb.2013.03.011; Soneson C, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-91; Spicer LJ, 2009, REPRODUCTION, V138, P329, DOI 10.1530/REP-08-0317; Sreenivasan R, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0001791; Sridevi P, 2011, GEN COMP ENDOCR, V174, P259, DOI 10.1016/j.ygcen.2011.08.015; Sun FY, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0068452; Tao WJ, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0063604; Tomy S, 2007, J NEUROENDOCRINOL, V19, P643, DOI 10.1111/j.1365-2826.2007.01572.x; Tomy S, 2009, DEV NEUROBIOL, V69, P299, DOI 10.1002/dneu.20705; Trabzuni D, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms3771; Uchida D, 2004, COMP BIOCHEM PHYS A, V137, P11, DOI 10.1016/S1095-6433(03)00178-8; Valenzuela N, 2003, AM NAT, V161, P676, DOI 10.1086/368292; Vizziano-Cantonnet D, 2011, GEN COMP ENDOCR, V170, P346, DOI 10.1016/j.ygcen.2010.10.009; Wang DS, 2002, BIOCHEM BIOPH RES CO, V297, P632, DOI 10.1016/S0006-291X(02)02252-0; Wang DS, 2004, BIOCHEM BIOPH RES CO, V320, P83, DOI 10.1016/j.bbrc.2004.05.133; Wang SM, 2010, CELL MOL LIFE SCI, V67, P123, DOI 10.1007/s00018-009-0167-3; Wang Z, 2009, NAT REV GENET, V10, P57, DOI 10.1038/nrg2484; Wijgerde M, 2005, ENDOCRINOLOGY, V146, P3558, DOI 10.1210/en.2005-0311; Wu GC, 2008, BIOL REPROD, V78, P200, DOI 10.1095/biolreprod.107.062612; Wu GC, 2010, BIOL REPROD, V83, P443, DOI 10.1095/biolreprod.110.084681; Wu GC, 2008, BIOL REPROD, V79, P1111, DOI 10.1095/biolreprod.108.069146; Wu GC, 2013, FISH PHYSIOL BIOCHEM, V39, P33, DOI 10.1007/s10695-012-9618-0; Wu GC, 2010, GEN COMP ENDOCR, V167, P417, DOI 10.1016/j.ygcen.2009.11.003; Wu GC, 2009, BIOL REPROD, V81, P1073, DOI 10.1095/biolreprod.109.077362; Yano A, 2012, CURR BIOL, V22, P1423, DOI 10.1016/j.cub.2012.05.045; Yoshimoto S, 2008, P NATL ACAD SCI USA, V105, P2469, DOI 10.1073/pnas.0712244105; Zarkower D, 2013, CURR TOP DEV BIOL, V102, P327, DOI 10.1016/B978-0-12-416024-8.00012-X; Zdobnov EM, 2001, BIOINFORMATICS, V17, P847, DOI 10.1093/bioinformatics/17.9.847; Zerbino DR, 2008, GENOME RES, V18, P821, DOI 10.1101/gr.074492.107; Zhang ZP, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018379; Zheng XW, 2012, BIOINFORMATICS, V28, P3326, DOI 10.1093/bioinformatics/bts606; Zhou LY, 2012, BMC DEV BIOL, V12, DOI 10.1186/1471-213X-12-36 120 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1471-2164 BMC GENOMICS BMC Genomics AUG 6 2014 15 655 10.1186/1471-2164-15-655 16 Biotechnology & Applied Microbiology; Genetics & Heredity Biotechnology & Applied Microbiology; Genetics & Heredity AN5PN WOS:000340643900001 J Bertoldi, G; Della Chiesa, S; Notarnicola, C; Pasolli, L; Niedrist, G; Tappeiner, U Bertoldi, Giacomo; Della Chiesa, Stefano; Notarnicola, Claudia; Pasolli, Luca; Niedrist, Georg; Tappeiner, Ulrike Estimation of soil moisture patterns in mountain grasslands by means of SAR RADARSAT2 images and hydrological modeling JOURNAL OF HYDROLOGY English Article Soil moisture; Hydrological models; Remote sensing; RADARSAT 2 SAR; Grasslands; Mountain regions MICROWAVE DIELECTRIC BEHAVIOR; SCALING CHARACTERISTICS; SPATIAL-ORGANIZATION; SURFACE-TEMPERATURE; COMPLEX TERRAIN; WET SOIL; RETRIEVAL; VEGETATION; WATER; PARAMETERS This paper analyzes the spatial patterns of surface soil moisture of alpine meadows and pastures in the Matsch/Mazia Valley in the Italian Alps by comparing estimations from three different sources of information: (I) RADARSAT 2 synthetic aperture radar (SAR) images; (II) simulations by using the GEOtop hydrological model and (III) ground observations, derived from a network of fixed stations and field campaigns with mobile devices. The aim of this paper is to assess the added value of RADARSAT 2 products with respect to a distributed hydrological model in capturing soil moisture patterns in mountain areas, which is a challenging environment with a high degree of spatial variability. Moreover, the physical controls of the observed soil moisture patterns are analyzed by using the hydrological model. Results show that the model, once calibrated for soil and vegetation parameters, predicts the plot-scale temporal dynamic in station locations and the spatial averages with sufficient accuracy. However, the model output shows lower spatial variability with respect to the ground surveys, with a limited capability of reproducing moist areas in irrigated meadows. Differences arise due to difficulties in knowing soil model parameters and irrigation amounts with accurate spatial detail. RADARSAT 2 soil moisture maps well reproduce the spatial ground surveys, as well as over-irrigated meadows. However, SAR products are limited to slopes with a favorable viewing angle, to bare soil or to grassland areas. Moreover, the signal penetration depth is restricted to the soil surface layer. The major control on RADARSAT 2 patterns is land use. Irrigated meadows in the bottom of the valley have moister conditions, with respect to pastures along the upper hillslopes. In this case, model simulations suggest that differences in soil type could have a relevant impact on soil moisture estimation. A secondary control is topography, with increased moisture in convergent locations with a high topographic wetness index. Results suggest that the capability of RADARSAT 2 products to reproduce small-scale (20 m pixels size) surface soil moisture patterns in mountain grassland areas could complement the ability of the hydrological model to predict variations of soil moisture continuously in space and time. Therefore, RADARSAT 2 products can give useful information to improve spatial parameterization and validation of distributed hydrological models in mountain grassland areas, also in the perspective of implementing data integration procedures for operational soil moisture monitoring. (C) 2014 Elsevier B.V. All rights reserved. [Bertoldi, Giacomo; Della Chiesa, Stefano; Niedrist, Georg; Tappeiner, Ulrike] EURAC, Inst Alpine Environm, I-39100 Bolzano, Italy; [Della Chiesa, Stefano; Tappeiner, Ulrike] Univ Innsbruck, Inst Ecol, A-6020 Innsbruck, Austria; [Notarnicola, Claudia; Pasolli, Luca] EURAC, Inst Appl Remote Sensing, I-39100 Bolzano, Italy Bertoldi, G (reprint author), EURAC, Inst Alpine Environm, VialeDruso 1, I-39100 Bolzano, Italy. giacomo.bertoldi@eurac.edu "HiResAlp" - Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo Studio, Universita e Ricerca Scientifica; "HydroAlp" - Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo Studio, Universita e Ricerca Scientifica This study was supported by the projects "HiResAlp" and "HydroAlp" financed by Provincia Autonoma di Bolzano, Alto Adige, Ripartizione Diritto allo Studio, Universita e Ricerca Scientifica. The RADARSAT 2 images were made available through the project ESA AO 6820 in the framework of the SOAR program. We thank Dr. Stefano Endrizzi for his efforts in developing the GEOtop source code, Dr. Emanuele Cordano for his suggestions on model parameterization strategy and Davide Castelletti for his support on the retrieval algorithm. Anguela TP, 2010, IEEE T GEOSCI REMOTE, V48, P874, DOI 10.1109/TGRS.2009.2028019; BALDOCCHI DD, 1988, ECOLOGY, V69, P1331, DOI 10.2307/1941631; Balenzano A, 2011, INT GEOSCI REMOTE SE, P142; Barrett BW, 2009, REMOTE SENS-BASEL, V1, P210, DOI 10.3390/rs1030210; Bastiaanssen W. G. M., 1999, Irrigation and Drainage Systems, V13, P291, DOI 10.1023/A:1006355315251; Becker A, 2007, MT RES DEV, V27, P58, DOI 10.1659/0276-4741(2007)27[58:EALUSA]2.0.CO;2; Beniston M, 2012, J HYDROL, V412, P291, DOI 10.1016/j.jhydrol.2010.06.046; Benoudjit N, 2009, CHEMOMETR INTELL LAB, V95, P144, DOI 10.1016/j.chemolab.2008.10.001; Bertoldi G, 2010, ECOHYDROLOGY, V3, P189, DOI 10.1002/eco.129; Bertoldi G., 2010, P EGU GEN ASS VIENN; Bertoldi G, 2006, J HYDROMETEOROL, V7, P389, DOI 10.1175/JHM500.1; Beven K, 2001, J HYDROL, V249, P11, DOI 10.1016/S0022-1694(01)00421-8; Beven K.J., 1979, HYDROL SCI B, V24, P43, DOI [10.1080/02626667909491834, DOI 10.1080/02626667909491834]; BEVEN KJ, 1988, J HYDROL, V100, P353, DOI 10.1016/0022-1694(88)90192-8; Bixio A.C., 2000, SURFACE WATER SYSTEM, V2, P115; Bogataj LK, 2007, MANAGING WEATHER AND CLIMATE RISKS IN AGRICULTURE, P113, DOI 10.1007/978-3-540-72746-0_8; Brocca L., 2013, VADOSE ZONE HYDROL, V12, P1; Bruzzone L, 2005, IEEE T GEOSCI REMOTE, V43, P159, DOI 10.1109/TGRS.2004.839818; Dall'Amico M, 2011, CRYOSPHERE, V5, P469, DOI 10.5194/tc-5-469-2011; Dall'Amico M., 2011, GEOTOP USERS MANUAL; DEARDORFF JW, 1978, J GEOPHYS RES-OC ATM, V83, P1889, DOI 10.1029/JC083iC04p01889; Della Chiesa S., 2014, ECOHYDROLOGY; DOBSON MC, 1985, IEEE T GEOSCI REMOTE, V23, P35, DOI 10.1109/TGRS.1985.289498; Endrizzi S., 2013, GEOSCI MODEL DEV DIS, V6, P6279; Endrizzi S, 2010, HYDROL RES, V41, P471, DOI 10.2166/nh.2010.149; ERBS DG, 1982, SOL ENERGY, V28, P293, DOI 10.1016/0038-092X(82)90302-4; Ewen J, 2000, J HYDROL ENG, V5, P250, DOI 10.1061/(ASCE)1084-0699(2000)5:3(250); Famiglietti JS, 2008, WATER RESOUR RES, V44, DOI 10.1029/2006WR005804; Fatichi S., 2012, J ADV MODEL EARTH SY, V4, P1, DOI DOI 10.1029/2011MS000086; Feldman A.M., 1980, WELFARE EC SOCIAL CH; Gebremichael M, 2009, NONLINEAR PROC GEOPH, V16, P141; Grayson RB, 1997, WATER RESOUR RES, V33, P2897, DOI 10.1029/97WR02174; HALLIKAINEN MT, 1985, IEEE T GEOSCI REMOTE, V23, P25, DOI 10.1109/TGRS.1985.289497; Heathman GC, 2003, J HYDROL, V279, P1, DOI 10.1016/S0022-1694(03)00088-X; Hornacek M, 2012, IEEE J-STARS, V5, P1303, DOI 10.1109/JSTARS.2012.2190136; Hugget R.J., 1995, GEOECOLOGY; Iqbal M, 1983, INTRO SOLAR RAD; Ivanov VY, 2004, WATER RESOUR RES, V40, DOI 10.1029/2004WR003218; JARVIS PG, 1976, T R SOC LONDON B, V273, P593; Kollmann K., 2012, THESIS U INNSBRUCK; Leibundgut C., 2004, HIST MEADOW IRRIGATI, V286; Leitinger G, 2010, J HYDROL, V385, P95, DOI 10.1016/j.jhydrol.2010.02.006; Liston GE, 2006, J HYDROMETEOROL, V7, P217, DOI 10.1175/JHM486.1; Luckman AJ, 1998, IEEE T GEOSCI REMOTE, V36, P1830, DOI 10.1109/36.718651; Manfreda S, 2007, ADV WATER RESOUR, V30, P2145, DOI 10.1016/j.advwatres.2006.07.009; MOORE ID, 1993, SOIL SCI SOC AM J, V57, P443; MOORE ID, 1991, HYDROL PROCESS, V5, P3, DOI 10.1002/hyp.3360050103; Paloscia S, 2010, INT J REMOTE SENS, V31, P2265, DOI 10.1080/01431160902953891; Pasolli L, 2011, CAN J REMOTE SENS, V37, P535; Pasolli L, 2011, IEEE GEOSCI REMOTE S, V8, P1080, DOI 10.1109/LGRS.2011.2156759; Pasolli L, 2012, IEEE J-STARS, V5, P1495, DOI 10.1109/JSTARS.2012.2197178; PASOLLI L, 2011, APPL ENVIRON SOIL SC, DOI DOI 10.1155/2011/175473; Pasolli L., 2011, P IGARSS VANC CAN 25; Pierdicca N, 2010, REMOTE SENS ENVIRON, V114, P440, DOI 10.1016/j.rse.2009.10.001; Rigon R, 2006, J HYDROMETEOROL, V7, P371, DOI 10.1175/JHM497.1; Rodriguez-Iturbe I, 1999, WATER RESOUR RES, V35, P3709, DOI 10.1029/1999WR900255; RODRIGUEZ-ITURBE I, 1995, GEOPHYS RES LETT, V22, P2757, DOI 10.1029/95GL02779; Rudiger C, 2010, J HYDROL, V383, P319, DOI 10.1016/j.jhydrol.2009.12.046; Schaap MG, 2001, J HYDROL, V251, P163, DOI 10.1016/S0022-1694(01)00466-8; Seyfried MS, 2005, VADOSE ZONE J, V4, P1070, DOI 10.2136/vzj2004.0148; Srivastava HS, 2009, IEEE T GEOSCI REMOTE, V47, P2528, DOI 10.1109/TGRS.2009.2018448; Tasser E., 1998, HYDROLOGY WATER RESO, V248, P58; Ulaby F.T., 1986, MICROWAVE REMOTE SEN, VIII, P1797; ULABY FT, 1979, IEEE T GEOSCI REMOTE, V17, P33, DOI 10.1109/TGE.1979.294626; VANGENUCHTEN MT, 1980, SOIL SCI SOC AM J, V44, P892; Vapnik V, 1995, NATURE STAT LEARNING; Western AW, 1999, WATER RESOUR RES, V35, P797, DOI 10.1029/1998WR900065; WIGMOSTA MS, 1994, WATER RESOUR RES, V30, P1665, DOI 10.1029/94WR00436; Williams CJ, 2009, HYDROL EARTH SYST SC, V13, P1325; Zanotti F, 2004, HYDROL PROCESS, V18, P3667, DOI 10.1002/hyp.5794; Zribi M., 2014, IEEE GEOSCI REMOTE S, V11 71 2 2 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. AUG 4 2014 516 SI 245 257 10.1016/j.jhydrol.2014.02.018 13 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Engineering; Geology; Water Resources AL3NT WOS:000339036100023 J Mell, JN; van Knippenberg, D; van Ginkel, WP Mell, Julija N.; van Knippenberg, Daan; van Ginkel, Wendy P. THE CATALYST EFFECT: THE IMPACT OF TRANSACTIVE MEMORY SYSTEM STRUCTURE ON TEAM PERFORMANCE ACADEMY OF MANAGEMENT JOURNAL English Article GROUP DECISION-MAKING; DIVERSE WORK GROUPS; INFORMATION ELABORATION; UNSHARED INFORMATION; KNOWLEDGE NETWORKS; GROUP COGNITION; METAANALYSIS; MEDIATION; MODELS; ORGANIZATIONS Research on transactive memory systems (TMSs) implicitly assumes that metaknowledge (i.e., the "knowledge of who knows what") is uniformly distributed among team members. Relaxing this assumption results in a more realistic notion of team cognition in which the distribution of metaknowledge can take different forms. Demonstrating the importance of this conceptual shift, we compare teams in which metaknowledge is concentrated within one central member (a centralized TMS structure) with teams in which metaknowledge is distributed evenly among the members (a decentralized TMS structure). We predict that centralized metaknowledge can give teams a performance advantage over decentralized metaknowledge, because centralized metaknowledge can allow the central member to function as a catalyst for information exchange and integration. We propose this catalyst effect to be contingent on the extent to which the distribution of task information among members poses high coordination demands to effectively integrate members' knowledge. In a laboratory team decision-making experiment (n = 112), we found the predicted interaction effect between TMS structure and the distribution of task information. Furthermore, the experiment supported our hypotheses about the mediating role of the transactive retrieval process and the ensuing team information elaboration. [Mell, Julija N.; van Knippenberg, Daan; van Ginkel, Wendy P.] Erasmus Univ, Rotterdam Sch Management, Rotterdam, Netherlands Mell, JN (reprint author), Erasmus Univ, Rotterdam Sch Management, Rotterdam, Netherlands. jmell@rsm.nl; dvanknippenberg@rsm.nl; wginkel@rsm.nl Akgun AE, 2005, INFORM MANAGE-AMSTER, V42, P1105, DOI 10.1016/j.im.2005.01.001; Anderson CA, 1999, CURR DIR PSYCHOL SCI, V8, P3, DOI 10.1111/1467-8721.00002; Austin JR, 2003, J APPL PSYCHOL, V88, P866, DOI 10.1037/0021-9010.88.5.866; BANDURA A, 1965, J PERS SOC PSYCHOL, V1, P589, DOI 10.1037/h0022070; BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173; Bell ST, 2007, J APPL PSYCHOL, V92, P595, DOI 10.1037/0021-9010.92.3.595; Bliese P., 2000, MULTILEVEL THEORY RE, P349; Borgatti SP, 2003, MANAGE SCI, V49, P432, DOI 10.1287/mnsc.49.4.432.14428; Brandon DP, 2004, ORGAN SCI, V15, P633, DOI 10.1287/orsc.1040.0069; Cannon-Bowers JA, 2001, J ORGAN BEHAV, V22, P195, DOI 10.1002/job.82; Cross R, 2001, SOC NETWORKS, V23, P215, DOI 10.1016/S0378-8733(01)00041-7; DeChurch LA, 2010, J APPL PSYCHOL, V95, P32, DOI 10.1037/a0017328; De Dreu CKW, 2008, PERS SOC PSYCHOL REV, V12, P22, DOI 10.1177/1088868307304092; Dipboye R. L., 1990, INT REV IND ORG PSYC, V5, P1; Edmondson A, 1999, ADMIN SCI QUART, V44, P350, DOI 10.2307/2666999; Edwards JR, 2007, PSYCHOL METHODS, V12, P1, DOI 10.1037/1082-989X.12.1.1; Faraj S, 2000, MANAGE SCI, V46, P1554, DOI 10.1287/mnsc.46.12.1554.12072; Fraidin SN, 2004, ORGAN BEHAV HUM DEC, V93, P102, DOI 10.1016/j.obhdp.2003.12.003; Garner J. T., 2006, J TECHNICAL WRITING, V36, P329, DOI 10.2190/U636-4844-2323-W071; Gino F, 2010, ORGAN BEHAV HUM DEC, V111, P102, DOI 10.1016/j.obhdp.2009.11.002; Grant RM, 1996, STRATEGIC MANAGE J, V17, P109; Gruenfeld DH, 1996, ORGAN BEHAV HUM DEC, V67, P1, DOI 10.1006/obhd.1996.0061; Harrison DA, 2007, ACAD MANAGE REV, V32, P1199; Hayes A. F., 2013, INTRO MEDIATION MODE; Hayes AF, 2009, COMMUN MONOGR, V76, P408, DOI 10.1080/03637750903310360; He J, 2007, J MANAGE INFORM SYST, V24, P261, DOI 10.2753/MIS0742-1222240210; Hinsz VB, 1997, PSYCHOL BULL, V121, P43, DOI 10.1037/0033-2909.121.1.43; Hoever IJ, 2012, J APPL PSYCHOL, V97, P982, DOI 10.1037/a0029159; Hollingshead AB, 2001, J PERS SOC PSYCHOL, V81, P1080, DOI 10.1037//0022-3514.81.6.1080; Hollingshead AB, 1998, J EXP SOC PSYCHOL, V34, P423, DOI 10.1006/jesp.1998.1358; Homan AC, 2007, J APPL PSYCHOL, V92, P1189, DOI 10.1037/0021-9010.92.5.1189; Homan AC, 2008, ACAD MANAGE J, V51, P1204; Huber GP, 2010, ACAD MANAGE REV, V35, P6; Ilgen DR, 2005, ANNU REV PSYCHOL, V56, P517, DOI 10.1146/annurev.psych.56.091103.070250; Kameda T, 1997, J PERS SOC PSYCHOL, V73, P296, DOI 10.1037/0022-3514.73.2.296; Kearney E, 2009, J APPL PSYCHOL, V94, P77, DOI 10.1037/a0013077; Kearney E, 2009, ACAD MANAGE J, V52, P581; Kooij-de Bode HJM, 2008, GROUP DYN-THEOR RES, V12, P307, DOI 10.1037/1089-2699.12.4.307; LeBreton JM, 2008, ORGAN RES METHODS, V11, P815, DOI 10.1177/1094428106296642; LePine JA, 2008, PERS PSYCHOL, V61, P273, DOI 10.1111/j.1744-6570.2008.00114.x; LePine JA, 1997, J APPL PSYCHOL, V82, P803; Lewis K, 2007, ORGAN BEHAV HUM DEC, V103, P159, DOI 10.1016/j.obhdp.2007.01.005; Lewis K, 2003, J APPL PSYCHOL, V88, P587, DOI 10.1037/0021-9010.88.4.587; Lewis K, 2011, ORGAN SCI, V22, P1254, DOI 10.1287/orsc.1110.0647; Lewis K, 2004, MANAGE SCI, V50, P1519, DOI 10.1287/mnsc.1040.0257; LIANG DW, 1995, PERS SOC PSYCHOL B, V21, P384, DOI 10.1177/0146167295214009; Mesmer-Magnus JR, 2009, J APPL PSYCHOL, V94, P535, DOI 10.1037/a0013773; Moreland R. L., 1999, SHARED COGNITION ORG, P3; Muller D, 2005, J PERS SOC PSYCHOL, V89, P852, DOI 10.1037/0022-3514.89.6.852; Palazzolo ET, 2006, COMMUN THEOR, V16, P223, DOI 10.1111/j.1468-2885.2006.00269.x; Peltokorpi V, 2008, REV GEN PSYCHOL, V12, P378, DOI 10.1037/1089-2680.12.4.378; Peltokorpi V, 2012, EUR PSYCHOL, V17, P11, DOI 10.1027/1016-9040/a000044; PENNINGTON N, 1993, COGNITION, V49, P123, DOI 10.1016/0010-0277(93)90038-W; Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879; Preacher KJ, 2007, MULTIVAR BEHAV RES, V42, P185; Ren YQ, 2011, ACAD MANAG ANN, V5, P189, DOI 10.1080/19416520.2011.590300; Salas E., 2004, TEAM COGNITION UNDER; SHROUT PE, 1979, PSYCHOL BULL, V86, P420, DOI 10.1037//0033-2909.86.2.420; Soda G, 2012, STRATEGIC MANAGE J, V33, P751, DOI 10.1002/smj.1966; STASSER G, 1985, J PERS SOC PSYCHOL, V48, P1467, DOI 10.1037/0022-3514.48.6.1467; Stasser G, 2000, ORGAN BEHAV HUM DEC, V82, P102, DOI 10.1006/obhd.2000.2890; STASSER G, 1995, J EXP SOC PSYCHOL, V31, P244, DOI 10.1006/jesp.1995.1012; Steinel W, 2010, ORGAN BEHAV HUM DEC, V113, P85, DOI 10.1016/j.obhdp.2010.07.001; STEWART DD, 1995, J PERS SOC PSYCHOL, V69, P619, DOI 10.1037//0022-3514.69.4.619; Ten Velden FS, 2010, PERS SOC PSYCHOL B, V36, P1454, DOI 10.1177/0146167210383698; Tindale R. S., 2000, GROUP PROCESS INTERG, V3, P123, DOI DOI 10.1177/1368430200003002002; Toma C, 2009, PERS SOC PSYCHOL B, V35, P793, DOI 10.1177/0146167209333176; van Ginkel WP, 2008, ORGAN BEHAV HUM DEC, V105, P82, DOI 10.1016/j.obhdp.2007.08.005; van Ginkel WP, 2009, ORGAN BEHAV HUM DEC, V108, P218, DOI 10.1016/j.obhdp.2008.10.003; van Knippenberg D, 2004, J APPL PSYCHOL, V89, P1008, DOI 10.1037/0021-9010.89.6.1008; van Knippenberg D, 2013, ORGAN BEHAV HUM DEC, V121, P183, DOI 10.1016/j.obhdp.2013.03.003; Wegner D. M., 1985, COMPATIBLE INCOMPATI, P253, DOI [DOI 10.1007/978-1-4612-5044-9_12, 10.1007/978-1-4612-5044-9_12]; Wegner D. M., 1986, THEORIES GROUP BEHAV, P185; Wegner DM, 1995, SOC COGNITION, V13, P319, DOI 10.1521/soco.1995.13.3.319; Weingart LR, 1997, RES ORGAN BEHAV, V19, P189; Wittenbaum GM, 2004, COMMUN MONOGR, V71, P286, DOI 10.1080/0363452042000299894; Yuan YC, 2010, COMMUN RES, V37, P20, DOI 10.1177/009365020351469; Zhang ZX, 2007, J APPL PSYCHOL, V92, P1722, DOI 10.1037/0021-9010.92.6.1722 78 0 0 ACAD MANAGEMENT BRIARCLIFF MANOR PACE UNIV, PO BOX 3020, 235 ELM RD, BRIARCLIFF MANOR, NY 10510-8020 USA 0001-4273 1948-0989 ACAD MANAGE J Acad. Manage. J. AUG 2014 57 4 1154 1173 10.5465/amj.2012.0589 20 Business; Management Business & Economics AN2UM WOS:000340441400011 J Huang, JJ; Zhong, N; Yao, YY Huang, Jiajin; Zhong, Ning; Yao, Yiyu A UNIFIED FRAMEWORK OF TARGETED MARKETING USING CUSTOMER PREFERENCES COMPUTATIONAL INTELLIGENCE English Article Web intelligence; targeted marketing; customer preference; utility theory FILTERING RECOMMENDER SYSTEMS; MODELS One of the fundamental tasks of targeted marketing is to elicit associations between customers and products. Based on the results from information retrieval and utility theory, this article proposes a unified framework of targeted marketing. The customer judgments of products are formally described by preference relations and the connections of customers and products are quantitatively measured by market value functions. Two marketing strategies, known as the customer-oriented and product-oriented marketing strategies, are investigated. Four marketing models are introduced and examined. They represent, respectively, the relationships between a group of customers and a group of products, between a group of customers and a single product, between a single customer and a group of products, and between a single customer and a single product. Linear and bilinear market value functions are suggested and studied. The required parameters of a market value function can be estimated by exploring three types of information, namely, customer profiles, product profiles, and transaction data. Experiments on a real-world data set are performed to demonstrate the effectiveness of the proposed framework. [Huang, Jiajin; Zhong, Ning] Beijing Univ Technol, Int WIC Inst, Beijing 100124, Peoples R China; [Zhong, Ning] Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gumma 3710816, Japan; [Yao, Yiyu] Univ Regina, Dept Comp Sci, Regina, SK S4S 0A2, Canada Zhong, N (reprint author), Maebashi Inst Technol, Dept Life Sci & Informat, Maebashi, Gumma 3710816, Japan. zhong@maebashi-it.ac.jp Beijing Natural Science Foundation [4102007]; CAS/SAFEA International Partnership Program for Creative Research Teams; Doctor Program of Beijing University of Technology [X0002020201101] The authors are grateful to the anonymous reviewers for their constructive comments and suggestions and Mr. Yu Xin for his experimental contributions. This work is partially supported by Beijing Natural Science Foundation (4102007), the CAS/SAFEA International Partnership Program for Creative Research Teams, and the Doctor Program of Beijing University of Technology (X0002020201101). Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99; Associates D. S., 1999, NEW DIR MARK; Balabanovic M, 1997, COMMUN ACM, V40, P66, DOI 10.1145/245108.245124; Basilico J., 2004, P 21 INT C MACH LEAR; Bobadilla J, 2011, KNOWL-BASED SYST, V24, P1310, DOI 10.1016/j.knosys.2011.06.005; Bobadilla J, 2011, EXPERT SYST APPL, V38, P14609, DOI 10.1016/j.eswa.2011.05.021; Breese JS, 1998, P 14 C UNC ART INT, P43; Burke R, 2011, AI MAG, V32, P13; Cohen WW, 1999, J ARTIF INTELL RES, V10, P243; Fishburn F.C., 1970, UTILITY THEORY DECIS; FRENCH S., 1986, DECISION THEORY INTR; Han J. W., 2000, DATA MINING CONCEPTS; Herlocker J., 1999, P 22 ANN INT ACM SIG, V54, P230, DOI DOI 10.1145/312624.312682; Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772; HERNANDEZ F., 2008, EXPERT SYSTEMS APPL, V1, P790, DOI DOI 10.1016/J.ESWA.2007.07.047; Hofmann T, 2004, ACM T INFORM SYST, V22, P89, DOI 10.1145/963770.963774; Hu J, 2006, DATA MIN KNOWL DISC, V12, P127, DOI 10.1007/s10618-005-0018-2; Huang JJ, 2005, LECT NOTES COMPUT SC, V3528, P197; Huang JJ, 2004, LECT NOTES ARTIF INT, V3066, P743; Huang JJ, 2008, STUD COMPUT INTELL, V123, P171; Jin R., 2003, P 12 INT C INF KNOWL; KOITRIKA G., 2009, P 2009 INT C MAN DAT, P745; Ling C. X., 1998, Proceedings Fourth International Conference on Knowledge Discovery and Data Mining; Lu LY, 2012, PHYS REP, V519, P1, DOI 10.1016/j.physrep.2012.02.006; Masthoff J, 2011, RECOMMENDER SYSTEMS HANDBOOK, P677, DOI 10.1007/978-0-387-85820-3_21; Pazzani MJ, 1999, ARTIF INTELL REV, V13, P393, DOI 10.1023/A:1006544522159; Potharst R., 2001, NETWORKS BUSINESS TE, P89; Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1; ROBERTS F, 1979, MEASUREMENT THEORY; Robertson S. E., 1982, Information Technology: Research and Development, V1; Sai Y., 2001, P 2001 IEEE INT C DA, P497; Sarwar B., 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071; Vozalis M., 2006, Web Intelligence and Agent Systems, V4; WONG S. K. M., 1983, P 6 ANN INT ACMSIGIR, P107; WONG SKM, 1991, J AM SOC INFORM SCI, V42, P723, DOI 10.1002/(SICI)1097-4571(199112)42:10<723::AID-ASI5>3.0.CO;2-U; WONG SKM, 1990, J AM SOC INFORM SCI, V41, P334, DOI 10.1002/(SICI)1097-4571(199007)41:5<334::AID-ASI4>3.0.CO;2-2; Yao Y. Y., 2008, HDB GRANULAR COMPUTI, P401, DOI 10.1002/9780470724163.ch17; Yao Y. Y., 2000, P 4 ONL WORLD C SOFT, P339; Yao YY, 2003, LECT NOTES ARTIF INT, V2639, P165; Yao YY, 2002, INT J PATTERN RECOGN, V16, P1117, DOI 10.1142/S0218001402002180; Yao YY, 2002, STUD FUZZ SOFT COMP, V95, P102; YAO YY, 1995, J AM SOC INFORM SCI, V46, P133, DOI 10.1002/(SICI)1097-4571(199503)46:2<133::AID-ASI6>3.0.CO;2-Z; ZHAO Y., 2005, P 2005 IEEE WIC ACM, P147; Zhao Y, 2006, PROCEEDINGS OF THE FIFTH IEEE INTERNATIONAL CONFERENCE ON COGNITIVE INFORMATICS, VOLS 1 AND 2, P457; ZHONG N., 2003, WEB INTELLIGENCE; Zhong N, 2004, INTELLIGENT TECHNOLOGIES FOR INFORMATION ANALYSIS, P109; Zhong N, 2003, LECT NOTES ARTIF INT, V2663, P1; Zhou B, 2010, J INTELL INF SYST, V34, P227, DOI 10.1007/s10844-009-0096-5 48 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0824-7935 1467-8640 COMPUT INTELL-US Comput. Intell. AUG 2014 30 3 451 472 10.1111/coin.12003 22 Computer Science, Artificial Intelligence Computer Science AN3PW WOS:000340501500001 J Hsiao, KJ; Kulesza, A; Hero, AO Hsiao, Ko-Jen; Kulesza, Alex; Hero, Alfred O., III Social Collaborative Retrieval IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING English Article Machine learning algorithms; recommender systems; information retrieval Socially-based recommendation systems have recently attracted significant interest, and a number of studies have shown that social information can dramatically improve a system's predictions of user interests. Meanwhile, there are now many potential applications that involve aspects of both recommendation and information retrieval, and the task of collaborative retrieval-a combination of these two traditional problems-has recently been introduced. Successful collaborative retrieval requires overcoming severe data sparsity, making additional sources of information, such as social graphs, particularly valuable. In this paper we propose a new model for collaborative retrieval, and show that our algorithm outperforms current state-of-the-art approaches by incorporating information from social networks. We also provide empirical analyses of the ways in which cultural interests propagate along a social graph using a real-world music dataset. [Hsiao, Ko-Jen; Kulesza, Alex; Hero, Alfred O., III] Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA Hsiao, KJ (reprint author), Univ Michigan, Dept Elect Engn & Comp Sci, Ann Arbor, MI 48109 USA. coolmark@umich.edu; kulesza@umich.edu; hero@umich.edu ARO [W911NF-12-1-0443] This work was supported in part by the ARO under grant W911NF-12-1-0443. The guest editor coordinating the review of this manuscript and approving it for publication was Prof. Kwang-Cheng Chen. Bedi P., 2007, P IJCAI, P2677; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Breese JS, 1998, P 14 C UNC ART INT, P43; Cantador I., 2011, P 5 ACM C REC SYST R, P387; Carmel D., 2009, P 18 ACM C INF KNOWL, P1227, DOI DOI 10.1145/1645953.1646109; Chu C., 2007, ADV NEURAL INFORM PR, V19, P281; Jacomy M., 2011, FORCEATLAS2 GRAPH LA; Karatzoglou A., 2010, P 4 ACM C REC SYST R, P79, DOI 10.1145/1864708.1864727; Konstas I, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P195, DOI 10.1145/1571941.1571977; Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344; Ma H, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P203, DOI 10.1145/1571941.1571978; Ma H., 2011, P 4 ACM INT C WEB SE, P287, DOI DOI 10.1145/1935826.1935877]; Ma H., 2008, P 17 ACM C INF KNOWL, P931, DOI DOI 10.1145/1458082.145; Massa P, 2007, RECSYS 07: PROCEEDINGS OF THE 2007 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P17; Massa P, 2004, LECT NOTES COMPUT SC, V3290, P492; O'Donovan J., 2005, P 10 INT C INT US IN, P167, DOI DOI 10.1145/1040830.1040870; Purushotham S., 2012, ARXIV12064684; Salakhutdinov R., 2008, ADV NEURAL INFORM PR, V20, P1257; Sarwar B., 2001, P 10 INT C WORLD WID, P285, DOI DOI 10.1145/371920.372071; Seung D., 2001, ADV NEURAL INF PROCE, V13, P556; Su X, 2009, ADV ARTIF INTELL JAN, P4; Symeonidis P, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P43; Weston J, 2010, MACH LEARN, V81, P21, DOI 10.1007/s10994-010-5198-3; Weston J., 2012, P 29 INT C MACH LEAR; Xiong L., 2010, P SIAM DAT MIN; Zheng V. W., 2010, P 24 AAAI C ART INT; Zinkevich M., 2010, ADV NEURAL INF PROCE 27 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1932-4553 1941-0484 IEEE J-STSP IEEE J. Sel. Top. Signal Process. AUG 2014 8 4 680 689 10.1109/JSTSP.2014.2317286 10 Engineering, Electrical & Electronic Engineering AN4AB WOS:000340528900015 J Lindsey, DT; Grasso, L; Dostalek, JF; Kerkmann, J Lindsey, Daniel T.; Grasso, Louie; Dostalek, John F.; Kerkmann, Jochen Use of the GOES-R Split-Window Difference to Diagnose Deepening Low-Level Water Vapor JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY English Article BASE-LINE IMAGER; RETRIEVAL ALGORITHM; SOUNDER; MSG; GENERATION; PRODUCT; FIELDS The depth of boundary layer water vapor plays a critical role in convective cloud formation in the warm season, but numerical models often struggle with accurate predictions of above-surface moisture. Satellite retrievals of water vapor have been developed, but they are limited by the use of a model's first guess, instrument spectral resolution, horizontal footprint size, and vertical resolution. In 2016, Geostationary Operational Environmental Satellite-R (GOES-R), the first in a series of new-generation geostationary satellites, will be launched. Its Advanced Baseline Imager will provide unprecedented spectral, spatial, and temporal resolution. Among the bands are two centered at 10.35 and 12.3 mu m. The brightness temperature difference between these bands is referred to as the split-window difference, and has been shown to provide information about atmospheric column water vapor. In this paper, the split-window difference is reexamined from the perspective of GOES-R and radiative transfer model simulations are used to better understand the factors controlling its value. It is shown that the simple split-window difference can provide useful information for forecasters about deepening low-level water vapor in a cloud-free environment. [Lindsey, Daniel T.] NOAA, Ctr Satellite Applicat & Res, Ft Collins, CO USA; [Grasso, Louie; Dostalek, John F.] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA; [Kerkmann, Jochen] EUMETSAT, Darmstadt, Germany Lindsey, DT (reprint author), Colorado State Univ, CIRA, 1375 Campus Delivery, Ft Collins, CO 80523 USA. dan.lindsey@noaa.gov National Oceanic and Atmospheric Administration [NA090AR4320074]; GOES-R Risk Reduction program This material is based on work supported by the National Oceanic and Atmospheric Administration under Grant NA090AR4320074, as well as the GOES-R Risk Reduction program. The authors thank Mat Gunshor from CIMSS for help with Fig. 1, Mark DeMaria for providing a very useful review, and helpful comments from three anonymous reviewers. The views, opinions, and findings in this report are those of the authors, and should not be construed as an official NOAA and or U.S. government position, policy, or decision. Bikos D, 2012, WEATHER FORECAST, V27, P784, DOI 10.1175/WAF-D-11-00130.1; CHESTERS D, 1983, J CLIM APPL METEOROL, V22, P725, DOI 10.1175/1520-0450(1983)022<0725:LLWVFF>2.0.CO;2; Dostalek JF, 2001, WEATHER FORECAST, V16, P573, DOI 10.1175/1520-0434(2001)016<0573:TPWMFG>2.0.CO;2; GRIFFITH PC, 2011, GOES US C BIRM AL NO; HAN Y, 2006, 122 NESDIS NOAA; Jin X, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010040; Kain JS, 2010, WEATHER FORECAST, V25, P1536, DOI 10.1175/2010WAF2222430.1; KLEESPIES JT, 1990, J APPL METEOR, V29, P851, DOI DOI 10.1175/1520-0450(1990)029<0851:ROPWFO>2.0.CO;2; Koenig M, 2009, WEATHER FORECAST, V24, P272, DOI 10.1175/2008WAF2222141.1; Lee YK, 2014, J ATMOS OCEAN TECH, V31, P3, DOI 10.1175/JTECH-D-13-00028.1; Li ZL, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL032797; Lindsey DT, 2012, J APPL REMOTE SENS, V6, DOI 10.1117/1.JRS.6.063598; Ma XL, 1999, J APPL METEOROL, V38, P501, DOI 10.1175/1520-0450(1999)038<0501:ANPRAI>2.0.CO;2; MENZEL WP, 1994, B AM METEOROL SOC, V75, P757, DOI 10.1175/1520-0477(1994)075<0757:IGITFO>2.0.CO;2; MOLLER AR, 2001, METEOR MONOGR AM MET, V50, P433; Schmetz J, 2002, B AM METEOROL SOC, V83, P977, DOI 10.1175/BAMS-83-7-Schmetz-2; Schmit TJ, 2005, B AM METEOROL SOC, V86, P1079, DOI 10.1175/BAMS-86-8-1079; Schmit TJ, 2008, J APPL METEOROL CLIM, V47, P2696, DOI 10.1175/2008JAMC1858.1; Schroedter-Homscheidt M, 2008, REMOTE SENS ENVIRON, V112, P249, DOI 10.1016/j.rse.2007.05.006; Seemann SW, 2008, J APPL METEOROL CLIM, V47, P108, DOI 10.1175/2007JAMC1590.1; Sieglaff JM, 2009, J ATMOS OCEAN TECH, V26, P1527, DOI 10.1175/2009JTECHA1210.1 21 0 0 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693 USA 1558-8424 1558-8432 J APPL METEOROL CLIM J. Appl. Meteorol. Climatol. AUG 2014 53 8 2005 2016 10.1175/JAMC-D-14-0010.1 12 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN3TS WOS:000340512200011 J Martin, AE; Nieuwland, MS; Carreiras, M Martin, Andrea E.; Nieuwland, Mante S.; Carreiras, Manuel Agreement attraction during comprehension of grammatical sentences: ERP evidence from ellipsis BRAIN AND LANGUAGE English Article Sentence processing; Memory; Interference; Retrieval cues; ERPs; Anterior negativity; NRef; P600; Ellipsis; Agreement attraction VERB-PHRASE ELLIPSIS; BRAIN POTENTIALS; LANGUAGE COMPREHENSION; INDIVIDUAL-DIFFERENCES; WORKING-MEMORY; INTERFERENCE; RESOLUTION; CONSTRAINTS; PERSPECTIVE; SPANISH Successful dependency resolution during language comprehension relies on accessing certain representations in memory, and not others. We recently reported event-related potential (ERP) evidence that syntactically unavailable, intervening attractor-nouns interfered during comprehension of Spanish noun-phrase ellipsis (the determiner otra/otro): grammatically correct determiners that mismatched the gender of attractor-nouns elicited a sustained negativity as also observed for incorrect determiners (Martin, Nieuwland, & Carreiras, 2012). The current study sought to extend this novel finding in sentences containing object-extracted relative clauses, where the antecedent may be less prominent. Whereas correct determiners that matched the gender of attractor-nouns now elicited an early anterior negativity as also observed for mismatching determiners, the previously reported interaction pattern was replicated in P600 responses to subsequent words. Our results suggest that structural and gender information is simultaneously taken into account, providing further evidence for retrieval interference during comprehension of grammatical sentences. (C) 2014 Elsevier Inc. All rights reserved. [Martin, Andrea E.; Nieuwland, Mante S.] Univ Edinburgh, Dept Psychol, Sch Philosophy Psychol & Language Sci, Edinburgh EH8 9JZ, Midlothian, Scotland; [Martin, Andrea E.; Nieuwland, Mante S.; Carreiras, Manuel] Basque Ctr Cognit Brain & Language, Donostia San Sebastian, Spain; [Carreiras, Manuel] Basque Fdn Sci, Ikerbasque Fdn, Bilbao, Spain Martin, AE (reprint author), Univ Edinburgh, Dept Psychol, Sch Philosophy Psychol & Language Sci, 7 George Sq, Edinburgh EH8 9JZ, Midlothian, Scotland. andrea.martin@ed.ac.uk Spanish Ministry of Science and Innovation (MICINN) [CONSOLIDER-INGENIO2010CSD2008-00048]; ESRC; Economic and Social Research Council [RA2553] We thank Eri Takahashi, Saioa Larraza, and Brendan Costello. AEM was supported by a Juan de la Cierva fellowship from the Spanish Ministry of Science and Innovation (MICINN) and a ESRC Future Research Leaders fellowship. This work was supported by the Economic and Social Research Council [Grant number RA2553], to AEM. MSN was supported by a Ramon y Cajal fellowship and a Plan Nacional research grant from MICINN, and MC was supported by CONSOLIDER-INGENIO2010CSD2008-00048 from MICINN. Anderson M. C., 1996, HDB PERCEPTION MEMOR, V10, P237; Badecker W, 2002, J EXP PSYCHOL LEARN, V28, P748, DOI 10.1037//0278-7393.28.4.748; Barber H, 2005, J COGNITIVE NEUROSCI, V17, P137, DOI 10.1162/0898929052880101; BOCK K, 1991, COGNITIVE PSYCHOL, V23, P45, DOI 10.1016/0010-0285(91)90003-7; Bornkessel I, 2006, PSYCHOL REV, V113, P787, DOI 10.1037/0033-295X.113.4.787; Bornkessel-Schlesewsky I., 2009, LANGUAGE LINGUISTICS, V3, P19, DOI 10.1111/j.1749-818X.2008.00099.x; Burkhardt P, 2006, BRAIN LANG, V98, P159, DOI 10.1016/j.bandl.2006.04.005; Chomsky N., 1981, LECTURES ON GOVERNME; Cunnings I, 2013, LANG COGNITIVE PROC, V28, P188, DOI 10.1080/01690965.2010.548391; Dillon B., 2013, JOURNAL OF MEMORY AN; Eguren L, 2010, LINGUA, V120, P435, DOI 10.1016/j.lingua.2009.05.004; Gerrig RJ, 2005, DISCOURSE PROCESS, V39, P225, DOI 10.1207/s15326950dp3902&3_7; GILLUND G, 1984, PSYCHOL REV, V91, P1, DOI 10.1037/0033-295X.91.1.1; Gordon PC, 2006, J EXP PSYCHOL LEARN, V32, P1304, DOI 10.1037/0278-7393.32.6.1304; Hagoort P., 1999, NEUROCOGNITION LANGU, P273; Hanulikova A, 2012, J COGNITIVE NEUROSCI, V24, P878, DOI 10.1162/jocn_a_00103; Kaan E, 2002, J PSYCHOLINGUIST RES, V31, P165, DOI 10.1023/A:1014978917769; Kaan E, 2003, J COGNITIVE NEUROSCI, V15, P98, DOI 10.1162/089892903321107855; Kaan E, 2007, BRAIN RES, V1146, P199, DOI 10.1016/j.brainres.2006.09.060; KING JW, 1995, J COGNITIVE NEUROSCI, V7, P376, DOI 10.1162/jocn.1995.7.3.376; Martin AE, 2009, J EXP PSYCHOL LEARN, V35, P1231, DOI 10.1037/a0016271; Martin AE, 2008, J MEM LANG, V58, P879, DOI 10.1016/j.jml.2007.06.010; Martin AE, 2012, NEUROIMAGE, V59, P1859, DOI 10.1016/j.neuroimage.2011.08.057; Martin AE, 2011, J MEM LANG, V64, P327, DOI 10.1016/j.jml.2010.12.006; McElree B, 2003, J MEM LANG, V48, P67, DOI 10.1016/S0749-596X(02)00515-6; McElree B., 2006, THE PSYCHOLOGY OF LE, V46; Nairne JS, 2002, MEMORY, V10, P389, DOI 10.1080/09658210244000216; Nicol JL, 1997, J MEM LANG, V36, P569, DOI 10.1006/jmla.1996.2497; Nieuwland M. S., 2008, LANGUAGE LINGUISTICS, V2, P603, DOI 10.1111/j.1749-818X.2008.00070.x; Nieuwland MS, 2006, BRAIN RES, V1118, P155, DOI 10.1016/j.brainres.2006.08.022; Nieuwland MS, 2008, BRAIN LANG, V106, P119, DOI 10.1016/j.bandl.2008.05.001; Osterhout L, 1997, BRAIN LANG, V59, P494, DOI 10.1006/brln.1997.1793; Osterhout L, 1995, J MEM LANG, V34, P739, DOI 10.1006/jmla.1995.1033; OSTERHOUT L, 1994, J EXP PSYCHOL LEARN, V20, P786, DOI 10.1037//0278-7393.20.4.786; OSTERHOUT L, 1992, J MEM LANG, V31, P785, DOI 10.1016/0749-596X(92)90039-Z; Pearlmutter NJ, 1999, J MEM LANG, V41, P427, DOI 10.1006/jmla.1999.2653; Phillips C., 2010, EXPERIMENTS AT THE I, V37; Silva-Pereyra J, 2012, PSYCHOPHYSIOLOGY, V49, P1401, DOI 10.1111/j.1469-8986.2012.01446.x; Staub A, 2009, J MEM LANG, V60, P308, DOI 10.1016/j.jml.2008.11.002; Sturt P, 2003, J MEM LANG, V48, P542, DOI 10.1016/S0749-596X(02)00536-3; Suner M, 1998, LANGUAGE, V74, P335, DOI 10.2307/417870; Tanner D, 2012, PROC ANN BUCLD, P594; van Berkum JJA, 1999, J MEM LANG, V41, P147, DOI 10.1006/jmla.1999.2641; Van Berkum JJA, 2007, BRAIN RES, V1146, P158, DOI 10.1016/j.brainres.2006.06.091; Van Dyke JA, 2003, J MEM LANG, V49, P285, DOI 10.1016/S0749-596X(03)00081-0; Van Dyke JA, 2011, J MEM LANG, V65, P247, DOI 10.1016/j.jml.2011.05.002; Van Dyke JA, 2007, J EXP PSYCHOL LEARN, V33, P407, DOI 10.1037/0278-7393.33.2.407; Wagers MW, 2009, J MEM LANG, V61, P206, DOI 10.1016/j.jml.2009.04.002 48 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0093-934X 1090-2155 BRAIN LANG Brain Lang. AUG 2014 135 42 51 10.1016/j.bandl.2014.05.001 10 Audiology & Speech-Language Pathology; Linguistics; Neurosciences; Psychology, Experimental Audiology & Speech-Language Pathology; Linguistics; Neurosciences & Neurology; Psychology AN1AZ WOS:000340316400005 J Santos-Ribeiro, S; Polyzos, NP; Haentjens, P; Smitz, J; Camus, M; Tournaye, H; Blockeel, C Santos-Ribeiro, S.; Polyzos, N. P.; Haentjens, P.; Smitz, J.; Camus, M.; Tournaye, H.; Blockeel, C. Live birth rates after IVF are reduced by both low and high progesterone levels on the day of human chorionic gonadotrophin administration HUMAN REPRODUCTION English Article ovarian stimulation; progesterone; in vitro fertilization; live birth rate IN-VITRO FERTILIZATION; ELEVATED SERUM PROGESTERONE; CONTROLLED OVARIAN HYPERSTIMULATION; HUMAN MENOPAUSAL GONADOTROPINS; ASSISTED REPRODUCTION CYCLES; HIGHER PREGNANCY RATE; PREMATURE LUTEINIZATION; EMBRYO-TRANSFER; FOLLICULAR PHASE; HORMONE AGONIST Are low serum progesterone levels on the day of human chorionic gonadotrophin (hCG) administration detrimental for live birth delivery rates during in vitro fertilization (IVF)? Progesterone levels a parts per thousand currency sign0.5 ng/ml on the day of hCG administration hinder live birth rates. Fundamental research has shown that the presence of late follicular phase progesterone is essential for follicular development, ovulation and endometrial receptivity. However, previous studies in patients undergoing ovarian stimulation have only assessed if progesterone levels in the higher range are detrimental for pregnancy or not. That said, information on the effect of the full range of late follicular progesterone on IVF outcomes is still lacking. This was a retrospective, single-centre cohort study with 2723 cycles performed in patients aged between 19 and 36 and undergoing controlled ovarian stimulation between January 2006 and March 2012 for their first or second attempt of IVF followed by a fresh embryo transfer (ET). All patients underwent ovarian stimulation using a gonadotrophin-releasing hormone (GnRH) antagonist for pituitary down-regulation. Final oocyte maturation was triggered with hCG 36 h before oocyte retrieval. On the day of hCG administration, serum progesterone evaluation was performed. Live birth delivery rates were compared amongst various ordinal and regular progesterone intervals (a parts per thousand currency sign0.50, 0.50-0.75, 0.75-1.00, 1.00-1.25, 1.25-1.50, > 1.50 ng/ml) using logistic regression. The average age of our sample was 30.5 years. Almost 82% of all embryo transfers were of a single embryo and 51.8% were performed with a Day 5 embryo. The average value (+/- standard deviation) of progesterone on the day of hCG administration was 1.02 +/- 0.50 ng/ml and the live birth rate was 23.4%. The live birth rates (according to the above-described ordinal serum progesterone intervals) were 17.1, 25.1, 26.7, 25.5, 21.9 and 16.6%, respectively. The live birth rates were significantly lower in patients with both low (a parts per thousand currency sign0.5 ng/ml) and high (> 1.5 ng/ml) late follicular progesterone levels (P < 0.05). The main limitation of our study was its retrospective nature. Furthermore, our study was restricted to patients under GnRH antagonist pituitary suppression and requires confirmation in a GnRH agonist setting. This study comprehensively assessed the relationship between live birth delivery rates and progesterone levels on the day of hCG administration during ovarian stimulation for IVF. Clinically relevant lower (a parts per thousand currency sign0.5 ng/ml) and higher (> 1.5 ng/ml) progesterone level limits were determined. No funding was received for this study and the authors have no conflicts of interest to declare. [Santos-Ribeiro, S.; Polyzos, N. P.; Haentjens, P.; Smitz, J.; Camus, M.; Tournaye, H.; Blockeel, C.] Vrije Univ Brussel, Univ Ziekenhuis Brussel, Ctr Reprod Med, B-1090 Brussels, Belgium; [Santos-Ribeiro, S.] Hosp Univ Santa Maria, Dept Obstet Gynaecol & Reprod Med, P-1649035 Lisbon, Portugal Santos-Ribeiro, S (reprint author), Vrije Univ Brussel, Univ Ziekenhuis Brussel, Ctr Reprod Med, Laarbeeklaan 101, B-1090 Brussels, Belgium. samueldsribeiro@gmail.com Al-Azemi M, 2012, REPROD BIOMED ONLINE, V24, P381, DOI 10.1016/j.rbmo.2012.01.010; [Anonymous], 2009, HUM REPROD, V23, P24; [Anonymous], 1996, FERTIL STERIL, V191, P65; BARILE G, 1979, EUR J OBSTET GYN R B, V9, P243, DOI 10.1016/0028-2243(79)90062-5; Borman SM, 2004, BIOL REPROD, V71, P366, DOI 10.1095/biolreprod.103.023390; Bosch E, 2008, HUM REPROD UPDATE, V14, P194, DOI 10.1093/humupd/dmm046; Bosch E, 2003, FERTIL STERIL, V80, P1444, DOI 10.1016/j.fertnstert.2003.07.002; Bosch E, 2010, HUM REPROD, V25, P2092, DOI 10.1093/humrep/deq125; Bosch E, 2011, HUM REPROD, V26, P499, DOI 10.1093/humrep/deq356; Bridges PJ, 2006, ENDOCRINOLOGY, V147, P4713, DOI 10.1210/en.2005-1575; Bustillo M, 1995, HUM REPROD, V10, P2862; CHAFFKIN LM, 1992, J CLIN ENDOCR METAB, V75, P1404, DOI 10.1210/jc.75.6.1404; CHECK JH, 1994, FERTIL STERIL, V61, P262; CHECK JH, 1993, EUR J OBSTET GYN R B, V52, P205, DOI 10.1016/0028-2243(93)90073-L; Chetkowski RJ, 1997, FERTIL STERIL, V68, P292, DOI 10.1016/S0015-0282(97)81518-X; COLLINS RL, 1986, J CLIN ENDOCR METAB, V63, P1270; COUZINET B, 1992, J CLIN ENDOCR METAB, V74, P374, DOI 10.1210/jc.74.2.374; De Geyter C, 2002, HUM REPROD, V17, P933, DOI 10.1093/humrep/17.4.933; de Ziegler D, 2008, HUM REPROD UPDATE, V14, P393, DOI 10.1093/humupd/dmn020; DIRNFELD M, 1993, J ASSIST REPROD GEN, V10, P126, DOI 10.1007/BF01207734; Doldi N, 1999, HUM REPROD, V14, P601, DOI 10.1093/humrep/14.3.601; EDELSTEIN MC, 1990, FERTIL STERIL, V54, P853; Elnashar AM, 2010, J ASSIST REPROD GEN, V27, P149, DOI 10.1007/s10815-010-9393-8; Fanchin R, 1997, FERTIL STERIL, V68, P799, DOI 10.1016/S0015-0282(97)00337-3; Fanchin R, 1996, FERTIL STERIL, V65, P1178; FANCHIN R, 1993, FERTIL STERIL, V59, P1090; Fanchin R, 1997, FERTIL STERIL, V68, P648, DOI 10.1016/S0015-0282(97)80464-5; Felberbaum R, 1999, HUM REPROD, V14, P207; FELDBERG D, 1989, J IN VITRO FERTIL EM, V6, P11, DOI 10.1007/BF01134575; Filicori M, 2005, FERTIL STERIL, V84, P394, DOI 10.1016/j.fertnstert.2005.02.036; Fleming R, 2008, HUM REPROD UPDATE, V14, P391, DOI 10.1093/humupd/dmn019; GIVENS CR, 1994, FERTIL STERIL, V62, P1011; HARADA T, 1995, HUM REPROD, V10, P1060; Harada T, 1996, FERTIL STERIL, V65, P594; Hibbert ML, 1996, P NATL ACAD SCI USA, V93, P1897, DOI 10.1073/pnas.93.5.1897; HILDPETITO S, 1988, ENDOCRINOLOGY, V123, P2896; Hofmann GE, 1996, FERTIL STERIL, V66, P980; Huang JC, 1996, J ASSIST REPROD GEN, V13, P617, DOI 10.1007/BF02069639; Ioannidis G, 2005, HUM REPROD, V20, P741, DOI 10.1093/humrep/deh644; Kilicdag EB, 2010, ARCH GYNECOL OBSTET, V281, P747, DOI 10.1007/s00404-009-1248-0; Kolibianakis EM, 2012, CURR PHARM BIOTECHNO, V13, P464; Labarta E, 2011, HUM REPROD, V26, P1813, DOI 10.1093/humrep/der126; LEGRO RS, 1993, HUM REPROD, V8, P1506; LESSEY BA, 1988, J CLIN ENDOCR METAB, V67, P334; LEVY MJ, 1995, J ASSIST REPROD GEN, V12, P161, DOI 10.1007/BF02211792; Li R, 2008, REPROD BIOMED ONLINE, V16, P627; Luke B, 2012, NEW ENGL J MED, V366, P2483, DOI 10.1056/NEJMoa1110238; Malizia BA, 2009, NEW ENGL J MED, V360, P236, DOI 10.1056/NEJMoa0803072; Martinez F, 2004, REPROD BIOMED ONLINE, V8, P183; Melo MAB, 2006, HUM REPROD, V21, P1503, DOI 10.1093/humrep/dei474; Miller KF, 1996, J ASSIST REPROD GEN, V13, P698, DOI 10.1007/BF02066420; MIO Y, 1992, FERTIL STERIL, V58, P159; Moffitt DV, 1997, FERTIL STERIL, V67, P296, DOI 10.1016/S0015-0282(97)81914-0; NATRAJ U, 1993, ENDOCRINOLOGY, V133, P761, DOI 10.1210/en.133.2.761; Ozcakir HT, 2004, J OBSTET GYNAECOL RE, V30, P100, DOI 10.1111/j.1447-0756.2003.00166.x; Randall GW, 1996, J ASSIST REPROD GEN, V13, P459, DOI 10.1007/BF02066524; RAVN V, 1994, CELL TISSUE RES, V276, P419; Saleh HA, 2009, J ASSIST REPROD GEN, V26, P239, DOI 10.1007/s10815-009-9309-7; SCHOOLCRAFT W, 1991, FERTIL STERIL, V55, P563; Shulman A, 1996, J ASSIST REPROD GEN, V13, P207, DOI 10.1007/BF02065937; SILVERBERG KM, 1991, J CLIN ENDOCR METAB, V73, P797; SMITZ J, 1992, HUM REPROD, V7, P49; Stouffer RL, 2007, FRONT BIOSCI, V12, P297, DOI 10.2741/2065; Ubaldi F, 1996, FERTIL STERIL, V66, P275; Venetis CA, 2013, HUM REPROD UPDATE, V19, P433, DOI 10.1093/humupd/dmt014; Venetis CA, 2007, HUM REPROD UPDATE, V13, P343, DOI 10.1093/humupd/dmm007; Wu Z, 2012, REPROD BIOMED ONLINE, V24, P511, DOI 10.1016/j.rbmo.2012.02.003; Xu B, 2012, FERTIL STERIL, V97, P1321, DOI 10.1016/j.fertnstert.2012.03.014; Younis JS, 2011, HUM REPROD, V26, P498, DOI 10.1093/humrep/deq355; Younis JS, 2001, FERTIL STERIL, V76, P294, DOI 10.1016/S0015-0282(01)01918-5; Younis JS, 1998, FERTIL STERIL, V69, P461, DOI 10.1016/S0015-0282(97)00561-X; YOVEL I, 1995, FERTIL STERIL, V64, P128; ZELINSKIWOOTEN MB, 1994, FERTIL STERIL, V61, P1147 73 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0268-1161 1460-2350 HUM REPROD Hum. Reprod. AUG 2014 29 8 1698 1705 10.1093/humrep/deu151 8 Obstetrics & Gynecology; Reproductive Biology Obstetrics & Gynecology; Reproductive Biology AM7TG WOS:000340071000014 J Zheng, L; Wang, SJ; Tian, Q Zheng, Liang; Wang, Shengjin; Tian, Qi Lp-Norm IDF for Scalable Image Retrieval IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Image retrieval; Lp-norm IDF; burstiness; visual word frequency SCALE OBJECT RETRIEVAL; SEARCH; CLASSIFICATION; VOCABULARY; CATEGORIZATION; SIMILARITY; GEOMETRY; FEATURES; KERNEL; CODES The inverse document frequency (IDF) is prevalently utilized in the bag-of-words-based image retrieval application. The basic idea is to assign less weight to terms with high frequency, and vice versa. However, in the conventional IDF routine, the estimation of visual word frequency is coarse and heuristic. Therefore, its effectiveness is largely compromised and far from optimal. To address this problem, this paper introduces a novel IDF family by the use of Lp-norm pooling technique. Carefully designed, the proposed IDF considers the term frequency, document frequency, the complexity of images, as well as the codebook information. We further propose a parameter tuning strategy, which helps to produce optimal balancing between TF and pIDF weights, yielding the so-called Lp-norm IDF (pIDF). We show that the conventional IDF is a special case of our generalized version, and two novel IDFs, i.e., the average IDF and the max IDF, can be defined from the concept of pIDF. Further, by counting for the term-frequency in each image, the proposed pIDF helps to alleviate the visual word burstiness phenomenon. Our method is evaluated through extensive experiments on four benchmark data sets (Oxford 5K, Paris 6K, Holidays, and Ukbench). We show that the pIDF works well on large scale databases and when the codebook is trained on irrelevant data. We report an mean average precision improvement of as large as +13.0% over the baseline TF-IDF approach on a 1M data set. In addition, the pIDF has a wide application scope varying from buildings to general objects and scenes. When combined with postprocessing steps, we achieve competitive results compared with the state-of-the-art methods. In addition, since the pIDF is computed offline, no extra computation or memory cost is introduced to the system at all. [Zheng, Liang; Wang, Shengjin] Tsinghua Univ, Dept Elect Engn, State Key Lab Intelligent Technol & Syst, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China; [Tian, Qi] Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA Wang, SJ (reprint author), Tsinghua Univ, Dept Elect Engn, State Key Lab Intelligent Technol & Syst, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China. zheng-l06@mails.tsinghua.edu.cn; wgsgj@tsinghua.edu.cn; qitian@cs.utsa.edu National High Technology Research and Development Program of China 863 Program [2012AA011004]; National Science and Technology Support Program [2013BAK02B04]; Army Research Office [W911NF-12-1-0057]; Faculty Research Award through NEC Laboratories of America, Inc.; University of Texas at San Antonio START Research Award; National Science Foundation of China [61128007] This work was supported in part by the National High Technology Research and Development Program of China 863 Program under Grant 2012AA011004, in part by the National Science and Technology Support Program under Grant 2013BAK02B04. The work of Q. Tian was supported in part by the Army Research Office under Grant W911NF-12-1-0057, in part by the Faculty Research Award through NEC Laboratories of America, Inc., in 2012, in part by the University of Texas at San Antonio START Research Award, and in part by the National Science Foundation of China, under Grant 61128007. The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Bulent Sankur. (Corresponding authors: Shengjin Wang and Qi Tian.) Arandjelovic R, 2012, PROC CVPR IEEE, P2911, DOI 10.1109/CVPR.2012.6248018; Baeza-Yates R, 1999, MODERN INFORM RETRIE, V463; Bu S., 2014, P IEEE ICIP; Cai J., 2013, P 3 ACM C ICMR, P33; Cai Y., 2012, P 2 ACM ICMR, P16; Chum O., 2007, P IEEE INT C COMP VI, P1; Csurka G., 2004, WORKSH STAT LEARN CO, V1, P1; Dean T, 2013, PROC CVPR IEEE, P1814, DOI 10.1109/CVPR.2013.237; Elkan C, 2005, LECT NOTES COMPUT SC, V3772, P295; Feng J., 2011, P IEEE C COMP VIS PA, P2609; He F., 2011, P 4 INT CISP OCT, V3, P1485; Jegou H, 2010, INT J COMPUT VISION, V87, P316, DOI 10.1007/s11263-009-0285-2; Jegou H, 2009, PROC CVPR IEEE, P1169; Jegou H, 2008, LECT NOTES COMPUT SC, V5302, P304, DOI 10.1007/978-3-540-88682-2_24; Ji RR, 2012, IEEE T IMAGE PROCESS, V21, P2282, DOI 10.1109/TIP.2011.2176950; Ji RR, 2009, PROC CVPR IEEE, P1161; Jiang Y, 2007, P ACM INT C IM VID R, P494; Jiang YG, 2013, IEEE T MULTIMEDIA, V15, P442, DOI 10.1109/TMM.2012.2231061; Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526; Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Li X., 2014, P IEEE ICIP; Li YL, 2014, IEEE T IMAGE PROCESS, V23, P1858, DOI 10.1109/TIP.2014.2307432; Liu Z., 2014, P INT C AC SPEECH SI, P6909; Liu Z, 2014, IEEE T IMAGE PROCESS, V23, P1606, DOI 10.1109/TIP.2014.2305072; Liu Z, 2014, IEEE T IMAGE PROCESS, V23, P2047, DOI 10.1109/TIP.2014.2312283; Lopez-Sastre RJ, 2013, IEEE T CIRC SYST VID, V23, P1358, DOI 10.1109/TCSVT.2013.2243058; Lopez-Sastre RJ, 2011, COMPUT VIS IMAGE UND, V115, P415, DOI 10.1016/j.cviu.2010.10.009; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Lu W., 2011, P ACM MULT, P513; Mikulik A, 2010, LECT NOTES COMPUT SC, V6313, P1; Niester D., 2006, P IEEE COMP SOC C CV, P2161; Perd'och M, 2009, PROC CVPR IEEE, P9; Perronnin F, 2010, PROC CVPR IEEE, P3384, DOI 10.1109/CVPR.2010.5540009; Perronnin F, 2010, LECT NOTES COMPUT SC, V6314, P143, DOI 10.1007/978-3-642-15561-1_11; Philbin J, 2007, P IEEE C COMP VIS PA, P1; Philbin J., 2008, P IEEE C COMP VIS PA, P1; Qin DF, 2011, PROC CVPR IEEE, P777; Qin DF, 2013, PROC CVPR IEEE, P1610, DOI 10.1109/CVPR.2013.211; Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232; Shen XH, 2012, PROC CVPR IEEE, P3013; Simonyan K, 2014, IEEE T PATTERN ANAL, V36, P1573, DOI 10.1109/TPAMI.2014.2301163; Simonyan K., 2012, P 12 ECCV, P243; Singhal A., 2001, IEEE DATA ENG B, V24, P35; Sivic J., 2003, P ICCV, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663]; Su B., 2013, P 12 INT C DOC AN RE, P1250; Su Y, 2012, INT J COMPUT VISION, V100, P59, DOI 10.1007/s11263-012-0529-4; Tuytelaars T., 2007, P IEEE INT C COMP VI, P1; van Gemert JC, 2010, IEEE T PATTERN ANAL, V32, P1271, DOI 10.1109/TPAMI.2009.132; van Gemert JC, 2008, LECT NOTES COMPUT SC, V5304, P696, DOI 10.1007/978-3-540-88690-7_52; Wang BK, 2012, J ZHEJIANG U-SCI C, V13, P649, DOI 10.1631/jzus.C1100373; Wang J, 2012, PROC CVPR IEEE, P3037; Wang TQ, 2014, IEEE T CIRC SYST VID, V24, P277, DOI 10.1109/TCSVT.2013.2276856; WANG XY, 2011, P IEEE INT C COMP VI, P209; Wengert C., 2011, P 19 ACM INT C MULT, P1437; Xie LX, 2014, COMPUT VIS IMAGE UND, V124, P31, DOI 10.1016/j.cviu.2013.12.011; Xie LX, 2014, IEEE T IMAGE PROCESS, V23, P1994, DOI 10.1109/TIP.2014.2310117; Xie Y, 2013, NEUROCOMPUTING, V119, P478, DOI 10.1016/j.neucom.2013.03.004; Yang JC, 2009, PROC CVPR IEEE, P1794; Yang X, 2014, IEEE T IMAGE PROCESS, V23, P2854, DOI 10.1109/TIP.2014.2321506; Zhang L, 2013, PROCEEDINGS OF THE EIGHTH INTERNATIONAL SYMPOSIUM ON VITICULTURE AND ENOLOGY (2013), P123; Zhang SG, 2010, PROCEEDINGS OF THE ASME JOINT RAIL CONFERENCE, VOL 2, P501, DOI 10.1145/1873951.1874018; Zhang S., 2012, P EUR C COMP VIS ECC, P660; Zhang S., 2013, P IEEE ICCV DEC, P1673; Zhang Shuhong, 2009, Proceedings of the 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2009), DOI 10.1109/FSKD.2009.456; Zhang YM, 2011, PROC CVPR IEEE, P809; Zheng L, 2013, IEEE SIGNAL PROC LET, V20, P391, DOI 10.1109/LSP.2013.2249513; Zheng L, 2013, PROC CVPR IEEE, P1626, DOI 10.1109/CVPR.2013.213; Zheng L., 2014, P IEEE C CVPR JUN; Zheng L., 2014, ARXIV14060132; Zhou WG, 2013, ACM T MULTIM COMPUT, V9, DOI 10.1145/2422956.2422960; Zhou WG, 2014, IEEE T MULTIMEDIA, V16, P601, DOI 10.1109/TMM.2014.2301979; Zhou WG, 2012, IEEE T IMAGE PROCESS, V21, P4269, DOI 10.1109/TIP.2012.2199506; Zhu C.-Z., 2013, P ICCV 73 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. AUG 2014 23 8 3604 3617 10.1109/TIP.2014.2329182 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AM8BK WOS:000340094000001 J Yazdi, SMHT; Savari, SA Yazdi, S. M. Hossein Tabatabaei; Savari, Serap A. On the Relationships Among Optimal Symmetric Fix-Free Codes IEEE TRANSACTIONS ON INFORMATION THEORY English Article Source coding; fix-free codes; reversible-variable-length codes; minimum-redundancy codes VARIABLE-LENGTH CODES; VIDEO CODING STANDARD; CONSTRUCTION Symmetric fix-free codes are prefix condition codes in which each codeword is required to be a palindrome. Their study is motivated by the topic of joint source-channel coding and by some information retrieval problems. Although they have been considered by a few communities they are not well understood. In earlier work, we used a collection of instances of Boolean satisfiability problems as a tool in the generation of all optimal binary symmetric fix-free codes with n codewords and observed that the number of different optimal codelength sequences grows slowly compared with the corresponding number for prefix condition codes. We demonstrate that all optimal symmetric fix-free codes can alternatively be obtained by sequences of codes generated by simple manipulations starting from one particular code. We also discuss simplifications in the process of searching for this set of codes as well as a conjecture, which if correct, together with the other results leads to a relatively fast algorithm which has been implemented in MATLAB to construct all optimal binary symmetric fix-free codes. [Yazdi, S. M. Hossein Tabatabaei; Savari, Serap A.] Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA Yazdi, SMHT (reprint author), Texas A&M Univ, Dept Elect & Comp Engn, College Stn, TX 77843 USA. hussein.ty@gmail.com; savari@ece.tamu.edu National Science Foundation [CCF-1017303] This work was supported by the National Science Foundation under Grant CCF-1017303. This paper was presented in part at the 2013 Data Compression Conference. Abedini N., 2010, P IEEE DAT COMPR C S, P169; Bauer R., 2000, Proceedings DCC 2000. Data Compression Conference, DOI 10.1109/DCC.2000.838149; Bauer R, 2001, IEEE DATA COMPR CONF, P273; Berstel J., 1985, THEORY CODES; FRAENKEL AS, 1990, COMPUT J, V33, P296, DOI 10.1093/comjnl/33.4.296; GILBERT EN, 1971, IEEE T INFORM THEORY, V17, P304, DOI 10.1109/TIT.1971.1054638; GILBERT EN, 1959, AT&T TECH J, V38, P933; Huang YM, 2010, IEEE T COMMUN, V58, P3175, DOI 10.1109/TCOMM.2010.091710.0901872; Knuth D. E., 1997, FUNDAMENTAL ALGORITH, V1; Lakovic K, 2003, IEEE COMMUN LETT, V7, P391, DOI 10.1109/LCOMM.2003.815660; Marpe D, 2006, IEEE COMMUN MAG, V44, P134, DOI 10.1109/MCOM.2006.1678121; Parker DS, 1999, SIAM J COMPUT, V28, P1875, DOI 10.1137/S0097539796311077; Savari S. A., 2009, P IEEE DAT COMPR C S, P3; Savari S. A., 2009, P INF THEOR APPL WOR, P311; Savari SA, 2012, IEEE T INFORM THEORY, V58, P5112, DOI 10.1109/TIT.2012.2196490; Schnettler H., 2007, ARXIV07092598V1CSIT; SCHUTZENBERGER MP, 1956, IRE T INFORM THEOR, V2, P47, DOI 10.1109/TIT.1956.1056809; SHANNON CE, 1948, AT&T TECH J, V27, P623; Sloane N. J. A., ON LINE ENCY INTEGER; TAKISHIMA Y, 1995, IEEE T COMMUN, V43, P158, DOI 10.1109/26.380026; Tsai CW, 2001, IEEE T INFORM THEORY, V47, P2543; Tsai CW, 2001, IEEE T COMMUN, V49, P1506; Tseng HW, 2003, COMPUT J, V46, P100, DOI 10.1093/comjnl/46.1.100; Wiegand T, 2003, IEEE T CIRC SYST VID, V13, P560, DOI 10.1109/TCSVT.2003.815165; Yazdi S. M. H. T., 2012, ARXIV12112723V1CSIT; Yazdi SMHT, 2013, IEEE DATA COMPR CONF, P391, DOI 10.1109/DCC.2013.47 26 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0018-9448 1557-9654 IEEE T INFORM THEORY IEEE Trans. Inf. Theory AUG 2014 60 8 4567 4583 10.1109/TIT.2014.2330839 17 Computer Science, Information Systems; Engineering, Electrical & Electronic Computer Science; Engineering AN0KO WOS:000340273700012 J Su, L; Yeh, CCM; Liu, JY; Wang, JC; Yang, YH Su, Li; Yeh, Chin-Chia Michael; Liu, Jen-Yu; Wang, Ju-Chiang; Yang, Yi-Hsuan A Systematic Evaluation of the Bag-of-Frames Representation for Music Information Retrieval IEEE TRANSACTIONS ON MULTIMEDIA English Article Bag-of-frames model; music information retrieval; sparse coding; unsupervised feature learning SPARSE REPRESENTATION; PATTERN-RECOGNITION; CLASSIFICATION; REGRESSION There has been an increasing attention on learning feature representations from the complex, high-dimensional audio data applied in various music information retrieval (MIR) problems. Unsupervised feature learning techniques, such as sparse coding and deep belief networks have been utilized to represent music information as a term-document structure comprising of elementary audio codewords. Despite the widespread use of such bag-of-frames (BoF) model, few attempts have been made to systematically compare different component settings. Moreover, whether techniques developed in the text retrieval community are applicable to audio codewords is poorly understood. To further our understanding of the BoF model, we present in this paper a comprehensive evaluation that compares a large number of BoF variants on three different MIR tasks, by considering different ways of low-level feature representation, codebook construction, codeword assignment, segment-level and song-level feature pooling, tf-idf term weighting, power normalization, and dimension reduction. Our evaluations lead to the following findings: 1) modeling music information by two levels of abstraction improves the result for difficult tasks such as predominant instrument recognition, 2) tf-idf weighting and power normalization improve system performance in general, 3) topic modeling methods such as latent Dirichlet allocation does not work for audio codewords. [Su, Li; Yeh, Chin-Chia Michael; Liu, Jen-Yu; Yang, Yi-Hsuan] Acad Sinica, Res Ctr Informat Technol Innovat, Taipei 11564, Taiwan; [Wang, Ju-Chiang] Acad Sinica, Inst Informat Sci, Taipei 11564, Taiwan Su, L (reprint author), Acad Sinica, Res Ctr Informat Technol Innovat, Taipei 11564, Taiwan. lisu@citi.sinica.edu.tw; mcyeh@citi.sinica.edu.tw; ciaua@citi.sinica.edu.tw; asriver@iis.sinica.edu.tw; yang@citi.sinica.edu.tw National Science Council of Taiwan [NSC 101-2221-E-001-017, NSC 102-2221-E-001-004-MY3]; Academia Sinica Career Development Award This work was supported in part by the National Science Council of Taiwan under Grants NSC 101-2221-E-001-017, NSC 102-2221-E-001-004-MY3 and in part by the Academia Sinica Career Development Award. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Tao Li. Aharon M, 2006, IEEE T SIGNAL PROCES, V54, P4311, DOI 10.1109/TSP.2006.881199; Aryafar K., 2011, P 1 ACM INT WORKSH M, P33; Aucouturier JJ, 2007, J ACOUST SOC AM, V122, P881, DOI 10.1121/1.2750160; Battenberg E., 2012, P ISMIR, P37; Bengio Yoshua, 2009, Foundations and Trends in Machine Learning, V2, DOI 10.1561/2200000006; Berenzweig A, 2004, COMPUT MUSIC J, V28, P63, DOI 10.1162/014892604323112257; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Boureau Y.-L., 2010, P ICML; Chang C.-C., 2001, ACM T INTELL SYST TE; Chen SSB, 1998, SIAM J SCI COMPUT, V20, P33, DOI 10.1137/S1064827596304010; Coates A., 2011, P INT C MACH LEARN I, P921; Coates A., 2011, P AISTATS; Corey P. D. Kereliuk, 2011, P INT C DIG AUD EFF, P81; Coviello E., 2012, P INT S MUS INF RETR, P547; Dieleman S., 2011, P INT S MUS INF RETR, P669; Efron B, 2004, ANN STAT, V32, P407; Fu ZY, 2011, PATTERN RECOGN LETT, V32, P1768, DOI 10.1016/j.patrec.2011.06.026; Fuhrmann F., 2012, THESIS U POMPEU FABR; Gemmeke JF, 2011, IEEE T AUDIO SPEECH, V19, P2067, DOI 10.1109/TASL.2011.2112350; Gersho A., 1991, VECTOR QUANTIZATION; Hamel P., 2011, P 12 INT C MUS INF R, P729; Hamel P., 2010, P 11 ISMIR AUG, P339; Haro M, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0033993; Henaff M., 2011, P INT S MUS INF RETR, P681; Humphrey E. J., 2012, P ISMIR, P403; Humphrey EJ, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P453; Jegou H, 2012, IEEE T PATTERN ANAL, V34, P1704, DOI 10.1109/TPAMI.2011.235; Lacoste A., 2007, EURASIP J ADV SIG PR, P1; Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110; Lee C.-T., IEEE T MULT IN PRESS; Lee H., 2009, P NIPS, P1096; Lyon RF, 2010, NEURAL COMPUT, V22, P2390, DOI 10.1162/NECO_a_00011; Mairal J., 2009, P 26 ANN INT C MACH, P689; Maji S., 2008, P IEEE C COMP VIS PA, P1; Manzagol P., 2008, P INT C MUS INF RETR, P14; McFee B., 2012, IEEE T AUDIO SPEECH, V20; Muller M, 2011, IEEE J-STSP, V5, P1088, DOI 10.1109/JSTSP.2011.2112333; Nam J., 2012, P INT SOC MUS INF RE, P565; Nam J., 2011, P ISMIR MIAM FL US, P175; Pampalk E., 2002, P 10 ACM INT C MULT, P570; Plumbley MD, 2010, P IEEE, V98, P995, DOI 10.1109/JPROC.2009.2030345; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Riley M., 2008, P ISMIR PHIL PA US, P295; Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582]; Robertson S. E., 1995, P 4 TEXT RETRIEVAL C, P73; Schedl M., 2011, ACM T INFORM SYSTEMS, V29; Schluter J., 2011, Proceedings of the 2011 Tenth International Conference on Machine Learning and Applications (ICMLA 2011), DOI 10.1109/ICMLA.2011.102; Schmidt E. M., 2012, P INT SOC MUS INF RE, P325; Scholler S, 2011, IEEE J-STSP, V5, P933, DOI 10.1109/JSTSP.2011.2161264; Sculley D., 2010, P 19 INT C WORLD WID, P1177, DOI 10.1145/1772690.1772862; Seyerlehner K., 2008, P INT C DIG AUD EFF; Smith EC, 2006, NATURE, V439, P978, DOI 10.1038/nature04485; Sturm B. L., 2012, P INT ACM WORKSH MIR, P7; Tibshirani R, 1996, J ROY STAT SOC B MET, V58, P267; Turnbull D., 2007, P 30 ANN INT ACM SIG, P439, DOI 10.1145/1277741.1277817; Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560; Wang J.-C., 2011, P ISMIR; Weiss R. J., 2011, P ISMIR, P123; Wright J, 2010, P IEEE, V98, P1031, DOI 10.1109/JPROC.2010.2044470; Wulfing J., 2012, P INT SOC MUS INF RE, P139; Yang JC, 2009, PROC CVPR IEEE, P1794; Yeh C.-C. M., 2012, P ACM ICMR; Yeh C.-C. M., 2013, P IEEE ICASSP 63 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia AUG 2014 16 5 1188 1200 10.1109/TMM.2014.2311016 13 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AN0SZ WOS:000340295600002 J Lee, K; Lee, K Lee, Kibeom; Lee, Kyogu Using Dynamically Promoted Experts for Music Recommendation IEEE TRANSACTIONS ON MULTIMEDIA English Article Algorithm Design and Analysis; Information Retrieval; Recommender Systems SYSTEMS Recommender systems have become an invaluable asset to online services with the ever-growing number of items and users. Most systems focused on recommendation accuracy, predicting likable items for each user. Such methods tend to generate popular and safe recommendations, but fail to introduce users to potentially risky, yet novel items that could help in increasing the variety of items consumed by the users. This is known as popularity bias, which is predominant in methods that adopt collaborative filtering. Recently, however, recommenders have started to improve their methods to generate lists that encompass diverse items that are both accurate and novel through specific novelty-driven algorithms or hybrid recommender systems. In this paper, we propose a recommender system that uses the concepts of Experts to find both novel and relevant recommendations. By analyzing the ratings of the users, the algorithm promotes special Experts from the user population to create novel recommendations for a target user. Thus, different users are promoted dynamically to Experts depending on who the recommendations are for. The system used data collected from Last.fm and was evaluated with several metrics. Results show that the proposed system outperforms matrix factorization methods in finding novel items and performs on par in finding simultaneously novel and relevant items. This system can also provide a means to popularity bias while preserving the advantages of collaborative filtering. [Lee, Kibeom; Lee, Kyogu] Seoul Natl Univ, Dept Transdisciplinary Studies, Seoul, South Korea Lee, K (reprint author), Seoul Natl Univ, Dept Transdisciplinary Studies, Seoul, South Korea. kiblee@snu.ac.kr; kglee@snu.ac.kr Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education, Science and Technology [2011-0013476] This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2011-0013476). The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Tao Li. Akiyama T., 2010, P WORKSH PRACT US RE, P3; Amatriain X, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P532, DOI 10.1145/1571941.1572033; Aucouturier J.-J., 2002, P 3 INT S MUS INF OC; Bu J., 2010, P INT C MULT, P391, DOI 10.1145/1873951.1874005; Burke R, 2002, USER MODEL USER-ADAP, V12, P331, DOI 10.1023/A:1021240730564; Celma O., 2008, P 2 KDD WORKSH LARG, P1, DOI 10.1145/1722149.1722154; Dhillon I. S., 2003, P 9 ACM SIGKDD INT C, P89; GOLDBERG D, 1992, COMMUN ACM, V35, P61, DOI 10.1145/138859.138867; Herlocker J. L., 2000, CSCW 2000. ACM 2000 Conference on Computer Supported Cooperative Work; Hoffman M., 2008, P INT C MUS INF RETR, P349; Kamahara J., 2005, P 11 INT MULT MOD C, P433; Kim SW, 2009, ONLINE INFORM REV, V33, P584, DOI 10.1108/14684520910969970; Konstan JA, 1997, COMMUN ACM, V40, P77, DOI 10.1145/245108.245126; Koren Y, 2009, COMPUTER, V42, P30, DOI 10.1109/MC.2009.263; Kumar A., 2012, INT J COMPUT APPL JA, V37, P7; Lee K., 2010, P IEEE SIL NAN WORKS, P47; Lee K., 2013, J COMPUT SCI ENG, V7, P21; Lin C., 2012, P 21 ACM C INF KNOWL, P1607; Linden G, 2003, IEEE INTERNET COMPUT, V7, P76, DOI 10.1109/MIC.2003.1167344; Logan B., 2001, P IEEE INT C MULT EX, P745; McFee B, 2011, J MACH LEARN RES, V12, P491; Mcnee S. M., 2002, P ACM 2002 C COMP SU, P116; McNee S. M., 2006, EXT ABSTR 2006 ACM C; MCNEE SM, 2006, CHI 06, P1097, DOI DOI 10.1145/1125451.1125659; Murakami T, 2008, LECT NOTES ARTIF INT, V4914, P40; Nanopoulos A, 2010, IEEE T AUDIO SPEECH, V18, P407, DOI 10.1109/TASL.2009.2033973; Onuma K, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P657; Resnick P., 1994, P ACM C COMP SUPP CO, P175, DOI DOI 10.1145/192844.192905; Shan HH, 2008, IEEE DATA MINING, P530, DOI 10.1109/ICDM.2008.91; Shao B, 2009, IEEE T AUDIO SPEECH, V17, P1602, DOI 10.1109/TASL.2009.2020893; Slaney M., 2008, P INT C MUS INF RETR, P313; Vargas S., 2011, P 5 ACM C REC SYST R, P109; Vozalis M. G., 2005, Proceedings. 5th International Conference on Intelligent Systems Design and Applications; Zhang Y. C., 2012, P 5 ACM INT C WEB SE, P13; Zhou T, 2010, P NATL ACAD SCI USA, V107, P4511, DOI 10.1073/pnas.1000488107; Ziegler CN, 2005, P 14 INT WORLD WID W, P22, DOI DOI 10.1145/1060745.1060754 36 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia AUG 2014 16 5 1201 1210 10.1109/TMM.2014.2311012 10 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AN0SZ WOS:000340295600003 J Smith, JBL; Chuan, CH; Chew, E Smith, Jordan B. L.; Chuan, Ching-Hua; Chew, Elaine Audio Properties of Perceived Boundaries in Music IEEE TRANSACTIONS ON MULTIMEDIA English Article Boundaries; corpus analysis; music analysis; music information retrieval TONAL MUSIC; GENERATIVE THEORY; SEGMENTATION; LERDAHL; SIGNALS; MELODY; RULES Data mining tasks such as music indexing, information retrieval, and similarity search, require an understanding of how listeners process music internally. Many algorithms for automatically analyzing the structure of recorded music assume that a large change in one or another musical feature suggests a section boundary. However, this assumption has not been tested: while our understanding of how listeners segment melodies has advanced greatly in the past decades, little is known about how this process works with more complex, full-textured pieces of music, or how stable this process is across genres. Knowing how these factors affect how boundaries are perceived will help researchers to judge the viability of algorithmic approaches with different corpora of music. We present a statistical analysis of a large corpus of recordings whose formal structure was annotated by expert listeners. We find that the acoustic properties of boundaries in these recordings corroborate findings of previous perceptual experiments. Nearly all boundaries correspond to peaks in novelty functions, which measure the rate of change of a musical feature at a particular time scale. Moreover, most of these boundaries match peaks in novelty for several features at several time scales. We observe that the boundary-novelty relationship can vary with listener, time scale, genre, and musical feature. Finally, we show that a boundary profile derived from a collection of novelty functions correlates with the estimated salience of boundaries indicated by listeners. [Smith, Jordan B. L.; Chew, Elaine] Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England; [Chuan, Ching-Hua] Univ N Florida, Coll Comp Engn & Construct, Jacksonville, FL 32224 USA Smith, JBL (reprint author), Queen Mary Univ London, Sch Elect Engn & Comp Sci, London E1 4NS, England. jblsmith@eecs.qmul.ac.uk; c.chuan@unf.edu; elaine.chew@eecs.qmul.ac.uk Social Sciences and Humanities Research Council of Canada; National Science Foundation of the United States; JISC of the United Kingdom We thank Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and Stephen J. Downie, who with the first author assembled the SALAMI data set. The SALAMI project was supported by the Social Sciences and Humanities Research Council of Canada, the National Science Foundation of the United States and JISC of the United Kingdom. Aucouturier JJ, 2005, IEEE T MULTIMEDIA, V7, P1028, DOI 10.1109/TMM.2005.858380; Bruderer M., 2008, P C INT MUS THESS GR; Bruderer MJ, 2009, MUSIC SCI, V13, P273, DOI 10.1177/102986490901300204; Cambouropoulos E., 2001, P ICMC HAV CUB; Chew E, 2005, J NEW MUSIC RES, V34, P341, DOI 10.1080/09298210600578147; Chew E., 2000, THESIS MIT CAMBRIDGE; Chuan C.-H., 2007, EURASIP J ADV SIGNAL; CLARKE EF, 1990, MUSIC PERCEPT, V7, P213; DELIEGE I, 1987, MUSIC PERCEPT, V4, P325; Foote J., 2000, P IEEE INT C MULT EX, P452; Frankland BW, 2004, MUSIC PERCEPT, V21, P499, DOI 10.1525/mp.2004.21.4.499; Goto M, 2006, IEEE T AUDIO SPEECH, V14, P1783, DOI 10.1109/TSA.2005.863204; Grosche P., 2012, P ISMIR PORT PORT, P55; Hamanaka M, 2006, J NEW MUSIC RES, V35, P249, DOI 10.1080/09298210701563238; Landone C., QMVAMP PLUGINS 2011; Lerdahl F., 1983, GENERATIVE THEORY TO; Margulis EH, 2012, MUSIC PERCEPT, V29, P377, DOI 10.1525/MP.2012.29.4.377; Muller M., 2007, EURASIP J APPL SIGNA; Pampalk E., 2002, P 10 ACM INT C MULT, P570; Pampalk E, 2004, COMPUT MUSIC J, V28, P49, DOI 10.1162/014892604323112248; Pampalk E., 2004, P ISMIR, P254; Paulus J., 2008, P 11 INT C DIG AUD E, P309; Paulus J., 2010, P 11 INT C MUS INF R, P625; Peeters G., 2004, COMPUTER MUSIC MODEL, P169; Sanden C, 2012, J NEW MUSIC RES, V41, P277, DOI 10.1080/09298215.2012.666556; Smith J. B. L., 2011, P INT SOC MUS INF RE, P555; Temperley D., 2001, COGNITION BASIC MUSI; Turnbull D., 2007, P 8 INT C MUS INF RE, P51 28 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia AUG 2014 16 5 1219 1228 10.1109/TMM.2014.2310706 10 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AN0SZ WOS:000340295600005 J Serra, J; Muller, M; Grosche, P; Arcos, JL Serra, Joan; Mueller, Meinard; Grosche, Peter; Arcos, Josep Ll Unsupervised Music Structure Annotation by Time Series Structure Features and Segment Similarity IEEE TRANSACTIONS ON MULTIMEDIA English Article Music information retrieval; Time series analysis; Unsupervised learning; Content-based retrieval AUDIO Automatically inferring the structural properties of raw multimedia documents is essential in today's digitized society. Given its hierarchical and multi-faceted organization, musical pieces represent a challenge for current computational systems. In this article, we present a novel approach to music structure annotation based on the combination of structure features with time series similarity. Structure features encapsulate both local and global properties of a time series, and allow us to detect boundaries between homogeneous, novel, or repeated segments. Time series similarity is used to identify equivalent segments, corresponding to musically meaningful parts. Extensive tests with a total of five benchmark music collections and seven different human annotations show that the proposed approach is robust to different ground truth choices and parameter settings. Moreover, we see that it outperforms previous approaches evaluated under the same framework. [Serra, Joan; Arcos, Josep Ll] IIIA CSIC, Bellaterra 08193, Spain; [Mueller, Meinard] Int Audio Labs Erlangen, D-91058 Erlangen, Germany; [Grosche, Peter] Univ Saarland, D-66123 Saarbrucken, Germany; [Grosche, Peter] Max Planck Inst Informat, D-66123 Saarbrucken, Germany Serra, J (reprint author), IIIA CSIC, Campus UAB S-N, Bellaterra 08193, Spain. jserra@iiia.csic.es; meinard.mueller@audiolabs-erlangen.de; pgrosche@mpi-inf.mpg.de; arcos@iiia.csic.es EU Feder; Cluster of Excellence on Multimodal Computing and Interaction at Saarland University; DFG MU [2682/5-1]; [ICT -2011-8-318770]; [2009-SGR-1434]; [JAEDOC069/2010] The work of J. Serra and J. Ll. Arcos was supported by ICT -2011-8-318770 and 2009-SGR-1434. The work of J. Serra also was supported by JAEDOC069/2010 and EU Feder funds. The work of M. Muller and P. Grosche was supported by the Cluster of Excellence on Multimodal Computing and Interaction at Saarland University and DFG MU 2682/5-1. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Mitsunori Ogihara. [Anonymous], 2007, P INT C MUS INF RETR, P35; Arnold D., 2012, OXFORD COMPANION MUS; Ball P., 2010, MUSIC INSTINCT MUSIC; Barrington L, 2010, IEEE T AUDIO SPEECH, V18, P602, DOI 10.1109/TASL.2009.2036306; Bimbot F., 2012, P INT SOC MUS INF RE, P235; Boutard G., 2006, P 1 WORKSH LEARN SEM, P87; Chen R., 2011, P 12 INT C MUS INF R, P477; Dannenberg R. B., 2002, P ISMIR, P63; Dannenberg R. B., 2008, HDB SIGNAL PROCESSIN, V1, P305; Downie JS, 2010, STUD COMPUT INTELL, V274, P93; Ewert S, 2009, INT CONF ACOUST SPEE, P1869, DOI 10.1109/ICASSP.2009.4959972; Foote J., 2000, P IEEE INT C MULT EX, P452; Gomez E, 2006, INFORMS J COMPUT, V18, P294, DOI 10.1287/ijoc.1040.0126; Goto M., 2002, P 3 INT C MUS INF RE, P287; Goto M, 2006, IEEE T AUDIO SPEECH, V14, P1783, DOI 10.1109/TSA.2005.863204; Jensen K, 2007, EURASIP J ADV SIG PR, DOI 10.1155/2007/73205; Kaiser F., 2012, MUSIC INFORM RETRIEV; Kantz H., 2004, NONLINEAR TIME SERIE; Levy M, 2008, IEEE T AUDIO SPEECH, V16, P318, DOI 10.1109/TASL.2007.910781; Lu L, 2004, P 6 ACM SIGMM INT WO, P275, DOI 10.1145/1026711.1026756; Lukashevich H., 2008, P 9 INT C MUS INF RE, P375; Maddage NC, 2006, IEEE MULTIMEDIA, V13, P65, DOI 10.1109/MMUL.2006.3; Martin B., 2011, MUSIC INFORM RETRIEV; Marwan N, 2007, PHYS REP, V438, P237, DOI 10.1016/j.physrep.2006.11.001; Mauch M., 2009, P 10 INT SOC MUS INF, P231; Muller M., 2007, INFORM RETRIEVAL MUS; Muller M, 2007, EURASIP J ADV SIG PR, DOI 10.1155/2007/89686; Muller M., 2011, P 12 INT C MUS INF R, P615; Muller M, 2011, IEEE J-STSP, V5, P1088, DOI 10.1109/JSTSP.2011.2112333; Ong B. S., 2006, THESIS U POMPEU FABR; Patel A. D., 2007, MUSIC LANGUAGE BRAIN; Paulus J, 2009, IEEE T AUDIO SPEECH, V17, P1159, DOI 10.1109/TASL.2009.2020533; Paulus J., 2010, P 11 INT C MUS INF R, P625; Peeters G, 2004, LECT NOTES COMPUT SC, V2771, P143; Peeters G., 2009, P WORKSH LEARN SEM A, P75; Peiszer E., 2007, THESIS VIENNA U TECH; Sargent G., 2011, P 12 INT SOC MUS INF, P483; Serra J., 2012, P AAAI INT C ART INT, P1613; Serra J, 2009, NEW J PHYS, V11, DOI 10.1088/1367-2630/11/9/093017; Serra J, 2008, IEEE T AUDIO SPEECH, V16, P1138, DOI 10.1109/TASL.2008.924595; Simonoff J. S., 1996, SMOOTHING METHODS ST; Smith J. B. L., 2010, THESIS MCGILL U MONT; Turnbull D., 2007, P 8 INT C MUS INF RE, P51; Weiss RJ, 2011, IEEE J-STSP, V5, P1240, DOI 10.1109/JSTSP.2011.2145356 44 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia AUG 2014 16 5 1229 1240 10.1109/TMM.2014.2310701 12 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AN0SZ WOS:000340295600006 J Tullis, JG; Benjamin, AS; Ross, BH Tullis, Jonathan G.; Benjamin, Aaron S.; Ross, Brian H. The Reminding Effect: Presentation of Associates Enhances Memory for Related Words in a List JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL English Article reminding; study-phase retrieval; spacing effect; lag effect STUDY-PHASE RETRIEVAL; FREE-RECALL; RECURSIVE REMINDINGS; ANALOGICAL TRANSFER; RECOGNITION MEMORY; TEMPORAL CONTEXT; REPETITION; JUDGMENTS; RECENCY; INFORMATION One aspect of successful cognition is the efficient use of prior relevant knowledge in novel situations. Remindings-stimulus-guided retrievals of prior events-allow us to link prior knowledge to current problems by prompting us to retrieve relevant knowledge from events that are distant from the present. Theorizing in research on higher cognition makes much use of the concept of remindings, yet many basic mnemonic consequences of remindings are untested. Here we consider implications of reminding-based theories of the effects of repetition on memory (Benjamin & Tullis, 2010; Hintzman, 2011). Those theories suggest that the spacing of repeated presentations of material benefits memory when the later experience reminds the learner of the earlier one. When applied to memory for related, rather than repeated, material, these theories predict a reminding effect: a mnemonic boost caused by a nearby presentation of a related item. In 7 experiments, we assessed this prediction by having learners study lists of words that contained related word pairs. Recall performance for the first presentation in related pairs was higher than for equivalent items in unrelated pairs, while recognition performance for items in related pairs did not differ from those in unrelated pairs. Remindings benefit only the recollection of the retrieved episodes. [Tullis, Jonathan G.] Indiana Univ, Dept Psychol & Brain Sci, Bloomington, IN 47401 USA; [Benjamin, Aaron S.; Ross, Brian H.] Univ Illinois, Dept Psychol, Urbana, IL 61801 USA Tullis, JG (reprint author), Indiana Univ, Dept Psychol & Brain Sci, 1101 East 10th St, Bloomington, IN 47401 USA. jonathantullis@gmail.com Appleton-Knapp SL, 2005, J CONSUM RES, V32, P266, DOI 10.1086/432236; Bauml KH, 1997, PSYCHON B REV, V4, P260, DOI 10.3758/BF03209403; Benjamin A. S., 2010, SUCCESSFUL REMEMBERI, P71; Benjamin A. S., 2008, HDB MEMORY METAMEMOR, P73; Benjamin AS, 2010, COGNITIVE PSYCHOL, V61, P228, DOI 10.1016/j.cogpsych.2010.05.004; Benjamin AS, 1998, J EXP PSYCHOL GEN, V127, P55, DOI 10.1037/0096-3445.127.1.55; Benjamin AS, 2003, MEM COGNITION, V31, P297, DOI 10.3758/BF03194388; Braun K, 1998, MEMORY, V6, P37; Chan JCK, 2007, J EXP PSYCHOL LEARN, V33, P431, DOI 10.1037/0278-7393.33.2.431; Cohn M, 2007, J MEM LANG, V57, P437, DOI 10.1016/j.jml.2007.06.006; Delaney PF, 2010, PSYCHOL LEARN MOTIV, V53, P63, DOI 10.1016/S0079-7421(10)53003-2; Frederick S., 2002, HEURISTICS BIASES PS, P49, DOI DOI 10.1017/CB09780511808098.004; Friedman WJ, 2010, MEM COGNITION, V38, P1122, DOI 10.3758/MC.38.8.1122; GICK ML, 1983, COGNITIVE PSYCHOL, V15, P1, DOI 10.1016/0010-0285(83)90002-6; GLANZER M, 1969, J VERB LEARN VERB BE, V8, P105, DOI 10.1016/S0022-5371(69)80018-6; Green D. M., 1966, SIGNAL DETECTION THE; GREENE RL, 1990, J EXP PSYCHOL LEARN, V16, P1004, DOI 10.1037//0278-7393.16.6.1004; Hintzman DL, 2008, BEHAV BRAIN SCI, V31, P656, DOI 10.1017/S0140525X08005724; Hintzman DL, 2004, MEM COGNITION, V32, P336, DOI 10.3758/BF03196863; Hintzman DL, 2011, PERSPECT PSYCHOL SCI, V6, P253, DOI 10.1177/1745691611406924; Hintzman DL, 2010, MEM COGNITION, V38, P102, DOI 10.3758/MC.38.1.102; HINTZMAN DL, 1973, J EXP PSYCHOL, V97, P220, DOI 10.1037/h0033884; HINTZMAN DL, 1975, J EXPT PSYCHOL HUMAN, V1, P31, DOI DOI 10.1037/0278-7393.1.1.31; Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388; Howard MW, 2006, PSYCHON B REV, V13, P439, DOI 10.3758/BF03193867; HYDE TS, 1969, J EXP PSYCHOL, V82, P472, DOI 10.1037/h0028372; JACOBY LL, 1974, J VERB LEARN VERB BE, V13, P483, DOI 10.1016/S0022-5371(74)80001-0; Jacoby LL, 2013, MEM COGNITION, V41, P638, DOI 10.3758/s13421-013-0313-x; Jacoby LL, 2013, MEM COGNITION, V41, P625, DOI 10.3758/s13421-013-0298-5; Kausler D. H., 1974, PSYCHOL VERBAL LEARN; Lepage M, 2000, P NATL ACAD SCI USA, V97, P506, DOI 10.1073/pnas.97.1.506; LOFTUS GR, 1994, PSYCHON B REV, V1, P476, DOI 10.3758/BF03210951; Mace J. H., 2007, INVOLUNTARY MEMORY, DOI [10.1002/9780470774069, DOI 10.1002/9780470774069]; Masson MEJ, 2009, J EXP PSYCHOL LEARN, V35, P509, DOI 10.1037/a0014876; McNamara DS, 1996, DISCOURSE PROCESS, V22, P247; MEDIN DL, 1978, PSYCHOL REV, V85, P207, DOI 10.1037//0033-295X.85.3.207; MELTON AW, 1970, J VERB LEARN VERB BE, V9, P596, DOI 10.1016/S0022-5371(70)80107-4; MORRIS CD, 1977, J VERB LEARN VERB BE, V16, P519, DOI 10.1016/S0022-5371(77)80016-9; Nelson DL, 2004, BEHAV RES METH INS C, V36, P402, DOI 10.3758/BF03195588; PUFF CR, 1970, J EXP PSYCHOL, V86, P384, DOI 10.1037/h0030189; RATCLIFF R, 1990, J EXP PSYCHOL LEARN, V16, P163, DOI 10.1037/0278-7393.16.2.163; REEVES LM, 1994, PSYCHOL BULL, V115, P381, DOI 10.1037//0033-2909.115.3.381; ROEDIGER HL, 1995, J EXP PSYCHOL LEARN, V21, P803, DOI 10.1037/0278-7393.21.4.803; ROGERS TB, 1977, J PERS SOC PSYCHOL, V35, P677, DOI 10.1037//0022-3514.35.9.677; ROSS BH, 1990, COGNITIVE PSYCHOL, V22, P460, DOI 10.1016/0010-0285(90)90010-2; ROSS BH, 1990, J EXP PSYCHOL LEARN, V16, P42, DOI 10.1037/0278-7393.16.1.42; ROSS BH, 1994, MEM COGNITION, V22, P591, DOI 10.3758/BF03198398; RUNDUS D, 1971, J EXP PSYCHOL, V89, P63, DOI 10.1037/h0031185; Sahakyan L, 2007, J EXP PSYCHOL LEARN, V33, P1035, DOI 10.1037/0278-7393.33.6.1035; SCHMIDT SR, 1989, MEM COGNITION, V17, P359, DOI 10.3758/BF03198475; SHIFFRIN RM, 1990, J EXP PSYCHOL LEARN, V16, P179, DOI 10.1037/0278-7393.16.2.179; SNODGRASS JG, 1988, J EXP PSYCHOL GEN, V117, P34, DOI 10.1037//0096-3445.117.1.34; THIOS SJ, 1976, J VERB LEARN VERB BE, V15, P529, DOI 10.1016/0022-5371(76)90047-5; THIOS SJ, 1972, J VERB LEARN VERB BE, V11, P789, DOI 10.1016/S0022-5371(72)80014-8; Toppino TC, 2002, MEM COGNITION, V30, P601, DOI 10.3758/BF03194961; Tullis JG, 2014, PSYCHON B REV, V21, P107, DOI 10.3758/s13423-013-0476-2; TZENG OJL, 1980, J EXP PSYCHOL-HUM L, V6, P705, DOI 10.1037/0278-7393.6.6.705; Wahlheim CN, 2013, MEM COGNITION, V41, P1, DOI 10.3758/s13421-012-0246-9; WALKER HJ, 1971, J EXP PSYCHOL, V88, P333, DOI 10.1037/h0030912; WINOGRAD E, 1985, J EXP PSYCHOL LEARN, V11, P262 60 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0096-3445 1939-2222 J EXP PSYCHOL GEN J. Exp. Psychol.-Gen. AUG 2014 143 4 1526 1540 10.1037/a0036036 15 Psychology, Experimental Psychology AN0TF WOS:000340296200009 J Mayo, R; Schul, Y; Rosenthal, M Mayo, Ruth; Schul, Yaacov; Rosenthal, Meytal If You Negate, You May Forget: Negated Repetitions Impair Memory Compared With Affirmative Repetitions JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL English Article negation; memory; inhibition; false memory FALSE MEMORIES; INHIBITORY MECHANISMS; TEXT INFORMATION; LEXICAL MEMORY; HYPERMNESIA; RETRIEVAL; RECALL; WORDS; MISINFORMATION; REMINISCENCE One of the most robust laws of memory is that repeated activation improves memory. Our study shows that the nature of repetition matters. Specifically, although both negated repetition and affirmative repetition improve memory compared with no repetition, negated repetition hinders memory compared with affirmative repetition. After showing participants different entities, we asked them about features of these entities, leading to either "yes" or "no" responses. Our findings show that correctly negating an incorrect feature of an entity elicits an active forgetting effect compared with correctly affirming its true features. For example, after seeing someone drink a glass of white wine, answering "no" to "was it red wine?" may lead one to greater memory loss of the individual drinking wine at all compared with answering "yes" to "was it white wine?" We find this negation-induced forgetting effect in 4 experiments that differ in (a) the meaning given for the negation, (b) the type of stimuli (visual or verbal), and (c) the memory measure (recognition or free recall). We discuss possible underlying mechanisms and offer theoretical and applied implications of the negation-induced forgetting effect in relation to other known inhibition effects. [Mayo, Ruth; Schul, Yaacov; Rosenthal, Meytal] Hebrew Univ Jerusalem, Dept Psychol, IL-91905 Jerusalem, Israel Mayo, R (reprint author), Hebrew Univ Jerusalem, Dept Psychol, IL-91905 Jerusalem, Israel. ruti.mayo@huji.ac.il ANDERSON MC, 1995, PSYCHOL REV, V102, P68, DOI 10.1037/0033-295X.102.1.68; ANDERSON MC, 1994, J EXP PSYCHOL LEARN, V20, P1063, DOI 10.1037/0278-7393.20.5.1063; Ayers MS, 1998, PSYCHON B REV, V5, P1, DOI 10.3758/BF03209454; BAHRICK HP, 1991, APPL COGNITIVE PSYCH, V5, P1, DOI 10.1002/acp.2350050102; BELMORE SM, 1981, J EXP PSYCHOL-HUM L, V7, P191, DOI 10.1037//0278-7393.7.3.191; Bjork E. L., 1996, MEMORY, P237, DOI [10.1016/B978-012102570-0/50010-0, DOI 10.1111/1467-9280.00325]; Bjork R. A., 1989, VARIETIES MEMORY CON, P195; Brainerd CJ, 1998, J EXP CHILD PSYCHOL, V71, P81, DOI 10.1006/jecp.1998.2464; CARPENTER PA, 1975, PSYCHOL REV, V82, P45, DOI 10.1037/h0076248; Anderson Michael C., 1994, P265; Ciranni MA, 1999, J EXP PSYCHOL LEARN, V25, P1403, DOI 10.1037/0278-7393.25.6.1403; CLARK HH, 1972, COGNITIVE PSYCHOL, V3, P472, DOI 10.1016/0010-0285(72)90019-9; Deutsch R, 2006, J PERS SOC PSYCHOL, V91, P385, DOI 10.1037/0022-3514.91.3.385; ERDELYI MH, 1974, COGNITIVE PSYCHOL, V6, P159, DOI 10.1016/0010-0285(74)90008-5; ERDELYI MH, 1978, J EXP PSYCHOL-HUM L, V4, P275, DOI 10.1037/0278-7393.4.4.275; Fiedler K, 1996, J EXP SOC PSYCHOL, V32, P484, DOI 10.1006/jesp.1996.0022; FREEDMAN JL, 1971, J VERB LEARN VERB BE, V10, P107, DOI 10.1016/S0022-5371(71)80001-4; Frenda SJ, 2011, CURR DIR PSYCHOL SCI, V20, P20, DOI 10.1177/0963721410396620; GILBERT DT, 1991, AM PSYCHOL, V46, P107, DOI 10.1037//0003-066X.46.2.107; GILBERT DT, 1993, J PERS SOC PSYCHOL, V65, P221, DOI 10.1037/0022-3514.65.2.221; GIORA R, 2007, DISCOURSE PROCESS, V43, P153, DOI DOI 10.1207/S15326950DP4302_3; Grant SJ, 2004, J CONSUM RES, V31, P583, DOI 10.1086/425093; Groninger LD, 2004, MEMORY, V12, P351, DOI 10.1080/09658210344000044; Horn L. R., 1989, NATURAL HIST NEGATIO; Johnson-Laird PN, 1999, COGNITION, V71, P191, DOI 10.1016/S0010-0277(99)00015-3; JUST MA, 1976, MEM COGNITION, V4, P318, DOI 10.3758/BF03213183; Kaup B, 2003, J EXP PSYCHOL LEARN, V29, P439, DOI 10.1037/0278-7393.29.3.439; Kaup B, 2001, MEM COGNITION, V29, P960, DOI 10.3758/BF03195758; Kelley MR, 2003, Q J EXP PSYCHOL-A, V56, P577, DOI 10.1080/02724980244000530; Kessler K., 2004, Visual Cognition, V11, DOI 10.1080/13506280444000012; Lea RB, 2002, J EXP PSYCHOL LEARN, V28, P303, DOI 10.1037//0278-7393.28.2.303; Loehr JD, 2006, MEMORY, V14, P17, DOI 10.1080/0965821044400467; Loftus E, 2003, NAT REV NEUROSCI, V4, P231, DOI 10.1038/nrn1054; LOFTUS EF, 1978, J EXP PSYCHOL-HUM L, V4, P19, DOI 10.1037//0278-7393.4.1.19; LOFTUS EF, 1973, J EXP PSYCHOL, V97, P70, DOI 10.1037/h0033782; LOFTUS EF, 1995, PSYCHIAT ANN, V25, P720; Loftus EF, 2005, LEARN MEMORY, V12, P361, DOI 10.1101/lm.94705; MACDONALD MC, 1989, J EXP PSYCHOL LEARN, V15, P633, DOI 10.1037//0278-7393.15.4.633; MACLEOD CM, 1989, J EXP PSYCHOL LEARN, V15, P13, DOI 10.1037/0278-7393.15.1.13; MacLeod MD, 2001, PSYCHOL SCI, V12, P148, DOI 10.1111/1467-9280.00325; Macrae CN, 1999, J PERS SOC PSYCHOL, V77, P463, DOI 10.1037//0022-3514.77.3.463; Mayo R, 2004, J EXP SOC PSYCHOL, V40, P433, DOI 10.1016/j.jesp.2003.07.008; Mulligan NW, 2002, J EXP PSYCHOL LEARN, V28, P541, DOI 10.1037//0278-7393.28.3.541; Mulligan NW, 2001, J EXP PSYCHOL LEARN, V27, P436, DOI 10.1037//0278-7393.27.2.436; Mulligan NW, 2006, MEMORY, V14, P502, DOI 10.1080/09658210500513438; NEELY JH, 1976, MEM COGNITION, V4, P648, DOI 10.3758/BF03213230; NEELY JH, 1977, J EXP PSYCHOL GEN, V106, P226, DOI 10.1037/0096-3445.106.3.226; NEILL WT, 1977, J EXP PSYCHOL HUMAN, V3, P444, DOI 10.1037//0096-1523.3.3.444; OTANI H, 1991, AM J PSYCHOL, V104, P101, DOI 10.2307/1422853; PAYNE DG, 1987, PSYCHOL BULL, V101, P5, DOI 10.1037//0033-2909.101.1.5; Pennebaker JW, 2003, ANNU REV PSYCHOL, V54, P547, DOI 10.1146/annurev.psych.54.101601.145041; POPKIN SJ, 1979, B PSYCHONOMIC SOC, V13, P378; ROEDIGER HL, 1982, J EXP PSYCHOL-HUM L, V8, P66, DOI 10.1037//0278-7393.8.1.66; ROEDIGER HL, 1995, J EXP PSYCHOL LEARN, V21, P803, DOI 10.1037/0278-7393.21.4.803; ROSENTHAL R, 1978, PSYCHOL BULL, V85, P185, DOI 10.1037//0033-2909.85.1.185; Searle JR, 2005, LOGIC EPISTEMOL UNIT, V2, P109, DOI 10.1007/1-4020-3167-X_5; SHAW JS, 1995, PSYCHON B REV, V2, P249, DOI 10.3758/BF03210965; Skurnik I, 2005, J CONSUM RES, V31, P713, DOI 10.1086/426605; Tettamanti M, 2008, NEUROIMAGE, V43, P358, DOI 10.1016/j.neuroimage.2008.08.004; Tipper SP, 2001, Q J EXP PSYCHOL-A, V54, P321, DOI 10.1080/02724980042000183; TIPPER SP, 1985, Q J EXP PSYCHOL-A, V37, P571; Tipper SP, 2003, PSYCHOL SCI, V14, P19, DOI 10.1111/1467-9280.01413; Vandeberg L, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0036154; WHEELER MA, 1992, PSYCHOL SCI, V3, P240, DOI 10.1111/j.1467-9280.1992.tb00036.x; Williams SJ, 2002, APPL COGNITIVE PSYCH, V16, P651, DOI 10.1002/acp.821; Wright DB, 2001, APPL COGNITIVE PSYCH, V15, P471, DOI 10.1002/acp.719.abs; Wundt W., 1902, GRUNDZUGE PHYSL PSYC, V2 67 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0096-3445 1939-2222 J EXP PSYCHOL GEN J. Exp. Psychol.-Gen. AUG 2014 143 4 1541 1552 10.1037/a0036122 12 Psychology, Experimental Psychology AN0TF WOS:000340296200010 J Jeong, JW; Lee, DH Jeong, Jin-Woo; Lee, Dong-Ho Automatic image annotation using affective vocabularies: Attribute-based learning approach JOURNAL OF INFORMATION SCIENCE English Article Affective image search; attribute-affect association; attribute-based learning; concept-affect association; image representation INFORMATION; RETRIEVAL; COLOR To improve image search results, understanding and exploiting the subjective aspects of an image is critical. However, how to effectively extract these subjective aspects (e.g. feeling, emotion, and so on) from an image is a challenging problem. In this paper, we propose a novel approach for predicting affective aspects, one of the most interesting subjective aspects, of concepts in images by learning the semantic attributes of the concept and mining the association between the attributes and affective aspects. The main idea of the proposed approach comes from the assumption that semantic attributes of a concept will influence the user's affect towards the concept (e.g. an animal with the semantic attributes small', furry', white' can be associated with the affective term cute'). Based on this assumption, we build a multi-layer affect learning framework that consists of (1) an attribute learning layer that predicts semantic attributes of a concept and (2) an affect learning layer that exploits the outputs from the attribute learning layer for predicting the affective aspects of the concept. Through the experimental results on the Animals with Attributes dataset, we show that the proposed approach outperforms traditional approaches by up to 25% in terms of precision and successfully predicts the affect of concepts in images according to different user preferences. [Jeong, Jin-Woo; Lee, Dong-Ho] Hanyang Univ, Dept Comp Sci & Engn, KDE Lab, Ansan 426791, Kyeonggi Do, South Korea Lee, DH (reprint author), Hanyang Univ, Sa 3 Dong, Ansan 426791, Kyeonggi Do, South Korea. dhlee72@hanyang.ac.kr Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2013R1A1A2059663] This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2013R1A1A2059663). Bosch A, 2006, P 6 ACM INT C IM VID, P401; Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199; Chatzichristofis SA, P 6 INT C COMP VIS S, P312; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; Dellagiacoma M, P INT WORKSH DET EXP, P17; Dunker P, P 1 ACM INT C MULT I, P97; Jeong JW, 2013, MULTIMED TOOLS APPL, V62, P451, DOI 10.1007/s11042-011-0903-1; Kemp C, 1921, P 21 NAT C ART INT A, P381; Lampert CH, P IEEE INT C COMP VI, P951; Lee DH, 2001, J SYST SOFTWARE, V56, P165, DOI 10.1016/S0164-1212(00)00095-9; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Li X, P 1 ACM INT C MULT I, P180; Lindstaedt S, 2009, MULTIMED TOOLS APPL, V42, P97, DOI 10.1007/s11042-008-0247-7; Liu D, P 18 INT C WORLD WID, P351; Liu N, 2004, P 4 INT C AFF COMP I, P195; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Machajdik J, P ACM INT C MULT MM, P83; Ooi BC, 1998, VLDB J, V7, P115, DOI 10.1007/s007780050057; OSHERSON DN, 1991, COGNITIVE SCI, V15, P251, DOI 10.1207/s15516709cog1502_3; Siersdorfer S, P ACM INT C MULT MM, P715; Sikora T, 2011, IEEE T CIRCUITS SYST, V11, P696; Smith J. R., 1996, Proceedings ACM Multimedia 96, DOI 10.1145/244130.244151; Teixeira RMA, 2012, MULTIMED TOOLS APPL, V61, P21, DOI 10.1007/s11042-010-0702-0; VALDEZ P, 1994, J EXP PSYCHOL GEN, V123, P394, DOI 10.1037/0096-3445.123.4.394; Wang W-n, P IEEE INT C SYST MA, P3534; Wei K, P INT C ADV DAT MIN, P485; Wu Q, P INT C AFF COMP INT, P239; Yanulevskaya V, 2015, P 15 IEEE INT C IM P, P101 28 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. AUG 2014 40 4 426 445 10.1177/0165551513501267 20 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UJ WOS:000340152900002 J Irfan, R; Khan, S; Qamar, AM; Bloodsworth, PC Irfan, Rabia; Khan, Sharifullah; Qamar, Ali Mustafa; Bloodsworth, Peter Charles Refining Kea plus plus automatic keyphrase assignment JOURNAL OF INFORMATION SCIENCE English Article Keyphrase assignment; keyphrase extraction; keyphrase indexing; subject classification; taxonomy DIGITAL LIBRARIES; EXTRACTION; RETRIEVAL; DOCUMENTS; TAXONOMY Keyphrases facilitate finding the right information in digital sources. Keyphrase assignment is the alignment of documents or text with keyphrases of any standard taxonomy/classification system. Kea++ is an automatic keyphrase assignment tool using a machine learning-based technique. However, it does not effectively exploit the hierarchical relations that exist in its input taxonomy and returns noise in its results. The refinement methodology was designed as a top layer of Kea++ in order to fine tune its results. It was an initial step and focused on a single Computing domain. It was neither validated on multiple domains nor evaluated to determine whether the improvement in the results is significant or not. The aim of this task was to solidify the refinement methodology. The main contributions of this work are (a) to extend the methodology for multiple domains and (b) to statistically verify that the improvement in the Kea++ results is significant. [Irfan, Rabia; Khan, Sharifullah; Qamar, Ali Mustafa; Bloodsworth, Peter Charles] Natl Univ Sci & Technol, Islamabad 74400, Pakistan Irfan, R (reprint author), Natl Univ Sci & Technol, Sch Elect Engn & Comp Sci, H-12 Campus, Islamabad 74400, Pakistan. 12phdrirfan@seecs.nust.edu.pk National University of Sciences and Technology, School of Electrical Engineering and Computer Science, Islamabad, Pakistan under Mega IT Funds This work was made possible by the funding support provided by National University of Sciences and Technology, School of Electrical Engineering and Computer Science, Islamabad, Pakistan under Mega IT Funds. Arampatzis AT, 1998, INFORM PROCESS MANAG, V34, P693, DOI 10.1016/S0306-4573(98)00030-2; Assem M., 2006, SEMANTIC WEB RES APP, V4011, P95, DOI 10.1007/11762256_10; Bluman AG, 1998, ELEMENTARY STAT STEP; Carpineto C, 2012, INFORM PROCESS MANAG, V48, P358, DOI 10.1016/j.ipm.2011.08.004; Do H, 2002, P 2 INT WORKSH WEB D, P221; Ercan G, 2007, INFORM PROCESS MANAG, V43, P1705, DOI 10.1016/j.ipm.2007.01.015; Euzenat J, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P348; Fatima I, 2009, 2009 IEEE THIRD INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2009), P281, DOI 10.1109/ICSC.2009.45; Fatima I, 2011, COMM COM INF SC, V184, P47; Feather J, 1996, INT ENCY INFORM LIB; Frank E, 1999, P 16 INT JOINT C ART, P668; Gutwin C, 1999, DECIS SUPPORT SYST, V27, P81, DOI 10.1016/S0167-9236(99)00038-X; Gutwin C, 1998, TECHNICAL REPORT IMP; Hull D, 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758; Hulth A, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P216; Irfan R, 2012, THESIS NATL U SCI TE; Jones S, 2003, J DIGITAL INFORM, V4; Khan Sharifullah, 2011, Journal of Digital Information Management, V9; Medelyan O, 2005, THESIS U FREIBURG GE; Medelyan O, 2006, P 6 ACM IEEE CS JOIN, P296, DOI 10.1145/1141753.1141819; Paukkeri MS, 2012, APPL SOFT COMPUT, V12, P1138, DOI 10.1016/j.asoc.2011.11.009; Salkind NJ, 2004, STAT PEOPLE WHO THIN; Tomokiyo T, 2003, P ACL WORKSH MULT EX, P33, DOI DOI 10.3115/1119282.1119287; Turney PD, 1999, LEARNING EXTRACT KEY; Witten IH, 1999, P 4 ACM C DIG LIB, P254, DOI 10.1145/313238.313437 25 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. AUG 2014 40 4 446 459 10.1177/0165551514529054 14 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UJ WOS:000340152900003 J Shah, C Shah, Chirag Evaluating collaborative information seeking - synthesis, suggestions, and structure JOURNAL OF INFORMATION SCIENCE English Article Collaboration; evaluation; framework; information seeking RETRIEVAL; AWARENESS; VALIDATION; SEARCH Evaluating the performance of collaborative information seeking (CIS) systems and users can be challenging, often more so than individual information-seeking environments. This can be attributed to the complex and dynamic interactions that take place among various users and systems processes in a CIS environment. While some of the aspects of a CIS system or user could be measured by typical assessment techniques from single-user information retrieval/seeking (IR/IS), one often needs to go beyond them to provide a meaningful evaluation, helping to provide not only a sense of performance, but also insights into design decisions (regarding systems) and behavioural trends (regarding users). This article first provides an overview of existing methods and techniques for evaluating CIS (synthesis). It then extracts valuable directives and advice from the literature that inform evaluation choices (suggestions). Finally, the article presents a framework for CIS evaluation with two major parts: system-based and user-based (structure). The proposed framework incorporates various instruments taken from computer and social sciences literature as applicable to CIS evaluations. The lessons from the literature and the framework could serve as important starting points for designing experiments and systems, as well as evaluating system and user performances in CIS and related research areas. Rutgers State Univ, New Brunswick, NJ 08873 USA Shah, C (reprint author), Rutgers State Univ, 4 Huntington St, New Brunswick, NJ 08873 USA. chirags@rutgers.edu Institute of Museum and Library Services (IMLS) Early Career Development grant [RE-04-12-0105-12] This work was supported by The Institute of Museum and Library Services (IMLS) Early Career Development grant # RE-04-12-0105-12. Amershi S, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P1647; Aneiros M., 2005, Proceedings. The 2005 IEEE/WIC/ACM International Conference on Web Intelligence; Baeza-Yates R, 1997, P INT ACM SIGGROUP C, P56, DOI 10.1145/266838.266860; BATES MJ, 1979, J AM SOC INFORM SCI, V30, P205, DOI 10.1002/asi.4630300406; BELKIN NJ, 1993, INFORM PROCESS MANAG, V29, P325, DOI 10.1016/0306-4573(93)90059-M; Birnholtz JP, 2005, P CHI 2005, P21; Blackwell AF, 2004, P ACM SIGCHI C HUM F, P1473; Charmaz K, 2006, CONSTRUCTING GROUNDE, P208; Evans BM, 2010, INFORM PROCESS MANAG, V46, P656, DOI 10.1016/j.ipm.2009.10.012; Fidel R, 2004, J AM SOC INF SCI TEC, V55, P939, DOI 10.1002/asi.20041; Foster J, 2006, ANNU REV INFORM SCI, V40, P329, DOI 10.1002/aris.1440400115; Fox S, 2005, ACM T INFORM SYST, V23, P147, DOI 10.1145/1059981.1059982; Ghani J. A., 1991, Proceedings of the Twelfth International Conference on Information Systems; Golovchinsky G, 2008, P JCDL 2008 WORKSH C; Gonzalez-Ibanez R, 2013, INFORM PROCESS MANAG, V49, P1165, DOI 10.1016/j.ipm.2012.12.008; Gonzalez-Ibanez R, 2012, ANN M AM SOC INF SCI; Gonzalez-Ibanez R, 2011, P AM SOC INF SCI TEC; Govern JM, 2001, CONSCIOUS COGN, V10, P366, DOI 10.1006/ccog.2001.0506; Gray Barbara, 1989, COLLABORATING FINDIN; Hart S.G., 1988, HUM MENT WORKLOAD, V1, P239; Haseki M, 2012, P ACM 2012 C COMP SU, P95; Heath C., 2002, Computer Supported Cooperative Work: The Journal of Collaborative Computing, V11, DOI 10.1023/A:1021247413718; Hmelo-Silver CE, 2006, P INT C LEARN SCI; Karamuftuoglu M, 1998, J AM SOC INFORM SCI, V49, P1070, DOI 10.1002/(SICI)1097-4571(1998)49:12<1070::AID-ASI3>3.0.CO;2-S; Laurillau Y, 2002, P 2002 ACM C COMP SU, P236; LEWIS JR, 1995, INT J HUM-COMPUT INT, V7, P57; Liechti O, 2002, INT J HUM-COMPUT ST, V56, P1, DOI 10.1006/ijhc.2001.0512; Marchionini G, 1995, INFORM SEEKING ELECT; McDonald DW, 2003, P SIGCHI C HUM FACT, P593, DOI DOI 10.1145/642611.642714; McNally K, 2011, INFORM SYST J, P387; Morris MR, 2007, UIST 2007: PROCEEDINGS OF THE 20TH ANNUAL ACM SYMPOSIUM ON USER INTERFACE SOFTWARE AND TECHNOLOGY, P3; Morris MR, 2007, MSRTR200711 MICR, P9; Nielsen J, 1994, C COMP HUM FACT COMP, P413, DOI 10.1145/259963.260531; O'Brien HL, 2008, J AM SOC INF SCI TEC, V59, P938, DOI 10.1002/asi.20801; Olivares R, 2006, EVALUACION IMPACTO J; Olson G. M., 1992, Human-Computer Interaction, V7, DOI 10.1207/s15327051hci0704_1; Paul SA, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1771; Pickens J, 2008, P ANN ACM C RES DEV; Prekop P, 2002, J DOC, V58, P538; Reddy MC, 2008, INFORM PROCESS MANAG, V44, P242, DOI 10.1016/j.ipm.2006.12.003; Saracevic T, 1995, P 18 ANN INT ACM SIG, P138, DOI 10.1145/215206.215351; SARACEVIC T, 1996, P ASIS ANNU MEET, V33, P3; Schmidt K., 2002, Computer Supported Cooperative Work: The Journal of Collaborative Computing, V11, DOI 10.1023/A:1021272909573; Shah C, 2011, P ANN ACM C RES DEV, P913; Shah C, 2013, J AM SOC INF SCI TEC, V64, P1122, DOI 10.1002/asi.22819; Shah C, 2012, J INF SCI, V38, P333, DOI 10.1177/0165551512438356; Shah C, 2010, INFORM PROCESS MANAG, V46, P773, DOI 10.1016/j.ipm.2009.10.002; Shah C, 2012, INFORM RETRIEVAL SER, P185; Shah C, 2008, P JCDL 2008 WORKSH C; Shah C, 2010, FRAMEWORK SUPPORT US; Shah C, 2010, J AM SOC INF SCI TEC, V61, P1970, DOI 10.1002/asi.21379; Shah C, 2010, P COLL INF RETR WORK; Smeaton AF, 2006, MULTIMEDIA SYSTEMS J, V12, P375; Smyth B, 2003, P 18 INT JOINT C ART, P1417; Smyth B, 2005, P INT JOINT C ART IN; Strijbos JW, 2004, SMALL GR RES, V35, P195, DOI 10.1177/1046496403260843; Surowiecki J., 2004, WISDOM CROWDS WHY MA; Tao Y, 2013, P 35 EUR C INF RETR, P26; Twidale MB, 1995, PROCEEDINGS OF CSCL '95 - THE FIRST INTERNATIONAL CONFERENCE ON COMPUTER SUPPORT FOR COLLABORATIVE LEARNING, P367; WAGNER RA, 1974, J ACM, V21, P168, DOI 10.1145/321796.321811; WATSON D, 1988, J PERS SOC PSYCHOL, V54, P1063, DOI 10.1037/0022-3514.54.6.1063; White RW, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P587; Wilson ML, 2008, WORKSH COLL INF RETR 63 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. AUG 2014 40 4 460 475 10.1177/0165551514530651 16 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UJ WOS:000340152900004 J Sadeghi, M; Vegas, J Sadeghi, Mohammad; Vegas, Jesus Automatic identification of light stop words for Persian information retrieval systems JOURNAL OF INFORMATION SCIENCE English Article Information retrieval; information theory; natural language processing; Persian language; stop words MATHEMATICAL-THEORY; COMMUNICATION Stop word identification is one of the most important tasks for many text processing applications such as information retrieval. Stop words occur too frequently in documents in a collection and do not contribute significantly to determining the context or information about the documents. These words are worthless as index terms and should be removed during indexing as well as before querying by an information retrieval system. In this paper, we propose an automatic aggregated methodology based on term frequency, normalized inverse document frequency and information model to extract the light stop words from Persian text. We define a light stop word' as a stop word that has few letters and is not a compound word. In the Persian language, a complete stop word list can be derived by combining the light stop words. The evaluation results, using a standard corpus, show a good percentage of coincidence between the Persian and English stop words and a significant improvement in the number of index terms. Specifically, the first 32 Persian light stop words have a great impact on the index size reduction and the set of stop words can reduce the number of index terms by about 27%. [Sadeghi, Mohammad; Vegas, Jesus] Univ Valladolid, Dept Comp Sci, E-47011 Valladolid, Spain Sadeghi, M (reprint author), Univ Valladolid, Dept Informat, Campus Miguel Delibes S-N, E-47011 Valladolid, Spain. msadeghi@infor.uva.es project entitled 'Compresioen y Recuperacioen de Contenidos Multilinges' - National Plan I + D/I + D + I [TIN2009-14009-C02-02] This work has been partially supported by the project entitled 'Compresioen y Recuperacioen de Contenidos Multilinges' financed by the National Plan I + D/I + D + I (TIN2009-14009-C02-02). Alajmi A, 2012, INT J COMPUTER APPL, V46; AleAhmad A, 2009, KNOWL-BASED SYST, V22, P382, DOI 10.1016/j.knosys.2009.05.002; Davarpanah MR, 2009, LIBR HI TECH, V27, P435, DOI 10.1108/07378830910988559; El-Khair IA, 2006, INT J COMPUTING INFO, V4; Francis W.N., 1982, FREQUENCY ANAL ENGLI; Kalbasi I., 1990, J LINGUIST, V7, P61; Kalbasi I., 2001, DERIVATIONAL STRUCTU; Korfhage R.R., 1997, INFORM STORAGE RETRI; Lo RT-W, 2005, P 5 DUTCH BELG INF R; LUHN HP, 1957, IBM J RES DEV, V1, P309; Megerdoomian K, 2000, MEMORANDA COMPUTER C; Myerson RB, 1996, FUNDAMENTALS SOCIAL; Neshati M, 2007, P 2007 IEEE ACS INT; Qasemizadeh B, 2006, 11 COMP SOC IR COMP; Rezaie S., 2001, ARABIC LANGUAGE PROC; ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302; SHANNON CE, 1948, AT&T TECH J, V27, P623; Taghva K, 2003, P SDIUT 03 S DOC IM; Taghva K, 2003, 200301 U NEV INF SCI; Van Rijsbergen C. J., 1979, INFORM RETRIEVAL; Witten I.H., 1999, MANAGING GIGABYTES C; Zipf H., 1949, HUMAN BEHAV PRINCIPL; Zou F, 2006, P 5 WSEAS INT C APPL, P1010 23 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. AUG 2014 40 4 476 487 10.1177/0165551514530655 12 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UJ WOS:000340152900005 J Bhatnagar, P; Pareek, N Bhatnagar, Pragati; Pareek, Narendra Improving pseudo relevance feedback based query expansion using genetic fuzzy approach and semantic similarity notion JOURNAL OF INFORMATION SCIENCE English Article Genetic fuzzy algorithm; information retrieval; pseudo relevance feedback; query expansion; semantic similarity DOCUMENT-RETRIEVAL SYSTEMS; INFORMATION-RETRIEVAL; COOCCURRENCE DATA; LOCAL CONTEXT; ALGORITHMS; OPTIMIZATION Pseudo relevance feedback-based query expansion is a popular automatic query expansion technique. However, a survey of work done in the area shows that it has a mixed chance of success. This paper captures the limitations of pseudo relevance feedback (PRF)-based query expansion and proposes a method of enhancing its performance by hybridizing corpus-based information, with a genetic fuzzy approach and semantic similarity notion. First the paper suggests use of a genetic fuzzy approach to select an optimal combination of query terms from a pool of terms obtained using PRF-based query expansion. The query terms obtained are further ranked on the basis of semantic similarity with original query terms. The experiments were performed on CISI collection, a benchmark dataset for information retrieval. It was found that the results were better in both terms of recall and precision. The main observation is that the hybridization of various techniques of query expansion in an intelligent way allows us to incorporate the good features of all of them. As this is a preliminary attempt in this direction, there is a large scope for enhancing these techniques. [Bhatnagar, Pragati; Pareek, Narendra] ML Sukhadia Univ, Dept Comp Sci, Udaipur, Rajasthan, India Bhatnagar, P (reprint author), ML Sukhadia Univ, Udaipur, Rajasthan, India. pragatibhat@gmail.com Araujo L, 2008, LECT NOTES COMPUT SC, V4972, P182, DOI 10.1007/978-3-540-78604-7_16; Booker L., 1987, IMPROVING SEARCH IN; Borkar Priya I, 2013, INTERNATIONAL JOURNA, V2; Calumby RT, 2012, MULTIMEDIA TOOLS AND; Cao G., 2008, P 31 ANN INT ACM SIG, P243, DOI 10.1145/1390334.1390377; Carpineto C, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2071389.2071390; Cecchini RL, 2008, INFORM PROCESS MANAG, V44, P1863, DOI 10.1016/j.ipm.2007.12.012; Chaturvedi DK, 2001, INSTITUTION OF ENGIN, V82, P23; Eleftherios K, 2012, PROCEEDINGS OF 2ND A; Fogarty TC., 1981, P 3 INT C GEN ALG AP, P104; Furnas GW, 1987, COMM ACM NOV, P964; GREFENSTETTE JJ, 1986, IEEE T SYST MAN CYB, V16, P122, DOI 10.1109/TSMC.1986.289288; Grossman D, 2004, INFORMATION RETREIVA; Hazra I, 2010, INTERNATIONAL JOURNA, V7, P52; Horng JT, 2000, INFORM PROCESS MANAG, V36, P737, DOI 10.1016/S0306-4573(00)00008-X; Jiang J, 1998, INTERNATIONAL CONFER; Leacock C, 1998, LANG SPEECH & COMMUN, P265; LESK ME, 1969, AM DOC, V20, P27, DOI 10.1002/asi.4630200106; Lin D., 1998, PROCEEDINGS OF 15TH; Liu S, 2004, PROCEEDINGS OF THE A; Mandala TR, 1988, THE USE OF WORDNET I, P469; Pathak P, 2000, PROCEEDINGS OF 33 HA; PEAT HJ, 1991, J AM SOC INFORM SCI, V42, P378, DOI 10.1002/(SICI)1097-4571(199106)42:5<378::AID-ASI8>3.0.CO;2-8; Resnik P, 1995, INT JOINT CONF ARTIF, P448; Robertson SE, 1995, THE THIRD TEXT RETRI; Robertson SE, 2000, P 9 TEXT RETR C TREC, P361; Saini A, 2006, INTERNATIONAL JOURNA, V5; Schaffer JD, 1981, P 3 INT C GEN ALG AP, P51; Schuster P., 1985, COMPLEX SYSTEM OPERA; Stairmand MA, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P140, DOI 10.1145/258525.258552; Tverskey, 1977, PSYCHOL REV, V84, P327; VANRIJSBERGEN CJ, 1977, J DOC, V33, P106; Verelas Voutsakis E, 2005, WEB INFORMATION AND, P10; Voorhees E. M., 1994, P 17 ANN INT ACM SIG, P61; Wu Z, 1994, ACL, P133; Xu J., 1997, THESIS; Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 37 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. AUG 2014 40 4 523 537 10.1177/0165551514533771 15 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UJ WOS:000340152900009 J Hunt, RR; Smith, RE Hunt, R. Reed; Smith, Rebekah E. How distinctive processing enhances hits and reduces false alarms JOURNAL OF MEMORY AND LANGUAGE English Article False memory; Monitoring and retrieval constraints; Criterial recollection paradigm; Distinctive processing CONSTRAINED RETRIEVAL; RECOGNITION MEMORY; EPISODIC MEMORY; SIMILARITY; RECOLLECTION; INFORMATION; MODALITY; RECALL; DIFFERENTIATION; DIFFERENCE Distinctive processing is a concept designed to account for precision in memory, both correct responses and avoidance of errors. The principal question addressed in two experiments is how distinctive processing of studied material reduces false alarms to familiar distractors. Jacoby, Kelley, and McElree (1999) has used the metaphors early selection and late correction to describe two different types of control processes. Early selection refers to limitations on access whereas late correction describes controlled monitoring of accessed information. The two types of processes are not mutually exclusive, and previous research has provided evidence for the operation of both. The data reported here extend previous work to a criterial recollection paradigm and to a recognition memory test. The results of both experiments show that variables that reduce false memory for highly familiar distracters continue to exert their effect under conditions of minimal post-access monitoring. Level of monitoring was reduced in the first experiment through test instructions and in the second experiment through speeded test responding. The results were consistent with the conclusion that both early selection and late correction operate to control accuracy in memory. (C) 2014 Elsevier Inc. All rights reserved. [Hunt, R. Reed; Smith, Rebekah E.] Univ Texas San Antonio, San Antonio, TX 78249 USA Hunt, RR (reprint author), Univ Texas San Antonio, Dept Psychol, 1 UTSA Circle, San Antonio, TX 78249 USA. reed.hunt@utsa.edu National Institute on Aging [AG034965] R. Reed Hunt and Rebekah E. Smith, Department of Psychology, The University of Texas at San Antonio. Support for this project was provided in part by Grant AG034965 from the National Institute on Aging to RES. The authors thank Marisa Aragon, Ryan Brigante, Andrew Bolisay, Ross DeForrest, Nadia Khoja, Sheila Meldrum, Bridget Miller, Florence Mizutani, Brittany Murray, Julie Niziuski, Eric Olguin, Brandon Oscarson, Katrina Presswood, Gabriel Tellez, Harvir Virk, Verlinda Wilkerson, and Manuel Zepeda for assistance with data collection. Joe Tidwell assisted with programming as well as data collection. Jason Arndt, David Gallo, and two anonymous reviewers provided valuable comments on an earlier version of the manuscript. Alban MW, 2012, MEM COGNITION, V40, P681, DOI 10.3758/s13421-012-0185-5; Arndt J, 2012, J EXP PSYCHOL LEARN, V38, P747, DOI 10.1037/a0026375; Arndt J, 2003, J MEM LANG, V48, P1, DOI 10.1016/S0749-596X(02)00518-1; Benjamin AS, 2001, J EXP PSYCHOL LEARN, V27, P941, DOI 10.1037//0278-7393.27.4.941; Brainerd CJ, 1998, PSYCHOL SCI, V9, P484, DOI 10.1111/1467-9280.00089; Criss AH, 2006, J MEM LANG, V55, P461, DOI 10.1016/j.jml.2006.08.003; Criss AH, 2010, J EXP PSYCHOL LEARN, V36, P484, DOI 10.1037/a0018435; Danckert SL, 2011, MEM COGNITION, V39, P1374, DOI 10.3758/s13421-011-0117-9; Dobbins IG, 1998, J MEM LANG, V38, P381, DOI 10.1006/jmla.1997.2554; Dodson CS, 2005, PSYCHON B REV, V12, P726, DOI 10.3758/BF03196764; Gallo DA, 2004, J MEM LANG, V51, P473, DOI 10.1016/j.jml.2004.06.002; GENTNER D, 1994, PSYCHOL SCI, V5, P152, DOI 10.1111/j.1467-9280.1994.tb00652.x; Gruppuso V, 1997, J EXP PSYCHOL LEARN, V23, P259, DOI 10.1037/0278-7393.23.2.259; Halamish V, 2012, J EXP PSYCHOL LEARN, V38, P1, DOI 10.1037/a0025053; Hege ACG, 2004, J EXP PSYCHOL LEARN, V30, P787, DOI 10.1037/0278-7393.30.4.787; Huff MJ, 2013, J EXP PSYCHOL LEARN, V39, P1246, DOI 10.1037/a0031338; HUNT RR, 1993, J MEM LANG, V32, P421, DOI 10.1006/jmla.1993.1023; Hunt R. R., 2006, DISTINCTIVENESS MEMO, P1; Hunt RR, 2011, J MEM LANG, V65, P378, DOI 10.1016/j.jml.2011.06.003; Hunt RR, 2003, J MEM LANG, V48, P811, DOI 10.1016/S0749-596X(03)00018-4; Hunt RR, 2011, J MEM LANG, V65, P390, DOI 10.1016/j.jml.2011.07.001; Hunt RR, 2012, PSYCHOL LEARN MOTIV, V56, P1, DOI 10.1016/13978-0-12-394393-4.00001-7; Jacoby L. L, 2005, CANADIAN J EXPT PSYC, V59, P17; Jacoby LL, 1999, DUAL-PROCESS THEORIES IN SOCIAL PSYCHOLOGY, P383; Jacoby LL, 2005, PSYCHON B REV, V12, P852, DOI 10.3758/BF03196776; JACOBY LL, 1990, J MEM LANG, V29, P433, DOI 10.1016/0749-596X(90)90065-8; JOHNSON MK, 1993, PSYCHOL BULL, V114, P3, DOI 10.1037//0033-2909.114.1.3; Klein KA, 2007, FOUNDATIONS OF REMEMBERING: ESSAYS IN HONOR OF HENRY L. ROEDIGER, III, P171; Koriat A, 1996, PSYCHOL REV, V103, P490, DOI 10.1037/0033-295X.103.3.490; Marsh RL, 2009, J MEM LANG, V61, P470, DOI 10.1016/j.jml.2009.06.005; McClelland J. L, 1998, PSYCHOL REV, V105, P734, DOI 10.1037//0033-295X.105.4.734-760; Pierce BH, 2005, MEM COGNITION, V33, P1407, DOI 10.3758/BF03193373; Roediger HL, 2001, PSYCHON B REV, V8, P385, DOI 10.3758/BF03196177; Schacter DL, 1999, J MEM LANG, V40, P1, DOI 10.1006/jmla.1998.2611; Schacter DL, 2001, PSYCHON B REV, V8, P827, DOI 10.3758/BF03196224; Scimeca A. M., 2011, J MEM LANG, V65, P373; Shiffrin RM, 1997, PSYCHON B REV, V4, P145, DOI 10.3758/BF03209391; Smith RE, 1998, PSYCHON B REV, V5, P710, DOI 10.3758/BF03208850; Smith RE, 2008, MEM COGNITION, V36, P1439, DOI 10.3758/MC.36.8.1439; TOTH JP, 1994, J EXP PSYCHOL LEARN, V20, P290, DOI 10.1037//0278-7393.20.2.290; Van Overschelde JP, 2004, J MEM LANG, V50, P289, DOI 10.1016/j.jml.2003.10.003 41 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0749-596X 1096-0821 J MEM LANG J. Mem. Lang. AUG 2014 75 45 57 10.1016/j.jml.2014.04.007 13 Linguistics; Psychology; Psychology, Experimental Linguistics; Psychology AN1CZ WOS:000340321600004 J Bosker, HR; Quene, H; Sanders, T; de Jong, NH Bosker, Hans Rutger; Quene, Hugo; Sanders, Ted; de Jong, Nivja H. Native 'um's elicit prediction of low-frequency referents, but non-native 'um's do not JOURNAL OF MEMORY AND LANGUAGE English Article Prediction; Disfluency bias; Disfluency; Hesitation; Non-native speech; Speech comprehension SPOKEN-LANGUAGE COMPREHENSION; VISUAL WORLD PARADIGM; SPONTANEOUS SPEECH; LEXICAL ACCESS; REFERENCE RESOLUTION; FILLED PAUSES; LISTENERS; DISFLUENCY; WORDS; INFORMATION Speech comprehension involves extensive use of prediction. Linguistic prediction may be guided by the semantics or syntax, but also by the performance characteristics of the speech signal, such as disfluency. Previous studies have shown that listeners, when presented with the filler uh, exhibit a disfluency bias for discourse-new or unknown referents, drawing inferences about the source of the disfluency. The goal of the present study is to study the contrast between native and non-native disfluencies in speech comprehension. Experiment 1 presented listeners with pictures of high-frequency (e.g., a hand) and low-frequency objects (e.g., a sewing machine) and with fluent and disfluent instructions. Listeners were found to anticipate reference to low-frequency objects when encountering disfluency, thus attributing disfluency to speaker trouble in lexical retrieval. Experiment 2 showed that, when participants listened to disfluent non-native speech, no anticipation of low-frequency referents was observed. We conclude that listeners can adapt their predictive strategies to the (non-native) speaker at hand, extending our understanding of the role of speaker identity in speech comprehension. (C) 2014 Elsevier Inc. All rights reserved. [Bosker, Hans Rutger] Max Planck Inst Psycholinguist, NL-6500 AH Nijmegen, Netherlands; [Quene, Hugo; Sanders, Ted; de Jong, Nivja H.] Univ Utrecht, Utrecht Inst Linguist OTS, NL-3512 JK Utrecht, Netherlands Bosker, HR (reprint author), Max Planck Inst Psycholinguist, POB 310, NL-6500 AH Nijmegen, Netherlands. HansRutger.Bosker@mpi.nl Pearson Language Testing; Utrecht institute of Linguistics OTS (UiL OTS), Utrecht University, The Netherlands This work was carried out at the Utrecht institute of Linguistics OTS (UiL OTS), Utrecht University, The Netherlands, and was supported by Pearson Language Testing by means of a grant awarded to Nivja H. de Jong ('Oral fluency: production and perception'). Almeida J, 2007, PSYCHON B REV, V14, P1177, DOI 10.3758/BF03193109; Altmann GTM, 1999, COGNITION, V73, P247, DOI 10.1016/S0010-0277(99)00059-1; Arnold JE, 2000, LANGUAGE, V76, P28, DOI 10.2307/417392; Arnold JE, 2007, J EXP PSYCHOL LEARN, V33, P914, DOI 10.1037/0278-7393.33.5.914; Arnold JE, 2004, PSYCHOL SCI, V15, P578, DOI 10.1111/j.0956-7976.2004.00723.x; Arnold JE, 2003, J PSYCHOLINGUIST RES, V32, P25, DOI 10.1023/A:1021980931292; Baayen RH, 2008, J MEM LANG, V59, P390, DOI 10.1016/j.jml.2007.12.005; Barr DJ, 2008, COGNITION, V109, P18, DOI 10.1016/j.cognition.2008.07.005; Barr DJ, 2010, LANG COGNITIVE PROC, V25, P441, DOI 10.1080/01690960903047122; Barr DJ, 2008, J MEM LANG, V59, P457, DOI 10.1016/j.jml.2007.09.002; Bates D., 2012, IME4 LINEAR MIXED EF; BEATTIE GW, 1979, LANG SPEECH, V22, P201; Belke E, 2005, COGNITION, V96, pB45, DOI 10.1016/j.cognition.2004.11.006; Bortfeld H, 2001, LANG SPEECH, V44, P123; Bosker R. J., 1999, MULTILEVEL ANAL INTR; Brennan SE, 2001, J MEM LANG, V44, P274, DOI 10.1006/jmla.2000.2753; Brunelliere A, 2013, BRAIN LANG, V125, P82, DOI 10.1016/j.bandl.2013.01.007; Caramazza A, 1997, COGNITIVE NEUROPSYCH, V14, P177, DOI 10.1080/026432997381664; Collard P, 2008, J EXP PSYCHOL LEARN, V34, P696, DOI 10.1037/0278-7393.34.3.696; Corley M, 2007, COGNITION, V105, P658, DOI 10.1016/j.cognition.2006.10.010; Corley M, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0019792; Dahan D, 2002, J MEM LANG, V47, P292, DOI 10.1016/S0749-596X(02)00001-3; Davies A., 2003, NATIVE SPEAKAR MYTH; DEBOT K, 1992, APPL LINGUIST, V13, P1, DOI 10.1093/applin/13.1.1; DeLong KA, 2005, NAT NEUROSCI, V8, P1117, DOI 10.1038/nn1504; Duyck W, 2008, PSYCHON B REV, V15, P850, DOI 10.3758/PBR.15.4.850; Tree JEF, 1995, J MEM LANG, V34, P709, DOI 10.1006/jmla.1995.1032; Fraundorf SH, 2011, J MEM LANG, V65, P161, DOI 10.1016/j.jml.2011.03.004; Gollan TH, 2008, J MEM LANG, V58, P787, DOI 10.1016/j.jml.2007.07.001; Hanulikova A, 2012, J COGNITIVE NEUROSCI, V24, P878, DOI 10.1162/jocn_a_00103; Hartsuiker RJ, 2010, EXP PSYCHOL, V57, P169, DOI 10.1027/1618-3169/a000021; Hox J. J, 2010, MULTILEVEL ANAL TECH; Huettig F, 2011, ACTA PSYCHOL, V137, P151, DOI 10.1016/j.actpsy.2010.11.003; JESCHENIAK JD, 1994, J EXP PSYCHOL LEARN, V20, P824, DOI 10.1037/0278-7393.20.4.824; Kahng J., 2013, NEW SOUNDS 2013 C MO; Kircher TTJ, 2004, NEUROIMAGE, V21, P84, DOI 10.1016/j.neuroimage.2003.09.041; Kutas M., 2011, USING OUR GENERATE F, P190; Levelt W. J. M., 1989, SPEAKING INTENTION A; LEVELT WJM, 1983, COGNITION, V14, P41, DOI 10.1016/0010-0277(83)90026-4; Levelt WJM, 1999, BEHAV BRAIN SCI, V22, P1; MacGregor LJ, 2010, NEUROPSYCHOLOGIA, V48, P3982, DOI 10.1016/j.neuropsychologia.2010.09.024; MacGregor LJ, 2009, BRAIN LANG, V111, P36, DOI 10.1016/j.bandl.2009.07.003; MACLAY H, 1959, WORD, V15, P19; MARTIN JG, 1968, PERCEPT PSYCHOPHYS, V3, P427, DOI 10.3758/BF03205750; McQueen JM, 2012, J ACOUST SOC AM, V131, P509, DOI 10.1121/1.3664087; Meyer AS, 2003, J MEM LANG, V48, P131, DOI 10.1016/S0749-596X(02)00509-0; Mirman D, 2008, J MEM LANG, V59, P475, DOI 10.1016/j.jml.2007.11.006; Morrison CM, 2000, BRIT J PSYCHOL, V91, P167, DOI 10.1348/000712600161763; Munro MJ, 1995, LANG SPEECH, V38, P289; Oostdijk N., 2000, ELRA NEWSLETTER, V5, P4; Pickering MJ, 2007, TRENDS COGN SCI, V11, P105, DOI 10.1016/j.tics.2006.12.002; Pinheiro JC, 2000, MIXED EFFECTS MODELS; Quene H, 2010, SPEECH COMMUN, V52, P911, DOI 10.1016/j.specom.2010.03.005; Quene H, 2008, J MEM LANG, V59, P413, DOI 10.1016/j.jml.2008.02.002; Quene H, 2004, SPEECH COMMUN, V43, P103, DOI 10.1016/j.specom 2004.02.004; R Development Core Team, 2012, R LANG ENV STAT COMP; Riazantseva A., 2001, STUDIES 2 LANGUAGE A, V23, P497, DOI 10.1017/S027226310100403X; Schnadt M. J., 2006, P 28 ANN M COGN SCI, P750; Segalowitz N., 2010, COGNITIVE BASES 2 LA; Severens E, 2005, ACTA PSYCHOL, V119, P159, DOI 10.1016/j.actpsy.2005.01.002; Skehan P., 2007, COMPLEXITY ACCURACY, P207; Skehan P, 2009, APPL LINGUIST, V30, P510, DOI 10.1093/applin/amp047; TANENHAUS MK, 1995, SCIENCE, V268, P1632, DOI 10.1126/science.7777863; Tavakoli P, 2011, ELT J, V65, P71, DOI 10.1093/elt/ccq020; Tree JEF, 2001, MEM COGNITION, V29, P320; Van Berkum JJA, 2005, J EXP PSYCHOL LEARN, V31, P443, DOI 10.1037/0278-7393.31.3.443; Van Berkum JJA, 2008, J COGNITIVE NEUROSCI, V20, P580, DOI 10.1162/jocn.2008.20054; van Wijngaarden SJ, 2001, SPEECH COMMUN, V35, P103, DOI 10.1016/S0167-6393(00)00098-4; Veenker T. J. G, 2012, ZEP EXPT CONTROL APP; Watanabe M, 2008, SPEECH COMMUN, V50, P81, DOI 10.1016/j.specom.2007.06.002; Weber A., 2006, COGNITION, V99, P63; Weenink David, 2012, PRAAT DOING PHONETIC; Wicha NYY, 2004, J COGNITIVE NEUROSCI, V16, P1272, DOI 10.1162/0898929041920487 73 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0749-596X 1096-0821 J MEM LANG J. Mem. Lang. AUG 2014 75 104 116 10.1016/j.jml.2014.05.004 13 Linguistics; Psychology; Psychology, Experimental Linguistics; Psychology AN1CZ WOS:000340321600008 J Xi, Y; Schwiebert, L; Shi, WS Xi, Yong; Schwiebert, Loren; Shi, Weisong Privacy preserving shortest path routing with an application to navigation PERVASIVE AND MOBILE COMPUTING English Article Location privacy; Shortest path; Navigation INFORMATION-RETRIEVAL Mobile navigation is a frequently used application, especially with the increasing proliferation of online geographical data. However, the origin and destination are often private information closely tied to a user's personal life. Sharing those with an online map provider greatly increases the chance of the user being profiled. Contrary to existing location privacy problems, the origin and the destination are essential for finding the shortest path in a realtime traffic setting. In this paper, we show that the problem can be solved with Private Information Retrieval (PIR) techniques without disclosing the origin or the destination. We analyze the cost associated with this approach and propose a practical solution with the assumption of a semi-honest third party to improve the efficiency. The proposed practical solution only introduces encryption overhead over the plain scenario where the path is returned by knowing the origin and destination. (C) 2013 Elsevier B.V. All rights reserved. [Xi, Yong; Schwiebert, Loren; Shi, Weisong] Wayne State Univ, Dept Comp Sci, Detroit, MI 48202 USA Xi, Y (reprint author), Wayne State Univ, Dept Comp Sci, Detroit, MI 48202 USA. yongxi@wayne.edu; loren@wayne.edu; weisong@wayne.edu Amini S., 2011, P 9 INT C MOB SYST A; Bellovin S.M., 2004, PRIVACY ENHANCED SEA; Bettini C, 2005, LECT NOTES COMPUT SC, V3674, P185; Boneh D, 2004, LECT NOTES COMPUT SC, V3027, P506; Chen Y., 2008, P 2 INT WORKSH PRIV, P88; Chor B, 1998, J ACM, V45, P965, DOI 10.1145/293347.293350; FLOYD RW, 1962, COMMUN ACM, V5, P345, DOI 10.1145/367766.368168; Krumm J, 2009, PERS UBIQUIT COMPUT, V13, P391, DOI 10.1007/s00779-008-0212-5; Kushilevitz E., 1997, Proceedings. 38th Annual Symposium on Foundations of Computer Science (Cat. No.97CB36150), DOI 10.1109/SFCS.1997.646125; Meyerowitz J, 2009, FIFTEENTH ACM INTERNATIONAL CONFERENCE ON MOBILE COMPUTING AND NETWORKING (MOBICOM 2009), P345; Olumofin F., 2011, FINANCIAL CRYPTOGRAP; Pagey H, 2009, MDM: 2009 10TH INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT, P238; Sion R., 2007, NDSS; Song DXD, 2000, P IEEE S SECUR PRIV, P44; Williams P., 2008, NDSS; Yekhanin S, 2010, COMMUN ACM, V53, P68, DOI 10.1145/1721654.1721674 16 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1574-1192 1873-1589 PERVASIVE MOB COMPUT Pervasive Mob. Comput. AUG 2014 13 142 149 10.1016/j.pmcj.2013.06.002 8 Computer Science, Information Systems; Telecommunications Computer Science; Telecommunications AM7AF WOS:000340016400010 J Pica, G; Pierro, A; Belanger, JJ; Kruglanski, AW Pica, Gennaro; Pierro, Antonio; Belanger, Jocelyn J.; Kruglanski, Arie W. THE ROLE OF NEED FOR COGNITIVE CLOSURE IN RETRIEVAL-INDUCED FORGETTING AND MISINFORMATION EFFECTS IN EYEWITNESS MEMORY SOCIAL COGNITION English Article EXECUTIVE DEFICIT HYPOTHESIS; INDIVIDUAL-DIFFERENCES; INHIBITORY ACCOUNT; CONSEQUENCES; DYNAMICS The present research examined the role of the need for cognitive closure (NfCC) in the phenomenon of retrieval-induced forgetting (i.e., the forgetting of non-retrieved information induced by selective retrieval) and in subsequent post-event misinformation effects (i.e., the tendency to recall misleading post-event information in preference to originally presented items). In three experiments, it was shown that NfCC augments retrieval-induced forgetting which in turn magnifies misinformation effects in eyewitness situations. Overall, the present work highlights the crucial role of motivation in retrieval-induced forgetting and discusses the implications this has for eyewitness research. [Pica, Gennaro; Pierro, Antonio] Univ Roma La Sapienza, I-00185 Rome, Italy; [Belanger, Jocelyn J.; Kruglanski, Arie W.] Univ Maryland, College Pk, MD 20742 USA Pica, G (reprint author), Univ Roma La Sapienza, Fac Psicol, Via Marsi 78, I-00185 Rome, Italy. gennaro.pica@uniroma1.it Adorno T., 1950, AUTHORITARIAN PERSON; Anderson MC, 2003, J MEM LANG, V49, P415, DOI 10.1016/j.jml.2003.08.006; ANDERSON MC, 1994, J EXP PSYCHOL LEARN, V20, P1063, DOI 10.1037/0278-7393.20.5.1063; Aslan A, 2011, J EXP PSYCHOL LEARN, V37, P264, DOI 10.1037/a0021324; Camp G, 2007, J EXP PSYCHOL LEARN, V33, P950, DOI 10.1037/0278-7393.33.5.950; Garcia-Bajos E, 2009, ACTA PSYCHOL, V131, P63, DOI 10.1016/j.actpsy.2009.02.007; Garcia-Bajos E, 2009, MEMORY, V17, P92, DOI 10.1080/09658210802572454; Hayes A. F., 2012, PROCESS VERSATILE CO; Jakab E, 2009, J EXP PSYCHOL LEARN, V35, P607, DOI 10.1037/a0015264; Koessler S, 2009, PSYCHOL SCI, V20, P1356, DOI 10.1111/j.1467-9280.2009.02450.x; Koriat A, 2000, ANNU REV PSYCHOL, V51, P481, DOI 10.1146/annurev.psych.51.1.481; Kossowska M, 2007, PERS INDIV DIFFER, V42, P1117, DOI 10.1016/j.paid.2006.09.026; Kruglanski A. W., 2004, PSYCHOL CLOSED MINDE; Kruglanski A. W., 1989, LAY EPISTEMICS HUMAN; Kruglanski A. W., 2010, SOCIAL PERSONALITY P, V4, P939; Kruglanski A. W., 2005, REVISED NEED C UNPUB; Kruglanski AW, 2006, PSYCHOL REV, V113, P84, DOI 10.1037/0033-295X.113.1.84; Kruglanski AW, 1996, PSYCHOL REV, V103, P263, DOI 10.1037//0033-295X.103.2.263; Kruglanski AW, 2012, PSYCHOL REV, V119, P1, DOI 10.1037/a0025488; Levy BJ, 2008, ACTA PSYCHOL, V127, P623, DOI 10.1016/j.actpsy.2007.12.004; LOFTUS EF, 1989, J EXP PSYCHOL GEN, V118, P100; LOFTUS EF, 1978, J EXP PSYCHOL-HUM L, V4, P19, DOI 10.1037//0278-7393.4.1.19; Macleod M, 2002, APPL COGNITIVE PSYCH, V16, P135, DOI 10.1002/acp.782; MacLeod MD, 2005, J EXP PSYCHOL LEARN, V31, P964, DOI 10.1037/0278-7393.31.5.964; MacLeod MD, 2008, CURR DIR PSYCHOL SCI, V17, P26, DOI 10.1111/j.1467-8721.2008.00542.x; MacLeod MD, 2001, PSYCHOL SCI, V12, P148, DOI 10.1111/1467-9280.00325; Migueles M, 2007, APPL COGNITIVE PSYCH, V21, P1157, DOI 10.1002/acp.1323; Ortega A, 2012, J EXP PSYCHOL LEARN, V38, P178, DOI 10.1037/a0024510; CACIOPPO JT, 1982, J PERS SOC PSYCHOL, V42, P116, DOI 10.1037//0022-3514.42.1.116; Pica G, 2013, PERS SOC PSYCHOL B, V39, P1530, DOI 10.1177/0146167213499237; Roman P, 2009, PSYCHOL SCI, V20, P1053, DOI 10.1111/j.1467-9280.2009.02415.x; Saunders J, 2002, J EXP PSYCHOL-APPL, V8, P127, DOI 10.1037//1076-898X.8.2.127; Scheck B., 2001, ACTUAL INNOCENCE JUS; Shah JY, 2002, J PERS SOC PSYCHOL, V83, P1261, DOI 10.1037//0022-3514.83.6.1261; SHAW JS, 1995, PSYCHON B REV, V2, P249, DOI 10.3758/BF03210965; Storm BC, 2006, PSYCHON B REV, V13, P1023, DOI 10.3758/BF03213919; Storm BC, 2012, MEM COGNITION, V40, P827, DOI 10.3758/s13421-012-0211-7; WEBSTER DM, 1994, J PERS SOC PSYCHOL, V67, P1049, DOI 10.1037/0022-3514.67.6.1049; Wright DB, 1998, J EXP CHILD PSYCHOL, V71, P155, DOI 10.1006/jecp.1998.2467 39 0 0 GUILFORD PUBLICATIONS INC NEW YORK 72 SPRING STREET, NEW YORK, NY 10012 USA 0278-016X SOC COGNITION Soc. Cogn. AUG 2014 32 4 337 359 23 Psychology, Social Psychology AM9QA WOS:000340214900002 J Kong, XY; Liu, XX; Hong, XY; Liu, J; Li, QP; Feng, ZC Kong, Xiang-Yong; Liu, Xiu-Xiang; Hong, Xiao-Yang; Liu, Jing; Li, Qiu-Ping; Feng, Zhi-Chun Improved outcomes of transported neonates in Beijing: the impact of strategic changes in perinatal and regional neonatal transport network services WORLD JOURNAL OF PEDIATRICS English Article morbidity; mortality; neonatal transport network; outcome BIRTH-WEIGHT INFANTS; PRETERM INFANTS; MORTALITY; INBORN; TRENDS; CARE Background: Infants born outside perinatal centers may have compromised outcomes due to the transfer speed and efficiency to an appropriate tertiary center. This study aimed to evaluate the impact of regional coordinated changes in perinatal supports and retrieval services on the outcome of transported neonates in Beijing, China. Methods: Information about transported newborns between phase 1 (July 1, 2004 to June 30, 2006) and phase 2 (July 1, 2007 to June 30, 2009) was collected. The strategic changes during phase 2 included standardized neonatal transport procedures, skilled attendants, a perinatal consulting service, and preferential admission of transported neonates to the intensive care unit of the tertiary care center. Data from phase 2 (after- strategic changes) were compared with those of phase 1 (the period of pre-strategic changes) after a 12-month washout period, especially regarding the reduction in mortality and selected morbidity. Results: There was a large increase in the number of transported infants in phase 2 compared with phase 1 (2797 vs. 567 patients). The average monthly rate of increase of transported infants was 383.3% (from 24 infants per month to 116 infants per month). The mortality rate of transported neonates reduced significantly from phase 1 to phase 2 (5.11% vs. 2.82%; P=0.005), particularly for preterm infants (8.47% vs. 4.34%; P=0.006). In addition, transported neonates during phase 2 had significantly decreased morbidities. Conclusions: Regional coordinated strategies optimizing the perinatal services and transport of outborn sick and preterm infants to tertiary care centers improved survival outcomes considerably. These findings have vital implications for health outcomes and resource planning. [Kong, Xiang-Yong; Hong, Xiao-Yang; Liu, Jing; Li, Qiu-Ping; Feng, Zhi-Chun] Bayi Childrens Hosp, Newborn Care Ctr, Beijing 100700, Peoples R China; [Kong, Xiang-Yong; Hong, Xiao-Yang; Liu, Jing; Li, Qiu-Ping; Feng, Zhi-Chun] Mil Gen Hosp Beijing, Beijing 100700, Peoples R China; [Kong, Xiang-Yong; Hong, Xiao-Yang; Liu, Jing; Li, Qiu-Ping; Feng, Zhi-Chun] Peoples Liberat Army, Beijing 100700, Peoples R China; [Liu, Xiu-Xiang] Hosp Binzhou Med Univ, Dept Pediat, Binzhou, Shandong, Peoples R China Feng, ZC (reprint author), Bayi Childrens Hosp, 5 Nanmen Chang, Beijing 100700, Peoples R China. zhjfengzc@126.com Capital Medical Development Funding committee [2005-3044] This study was supported by a grant from Capital Medical Development Funding committee (2005-3044). Arad I, 2008, ISR MED ASSOC J, V10, P457; Behrman RE, 2004, NELSON TXB PEDIAT, P249; Bhutta ZA, 2005, PEDIATRICS, V115, P519, DOI 10.1542/peds.2004-1441; Chien LY, 2001, OBSTET GYNECOL, V98, P247, DOI 10.1016/S0029-7844(01)01438-7; China Population and Development Research Center, POP NAT DYN PROV CHI; Chung MY, 2009, PEDIATR INT, V51, P233, DOI 10.1111/j.1442-200X.2008.02734.x; Feng ZC, 1999, CHIN J CONT PEDIAT, V1, P214; Gomella TL, 2009, NEONATOLOGY MANAGEME; Horbar JD, 2002, PEDIATRICS, V110, P143, DOI 10.1542/peds.110.1.143; James Andrew G., 1993, Current Opinion in Pediatrics, V5, P150, DOI 10.1097/00008480-199304000-00003; Lawn JE, 2005, LANCET, V365, P891, DOI 10.1016/S0140-6736(05)71048-5; Lee SK, 2000, PEDIATRICS, V106, P1070, DOI 10.1542/peds.106.5.1070; Lu Q., 2001, XIAO ER JI JIU YI XU, V8, P98; Lui K, 2006, PEDIATRICS, V118, P2076, DOI 10.1542/peds.2006-1540; Lupton Brian A, 2004, Semin Neonatol, V9, P125, DOI 10.1016/j.siny.2003.08.007; McNamara P J, 2005, J Perinatol, V25, P309, DOI 10.1038/sj.jp.7211263; Murray CJL, 2007, LANCET, V370, P1040, DOI 10.1016/S0140-6736(07)61478-0; Noone D, 2011, Ir Med J, V104, P232; Rashid A, 1999, ARCH DIS CHILD, V80, P488; Samuelson JL, 2002, PAEDIATR PERINAT EP, V16, P305, DOI 10.1046/j.1365-3016.2002.00450.x; Spector JM, 2008, TROP DOCT, V38, P68, DOI 10.1258/td.2006.006223; Spector JM, 2009, J PERINATOL, V29, P512, DOI 10.1038/jp.2009.20; Towers CV, 2000, OBSTET GYNECOL, V95, P291, DOI 10.1016/S0029-7844(99)00528-1; Zhang Xue-Feng, 2012, Zhongguo Dang Dai Er Ke Za Zhi, V14, P101 24 0 0 ZHEJIANG UNIV SCH MEDICINE HANGZHOU CHILDRENS HOSPITAL, 57 ZHUGAN XIANG, HANGZHOU, 310003, PEOPLES R CHINA 1708-8569 1867-0687 WORLD J PEDIATR World Journal of Pediatrics AUG 2014 10 3 251 255 10.1007/s12519-014-0501-1 5 Pediatrics Pediatrics AM7WP WOS:000340079700009 J Liu, L; Peng, T Liu, Lu; Peng, Tao Clustering-based topical Web crawling using CFu-tree guided by link-context FRONTIERS OF COMPUTER SCIENCE English Article topical Web crawling; comparison variation (CV); cluster impurity (CIP); CFu-tree; link-context; clustering ALGORITHM; TEXT Topical Web crawling is an established technique for domain-specific information retrieval. However, almost all the conventional topical Web crawlers focus on building crawlers using different classifiers, which needs a lot of labeled training data that is very difficult to labelmanually. This paper presents a novel approach called clustering-based topical Web crawling which is utilized to retrieve information on a specific domain based on link-context and does not require any labeled training data. In order to collect domain-specific content units, a novel hierarchical clustering method called bottom-up approach is used to illustrate the process of clustering where a new data structure, a linked list in combination with CFu-tree, is implemented to store cluster label, feature vector and content unit. During clustering, four metrics are presented. First, comparison variation (CV) is defined to judge whether the closest pair of clusters can be merged. Second, cluster impurity (CIP) evaluates the cluster error. Then, the precision and recall of clustering are also presented to evaluate the accuracy and comprehensive degree of the whole clustering process. Link-context extraction technique is used to expand the feature vector of anchor text which improves the clustering accuracy greatly. Experimental results show that the performance of our proposed method overcomes conventional focused Web crawlers both in Harvest rate and Target recall. [Liu, Lu; Peng, Tao] Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China; [Liu, Lu; Peng, Tao] Univ Illinois, Dept Comp Sci, Urbana, IL 61801 USA Peng, T (reprint author), Jilin Univ, Coll Comp Sci & Technol, Changchun 130012, Peoples R China. taopeng@illinois.edu Attardi G, 1999, P 1 EUR S TEL HYP AR, V99, P105; Boser B, 1992, P 5 ANN WORKSH COMP, V5, P144, DOI DOI 10.1145/130385.130401; Bouras C, 2012, KNOWL-BASED SYST, V36, P115, DOI 10.1016/j.knosys.2012.06.015; Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X; Chakrabarti S, 1998, COMPUT NETWORKS ISDN, V30, P65, DOI 10.1016/S0169-7552(98)00087-7; Chou CH, 2009, COMPUT J, V52, P890, DOI 10.1093/comjnl/bxn049; Cota RG, 2010, J AM SOC INF SCI TEC, V61, P1853, DOI 10.1002/asi.21363; Fu TJ, 2010, J AM SOC INF SCI TEC, V61, P1213, DOI 10.1002/asi.21323; Fung BCM, 2003, SIAM PROC S, P59; Hao HW, 2011, IEEE SYS MAN CYBERN, P850; Hersovici M, 1998, COMPUT NETWORKS ISDN, V30, P317, DOI 10.1016/S0169-7552(98)00038-5; Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504; Li J, 2005, P 14 INT WORLD WID W, P1190; Li JQ, 2012, J INTELL INF SYST, V39, P763, DOI 10.1007/s10844-012-0211-x; Liu B, 1998, P 4 INT C KNOWL DISC, P80; Liu HY, 2012, COMPUT INTELL-US, V28, P289, DOI 10.1111/j.1467-8640.2012.00411.x; Liu YL, 2013, J WEB ENG, V12, P203; McCallum A, 1998, AAAI 98 WORKSH LEARN, V752, P41; Pant G, 2006, IEEE T KNOWL DATA EN, V18, P107; Pant G, 2004, P 4 ACM IEEE CS JOIN, P142, DOI 10.1145/996350.996384; Pant G, 2003, P 8 ACM SIGMOD WORKS, P49; Peng T, 2008, CONCURR COMP-PRACT E, V20, P61, DOI 10.1002/cpe.1211; Qi GJ, 2012, IEEE T PATTERN ANAL, V34, P850, DOI 10.1109/TPAMI.2011.191; Rangrej A, 2011, P 20 INT C COMP WORL, P111; Spanakis G, 2012, COMPUT J, V55, P299, DOI 10.1093/comjnl/bxr024; Steinbach M., 2000, P KDD WORKSH TEXT MI, V400, P525; Sun Y, 2012, MINING HETEROGENEOUS; Trivedi A, 2012, ACM T INTEL SYST TEC, V3, DOI 10.1145/2337542.2337552; Wang X, 2011, P 2011 IEEE 11 INT C, P804; Wu M, 2012, J AM SOC INFORM SCI, V63, P1234; Yu HL, 2005, LECT NOTES ARTIF INT, V3789, P824; Zhang HX, 2010, APPL SOFT COMPUT, V10, P490, DOI 10.1016/j.asoc.2009.08.017 32 0 0 HIGHER EDUCATION PRESS BEIJING SHATANHOU ST 55, BEIJING 100009, PEOPLES R CHINA 2095-2228 2095-2236 FRONT COMPUT SCI-CHI Front.. Comput. Sci. AUG 2014 8 4 581 595 10.1007/s11704-014-3050-9 15 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods Computer Science AM4IR WOS:000339817700004 J Makar, M; Chandrasekhar, V; Tsai, SS; Chen, D; Girod, B Makar, Mina; Chandrasekhar, Vijay; Tsai, Sam S.; Chen, David; Girod, Bernd Interframe Coding of Feature Descriptors for Mobile Augmented Reality IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Augmented reality; canonical patch coding; descriptor coding; image matching VISUAL-SEARCH; TRACKING Streaming mobile augmented reality applications require both real-time recognition and tracking of objects of interest in a video sequence. Typically, local features are calculated from the gradients of a canonical patch around a keypoint in individual video frames. In this paper, we propose a temporally coherent keypoint detector and design efficient interframe predictive coding techniques for canonical patches, feature descriptors, and keypoint locations. In the proposed system, we strive to transmit each patch or its equivalent feature descriptor with as few bits as possible by modifying a previously transmitted patch or descriptor. Our solution enables server-based mobile augmented reality where a continuous stream of salient information, sufficient for image-based retrieval, and object localization, is sent at a bit-rate that is practical for today's wireless links and less than one-tenth of the bit-rate needed to stream the compressed video to the server. [Makar, Mina] Qualcomm Inc, San Diego, CA 92121 USA; [Makar, Mina; Chandrasekhar, Vijay; Tsai, Sam S.; Chen, David; Girod, Bernd] Stanford Univ, Dept Elect Engn, Stanford, CA 94305 USA; [Chandrasekhar, Vijay] Inst Infocomm Res, Singapore 138632, Singapore Makar, M (reprint author), Qualcomm Inc, San Diego, CA 92121 USA. minamakar@alumni.stanford.edu; vijay.cmu@gmail.com; sstsai@stanford.edu; dmchen@stanford.edu; bgirod@stanford.edu [Anonymous], 2012, STANFORD STREAMING M; Bay H., 2006, P EUR C COMP VIS GRA; Chandrasekhar V., 2011, INT J COMPUT VISION, V94, P348; Chandrasekhar V., 2010, P IEEE COMP SOC C CO; Chandrasekhar V., 2010, P 2 INT WORKSH MOB M; Chandrasekhar V., 2009, P IEEE C COMP VIS PA, P2504; Chandrasekhar V., 2013, THESIS STANFORD U ST; Chandrasekhar V., 2012, JTC1SC29WG11M23580 I; FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692; Francini G., 2011, JTC1SC29WG11N12367 I; Francini G, 2013, SIGNAL PROCESS-IMAGE, V28, P311, DOI 10.1016/j.image.2012.11.002; Girod B, 2011, IEEE MULTIMEDIA, V18, P86, DOI 10.1109/MMUL.2011.48; Girod B, 2011, IEEE SIGNAL PROC MAG, V28, P61, DOI 10.1109/MSP.2011.940881; Huiskes M. J., 2010, P ACM INT C MULT INF; JAIN JR, 1981, IEEE T COMMUN, V29, P1799, DOI 10.1109/TCOM.1981.1094950; Lin J., 2013, JTC1SC29WG11M28061 I; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Lucas B. D., 1981, P 7 INT JOINT C ART, V2, P674; Makar Mina, 2013, International Journal of Semantic Computing, V7, DOI 10.1142/S1793351X13400011; Makar M, 2012, IEEE IMAGE PROC, P2505, DOI 10.1109/ICIP.2012.6467407; Makar M, 2009, INT CONF ACOUST SPEE, P821, DOI 10.1109/ICASSP.2009.4959710; Makar M., 2012, P IEEE INT S MULT IR; Nister D., 2006, P IEEE COMP SOC C CO; Oehler K. L., 1993, P IEEE INT C AC SPEE, P241; Paschalakis S., 2012, JTC1SC29WG11M25929 I; Reznik Y., 2011, JTC1SC29WG11N12202 I; Sivic J., 2003, P ICCV, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663]; Takacs G, 2013, SIGNAL PROCESS-IMAGE, V28, P334, DOI 10.1016/j.image.2012.11.004; Takacs G., 2012, THESIS STANFORD U ST; Tourapis H.-Y. C., 2003, P INT C MULT EXP BAL; Tsai S. S., 2012, P SPIE SAN DIEG CA U; Tsai S. S., 2009, P 5 INT MOB MULT COM; Wiegand T, 2003, CIRCUITS SYSTEMS VID, V13, P560; Yilmaz A, 2006, ACM COMPUT SURV, V38, DOI 10.1145/1177352.1177355 34 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. AUG 2014 23 8 3352 3367 10.1109/TIP.2014.2331136 16 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AM8DE WOS:000340099800002 J Roa-Valverde, AJ; Sicilia, MA Roa-Valverde, Antonio J.; Sicilia, Miguel-Angel A survey of approaches for ranking on the web of data INFORMATION RETRIEVAL English Article Linked data; Information retrieval; Semantic search; Ranking algorithms; Link analysis; Semantic web data management COMPLEX SEMANTIC RELATIONSHIPS; SEARCH ENGINE; ONTOLOGIES; TRACK Ranking information resources is a task that usually happens within more complex workflows and that typically occurs in any form of information retrieval, being commonly implemented by Web search engines. By filtering and rating data, ranking strategies guide the navigation of users when exploring large volumes of information items. There exist a considerable number of ranking algorithms that follow different approaches focusing on different aspects of the complex nature of the problem, and reflecting the variety of strategies that are possible to apply. With the growth of the web of linked data, a new problem space for ranking algorithms has emerged, as the nature of the information items to be ranked is very different from the case of Web pages. As a consequence, existing ranking algorithms have been adapted to the case of Linked Data and some specific strategies have started to be proposed and implemented. Researchers and organizations deploying Linked Data solutions thus require an understanding of the applicability, characteristics and state of evaluation of ranking strategies and algorithms as applied to Linked Data. We present a classification that formalizes and contextualizes under a common terminology the problem of ranking Linked Data. In addition, an analysis and contrast of the similarities, differences and applicability of the different approaches is provided. We aim this work to be useful when comparing different approaches to ranking Linked Data and when implementing new algorithms. [Roa-Valverde, Antonio J.; Sicilia, Miguel-Angel] STI Innsbruck, Innsbruck, Austria Roa-Valverde, AJ (reprint author), STI Innsbruck, Innsbruck, Austria. antonio.roa@sti2.at; msicilia@uah.es Alani H, 2006, LECT NOTES COMPUT SC, V4273, P1; Alexander K., VOID GUIDE USING VOC; [Anonymous], 2004, CNSR 04 2 ANN C COMM, P305; Anyanwu K., 2005, SEMRANK RANKING COMP, P117; Artiles J., 2008, WWW 08 P 17 INT C WO, P1071; Baeza-Yates R., 2004, P 13 INT WORLD WID W, P328, DOI 10.1145/1013367.1013459; Balmin A., 2004, VLDB, P564; Balog K., 2010, TREC; Balog K., 2011, TREC; Balog K., 2009, TREC; Balog K., 2012, SIGIR FORUM, V46, P87; Balog K., 2013, P 36 INT ACM SIGIR C, P737, DOI [10.1145/2484028.2484165, DOI 10.1145/2484028.2484165]; Berners-Lee T., 2006, LINKED DATA DESIGN I; Bhalotia G, 2002, PROC INT CONF DATA, P431, DOI 10.1109/ICDE.2002.994756; BIZER C, 2009, INT J SEMANT WEB INF, V5, P1; Blanco R, 2011, LECT NOTES COMPUT SC, V7031, P83, DOI 10.1007/978-3-642-25073-6_6; Brickley D., 2014, RDF VOCABULARY DESCR; Brin S, 1998, COMPUT NETWORKS ISDN, V30, P107, DOI 10.1016/S0169-7552(98)00110-X; Broder A, 2000, COMPUT NETW, V33, P309, DOI 10.1016/S1389-1286(00)00083-9; Campinas S., 2012, EKAW, P200; Chen N, 2012, INT J SEMANT WEB INF, V8, P1, DOI 10.4018/jswis.2012100101; Cheng G, 2009, INT J SEMANT WEB INF, V5, P49, DOI 10.4018/jswis.2009081903; Coffman J., 2010, CIKM, P729; Cyganiak R., 2008, N QUADS EXTENDING N; Dali L., 2012, ESWC, P484; Dbpedia spotlight, 2011, P 7 INT C SEM SYST 1; Delbru R, 2010, LECT NOTES COMPUT SC, V6089, P225, DOI 10.1007/978-3-642-13489-0_16; Demartini G, 2009, LECT NOTES COMPUT SC, V5631, P243; Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26; Fellbaum C, 1998, COMPUT HUMANITIES, V32, P209, DOI 10.1023/A:1001181927857; Fernandez M., 2008, P 2 IEEE INT C SEM C, P253, DOI [10.1109/ICSC.2008.52, DOI 10.1109/ICSC.2008.52]; Finin T., 2004, P 13 ACM C INF KNOWL, P652; Franz T., 2009, INT SEM WEB C ISWC; Franz T, 2009, LECT NOTES COMPUT SC, V5823, P213, DOI 10.1007/978-3-642-04930-9_14; Getoor L., 2005, ACM SIGKDD EXPLORATI, V7, P3, DOI 10.1145/1117454.1117456; Halpin H., 2010, P INT WORKSH EV SEM; Harth A, 2009, LECT NOTES COMPUT SC, V5823, P277, DOI 10.1007/978-3-642-04930-9_18; He H., 2007, SIGMOD C, P305, DOI 10.1145/1247480.1247516; Hildebrand M., 2007, ANAL SEARCH BASED US; Hoffart J., 2011, P 20 INT C COMP WORL, P229; Hogan A., 2006, 2 WORKSH SCAL SEM WE; Hogan A, 2011, J WEB SEMANT, V9, P365, DOI 10.1016/j.websem.2011.06.004; Hristidis V., 2003, VLDB, P850; Hristidis V., 2002, VLDB, P670, DOI 10.1016/B978-155860869-6/50065-2; Jansen BJ, 2006, INFORM PROCESS MANAG, V42, P248, DOI 10.1016/j.ipm.2004.10.007; Kacholia V., 2005, VLDB, P505; Kamps J., 2008, INEX 2008 WORKSH PRE, P1; Kasneci G, 2008, PROC INT CONF DATA, P953, DOI 10.1109/ICDE.2008.4497504; Kleinberg J., 1998, P 9 ANN ACM SIAM S D; Klyne G., 2004, RESOURCE DESCRIPTION; Lassila O., 2007, THESIS HELSINKI U TE; LEI YG, 2006, EKAW LECT NOTES COMP, V4248, P238; Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041; Liu F., 2006, SIGMOD, P563; Liu T., 2009, FDN TRENDS INFORM RE, V3, P225, DOI DOI 10.1561/1500000016; Liu X., 2005, ANNU REV INFORM SCI, V39, P1, DOI 10.1002/aris.1440390108; Manning C, 2008, INTRO INFORM RETRIEV; May W., 1999, 131 U FREIB I FUER I; McGuinness D., 2004, OWL WEB ONTOLOGY LAN; Mirizzi R, 2010, LECT NOTES COMPUT SC, V6189, P337, DOI 10.1007/978-3-642-13911-6_23; Perez-Aguera J. R., 2010, P 3 INT SEM SEARCH W, P2, DOI 10.1145/1863879.1863881; Pound J., 2010, P 19 INT C WORLD WID, P771, DOI DOI 10.1145/1772690.1772769; Roa-Valverde A. J., 2011, P 7 INT C SEM SYST I, P230; Sabou M., 2007, ESWC 2007; Sawant U., 2013, FEATURES AGGREGATORS; Schenkel F. S. R., 2007, YAWN SEMANTICALLY AN; Sheth A, 2004, STUD FUZZ SOFT COMP, V139, P63; Sicilia MA, 2012, EXPERT SYST APPL, V39, P6706, DOI 10.1016/j.eswa.2011.11.094; Soboroff I., 2006, TREC; Suchanek F.M., 2007, P 16 INT C WORLD WID, P697, DOI DOI 10.1145/1242572.1242667; Tonon Alberto, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348304; Tummarello G., 2007, LNCS, V4825, P547; Tummarello G, 2010, J WEB SEMANT, V8, P355, DOI 10.1016/j.websem.2010.08.003; Vries A. P., 2008, FOCUSED ACCESS XML D, P245, DOI [10.1007/978-3-540-85902-4_22, DOI 10.1007/978-3-540-85902-4_22]; Wang Q., 2012, COMMUNITY ANNOTATION, P1; Wei W., 2009, THESIS U NOTTINGHAM; Xue G. R., 2005, SIGIR 05, P186; Yu J. X., 2010, IEEE DATA ENG B, V33, P67; Zhu X., 2007, P N AM CHAPT ASS COM, P97 79 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1386-4564 1573-7659 INFORM RETRIEVAL Inf. Retr. AUG 2014 17 4 295 325 10.1007/s10791-014-9240-0 31 Computer Science, Information Systems Computer Science AM4LA WOS:000339824800001 J Lee, Y; Lee, JH Lee, Yeha; Lee, Jong-Hyeok Identifying top news stories based on their popularity in the blogosphere INFORMATION RETRIEVAL English Article Blog retrieval; Blogosphere; Top news stories identification; Language model approach A huge volume of news stories are reported by various news channels, on a daily basis. Subscribing to all the stories and keeping track of the important ones day after day is very time-consuming. This paper proposes several approaches to identify important news stories. To this end, we take advantage of the blogosphere as an information source to evaluate the importance of news stories. Blogs reflect the diverse opinions of bloggers about news stories, and the attention that these stories receive can help estimate the importance of the stories. In this paper, we define the popularity of a news story in the blogosphere as the attention it attracts from users. We measure popularity of the stories in the blogosphere from two viewpoints: content and a timeline. In terms of content, we suggest several approaches to estimate language models for a news story and blog posts, and we evaluate the importance of the story using these language models. Furthermore, we generate a temporal profile of a news story by exploring the timeline of blog posts related to the story, and evaluate its importance based on the temporal profile. We experimentally verify the effectiveness of the proposed approaches for identifying top news stories. [Lee, Yeha; Lee, Jong-Hyeok] POSTECH, Div Elect & Comp Engn, Pohang, South Korea Lee, Y (reprint author), POSTECH, Div Elect & Comp Engn, Pohang, South Korea. sion@postech.ac.kr; jhlee@postech.ac.kr Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290954; Allan J., 2000, P TREC 9; Aslam J. A., 2008, TECHNICAL REPORT; Becker H., 2010, P 3 ACM INT C WEB SE, P291, DOI 10.1145/1718487.1718524; Bendersky M., 2008, P 31 ANN INT ACM SIG, P491, DOI 10.1145/1390334.1390419; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Brants T., 2003, P 26 ANN INT ACM SIG, P330; Chen CC, 2003, LECT NOTES ARTIF INT, V2837, P47; Chen KY, 2007, IEEE T KNOWL DATA EN, V19, P1016, DOI 10.1109/TKDE.2007.1040; Chieu H. L., 2004, P 27 ANN INT ACM SIG, P425, DOI 10.1145/1008992.1009065; Del Corso G. M., 2005, P 14 INT C WORLD WID, P97, DOI 10.1145/1060745.1060764; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; He Q., 2007, P 30 ANN INT ACM SIG, P207, DOI DOI 10.1145/1277741.1277779; Hofmann T, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P50; Hu M., 2008, P IEEE INT C PATT RE, P1, DOI 10.1145/1458502.1458504; Jones R, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1247715.1247720; Kleinberg J, 2002, P 8 ACM SIGKDD INT C, P91, DOI [10.1145/775047.775061, DOI 10.1023/A:1024940629314]; Kumaran G., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009044; Lee Y, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P395; Leskovec J, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P497; Lin Y. F., 2011, P TREC 2010; Macdonald C., 2010, P TREC 2009; Macdonald C, 2009, COMPUT J, V52, P729, DOI 10.1093/comjnl/bxm112; McCreadie R., 2011, P CSDM 2010; McCreadie R. M. C., 2010, ADAPTIVITY PERSONALI, P40; MediaCollege, 2009, WHAT MAK STOR NEWSW; Mei Q., 2006, P 15 INT C WORLD WID, P533, DOI 10.1145/1135777.1135857; Mishne G, 2006, LECT NOTES COMPUT SC, V3936, P289; Nam SH, 2009, LECT NOTES COMPUT SC, V5478, P791; Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022; Sun A., 2008, P 31 ANN INT ACM SIG, P729, DOI 10.1145/1390334.1390474; Thelwall M., 2006, WWE 3 ANN WORKSH WEB; Tsagkias M., 2011, P INT C WEB SEARCH D, P565, DOI DOI 10.1145/1935826.1935906; Wang C., 2008, P 17 ACM C INF KNOWL, P1033, DOI 10.1145/1458082.1458219; Weerkamp W., 2010, P TREC 2009; Yang Y., 1998, P 21 ANN INT ACM SIG, P28, DOI DOI 10.1145/290941.290953; Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI 10.1145/502585.502654; Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322; Zhang K., 2007, P SIGIR 2007, P215, DOI 10.1145/1277741.1277780; Zhang Y., 2002, P 25 ANN INT ACM SIG, P81 40 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1386-4564 1573-7659 INFORM RETRIEVAL Inf. Retr. AUG 2014 17 4 326 350 10.1007/s10791-014-9241-z 25 Computer Science, Information Systems Computer Science AM4LA WOS:000339824800002 J Hall, MM; Fernando, S; Clough, PD; Soroa, A; Agirre, E; Stevenson, M Hall, Mark M.; Fernando, Samuel; Clough, Paul D.; Soroa, Aitor; Agirre, Eneko; Stevenson, Mark Evaluating hierarchical organisation structures for exploring digital libraries INFORMATION RETRIEVAL English Article Evaluation; Hierarchical structures; Exploratory search; Interactive information retrieval; Browsing INFORMATION-RETRIEVAL; SEARCH; SYSTEMS; WEB Search boxes providing simple keyword-based search are insufficient when users have complex information needs or are unfamiliar with a collection, for example in large digital libraries. Browsing hierarchies can support these richer interactions, but many collections do not have a suitable hierarchy available. In this paper we present a number of approaches for automatically creating hierarchies and mapping items into them, including a novel technique which automatically adapts a Wikipedia-based taxonomy to the target collection. These approaches are applied to a large collection of cultural heritage items which is formed through the aggregation of other collections and for which no unified hierarchy is available. We investigate a number of novel user-evaluated metrics to quantify the hierarchies' quality and performance, showing that the proposed technique is preferred by users. From this we draw a number of conclusions as to what makes a hierarchy useful to the user. [Hall, Mark M.] Edge Hill Univ, Dept Comp, Ormskirk L39 4QP, Lancs, England; [Fernando, Samuel; Stevenson, Mark] Univ Sheffield, Dept Comp Sci, Sheffield S1 4DP, S Yorkshire, England; [Clough, Paul D.] Univ Sheffield, Informat Sch, Sheffield S1 4DP, S Yorkshire, England; [Soroa, Aitor; Agirre, Eneko] Univ Basque Country, IXA NLP Grp, Donostia San Sebastian 20018, Basque Country, Spain Hall, MM (reprint author), Edge Hill Univ, Dept Comp, Ormskirk L39 4QP, Lancs, England. Mark.Hall@edgehill.ac.uk; s.fernando@sheffield.ac.uk; p.d.clough@sheffield.ac.uk; a.soroa@ehu.es; e.agirre@ehu.es; mark.stevenson@sheffield.ac.uk PATHS project - European Community [270082] The research leading to these results was supported by the PATHS project (http://paths-project.eu) funded by the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant Agreement No. 270082. Anick P. G., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312670; Atserias J., 2004, P 2 GLOB WORDNET C G, P23; Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722; Azzopardi L, 2004, IEEE IJCNN, P3281; Blei D. M., 2003, NIPS; Borlund P, 1997, J DOC, V53, P225, DOI 10.1108/EUM0000000007198; Brewster C., 2004, P INT C LANG RES EV; Carterette B, 2008, LECT NOTES COMPUT SC, V4956, P16; Chang J., 2009, NIPS; Chen M., 1999, PARASITOL INT, P11; Falleti MG, 2006, J CLIN EXP NEUROPSYC, V28, P1095, DOI 10.1080/13803390500205718; Fellbaum C., 1998, WORDNET ELECT DATABA; Fernando S., 2012, P COLING 2012, P879; GomezPerez A, 1996, EXPERT SYST APPL, V11, P519, DOI 10.1016/S0957-4174(96)00067-X; Hall M. M., 2013, CLEF 2013 INF ACC EV, P17, DOI [10.1007/978-3-642-40802-1_3, DOI 10.1007/978-3-642-40802-1_3]; Hearst M., 2006, P 29 ANN INT ACM SIG; Hearst MA, 2006, COMMUN ACM, V49, P59, DOI 10.1145/1121949.1121983; Hoffart J., 2011, P 20 INT C COMP WORL, P229; Hornbaek K, 2011, INT J HUM-COMPUT ST, V69, P509, DOI 10.1016/j.ijhcs.2011.02.007; Horvat M, 2012, FRONT ARTIF INTEL AP, V243, P585, DOI 10.3233/978-1-61499-105-2-585; Jorgensen C, 2004, J AM SOC INF SCI TEC, V55, P462, DOI 10.1002/asi.10396; Kelly D, 2013, J AM SOC INF SCI TEC, V64, P745, DOI 10.1002/asi.22799; Lau J., 2011, P 49 ANN M ASS COMP, P1536; Lawrie D., 2001, P 24 ANN INT ACM SIG, P349, DOI 10.1145/383952.384022; Liu X., 2012, P 18 ACM SIGKDD INT, P1433, DOI [10.1145/2339530.2339754, DOI 10.1145/2339530.2339754]; Maedche A., 2002, KNOWLEDGE ENG KNOWLE, P15; Magnini B., 2000, P LREC 2000 2 INT C, P1413; Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979; Markkula M., 2000, Information Retrieval, V1, DOI 10.1023/A:1009995816485; Milne D., 2008, P 17 ACM C INF KNOWL, P509, DOI DOI 10.1145/1458082.1458150; Milne D. N., 2007, P 16 ACM C INF KNOWL, P445, DOI 10.1145/1321440.1321504; Navigli R, 2003, IEEE INTELL SYST, V18, P22, DOI 10.1109/MIS.2003.1179190; Nevill-Manning C., 1999, INT J DIGITAL LIB, V2, P111, DOI 10.1007/s007990050041; Padro L., 2010, PRINC CONSTR APPL MU, P99; Pirolli P., 1996, P ACM SIGCHI C HUM F, P213, DOI 10.1145/238386.238489; Pirolli P, 2009, COMPUTER, V42, P33, DOI 10.1109/MC.2009.94; Ponzetto SP, 2011, ARTIF INTELL, V175, P1737, DOI 10.1016/j.artint.2011.01.003; Pratt W., 1999, P 16 ANN C ART INT A; RAO R, 1995, COMMUN ACM, V38, P29, DOI 10.1145/205323.205326; Rosenfeld L., 2002, INFORM ARCHITECTURE; Sanderson M., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312679; Shiri AA, 2002, J INFORM SCI, V28, P111, DOI 10.1177/0165551024234011; Singer G., 2012, J INF SCI, V39, P346; Skov M., 2008, P 2 INT S INF INT CO, P110, DOI 10.1145/1414694.1414719; Stoica E., 2007, HLT NAACL ASS COMP L, P244; Tang L., 2006, P 12 ACM SIGKDD INT, P384, DOI [10.1145/1150402.1150446, DOI 10.1145/1150402.1150446]; Toms EG, 2013, J INF SCI, V39, P15, DOI 10.1177/0165551512469929; Treeratpituk P., 2006, P 2006 ACM INT C DIG, P167, DOI 10.1145/1146598.1146650; Wang ZH, 2014, J ASSOC INF SCI TECH, V65, P948, DOI 10.1002/asi.23017; Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204; White Ryen W., 2006, COMMUN ACM, V49, P36, DOI 10.1145/1121949.1121978; Yakel E., 2007, D LIB MAGAZINE, V13, DOI [10.1045/may2007-yakel, DOI 10.1045/MAY2007-YAKEL]; Yu J., 2007, P 16 ACM C INF KNOWL, P223, DOI 10.1145/1321440.1321474 53 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1386-4564 1573-7659 INFORM RETRIEVAL Inf. Retr. AUG 2014 17 4 351 379 10.1007/s10791-014-9242-y 29 Computer Science, Information Systems Computer Science AM4LA WOS:000339824800003 J Vuurens, JBP; de Vries, AP Vuurens, Jeroen B. P.; de Vries, Arjen P. Distance matters! Cumulative proximity expansions for ranking documents INFORMATION RETRIEVAL English Article Term dependency; Term proximity; Query expansion TERM PROXIMITY; INFORMATION-RETRIEVAL; MODELS In the information retrieval process, functions that rank documents according to their estimated relevance to a query typically regard query terms as being independent. However, it is often the joint presence of query terms that is of interest to the user, which is overlooked when matching independent terms. One feature that can be used to express the relatedness of co-occurring terms is their proximity in text. In past research, models that are trained on the proximity information in a collection have performed better than models that are not estimated on data. We analyzed how co-occurring query terms can be used to estimate the relevance of documents based on their distance in text, which is used to extend a unigram ranking function with a proximity model that accumulates the scores of all occurring term combinations. This proximity model is more practical than existing models, since it does not require any co-occurrence statistics, it obviates the need to tune additional parameters, and has a retrieval speed close to competing models. We show that this approach is more robust than existing models, on both Web and newswire corpora, and on average performs equal or better than existing proximity models across collections. [Vuurens, Jeroen B. P.] Hague Univ Appl Sci, The Hague, Netherlands; [Vuurens, Jeroen B. P.; de Vries, Arjen P.] Delft Univ Technol, Delft, Netherlands; [de Vries, Arjen P.] CWI, NL-1009 AB Amsterdam, Netherlands Vuurens, JBP (reprint author), Hague Univ Appl Sci, The Hague, Netherlands. j.b.p.vuurens@tudelft.nl; arjen@acm.org Beeferman D., 1997, P 35 ANN M ASS COMP, P373; Bendersky M., 2010, P 3 ACM INT C WEB SE, P31, DOI DOI 10.1145/1718487.1718492; Bendersky Michael, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348408; Buttcher S., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148285; Carterette B., 2008, P 31 ANN INT ACM SIG, P651, DOI 10.1145/1390334.1390445; Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5; Collins-Thompson K., 2007, P SIGIR 2007, P303, DOI 10.1145/1277741.1277795; Croft W. B., 1991, P 14 ANN INT ACM SIG, P32, DOI DOI 10.1145/122860.122864; Cummins R, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P251, DOI 10.1145/1571941.1571986; de Kretser O., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312664; Fagan J. L., 1987, Proceedings of the Tenth Annual International ACMSIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/42005.42016; Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024; Hawking D., 1995, P 4 TEXT RETR C TREC, P131; He B, 2011, INFORM SCIENCES, V181, P3017, DOI 10.1016/j.ins.2011.03.007; KEEN EM, 1991, J DOC, V47, P1, DOI 10.1108/eb026869; Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI 10.1145/383952.383972; Liu X., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002; LV YH, 2009, P 32 ANN INT ACM, P299; Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796; Metzler D., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115; Miao J., 2012, P 35 INT ACM SIGIR C, P535; Nallapati R., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002; Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207; Sakai T., 2005, ACM T ASIAN LANG INF, V2, P111; Shi L., 2010, P 19 ACM INT C INF K, P1493, DOI 10.1145/1871437.1871655; Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022; Song RH, 2008, LECT NOTES COMPUT SC, V4956, P346; Svore KM, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P154; Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794; Tellex S., 2003, P 26 ANN INT ACM SIG, P41; VANRIJSBERGEN CJ, 1977, J DOC, V33, P106; Vechtomova O, 2006, J INF SCI, V32, P324, DOI 10.1177/0165551506065787; Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322; Zhao JL, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P291, DOI 10.1145/1571941.1571993 34 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1386-4564 1573-7659 INFORM RETRIEVAL Inf. Retr. AUG 2014 17 4 380 406 10.1007/s10791-014-9243-x 27 Computer Science, Information Systems Computer Science AM4LA WOS:000339824800004 J Braga, RC; Vila, DA Braga, Ramon Campos; Vila, Daniel Alejandro Investigating the Ice Water Path in Convective Cloud Life Cycles to Improve Passive Microwave Rainfall Retrievals JOURNAL OF HYDROMETEOROLOGY English Article RADAR; CLASSIFICATION; PRODUCTS; ISCCP This study focuses on the possible relationship between ice water path (IWP) retrievals using high-frequency channels (89 and 150 GHz) from the Advanced Microwave Sounding Unit-B and Moisture Humidity Sounder sensors (NOAA-16-NOAA-19) and the life cycle stage of convective clouds. In the first part of this study, the relationship between IWP and the cloud area expansion rate is analyzed using the 235-K isotherm from Geostationary Operational Environmental Satellite-12 (GOES-12) thermal infrared images (10.7 mu m). Next, the relationships between cloud convective fraction, rain rates (from ground radar), and cloud life cycle are analyzed. The selected area and time period coincide with the research activities of the Cloud Processes of the Main Precipitation Systems in Brazil: A Contribution to Cloud Resolving Modeling and to the Global Precipitation Measurement (CHUVA)-Geostationary Lightning Mapper (GLM) project at Sao Jose dos Campos, Brazil. The results show that 84% of precipitating clouds contain ice, according to the Microwave Surface and Precipitation Products System (MSPPS) algorithm. Convective systems in the intensifying stage (when the area is expanding and the minimum temperature is decreasing) tend to have larger IWPs than systems in the dissipating stage. Larger rain rates and convective fractions are also observed from radar retrievals in the early stage of convection compared with mature systems. Hydrometeor retrieval data from polarimetric X-band radar suggest that particle effective diameter D-e and IWP patterns inferred with the MSPPS algorithm could be used to determine the life cycle stage of a given convective system. Using this information, a new set of equations is evaluated for rainfall retrievals using D-e and IWP from the current retrieval algorithm. This new approach outperforms the current algorithm in the studied region. [Braga, Ramon Campos; Vila, Daniel Alejandro] Natl Inst Space Res, Ctr Weather Forecast & Climate Studies, Satellite & Environm Syst Div, Sao Paulo, Brazil Braga, RC (reprint author), PTEC INPE, Rod Pres Dutra Km 40, BR-12630000 Sao Paulo, Brazil. ramon.braga@cptec.inpe.br Center for Weather Forecast and Climate Studies; National Institute for Space and Research (CPTEC/INPE); CHUVA project (FAPESP Grant) [2009/15235-8]; Brazilian National Science Council (CNPq) The authors acknowledge the Center for Weather Forecast and Climate Studies, the National Institute for Space and Research (CPTEC/INPE) and the CHUVA project (FAPESP Grant 2009/15235-8) for the data and infrastructure for the development of this research. The first author also acknowledges the financial support of the Brazilian National Science Council (CNPq) during his master's degree studies. Both authors also thank Thiago Biscaro for the radar data support. Awaka J., 1998, P 8 URSI COMM F OP S, P143; Bennartz R, 2000, J ATMOS OCEAN TECH, V17, P1215, DOI 10.1175/1520-0426(2000)017<1215:OCOABT>2.0.CO;2; Biggerstaff MI, 2000, J APPL METEOROL, V39, P2129, DOI 10.1175/1520-0450(2001)040<2129:AISFCS>2.0.CO;2; Bringi V. N., 2007, DUAL POLARIZATION WE; Ferraro RR, 2005, IEEE T GEOSCI REMOTE, V43, P1036, DOI 10.1109/TGRS.2004.843249; Gorgucci E, 2006, J ATMOS OCEAN TECH, V23, P1668, DOI 10.1175/JTECH1950.1; Keenan T, 2003, AUST METEOROL MAG, V52, P23; Liu GS, 1998, J APPL METEOROL, V37, P337, DOI 10.1175/1520-0450(1998)037<0337:RSOIWC>2.0.CO;2; Machado LAT, 1998, MON WEATHER REV, V126, P1630, DOI 10.1175/1520-0493(1998)126<1630:LCVOMC>2.0.CO;2; MACHADO LAT, 1993, MON WEATHER REV, V121, P3234, DOI 10.1175/1520-0493(1993)121<3234:SCARPO>2.0.CO;2; Mattos EV, 2011, ATMOS RES, V99, P377, DOI 10.1016/j.atmosres.2010.11.007; Rossow WB, 1999, B AM METEOROL SOC, V80, P2261, DOI 10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2; ROSSOW WB, 1991, B AM METEOROL SOC, V72, P2, DOI 10.1175/1520-0477(1991)072<0002:ICDP>2.0.CO;2; Satyamurty P., 2008, Revista Brasileira de Meteorologia, V23, P404, DOI 10.1590/S0102-77862008000400003; STEINER M, 1995, J APPL METEOROL, V34, P1978, DOI 10.1175/1520-0450(1995)034<1978:CCOTDS>2.0.CO;2; Sun NH, 2012, J APPL METEOROL CLIM, V51, P366, DOI 10.1175/JAMC-D-11-021.1; VIVEKANANDAN J, 1991, J APPL METEOROL, V30, P1407, DOI 10.1175/1520-0450(1991)030<1407:IWPEAC>2.0.CO;2; Wang JR, 1997, J ATMOS OCEAN TECH, V14, P13, DOI 10.1175/1520-0426(1997)014<0013:SAMRSI>2.0.CO;2; Wang NY, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017812; Weng FZ, 2000, J ATMOS SCI, V57, P1069, DOI 10.1175/1520-0469(2000)057<1069:ROICPU>2.0.CO;2 20 0 0 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693 USA 1525-755X 1525-7541 J HYDROMETEOROL J. Hydrometeorol. AUG 2014 15 4 1486 1497 10.1175/JHM-D-13-0206.1 12 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AM2QX WOS:000339697000010 J Goh, KL; Patchmuthu, RK; Singh, AK Goh, Kwang Leng; Patchmuthu, Ravi Kumar; Singh, Ashutosh Kumar Link-based web spam detection using weight properties JOURNAL OF INTELLIGENT INFORMATION SYSTEMS English Article Host level; Link spam; Adversarial information retrieval; Weight properties; Web spam Link spam is created with the intention of boosting one target's rank in exchange of business profit. This unethical way of deceiving Web search engines is known as Web spam. Since then many anti-link spam detection techniques have constantly being proposed. Web spam detection is a crucial task due to its devastation towards Web search engines and global cost of billion dollars annually. In this paper, we proposed a novel technique by incorporating weight properties to enhance the Web spam detection algorithms. Weight properties can be defined as the influences of one Web node towards another Web node. We modified existing Web spam detection algorithms with our novel technique to evaluate the performances on a large public Web spam dataset - WEBSPAM-UK2007. The overall performance have shown that the modified algorithms outperform the benchmark algorithms up to 30.5 % improvement at host level and 6.11 % improvement at page level. [Goh, Kwang Leng; Patchmuthu, Ravi Kumar] Curtin Univ, Dept Elect & Comp Engn, Sarawak, Malaysia; [Singh, Ashutosh Kumar] Natl Inst Technol, Dept Comp Applicat, Kurukshetra, Haryana, India Goh, KL (reprint author), Curtin Univ, Dept Elect & Comp Engn, Sarawak Campus, Sarawak, Malaysia. alex.goh@curtin.edu.my; ravi2266@gmail.com; ashutosh@nitkkr.ac.in [Anonymous], 2004, CNSR 04 2 ANN C COMM, P305; Becchetti L., 2006, P WORKSH WEB MIN WEB; Becchetti L, 2008, ACM T WEB, V2, DOI 10.1145/1326561.1326563; Borodin A., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052942; Brinkmeier M., 2006, ACM Transactions on Internet Technology, V6, DOI 10.1145/1151087.1151090; Castillo C., 2006, SIGIR FORUM, V40; Eiron N., 2004, P 13 INT C WORLD WID; Fetterly D., 2004, P 7 INT WORKSH WEB D; Gyongyi Z., 2006, P 32 INT C VER LARG, P439, DOI Seoul, Korea; Gyongyi Z., 2005, P 1 INT WORKSH ADV I, P39; Gyongyi Z., 2004, P 30 INT C VER LARG, P576, DOI 10.1016/B978-012088469-8/50052-8; Henzinger M. R., 2002, SIGIR Forum, V36; Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140; Krishnan V., 2006, P 2 INT WORKSH ADV I, P37; Lempel R, 2001, ACM T INFORM SYST, V19, P131, DOI 10.1145/382979.383041; Leng AGK, 2012, 2012 2ND INTERNATIONAL CONFERENCE ON UNCERTAINTY REASONING AND KNOWLEDGE ENGINEERING (URKE), P18; Leng AGK, 2012, CYBERNET SYST, V43, P459, DOI 10.1080/01969722.2012.707491; Li L., 2002, P 11 INT C WORLD WID, P527; Liang C., 2007, J COMPUTATIONAL INFO, V3, P1705; Nemirovsky D., 2008, WEIGHTED PAGERANK CL; Nie L., 2007, P 30 ANN INT ACM SIG, P869, DOI 10.1145/1277741.1277950; Noi L.D., 2010, P 20 INT C ART NEU 2, P372; Qi C., 2008, P INT C COMP SCI SOF, P1004, DOI [10.1109/csse.2008.1099., DOI 10.1109/CSSE.2008.1099]; Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P81, DOI 10.1109/TNN.2008.2005141; Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P61, DOI 10.1109/TNN.2008.2005605; Sobek M., 2002, PR0 GOOGLES PAGERANK; [王学春 WANG Xuechun], 2008, [干旱地区农业研究, Agricultural Research in the Arid Areas], V26, P1, DOI 10.1145/1344411.1344416; Wu B., 2005, P 14 INT WORLD WID W, P820, DOI 10.1145/1062745.1062762.; Wu B., 2006, WORLD WID WEB WWW200; Wu B., 2005, P 1 INT WORKSH ADV I, P39; Wu B., 2006, P 15 INT C WORLD WID, P63, DOI 10.1145/1135777.1135792; Yahoo!, 2007, WEB SPAM COLL; Yang H., 2007, P 30 ANN INT ACM SIG, P431, DOI 10.1145/1277741.1277815; Zhang X., 2011, P 25 C ART INT AAAI, P1292; Zhang Y., 2009, P 18 ACM C INF KNOWL, P1839, DOI 10.1145/1645953.1646244 35 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0925-9902 1573-7675 J INTELL INF SYST J. Intell. Inf. Syst. AUG 2014 43 1 129 145 10.1007/s10844-014-0310-y 17 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AM5DA WOS:000339875000006 J Torres-Parejo, U; Campana, JR; Vila, MA; Delgado, M Torres-Parejo, Ursula; Campana, Jesus R.; Amparo Vila, M.; Delgado, Miguel A theoretical model for the automatic generation of tag clouds KNOWLEDGE AND INFORMATION SYSTEMS English Article Semantic search; Knowledge visualization; Multi-term; Tag cloud; Unstructured databases; Content identification; Set algebra; Lattices PATTERNS This paper presents a new approach to information retrieval from non-structured attributes in databases, which involves the processing of text attributes. To make retrieval more effective, frequent text sequences are extracted and mathematically represented as intermediate forms which permit a clearer and more precise definition of operations on texts. These intermediate forms appear to users in the form of tag clouds to facilitate content identification, exploration, and querying. In this sense, tag cloud visualization is a simple, user-friendly visual interface to data. This paper proposes a theoretical model for the representation of frequent text sequences and their operations as well as a general procedure for generating tag clouds from text attributes in databases. The tag clouds thus obtained were compared with conventional tag clouds composed of single terms. Our study showed that automatically generated multi-term tag clouds provide better results than mono-term tag clouds. [Torres-Parejo, Ursula; Campana, Jesus R.; Amparo Vila, M.; Delgado, Miguel] Univ Granada, ETSIIT, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain Torres-Parejo, U (reprint author), Univ Granada, ETSIIT, Dept Comp Sci & Artificial Intelligence, E-18071 Granada, Spain. ursula@decsai.ugr.es; jesuscg@decsai.ugr.es; vila@decsai.ugr.es; mdelgado@decsai.ugr.es "Consejeria de Economia, Innovacion, y Ciencia de Andalucia" (Spain) [P07-TIC-02786, P10-TIC-6109, P11-TIC-7460] This work has been partially supported by the "Consejeria de Economia, Innovacion, y Ciencia de Andalucia" (Spain) under research projects P07-TIC-02786, P10-TIC-6109, and P11-TIC-7460. Agili A, 2008, P 6 INT LANG RES EV; Agrawal R., 1994, P 20 INT C VER LARG, V1215, P487; AGRAWAL R, 1995, PROC INT CONF DATA, P3, DOI 10.1109/ICDE.1995.380415; Balachandran V, 2012, KNOWL INF SYST, V32, P475, DOI 10.1007/s10115-011-0446-9; Bar-Ilan J, 2008, ONLINE INFORM REV, V32, P635, DOI 10.1108/14684520810914016; Begelman G, 2006, COLL WEB TAGG WORKSH; Campana JR, 2009, LECT NOTES ARTIF INT, V5822, P488, DOI 10.1007/978-3-642-04957-6_42; Campana JR, 2011, LECT NOTES ARTIF INT, V7022, P84, DOI 10.1007/978-3-642-24764-4_8; Don A, 2007, P 16 ACM C INF KNOWL, P213, DOI 10.1145/1321440.1321473; Durao Frederico, 2012, Current Trends in Web Engineering. Workshops, Doctoral Symposium, and Tutorials Held at ICWE 2011. Revised Selected Papers, DOI 10.1007/978-3-642-27997-3_14; Garcia-Silva A, 2012, KNOWL ENG REV, V27, P57, DOI 10.1017/S026988891100018X; Grahl M, 2007, P I KNOW, V7, P5; Han JW, 2000, SIGMOD RECORD, V29, P1; Hassan-Montero Y, 2006, INT C MULT INF SCI T, P25; Hearst M, 2008, HAW INT C SYST SCI P, P160; Helic Denis, 2011, International Journal of Social Computing and Cyber-Physical Systems, V1, DOI 10.1504/IJSCCPS.2011.043603; Heymann P, 2006, TECHNICAL REPORT; Hipp J, 2000, SIGKDD EXPLORATIONS, V2, P58; Howard H, 2009, ONLINE NOTES COMPUTE; Hsieh W T, 2006, CURRENT DEV TECHNOLO, V2, P1364; Koutrika G, 2009, P 12 INT C EXT DAT T, P391, DOI 10.1145/1516360.1516406; Kuo B, 2007, P 16 INT C WORLD WID, P1204; Leone S, 2011, LECT NOTES BUS INF P, V72, P15; Marin N, 2006, LECT NOTES COMPUT SC, V4027, P613; Marinho L, 2012, RECOMMENDER SYSTEMS, P3; Martin-Bautista MJ, 2008, LECT NOTES COMPUT SC, V5182, P347, DOI 10.1007/978-3-540-85836-2_33; Martinez-Folgoso S, 2008, THESIS U GRANADA SPA; Milgram S, 1976, ENV PSYCHOL PEOPLE T, P104; Morik K, 2012, KNOWL INF SYST, V30, P715, DOI 10.1007/s10115-011-0431-3; Panunzi A, 2006, P 5 INT LANG RES EV, P1917; Savasere A, 1995, P 21 INT C VER LARG, P432; Schmitz P, 2006, COLL WEB TAGG WORKSH, P210; Sinclair J, 2008, J INF SCI, V34, P15, DOI 10.1177/0165551506078083; Tao F, 2003, P 9 ACM SIGKDD INT C, P661; Torres-Parejo U, 2010, THESIS U GRANADA SPA; Torres-Parejo U, 2012, COMMUN COMPUT PHYS, V297, P390; Venetis P, 2011, P 4 ACM INT C WEB SE, P835, DOI 10.1145/1935826.1935855; Viegas FB, 2009, IEEE T VIS COMPUT GR, V15, P1137, DOI 10.1109/TVCG.2009.171; Viegas FB, 2008, INTERACTIONS, V15, P49, DOI 10.1145/1374489.1374501; Watters D, 2008, ONLINE NOTES; Xexeo G, 2009, 24 S BRAS BANC DAT; Zaki MJ, 1997, 3 INTL C KNOWL DISC 42 1 1 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0219-1377 0219-3116 KNOWL INF SYST Knowl. Inf. Syst. AUG 2014 40 2 315 347 10.1007/s10115-013-0651-9 33 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AM5FW WOS:000339883200003 J Zhong, JF; Wu, W; Gao, XF; Shi, Y; Yue, XD Zhong, Jiaofei; Wu, Wei; Gao, Xiaofeng; Shi, Yan; Yue, Xiaodong Evaluation and comparison of various indexing schemes in single-channel broadcast communication environment KNOWLEDGE AND INFORMATION SYSTEMS English Article Data broadcasting; Indexing scheme; Access latency; Tuning time WIRELESS DATA BROADCAST; ENERGY-EFFICIENT; ACCESS; ALLOCATION; RETRIEVAL; TIME Wireless Data Broadcasting is a newly developed data dissemination method for spreading public information to a tremendous number of mobile subscribers. Access Latency and Tuning Time are two main criteria to evaluate the performance of such system. With the help of indexing technology, clients can reduce tuning time significantly by searching indices first and turning to doze mode during waiting period. Different indexing schemes perform differently, so we can hardly compare the efficiency of different indexing schemes. In this paper, we redesigned several most popular indexing schemes for data broadcasting systems, i.e., distributed index, exponential index, hash table, and Huffman tree index. We created a unified communication model and constructed a novel evaluation strategy by using the probability theory to formulate the performance of each scheme theoretically and then conducted simulations to compare their performance by numerical experiments. This is the first work to provide scalable communication environment and accurate evaluation strategies. Our communication model can easily be modified to meet specific requirements. Our comparison model can be used by the service providers to evaluate other indexing schemes to choose the best one for their systems. [Zhong, Jiaofei; Yue, Xiaodong] Cent Missouri State Univ, Dept Math & Comp Sci, Warrensburg, MO 64093 USA; [Wu, Wei] Univ Texas Dallas, Dept Comp Sci, Richardson, TX 75083 USA; [Gao, Xiaofeng] Shanghai Jiao Tong Univ, Dept Comp Sci & Engn, Shanghai 200030, Peoples R China; [Shi, Yan] Univ Wisconsin Platteville, Dept Comp Sci & Software Engn, Platteville, WI USA Zhong, JF (reprint author), Cent Missouri State Univ, Dept Math & Comp Sci, Warrensburg, MO 64093 USA. zhong@ucmo.edu; gao-xf@cs.sjtu.edu.cn U.S. National Science Foundation [CNS-0831579, CNS-1016320, CCF-0829993]; Shanghai Educational Development Foundation (Chenguang Grant) [12CG09]; Natural Science Foundation of Shanghai [12ZR1445000]; National Natural Science Foundation of China [61202024, 61033002] This work was supported in part by the U.S. National Science Foundation under Grant CNS-0831579, CNS-1016320, and CCF-0829993, partially supported by Shanghai Educational Development Foundation (Chenguang Grant No. 12CG09), the Natural Science Foundation of Shanghai (Grant No. 12ZR1445000), the National Natural Science Foundation of China (Grant numbers 61202024 and 61033002). Chen CC, 2009, INFORM SYST, V34, P164, DOI 10.1016/j.is.2008.05.004; Chen MS, 1997, INT CON DISTR COMP S, P124; Gao X, 2012, P 21 INT C SOFTW ENG; Hsu CH, 2002, MDM 2002: THIRD INTERNATIONAL CONFERENCE ON MOBILE DATA MANAGEMENT, PROCEEDINGS, P87; Hu QL, 2001, DISTRIB PARALLEL DAT, V9, P151, DOI 10.1023/A:1018944523033; HU TC, 1971, SIAM J APPL MATH, V21, P514, DOI 10.1137/0121057; Im S, 2012, COMPUT MATH APPL, V64, P1242, DOI 10.1016/j.camwa.2012.03.068; Imielinski T, 1994, P 4 INT C EXT DAT TE, P245; Imielinski T, 1997, IEEE T KNOWL DATA EN, V9, P353, DOI 10.1109/69.599926; Jung SE, 2005, IEEE T KNOWL DATA EN, V17, P311; Lee WC, 1996, DISTRIB PARALLEL DAT, V4, P205, DOI 10.1007/BF00140950; Lee WC, 2005, PROC INT CONF DATA, P417; Lu X, 2013, P ACM INT C UB INF M; Lu Z, 2012, IEEE T MOBILE COM, P1, DOI [10.1109/TC.2012.139, DOI 10.4018/978-1-4666-0936-5]; Manning C., 1999, FDN STAT NATURAL LAN; Pichevar R, 2011, SPEECH COMMUN, V53, P643, DOI 10.1016/j.specom.2010.09.008; Shen J, 2008, THESIS NATL SUN YS U; Shi Y, 2010, LECT NOTES COMPUT SC, V6262, P80; Shivakumar N, 1996, J MOBILE NETWORKS AP, V1, P433; Vaidya NH, 1999, WIREL NETW, V5, P171, DOI 10.1023/A:1019142809816; Vijayalakshmi Muthuswamy, 2008, Journal of Computing and Information Technology - CIT, V16, DOI 10.2498/cit.1001112; Wang JY, 2012, INFORM SCIENCES, V199, P93, DOI 10.1016/j.ins.2012.02.038; Wang S, 2007, P INT C ADV INF NETW; Xu JL, 2006, IEEE T KNOWL DATA EN, V18, P392; Yang X, 2002, LECT NOTES COMPUT SC, V2287, P553; Yao YX, 2006, IEEE T KNOWL DATA EN, V18, P1111; Yee WG, 2002, IEEE T COMPUT, V51, P1231; Zheng BH, 2009, IEEE T KNOWL DATA EN, V21, P1783, DOI 10.1109/TKDE.2009.43; Zhong J, 2012, THESIS U TEXAS DALLA; Zhong J, 2013, IEEE WIR COMM NETW C; Zhong J, 2012, P 21 INT C SOFTW ENG; Zhong JF, 2011, LECT NOTES COMPUT SC, V6588, P335; Zomaya A, 2007, P ACM INT WORKSH MOB, P112 33 0 0 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0219-1377 0219-3116 KNOWL INF SYST Knowl. Inf. Syst. AUG 2014 40 2 375 409 10.1007/s10115-013-0643-9 35 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AM5FW WOS:000339883200005 J Sjolund, LA; Erdman, M; Kelly, JW Sjolund, Lori A.; Erdman, Matthew; Kelly, Jonathan W. Collaborative inhibition in spatial memory retrieval MEMORY & COGNITION English Article Collaborative inhibition; Spatial cognition; Memory; Reference frames RECALL; ENVIRONMENTS; PERSPECTIVE; FRAMES Collaborative inhibition refers to the finding that pairs of people working together to retrieve information from memory-a collaborative group-often retrieve fewer unique items than do nominal pairs, who retrieve individually but whose performance is pooled. Two experiments were designed to explore whether collaborative inhibition, which has heretofore been studied using traditional memory stimuli such as word lists, also characterizes spatial memory retrieval. In the present study, participants learned a layout of objects and then reconstructed the layout from memory, either individually or in pairs. The layouts created by collaborative pairs were more accurate than those created by individuals, but less accurate than those of nominal pairs, providing evidence for collaborative inhibition in spatial memory retrieval. Collaborative inhibition occurred when participants were allowed to dictate the order of object placement during reconstruction (Exp. 1), and also when object order was imposed by the experimenter (Exp. 2), which was intended to disrupt the retrieval processes of pairs as well as of individuals. Individual tests of perspective taking indicated that the underlying representations of pair members were no different than those of individuals; in all cases, spatial memories were organized around a reference frame aligned with the studied perspective. These results suggest that inhibition is caused by the product of group recall (i.e., seeing a partner's object placement), not by the process of group recall (i.e., taking turns choosing an object to place). The present study has implications for how group performance on a collaborative spatial memory task may be optimized. [Sjolund, Lori A.; Erdman, Matthew; Kelly, Jonathan W.] Iowa State Univ, Dept Psychol, Ames, IA 50011 USA Kelly, JW (reprint author), Iowa State Univ, Dept Psychol, W112 Lagomarcino Hall, Ames, IA 50011 USA. jonkelly@iastate.edu Kelly, Jonathan/A-4793-2013 ANDERSSON J, 1995, APPL COGNITIVE PSYCH, V9, P199, DOI 10.1002/acp.2350090303; Basden BH, 1997, J EXP PSYCHOL LEARN, V23, P1176, DOI 10.1037/0278-7393.23.5.1176; Batschelet E., 1981, CIRCULAR STAT BIOL; Blumen HM, 2008, MEMORY, V16, P231, DOI 10.1080/09658210701804495; Ekeocha JO, 2008, MEMORY, V16, P245, DOI 10.1080/09658210701807480; Finlay F, 2000, J EXP PSYCHOL LEARN, V26, P1556, DOI 10.1037//0278-7393.26.6.1556; Galati A, 2013, J MEM LANG, V68, P140, DOI 10.1016/j.jml.2012.10.001; Greenauer N, 2008, PSYCHON B REV, V15, P1015, DOI 10.3758/PBR.15.5.1015; Kelly JW, 2013, COGNITION, V126, P459, DOI 10.1016/j.cognition.2012.11.007; Kelly JW, 2011, PSYCHON B REV, V18, P774, DOI 10.3758/s13423-011-0100-2; Kelly JW, 2009, PSYCHON B REV, V16, P295, DOI 10.3758/PBR.16.2.295; Kelly JW, 2008, PSYCHON B REV, V15, P322, DOI 10.3758/PBR/15.2.322; Klatzky R., 1998, LECT NOTES ARTIF INT, V1404, P1; Marchette SA, 2011, MEM COGNITION, V39, P1401, DOI 10.3758/s13421-011-0108-x; MONTELLO DR, 1991, ENVIRON BEHAV, V23, P47, DOI 10.1177/0013916591231003; Mou WM, 2002, J EXP PSYCHOL LEARN, V28, P162, DOI 10.1037//0278-7393.28.1.162; NICKERSON RS, 1984, MEM COGNITION, V12, P531, DOI 10.3758/BF03213342; Peker M, 2009, SOC PSYCHOL-GERMANY, V40, P111, DOI 10.1027/1864-9335.40.3.111; Rajaram S, 2011, CURR DIR PSYCHOL SCI, V20, P76, DOI 10.1177/0963721411403251; Rajaram S, 2010, PERSPECT PSYCHOL SCI, V5, P649, DOI 10.1177/1745691610388763; Richard L, 2013, J EXP PSYCHOL LEARN, V39, P1914, DOI 10.1037/a0032995; Shelton AL, 2004, MEM COGNITION, V32, P416, DOI 10.3758/BF03195835; Shelton AL, 2001, COGNITIVE PSYCHOL, V43, P274, DOI 10.1006/cogp.2001.0758; Thorley C, 2007, EUR J COGN PSYCHOL, V19, P867, DOI 10.1080/09541440600872068; TOBLER WR, 1994, GEOGR ANAL, V26, P187; Waller D, 2004, PSYCHON B REV, V11, P157, DOI 10.3758/BF03206476; Weldon MS, 2000, J EXP PSYCHOL LEARN, V26, P1568, DOI 10.1037//0278-7393.26.6.1568; Weldon MS, 1997, J EXP PSYCHOL LEARN, V23, P1160, DOI 10.1037/0278-7393.23.5.1160; Werner S., 1999, SPAT COGN COMPUT, V1, P447, DOI 10.1023/A:1010095831166; Wright DB, 2004, PSYCHON B REV, V11, P1080, DOI 10.3758/BF03196740 30 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0090-502X 1532-5946 MEM COGNITION Mem. Cogn. AUG 2014 42 6 876 885 10.3758/s13421-014-0407-0 10 Psychology, Experimental Psychology AM6FA WOS:000339957500004 J Rowland, CA; Littrell-Baez, MK; Sensenig, AE; DeLosh, EL Rowland, Christopher A.; Littrell-Baez, Megan K.; Sensenig, Amanda E.; DeLosh, Edward L. Testing effects in mixed- versus pure-list designs MEMORY & COGNITION English Article Memory; Recall; Testing; Retrieval LONG-TERM RETENTION; FREE-RECALL; RECOGNITION MEMORY; ORDER INFORMATION; WORD-FREQUENCY; GENERATION; ITEM; REPETITION; SUPPORT; EXPLANATION In the present study, we investigated the role of list composition in the testing effect. Across three experiments, participants learned items through study and initial testing or study and restudy. List composition was manipulated, such that tested and restudied items appeared either intermixed in the same lists (mixed lists) or in separate lists (pure lists). In Experiment 1, half of the participants received mixed lists and half received pure lists. In Experiment 2, all participants were given both mixed and pure lists. Experiment 3 followed Erlebacher's (Psychological Bulletin, 84, 212-219, 1977) method, such that mixed lists, pure tested lists, and pure restudied lists were given to independent groups. Across all three experiments, the final recall results revealed significant testing effects for both mixed and pure lists, with no reliable difference in the magnitude of the testing advantage across list designs. This finding suggests that the testing effect is not subject to a key boundary condition-list design-that impacts other memory phenomena, including the generation effect. [Rowland, Christopher A.; DeLosh, Edward L.] Colorado State Univ, Dept Psychol, Ft Collins, CO 80521 USA; [Littrell-Baez, Megan K.] Univ Colorado, Boulder, CO 80309 USA; [Sensenig, Amanda E.] Bluffton Univ, Bluffton, OH USA Rowland, CA (reprint author), Colorado State Univ, Dept Psychol, Ft Collins, CO 80521 USA. rowlandc@colostate.edu BEGG I, 1989, J EXP PSYCHOL LEARN, V15, P977, DOI 10.1037//0278-7393.15.5.977; Brewer GA, 2010, MEMORY, V18, P385, DOI 10.1080/09658211003702163; BURNS DJ, 1992, J MEM LANG, V31, P615, DOI 10.1016/0749-596X(92)90031-R; Burns DJ, 1996, AM J PSYCHOL, V109, P567, DOI 10.2307/1423395; Butler AC, 2007, J EXP PSYCHOL-APPL, V13, P273, DOI 10.1037/1076-898X.13.4.273; Carpenter SK, 2005, APPL COGNITIVE PSYCH, V19, P619, DOI 10.1002/acp.1101; Carpenter SK, 2006, PSYCHON B REV, V13, P826, DOI 10.3758/BF03194004; Carpenter SK, 2007, PSYCHON B REV, V14, P474, DOI 10.3758/BF03194092; Carpenter SK, 2009, APPL COGNITIVE PSYCH, V23, P760, DOI 10.1002/acp.1507; Carpenter SK, 2006, MEM COGNITION, V34, P268, DOI 10.3758/BF03193405; Carpenter SK, 2008, MEM COGNITION, V36, P438, DOI 10.3758/MC.36.2.438; CARRIER M, 1992, MEM COGNITION, V20, P633, DOI 10.3758/BF03202713; Chan JCK, 2007, J EXP PSYCHOL LEARN, V33, P431, DOI 10.1037/0278-7393.33.2.431; Cohen J, 1988, STAT POWER ANAL BEHA; CUDDY LJ, 1982, J VERB LEARN VERB BE, V21, P451, DOI 10.1016/S0022-5371(82)90727-7; Delaney PF, 2009, J EXP PSYCHOL LEARN, V35, P1148, DOI 10.1037/a0016380; DeLosh EL, 1996, J EXP PSYCHOL LEARN, V22, P1136, DOI 10.1037//0278-7393.22.5.1136; ERLEBACHER A, 1977, PSYCHOL BULL, V84, P212, DOI 10.1037//0033-2909.84.2.212; HALL JW, 1992, J EXP PSYCHOL LEARN, V18, P608, DOI 10.1037//0278-7393.18.3.608; HIRSHMAN E, 1988, J EXP PSYCHOL LEARN, V14, P484, DOI 10.1037/0278-7393.14.3.484; JACOBY LL, 1978, J VERB LEARN VERB BE, V17, P649, DOI 10.1016/S0022-5371(78)90393-6; Kang SHK, 2007, EUR J COGN PSYCHOL, V19, P528, DOI 10.1080/09541440601056620; Karpicke JD, 2010, J MEM LANG, V62, P227, DOI 10.1016/j.jml.2009.11.010; MacLeod CM, 2010, J EXP PSYCHOL LEARN, V36, P671, DOI 10.1037/a0018785; Malmberg KJ, 2005, J EXP PSYCHOL LEARN, V31, P322, DOI 10.1037/0278-7393.31.2.322; McDaniel MA, 2008, PSYCHON B REV, V15, P237, DOI 10.3758/PBR.15.2.237; MCDANIEL MA, 1995, J EXP PSYCHOL LEARN, V21, P422, DOI 10.1037//0278-7393.21.2.422; McDaniel MA, 2007, EUR J COGN PSYCHOL, V19, P494, DOI 10.1080/09541440701326154; Merritt PS, 2006, MEM COGNITION, V34, P1615, DOI 10.3758/BF03195924; NAIRNE JS, 1991, J EXP PSYCHOL LEARN, V17, P702, DOI 10.1037//0278-7393.17.4.702; RATCLIFF R, 1990, J EXP PSYCHOL LEARN, V16, P163, DOI 10.1037/0278-7393.16.2.163; Roediger HL, 2000, MEMORY, CONSCIOUSNESS, AND THE BRAIN, P52; Roediger HL, 2006, PERSPECT PSYCHOL SCI, V1, P181, DOI 10.1111/j.1745-6916.2006.00012.x; Roediger HL, 2006, PSYCHOL SCI, V17, P249, DOI 10.1111/j.1467-9280.2006.01693.x; Roediger III H. L., 1996, HUMAN MEMORY, P197; Rose RJ, 1996, CAN J EXP PSYCHOL, V50, P261, DOI 10.1037/1196-1961.50.3.261; Rowland C. A., 2014, MEMORY, DOI [10.1080/09658211.2014.889710, DOI 10.1080/09658211.2014.889710]; RUNQUIST WN, 1983, MEM COGNITION, V11, P641, DOI 10.3758/BF03198289; RUNQUIST WN, 1986, CAN J PSYCHOL, V40, P65, DOI 10.1037/h0080086; Sensenig AE, 2011, MEMORY, V19, P664, DOI 10.1080/09658211.2011.599935; SERRA M, 1993, MEM COGNITION, V21, P34, DOI 10.3758/BF03211162; SLAMECKA NJ, 1987, J MEM LANG, V26, P589, DOI 10.1016/0749-596X(87)90104-5; Toppino TC, 1999, J EXP PSYCHOL LEARN, V25, P1071, DOI 10.1037/0278-7393.25.4.1071; TULVING E, 1972, J EXP PSYCHOL, V92, P297, DOI 10.1037/h0032367; Verkoeijen PPJL, 2011, EXP PSYCHOL, V58, P490, DOI 10.1027/1618-3169/a000117; WILSON M, 1988, BEHAV RES METH INSTR, V20, P6, DOI 10.3758/BF03202594 46 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0090-502X 1532-5946 MEM COGNITION Mem. Cogn. AUG 2014 42 6 912 921 10.3758/s13421-014-0404-3 10 Psychology, Experimental Psychology AM6FA WOS:000339957500007 J Wahlheim, CN Wahlheim, Christopher N. Proactive effects of memory in young and older adults: The role of change recollection MEMORY & COGNITION English Article Aging; Change detection; Change recollection; Proactive interference; Reminding; Metacognition RECURSIVE REMINDINGS; COGNITIVE CONTROL; AGE-DIFFERENCES; RETRIEVAL; INTERFERENCE; JUDGMENTS; REPETITION; RECENCY; DEFICIT; MODELS Age-related deficits in episodic memory are sometimes attributed to older adults being more susceptible to proactive interference. These deficits have been explained by impaired abilities to inhibit competing information and to recollect target information. In the present article, I propose that a change recollection deficit also contributes to age differences in proactive interference. Change recollection occurs when individuals can remember how information changed across episodes, and this counteracts proactive interference by preserving the temporal order of information. Three experiments were conducted to determine whether older adults are less likely to counteract proactive interference by recollecting change. Paired-associate learning paradigms with two lists of word pairs included pairs that repeated across lists, pairs that only appeared in List 2 (control items), and pairs with cues that repeated and responses that changed across lists. Young and older adults' abilities to detect changed pairs in List 2 and to later recollect those changes at test were measured, along with cued recall of the List 2 responses and confidence in recall performance. Change recollection produced proactive facilitation in the recall of changed pairs, whereas the failure to recollect change resulted in proactive interference. Confidence judgments were sensitive to these effects. The critical finding was that older adults recollected change less than did young adults, and this partially explained older adults' greater susceptibility to proactive interference. These findings have theoretical implications, showing that a change recollection deficit contributes to age-related deficits in episodic memory. Washington Univ, Dept Psychol, St Louis, MO 63130 USA Wahlheim, CN (reprint author), Washington Univ, Dept Psychol, 1 Brookings Dr, St Louis, MO 63130 USA. cnwahlheim@gmail.com Anderson MC, 1999, J EXP PSYCHOL LEARN, V25, P608, DOI 10.1037//0278-7393.25.3.608; Balota D. A., 2000, OXFORD HDB MEMORY, P395; Balota DA, 2007, BEHAV RES METHODS, V39, P445, DOI 10.3758/BF03193014; BARNES JM, 1959, J EXP PSYCHOL, V58, P97, DOI 10.1037/h0047507; Campbell KL, 2010, PSYCHOL SCI, V21, P399, DOI 10.1177/0956797609359910; Craik F. I. M., 1992, HDB AGING COGNITION, P51; Grady CL, 2000, CURR OPIN NEUROBIOL, V10, P224, DOI 10.1016/S0959-4388(00)00073-8; Hasher L., 1988, PSYCHOL LEARN MOTIV, V22, P193, DOI DOI 10.1016/S0079-7421(08)60041-9; Hay JF, 1999, PSYCHOL AGING, V14, P122, DOI 10.1037/0882-7974.14.1.122; Healey MK, 2013, PSYCHOL AGING, V28, P721, DOI 10.1037/a0033003; Hintzman DL, 2010, MEM COGNITION, V38, P102, DOI 10.3758/MC.38.1.102; Jacoby L., 1989, VARIETIES MEMORY CON, P391; Jacoby L. L., 2014, MEMORY FLIP FLOPPING; Jacoby LJ, 2001, J EXP PSYCHOL LEARN, V27, P686, DOI 10.1037/0278-7393.27.3.686; Jacoby LL, 2010, MEM COGNITION, V38, P820, DOI 10.3758/MC.38.6.820; JACOBY LL, 1981, J EXP PSYCHOL GEN, V110, P306, DOI 10.1037/0096-3445.110.3.306; Jacoby LL, 2005, J EXP PSYCHOL GEN, V134, P131, DOI 10.1037/0096-3445.134.2.131; Jacoby LL, 2013, MEM COGNITION, V41, P638, DOI 10.3758/s13421-013-0313-x; Jacoby LL, 2013, MEM COGNITION, V41, P625, DOI 10.3758/s13421-013-0298-5; Jacoby LL, 2005, PSYCHON B REV, V12, P852, DOI 10.3758/BF03196776; Jacoby LL, 1999, J EXP PSYCHOL LEARN, V25, P3, DOI 10.1037//0278-7393.25.1.3; Kane M. J., 2002, ENCY AGING, P514; Kausler D. H., 1994, LEARNING MEMORY NORM; Koriat A, 1996, PSYCHOL REV, V103, P490, DOI 10.1037/0033-295X.103.3.490; LIGHT LL, 1991, ANNU REV PSYCHOL, V42, P333, DOI 10.1146/annurev.ps.42.020191.002001; Lindenberger U, 2009, PSYCHOL AGING, V24, P1, DOI 10.1037/a0014986; Logan JM, 2003, PSYCHOL AGING, V18, P537, DOI 10.1037/0882-7974.18.3.537; Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766; METCALFE J, 1993, J EXP PSYCHOL LEARN, V19, P851, DOI 10.1037//0278-7393.19.4.851; Metcalfe J., 2009, METACOGNITION; Naveh-Benjamin M, 2000, J EXP PSYCHOL LEARN, V26, P1170, DOI 10.1037//0278-7393.26.2.1170; Nelson D. L., 1998, U S FLORIDA WORD ASS; POSTMAN L, 1964, J VERB LEARN VERB BE, V3, P437, DOI 10.1016/S0022-5371(64)80014-1; Radvansky GA, 2005, J GERONTOL B-PSYCHOL, V60, pP276; RADVANSKY GA, 1991, J EXP PSYCHOL LEARN, V17, P940, DOI 10.1037//0278-7393.17.5.940; Salthouse TA, 1996, PSYCHOL REV, V103, P403, DOI 10.1037/0033-295X.103.3.403; Shipley W. C, 1986, SHIPLEY I LIVING SCA; TZENG OJL, 1980, J EXP PSYCHOL-HUM L, V6, P705, DOI 10.1037/0278-7393.6.6.705; Wahlheim CN, 2013, MEM COGNITION, V41, P1, DOI 10.3758/s13421-012-0246-9; Wahlheim CN, 2011, MEM COGNITION, V39, P185, DOI 10.3758/s13421-010-0017-4; Wahlheim CN, 2011, MEM COGNITION, V39, P827, DOI 10.3758/s13421-010-0065-9; WILSON M, 1988, BEHAV RES METH INSTR, V20, P6, DOI 10.3758/BF03202594; WINOCUR G, 1983, J GERONTOL, V38, P455; WINOGRAD E, 1985, J EXP PSYCHOL LEARN, V11, P262; Zacks R. T., 2006, LIFESPAN COGNITION M, P162 45 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0090-502X 1532-5946 MEM COGNITION Mem. Cogn. AUG 2014 42 6 950 964 10.3758/s13421-014-0411-4 15 Psychology, Experimental Psychology AM6FA WOS:000339957500011 J Pyc, MA; Balota, DA; McDermott, KB; Tully, T; Roediger, HL Pyc, Mary A.; Balota, David A.; McDermott, Kathleen B.; Tully, Tim; Roediger, Henry L., III Between-list lag effects in recall depend on retention interval MEMORY & COGNITION English Article Memory; Recall; Spacing effects; Lag effects LONG-TERM RETENTION; RETRIEVAL PRACTICE; PAIRED-ASSOCIATE; MEMORY; STRATEGIES; BENEFITS; STUDENTS; REPETITIONS; MAINTENANCE; VOCABULARY Although the benefits of spaced retrieval for long-term retention are well established, the majority of this work has involved spacing over relatively short intervals (on the order of seconds or minutes). In the present experiments, we evaluated the effectiveness of spaced retrieval across relatively short intervals (within a single session), as compared to longer intervals (between sessions spaced a day apart), for long-term retention (i.e., one day or one week). Across a series of seven experiments, participants (N = 536) learned paired associates to a criterion of 70 % accuracy and then received one test-feedback trial for each item. The test-feedback trial occurred within 10 min of reaching criterion (short lag) or one day later (long lag). Then, a final test occurred one day (Exps. 1-3) or one week (Exps. 4 and 5) after the test-feedback trial. Across the different materials and methods in Experiments 1-3, we found little benefit for the long-lag relative to the short-lag schedule in final recall performance-that is, no lag effect-but large effects on the retention of information from the test-feedback to the final test phase. The results from the experiments with the one-week retention interval (Exps. 4 and 5) indicated a benefit of the long-lag schedule on final recall performance (a lag effect), as well as on retention. This research shows that even when the benefits of lag are eliminated at a (relatively long) one-day retention interval, the lag effect reemerges after a one-week retention interval. The results are interpreted within an extension of the bifurcation model to the spacing effect. [Pyc, Mary A.; Balota, David A.; McDermott, Kathleen B.; Roediger, Henry L., III] Washington Univ, Dept Psychol, St Louis, MO 63130 USA; [Tully, Tim] Dart Neurosci, San Diego, CA USA Pyc, MA (reprint author), Washington Univ, Dept Psychol, 1 Brookings Dr,Box 1125, St Louis, MO 63130 USA. mpyc@wustl.edu Arnold KM, 2013, J EXP PSYCHOL LEARN, V39, P940, DOI 10.1037/a0029199; Arnold KM, 2013, PSYCHON B REV, V20, P507, DOI 10.3758/s13423-012-0370-3; BAHRICK HP, 1993, PSYCHOL SCI, V4, P316, DOI 10.1111/j.1467-9280.1993.tb00571.x; BALOTA DA, 1989, PSYCHOL AGING, V4, P3, DOI 10.1037//0882-7974.4.1.3; Balota DA, 2006, PSYCHOL AGING, V21, P19, DOI 10.1037/0882-7974.21.1.19; Bjork R. A., 1994, METACOGNITION KNOWIN, P185; Cepeda NJ, 2006, PSYCHOL BULL, V132, P354, DOI 10.1037/0033-2909.132.3.354; Cepeda NJ, 2008, PSYCHOL SCI, V19, P1095, DOI 10.1111/j.1467-9280.2008.02209.x; Crowder R. G., 1976, PRINCIPLES LEARNING; Cull WL, 2000, APPL COGNITIVE PSYCH, V14, P215, DOI 10.1002/(SICI)1099-0720(200005/06)14:3<215::AID-ACP640>3.0.CO;2-1; Delaney PF, 2010, PSYCHOL LEARN MOTIV, V53, P63, DOI 10.1016/S0079-7421(10)53003-2; Donovan JJ, 1999, J APPL PSYCHOL, V84, P795, DOI 10.1037/0021-9010.84.5.795; Ebbinghaus H, 1913, MEMORY CONTRIBUTION; GLENBERG AM, 1980, MEM COGNITION, V8, P528, DOI 10.3758/BF03213772; Goverover Y, 2011, MULT SCLER J, V17, P1488, DOI 10.1177/1352458511406310; Greene R. L., 2008, COGNITIVE PSYCHOL ME, V2, P65; Halamish V, 2011, J EXP PSYCHOL LEARN, V37, P801, DOI 10.1037/a0023219; Hartwig MK, 2012, PSYCHON B REV, V19, P126, DOI 10.3758/s13423-011-0181-y; IZAWA C, 1966, PSYCHOL REP, V18, P879; Karpicke JD, 2011, J EXP PSYCHOL LEARN, V37, P1250, DOI 10.1037/a0023436; Karpicke JD, 2009, MEMORY, V17, P471, DOI 10.1080/09658210802647009; Kornell N, 2011, J MEM LANG, V65, P85, DOI 10.1016/j.jml.2011.04.002; Kornell N, 2010, PSYCHOL AGING, V25, P498, DOI 10.1037/a0017807; Kornell N, 2007, PSYCHON B REV, V14, P219, DOI 10.3758/BF03194055; Kupper-Tetzel CE, 2012, MEMORY, V20, P37, DOI 10.1080/09658211.2011.631550; Landauer TK, 1978, PRACTICAL ASPECTS ME, P625; Litman L, 2008, LEARN MEMORY, V15, P711, DOI 10.1101/lm.1132008; Maddox GB, 2012, AGING NEUROPSYCHOL C, V19, P620, DOI 10.1080/13825585.2011.640658; MADIGAN SA, 1969, J VERB LEARN VERB BE, V8, P828, DOI 10.1016/S0022-5371(69)80050-2; Mason W, 2012, BEHAV RES METHODS, V44, P1, DOI 10.3758/s13428-011-0124-6; MELTON AW, 1970, J VERB LEARN VERB BE, V9, P596, DOI 10.1016/S0022-5371(70)80107-4; MELTON AW, 1967, SCIENCE, V158, P532, DOI 10.1126/science.158.3800.532-b; PETERSON LR, 1963, J EXP PSYCHOL, V66, P206, DOI 10.1037/h0046694; Pyc M. A., 2014, IS THERE BENEFIT 24; Pyc MA, 2009, J MEM LANG, V60, P437, DOI 10.1016/j.jml.2009.01.004; Rawson KA, 2005, J EDUC PSYCHOL, V97, P70, DOI 10.1037/0022-0663.97.1.70; Rawson KA, 2011, J EXP PSYCHOL GEN, V140, P283, DOI 10.1037/a0023956; ROBBINS D, 1973, J EXP PSYCHOL, V97, P344, DOI 10.1037/h0034133; Roediger HL, 2011, TRENDS COGN SCI, V15, P20, DOI 10.1016/j.tics.2010.09.003; Roediger HL, 2006, PSYCHOL SCI, V17, P249, DOI 10.1111/j.1467-9280.2006.01693.x; Sargis E. G., 2013, SOCIAL NET UNDERSTAN, P253; Simone PM, 2013, J GERONTOL B-PSYCHOL, V68, P674, DOI 10.1093/geronb/gbs096; Sobel HS, 2011, APPL COGNITIVE PSYCH, V25, P763, DOI 10.1002/acp.1747; Toppino TC, 2009, MEM COGNITION, V37, P316, DOI 10.3758/MC.37.3.316; TULLY T, 1994, CELL, V79, P35, DOI 10.1016/0092-8674(94)90398-0; Wissman KT, 2012, MEMORY, V20, P568, DOI 10.1080/09658211.2012.687052; Zacks JM, 2007, CURR DIR PSYCHOL SCI, V16, P80, DOI 10.1111/j.1467-8721.2007.00480.x; Zacks JM, 2007, PSYCHOL BULL, V133, P273, DOI 10.1037/0033-2909.133.2.273 48 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0090-502X 1532-5946 MEM COGNITION Mem. Cogn. AUG 2014 42 6 965 977 10.3758/s13421-014-0406-1 13 Psychology, Experimental Psychology AM6FA WOS:000339957500012 J Aasen, H; Gnyp, ML; Miao, YX; Bareth, G Aasen, Helge; Gnyp, Martin Leon; Miao, Yuxin; Bareth, Georg Automated Hyperspectral Vegetation Index Retrieval from Multiple Correlation Matrices with HyperCor PHOTOGRAMMETRIC ENGINEERING AND REMOTE SENSING English Article DIFFERENT GROWTH-STAGES; PRECISION AGRICULTURE; NITROGEN STATUS; RICE CANOPIES; PADDY RICE; REFLECTANCE; BIOMASS; CHLOROPHYLL; MANAGEMENT; DISCRIMINATION Hyperspectral vegetation indices have shown high potential for characterizing, classifying, monitoring, and modeling of vegetation and agricultural crops. Correlation matrices from hyperspectral vegetation indices and plant growth parameters help select important wavelength domains and identify redundant bands. We introduce the software HyperCor for automated preprocessing of narrowband hyperspectral field data and computation of correlation matrices. In addition, we propose a multi-correlation matrix strategy which combines multiple correlation matrices from different datasets and uses more information from each matrix. We apply this method to a large multi-temporal spectral library to derive vegetation indices and related regression models for rice biomass detection in the tillering, stem elongation: heading and across all growth stages. The models are calibrated with data from three consecutive years and validated with two other years. The results reveal that the multi-correlation matrix strategy can improve the model performance by 10 to 62 percent, depending on the growth stage. [Aasen, Helge; Gnyp, Martin Leon; Bareth, Georg] Univ Cologne, Inst Geog, D-50923 Cologne, Germany; [Gnyp, Martin Leon] Yara Int, Int Ctr Agroinformat & Sustainable Dev, D-48249 Duelmen, Germany; [Gnyp, Martin Leon] Yara Int, Res Ctr Hanninghof, D-48249 Duelmen, Germany; [Miao, Yuxin] China Agr Univ, Coll Resources & Environm Sci, Beijing 100193, Peoples R China; [Miao, Yuxin; Bareth, Georg] Int Ctr Agroinformat & Sustainable Dev, D-48249 Duelmen, Germany Aasen, H (reprint author), Univ Cologne, Inst Geog, Magnus Pl, D-50923 Cologne, Germany. helge.aasen@uni-koeln.de German Federal Ministry of Education and Research (BMBF) [CHN 08/051]; Natural Science Foundation of China [31071859]; National Science and Technology Support Project [2012BAD04B01-06-03]; Ministry for Innovation; Science and Research (MIWF) of the state North Rhine Westphalia (NRW); European Union Funds for regional development (EFRE) [005-1103-0018] We would like to thank the students of the China Agricultural University and the Institute of Geography, University of Cologne, for their help with data acquisition in the field. We would also like to thank Dr. Fei Yuan from Minnesota State University for proofreading and the anonymous reviewers for their constructive feedback improving the paper. This research was funded by the German Federal Ministry of Education and Research (BMBF, project number CHN 08/051), the Natural Science Foundation of China (31071859) and the National Science and Technology Support Project (2012BAD04B01-06-03). Also, the authors acknowledge the funding of the CROP.SENSe.net project in the context of the Ziel 2-Programms NRW 2007-2013 "Regionale Wettbewerbsfahigkeit und Beschaftigung" by the Ministry for Innovation, Science and Research (MIWF) of the state North Rhine Westphalia (NRW) and European Union Funds for regional development (EFRE) (005-1103-0018) during preparation of the manuscript. Bajwa S.G., 2011, HYPERSPECTRAL REMOTE, P94; Cao Q, 2013, FIELD CROP RES, V154, P133, DOI 10.1016/j.fcr.2013.08.005; Chauhan H.J., 2013, J REMOTE SENSING TEC, V1, P9; Dorigo W., 2006, AS TOOLBOX PROCESSIN; Fagiera N.K., 2013, J PLANT NUTR, V36, P1841; Galvao L.S., 2011, HYPERSPECTRAL REMOTE, P397; Garfagnoli F, 2013, EARTH SCI INFORM, V6, P227, DOI 10.1007/s12145-013-0124-4; Gitelson A.A., 2011, HYPERSPECTRAL REMOTE, P329; Gitelson AA, 1996, J PLANT PHYSIOL, V148, P494; Gnyp ML, 2013, PHOTOGRAMM FERNERKUN, P351, DOI 10.1127/1432-8364/2013/0182; Gnyp ML, 2014, FIELD CROP RES, V155, P42, DOI 10.1016/j.fcr.2013.09.023; Haboudane D, 2002, REMOTE SENS ENVIRON, V81, P416, DOI 10.1016/S0034-4257(02)00018-4; Haboudane D, 2004, REMOTE SENS ENVIRON, V90, P337, DOI 10.1016/j.rse.2003.12.013; Hansen PM, 2003, REMOTE SENS ENVIRON, V86, P542, DOI 10.1016/S0034-4257(03)00131-7; Hatfield JL, 2010, REMOTE SENS-BASEL, V2, P562, DOI 10.3390/rs2020562; Huang JF, 2004, COMMUN SOIL SCI PLAN, V35, P2689, DOI 10.1081/LCSS-200036401; HUETE A, 1994, REMOTE SENS ENVIRON, V49, P224, DOI 10.1016/0034-4257(94)90018-3; Jensen R.R., 2007, REMOTE SENSING ENV; JORDAN CF, 1969, ECOLOGY, V50, P663, DOI 10.2307/1936256; Koppe W, 2010, PHOTOGRAMM FERNERKUN, P171, DOI 10.1127/1432-8364/2010/0047; Koppe W, 2013, INT J APPL EARTH OBS, V21, P568, DOI 10.1016/j.jag.2012.07.016; Laudien R, 2006, ZUCKERINDUSTRIE, V131, P164; Li F, 2010, PRECIS AGRIC, V11, P335, DOI 10.1007/s11119-010-9165-6; Ma W., 2013, P SPIE, V8910; Mac Arthur A, 2012, IEEE T GEOSCI REMOTE, V50, P3892, DOI 10.1109/TGRS.2012.2185055; Mahlein AK, 2013, REMOTE SENS ENVIRON, V128, P21, DOI 10.1016/j.rse.2012.09.019; Mariotto I, 2013, REMOTE SENS ENVIRON, V139, P291, DOI 10.1016/j.rse.2013.08.002; Miao YX, 2009, PRECIS AGRIC, V10, P45, DOI 10.1007/s11119-008-9091-z; Milton E.J., 2009, REMOTE SENS ENVIRON, V113, P92; Moran MS, 1997, REMOTE SENS ENVIRON, V61, P319, DOI 10.1016/S0034-4257(97)00045-X; Mulla DJ, 2013, BIOSYST ENG, V114, P358, DOI 10.1016/j.biosystemseng.2012.08.009; Mutanga O, 2004, REMOTE SENS ENVIRON, V89, P393, DOI 10.1016/j.rse.2003.11.001; Mutanga O, 2004, INT J REMOTE SENS, V25, P3999, DOI 10.1080/01431160310001654923; Rao NR, 2007, PRECIS AGRIC, V8, P173, DOI 10.1007/s11119-007-9037-x; Roberts D.A., 2011, HYPERSPECTRAL REMOTE, P309; Rouse J.W., 1974, P 3 EARTH RES TECHN, V1, P309; RUNNING SW, 1994, INT J REMOTE SENS, V15, P3587; SHIBAYAMA M, 1989, REMOTE SENS ENVIRON, V27, P119, DOI 10.1016/0034-4257(89)90011-4; Stroppiana D, 2009, FIELD CROP RES, V111, P119, DOI 10.1016/j.fcr.2008.11.004; Thenkabail P.S., 2000, REMOTE SENS ENVIRON, V71, P152; Thenkabail PS, 2012, PHOTOGRAMM ENG REM S, V78, P773; Thenkabail PS, 2004, REMOTE SENS ENVIRON, V90, P23, DOI 10.1016/j.rse.2003.11.018; Thenkabail P.S., 2011, HYPERSPECTRAL REMOTE, P3; Thenkabail PS, 2013, IEEE J-STARS, V6, P427, DOI 10.1109/JSTARS.2013.2252601; Thenkabail PS, 2004, REMOTE SENS ENVIRON, V91, P354, DOI 10.1016/j.rse.2004.03.013; TUCKER CJ, 1979, REMOTE SENS ENVIRON, V8, P127, DOI 10.1016/0034-4257(79)90013-0; Van Niel TG, 2004, AUST J AGR RES, V55, P155, DOI 10.1071/AR03149; Viacheslav I., 2010, PRECISION CROP PROTE, P3; WIEGAND C, 1989, JPN J CROP SCI, V58, P673; Yao H., 2011, HYPERSPECTRAL REMOTE, P591; Yoshida S, 1981, FUNDAMENTALS RICE CR; Yu K, 2013, ISPRS J PHOTOGRAMM, V78, P102, DOI 10.1016/j.isprsjprs.2013.01.008; Zhu Y., 2011, HYPERSPECTRAL REMOTE, P187 53 0 0 AMER SOC PHOTOGRAMMETRY BETHESDA 5410 GROSVENOR LANE SUITE 210, BETHESDA, MD 20814-2160 USA 0099-1112 PHOTOGRAMM ENG REM S Photogramm. Eng. Remote Sens. AUG 2014 80 8 785 795 11 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology AM6QB WOS:000339988600012 J Cai, ZN; Liu, Y; Yang, DX Cai ZhaoNan; Liu Yi; Yang DongXu Analysis of XCO2 retrieval sensitivity using simulated Chinese Carbon Satellite (TanSat) measurements SCIENCE CHINA-EARTH SCIENCES English Article TanSat; retrieval sensitivity; retrieval error; simulation; XCO2 GASES OBSERVING SATELLITE; ATMOSPHERIC CO2; SCATTERING; ALGORITHM; SUNLIGHT; MISSION We present a study on the retrieval sensitivity of the column-averaged dry-air mole fraction of CO2 (XCO2) for the Chinese carbon dioxide observation satellite (TanSat) with a full physical forward model and the optimal estimation technique. The forward model is based on the vector linearized discrete ordinate radiative transfer model (VLIDORT) and considers surface reflectance, gas absorption, and the scattering of air molecules, aerosol particles, and cloud particles. XCO2 retrieval errors from synthetic TanSat measurements show solar zenith angle (SZA), albedo dependence with values varying from 0.3 to 1 ppm for bright land surface in nadir mode and 2 to 8 ppm for dark surfaces like snow. The use of glint mode over dark oceans significantly improves the CO2 information retrieved. The aerosol type and profile are more important than the aerosol optical depth, and underestimation of aerosol plume height will introduce a bias of 1.5 ppm in XCO2. The systematic errors due to radiometric calibration are also estimated using a forward model simulation approach. [Cai ZhaoNan; Liu Yi; Yang DongXu] Chinese Acad Sci, Key Lab Middle Atmosphere & Global Environm Obser, Inst Atmospher Phys, Beijing 100029, Peoples R China Liu, Y (reprint author), Chinese Acad Sci, Key Lab Middle Atmosphere & Global Environm Obser, Inst Atmospher Phys, Beijing 100029, Peoples R China. liuyi@mail.iap.ac.cn Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues [XDA05040200]; National High-tech Research and Development Program of China [2011AA12A104] This study was supported by the Strategic Priority Research Program-Climate Change: Carbon Budget and Relevant Issues (Grant No. XDA05040200) and the National High-tech Research and Development Program of China (Grant No. 2011AA12A104). We thank Dr. R. J. D. Spurr for providing VLIDORT code. Aben I, 2007, J QUANT SPECTROSC RA, V104, P450, DOI 10.1016/j.jqrst.2006.09.013; Baldridge AM, 2009, REMOTE SENS ENVIRON, V113, P711, DOI 10.1016/j.rse.2008.11.007; Boesch H, 2011, REMOTE SENS-BASEL, V3, P270, DOI 10.3390/rs3020270; Butz A, 2009, APPL OPTICS, V48, P3322, DOI 10.1364/AO.48.003322; Butz A, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047888; Cai ZN, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD017096; Chandrasekhar S., 1950, RAD TRANSFER; Connor BJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2006JD008336; Crisp D, 2004, ADV SPACE RES-SERIES, V34, P700, DOI 10.1016/j.asr.2003.08.062; Hartmann J M, 2009, ATMOS CHEM PHYS DISC, V9, P4873; IPCC (Intergovernmental Panel on Climate Change), 2007, CONTRIBUTION WORKING; Kuang ZM, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014298; Kuze A, 2009, APPL OPTICS, V48, P6716, DOI 10.1364/AO.48.006716; Liu Y, 2011, 2011 AGU FALL M; Min M, 2011, ADV ATMOS SCI, V28, P653, DOI 10.1007/s00376-010-0049-5; Mishchenko MI, 2002, SCATTERING ABSORPTIO; O'Dell CW, 2012, ATMOS MEAS TECH, V5, P99, DOI 10.5194/amt-5-99-2012; Rayner PJ, 2001, GEOPHYS RES LETT, V28, P175, DOI 10.1029/2000GL011912; Rodgers C. D., 2000, INVERSE METHODS ATMO; Rothman LS, 2009, J QUANT SPECTROSC RA, V110, P533, DOI 10.1016/j.jqsrt.2009.02.013; Spurr RJD, 2006, J QUANT SPECTROSC RA, V102, P316, DOI 10.1016/j.jqsrt.2006.05.005; Yang Dongxu, 2013, [Atmospheric and Oceanic Science Letters, 大气和海洋科学快报], V6, P60; Yokota T, 2009, SOLA, V5, P160, DOI 10.2151/sola.2009-041; Yoshida Y, 2011, ATMOS MEAS TECH, V4, P717, DOI 10.5194/amt-4-717-2011 24 1 1 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-7313 1869-1897 SCI CHINA EARTH SCI Sci. China-Earth Sci. AUG 2014 57 8 1919 1928 10.1007/s11430-013-4707-1 10 Geosciences, Multidisciplinary Geology AM4JC WOS:000339818800021 J Garcia-Cuesta, E; de Castro, AJ; Galvan, IM; Lopez, F Garcia-Cuesta, Esteban; de Castro, Antonio J.; Galvan, Ines M.; Lopez, Fernando Temperature Profile Retrieval in Axisymmetric Combustion Plumes Using Multi layer Perceptron Modeling and Spectral Feature Selection in the Infrared CO2 Emission Band APPLIED SPECTROSCOPY English Article Combustion monitoring; Flame temperature; Infrared remote sensing; Feature selection; Multilayer perceptron; Principal component analysis FLAMES; SENSORS In this work, a methodology based on the combined use of a multilayer perceptron model fed using selected spectral information is presented to invert the radiative transfer equation (RTE) and to recover the spatial temperature profile inside an axisymmetric flame. The spectral information is provided by the measurement of the infrared CO2 emission band in the 3-5 mu m spectral region. A guided spectral feature selection was carried out using a joint criterion of principal component analysis and a priori physical knowledge of the radiative problem. After applying this guided feature selection, a subset of 17 wavenumbers was selected. The proposed methodology was applied over synthetic scenarios. Also, an experimental validation was carried out by measuring the spectral emission of the exhaust hot gas plume in a microjet engine with a Fourier transform-based spectroradiometer. Temperatures retrieved using the proposed methodology were compared with classical thermocouple measurements, showing a good agreement between them. Results obtained using the proposed methodology are very promising and can encourage the use of sensor systems based on the spectral measurement of the CO2 emission band in the 3-5 pm spectral window to monitor combustion processes in a nonintrusive way. [Garcia-Cuesta, Esteban; de Castro, Antonio J.; Lopez, Fernando] Univ Carlos III Madrid, Dept Fis, LIR Lab, Leganes 28911, Madrid, Spain; [Galvan, Ines M.] Univ Carlos III Madrid, Dept Informat, Leganes 28911, Madrid, Spain de Castro, AJ (reprint author), Univ Carlos III Madrid, Dept Fis, LIR Lab, Avda Univ 30, Leganes 28911, Madrid, Spain. decastro@fis.uc3m.es Spanish Ministry of Education [TRA2005-08892-C02-01] The authors acknowledge the Spanish Ministry of Education for financial support under the project TRA2005-08892-C02-01. Afgan NH, 2006, APPL THERM ENG, V26, P766, DOI [10.1016/j.applthermaleng.2005.04.020, 10.1016/j.applthermateng.2005.04.020]; Bellmann R.E., 1961, ADAPTIVE CONTROL PRO; Bishop C, 1995, NEURAL NETWORKS PATT; Cybenko G., 1989, Mathematics of Control, Signals, and Systems, V2, DOI 10.1007/BF02551274; Docquier N, 2002, PROG ENERG COMBUST, V28, P107, DOI 10.1016/S0360-1285(01)00009-0; Dupuy JL, 2007, INT J WILDLAND FIRE, V16, P324, DOI 10.1071/WF06043; Garcia-Cuesta E., 2003, THESIS U CARLOS 3 MA; Goldstein N., 2003, ASME TURBO EXPO; HORNIK K, 1989, NEURAL NETWORKS, V2, P359, DOI 10.1016/0893-6080(89)90020-8; Jirasuwankul N, 2009, COMBUST SCI TECHNOL, V181, P191, DOI 10.1080/00102200802388184; Jollife I. T., 2002, PRINCIPAL COMPONENT; Liu LH, 2001, J QUANT SPECTROSC RA, V70, P207, DOI 10.1016/S0022-4073(00)00133-3; Romero C, 2005, APPL THERM ENG, V25, P659, DOI 10.1016/j.applthermaleng.2004.07.020; Rothman LS, 2003, J QUANT SPECTROSC RA, V82, P5, DOI 10.1016/S0022-4073(03)00146-8; Schumann U, 1998, ATMOS ENVIRON, V32, P3097, DOI 10.1016/S1352-2310(97)00455-X; Thakur M, 2001, OPT LASER ENG, V36, P373, DOI 10.1016/S0143-8166(01)00056-2; Webber ME, 2000, P COMBUST INST, V28, P407; Zhou X, 2007, AIAA J, V45, P420, DOI 10.2514/1.26624 18 0 0 SOC APPLIED SPECTROSCOPY FREDERICK 5320 SPECTRUM DRIVE SUITE C, FREDERICK, MD 21703 USA 0003-7028 1943-3530 APPL SPECTROSC Appl. Spectrosc. AUG 2014 68 8 900 908 10.1366/13-07185 9 Instruments & Instrumentation; Spectroscopy Instruments & Instrumentation; Spectroscopy AM1YE WOS:000339644400011 J Splendiani, B; Ribera, M; Garcia, R; Termens, M Splendiani, Bruno; Ribera, Mireia; Garcia, Roberto; Termens, Miquel Do Physicians Make Their Articles Readable for Their Blind or Low-Vision Patients? An Analysis of Current Image Processing Practices in Biomedical Journals from the Point of View of Accessibility JOURNAL OF DIGITAL IMAGING English Article Medical images; Publishing; Biomedical journals; Accessibility policies; Image description; Alternative text; Visual impairment; Disabilities; Publications HEALTH INFORMATION; FUTURE; PEOPLE; INTERNET; SYSTEMS Visual content in biomedical academic papers is a growing source of critical information, but it is not always fully readable for people with visual impairments. We aimed to assess current image processing practices, accessibility policies, and submission policies in a sample of 12 highly cited biomedical journals. We manually checked the application of text-based alternative image descriptions for every image in 12 articles (one for each journal). We determined whether the journals claimed to follow an accessibility policy and we reviewed their submission policy and their guidelines related to the visual content. We identified important features concerning the processing of images and the characteristics of the visual and the retrieval options of visual content offered by the publishers. The evaluation shows that the actual practices of textual image description in highly cited biomedical journals do not follow general guidelines on accessibility. The images within the articles analyzed lack alternative descriptions or have uninformative descriptions, even in the case of journals claiming to follow an accessibility policy. Consequently, the visual information of scientific articles is not accessible to people with severe visual disabilities. Instructions on image submission are heterogeneous and a declaration of accessibility guidelines was only found in two thirds of the sample of journals, with one third not explicitly following any accessibility policy, although they are required to by law. [Splendiani, Bruno; Ribera, Mireia; Termens, Miquel] Univ Barcelona, Lib & Informat Sci Dept, Barcelona 08014, Spain; [Garcia, Roberto] Univ Lleida, Comp Sci & Ind Engn Dept, Lleida 25001, Spain Termens, M (reprint author), Univ Barcelona, Lib & Informat Sci Dept, Melcior Palau 140, Barcelona 08014, Spain. splendiani@ub.edu; ribera@ub.edu; roberto.garcia@udl.cat; termens@ub.edu Anderson K, 2001, J ELECT PUBLISHING, V6, DOI [10.3998/3336451.0006.303, DOI 10.3998/3336451.0006.303]; [Anonymous], 2012, 405002012 ISOIEC; Association of Directors of Social Services, 2002, PROGR SIGHT NAT STAN; Beverley CA, 2011, ASLIB PROC, V63, P256, DOI 10.1108/00012531111135691; Blind Citizens Australia (BCA), 2012, ACC HLTH SERV PEOPL; Clark L, 2002, LIVERPOOL CENTRAL PR; Codogno P, 2012, NAT REV MOL CELL BIO, V13, P7, DOI 10.1038/nrm3249; Dillon A, 2004, DESIGNING USABLE ELE; Draffan EA, 2013, HARDWARE READING; Fox S, 2008, ENGAGED E PATIENT PO; Fox S, 2007, E PATIENT DISABILITY; Fox S, 2006, ONLINE HLTH SEARCH 2; Gardner J, 2009, LEARN PUBL, V22, P314, DOI 10.1087/20090408; Hersh W, 2009, J DIGIT IMAGING, V22, P648, DOI 10.1007/s10278-008-9154-8; Hughes B, 2008, J MED INTERNET RES, V10, DOI 10.2196/jmir.1056; Iezzoni LI, 2005, MORE RAMPS GUIDEIMPR; International Committee of Medical Journal Editors (ICMJE), 2010, BIOMEDICAL J UNPUB; Jackson GW, 2001, J DIGIT IMAGING, V14, P107, DOI 10.1007/s10278-001-0008-x; Kahn CE, 2012, J DIGIT IMAGING, V25, P37, DOI 10.1007/s10278-011-9399-5; Kim E, 2010, SPIE P, V7628; Levine D, 2010, RADIOLOGY, V257, P603, DOI 10.1148/radiol.10091423; Liang HG, 2011, INT J MED INFORM, V80, P745, DOI 10.1016/j.ijmedinf.2011.08.003; Lo B, 2010, J LAW MED ETHICS, V38, P17, DOI 10.1111/j.1748-720X.2010.00462.x; Luchtenberg M, 2008, OPHTHALMOLOGICA, V222, P187, DOI 10.1159/000126082; Marschollek M, 2007, MED INFORM INTERNET, V32, P251, DOI 10.1080/14639230701692736; Moreno RA, 2007, IEEE T INF TECHNOL B, V11, P583, DOI 10.1109/TITB.2006.884373; Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024; Nature Publishing Group, 2012, SUBM NAT GEN; Ortiz Hojas A, 2008, INSTRUCCIONES CRITER; Parmanto B, 2005, J AM SOC INF SCI TEC, V56, P1394, DOI 10.1002/asi.20233; Perera C, 2012, J MOB TECHNOL MED, V1, P1; Purcell GP, 2002, BRIT MED J, V324, P557, DOI 10.1136/bmj.324.7337.557; Rausch T, 2012, CELL, V148, P59, DOI 10.1016/j.cell.2011.12.013; RNIB, 2013, CREAT ACC EBOOKS; Royal National Institute of Blind People (RNIB), 2011, SUPP BLIND PART SIGH; Sedghi S, 2011, ASLIB PROC, V63, P570, DOI 10.1108/00012531111187225; Sedghi S, 2008, PATTERN RECOGN LETT, V29, P2046, DOI 10.1016/j.patrec.2008.07.003; Siegel R, 2012, CA-CANCER J CLIN, V62, P10, DOI 10.3322/caac.20138; Smith R, 2006, J ROY SOC MED, V99, P115, DOI 10.1258/jrsm.99.3.115; Srinivasarao V, 2011, LECT NOTES COMPUT SC, V6612, P237, DOI 10.1007/978-3-642-20291-9_24; Steinman RM, 2012, ANNU REV IMMUNOL, V30, P1, DOI 10.1146/annurev-immunol-100311-102839; Sutton J, 2002, GUIDE MAKING DOCUMEN; Voces R, 2008, PROF INFORM, V17, P205, DOI 10.3145/epi.2008.mar.11; World Wide Web Consortium (W3C), 2000, ACC FEAT SVG; World Wide Web Consortium (W3C), SHAR WEB EXP BARR CO; World Wide Web Consortium (W3C), 2008, WEB CONT ACC GUID WC; World Wide Web Consortium (W3C), 2012, UND WCAG 2 0; You D, 2011, SPIE P, V7874 48 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0897-1889 1618-727X J DIGIT IMAGING J. Digit. Imaging AUG 2014 27 4 419 442 10.1007/s10278-014-9674-3 24 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging AL7WM WOS:000339347400001 J Joshi, V; Narra, VR; Joshi, K; Lee, K; Melson, D Joshi, Vivek; Narra, Vamsi R.; Joshi, Kailash; Lee, Kyootai; Melson, David PACS Administrators' and Radiologists' Perspective on the Importance of Features for PACS Selection JOURNAL OF DIGITAL IMAGING English Article Picture archiving and communication system; PACS; Analytical hierarchy process; AHP; RIS; Structured reporting; Voice recognition; Transcription; Open systems; Proprietary systems; Display quality; System continuity; Security; Backup; Recovery; Downtime prevention; PACS performance monitoring; Configuration; Upgrade; Cardiology images; Pathology images; System architecture and performance; User interface for image manipulation; User interface workflow management; Worklist management COMPUTER-AIDED DETECTION; INFORMATION-SYSTEM; VISUALIZATION; RECOGNITION; MAMMOGRAPHY; WORKFLOW; ISSUES Picture archiving and communication systems (PACS) play a critical role in radiology. This paper presents the criteria important to PACS administrators for selecting a PACS. A set of criteria are identified and organized into an integrative hierarchical framework. Survey responses from 48 administrators are used to identify the relative weights of these criteria through an analytical hierarchy process. The five main dimensions for PACS selection in order of importance are system continuity and functionality, system performance and architecture, user interface for workflow management, user interface for image manipulation, and display quality. Among the subdimensions, the highest weights were assessed for security, backup, and continuity; tools for continuous performance monitoring; support for multispecialty images; and voice recognition/transcription. PACS administrators' preferences were generally in line with that of previously reported results for radiologists. Both groups assigned the highest priority to ensuring business continuity and preventing loss of data through features such as security, backup, downtime prevention, and tools for continuous PACS performance monitoring. PACS administrators' next high priorities were support for multispecialty images, image retrieval speeds from short-term and long-term storage, real-time monitoring, and architectural issues of compatibility and integration with other products. Thus, next to ensuring business continuity, administrators' focus was on issues that impact their ability to deliver services and support. On the other hand, radiologists gave high priorities to voice recognition, transcription, and reporting; structured reporting; and convenience and responsiveness in manipulation of images. Thus, radiologists' focus appears to be on issues that may impact their productivity, effort, and accuracy. [Joshi, Vivek] Icahn Sch Med Mt Sinai, Dept Radiol, New York, NY 10029 USA; [Narra, Vamsi R.; Melson, David] Washington Univ, Sch Med, Mallinckrodt Inst Radiol, St Louis, MO USA; [Joshi, Kailash] Univ Missouri, St Louis, MO 63121 USA; [Lee, Kyootai] Sogang Univ, Grad Sch Technol Management, Seoul, South Korea Joshi, V (reprint author), Icahn Sch Med Mt Sinai, Dept Radiol, One Gustave L Levy Pl, New York, NY 10029 USA. vjoshiMD@gmail.com; narrav@mir.wustl.edu; joshi@umsl.edu; kyootai@gmail.com; dlm2777@bjc.org Birdwell RL, 2009, RADIOLOGY, V253, P9, DOI 10.1148/radiol.2531090611; Branstetter IV, 2007, RADIOLOGY, V243, P656; Branstetter IV BF, 2007, RADIOLOGY, V244, P78; Duncan LD, 2010, AM SURGEON, V76, P982; Dykema J, 2013, EVAL HEALTH PROF, V36, P352, DOI 10.1177/0163278713496630; Franklin MA, 2013, J AM COLL RADIOL, V10, P279, DOI 10.1016/j.jacr.2012.10.004; Geis JR, 2007, J DIGIT IMAGING, V20, P99, DOI 10.1007/s10278-007-9010-2; Joshi V, 2011, J DIGIT IMAGING, V24, P700, DOI 10.1007/s10278-010-9332-3; Khorasani Ramin, 2008, J Am Coll Radiol, V5, P144, DOI 10.1016/j.jacr.2007.11.002; Kolowitz BJ, 2012, J DIGIT IMAGING, V25, P744, DOI 10.1007/s10278-012-9504-4; Krupinski EA, 2001, ACAD RADIOL, V8, P1127, DOI 10.1016/S1076-6332(03)80725-0; Krupinski EA, 2007, RADIOLOGY, V242, P671, DOI 10.1148/radiol.2423051403; Lai VS, 1999, INFORM MANAGE, V36, P221, DOI 10.1016/S0378-7206(99)00021-X; Lam K, 1998, INT J QUALITY RELIAB, V15, P389, DOI 10.1108/02656719810196351; Langer S, 2002, J DIGIT IMAGING, V15, P91, DOI 10.1007/s10278-002-0010-y; Langer S, 2009, J DIGIT IMAGING, V22, P48, DOI 10.1007/s10278-008-9125-0; Cheng E. W. L., 2001, Information Management & Computer Security, V9, DOI 10.1108/09685220110388827; Luo H, 2006, IEEE T INF TECHNOL B, V10, P302, DOI 10.1109/TITB.2005.859872; Mehta A, 1998, J DIGIT IMAGING, V11, P20; Nance JW, 2013, AM J ROENTGENOL, V200, P1064, DOI 10.2214/AJR.12.10326; Perez F, 2013, COMPUT METH PROG BIO, V110, P399, DOI [10.1016/j.cmpb.2012.12.002, 10.1016/j.cmpb.2013.01.002]; Reiner BI, 2009, J DIGIT IMAGING, V22, P562, DOI 10.1007/s10278-009-9239-z; Rosenthal DI, 1998, AM J ROENTGENOL, V170, P23; Saaty T.L., 2005, THEORY APPL ANAL NET; Sadaf A, 2011, EUR J RADIOL, V77, P457, DOI 10.1016/j.ejrad.2009.08.024; Teltumbde A, 2000, INT J PROD RES, V38, P4507, DOI 10.1080/00207540050205262; Thrall JH, 2005, RADIOLOGY, V237, P15, DOI 10.1148/radiol.2371050258; D'Asseler Y, 2000, Technol Health Care, V8, P35; Wang JH, 2009, MED PHYS, V36, P3682, DOI 10.1118/1.3173816; Weiss David L, 2006, J Am Coll Radiol, V3, P265, DOI 10.1016/j.jacr.2005.10.016 30 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0897-1889 1618-727X J DIGIT IMAGING J. Digit. Imaging AUG 2014 27 4 486 495 10.1007/s10278-014-9682-3 10 Radiology, Nuclear Medicine & Medical Imaging Radiology, Nuclear Medicine & Medical Imaging AL7WM WOS:000339347400009 J Walia, E; Pal, A Walia, Ekta; Pal, Aman Fusion framework for effective color image retrieval JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION English Article Content based image retrieval; Lab color space; Color Difference Histogram; Angular Radial Transform; Borda Count; MM-max normalization; Z-score normalization; Fusion DESCRIPTOR; FEATURES; WAVELET; SYSTEM This paper presents a novel framework for color image retrieval through combination of all the low level features, which gives higher retrieval accuracy. The Color Difference Histogram (CDH) and Angular Radial Transform (ART) features are exploited to capture color, texture and shape information of an image. The CDH algorithm is modified in order to make the proposed system more effective. The proposed fusion framework combines the ranking results of the aforementioned descriptors through various post-classification methods i.e. Borda Count method, Min-max and Z-score normalization. The maximum retrieval accuracy attained in terms of average precision using Min-max normalization on Wang's database is 78.3% when ART is applied on non-overlapping regions of the images. The proposed fusion framework is recommended because it improves the average retrieval accuracy by approximately 16% and 14% over CDH and ART respectively. Extensive experiments are carried out on different databases to establish the efficacy of the proposed scheme. (C) 2014 Elsevier Inc. All rights reserved. [Walia, Ekta; Pal, Aman] South Asian Univ, Dept Comp Sci, New Delhi, India Walia, E (reprint author), South Asian Univ, Dept Comp Sci, New Delhi, India. wekta@yahoo.com; aman.pal2610@yahoo.in Amanatiadis A, 2011, IET IMAGE PROCESS, V5, P493, DOI 10.1049/iet-ipr.2009.0246; Banerjee M, 2009, FUZZY SET SYST, V160, P3323, DOI 10.1016/j.fss.2009.02.024; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; ElAlami ME, 2014, APPL SOFT COMPUT, V14, P407, DOI 10.1016/j.asoc.2013.10.003; ElAlami ME, 2011, KNOWL-BASED SYST, V24, P23, DOI 10.1016/j.knosys.2010.06.001; Gali R., 2012, 4 INT C COMP INT COM, P243; Gonzalez R. C., 2007, DIGITAL IMAGE PROCES; Goyal A., 2012, INT J IMAGING ROBOTI, V7, P44; Guo JM, 2013, J VIS COMMUN IMAGE R, V24, P1360, DOI 10.1016/j.jvcir.2013.09.005; He ZY, 2009, SIGNAL PROCESS, V89, P1501, DOI 10.1016/j.sigpro.2009.01.021; Hiremath PS, 2007, ADCOM 2007: PROCEEDINGS OF THE 15TH INTERNATIONAL CONFERENCE ON ADVANCED COMPUTING AND COMMUNICATIONS, P780, DOI 10.1109/ADCOM.2007.21; Huang J, 1997, PROC CVPR IEEE, P762; Huang Z.C., 2010, ICMLC INT C MACH LEA, V2, P719; Irtaza A., 2013, MULTIMED TOOLS APPL, P1; Jain A, 2005, PATTERN RECOGN, V38, P2270, DOI 10.1016/j.patcog.2005.01.012; Jalab H.A., 2011, IEEE C OP SYST ICOS, V1; Kang JY, 2012, CHIN CONT DECIS CONF, P1326; Kim WY, 2000, SIGNAL PROCESS-IMAGE, V16, P95, DOI 10.1016/S0923-5965(00)00019-9; Liu GH, 2008, PATTERN RECOGN, V41, P3521, DOI 10.1016/j.patcog.2008.06.010; Liu GH, 2013, PATTERN RECOGN, V46, P188, DOI 10.1016/j.patcog.2012.06.001; Liu GH, 2010, PATTERN RECOGN, V43, P2380, DOI 10.1016/j.patcog.2010.02.012; Lu TC, 2007, INFORM PROCESS MANAG, V43, P461, DOI 10.1016/j.ipm.2006.07.014; Manjunath BS, 2001, IEEE T CIRC SYST VID, V11, P703, DOI 10.1109/76.927424; Park D.K., 2000, P 2000 ACM WORKSH MU; Qi XJ, 2005, PATTERN RECOGN, V38, P2449, DOI 10.1016/j.patcog.2005.04.005; Rahimi M., 2013, CONTENT BASED IMAGE, P1; Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138; SHEN GX, 2013, CARDIOVASC HEMATOL D, V13, P1; Singh C, 2012, OPT LASER TECHNOL, V44, P2249, DOI 10.1016/j.optlastec.2012.02.030; Singha M., 2012, INT J SIGNAL IMAGE P, V3, P39; Subrahmanyam M., 2012, COMPUT ELECTR ENG, V39, P762; Subrahmanyam M, 2012, EXPERT SYST APPL, V39, P5104, DOI 10.1016/j.eswa.2011.11.029; Ting H.C., 1998, P INT C IM PROC ICIP, V2, P545; Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109; Wang XY, 2013, J VIS COMMUN IMAGE R, V24, P63, DOI 10.1016/j.jvcir.2012.10.003; Wang XY, 2011, COMPUT STAND INTER, V33, P59, DOI 10.1016/j.csi.2010.03.004; Yue J, 2011, MATH COMPUT MODEL, V54, P1121, DOI 10.1016/j.mcm.2010.11.044; Zhang DS, 2002, SIGNAL PROCESS-IMAGE, V17, P825, DOI 10.1016/S0923-5965(02)00084-X 38 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1047-3203 1095-9076 J VIS COMMUN IMAGE R J. Vis. Commun. Image Represent. AUG 2014 25 6 1335 1348 10.1016/j.jvcir.2014.05.005 14 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AM0LW WOS:000339538100005 J Guldogan, E; Olsson, T; Lagerstam, E; Gabbouj, M Guldogan, Esin; Olsson, Thomas; Lagerstam, Else; Gabbouj, Moncef Instance based personalized multi-form image browsing and retrieval MULTIMEDIA TOOLS AND APPLICATIONS English Article Content-based image indexing and retrieval; Image browsing; Implicit feedback; Personalized and adaptive image image browsing RELEVANCE FEEDBACK; SYSTEM It is important to adapt and personalize image browsing and retrieval systems based on users' preferences for improved user experience and satisfaction. In this paper, we present a novel instance based personalized multi-form image representation with implicit relevance feedback and adaptive weighting approach for image browsing and retrieval systems. In the proposed system, images are grouped into forms, which represent different information on images such as location, content etc. We conducted user interviews on image browsing, sharing and retrieval systems for understanding image browsing and searching behaviors of users. Based on the insights gained from the user interview study we propose an adaptive weighting method and implicit relevance feedback for multi-form structures that aim to improve the efficiency and accuracy of the system. Statistics of the past actions are considered for modeling the target of the users. Thus, on each iteration weights of the forms are updated adaptively. Moreover, retrieval results are modified according to the users' preferences on iterations in order to improve personalized user experience. The proposed method has been evaluated and results are illustrated in the paper. It is shown that, satisfactory improvements can be achieved with proposed approaches in the multi-form scheme. [Guldogan, Esin; Gabbouj, Moncef] Tampere Univ Technol, Dept Signal Proc, FIN-33101 Tampere, Finland; [Olsson, Thomas] Tampere Univ Technol, Unit Human Ctr Technol, FIN-33101 Tampere, Finland; [Lagerstam, Else] Tampere Univ Technol, Unit Human Ctr Technol, Dept Software Syst, FIN-33101 Tampere, Finland Guldogan, E (reprint author), Tampere Univ Technol, Dept Signal Proc, FIN-33101 Tampere, Finland. esin.guldogan@tut.fi; moncef.gabbouj@tut.fi Gabbouj, Moncef/G-4293-2014 Gabbouj, Moncef/0000-0002-9788-2323 Devices and Interoperability Ecosystem-DIEM-project; Finnish Funding Agency for Technology and Innovation This work is supported by Devices and Interoperability Ecosystem-DIEM-project is part of the Finnish ICT SHOK program coordinated by TIVIT and funded by Finnish Funding Agency for Technology and Innovation. Benbunan-Fich R, 2007, J STRATEGIC INF SYST, V16, P393, DOI [10.1016/j.jsis.2007.08.002, 10.1016/jjsis.2007.08.002]; Bockting S, 2008, P DUTCH BELG INF RET, P15; Covey DT, 2002, COUNCIL LIB INFORM R; Djordjevic D, 2007, IEEE T CIRC SYST VID, V17, P313, DOI 10.1109/TCSVT.2007.890634; Eakins JP, 2004, LECT NOTES COMPUT SC, V3115, P628; Fei-Fei L, 2004, IEEE CVPR 2004 WORKS; Gray WD, 2001, INT ENCY ERGONOMICS, V1, P387; Guldogan E, 2010, Proceedings 2010 5th International Workshop on Semantic Media Adaptation and Personalization (SMAP 2010), DOI 10.1109/SMAP.2010.5706855; Guldogan E, 2010, P INT WORKSH IM AN M, P1; Huiskes MJ, 2008, ACM INT C MULT INF R, P39; Jaimes A, 2006, P SOC PHOTO-OPT INS, V6061, P6103, DOI 10.1117/12.660255; Jing F, 2004, IEEE T CIRC SYST VID, V14, P672, DOI 10.1109/TCSVT.2004.826775; Kelly D, 2001, P 24 ANN INT ACM C R, P408, DOI 10.1145/383952.384045; Kim YH, P IEEE REG 10 C TENC, V1, P439; Kosch H, 2005, P INT ASS SCI TECHN; Kuniavsky M, 2003, OBSERVING USER EXPER, P129, DOI 10.1016/B978-155860923-5/50034-6; Laaksonen J, 2000, PATTERN RECOGN LETT, V21, P1199, DOI 10.1016/S0167-8655(00)00082-9; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Manavoglu E, 2003, ICDM 03 P 3 IEEE INT, P203; Moghaddam B, 2004, INT J COMPUT VISION, V56, P109, DOI 10.1023/B:VISI.0000004834.62090.74; Piras L, 2009, 2009 10TH INTERNATIONAL WORKSHOP ON IMAGE ANALYSIS FOR MULTIMEDIA INTERACTIVE SERVICES, P238, DOI 10.1109/WIAMIS.2009.5031477; Rao Y, 2006, LECT NOTES COMPUT SC, V4071, P350; Robertson S., 2001, Lectures on Information Retrieval. Third European Summer-School, ESSIR 2000. Revised Lectures (Lecture Notes in Computer Science Vol.1980); Sandhaus P, 2011, MULTIMED TOOLS APPL, V51, P5, DOI 10.1007/s11042-010-0673-1; Shen X, P 28 ANN INT ACM SIG, V43, P43; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Torres JM, 2000, P WORKSH COMP SEM NE; Weiss D, 2008, P 3 INT C DIG INT ME, V349, P281; Zhou XS, 2003, MULTIMEDIA SYST, V8, P536, DOI 10.1007/s00530-002-0070-3; Zhou XS, 2000, P SPIE IMAG VID COMM, P24 30 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. AUG 2014 71 3 1087 1104 10.1007/s11042-012-1249-z 18 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AL8KJ WOS:000339387000006 J Amato, G; Gennaro, C; Savino, P Amato, Giuseppe; Gennaro, Claudio; Savino, Pasquale MI-File: using inverted files for scalable approximate similarity search MULTIMEDIA TOOLS AND APPLICATIONS English Article Similarity searching; Access methods; Multimedia information retrieval NEAREST-NEIGHBOR; METRIC-SPACES; PROXIMITY; RETRIEVAL; TREES We propose a new efficient and accurate technique for generic approximate similarity searching, based on the use of inverted files. We represent each object of a dataset by the ordering of a number of reference objects according to their distance from the object itself. In order to compare two objects in the dataset, we compare the two corresponding orderings of the reference objects. We show that this representation enables us to use inverted files to obtain very efficiently a very small set of good candidates for the query result. The candidate set is then reordered using the original similarity function to obtain the approximate similarity search result. The proposed technique performs several orders of magnitude better than exact similarity searches, still guaranteeing high accuracy. To also demonstrate the scalability of the proposed approach, tests were executed with various dataset sizes, ranging from 200,000 to 100 million objects. [Amato, Giuseppe; Gennaro, Claudio; Savino, Pasquale] ISTI CNR, I-56124 Pisa, Italy Amato, G (reprint author), ISTI CNR, Via G Moruzzi 1, I-56124 Pisa, Italy. giuseppe.amato@isti.cnr.it; claudio.gennaro@isti.cnr.it; pasquale.savino@isti.cnr.it Amato G, 2008, INFOSCALE 08, P1; Amato G, 2003, ACM T INFORM SYST, V21, P192, DOI 10.1145/763693.763696; Andoni A, 2008, COMMUN ACM, V51, P117, DOI 10.1145/1327452.1327494; Bawa M, 2005, WWW, P651, DOI 10.1145/1060745.1060840; Beyer K, 1999, LECT NOTES COMPUT SC, V1540, P217; Bolettieri P, 2009, 2 WORKSH VER LARG DI, P43; Bozkaya T, 1997, SIGMOD 97, P357; Brin S., 1995, VLDB '95. Proceedings of the 21st International Conference on Very Large Data Bases; Chavez E, 2008, IEEE T PATTERN ANAL, V30, P1647, DOI 10.1109/TPAMI.2007.70815; Ciaccia P, 1997, VLDB 97, P426; Ciaccia P, 2000, ICDE, P244; Diaconis P, 1988, SER IMS LECT NOTES M, V11; Egecioglu O., 2000, Proceedings of the Ninth International Conference on Information and Knowledge Management. CIKM 2000, DOI 10.1145/354756.354822; Esuli A, 2012, INFORM PROCESS MANAG, V48, P889, DOI 10.1016/j.ipm.2010.11.011; Faloutsos C, 1995, P 1995 ACM SIGMOD IN, P163, DOI 10.1145/223784.223812; Ferhatosmanoglu H, 2001, PROC INT CONF DATA, P503, DOI 10.1109/ICDE.2001.914864; Gennaro C, 2010, LECT NOTES COMPUT SC, V6273, P55, DOI 10.1007/978-3-642-15464-5_8; Hjaltason GR, 2003, ACM T DATABASE SYST, V28, P517, DOI 10.1145/958942.958948; Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Ogras UY, 2003, P 12 INT C INF KNOWL, P99; Patella M, 2009, J DISCRETE ALGORITHM, V7, P36, DOI DOI 10.1016/J.JDA.2008.09.014; Salton G., 1983, INTRO MODERN INFORM; SAPIR, 2009, SEARCH AUD VIS CONT; Seward HH, 1954, THESIS MIT; SHAPIRO M, 1977, COMMUN ACM, V20, P339, DOI 10.1145/359581.359599; Skala M, 2009, J DISCRETE ALGORITHM, V7, P49, DOI DOI 10.1016/J.JDA.2008.09.011; UHLMANN JK, 1991, INFORM PROCESS LETT, V40, P175, DOI 10.1016/0020-0190(91)90074-R; Wang X, 2000, KNOWL INF SYST, V2, P161, DOI 10.1007/s101150050009; Weber R, 1998, VLDB, P194; Weber R, 2000, LECT NOTES COMPUT SC, V1777, P21; Weiss Y, 2008, NIPS, P1753; Witten IH, 1999, BELL MANAGING GIGABY; Yianilos PN, 1993, SODA, P311; Zezula P, 2006, SER ADV DATABASE SYS, V32; Zezula P, 1998, VLDB J, V7, P275, DOI 10.1007/s007780050069 36 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. AUG 2014 71 3 1333 1362 10.1007/s11042-012-1271-1 30 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AL8KJ WOS:000339387000017 J Gao, GY; Ma, HD Gao, Guangyu; Ma, Huadong To accelerate shot boundary detection by reducing detection region and scope MULTIMEDIA TOOLS AND APPLICATIONS English Article Shot boundary detection; Skipping interval; Mutual information; Camera motion; Corner distribution MACROBLOCK TYPE INFORMATION; SCENE-CHANGE DETECTION; COMPRESSED VIDEO; DISSOLVE DETECTION; SEGMENTATION; RETRIEVAL Video Shot Boundary Detection (SBD) is the fundamental process towards video summarization and retrieval. A fast and efficient SBD algorithm is necessary for real-time video processing applications. Extensive work has focused on accurate shot boundary detection at the expense of demanding computational costs. In this paper, we propose a fast SBD approach that reduces the computation pixel-wise and frame-wise while still giving satisfactory accuracy. The proposed approach substantially speeds up the computation through reducing both detection region and scope. Color histogram and mutual information are used together to measure the difference between frames. Corner distribution of frames is utilized to exclude most of false boundaries. We conduct extensive experiments to evaluate the proposed approach, and the results show that our approach can not only speed up SBD, but also detect shot boundaries with high accuracy in both Cut (CUT) and Gradual Transition (GT) boundaries. [Gao, Guangyu; Ma, Huadong] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China Ma, HD (reprint author), Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China. mhd@bupt.edu.cn National Science Foundation for Distinguished Young Scholars of China [60925010]; Foundation for Innovative Research Groups of the National Natural Science Foundation of China [61121001]; Program for Changjiang Scholars and Innovative Research Team in University [IRT1049]; Beijing Committee of Education The work reported in this paper is supported by the National Science Foundation for Distinguished Young Scholars of China under Grant No. 60925010, the Foundation for Innovative Research Groups of the National Natural Science Foundation of China under Grant No. 61121001, the Program for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1049, the Co-sponsored Project of Beijing Committee of Education. Adjeroh D, 2009, EURASIP J IMAGE VIDE, V2009, P1; Cabedo XU, 1998, P NONL MOD BAS IM AN, P121; Chiu S-T, 2008, P 2008 3 INT C INN C, P173; Cotsaces C, 2005, P 2005 WORKSH AUD VI; COVER T., 1991, ELEMENTS INFORM THEO; Danisman T, 2007, P 2007 TREC VID RETR; Danisman T, 2006, P TREC VID RETR EV T; Derpanis KG, 2004, HARRIS CORNER DETECT; Han SH, 2000, 12 WORKSH IM P IM UN; Hanjalic A, 2002, IEEE T CIRC SYST VID, V12, P90, DOI 10.1109/76.988656; Henga WJ, 2001, VISUAL COMMUNICATION, V12, P217; Hu WM, 2011, IEEE T SYST MAN CY C, V41, P797, DOI 10.1109/TSMCC.2011.2109710; Huang CL, 2001, IEEE T CIRC SYST VID, V11, P1281; Huang CR, 2008, IEEE T MULTIMEDIA, V10, P1097, DOI 10.1109/TMM.2008.2001374; Huang XD, 2008, CISP 2008: FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOL 2, PROCEEDINGS, P276, DOI 10.1109/CISP.2008.425; Lefevre S, 2003, REAL-TIME IMAGING, V9, P73, DOI 10.1016/S1077-2014(02)00115-8; Li YN, 2009, IET IMAGE PROCESS, V3, P121, DOI 10.1049/iet-ipr.2007.0193; Lienhart R, 2001, P SOC PHOTO-OPT INS, V4315, P219, DOI 10.1117/12.410931; Ling X, 2008, CISP 2008: FIRST INTERNATIONAL CONGRESS ON IMAGE AND SIGNAL PROCESSING, VOL 2, PROCEEDINGS, P445; Mas J, 2003, P TREC VID RETR EV C; Pei SC, 2002, IEEE T MULTIMEDIA, V4, P309, DOI 10.1109/TMM.2002.802841; Pei SC, 1999, IEEE T MULTIMEDIA, V1, P321; Qin T, 2010, P 2010 IEEE INT C NE, P190; Ren W, 2001, INT C INF COMM SIGN; Su CW, 2005, IEEE T MULTIMEDIA, V7, P1106, DOI 10.1109/TMM.2005.858394; Tapu R., 2011, Proceedings of the 1st IEEE First International Conference on Consumer Electronics - Berlin (IEEE ICCE-Berlin 2011), DOI 10.1109/ICCE-Berlin.2011.6031875; Wolf MWW, 1998, P INT C IM PROC, V1, P893; Xia DY, 2007, Proceedings of the Fourth International Conference on Image and Graphics, P389, DOI 10.1109/ICIG.2007.11; Xiong W, 1998, COMPUT VIS IMAGE UND, V71, P166, DOI 10.1006/cviu.1998.0711; Yeo BL, 1995, IEEE T CIRC SYST VID, V5, P533; Yuan J, 2005, P 13 ANN ACM INT C M, P539, DOI 10.1145/1101149.1101271; Zhu SH, 2009, EXPERT SYST APPL, V36, P5976, DOI 10.1016/j.eswa.2008.07.009; Zuzana C, 2006, IEEE T CIRCUITS SYST, V16, P82 33 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. AUG 2014 71 3 1749 1770 10.1007/s11042-012-1301-z 22 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AL8KJ WOS:000339387000035 J Chen, CM; Chen, LH Chen, Chun-Min; Chen, Ling-Hwei A novel approach for semantic event extraction from sports webcast text MULTIMEDIA TOOLS AND APPLICATIONS English Article Semantic event detection; Webcast text; Information retrieval; Video retrieval VIDEO Semantic event extraction is helpful for video annotation and retrieval. For sports video, most previous works detect events by video content itself. Some useful external knowledge has been researched recently. In this paper, we proposed an unsupervised approach to extract semantic events from sports webcast text. First, unrelated words in the descriptions of webcast text are filtered out, and then the filtered descriptions are clustered into significant event categories. Finally, the keywords for each event category are extracted. According to our experimental results, the proposed approach actually extracts significant text events, which can be used for further video indexing and summarization. Furthermore, we also provide a hierarchical searching scheme for text event retrieval. [Chen, Chun-Min; Chen, Ling-Hwei] Natl Chiao Tung Univ, Dept Comp Sci, Hsinchu 300, Taiwan Chen, LH (reprint author), Natl Chiao Tung Univ, Dept Comp Sci, 1001 Univ Rd, Hsinchu 300, Taiwan. cmchen@debut.cis.nctu.edu.tw; lhchen@cc.nctu.edu.tw National Science Council of Republic of China [NSC-100-2221-E-009-140-MY2] This work is supported in part by National Science Council of Republic of China under grant NSC-100-2221-E-009-140-MY2. Chen YH, 2011, INT C GEN EV COMP IC; Hassan E, 2011, INT C COMP VIS WORKS; Kim HG, 2011, IEEE INT C MULT EXP; Manning C, 2008, INTRO INFORM RETRIEV, P27; Nitta N, 2005, MULTIMED TOOLS APPL, V25, P59, DOI 10.1023/B:MTAP.0000046382.62218.e1; Schreer O, 2010, MULTIMED TOOLS APPL, V48, P23, DOI 10.1007/s11042-009-0375-8; Shen JL, 2008, IEEE T CIRC SYST VID, V18, P1587, DOI 10.1109/TCSVT.2008.2005607; Shyu ML, 2008, IEEE T MULTIMEDIA, V10, P252, DOI 10.1109/TMM.2007.911830; Xu CS, 2008, IEEE T MULTIMEDIA, V10, P1342, DOI 10.1109/TMM.2008.2004912; Xu H, 2004, P WORKSH MULT INF RE; Zhou HY, 2010, NEUROCOMPUTING, V73, P1718, DOI 10.1016/j.neucom.2009.09.022; Zhu XQ, 2005, IEEE T KNOWL DATA EN, V17, P665 12 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. AUG 2014 71 3 1937 1952 10.1007/s11042-012-1323-6 16 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AL8KJ WOS:000339387000043 J Osarogiagbon, RU; Eke, R; Sareen, S; Leary, C; Coleman, L; Faris, N; Yu, XH; Spencer, D Osarogiagbon, Raymond U.; Eke, Ransome; Sareen, Srishti; Leary, Cynthia; Coleman, LaShundra; Faris, Nicholas; Yu, Xinhua; Spencer, David The impact of a novel lung gross dissection protocol on intrapulmonary lymph node retrieval from lung cancer resection specimens ANNALS OF DIAGNOSTIC PATHOLOGY English Article Non-small cell lung cancer; Pathologic nodal staging; Quality improvement PROGNOSTIC-SIGNIFICANCE; CELL; SURVIVAL; NUMBER; IDENTIFICATION; VALIDATION; CARCINOMA; TNM Although thorough pathologic nodal staging provides the greatest prognostic information in patients with potentially curable non-small cell lung cancer, N1 nodal metastasis is frequently missed. We tested the impact of corrective intervention with a novel pathology gross dissection protocol on intrapulmonary lymph node retrieval. This study is a retrospective review of consecutive lobectomy, or greater, lung resection specimens over a period of 15 months before and 15 months after training pathologist's assistants on the novel dissection protocol. One hundred forty one specimens were examined before and 121 specimens after introduction of the novel dissection protocol. The median number of intrapulmonary lymph nodes retrieved increased from 2 to 5 (P <.0001), and the 75th to 100th percentile range of detected intrapulmonary lymph node metastasis increased from 0 to 5 to 0 to 17 (P = .0003). In multivariate analysis, the extent of resection, examination period (preintervention or postintervention), and pathologic N1 (vs NO) status were most strongly associated with a higher number of intrapulmonary lymph nodes examined. A novel pathology dissection protocol is a feasible and effective means of improving the retrieval of intrapulmonary lymph nodes for examination. Further studies to enhance dissemination and implementation of this novel pathology dissection protocol are warranted. (C) 2014 Elsevier Inc. All rights reserved. [Osarogiagbon, Raymond U.; Sareen, Srishti; Coleman, LaShundra; Faris, Nicholas] Baptist Canc Ctr, Thorac Oncol Res Grp, Multidisciplinary Thorac Oncol Program, Memphis, TN 38120 USA; [Osarogiagbon, Raymond U.; Eke, Ransome; Yu, Xinhua] Univ Memphis, Sch Publ Hlth, Div Epidemiol & Biostat, Memphis, IN USA; [Leary, Cynthia; Spencer, David] Trumbull Labs LLC, Pathol Grp Mid South, Memphis, TN USA Osarogiagbon, RU (reprint author), Baptist Canc Ctr, Thorac Oncol Res Grp, 80 Humphreys Ctr Dr,Suite 220, Memphis, TN 38120 USA. rosarogi@bmhcc.org NIH [R01CA172253 (Osarogiagbon)] NIH R01CA172253 (Osarogiagbon). Le Chevalier T, 2004, NEW ENGL J MED, V350, P351; Farooq A, 2009, J CLIN ONCOL, V27, P15; Fukui T, 2006, J THORAC ONCOL, V1, P120, DOI 10.1097/01243894-200602000-00004; Gephardt GN, 1996, ARCH PATHOL LAB MED, V120, P922; Jonnalagadda S, 2011, CHEST, V140, P433, DOI 10.1378/chest.10-2885; Jonnalagadda S, 2011, CANCER-AM CANCER SOC, V117, P4724, DOI 10.1002/cncr.26093; Lee JG, 2008, ANN THORAC SURG, V85, P211, DOI 10.1016/j.athoracsur.2007.08.020; Ludwig MS, 2005, CHEST, V128, P1545, DOI 10.1378/chest.128.3.1545; Maeshima AM, 2012, CANCER-AM CANCER SOC, V118, P4512, DOI 10.1002/cncr.27424; Makary MA, 2007, SURGERY, V141, P450, DOI 10.1016/j.surg.2006.08.018; Molnar TF, 2007, EUR J CARDIO-THORAC, V31, P311, DOI 10.1016/j.ejcts.2006.11.047; Nwogu CE, 2012, ANN THORAC SURG, V93, P1614; Osaki T, 2004, LUNG CANCER-J IASLC, V43, P151, DOI 10.1016/j.lungcan.2003.08.020; Osarogiagbon RU, 2013, TRANSL LUNG CANC RES, V2, P364; Osarogiagbon RU, 2012, J THORAC DIS, V4, P214, DOI 10.3978/j.issn.2072-1439.2012.03.06; Osarogiagbon RU, 2012, J THORAC ONCOL, V7, P1798, DOI 10.1097/JTO.0b013e31827457db; Osarogiagbon RU, 2012, J THORAC ONCOL, V7, P1276, DOI 10.1097/JTO.0b013e318257fbe5; Osarogiagbon RU, 2013, ANN THORAC SURG, V96, P1975, DOI 10.1016/j.athoracsur.2013.07.009; Osarogiagbon RU, 2013, ANN THORAC SURG, V96, P1178, DOI 10.1016/j.athoracsur.2013.05.021; Osarogiagbon RU, 2013, ANN THORAC SURG; Osarogiagbon RU, 2011, ANN THORAC SURG, V91, P1486, DOI 10.1016/j.athoracsur.2010.11.065; Ou SHI, 2008, J THORAC ONCOL, V3, P880, DOI 10.1097/JTO.0b013e31817dfced; Pfannschmidt J, 2007, LUNG CANCER, V55, P371, DOI 10.1016/j.lungcan.2006.10.017; Pignon JP, 2008, J CLIN ONCOL, V26, P3552, DOI 10.1200/JCO.2007.13.9030; Ramirez RA, 2012, J CLIN ONCOL, V30, P2823, DOI 10.1200/JCO.2011.39.2589; Rena O, 2014, ANN THORAC SURG, V97, P987, DOI 10.1016/j.athoracsur.2013.11.051; Riquet M, 1999, ANN THORAC SURG, V67, P1572, DOI 10.1016/S0003-4975(99)00276-3; Rusch VW, 2007, J THORAC ONCOL, V2, P603, DOI 10.1097/JTO.0b013e31807ec803; Varlotto JM, 2009, CANCER, V115, P851, DOI 10.1002/cncr.23985; Wei SH, 2011, J THORAC ONCOL, V6, P310, DOI 10.1097/JTO.0b013e3181ff9b45; Winton T, 2005, NEW ENGL J MED, V352, P2589, DOI 10.1056/NEJMoa043623; Wu YC, 2003, EUR J CARDIO-THORAC, V24, P994, DOI 10.1016/S1010-7940(03)00567-0 32 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 1092-9134 1532-8198 ANN DIAGN PATHOL Ann. Diagn. Pathol. AUG 2014 18 4 220 226 10.1016/j.anndiagpath.2014.03.005 7 Pathology Pathology AL7JG WOS:000339309500006 J Oskouie, P; Alipour, S; Eftekhari-Moghadam, AM Oskouie, Payam; Alipour, Sara; Eftekhari-Moghadam, Amir-Masoud Multimodal feature extraction and fusion for semantic mining of soccer video: a survey ARTIFICIAL INTELLIGENCE REVIEW English Article Soccer video analysis; Event detection; Video summarization; Field object tracking; Semantic mining BROADCAST SPORTS VIDEO; MULTIPLE FIXED CAMERAS; BALL DETECTION; EVENT DETECTION; TRACKING; PLAYERS; RECOGNITION; INFORMATION; ALGORITHM; SEQUENCES This paper presents a classified review of soccer video analysis works. The existing approaches in the aspects of highlight event detection, video summarization and retrieval based on video stream, ball and player tracking for provision of match statistics, technical and tactical analysis and application of different sources in soccer video analysis have been surveyed. In addition, some major existing commercial softwares developed for video analysis are introduced and compared. With regard to the existing challenge for automatic and realtime provision of video analysis, different computer vision approaches are discussed and compared. Audio, video and text feature extraction methods have been investigated and the future trends for improvement of the reviewed systems have been introduced in terms of response time optimization, increase of precision and eliminating the need of human intervention for video analysis. [Oskouie, Payam; Alipour, Sara; Eftekhari-Moghadam, Amir-Masoud] Islamic Azad Univ, Qazvin Branch, Dept Elect Comp & IT Engn, Qazvin, Iran Oskouie, P (reprint author), Islamic Azad Univ, Qazvin Branch, Dept Elect Comp & IT Engn, Qazvin, Iran. p.oskouie@qiau.ac.ir; s.alipour@qiau.ac.ir; eftekhari@qiau.ac.ir Abreu P, 2010, IEEE C CYB INT SYST, P126; Ariki Y, 2006, P 8 IEEE INT S MULT; Assfalg J, 2003, COMPUTER VISION IMAG, V92, P285, DOI DOI 10.1016/J.CVIU.2003.06.004; Ballan L, 2011, MULTIMED TOOLS APPL, V51, P279, DOI 10.1007/s11042-010-0643-7; Ballan L, 2010, MULTIMED TOOLS APPL, V48, P313, DOI 10.1007/s11042-009-0342-4; Bayar M, 2010, IEEE INT CON MULTI, P578, DOI 10.1109/EUC.2010.93; Beetz M, 2006, P 5 INT JOINT C AUT, P42, DOI 10.1145/1160633.1160638; Chen SC, 2005, INNOVATIVE SHOT BOUN, P217; Cheng CC, 2006, IEEE T MULTIMEDIA, V8, P585, DOI 10.1109/TMM.2006.870726; Choi K, 2005, LECT NOTES COMPUT SC, V3617, P661; De Sousa J'unior SF, 2011, IEEE WORKSH APPL COM, P31; D'Orazio T, 2004, PATTERN RECOGN, V37, P393, DOI 10.1016/S0031-3203(03)00228-0; D'Orazio T, 2002, INT C PATT RECOG, P210; D'Orazio T, 2009, IEEE T CIRC SYST VID, V19, P1804, DOI 10.1109/TCSVT.2009.2026817; D'Orazio T, 2009, COMPUT VIS IMAGE UND, V113, P622, DOI 10.1016/j.cviu.2008.01.010; D'Orazio T, 2010, PATTERN RECOGN, V43, P2911, DOI 10.1016/j.patcog.2010.03.009; Du W, 2007, LECT NOTES COMPUT SC, V4843, P365; Du W, 2006, WORKSH COMP VIS BAS, P2; Duan LY, 2003, P 11 ACM INT C MULT, P33; Ekin A, 2003, IEEE T IMAGE PROCESS, V12, P796, DOI 10.1109/TIP.2003.812758; Eldib MY, 2009, IEEE INT C IM PROC, V43, P45; Figueroa PJ, 2006, COMPUT VIS IMAGE UND, V101, P122, DOI 10.1016/j.cviu.2005.07.006; Gao XB, 2011, NEUROCOMPUTING, V74, P540, DOI 10.1016/j.neucom.2010.09.013; Gao Y, 2009, MULTIMED TOOLS APPL, V42, P233, DOI 10.1007/s11042-008-0236-x; Gedikli S, 2007, P 5 INT C COMP VIS S, P21; Gonzales R., 2008, DIGITAL IMAGE PROCES; Hartley R., 2000, MULTIPLE VIEW GEOMET; Hashimoto S, 2006, P IEEE INT C MULT EX, P1889; Hossein-Khani J, 2011, 2011 NINTH IEEE INTERNATIONAL SYMPOSIUM ON PARALLEL AND DISTRIBUTED PROCESSING WITH APPLICATIONS WORKSHOPS (ISPAW), P147, DOI 10.1109/ISPAW.2011.41; Hu Sh, 2010, 2 INT C INF SCI ENG, P1; HU SH, 2010, P 11 PAC RIM C ADV M, V6297, P537; Huang CL, 2006, IEEE T MULTIMEDIA, V8, P749, DOI 10.1109/TMM.2006.876289; Jiang Sh, 2007, ICME07 C P IEEE JUL, P1095; Joo SW, 2007, IEEE T IMAGE PROCESS, V16, P2849, DOI 10.1109/TIP.2007.906254; Kang C, 2006, P INT C DAT MIN WORK, P377; Kang YL, 2004, IEEE IMAGE PROC, P1629; Khatoonabadi HS, 2009, IMAGE VISION COMPUT, V27, P469; Kim HG, 2005, P SOC PHOTO-OPT INS, V5682, P317, DOI 10.1117/12.586533; Kolekar MH, 2010, MULTIMED TOOLS APPL, V54, P27; Kolekar Maheshkumar H, 2009, Journal of Multimedia, V4, DOI 10.4304/jmm.4.5.298-312; Kolekar MH, 2009, WORKSH IEEE 12 INT C, P554; Leonardi R, 2004, IEEE T CIRC SYST VID, V14, P634, DOI 10.1109/TCSVT.2004.826751; Liu J, 2007, P BRIT MACH VIS C; Liu J, 2009, PATTERN RECOGN LETT, V30, P103, DOI 10.1016/j.patrec.2008.02.011; Liu Y, 2006, IMAGE VISION COMPUT, V24, P1146, DOI 10.1016/j.imavis.2006.04.001; Masui K, 2010, 2010 2nd European Workshop on Visual Information Processing (EUVIP 2010), DOI 10.1109/EUVIP.2010.5699109; Misu T, 2004, P INT C WSCG, P285; Misu T, 2007, INT CONF ACOUST SPEE, P937; Miura J, 2009, COMPUT VIS IMAGE UND, V113, P653, DOI 10.1016/j.cviu.2008.10.005; Miura J, 2008, P CIVR; Money AG, 2007, J VISUAL IMAGE REPRE, V19, P121; Nguyen VT, 2010, IEEE RIVF INT C COMP, P1; Nillius P, 2006, P IEEE INT C COMP VI, P2187; Nitta N, 2009, MULTIMED TOOLS APPL, V41, P1, DOI 10.1007/s11042-008-0217-0; Pallavi V, 2008, IEEE T MULTIMEDIA, V10, P794, DOI 10.1109/TMM.2008.922869; Pallavi V, 2008, J VIS COMMUN IMAGE R, V19, P426, DOI 10.1016/j.jvcir.2008.06.007; PAN H, 2001, ACOUST SPEECH SIG PR, P1649; Pei CK, 2009, 2009 THIRD INTERNATIONAL SYMPOSIUM ON INTELLIGENT INFORMATION TECHNOLOGY APPLICATION, VOL 2, PROCEEDINGS, P392, DOI 10.1109/IITA.2009.235; Ping Sh, 2009, 9 INT C MAN SERV SCI, P1; Poppe Ch, 2010, ACM AIEM P, V10, P51; Poppe Ch, 2010, 17 IEEE INT C ADV VI, P26; Qian X, 2011, MULTIMED TOOLS APPL, V55, P1; Qian X, 2010, 2 INT C MULT INF TEC, P138; Ren J, 2008, IEEE T CIRC SYST VID, V18, P350, DOI 10.1109/TCSVT.2008.918276; Ren JC, 2009, COMPUT VIS IMAGE UND, V113, P633, DOI 10.1016/j.cviu.2008.01.007; Ren JC, 2010, MACH VISION APPL, V21, P855, DOI 10.1007/s00138-009-0212-0; Seo K, 2007, IEEE T CIRCUITS SYST, V17, P1395; Shen JL, 2008, IEEE T CIRC SYST VID, V18, P1587, DOI 10.1109/TCSVT.2008.2005607; Shyu ML, 2008, IEEE T MULTIMEDIA, V10, P252, DOI 10.1109/TMM.2007.911830; Snoek CGM, 2005, THESIS; Spagnolo P, 2007, P INT C SIGN PROC MU, P129; Sullivan J, 2006, LECT NOTES COMPUT SC, V3953, P619; Sun L, 2009, INT CONF ACOUST SPEE, P1237, DOI 10.1109/ICASSP.2009.4959814; Taki T, 1996, INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, PROCEEDINGS - VOL III, P815; Tjondronegoro DW, 2009, IEEE T SYST MAN CYB, V40, P1009; Tjondronegoro DW, 2003, P IEEE INT C MULT EX, P579; Tong XF, 2004, INT C PATT RECOG, P795; Vandenbroucke N, 2003, COMPUT VIS IMAGE UND, V90, P190, DOI 10.1016/S1077-3142(03)00025-0; Wang F, 2005, P 11 INT MULT MOD C, P115; Wang J, 2004, P IEEE ICME, P27; Wickramaratna K, 2005, P IEEE INT S MULT, P21; Xie LX, 2004, PATTERN RECOGN LETT, V25, P767, DOI 10.1016/j.patrec.2004.01.005; XIE LX, 2003, 2003 INT C MULT EXP, P29; Xie1 Z, 2007, IEEE INT C MULT EXP, P2026; Xu Ch, 2009, J MULTIMEDIA, V4, P69; Xu CS, 2008, IEEE T MULTIMEDIA, V10, P1342, DOI 10.1109/TMM.2008.2004912; Xu CS, 2008, IEEE T MULTIMEDIA, V10, P421, DOI 10.1109/TMM.2008.917346; Xu M, 2005, IEE P-VIS IMAGE SIGN, V152, P232, DOI 10.1049/ip-vis:20041257; Xu W, 2011, IEEE SIGNAL PROC LET, V18, P509, DOI 10.1109/LSP.2011.2161287; Xu ZF, 2005, Proceedings of 2005 IEEE International Workshop on VLSI Design and Video Technology, P369; Yang Y., 2007, IEEE POW ENG SOC GEN, P1; Yang YQ, 2004, PROCEEDINGS OF THE 2004 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7, P3759; Yilmaz A, 2006, ACM COMPUT SURV, V38, DOI 10.1145/1177352.1177355; Yong LH, 2009, INT JOINT C ART INT, P128; Yoon HS, 2002, ETRI J, V24, P443, DOI 10.4218/etrij.02.0102.0005; Yu X, 2003, ACM MM03 BERK, P11; Yu XG, 2009, J VIS COMMUN IMAGE R, V20, P117, DOI 10.1016/j.jvcir.2008.12.004; Yu XG, 2006, IEEE T MULTIMEDIA, V8, P1164, DOI 10.1109/TMM.2006.884621; Zhu G, 2007, P 15 ACM INT C MULT, P58, DOI 10.1145/1291233.1291250; Zhu G, 2008, P ACM INT C IM VID R, P515, DOI 10.1145/1386352.1386418; Zhu GY, 2009, IEEE T MULTIMEDIA, V11, P49, DOI 10.1109/TMM.2008.2008918 101 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0269-2821 1573-7462 ARTIF INTELL REV Artif. Intell. Rev. AUG 2014 42 2 173 210 10.1007/s10462-012-9332-4 38 Computer Science, Artificial Intelligence Computer Science AL4LI WOS:000339104100002 J Paegert, M; Stassun, KG; Burger, DM Paegert, Martin; Stassun, Keivan G.; Burger, Dan M. THE EB FACTORY PROJECT. I. A FAST, NEURAL-NET-BASED, GENERAL PURPOSE LIGHT CURVE CLASSIFIER OPTIMIZED FOR ECLIPSING BINARIES ASTRONOMICAL JOURNAL English Article binaries: eclipsing; stars: variables: general SKY AUTOMATED SURVEY; GRAVITATIONAL LENSING EXPERIMENT; VARIABLE-STARS; DATA RELEASE; SUPERVISED CLASSIFICATION; CATALOG; METHODOLOGY We describe a new neural-net-based light curve classifier and provide it with documentation as a ready-to-use tool for the community. While optimized for identification and classification of eclipsing binary stars, the classifier is general purpose, and has been developed for speed in the context of upcoming massive surveys such as the Large Synoptic Survey Telescope. A challenge for classifiers in the context of neural-net training and massive data sets is to minimize the number of parameters required to describe each light curve. We show that a simple and fast geometric representation that encodes the overall light curve shape, together with a chi-square parameter to capture higher-order morphology information results in efficient yet robust light curve classification, especially for eclipsing binaries. Testing the classifier on the ASAS light curve database, we achieve a retrieval rate of 98% and a false-positive rate of 2% for eclipsing binaries. We achieve similarly high retrieval rates for most other periodic variable-star classes, including RR Lyrae, Mira, and delta Scuti. However, the classifier currently has difficulty discriminating between different sub-classes of eclipsing binaries, and suffers a relatively low (similar to 60%) retrieval rate for multi-mode delta Cepheid stars. We find that it is imperative to train the classifier's neural network with exemplars that include the full range of light curve quality to which the classifier will be expected to perform; the classifier performs well on noisy light curves only when trained with noisy exemplars. The classifier source code, ancillary programs, a trained neural net, and a guide for use, are provided. [Paegert, Martin; Stassun, Keivan G.; Burger, Dan M.] Vanderbilt Univ, Dept Phys & Astron, Nashville, TN 37235 USA; [Stassun, Keivan G.] Fisk Univ, Dept Phys, Nashville, TN 37208 USA Paegert, M (reprint author), Vanderbilt Univ, Dept Phys & Astron, Vu Stn B 1807, Nashville, TN 37235 USA. NASA ADAP [NNX12AE22G] This work has been funded by the NASA ADAP grant NNX12AE22G. We thank Dr. Joshua Pepper and Dr. Nathan De Lee for valuable discussions and help. Blomme J, 2011, MON NOT R ASTRON SOC, V418, P96, DOI 10.1111/j.1365-2966.2011.19466.x; Borucki WJ, 2010, SCIENCE, V327, P977, DOI 10.1126/science.1185402; Coughlin JL, 2011, ASTRON J, V141, DOI 10.1088/0004-6256/141/3/78; Debosscher J, 2007, ASTRON ASTROPHYS, V475, P1159, DOI 10.1051/0004-6361:20077638; Debosscher J, 2009, ASTRON ASTROPHYS, V506, P519, DOI 10.1051/0004-6361/200911618; Eyer L, 2005, MON NOT R ASTRON SOC, V358, P30, DOI 10.1111/j.1365-2966.2005.08651.x; Guinan EF, 1998, ASTROPHYS J, V509, pL21, DOI 10.1086/311760; Hebb DO, 1949, ORG BEHAV; Matijevic G, 2012, ASTRON J, V143, DOI 10.1088/0004-6256/143/5/123; McCulloch Warren S., 1943, BULL MATH BIOPHYS, V5, P115, DOI 10.1007/BF02459570; Paczynski B, 2006, MON NOT R ASTRON SOC, V368, P1311, DOI 10.1111/j.1365-2966.2006.10223.x; Pojmanski G, 2002, ACTA ASTRONOM, V52, P397; Prsa A, 2011, ASTRON J, V141, DOI 10.1088/0004-6256/141/3/83; Prsa A, 2011, ASTRON J, V142, DOI 10.1088/0004-6256/142/2/52; Prsa A, 2008, ASTROPHYS J, V687, P542, DOI 10.1086/591783; Richards JW, 2012, ASTROPHYS J SUPPL S, V203, DOI 10.1088/0067-0049/203/2/32; Ricker G. R., 2010, BAAS, V42; Rimoldini L, 2012, MON NOT R ASTRON SOC, V427, P2917, DOI 10.1111/j.1365-2966.2012.21752.x; RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0; Sarro LM, 2009, ASTRON ASTROPHYS, V494, P739, DOI 10.1051/0004-6361:200809918; Slawson RW, 2011, ASTRON J, V142, DOI 10.1088/0004-6256/142/5/160; Stassun K., 2013, BAAS, V221; Stassun K. G., 2009, IAU S, V258, P161; Thompson IB, 2010, ASTRON J, V139, P329, DOI 10.1088/0004-6256/139/2/329; Torres G, 2010, ASTRON ASTROPHYS REV, V18, P67, DOI 10.1007/s00159-009-0025-1; Udalski A, 2002, ACTA ASTRONOM, V52, P217; Zebrun K, 2001, ACTA ASTRONOM, V51, P317 27 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-6256 1538-3881 ASTRON J Astron. J. AUG 2014 148 2 31 10.1088/0004-6256/148/2/31 16 Astronomy & Astrophysics Astronomy & Astrophysics AL7OQ WOS:000339324400007 J Joe, YY; Gan, OP; Lewis, FL Joe, Yen Yen; Gan, Oon Peen; Lewis, Frank L. Multi-commodity flow dynamic resource assignment and matrix-based job dispatching for multi-relay transfer in complex material handling systems (MHS) JOURNAL OF INTELLIGENT MANUFACTURING English Article Material handling system; Multi-commodity flow; Resource assignment; Matrix-based discrete-event control; Job dispatching RETRIEVAL-SYSTEMS; AUTOMATED STORAGE; DESIGN; MODEL; PICKING Mass management and production of customized products requires material handling systems (MHS) which are flexible and responsive enough to accommodate dynamic and real-time changes in material handling tasks. Towards this goal, we develop a novel control framework to improve the flexibility and responsiveness of material handling systems. Flexibility is achieved by using multi-commodity flow network optimization to find the most optimized job sequence in terms of minimum transfer steps. Responsiveness is achieved by the use of a matrix-based discrete event (DE) supervisory controller to dispatch equipment control commands in real-time based on real-time sensor information, according to the optimized sequence. By modeling the MHS network as multi-commodity flow network to define job routes, and using the matrix-based DE controller to implement the job routes in real-time, the users achieve a seamlessly integrated solution to control the execution of transfer jobs that covers the supervisory planning stage through the real-time actual dispatching decisions. The proposed control framework is evaluated on an industrial case study of airfreight terminal material handling and simulation results show its effectiveness. [Joe, Yen Yen; Gan, Oon Peen] Singapore Inst Mfg Technol, Singapore 638075, Singapore; [Lewis, Frank L.] Univ Texas Arlington, Automat & Robot Res Inst, Ft Worth, TX 76118 USA Joe, YY (reprint author), Singapore Inst Mfg Technol, 71 Nanyang Dr, Singapore 638075, Singapore. yyjoe@simtech.a-star.edu.sg; opgan@simtech.a-star.edu.sg; lewis@uta.edu National Science Foundation [ECS-1128050]; Army Research Office [W91NF-05-1-0314]; Air Force Office of Scientific Research [FA9550-09-1-0278] This work was supported by the National Science Foundation ECS-1128050, the Army Research Office W91NF-05-1-0314 and the Air Force Office of Scientific Research FA9550-09-1-0278. Ahuja R.K., 1993, NETWORK FLOWS THEORY; Andriansyah R, 2011, COMPUT IND, V62, P292, DOI 10.1016/j.compind.2010.09.005; Babiceanu R. F., 2005, ROBOTICS COMPUTER IN, V23, P441; Basile F., 2011, P IEEE INT C AUT SCI; Bessenouci HN, 2012, J INTELL MANUF, V23, P1157, DOI 10.1007/s10845-010-0432-1; Bogdan S., 2006, MANUFACTURING SYSTEM; Bright G., 2007, P 2007 AUSTR C ROB A; Choe R, 2012, J INTELL MANUF, V23, P2179, DOI 10.1007/s10845-011-0564-y; Confessore G, 2013, J INTELL MANUF, V24, P405, DOI 10.1007/s10845-011-0612-7; Cormen T. H., 2001, INTRO ALGORITHMS, P788; Daoud S, 2014, J INTELL MANUF, V25, P27, DOI 10.1007/s10845-012-0668-z; de Koster R, 2007, EUR J OPER RES, V182, P481, DOI 10.1016/j.ejor.2006.07.009; Dotoli M, 2005, INT J COMP INTEG M, V18, P122, DOI 10.1080/0951192052000288233; Even S., 1976, SIAM Journal on Computing, V5, DOI 10.1137/0205048; Giordano V, 2008, IEEE T AUTOM SCI ENG, V5, P53, DOI 10.1109/TASE.2007.891472; Gu JX, 2007, EUR J OPER RES, V177, P1, DOI 10.1016/j.ejor.2006.02.025; Gurel A, 2000, IEEE T AUTOMAT CONTR, V45, P2086, DOI 10.1109/9.887631; Harris B, 2002, J INTELL MANUF, V13, P239, DOI 10.1023/A:1016060009229; Harris B., 2000, J INTELLIGENT SYSTEM, V10, P279; Harris B, 1998, J INTELL MANUF, V9, P413, DOI 10.1023/A:1008888215052; Heineman G.T., 2009, ALGORITHMS NUTSHELL; Jung SH, 2006, J INTELL MANUF, V17, P479, DOI 10.1007/s10845-005-0020-y; Kusiak A., 1992, Intelligent design and manufacturing; Le-Anh T, 2010, INT J PROD RES, V48, P7219, DOI 10.1080/00207540903443279; Lin L, 2006, J INTELL MANUF, V17, P465, DOI 10.1007/s10845-005-0019-4; Luo M., 2005, P 31 ANN C IEEE IND, P2325; McAree P, 2002, NETWORKS, V39, P107, DOI 10.1002/net.10017; Medina L., 2009, P 2009 WINT SIM C; Mireles J, 2001, IEEE T IND ELECTRON, V48, P1087, DOI 10.1109/41.969387; Nishi T, 2006, ROBOT CIM-INT MANUF, V22, P154, DOI 10.1016/j.rcim.2005.02.010; Ouelhadj D, 2009, J SCHED, V12, P417, DOI 10.1007/s10951-008-0090-8; Roodbergen KJ, 2009, EUR J OPER RES, V194, P343, DOI 10.1016/j.ejor.2008.01.038; Rubrico JIU, 2011, ROBOT CIM-INT MANUF, V27, P62, DOI 10.1016/j.rcim.2010.06.011; STEWARD DV, 1981, IEEE T ENG MANAGE, V28, P71; Tacconi DA, 1997, IEEE CONTR SYST MAG, V17, P62, DOI 10.1109/37.621472; van der Meer J., 2000, THESIS ERIM TRAIL; Wang CN, 2012, J INTELL MANUF, V23, P2047, DOI 10.1007/s10845-011-0531-7; Wong M., 2008, ROBOTICS COMPUTER IN, V23, P294; Yang J.-W., 2008, IEEE INT C SYST MAN, P1608 39 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0956-5515 1572-8145 J INTELL MANUF J. Intell. Manuf. AUG 2014 25 4 681 697 10.1007/s10845-012-0713-y 17 Computer Science, Artificial Intelligence; Engineering, Manufacturing Computer Science; Engineering AL8GF WOS:000339375400004 J El-Sappagh, SH; El-Masri, S; Elmogy, M; Riad, AM; Saddik, B El-Sappagh, Shaker H.; El-Masri, Samir; Elmogy, Mohammed; Riad, A. M.; Saddik, Basema An Ontological Case Base Engineering Methodology for Diabetes Management JOURNAL OF MEDICAL SYSTEMS English Article Case based reasoning; Ontology engineering; Case representation; Knowledge management and clinical decision support system SYSTEMS; INFORMATION Ontology engineering covers issues related to ontology development and use. In Case Based Reasoning (CBR) system, ontology plays two main roles; the first as case base and the second as domain ontology. However, the ontology engineering literature does not provide adequate guidance on how to build, evaluate, and maintain ontologies. This paper proposes an ontology engineering methodology to generate case bases in the medical domain. It mainly focuses on the research of case representation in the form of ontology to support the case semantic retrieval and enhance all knowledge intensive CBR processes. A case study on diabetes diagnosis case base will be provided to evaluate the proposed methodology. [El-Sappagh, Shaker H.] King Saud Univ, Coll Sci, Dept Math, Riyadh 11451, Saudi Arabia; [El-Masri, Samir] King Saud Univ, Coll Comp & Informat Sci, Dept Informat Syst, Riyadh, Saudi Arabia; [El-Sappagh, Shaker H.; Elmogy, Mohammed; Riad, A. M.] Mansoura Univ, Fac Comp & Informat, Mansoura, Egypt; [Saddik, Basema] Univ King Saud Bin Abdulaziz Univ Hlth Sci, Coll Publ Hlth & Hlth Informat, Riyadh, Saudi Arabia El-Masri, S (reprint author), King Saud Univ, Coll Comp & Informat Sci, Dept Informat Syst, Riyadh, Saudi Arabia. selmasri@ksu.edu.sa King Saud University, Deanship of Scientific Research, College of Sciences Research Centre This project was supported by King Saud University, Deanship of Scientific Research, College of Sciences Research Centre. AACE Diabetes care plan Guidelines, 2011, ENDOCRINE PRACTICE S, V17; Abou Assali A, 2010, STUD COMPUT INTELL, V305, P97; Aissam B., 2012, ICWIT CEUR WORKSHOP, V867, P330; AlJarullah A., 2011, INT C INN INF TECHN, P303; American diabetes association, 2013, STAND MED CAR DIAB, V36; [Anonymous], 2013, OWL 2 WEB ONTOLOGY L; [Anonymous], 2013, DATA MASTER; [Anonymous], 2013, PROTEGE ONTOLOGY EDI; [Anonymous], 2013, CANADIAN J DIABETES; [Anonymous], 2014, UP DATE CLIN PRACTIC; Batsakis S, 2011, LECT NOTES COMPUT SC, V6826, P242, DOI 10.1007/978-3-642-22546-8_19; Bechhofer S., 2001, DAML OIL IS NOT ENOU, P151; Branden M., 2011, J ARTIFICIAL INTELLI, V51, P117; Calvanese D., 2001, P INT SEM WEB WORK S, P663; Casellas N., 2011, TOOLS LANGUAGES ONTO, V3, P57; Castillo-Barrera FE, 2013, APPL INTELL, V38, P99, DOI 10.1007/s10489-012-0360-1; Changrui Y., 2012, ASPECTS ARTIFICIAL I, V6839, P349; Chen J., 2010, INT C IND INF SYST I, V1, P323; CHIKOFSKY EJ, 1990, IEEE SOFTWARE, V7, P13, DOI 10.1109/52.43044; Correa J., 1996, P 12 EUR C ART INT E, P298; Dufour-Lussier V, 2011, LECT NOTES ARTIF INT, V6880, P62, DOI 10.1007/978-3-642-23291-6_7; El-Sappagh S., 2014, P 2 IEEE IN IN PRESS; El-Sappagh S., 2012, IJCSI INT J COMPUTER, V9, P329; Fernandez M., 1997, SPRING S ONT ENG AAA, P33; Fernandez-Lopez M., 2013, DELIVERABLE 1 4 SURV; Forbes D., 2012, P WORKSH NEW TRENDS, P43; Gasevic Dragan, 2004, ACM P 13 INT WORLD W, P488, DOI 10.1145/1013367.1013539; Gawich M., 2012, INT J COMPUTER APPL, V56; Gil R, 2014, J INTELL INF SYST, V42, P415, DOI 10.1007/s10844-013-0296-x; Gomez-Perez A., 1999, P 11 EUR KNOWL ACQ W, V1621, P139; Gomez-Perez A., 2003, ONTOLOGICAL ENG EXAM, V67, P14; Gruninger M., 1995, INT JOINT C ART INT; Gu DX, 2010, J MED SYST, V34, P213, DOI 10.1007/s10916-008-9232-y; Guo Y, 2012, COMPUT AIDED DESIGN, V44, P496, DOI 10.1016/j.cad.2011.12.007; Haghighi PD, 2013, DECIS SUPPORT SYST, V54, P1192, DOI 10.1016/j.dss.2012.11.013; Horrocks I, 2004, SWRL SEMANTIC WEB RU; Iqbal R., 2013, RES J APPL SCI ENG T, V6, P2993; Jarrar M., 2009, ADV WEB SEMANTICS 1, V4891, P7, DOI 10.1007/978-3-540-89784-2_2; Jaya A., 2011, INT J COMPUTER APPL, V14, P47, DOI 10.5120/1805-2291; Juarez J., 2007, ACM P 25 IASTED INT, P168; Kerremans K., 2003, P 1 INT WORKSH REG O, V2889, P662; Kings N., 2009, SEMANTIC WEB KNOWLED, P103, DOI [10.1007/978-3-540-88845-1_8, DOI 10.1007/978-3-540-88845-1_8]; Lenat D., 1993, ARTIF INTELL, V61, P41; Lin Y., 2008, KOBE J MED SCI, V54, pE290; Meersman R., 1999, P C COOP DAT SYST CO, P1; Ministry of health in Malaysia, 2009, CLIN PRACT GUID MAN; Ml H., 2012, J MED SYST, V36, P407; Mukesh J., 2013, INT J SOFT COMPUTING, V2, P132; Noy F., 2001, SMI20010880 STANF U; O'Connor MJ, 2011, COMM COM INF SC, V127, P97; Pan F., 2004, P AAAI SPRING S SEM, P29; Rahimi A., 2012, 24 INT C EUR FED MED; Recio-Garcia JA, 2014, SCI COMPUT PROGRAM, V79, P126, DOI 10.1016/j.scico.2012.04.002; Sclano F., 2008, P 9 C TERM ART INT T; Sharaf-El-Deen DA, 2014, J MED SYST, V38, DOI 10.1007/s10916-014-0009-1; Sicilia M., 2014, HDB METADATA SEMANTI; Siorpaes K, 2010, WORLD WIDE WEB, V13, P33, DOI 10.1007/s11280-009-0078-0; Spyns P., 2005, OTM 05 P 2005 OTM CO, V3762, P710; Suarez-Figueroa M., 2012, ONTOLOGY ENG NETWORK, P9; Subhashini R., 2011, INT J ENTERPRISE COM, V1; Subirats L, 2011, LECT NOTES ARTIF INT, V7094, P549, DOI 10.1007/978-3-642-25324-9_47; Sure Y., 2004, HDB ONTOLOGIES, P117; Sutton D., 2011, P 1 INT WORKSH MAN I, P83; Uschold M., 1995, METHODOLOGY BUILDING, P15; VA/DOD Evidence-based Practice, 2010, VA DOD EVIDENCE BASE; Valente A, 1999, IEEE INTELL SYST APP, V14, P27, DOI 10.1109/5254.747903; W3C, 2013, TIM ONT OWL; Yang SY, 2013, EXPERT SYST APPL, V40, P3351, DOI 10.1016/j.eswa.2012.12.044; Zhao H., 2013, HLTH INFORM SCI, V7798, P53 69 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0148-5598 1573-689X J MED SYST J. Med. Syst. AUG 2014 38 8 67 10.1007/s10916-014-0067-4 14 Health Care Sciences & Services; Medical Informatics Health Care Sciences & Services; Medical Informatics AL4OD WOS:000339111400007 J Gipp, B; Meuschke, N; Breitinger, C Gipp, Bela; Meuschke, Norman; Breitinger, Corinna Citation-Based Plagiarism Detection: Practicability on a Large-Scale Scientific Corpus JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article The automated detection of plagiarism is an information retrieval task of increasing importance as the volume of readily accessible information on the web expands. A major shortcoming of current automated plagiarism detection approaches is their dependence on high character-based similarity. As a result, heavily disguised plagiarism forms, such as paraphrases, translated plagiarism, or structural and idea plagiarism, remain undetected. A recently proposed language-independent approach to plagiarism detection, Citation-based Plagiarism Detection (CbPD), allows the detection of semantic similarity even in the absence of text overlap by analyzing the citation placement in a document's full text to determine similarity. This article evaluates the performance of CbPD in detecting plagiarism with various degrees of disguise in a collection of 185,000 biomedical articles. We benchmark CbPD against two character-based detection approaches using a ground truth approximated in a user study. Our evaluation shows that the citation-based approach achieves superior ranking performance for heavily disguised plagiarism forms. Additionally, we demonstrate CbPD to be computationally more efficient than character-based approaches. Finally, upon combining the citation-based with the traditional character-based document similarity visualization methods in a hybrid detection prototype, we observe a reduction in the required user effort for document verification. [Gipp, Bela; Meuschke, Norman] Univ Calif Berkeley, Dept Stat, Berkeley, CA 94720 USA; [Breitinger, Corinna] Univ Calif Berkeley, SciPlore Res Grp, Berkeley, CA 94720 USA Gipp, B (reprint author), Univ Calif Berkeley, Dept Stat, 493 Evans Hall, Berkeley, CA 94720 USA. gipp@berkeley.edu; meuschke@berkeley.edu; breitinger@berkeley.edu Bao J., 2007, 461 U HERTF SCI TECH; Buckley C, 2007, INFORM RETRIEVAL, V10, P491, DOI 10.1007/s10791-007-9032-x; Chen YL, 2012, NEUROSCI LETT, V510, P62, DOI 10.1016/j.neulet.2012.01.005; GARFIELD E, 1955, SCIENCE, V122, P108, DOI 10.1126/science.122.3159.108; Gipp B., 2013, P 36 INT ACM SIGIR C; Gipp B., 2011, P 11 ACM S DOC ENG D; Gipp B., 2011, P 11 ACM IEEE CS JOI; Gipp B., 2013, THESIS OTTO VON GUER; Goan T, 2006, LECT NOTES COMPUT SC, V3975, P692; Grman J., 2011, P NOTEBOOK PAPERS CL; Grozea C., 2009, P 3 PAN WORKSH UNC P; GuttenPlag Wiki, 2011, THESIS; Kakkonen T, 2010, J EDUC COMPUT RES, V42, P135, DOI 10.2190/EC.42.2.a; Lachlan P., 2012, SHERLOCK PLAGIARISM; Potthast M., 2013, CLEF 2013 EV LABS WO; Potthast M, 2011, LANG RESOUR EVAL, V45, P45, DOI 10.1007/s10579-009-9114-z; Potthast M., 2012, CLEF ONLINE WORKING; Potthast M., 2011, CLEF NOTEBOOK PAPERS; Stein B., 2007, P 30 ANN C GERM CLAS; Stein B, 2011, LANG RESOUR EVAL, V45, P63, DOI 10.1007/s10579-010-9115-y; Sun Z., 2010, PLOS ONE, V5; Weber-Wulff D., 2013, TEST PLAGIARISM SOFT; Weber-Wulff D., 2010, PORTAL PLAGIAT SOFTW; Weber-Wulff D., 2010, P 4 INT PLAG C NEWC; Zhan S., 2008, P 3 INT C INN COMP I; zu Guttenberg K.-T, 2009, VERFASSUNG VERFASSUN 26 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH AUG 2014 65 8 1527 1540 10.1002/asi.23228 14 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AL3XY WOS:000339066500002 J Symonds, M; Bruza, P; Zuccon, G; Koopman, B; Sitbon, L; Turner, I Symonds, Michael; Bruza, Peter; Zuccon, Guido; Koopman, Bevan; Sitbon, Laurianne; Turner, Ian Automatic Query Expansion: A Structural Linguistic Perspective JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article INFORMATION-RETRIEVAL; SPACE; COOCCURRENCE; MODEL A user's query is considered to be an imprecise description of their information need. Automatic query expansion is the process of reformulating the original query with the goal of improving retrieval effectiveness. Many successful query expansion techniques model syntagmatic associations that infer two terms co-occur more often than by chance in natural language. However, structural linguistics relies on both syntagmatic and paradigmatic associations to deduce the meaning of a word. Given the success of dependency-based approaches to query expansion and the reliance on word meanings in the query formulation process, we argue that modeling both syntagmatic and paradigmatic information in the query expansion process improves retrieval effectiveness. This article develops and evaluates a new query expansion technique that is based on a formal, corpus-based model of word meaning that models syntagmatic and paradigmatic associations. We demonstrate that when sufficient statistical information exists, as in the case of longer queries, including paradigmatic information alone provides significant improvements in retrieval effectiveness across a wide variety of data sets. More generally, when our new query expansion approach is applied to large-scale web retrieval it demonstrates significant improvements in retrieval effectiveness over a strong baseline system, based on a commercial search engine. [Symonds, Michael; Bruza, Peter; Koopman, Bevan] Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4001, Australia; [Zuccon, Guido] CSIRO, Australian E Hlth Res Ctr, Brisbane, Qld 4001, Australia; [Sitbon, Laurianne] Queensland Univ Technol, Dept Comp Sci, Brisbane, Qld 4001, Australia; [Turner, Ian] Queensland Univ Technol, Dept Math, Brisbane, Qld 4001, Australia Symonds, M (reprint author), Queensland Univ Technol, Sch Informat Syst, Brisbane, Qld 4001, Australia. michael.symonds@qut.edu.au; p.bruza@qut.edu.au; guido.zuccon@csiro.au; bevan.koopman@csiro.au; laurianne.sitbon@qut.edu.au; i.turner@qut.edu.au Allan J., 2012, SIGIR FORUM, V46, P2; Balasubramanian N, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P571; Bendersky M., 2009, P 2009 WORKSH WEB SE, P8, DOI 10.1145/1507509.1507511; Billerbeck B., 2004, P AUSTR DAT C DUN NZ, V27, P69; Billhardt H, 2002, J AM SOC INF SCI TEC, V53, P236, DOI 10.1002/asi.10032; Bruza P., 2011, P 16 AUSTR DOC COMP, P87; Bruza P., 2013, 21 TEXT RETRIEVAL C; Buckley C., 1995, P 3 TEXT RETR C TREC, P69; Bullinaria JA, 2007, BEHAV RES METHODS, V39, P510, DOI 10.3758/BF03193020; Burgess C, 1998, DISCOURSE PROCESS, V25, P211; Chapelle O., 2009, P 18 ACM C INF KNOWL, P621, DOI 10.1145/1645953.1646033; Chung YM, 2001, J AM SOC INF SCI TEC, V52, P283, DOI 10.1002/1532-2890(2000)9999:9999<::AID-ASI1073>3.3.CO;2-X; Collins-Thompson K., 2009, P 18 ACM C INF KNOWL, P837, DOI 10.1145/1645953.1646059; Fisher D., 2000, P 19 TEXT RETRIEVAL; Greenberg J, 2001, J AM SOC INF SCI TEC, V52, P487, DOI 10.1002/asi.1093.abs; Grefenstette G., 1992, P 15 ANN INT ACM SIG, P89, DOI 10.1145/133160.133181; Harris ZS, 1954, WORD, V10, P146; HOENKAMP E, 2009, P 2 INT C THEOR INF, V5766, P116; Holland N. N., 1992, CRITICAL 1; Huston S, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P291; Jones MN, 2007, PSYCHOL REV, V114, P1, DOI 10.1037/0033-295X.114.1.1; Kanerva P., 2008, P 30 ANN M COGN SCI, P23; Keen M., 1966, FACTORS DETERMINING, V1; Keen M., 1966, FACTORS DETERMINING, V2; Kolda TG, 2009, SIAM REV, V51, P455, DOI 10.1137/07070111X; Koopman B., 2012, P 10 AUSTR LANGU TEC, P15; Krovetz R., 1993, P 16 ANN INT ACM SIG, P191, DOI 10.1145/160688.160718; Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211; Lavrenko V., 2004, THESIS U MASSACHUSET; Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI 10.1145/383952.383972; Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766; Lv Y., 2009, P 18 ACM C INF KNOWL, P1895, DOI 10.1145/1645953.1646259; Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579; Lyons J., 1968, INTRO THEORETICAL LI; Metzler D, 2009, LECT NOTES COMPUT SC, V5766, P42, DOI 10.1007/978-3-642-04417-5_5; Metzler D., 2007, P 30 ANN INT ACM SIG, P311, DOI 10.1145/1277741.1277796; Metzler D., 2011, P 34 INT ACM SIGIR C, P605, DOI [DOI 10.1145/2009916.2009998, 10.1145/2009916.2009998]; Metzler D., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115; Miller G., 1990, INT J LEXICOGR, V3, P235; Moffat A., 2012, P 17 AUSTR DOC COMP, P47; Nie J.-Y., 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725; Ogilvie P, 2009, INFORM RETRIEVAL, V12, P666, DOI 10.1007/s10791-009-9104-1; Pavel T. C., 2001, SPELL LANGUAGE POSTS; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220; Schutze H., 1993, ADV NEURAL INFORMATI, V5, P895; Bruza P. D., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002; Symonds M., 2012, P 17 AUSTR DOC COMP, P123; Symonds M., 2012, P 21 ACM INT C INF K, P2267; Tannenbaum P. H., 1957, MEASUREMENT MEANING; Turner I., 2011, P 25 PAC AS C LANG I, P313; Turney PD, 2010, J ARTIF INTELL RES, V37, P141; Voorhees E. M., 1994, P 17 ANN INT ACM SIG, P61; Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202; Xue X., 2013, ACM T INFORM SYSTEMS, V31, P1; Yilmaz E., 2008, P 31 ANN INT ACM SIG, P603, DOI 10.1145/1390334.1390437; Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI 10.1145/502585.502654; Zhai C., 2006, P 29 ANN INT ACM SIG, P115, DOI DOI 10.1145/1148170.1148193; Zuccon G., 2011, P 16 AUSTR DOC COMP 60 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH AUG 2014 65 8 1577 1596 10.1002/asi.23065 20 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AL3XY WOS:000339066500005 J Sarnikar, S; Zhang, Z; Zhao, JL Sarnikar, Surendra; Zhang, Zhu; Zhao, J. Leon Query-Performance Prediction for Effective Query Routing in Domain-Specific Repositories JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article INFORMATION-RETRIEVAL; LANGUAGE MODELS; WEB SEARCH; EXPANSION; RANKING; SYSTEMS; TRANSFORMATIONS; ENVIRONMENT; REGRESSION; PORTALS The effective use of corporate memory is becoming increasingly important because every aspect of e-business requires access to information repositories. Unfortunately, less-than-satisfying effectiveness in state-of-the-art information-retrieval techniques is well known, even for some of the best search engines such as Google. In this study, the authors resolve this retrieval ineffectiveness problem by developing a new framework for predicting query performance, which is the first step toward better retrieval effectiveness. Specifically, they examine the relationship between query performance and query context. A query context consists of the query itself, the document collection, and the interaction between the two. The authors first analyze the characteristics of query context and develop various features for predicting query performance. Then, they propose a context-sensitive model for predicting query performance based on the characteristics of the query and the document collection. Finally, they validate this model with respect to five real-world collections of documents and demonstrate its utility in routing queries to the correct repository with high accuracy. [Sarnikar, Surendra] Dakota State Univ, Coll Business & Informat Syst, Madison, SD 57042 USA; [Zhang, Zhu] Univ Arizona, Dept Management Informat Syst, Tucson, AZ 85721 USA; [Zhao, J. Leon] City Univ Hong Kong, Dept Informat Syst, Kowloon, Hong Kong, Peoples R China Sarnikar, S (reprint author), Dakota State Univ, Coll Business & Informat Syst, 820 North Washington Ave,EH7, Madison, SD 57042 USA. surendra.sarnikar@dsu.edu; zhuzhang@email.arizona.edu; jlzhao@cityu.edu.hk Alemayehu N, 2003, J AM SOC INF SCI TEC, V54, P379, DOI 10.1002/asi.10217; Amati G, 2004, LECT NOTES COMPUT SC, V2997, P127; Arazy O, 2007, MIS QUART, V31, P525; Avrahami TT, 2006, J AM SOC INF SCI TEC, V57, P347, DOI 10.1002/asi.20283; Bashir S, 2011, J AM SOC INF SCI TEC, V62, P1515, DOI 10.1002/asi.21549; Belkin NJ, 2000, COMMUN ACM, V43, P58, DOI 10.1145/345124.345143; Bhavnani SK, 2006, J AM SOC INF SCI TEC, V57, P4, DOI 10.1002/asi.20238; Billsus D., 2005, P IUI 2005 JAN 9, P159, DOI 10.1145/1040830.1040869; BREIMAN L, 1985, J AM STAT ASSOC, V80, P580, DOI 10.2307/2288473; Buckley C, 2009, INFORM RETRIEVAL, V12, P652, DOI 10.1007/s10791-009-9103-2; Carmel D., 2010, SYNTHESIS LECTURES I, V2, P1, DOI 10.2200/S00235ED1V01Y201004ICR015; Carmel D., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148238; Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860; Chang CC, 2011, ACM T INTEL SYST TEC, V2, DOI 10.1145/1961189.1961199; Chen SM, 2001, IEEE T SYST MAN CY B, V31, P111, DOI 10.1109/3477.907569; Collins-Thompson K, 2010, LECT NOTES COMPUT SC, V5993, P140, DOI 10.1007/978-3-642-12275-0_15; Cronen-Townsend S, 2006, INFORM RETRIEVAL, V9, P723, DOI 10.1007/s10791-006-9006-4; Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Dash M, 2002, 2002 IEEE INTERNATIONAL CONFERENCE ON DATA MINING, PROCEEDINGS, P115, DOI 10.1109/ICDM.2002.1183893; Davis JR, 2000, J AM SOC INFORM SCI, V51, P273, DOI 10.1002/(SICI)1097-4571(2000)51:3<273::AID-ASI6>3.0.CO;2-6; De Loupy C., 2000, P 2 INT C LANG RES E, P32; Dubin D., 1996, THESIS U PITTSBURGH; Efthimiadis EN, 2000, J AM SOC INFORM SCI, V51, P989, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1002>3.0.CO;2-B; ELHAMDOUCHI A, 1987, J INFORM SCI, V13, P361, DOI 10.1177/016555158701300607; Fan WG, 2005, J MANAGE INFORM SYST, V21, P37; Finn A, 2006, J AM SOC INF SCI TEC, V57, P1506, DOI 10.1002/asi.20427; Geng B, 2012, IEEE T KNOWL DATA EN, V24, P745, DOI 10.1109/TKDE.2010.252; Gray PH, 2005, J MANAGE INFORM SYST, V22, P159; Grivolla J., 2005, P SIGIR 2005 PRED QU; Harman D., 2004, P 27 ANN INT ACM SIG, P528, DOI 10.1145/1008992.1009104; Haveliwala TH, 2003, IEEE T KNOWL DATA EN, V15, P784, DOI 10.1109/TKDE.2003.1208999; He B, 2006, INFORM SYST, V31, P585, DOI 10.1016/j.is.2005.11.003; Hsu C.W., 2003, TECHNICAL REPORT; Hu X., 2003, P 26 ACM INT C RES D, P407; James C.F., 2001, P 10 INT C INF KNOWL, P199; Jin R., 2001, P 24 ACM INT C RES D, P83, DOI 10.1145/383952.383964; Jones R, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1247715.1247720; Kurland O, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1508850.1508851; Kwok K., 2005, P SIGIR 2005 PRED QU; Lee DL, 1997, IEEE SOFTWARE, V14, P67, DOI 10.1109/52.582976; Liang T., 2007, J MANAGE INFORM SYST, V23, P45, DOI 10.2753/MIS0742-1222230303; Liu DR, 2012, J AM SOC INF SCI TEC, V63, P2100, DOI 10.1002/asi.22705; Marcial LH, 2010, J AM SOC INF SCI TEC, V61, P2029, DOI 10.1002/asi.21339; Melucci M., 2008, ACM T INFORM SYST, V26, P1, DOI DOI 10.1145/1361684.1361687; Menczer F, 2003, DECIS SUPPORT SYST, V35, P195, DOI 10.1016/S0167-9236(02)00106-9; Miller G., 1990, INT J LEXICOGR, V3, P235; MINKER J, 1973, J AM SOC INFORM SCI, V24, P246, DOI 10.1002/asi.4630240404; Mothe J., 2005, ACT ACM SIGIR WORKSH, P7; Muresan S, 2013, J AM SOC INF SCI TEC, V64, P727, DOI 10.1002/asi.22787; Ogilvie P., 2001, P 2001 TEXT RETR C G, P103; Paltoglou G, 2011, INFORM PROCESS MANAG, V47, P18, DOI 10.1016/j.ipm.2010.02.004; Pattuelli MC, 2011, J AM SOC INF SCI TEC, V62, P314, DOI 10.1002/asi.21453; Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008; Rorvig M., 2000, P 2000 ANN M AM SOC, P372; Sanderson Mark, 2010, Foundations and Trends in Information Retrieval, V4, DOI 10.1561/1500000009; Shah C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1008996; Shiri A, 2006, J AM SOC INF SCI TEC, V57, P462, DOI 10.1002/asi.20319; Simeoni F, 2008, J AM SOC INF SCI TEC, V59, P12, DOI 10.1002/asi.20694; STEIN EW, 1995, INFORM SYST RES, V6, P85, DOI 10.1287/isre.6.2.85; Storey VC, 2008, INFORM SYST RES, V19, P3, DOI 10.1287/isre.1070.0140; Sullivan T., 2001, P 1 ACM IEEE CS JOIN, P251, DOI 10.1145/379437.379669; Tang XN, 2013, J AM SOC INF SCI TEC, V64, P1065, DOI 10.1002/asi.22813; TIBSHIRANI R, 1988, J AM STAT ASSOC, V83, P394, DOI 10.2307/2288855; Tojib DR, 2008, EUR J INFORM SYST, V17, P649, DOI 10.1057/ejis.2008.55; Van Rijsbergen C. J., 1979, INFORM RETRIEVAL; Vinay V., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148239; Wei CP, 2008, J MANAGEMENT INFORM, V24, P269; Xu P., 2009, J MANAGE INFORM SYST, V25, P277; Yang Y., 1997, P 14 INT C MACH LEAR, V97, P412; Yom-Tov E., 2005, P 13 TEXT RETRIEVAL; Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322; Zhen L, 2009, DECIS SUPPORT SYST, V48, P237, DOI 10.1016/j.dss.2009.08.002; Zhou Y., 2007, P 30 ANN INT ACM SIG; Zimmer J., 2008, J MANAGEMENT INFORM, V24, P297 74 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH AUG 2014 65 8 1597 1614 10.1002/asi.23072 18 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AL3XY WOS:000339066500006 J Chen, YN; Ke, HR Chen, Ya-Ning; Ke, Hao-Ren A Study on Mental Models of Taggers and Experts for Article Indexing Based on Analysis of Keyword Usage JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article INFORMATION-RETRIEVAL; SUBJECT-HEADINGS; FLICKR; PATTERNS; LIBRARY; TAGS; USER; COLLECTION; CITEULIKE; SCIENCE This article explores the mental models of article indexing of taggers and experts in keyword usage. Better understanding of the mental models of taggers and experts and their usage gap may inspire better selection of appropriate keywords for organizing information resources. Using a data set of 3,972 tags from CiteULike and 6,708 descriptors from Library and Information Science Abstracts (LISA) from 1,489 scholarly articles of 13 library and information science journals, social network analysis and frequent-pattern tree methods were used to capture and build up the mental models of article indexing of taggers and experts when using keywords, and to generalize their structures and patterns. When measured with respect to the terms used, a power-law distribution, a comparison of terms used as tags and descriptors, social network analysis (including centrality, overall structure, and role equivalent) and frequent-pattern tree analysis, little similarity was found between the mental models of taggers and experts. Twenty-five patterns of path-based rules and 12 identical rules of frequent-pattern trees were shared by taggers and experts. Title-and topic-related keyword categories were the most popular keyword categories used in path-based rules of frequent-pattern trees, and also the most popular members of 25 patterns and the starting point of the 12 identical rules. [Chen, Ya-Ning] Tamkang Univ, Dept Informat & Lib Sci, New Taipei City 25137, Taiwan; [Ke, Hao-Ren] Natl Taiwan Normal Univ, Grad Inst Lib & Informat Studies, Taipei 106, Taiwan Chen, YN (reprint author), Tamkang Univ, Dept Informat & Lib Sci, 151 Yingzhuan Rd, New Taipei City 25137, Taiwan. arthur@mail.tku.edu.tw; clavenke@ntnu.edu.tw Agarwal N., 2009, MODELING DATA MINING; Ahlstrom V., 2004, INFORM ORG PORTAL US; Angus E, 2008, ONLINE INFORM REV, V32, P89, DOI 10.1108/14684520810866001; Ansari M., 2005, Library Review, V54, DOI 10.1108/00242530510611901; Bar-Ilan J, 2008, ONLINE INFORM REV, V32, P635, DOI 10.1108/14684520810914016; BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722; Bischoff K., 2008, P 17 ACM C INF KNOWL, P203; BROOKES BC, 1980, J INFORM SCI, V2, P125, DOI 10.1177/016555158000200302; Bruce R., 2008, WEBOLOGY, V5; Carlyle A., 1989, Cataloging & Classification Quarterly, V10, DOI 10.1300/J104v10n01_04; Chan LM, 1998, LIBR RESOUR TECH SER, V42, P45; Cole C, 2007, J AM SOC INF SCI TEC, V58, P2092, DOI 10.1002/asi.20668; Fleiss JL, 1981, STAT METHODS RATES P; Frost C. O., 1989, Cataloging & Classification Quarterly, V10, DOI 10.1300/J104v10n01_11; Golder SA, 2006, J INF SCI, V32, P198, DOI 10.1177/0165551506062337; Gwet K, 2012, HDB INTERRATER RELIA; Han JW, 2000, SIGMOD RECORD, V29, P1; Haythornthwaite C., 1996, LIB INFORMATION SCI, V18, P323, DOI 10.1016/S0740-8188(96)90003-1; Heckner M., 2008, J DIGITAL INFORM, V9; Heery R., 2000, ARIADNE, V25; Heymann P., 2008, P INT C WEB SEARCH W, P195, DOI 10.1145/1341531.1341558; Holley R., 2010, TAGGING FULL TEXT SE; Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960; Inskip C, 2008, J DOC, V64, P687, DOI 10.1108/00220410810899718; Iyer H., 2011, INFORM RES, V16; Johnson- Laird P. N., 1989, FDN COGNITIVE SCI, P469; Kipp MEI, 2011, CAN J INFORM LIB SCI, V35, P17; Kipp MEI, 2011, KNOWL ORGAN, V38, P245; LANDIS JR, 1977, BIOMETRICS, V33, P159, DOI 10.2307/2529310; Lin X., 2006, P 17 AM SOC INF SCI; Lu CM, 2010, J INF SCI, V36, P763, DOI 10.1177/0165551510386173; Marlow C., 2006, P COLL WEB TAGG WORK; Maron M. E., 1977, J AM SOC INFORM SCI, V28, P261; Michell G, 1998, J EDUC LIBR INF SCI, V39, P275, DOI 10.2307/40324303; Mohammed S., 2000, ORGAN RES METHODS, V3, P123, DOI DOI 10.1177/109442810032001; Munk TB, 2007, KNOWL ORGAN, V34, P115; National Information Standards Organization, 2005, GUID CONSTR FORM MAN; Norman D. A., 1983, MENTAL MODELS, P7; Pisanski J, 2010, J DOC, V66, P668, DOI 10.1108/00220411011066781; Pisanski J, 2010, J DOC, V66, P643, DOI 10.1108/00220411011066772; Quintarelli E., 2005, M ISKO INT SOC KNOWL; Rafferty P, 2007, ASLIB PROC, V59, P397, DOI 10.1108/00012530710817591; Rentsch J. R., 1994, ADV INTERDISCIPLINAR, V1, P223; Rolla PJ, 2009, LIBR RESOUR TECH SER, V53, P174; Rorissa A, 2010, J AM SOC INF SCI TEC, V61, P2230, DOI 10.1002/asi.21401; Schaffernicht M, 2011, EUR J OPER RES, V210, P57, DOI 10.1016/j.ejor.2010.09.003; Smith G., 2008, TAGGING PEOPLE POWER; Spalding T., 2007, THINGOLOGY BLOG; Spiteri L. F., 2007, WEBOLOGY, V4; STAGGERS N, 1993, INT J MAN MACH STUD, V38, P587, DOI 10.1006/imms.1993.1028; Stevens A., 1983, MENTAL MODELS; Strader R. C., 2009, LIBR RESOUR TECH SER, V53, P243; Stvilia B, 2010, J AM SOC INF SCI TEC, V61, P2477, DOI 10.1002/asi.21432; Thomas M, 2009, LIBR HI TECH, V27, P411, DOI 10.1108/07378830910988540; Toker S., 2012, THESIS PROQUEST UK; Vander Wal T., 2007, FOLKSONOMY; Voorbij HJ, 1998, J DOC, V54, P466, DOI 10.1108/EUM0000000007178; Wang PL, 1998, J AM SOC INFORM SCI, V49, P115, DOI 10.1002/(SICI)1097-4571(1998)49:2<115::AID-ASI3>3.0.CO;2-1; Winn W., 2001, HDB RES ED COMMUNICA, P79; Yi K, 2009, J DOC, V65, P872, DOI 10.1108/00220410910998906; Zhang Y, 2008, J AM SOC INF SCI TEC, V59, P2087, DOI 10.1002/asi.20915; Zhang Y, 2008, INFORM PROCESS MANAG, V44, P1330, DOI 10.1016/j.ipm.2007.09.002 62 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH AUG 2014 65 8 1675 1694 10.1002/asi.23077 20 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AL3XY WOS:000339066500011 J Teeter, MG; Milner, JS; Naudie, DDR; MacDonald, SJ Teeter, Matthew G.; Milner, Jaques S.; Naudie, Douglas D. R.; MacDonald, Steven J. Surface extraction can provide a reference for micro-CT analysis of retrieved total knee implants KNEE English Article Total knee arthroplasty; Retrieval analysis; Polyethylene wear; Micro-computed tomography POLYETHYLENE WEAR; METROLOGY; INSERTS; VOLUME Background: Quantitative measurements of damage and wear in orthopaedic components retrieved from patients during revision surgery can provide valuable information. However, to perform these measurements there needs to be an estimate of the original, unworn geometry of the component, often requiring multiple scans of the various sizes of components that have been retrieved. The objective of this study was to determine whether the articular and backside surfaces could be independently segmented from a micro-CT reconstruction of a tibial insert, such that a tibial insert of one thickness could be used as a reference for a tibial insert of a different thickness. Methods: New tibial inserts of a single width but with six different thicknesses were obtained and scanned with micro-CT. An automated method was developed to computationally segment the articular and backside surfaces of the components. Variability between intact and extracted components was determined. Results: The deviations between the comparisons of the extracted surfaces (range, 0.0004 to 0.010 mm) were less (p < 0.001) than the baseline deviation between the intact surfaces (range, 0.0002 to 0.053 mm). Conclusions: An extracted surface from one insert thickness could be used to accurately represent the surface a an insert of a different thickness. This greatly enhances the feasibility of performing retrieval studies using micro-CT as a quantitative tool, by reducing the costs and time associated with acquiring, scanning, and reconstructing multiple reference tibial insert geometries. (C) 2014 Elsevier B.V. All rights reserved. [Teeter, Matthew G.; Naudie, Douglas D. R.; MacDonald, Steven J.] London Hlth Sci Ctr, Div Orthopaed Surg, London, ON, Canada; [Milner, Jaques S.] Robarts Res Inst, Imaging Res Labs, London, ON N6A 5C1, Canada Teeter, MG (reprint author), 339 Windermere Rd, London, ON N6A 5A5, Canada. matthew.teeter@lhsc.on.ca Canadian Institutes of Health Research The authors thank Hristo Nikolov for his assistance in generating the milled tibial inserts. MGT is supported by a Fellowship from the Canadian Institutes of Health Research. Bills P, 2005, J PHYS CONF SER, V13, P316, DOI 10.1088/1742-6596/13/1/074; Engh CA, 2013, CLIN ORTHOP RELAT R, V471, P86, DOI 10.1007/s11999-012-2513-2; HOOD RW, 1983, J BIOMED MATER RES, V17, P829, DOI 10.1002/jbm.820170510; Knowlton CB, 2013, J BIOMED MATER RES B, V101B, P449, DOI 10.1002/jbm.b.32782; McKellop HA, 2007, BIOMATERIALS, V28, P5049, DOI 10.1016/j.biomaterials.2007.07.040; Muratoglu OK, 2003, CLIN ORTHOP RELAT R, P155, DOI 10.1097/01.blo.0000063604.67412.04; Naudie DDR, 2007, J AM ACAD ORTHOP SUR, V15, P53; Teeter MG, 2013, P I MECH ENG H, V227, P884, DOI 10.1177/0954411913486755; Teeter MG, 2011, J ARTHROPLASTY, V26, P497, DOI 10.1016/j.arth.2010.01.096; Teeter MG, 2012, CLIN ORTHOP RELAT R, V470, P1847, DOI 10.1007/s11999-011-2143-0; Teeter MG, 2010, J ARTHROPLASTY, V25, P330, DOI 10.1016/j.arth.2009.11.001 11 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0968-0160 1873-5800 KNEE Knee AUG 2014 21 4 801 805 10.1016/j.knee.2014.04.009 5 Orthopedics; Sport Sciences; Surgery Orthopedics; Sport Sciences; Surgery AL5CG WOS:000339150500002 J Hall, S; Beatty, S Hall, Sue; Beatty, Sharon Assessing spiritual well-being in residents of nursing homes for older people using the FACIT-Sp-12: a cognitive interviewing study QUALITY OF LIFE RESEARCH English Article Aged; Nursing homes; Spirituality; Questionnaires; Cognition; Elderly persons QUALITY-OF-LIFE; PALLIATIVE CARE PATIENTS; LONG-TERM-CARE; SCALE FACIT-SP; FUNCTIONAL ASSESSMENT; CANCER-PATIENTS; HEALTH-STATUS; END; QUESTIONNAIRE; VALIDATION To detect any problems with completion of the Functional Assessment of Chronic Illness Therapy Spiritual Well-being Scale (FACIT-Sp-12), to analyse the causes of such problems and to propose solutions to overcome them. We audio-recorded face-to-face interviews with 17 older people living in one of three nursing homes in London, UK, while they completed FACIT-Sp-12. We used cognitive interviewing methods to explore residents' responses. Our analysis was based on the Framework approach to qualitative analysis. We developed the framework of themes a priori. These comprised: comprehension of the question; retrieval from memory of relevant information; decision processes; and response processes. Ten residents completed the FACIT-Sp-12 with no missing data. Most problems involved comprehension and/or selecting response options. Twelve residents had problems with comprehension of at least one question, particularly with abstract concepts (e.g. harmony, productivity), or where there were assumptions inherent in the questions (e.g. they had an illness). When residents had problems comprehending the question, they also found it difficult to select a response. Thirteen residents had difficulties selecting responses (e.g. categories did not reflect their views or were not meaningful in the context of the statement). Some chose not to respond, others responded to the question as they understood it. The FACIT-Sp-12 could provide valuable insights into the spiritual concerns of nursing home residents; however, data may be neither valid nor reliable if they do not comprehend the questions as intended and respond appropriately. Providing clear and detailed instructions, including definitions of abstract concepts, may improve the validity of this measure for this population. [Hall, Sue; Beatty, Sharon] Kings Coll London, Dept Palliat Care Policy & Rehabil, Cicely Saunders Inst, London SE5 9PJ, England Hall, S (reprint author), Kings Coll London, Dept Palliat Care Policy & Rehabil, Cicely Saunders Inst, Bessemer Rd, London SE5 9PJ, England. sue.hall@kcl.ac.uk Cicely Saunders International from Dunhill Medical Trust We are grateful to the nursing home staff for their help, to the residents who took part in this study, and to Diana Opio, Cassie Goddard and Sreeparna Chattopadhyay for conducting the interviews. This work was supported by Cicely Saunders International, from a grant from the Dunhill Medical Trust. Our financial sponsors played no role in the design, execution, analysis and interpretation of data, or writing of the study. Albers G, 2010, J PAIN SYMPTOM MANAG, V40, P290, DOI 10.1016/j.jpainsymman.2009.12.012; Baltes PB, 2003, GERONTOLOGY, V49, P123, DOI 10.1159/000067946; Bergh I, 2011, PALLIATIVE MED, V25, P716, DOI 10.1177/0269216310395985; Bradshaw SA, 2012, AGE AGEING, V41, P429, DOI 10.1093/ageing/afs069; Bredle JM, 2011, RELIGIONS, V2, P77, DOI 10.3390/rel2010077; Canada AL, 2008, PSYCHO-ONCOL, V17, P908, DOI 10.1002/pon.1307; CELLA DF, 1993, J CLIN ONCOL, V11, P570; Cobb M, 2012, J PAIN SYMPTOM MANAG, V43, P1105, DOI 10.1016/j.jpainsymman.2011.06.017; Cotton SP, 1999, PSYCHO-ONCOL, V8, P429, DOI 10.1002/(SICI)1099-1611(199909/10)8:5<429::AID-PON420>3.0.CO;2-P; Daaleman TP, 2008, MED CARE, V46, P85; Edwards A, 2010, PALLIATIVE MED, V24, P753, DOI 10.1177/0269216310375860; Frank L, 2001, GERONTOLOGIST, V41, P778; Gijsberts MJHE, 2011, J PALLIAT MED, V14, P852, DOI 10.1089/jpm.2010.0356; Gijsberts MJHE, 2013, J AM MED DIR ASSOC, V14, P679, DOI 10.1016/j.jamda.2013.04.001; Hales S, 2012, J PAIN SYMPTOM MANAG, V43, P195, DOI 10.1016/j.jpainsymman.2011.03.018; Haugan Gørill, 2014, Int J Older People Nurs, V9, P65, DOI 10.1111/opn.12018; Heyland DK, 2010, CAN MED ASSOC J, V182, pE747, DOI 10.1503/cmaj.100131; Housen P, 2008, GERONTOLOGIST, V48, P158; Hulme C, 2004, AGE AGEING, V33, P504, DOI 10.1093/ageing/afh178; KATZMAN R, 1983, AM J PSYCHIAT, V140, P734; Koenig HG, 1998, J NERV MENT DIS, V186, P513, DOI 10.1097/00005053-199809000-00001; Lunder Urška, 2011, Curr Opin Support Palliat Care, V5, P273, DOI 10.1097/SPC.0b013e3283499b20; MacKinlay E., 2006, SPIRITUAL GROWTH CAR; Magaziner J, 2000, GERONTOLOGIST, V40, P663; Magsi H, 2005, J AM GERIATR SOC, V53, P295, DOI 10.1111/j.1532-5415.2005.53117.x; MAHONEY F I, 1965, Md State Med J, V14, P61; Mallinson S, 2002, SOC SCI MED, V54, P11, DOI 10.1016/S0277-9536(01)00003-X; Murtagh FEM, 2007, PALLIATIVE MED, V21, P87, DOI 10.1177/0269216306075367; Nolan S, 2011, EJPC, V18, P86; Orchard H., 2012, INT J PALLIATIVE NUR, V7, P541; OTOOLE DM, 1991, WESTERN J MED, V155, P384; Pargament KI, 2000, J CLIN PSYCHOL, V56, P519, DOI 10.1002/(SICI)1097-4679(200004)56:4<519::AID-JCLP6>3.0.CO;2-1; Pargament KI, 1998, J SCI STUD RELIG, V37, P710, DOI 10.2307/1388152; Paterson C, 2004, QUAL LIFE RES, V13, P871, DOI 10.1023/B:QURE.0000025586.51955.78; Peterman AH, 2002, ANN BEHAV MED, V24, P49, DOI 10.1207/S15324796ABM2401_06; Ritchie J., 1993, RES SOCIAL LIFE; Selman L, 2011, J PAIN SYMPTOM MANAG, V41, P728, DOI 10.1016/j.jpainsymman.2010.06.023; Sorensen L, 2001, INT J GERIATR PSYCH, V16, P615, DOI 10.1002/gps.390; Stefanek M, 2005, PSYCHO-ONCOL, V14, P450, DOI 10.1002/pon.861; Tanur J., 1984, COGNITIVE ASPECTS SU, P73; Tanur J. M., 1992, QUESTIONS QUESTIONS; Vivat B, 2008, PALLIATIVE MED, V22, P859, DOI 10.1177/0269216308095990; Weaver AJ, 2006, J RELIG HEALTH, V45, P208, DOI 10.1007/s10943-006-9011-3; Whitford HS, 2008, PSYCHO-ONCOLOGY, V17, P1121, DOI 10.1002/pon.1322; Willis GB, 2005, COGNITIVE INTERVIEWI 45 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0962-9343 1573-2649 QUAL LIFE RES Qual. Life Res. AUG 2014 23 6 1701 1711 10.1007/s11136-014-0627-6 11 Health Care Sciences & Services; Health Policy & Services; Public, Environmental & Occupational Health Health Care Sciences & Services; Public, Environmental & Occupational Health AL6YU WOS:000339280700004 J Abilhoa, WD; de Castro, LN Abilhoa, Willyan D.; de Castro, Leandro N. A keyword extraction method from twitter messages represented as graphs APPLIED MATHEMATICS AND COMPUTATION English Article Knowledge discovery; Text mining; Keyword extraction; Graph theory; Centrality measures; Twitter data CENTRALITY; TEXT Twitter is a microblog service that generates a huge amount of textual content daily. All this content needs to be explored by means of text mining, natural language processing, information retrieval, and other techniques. In this context, automatic keyword extraction is a task of great usefulness. A fundamental step in text mining techniques consists of building a model for text representation. The model known as vector space model, VSM, is the most well-known and used among these techniques. However, some difficulties and limitations of VSM, such as scalability and sparsity, motivate the proposal of alternative approaches. This paper proposes a keyword extraction method for tweet collections that represents texts as graphs and applies centrality measures for finding the relevant vertices (keywords). To assess the performance of the proposed approach, three different sets of experiments are performed. The first experiment applies TKG to a text from the Time magazine and compares its performance with that of the literature. The second set of experiments takes tweets from three different TV shows, applies TKG and compares it with TFIDF and KEA, having human classifications as benchmarks. Finally, these three algorithms are applied to tweets sets of increasing size and their computational running time is measured and compared. Altogether, these experiments provide a general overview of how TKG can be used in practice, its performance when compared with other standard approaches, and how it scales to larger data instances. The results show that TKG is a novel and robust proposal to extract keywords from texts, particularly from short messages, such as tweets. (C) 2014 Elsevier Inc. All rights reserved. [Abilhoa, Willyan D.; de Castro, Leandro N.] Univ Prebiteriana Mackenzie, Nat Comp Lab, Sao Paulo, Brazil Abilhoa, WD (reprint author), Univ Prebiteriana Mackenzie, Nat Comp Lab, Sao Paulo, Brazil. abilhoa.willyan@gmail.com; lnunes@mackenzie.br Mackenzie University; Mackpesquisa; CNPq; Capes [9315/13-6]; FAPESP The authors thank Mackenzie University, Mackpesquisa, CNPq, Capes (Proc. n. 9315/13-6) and FAPESP for the financial support. Asur S., 2010, 2010 IEEE WIC ACM IN, V1, P492; Bermingham A., 2011, USING TWITTER MONITO, P2; Berry M.W., 2004, SURVEY TEXT MINING; Chahine CA, 2008, SITIS 2008: 4TH INTERNATIONAL CONFERENCE ON SIGNAL IMAGE TECHNOLOGY AND INTERNET BASED SYSTEMS, PROCEEDINGS, P692, DOI 10.1109/SITIS.2008.47; Chowdhury GG, 2003, ANNU REV INFORM SCI, V37, P51, DOI 10.1002/aris.1440370103; Cohen AM, 2005, BRIEF BIOINFORM, V6, P57, DOI 10.1093/bib/6.1.57; Corley CD, 2010, INT J ENV RES PUB HE, V7, P596, DOI 10.3390/ijerph7020596; Datasift, 2012, BROWS DAT SOURC TWIT; Dennis S.F., 1967, INFORM RETRIEVAL CRI; Earle PS, 2011, ANN GEOPHYS-ITALY, V54, P708, DOI 10.4401/ag-5364; Ediger D., 2010, 2010 39 INT C PAR PR, P583, DOI 10.1109/ICPP.2010.66; Erckan G., 2007, INF PROCESSING MANAG; Feldman R, 2007, TEXT MINING HDB ADV; Frakes W. B., 1992, INFORM RETRIEVAL DAT; Frank E., 1999, P 16 INT JOINT C ART; Gross J.L., 2006, GRAPH THEORY ITS APP; Gupta Vishal, 2009, Journal of Emerging Technologies in Web Intelligence, V1, DOI 10.4304/jetwi.1.1.60-76; HAGE P, 1995, SOC NETWORKS, V17, P57, DOI 10.1016/0378-8733(94)00248-9; Han J., 2001, DATA MINING CONCEPTS; Hensman S., 2004, P STUD RES WORKSH HL, P49, DOI 10.3115/1614038.1614047; Hirschman L., 1997, CAMBRIDGE STUDIES NA, VXII-XIII, P409; Hotho A., 2005, LDV FORUM, V20, P19; Hulth A, 2003, PROCEEDINGS OF THE 2003 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P216; Jin W, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P807; Kaplan AM, 2010, BUS HORIZONS, V53, P59, DOI 10.1016/j.bushor.2009.09.003; Kietzmann JH, 2011, BUS HORIZONS, V54, P241, DOI 10.1016/j.bushor.2011.01.005; Kowalski G, 2011, INFORMATION RETRIEVAL ARCHITECTURE AND ALGORITHMS, P1, DOI 10.1007/978-1-4419-7716-8; Litvak M., 2008, P WORKSH MULT MULT I, P17, DOI 10.3115/1613172.1613178; Lott B., 2012, SURVEY KEYWORD EXTRA; Luhn H.P., 1957, IBM J RES DEV; Manning C., 1999, FDN STAT NATURAL LAN; Manning CD., 2008, INTRO INFORM RETRIEV, V1, P6; Matsuo Y., 2004, INT J ARTIF INTELL T, V4; NIEMINEN J, 1974, SCAND J PSYCHOL, V15, P332, DOI 10.1111/j.1467-9450.1974.tb00598.x; Ohsawa Y, 1998, P IEEE INT FORUM RES, P12, DOI 10.1109/ADL.1998.670375; Palshikar GK, 2007, LECT NOTES COMPUT SC, V4815, P503; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Rose S., 2010, AUTOMATIC KEYWORD EX; Russell M.A., 2013, MINING SOCIAL WEB DA; Safko L., 2010, SOCIAL MEDIA BIBLE T; Salton G., 1983, INTRO MODERN INFORM; Salton G., 1975, J AM SOC INF SCI; Schenker A., 2003, P 7 INT C DOC AN REC; Turney P.D., 1999, ERB1057 NRC, P1; Wasserman S, 1995, SOCIAL NETWORK ANAL; Witten IH, 1999, P 4 ACM C DIG LIB, P254, DOI 10.1145/313238.313437; Yoshida M., 2010, C MULT MULT INF ACC; Zhang C., 2008, J COMPUTATIONAL INFO, P1169; Zhang K., 2006, P 7 INT C WEB AG INF; ZHAO WX, 2011, P 49 ANN M ASS COMP, V1, P379; Zhou F., 2010, 2010 INT C NAT LANG, V1, P21; Zoie, 2008, P INT C WEB SEARCH W, DOI DOI 10.1145/1341531.1341557 52 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0096-3003 1873-5649 APPL MATH COMPUT Appl. Math. Comput. AUG 1 2014 240 308 325 10.1016/j.amc.2014.04.090 18 Mathematics, Applied Mathematics AK7LB WOS:000338608700027 J Lee, J; Kim, J; Lee, YG Lee, Jaehwa; Kim, Jhoon; Lee, Yun Gon Simultaneous retrieval of aerosol properties and clear-sky direct radiative effect over the global ocean from MODIS ATMOSPHERIC ENVIRONMENT English Article Aerosol; Direct radiative effect; Radiative forcing; Ocean; MODIS OPTICAL-PROPERTIES; ATMOSPHERE; CERES; TOP; INSTRUMENT; AERONET A unified satellite algorithm is presented to simultaneously retrieve aerosol properties (aerosol optical depth; AOD and aerosol type) and clear-sky shortwave direct radiative effect (hereafter, DREA) over ocean. The algorithm is applied to Moderate Resolution Imaging spectroradiometer (MODIS) observations for a period from 2003 to 2010 to assess the DREA over the global ocean. The simultaneous retrieval utilizes lookup table (LUT) containing both spectral reflectances and solar irradiances calculated using a single radiative transfer model with the same aerosol input data. This study finds that aerosols cool the top-of-atmosphere (TOA) and bottom-of-atmosphere (BOA) by 5.2 +/- 0.5 W/m(2) and 8.3 W/m(2), respectively, and correspondingly warm the atmosphere (hereafter, ATM) by 3.1 W/m(2). These quantities, solely based on the MODIS observations, are consistent with those of previous studies incorporating chemical transport model simulations and satellite observations. However, the DREAs at BOA and ATM are expected to be less accurate compared to that of TOA due to low sensitivity in retrieving aerosol type information, which is related with the atmospheric heating by aerosols, particularly in low AOD conditions; consequently, the uncertainties could not be quantified. Despite the issue in the aerosol type information, the present method allows us to confine the DREA attributed only to fine-mode dominant aerosols, which are expected to be mostly anthropogenic origin, in the range from -1.1 W/m(2) to -1.3 W/m(2) at TOA. Improvements in size-resolved AOD and SSA retrievals from current and upcoming satellite instruments are suggested to better assess the DREA, particularly at BOA and ATM, where aerosol absorptivity induces substantial uncertainty. (C) 2014 Elsevier Ltd. All rights reserved. [Lee, Jaehwa; Kim, Jhoon; Lee, Yun Gon] Yonsei Univ, Inst Earth Astron & Atmosphere, Dept Atmospher Sci, Brain Korea Program 21, Seoul 120749, South Korea; [Lee, Jaehwa] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA; [Lee, Jaehwa] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Lee, Yun Gon] Seoul Natl Univ, Sch Earth & Environm Sci, Seoul, South Korea Kim, J (reprint author), Yonsei Univ, Inst Earth Astron & Atmosphere, Dept Atmospher Sci, Brain Korea Program 21, Seoul 120749, South Korea. jhoonkim1@gmail.com Korea Meteorological Administration Research and Development Program [CATER 2012-2065]; Brain Korea 21 (BK21) program We thank the MODIS science team for providing valuable data used in this study. We also thank the principal investigators and their staff for establishing and maintaining the AERONET sites used in this investigation. This work was supported by the Korea Meteorological Administration Research and Development Program under Grant CATER 2012-2065. This research was partially supported by the Brain Korea 21 (BK21) program for Jhoon Kim and Jaehwa Lee. Chen L, 2011, ADV ATMOS SCI, V28, P973, DOI 10.1007/s00376-010-9210-4; Choi YS, 2009, ATMOS ENVIRON, V43, P5654, DOI 10.1016/j.atmosenv.2009.07.036; Chou MD, 2002, J ATMOS SCI, V59, P748, DOI 10.1175/1520-0469(2002)059<0748:ARFDFS>2.0.CO;2; Chou M.-D., 1992, J ATMOS SCI, V50, P673; Christopher SA, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL020510; Christopher SA, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2002GL014803; Chung CE, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD006356; COX C, 1954, J OPT SOC AM, V44, P838, DOI 10.1364/JOSA.44.000838; Dubovik O, 2002, J ATMOS SCI, V59, P590, DOI 10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2; Holben BN, 1998, REMOTE SENS ENVIRON, V66, P1, DOI 10.1016/S0034-4257(98)00031-5; Kaufman YJ, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023125; Kaufman YJ, 2005, IEEE T GEOSCI REMOTE, V43, P2886, DOI 10.1109/TGRS.2005.858430; Kaufman YJ, 2001, GEOPHYS RES LETT, V28, P3251, DOI 10.1029/2001GL013312; Kim J, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008201; Lee J, 2012, ATMOS CHEM PHYS, V12, P7087, DOI 10.5194/acp-12-7087-2012; Loeb NG, 2002, J CLIMATE, V15, P1474, DOI 10.1175/1520-0442(2002)015<1474:TOADRE>2.0.CO;2; Loeb NG, 2005, J CLIMATE, V18, P3506, DOI 10.1175/JCLI3504.1; Monahan AH, 2006, J CLIMATE, V19, P497, DOI 10.1175/JCLI3640.1; NAKAJIMA T, 1986, J QUANT SPECTROSC RA, V35, P13, DOI 10.1016/0022-4073(86)90088-9; Oh HR, 2013, J ATMOS SOL-TERR PHY, V102, P311, DOI 10.1016/j.jastp.2013.06.009; Remer LA, 2005, J ATMOS SCI, V62, P947, DOI 10.1175/JAS3385.1; Remer LA, 2006, ATMOS CHEM PHYS, V6, P237; Solomon S, 2007, CLIMATE CHANGE 2007: THE PHYSICAL SCIENCE BASIS, P19; Yu H, 2006, ATMOS CHEM PHYS, V6, P613; ZHANG J, 2005, J GEOPHYS RES, V110 25 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 1352-2310 1873-2844 ATMOS ENVIRON Atmos. Environ. AUG 2014 92 309 317 10.1016/j.atmosenv.2014.04.021 9 Environmental Sciences; Meteorology & Atmospheric Sciences Environmental Sciences & Ecology; Meteorology & Atmospheric Sciences AL0IO WOS:000338810800033 J Klug, H; Kmoch, A Klug, Hermann; Kmoch, Alexander A SMART groundwater portal: An OGC web services orchestration framework for hydrology to improve data access and visualisation in New Zealand COMPUTERS & GEOSCIENCES English Article WebGIS; Interoperability; Harmonisation; Metadata; CSW; SDI SEMANTIC WEB; WATER DATA; FUTURE; INTEROPERABILITY; REQUIREMENTS; DISCOVERY; RETRIEVAL; ONTOLOGY; GEODATA; SKOS Transboundary and cross-catchment access to hydrological data is the key to designing successful environmental policies and activities. Electronic maps based on distributed databases are fundamental for planning and decision making in all regions and for all spatial and temporal scales. Freshwater is an essential asset in New Zealand (and globally) and the availability as well as accessibility of hydrological information held by or held for public authorities and businesses are becoming a crucial management factor. Access to and visual representation of environmental information for the public is essential for attracting greater awareness of water quality and quantity matters. Detailed interdisciplinary knowledge about the environment is required to ensure that the environmental policy-making community of New Zealand considers regional and local differences of hydrological statuses, while assessing the overall national situation. However, cross-regional and inter-agency sharing of environmental spatial data is complex and challenging. In this article, we firstly provide an overview of the state of the art standard compliant techniques and methodologies for the practical implementation of simple, measurable, achievable, repeatable, and time-based (SMART) hydrological data management principles. Secondly, we contrast international state of the art data management developments with the present status for groundwater information in New Zealand. Finally, for the topics (i) data access and harmonisation, (ii) sensor web enablement and (iii) metadata, we summarise our findings, provide recommendations on future developments and highlight the specific advantages resulting from a seamless view, discovery, access, and analysis of interoperable hydrological information and metadata for decision making. (C) 2014 Elsevier Ltd. All rights reserved. [Klug, Hermann] Salzburg Univ, Interfac Dept Geoinformat Z GIS, A-5020 Salzburg, Austria; [Kmoch, Alexander] AUT Univ, GRC, Manukau 2025, New Zealand Klug, H (reprint author), Salzburg Univ, Interfac Dept Geoinformat Z GIS, Schillerstr 30,Bldg 15,3rd Floor, A-5020 Salzburg, Austria. hermann.klug@sbg.ac.at Ministry of Business, Innovation, and Employment (MBIE), New Zealand [07/2011-06/2017] The authors wish to acknowledge the six year funding (07/2011-06/2017) of the Ministry of Business, Innovation, and Employment (MBIE), New Zealand. We are also very thankful to the SMART project participants and volunteering regional councils that provide a variety of information and datasets. Works were mainly based on data from the three New Zealand regions Manawatu-Wanganui, Waikato, and Bay of Plenty. Furthermore we would like to acknowledge the on-going collaborations, e.g. through the Google Summer of Code programme, with the 52 degrees North initiative, Germany. Ames DP, 2012, ENVIRON MODELL SOFTW, V37, P146, DOI 10.1016/j.envsoft.2012.03.013; [Anonymous], 2003, 191152003 ISO, P140; [Anonymous], 2011, 19118 ISO, P69; [Anonymous], 2007, 19139 ISO, P111; [Anonymous], 2007, 19136 ISO, P394; [Anonymous], 2005, 19128 ISO, P76; [Anonymous], 2011, 19156 ISO, P46; [Anonymous], 2005, 19119 ISOTC 211, P67; Behr J., 2009, P 14 INT C 3D WEB TE, P127, DOI DOI 10.1145/1559764.1559784; Beran B, 2009, COMPUT GEOSCI-UK, V35, P753, DOI 10.1016/j.cageo.2008.02.017; Bermudez L, 2009, PROCEEDINGS OF THE 2009 INTERNATIONAL SYMPOSIUM ON COLLABORATIVE TECHNOLOGIES AND SYSTEMS, P36; Boisvert E, 2012, J HYDROINFORM, V14, P93, DOI 10.2166/hydro.2011.172; Brodaric B., 2011, GEOHYDRO 2011 QUEB C; Brodaric B., 2011, 10194R3 OGC; Broring A, 2011, SENSORS-BASEL, V11, P2652, DOI 10.3390/s110302652; Buccella A, 2011, COMPUT GEOSCI-UK, V37, P893, DOI 10.1016/j.cageo.2011.02.022; Cade-Menun BJ, 1997, COMMUN SOIL SCI PLAN, V28, P651, DOI 10.1080/00103629709369818; Carleton CJ, 2005, COMPUT GEOSCI-UK, V31, P393, DOI 10.1016/j.cageo.2004.10.007; Cruz SAB, 2012, COMPUT GEOSCI-UK, V47, P60, DOI 10.1016/j.cageo.2011.11.020; Erickson J, 2008, J DATABASE MANAGE, V19, P42, DOI 10.4018/jdm.2008070103; Fils D, 2009, COMPUT GEOSCI-UK, V35, P774, DOI 10.1016/j.cageo.2008.02.035; Goodall JL, 2008, ENVIRON MODELL SOFTW, V23, P404, DOI 10.1016/j.envsoft.2007.01.005; Hildebrandt D, 2010, COMPUT ENVIRON URBAN, V34, P484, DOI 10.1016/j.compenvurbsys.2010.05.003; Horrocks I., 2003, WEB SEMANTICS SCI SE, V1, P7, DOI DOI 10.1016/J.WEBSEM.2003.07.001; Horsburgh JS, 2009, ENVIRON MODELL SOFTW, V24, P879, DOI 10.1016/j.envsoft.2009.01.002; Huang MT, 2011, ENVIRON MODELL SOFTW, V26, P1309, DOI 10.1016/j.envsoft.2011.05.008; ISO/IEC, 1997, 1477211997VRML97 ISO; ISO/IEC, 2008, 19775122008X3D ISOIE; Kao S., 2011, J ENV MODELL SOFTW, V26, P1767; Kiehle C, 2006, COMPUT GEOSCI-UK, V32, P1746, DOI 10.1016/j.cageo.2006.04.002; Klug H., 2012, DISCOVER INSPIRE COM, P60; Klug H., 2014, J HYDROLOGY IN PRESS; Lacasta J, 2007, INFORM TECHNOL LIBR, V26, P39; Laniak GF, 2013, ENVIRON MODELL SOFTW, V39, P3, DOI 10.1016/j.envsoft.2012.09.006; LINZ LINZ), 2007, COORD APPR LOC INF; Ma XG, 2011, COMPUT GEOSCI-UK, V37, P1602, DOI 10.1016/j.cageo.2011.02.011; Morvan X, 2008, SCI TOTAL ENVIRON, V391, P1, DOI 10.1016/j.scitotenv.2007.10.046; OGC, 2011, OBS MEAS XML IMPL O; OGC, 2007, OPENGIS WEB PROC SER; OGC, 2010, OGC WCS 2 0 INT STAN; OGC, 2007, 19115 OGC ISO; OGC, 2010, 19142 OGC ISO; OGC, 2006, 19128 OGC ISO; OGC, 2007, OBS MEAS 2, P46; OGC, 2007, 19136 OGC ISO; OGC, 2012, OGC SENS OBS SERV IN; OGC, 2007, SENS MOD LANG SENSOR; OGC, 2011, OGC SWE COMM DAT MOD; Pourabdollah A, 2012, COMPUT GEOSCI-UK, V45, P270, DOI 10.1016/j.cageo.2011.11.026; Pulido JRG, 2006, KNOWL-BASED SYST, V19, P489, DOI 10.1016/j.knosys.2006.04.013; Ranatunga K, 2011, ENVIRON MODELL SOFTW, V26, P549, DOI 10.1016/j.envsoft.2010.10.005; Schmidt B., 2012, TRITURUS JAVA BASED; Stock K, 2012, COMPUT GEOSCI-UK, V45, P98, DOI 10.1016/j.cageo.2011.10.021; Tian Y, 2012, EXPERT SYST APPL, V39, P12522, DOI 10.1016/j.eswa.2012.04.061; White P.A., 2007, NZ GROUNDWATER UNPUB; White P.A., 2002, 200279 GNS SCI CLIEN; White P.A., 2011, NZ HYDR SOC C WELL; White P.A., 2006, J HYDROL-N Z, V45, P63; White P.A., 2010, 2009310 GNS SCI CONS; White P.A., 1999, J HYDROL NZ, V40, P49; White P.A, 2001, GROUNDWATER RESOURCE, P47; WOJDA P, 2013, J ENV MODELL SOFTW, V43, P109; X3DOM, 2012, X3DOM EXP OP SOURC F; Yang CW, 2010, COMPUT ENVIRON URBAN, V34, P264, DOI 10.1016/j.compenvurbsys.2010.04.001; Zealand M.f.t.E.N, 2013, 1114 MFTEN, P51; Zhang Y., 2011, P 19 INT C GEOINF GE; Zhao PS, 2009, COMPUT GEOSCI-UK, V35, P798, DOI 10.1016/j.cageo.2008.03.013 67 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0098-3004 1873-7803 COMPUT GEOSCI-UK Comput. Geosci. AUG 2014 69 78 86 10.1016/j.cageo.2014.04.016 9 Computer Science, Interdisciplinary Applications; Geosciences, Multidisciplinary Computer Science; Geology AL0KH WOS:000338815300009 J Zhang, TW; Yang, RS; Milliken, KL; Ruppel, SC; Pottorf, RJ; Sun, X Zhang, Tongwei; Yang, Rongsheng; Milliken, Kitty L.; Ruppel, Stephen C.; Pottorf, Robert J.; Sun, Xun Chemical and isotopic composition of gases released by crush methods from organic rich mudrocks ORGANIC GEOCHEMISTRY English Article Barnett Shale; Carbon isotopes; Gas storage; Mudrocks; Shale gas; Thermal maturity FORT-WORTH BASIN; MISSISSIPPIAN BARNETT SHALE; THERMOCHEMICAL SULFATE REDUCTION; OPEN-SYSTEM PYROLYSIS; NORTH-CENTRAL TEXAS; NATURAL-GAS; METHANE ADSORPTION; PETROLEUM GENERATION; THERMAL ALTERATION; HYDROCARBON GASES We report on the composition of mudrock gases released under vacuum by ball-mill rock crushing and pressure induced fracturing. Nine core samples from organic rich Barnett Shale were used in this study. TOC content varies from 3.3-7.9%; thermal maturity varies from 0.58-2.07 % Ro. Our results show that both thermal maturity and gas desorption contributes to changes in the CH4/CO2 ratio of gases released during rock crushing. CH4/CO2 ratios of these gases are lower at low thermal maturities and higher at high thermal maturities because more CH4 rich gas is generated at higher maturity levels. CH4/CO2 ratios decrease with longer rock crushing times because of the increase in the CO2 rich gas contribution. However, no obvious compositional fractionation occurs among C-1, C-2 and C-3 of crushed-rock gas and C1/C-2 and C-2/C-3 ratios remain nearly constant during crushing although these ratios are greatly increased overall when the level of thermal maturity is high. Gas geochemical parameters (C-1/C-2, C-2/C-3, and i-C-4/n-C-4) of released gas are good indicators of thermal maturation of organic rich shales. The CH4/CO2 ratio is a function of selectivity, partition coefficients and (possibly but less likely) sorption kinetics between CH4 and CO2 molecules in shales. Trends in released gas yield and gas chemistry during rock crushing relate to gas storage states and pore connectivity. The d C-13(1), d C-13(2) and d C-13(3) values of gas released from particles of coarser size (> 250 lm) are similar to values of gas produced from Barnett shales after hydraulic fracturing. CH4 dominated gas appears to be stored in larger connected pores and is therefore released during the initial stages of crushing. The carbon isotope values of methane, ethane and propane are heavier in the more thermally mature samples, suggesting that this released gas is representative of the gas chemistry of subsurface rocks. Retrieval of gas chemistry data from existing core samples provides information of great relevance for understanding deep shale gas reservoirs. (C) 2014 Elsevier Ltd. All rights reserved. [Zhang, Tongwei; Yang, Rongsheng; Milliken, Kitty L.; Ruppel, Stephen C.; Sun, Xun] Univ Texas Austin, Bur Econ Geol, Austin, TX 78712 USA; [Yang, Rongsheng] C&C Reservoirs Inc, Houston, TX USA; [Pottorf, Robert J.] ExxonMobil Upstream Res Co, Houston, TX USA Zhang, TW (reprint author), Univ Texas Austin, Bur Econ Geol, Austin, TX 78712 USA. tongwei.zhang@beg.utexas.edu ExxonMobil and the Jackson School of Geosciences; The University of Texas at Austin; Major State Basic Research Development Program of China [2012CB214701]; National Natural Science Foundation of China [41072092]; UT's Jackson School of Geosciences This article results from research results by the ExxonMobil/BEG unconventional reservoirs project, which is financially supported by ExxonMobil and the Jackson School of Geosciences, The University of Texas at Austin, the Major State Basic Research Development Program of China (Grant No. 2012CB214701), the National Natural Science Foundation of China (Grant No. 41072092). The construction of an in-house gas-tight rock crushing cell was made possible by start- up funds provided by UT's Jackson School of Geosciences. The Bureau's Mudrock Systems Research Laboratory (MSRL) consortium provided access to Barnett Shale cores and TOC and Rock- Eval data. In particular, we thank Dr. Bernhard Krooss and an anonymous reviewer for constructive suggestions that significantly improved the quality of the manuscript. We also thank, Associate Editor, Dr. Kenneth Peters for his constructive comments for the revised version. Publication authorized by the Director, Bureau of Economic Geology. Ambrose R.J., 2010, 131772 SPE; Ambrose R.J., 2011, SPE141416; Bernard S, 2012, MAR PETROL GEOL, V31, P70, DOI 10.1016/j.marpetgeo.2011.05.010; Bernard S, 2012, INT J COAL GEOL, V103, P3, DOI 10.1016/j.coal.2012.04.010; Berner U, 1996, ORG GEOCHEM, V24, P947, DOI 10.1016/S0146-6380(96)00090-3; BURNHAM AK, 1989, GEOCHIM COSMOCHIM AC, V53, P2649, DOI 10.1016/0016-7037(89)90136-1; Burruss RC, 2010, ORG GEOCHEM, V41, P1285, DOI 10.1016/j.orggeochem.2010.09.008; Chalmers GR, 2012, AAPG BULL, V96, P1099, DOI 10.1306/10171111052; Chen JH, 1996, ORG GEOCHEM, V25, P179, DOI 10.1016/S0146-6380(96)00125-8; CHUNG HM, 1988, CHEM GEOL, V71, P97, DOI 10.1016/0009-2541(88)90108-8; CLAYTON C, 1991, MAR PETROL GEOL, V8, P232, DOI 10.1016/0264-8172(91)90010-X; Curtis JB, 2002, AAPG BULL, V86, P1921; Dai JX, 2004, ORG GEOCHEM, V35, P405, DOI 10.1016/j.orggeochem.2004.01.006; Desbois G., 2009, E EARTH, V4, P1; ESPITALIE J, 1977, REV I FR PETROL, V32, P23; Gensterblum Y, 2014, FUEL, V115, P581, DOI 10.1016/j.fuel.2013.07.014; Hill RJ, 2007, AAPG BULL, V91, P445, DOI 10.1306/11030606014; HUIZINGA BJ, 1987, ORG GEOCHEM, V11, P591, DOI 10.1016/0146-6380(87)90012-X; JAMES AT, 1983, AAPG BULL, V67, P1176; Jarvie D.M., 2005, OKLAHOMA GEOLOGICAL, V110, P37; Jarvie DM, 2007, AAPG BULL, V91, P475, DOI 10.1306/12190606068; Javadpour F, 2007, J CAN PETROL TECHNOL, V46, P55; Javadpour F, 2009, J CAN PETROL TECHNOL, V48, P16; Ji LM, 2012, APPL GEOCHEM, V27, P2533, DOI 10.1016/j.apgeochem.2012.08.027; KROUSE HR, 1988, NATURE, V333, P415, DOI 10.1038/333415a0; LaFollette R.F., 2011, 140524 SPE; LEWAN MD, 1985, PHILOS T R SOC A, V315, P123, DOI 10.1098/rsta.1985.0033; Lewan MD, 2002, ORG GEOCHEM, V33, P1457, DOI 10.1016/S0146-6380(02)00182-1; Lorant F, 1998, CHEM GEOL, V147, P249, DOI 10.1016/S0009-2541(98)00017-5; Loucks RG, 2007, AAPG BULL, V91, P579, DOI 10.1306/11020606059; Loucks RG, 2009, J SEDIMENT RES, V79, P848, DOI 10.2110/jsr.2009.092; Loucks RG, 2012, AAPG BULL, V96, P1071, DOI 10.1306/08171111061; Milliken KL, 2012, AAPG BULL, V96, P1553, DOI 10.1306/12011111129; Milliken KL, 2013, AAPG BULL, V97, P177, DOI 10.1306/07231212048; Modica CJ, 2012, AAPG BULL, V96, P87, DOI 10.1306/04111110201; Mohr SH, 2011, ENERG POLICY, V39, P5550, DOI 10.1016/j.enpol.2011.04.066; Montgomery SL, 2005, AAPG BULL, V89, P155, DOI 10.1306/09170404042; Mosher K, 2013, INT J COAL GEOL, V109, P36, DOI 10.1016/j.coal.2013.01.001; Pan CC, 2010, ORG GEOCHEM, V41, P611, DOI 10.1016/j.orggeochem.2010.04.011; PETERS KE, 1986, AAPG BULL, V70, P318; Potter J, 2007, CHEM GEOL, V244, P186, DOI 10.1016/j.chemgeo.2007.06.014; Rodriguez ND, 2010, AAPG BULL, V94, P1641, DOI 10.1306/04061009119; Rooney MA, 1995, CHEM GEOL, V126, P219, DOI 10.1016/0009-2541(95)00119-0; Rooney M.A., 1995, ORGANIC GEOCHEMISTRY, P523; Ross DJK, 2009, MAR PETROL GEOL, V26, P916, DOI 10.1016/j.marpetgeo.2008.06.004; Rowe HD, 2008, CHEM GEOL, V257, P16, DOI 10.1016/j.chemgeo.2008.08.006; Ruppel S.C., 2008, SEDIMENTARY RECORD, V6, P4; SCHOELL M, 1983, AAPG BULL, V67, P2225; Sondergeld C.H., 2010, 131771 SPE; Strapoc D, 2010, AAPG BULL, V94, P1713, DOI 10.1306/06301009197; Tang Y, 2000, GEOCHIM COSMOCHIM AC, V64, P2673, DOI 10.1016/S0016-7037(00)00377-X; TANNENBAUM E, 1986, AAPG BULL, V70, P1156; Tilley B, 2011, AAPG BULL, V95, P1399; Tilley B, 2013, CHEM GEOL, V339, P194, DOI 10.1016/j.chemgeo.2012.08.002; Weniger P, 2012, INT J COAL GEOL, V93, P23, DOI 10.1016/j.coal.2012.01.009; Xia XY, 2012, GEOCHIM COSMOCHIM AC, V77, P489, DOI 10.1016/j.gca.2011.10.014; Zhang TW, 2008, ORG GEOCHEM, V39, P308, DOI 10.1016/j.orggeochem.2007.12.007; Zhang TW, 2007, ORG GEOCHEM, V38, P897, DOI 10.1016/j.orggeochem.2007.02.004; Zhang TW, 2012, ORG GEOCHEM, V47, P120, DOI 10.1016/j.orggeochem.2012.03.012; Zhang TW, 2001, GEOCHIM COSMOCHIM AC, V65, P2723, DOI 10.1016/S0016-7037(01)00601-9; Zhao H, 2007, AAPG BULL, V91, P535, DOI 10.1306/10270606060; Zumberge J, 2012, MAR PETROL GEOL, V31, P43, DOI 10.1016/j.marpetgeo.2011.06.009 62 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0146-6380 ORG GEOCHEM Org. Geochem. AUG 2014 73 16 28 10.1016/j.orggeochem.2014.05.003 13 Geochemistry & Geophysics Geochemistry & Geophysics AL2PM WOS:000338967500003 J Saneifar, H; Bonniol, S; Poncelet, P; Roche, M Saneifar, Hassan; Bonniol, Stephane; Poncelet, Pascal; Roche, Mathieu Enhancing passage retrieval in log files by query expansion based on explicit and pseudo relevance feedback COMPUTERS IN INDUSTRY English Article Information retrieval; Passage retrieval; Question answering; Query enrichment; Context learning; Log files Passage retrieval is usually defined as the task of searching for passages which may contain the answer for a given query. While these approaches are very efficient when dealing with texts, applied to log files (i.e. semi-structured data containing both numerical and symbolic information) they usually provide irrelevant or useless results. Nevertheless one appealing way for improving the results could be to consider query expansions that aim at adding automatically or semi-automatically additional information in the query to improve the reliability and accuracy of the returned results. In this paper, we present a new approach for enhancing the relevancy of queries during a passage retrieval in log files. It is based on two relevance feedback steps. In the first one, we determine the explicit relevance feedback by identifying the context of the requested information within a learning process. The second step is a new kind of pseudo relevance feedback. Based on a novel term weighting measure it aims at assigning a weight to terms according to their relatedness to queries. This measure, called TRQ (TERM RELATEDNESS TO QUERY), is used to identify the most relevant expansion terms. The main advantage of our approach is that is can be applied both on log files and documents from general domains. Experiments conducted on real data from logs and documents show that our query expansion protocol enables retrieval of relevant passages. (C) 2014 Elsevier B.V. All rights reserved. [Saneifar, Hassan; Poncelet, Pascal; Roche, Mathieu] Univ Montpellier 2, LIRMM, CNRS, Montpellier, France; [Saneifar, Hassan; Bonniol, Stephane] Satin Technol, Montpellier, France; [Roche, Mathieu] AgroParisTech, Irstea, UMR TETIS Cirad, Montpellier, France Roche, M (reprint author), MTD UMR TETIS, 500 Rue JF Breton, F-34093 Montpellier 5, France. hassan.saneifar@gmail.com; stephane.bonniol@satin-tech.com; poncelet@lirmm.fr; mathieu.roche@cirad.fr Agichtein E., 2001, P 10 INT WORLD WID W, P169, DOI 10.1145/371920.371976; Berger A., 2000, P 23 ANN INT ACM SIG, P192, DOI 10.1145/345508.345576; Bernhard D., 2010, P 23 INT C COMP LING, P54; Brill E., 2001, P 10 TEXT RETR C TRE, P393; Buscaldi D, 2010, J INTELL INF SYST, V34, P113, DOI 10.1007/s10844-009-0082-y; Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860; Chalendar G., 2002, P 11 TEXT RETRIEVAL, P1; Clarke CLA, 2000, INFORM PROCESS MANAG, V36, P291, DOI 10.1016/S0306-4573(99)00017-5; Clarke C.L.A., 2001, P 24 ANN INT ACM SIG, P358, DOI 10.1145/383952.384024; Cui H., 2005, P 28 ANN INT ACM SIG, P400, DOI 10.1145/1076034.1076103; Daille B., 1996, BALANCING ACT COMBIN, P49; Fellbaum C, 1998, WORDNET ELECT LEXICA; Ferres D., 2006, P MULT QUEST ANSW WO, P69, DOI 10.3115/1708097.1708111; Gillard L, 2006, P 3 FRENCH INF RETR, P193; Grefenstette G., 1992, P 15 ANN INT ACM SIG, P89, DOI 10.1145/133160.133181; Guiasu S., 1977, INFORM THEORY APPL; Ittycheriah A., 2002, P 11 TEXT RETR C TRE, P1; J Wu, 2011, STUDY ONTOLOGY BASED; Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561; Keikha M, 2011, LECT NOTES COMPUT SC, V6611, P436, DOI 10.1007/978-3-642-20161-5_43; Khalid M., 2008, COLING 2008, P26, DOI 10.3115/1641451.1641455; Kosseim L, 2008, DATA KNOWL ENG, V66, P53, DOI 10.1016/j.datak.2007.07.010; Lamjiri A.K., 2007, 8 INT C COMP ASS INF, P659; Lee G.G., 2001, P TREC 10, P442; Lee KS, 2013, INFORM PROCESS MANAG, V49, P792, DOI 10.1016/j.ipm.2013.01.001; Li XY, 2008, LECT NOTES COMPUT SC, V4956, P463; Light M., 2001, NATURAL LANGUAGE ENG, V7, P325; Ligozat AL, 2012, LECT NOTES COMPUT SC, V7499, P689, DOI 10.1007/978-3-642-32790-2_84; Lin J, 2007, ACM T INFORM SYST, V25, DOI 10.1145/1229179.1229180; Llopis F., 2002, P C MULT SUMM QUEST, V19, P1, DOI 10.3115/1118845.1118851; Lv Y., 2009, P 18 ACM C INF KNOWL, P255, DOI 10.1145/1645953.1645988; Manning C, 2008, INTRO INFORM RETRIEV; Melucci M., 2011, ADV TOPICS INFORM RE; Monz C., 2003, THESIS U AMSTERDAM A; Nie J.-Y., 2005, P 14 ACM INT C INF K, P688, DOI 10.1145/1099554.1099725; OCONNOR J, 1975, INFORM PROCESS MANAG, V11, P155, DOI 10.1016/0306-4573(75)90004-7; Ofoghi B., 2006, P 29 AUSTR COMP SCI, P95; Pasca M.A., 2001, P 24 ANN INT ACM SIG, P366, DOI 10.1145/383952.384025; Rocchio J. J., 1971, SMART RETRIEVAL SYST; Roche Mathieu, 2009, JoDI - Journal of Digital Information, V10; Salton G., 1997, IMPROVING RETRIEVAL, P355; Salton G., 1987, TERM WEIGHTING APPRO; Salton G., 1986, INTRO MODERN INFORM; Saneifar H, 2011, COMM COM INF SC, V128, P121; Saneifar H., 2011, THESIS U MONTPELLIER, V2; Saneifar H, 2009, LECT NOTES COMPUT SC, V5690, P769; Soboroff I., 2004, P TREC 2004 13 TEXT, P1; Tellex S., 2003, P 26 ANN INT ACM SIG, P41; Tiedemann J., 2005, P EMNLP 2005 VANC, P939, DOI 10.3115/1220575.1220693; Tiedemann J., 2008, P 2 WORKSH INF RETR, P17; Tiedemann J., 2007, P C REC ADV NAT LANG, P1; Van der Plas L., 2008, P COLING WORKSH INF, P50; Van Rijsbergen C. J., 1979, INFORM RETRIEVAL; Voorhees Ellen M., 1999, P 8 TEXT RETR C TREC, P77; Wade C., 2005, PASSAGE RETRIEVAL EV; Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202; Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138; Yang H., 2002, P 11 TEXT RETR C TRE, P155 58 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0166-3615 1872-6194 COMPUT IND Comput. Ind. AUG 2014 65 6 937 951 10.1016/j.compind.2014.02.010 15 Computer Science, Interdisciplinary Applications Computer Science AK4QW WOS:000338410200003 J Flegal, KE; Marin-Gutierrez, A; Ragland, JD; Ranganath, C Flegal, Kristin E.; Marin-Gutierrez, Alejandro; Ragland, J. Daniel; Ranganath, Charan Brain Mechanisms of Successful Recognition through Retrieval of Semantic Context JOURNAL OF COGNITIVE NEUROSCIENCE English Article MEDIAL TEMPORAL-LOBES; EPISODIC MEMORY RETRIEVAL; POSTERIOR PARIETAL CORTEX; EVENT-RELATED FMRI; HIPPOCAMPAL ACTIVITY; COGNITIVE NEUROSCIENCE; RECOLLECTION; FAMILIARITY; ITEM; CONSCIOUSNESS Episodic memory is associated with the encoding and retrieval of context information and with a subjective sense of reexperiencing past events. The neural correlates of episodic retrieval have been extensively studied using fMRI, leading to the identification of a "general recollection network" including medial temporal, parietal, and prefrontal regions. However, in these studies, it is difficult to disentangle the effects of context retrieval from recollection. In this study, we used fMRI to determine the extent to which the recruitment of regions in the recollection network is contingent on context reinstatement. Participants were scanned during a cued recognition test for target words from encoded sentences. Studied target words were preceded by either a cue word studied in the same sentence ( thus congruent with encoding context) or a cue word studied in a different sentence ( thus incongruent with encoding context). Converging fMRI results from independently defined ROIs and whole-brain analysis showed regional specificity in the recollection network. Activity in hippocampus and parahippocampal cortex was specifically increased during successful retrieval following congruent context cues, whereas parietal and prefrontal components of the general recollection network were associated with confident retrieval irrespective of contextual congruency. Our findings implicate medial temporal regions in the retrieval of semantic context, contributing to, but dissociable from, recollective experience. [Flegal, Kristin E.; Ragland, J. Daniel; Ranganath, Charan] Univ Calif Davis, Davis, CA 95618 USA; [Marin-Gutierrez, Alejandro] Univ Salamanca, E-37008 Salamanca, Spain Flegal, KE (reprint author), Univ Calif Davis, Ctr Neurosci, 1544 Newton Ct, Davis, CA 95618 USA. kflegal@ucdavis.edu NIH [F32MH096469, R01MH083734, R01MH068721, R01MH084895]; Spanish Ministry of Science and Innovation [PSI2008-05607/PSIC] This work was supported by NIH grants F32MH096469, R01MH083734, R01MH068721, and R01MH084895. Alejandro Marin-Gutierrez was supported by a grant from the Spanish Ministry of Science and Innovation (Project PSI2008-05607/PSIC). We thank Tamara Swaab and Megan Boudewyn for helpful discussions and Maria Montchal and Manoj Doss for assistance with stimulus preparation and data collection. Aggleton JP, 1999, BEHAV BRAIN SCI, V22, P425; Berryhill ME, 2009, COGN NEUROPSYCHOL, V26, P606, DOI 10.1080/02643290903534150; Cabeza R, 2008, NEUROPSYCHOLOGIA, V46, P1813, DOI 10.1016/j.neuropsychologia.2008.03.019; Ciaramelli E, 2010, J NEUROSCI, V30, P4943, DOI 10.1523/JNEUROSCI.1209-09.2010; Dale AM, 1999, HUM BRAIN MAPP, V8, P109, DOI 10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W; Davachi L, 2006, CURR OPIN NEUROBIOL, V16, P693, DOI 10.1016/j.conb.2006.10.012; Davidson PSR, 2008, NEUROPSYCHOLOGIA, V46, P1743, DOI 10.1016/j.neuropsychologia.2008.01.011; Diana RA, 2013, J EXP PSYCHOL GEN, V142, P1287, DOI 10.1037/a0034029; Diana RA, 2012, NEUROPSYCHOLOGIA, V50, P3062, DOI 10.1016/j.neuropsychologia.2012.07.035; Diana RA, 2007, TRENDS COGN SCI, V11, P379, DOI 10.1016/j.tics.2007.08.001; Eacott MJ, 2005, Q J EXP PSYCHOL-B, V58, P202, DOI 10.1080/02724990444000203; Eichenbaum H, 2007, ANNU REV NEUROSCI, V30, P123, DOI 10.1146/annurev.neuro.30.051606.094328; Hannula DE, 2009, NEURON, V63, P592, DOI 10.1016/j.neuron.2009.08.025; Hayama HR, 2012, J COGNITIVE NEUROSCI, V24, P1127, DOI 10.1162/jocn_a_00202; Hsieh LT, 2014, NEURON, V81, P1165, DOI 10.1016/j.neuron.2014.01.015; Hutchinson JB, 2009, LEARN MEMORY, V16, P343, DOI 10.1101/lm.919109; Johnson JD, 2007, CEREB CORTEX, V17, P2507, DOI 10.1093/cercor/bhl156; JOHNSON MK, 1993, PSYCHOL BULL, V114, P3, DOI 10.1037//0033-2909.114.1.3; Kafkas A, 2012, NEUROPSYCHOLOGIA, V50, P3080, DOI 10.1016/j.neuropsychologia.2012.08.001; Kahn I, 2004, J NEUROSCI, V24, P4172, DOI 10.1523/JNEUROSCI.0624-04.2004; KINTSCH W, 1988, PSYCHOL REV, V95, P163, DOI 10.1037/0033-295X.95.2.163; LIGHT LL, 1970, J VERB LEARN VERB BE, V9, P1, DOI 10.1016/S0022-5371(70)80002-0; MAYES AR, 1985, CORTEX, V21, P167; MAYES AR, 1992, Q J EXP PSYCHOL-A, V45, P265; Montaldi D, 2010, HIPPOCAMPUS, V20, P1291, DOI 10.1002/hipo.20853; MOSCOVITCH M, 1995, J CLIN EXP NEUROPSYC, V17, P276, DOI 10.1080/01688639508405123; Nadel L., 2008, HIPPOCAMPAL PLACE FI, P3; Nelson D. L., 1998, U S FLORIDA WORD ASS; Quamme JR, 2004, NEUROPSYCHOLOGIA, V42, P672, DOI 10.1016/j.neuropsychologia.2003.09.008; Ranganath C, 2010, CURR DIR PSYCHOL SCI, V19, P131, DOI 10.1177/0963721410368805; Ranganath C, 2010, HIPPOCAMPUS, V20, P1263, DOI 10.1002/hipo.20852; Park H, 2008, CEREB CORTEX, V18, P868, DOI 10.1093/cercor/bhm130; Rugg MD, 2013, CURR OPIN NEUROBIOL, V23, P255, DOI 10.1016/j.conb.2012.11.005; Schacter DL, 1998, ANNU REV PSYCHOL, V49, P289, DOI 10.1146/annurev.psych.49.1.289; SCOVILLE WB, 1957, J NEUROL NEUROSUR PS, V20, P11, DOI 10.1136/jnnp.20.1.11; Shattuck DW, 2008, NEUROIMAGE, V39, P1064, DOI 10.1016/j.neuroimage.2007.09.031; Shimamura AP, 1995, ANN NY ACAD SCI, V769, P151, DOI 10.1111/j.1749-6632.1995.tb38136.x; Shimamura AP, 2011, COGN AFFECT BEHAV NE, V11, P277, DOI 10.3758/s13415-011-0031-4; Simons JS, 2008, NEUROPSYCHOLOGIA, V46, P1185, DOI 10.1016/j.neuropsychologia.2007.07.024; Simons JS, 2010, CEREB CORTEX, V20, P479, DOI 10.1093/cercor/bhp116; Smith CN, 2011, J NEUROSCI, V31, P15693, DOI 10.1523/JNEUROSCI.3438-11.2011; Spaniol J, 2009, NEUROPSYCHOLOGIA, V47, P1765, DOI 10.1016/j.neuropsychologia.2009.02.028; SQUIRE LR, 1992, PSYCHOL REV, V99, P195, DOI 10.1037//0033-295X.99.2.195; TULVING E, 1971, J EXP PSYCHOL, V87, P116, DOI 10.1037/h0030186; Tulving E, 1983, ELEMENTS EPISODIC ME; Tulving E, 2002, ANNU REV PSYCHOL, V53, P1, DOI 10.1146/annurev.psych.53.100901.135114; TULVING E, 1973, PSYCHOL REV, V80, P352, DOI 10.1037/h0020071; TULVING E, 1985, CAN PSYCHOL, V26, P1, DOI 10.1037/h0080017; UNDERWOOD BJ, 1965, J EXP PSYCHOL, V70, P122, DOI 10.1037/h0022014; Vann SD, 2009, P NATL ACAD SCI USA, V106, P5442, DOI 10.1073/pnas.0812097106; Vilberg KL, 2008, NEUROPSYCHOLOGIA, V46, P1787, DOI 10.1016/j.neuropsychologia.2008.01.004; Wagner AD, 2005, TRENDS COGN SCI, V9, P445, DOI 10.1016/j.tics.2005.07.001; Wais PE, 2011, HIPPOCAMPUS, V21, P9, DOI 10.1002/hipo.20716; Wang WC, 2013, BEHAV BRAIN RES, V254, P102, DOI 10.1016/j.bbr.2013.05.029; Wheeler M A, 1995, J Int Neuropsychol Soc, V1, P525; Wheeler ME, 2003, J NEUROSCI, V23, P3869; Wixted JT, 2011, TRENDS COGN SCI, V15, P210, DOI 10.1016/j.tics.2011.03.005; Yarkoni T, 2011, NAT METHODS, V8, P665, DOI [10.1038/nmeth.1635, 10.1038/NMETH.1635]; Yazar Y, 2012, CORTEX, V48, P1381, DOI 10.1016/j.cortex.2012.05.011; Yazar Y., CONTINUOUS THE UNPUB; YONELINAS AP, 1994, J EXP PSYCHOL LEARN, V20, P1341, DOI 10.1037/0278-7393.20.6.1341; Yonelinas AP, 2010, HIPPOCAMPUS, V20, P1178, DOI 10.1002/hipo.20864; Yonelinas AP, 2001, J EXP PSYCHOL GEN, V130, P361, DOI 10.1037//0096-3445.130.3.361; Yu SS, 2012, HIPPOCAMPUS, V22, P1429, DOI 10.1002/hipo.20982 64 0 0 MIT PRESS CAMBRIDGE 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA 0898-929X 1530-8898 J COGNITIVE NEUROSCI J. Cogn. Neurosci. AUG 2014 26 8 1694 1704 10.1162/jocn_a_00587 11 Neurosciences; Psychology, Experimental Neurosciences & Neurology; Psychology AK1RY WOS:000338194800008 J Cheong, H; Shu, LH Cheong, Hyunmin; Shu, L. H. Retrieving Causally Related Functions From Natural-Language Text for Biomimetic Design JOURNAL OF MECHANICAL DESIGN English Article biomimetic design; biologically inspired design; conceptual design; creativity and concept generation BIOLOGICALLY INSPIRED DESIGN; SIMILARITY; CONSTRAINT; SYSTEM Identifying biological analogies is a significant challenge in biomimetic (biologically inspired) design. This paper builds on our previous work on finding biological phenomena in natural-language text. Specifically, a rule-based computational technique is used to identify biological analogies that contain causal relations. Causally related functions describe how one function is enabled by another function, and support the transfer of functional structure from analogies to design solutions. The causal-relation retrieval method uses patterns of syntactic information that represent causally related functions in individual sentences, and scored F-measures of 0.73-0.85. In a user study, novice designers found that of the total search matches, proportionally more of the matches obtained with the causal-relation retrieval method were relevant to design problems than those obtained with a single verb-keyword search. In addition, matches obtained with the causal-relation retrieval method increased the likelihood of using functional association to develop design concepts. Finally, the causal-relation retrieval method enables automatic extraction of biological analogies at the sentence level from a large amount of natural-language sources, which could support other approaches to biologically inspired design that require the identification of interesting biological phenomena. [Cheong, Hyunmin; Shu, L. H.] Univ Toronto, Dept Mech & Ind Engn, Toronto, ON M5S 3G8, Canada Shu, LH (reprint author), Univ Toronto, Dept Mech & Ind Engn, 5 Kings Coll Rd, Toronto, ON M5S 3G8, Canada. cheong@mie.utoronto.ca; shu@mie.utoronto.ca Natural Sciences and Engineering Research Council of Canada The authors gratefully acknowledge the financial support of Natural Sciences and Engineering Research Council of Canada. AlGeddawy T, 2012, J MECH DESIGN, V134, DOI 10.1115/1.4006439; Altshuller G., 1984, CREATIVITY EXACT SCI; Bandi P, 2013, J MECH DESIGN, V135, DOI 10.1115/1.4024722; Bejgerowski W, 2009, J MECH DESIGN, V131, DOI 10.1115/1.3116258; Biomimicry Institute, 2008, ASK NAT BIOM DES POR; Borsci S, 2009, ASSIST TECHN RES SER, V25, P421, DOI 10.3233/978-1-60750-042-1-421; Cer D., 2010, P INT C LANG RES EV; Chakrabarti A, 2005, AI EDAM, V19, P113, DOI 10.1017/S0890060405050109; Charniak E., 2005, P 43 ANN M ASS COMP, P173, DOI 10.3115/1219840.1219862; Cheong H, 2014, AI EDAM, V28, P27, DOI 10.1017/S0890060413000486; Cheong H., 2011, J MECH DESIGN, V133; Cheong H., 2012, P ASME IDETC CHIC IL; Cheong HM, 2013, DESIGN STUD, V34, P706, DOI 10.1016/j.destud.2013.02.002; Cheong HM, 2013, CIRP ANN-MANUF TECHN, V62, P111, DOI 10.1016/j.cirp.2013.03.064; Chiu I, 2007, AI EDAM, V21, P45, DOI 10.1017/S0890060407070138; CLEMENT CA, 1991, COGNITIVE SCI, V15, P89, DOI 10.1207/s15516709cog1501_3; de Marneffe M-C., 2006, P INT C LANG RES EV; Delucchi KL, 2004, AM J PSYCHIAT, V161, P1159, DOI 10.1176/appi.ajp.161.7.1159; Egan PF, 2013, J MECH DESIGN, V135, DOI 10.1115/1.4024227; Garcia D, 1997, LECT NOTES ARTIF INT, V1319, P347; GENTNER D, 1983, COGNITIVE SCI, V7, P155, DOI 10.1207/s15516709cog0702_3; GENTNER D, 1993, COGNITIVE PSYCHOL, V25, P524, DOI 10.1006/cogp.1993.1013; Girju R., 2003, P ACL WORKSH MULT SU, P76; Goel A. K., 2011, COMPUT AIDED DESIGN, V44, P879; Goel AK, 2009, AI EDAM, V23, P23, DOI 10.1017/S0890060409000080; Hacco E., 2002, P ASME IDETC MONTR C; HOLYOAK KJ, 1989, COGNITIVE SCI, V13, P295, DOI 10.1207/s15516709cog1303_1; Jurafsky D., 2009, SPEECH LANGUAGE PROC; Ke J., 2009, P ASME IDETC SAN DIE; Ke J., 2010, P ASME IDETC MONTR C; Khoo C., 2000, P 38 ANN M ASS COMP; Lodish H, 2000, MOL CELL BIOL; Madangopal R, 2005, J MECH DESIGN, V127, P809, DOI 10.1115/1.1899690; Mak TW, 2008, RES ENG DES, V19, P21, DOI 10.1007/s00163-007-0041-y; Manning C., 1999, FDN STAT NATURAL LAN; Marcus M. P., 1993, ASS COMPUTATIONAL LI, V19, P313; MARKMAN AB, 1993, COGNITIVE PSYCHOL, V25, P431, DOI 10.1006/cogp.1993.1011; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; Nagel RL, 2008, J MECH DESIGN, V130, DOI 10.1115/1.2992062; Ozkan O, 2013, DESIGN STUD, V34, P161, DOI 10.1016/j.destud.2012.11.006; Purves WK, 2001, LIFE SCI BIOL; Sartori J, 2010, AI EDAM, V24, P483, DOI 10.1017/S0890060410000351; Shu LH, 2010, AI EDAM, V24, P507, DOI 10.1017/S0890060410000363; Shu LH, 2011, CIRP ANN-MANUF TECHN, V60, P673, DOI 10.1016/j.cirp.2011.06.001; Stanford B, 2012, J MECH DESIGN, V134, DOI 10.1115/1.4006438; Stone RB, 2000, J MECH DESIGN, V122, P359, DOI 10.1115/1.1289637; Ueda K, 2001, CIRP ANN-MANUF TECHN, V50, P319, DOI 10.1016/S0007-8506(07)62130-1; Vandevenne D., 2012, P ASME IDETC CHICA I; Vattam S., 2011, P ASME IDETC WASH DC; Vincent JFV, 2002, PHILOS T ROY SOC A, V360, P159, DOI 10.1098/rsta.2001.0923 50 0 0 ASME NEW YORK TWO PARK AVE, NEW YORK, NY 10016-5990 USA 1050-0472 J MECH DESIGN J. Mech. Des. AUG 2014 136 8 081008 10.1115/1.4027494 10 Engineering, Mechanical Engineering AK5ZV WOS:000338507300008 J Angelini, M; Ferro, N; Santucci, G; Silvello, G Angelini, Marco; Ferro, Nicola; Santucci, Giuseppe; Silvello, Gianmaria VIRTUE: A visual tool for information retrieval performance evaluation and failure analysis JOURNAL OF VISUAL LANGUAGES AND COMPUTING English Article Information retrieval; Experimental evaluation; Visual analytics; Performance analysis; Failure analysis GRADED RELEVANCE; IR; RANKING; SYSTEMS; HISTORY Objective: Information Retrieval (IR) is strongly rooted in experimentation where new and better ways to measure and interpret the behavior of a system are key to scientific advancement. This paper presents an innovative visualization environment: Visual Information Retrieval Tool for Upfront Evaluation (VIRTUE), which eases and makes more effective the experimental evaluation process. Methods: VIRTUE supports and improves performance analysis and failure analysis. Performance analysis: VIRTUE offers interactive visualizations based on well-known IR metrics allowing us to explore system performances and to easily grasp the main problems of the system. Failure analysis: VIRTUE develops visual features and interaction, allowing researchers and developers to easily spot critical regions of a ranking and grasp possible causes of a failure. Results: VIRTUE was validated through a user study involving IR experts. The study reports on (a) the scientific relevance and innovation and (b) the comprehensibility and efficacy of the visualizations. Conclusion: VIRTUE eases the interaction with experimental results, supports users in the evaluation process and reduces the user effort. Practice: VIRTUE will be used by IR analysts to analyze and understand experimental results. Implications: VIRTUE improves the state-of-the-art in the evaluation practice and integrates visualization and IR research fields in an innovative way. (C) 2014 Elsevier Ltd. All rights reserved. [Angelini, Marco; Santucci, Giuseppe] Univ Roma La Sapienza, Rome, Italy; [Ferro, Nicola; Silvello, Gianmaria] Univ Padua, I-35100 Padua, Italy Silvello, G (reprint author), Univ Padua, I-35100 Padua, Italy. silvello@dei.unipd.it PROMISE network of excellence4 project, as part of the 7th Framework Program of the European Commission [258191] The PROMISE network of excellence4 (Contract no. 258191) project, as part of the 7th Framework Program of the European Commission, has partially supported the reported work. The authors would like to thank the IR evaluation experts involved in the validation study, who provided valuable suggestions about how to improve VIRTUE. Agosti M., 2012, LECT NOTES COMPUTER, V7488, P88; Agosti M., 2012, SIGIR FORUM, V46; Agosti M., 2010, P 3 INT WORKSH EV IN, P16; Agosti M, 2009, CHANDOS INF PROF SER, P93; Angelini M., 2012, LECT NOTES COMPUTER, V7488, P112; Angelini M., 2012, P 4 S INF INT CONT I, P195; Angelini M, 2013, LECT NOTES COMPUT SC, V8138, P29, DOI 10.1007/978-3-642-40802-1_4; Balog K., 2012, TRENDS INF RETR, V6, P127; Banks D., 1999, Information Retrieval, V1, DOI 10.1023/A:1009984519381; Behrisch M., 2013, P 4 INT WORKSH VIS A; Buckley C., 2000, P 23 ANN INT ACM SIG, P33, DOI DOI 10.1145/345508.345543; Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009132; Buettcher S., 2010, INFORM RETRIEVAL IMP; Burnett S., 2006, ENTERPRISE SEARCH RE; Candela L., 2007, DELOS DIGITAL LIB RE; Carterette B, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P436, DOI 10.1145/1571941.1572017; Catarci T., 2012, LECT NOTES COMPUTER, V7488; Cleverdon C.W., 1997, READINGS INFORMATION, P47; Crestani F, 2004, INFORM PROCESS MANAG, V40, P269, DOI 10.1016/S0306-4573(02)00120-6; Croft W.B., 2009, SEARCH ENGINES INFOR; Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Derthick M., 2003, P INFOVIS 03 SEATTL, P137; Di Buccio E., 2011, P 1 EUR WORKSH HUM C, P11; Di Buccio E, 2011, LECT NOTES COMPUT SC, V6941, P119, DOI 10.1007/978-3-642-23708-9_14; Fan WG, 2004, INFORM PROCESS MANAG, V40, P587, DOI 10.1016/j.ipm.2003.08.001; Ferro N., 2011, P 2 INT WORKSH VIS A, P21; Ferro N, 2011, PROCEDIA COMPUT SCI, V4, P740, DOI 10.1016/j.procs.2011.04.078; Fowler R.H., 1991, P 14 ANN INT ACM SIG, P142, DOI 10.1145/122860.122874; Fox E.A., 2012, THEORETICAL FDN DIGI; Hansen P., 2012, CEUR WORKSHOP P, V909, P55; Harman D, 2009, INFORM RETRIEVAL, V12, P615, DOI 10.1007/s10791-009-9101-4; Harman D.K., 2005, TREC EXPT EVALUATION; Harman D.K., 2008, P 2 WORKSH AN NOIS U, P1, DOI 10.1145/1390749.1390750; Harman D.K., 2011, INFORM RETRIEVAL EVA; Hearst MA, 2011, COMMUN ACM, V54, P60, DOI [10.1145/2018396.2018114, 10.1145/2018396.2018414]; Hull D, 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Kando N., 2011, P 9 NTCIR WORKSH M E; Keen E.M., 1971, EVALUATION PARAMETER, P74; Kekalainen J, 2005, INFORM PROCESS MANAG, V41, P1019, DOI [10.1016/j.ipm.2005.01.004, 10.1016/j.ipm.2004.01.004]; Kekalainen J, 2002, J AM SOC INF SCI TEC, V53, P1120, DOI 10.1002/asi.10137; Kendall M. G., 1948, RANK CORRELATION MET; Keskustalo H., 2008, P 31 ANN INT ACM SIG, P675, DOI 10.1145/1390334.1390448; Koshman S, 2005, J AM SOC INF SCI TEC, V56, P824, DOI 10.1002/asi.20175; Lupu M., 2013, TRENDS INF RETR FNTI, V7, P1; Manning C, 2008, INTRO INFORM RETRIEV; MCGILL R, 1978, AM STAT, V32, P12, DOI 10.2307/2683468; Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U; Morse E, 2002, J AM SOC INF SCI TEC, V53, P28, DOI 10.1002/asi.10006; Robertson S, 2008, J INF SCI, V34, P439, DOI 10.1177/0165551507086989; Robertson S.E., 1981, INFORMATION RETRIEVA, P9; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Rowe B.R., 2010, EC IMPACT ASSESSMENT; Sakai T, 2007, INFORM PROCESS MANAG, V43, P531, DOI 10.1016/j.ipm.2006.07.020; Sakai T., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148261; Sanderson M., 2005, P 28 ANN INT ACM SIG, P162, DOI 10.1145/1076034.1076064; Savoy J, 1997, INFORM PROCESS MANAG, V33, P495, DOI 10.1016/S0306-4573(97)00027-7; Savoy J, 2007, APPLIED COMPUTING 2007, VOL 1 AND 2, P872, DOI 10.1145/1244002.1244193; Seo J., 2005, Information Visualization, V4, DOI 10.1057/palgrave.ivs.9500091; Sormunen E., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Sormunen E., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Teevan J, 2010, ACM T COMPUT-HUM INT, V17, DOI 10.1145/1721831.1721835; Tukey J. W., 1977, EXPLORATORY DATA ANA; Tukey J.W., 1970, EXPLORATORY DATA ANA; Voorhees E., 2001, P 24 ANN INT ACM SIG, P74, DOI 10.1145/383952.383963; Voorhees E.M., 1998, SPECIAL PUBLICATION; Vredenburg K., 2002, P SIGCHI C HUM FACT, P471; Witten I.H., 2009, BUILD DIGITAL LIB; Zhang J, 2001, INFORM PROCESS MANAG, V37, P639, DOI 10.1016/S0306-4573(00)00042-X; Zhang J, 2008, INFORM RETRIEVAL SER, V23, P1 70 0 0 ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD LONDON 24-28 OVAL RD, LONDON NW1 7DX, ENGLAND 1045-926X 1095-8533 J VISUAL LANG COMPUT J. Vis. Lang. Comput. AUG 2014 25 4 SI 394 413 10.1016/j.jvlc.2013.12.003 20 Computer Science, Software Engineering Computer Science AK5QZ WOS:000338482400010 J Zhang, KC Zhang, Kechen How to Compress Sequential Memory Patterns into Periodic Oscillations: General Reduction Rules NEURAL COMPUTATION English Article NEURAL-NETWORKS; TEMPORAL ASSOCIATION; REPRESENTATION; INTEGRATION; NEURONS; MODEL; TIME A neural network with symmetric reciprocal connections always admits a Lyapunov function, whose minima correspond to the memory states stored in the network. Networks with suitable asymmetric connections can store and retrieve a sequence of memory patterns, but the dynamics of these networks cannot be characterized as readily as that of the symmetric networks due to the lack of established general methods. Here, a reduction method is developed for a class of asymmetric attractor networks that store sequences of activity patterns as associative memories, as in a Hop-field network. The method projects the original activity pattern of the network to a low-dimensional space such that sequential memory retrievals in the original network correspond to periodic oscillations in the reduced system. The reduced system is self-contained and provides quantitative information about the stability and speed of sequential memory retrieval in the original network. The time evolution of the overlaps between the network state and the stored memory patterns can also be determined from extended reduced systems. The reduction procedure can be summarized by a few reduction rules, which are applied to several network models, including coupled networks and networks with time-delayed connections, and the analytical solutions of the reduced systems are confirmed by numerical simulations of the original networks. Finally, a local learning rule that provides an approximation to the connection weights involving the pseudoinverse is also presented. Johns Hopkins Univ, Sch Med, Dept Biomed Engn, Baltimore, MD 21205 USA Zhang, KC (reprint author), Johns Hopkins Univ, Sch Med, Dept Biomed Engn, Baltimore, MD 21205 USA. kzhang4@jhmi.edu [AFOSR FA9550-12-1-0018]; [NIH R01MH079511] This work was supported partially by grants AFOSR FA9550-12-1-0018 and NIH R01MH079511. Amit D. J., 1989, MODELING BRAIN FUNCT; Burgess N., 2007, HIPPOCAMPUS BOOK, P715; Buzsaki G., 2006, RHYTHMS BRAIN; COHEN MA, 1983, IEEE T SYST MAN CYB, V13, P815; Diesmann M, 1999, NATURE, V402, P529; Hahnloser RHR, 2002, NATURE, V419, P65, DOI 10.1038/nature00974; Hasselmo M.E., 2012, WE REMEMBER BRAIN ME; HERZ AVM, 1991, PHYS REV LETT, V66, P1370, DOI 10.1103/PhysRevLett.66.1370; Hirsch M. W., 1974, DIFFERENTIAL EQUATIO; HOPFIELD JJ, 1984, P NATL ACAD SCI-BIOL, V81, P3088, DOI 10.1073/pnas.81.10.3088; HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554; Hopfield JJ, 2001, P NATL ACAD SCI USA, V98, P1282, DOI 10.1073/pnas.031567098; Ikegaya Y, 2004, SCIENCE, V304, P559, DOI 10.1126/science.1093173; Jin DZZ, 2009, P NATL ACAD SCI USA, V106, P19156, DOI 10.1073/pnas.0909881106; KANTER I, 1987, PHYS REV A, V35, P380, DOI 10.1103/PhysRevA.35.380; KLEINFELD D, 1986, P NATL ACAD SCI USA, V83, P9469, DOI 10.1073/pnas.83.24.9469; KLEINFELD D, 1988, BIOPHYS J, V54, P1039; Knierim JJ, 2012, ANNU REV NEUROSCI, V35, P267, DOI 10.1146/annurev-neuro-062111-150351; KOHONEN T, 1973, IEEE T COMPUT, VC 22, P701; Kohonen T., 1977, ASS MEMORY SYSTEM TH; KOSKO B, 1988, IEEE T SYST MAN CYB, V18, P49, DOI 10.1109/21.87054; Kuhn R., 1995, MODELS NEURAL NETWOR, P221; Maass W, 2002, NEURAL COMPUT, V14, P2531, DOI 10.1162/089976602760407955; Mauk MD, 2004, ANNU REV NEUROSCI, V27, P307, DOI 10.1146/annurev.neuro.27.070203.144247; McNaughton BL, 2006, NAT REV NEUROSCI, V7, P663, DOI 10.1038/nrn1932; Personnaz L., 1985, Journal de Physique Lettres, V46, DOI 10.1051/jphyslet:01985004608035900; Press W., 2007, NUMERICAL RECIPES AR; SEJNOWSKI TJ, 1977, J MATH BIOL, V4, P303, DOI 10.1007/BF00275079; SOMPOLINSKY H, 1986, PHYS REV LETT, V57, P2861, DOI 10.1103/PhysRevLett.57.2861; Zhang K., 1994, 9402 U CAL DEP COGN 30 0 0 MIT PRESS CAMBRIDGE 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA 0899-7667 1530-888X NEURAL COMPUT Neural Comput. AUG 2014 26 8 1542 1599 10.1162/NECO_a_00618 58 Computer Science, Artificial Intelligence; Neurosciences Computer Science; Neurosciences & Neurology AK7GH WOS:000338596100002 J Islam, T; Srivastava, PK; Rico-Ramirez, MA; Dai, Q; Han, DW; Gupta, M Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei; Gupta, Manika An exploratory investigation of an adaptive neuro fuzzy inference system (ANFIS) for estimating hydrometeors from TRMM/TMI in synergy with TRMM/PR ATMOSPHERIC RESEARCH English Article Liquid water contents; Ice water contents; Global precipitation measurement (GPM); Rain rate retrieval; Passive microwave radiometry; Precipitation radar PRECIPITATION RADAR; PROFILING ALGORITHM; CLOUD PROPERTIES; NETWORK The authors have investigated an adaptive neuro fuzzy inference system (ANFIS) for the estimation of hydrometeors from the TRMM microwave imager (TMI). The proposed algorithm, named as Hydro-Rain algorithm, is developed in synergy with the TRMM precipitation radar (PR) observed hydrometeor information. The method retrieves rain rates by exploiting the synergistic relations between the TMI and PR observations in twofold steps. First, the fundamental hydrometeor parameters, liquid water path (LWP) and ice water path (IWP), are estimated from the TMI brightness temperatures. Next, the rain rates are estimated from the retrieved hydrometeor parameters (LWP and IWP). A comparison of the hydrometeor retrievals by the Hydro-Rain algorithm is done with the TRMM PR 2A25 and GPROF 2A12 algorithms. The results reveal that the Hydro-Rain algorithm has good skills in estimating hydrometeor paths LWP and IWP, as well as surface rain rate. An examination of the Hydro-Rain algorithm is also conducted on a super typhoon case, in which the Hydro-Rain has shown very good performance in reproducing the typhoon field. Nevertheless, the passive microwave based estimate of hydrometeors appears to suffer in high rain rate regimes, and as the rain rate increases, the discrepancies with hydrometeor estimates tend to increase as well. (C) 2014 Elsevier B.V. All rights reserved. [Islam, Tanvir] NOAA, NESDIS, Ctr Weather & Climate Predict, College Pk, MD USA; [Islam, Tanvir] Colorado State Univ, Cooperat Inst Res Atmosphere, Ft Collins, CO 80523 USA; [Islam, Tanvir; Srivastava, Prashant K.; Rico-Ramirez, Miguel A.; Dai, Qiang; Han, Dawei] Univ Bristol, Dept Civil Engn, Bristol, Avon, England; [Srivastava, Prashant K.] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Srivastava, Prashant K.] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA; [Gupta, Manika] Indian Inst Technol Delhi, Dept Civil Engn, New Delhi, India Islam, T (reprint author), NOAA, NESDIS, STAR, Ctr Weather & Climate Predict, 5830 Univ Res Ct, College Pk, MD 20740 USA. tanvir.islam@noaa.gov Islam, Tanvir/F-6922-2011 Brandau CL, 2010, ATMOS RES, V96, P366, DOI 10.1016/j.atmosres.2010.01.009; Chiu S., 1994, J INTELL FUZZY SYST, V2, P267; Crewell S., 2009, GEOPHYS RES LETT, V36; Du P, 2011, ATMOS RES, V101, P911, DOI 10.1016/j.atmosres.2011.05.018; Hazra A, 2013, J ATMOS SOL-TERR PHY, V93, P29, DOI 10.1016/j.jastp.2012.11.010; Iguchi T, 2000, J APPL METEOROL, V39, P2038, DOI 10.1175/1520-0450(2001)040<2038:RPAFTT>2.0.CO;2; Islam T, 2012, COMPUT GEOSCI-UK, V48, P20, DOI 10.1016/j.cageo.2012.05.028; Islam T, 2012, ATMOS RES, V108, P57, DOI 10.1016/j.atmosres.2012.01.013; Islam T, 2012, ADV SPACE RES, V50, P1383, DOI 10.1016/j.asr.2012.07.011; Islam T, 2012, J ATMOS SOL-TERR PHY, V77, P194, DOI 10.1016/j.jastp.2012.01.001; JANG JSR, 1993, IEEE T SYST MAN CYB, V23, P665, DOI 10.1109/21.256541; Kummerow C, 1998, J ATMOS OCEAN TECH, V15, P809, DOI 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2; Masunaga H, 2002, J APPL METEOROL, V41, P849, DOI 10.1175/1520-0450(2002)041<0849:CORPDF>2.0.CO;2; Michel Y, 2011, MON WEATHER REV, V139, P2994, DOI 10.1175/2011MWR3632.1; Saavedra P., 2012, J GEOPHYS RES ATMOS, V117; Shen XY, 2011, ATMOS RES, V99, P120, DOI 10.1016/j.atmosres.2010.09.011; Spencer R. W., 1989, Journal of Atmospheric and Oceanic Technology, V6, DOI 10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2; Van Weverberg K, 2012, Q J ROY METEOR SOC, V138, P2163, DOI 10.1002/qj.1933; You Y.L., 2012, J GEOPHYS RES ATMOS, V117; Zhou YS, 2012, ATMOS RES, V108, P1, DOI 10.1016/j.atmosres.2011.12.015 20 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0169-8095 1873-2895 ATMOS RES Atmos. Res. AUG-SEP 2014 145 57 68 10.1016/j.atmosres.2014.03.019 12 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AJ8UK WOS:000337983100005 J Salti, S; Tombari, F; Di Stefano, L Salti, Samuele; Tombari, Federico; Di Stefano, Luigi SHOT: Unique signatures of histograms for surface and texture description COMPUTER VISION AND IMAGE UNDERSTANDING English Article Surface matching; 3D descriptors; Object recognition; 3D reconstruction 3D OBJECT RECOGNITION; RANGE IMAGES; PERFORMANCE EVALUATION; SPIN IMAGES; REPRESENTATION; FEATURES; SCENES; REGISTRATION This paper presents a local 3D descriptor for surface matching dubbed SHOT. Our proposal stems from a taxonomy of existing methods which highlights two major approaches, referred to as Signatures and Histograms, inherently emphasizing descriptiveness and robustness respectively. We formulate a comprehensive proposal which encompasses a repeatable local reference frame as well as a 3D descriptor, the latter featuring an hybrid structure between Signatures and Histograms so as to aim at a more favorable balance between descriptive power and robustness. A quite peculiar trait of our method concerns seamless integration of multiple cues within the descriptor to improve distinctiveness, which is particularly relevant nowadays due to the increasing availability of affordable RGB-D sensors which can gather both depth and color information. A thorough experimental evaluation based on datasets acquired with different types of sensors, including a novel RGB-D dataset, vouches that SHOT outperforms state-of-the-art local descriptors in experiments addressing descriptor matching for object recognition, 3D reconstruction and shape retrieval. (C) 2014 Elsevier Inc. All rights reserved. [Salti, Samuele; Tombari, Federico; Di Stefano, Luigi] Univ Bologna, Dept Comp Sci & Engn, I-40126 Bologna, Italy Salti, S (reprint author), Univ Bologna, Dept Comp Sci & Engn, I-40126 Bologna, Italy. samuele.salti@unibo.it Akagunduz E, 2007, P IEEE 11 INT C COMP, P1; Aldoma A, 2012, IEEE ROBOT AUTOM MAG, V19, P80, DOI 10.1109/MRA.2012.2206675; ALDOMA A, 2012, ECCV; ALEXANDRE L, 2012, IROS WORKSH COL DEPT; Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014; BEHLEY J, 2012, INT C ROBOTICS AUTOM; Bro R, 2008, J CHEMOMETR, V22, P135, DOI 10.1002/cem.1122; Chen H, 2007, PATTERN RECOGN LETT, V28, P1252, DOI 10.1016/j.patrec.2007.02.009; Chua CS, 1997, INT J COMPUT VISION, V25, P63, DOI 10.1023/A:1007981719186; Conde C, 2006, LECT NOTES COMPUT SC, V4142, P317; Darom T, 2012, IEEE T IMAGE PROCESS, V21, P2758, DOI 10.1109/TIP.2012.2183142; DAVIS J, 2005, T PATTERN ANAL MACH, V27, P1615; DERRICO J, 2010, SURFACE FITTING USIN; Dorai C, 1997, IEEE T PATTERN ANAL, V19, P1115, DOI 10.1109/34.625113; FAIRCHILD M. D., 2005, COLOR APPEARANCE MOD; FIGUEROA N, 2012, INT C COMPUTER ROBOT; Frome A, 2004, LECT NOTES COMPUT SC, V3023, P224; Guo YL, 2013, INT J COMPUT VISION, V105, P63, DOI 10.1007/s11263-013-0627-y; HOPPE H, 1992, COMP GRAPH, V26, P71; IYER M, 2005, COMPUT AIDED DESIGN, V5, P509; Johnson AE, 1999, IEEE T PATTERN ANAL, V21, P433, DOI 10.1109/34.765655; Ke Y, 2004, PROC CVPR IEEE, P506; KNOPP J, 2010, ACM MULT 2010 WORKSH; KNOPP J, 2010, ECCV; Kohli P., 2011, P 24 ANN ACM S US IN, P559; Lai K, 2011, IEEE INT CONF ROBOT, P1817; LEIBE B, 2008, INT J COMPUT VISION, P17; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Mian A, 2010, INT J COMPUT VISION, V89, P348, DOI 10.1007/s11263-009-0296-z; Mian AS, 2006, INT J COMPUT VISION, V66, P19, DOI 10.1007/s11263-005-3221-0; Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188; Mitra NJ, 2004, INT J COMPUT GEOM AP, V14, P261, DOI 10.1142/S0218195904001470; Novatnack J, 2008, LECT NOTES COMPUT SC, V5304, P440, DOI 10.1007/978-3-540-88690-7_33; Ovsjanikov M, 2008, COMPUT GRAPH FORUM, V27, P1341, DOI 10.1111/j.1467-8659.2008.01273.x; PELE O, 2010, P EUR C COMP VIS ECC; Petrelli A, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P2244, DOI 10.1109/ICCV.2011.6126503; Proenca PF, 2013, LECT NOTES COMPUT SC, V8033, P385, DOI 10.1007/978-3-642-41914-0_38; Rodola E, 2013, INT J COMPUT VISION, V102, P129, DOI 10.1007/s11263-012-0568-x; RUBNER Y, 1998, P INT C COMP VIS ICC; RUSU R, 2009, P INT C ROB AUT ICRA; RUSU R, 2008, P INT C INT ROB SYST; SHILANE P, 2004, SHAPE MODELING INT; SOMANATH G, 2011, LECT NOTES COMPUTER, P483; STEIN F, 1992, IEEE T PATTERN ANAL, V14, P125, DOI 10.1109/34.121785; SUKNO F, 2012, INT S VISUAL COMPUTI; Sun JA, 2009, COMPUT GRAPH FORUM, V28, P1383; SUN Y, 2001, ICCV, V2, P263; Tangelder JH, 2008, MULTIMED TOOLS APPL, V39, P441, DOI 10.1007/s11042-007-0181-0; TOMBARI F, 2011, P 18 INT C IMAG PROC; TOMBARI F, 2010, P 11 EUR C COMP VIS; Tombari F, 2013, INT J COMPUT VISION, V102, P198, DOI 10.1007/s11263-012-0545-4; UNNIKRISHNAN R, 2008, P INT C COMP VIS PAT; WOHLKINGER W, 2012, INT C ROBOTICS AUTOM; Zaharescu A, 2012, INT J COMPUT VISION, V100, P78, DOI 10.1007/s11263-012-0528-5; Zhang L, 2003, PROC CVPR IEEE, P367; Zhao W, 2003, ACM COMPUT SURV, V35, P399, DOI 10.1145/954339.954342; ZHONG Y, 2009, P INT C COMP VIS ICC, P689 57 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1077-3142 1090-235X COMPUT VIS IMAGE UND Comput. Vis. Image Underst. AUG 2014 125 251 264 10.1016/j.cviu.2014.04.011 14 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AJ8CP WOS:000337930600018 J Zamani, S; Lee, SP; Shokripour, R; Anvik, J Zamani, Sima; Lee, Sai Peck; Shokripour, Rarnin; Anvik, John A noun-based approach to feature location using time-aware term-weighting INFORMATION AND SOFTWARE TECHNOLOGY English Article Feature location; Software change request; Time-metadata; Term-weighting; Noun usage INFORMATION-RETRIEVAL; TRACEABILITY LINKS; SOURCE CODE; SOFTWARE; MAINTENANCE; COMPREHENSION; EXECUTION Context: Feature location aims to identify the source code location corresponding to the implementation of a software feature. Many existing feature location methods apply text retrieval to determine the relevancy of the features to the text data extracted from the software repositories. One of the preprocessing activities in text retrieval is term-weighting, which is used to adjust the importance of a term within a document or corpus. Common term-weighting techniques may not be optimal to deal with text data from software repositories due to the origin of term-weighting techniques from a natural language context. Objective: This paper describes how the consideration of when the terms were used in the repositories, under the condition of weighting only the noun terms, can improve a feature location approach. Method: We propose a feature location approach using a new term-weighting technique that takes into account how recently a term has been used in the repositories. In this approach, only the noun terms are weighted to reduce the dataset volume and avoid dealing with dimensionality reduction. Results: An empirical evaluation of the approach on four open-source projects reveals improvements to the accuracy, effectiveness and performance up to 50%, 17%, and 13%, respectively, when compared to the commonly-used Vector Space Model approach. The comparison of the proposed term-weighting technique with the Term Frequency-Inverse Document Frequency technique shows accuracy, effectiveness, and performance improvements as much as 15%, 10%, and 40%, respectively. The investigation of using only noun terms, instead of using all terms, in the proposed approach also indicates improvements up to 28%, 21%, and 58% on accuracy, effectiveness, and performance, respectively. Conclusion: In general, the use of time in the weighting of terms, along with the use of only the noun terms, makes significant improvements to a feature location approach that relies on textual information. (C) 2014 Elsevier B.V. All rights reserved. [Zamani, Sima; Lee, Sai Peck; Shokripour, Rarnin] Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia; [Anvik, John] Cent Washington Univ, Dept Comp Sci, Ellensburg, WA USA Zamani, S (reprint author), Univ Malaya, Fac Comp Sci & Informat Technol, Kuala Lumpur, Malaysia. s.zamani@siswa.um.edu.my High Impact Research Grant - Ministry of Education, Malaysia [UM.C/625/1/HIR/MOHE/FCSIT/13] This work was carried out within the framework of a research project supported by the High Impact Research Grant with reference UM.C/625/1/HIR/MOHE/FCSIT/13, funded by the Ministry of Education, Malaysia. We would like to thank Reza M. Parizi for his helpful comments on improving the earlier versions of this paper; and the TraceLab Team19 for their support in using their framework. Abebe S.L., 2010, 2010 IEEE 18 INT C P, P156, DOI 10.1109/ICPC.2010.29; Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053; Anvik J., 2006, P 28 INT C SOFTW ENG, P361, DOI [10.1145/1134285.1134336, DOI 10.1145/1134285.1134336]; Bacchelli A., 2010, P 32 ACM IEEE INT C, V1, P375, DOI 10.1145/1806799.1806855; Bachmann A., 2010, P 18 ACM SIGSOFT INT, P97; Bassett B., 2013, P 35 ACM IEEE INT C; Biggers L.R., 2012, EMPIR SOFTW ENG, P1; BIGGERSTAFF TJ, 1993, PROC INT CONF SOFTW, P482, DOI 10.1109/ICSE.1993.346017; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Briand LC, 2009, INFORM SOFTWARE TECH, V51, P512, DOI 10.1016/j.infsof.2008.06.002; Butler S., 2011, ECOOP 2011 OBJ OR PR, P130; Capobianco G, 2009, INT C PROGRAM COMPRE, P148; Capobianco G, 2013, J SOFTW-EVOL PROC, V25, P743, DOI 10.1002/smr.1564; Chen KR, 2000, PROG COMPREHEN, P241, DOI 10.1109/WPC.2000.852498; Cleary B, 2009, EMPIR SOFTW ENG, V14, P93, DOI 10.1007/s10664-008-9095-3; COHEN J, 1992, PSYCHOL BULL, V112, P155, DOI 10.1037//0033-2909.112.1.155; Corley C.S., 2011, P 6 INT WKS TRAC EM, P31, DOI DOI 10.1145/1987856.1987863; Crain S.P., 2012, MINING TEXT DATA, P129; Cunningham H., 2002, P 40 ANN M ASS COMP, P168, DOI DOI 10.3115/1073083.1073112; Dias Jr L.D., 2012, INT J SOFTW ENG APPL, V3; Dit B, 2013, J SOFTW-EVOL PROC, V25, P53, DOI 10.1002/smr.567; Dit B, 2013, EMPIR SOFTW ENG, V18, P277, DOI 10.1007/s10664-011-9194-4; Dit B., 2012, 2012 IEEE 20th International Conference on Program Comprehension; Eisenberg AD, 2005, PROC IEEE INT CONF S, P337; Gay G, 2009, PROC IEEE INT CONF S, P351, DOI 10.1109/ICSM.2009.5306315; Gomez VU, 2009, IWPSE-EVOL 09: ERCIM WORKSHOP ON SOFTWARE EVOLUTION (EVOL) AND INTERNATIONAL WORKSHOP ON PRINCIPLES OF SOFTWARE EVOLUTION (IWPSE), P79; Haiduc S., 2010, 2010 ACM IEEE 32 INT, V2, P223; Hassan AE, 2005, PROC IEEE INT CONF S, P263; Hill E, 2009, PROC INT CONF SOFTW, P232, DOI 10.1109/ICSE.2009.5070524; Kagdi H, 2007, INT C PROGRAM COMPRE, P145; Kagdi H, 2012, J SOFTW-EVOL PROC, V24, P3, DOI 10.1002/smr.530; Liu D., 2007, P 22 IEEE ACM INT C, P234, DOI DOI 10.1145/1321631.1321667; Lukins SK, 2010, INFORM SOFTWARE TECH, V52, P972, DOI 10.1016/j.infsof.2010.04.002; Mader P., 2008, P ASE 08, P49; Mader P, 2012, J SYST SOFTWARE, V85, P2205, DOI 10.1016/j.jss.2011.10.023; Manning C.D., 2008, INTRO INFORM RETRIEV, V1; Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10; Marcus A., 2013, SOFTWARE ENG, P126; Omoronyia I, 2011, INFORM SOFTWARE TECH, V53, P851, DOI 10.1016/j.infsof.2011.03.001; Petrenko M, 2008, INT C PROGRAM COMPRE, P13, DOI 10.1109/ICPC.2008.14; Petrenko M, 2013, INFORM SOFTWARE TECH, V55, P651, DOI 10.1016/j.infsof.2012.09.013; Poshyvanyk D., 2006, 14 IEEE INT C PROGR; Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016.; Rao S., 2011, P 8 WORK C MIN SOFTW, P43; Ratanotayanon S., 2010, P 25 IEEE ACM INT C, P341, DOI 10.1145/1858996.1859066; Ratiu D, 2007, INT C PROGRAM COMPRE, P91; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Romano J., 2006, ANN M FLOR ASS I RES; SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0; Sarawagi S., 2008, TRENDS DATABASES, V1, P261; Shepherd D., 2007, P 6 INT C ASP OR SOF, P212, DOI 10.1145/1218563.1218587; Shokripour R., 2013, P 10 INT WORKSH MIN, P2; Sillito J, 2008, IEEE T SOFTWARE ENG, V34, P434, DOI 10.1109/TSE.2008.26; Sisman B., 2012, 9 IEEE WORK C MIN SO, P50; Voinea L., 2006, P 3 INT WORKSH MIN S, P33, DOI 10.1145/1137983.1137993; WILDE N, 1995, J SOFTW MAINT-RES PR, V7, P49, DOI 10.1002/smr.4360070105; Winkler S, 2010, SOFTW SYST MODEL, V9, P529, DOI 10.1007/s10270-009-0145-0; Wohlin C, 2012, EXPT SOFTWARE ENG; Zhou J, 2012, PROC INT CONF SOFTW, P14; Zimmermann T, 2004, PROC INT CONF SOFTW, P563, DOI 10.1109/ICSE.2004.1317478 60 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-5849 1873-6025 INFORM SOFTWARE TECH Inf. Softw. Technol. AUG 2014 56 8 991 1011 10.1016/j.infsof.2014.03.007 21 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AI9OL WOS:000337261800010 J George, J; Shamir, L George, Joe; Shamir, Lior Computer analysis of similarities between albums in popular music PATTERN RECOGNITION LETTERS English Article Music; Machine perception; Music information retrieval GENRE CLASSIFICATION; FEATURES; RECOGNITION; RETRIEVAL; SIGNALS; IMAGES Analysis of musical styles is a complex cognitive task normally performed by music fans and critics, and due to the multi-dimensional nature of music data can be considered a challenging task for computing machines. Here we propose an automatic quantitative method that can analyze similarities between the sound of popular music albums in an unsupervised fashion. The method works by first converting the music samples into two-dimensional spectrograms, and then extracting a large set of 2883 2D numerical content descriptors from the raw spectrograms as well as 2D transforms and compound transforms of the spectrograms. The similarity between each pair of samples is computed using a variation of the Weighted K-Nearest Neighbor scheme, and a phylogeny is then used to visualize the differences between the albums. Experimental results show that the method was able to automatically organize the albums of The Beatles by their chronological order, and also unsupervisely arranged albums of musicians such as U2, Queen, ABBA, and Tears for Fears in a fashion that is largely in agreement with their chronological order and musical styles. (C) 2014 Elsevier B.V. All rights reserved. [George, Joe; Shamir, Lior] Lawrence Technol Univ, Southfield, MI 48075 USA Shamir, L (reprint author), Lawrence Technol Univ, 21000 W Ten Mile Rd, Southfield, MI 48075 USA. lshamir@mtu.edu National Science Foundation [1157162] This work was supported in part by Grant 1157162 of the National Science Foundation. ALTES RA, 1980, J ACOUST SOC AM, V67, P1232, DOI 10.1121/1.384165; Bagci U, 2007, IEEE SIGNAL PROC LET, V14, P521, DOI 10.1109/LSP.2006.891320; Bainbridge D, 2005, INFORM PROCESS MANAG, V41, P41, DOI 10.1016/j.ipm.2004.04.001; Bishop C. M., 2006, PATTERN RECOGNITION; Casey MA, 2008, P IEEE, V96, P668, DOI 10.1109/JPROC.2008.916370; Clifford R., 2006, INT C MUS INF RETR, P150; Costa Y.M.G., 2011, 18 INT C SYST SIGN I, P1; Deshpande H., 2001, P COST G 6 C DIG AUD; Downie S., 2008, ACOUST SCI TECHNOL, V29, P247; Duggan D., 2009, THESIS DUBLIN I TECH; Eno B., 1991, ROLLING STONE; Felsenstein J, 2004, PHYLIP PHYLOGENY INF; Gabor D., 1946, Journal of the Institution of Electrical Engineers. III. Radio and Communication Engineering, V93; Gradshtein I., 1994, TABLE INTEGRALS SERI, P1054; GRAY SB, 1971, IEEE T COMPUT, VC 20, P551, DOI 10.1109/T-C.1971.223289; Gregorescu C., 2002, IEEE T IMAGE PROCESS, V11, P1160; Guo GD, 2003, IEEE T NEURAL NETWOR, V14, P209, DOI 10.1109/TNN.2002.806626; Hadjidementriou E., 2001, IEEE C COMPUTER VISI, V1, P702; Hanna P., 2008, INT S COMP MUS MOD R, P244; Haralick R.M., 1973, IEEE T SYST MAN CYB, V6, P269; Holden S., 1990, NY TIMES; Holzapfel A, 2008, IEEE T AUDIO SPEECH, V16, P424, DOI 10.1109/TASL.2007.909434; Li T., 2003, SIGIR 03, P282; Lim J.S., 1990, 2 DIMENSIONAL SIGNAL, P42; Manders AJ, 2012, IEEE T AUDIO SPEECH, V20, P1734, DOI 10.1109/TASL.2012.2188513; Mauch M., 2010, P 11 INT SOC MUS INF; McFee B, 2012, IEEE T AUDIO SPEECH, V20, P2207, DOI 10.1109/TASL.2012.2199109; McKay C., 2010, THESIS MCGILL U; Miccio A., 2011, STYLUS MAGAZINE 0531; MONGEAU M, 1990, COMPUT HUMANITIES, V24, P161, DOI 10.1007/BF00117340; O'hara K., 2004, HOUSTON CHRONICLE, P5; Orlov N, 2008, PATTERN RECOGN LETT, V29, P1684, DOI 10.1016/j.patrec.2008.04.013; OTSU N, 1979, IEEE T SYST MAN CYB, V9, P62; Pachet F., 2002, INT S C MUS INF RETR; PREWITT J. M. S., 1970, PICTURE PROCESSING P, P75; Reynolds R., 2004, CITY MON MAG; Rocamora M, 2014, PATTERN RECOGN LETT, V36, P272, DOI 10.1016/j.patrec.2013.04.006; Serr Y., 2012, IEEE T AUDIO SPEECH, V20, P514; Shamir L., 2008, SOURCE CODE BIOL MED, V46, P943; Shamir L, 2009, MON NOT R ASTRON SOC, V399, P1367, DOI 10.1111/j.1365-2966.2009.15366.x; Shamir L., 2012, ACM J COMPUT CULTURA, V5; Shamir L, 2012, LEONARDO, V45, P149, DOI 10.1162/LEON_a_00281; Shamir L., 2009, INT C IM PROC COMP V, P37; Shamir L, 2010, PLOS COMPUT BIOL, V6, DOI 10.1371/journal.pcbi.1000974; Shamir L, 2009, OSTEOARTHR CARTILAGE, V17, P1307, DOI 10.1016/j.joca.2009.04.010; Shamir L, 2009, IEEE T BIO-MED ENG, V56, P407, DOI 10.1109/TBME.2008.2006025; Shamir Lior, 2008, Source Code Biol Med, V3, P13, DOI 10.1186/1751-0473-3-13; Shamir L, 2010, ACM T APPL PERCEPT, V7, DOI 10.1145/1670671.1670672; Shamir L, 2008, INT J COMPUT VISION, V79, P225, DOI 10.1007/s11263-008-0143-7; Sheffield R., 2004, ROLLING STONE REV; Sinclair T., 1995, ENTERTAINMENT W 1013; SOX, 2013, SOX SOUND EXCH; TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999; TEAGUE MR, 1980, J OPT SOC AM, V70, P920, DOI 10.1364/JOSA.70.000920; Thrills A., 1990, TEARS FEARS SEEDS LO; Tsai W.H., 2013, PATTERN RECOGNIT LET, V33, P2285; Typke R., 2004, ACM INT C MULT, P128; Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560; Uitdenbogerd A., 1999, IEEE ACM INT C MULT, P57; Urbano J, 2011, LECT NOTES COMPUT SC, V6684, P338, DOI 10.1007/978-3-642-23126-1_21; WU CM, 1992, IEEE T MED IMAGING, V11, P141; YANG YH, 2008, ADV MULTIMEDIA INFOR, V5353, P70, DOI 10.1007/978-3-540-89796-5_8; ZHANG BJ, 2009, SIGIR09 JUL 2009, P403; Zlatintsi A, 2013, IEEE T AUDIO SPEECH, V21, P737, DOI 10.1109/TASL.2012.2231073 64 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. AUG 1 2014 45 78 84 10.1016/j.patrec.2014.02.021 7 Computer Science, Artificial Intelligence Computer Science AI9AW WOS:000337219200011 J Le Merrer, E; Le Scouarnec, N; Tredan, G Le Merrer, Erwan; Le Scouarnec, Nicolas; Tredan, Gilles Heuristical top-k: fast estimation of centralities in complex networks INFORMATION PROCESSING LETTERS English Article Information retrieval; Network analysis; Centralities; Complex networks Centrality metrics have proven to be of a major interest when analyzing the structure of networks. Given modern-day network sizes, fast algorithms for estimating these metrics are needed. This paper proposes a computation framework (named Filter-Compute-Extract) that returns an estimate of the top-k most important nodes in a given network. We show that considerable savings in computation time can be achieved by first filtering the input network based on correlations between cheap and more costly centrality metrics. Running the costly metric on the smaller resulting filtered network yields significant gains in computation time. We examine the complexity improvement due to this heuristic for classic centrality measures, as well as experimental results on well-studied public networks. (C) 2014 Elsevier B.V. All rights reserved. [Le Merrer, Erwan; Le Scouarnec, Nicolas] Technicolor, Rennes, France; [Tredan, Gilles] LAAS CNRS, Toulouse, France Le Merrer, E (reprint author), Technicolor, Rennes, France. Avin C., ARXIV11113374CSSI; Bahmani B., 2010, VLDB; Bianchini M., 2005, ACM Transactions on Internet Technology, V5, DOI 10.1145/1052934.1052938; Brandes U, 2001, J MATH SOCIOL, V25, P163; Brandes U., 2007, INT J BIFURC CHAOS A; Chapanond A., 2005, Computational & Mathematical Organization Theory, V11, DOI 10.1007/s10588-005-5381-4; Dolev S, 2009, INFORM PROCESS LETT, V109, P1172, DOI 10.1016/j.ipl.2009.07.019; Dolev S., 2010, J ACM, V57, P25; Fortunato S, 2008, LECT NOTES COMPUT SC, V4936, P59, DOI 10.1007/978-3-540-78808-9_6; Ghoshal G, 2011, NATURE COMMUNICATION, V2, P1; Kiss C, 2008, DECIS SUPPORT SYST, V46, P233, DOI 10.1016/j.dss.2008.06.007; Martinez C., 2004, PARTIAL QUICKSORT; Okamoto K., 2008, RANKING CLOSENESS CE; Sterbenz J., 2011, TELECOMMUN SYST, P1; Valente T. W., 2008, CONNECTIONS, V28, P16; Wen S, 2012, IEEE COMMUN LETT, V16, P560, DOI 10.1109/LCOMM.2012.030512.112452 16 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0020-0190 1872-6119 INFORM PROCESS LETT Inf. Process. Lett. AUG 2014 114 8 432 436 10.1016/j.ipl.2014.03.006 5 Computer Science, Information Systems Computer Science AI2OZ WOS:000336699200009 J Figueroa, A; Neumann, G Figueroa, Alejandro; Neumann, Guenter Category-specific models for ranking effective paraphrases in community Question Answering EXPERT SYSTEMS WITH APPLICATIONS English Article Community-based Question Answering; Learning to rank; Question paraphrases; Question categories Platforms for community-based Question Answering (cQA) are playing an increasing role in the synergy of information-seeking and social networks. Being able to categorize user questions is very important, since these categories are good predictors for the underlying question goal, viz, informational or subjective. Furthermore, an effective cQA platform should be capable of detecting similar past questions and relevant answers, because it is known that a high number of best answers are reusable. Therefore, question paraphrasing is not only a useful but also an essential ingredient for effective search in cQA. However, the generated paraphrases do not necessarily lead to the same answer set, and might differ in their expected quality of retrieval, for example, in their power of identifying and ranking best answers higher. We propose a novel category-specific learning to rank approach for effectively ranking paraphrases for cQA. We describe a number of different large-scale experiments using logs from Yahoo! Search and Yahoo! Answers, and demonstrate that the subjective and objective nature of cQA questions dramatically affect the recall and ranking of past answers, when fine-grained category information is put into its place. Then, category-specific models are able to adapt well to the different degree of objectivity and subjectivity of each category, and the more specific the models are, the better the results, especially when benefiting from effective semantic and syntactic features. (C) 2014 Elsevier Ltd. All rights reserved. [Figueroa, Alejandro] Yahoo, Res Latin Amer, Santiago, Chile; [Figueroa, Alejandro] Univ Diego Port, Escuela Ingn Informat, Santiago, Chile; [Neumann, Guenter] DFKI GmbH, D-66123 Saarbrucken, Germany Neumann, G (reprint author), DFKI GmbH, D-66123 Saarbrucken, Germany. afiguero@yahoo-inc.com; neumann@dfici.de FONDEF-IdeA [CA12I10081]; Fondecyt "Bridging the Gap between Askers and Answers in Community Question Answering Services - Chilean Government [11130094]; European Community's Seventh Framework Programme (FP7) [287923] This work was partially supported by the projects FONDEF-IdeA (CA12I10081) and Fondecyt "Bridging the Gap between Askers and Answers in Community Question Answering Services" (11130094) funded by the Chilean Government, and the European Community's Seventh Framework Programme (FP7/2007-2013) under Grant agreement No. 287923 (EXCITEMENT). Atkinson J, 2013, EXPERT SYST APPL, V40, P7060, DOI 10.1016/j.eswa.2013.06.017; Bian J., 2008, WWW 08, P467; Blooma M. J., 2011, Proceedings of the 2011 Eighth International Conference on Information Technology: New Generations (ITNG), DOI 10.1109/ITNG.2011.108; Blooma M. J., 2012, P 2012 PAC AS C INF; Cao Y., 2008, P 17 INT C WORLD WID, P81, DOI 10.1145/1367497.1367509; Chen L., 2012, P 21 INT C COMP WORL, P823; Ferrandez O., 2011, WEB SEMANTICS SCI SE, V9, P137, DOI DOI 10.1016/J.WEBSEM.2011.01.002; Figueroa A., 2013, AAAI 2013; Harper FM, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P759; Harper FM, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P865; Hovy E., 2000, P TREC 9 C; JEON J, 2006, P 29 ANN INT ACM SIG, P228, DOI 10.1145/1148170.1148212; Joachims T., 2006, P ACM C KNOWL DISC D; John BM, 2011, IEEE INTERNET COMPUT, V15, P66, DOI 10.1109/MIC.2011.23; Li B., 2008, P 31 ANN INT ACM SIG; Lin C.-Y., 2008, P WORKSH QUEST GEN S, P929; Liu MR, 2010, LECT NOTES COMPUT SC, V6184, P127; Liu Y., 2008, INT C COMP LING, P497; Maleq Khan M. A., 2001, FAST DISTANCE METRIC; Rechavi A., 2012, 2012 45th Hawaii International Conference on System Sciences (HICSS), DOI 10.1109/HICSS.2012.398; Rose Daniel E., 2004, P 13 INT C WORLD WID, P13, DOI DOI 10.1145/988672.988675; Shtok A., 2012, P 21 INT C WORLD WID, P759; Surdeanu M, 2011, COMPUT LINGUIST, V37, P351, DOI 10.1162/COLI_a_00051; Surdeanu Mihai, 2008, P 46 ANN M ASS COMP, P719; SURYANTO MA, 2009, P 2 ACM INT C WEB SE, P142, DOI 10.1145/1498759.1498820; Wanga K., 2009, RES DEV INFORM RETRI, P187; Wen JR, 2002, ACM T INFORM SYST, V20, P59, DOI 10.1145/503104.503108; Xue X., 2008, RES DEV INFORM RETRI, P475; Yang L., 2011, AAAI; Zhao S., 2009, P JOINT C 47 ANN M A, P834, DOI 10.3115/1690219.1690263; Zhao S., 2011, P 5 INT JOINT C NAT, P929; Zhao S., 2010, P 23 INT C COMP LING, P1317; Zhou Z.-M., 2012, P 21 INT C COMP WORL, P767; Zoie, 2008, P INT C WEB SEARCH W, DOI DOI 10.1145/1341531.1341557 34 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. AUG 2014 41 10 4730 4742 10.1016/j.eswa.2014.02.004 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AG7WM WOS:000335629500019 J Hawalah, A; Fasli, M Hawalah, Ahmad; Fasli, Maria Utilizing contextual ontological user profiles for personalized recommendations EXPERT SYSTEMS WITH APPLICATIONS English Article User profiles; Context-aware systems; Web personalization; Recommender systems SPREADING ACTIVATION TECHNIQUES; SYSTEMS; INFORMATION; RETRIEVAL; KNOWLEDGE; SEARCH As users may have different needs in different situations and contexts, it is increasingly important to consider user context data when filtering information. In the field of web personalization and recommender systems, most of the studies have focused on the process of modelling user profiles and the personalization process in order to provide personalized services to the user, but not on contextualized services. Rather limited attention has been paid to investigate how to discover, model, exploit and integrate context information in personalization systems in a generic way. In this paper, we aim at providing a novel model to build, exploit and integrate context information with a web personalization system. A context-aware personalization system (CAPS) is developed which is able to model and build contextual and personalized ontological user profiles based on the user's interests and context information. These profiles are then exploited in order to infer and provide contextual recommendations to users. The methods and system developed are evaluated through a user study which shows that considering context information in web personalization systems can provide more effective personalization services and offer better recommendations to users. (C) 2014 Elsevier Ltd. All rights reserved. [Hawalah, Ahmad; Fasli, Maria] Univ Essex, Sch Comp Sci & Elect Engn, Colchester CO4 3SQ, Essex, England; [Hawalah, Ahmad] Univ Taibah, Coll Comp Sci & Engn, Medina, Saudi Arabia Hawalah, A (reprint author), Univ Essex, Sch Comp Sci & Elect Engn, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England. ahawalah@taibahu.edu.sa; mfasli@essex.ac.uk Abbar S., 2010, P 12 INT C INF INT W, P155; Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99; Adomavicius G, 2011, AI MAG, V32, P67; Adomavicius G, 2005, ACM T INFORM SYST, V23, P103, DOI 10.1145/1055709.1055714; Aho A. V., 1973, P 5 ANN ACM S THEOR, P253, DOI DOI 10.1145/800125.804056; Anand S. S., 2007, ACM T INTERNET TECHN, V7; Anyanwu K., 2003, P 12 INT WORLD WID W, P690, DOI DOI 10.1145/775152.775249; Bazire M, 2005, LECT NOTES ARTIF INT, V3554, P29; Blanco-Fernandez Y, 2011, INFORM SCIENCES, V181, P4823, DOI 10.1016/j.ins.2011.06.016; Blanco-Fernandez Y, 2010, SMART INNOV SYS, V6, P1; Borlund P, 2003, INFORM RES, P8; Cantador I, 2008, LECT NOTES COMPUT SC, V5149, P279, DOI 10.1007/978-3-540-70987-9_34; Cantador I, 2008, STUD COMP INTELL, V93, P25, DOI 10.1007/978-3-540-76361_2; Challam V., 2007, LARGE SCALE SEMANTIC, P612; COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407; Crestani F, 1997, ARTIF INTELL REV, V11, P453, DOI 10.1023/A:1006569829653; Crestani F, 1999, P IEEE INT FORUM RES, P163, DOI 10.1109/ADL.1999.777711; Daoud M., 2008, P 2 INT IIIX S IIIX, P57, DOI 10.1145/1414694.1414708; Eirinaki M, 2006, LECT NOTES COMPUT SC, V4289, P147; Gao Q, 2008, IFIP INT C NETW PARA, P488, DOI 10.1109/NPC.2008.74; Gorgoglione M, 2006, IEEE DATA MINING, P222; Hawalah A., 2011, P INT C WEB INT MIN; Hawalah A, 2011, LECT NOTES BUS INF P, V85, P282; Huang Z, 2004, ACM T INFORM SYST, V22, P116, DOI 10.1145/963770.963775; Jiang J. J., 1997, INT C RES COMP LING, P9008; Jih W., 2007, J COMPUTERS, V18, P45; Kelly Diane, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000012; Lee D, 2010, STUD COMP INTELL, V317, P121; Liang TP, 2008, DECIS SUPPORT SYST, V45, P401, DOI 10.1016/j.dss.2007.05.004; Lin D., 1998, P 15 INT C MACH LEAR, V1, P296; Maguitman A., 2005, P 14 INT C WORLD WID, P107, DOI 10.1145/1060745.1060765; Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3; Middleton SE, 2004, ACM T INFORM SYST, V22, P54, DOI 10.1145/963770.963773; Mohammed NU, 2010, LECT NOTES ARTIF INT, V6422, P490, DOI 10.1007/978-3-642-16732-4_52; Montaner M, 2001, TECHNICAL REPORT; Mooney R. J., 1999, P SIGIR WORKSH REC S; Mooney R. J., 1998, WORKSH REC SYST, P49; Palmisano C, 2008, IEEE T KNOWL DATA EN, V20, P1535, DOI 10.1109/TKDE.2008.110; Pignotti E, 2004, FR ART INT, V110, P1077; Popescul A, 2001, P 17 C UNC ART INT U, P437; Prahalad C.K., 2004, CRM CK PRAHALAD PRED; RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528; Resnik P, 1999, J ARTIF INTELL RES, V11, P95; Ricci F, 2011, RECOMMENDER SYSTEMS HANDBOOK, P1, DOI 10.1007/978-0-387-85820-3_1; Salton G., 1988, P 11 ANN INT ACM SIG, P147, DOI 10.1145/62437.62447; Shani G, 2011, RECOMMENDER SYSTEMS HANDBOOK, P257, DOI 10.1007/978-0-387-85820-3_8; Shi Y., 2010, P WORKSH CONT AW MOV, P34, DOI 10.1145/1869652.1869658; Sieg A., 2007, IEEE WIC ACM INT C W, P91; Song L, 2007, ADVANCES AND INNOVATIONS IN SYSTEMS, COMPUTING SCIENCES AND SOFTWARE ENGINEERING, P275, DOI 10.1007/978-1-4020-6264-3_49; Sussna M., 1993, P 2 INT C INF KNOWL, P67, DOI 10.1145/170088.170106; Trajkova J., 2004, P RECH INF ASS ORD R, P380; Weng SS, 2008, EXPERT SYST APPL, V34, P1857, DOI 10.1016/j.eswa.2007.02.023; Wu K. L., 2001, P 3 INT WORKSH ADV I, P12; Wu Z., 1994, P 32 ANN M ASS COMP, V94, P133, DOI DOI 10.3115/981732.981751; Xiang BA, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P451; Xu K., 2008, P 1 INT C AMB MED SY, P1, DOI 10.1145/1509315.1509413; Yang Y., 2005, J ELECTRON COMMER RE, V6, P112; Zhang Y., 2010, P IEEE COMP SCI ED I, P362; Ziegler C. N., 2006, P 15 ACM INT C INF K, P465, DOI 10.1145/1183614.1183682 59 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. AUG 2014 41 10 4777 4797 10.1016/j.eswa.2014.01.039 21 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AG7WM WOS:000335629500023 J Paulik, C; Dorigo, W; Wagner, W; Kidd, R Paulik, Christoph; Dorigo, Wouter; Wagner, Wolgang; Kidd, Richard Validation of the ASCAT Soil Water Index using in situ data from the International Soil Moisture Network INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION English Article Soil moisture; Remote sensing; Validation; Soil Water Index ANTECEDENT WETNESS CONDITIONS; AMSR-E; SATELLITE; PRODUCTS; SURFACE; MODEL; ASSIMILATION; RETRIEVAL; PREDICTION; REANALYSES Soil moisture is an essential climate variable and a key parameter in hydrology, meteorology and agriculture. Surface Soil Moisture (SSM) can be estimated from measurements taken by ASCAT onboard Metop-A and have been successfully validated by several studies. Profile soil moisture, while equally important, cannot be directly measured by remote sensing but must be modeled. The Soil Water Index (SWI) product developed for near real time applications within the framework of the GMES project geoland2 aims to provide such a modeled profile estimate using satellite data as input. It is produced from ASCAT SSM estimates using a two-layer water balance model which describes the relationship between surface and profile soil moisture as a function of time. It provides daily global data about moisture conditions for eight characteristic time lengths representing different depths. The objective of this work was to assess the overall quality of the SWI data. Furthermore we tested the assumptions of the used water balance model and checked if ancillary information about topography, water fraction and noise information are useful for identifying observations of questionable quality. SWI data from January 1st 2007 until the end of 2011 was compared to in situ soil moisture data from 664 stations belonging to 23 observation networks which are available through the International Soil Moisture Network (ISMN). These stations delivered 2081 time series at different depths which were compared to the SWI values. The average of the significant Pearson correlation coefficients was 0.54 while being greater than 0.5 for 64.4% of all time series. It was found that the characteristic time length showing the highest correlation increases with in situ observation depth, thus confirming the SWI model assumptions. Relationships of the correlation coefficients with topographic complexity, water fraction, in situ observation depth, and soil moisture noise were found. (C) 2014 Elsevier B.V. All rights reserved. [Paulik, Christoph; Dorigo, Wouter; Wagner, Wolgang; Kidd, Richard] Vienna Univ Technol, A-1040 Vienna, Austria Paulik, C (reprint author), Vienna Univ Technol, Gusshausstr 27-29, A-1040 Vienna, Austria. christoph.paulik@geo.tuwien.ac.at European Community's Seventh Framework Program (FP7) [218795] The research leading to these results has received funding from the European Community's Seventh Framework Program (FP7/2007-2013) under grant agreement no 218795. Albergel C, 2008, HYDROL EARTH SYST SC, V12, P1323; Albergel C, 2012, REMOTE SENS ENVIRON, V118, P215, DOI 10.1016/j.rse.2011.11.017; Albergel C, 2009, HYDROL EARTH SYST SC, V13, P115; Albergel C, 2013, J HYDROMETEOROL, V14, P1259, DOI 10.1175/JHM-D-12-0161.1; Albergel C, 2013, REMOTE SENS ENVIRON, V138, P77, DOI 10.1016/j.rse.2013.07.009; Bartalis Z., 2008, ASCAT SOIL MOISTURE, V15; Beyrich F., 2007, BERICHTE DEUTSCHEN W; Brocca L, 2010, REMOTE SENS ENVIRON, V114, P2745, DOI 10.1016/j.rse.2010.06.009; Brocca L, 2009, J HYDROL, V364, P73, DOI 10.1016/j.jhydrol.2008.10.007; Brocca L, 2010, HYDROL EARTH SYST SC, V14, P1881, DOI 10.5194/hess-14-1881-2010; Brocca L, 2011, REMOTE SENS ENVIRON, V115, P3390, DOI 10.1016/j.rse.2011.08.003; Brocca L, 2008, HYDROL PROCESS, V22, P629, DOI 10.1002/hyp.6629; Calvet JC, 2007, INT GEOSCI REMOTE SE, P1196; Cappelaere B, 2009, J HYDROL, V375, P34, DOI 10.1016/j.jhydrol.2009.06.021; de Lange R, 2008, IEEE T GEOSCI REMOTE, V46, P4041, DOI 10.1109/TGRS.2008.2000796; de Rosnay P, 2009, J HYDROL, V375, P241, DOI 10.1016/j.jhydrol.2009.01.015; de Wit AM, 2007, AGR FOREST METEOROL, V146, P38, DOI 10.1016/j.agrformet.2007.05.004; Dharssi I., 2011, HYDROL EARTH SYST SC, V8, P4313, DOI DOI 10.5194/HESSD-8-4313-2011; Dorigo W., 2013, VADOSE ZONE J, V12; Dorigo W., 2012, GEOPHYS RES LETT, V39; Dorigo WA, 2011, HYDROL EARTH SYST SC, V15, P1675, DOI 10.5194/hess-15-1675-2011; Draper CS, 2009, REMOTE SENS ENVIRON, V113, P703, DOI 10.1016/j.rse.2008.11.011; Gouveia C, 2009, NAT HAZARD EARTH SYS, V9, P185; Gruber A., 2013, VADOSE ZONE J, V12; Hollinger S., 1994, J CLIMATE; Jackson TJ, 2010, IEEE T GEOSCI REMOTE, V48, P4256, DOI 10.1109/TGRS.2010.2051035; JACKSON TJ, 1993, HYDROL PROCESS, V7, P139, DOI 10.1002/hyp.3360070205; Kerr YH, 2010, P IEEE, V98, P666, DOI 10.1109/JPROC.2010.2043032; Kidd R., 2012, SOIL WATER INDEX FRE; Kidd R., 2011, FP7SPACE20071; Koike T, 2004, ANN J HYDRAULIC ENG, V48, P217; Leavesley G., 2008, AGU FALL M; Liu YY, 2012, REMOTE SENS ENVIRON, V123, P280, DOI 10.1016/j.rse.2012.03.014; Loew A., 2009, ESA SPECIAL PUBLICAT, V12, P9706; Mahfouf JF, 2010, Q J ROY METEOR SOC, V136, P784, DOI 10.1002/qj.602; Marczewski W., 2010, HYDROL EARTH SYST SC, V7, P7007, DOI DOI 10.5194/HESSD-7-7007-2010; Mougin E, 2009, J HYDROL, V375, P14, DOI 10.1016/j.jhydrol.2009.06.045; Naeimi V., 2012, IEEE T GEOSCIENCE RE; Naeimi V, 2009, IEEE T GEOSCI REMOTE, V47, P1999, DOI [10.1109/TGRS.2008.2011617, 10.1109/TGRS.2009.2011617]; Njoku EG, 2003, IEEE T GEOSCI REMOTE, V41, P215, DOI 10.1109/TGRS.2002.808243; Owe M, 2001, IEEE T GEOSCI REMOTE, V39, P1643, DOI 10.1109/36.942542; Pellarin T, 2009, J HYDROL, V375, P262, DOI 10.1016/j.jhydrol.2008.12.003; Penna D, 2009, J HYDROL, V364, P311, DOI 10.1016/j.jhydrol.2008.11.009; Rautiainen K, 2012, IEEE T GEOSCI REMOTE, V50, P1483, DOI 10.1109/TGRS.2011.2167755; Rudiger C, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005837; Sanchez N, 2012, IEEE T GEOSCI REMOTE, V50, P1602, DOI 10.1109/TGRS.2012.2186971; Scipal K, 2008, ADV WATER RESOUR, V31, P1101, DOI 10.1016/j.advwatres.2008.04.013; Su CH, 2013, GEOPHYS RES LETT, V40, P3624, DOI 10.1002/grl.50695; Su Z, 2011, HYDROL EARTH SYST SC, V15, P2303, DOI 10.5194/hess-15-2303-2011; Wagner W., 1998, SOIL MOISTURE RETRIE; Young R., 2008, SOIL MOISTURE METEOR; Zhao DM, 2006, ADV ATMOS SCI, V23, P299, DOI 10.1007/s00376-006-0299-4; Zreda M., 2012, HYDROL EARTH SYST SC, V9, P4505, DOI DOI 10.5194/HESSD-9-4505-2012 53 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0303-2434 INT J APPL EARTH OBS Int. J. Appl. Earth Obs. Geoinf. AUG 2014 30 1 8 10.1016/j.jag.2014.01.007 8 Remote Sensing Remote Sensing AG7UT WOS:000335625000001 J van Meeuwen, LW; Jarodzka, H; Brand-Gruwel, S; Kirschner, PA; de Bock, JJPR; van Merrienboer, JJG van Meeuwen, Ludo W.; Jarodzka, Halszka; Brand-Gruwel, Saskia; Kirschner, Paul A.; de Bock, Jeano J. P. R.; van Merrienboer, Jeroen J. G. Identification of effective visual problem solving strategies in a complex visual domain LEARNING AND INSTRUCTION English Article Expertise; Eye-tracking; Perceptual task; Visual problem solving; Solution similarity COGNITIVE-LOAD; EYE-MOVEMENT; DYNAMIC VISUALIZATIONS; INSTRUCTIONAL-DESIGN; SELF-EFFICACY; EXPERTISE; STUDENTS; ARCHITECTURE; PERSPECTIVE; PERFORMANCE Students in complex visual domains must acquire visual problem solving strategies that allow them to make fast decisions and come up with good solutions to real-time problems. In this study, 31 air traffic controllers at different levels of expertise (novice, intermediate, expert) were confronted with 9 problem situations depicted on a radar screen. Participants were asked to provide the optimal order of arrival of all depicted aircrafts. Eye-movements, time-on-task, perceived mental effort, and task performance were recorded. Eye-tracking data revealed that novices use inefficient means-end visual problem solving strategies in which they primarily focus on the destination of aircraft. Higher levels of expertise yield visual problem solving strategies characterized by more efficient retrieval of relevant information and more efficient scan paths. Furthermore, experts' solutions were more similar than intermediates' solutions and intermediates' solutions were more similar than novices' solutions. Performance measures showed that experts and intermediates reached better solutions than novices, and that experts were faster and invested less mental effort than intermediates and novices. These findings may help creating eye-movement modeling examples for the teaching of visual problem solving strategies in complex visual domains. (C) 2014 Elsevier Ltd. All rights reserved. [van Meeuwen, Ludo W.; Jarodzka, Halszka; Brand-Gruwel, Saskia; Kirschner, Paul A.; van Merrienboer, Jeroen J. G.] Open Univ Netherlands, NL-6419 AT Heerlen, Netherlands; [van Meeuwen, Ludo W.; de Bock, Jeano J. P. R.] Air Traff Control Netherlands, Schiphol, Netherlands; [van Merrienboer, Jeroen J. G.] Maastricht Univ, Maastricht, Netherlands van Meeuwen, LW (reprint author), Open Univ Netherlands, Valkenburgerweg 177, NL-6419 AT Heerlen, Netherlands. Ludo.vanMeeuwen@ou.nl Bellenkes AH, 1997, AVIAT SPACE ENVIR MD, V68, P569; Berliner D., 1986, EDUC RES, V15, P5, DOI 10.3102/0013189X015007007; Boshuizen HC, 2008, CLIN REASONING HLTH, P113; BOSHUIZEN HPA, 1992, COGNITIVE SCI, V16, P153, DOI 10.1207/s15516709cog1602_1; Chi M, 1988, NATURE EXPERTISE, P311; Chi M. T. H., 1982, ADV PSYCHOL HUMAN IN, V1, P7; De Groot A. D., 1978, THOUGHT CHOICE CHESS; Dreyfus HL, 2005, ORGAN STUD, V26, P779, DOI 10.1177/0170840605053102; Endsley MR, 2006, CAMBRIDGE HANDBOOK OF EXPERTISE AND EXPERT PERFORMANCE, P633; ENDSLEY MR, 1995, HUM FACTORS, V37, P32, DOI 10.1518/001872095779049543; Ericsson KA, 1996, ANNU REV PSYCHOL, V47, P273, DOI 10.1146/annurev.psych.47.1.273; Ericsson KA, 2006, CAMBRIDGE HDB EXPERT, P685; Feldon DF, 2007, EDUC PSYCHOL REV, V19, P91, DOI 10.1007/s10648-006-9009-0; Gegenfurtner A, 2011, EDUC PSYCHOL REV, V23, P523, DOI 10.1007/s10648-011-9174-7; Gegenfurtner A, 2013, VOCAT LEARN, V6, P37, DOI 10.1007/s12186-012-9088-7; Gobet E, 1998, MEMORY, V6, P225; Goldstone RL, 1998, ANNU REV PSYCHOL, V49, P585, DOI 10.1146/annurev.psych.49.1.585; Gronlund SD, 2005, INT J AVIAT PSYCHOL, V15, P269, DOI 10.1207/s15327108ijap1503_4; Haider H, 1999, J EXP PSYCHOL LEARN, V25, P172, DOI 10.1037/0278-7393.25.1.172; HOFFMAN RR, 1987, AI MAG, V8, P53; Holmqvist K., 2011, EYE TRACKING COMPREH; Jarodzka H, 2013, LEARN INSTR, V25, P62, DOI 10.1016/j.learninstruc.2012.11.004; Jarodzka H, 2010, LEARN INSTR, V20, P146, DOI 10.1016/j.learninstruc.2009.02.019; Jarodzka H, 2012, INSTR SCI, V40, P813, DOI 10.1007/s11251-012-9218-5; Jongman R. W., 1968, OOG MEESTER; Kasarskis P., 2001, 11 INT S AV PSYCH CO; Klingner J, 2011, PSYCHOPHYSIOLOGY, V48, P323, DOI 10.1111/j.1469-8986.2010.01069.x; Levenshtein VI, 1966, SOV PHYS DOKL, V10, P707; Lodewyk KR, 2005, J EDUC PSYCHOL, V97, P3, DOI 10.1037/0022-0663.97.1.3; Lowe RK, 2003, LEARN INSTR, V13, P157, DOI 10.1016/S0959-4752(02)00018-X; MAY JG, 1990, ACTA PSYCHOL, V75, P75, DOI 10.1016/0001-6918(90)90067-P; Mayer R. E., 2005, CAMBRIDGE HDB MULTIM, P31, DOI DOI 10.1017/CB09780511816819.004; Mayer RE, 2003, EDUC PSYCHOL-US, V38, P43, DOI 10.1207/S15326985EP3801_6; Medin DL, 1997, COGNITIVE PSYCHOL, V32, P49, DOI 10.1006/cogp.1997.0645; Medin DL, 2006, COGNITION, V99, P237, DOI 10.1016/j.cognition.2003.12.005; Mumford M. D., 2001, REV GEN PSYCHOL, V5, P213, DOI 10.1037/1089-2680.5.3.213; Oprins E., 2003, HUFAG NIEUWSBRIEF, V6, P2; PAAS FGWC, 1992, J EDUC PSYCHOL, V84, P429, DOI 10.1037/0022-0663.84.4.429; PINTRICH PR, 1990, J EDUC PSYCHOL, V82, P33, DOI 10.1037/0022-0663.82.1.33; Reingold EM, 2011, OXFORD HDB EYE MOVEM, P523; Scheiter K, 2009, LEARN INSTR, V19, P481, DOI 10.1016/j.learninstruc.2008.08.001; SCHMIDT HG, 1990, ACAD MED, V65, P611, DOI 10.1097/00001888-199010000-00001; SCHUNK DH, 1985, PSYCHOL SCHOOLS, V22, P208, DOI 10.1002/1520-6807(198504)22:2<208::AID-PITS2310220215>3.0.CO;2-7; SIMON HA, 1975, COGNITIVE PSYCHOL, V7, P268, DOI 10.1016/0010-0285(75)90012-2; Spanjers IAE, 2010, EDUC PSYCHOL REV, V22, P411, DOI 10.1007/s10648-010-9135-6; Spivey M. J., 2011, OXFORD HDB EYE MOVEM, P551; Sweller J, 2004, INSTR SCI, V32, P9, DOI 10.1023/B:TRUC.0000021808.72598.4d; Sweller J, 1998, EDUC PSYCHOL REV, V10, P251, DOI 10.1023/A:1022193728205; Van Cog T., 2009, COMPUT HUM BEHAV, V25, P785; Van Gog T., 2010, EDUC PSYCHOL REV, V22, P155, DOI DOI 10.1007/S10648-010-9134-7; Van Gog T., 2008, LEARN INSTR, V18, P211, DOI DOI 10.1016/J.LEARNINSTRUC.2007.03.003; Van Gog T., 2006, LEARN INSTR, V16, P154, DOI DOI 10.1016/J.LEARNINSTRUC.2006.02.003; Van Merrienboer JJG, 2013, 10 STEPS COMPLEX LEA; van Merrienboer JJG, 2002, ETR&D-EDUC TECH RES, V50, P39 54 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0959-4752 LEARN INSTR Learn Instr. AUG 2014 32 10 21 10.1016/j.learninstruc.2014.01.004 12 Education & Educational Research; Psychology, Educational Education & Educational Research; Psychology AG0NJ WOS:000335111900002 J Zhang, MW; Ma, RH; Li, JS; Zhang, B; Duan, HT Zhang, Minwei; Ma, Ronghua; Li, Junsheng; Zhang, Bing; Duan, Hongtao A Validation Study of an Improved SWIR Iterative Atmospheric Correction Algorithm for MODIS-Aqua Measurements in Lake Taihu, China IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Atmosphere; lakes; remote sensing; water pollution TURBID PRODUCTIVE WATERS; CHLOROPHYLL-A CONCENTRATION; AEROSOL OPTICAL-THICKNESS; OCEAN COLOR; SEAWIFS IMAGERY; LEAVING REFLECTANCE; REMOTE ESTIMATION; PRACTICAL METHOD; INFRARED BANDS; INLAND WATERS We have presented an improved short-wave infrared (SWIR)-based iterative algorithm for the atmospheric correction (AC) of Moderate Resolution Imaging Spectroradiometer (MODIS) data over Lake Taihu, China. The algorithm was validated by means of matchup comparison between MODIS-retrieved and in situ remote sensing reflectances (R-rs). Four examples of the matchup comparison were first carried out for the observation stations within a +/- 5-min time window of MODIS overpass and field measurements. It is shown in the examples that the retrieved R-rs spectra compare reasonably well with the in situ measurements not only over relatively clear waters (with R-rs(859) about 0.0014 sr(-1)) but also over turbid waters (with R-rs(859) about 0.013 sr(-1)). The matchup comparison was further carried out for a total of 54 observation stations within a +/- 2-h time window, indicating that the AC algorithm has good performance for producing water spectra from MODIS data over Lake Taihu. The development of an algal bloom event has been monitored using MODIS-measured R-rs(443) and R-rs(859), showing that MODIS data, combined with the AC algorithm, can be a useful tool for monitoring the water quality of Lake Taihu. The SWIR iterative algorithm, along with the chlorophyll-a concentration (Chl-a) retrieval model using red to near-infrared bands, has the potential of monitoring Chl-a quantitatively and providing useful information for decision makers to manage the water environment and to prepare for events as algal blooms. [Zhang, Minwei; Li, Junsheng; Zhang, Bing] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Ma, Ronghua; Duan, Hongtao] Chinese Acad Sci, Nanjing Inst Geog & Limnol, State Key Lab Lake Sci & Environm, Nanjing 210008, Jiangsu, Peoples R China Zhang, MW (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. zhangminwei2004@126.com Duan, Hongtao/B-7210-2011 Duan, Hongtao/0000-0002-1985-2292 National Natural Science Foundation of China [41106154, 40906090, 40901174] This work was supported by the National Natural Science Foundation of China under Grants 41106154, 40906090, and 40901174. Arnone R. A., 1996, P SPIE OCEAN OPT, V2963, P222; Austin R. W., 1981, OCEANOGRAPHY SPACE; Bailey SW, 2010, OPT EXPRESS, V18, P7521, DOI 10.1364/OE.18.007521; Bailey SW, 2006, REMOTE SENS ENVIRON, V102, P12, DOI 10.1016/j.rse.2006.01.015; Chen J, 2013, ENVIRON MONIT ASSESS, V185, P2243, DOI 10.1007/s10661-012-2705-y; Dall'Olmo G, 2005, APPL OPTICS, V44, P412, DOI 10.1364/AO.44.000412; Dall'Olmo G, 2005, REMOTE SENS ENVIRON, V96, P176, DOI 10.1016/j.rse.2005.02.007; Dekker AG, 2001, SCI TOTAL ENVIRON, V268, P197, DOI 10.1016/S0048-9697(00)00679-3; Ding J., 2006, J REMOTE SENSING, V10, P732; Duan Hongtao, 2008, Hupo Kexue, V20, P145; GITELSON A, 1992, INT J REMOTE SENS, V13, P3367; GORDON HR, 1994, APPL OPTICS, V33, P443, DOI 10.1364/AO.33.000443; Gordon HR, 1995, APPL OPTICS, V34, P8363, DOI 10.1364/AO.34.008363; Goyens C, 2013, REMOTE SENS ENVIRON, V131, P63, DOI 10.1016/j.rse.2012.12.006; Gurlin D, 2011, REMOTE SENS ENVIRON, V115, P3479, DOI 10.1016/j.rse.2011.08.011; HALE GM, 1973, APPL OPTICS, V12, P555, DOI 10.1364/AO.12.000555; He XQ, 2003, P SOC PHOTO-OPT INS, V4892, P494, DOI 10.1117/12.466084; Hu CH, 2006, CHINESE SCI BULL, V51, P731, DOI 10.1007/s11434-006-0731-2; Hu CM, 2000, REMOTE SENS ENVIRON, V74, P195, DOI 10.1016/S0034-4257(00)00080-8; Hu CM, 2004, REMOTE SENS ENVIRON, V93, P423, DOI 10.1016/j.rse.2004.08.007; Jamet C, 2011, REMOTE SENS ENVIRON, V115, P1955, DOI 10.1016/j.rse.2011.03.018; [孔维娟 KONG Wei-juan], 2009, [遥感信息, Remote Sensing Information], P80; Lavender SJ, 2005, CONT SHELF RES, V25, P539, DOI 10.1016/j.csr.2004.10.007; Le CF, 2009, REMOTE SENS ENVIRON, V113, P1175, DOI 10.1016/j.rse.2009.02.005; Ma R, 2006, INT J REMOTE SENS, V27, P4277, DOI 10.1080/01431160600851835; Ma Ronghua, 2008, Hupo Kexue, V20, P687; Ma RH, 2008, SENSORS-BASEL, V8, P3988, DOI 10.3390/s8063988; Meister G., 2011, P MODIS SCI TEAM M A, P1; Mobley C. D., 1995, HYDROLIGHT 3 0 USERS; Mobley CD, 1999, APPL OPTICS, V38, P7442, DOI 10.1364/AO.38.007442; Moore GF, 1999, INT J REMOTE SENS, V20, P1713, DOI 10.1080/014311699212434; MOREL A, 1993, APPL OPTICS, V32, P6864, DOI 10.1364/AO.32.006864; MOREL A, 1977, LIMNOL OCEANOGR, V22, P709; Mueller J. L., 2003, OCEAN OPTICS PROTOCO, P21; O'Reilly JE, 1998, J GEOPHYS RES-OCEANS, V103, P24937, DOI 10.1029/98JC02160; Rijkeboer M., 1998, AQUAT ECOL, V31, P313, DOI DOI 10.1023/A:1009916501492; Ruddick KG, 2000, APPL OPTICS, V39, P897, DOI 10.1364/AO.39.000897; Ruiz-Verdu A., 2008, REMOTE SENS ENVIRON, V112, P3993; Sathyendranath S, 2001, INT J REMOTE SENS, V22, P249, DOI 10.1080/014311601449925; Shi W, 2009, REMOTE SENS ENVIRON, V113, P1587, DOI 10.1016/j.rse.2009.03.011; Siegel DA, 2000, APPL OPTICS, V39, P3582, DOI 10.1364/AO.39.003582; Song XL, 2010, AQUAT ECOL, V44, P41, DOI 10.1007/s10452-009-9258-3; Stumpf R. P., 2003, NASATM2003206892, P51; Trees C. C., 2003, OCEAN OPTICS PROTOCO, P15; Wang M., 2005, GEOPHYS RES LETT, V32; Wang M. H., 2007, GEOPHYS RES LETT, V34; Wang MH, 2007, APPL OPTICS, V46, P1535, DOI 10.1364/AO.46.001535; Wang MH, 2011, REMOTE SENS ENVIRON, V115, P841, DOI 10.1016/rse.2010.11.012; [王艳红 WANG Yanhong], 2007, [环境科学学报, Acta Scientiae Circumstantiae], V27, P509; Xia XA, 2004, J ENVIRON SCI-CHINA, V16, P832; Xing Xiao-Gang, 2007, Ocean Science Journal, V42, P49; Zhang M., 2010, J GEOPHYS RES, V115; Zhang MW, 2010, REMOTE SENS ENVIRON, V114, P392, DOI 10.1016/j.rse.2009.09.016; Zhang YL, 2010, J PLANKTON RES, V32, P1023, DOI 10.1093/plankt/fbq039; Zhao FS, 1997, APPL OPTICS, V36, P6949, DOI 10.1364/AO.36.006949; Zhou Guanhua, 2008, Hupo Kexue, V20, P153; [周艺 Zhou Yi], 2004, [水科学进展, Advances in Water Science], V15, P312 57 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2014 52 8 4686 4695 10.1109/TGRS.2013.2283523 10 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5ZC WOS:000332598500017 J Indu, J; Kumar, DN Indu, J.; Kumar, D. Nagesh Copula-Based Modeling of TMI Brightness Temperature With Rainfall Type IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Copula; quantile regression; river basin; Tropical Rainfall Measuring Mission (TRMM) BIVARIATE DISTRIBUTIONS; MICROWAVE MEASUREMENT; RETRIEVAL ALGORITHMS; PRECIPITATION; LAND; VALIDATION; MARGINALS; HYDROLOGY; FAMILY; IMAGER Overland rain retrieval using spaceborne microwave radiometer offers a myriad of complications as land presents itself as a radiometrically warm and highly variable background. Hence, land rainfall algorithms of the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in the TMI ocean algorithm). In this paper, sensitivity analysis is conducted using the Spearman rank correlation coefficient as benchmark, to estimate the best combination of TMI low-frequency channels that are highly sensitive to the near surface rainfall rate from the TRMM Precipitation Radar (PR). Results indicate that the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors but also aid in surface noise reduction over a predominantly vegetative land surface background. Furthermore, the variations of rainfall signature in these channel combinations are not understood properly due to their inherent uncertainties and highly nonlinear relationship with rainfall. Copula theory is a powerful tool to characterize the dependence between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this paper proposes a regional model using Archimedean copulas, to study the dependence of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from the passive and active sensors on board TRMM, namely, TMI and PR. Studies conducted for different rainfall regimes over the study area show the suitability of Clayton and Gumbel copulas for modeling convective and stratiform rainfall types for the majority of the intraseasonal months. Furthermore, large ensembles of TMI Tb (from the most sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, and 95th) of the convective and the stratiform rainfall. Comparatively greater ambiguity was observed to model extreme values of the convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal the superior performance of the proposed copula-based technique. [Indu, J.; Kumar, D. Nagesh] Indian Inst Sci, Dept Civil Engn, Bangalore 560012, Karnataka, India Indu, J (reprint author), Indian Inst Sci, Dept Civil Engn, Bangalore 560012, Karnataka, India. IBM; Ministry of Earth Sciences, Government of India [MOES/ATMOS/PP-IX/09] The work of D. N. Kumar was supported by IBM through IBM Faculty Award 2012 and the Ministry of Earth Sciences, Government of India, through the project MOES/ATMOS/PP-IX/09. AghaKouchak A, 2010, HYDROL PROCESS, V24, P2111, DOI 10.1002/hyp.7632; Aonashi K, 2009, J METEOROL SOC JPN, V87A, P119, DOI 10.2151/jmsj.87A.119; Bosq D., 1998, LECT NOTES STAT, P210; Bouye E, 2000, COPULAS FINANCE READ; Chollete L, 2009, J FINANC ECONOMET, V7, P437, DOI 10.1093/jjfinec/nbp014; De Michele C., 2003, J GEOPHYS RES, V108; Dinku T, 2005, J APPL METEOROL, V44, P189, DOI 10.1175/JAM2186.1; Dupuis DJ, 2007, J HYDROL ENG, V12, P381, DOI 10.1061/(ASCE)1084-0699(2007)12:4(381); Favre A. C., 2004, WATER RESOUR RES, V40; Ferraro R., 1994, REMOTE SENS REV, V11, P195; Ferraro R, 2005, WEATHER FORECAST, V20, P465, DOI 10.1175/WAF861.1; FERRARO RR, 1995, J ATMOS OCEAN TECH, V12, P755, DOI 10.1175/1520-0426(1995)012<0755:TDOSRR>2.0.CO;2; Frank MJ, 1979, AEQUATIONES MATH, V19, P194, DOI DOI 10.1007/BF02189866; Frees E. W., 1998, N AM ACTUARIAL J, V2, P1, DOI DOI 10.1080/10920277.1998.10595667; Gao H, 2006, J HYDROMETEOROL, V7, P23, DOI 10.1175/JHM473.1; GENEST C, 1993, J AM STAT ASSOC, V88, P1034, DOI 10.2307/2290796; GENEST C, 1987, BIOMETRIKA, V74, P549, DOI 10.1093/biomet/74.3.549; GENEST C, 1986, AM STAT, V40, P280, DOI 10.2307/2684602; Gopalan K, 2010, J ATMOS OCEAN TECH, V27, P1343, DOI 10.1175/2010JTECHA1454.l; Grecu M, 2004, J APPL METEOROL, V43, P562, DOI 10.1175/1520-0450(2004)043<0562:ROPPFM>2.0.CO;2; GRODY NC, 1991, J GEOPHYS RES-ATMOS, V96, P7423, DOI 10.1029/91JD00045; GUMBEL EJ, 1960, J AM STAT ASSOC, V55, P698, DOI 10.2307/2281591; Hafner CM, 2012, J APPL ECONOMET, V27, P269, DOI 10.1002/jae.1197; Huffman GJ, 2007, J HYDROMETEOROL, V8, P38, DOI 10.1175/JHM560.1; Joe H, 1997, MULTIVARIATE MODELS; Kidd C., 1998, INT J REMOTE SENS; Kimberling C. H., 1974, AEQUATIONES MATH, V10, P152; Koenker R., 2005, QUANTILE REGRESSION; Kummerow C, 2001, J APPL METEOROL, V40, P1801, DOI 10.1175/1520-0450(2001)040<1801:TEOTGP>2.0.CO;2; Kummerow C, 1998, J ATMOS OCEAN TECH, V15, P809, DOI 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2; Lall U, 1996, WATER RESOUR RES, V32, P2803, DOI 10.1029/96WR00565; LALL U, 1995, REV GEOPHYS, V33, P1093, DOI 10.1029/95RG00343; LIU GS, 1992, J GEOPHYS RES-ATMOS, V97, P9959; Maity R., 2008, J GEOPHYS RES, V113; Mishra A., 2012, ISRN GEOPHYS, V2012, P1; MUGNAI A, 1990, B AM METEOROL SOC, V71, P2, DOI 10.1175/1520-0477(1990)071<0002:SOMBTO>2.0.CO;2; Mukhopadhyay S, 2009, IEEE T COMPUT AID D, V28, P1038, DOI 10.1109/TCAD.2009.2017429; Nelsen R. B., 2006, INTRO COPULAS, P269; Nelsen R. B., 1999, INTRO COPULAS; NELSEN RB, 1986, COMMUN STAT THEORY, V15, P3277, DOI 10.1080/03610928608829309; Patton AJ, 2006, J APPL ECONOM, V21, P147, DOI 10.1002/jae.865; Prabhakara C., 2005, Journal of the Meteorological Society of Japan, V83, P595, DOI 10.2151/jmsj.83.595; Rapp AD, 2009, J APPL METEOROL CLIM, V48, P1981, DOI 10.1175/2009JAMC2155.1; Sarma DK, 2008, IEEE T GEOSCI REMOTE, V46, P1689, DOI 10.1109/TGRS.2008.916469; Sarma DK, 2005, IEEE T GEOSCI REMOTE, V43, P2879, DOI 10.1109/TGRS.2005.857910; Scott D. W., 1992, MULTIVARIATE DENSITY; Sharma A, 2000, J HYDROL, V239, P249, DOI 10.1016/S0022-1694(00)00348-6; Sklar A., 1959, FONCTIONS REPARTITIO, P229; Spencer R. W., 1989, Journal of Atmospheric and Oceanic Technology, V6, DOI 10.1175/1520-0426(1989)006<0254:PROLAO>2.0.CO;2; Tarboton DG, 1998, WATER RESOUR RES, V34, P107, DOI 10.1029/97WR02429; Venter G. G., 2002, P CAS ACT SOC, P68; Viltard N, 2006, J APPL METEOROL CLIM, V45, P455, DOI 10.1175/JAM2346.1; Wang NY, 2009, J METEOROL SOC JPN, V87A, P237, DOI 10.2151/jmsj.87A.237; WILHEIT TT, 1986, B AM METEOROL SOC, V67, P1226, DOI 10.1175/1520-0477(1986)067<1226:SCOPMM>2.0.CO;2; You YL, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015345; Zhang L, 2006, J HYDROL ENG, V11, P150, DOI 10.1061/(ASCE)1084-0699(2006)11:2(150) 56 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2014 52 8 4832 4845 10.1109/TGRS.2013.2285225 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5ZC WOS:000332598500029 J Jimenez, S; Malo, J Jimenez, Sandra; Malo, Jesus The Role of Spatial Information in Disentangling the Irradiance-Reflectance-Transmittance Ambiguity IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Complexity of irradiance-reflectance-transmittance; curvature analysis; inverse problems in remote sensing; spatial information; spatio-spectral principal component analysis (PCA) IMAGING SPECTROMETRY DATA; SURFACE REFLECTANCE; RADIATIVE-TRANSFER; INVERSION; RETRIEVAL; LAND; MODEL; ATMOSPHERE In the satellite hyperspectral measures, the contributions of light, surface, and atmosphere are mixed. Applications need separate access to the sources. Conventional inversion techniques usually take a pixelwise spectral-only approach. However, recent improvements in retrieving surface and atmosphere characteristics use heuristic spatial smoothness constraints. In this paper, we theoretically justify such heuristics by analyzing the impact of spatial information on the uncertainty of the solution. The proposed analysis allows to assess in advance the uniqueness (or robustness) of the solution depending on the curvature of a likelihood surface. In situations where pixel-based approaches become unreliable, it turns out that the consideration of spatial information always makes the problem to be better conditioned. With the proposed analysis, this is easily understood since the curvature is consistent with the complexity of the sources measured in terms of the number of significant eigenvalues (or free parameters in the problem). In agreement with recent results in hyperspectral image coding, spatial correlations in the sources imply that the intrinsic complexity of the spatio-spectral representation of the signal is always lower than its spectral-only counterpart. According to this, the number of free parameters in the spatio-spectral inverse problem is smaller, so the spatio-spectral approaches are always better than spectral-only approaches. Experiments using ensembles of actual reflectance values and realistic MODTRAN irradiance and atmosphere radiance and transmittance values show that the proposed analysis successfully predicts the practical difficulty of the problem and the improved quality of spatio-spectral retrieval. [Jimenez, Sandra; Malo, Jesus] Univ Valencia, Image Proc Lab, Valencia 46980, Spain Jimenez, S (reprint author), Univ Valencia, Image Proc Lab, Valencia 46980, Spain. sjimenez@uv.es; jmalo@uv.es [TIN2012-38102-C03-01] This work was supported in part by Project TIN2012-38102-C03-01. Abousleman P., 1995, IEEE T GEOSCI REMOTE, V33, P26; Asrar G., 1989, THEORY APPL OPTICAL; Beal D, 2007, INT J REMOTE SENS, V28, P761, DOI 10.1080/01431160600821085; Camps G., 2011, REM SENS IM PROC SYN; Clarke R. J., 1981, P IEE F, V128, P359; de Valencia Universitat, 2013, MAT ROLE SPATIAL I S; Dorigo W., 2007, AGRISAR EAGLE CAMP F; Dubovik O, 2004, NATO SCI SER II MATH, V161, P65; Enting I. T, 2002, INVERSE PROBLEMS ATM; Guanter L, 2008, REMOTE SENS ENVIRON, V112, P2898, DOI 10.1016/j.rse.2008.02.001; Hajnsek I., 2007, GEOPH RES ABSTR, V9; Hedley J, 2009, REMOTE SENS ENVIRON, V113, P2527, DOI 10.1016/j.rse.2009.07.008; Hoyningen W., 2006, ADV SPACE RES, V37, P2172; Jia XP, 2013, P IEEE, V101, P676, DOI 10.1109/JPROC.2012.2229082; Jolliffe IT, 2002, PRINCIPAL COMPONENT; KAUFMAN YJ, 1982, J GEOPHYS RES-OC ATM, V87, P4137, DOI 10.1029/JC087iC06p04137; Lauvernet C, 2008, REMOTE SENS ENVIRON, V112, P851, DOI 10.1016/j.rse.2007.06.027; Lauvernet C., 2005, INT S PHYS MEAS SIG; Lavergne T, 2007, REMOTE SENS ENVIRON, V107, P362, DOI 10.1016/j.rse.2006.05.021; MEKLER Y, 1982, APPL OPTICS, V21, P310, DOI 10.1364/AO.21.000310; Penna B, 2007, IEEE T GEOSCI REMOTE, V45, P1408, DOI 10.1109/TGRS.2007.894565; POGGIO T, 1985, NATURE, V317, P314, DOI 10.1038/317314a0; Press W. H., 1992, NUMERICAL RECIPES C; Qu Z, 2003, IEEE T GEOSCI REMOTE, V41, P1223, DOI 10.1109/TGRS.2003.813125; Richter R, 2002, INT J REMOTE SENS, V23, P2631, DOI 10.1080/01431160110115834; SAGHRI JA, 1995, IEEE SIGNAL PROC MAG, V12, P32, DOI 10.1109/79.363506; Sanchez J. G., 2007, AGRISAR EAGLE CAMP F; Schlapfer D, 2002, INT J REMOTE SENS, V23, P2609, DOI 10.1080/01431160110115825; Singh B., 2003, P 3 INT S 3 DIM EL F, P1; Tarantola A., 2005, INVERSE PROBLEM THEO; Timofeyev YM, 2003, APPL OPTICS, V42, P2635, DOI 10.1364/AO.42.002635; Tuia D, 2013, IEEE T GEOSCI REMOTE, V51, P329, DOI 10.1109/TGRS.2012.2200045; Tuia D, 2011, REMOTE SENS ENVIRON, V115, P2232, DOI 10.1016/j.rse.2011.04.022; Wang YF, 2007, REMOTE SENS ENVIRON, V111, P36, DOI 10.1016/j.rse.2007.03.007 34 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2014 52 8 4881 4894 10.1109/TGRS.2013.2285731 14 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5ZC WOS:000332598500033 J Song, ZL; Zhou, SG; Guan, JH Song, Zhili; Zhou, Shuigeng; Guan, Jihong A Novel Image Registration Algorithm for Remote Sensing Under Affine Transformation IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Histogram; image registration; remote sensing; robust estimation; triangle area representation (TAR) RANDOM SAMPLE CONSENSUS; RANDOMIZED RANSAC; SIFT; REPRESENTATION; SEGMENTATION; TRAJECTORIES; DESCRIPTORS; INFORMATION; RETRIEVAL; FEATURES With the help of the histogram of triangle area representation (TAR) and feature matching strategy, a new effective image registration approach for remote sensing is proposed in this paper. This approach is based on a robust transformation parameter estimation algorithm called the histogram of TAR sample consensus (HTSC in short). The HTSC algorithm can replace the existing random sample consensus (RANSAC) and progressive sample consensus (PROSAC) methods that have been widely used in the transformation parameter estimation step of remote-sensing image registration, for it can efficiently calculate the consensus set with a higher accuracy. This paper lays down a new way to build a robust transformation parameter estimator based on the invariance constraint for remote-sensing image registration. Analogous to the two types of well-known existing transformation parameter estimation methods RANSAC and PROSAC, HTSC can serve as a new type (or the third type if we treat RANSAC and PROSAC as the first and the second types) of such methods, as it adopts the transformation-invariance information to find the consensus. [Song, Zhili] Shanghai Inst Technol, Sch Comp Sci, Shanghai 201418, Peoples R China; [Zhou, Shuigeng] Fudan Univ, Shanghai Key Lab Intelligent Informat Proc, Shanghai 200433, Peoples R China; [Zhou, Shuigeng] Fudan Univ, Sch Comp Sci, Shanghai 200433, Peoples R China; [Guan, Jihong] Tongji Univ, Dept Comp Sci & Technol, Shanghai 201802, Peoples R China Guan, JH (reprint author), Tongji Univ, Dept Comp Sci & Technol, Shanghai 201802, Peoples R China. zlsong@fudan.edu.cn; sgzhou@fudan.edu.cn; jhguan@tongji.edu.cn Natural Science Foundation of Shanghai Municipality of China [12ZR1431000]; Science Research Program Foundation of the Shanghai Institute of Technology of China [YJ2011-68]; Research Innovation Program of Shanghai Municipal Education Committee [13ZZ003]; National Natural Science Foundation of China [61373036] The work of Z. Song was supported in part by the Natural Science Foundation of Shanghai Municipality of China under Grant 12ZR1431000 and in part by the Science Research Program Foundation of the Shanghai Institute of Technology of China under Grant YJ2011-68. The work of S. Zhou was supported by the Research Innovation Program of Shanghai Municipal Education Committee under Grant 13ZZ003. The work of J. Guan was supported by the National Natural Science Foundation of China under Grant 61373036. (Corresponding author: J. Guan.) Alajlan N, 2007, PATTERN RECOGN, V40, P1911, DOI 10.1016/j.patcog.2006.12.005; Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014; Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558; Bin TJ, 2008, IMAGE VISION COMPUT, V26, P563, DOI 10.1016/j.imavis.2007.07.003; Brown L.G., 1992, ACM COMPUT SURV, V24, P325, DOI DOI 10.1145/146370.146374; Capel D., 2005, P BRIT MACH VIS C BM, P629; Chum O, 2005, PROC CVPR IEEE, P220; Chum O, 2008, IEEE T PATTERN ANAL, V30, P1472, DOI 10.1109/TPAMI.2007.70787; Chum O, 2003, LECT NOTES COMPUT SC, V2781, P236; Ding MT, 2010, IEEE GEOSCI REMOTE S, V7, P761, DOI 10.1109/LGRS.2010.2047241; FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692; Goncalves H, 2011, IEEE T GEOSCI REMOTE, V49, P2589, DOI 10.1109/TGRS.2011.2109389; Goncalves H, 2009, IEEE GEOSCI REMOTE S, V6, P292, DOI 10.1109/LGRS.2008.2012441; Hartley R, 2003, MULTIPLE VIEW GEOMET; Heo YS, 2011, IEEE T PATTERN ANAL, V33, P807, DOI 10.1109/TPAMI.2010.136; Kachenoura N, 2011, MAGN RESON IMAGING, V29, P853, DOI 10.1016/j.mri.2011.02.020; Ke Y, 2004, PROC CVPR IEEE, P506; Lee D, 2009, PROC CVPR IEEE, P186; Leprince S, 2007, IEEE T GEOSCI REMOTE, V45, P1529, DOI 10.1109/TGRS.2006.888937; Li ST, 2008, IMAGE VISION COMPUT, V26, P971, DOI 10.1016/j.imavis.2007.10.012; Loeckx D, 2010, IEEE T MED IMAGING, V29, P19, DOI 10.1109/TMI.2009.2021843; Lowe D., 1999, P 7 IEEE INT C COMP, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410; Maintz J B, 1998, Med Image Anal, V2, P1, DOI 10.1016/S1361-8415(01)80026-8; MANDAVA VR, 1989, IEEE T MED IMAGING, V8, P251, DOI 10.1109/42.34714; Matas J, 2004, IMAGE VISION COMPUT, V22, P837, DOI 10.1016/j.imavis.2004.02.009; Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188; Mikolajczyk K., 2001, P ICCV, V1, P525, DOI DOI 10.1109/ICCV.2001.937561; Palenichka RM, 2010, IEEE T GEOSCI REMOTE, V48, P2864, DOI 10.1109/TGRS.2010.2043677; Raguram R, 2008, LECT NOTES COMPUT SC, V5303, P500, DOI 10.1007/978-3-540-88688-4_37; Rajwade A, 2009, IEEE T PATTERN ANAL, V31, P475, DOI 10.1109/TPAMI.2008.97; Rohde GK, 2003, IEEE T MED IMAGING, V22, P1470, DOI 10.1109/TMI.2003.819299; Schmid C, 1997, IEEE T PATTERN ANAL, V19, P530, DOI 10.1109/34.589215; Shah CA, 2008, IEEE T GEOSCI REMOTE, V46, P3908, DOI 10.1109/TGRS.2008.2000636; Soille P, 2006, IEEE T PATTERN ANAL, V28, P673, DOI 10.1109/TPAMI.2006.99; Song ZL, 2010, OPT EXPRESS, V18, P513, DOI 10.1364/OE.18.000513; Song ZL, 2010, IEEE GEOSCI REMOTE S, V7, P491, DOI 10.1109/LGRS.2009.2039917; Stewart CV, 1999, SIAM REV, V41, P513, DOI 10.1137/S0036144598345802; Suri S, 2010, IEEE T GEOSCI REMOTE, V48, P939, DOI 10.1109/TGRS.2009.2034842; Tan QL, 2005, INT GEOSCI REMOTE SE, P2866; Tordoff B, 2002, LECT NOTES COMPUT SC, V2350, P82; Torr PHS, 2000, COMPUT VIS IMAGE UND, V78, P138, DOI 10.1006/cviu.1999.0832; Tsai CL, 2010, IEEE T MED IMAGING, V29, P636, DOI 10.1109/TMI.2009.2030324; Tzimiropoulos G, 2010, IEEE T PATTERN ANAL, V32, P1899, DOI 10.1109/TPAMI.2010.107; van de Sande K., 2008, P IEEE CVPR, P1; Wong A, 2007, IEEE T GEOSCI REMOTE, V45, P1483, DOI 10.1109/TGRS.2007.892601; Wong A, 2008, IEEE T GEOSCI REMOTE, V46, P3917, DOI 10.1109/TGRS.2008.2001685; Zheng JA, 2011, IEEE T INF TECHNOL B, V15, P221, DOI 10.1109/TITB.2010.2091145; Zitova B, 2003, IMAGE VISION COMPUT, V21, P977, DOI 10.1016/S0262-8856(03)00137-9 48 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2014 52 8 4895 4912 10.1109/TGRS.2013.2285814 18 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5ZC WOS:000332598500034 J Salmon, BP; Kleynhans, W; van den Bergh, F; Olivier, JC; Marais, WJ; Grobler, TL; Wessels, KJ Salmon, B. P.; Kleynhans, W.; van den Bergh, F.; Olivier, J. C.; Marais, W. J.; Grobler, T. L.; Wessels, K. J. Meta-Optimization of the Extended Kalman Filter's Parameters Through the Use of the Bias Variance Equilibrium Point Criterion IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Classification algorithms and geospatial analysis; Kalman Filters; time series analysis; unsupervised learning LAND-COVER CLASSIFICATION; NOISE COVARIANCES; TIME-SERIES; MODIS; IDENTIFICATION; REFLECTANCE; CLASSIFIERS; ALGORITHMS; RETRIEVAL; ALBEDO The extraction of information on land cover classes using unsupervised methods has always been of relevance to the remote sensing community. In this paper, a novel criterion is proposed, which extracts the inherent information in an unsupervised fashion from a time series. The criterion is used to fit a parametric model to a time series, derive the corresponding covariance matrices of the parameters for the model, and estimate the additive noise on the time series. The proposed criterion uses both spatial and temporal information when estimating the covariance matrices and can be extended to incorporate spectral information. The algorithm used to estimate the parameters for the model is the extended Kalman filter (EKF). An unsupervised search algorithm, specifically designed for this criterion, is proposed in conjunction with the criterion that is used to rapidly and efficiently estimate the variables. The search algorithm attempts to satisfy the criterion by employing density adaptation to the current candidate system. The application in this paper is the use of an EKF to model Moderate Resolution Imaging Spectroradiometer time series with a triply modulated cosine function as the underlying model. The results show that the criterion improved the fit of the triply modulated cosine function by an order of magnitude on the time series over all seven spectral bands when compared with the other methods. The state space variables derived from the EKF are then used for both land cover classification and land cover change detection. The method was evaluated in the Gauteng province of South Africa where it was found to significantly improve on land cover classification and change detection accuracies when compared with other methods. [Salmon, B. P.; Kleynhans, W.] Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0028 Pretoria, South Africa; [Salmon, B. P.; Kleynhans, W.; van den Bergh, F.; Grobler, T. L.] CSIR, Meraka Inst, Remote Sensing Res Unit, ZA-0001 Pretoria, South Africa; [Olivier, J. C.] Univ Tasmania, Sch Engn, Hobart, Tas 7001, Australia; [Marais, W. J.] Univ Wisconsin, Space Sci & Engn Ctr, Madison, WI 53706 USA; [Grobler, T. L.] Univ Pretoria, ZA-0028 Pretoria, South Africa Salmon, BP (reprint author), Univ Pretoria, Dept Elect Elect & Comp Engn, ZA-0028 Pretoria, South Africa. brian.salmon@gmail.com ARNOLD WF, 1984, P IEEE, V72, P1746, DOI 10.1109/PROC.1984.13083; Bishop C, 1995, NEURAL NETWORKS PATT; CAREW B, 1973, IEEE T AUTOMAT CONTR, VAC18, P582, DOI 10.1109/TAC.1973.1100420; Chen M, 2008, ECOL MODEL, V219, P317, DOI 10.1016/j.ecolmodel.2008.07.013; Coppin P, 2004, INT J REMOTE SENS, V25, P1565, DOI 10.1080/0143116031000101675; DAILY GC, 1992, BIOSCIENCE, V42, P761, DOI 10.2307/1311995; DeFries RS, 2000, REMOTE SENS ENVIRON, V74, P503, DOI 10.1016/S0034-4257(00)00142-5; Friedl MA, 2010, REMOTE SENS ENVIRON, V114, P168, DOI 10.1016/j.rse.2009.08.016; Kleynhans W, 2010, IEEE GEOSCI REMOTE S, V7, P381, DOI 10.1109/LGRS.2009.2036578; Lhermitte S, 2008, REMOTE SENS ENVIRON, V112, P506, DOI 10.1016/j.rse.2007.05.018; Lu D, 2007, INT J REMOTE SENS, V28, P823, DOI 10.1080/01431160600746456; Lunetta RS, 2006, REMOTE SENS ENVIRON, V105, P142, DOI 10.1016/j.rse.2006.06.018; MEHRA RK, 1970, IEEE T AUTOMAT CONTR, VAC15, P175, DOI 10.1109/TAC.1970.1099422; Nikoukhah R., 1991, 07040188 MIT LAB INF; Nikulin MS, 2010, STAT IND TECHNOL, P1, DOI 10.1007/978-0-8176-4924-1; Noriega G, 1997, IEEE T GEOSCI REMOTE, V35, P1146, DOI 10.1109/36.628782; Odelson BJ, 2006, AUTOMATICA, V42, P303, DOI 10.1016/j.automatica.2005.09.006; PAOLA JD, 1995, IEEE T GEOSCI REMOTE, V33, P981, DOI 10.1109/36.406684; Rajamani MR, 2009, AUTOMATICA, V45, P142, DOI 10.1016/j.automatica.2008.05.032; Ristic B., 2004, KALMAN FILTER PARTIC; Salmon BP, 2011, INT J APPL EARTH OBS, V13, P873, DOI 10.1016/j.jag.2011.06.007; Salzberg SL, 1997, DATA MIN KNOWL DISC, V1, P317, DOI 10.1023/A:1009752403260; Samain O, 2008, REMOTE SENS ENVIRON, V112, P1337, DOI 10.1016/j.rse.2007.07.007; Schaaf CB, 2002, REMOTE SENS ENVIRON, V83, P135, DOI 10.1016/S0034-4257(02)00091-3; Shumway R. H., 1982, Journal of Time Series Analysis, V3, DOI 10.1111/j.1467-9892.1982.tb00349.x; Vitousek PM, 1997, SCIENCE, V277, P494, DOI 10.1126/science.277.5325.494; Wanner W, 1997, J GEOPHYS RES-ATMOS, V102, P17143, DOI 10.1029/96JD03295 27 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing AUG 2014 52 8 5072 5087 10.1109/TGRS.2013.2286821 16 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5ZC WOS:000332598500047 J Ambrico, PF; Ambrico, M; Schiavulli, L; De Benedictis, S Ambrico, Paolo F.; Ambrico, Marianna; Schiavulli, Luigi; De Benedictis, Santolo 2D Thermoluminescence imaging of dielectric surface long term charge memory of plasma surface interaction in DBD discharges JOURNAL OF PHYSICS D-APPLIED PHYSICS English Article DBD; thermoluminescence; charge trapping; plasma BARRIER DISCHARGE; ATMOSPHERIC-PRESSURE; AIR The charge trapping effect due to the exposure of alumina surfaces to plasma has been studied in a volume dielectric barrier discharge (DBD) in Ar and He noble gases. The long lasting charge trapping of alumina dielectric plates, used as barriers in DBDs, is evidenced by an ex situ thermoluminescence (TL) experiment performed with a standard and a custom two-dimensional (2D)-TL apparatus. The spatial density of trapped surface charges is found to be strongly correlated to the plasma morphology, and the surface spatial memory lasted for several minutes to hours after plasma exposure. In the case of Ar, the plasma channel impact signature on the surface shows a higher equivalent radiation dose with respect to the surface plasma wave and the post-discharge species signature. As a consequence, for the development of discharges, inside the dielectric surface the availability of lower energy trapped electrons is larger in the first region of plasma impact. The reported spatial memory increases the likelihood of the occurrence of plasma filaments in the same position in different runs. In He plasmas, the dielectric barrier shows an almost uniform distribution of trapped charges, meaning that there is no preferred region for the development of the discharge. In all cases a slight asymmetry was shown in the direction of the gas flow. This can be interpreted as being due to the long-living species moving in the direction of the gas flow, corresponding with the TL side experiment on the sample exposed to the plasma afterglow. The maximum values and the integral of the 2D-TL images showed a linear relation with the total charge per ac cycle, corresponding with findings for the TL glow curve. In conclusion, 2D-TL images allow the retrieval of information regarding the plasma surface interaction such as the plasma morphology, trap sites and their activation temperature. [Ambrico, Paolo F.; Ambrico, Marianna; De Benedictis, Santolo] CNR, Ist Metodol Inorgan & Plasmi, Dipartimento Chim, I-70126 Bari, Italy; [Ambrico, Paolo F.; Ambrico, Marianna; Schiavulli, Luigi] Ist Nazl Fis Nucl, Unita Bari, Dipartimento Interateneo Fis, I-70126 Bari, Italy; [Schiavulli, Luigi] Univ Bari, Dipartimento Interateneo Fis, I-70126 Bari, Italy Ambrico, PF (reprint author), CNR, Ist Metodol Inorgan & Plasmi, Dipartimento Chim, Via Orabona 4, I-70126 Bari, Italy. paolofrancesco.ambrico@cnr.it Ambrico PF, 2009, APPL PHYS LETT, V94, DOI 10.1063/1.3076122; Ambrico PF, 2010, J PHYS D APPL PHYS, V43, DOI 10.1088/0022-3727/43/32/325201; Ambrico PF, 2009, APPL PHYS LETT, V94, DOI 10.1063/1.3152284; Ambrico PF, 2008, PLASMA CHEM PLASMA P, V28, P299, DOI 10.1007/s11090-008-9131-5; Baldacchini G, 2008, PHYS SOLID STATE+, V50, P1747, DOI 10.1134/S106378340809031X; Belmonte T, 2002, J PHYS D APPL PHYS, V35, P1919, DOI 10.1088/0022-3727/35/16/304; Braun D, 1992, PLASMA SOURCES SCI T, V1, P166, DOI 10.1088/0963-0252/1/3/004; Chirokov A, 2004, PLASMA SOURCES SCI T, V13, P623, DOI 10.1088/0963-0252/13/4/011; Chirokov A, 2005, IEEE T PLASMA SCI, V33, P300, DOI 10.1109/TPS.2005.845108; Chirokov A, 2005, PURE APPL CHEM, V77, P487, DOI 10.1351/pac200577020487; Duran M, 2001, SURF COAT TECH, V142, P743, DOI 10.1016/S0257-8972(01)01159-8; Gibalov VI, 2012, PLASMA SOURCES SCI T, V21, DOI 10.1088/0963-0252/21/2/024010; Golubovskii YB, 2002, J PHYS D APPL PHYS, V35, P751, DOI 10.1088/0022-3727/35/8/306; Guaitella O, 2011, APPL PHYS LETT, V98, DOI 10.1063/1.3552965; Horowitz YS, 2013, RADIAT PROT DOSIM, V153, P1, DOI 10.1093/rpd/ncs242; Kubota S, 2009, PLASMA SOURCES SCI T, V18, DOI 10.1088/0963-0252/18/3/034003; Li M, 2008, APPL PHYS LETT, V92, DOI 10.1063/1.2838340; McKeever S. W. S., 1985, THERMOLUMINESCENCE S; Puchalska M, 2006, RADIAT MEAS, V41, P659, DOI 10.1016/j.radmeas.2006.03.008; Rahel J, 2005, J PHYS D APPL PHYS, V38, P547, DOI 10.1088/0022-3727/38/4/006; Sakurai T, 2007, PLASMA SOURCES SCI T, V16, pS101, DOI 10.1088/0963-0252/16/1/S11 21 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0022-3727 1361-6463 J PHYS D APPL PHYS J. Phys. D-Appl. Phys. JUL 30 2014 47 30 305201 10.1088/0022-3727/47/30/305201 13 Physics, Applied Physics AL1BW WOS:000338861800008 J Autry, KS; Levine, WH Autry, Kevin S.; Levine, William H. A fan effect in anaphor processing: effects of multiple distractors FRONTIERS IN PSYCHOLOGY English Article comprehension; memory; fan effect; reading; anaphor resolution; antecedent; distractor PRONOUN RESOLUTION; SENTENCE COMPREHENSION; READING-COMPREHENSION; CATEGORICAL ANAPHORS; MEMORY RETRIEVAL; SITUATION MODELS; TERM-MEMORY; INFORMATION; SEARCH; INTERFERENCE Research suggests that the presence of a non-referent from the same category as the referent interferes with anaphor resolution. In five experiments, the hypothesis that multiple non-referents would produce a cumulative interference effect (i.e., a fan effect) was examined. This hypothesis was supported in Experiments 1A and 1B, with subjects being less accurate and slower to recognize referents (1A) and non-referents (1B) as the number of potential referents increased from two to five. Surprisingly, the number of potential referents led to a decrease in anaphor reading times. The results of Experiments 2A and 2B replicated the probe-recognition results in a completely within-subjects design and ruled out the possibility that a speeded-reading strategy led to the fan-effect findings. The results of Experiment 3 provided evidence that subjects were resolving the anaphors. These results suggest that multiple non-referents do produce a cumulative interference effect; however, additional research is necessary to explore the effect on anaphor reading times. [Autry, Kevin S.; Levine, William H.] Univ Arkansas, Dept Psychol Sci, Fayetteville, AR 72701 USA Autry, KS (reprint author), Univ Arkansas, Dept Psychol Sci, Mem Hall 216, Fayetteville, AR 72701 USA. ksautry@gmail.com; whlevine@uark.edu Almor A, 1999, PSYCHOL REV, V106, P748, DOI 10.1037/0033-295X.106.4.748; ANDERSON JR, 1983, J VERB LEARN VERB BE, V22, P261, DOI 10.1016/S0022-5371(83)90201-3; ANDERSON JR, 1974, COGNITIVE PSYCHOL, V6, P451, DOI 10.1016/0010-0285(74)90021-8; Anderson JR, 2005, COGNITIVE SCI, V29, P313, DOI 10.1207/s15516709cog0000_22; Anderson JR, 1999, J EXP PSYCHOL GEN, V128, P186, DOI 10.1037//0096-3445.128.2.186; Badecker W, 2002, J EXP PSYCHOL LEARN, V28, P748, DOI 10.1037//0278-7393.28.4.748; Balota DA, 2007, BEHAV RES METHODS, V39, P445, DOI 10.3758/BF03193014; Bower G. H., 1973, HUMAN ASS MEMORY; Chambers CG, 1998, J MEM LANG, V39, P593, DOI 10.1006/jmla.1998.2575; Chow WY, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00630; COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407; CORBETT AT, 1984, J VERB LEARN VERB BE, V23, P683, DOI 10.1016/S0022-5371(84)90418-3; CORBETT AT, 1983, MEM COGNITION, V11, P283, DOI 10.3758/BF03196975; DELL GS, 1983, J VERB LEARN VERB BE, V22, P121, DOI 10.1016/S0022-5371(83)80010-3; Dillon B, 2013, J MEM LANG, V69, P85, DOI 10.1016/j.jml.2013.04.003; Ditman T, 2007, LANG COGNITIVE PROC, V22, P793, DOI 10.1080/01690960601057126; Estes WK, 1997, PSYCHOL REV, V104, P148, DOI 10.1037/0033-295X.104.1.148; FOERTSCH J, 1994, DISCOURSE PROCESS, V18, P271; Foraker S, 2007, J MEM LANG, V56, P357, DOI 10.1016/j.jml.2006.07.004; Garrod S., 1981, ATTENTION PERFORM, P331; GERNSBACHER MA, 1989, COGNITION, V32, P99, DOI 10.1016/0010-0277(89)90001-2; Gerrig RJ, 1998, DISCOURSE PROCESS, V26, P67; GILLUND G, 1984, PSYCHOL REV, V91, P1, DOI 10.1037/0033-295X.91.1.1; GREENE SB, 1994, J MEM LANG, V33, P511, DOI 10.1006/jmla.1994.1024; GREENE SB, 1992, J EXP PSYCHOL LEARN, V18, P266, DOI 10.1037/0278-7393.18.2.266; HINTZMAN DL, 1986, PSYCHOL REV, V93, P411, DOI 10.1037//0033-295X.93.4.411; Horton WS, 2005, DISCOURSE PROCESS, V40, P1, DOI 10.1207/s15326950dp4001_1; JUST MA, 1982, J EXP PSYCHOL GEN, V111, P228, DOI 10.1037/0096-3445.111.2.228; Kennison SM, 2003, J MEM LANG, V49, P335, DOI 10.1016/S0749-596X(03)00071-8; KINTSCH W, 1988, PSYCHOL REV, V95, P163, DOI 10.1037/0033-295X.95.2.163; Klin CM, 2006, J MEM LANG, V54, P131, DOI 10.1016/j.jml.2005.09.001; Klin CM, 2004, MEM COGNITION, V32, P511, DOI 10.3758/BF03195843; Levine WH, 2008, INTERCULT PRAGMAT, V5, P471, DOI 10.1515/IPRG.2008.023; Levine WH, 2000, J MEM LANG, V43, P594, DOI 10.1006/jmla.2000.2719; Lewis RL, 2005, COGNITIVE SCI, V29, P375, DOI 10.1207/s15516709cog0000_25; Lewis RL, 1996, J PSYCHOLINGUIST RES, V25, P93, DOI 10.1007/BF01708421; LOFTUS GR, 1994, PSYCHON B REV, V1, P476, DOI 10.3758/BF03210951; LORCH RF, 1990, J EXP PSYCHOL LEARN, V16, P149, DOI 10.1037//0278-7393.16.1.149; Love J, 2011, J EXP PSYCHOL LEARN, V37, P874, DOI 10.1037/a0022932; Lund K, 1996, BEHAV RES METH INSTR, V28, P203, DOI 10.3758/BF03204766; Mason R. A., 1997, THESIS U MASSACHUSET; McElree B, 2000, J PSYCHOLINGUIST RES, V29, P111, DOI 10.1023/A:1005184709695; MYERS JL, 1984, COGNITIVE PSYCHOL, V16, P217, DOI 10.1016/0010-0285(84)90008-2; Myers JL, 1998, DISCOURSE PROCESS, V26, P131; NAIRNE JS, 1990, MEM COGNITION, V18, P251, DOI 10.3758/BF03213879; OBRIEN EJ, 1990, J EXP PSYCHOL LEARN, V16, P241, DOI 10.1037//0278-7393.16.2.241; OBRIEN EJ, 1987, J EXP PSYCHOL LEARN, V13, P278, DOI 10.1037//0278-7393.13.2.278; Radvansky G, 1998, J EXP PSYCHOL LEARN, V24, P1224, DOI 10.1037//0278-7393.24.5.1224; Radvansky GA, 1998, PSYCHON B REV, V5, P283, DOI 10.3758/BF03212952; RATCLIFF R, 1978, PSYCHOL REV, V85, P59, DOI 10.1037//0033-295X.85.2.59; REINHART T, 1983, LINGUIST PHILOS, V6, P47, DOI 10.1007/BF00868090; Rohde D., 2003, LINGER FLEXIBLE PROG; Smithson M., 2003, CONFIDENCE INTERVALS; STERNBER.S, 1966, SCIENCE, V153, P652, DOI 10.1126/science.153.3736.652; Sturt P, 2003, J MEM LANG, V48, P542, DOI 10.1016/S0749-596X(02)00536-3; Townsend JT, 2004, PERCEPT PSYCHOPHYS, V66, P953, DOI 10.3758/BF03194987; Tukey J. W., 1977, EXPLORATORY DATA ANA; van den Broek P, 2005, DISCOURSE PROCESS, V39, P299; Van Dyke JA, 2006, J MEM LANG, V55, P157, DOI 10.1016/j.jml.2006.03.007; van Gompel RPG, 2004, COGNITION, V90, P255, DOI 10.1016/S0010-0277(03)00161-6; Wiley J, 2001, J EXP PSYCHOL LEARN, V27, P1238, DOI 10.1037//0278-7393.27.5.1238 61 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUL 29 2014 5 818 10.3389/fpsyg.2014.00818 15 Psychology, Multidisciplinary Psychology AM4HT WOS:000339815200002 J Craik, FIM Craik, Fergus I. M. Effects of distraction on memory and cognition: a commentary FRONTIERS IN PSYCHOLOGY English Article attention; distraction; domain-general suppression; domain-specific interference; aging DIVIDED ATTENTION; INTERFERENCE; RECOGNITION; RETRIEVAL; AGE This commentary is a review of the findings and ideas reported in the preceding nine articles on the effects of distraction on aspects of cognitive performance. The articles themselves deal with the disruptive effects of distraction on recall of words, objects and events, also on visual processing, category formation and other cognitive tasks. The commentary assesses the part played by "domain-general" suppression of distracting information and the "domain-specific" competition arising when tasks and distraction involve very similar material. Some forms of distraction are meaningfully relevant to the ongoing task, and Treisman's (1964) model of selective attention is invoked to provide an account of findings in this area. Finally, individual differences to vulnerability to distraction are discussed; older adults are particularly affected by distracting stimuli although the failure to repress distraction can sometimes prove beneficial to later cognitive performance. Rotman Res Inst Baycrest, Baycrest Ctr, Toronto, ON M6A 2E1, Canada Craik, FIM (reprint author), Rotman Res Inst Baycrest, Baycrest Ctr, 3560 Bathurst St, Toronto, ON M6A 2E1, Canada. fcraik@rotman-baycrest.on.ca Baddeley A. D., 1975, ATTENTION PERFORM, P205; Beaman P., 2014, FRONT PSYCHOL, V5, P439, DOI [10.3389/fpsyg.2014.00439, DOI 10.3389/FPSYG.2014.00439]; Benjamin AS, 2010, PSYCHOL REV, V117, P1055, DOI 10.1037/a0020810; BRITTON BK, 1983, DISCOURSE PROCESS, V6, P39; Buchanan H, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00671; Craik F. I. M., 1986, HUMAN MEMORY COGNITI, P409; Craik FIM, 1996, J EXP PSYCHOL GEN, V125, P159, DOI 10.1037/0096-3445.125.2.159; CRAIK FIM, 1987, J EXP PSYCHOL LEARN, V13, P474, DOI 10.1037//0278-7393.13.3.474; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; Fernandes MA, 2000, J EXP PSYCHOL GEN, V129, P155, DOI 10.1037/0096-3445.129.2.155; Glenberg AM, 1998, MEM COGNITION, V26, P651, DOI 10.3758/BF03211385; GOODALE MA, 1992, TRENDS NEUROSCI, V15, P20, DOI 10.1016/0166-2236(92)90344-8; GOSCHKE T, 1993, J EXP PSYCHOL LEARN, V19, P1211, DOI 10.1037/0278-7393.19.5.1211; Hasher L., 1988, PSYCHOL LEARN MOTIV, V22, P193, DOI DOI 10.1016/S0079-7421(08)60041-9; Hasher L, 1999, ATTENTION PERFORM, V17, P653; Hyman Ira E Jr, 2014, Front Psychol, V5, P356, DOI 10.3389/fpsyg.2014.00356; Kyriakidou M, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00448; LOGIE RH, 1990, ACTA PSYCHOL, V75, P55, DOI 10.1016/0001-6918(90)90066-O; Mastroberardino S, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00241; Rae PJL, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00362; Scheiter Katharina, 2014, Front Psychol, V5, P268, DOI 10.3389/fpsyg.2014.00268; TREISMAN AM, 1964, AM J PSYCHOL, V77, P206, DOI 10.2307/1420127; Vredeveldt A, 2011, MEM COGNITION, V39, P1253, DOI 10.3758/s13421-011-0098-8; Wais PE, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00280; Weeks Jennifer C, 2014, Front Psychol, V5, P133, DOI 10.3389/fpsyg.2014.00133 25 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUL 29 2014 5 841 10.3389/fpsyg.2014.00841 5 Psychology, Multidisciplinary Psychology AM4HZ WOS:000339815900001 J Qiu, JX; Crow, WT; Nearing, GS; Mo, XG; Liu, SX Qiu, Jianxiu; Crow, Wade T.; Nearing, Grey S.; Mo, Xingguo; Liu, Suxia The impact of vertical measurement depth on the information content of soil moisture times series data GEOPHYSICAL RESEARCH LETTERS English Article SEQUENTIAL ASSIMILATION; ERS SCATTEROMETER; NEAR-SURFACE; VALIDATION; PRODUCTS; FILTER Using a decade of ground-based soil moisture observations acquired from the United States Department of Agriculture's Soil Climate Analysis Network (SCAN), we calculate the mutual information (MI) content between multiple soil moisture variables and near-future vegetation condition to examine the existence of emergent drought information in vertically integrated (surface to 60 cm) soil moisture observations (theta(0-60) ([cm])) not present in either superficial soil moisture observations (theta(5) ([cm])) or a simple low-pass transformation of theta(5). Results suggest that while theta(0-60) is indeed more valuable than theta(5) for predicting near-future vegetation anomalies, the enhanced information content in theta(0-60) soil moisture can be effectively duplicated by the low-pass transformation of theta(5). This implies that, for drought monitoring applications, the shallow vertical penetration depth of microwave-based theta(5) retrievals does not represent as large a practical limitation as commonly perceived. [Qiu, Jianxiu; Mo, Xingguo; Liu, Suxia] Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing, Peoples R China; [Qiu, Jianxiu; Crow, Wade T.] ARS, USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA; [Nearing, Grey S.] NASA Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD USA Crow, WT (reprint author), ARS, USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA. Wade.Crow@ars.usda.gov Natural Science Foundation of China [31171451]; Key Project for the Strategic Science Plan in IGSNRR, CAS [2012ZD003]; Chinese Scholarship Council; NASA Terrestrial Ecology Program [NNH09ZDA001N] The soil moisture data set for this paper is available at USDA SCAN website (see http://www.wcc.nrcs.usda.gov/scan/), and MODIS surface reflectance and land cover data are available at http://modis.gsfc.nasa.gov. Thanks to the Natural Science Foundation of China grant (31171451), the Key Project for the Strategic Science Plan in IGSNRR, CAS (2012ZD003), and the Chinese Scholarship Council for supporting the first author to conduct research at the USDA-ARS Hydrology and Remote Sensing Laboratory. This work was also partially supported by a grant from the NASA Terrestrial Ecology Program (NNH09ZDA001N) to W. T. Crow. Albergel C, 2008, HYDROL EARTH SYST SC, V12, P1323; Arora V. K., 2003, EARTH INTERACT, V7, P1, DOI DOI 10.1175/1087-3562(2003)007<0001:AROVRD>2.0.CO;2; Bolten JD, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL053470; Brocca L, 2011, REMOTE SENS ENVIRON, V115, P3390, DOI 10.1016/j.rse.2011.08.003; Ceballos A, 2005, HYDROL PROCESS, V19, P1549, DOI 10.1002/hyp.5585; COVER T., 1991, ELEMENTS INFORM THEO; Crow WT, 2006, J HYDROMETEOROL, V7, P421, DOI 10.1175/JHM499.1; DEAN RB, 1951, ANAL CHEM, V23, P636, DOI 10.1021/ac60052a025; De Kauwe MG, 2011, REMOTE SENS ENVIRON, V115, P767, DOI 10.1016/j.rse.2010.11.004; Entekhabi D, 2010, P IEEE, V98, P704, DOI 10.1109/JPROC.2010.2043918; Escorihuela MJ, 2010, REMOTE SENS ENVIRON, V114, P995, DOI 10.1016/j.rse.2009.12.011; Jackson TJ, 2010, IEEE T GEOSCI REMOTE, V48, P4256, DOI 10.1109/TGRS.2010.2051035; KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694; Kumar SV, 2009, J HYDROMETEOROL, V10, P1534, DOI 10.1175/2009JHM1134.1; Li J, 1999, J HYDROL, V220, P86, DOI 10.1016/S0022-1694(99)00066-9; Mo XG, 2011, IAHS-AISH P, V343, P118; Nearing GS, 2013, WATER RESOUR RES, V49, P2164, DOI 10.1002/wrcr.20177; Paninski L, 2003, NEURAL COMPUT, V15, P1191, DOI 10.1162/089976603321780272; Pollacco JAP, 2012, VADOSE ZONE J, V11, DOI 10.2136/vzj2011.0167; Reichle RH, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2007GL031986; Reichle RH, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008033; Sabater JM, 2007, J HYDROMETEOROL, V8, P194, DOI 10.1175/JHM571.1; Scott DW, 2004, HANDBOOK OF COMPUTATIONAL STATISTICS: CONCEPTS AND METHODS, P517; SHANNON CE, 1948, AT&T TECH J, V27, P623; Theil H., 1967, EC INFORM THEORY; Wagner W, 1999, REMOTE SENS ENVIRON, V70, P191, DOI 10.1016/S0034-4257(99)00036-X; Wolfe RE, 2002, REMOTE SENS ENVIRON, V83, P31, DOI 10.1016/S0034-4257(02)00085-8; Zhou XB, 2005, REMOTE SENS ENVIRON, V94, P214, DOI 10.1016/j.rse.2004.10.007; Zribi M, 2010, WATER RESOUR RES, V46, DOI 10.1029/2009WR008196 29 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. JUL 28 2014 41 14 4997 5004 10.1002/2014GL060017 8 Geosciences, Multidisciplinary Geology AN4CU WOS:000340536000025 J O'Malley, RT; Behrenfeld, MJ; Westberry, TK; Milligan, AJ; Shang, SL; Yan, J O'Malley, Robert T.; Behrenfeld, Michael J.; Westberry, Toby K.; Milligan, Allen J.; Shang, Shaoling; Yan, Jing Geostationary satellite observations of dynamic phytoplankton photophysiology GEOPHYSICAL RESEARCH LETTERS English Article COLOR IMAGER GOCI; THALASSIOSIRA-WEISSFLOGII; CHLOROPHYLL FLUORESCENCE; NATURAL PHYTOPLANKTON; SOUTHERN-OCEAN; LIGHT; PHOTOSYNTHESIS; ABSORPTION; LIMITATION; PHYSIOLOGY Since June 2010, the Geostationary Ocean Color Imager (GOCI) has been collecting the first diurnally resolved satellite ocean measurements. Here GOCI retrievals of phytoplankton chlorophyll concentration and fluorescence are used to evaluate daily to seasonal changes in photophysiological properties. We focus on nonphotochemical quenching (NPQ) processes that protect phytoplankton from high light damage and cause strong diurnal cycles in fluorescence emission. This NPQ signal varies seasonally, with maxima in winter and minima in summer. Contrary to expectations from laboratory studies under constant light conditions, this pattern is highly consistent with an earlier conceptual model and recent field observations. The same seasonal cycle is registered in fluorescence data from the polar-orbiting Moderate Resolution Imaging Spectroradiometer Aqua satellite sensor. GOCI data reveal a strong correlation between mixed layer growth irradiance and fluorescence-derived phytoplankton photoacclimation state that can provide a path for mechanistically accounting for NPQ variability and, subsequently, retrieving information on iron stress in global phytoplankton populations. [O'Malley, Robert T.; Behrenfeld, Michael J.; Westberry, Toby K.; Milligan, Allen J.] Oregon State Univ, Dept Bot & Plant Pathol, Corvallis, OR 97331 USA; [Shang, Shaoling; Yan, Jing] Xiamen Univ, State Key Lab Marine Environm Sci, Xiamen, Peoples R China Behrenfeld, MJ (reprint author), Oregon State Univ, Dept Bot & Plant Pathol, Corvallis, OR 97331 USA. mjb@science.oregonstate.edu National Aeronautics and Space Administration Ocean Biology and Geochemistry Program GOCI data was provided by the Korea Ocean Satellite Center (KOSC)/KORDI. This study was supported by the National Aeronautics and Space Administration Ocean Biology and Geochemistry Program. Abbott M. R., 1999, ALGORITHM THEORETICA; Ahn JH, 2012, OCEAN SCI J, V47, P247, DOI 10.1007/s12601-012-0026-2; Behrenfeld MJ, 2005, GLOBAL BIOGEOCHEM CY, V19, DOI 10.1029/2004GB002299; Behrenfeld MJ, 2004, J PHYCOL, V40, P4, DOI 10.1046/j.1529-8817.2004.03083.x; Behrenfeld MJ, 2009, BIOGEOSCIENCES, V6, P779; BRICAUD A, 1995, J GEOPHYS RES-OCEANS, V100, P13321, DOI 10.1029/95JC00463; Browning TJ, 2014, GLOBAL BIOGEOCHEM CY, V28, P510, DOI 10.1002/2013GB004773; Garcia-Mendoza E, 2007, NEW PHYTOL, V173, P526, DOI 10.1111/j.1469-8137.2006.01951.x; Garcia-Mendoza E, 2002, PHOTOSYNTH RES, V74, P303, DOI 10.1023/A:1021230601077; Gower JFR, 1999, INT J REMOTE SENS, V20, P1771, DOI 10.1080/014311699212470; JASSBY AD, 1976, LIMNOL OCEANOGR, V21, P540; Kautsky H., 1931, NATURWISSENSCHAFTEN, V19, P964, DOI DOI 10.1007/BF01516164; KIEFER DA, 1973, MAR BIOL, V22, P263, DOI 10.1007/BF00389180; Laney SR, 2005, LIMNOL OCEANOGR, V50, P1499; Letelier RM, 1997, GEOPHYS RES LETT, V24, P409, DOI 10.1029/97GL00205; Levy O, 2004, MAR ECOL PROG SER, V268, P105, DOI 10.3354/meps268105; Milligan AJ, 2012, MAR ECOL PROG SER, V448, P67, DOI 10.3354/meps09544; Morrison JR, 2003, LIMNOL OCEANOGR, V48, P618; Morrison JR, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2009GL041799; Muller P, 2001, PLANT PHYSIOL, V125, P1558, DOI 10.1104/pp.125.4.1558; Neale P.J., 1987, PHOTOINHIBITION, P39; Niyogi KK, 1997, PLANT CELL, V9, P1369; Oh E, 2012, PROC SPIE, V8533, DOI 10.1117/12.974455; Ragni M, 2008, J PHYCOL, V44, P670, DOI 10.1111/j.1529-8817.2008.00524.x; Ryan-Keogh TJ, 2012, J PHYCOL, V48, P145, DOI 10.1111/j.1529-8817.2011.01092.x; Ryu JH, 2012, OCEAN SCI J, V47, P223, DOI 10.1007/s12601-012-0024-4; Schallenberg C, 2008, J GEOPHYS RES-OCEANS, V113, DOI 10.1029/2007JC004355; Schrader PS, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0018753; STITT M, 1986, PLANT PHYSIOL, V81, P1115, DOI 10.1104/pp.81.4.1115 29 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0094-8276 1944-8007 GEOPHYS RES LETT Geophys. Res. Lett. JUL 28 2014 41 14 5052 5059 10.1002/2014GL060246 8 Geosciences, Multidisciplinary Geology AN4CU WOS:000340536000032 J Alexander, P; de la Torre, A; Llamedo, P; Hierro, R Alexander, P.; de la Torre, A.; Llamedo, P.; Hierro, R. Precision estimation in temperature and refractivity profiles retrieved by GPS radio occultations JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article MISSION The Constellation Observing System for Meteorology Ionosphere and Climate (COSMIC) is a six-satellite Global Positioning System (GPS) radio occultation (RO) mission that started in April 2006. The close proximity of these satellites during some months after launch provided a unique opportunity to evaluate the precision of GPS RO temperature and refractivity profile retrievals in the neutral atmosphere from nearly collocated and simultaneous observations. In order to work with nearly homogeneous sets, data are divided into five groups according to latitude bands during 20 days of July. For all latitude bands and variables, the best precision values (about 0.1%) are found somewhere between 8 and 25 km height. In general, we find that precision degrades significantly with height above 30 km and its performance becomes there worse than 1%. Temperature precision assessment has been generally excluded in previous studies. Refractivity has here, in general, a precision similar to dry temperature but worse than wet temperature in the lower atmosphere and above 30 km. However, it has been shown that the better performance of wet temperature is an artificial effect produced by the use of the same background information in nearly collocated wet retrievals. Performance in refractivity around 1% is found in the Northern Hemisphere at the lowest heights and significantly worse in the southern polar zone above 30 km. There is no strong dependence of the estimated precision in terms of height on day and night, on latitude, on season, or on the homogeneity degree of each group of profiles. This reinforces the usual claim that GPS RO precision is independent of the atmospheric conditions. The roughly 0.1% precision in the 8-25 km height interval should suffice to distinguish between day and night average values, but no significant differences are found through a Student t test for both populations at all heights in each latitude band. It was then shown that the present spatial density of GPS RO does not allow to analyze smaller latitudinal bands, which could lead to smaller dispersions associated with the day and night means, where it would then be potentially possible to detect significant statistical differences among both categories. We studied the uncertainties associated with the background conditions used in the retrievals and found that their contribution is negligible at all latitudes and heights. However, they force an artificial improvement of wet temperature precision as compared to the dry counterpart at the lowest and highest altitudes studied. In addition, we showed that there is no detectable dubious behavior of COSMIC data prior to day 194 of year 2006 as warned by the data providers, but our result applies only to the precision issue and cannot be extended to other features of data quality. Regarding accuracy, we estimated an average bias of 0.1 K for GPS RO temperature between about 10 and 30 km height and somewhat larger at lower altitudes. We expect a roughly -0.5 K bias above 35 kmaltitude. Regarding refractivity, a -0.2% bias of the measurements was estimated below about 8 km height. [Alexander, P.] Consejo Nacl Invest Cient & Tecn, Inst Fis Buenos Aires, RA-1033 Buenos Aires, DF, Argentina; [de la Torre, A.; Llamedo, P.; Hierro, R.] Univ Austral, Fac Ingn, Buenos Aires, DF, Argentina Alexander, P (reprint author), Consejo Nacl Invest Cient & Tecn, Inst Fis Buenos Aires, Ciudad Univ Pabellon 1, RA-1033 Buenos Aires, DF, Argentina. peter@df.uba.ar grant CONICET [PIP 11220090100649] Manuscript prepared under grant CONICET PIP 11220090100649. P. Alexander, A. de la Torre, P. Llamedo, and R. Hierro are members of CONICET. Data were downloaded from cdaac-ftp. cosmic.ucar.edu. We thank unknown referees for very helpful suggestions. Alexander P, 2010, ADV SPACE RES, V45, P1231, DOI 10.1016/j.asr.2009.12.015; Alexander P, 2010, ANN GEOPHYS-GERMANY, V28, P587; Anthes RA, 2008, B AM METEOROL SOC, V89, P313, DOI 10.1175/BAMS-89-3-313; Das U, 2014, ATMOS MEAS TECH, V7, P731, DOI 10.5194/amt-7-731-2014; Foelsche U, 2004, OCCULTATIONS FOR PROBING ATMOSPHERE AND CLIMATE, P127; Gorbunov ME, 2011, J ATMOS OCEAN TECH, V28, P737, DOI 10.1175/2011JTECHA1489.1; Hajj GA, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003909; Healy SB, 2000, Q J ROY METEOR SOC, V126, P1661, DOI 10.1256/smsqj.56606; KITCHEN M, 1989, Q J ROY METEOR SOC, V115, P673, DOI 10.1002/qj.49711548713; Kuo YH, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2004GL021443; Kuo YH, 2004, J METEOROL SOC JPN, V82, P507, DOI 10.2151/jmsj.2004.507; Kursinski ER, 1997, J GEOPHYS RES-ATMOS, V102, P23429, DOI 10.1029/97JD01569; Liou YA, 2007, IEEE T GEOSCI REMOTE, V45, P3813, DOI 10.1109/TGRS.2007.903365; Poli P, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000935; Schreiner W, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL027557; Sokolovskiy S, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014058; Staten PW, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL041046; Steiner AK, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005251 18 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUL 27 2014 119 14 8624 8638 10.1002/2013JD021016 15 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN2GQ WOS:000340402800007 J Yu, FF; Wu, XQ; Grotenhuis, M; Qian, HF Yu, Fangfang; Wu, Xiangqian; Grotenhuis, Michael; Qian, Haifeng Intercalibration of GOES Imager visible channels over the Sonoran Desert JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article HIGH-RESOLUTION RADIOMETER; REFLECTIVE SOLAR BANDS; OPERATIONAL CALIBRATION; CROSS-CALIBRATION; SATELLITE DATA; AMERICA; RETRIEVAL; SYSTEM; SITES; TERRA The Geostationary Operational Environmental Satellites (GOES) have been observing the Western Hemisphere since the late 1970s, providing valuable information for weather forecast and climate change studies. Due to the lack of an onboard calibration device for the visible channel, accurate reflectance of the visible channel data depends on vicarious calibration methods to provide postlaunch calibration coefficients to compensate for the degraded responsivity. In this study, the Sonoran Desert, which can be viewed by both GOES-East and GOES-West satellites, is used to intercalibrate the visible channels on board the three-axis stabilized GOES satellite Imagers traceable to the AquaModerate Resolution Imaging Spectroradiometer (MODIS) Collection 6 (C6) calibration standard. It was found that when the anomalous reflectance in 2004 and 2005 are excluded, the Sonoran Desert is radiometrically, spatially, and spectrally stable at the GOES viewing geometries and thus can be considered as a pseudo-invariant calibration site to develop long-term GOES Imager visible data set. To characterize the desert target reflectance with the MODIS data, GOES observations over 1 year period are used to convert the MODIS reflectance to the GOES viewing and solar illumination geometries. The spectral band adjustment factor for each GOES Imager visible channel is generated with a set of clear-sky Hyperion measurements. A trending algorithm, which consists of a polynomial function for the description of instrument degradation performance and two sine terms for the impacts of the seasonal variations of the solar zenith angle and atmospheric components, is applied to fit the time series of prelaunch calibrated reflectance. The combined calibration uncertainty of the desert calibration method is less than 4% at the Aqua MODIS C6 calibration standard. The difference of the postlaunch calibration coefficients between the desert calibration and the current GOES visible operational calibration methods is mainly within 5%. [Yu, Fangfang; Grotenhuis, Michael] Earth Resources Technol Inc, Laurel, MD 20707 USA; [Wu, Xiangqian] NOAA, NESDIS, STAR, College Pk, MD USA; [Qian, Haifeng] IM Syst Grp, Rockville, MD USA Yu, FF (reprint author), Earth Resources Technol Inc, Laurel, MD 20707 USA. Fangfang.Yu@noaa.gov NOAA/NESDIS/STAR cal/val project This work was supported by the NOAA/NESDIS/STAR cal/val project. We would like to thank Yong Chen for the LBLRTM simulated atmospheric transmission data, Chenyang Xiao for the help with statistical analysis, and Wenhui Wang and Mike Kalb for the valuable review comments. We also want to thank the three anonymous reviewers for their critical reviews with constructive comments and suggestions to help improve the quality of this paper. The GOES data for this paper are available at NOAA's Comprehensive Large Array-Data Stewardship System (CLASS) (http://www.class.ncdc.noaa.gov/), Aqua MODIS C6 data were downloaded from NASA Goddard Space Flight Center Level 1 and Atmosphere Archive and Distribution system (http://ladsweb.nascom.nasa.gov/), and Hyperion data were obtained from the USGS EarthExplorer webpage (http://earthexplorer.usgs.gov). The manuscript contents are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U. S. government. Angal A., 2011, J APPL REMOTE SENS, V5; Angal A., 2010, J APPL REMOTE SENS, V4; [Anonymous], 2012, NASA REPORT GSICS EX; Barry P., 2001, EO 1 HYPERION SCI DA; Bremer JC, 1998, P SOC PHOTO-OPT INS, V3439, P145, DOI 10.1117/12.325622; Chander G, 2013, IEEE T GEOSCI REMOTE, V51, P1267, DOI 10.1109/TGRS.2012.2228007; Chander G, 2013, IEEE T GEOSCI REMOTE, V51, P1282, DOI 10.1109/TGRS.2012.2228008; Chang I.-L., 2005, P SPIE EARTH OBSERVI, V5882, DOI 10.1117/12.614601; Chepfer H, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2000JD000240; Cosnefroy HN, 1996, REMOTE SENS ENVIRON, V58, P101, DOI 10.1016/0034-4257(95)00211-1; Dean C, 2012, PROC SPIE, V8510, DOI 10.1117/12.929036; Doelling D., 2004, MSG RAO WORKSH SALZB, P100; Doelling DR, 2004, P SOC PHOTO-OPT INS, V5542, P281, DOI 10.1117/12.560047; Ellrod GP, 2007, WEATHER FORECAST, V22, P160, DOI 10.1175/WAF984.1; Fougnie B., 2002, P OC OPT 16 SANT FE; Fox N., 2010, QA4EOQAEOGENDQK003; Goldberg M, 2011, B AM METEOROL SOC, V92, P467, DOI 10.1175/2010BAMS2967.1; Govaerts YM, 2004, IEEE T GEOSCI REMOTE, V42, P1900, DOI 10.1109/TGRS.2004.831882; Hagolle O, 2004, IEEE T GEOSCI REMOTE, V42, P1472, DOI 10.1109/TGRS.2004.826805; HAYDEN CM, 1988, J APPL METEOROL, V27, P705, DOI 10.1175/1520-0450(1988)027<0705:GVSTMR>2.0.CO;2; Hayden CM, 1996, J APPL METEOROL, V35, P153, DOI 10.1175/1520-0450(1996)035<0153:DPIF>2.0.CO;2; Henry P, 2013, IEEE T GEOSCI REMOTE, V51, P1297, DOI 10.1109/TGRS.2012.2228210; Kieffer HH, 2005, ASTRON J, V129, P2887, DOI 10.1086/430185; Knapp KR, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000505; Knapp KR, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD002001; Maturi E, 2008, B AM METEOROL SOC, V89, P1877, DOI 10.1175/2008BAMS2528.1; Pinker RT, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003301; Prins E. M., 1996, INT FOR FIRE NEWS, P49; Rao C. R. N., 2001, IMPLEMENTATION POSTL; RAO CRN, 1995, INT J REMOTE SENS, V16, P1931; Romanov P, 2000, J APPL METEOROL, V39, P1866, DOI 10.1175/1520-0450(2000)039<1866:AMOSCO>2.0.CO;2; Santer BD, 2000, J GEOPHYS RES-ATMOS, V105, P7337, DOI 10.1029/1999JD901105; Smith DL, 2013, IEEE T GEOSCI REMOTE, V51, P1370, DOI 10.1109/TGRS.2012.2230333; Smith G. R., 1988, Journal of Atmospheric and Oceanic Technology, V5, DOI 10.1175/1520-0426(1988)005<0631:COTSCO>2.0.CO;2; Sun JQ, 2012, PROC SPIE, V8528, DOI 10.1117/12.979733; Thuillier G., 2003, SOL PHYS, V204, P1; Uprety S., 2012, J APPL REMOTE SENS, V6; Wu A, 2008, INT J REMOTE SENS, V29, P1997, DOI 10.1080/01431160701355272; Wu AS, 2013, IEEE T GEOSCI REMOTE, V51, P4330, DOI 10.1109/TGRS.2012.2226588; Wu X., 2005, P SOC PHOTO-OPT INS, V5582, DOI [10.1117/12.615401, DOI 10.1117/12.615401]; WU XQ, 2006, P SOC PHOTO-OPT INS, V6296, DOI DOI 10.1117/12.681591; Wu XQ, 2011, INT GEOSCI REMOTE SE, P1033; Wu XQ, 1999, B AM METEOROL SOC, V80, P1127, DOI 10.1175/1520-0477(1999)080<1127:EOSSTU>2.0.CO;2; Wu XQ, 2010, CAN J REMOTE SENS, V36, P602; Xiong X., 2009, P SOC PHOTO-OPT INS, V7452, DOI [10.1117/12.824761, DOI 10.1117/12.824761]; Xiong X., 2003, METROLOGIA, V40, P89; Xiong XX, 2007, IEEE T GEOSCI REMOTE, V45, P879, DOI 10.1109/TGRS.2006.890567; Yu F., 2013, 2013 EUMETSAT SAT C; Yu F., 2010, J ATMOS OCEAN TECH, V26, P1354; Zhang XY, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017459 50 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUL 27 2014 119 14 8639 8658 10.1002/2013JD020702 20 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN2GQ WOS:000340402800008 J Xie, Y; Liu, YG; Long, CN; Min, QL Xie, Yu; Liu, Yangang; Long, Charles N.; Min, Qilong Retrievals of cloud fraction and cloud albedo from surface-based shortwave radiation measurements: A comparison of 16 year measurements JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article SOUTHERN GREAT-PLAINS; MIDLATITUDE CONTINENTAL CLOUDS; GENERAL-CIRCULATION MODEL; SGP CENTRAL FACILITY; OPTICAL DEPTH; SKY COVER; CLIMATOLOGY; RADIOMETER; SATELLITE; RADAR Ground-based radiation measurements have been widely conducted to gain information on clouds and the surface radiation budget. To examine the existing techniques of cloud property retrieval and explore the underlying reasons for uncertainties, a newly developed approach that allows for simultaneous retrievals of cloud fraction and cloud albedo from ground-based shortwave broadband radiation measurements, XL2013, is used to derive cloud fraction and cloud albedo from ground-based shortwave broadband radiation measurements at the Department of Energy Atmospheric Radiation Measurement Southern Great Plains site. The new results are compared with the separate retrieval of cloud fraction and cloud albedo using Long2006 and Liu2011, respectively. The retrievals from the broadband radiation measurements are further compared with those based on shortwave spectral measurements (Min2008). The comparison shows overall good agreement between the retrievals of both cloud fraction and cloud albedo, with noted differences, however. The Long2006 and Min2008 cloud fractions are greater on average than the XL2013 values. Compared to Min2008 and Liu2011, the XL2013 cloud albedo tends to be greater for thin clouds but smaller for thick clouds, with the differences decreasing with increasing cloud fraction; the neglect of land surface albedo and cloud absorption by Liu2011 also contributes the difference in cloud albedo. Further analysis reveals that the approaches that retrieve cloud fraction and cloud albedo separately may suffer from mutual contamination of errors in retrieved cloud fraction and cloud albedo. [Xie, Yu; Liu, Yangang] Brookhaven Natl Lab, Upton, NY 11973 USA; [Xie, Yu] Natl Renewable Energy Lab, Golden, CO USA; [Long, Charles N.] Pacific NW Natl Lab, Richland, WA 99352 USA; [Min, Qilong] SUNY Albany, Atmospher Sci Res Ctr, Albany, NY 12222 USA Xie, Y (reprint author), Brookhaven Natl Lab, Upton, NY 11973 USA. Yu.Xie@nrel.gov U.S. Department of Energy's Earth Systems Modeling (ESM) program via the FASTER project; Office of Biological and Environmental Research; Atmospheric Systems Research (ASR) Program This work is supported by the U.S. Department of Energy's Earth Systems Modeling (ESM) program via the FASTER project (www.bnl.gov/faster), Office of Biological and Environmental Research, and the Atmospheric Systems Research (ASR) Program. The work uses data from the Atmospheric Radiation Measurement (ARM) Climate Research Facility (http://www.arm.gov/data/vaps/swfluxanal). Betts AK, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005702; Betts AK, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008191; Bouniol D, 2010, J APPL METEOROL CLIM, V49, P1971, DOI 10.1175/2010JAMC2333.1; CHARLOCK TP, 1985, J ATMOS SCI, V42, P1408, DOI 10.1175/1520-0469(1985)042<1408:TAFACR>2.0.CO;2; Dong XQ, 2005, J CLIMATE, V18, P1391, DOI 10.1175/JCLI3342.1; Dong XQ, 2006, J CLIMATE, V19, P1765, DOI 10.1175/JCLI3710.1; Gautier C, 1997, J ATMOS SCI, V54, P1289, DOI 10.1175/1520-0469(1997)054<1289:SSRFAC>2.0.CO;2; Han QY, 1998, J CLIMATE, V11, P1516, DOI 10.1175/1520-0442(1998)011<1516:GSOTRO>2.0.CO;2; HARRISON L, 1994, APPL OPTICS, V33, P5118, DOI 10.1364/AO.33.005118; HENDERSON-SELLERS A, 1990, INT J REMOTE SENS, V11, P543; Hinkelman LM, 1999, J GEOPHYS RES-ATMOS, V104, P19535, DOI 10.1029/1999JD900120; Hogan RJ, 2001, J APPL METEOROL, V40, P513, DOI 10.1175/1520-0450(2001)040<0513:COEWSC>2.0.CO;2; Kassianov E, 2005, J APPL METEOROL, V44, P86, DOI 10.1175/JAM-2184.1; Kennedy A. D., 2013, THEOR APPL CLIMATOL, DOI [10.1007/s00704-00013-00853-00709, DOI 10.1007/S00704-00013-00853-00709]; Lazarus SM, 2000, J CLIMATE, V13, P1762, DOI 10.1175/1520-0442(2000)013<1762:ACCOTS>2.0.CO;2; Liu Y, 2011, ATMOS CHEM PHYS, V11, P7155, DOI 10.5194/acp-11-7155-2011; Long C., 2004, ATMOS RAD MEAS PROG; Long CN, 2000, J GEOPHYS RES-ATMOS, V105, P15609, DOI 10.1029/2000JD900077; Long CN, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006475; Marshak A., 2005, 3D RAD TRANSFER CLOU; McFarlane SA, 2013, J APPL METEOROL CLIM, V52, P996, DOI 10.1175/JAMC-D-12-0189.1; Min QL, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010278; Min QL, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1023/2003JD003964; Min QL, 1996, GEOPHYS RES LETT, V23, P1641, DOI 10.1029/96GL01488; Minnis P., 2008, P SPIE EUR REM SENS; Mlawer EJ, 1997, J GEOPHYS RES-ATMOS, V102, P16663, DOI 10.1029/97JD00237; Ohmura A, 1998, B AM METEOROL SOC, V79, P2115, DOI 10.1175/1520-0477(1998)079<2115:BSRNBW>2.0.CO;2; Oreopoulos L, 1999, Q J ROY METEOR SOC, V125, P301, DOI 10.1256/smsqj.55314; Paquin-Ricard D, 2010, MON WEATHER REV, V138, P818, DOI 10.1175/2009MWR2745.1; Qian Y, 2012, ATMOS CHEM PHYS, V12, P1785, DOI 10.5194/acp-12-1785-2012; SAGAN C, 1967, J GEOPHYS RES, V72, P469, DOI 10.1029/JZ072i002p00469; Sengupta M, 2004, J CLIMATE, V17, P4760, DOI 10.1175/JCLI-3231.1; Song H, 2013, J CLIMATE, V26, P5467, DOI 10.1175/JCLI-D-12-00263.1; Warren S. G., 1986, NCARTN273STR; WIGLEY TML, 1990, J GEOPHYS RES-ATMOS, V95, P1943, DOI 10.1029/JD095iD02p01943; Wu W, 2014, J GEOPHYS RES-ATMOS, V119, P3438, DOI 10.1002/2013JD019813; Wyant MC, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2005GL025464; Xie Y., 2013, ENVIRON RES LETT, V8, DOI [10.1088/1748-9326/1088/1084/044023, DOI 10.1088/1748-9326/1088/1084/044023]; Zhang MH, 2012, J ADV MODEL EARTH SY, V4, DOI 10.1029/2012MS000182 39 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUL 27 2014 119 14 8925 8940 10.1002/2014JD021705 16 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN2GQ WOS:000340402800024 J Wood, NB; L'Ecuyer, TS; Heymsfield, AJ; Stephens, GL; Hudak, DR; Rodriguez, P Wood, Norman B.; L'Ecuyer, Tristan S.; Heymsfield, Andrew J.; Stephens, Graeme L.; Hudak, David R.; Rodriguez, Peter Estimating snow microphysical properties using collocated multisensor observations JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article PARTICLE TERMINAL VELOCITIES; ICE-CLOUD PARTICLES; VIDEO DISDROMETER; FALL SPEEDS; PRECIPITATION; CRYSTALS; MICROWAVE; UNCERTAINTIES; CIRRUS; AREA The ability of ground-based in situ and remote sensing observations to constrain microphysical properties for dry snow is examined using a Bayesian optimal estimation retrieval method. Power functions describing the variation of mass and horizontally projected area with particle size and a parameter related to particle shape are retrieved from near-Rayleigh radar reflectivity, particle size distribution, snowfall rate, and size-resolved particle fall speeds. Algorithm performance is explored in the context of instruments deployed during the Canadian CloudSat CALIPSO Validation Project, but the algorithm is adaptable to other similar combinations of sensors. Critical estimates of observational and forward model uncertainties are developed and used to quantify the performance of the method using synthetic cases developed from actual observations of snow events. In addition to illustrating the technique, the results demonstrate that this combination of sensors provides useful constraints on the mass parameters and on the coefficient of the area power function but only weakly constrains the exponent of the area power function and the shape parameter. Information content metrics show that about two independent quantities are measured by the suite of observations and that the method is able to resolve about eight distinct realizations of the state vector containing the mass and area power function parameters. Alternate assumptions about observational and forward model uncertainties reveal that improved modeling of particle fall speeds could contribute substantial improvements to the performance of the method. [Wood, Norman B.] Univ Wisconsin, Cooperat Inst Meteorol Satellite Studies, Madison, WI 53706 USA; [L'Ecuyer, Tristan S.] Univ Wisconsin, Dept Atmospher & Ocean Sci, Madison, WI USA; [Heymsfield, Andrew J.] Natl Ctr Atmospher Res, Boulder, CO 80307 USA; [Stephens, Graeme L.] NASA, Jet Prop Lab, Pasadena, CA USA; [Hudak, David R.; Rodriguez, Peter] Environm Canada, Cloud Phys & Severe Weather Res Sect, King City, ON, Canada Wood, NB (reprint author), Univ Wisconsin, Cooperat Inst Meteorol Satellite Studies, Madison, WI 53706 USA. norman.wood@ssec.wisc.edu National Aeronautics and Space Administration; JPL CloudSat Office; NASA Global Precipitation Measurement program [NN-X13AH73G] Parts of this research by N.B.W. and T. S. L. were performed at the University of Wisconsin-Madison and at Colorado State University for the Jet Propulsion Laboratory, California Institute of Technology, sponsored by the National Aeronautics and Space Administration. A.J.H. acknowledges support from the JPL CloudSat Office and NASA Global Precipitation Measurement program contract NN-X13AH73G. Thanks to G.-J. Huang of Colorado State University (2DVD data), F. Fabry of McGill University (VertiX data), and L. Bliven of NASA Goddard Space Flight Center (SVI data) for making their C3VP data sets available and sharing their expertise. Other data used in the analyses, including the relevant FD12P and DFIR observations, are available from the authors. Thanks to two anonymous reviewers and S. Nesbitt of University of Illinois Urbana-Champaign for their thoughtful comments and suggestions. ATLAS D, 1953, J ATMOS TERR PHYS, V3, P108, DOI 10.1016/0021-9169(53)90093-2; AUER AH, 1970, J ATMOS SCI, V27, P919, DOI 10.1175/1520-0469(1970)027<0919:TDOICI>2.0.CO;2; BOHM HP, 1989, J ATMOS SCI, V46, P2419, DOI 10.1175/1520-0469(1989)046<2419:AGEFTT>2.0.CO;2; Brandes EA, 2008, J APPL METEOROL CLIM, V47, P2729, DOI 10.1175/2008JAMC1869.1; Brandes EA, 2007, J APPL METEOROL CLIM, V46, P634, DOI 10.1175/JAM2489.1; Davis C. I., 1974, THESIS U WYOMING LAR; DRAINE BT, 1994, J OPT SOC AM A, V11, P1491, DOI 10.1364/JOSAA.11.001491; FABRY F, 1995, J ATMOS SCI, V52, P838, DOI 10.1175/1520-0469(1995)052<0838:LTROOT>2.0.CO;2; Goodison B. E., 1998, 87288 WMOTD; HEYMSFIE.A, 1972, J ATMOS SCI, V29, P1348, DOI 10.1175/1520-0469(1972)029<1348:ICTV>2.0.CO;2; Heymsfield AJ, 2003, J ATMOS SCI, V60, P936, DOI 10.1175/1520-0469(2003)060<0936:PFTCSA>2.0.CO;2; Heymsfield AJ, 2010, J ATMOS SCI, V67, P2469, DOI 10.1175/2010JAS3379.1; Heymsfield AJ, 2002, J ATMOS SCI, V59, P3, DOI 10.1175/1520-0469(2002)059<0003:AGAFDT>2.0.CO;2; Huang GJ, 2010, J ATMOS OCEAN TECH, V27, P637, DOI 10.1175/2009JTECHA1284.1; Hudak D., 2012, P MET SAT C SOP POL; Hudak D., 2006, P 2006 EUR C RAD MET, P9; Hudak D., 2006, P 4 EUR C RAD HYDR M, P609; KAJIKAWA M, 1982, J METEOROL SOC JPN, V60, P797; Kajikawa M., 1972, J METEOR SOC JAPAN, V50, P577; Kajikawa M., 1975, J METEOR SOC JAPAN, V53, P476; Korolev A, 2003, J ATMOS SCI, V60, P1795, DOI 10.1175/1520-0469(2003)060<1795:RAAROP>2.0.CO;2; Kulie MS, 2010, J ATMOS SCI, V67, P3471, DOI 10.1175/2010JAS3520.1; L'Ecuyer TS, 2006, J APPL METEOROL CLIM, V45, P20, DOI 10.1175/JAM2326.1; LIST R, 1971, J ATMOS SCI, V28, P110, DOI 10.1175/1520-0469(1971)028<0110:FFBOPS>2.0.CO;2; Liu GS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009766; LOCATELL.JD, 1974, J GEOPHYS RES, V79, P2185, DOI 10.1029/JC079i015p02185; Magono C., 1995, J METEOROL SOC JAPAN, V43, P139; Matrosov SY, 1998, J APPL METEOROL, V37, P1510, DOI 10.1175/1520-0450(1998)037<1510:ADWRMT>2.0.CO;2; Matrosov SY, 2007, J ATMOS SCI, V64, P1727, DOI 10.1175/JAS3904.1; Mitchell DL, 2005, J ATMOS SCI, V62, P1637, DOI 10.1175/JAS3413.1; Mitchell DL, 1996, J ATMOS SCI, V53, P1710, DOI 10.1175/1520-0469(1996)053<1710:UOMAAD>2.0.CO;2; MITCHELL DL, 1990, J APPL METEOROL, V29, P153, DOI 10.1175/1520-0450(1990)029<0153:MDRFIP>2.0.CO;2; Mory M., 2011, FLUID MECH CHEM ENG; Nakaya U., 1935, J FAC SCI HOKKAIDO 2, V1, P191; Newman AJ, 2009, J ATMOS OCEAN TECH, V26, P167, DOI 10.1175/2008JTECHA1148.1; Petersen W. A., 2011, EARTH OBSERVER, V23, P21; Rodgers C. D., 2000, INVERSE METHODS ATMO; Shannon C, 1949, MATH THEORY COMMUNIC; Sheppard BE, 2008, J ATMOS OCEAN TECH, V25, P196, DOI 10.1175/2007JTECHA957.1; Skofronick-Jackson GM, 2004, IEEE T GEOSCI REMOTE, V42, P1047, DOI 10.1109/TGRS.2004.825585; Sutherland W., 1893, PHILOS MAG, V5, P507; Thurai M, 2005, J ATMOS OCEAN TECH, V22, P966, DOI 10.1175/JTECH1767.1; Vaisala O., 2002, WEATHER SENSOR FD12P; WARREN SG, 1984, APPL OPTICS, V23, P1206; Wood N. B., 2011, THESIS COLORADO STAT; Wood NB, 2013, ATMOS MEAS TECH, V6, P3635, DOI 10.5194/amt-6-3635-2013; ZIKMUNDA J, 1977, J ATMOS SCI, V34, P1675; ZIKMUNDA J, 1972, J ATMOS SCI, V29, P1334, DOI 10.1175/1520-0469(1972)029<1334:FPAFVO>2.0.CO;2 48 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUL 27 2014 119 14 8941 8961 10.1002/2013JD021303 21 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN2GQ WOS:000340402800025 J Shao, ZF; Zhou, WX; Zhang, L; Hou, JH Shao, Zhenfeng; Zhou, Weixun; Zhang, Lei; Hou, Jihu Improved color texture descriptors for remote sensing image retrieval JOURNAL OF APPLIED REMOTE SENSING English Article remote sensing image retrieval; texture feature; color texture; grayscale texture; Gabor wavelets CLASSIFICATION; FEATURES; RECOGNITION; REPRESENTATION; SYSTEM Texture features are widely used in image retrieval literature. However, conventional texture features are extracted from grayscale images without taking color information into consideration. We present two improved texture descriptors, named color Gabor wavelet texture (CGWT) and color Gabor opponent texture (CGOT), respectively, for the purpose of remote sensing image retrieval. The former consists of unichrome features computed from color channels independently and opponent features computed across different color channels at different scales, while the latter consists of Gabor texture features and opponent features mentioned above. The two representations incorporate discriminative information among color bands, thus describing well the remote sensing images that have multiple objects. Experimental results demonstrate that CGWT yields better performance compared to other state-of-the-art texture features, and CGOT not only improves the retrieval results of some image classes that have unsatisfactory performance using CGWT representation, but also increases the average precision of all queried images further. In addition, a similarity measure function for proposed representation CGOT has been defined to give a convincing evaluation. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [Shao, Zhenfeng; Zhou, Weixun; Zhang, Lei; Hou, Jihu] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China Zhou, WX (reprint author), Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430079, Peoples R China. weixunzhou1990@whu.edu.cn National Science and Technology Specific Projects [2012YQ16018505]; National Natural Science Foundation of China [61172174] The author would like to thank Shawn Newsam for his LULC data set and the anonymous reviewers for their comments and corrections. This work was supported in part by National Science and Technology Specific Projects under Grant No. 2012YQ16018505 and National Natural Science Foundation of China under Grant No. 61172174. Aptoula E., 2013, IEEE T GEOSCI REMOTE, V52, P3023; Chang T, 1993, IEEE T IMAGE PROCESS, V2, P429, DOI 10.1109/83.242353; Choi JY, 2012, IEEE T IMAGE PROCESS, V21, P1366, DOI 10.1109/TIP.2011.2168413; Chun YD, 2008, IEEE T MULTIMEDIA, V10, P1073, DOI 10.1109/TMM.2008.2001357; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; DUBUF JMH, 1990, PATTERN RECOGN, V23, P291, DOI 10.1016/0031-3203(90)90017-F; FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146; HARALICK RM, 1973, IEEE T SYST MAN CYB, VSMC3, P610, DOI 10.1109/TSMC.1973.4309314; HURVICH LM, 1957, PSYCHOL REV, V64, P384, DOI 10.1037/h0041403; Jain A, 1998, IEEE T IMAGE PROCESS, V7, P124, DOI 10.1109/83.650858; Liapis S, 2004, IEEE T MULTIMEDIA, V6, P676, DOI 10.1109/TMM.2004.834858; Lin CH, 2009, IMAGE VISION COMPUT, V27, P658, DOI 10.1016/j.imavis.2008.07.004; Liu CJ, 2002, IEEE T IMAGE PROCESS, V11, P467, DOI 10.1109/TIP.2002.999679; Maenpaa T, 2004, PATTERN RECOGN, V37, P1629, DOI 10.1016/j.patcog.2003.11.011; Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803; Muller H, 2002, LECT NOTES COMPUT SC, V2383, P38; Niblack C. W., 1993, P SOC PHOTO-OPT INS, V1908, P173, DOI 10.1117/12.143648; Ojala T, 1996, PATTERN RECOGN, V29, P51, DOI 10.1016/0031-3203(95)00067-4; Ojala T, 2002, IEEE T PATTERN ANAL, V24, P971, DOI 10.1109/TPAMI.2002.1017623; Risojevic V, 2013, IEEE GEOSCI REMOTE S, V10, P836, DOI 10.1109/LGRS.2012.2225596; Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054; Shi MH, 2003, IEEE T GEOSCI REMOTE, V41, P1090, DOI 10.1109/TGRS.2003.811076; Singha M., 2012, SIGNAL IMAGE PROCESS, V3, P39; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Tsai M. H., 2010, MATH PROBL ENG, V2009, P1; Yang Y, 2013, IEEE T GEOSCI REMOTE, V51, P818, DOI 10.1109/TGRS.2012.2205158; Yue J, 2011, MATH COMPUT MODEL, V54, P1121, DOI 10.1016/j.mcm.2010.11.044 27 0 0 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. JUL 24 2014 8 083584 10.1117/1.JRS.8.083584 13 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AN0UI WOS:000340299100001 J Liu, JL; Li, Z; Huang, L; Tian, BS Liu, Jiuliang; Li, Zhen; Huang, Lei; Tian, Bangsen Hemispheric-scale comparison of monthly passive microwave snow water equivalent products JOURNAL OF APPLIED REMOTE SENSING English Article snow water equivalent; passive microwave instrument; remote sensing; the Northern Hemisphere RADIOMETER DATA; SEASONAL SNOW; DEPTH; COVER; RETRIEVALS; VARIABILITY; UNCERTAINTY; PLATEAU; BOREAL; MODEL The snow water equivalent (SWE) products from passive microwave remote sensing are useful in global climate change studies due to the long-time and all-weather imaging capabilities of passive microwave radiometry at the hemisphere scale. Northern Hemisphere SWE products, including products from the National Snow and Ice Data Center (NSIDC) and GlobSnow from the European Space Agency (ESA), have been providing long-time series information since 1979. However, the different algorithms used to produce the NSIDC and GlobSnow products lead to discrepancies in the data. To determine which product might be superior, this paper assesses their hemisphere-scale quality for the time period 1979-2010. By comparing the data with historical snow depth measurements obtained from 7388 meteorological stations in the Northern Hemisphere, the accuracies of the different SWE products are analyzed for the period and for different snow types. The results show that for SWEs above 30 mm but below 200 mm, GlobSnow estimates maintain a better linear relation with the ground measurements. NSIDC products are more influenced by microwave "saturation," producing obvious underestimations for SWEs over 120 mm. However, for shallow snow (SWE less than 30 mm), the slight overestimate produced by GlobSnow is more obvious than that of the other NSIDC products. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [Liu, Jiuliang; Li, Zhen; Huang, Lei; Tian, Bangsen] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China; [Liu, Jiuliang] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Li, Z (reprint author), Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Key Lab Digital Earth Sci, Beijing 100094, Peoples R China. zli@ceode.ac.cn Chinese Ministry of Science and Technology [2010CB951403, 2011AA120403] The authors would like to thank the anonymous reviewers for their constructive advice. This research was supported by the Chinese Ministry of Science and Technology (Grant Nos. 2010CB951403 and 2011AA120403). Armstrong R. L., 2007, GLOBAL MONTHLY EASE; Armstrong RL, 2001, GEOPHYS RES LETT, V28, P3673, DOI 10.1029/2000GL012556; Armstrong RL, 2002, ANN GLACIOL-SER, V34, P38, DOI 10.3189/172756402781817428; Bartholome E, 2005, INT J REMOTE SENS, V26, P1959, DOI 10.1080/01431160412331291297; Brasnett B, 1999, J APPL METEOROL, V38, P726, DOI 10.1175/1520-0450(1999)038<0726:AGAOSD>2.0.CO;2; Brown RD, 2011, CRYOSPHERE, V5, P219, DOI 10.5194/tc-5-219-2011; Brown RD, 1998, ATMOS OCEAN, V36, P37; Brown RD, 2003, ATMOS OCEAN, V41, P1, DOI 10.3137/ao.410101; Brown RD, 1996, J CLIMATE, V9, P1299, DOI 10.1175/1520-0442(1996)009<1299:IVIRCS>2.0.CO;2; CHANG ATC, 1987, ALLNALS GLACIOLOGY, V0009; Chang ATC, 1996, HYDROL PROCESS, V10, P1565, DOI 10.1002/(SICI)1099-1085(199612)10:12<1565::AID-HYP501>3.0.CO;2-5; Che T, 2014, REMOTE SENS ENVIRON, V143, P54, DOI 10.1016/j.rse.2013.12.009; Chunlin Huang, 2012, IEEE Transactions on Geoscience and Remote Sensing, V50, DOI 10.1109/TGRS.2012.2192480; Clifford D, 2010, INT J REMOTE SENS, V31, P3707, DOI 10.1080/01431161.2010.483482; Colbeck S. C., 1987, IAHS PUBL, V162, P3; Dai LY, 2012, REMOTE SENS ENVIRON, V127, P14, DOI 10.1016/j.rse.2011.08.029; Derksen C, 2012, REMOTE SENS ENVIRON, V117, P236, DOI 10.1016/j.rse.2011.09.021; Derksen C, 2005, REMOTE SENS ENVIRON, V96, P315, DOI 10.1016/j.rse.2005.02.014; Dietz AJ, 2012, INT J REMOTE SENS, V33, P4094, DOI 10.1080/01431161.2011.640964; Erxleben J, 2002, HYDROL PROCESS, V16, P3627, DOI 10.1002/hyp.1239; Foster, 2005, REMOTE SENS ENVIRON, V94, P187; Grippa M, 2004, REMOTE SENS ENVIRON, V93, P30, DOI 10.1016/j.rse.2004.06.012; Hall DK, 2002, ANN GLACIOL, V34, P24, DOI 10.3189/172756402781817770; Hancock S, 2013, REMOTE SENS ENVIRON, V128, P107, DOI 10.1016/j.rse.2012.10.004; Jiang L. M., 2014, SCI CHINA EARTH SCI, P1; Kelly R., 2004, AMSR E AQUA DAILY L3; Krenke A., 2004, FORMER SOVIET UNION; Lemmetyinen J, 2010, IEEE T GEOSCI REMOTE, V48, P2781, DOI 10.1109/TGRS.2010.2041357; Li Z, 2012, INT J DIGIT EARTH, V5, P516, DOI 10.1080/17538947.2011.594099; Luojus K., 2010, GLOBAL SNOW MONITORI, V1; Luojus K., 2010, GEOSC REM SENS S IGA, P4851, DOI DOI 10.1109/IGARSS.2010.5741987; Mognard N. M., 1998, 27 INT S REM SENS EN, P333; Mote T. L., 2003, WATER RESOUR RES, V39; Nolin AW, 2010, J GLACIOL, V56, P1141; Pulliainen J, 2006, REMOTE SENS ENVIRON, V101, P257, DOI 10.1016/j.rse.2006.01.002; Pulliainen J, 2001, REMOTE SENS ENVIRON, V75, P76, DOI 10.1016/S0034-4257(00)00157-7; Pulliainen JT, 1999, IEEE T GEOSCI REMOTE, V37, P1378, DOI 10.1109/36.763302; Rees A, 2006, HYDROL PROCESS, V20, P1019, DOI 10.1002/hyp.6076; Savoie MH, 2009, REMOTE SENS ENVIRON, V113, P2661, DOI 10.1016/j.rse.2009.08.006; Steven H., 2013, REMOTE SENS ENVIRON, V128, P107; Sturm M, 2010, J HYDROMETEOROL, V11, P1380, DOI 10.1175/2010JHM1202.1; STURM M, 1995, J CLIMATE, V8, P1261, DOI 10.1175/1520-0442(1995)008<1261:ASSCCS>2.0.CO;2; Takala M, 2011, REMOTE SENS ENVIRON, V115, P3517, DOI 10.1016/j.rse.2011.08.014; Takala M, 2009, IEEE T GEOSCI REMOTE, V47, P2996, DOI 10.1109/TGRS.2009.2018442; Tedesco M, 2010, IEEE J-STARS, V3, P141, DOI 10.1109/JSTARS.2010.2040462; Ulaby F. T., 1986, ADV SYSTEMS APPL INC, V3, P1797; WALKER AE, 1993, ANN GLACIOL, V0017 47 0 0 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. JUL 22 2014 8 084688 10.1117/1.JRS.8.084688 16 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AN0TW WOS:000340297900001 J Sutcliffe, JS; Beaumont, V; Watson, JM; Chew, CS; Beconi, M; Hutcheson, DM; Dominguez, C; Munoz-Sanjuan, I Sutcliffe, Jane S.; Beaumont, Vahri; Watson, James M.; Chew, Chang Sing; Beconi, Maria; Hutcheson, Daniel M.; Dominguez, Celia; Munoz-Sanjuan, Ignacio Efficacy of Selective PDE4D Negative Allosteric Modulators in the Object Retrieval Task in Female Cynomolgus Monkeys (Macaca fascicularis) PLOS ONE English Article QUINOLINIC ACID MODEL; CREB-BINDING-PROTEIN; R6/2 MOUSE MODEL; HUNTINGTONS-DISEASE; PREFRONTAL CORTEX; INHIBITOR ROLIPRAM; MOTOR DEFICITS; PHOSPHODIESTERASE INHIBITORS; DARPP-32 PHOSPHORYLATION; STRIATAL EXCITOTOXICITY Cyclic adenosine monophosphate (cAMP) signalling plays an important role in synaptic plasticity and information processing in the hippocampal and basal ganglia systems. The augmentation of cAMP signalling through the selective inhibition of phosphodiesterases represents a viable strategy to treat disorders associated with dysfunction of these circuits. The phosphodiesterase (PDE) type 4 inhibitor rolipram has shown significant pro-cognitive effects in neurological disease models, both in rodents and primates. However, competitive non-isoform selective PDE4 inhibitors have a low therapeutic index which has stalled their clinical development. Here, we demonstrate the pro-cognitive effects of selective negative allosteric modulators (NAMs) of PDE4D, D159687 and D159797 in female Cynomolgous macaques, in the object retrieval detour task. The efficacy displayed by these NAMs in a primate cognitive task which engages the corticostriatal circuitry, together with their suitable pharmacokinetic properties and safety profiles, suggests that clinical development of these allosteric modulators should be considered for the treatment of a variety of brain disorders associated with cognitive decline. [Sutcliffe, Jane S.; Watson, James M.; Chew, Chang Sing; Hutcheson, Daniel M.] Maccine Pte Ltd, Dept Neurosci & CNS Safety Pharmacol, Singapore, Singapore; [Beaumont, Vahri; Beconi, Maria; Dominguez, Celia; Munoz-Sanjuan, Ignacio] CHDI Management Inc, CHDI Fdn, Los Angeles, CA 90045 USA Munoz-Sanjuan, I (reprint author), CHDI Management Inc, CHDI Fdn, Los Angeles, CA 90045 USA. ignacio.munoz@chdifoundation.org CHDI Foundation CHDI Foundation is a privately-funded not-for-profit biomedical research organization exclusively dedicated to discovering and developing therapeutics that slow the progression of Huntington's disease. CHDI Foundation conducts research in a number of different ways; for the purposes of this manuscript, research was conducted at the contract research organization Maccine Pte Ltd. under a fee-for-service agreement. The authors listed all contributed to the conception, study design, data collection and analysis, decision to publish, and preparation of the manuscript. The specific roles of these authors are articulated in the 'uthor contributions' section. ASANUMA M, 1993, ARCH GERONTOL GERIAT, V16, P191, DOI 10.1016/0167-4943(93)90009-7; Ballard TM, 2009, PSYCHOPHARMACOLOGY, V202, P207, DOI 10.1007/s00213-008-1357-7; Bateup HS, 2008, NAT NEUROSCI, V11, P932, DOI 10.1038/nn.2153; BERTOLINO A, 1988, INT CLIN PSYCHOPHARM, V3, P245, DOI 10.1097/00004850-198807000-00006; Bruno O, 2011, BRIT J PHARMACOL, V164, P2054, DOI 10.1111/j.1476-5381.2011.01524.x; Burgin AB, 2010, NAT BIOTECHNOL, V28, P63, DOI 10.1038/nbt.1598; Chiang MC, 2005, J BIOL CHEM, V280, P14331, DOI 10.1074/jbc.M413279200; DeMarch Z, 2008, NEUROBIOL DIS, V30, P375, DOI 10.1016/j.nbd.2008.02.010; DeMarch Z, 2007, NEUROBIOL DIS, V25, P266, DOI 10.1016/j.nbd.2006.09.006; Di L, 2013, J MED CHEM, V56, P2, DOI 10.1021/jm301297f; DIAMOND A, 1989, BEHAV NEUROSCI, V103, P526, DOI 10.1037/0735-7044.103.3.526; Dias R, 1996, BEHAV NEUROSCI, V110, P872, DOI 10.1037/0735-7044.110.5.872; Diehl KH, 2001, J APPL TOXICOL, V21, P15, DOI 10.1002/jat.727; FREY U, 1993, SCIENCE, V260, P1661, DOI 10.1126/science.8389057; Friden M, 2009, J MED CHEM, V52, P6233, DOI 10.1021/jm901036q; Giampa C, 2009, EUR J NEUROSCI, V29, P902, DOI 10.1111/j.1460-9568.2009.06649.x; Giampa C, 2009, NEUROBIOL DIS, V34, P450, DOI 10.1016/j.nbd.2009.02.014; Gines S, 2003, HUM MOL GENET, V12, P497, DOI 10.1093/hmg/ddg046; Gong B, 2004, J CLIN INVEST, V114, P1624, DOI 10.1172/JCI200422831; Gray RA, 2006, AIDS RES HUM RETROV, V22, P1031, DOI 10.1089/aid.2006.22.1031; Hebb ALO, 2007, CURR OPIN PHARMACOL, V7, P86, DOI 10.1016/j.coph.2006.08.014; HEBENSTREIT GF, 1989, PHARMACOPSYCHIATRY, V22, P156, DOI 10.1055/s-2007-1014599; Houslay MD, 2005, DRUG DISCOV TODAY, V10, P1503, DOI 10.1016/S1359-6446(05)03622-6; Huang Z, 2007, BIOCHEM PHARMACOL, V73, P1971, DOI 10.1016/j.bcp.2007.03.010; Imanishi T, 1997, EUR J PHARMACOL, V321, P273, DOI 10.1016/S0014-2999(96)00969-7; Jentsch JD, 1997, SCIENCE, V277, P953, DOI 10.1126/science.277.5328.953; Jentsch JD, 1999, PSYCHOPHARMACOLOGY, V142, P78, DOI 10.1007/s002130050865; Jung J, 2007, SCIENCE, V315, P1857, DOI 10.1126/science.1139517; Kleiman RJ, 2011, J PHARMACOL EXP THER, V336, P64, DOI 10.1124/jpet.110.173294; Kobayashi M, 2011, INT IMMUNOPHARMACOL, V11, P732, DOI 10.1016/j.intimp.2011.01.023; Kuhn A, 2007, HUM MOL GENET, V16, P1845, DOI 10.1093/hmg/ddm133; Kuroiwa M, 2011, PSYCHOPHARMACOLOGY B; Li YF, 2009, NEUROPSYCHOPHARMACOL, V34, P2404, DOI 10.1038/npp.2009.66; Li YF, 2011, J NEUROSCI, V31, P172, DOI 10.1523/JNEUROSCI.5236-10.2011; Mantamadiotis T, 2002, NAT GENET, V31, P47, DOI 10.1038/ng882; Marte A, 2008, J NEUROSCI RES, V86, P3338, DOI 10.1002/jnr.21788; Munoz-Sanjuan I, 2011, J CLIN INVEST, V121, P476, DOI 10.1172/JCI45364; Navakkode S, 2004, J NEUROSCI, V24, P7740, DOI 10.1523/JNEUROSCI.1796-04.2004; Nishi A, 2005, P NATL ACAD SCI USA, V102, P1199, DOI 10.1073/pnas.0409138102; Nishi A, 2008, J NEUROSCI, V28, P10460, DOI 10.1523/JNEUROSCI.2518-08.2008; Obrietan K, 2004, J NEUROSCI, V24, P791, DOI 10.1523/JNEUROSCI.3493-03.2004; Oliveira RF, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0011725; Palfi SP, 1996, J NEUROSCI, V16, P3019; Puerta E, 2010, NEUROBIOL DIS, V38, P237, DOI 10.1016/j.nbd.2010.01.013; Ramos BP, 2003, NEURON, V40, P835, DOI 10.1016/S0896-6273(03)00694-9; Reneerkens OAH, 2009, PSYCHOPHARMACOLOGY, V202, P419, DOI 10.1007/s00213-008-1273-x; Richter W, 2013, EXPERT OPIN THER TAR, V17, P1011, DOI 10.1517/14728222.2013.818656; Roberts AC, 2000, CEREB CORTEX, V10, P252, DOI 10.1093/cercor/10.3.252; Robichaud A, 2002, BRIT J PHARMACOL, V135, P113, DOI 10.1038/sj.bjp.0704457; Robichaud A, 1999, NEUROPHARMACOLOGY, V38, P289, DOI 10.1016/S0028-3908(98)00190-7; Robichaud A, 2002, J CLIN INVEST, V110, P1045, DOI 10.1172/JCI200215506; Rodefer JS, 2011, NEUROPHARMACOLOGY; Roitberg BZ, 2002, NEUROSURGERY, V50, P137, DOI 10.1097/00006123-200201000-00022; Rosas HD, 2011, MOVEMENT DISORD, V26, P1691, DOI 10.1002/mds.23762; Rosas HD, 2010, NEUROIMAGE, V49, P2995, DOI 10.1016/j.neuroimage.2009.10.015; Rose GM, 2005, CURR PHARM DESIGN, V11, P3329, DOI 10.2174/138161205774370799; Rutten K, 2006, NEUROBIOL LEARN MEM, V85, P132, DOI 10.1016/j.nlni.2005.09.002; Rutten K, 2008, PSYCHOPHARMACOLOGY, V196, P643, DOI 10.1007/s00213-007-0999-1; Rutten K, 2007, PSYCHOPHARMACOLOGY, V192, P275, DOI 10.1007/s00213-006-0697-4; Sanderson TM, 2013, NEUROPHARMACOLOGY, V74, P86, DOI 10.1016/j.neuropharm.2013.01.011; Scahill RI, 2011, HUM BRAIN MAPP; Sharma S, 2013, EUR J PHARMACOL, V714, P486, DOI 10.1016/j.ejphar.2013.06.038; Sierksma A. S., 2013, NEUROPHARMACOLOGY; Smith DA, 2010, NAT REV DRUG DISCOV, V9, P929, DOI 10.1038/nrd3287; Sugars KL, 2004, J BIOL CHEM, V279, P4988, DOI 10.1074/jbc.M310226200; Tabrizi SJ, 2012, LANCET NEUROL, V11, P42, DOI 10.1016/S1474-4422(11)70263-0; TAYLOR JR, 1990, BRAIN, V113, P617, DOI 10.1093/brain/113.3.617; Threlfell S, 2009, J PHARMACOL EXP THER, V328, P785, DOI 10.1124/jpet.108.146332; Tinsley M, 2007, COGNITION ENHANCING; Walker SC, 2006, EUR J NEUROSCI, V23, P3119, DOI 10.1111/j.1460-9568.2006.04826.x; Wang HC, 2007, BIOCHEM J, V408, P193, DOI 10.1042/BJ20070970; Zhang KYJ, 2004, MOL CELL, V15, P279, DOI 10.1016/j.molcel.2004.07.005 72 0 0 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One JUL 22 2014 9 7 e102449 10.1371/journal.pone.0102449 16 Multidisciplinary Sciences Science & Technology - Other Topics AM0QO WOS:000339551100037 J Song, W; Liang, JZ; Park, SC Song, Wei; Liang, Jiu Zhen; Park, Soon Cheol Fuzzy control GA with a novel hybrid semantic similarity strategy for text clustering INFORMATION SCIENCES English Article Clustering; WordNet; Hybrid semantic similarity; Fuzzy control; Evolutionary computation; Genetic algorithm GENETIC ALGORITHM; INFORMATION-RETRIEVAL; OPTIMIZATION; INDEXES This paper proposes a fuzzy control genetic algorithm (GA) in conjunction with a novel hybrid semantic similarity measure for document clustering. Since the common clustering algorithms use vector space model (VSM) to represent document, the conceptual relationships between related terms being ignored, we use semantic similarity measures to solve this problem. In general, the semantic similarity measures can be extensively categorized into two kinds: thesaurus-based methods and corpus-based methods. However, in practice the corpus-based method is rather complicated to tackle. We propose and demonstrate a semantic space model (SSM) as the corpus-based method, where the appropriately reduced dimensions in SSM can capture the true relationship between documents in terms of concepts, rather than specific terms. Thus, the thesaurus-based method is combined with our SSM as a hybrid strategy to represent the semantic similarity measure. In GA field, the balance between the capability to converge to an optimum and the capacity to explore new solutions affects the success of search for the global optimum. We utilize a fuzzy control GA to adaptively adjust the influence between these two factors. Two textual data sets from Reuter document collection and 20-newsgroup corpus are tested in our experiments, and the results show that our fuzzy control GA combined with the hybrid semantic similarity strategy apparently outperforms the conventional GA, FCM and K-means with the traditional cosine similarity in VSM. Moreover, the superiorities of the fuzzy control GA and our hybrid semantic strategy are demonstrated by their better performance, in comparison with conventional GA with the same similarity measures. (c) 2014 Elsevier Inc. All rights reserved. [Song, Wei; Liang, Jiu Zhen] Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Jiangsu, Peoples R China; [Song, Wei; Park, Soon Cheol] Chonbuk Natl Univ, Sch Informat & Commun Engn, Jeonju 561756, Jeonbuk, South Korea Song, W (reprint author), Jiangnan Univ, Sch Internet Things Engn, Wuxi 214122, Jiangsu, Peoples R China. songwei@jiangnan.edu.cn National Natural Science Foundation of China [61103129]; Natural Science Foundation of Jiangsu Province [SBK201122266]; SRF; SEM, Brain Korea 21; Specialized Research Fund for the Doctoral Program of Higher Education [20100093120004] The authors thank the Editor in Chief and the reviewers for providing very helpful comments and suggestions. Their insight and comments led to a better presentation of the ideas in this paper. This work was sponsored by National Natural Science Foundation of China (61103129), Natural Science Foundation of Jiangsu Province (SBK201122266), SRF for ROCS, SEM, Brain Korea 21, and the Specialized Research Fund for the Doctoral Program of Higher Education (20100093120004). ANKERST M, 1999, P ACM SIGMOD INT C M, P49, DOI 10.1145/304182.304187; Antanas Z., 2003, INFORMATICA, V14, P121; Bandyopadhyay S, 2001, IEEE T SYST MAN CY C, V31, P120, DOI 10.1109/5326.923275; Bandyopadhyay S, 2002, INFORM SCIENCES, V146, P221, DOI 10.1016/S0020-0255(02)00208-6; Bandyopadhyay S, 2004, IEEE T SYST MAN CY B, V34, P2088, DOI 10.1109/TSMCB.2004.834438; Bellegarda JR, 1996, INT CONF ACOUST SPEE, P172, DOI 10.1109/ICASSP.1996.540318; Berry MW, 1995, SIAM REV, V37, P573, DOI 10.1137/1037127; Davies D., 1979, IEEE T PATTERN ANAL, P224; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; Francis W., 1979, BROWN CORPUS MANUAL; Goldberg D. E., 1989, GENETIC ALGORITHMS S; Gong DW, 2011, INFORM SCIENCES, V181, P4124, DOI 10.1016/j.ins.2011.05.011; Gurrutxaga I, 2010, PATTERN RECOGN, V43, P3364, DOI 10.1016/j.patcog.2010.04.021; He B, 2011, INFORM SCIENCES, V181, P3017, DOI 10.1016/j.ins.2011.03.007; Hotho A., 1995, P 26 ANN INT ACM SIG; Ji ZX, 2011, PATTERN RECOGN, V44, P999, DOI 10.1016/j.patcog.2010.11.017; Kim DH, 2007, INFORM SCIENCES, V177, P3918, DOI 10.1016/j.ins.2007.04.002; Koontz W., 1975, IEEE T COMPUT C, V25, P936; KOONTZ WLG, 1975, IEEE T COMPUT, V24, P908, DOI 10.1109/T-C.1975.224336; Lefever E, 2010, INFORM SCIENCES, V180, P3192, DOI 10.1016/j.ins.2010.05.018; Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005; Liu WY, 2010, INFORM SCIENCES, V180, P4031, DOI 10.1016/j.ins.2010.06.021; Maulik U, 2000, PATTERN RECOGN, V33, P1455, DOI 10.1016/S0031-3203(99)00137-5; Michael W.B., 1999, UNDERSTANDING SEARCH; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; Nguyen CD, 2008, INFORM SCIENCES, V178, P4205, DOI 10.1016/j.ins.2008.07.016; Noorinaeini A., 2006, International Journal of Human Factors Modelling and Simulation, V1, DOI 10.1504/IJHFMS.2006.011684; Nottelmann H, 2005, LECT NOTES COMPUT SC, V3408, P260; Paltoglou G, 2010, INFORM SCIENCES, V180, P2763, DOI 10.1016/j.ins.2010.03.020; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528; Resnik P, 1995, P 14 INT JOINT C ART; Robertson S.E., 1992, P TEXT RETR C TREC, P21; Saha S, 2010, PATTERN RECOGN, V43, P738, DOI 10.1016/j.patcog.2009.07.004; Saha S, 2009, INFORM SCIENCES, V179, P3230, DOI 10.1016/j.ins.2009.06.013; Sanchis J, 2008, INFORM SCIENCES, V178, P931, DOI 10.1016/j.ins.2007.09.018; Savio L.Y., 1999, P DA SFAA 299 6 IEEE, P195; SELIM SZ, 1984, IEEE T PATTERN ANAL, V6, P81; SHEPARD RN, 1987, SCIENCE, V237, P1317, DOI 10.1126/science.3629243; Song W, 2006, LECT NOTES COMPUT SC, V4221, P779; Sun J.T., 2004, P ICDM 2004, P535; Tarazaga P, 1998, COMP SCI STAT, V30, P292; Vozalis MG, 2007, INFORM SCIENCES, V177, P3017, DOI 10.1016/j.ins.2007.02.036; WOLFE JH, 1970, MULTIVAR BEHAV RES, V5, P329, DOI 10.1207/s15327906mbr0503_6; Xia HX, 2006, J SYST SCI SYST ENG, V15, P474, DOI 10.1007/s11518-006-5029-z; Yany Y., 1995, P 18 ACM INT C REX D, P256; Zhang L, 2011, INFORM SCIENCES, V181, P4658, DOI 10.1016/j.ins.2010.11.005 47 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. JUL 20 2014 273 156 170 10.1016/j.ins.2014.03.024 15 Computer Science, Information Systems Computer Science AI2PM WOS:000336700500009 J Parapar, J; Presedo-Quindimil, MA; Barreiro, A Parapar, Javier; Presedo-Quindimil, Manuel A.; Barreiro, Alvaro Score distributions for Pseudo Relevance Feedback INFORMATION SCIENCES English Article Information Retrieval; Pseudo Relevance Feedback; Score distribution; Pseudo Relevance Feedback set; Relevance Model INFORMATION-RETRIEVAL; DOCUMENT-RETRIEVAL; QUERY EXPANSION; SYSTEMS; MODELS Relevance-Based Language Models, commonly known as Relevance Models, are successful approaches to explicitly introduce the concept of relevance in the statistical language modelling framework of Information Retrieval. These models achieve state-of-the-art retrieval performance in the Pseudo Relevance Feedback task. It is known that one of the factors that more affect to the Pseudo Relevance Feedback robustness is the selection for some queries of harmful expansion terms. In order to minimise this effect in these methods a crucial point is to reduce the number of non-relevant documents in the pseudo relevant set. In this paper, we propose an original approach to tackle this problem. We try to automatically determine for each query how many documents we should select as pseudo-relevant set. For achieving this objective we will study the score distributions of the initial retrieval and trying to discern in base of their distribution between relevant and non-relevant documents. Evaluation of our proposal showed important improvements in terms of robustness. (c) 2014 Elsevier Inc. All rights reserved. [Parapar, Javier; Presedo-Quindimil, Manuel A.; Barreiro, Alvaro] Univ A Coruna, Dept Comp Sci, Informat Retrieval Lab, La Coruna 15071, Spain Parapar, J (reprint author), Univ A Coruna, Dept Comp Sci, Informat Retrieval Lab, Campus Elvina, La Coruna 15071, Spain. javierparapar@udc.es; mpresedo@udc.es; barreiro@udc.es Ministerio de Economia y Competitividad of the Kingdom of Spain [TIN2012-33867] This paper has been funded by the Ministerio de Economia y Competitividad of the Kingdom of Spain under research project ref. TIN2012-33867. Abdul-jaleel N., 2004, NIST SPECIAL PUBLICA; Amati G, 2004, LECT NOTES COMPUT SC, V2997, P127; Arampatzis A, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P524, DOI 10.1145/1571941.1572031; Arampatzis A., 2000, NIST SPECIAL PUBLICA; Banerjee A, 2005, J MACH LEARN RES, V6, P1705; Baumgarten C., 1999, Proceedings of SIGIR '99. 22nd International Conference on Research and Development in Information Retrieval, DOI 10.1145/312624.312685; BOOKSTEIN A, 1977, INFORM PROCESS MANAG, V13, P377, DOI 10.1016/0306-4573(77)90057-7; Carmel D., 2006, P 29 ANN INT ACM SIG; Croft W.B., 2009, SEARCH ENGINES INFOR; CROFT WB, 1979, J DOC, V35, P285, DOI 10.1108/eb026683; Cronen-Townsend S., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Dai K., 2012, P 34 EUR C ADV INF R, P293; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Garcia V, 2010, SIGNAL PROCESS, V90, P3197, DOI 10.1016/j.sigpro.2010.05.024; Huang Q, 2008, LECT NOTES COMPUT SC, V4956, P547; Kanoulas E, 2009, LECT NOTES COMPUT SC, V5766, P152, DOI 10.1007/978-3-642-04417-5_14; Kanoulas E, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P242; Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI 10.1145/383952.383972; Li XY, 2008, INFORM PROCESS MANAG, V44, P991, DOI 10.1016/j.ipm.2007.07.005; Lv Y., 2009, P 18 ACM C INF KNOWL, P255, DOI 10.1145/1645953.1645988; Lv Y., 2009, P 18 ACM C INF KNOWL, P1895, DOI 10.1145/1645953.1646259; Madigan D, 2006, INFORM RETRIEVAL, V9, P273, DOI 10.1007/s10791-006-0882-4; Manmatha R., 2001, P 24 ANN INT ACM SIG, P267, DOI DOI 10.1145/383952.384005; Mitra M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290995; Nielsen F., 2009, ABS09114863 CORR; Ounis C.C.V., 2007, NOVATICA UPGRADE SPE; Robertson S, 2007, LECT NOTES COMPUT SC, V4425, P40; Robertson S. E., 1997, READINGS INFORM RETR, P281; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638; Sakai T., 2001, P 24 ANN INT ACM SIG, P396, DOI 10.1145/383952.384035; Sakai T., 2005, ACM T ASIAN LANGUAGE, V4, P111, DOI 10.1145/1105696.1105699; Salton G., 1971, SMART RETRIEVAL SYST; Shtok A, 2009, LECT NOTES COMPUT SC, V5766, P305, DOI 10.1007/978-3-642-04417-5_30; SWETS JA, 1963, SCIENCE, V141, P245, DOI 10.1126/science.141.3577.245; SWETS JA, 1969, AM DOC, V20, P72, DOI 10.1002/asi.4630200110; Voorhees Ellen M., 2005, TREC EXPT EVALUATION, V63; Winaver M., 2007, P SIGIR, P729, DOI 10.1145/1277741.1277880; Xu Jinxi, 1996, P 19 ANN INT ACM SIG, P4, DOI 10.1145/243199.243202; Zhai CX, 2004, ACM T INFORM SYST, V22, P179, DOI 10.1145/984321.984322; Zhang P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P107, DOI 10.1145/1571941.1571962 41 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. JUL 20 2014 273 171 181 10.1016/j.ins.2014.03.034 11 Computer Science, Information Systems Computer Science AI2PM WOS:000336700500010 J Millane, RP; Chen, JPJ Millane, Rick P.; Chen, Joe P. J. Aspects of direct phasing in femtosecond nanocrystallography PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES English Article X-ray free-electron lasers; phase retrieval; crystallography; imaging X-RAY CRYSTALLOGRAPHY; PROTEIN NANOCRYSTALLOGRAPHY; DIFFRACTION-PATTERN; RETRIEVAL; LASER X-ray free-electron laser diffraction patterns from protein nanocrystals provide information on the diffracted amplitudes between the Bragg reflections, offering the possibility of direct phase retrieval without the use of ancillary experimental data. Proposals for implementing direct phase retrieval are reviewed. These approaches are limited by the signal-to-noise levels in the data and the presence of different and incomplete unit cells in the nanocrystals. The effects of low signal to noise can be ameliorated by appropriate selection of the intensity data samples that are used. The effects of incomplete unit cells may be small in some cases, and a unique solution is likely if there are four or fewer molecular orientations in the unit cell. [Millane, Rick P.; Chen, Joe P. J.] Univ Canterbury, Computat Imaging Grp, Dept Elect & Comp Engn, Christchurch 1, New Zealand Millane, RP (reprint author), Univ Canterbury, Computat Imaging Grp, Dept Elect & Comp Engn, Christchurch 1, New Zealand. rick.millane@canterbury.ac.nz James Cook Research Fellowship; UC Doctoral Scholarship; R.H.T. Bates Postgraduate Scholarship This work was supported by a James Cook Research Fellowship to R. P. M., and a UC Doctoral Scholarship and an R.H.T. Bates Postgraduate Scholarship to J.P.J.C. Barends TRM, 2014, NATURE, V505, P244, DOI 10.1038/nature12773; Barty A, 2012, NAT PHOTONICS, V6, P35, DOI [10.1038/nphoton.2011.297, 10.1038/NPHOTON.2011.297]; Chapman HN, 2009, NAT MATER, V8, P299, DOI 10.1038/nmat2402; Chapman HN, 2011, NATURE, V470, P73, DOI 10.1038/nature09750; Chen JPJ, 2014, ACTA CRYSTALLOGR A, V70, P143, DOI 10.1107/S2053273313032038; Chen JPJ, 2013, J OPT SOC AM A, V30, P2627, DOI 10.1364/JOSAA.30.002627; Chen JPJ, 2014, ACTA CRYSTALLOGR A, V70, P154, DOI 10.1107/S2053273313032725; Dilanian RA, 2013, ACTA CRYSTALLOGR A, V69, P108, DOI 10.1107/S0108767312042535; Eden M, 1961, P 4 BERK S MATH STAT, V4, P223; Elser V, 2003, J OPT SOC AM A, V20, P40, DOI 10.1364/JOSAA.20.000040; Elser V, 2007, P NATL ACAD SCI USA, V104, P418, DOI 10.1073/pnas.0606359104; Elser V, 2013, ACTA CRYSTALLOGR A, V69, P559, DOI 10.1107/S0108767313023362; Elser V, 2008, ACTA CRYSTALLOGR A, V64, P273, DOI 10.1107/S0108767307050684; KAM Z, 1977, MACROMOLECULES, V10, P927, DOI 10.1021/ma60059a009; Kang HJ, 2013, INT J BIOCHEM CELL B, V45, P636, DOI 10.1016/j.biocel.2012.12.018; Kirian RA, 2014, PHILOS T R SOC B, V369, DOI 10.1098/rstb.2013.0331; Kirian RA, 2010, OPT EXPRESS, V18, P5713, DOI 10.1364/OE.18.005713; Kirian RA, 2012, J PHYS B-AT MOL OPT, V45, DOI 10.1088/0953-4075/45/22/223001; Liu Haiguang, 2014, IUCrJ, V1, P19, DOI 10.1107/S2052252513025530; Marchesini S, 2007, REV SCI INSTRUM, V78, DOI 10.1063/1.2403783; Martin AV, 2012, OPT EXPRESS, V20, P16650, DOI 10.1364/OE.20.016650; Miao J, 2000, ACTA CRYSTALLOGR A, V56, P596, DOI 10.1107/S010876730001031X; Miao J, 1998, J OPT SOC AM A, V15, P1662, DOI 10.1364/JOSAA.15.001662; Miao JW, 1999, NATURE, V400, P342, DOI 10.1038/22498; MILLANE RP, 1990, J OPT SOC AM A, V7, P394, DOI 10.1364/JOSAA.7.000394; Neutze R, 2000, NATURE, V406, P752, DOI 10.1038/35021099; Redecke L, 2013, SCIENCE, V339, P227, DOI 10.1126/science.1229663; Ren G, 2000, J MOL BIOL, V301, P369, DOI 10.1006/jmbi.2000.3949; Saldin DK, 2011, PHYS REV LETT, V106, DOI 10.1103/PhysRevLett.106.115501; SAYRE D, 1952, ACTA CRYSTALLOGR, V5, P843, DOI 10.1107/S0365110X52002276; Seibert MM, 2011, NATURE, V470, P78, DOI 10.1038/nature09748; Son SK, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.218102; Spence JCH, 2012, REP PROG PHYS, V75, DOI 10.1088/0034-4885/75/10/102601; Spence JCH, 2011, OPT EXPRESS, V19, P2866, DOI 10.1364/OE.19.002866; Starodub D, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2288; Weierstall U, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.3693040; White TA, 2013, ACTA CRYSTALLOGR D, V69, P1231, DOI 10.1107/S0907444913013620 37 2 2 ROYAL SOC LONDON 6-9 CARLTON HOUSE TERRACE, LONDON SW1Y 5AG, ENGLAND 0962-8436 1471-2970 PHILOS T R SOC B Philos. Trans. R. Soc. B-Biol. Sci. JUL 17 2014 369 1647 20130498 10.1098/rstb.2013.0498 7 AJ0SZ WOS:000337367600022 J Guo, ZY; Song, Y; Wang, J; Li, ZH; He, AZ Guo, Zhenyan; Song, Yang; Wang, Jia; Li, Zhenhua; He, Anzhi Two-step spatial phase-shifting lateral shearing interferometry by triple gratings OPTICS COMMUNICATIONS English Article Linear gratings; Phase shift; Frequency filtering; Phase retrieval; Tomographic image processing; Moire techniques MOIRE DEFLECTOMETRY; DEFLECTION TOMOGRAPHY; THEORETICAL-ANALYSIS; OPTICAL TOMOGRAPHY; FLOW-FIELDS; ALGORITHM To acquire phase projection information for moire tomography, a new spatial phase-shifting lateral shearing interferometry is presented in this paper. The system is very simple and contains only three linear gratings and two filters. No wave plates or polarization elements are introduced. Moreover, via using a 4-1 system, the optical path is greatly shortened and two complete spatial phase-shifted lateral shearing interferograms can be obtained simultaneously. A corresponding two-step phase-shifting algorithm is used for phase retrieval and the interferometry is used to extract the first-order partial derivative of the spherical wave. The results show that the proposed method is not only feasible but also has high accuracy. Propane flame phase information is measured by the optical system. Finally, error analysis of phase projection extracted by two-step spatial phase-shifting method is mathematically co ncl ucted. (C) 2014 Elsevier B.V. All rights reserved. [Guo, Zhenyan; Song, Yang; Wang, Jia; Li, Zhenhua; He, Anzhi] Nanjing Univ Sci & Technol, Dept Informat Phys & Engn, Nanjing 210094, Jiangsu, Peoples R China Song, Y (reprint author), Nanjing Univ Sci & Technol, Dept Informat Phys & Engn, Nanjing 210094, Jiangsu, Peoples R China. guozhenyan15@163.com; sy0204@mail.njust.edu.cn National Natural Science Foundation of China [10804052] This work is supported by the National Natural Science Foundation of China (Grant no. 10804052). Bruning H., 1974, APPL OPTICS, V13, P2693; Chen YY, 2011, OPT COMMUN, V284, P2648, DOI 10.1016/j.optcom.2011.01.087; Chen YY, 2012, OPT LETT, V37, P2721, DOI 10.1364/OL.37.002721; Faris GW, 2000, OPT EXPRESS, V7, P447; FARIS GW, 1988, APPL OPTICS, V27, P5202, DOI 10.1364/AO.27.005202; Guo ZY, 2013, J OPT SOC AM A, V30, P1535, DOI 10.1364/JOSAA.30.001535; Hettwer A, 2000, OPT ENG, V39, P960, DOI 10.1117/1.602453; JEFFRIES RA, 1970, PHYS FLUIDS, V13, P210, DOI 10.1063/1.1692793; KAFRI O, 1980, OPT LETT, V5, P555, DOI 10.1364/OL.5.000555; KEREN E, 1981, APPL OPTICS, V20, P4263, DOI 10.1364/AO.20.004263; KWON OY, 1984, OPT LETT, V9, P59, DOI 10.1364/OL.9.000059; Lv W, 2011, APPL OPTICS, V50, P3924, DOI 10.1364/AO.50.003924; Meng XF, 2009, OPT LETT, V34, P1210, DOI 10.1364/OL.34.001210; Pritt MD, 1996, IEEE T GEOSCI REMOTE, V34, P728, DOI 10.1109/36.499752; Quiroga JA, 1999, OPT ENG, V38, P974, DOI 10.1117/1.602138; Rodriguez-Zurita Gustavo, 2008, Optics Express, V16, DOI 10.1364/OE.16.007806; Schreiber H, 1997, APPL OPTICS, V36, P5321, DOI 10.1364/AO.36.005321; SERVIN M, 1990, APPL OPTICS, V29, P3266, DOI 10.1364/AO.29.003266; SMYTHE R, 1984, OPT ENG, V23, P361; SNYDER R, 1988, OPT LETT, V13, P87, DOI 10.1364/OL.13.000087; Song Y, 2009, J OPT SOC AM A, V26, P882; Song Y, 2012, OPT LETT, V37, P1922, DOI 10.1364/OL.37.001922; Song Y, 2006, APPL OPTICS, V45, P8092, DOI 10.1364/AO.45.008092; Song Y, 2009, OPT EXPRESS, V17, P20415, DOI 10.1364/OE.17.020415; STRICKER J, 1983, AIAA J, V21, P1767, DOI 10.2514/3.8326; Sun N, 2012, APPL OPTICS, V51, P8081, DOI 10.1364/AO.51.008081; Toto-Arellano NI, 2010, APPL OPTICS, V49, P6402, DOI 10.1364/AO.49.006402; Vannoni M, 2011, PROC SPIE, V8082, DOI 10.1117/12.888981; Vlad V., 1988, SPIE OPT TEST METROL, V954, P145; Wang J, 2013, OPT LETT, V38, P1116, DOI 10.1364/OL.38.001116; Wang M, 2002, OPT LASER TECHNOL, V34, P679, DOI 10.1016/S0030-3992(02)00099-3; Xie X, 2013, APPL OPTICS, V52, P4063, DOI 10.1364/AO.52.004063 32 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0030-4018 1873-0310 OPT COMMUN Opt. Commun. JUL 15 2014 323 110 118 10.1016/j.optcom.2014.02.057 9 Optics Optics AH1NM WOS:000335887700017 J Flaherty, S; Lurz, PWW; Patenaude, G Flaherty, Silvia; Lurz, Peter W. W.; Patenaude, Genevieve Use of LiDAR in the conservation management of the endangered red squirrel (Sciurus vulgaris L.) JOURNAL OF APPLIED REMOTE SENSING English Article LiDAR; general linear model; geographic information system; habitat mapping; red squirrel AIRBORNE LIDAR; HABITAT USE; FOREST; TREE; HEIGHT; PREDICTION; VARIABLES; VOLUME; COVER LiDAR remote sensing allows the direct retrieval of vegetation structure parameters and has been widely used to assess habitat quality for various species. The aim of this study is to test whether LiDAR can help in providing estimates of habitat suitability over larger scales and inform conservation management planning in stronghold areas of an endangered forest mammal, the red squirrel (Sciurus vulgaris L.). The Eurasian red squirrel is endangered in the UK and under strict legal protection. Hence, long-term habitat management is a key goal of the UK conservation strategy. This involves understanding habitat preferences of the species. In a previous study, we demonstrated the importance of forest structure for red squirrels' habitat preference. We used a general linear model (GLM) to relate the distribution and abundance of squirrel feeding signs to mean canopy closure, mean tree height, and the total number of trees at the plot level. However, this analysis was limited to a few sample areas. In the current study, we implement the GLM using LiDAR-derived explanatory variables in Abernethy Forest. Results suggest that when forest structure is considered, only 27% of the total forest area is highly suitable for red squirrel. Implications for management are discussed. (C) 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [Flaherty, Silvia] Consejo Nacl Invest Cient & Tecn, CENPAT, Ctr Nacl Patagon, RA-9120 Puerto Madryn, Chubut, Argentina; [Patenaude, Genevieve] Univ Edinburgh, Sch Geosci, Edinburgh EH8 9XP, Midlothian, Scotland Flaherty, S (reprint author), Consejo Nacl Invest Cient & Tecn, CENPAT, Ctr Nacl Patagon, Bv Brown 2915, RA-9120 Puerto Madryn, Chubut, Argentina. flaherty@cenpat.edu.ar University of Edinburgh; Forestry Commission Scotland We would like to acknowledge both the Natural Environment Research Council (NERC) and the Airborne Research and Survey Facility (ARSF) for acquiring and providing the LiDAR data. Special thanks go to Dr Rachel Gaulton for her help with the local maxima algorithm and Dr Pablo Rosso for reading the manuscript and providing valuable feedback. This research was funded by the University of Edinburgh and Forestry Commission Scotland. Arlot S, 2010, STAT SURVEYS, V4, P40, DOI DOI 10.1214/09-SS054; Bosch S., 2012, EURASIAN RED SQUIRRE; Bradbury RB, 2005, IBIS, V147, P443, DOI 10.1111/j.1474-919x.2005.00438.x; Clawges R, 2008, REMOTE SENS ENVIRON, V112, P2064, DOI 10.1016/j.rse.2007.08.023; Crawley M.J., 2007, R BOOK; Doumas SL, 2013, RESTOR ECOL, V21, P133, DOI 10.1111/j.1526-100X.2012.00864.x; Faraway JJ, 2006, EXTENDING LINEAR MOD; Flaherty S., 2012, THESIS U EDINBURGH; Flaherty S, 2012, FORESTRY, V85, P437, DOI 10.1093/forestry/cps042; Garcia R, 2007, LECT NOTES GEOINF CA, P55, DOI 10.1007/978-3-540-72385-1_4; Gaulton R, 2010, INT J REMOTE SENS, V31, P1193, DOI 10.1080/01431160903380565; Gaulton R., 2008, THESIS U EDINBURGH; Goetz SJ, 2010, ECOLOGY, V91, P1569, DOI 10.1890/09-1670.1; Graf RF, 2009, FOREST ECOL MANAG, V257, P160, DOI 10.1016/j.foreco.2008.08.021; GURNELL J, 1983, MAMMAL REV, V13, P133, DOI 10.1111/j.1365-2907.1983.tb00274.x; Gurnell J, 2004, J ANIM ECOL, V73, P26, DOI 10.1111/j.1365-2656.2004.00791.x; Gurnell J., 1987, NATURAL HIST SQUIRRE; Gurnell J, 2002, BIOL CONSERV, V105, P53, DOI 10.1016/S0006-3207(01)00179-3; Hill RA, 2004, INT J REMOTE SENS, V25, P4851, DOI 10.1080/0143116031000139962; Holmgren J, 2004, SCAND J FOREST RES, V19, P543, DOI 10.1080/02827580410019472; Hopkinson C, 2008, REMOTE SENS ENVIRON, V112, P1168, DOI 10.1016/j.rse.2007.07.020; Jennings SB, 1999, FORESTRY, V72, P59, DOI 10.1093/forestry/72.1.59; Korhonen L, 2011, REMOTE SENS ENVIRON, V115, P1065, DOI 10.1016/j.rse.2010.12.011; Koukoulas S, 2005, INT J REMOTE SENS, V26, P431, DOI 10.1080/0143116042000298289; Lee H, 2010, INT J REMOTE SENS, V31, P117, DOI 10.1080/01431160902882561; Lefsky MA, 2002, BIOSCIENCE, V52, P19, DOI 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2; Lurz PWW, 1995, FOREST ECOL MANAG, V79, P79, DOI 10.1016/0378-1127(95)03617-2; Lurz Peter W. W., 2000, Journal of Zoology (London), V251, P167, DOI 10.1017/S0952836900006038; Maltamo M, 2004, CAN J FOREST RES, V34, P1791, DOI 10.1139/X04-055; Martinuzzi S, 2009, REMOTE SENS ENVIRON, V113, P2533, DOI 10.1016/j.rse.2009.07.002; McGaughey R. J., 2009, FUSION LDV SOFTWARE; Nelson R, 2005, REMOTE SENS ENVIRON, V96, P292, DOI 10.1016/j.rse.2005.02.012; Patenaude G, 2004, REMOTE SENS ENVIRON, V93, P368, DOI 10.1016/j.rse.2004.07.016; Popescu SC, 2003, CAN J REMOTE SENS, V29, P564; Poulsom L., 2005, 089 SCOTT NAT HER CO; Rees D. G., 1995, ESSENTIAL STAT; SHORTEN M, 1953, J ANIM ECOL, V22, P134, DOI 10.2307/1695; Summers RW, 1999, FOREST ECOL MANAG, V118, P173, DOI 10.1016/S0378-1127(98)00496-4; Turner W, 2003, TRENDS ECOL EVOL, V18, P306, DOI 10.1016/S0169-5347(03)00070-3; Vauhkonen J, 2012, FORESTRY, V85, P27, DOI 10.1093/forestry/cpr051; Vierling KT, 2008, FRONT ECOL ENVIRON, V6, P90, DOI 10.1890/070001; Zuur AF, 2007, STAT BIOL HEALTH, P1; Zuur A. J., 2013, BEGINNERS GUIDE GLM, P49 43 0 0 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. JUL 14 2014 8 083952 10.1117/1.JRS.8.083592 15 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AL5GI WOS:000339161200001 J Galashan, D; Fehr, T; Kreiter, AK; Herrmann, M Galashan, Daniela; Fehr, Thorsten; Kreiter, Andreas K.; Herrmann, Manfred Human area MT+ shows load-dependent activation during working memory maintenance with continuously morphing stimulation BMC NEUROSCIENCE English Article fMRI; Human; hMT; V5; Retention SHORT-TERM-MEMORY; FALSE DISCOVERY RATE; SELECTIVE ATTENTION; PREFRONTAL CORTEX; TEMPORAL CORTEX; STATIC IMAGES; VISUAL-FIELD; HUMAN BRAIN; MOTION; TASK Background: Initially, human area MT+ was considered a visual area solely processing motion information but further research has shown that it is also involved in various different cognitive operations, such as working memory tasks requiring motion-related information to be maintained or cognitive tasks with implied or expected motion. In the present fMRI study in humans, we focused on MT+ modulation during working memory maintenance using a dynamic shape-tracking working memory task with no motion-related working memory content. Working memory load was systematically varied using complex and simple stimulus material and parametrically increasing retention periods. Activation patterns for the difference between retention of complex and simple memorized stimuli were examined in order to preclude that the reported effects are caused by differences in retrieval. Results: Conjunction analysis over all delay durations for the maintenance of complex versus simple stimuli demonstrated a wide-spread activation pattern. Percent signal change (PSC) in area MT+ revealed a pattern with higher values for the maintenance of complex shapes compared to the retention of a simple circle and with higher values for increasing delay durations. Conclusions: The present data extend previous knowledge by demonstrating that visual area MT+ presents a brain activity pattern usually found in brain regions that are actively involved in working memory maintenance. [Galashan, Daniela; Fehr, Thorsten; Herrmann, Manfred] Univ Bremen, Ctr Cognit Sci ZKW, Dept Neuropsychol & Behav Neurobiol, D-28359 Bremen, Germany; [Galashan, Daniela; Fehr, Thorsten; Kreiter, Andreas K.; Herrmann, Manfred] Univ Bremen, Ctr Adv Imaging, D-28359 Bremen, Germany; [Kreiter, Andreas K.] Univ Bremen, Inst Brain Res, D-28359 Bremen, Germany; [Fehr, Thorsten] Univ Magdeburg, Dept Neurol, D-39106 Magdeburg, Germany Galashan, D (reprint author), Univ Bremen, Ctr Cognit Sci ZKW, Dept Neuropsychol & Behav Neurobiol, Cognium Bldg,Hochschulring 18, D-28359 Bremen, Germany. galashan@uni-bremen.de Herrmann, Manfred/H-3931-2011 BMBF Neuroimaging programme from the Center for Advanced Imaging (CAI) - Magdeburg/Bremen [01GO0506] DG was supported by a grant of the BMBF Neuroimaging programme (01GO0506) from the Center for Advanced Imaging (CAI) - Magdeburg/Bremen (MH and AKK). We thank Wolf Zinke for helping to create stimuli, and comments on the experimental design, Manfred Fahle for providing the MT+ localizer stimulus, Katja Schmuck for her assistance in data acquisition and analysis, and Fingal Orlando Galashan and Ekkehard Kustermann for helpful comments on data analysis. ALLMAN JM, 1971, BRAIN RES, V31, P85, DOI 10.1016/0006-8993(71)90635-4; Barch DM, 1997, NEUROPSYCHOLOGIA, V35, P1373, DOI 10.1016/S0028-3932(97)00072-9; BENJAMINI Y, 1995, J ROY STAT SOC B MET, V57, P289; Bisley JW, 2004, J NEUROPHYSIOL, V91, P286, DOI 10.1152/jn.00870.2003; Bisley JW, 2001, J NEUROPHYSIOL, V85, P187; Brett M, 2002, NAT REV NEUROSCI, V3, P243, DOI 10.1038/nrn756; Brett M., 2002, NEUROIMAGE, V16; Cairo TA, 2004, COGNITIVE BRAIN RES, V21, P377, DOI 10.1016/j.cogbrainres.2004.06.014; Chafee MV, 1998, J NEUROPHYSIOL, V79, P2919; Chawla D, 1999, NAT NEUROSCI, V2, P671, DOI 10.1038/10230; Constantinidis C, 1996, J NEUROPHYSIOL, V76, P1352; Coventry KR, 2013, PSYCHOL SCI, V24, P1379, DOI 10.1177/0956797612469209; de Fockert JW, 2001, SCIENCE, V291, P1803, DOI 10.1126/science.1056496; Della-Maggiore V, 2002, NEUROIMAGE, V17, P19, DOI 10.1006/nimg.2002.1113; Denys K, 2004, J NEUROSCI, V24, P2551, DOI 10.1523/JNEUROSCI.3569-03.2004; Downing PE, 2007, J NEUROSCI, V27, P226, DOI 10.1523/JNEUROSCI.3619-06.2007; Druzgal TJ, 2003, J COGNITIVE NEUROSCI, V15, P771, DOI 10.1162/089892903322370708; Eng HY, 2005, PSYCHON B REV, V12, P1127, DOI 10.3758/BF03206454; Friston KJ, 1999, NEUROIMAGE, V10, P385, DOI 10.1006/nimg.1999.0484; FUNAHASHI S, 1989, J NEUROPHYSIOL, V61, P331; Fuster JM, 2001, NEURON, V30, P319, DOI 10.1016/S0896-6273(01)00285-9; Gazzaley Adam, 2004, Cognitive Affective & Behavioral Neuroscience, V4, P580, DOI 10.3758/CABN.4.4.580; Genovese CR, 2002, NEUROIMAGE, V15, P870, DOI 10.1006/nimg.2001.1037; Goebel R, 1998, EUR J NEUROSCI, V10, P1563, DOI 10.1046/j.1460-9568.1998.00181.x; Herrington J, 2012, NEUROIMAGE, V63, P581, DOI 10.1016/j.neuroimage.2012.06.077; Holmes AP, 1998, NEUROIMAGE, V7, pS754; Johnstone T, 2006, HUM BRAIN MAPP, V27, P779, DOI 10.1002/hbm.20219; Katzner Steffen, 2009, Front Syst Neurosci, V3, P12, DOI 10.3389/neuro.06.012.2009; Kayser AS, 2010, J NEUROSCI, V30, P15778, DOI 10.1523/JNEUROSCI.3163-10.2010; Kourtzi Z, 2000, J COGNITIVE NEUROSCI, V12, P48, DOI 10.1162/08989290051137594; Kourtzi Z, 2002, NAT NEUROSCI, V5, P17, DOI 10.1038/nn780; Lavie N, 2004, J EXP PSYCHOL GEN, V133, P339, DOI 10.1037/0096-3445.133.3.339; Linden DEJ, 2003, NEUROIMAGE, V20, P1518, DOI 10.1016/j.neuroimage.2003.07.021; Liu TS, 2011, VISION RES, V51, P26, DOI 10.1016/j.visres.2010.09.023; Manoach DS, 2003, NEUROIMAGE, V20, P1670, DOI 10.1016/j.neuroimage.2003.08.002; McKeefry DJ, 2007, VISION RES, V47, P2418, DOI 10.1016/j.visres.2007.05.011; Miller EK, 2001, ANNU REV NEUROSCI, V24, P167, DOI 10.1146/annurev.neuro.24.1.167; MILLER EK, 1993, J NEUROSCI, V13, P1460; MIYASHITA Y, 1988, NATURE, V331, P68, DOI 10.1038/331068a0; Nichols T, 2005, NEUROIMAGE, V25, P653, DOI 10.1016/j.neuroimage.2004.12.005; OCraven KM, 1997, NEURON, V18, P591, DOI 10.1016/S0896-6273(00)80300-1; O'Craven KM, 1999, NATURE, V401, P584; OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4; Owen AM, 2005, HUM BRAIN MAPP, V25, P46, DOI 10.1002/hbm.20131; Postle BR, 2006, NEUROSCIENCE, V139, P23, DOI 10.1016/j.neuroscience.2005.06.005; Postle BR, 2003, CORTEX, V39, P927, DOI 10.1016/S0010-9452(08)70871-2; Rickham PP, 1964, BRIT MED J, V2, P177; Riggall AC, 2012, J NEUROSCI, V32, P12990, DOI 10.1523/JNEUROSCI.1892-12.2012; Schoenfeld MA, 2003, P NATL ACAD SCI USA, V100, P11806, DOI 10.1073/pnas.1932820100; Sereno ME, 2002, NEURON, V33, P635, DOI 10.1016/S0896-6273(02)00598-6; Shelton AL, 2006, COGN AFFECT BEHAV NE, V6, P323, DOI 10.3758/CABN.6.4.323; Sohn W, 2004, VISION RES, V44, P1437, DOI 10.1016/j.visres.2003.12.010; Taylor K, 2005, CEREB CORTEX, V15, P1424, DOI 10.1093/cercor/bhi023; Treue S, 1996, NATURE, V382, P539, DOI 10.1038/382539a0; Umla-Runge K, 2011, BRAIN RES, V1382, P206, DOI 10.1016/j.brainres.2011.01.052; Vanduffel W, 2002, SCIENCE, V298, P413, DOI 10.1126/science.1073574; WATSON JDG, 1993, CEREB CORTEX, V3, P79, DOI 10.1093/cercor/3.2.79; Wegener D, 2004, J NEUROSCI, V24, P6106, DOI 10.1523/JNEUROSCI.1459-04.2004; Wilms M, 2005, ANAT EMBRYOL, V210, P485, DOI 10.1007/s00429-005-0064-y; Zarahn E, 2005, CEREB CORTEX, V15, P303, DOI 10.1093/cercor/bhh132; ZEKI S, 1991, J NEUROSCI, V11, P641 61 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1471-2202 BMC NEUROSCI BMC Neurosci. JUL 11 2014 15 85 10.1186/1471-2202-15-85 10 Neurosciences Neurosciences & Neurology AL7XZ WOS:000339351700001 J Howard, D Howard, David Determining membership with 2 simultaneous queries THEORETICAL COMPUTER SCIENCE English Article Searching; Sorting; Information retrieval; Cell-probe; Membership TIME Alice and Bob are playing a cooperative game in which Alice must devise a scheme to store n elements in an array from a universe U of size m. Her goal is to store in such a way that for every x is an element of U Bob can observe the values of two positions (dependent on x) in the array and determine whether x is in the array or not. Alice may share her storage scheme with Bob and they win if such an arrangement is made. The question is how large can the universe U be in terms of n so that Alice and Bob can win? In this paper we give upper and lower bounds on this question for general n and the special case when n = 3. We also pose conjectures and further questions for research. (C) 2014 Elsevier B.V. All rights reserved. Colgate Univ, Dept Math, Hamilton, NY 13346 USA Howard, D (reprint author), Colgate Univ, Dept Math, Hamilton, NY 13346 USA. dmhoward@colgate.edu Alon N, 2009, PROCEEDINGS OF THE TWENTIETH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P346; Brodnik A, 1999, SIAM J COMPUT, V28, P1627, DOI 10.1137/S0097539795294165; Buhrman H, 2002, SIAM J COMPUT, V31, P1723, DOI 10.1137/S0097539702405292; Kezdy Andre, COMMUNICATION; Knuth D.E., 1973, ADDISON WESLEY SER C, V3; Nicholson P.K., 2013, LECT NOTES COMPUT SC, V8066, P303; Nicholson P.K., 2013, THESIS U WATERLOO; Pagh R., 2001, P 33 ANN ACM S THEOR, P425, DOI 10.1145/380752.380836; Pagh R, 2001, SIAM J COMPUT, V31, P353, DOI 10.1137/S0097539700369909; Patrascu Succincter M., 2008, P 49 ANN IEEE S FDN, P305; Radhakrishnan J., 2001, LECT NOTES COMPUT SC, V2161, P290; Radhakrishnan J, 2010, LECT NOTES COMPUT SC, V6347, P159, DOI 10.1007/978-3-642-15781-3_14; Srinivasa Rao S., 2001, THESIS MADRAS U; Ta-Shma A, 2002, INFORM PROCESS LETT, V83, P267, DOI 10.1016/S0020-0190(02)00206-5; Viola E, 2012, SIAM J COMPUT, V41, P1593, DOI 10.1137/090766619; YAO ACC, 1981, J ACM, V28, P615, DOI 10.1145/322261.322274 16 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0304-3975 1879-2294 THEOR COMPUT SCI Theor. Comput. Sci. JUL 10 2014 543 112 119 10.1016/j.tcs.2014.05.020 8 Computer Science, Theory & Methods Computer Science AM2TB WOS:000339702600010 J Saive, AL; Royet, JP; Plailly, J Saive, Anne-Lise; Royet, Jean-Pierre; Plailly, Jane A review on the neural bases of episodic odor memory: from laboratory-based to autobiographical approaches FRONTIERS IN BEHAVIORAL NEUROSCIENCE English Review episodic memory; recognition memory; autobiographical memory; olfaction; behavior; approaches; neural bases; human HUMAN OLFACTORY CORTEX; MEDIAL TEMPORAL-LOBE; LONG-TERM-MEMORY; ABSTRACT VISUAL-STIMULI; RECOGNITION MEMORY; RECOLLECTIVE EXPERIENCE; FUNCTIONAL NEUROANATOMY; VERBAL ASSOCIATIONS; ENTORHINAL CORTEX; SEMANTIC FACTORS Odors are powerful cues that trigger episodic memories. However, in light of the amount of behavioral data describing the characteristics of episodic odor memory, the paucity of information available on the neural substrates of this function is startling. Furthermore, the diversity of experimental paradigms complicates the identification of a generic episodic odor memory network. We conduct a systematic review of the literature depicting the current state of the neural correlates of episodic odor memory in healthy humans by placing a focus on the experimental approaches. Functional neuroimaging data are introduced by a brief characterization of the memory processes investigated. We present and discuss laboratory-based approaches, such as odor recognition and odor associative memory, and autobiographical approaches, such as the evaluation of odor familiarity and odor-evoked autobiographical memory. We then suggest the development of new laboratory-ecological approaches allowing for the controlled encoding and retrieval of specific multidimensional events that could open up new prospects for the comprehension of episodic odor memory and its neural underpinnings. While large conceptual differences distinguish experimental approaches, the overview of the functional neuroimaging findings suggests relatively stable neural correlates of episodic odor memory. [Saive, Anne-Lise; Royet, Jean-Pierre; Plailly, Jane] Univ Lyon 1, Olfact Coding Memory Team, Lyon Neurosci Res Ctr, CNRS,UMR 5292,INSERM,U1028, F-69366 Lyon 07, France Plailly, J (reprint author), Univ Lyon 1, Olfact Coding Memory Team, Lyon Neurosci Res Ctr, CNRS,UMR 5292,INSERM,U1028, 50 Ave Tony Garnier, F-69366 Lyon 07, France. plailly@olfac.univ-lyon1.fr Centre National de la Recherche Scientifique (CNRS); LABEX Cortex of Universit de Lyon within the program" Investissements d'Avenir" [NR-11-LABX-0042, ANR-11-IDEX-0007]; Region Rhone-Alpes [CIBLE 10 015 772 01]; Roudnitska Foundation This work was supported by the Centre National de la Recherche Scientifique (CNRS), the LABEX Cortex (NR-11-LABX-0042) of Universit de Lyon within the program" Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR) and research grants from the Region Rhone-Alpes (CIBLE 10 015 772 01). Anne-Lise Saive was funded by the Roudnitska Foundation. Aggleton JP, 1999, BRIT J PSYCHOL, V90, P1, DOI 10.1348/000712699161170; Arshamian A, 2013, NEUROPSYCHOLOGIA, V51, P123, DOI 10.1016/j.neuropsychologia.2012.10.023; Ayabe-Kanamura S, 1998, CHEM SENSES, V23, P31; Babb SJ, 2006, CURR BIOL, V16, P1317, DOI 10.1016/j.cub.2006.05.025; Bhalla M, 2000, PSYCHOL REP, V86, P565, DOI 10.2466/PR0.86.2.565-574; Broman DA, 2001, CHEM SENSES, V26, P1187, DOI 10.1093/chemse/26.9.1187; Buchanan TW, 2003, LEARN MEMORY, V10, P319, DOI 10.1101/lm.62303; Burianova H, 2007, J COGNITIVE NEUROSCI, V19, P1520, DOI 10.1162/jocn.2007.19.9.1520; Burwell RD, 1998, J COMP NEUROL, V398, P179, DOI 10.1002/(SICI)1096-9861(19980824)398:2<179::AID-CNE3>3.0.CO;2-Y; Cabeza R, 2007, TRENDS COGN SCI, V11, P219, DOI 10.1016/j.tics.2007.02.005; Cabeza R, 2004, J COGNITIVE NEUROSCI, V16, P1583, DOI 10.1162/0898929042568578; CAIN WS, 1979, SCIENCE, V203, P467, DOI 10.1126/science.760202; CARMICHAEL ST, 1994, J COMP NEUROL, V346, P403, DOI 10.1002/cne.903460306; CARROLL B, 1993, BRAIN COGNITION, V22, P230, DOI 10.1006/brcg.1993.1036; Cerf-Ducastel B, 2006, NEUROIMAGE, V31, P386, DOI 10.1016/j.neuroimage.2005.11.009; Chu S, 2002, MEM COGNITION, V30, P511, DOI 10.3758/BF03194952; Chu S, 2000, CHEM SENSES, V25, P111, DOI 10.1093/chemse/25.1.111; Clayton NS, 1998, NATURE, V395, P272, DOI 10.1038/26216; Clayton NS, 2003, NAT REV NEUROSCI, V4, P685, DOI 10.1038/nrn1180; Cohen N. J., 1993, MEMORY AMNESIA HIPPO; Crystal JD, 2009, BEHAV PROCESS, V80, P269, DOI 10.1016/j.beproc.2008.09.009; Dade LA, 2002, BRAIN, V125, P86, DOI 10.1093/brain/awf003; Davachi L, 2006, CURR OPIN NEUROBIOL, V16, P693, DOI 10.1016/j.conb.2006.10.012; DAVIS RG, 1977, J EXP PSYCHOL-HUM L, V3, P37, DOI 10.1037//0278-7393.3.1.37; DAVIS RG, 1975, J EXP PSYCHOL-HUM L, V104, P134; Delplanque S, 2008, CHEM SENSES, V33, P469, DOI 10.1093/chemse/bjn014; Diana RA, 2007, TRENDS COGN SCI, V11, P379, DOI 10.1016/j.tics.2007.08.001; Distel H, 1999, CHEM SENSES, V24, P191, DOI 10.1093/chemse/24.2.191; Eacott MJ, 2010, NEUROPSYCHOLOGIA, V48, P2273, DOI 10.1016/j.neuropsychologia.2009.11.002; Easton A, 2012, LEARN MEMORY, V19, P146, DOI 10.1101/lm.025676.112; Eichenbaum H, 2007, ANNU REV NEUROSCI, V30, P123, DOI 10.1146/annurev.neuro.30.051606.094328; Easton A, 2008, HBK BEHAV NEUROSCI, V18, P185, DOI 10.1016/S1569-7339(08)00211-7; ENGEN T, 1960, J EXP PSYCHOL, V59, P214, DOI 10.1037/h0043912; ENGEN T, 1987, AM SCI, V75, P497; ENGEN T, 1973, J EXP PSYCHOL, V100, P221, DOI 10.1037/h0035492; Ferdenzi C, 2013, CHEM SENSES, V38, P175, DOI 10.1093/chemse/bjs083; Frank RA, 2011, CHEM SENSES, V36, P29, DOI 10.1093/chemse/bjq095; Gilbert PE, 2008, EXP AGING RES, V34, P437, DOI 10.1080/03610730802271914; Gilbert PE, 2006, J GERONTOL B-PSYCHOL, V61, pP58; Goddard L, 2005, MEMORY, V13, P79, DOI 10.1080/09658210344000594; GOLDMAN WP, 1992, AM J PSYCHOL, V105, P549, DOI 10.2307/1422910; Goodrich-Hunsaker NJ, 2009, CHEM SENSES, V34, P513, DOI 10.1093/chemse/bjp026; Gottfried JA, 2004, NEURON, V42, P687, DOI 10.1016/S0896-6273(04)00270-3; Griffiths DP, 2001, PHYSIOL BEHAV, V73, P755, DOI 10.1016/S0031-9384(01)00532-7; Haberly LB, 2001, CHEM SENSES, V26, P551, DOI 10.1093/chemse/26.5.551; Hernandez RJ, 2008, GERONTOLOGY, V54, P187, DOI 10.1159/000121377; Herz RS, 2012, ADV CONSC RES, V85, P95; Herz RS, 2004, CHEM SENSES, V29, P217, DOI 10.1093/chemse/bjh025; Herz RS, 1996, PSYCHON B REV, V3, P300, DOI 10.3758/BF03210754; HERZ RS, 1995, CHEM SENSES, V20, P517, DOI 10.1093/chemse/20.5.517; Herz RS, 2002, AM J PSYCHOL, V115, P21, DOI 10.2307/1423672; Herz RS, 2004, NEUROPSYCHOLOGIA, V42, P371, DOI 10.1016/j.neuropsychologia.2003.08.009; HERZ RS, 1992, CHEM SENSES, V17, P519, DOI 10.1093/chemse/17.5.519; Herz RS, 1998, ANN NY ACAD SCI, V855, P670, DOI 10.1111/j.1749-6632.1998.tb10643.x; HINTON PB, 1993, B PSYCHONOMIC SOC, V31, P595; Holland SM, 2011, ANIM COGN, V14, P95, DOI 10.1007/s10071-010-0346-5; Hudry J, 2003, BRAIN, V126, P1851, DOI 10.1093/brain/awg192; Insausti R, 1997, HIPPOCAMPUS, V7, P146, DOI 10.1002/(SICI)1098-1063(1997)7:2<146::AID-HIPO4>3.0.CO;2-L; Jacquet C., 2014, ART OLFACTI IN PRESS; Jehl C, 1997, PERCEPT PSYCHOPHYS, V59, P100, DOI 10.3758/BF03206852; JELLINEK JS, 1983, J SOC COSMET CHEM, V34, P83; JONES FN, 1978, PERCEPT PSYCHOPHYS, V24, P2, DOI 10.3758/BF03202967; JONESGOTMAN M, 1993, BRAIN COGNITION, V22, P182, DOI 10.1006/brcg.1993.1033; Laing D. G., 1992, HUMAN SENSE SMELL, P217; Larsson M, 2009, EXP PSYCHOL, V56, P375, DOI 10.1027/1618-3169.56.6.375; Larsson M, 1997, MEMORY, V5, P361; Larsson M, 2006, PSYCHOL RES-PSYCH FO, V70, P68, DOI 10.1007/s00426-004-0190-9; Larsson M, 2009, ANN NY ACAD SCI, V1170, P318, DOI 10.1111/j.1749-6632.2009.03934.x; Larsson M, 2003, ACTA PSYCHOL, V112, P89, DOI 10.1016/S0001-6918(02)00092-6; Larsson M, 1997, CHEM SENSES, V22, P623, DOI 10.1093/chemse/22.6.623; LAWLESS H, 1977, J EXP PSYCHOL-HUM L, V3, P52, DOI 10.1037//0278-7393.3.1.52; LAWLESS HT, 1975, CHEM SENS FLAV, V1, P331, DOI 10.1093/chemse/1.3.331; LAWLESS HT, 1978, PERCEPT PSYCHOPHYS, V24, P493, DOI 10.3758/BF03198772; Lehn H, 2013, HIPPOCAMPUS, V23, P122, DOI 10.1002/hipo.22073; Lesschaeve I, 1996, CHEM SENSES, V21, P699, DOI 10.1093/chemse/21.6.699; Levy DA, 2004, LEARN MEMORY, V11, P794, DOI 10.1101/lm.82504; Litaudon P, 1997, EUR J NEUROSCI, V9, P1593, DOI 10.1111/j.1460-9568.1997.tb01517.x; LOCKHART RS, 1970, PSYCHOL BULL, V74, P100, DOI 10.1037/h0029536; LYMAN BJ, 1990, J EXP PSYCHOL LEARN, V16, P656, DOI 10.1037/0278-7393.16.4.656; LYMAN BJ, 1986, Q J EXP PSYCHOL-A, V38, P753; MANDLER G, 1980, PSYCHOL REV, V87, P252, DOI 10.1037//0033-295X.87.3.252; MARR D, 1971, PHILOS T ROY SOC B, V262, P23, DOI 10.1098/rstb.1971.0078; McDermott KB, 2009, NEUROPSYCHOLOGIA, V47, P2290, DOI 10.1016/j.neuropsychologia.2008.12.025; Meunier D, 2014, NEUROIMAGE, V95, P264, DOI 10.1016/j.neuroimage.2014.03.041; Miles AN, 2011, MEMORY, V19, P930, DOI 10.1080/09658211.2011.613847; MILNER B, 1968, NEUROPSYCHOLOGIA, V6, P215, DOI 10.1016/0028-3932(68)90021-3; Milton F, 2011, MEMORY, V19, P733, DOI 10.1080/09658211.2011.552185; Mitchell KJ, 2009, PSYCHOL BULL, V135, P638, DOI 10.1037/a0015849; MURPHY C, 1991, AM J PSYCHOL, V104, P161, DOI 10.2307/1423153; Nadel L, 1998, NEUROPHARMACOLOGY, V37, P431, DOI 10.1016/S0028-3908(98)00057-4; Nadel L, 1997, CURR OPIN NEUROBIOL, V7, P217, DOI 10.1016/S0959-4388(97)80010-4; Olsson MJ, 2009, CHEMOSENS PERCEPT, V2, P161, DOI 10.1007/s12078-009-9051-7; Pause BM, 2013, FRONT BEHAV NEUROSCI, V7, DOI 10.3389/fnbeh.2013.00033; Pause BM, 2010, J NEUROSCI METH, V189, P88, DOI 10.1016/j.jneumeth.2010.03.016; Pirogovsky E, 2009, DEVELOPMENTAL SCI, V12, P1054, DOI 10.1111/j.1467-7687.2009.00857.x; Pirogovsky E, 2006, DEV NEUROPSYCHOL, V30, P739, DOI 10.1207/s15326942dn3002_5; Plailly J, 2005, NEUROIMAGE, V24, P1032, DOI 10.1016/j.neuroimage.2004.10.028; Plailly J, 2007, CEREB CORTEX, V17, P2650, DOI 10.1093/cercor/bhl173; Plailly J, 2011, APPETITE, V57, P615, DOI 10.1016/j.appet.2011.07.006; Poellinger A, 2001, NEUROIMAGE, V13, P547, DOI 10.1006/nimg.2000.0713; RABIN MD, 1984, J EXP PSYCHOL LEARN, V10, P316, DOI 10.1037/0278-7393.10.2.316; RAUSCH R, 1977, CORTEX, V13, P445; ROBINSON JA, 1976, COGNITIVE PSYCHOL, V8, P578, DOI 10.1016/0010-0285(76)90020-7; Royet Jean-Pierre, 2011, Front Hum Neurosci, V5, P65, DOI 10.3389/fnhum.2011.00065; Royet JP, 1999, J COGNITIVE NEUROSCI, V11, P94, DOI 10.1162/089892999563166; Royet JP, 2004, CHEM SENSES, V29, P731, DOI 10.1093/chemse/bjh067; Royet JP, 2001, NEUROIMAGE, V13, P506, DOI 10.1006/nimg.2000.0704; RUBIN DC, 1984, AM J PSYCHOL, V97, P493, DOI 10.2307/1422158; Saive AL, 2013, J NEUROSCI METH, V213, P22, DOI 10.1016/j.jneumeth.2012.11.010; Saive AL, 2014, FRONT BEHAV NEUROSCI, V8, DOI 10.3389/fnbeh.2014.00203; Savic I, 2004, HUM BRAIN MAPP, V21, P271, DOI 10.1002/hbm.20009; Savic I, 2000, NEURON, V26, P735, DOI 10.1016/S0896-6273(00)81209-X; SCHAB FR, 1991, PSYCHOL BULL, V109, P242, DOI 10.1037/0033-2909.109.2.242; Shepherd G. M., 1998, SYNAPTIC ORG BRAIN, P377; Sobel N, 1998, NATURE, V392, P282, DOI 10.1038/32654; Zelano C, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0004965; Squire L, 1984, MEMORY CONSOLIDATION, P185; SQUIRE LR, 1992, PSYCHOL REV, V99, P195, DOI 10.1037//0033-295X.99.2.195; Squire LR, 2004, ANNU REV NEUROSCI, V27, P279, DOI 10.1146/annurev.neuro.27.070203.144130; Sulmont C, 2002, CHEM SENSES, V27, P307, DOI 10.1093/chemse/27.4.307; SUZUKI WA, 1994, J NEUROSCI, V14, P1856; Swets J. A., 1964, SIGNAL DETECTION REC; Takahashi M, 2003, AM J PSYCHOL, V116, P527, DOI 10.2307/1423659; Tulving E, 1983, ELEMENTS EPISODIC ME; Tulving E, 2002, ANNU REV PSYCHOL, V53, P1, DOI 10.1146/annurev.psych.53.100901.135114; TULVING E, 1985, CAN PSYCHOL, V26, P1, DOI 10.1037/h0080017; Tulving E, 2001, PHILOS T ROY SOC B, V356, P1505, DOI 10.1098/rstb.2001.0937; Tulving E., 1972, ORG MEMORY, P381; Willander J, 2007, MEM COGNITION, V35, P1659, DOI 10.3758/BF03193499; Willander J, 2006, PSYCHON B REV, V13, P240, DOI 10.3758/BF03193837; Wilson DA, 2003, NEUROSCI BIOBEHAV R, V27, P307, DOI 10.1016/S0149-7634(03)00050-2; Witter MP, 2000, HIPPOCAMPUS, V10, P398; Yeshurun Y, 2009, CURR BIOL, V19, P1869, DOI 10.1016/j.cub.2009.09.066; Yousem DM, 1997, RADIOLOGY, V204, P833; ZATORRE RJ, 1992, NATURE, V360, P339, DOI 10.1038/360339a0 135 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1662-5153 FRONT BEHAV NEUROSCI Front. Behav. Neurosci. JUL 7 2014 8 240 10.3389/fnbeh.2014.00240 13 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AK6HU WOS:000338529500001 J Zhang, KW; Hu, BX; Robinson, J Zhang, Kongwen; Hu, Baoxin; Robinson, Justin Early detection of emerald ash borer infestation using multisourced data: a case study in the town of Oakville, Ontario, Canada JOURNAL OF APPLIED REMOTE SENSING English Article emerald ash borer; early detection; hyperspectral; data analysis; remote sensing CHLOROPHYLL CONTENT; PRECISION AGRICULTURE; VEGETATION INDEXES; MAPLE LEAVES; REFLECTANCE; MODEL; BUPRESTIDAE; COLEOPTERA; RETRIEVAL; INVERSION The emerald ash borer (EAB) poses a significant economic and environmental threat to ash trees in southern Ontario, Canada, and the northern states of the USA. It is critical that effective technologies are urgently developed to detect, monitor, and control the spread of EAB. This paper presents a methodology using multisourced data to predict potential infestations of EAB in the town of Oakville, Ontario, Canada. The information combined in this study includes remotely sensed data, such as high spatial resolution aerial imagery, commercial ground and airborne hyper-spectral data, and Google Earth imagery, in addition to nonremotely sensed data, such as archived paper maps and documents. This wide range of data provides extensive information that can be used for early detection of EAB, yet their effective employment and use remain a significant challenge. A prediction function was developed to estimate the EAB infestation states of individual ash trees using three major attributes: leaf chlorophyll content, tree crown spatial pattern, and prior knowledge. Comparison between these predicted values and a ground-based survey demonstrated an overall accuracy of 62.5%, with 22.5% omission and 18.5% commission errors. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. [Zhang, Kongwen; Hu, Baoxin] York Univ, Dept Earth & Space Sci & Engn, Toronto, ON M3J 1P3, Canada; [Zhang, Kongwen; Robinson, Justin] Selkirk Geospatial Res Ctr, Castlegar, BC V1N 4L3, Canada Zhang, KW (reprint author), York Univ, Dept Earth & Space Sci & Engn, 4700 Keele St, Toronto, ON M3J 1P3, Canada. conwen@yorku.ca Natural Sciences and Engineering Research Council (NSERC) of Canada The authors would like to thank Ziya He and Meaghan Eastwood of Toronto and Region Conservation for their contributions in the field and in consulting this research, and the town of Oakville, AMEC Inc., and the York University library for kindly supplying the data and technical support. The authors are grateful for financial support provided by the Natural Sciences and Engineering Research Council (NSERC) of Canada. Anulewicz Andrea C., 2007, Arboriculture & Urban Forestry, V33, P338; BARNES JD, 1992, ENVIRON EXP BOT, V32, P85, DOI 10.1016/0098-8472(92)90034-Y; BenDor T. K., 2006, ECOL MODEL, V197, P222; Carter G. A., 1994, J INT REMOTE SENS, V15, P697; CARTER GA, 1994, REMOTE SENS ENVIRON, V50, P295, DOI 10.1016/0034-4257(94)90079-5; Cosmopoulos P., 2008, INT J REMOTE SENS, V12, P2259; GAMON JA, 1992, REMOTE SENS ENVIRON, V41, P35, DOI 10.1016/0034-4257(92)90059-S; GITELSON A, 1994, J PHOTOCH PHOTOBIO B, V22, P247, DOI 10.1016/1011-1344(93)06963-4; Gould J. S., 2013, EMERALD ASH BORER AG; Haboudane D, 2002, REMOTE SENS ENVIRON, V81, P416, DOI 10.1016/S0034-4257(02)00018-4; Haboudane D, 2004, REMOTE SENS ENVIRON, V90, P337, DOI 10.1016/j.rse.2003.12.013; Hanou I., 2010, TOWN OAKVILLE HYPERS; Herms D. A., 2009, N CTR IPM CTR B; Jacquemoud S., 2009, REMOTE SENS ENVIRON, V113, pS55; JACQUEMOUD S, 1990, REMOTE SENS ENVIRON, V34, P75, DOI 10.1016/0034-4257(90)90100-Z; Jing L., 2013, P 35 INT S REM SENS, P22; Kovacs KF, 2010, ECOL ECON, V69, P569, DOI 10.1016/j.ecolecon.2009.09.004; le Maire G, 2004, REMOTE SENS ENVIRON, V89, P1, DOI 10.1016/j.rse.2003.09.004; Maloney K., 2006, EMERALD ASH BORER 20; McCullough D. G., 2004, NAPR0204 USDA; Merzlyak MN, 1999, PHYSIOL PLANTARUM, V106, P135, DOI 10.1034/j.1399-3054.1999.106119.x; Omari K, 2013, IEEE J-STARS, V6, P715, DOI 10.1109/JSTARS.2013.2240264; Pontius J, 2008, REMOTE SENS ENVIRON, V112, P2665, DOI 10.1016/j.rse.2007.12.011; Rose J. R., 2010, LANDOWNERS GUIDE WOO; Ryall KL, 2011, ENVIRON ENTOMOL, V40, P679, DOI 10.1603/EN10310; Sims DA, 2002, REMOTE SENS ENVIRON, V81, P337, DOI 10.1016/S0034-4257(02)00010-X; Smith A. M., 2005, P 26 CAN S REM SENS; Smitley D, 2008, J ECON ENTOMOL, V101, P1643, DOI 10.1603/0022-0493(2008)101[1643:POACTA]2.0.CO;2; Souci JS, 2009, PHOTOGRAMM ENG REM S, V75, P905; Sydnor T. D., 2007, INT SOC ARBORICULTUR, V1, P45; Thenkabail S., 2012, HYPERSPECTRAL REMOTE; VERHOEF W, 1984, REMOTE SENS ENVIRON, V16, P125, DOI 10.1016/0034-4257(84)90057-9; VOGELMANN JE, 1993, INT J REMOTE SENS, V14, P1563; Zarco-Tejada PJ, 2001, IEEE T GEOSCI REMOTE, V39, P1491, DOI 10.1109/36.934080; Zhang KW, 2011, CAN J REMOTE SENS, V37, P643, DOI 10.5589/m12-006; Zhang KW, 2012, REMOTE SENS-BASEL, V4, P1741, DOI 10.3390/rs4061741; Zhang YQ, 2008, REMOTE SENS ENVIRON, V112, P3234, DOI 10.1016/j.rse.2008.04.005 37 0 0 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. JUL 7 2014 8 10.1117/1.JRS.8.083602 19 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AK5ZJ WOS:000338505800001 J Yang, L; Yang, GP; Yin, YL; Xi, XM Yang, Lu; Yang, Gongping; Yin, Yilong; Xi, Xiaoming Exploring soft biometric trait with finger vein recognition NEUROCOMPUTING English Article Finger vein recognition; Soft biometric trait; Width of phalangeal joint; Hybrid framework PERSONAL IDENTIFICATION; LEVEL FUSION; EXTRACTION; CURVATURE; RETRIEVAL; SYSTEMS Soft biometric trait has been used as ancillary information to enhance the recognition accuracy for face, fingerprint, gait, iris, etc. In this paper, we present a new investigation of soft biometric trait to improve the performance of finger vein recognition. We first propose some extraction criteria of soft biometric trait for comprehensively understanding this kind of ancillary information. And then based on these criteria, the width of phalangeal joint is employed as a novel soft biometric trait, which can be directly extracted from finger vein image. Finally, three frameworks are developed to conduct the combination of the width measurement and finger vein pattern, i.e., the fusion framework, the filter framework and the hybrid framework. We perform rigorous experiments both on the open and self-built finger vein databases, and experimental results illustrate that soft biometric trait can make promising improvement of finger vein recognition performance. (c) 2014 Elsevier B.V. All rights reserved. [Yang, Lu; Yang, Gongping; Yin, Yilong; Xi, Xiaoming] Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China Yang, GP (reprint author), Shandong Univ, Sch Comp Sci & Technol, Jinan 250101, Peoples R China. gpyang@sdu.edu.cn National Natural Science Foundation of China [61173069, 61070097]; Program for New Century Excellent Talents in University of Ministry of Education of China [NCET-11-0315]; Shandong Natural Science Funds for Distinguished Young Scholar [JQ201316] This work is supported by the National Natural Science Foundation of China under Grant nos. 61173069 and 61070097, the Program for New Century Excellent Talents in University of Ministry of Education of China under Grant no. NCET-11-0315 and the Shandong Natural Science Funds for Distinguished Young Scholar under Grant no. JQ201316. The authors would particularly like to thank the anonymous reviewers for their helpful suggestions. Ailisto H, 2006, PATTERN RECOGN LETT, V27, P325, DOI 10.1016/j.patrec.2005.08.018; Giot Romain, 2012, International Journal of Information Technology and Management, V11, DOI 10.1504/IJITM.2012.044062; Hashimoto J., 2006, P S VLSI CIRC HON HI, P25; He MX, 2010, PATTERN RECOGN, V43, P1789, DOI 10.1016/j.patcog.2009.11.018; Jain A., 2012, INT J APPL SCI ADV T, V1, P55; Jain AK, 2004, LECT NOTES COMPUT SC, V3072, P731; Jain AK, 2009, 2009 16TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOLS 1-6, P37, DOI 10.1109/ICIP.2009.5413921; Jain AK, 2012, IEEE MULTIMEDIA, V19, P20, DOI 10.1109/MMUL.2012.4; Jain AK, 2004, P SOC PHOTO-OPT INS, V5404, P561, DOI 10.1117/12.542890; Kang BJ, 2010, IET COMPUT VIS, V4, P209, DOI 10.1049/iet-cvi.2009.0081; Kumar A, 2012, IEEE T IMAGE PROCESS, V21, P2228, DOI 10.1109/TIP.2011.2171697; Lee EC, 2011, SENSORS-BASEL, V11, P2319, DOI 10.3390/s110302319; Lyle J. R., 2010, P IEEE INT C BIOM TH, P1; Miura N, 2004, MACH VISION APPL, V15, P194, DOI 10.1007/s00138-004-0149-2; Miura N, 2007, IEICE T INF SYST, VE90D, P1185, DOI 10.1093/ietisy/e90-d.8.1185; Moustakas K, 2010, IEEE SIGNAL PROC LET, V17, P367, DOI 10.1109/LSP.2010.2040927; Park U, 2010, IEEE T INF FOREN SEC, V5, P406, DOI 10.1109/TIFS.2010.2049842; Rosdi BA, 2011, SENSORS-BASEL, V11, P11357, DOI 10.3390/s111211357; Song W, 2011, PATTERN RECOGN LETT, V32, P1541, DOI 10.1016/j.patrec.2011.04.021; Sunder Manisha Sam, 2010, Proceedings of the 2010 20th International Conference on Pattern Recognition (ICPR 2010), DOI 10.1109/ICPR.2010.328; Wayman J.L., 1997, P CARD TECH SEC TECH; Wu JD, 2011, EXPERT SYST APPL, V38, P5423, DOI 10.1016/j.eswa.2010.10.013; Wu JD, 2011, EXPERT SYST APPL, V38, P14284, DOI 10.1016/j.eswa.2011.05.086; Yang GP, 2012, SENSORS-BASEL, V12, P1738, DOI 10.3390/s120201738; Yang G.P., 2012, J BIOMED BIOTECHNOL, V2012, P1; Yang JF, 2012, PATTERN RECOGN LETT, V33, P1569, DOI 10.1016/j.patrec.2012.04.018; Yang JF, 2012, PATTERN RECOGN LETT, V33, P623, DOI 10.1016/j.patrec.2011.11.002; Yang L, 2013, SENSORS-BASEL, V13, P3799, DOI 10.3390/s130303799 28 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUL 5 2014 135 SI 218 228 10.1016/j.neucom.2013.12.029 11 Computer Science, Artificial Intelligence Computer Science AH1HM WOS:000335871200025 J Romero-Tris, C; Castella-Roca, J; Viejo, A Romero-Tris, Cristina; Castella-Roca, Jordi; Viejo, Alexandre Distributed system for private web search with untrusted partners COMPUTER NETWORKS English Article Privacy; Cryptography; Web search engines; Distributed system; Private information retrieval PERMUTATION NETWORK; SOCIAL NETWORKS; ENGINES; SCHEME Web search engines (WSEs) allow information retrieval from the Internet, a really useful service which is not provided without cost: users' queries and related information (e.g., query time, browser type, etc.) are stored and analyzed in the WSE database. The stored logs may contain sensitive information (e.g., health issues, location, religion, etc.) and identifiers (e.g., full name, IP address, cookies, etc.), which poses a serious threat to users' privacy. In the literature, there are several proposals that try to address this situation. In general, current schemes consider the WSE as the only adversary, and do not address the presence of other attackers or, if addressed, the introduced query delay is unaffordable in real environments. In this paper, we propose a distributed protocol, where a group of users collaborate to protect their privacy in front of WSEs and dishonest users, while introducing a reasonable delay. The performance of the new scheme is evaluated in terms of privacy level and delay. The former is analyzed using a set of query logs belonging to real users and provided by AOL. The latter involves the implementation, deployment and evaluation of the protocol in a real environment. (C) 2014 Elsevier B.V. All rights reserved. [Romero-Tris, Cristina; Castella-Roca, Jordi; Viejo, Alexandre] Univ Rovira & Virgili, UNESCO Chair Data Privacy, Dept Engn Informat & Matemat, E-43007 Tarragona, Spain Romero-Tris, C (reprint author), Univ Rovira & Virgili, UNESCO Chair Data Privacy, Dept Engn Informat & Matemat, Av Paisos Catalans 26, E-43007 Tarragona, Spain. cristina.romero@urv.cat Spanish Ministry of Science, and Innovation [TSI2007-65406-C03-01, TIN2011-27076-C03-01, CSD2007-00004, IPT-430000-2010-31]; Spanish Ministry of Industry, Commerce and Tourism [TSI-020100-2011-39, TSI-020302-2010-153]; Government of Catalonia [SGR 1135] Authors are solely responsible for the views expressed in this paper, which do not necessarily reflect the position of UNESCO nor commit, that organization. This work was partly supported by the Spanish Ministry of Science, and Innovation (through projects eAEGIS TSI2007-65406-C03-01, CO-PRIVACY TIN2011-27076-C03-01, ARES-CONSOLIDER INGENIO 2010 CSD2007-00004 and Audit Transparency Voting Process IPT-430000-2010-31), by the Spanish Ministry of Industry, Commerce and Tourism (through projects eVerification2 TSI-020100-2011-39 and SeCloud TSI-020302-2010-153) and by the Government of Catalonia (under Grant 2009 SGR 1135). Abe M, 1999, LECT NOTES COMPUT SC, V1716, P258; Balsa E, 2012, P IEEE S SECUR PRIV, P491, DOI 10.1109/SP.2012.36; Barbaro M., 2005, NY TIMES; Castella-Roca J, 2009, COMPUT COMMUN, V32, P1541, DOI 10.1016/j.comcom.2009.05.009; Chaum D., 1992, ADV CRYPTOLOGY CRYPT, V740, P89; Chow R., 2009, PROC 8TH ACM WORKSHO, P105, DOI 10.1145/1655188.1655204; Cooper A., 2008, ACM T WEB, V2; Cover T. M., 2012, ELEMENTS INFORM THEO; DESMEDT Y, 1990, LECT NOTES COMPUT SC, V435, P307; Dingledine R., 2004, P 13 C USENIX SEC S, P21; Domingo-Ferrer J., 2009, J ONLINE INFORM REV, V33, P1468; Eickhoff C., 2013, P 35 EUR C ADV INF R, P701; ELGAMAL T, 1985, IEEE T INFORM THEORY, V31, P469, DOI 10.1109/TIT.1985.1057074; Erola A, 2011, J SYST SOFTWARE, V84, P1734, DOI 10.1016/j.jss.2011.05.009; Erola A, 2011, SORT-STAT OPER RES T, P41; Hafner K., 2006, GOOGLE RESISTS US SU; Jakobsson M., 1999, 9933 DIMACS; Kamvar M., 2006, P SIGCHI C HUM FACT, P701, DOI 10.1145/1124772.1124877; Lindell Y, 2010, LECT NOTES COMPUT SC, V6205, P220, DOI 10.1007/978-3-642-14527-8_13; Manning C., 1999, FDN STAT NATURAL LAN; NIST, 2007, NIST SPEC PUBL 1; OPFERMAN DC, 1971, AT&T TECH J, V50, P1579; Peddinti ST, 2010, LECT NOTES COMPUT SC, V6205, P19, DOI 10.1007/978-3-642-14527-8_2; Rebollo-Monedero D, 2010, IEEE T INFORM THEORY, V56, P4631, DOI 10.1109/TIT.2010.2054471; Reiter M., 1998, ACM T INFORM SYST, V1, P66, DOI 10.1145/290163.290168; Romero-Tris C., 2011, 7 INT ICST C SEC PRI; Sanchez D, 2013, INFORM SCIENCES, V218, P17, DOI 10.1016/j.ins.2012.06.025; Schnorr C. P., 1991, Journal of Cryptology, V4, DOI 10.1007/BF00196725; Soo WH, 2002, LECT NOTES COMPUT SC, V2433, P446; Steel E, 2010, WALL STREET J; Viejo A, 2010, COMPUT NETW, V54, P1343, DOI 10.1016/j.comnet.2009.11.003; WAKSMAN A, 1968, J ACM, V15, P159, DOI 10.1145/321439.321449; Wright M. K., 2004, ACM Transactions on Information and Systems Security, V7, DOI 10.1145/1042031.1042032; Yang Z., 2005, P 11 ACM SIGKDD INT, P334, DOI 10.1145/1081870.1081909 34 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1389-1286 1872-7069 COMPUT NETW Comput. Netw. JUL 4 2014 67 26 42 10.1016/j.comnet.2014.03.022 17 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Computer Science; Engineering; Telecommunications AJ7FN WOS:000337862700003 J Li, JQ; Yang, JJ; Liu, CC; Zhao, Y; Liu, B; Shi, YL Li, JianQiang; Yang, Ji-Jiang; Liu, Chunchen; Zhao, Yu; Liu, Bo; Shi, Yuliang Exploiting semantic linkages among multiple sources for semantic information retrieval ENTERPRISE INFORMATION SYSTEMS English Article linked data; knowledge management; enterprise application; Semantic Web; enterprise search TEXT CATEGORIZATION; ENTERPRISE SYSTEMS; SEARCH; SIMILARITY; WORDNET; MODEL; ENVIRONMENT; ALGORITHM; GRAPH The vision of the Semantic Web is to build a global Web of machine-readable data to be consumed by intelligent applications. As the first step to make this vision come true, the initiative of linked open data has fostered many novel applications aimed at improving data accessibility in the public Web. Comparably, the enterprise environment is so different from the public Web that most potentially usable business information originates in an unstructured form (typically in free text), which poses a challenge for the adoption of semantic technologies in the enterprise environment. Considering that the business information in a company is highly specific and centred around a set of commonly used concepts, this paper describes a pilot study to migrate the concept of linked data into the development of a domain-specific application, i.e. the vehicle repair support system. The set of commonly used concepts, including the part name of a car and the phenomenon term on the car repairing, are employed to build the linkage between data and documents distributed among different sources, leading to the fusion of documents and data across source boundaries. Then, we describe the approaches of semantic information retrieval to consume these linkages for value creation for companies. The experiments on two real-world data sets show that the proposed approaches outperform the best baseline 6.3-10.8% and 6.4-11.1% in terms of top five and top 10 precisions, respectively. We believe that our pilot study can serve as an important reference for the development of similar semantic applications in an enterprise environment. [Li, JianQiang; Shi, Yuliang] Beijing Univ Technol, Sch Software Engn, Beijing 100124, Peoples R China; [Yang, Ji-Jiang] Tsinghua Univ, Tsinghua Natl Lab Info Sci & Technol, Beijing 100084, Peoples R China; [Liu, Chunchen; Liu, Bo] NEC Labs, Semant Comp Dept, Beijing, Peoples R China; [Zhao, Yu] Douban Inc, Beijing, Peoples R China Li, JQ (reprint author), Beijing Univ Technol, Sch Software Engn, Beijing 100124, Peoples R China. jianqiangli.email@gmail.com National 973 project [2011CB302505] This research was also supported by the National 973 project [grant no. 2011CB302505]. B Hu, 2010, ISWC; Berners-Lee T., 2001, SCI AM; BIZER C, 2009, INT J SEMANT WEB INF, V5, P1; Brauer F., 2010, P WWW RAL NC APR 26; Brauer F., 2009, P VLDB END; Broder AZ, 2004, IBM SYST J, V43, P451; Buscaldi D., 2005, CLEF WORKSH VIENN SE; Chen JQ, 2010, LECT NOTES COMPUT SC, V6488, P175; Chien BC, 2010, CYBERNET SYST, V41, P4, DOI 10.1080/01969720903408565; Cstells P., 2007, IEEE T KNOWL DATA EN, V19, P261; Daoud M, 2010, LECT NOTES COMPUT SC, V6075, P171, DOI 10.1007/978-3-642-13470-8_17; Davies J., 2004, 37 HAW INT C SYST SC; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; Dietz J.L.G., 2006, ENTERPRISE ONTOLOGY; Dong H, 2009, LECT NOTES COMPUT SC, V5872, P848; Duan L, 2012, IEEE T IND INFORM, V8, P679, DOI 10.1109/TII.2012.2188804; Egozi O., 2011, ACM T INFORM SYSTEMS, V29, P1; Eiron N., 2003, P 26 ANN INT ACM SIG, P459; Fernandez M, 2011, J WEB SEMANT, V9, P434, DOI 10.1016/j.websem.2010.11.003; Gong ZG, 2010, ENTERP INFORM SYST, V4, P1, DOI 10.1080/17517570903502856; Guo JZ, 2012, IEEE T IND INFORM, V8, P647, DOI 10.1109/TII.2012.2188899; Guo JZ, 2012, IEEE T SYST MAN CY A, V42, P316, DOI 10.1109/TSMCA.2011.2162946; Hao T., 2008, P ICUIMC 08 SUW JAN; He W, 2014, IEEE T IND INFORM, V10, P35, DOI 10.1109/TII.2012.2189221; Heflin J, 2000, ARTIF INTELL, P35; Henzinger M, 2005, ACM HYP SALZB SEPT 6; Hu Y., 2005, P 28 ANN INT ACM SIG, P250, DOI 10.1145/1076034.1076079; Jindal V., 2013, INFORM PROCESSING MA; Kallipolitis L, 2012, KNOWL-BASED SYST, V27, P38, DOI 10.1016/j.knosys.2011.12.007; Kara S, 2012, INFORM SYST, V37, P294, DOI 10.1016/j.is.2011.09.004; Khan L, 2004, VLDB J, V13, P71, DOI 10.1007/s00778-003-0105-1; Kleinberg JM, 1999, J ACM, V46, P604, DOI 10.1145/324133.324140; Lamberti F, 2009, IEEE T KNOWL DATA EN, V21, P123, DOI 10.1109/TKDE.2008.113; Lee DD, 1999, NATURE, V401, P788; Li JQ, 2012, WORLD WIDE WEB, V15, P257, DOI 10.1007/s11280-011-0133-5; Li JQ, 2009, LECT NOTES COMPUT SC, V5839, P1; Li JQ, 2012, J INTELL INF SYST, V39, P763, DOI 10.1007/s10844-012-0211-x; Li YH, 2003, IEEE T KNOWL DATA EN, V15, P871, DOI 10.1109/TKDE.2003.1209005; Liu B, 2012, ADV ENG SOFTW, V45, P380, DOI 10.1016/j.advengsoft.2011.10.015; Maedche A, 2003, SPINNING THE SEMANTIC WEB, P317; Makela E., 2005, P SEM KNOWL MAN SEM; Moldovan DI, 2000, IEEE INTERNET COMPUT, V4, P34, DOI 10.1109/4236.815847; Page S., 1998, PAGERANK CITATION RA; Rinaldi A. M., 2009, ACM T INTERNET TECHN, V9; Robertson S. E., 1992, TEXT RETR C GAITH MD; Salton G., 1983, INTRO MODERN IR; Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283; Servant F.-P., 2008, LINK DAT WEB WORKSH; Shilakes C. C., 1998, ENTERPRISE INFORM PO; Tran T, 2007, LECT NOTES COMPUT SC, V4825, P523; Vallet D., 2005, P 2 EUR SEM WEB C ES, P455; Vapnik V, 1995, NATURE STAT LEARNING; Wang CG, 2008, INFORM SYST FRONT, V10, P589, DOI 10.1007/s10796-008-9112-5; Wang HF, 2008, LECT NOTES COMPUT SC, V5021, P584; Wang P., 2008, P KDD; Wang S, 2012, INFORM TECHNOL MANAG, V13, P233, DOI 10.1007/s10799-012-0119-8; Yang L, 2012, ENTERP INF SYST-UK, V6, P419, DOI 10.1080/17517575.2012.665483; You MY, 2009, ADV INTELL SOFT COMP, V62, P271; Zhao F, 2012, INT J SOFTW ENG KNOW, V22, P305, DOI 10.1142/S0218194012500088; Zuccon G., 2012, P 17 AUSTR DOC COMP, P111 60 0 0 TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 1751-7575 1751-7583 ENTERP INF SYST-UK Enterp. Inf. Syst. JUL 4 2014 8 4 464 489 10.1080/17517575.2013.879923 26 Computer Science, Information Systems Computer Science AG0PO WOS:000335117600002 J Bruza, P; Chang, V Bruza, Peter; Chang, Vivien Perceptions of document relevance FRONTIERS IN PSYCHOLOGY English Article document relevance; quantum cognition; information retrieval; cognitive modeling; user modeling PROBABILITY JUDGMENT; QUANTUM; EXPLANATION; VIOLATIONS; DEFINITION This article presents a study of how humans perceive and judge the relevance of documents. Humans are adept at making reasonably robust and quick decisions about what information is relevant to them, despite the ever increasing complexity and volume of their surrounding information environment. The literature on document relevance has identified various dimensions of relevance (e.g., topicality, novelty, etc.), however little is understood about how these dimensions may interact. We performed a crowdsourced study of how human subjects judge two relevance dimensions in relation to document snippets retrieved from an internet search engine. The order of the judgment was controlled. For those judgments exhibiting an order effect, a q-test was model performed to determine whether the order effects can be explained by a quantum decision based on in compatible decision perspectives. Some evidence of incompatibility was found which suggests incompatible decision perspectives is appropriate for explaining interacting dimensions of relevance in such instances. [Bruza, Peter; Chang, Vivien] Queensland Univ Technol, Fac Sci & Engn, Informat Syst Sch, Brisbane, Qld 4001, Australia Bruza, P (reprint author), Queensland Univ Technol, Fac Sci & Engn, Informat Syst Sch, GPO Box 2434, Brisbane, Qld 4001, Australia. p.bruza@qut.edu.au Aerts D, 2009, J MATH PSYCHOL, V53, P314, DOI 10.1016/j.jmp.2009.04.005; Aerts D, 2013, TOP COGN SCI, V5, P737, DOI 10.1111/tops.12042; Atmanspacher H, 2010, J MATH PSYCHOL, V54, P314, DOI 10.1016/j.jmp.2009.12.001; Barry C., 1994, J AM SOC INFORM SCI, V45, P145; Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2; Blutner R, 2013, TOP COGN SCI, V5, P711, DOI 10.1111/tops.12041; Bordley RF, 1998, OPER RES, V46, P923, DOI 10.1287/opre.46.6.923; Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286; Bruza P, 2009, J MATH PSYCHOL, V53, P303, DOI 10.1016/j.jmp.2009.06.002; Busemeyer J., 2012, QUANTUM COGNITION DE, DOI [10.1017/CBO9780511997716, DOI 10.1017/CB09780511997716]; Busemeyer JR, 2011, PSYCHOL REV, V118, P193, DOI 10.1037/a0022542; Chu HT, 2011, J DOC, V67, P264, DOI 10.1108/00220411111109467; Conte E., 2007, CHAOS SOLITON FRACT, V33, P1076, DOI 10.1016/j.chaos.2005.09.061; Conte E., 2011, CHAOS COMPLEXITY LET, V4, P123; Conte E., 2012, ADV APPL QUANTUM MEC; COOPER WS, 1971, INFORM STORAGE RET, V7, P19, DOI 10.1016/0020-0271(71)90024-6; Dzhafarov R., 2012, J MATH PSYCHOL, V56, P54, DOI [10.1016/j.jmp.2011.12.003, DOI 10.1016/J.JMP.2011.12.003]; Gabora L, 2008, ECOL PSYCHOL, V20, P84, DOI 10.1080/10407410701766676; Gabora L, 2002, J EXP THEOR ARTIF IN, V14, P327, DOI 10.1080/09528130210162253; Graben PB, 2013, INT J THEOR PHYS, V52, P723, DOI 10.1007/s10773-012-1381-6; Haven E., 2013, QUANTUM SOCIAL SCI, DOI [10.1017/CB09781139003261, DOI 10.1017/CB09781139003261]; Khrennikov A, 2010, UBIQUITOUS QUANTUM STRUCTURE: FROM PSYCHOLOGY TO FINANCE, P185, DOI 10.1007/978-3-642-05101-2_12; Kim J., 2013, P 36 ANN ACM C RES D, P913; Lin C., 2009, P 18 ACM C INF KNOWL, P375, DOI DOI 10.1145/1645953.1646003; Mizzaro S, 1997, J AM SOC INFORM SCI, V48, P810, DOI 10.1002/(SICI)1097-4571(199709)48:9<810::AID-ASI6>3.0.CO;2-U; Pothos EM, 2009, P R SOC B, V276, P2171, DOI 10.1098/rspb.2009.0121; SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C; Trueblood JS, 2011, COGNITIVE SCI, V35, P1518, DOI 10.1111/j.1551-6709.2011.01197.x; TVERSKY A, 1983, PSYCHOL REV, V90, P293, DOI 10.1037//0033-295X.90.4.293; Wang J., 2013, TOP COGN SCI, V5, P689, DOI [10.1111/tops.12040, DOI 10.1111/TOPS.12040]; White R., 2013, P 36 ANN ACM C RES D, P3 31 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUL 2 2014 5 612 10.3389/fpsyg.2014.00612 8 Psychology, Multidisciplinary Psychology AK9KS WOS:000338746600001 J Pecina, P; Dusek, O; Goeuriot, L; Hajic, J; Hlavacova, J; Jones, GJF; Kelly, L; Leveling, J; Marecek, D; Novak, M; Popel, M; Rosa, R; Tamchyna, A; Uresova, Z Pecina, Pavel; Dusek, Ondrej; Goeuriot, Lorraine; Hajic, Jan; Hlavacova, Jaroslava; Jones, Gareth J. F.; Kelly, Liadh; Leveling, Johannes; Marecek, David; Novak, Michal; Popel, Martin; Rosa, Rudolf; Tamchyna, Ales; Uresova, Zdenka Adaptation of machine translation for multilingual information retrieval in the medical domain ARTIFICIAL INTELLIGENCE IN MEDICINE English Article Statistical machine translation; Domain adaptation of statistical machine translation; Intelligent training data selection for machine translation; Compound splitting; Cross-language information retrieval; Medical query translation PERFORMANCE; EXPANSION; SYSTEMS; CORPUS; WEB Objective: We investigate machine translation (MT) of user search queries in the context of cross-lingual information retrieval (IR) in the medical domain. The main focus is on techniques to adapt MT to increase translation quality; however, we also explore MT adaptation to improve effectiveness of cross-lingual IR. Methods and data: Our MT system is Moses, a state-of-the-art phrase-based statistical machine translation system. The IR system is based on the BM25 retrieval model implemented in the Lucene search engine. The MT techniques employed in this work include in-domain training and tuning, intelligent training data selection, optimization of phrase table configuration, compound splitting, and exploiting synonyms as translation variants. The IR methods include morphological normalization and using multiple translation variants for query expansion. The experiments are performed and thoroughly evaluated on three language pairs: Czech-English, German-English, and French-English. MT quality is evaluated on data sets created within the Khresmoi project and IR effectiveness is tested on the CLEF eHealth 2013 data sets. Results: The search query translation results achieved in our experiments are outstanding - our systems outperform not only our strong baselines, but also Google Translate and Microsoft Bing Translator in direct comparison carried out on all the language pairs. The baseline BLEU scores increased from 26.59 to 41.45 for Czech-English, from 23.03 to 40.82 for German-English, and from 32.67 to 40.82 for French-English. This is a 55% improvement on average. In terms of the IR performance on this particular test collection, a significant improvement over the baseline is achieved only for French-English. For Czech-English and German-English, the increased MT quality does not lead to better IR results. Conclusions: Most of the MT techniques employed in our experiments improve MT of medical search queries. Especially the intelligent training data selection proves to be very successful for domain adaptation of MT. Certain improvements are also obtained from German compound splitting on the source language side. Translation quality, however, does not appear to correlate with the IR performance - better translation does not necessarily yield better retrieval. We discuss in detail the contribution of the individual techniques and state-of-the-art features and provide future research directions.. (C) 2014 Elsevier B.V. All rights reserved. [Pecina, Pavel; Dusek, Ondrej; Hajic, Jan; Hlavacova, Jaroslava; Marecek, David; Novak, Michal; Popel, Martin; Rosa, Rudolf; Tamchyna, Ales; Uresova, Zdenka] Charles Univ Prague, Fac Math & Phys, Inst Formal & Appl Linguist, CR-11800 Prague 1, Czech Republic; [Goeuriot, Lorraine; Jones, Gareth J. F.; Kelly, Liadh; Leveling, Johannes] Dublin City Univ, CNGL Ctr Global Intelligent Content, Sch Comp, Dublin 9, Ireland Pecina, P (reprint author), Charles Univ Prague, Fac Math & Phys, Inst Formal & Appl Linguist, Malostranske Nam 25, CR-11800 Prague 1, Czech Republic. pecina@ufal.mff.cuni.cz EU [257528]; Czech Science Foundation [P103/12/G084]; Science Foundation Ireland as part of the Centre for Next Generation Localisation at Dublin City University [07/CE/I1142]; ESF project ELIAS; MEYS of the Czech Republic [LM2010013] This work was supported by the EU FP7 project Khresmoi (contract no. 257528), the Czech Science Foundation (grant no. P103/12/G084), the Science Foundation Ireland (grant no. 07/CE/I1142) as part of the Centre for Next Generation Localisation at Dublin City University, and by the ESF project ELIAS.The work described herein uses language resources hosted by the LINDAT/CLARIN repository,20 funded by the project LM2010013 of the MEYS of the Czech Republic. Alfonseca E, 2008, P 46 ANN M ASS COMP, P253, DOI 10.3115/1557690.1557763; ATTAR R, 1977, J ACM, V24, P397, DOI 10.1145/322017.322021; Axelrod A, 2011, P EMNLP, P355; Ballesteros L., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290958; Ballesteros L, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P84, DOI 10.1145/258525.258540; Banerjee P, 2012, P 16 ANN M EUR ASS M, P169; Bertoldi N, 2009, P 4 WORKSH STAT MACH, P182, DOI DOI 10.3115/1626431.1626468; Bertoldi N., 2009, PRAGUE B MATH LINGUI, V91, P7; Bisazza A, 2012, P 13 C EUR CHAPT ASS, P439; Bisazza A, 2011, P INT WORKSH SPOK LA, P136; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Bojar O, 2011, P 6 WORKSH STAT MACH, P1; Bojar O, 2013, PRAGUE B MATH LINGUI, V100, P39; BOJAR O, 2012, P 8 INT C LANG RES E, P3921; Bojar O, 2013, P 8 WORKSH STAT MACH, P1; Bojar O, 2013, P 8 WORKSH STAT MACH, P92; Bouayad-Agha N, 2000, INFORM DESIGN J, V9, P161; Boyer C, 2012, USE CASE DEFINITION; Boyer C, 2012, PROTOTYPE 1 SEARCH S; Buitelaar P, 2003, MULTILINGUAL CONCEPT; Byrne W, 2004, IEEE T SPEECH AUDI P, V12, P420, DOI 10.1109/TSA.2004.828702; Callison-Burch C, 2011, P 6 WORKSH STAT MACH, P22; Callison-Burch C, 2012, P 7 WORKSH STAT MACH, P10; Carpuat M, 2012, 2012 J HOPK SUMM WOR, P61; Ceausu A, 2011, P 15 ANN M EUR ASS M, P21; Chen A, 2002, LECT NOTES COMPUTER, V2785, P28; Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671; Costa-jussa MR, 2012, P 1 VIRT INT C ADV R, P1995; Darwish K, 2003, P 26 ANN INT ACM SIG, P338; Daume III H, 2011, P 49 ANN M ASS COMP, P407; Dickersin K, 2002, EVAL HEALTH PROF, V25, P38, DOI 10.1177/016327870202500104; Dyer C, 2013, P 2013 C N AM CHAPT, P644; Eck M, 2004, COLING 2004 P 20 INT, P792; Eck M, 2004, P LREC, P327; Eichmann D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290959; Eisele A, 2010, P LREC, P2868; Federico M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Federico M., 2007, P 45 ANN M ASS COMP, P177, DOI 10.3115/1557769.1557821; Fishel M, 2012, P 16 ANN C EUR ASS M, P3; Forcada Mikel L., 2011, Machine Translation, V25, DOI 10.1007/s10590-011-9090-0; Fox S, 2011, TECHNICAL REPORT; Gao J, 2006, P 29 ANN INT ACM SIG, P194, DOI 10.1145/1148170.1148207; Goeuriot L, 2013, ONLINE WORKING NOTES; Goeuriot L, 2013, P 5 INT WORKSH EV IN, P29; Griffon N, 2012, BMC MED INFORM DECIS, V12, DOI 10.1186/1472-6947-12-12; Hajic J, 2004, DISAMBIGUATION RICH; Han X, 2009, P 2 WORKSH BUILD US, P27, DOI 10.3115/1690339.1690348; Heafield K, 2011, P 6 WORKSH STAT MACH, P187; Hersh W, 1994, P 17 ANN INT ACM SIG, P192; Hildebrand AS, 2005, P 10 ANN C EUR ASS M, P133; Hollink V, 2004, INFORM RETRIEVAL, V7, P33, DOI 10.1023/B:INRT.0000009439.19151.4c; Hull D, 1993, P 16 ANN INT ACM SIG, P329, DOI 10.1145/160688.160758; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Jelinek F., 1997, STAT METHODS SPEECH; Jimeno Yepes A, 2013, BMC BIOINFORMATICS, V14, P1; Jones GJ, 2008, CLIA 2008 2 INT WORK, P34; Kim J-D, 2003, BIOINFORMATICS S1, V19, pi180, DOI DOI 10.1093/BIOINFORMATICS/BTG1023; Kneser R, 1995, P IEEE INT C AC SPEE, V1, P181; Knox C, 2011, NUCLEIC ACIDS RES, V39, pD1035, DOI 10.1093/nar/gkq1126; Koehn P, 2007, P 2 WORKSH STAT MACH, P224, DOI 10.3115/1626355.1626388; Koehn P, 2004, P C EMP METH NAT LAN, P388; Koehn P, 2003, P 10 C EUR CHAPT ASS, P187; Koehn P, 2005, C P 10 MACH TRANSL S, P79; Koehn P, 2005, P INT WORKSH SPOK LA, P78; Langlais P, 2002, COLING 02 COMPUTERM, V14, P1, DOI 10.3115/1118771.1118776; Bizer C, 2009, J WEB SEMANT, V7, P154, DOI 10.1016/j.websem.2009.07.002; Leveling J, 2012, TEXT RETR C TREC 201, P1; Levenshtein VI, 1966, SOV PHYS DOKL, V10, P707; Lopes CT, 2013, J AM SOC INF SCI TEC, V64, P951, DOI 10.1002/asi.22812; Maeda A, 2000, P 5 INT WORKSH INF R, P25, DOI 10.1145/355214.355218; Magdy W, 2011, P 20 ACM INT C INF K, P1925; Majlis M, 2012, P STUD RES WORKSH 13, P46; Manning C, 2008, INTRO INFORM RETRIEV; Mansour S, 2011, INT WORKSH SPOK LANG, P222; Marko K, 2007, ST HEAL T, V129, P392; Marko K, 2005, METHOD INFORM MED, V44, P9; Dejean H, 2005, ARTIF INTELL MED, V33, P111, DOI 10.1016/j.artmed.2004.07.015; Meats E, 2007, J MED LIBR ASSOC, V95, P156, DOI 10.3163/1536-5050.95.2.156; Mooers CE, 1950, AM DOC, V1, P225, DOI 10.1002/asi.5090010409; Moore RC, 2010, P ACL, P220; Munteanu DS, 2005, COMPUT LINGUIST, V31, P477, DOI 10.1162/089120105775299168; Nakayama K, 2008, P ANN WIK C WIK; Nakov P, 2008, P 3 WORKSH STAT MACH, P147, DOI 10.3115/1626394.1626414; Nie J-Y, 2010, CROSS LANGUAGE INFOR; Niessen S, 2000, P 18 C COMP LING SAA, V2, P1081, DOI 10.3115/992730.992809; Nikoulina V, 2012, P 13 C EUR CHAPT ASS, P109; Oard DW, 2001, LNCS, V2069, P176; Och F. J., 2003, Computational Linguistics, V29, DOI 10.1162/089120103321337421; Och FJ, 2003, P 41 ANN M ASS COMP, P160; Papineni K., 2002, P 40 ANN M ASS COMP, P311; Parker R., 2011, ENGLISH GIGAWORD; Pecina P, 2011, P 15 ANN C EUR ASS M, P297; Pecina P, 2012, EAMT 2012 P 16 ANN C, P145; Pecina P, 2012, P 24 INT C COMP LING, P2209; Peters C, 2012, MULTILINGUAL INFORM; Pirkola A., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290957; Popel M, 2009, PRAGUE B MATH LINGUI, V92, P1; Popel M, 2010, LECT NOTES ARTIF INT, V6233, P293, DOI 10.1007/978-3-642-14770-8_33; POPOVIC M, 2006, P 5 INT C ADV NAT LA, V4139, P616; Porter M. F., 2001, SNOWBALL LANGUAGE ST; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; Pouliquen B, 2011, P 13 MACH TRANSL SUM, P24; Roberts PM, 2009, INFORM RETRIEVAL, V12, P81, DOI 10.1007/s10791-008-9072-x; Robertson SE, 1998, 7 TEXT RETRIEVAL C T, P253; Robertson SE, 1976, J AM SOC INFORM SCI, V27, P143; ROBERTSON SE, 1990, J DOC, V46, P359, DOI 10.1108/eb026866; Robertson SE, 1995, OV 3 TEXT RETR C TRE, P109; ROGERS FB, 1963, B MED LIBR ASSOC, V51, P114; Rosemblat Graciela, 2003, AMIA Annu Symp Proc, P564; ROSSE C, 2008, COMPU BIOL, P59; Roukos S, 1995, HANSARD CORPUS PARAL; Ruiz M, 1999, 8 TEXT RETRIEVAL C T, P597; Salton G., 1971, SMART RETRIEVAL SYST; Sanchis-Trilles G, 2010, P 23 INT C COMP LING, P1077; Schmid H., 1994, P INT C NEW METH LAN, V12, P44; Shuyo N, 2010, LANGUAGE DETECTION L; Smith JR, 2013, P 51 ANN M ASS COMP, V1, P1374; Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R; Spoustova D, 2007, P WORKSH BALT SLAV N, P67, DOI 10.3115/1567545.1567558; Steinberger R, 2006, P 5 INT C LANG RES E, P2141; Stolcke A., 2002, P INT C SPOK LANG PR, V2, P901; Suominen H, 2013, LECT NOTES COMPUT SC, V8138, P212, DOI 10.1007/978-3-642-40802-1_24; Thompson P, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-349; Tiedemann J, 2009, RECENT ADV NATURAL L, V5, P237; Tillmann C, 1997, P 5 EUR C SPEECH COM, P2667; Tran TD, 2004, ST HEAL T, V107, P946; U.S. National Library of Medicine, 2009, UMLS REF MAN; Volk M, 2002, INT J MED INFORM, V67, P97, DOI 10.1016/S1386-5056(02)00058-8; Voorhees EM, 2005, DIGITAL LIB ELECT PU, V63; Voorhees EM, 2011, 11 TEXT RETR C TREC, P1; Waschle K, 2012, LNCS, V7356, P12; Wu C, 2011, AMIA ANN S P 2011, P1290; Wu H, 2003, P 2 INT WORKSH PAR, V16, P72, DOI 10.3115/1118984.1118994; Wu H, 2004, LECT NOTES COMPUT SC, V3265, P262; Zhou D, 2012, ACM COMPUT SURV, V45, P1 135 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0933-3657 1873-2860 ARTIF INTELL MED Artif. Intell. Med. JUL 2014 61 3 SI 165 185 10.1016/j.artmed.2014.01.004 21 Computer Science, Artificial Intelligence; Engineering, Biomedical; Medical Informatics Computer Science; Engineering; Medical Informatics AM9XG WOS:000340233700006 J Zhang, YZ; Luo, XF; Zhang, H; Sutherland, JW Zhang, Yingzhong; Luo, Xiaofang; Zhang, Hong; Sutherland, John W. A knowledge representation for unit manufacturing processes INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY English Article Unit manufacturing process; Process knowledge; Knowledge representation; Ontology SYSTEM; ONTOLOGY; DESIGN; MODEL A unit manufacturing process is a fundamental operation unit in the manufacturing process of a mechanical product. It requires extensive knowledge of various processes to select a suitable unit process in process planning and manufacturing services. In the face of a large number of unit processes, it is very necessary to research and develop the knowledge representation and information processing approach for unit processes. In this paper, the basic unit manufacturing process concepts and relationships between them are analyzed in detail. The semantic Web technology is introduced into the knowledge modeling for unit processes, and a new ontology-based knowledge representation model is presented. A formalization for taxonomic semantics and descriptions of manufacturing capability knowledge is presented. A rule base that consists of a set of rules using Semantic Web Rule Language (SWRL) is built, which is used to enable reasoning on taxonomic semantics and manufacturing knowledge of unit processes. A knowledge retrieval approach that integrates Web Ontology Language (OWL) ontologies and SWRL rules is proposed. Some knowledge retrieval examples employing Semantic Query-enhanced Web Rule Language (SQWRL) are given. A knowledge prototype system for unit processes is developed. This research is a new attempt to construct an open, scalable, and shared manufacturing process knowledge system. [Zhang, Yingzhong; Luo, Xiaofang; Zhang, Hong] Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China; [Sutherland, John W.] Purdue Univ, Div Environm & Ecol Engn, W Lafayette, IN 47907 USA Zhang, YZ (reprint author), Dalian Univ Technol, Sch Mech Engn, Dalian 116024, Peoples R China. zhangyz@dlut.edu.cn National Science Foundation of China [60773214, 51375069] This work is supported by the National Science Foundation of China (grant nos. 60773214 and 51375069). The authors thank anonymous reviewers for their helpful suggestions in this study. Brachman RJ, 2004, KNOWLEDGE REPRESENTA; Chang T-C, 1990, EXPERT PROCESS PLANN; Chen WL, 2011, COMPUT IND, V62, P9, DOI 10.1016/j.compind.2010.05.016; Er A, 2000, KNOWL-BASED SYST, V13, P225, DOI 10.1016/S0950-7051(00)00075-7; Friedman-Hill E, 2013, JESS RULE ENGINE JAV; Garcia-Crespo A, 2010, COMPUT IND, V61, P595, DOI 10.1016/j.compind.2010.01.004; Gay JC, 2007, INTRO APPL DIN 8580; GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008; Gruninger M, 2003, AI MAG, V24, P63; Guerra-Zubiaga DA, 2008, INT J COMPUT INTEG M, V21, P526, DOI 10.1080/09511920701258040; Hao YT, 2006, COMPUT IND, V57, P297, DOI 10.1016/j.compind.2005.09.006; HAYES C, 1989, J MANUF SYST, V8, P1, DOI 10.1016/0278-6125(89)90015-0; Horrocks I, 2004, SWRL SEMANTIC WEB RU; Knublauch H, 2004, LECT NOTES COMPUT SC, V3298, P229; Lemaignan S, 2006, P INT IEEE WORKSH DI; Liu ZK, 2007, COMPUT IND, V58, P295, DOI 10.1016/j.compind.2006.07.003; Martinez-Pellitero S, 2011, INT J ADV MANUF TECH, V57, P325, DOI 10.1007/s00170-011-3285-7; Mikos WL, 2011, J MANUF SYST, V30, P133, DOI 10.1016/j.jmsy.2011.06.001; Naish JC, 1996, J MATER PROCESS TECH, V61, P124, DOI 10.1016/0924-0136(96)02476-4; National Research Council, 1995, UN MAN PROC ISS OPP; O'Connor M, 2009, P 5 INT WORKSH OWL E; Qiao LH, 2011, INT J ADV MANUF TECH, V55, P549, DOI 10.1007/s00170-010-3115-3; Smith B, 1996, DATA KNOWL ENG, V20, P287, DOI 10.1016/S0169-023X(96)00015-8; Sormaz DN, 1997, INT J COMP INTEG M, V10, P92, DOI 10.1080/095119297131219; Todd RH, 1994, FUNDAMENTAL PRINCIPL; Xu HM, 2009, INT J ADV MANUF TECH, V44, P161, DOI 10.1007/s00170-008-1804-y; Xun Xu, 2011, International Journal of Computer Integrated Manufacturing, V24, DOI 10.1080/0951192X.2010.518632; Zhu LJ, 2012, J COMPUT INF SCI ENG, V12, DOI 10.1115/1.3647878 28 0 0 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0268-3768 1433-3015 INT J ADV MANUF TECH Int. J. Adv. Manuf. Technol. JUL 2014 73 5-8 1011 1031 10.1007/s00170-014-5864-x 21 Automation & Control Systems; Engineering, Manufacturing Automation & Control Systems; Engineering AN2MQ WOS:000340420000037 J Martinet, P; Lavanant, L; Fourrie, N; Rabier, F; Gambacorta, A Martinet, P.; Lavanant, L.; Fourrie, N.; Rabier, F.; Gambacorta, A. Evaluation of a revised IASI channel selection for cloudy retrievals with a focus on the Mediterranean basin QUARTERLY JOURNAL OF THE ROYAL METEOROLOGICAL SOCIETY English Article IASI; microphysical variables; 1D-Var; DFS; channel selection; Mediterranean Sea NUMERICAL WEATHER PREDICTION; SOUNDING INTERFEROMETER RADIANCES; BACKGROUND-ERROR COVARIANCES; SATELLITE INFRARED RADIANCES; DIRECT 4D-VAR ASSIMILATION; ICE CLOUDS; ECMWF; MICROPHYSICS; MESOSCALE; SOUNDERS The Infrared Atmospheric Sounding Interferometer (IASI) provides 8461 channels in the infrared spectrum. In current numerical weather prediction (NWP) models, it is not feasible to assimilate all channels and it is known that the information content between adjacent channels is redundant. This issue has been addressed in NWP centres by employing a channel selection strategy. The goal of this article is to add new channels to the existing IASI operational channel selection, aimed at improving the data assimilation in cloudy conditions and the simultaneous retrieval of cloud microphysical variables, specifically liquid and ice water contents. Cloudy profiles from the French convective-scale model Applications of Research to Operations at MEsoscale (AROME) are used in the study to focus on the retrieval of cloud variables over the Mediterranean region. Three channel selection methodologies were evaluated in this study: a statistical approach based on the degrees of freedom of the signal (DFS), a physical method based on the channel spectral sensitivity to the cloud variables and a random selection. To validate the new selections, an idealized framework is used with observing system simulation experiments (OSSE) in the context of one-dimensional variational retrievals. The current operational IASI selection has already been shown to provide good retrievals of cloud variables. However, all the different channel selections improve the results with small differences in the 1D-Var retrievals. Based on the physical and DFS methods, the final sets of 134 channels sensitive to cloud variables are proposed for future investigation in operational implementation. Additional tests on temperature and water-vapour retrieval results, air-mass dependence and cloud microphysical parametrization have also been conducted. [Martinet, P.; Fourrie, N.; Rabier, F.] Meteo France, F-31057 Toulouse, France; [Martinet, P.; Fourrie, N.; Rabier, F.] CNRS CNRM GAME, Toulouse, France; [Lavanant, L.] Meteo France, Ctr Meteorol Spatiale, Lannion, France; [Gambacorta, A.] NOAA, IM Syst Grp, NESDIS, STAR, College Pk, MD USA Martinet, P (reprint author), Meteo France, CNRM GMAP, 42 Ave Coriolis, F-31057 Toulouse, France. pauline.martinet@meteo.fr Bauer P, 2010, Q J ROY METEOR SOC, V136, P1868, DOI 10.1002/qj.659; Berre L, 2000, MON WEATHER REV, V128, P644, DOI 10.1175/1520-0493(2000)128<0644:EOSAMF>2.0.CO;2; Boudala FS, 2002, INT J CLIMATOL, V22, P1267, DOI 10.1002/joc.774; Cayla F-R., 2001, IATN00002092CNE CNES; Chalon G, 2001, P 52 C IAF 1 5 OCT 2; Chedin A, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2001JD001443; Chevallier F., 2006, NWPSAFECVS013; Chevallier F, 2004, Q J ROY METEOR SOC, V130, P917, DOI 10.1256/qj.03.113; Clerbaux C, 1998, J GEOPHYS RES-ATMOS, V103, P18999, DOI 10.1029/98JD01422; Collard AD, 2009, Q J ROY METEOR SOC, V135, P1044, DOI 10.1002/qj.410; Collard AD, 2007, Q J ROY METEOR SOC, V133, P1977, DOI 10.1002/qj.178; Desroziers G, 2008, 68 HIRLAM; Eresmaa R, 2012, NWPSAFECTR015; Faijan F, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017962; Fourrie N, 2003, Q J ROY METEOR SOC, V129, P2425, DOI 10.1256/qj.02.210; Fourrie N, 2004, Q J R METEOROL SOC, V128, P2551; Gambacorta A, 2012, IEEE T GEOSCI REMOTE, V51, P3207, DOI 10.1109/TGRS.2012.2220369; Geer AJ, 2010, Q J ROY METEOR SOC, V136, P1886, DOI 10.1002/qj.681; Geer AJ, 2008, Q J ROY METEOR SOC, V134, P1513, DOI 10.1002/qj.304; Guidard V, 2011, Q J ROY METEOR SOC, V137, P1975, DOI 10.1002/qj.928; Hocking J, 2010, NWPSAFMOUD023 EUMETS; Huang HL, 2004, J APPL METEOROL, V43, P795, DOI 10.1175/2090.1; KAPLAN LD, 1977, APPL OPTICS, V16, P322, DOI 10.1364/AO.16.000322; Lavanant L, 2011, Q J ROY METEOR SOC, V137, P1988, DOI 10.1002/qj.917; Martinet P, 2013, Q J ROY METEOR SOC, V139, P1402, DOI 10.1002/qj.2046; Matricardi M., 2005, INCLUSION AEROSOLS C; McFarquhar GM, 2003, J CLIMATE, V16, P1643, DOI 10.1175/1520-0442(2003)016<1643:SSOTIC>2.0.CO;2; McNally AP, 2002, Q J ROY METEOR SOC, V128, P2551, DOI 10.1256/qj.01.206; McNally AP, 2009, Q J ROY METEOR SOC, V135, P1214, DOI 10.1002/qj.426; Michel Y, 2011, MON WEATHER REV, V139, P2994, DOI 10.1175/2011MWR3632.1; Montmerle T, 2010, Q J ROY METEOR SOC, V136, P1408, DOI 10.1002/qj.655; Okamoto K, 2012, NWPSAFECVS022 CLOUD; OU SC, 1995, ATMOS RES, V35, P127, DOI 10.1016/0169-8095(94)00014-5; Pangaud T, 2009, MON WEATHER REV, V137, P4276, DOI 10.1175/2009MWR3020.1; Pavelin EG, 2009, NWPSAFMOUD006; Pavelin EG, 2008, Q J ROY METEOR SOC, V134, P737, DOI 10.1002/qj.243; Rabier F, 2002, Q J ROY METEOR SOC, V128, P1011, DOI 10.1256/0035900021643638; Rodgers C. D., 2000, INVERSE METHODS ATMO; Seity Y, 2010, MON WEATHER REV, V139, P976, DOI DOI 10.1175/2010MWR3425.1; Stengel M, 2010, Q J ROY METEOR SOC, V136, P1064, DOI 10.1002/qj.621; Susskind J, 2003, IEEE REMOTE SENSING, V136, P1064; Wei HL, 2004, IEEE T GEOSCI REMOTE, V42, P2254, DOI 10.1109/TGRS.2004.833780; Wyser K, 1998, J CLIMATE, V11, P1793, DOI 10.1175/1520-0442(1998)011<1793:TERIIC>2.0.CO;2 43 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0035-9009 1477-870X Q J ROY METEOR SOC Q. J. R. Meteorol. Soc. JUL 2014 140 682 A 1563 1577 10.1002/qj.2239 15 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN1SN WOS:000340364000012 J Su, H; Huang, QX; Mitra, NJ; Li, YY; Guibas, L Su, Hao; Huang, Qixing; Mitra, Niloy J.; Li, Yangyan; Guibas, Leonidas Estimating Image Depth Using Shape Collections ACM TRANSACTIONS ON GRAPHICS English Article data-driven shape analysis; pose estimation; depth estimation; image retrieval; shape collections RECOGNITION; SCENE; MAPS Images, while easy to acquire, view, publish, and share, they lack critical depth information. This poses a serious bottleneck for many image manipulation, editing, and retrieval tasks. In this paper we consider the problem of adding depth to an image of an object, effectively 'lifting' it back to 3D, by exploiting a collection of aligned 3D models of related objects. Our key insight is that, even when the imaged object is not contained in the shape collection, the network of shapes implicitly characterizes a shape-specific deformation subspace that regularizes the problem and enables robust diffusion of depth information from the shape collection to the input image. We evaluate our fully automatic approach on diverse and challenging input images, validate the results against Kinect depth readings, and demonstrate several imaging applications including depth-enhanced image editing and image relighting. [Su, Hao; Huang, Qixing; Li, Yangyan; Guibas, Leonidas] Stanford Univ, Stanford, CA 94305 USA; [Mitra, Niloy J.] UCL, London WC1E 6BT, England Su, H (reprint author), Stanford Univ, Stanford, CA 94305 USA. NSF [IIS 1016324, DMS 1228304]; AFOSR grant [FA9550-12-1-0372]; NSFC [61202221]; Max Plack Center for Visual Computing and Communications; Google; Motorola; Marie Curie Career Integration Grant [303541]; ERC Starting Grant SmartGeometry [StG-2013335373] We thank the reviewers for their comments and suggestions on the paper. This work was supported in part by NSF grants IIS 1016324 and DMS 1228304, AFOSR grant FA9550-12-1-0372, NSFC grant 61202221, the Max Plack Center for Visual Computing and Communications, Google and Motorola research awards, a gift from HTC corporation, the Marie Curie Career Integration Grant 303541, the ERC Starting Grant SmartGeometry (StG-2013335373), and gifts from Adobe. Averkiou M., 2014, CGF; Chaudhuri S., 2011, ACM T GRAPHIC, V30; Chen DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669; Coifman RR, 2005, P NATL ACAD SCI USA, V102, P7426, DOI 10.1073/pnas.0500334102; Cyr CM, 2004, INT J COMPUT VISION, V57, P5, DOI 10.1023/B:VISI.0000013088.59081.4c; Dalal N, 2005, PROC CVPR IEEE, P886; Funkhouser T, 2004, ACM T GRAPHIC, V23, P652, DOI 10.1145/1015706.1015775; Goldman DB, 2005, IEEE I CONF COMP VIS, P341; Hoiem D, 2005, ACM T GRAPHIC, V24, P577, DOI 10.1145/1073204.1073232; Huang Q., 2011, ACM T GRAPHIC, V30, P6; Huang Q.-X., 2013, ACM T GRAPHIC, V32; Huang QX, 2013, COMPUT GRAPH FORUM, V32, P177, DOI 10.1111/cgf.12184; Kalogerakis E, 2012, ACM T GRAPHIC, V31, DOI 10.1145/2185520.2185551; Kazhdan M. M., 2006, ACM INT C P SERIES, V256, P61; Kim V. G., 2012, ACM T GRAPHIC, V31; Kim VG, 2013, ACM T GRAPHIC, V32, DOI 10.1145/2461912.2461933; Kim Y. M., 2012, ACM T GRAPHIC, V31; Lensch H. P. A., 2003, ACM TOG, V22; MARCATO Jr R. W., 1998, THESIS MIT; Mitra N, 2007, ACM T GRAPHIC, V63, P1; Mitra NJ, 2006, ACM T GRAPHIC, V25, P560, DOI 10.1145/1141911.1141924; Munich M. E., 1999, ICCV, V1; Nan LL, 2012, ACM T GRAPHIC, V31, DOI 10.1145/2366145.2366156; Oliva A, 2006, PROG BRAIN RES, V155, P23, DOI 10.1016/S0079-6123(06)55002-2; Oliva A, 2001, INT J COMPUT VISION, V42, P145, DOI 10.1023/A:1011139631724; Osada R, 2002, ACM T GRAPHIC, V21, P807, DOI 10.1145/571647.571648; Ovsjanikov M., 2011, ACM SIGGRAPH, V30; Rusinkiewicz S, 2001, THIRD INTERNATIONAL CONFERENCE ON 3-D DIGITAL IMAGING AND MODELING, PROCEEDINGS, P145; Saxena A, 2009, IEEE T PATTERN ANAL, V31, P824, DOI 10.1109/TPAMI.2008.132; Shen C.-H., 2012, ACM T GRAPHIC, V31; SORKINE O., 2004, CGF, P175; Sumner R. W., 2007, ACM TOG, V26, P3; Sun M., 2011, 3DIMPVT; Szeliski R, 2008, IEEE T PATTERN ANAL, V30, P1068, DOI 10.1109/TPAMI.2007.70844; Wang Y., 2013, ACM T GRAPHIC, V32; Wu T.-P., 2008, ACM T GRAPHIC; Xu K., 2011, ACM T GRAPHIC, V30; Zheng Y., 2012, ACM T GRAPHIC, V31; Zia Z., 2013, IEEE TPAMI, V35, p2608 39 0 0 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0730-0301 1557-7368 ACM T GRAPHIC ACM Trans. Graph. JUL 2014 33 4 37 10.1145/2601097.2601159 11 Computer Science, Software Engineering Computer Science AM6UG WOS:000340000100004 J Zhao, HW; Li, QL; Liu, PP Zhao, Hongwei; Li, Qingliang; Liu, Pingping Hierarchical Geometry Verification via Maximum Entropy Saliency in Image Retrieval ENTROPY English Article image retrieval; geometry verification; saliency detection; maximum entropy SCALE; ATTENTION; THRESHOLD; FEATURES; SEARCH; MODEL; CONSISTENCY We propose a new geometric verification method in image retrieval-Hierarchical Geometry Verification via Maximum Entropy Saliency (HGV)-which aims at filtering the redundant matches and remaining the information of retrieval target in images which is partly out of the salient regions with hierarchical saliency and also fully exploring the geometric context of all visual words in images. First of all, we obtain hierarchical salient regions of a query image based on the maximum entropy principle and label visual features with salient tags. The tags added to the feature descriptors are used to compute the saliency matching score, and the scores are regarded as the weight information in the geometry verification step. Second we define a spatial pattern as a triangle composed of three matched features and evaluate the similarity between every two spatial patterns. Finally, we sum all spatial matching scores with weights to generate the final ranking list. Experiment results prove that Hierarchical Geometry Verification based on Maximum Entropy Saliency can not only improve retrieval accuracy, but also reduce the time consumption of the full retrieval. [Zhao, Hongwei; Li, Qingliang; Liu, Pingping] Jilin Univ, Sch Comp Sci & Technol, Changchun 130012, Peoples R China; [Zhao, Hongwei; Liu, Pingping] Minist Educ, Key Lab Symbol Computat & Knowledge Engn, Changchun 130012, Peoples R China Liu, PP (reprint author), Jilin Univ, Sch Comp Sci & Technol, Changchun 130012, Peoples R China. zhaohw@jlu.edu.cn; lql_321@163.com; liupp@jlu.edu.cn Nature Science Foundation of China [6110115]; Jilin Province Science and Technology Development Program [20101504] This work was supported by the Nature Science Foundation of China, under Grants No. 6110115, Jilin Province Science and Technology Development Program, under Grants No. 20101504. We acknowledge Cliff and Tom of Flickr [33] who provides pictures in our datasets. We also acknowledge Zhimeng Nong who validated the experiment results of all the retrieval methods. Bhandari K., 2010, P INT C WORKSH EM TR, P253, DOI 10.1145/1741906.1741964; Chao DC, 2007, RESPIR CARE, V52, P159; Chum O, 2009, PROC CVPR IEEE, P17; Costa L. D. F., 2006, PHYSICS0603025 ARXIV; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; Fiala M., 2006, P 3 CAN C COMP ROB V; FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692; Gopalakrishnan V, 2009, PROC CVPR IEEE, P1698; Harel J, 2006, ADV NEURAL INFORM PR, V19, P545; Itti L, 1998, IEEE T PATTERN ANAL, V20, P1254, DOI 10.1109/34.730558; Jegou H, 2008, LECT NOTES COMPUT SC, V5302, P304, DOI 10.1007/978-3-540-88682-2_24; Kadir T, 2001, INT J COMPUT VISION, V45, P83, DOI 10.1023/A:1012460413855; KAPUR JN, 1985, COMPUT VISION GRAPH, V29, P273, DOI 10.1016/0734-189X(85)90125-2; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Luo G., 2010, P 2010 IEEE INT C IN, P243; Maki A., 1996, Proceedings of the 13th International Conference on Pattern Recognition, DOI 10.1109/ICPR.1996.547661; MILANESE R, 1994, 1994 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION, PROCEEDINGS, P781; Nister D, 2006, IEEE C COMP VIS PATT, V2, P2161; Philbin J, 2007, P IEEE C COMP VIS PA, P1; Philbin J., 2007, P 2007 IEEE COMP SOC; Rutishauser U, 2004, PROC CVPR IEEE, P37; Sivic J., 2003, P ICCV, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663]; Soares RD, 2012, PROC INT C TOOLS ART, P1070, DOI 10.1109/ICTAI.2012.151; Tsai SS, 2010, IEEE IMAGE PROC, P1029, DOI 10.1109/ICIP.2010.5648942; Walther D, 2006, NEURAL NETWORKS, V19, P1395, DOI 10.1016/j.neunet.2006.10.001; Wu Z, 2009, PROC CVPR IEEE, P25; Wuhib F, 2008, COMPUT NETW, V52, P1745, DOI 10.1016/j.comnet.2008.02.015; Xie HT, 2011, IEEE IMAGE PROC, P101; Yue L., 2009, P INT C IM AN SIGN P, P172; Zhang BC, 2011, J VIS COMMUN IMAGE R, V22, P516, DOI 10.1016/j.jvcir.2011.05.001; Zhang ZH, 2006, Proceedings of 2006 International Conference on Machine Learning and Cybernetics, Vols 1-7, P4013; Zhao WL, 2010, IEEE T MULTIMEDIA, V12, P448, DOI 10.1109/TMM.2010.2050651 33 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy JUL 2014 16 7 3848 3865 10.3390/e16073848 18 Physics, Multidisciplinary Physics AM6QX WOS:000339990800017 J Asshauer, KP; Klingenberg, H; Lingner, T; Meinicke, P Asshauer, Kathrin P.; Klingenberg, Heiner; Lingner, Thomas; Meinicke, Peter Exploring Neighborhoods in the Metagenome Universe INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES English Article metagenomics; functional profile; taxonomic profile; metagenome comparison FUNCTIONAL-ANALYSIS; CLASSIFICATION; SEARCH; SERVER The variety of metagenomes in current databases provides a rapidly growing source of information for comparative studies. However, the quantity and quality of supplementary metadata is still lagging behind. It is therefore important to be able to identify related metagenomes by means of the available sequence data alone. We have studied efficient sequence-based methods for large-scale identification of similar metagenomes within a database retrieval context. In a broad comparison of different profiling methods we found that vector-based distance measures are well-suitable for the detection of metagenomic neighbors. Our evaluation on more than 1700 publicly available metagenomes indicates that for a query metagenome from a particular habitat on average nine out of ten nearest neighbors represent the same habitat category independent of the utilized profiling method or distance measure. While for well-defined labels a neighborhood accuracy of 100% can be achieved, in general the neighbor detection is severely affected by a natural overlap of manually annotated categories. In addition, we present results of a novel visualization method that is able to reflect the similarity of metagenomes in a 2D scatter plot. The visualization method shows a similarly high accuracy in the reduced space as compared with the high-dimensional profile space. Our study suggests that for inspection of metagenome neighborhoods the profiling methods and distance measures can be chosen to provide a convenient interpretation of results in terms of the underlying features. Furthermore, supplementary metadata of metagenome samples in the future needs to comply with readily available ontologies for fine-grained and standardized annotation. To make profile-based k-nearest-neighbor search and the 2D-visualization of the metagenome universe available to the research community, we included the proposed methods in our CoMet-Universe server for comparative metagenome analysis. [Asshauer, Kathrin P.; Klingenberg, Heiner; Lingner, Thomas; Meinicke, Peter] Univ Gottingen, Dept Bioinformat, Inst Microbiol & Genet, D-37077 Gottingen, Germany Meinicke, P (reprint author), Univ Gottingen, Dept Bioinformat, Inst Microbiol & Genet, D-37077 Gottingen, Germany. kathrin@gobics.de; heiner@gobics.de; thomas@gobics.de; peter@gobics.de DFG [ME 3138] We would like to thank two anonymous reviewers for their comments. This work was partially funded by a DFG grant (ME 3138) to P.M. Abubucker S, 2012, PLOS COMPUT BIOL, V8, DOI 10.1371/journal.pcbi.1002358; Asshauer K.P., 2013, OPENACCESS SERIES IN, V34, P1; BRAY J. ROGER, 1957, ECOL MONOGR, V27, P325, DOI 10.2307/1942268; Brooksbank C., 2014, NUCLEIC ACIDS RES, V42, P18; Delmont TO, 2011, ISME J, V5, P1837, DOI 10.1038/ismej.2011.61; Diaconis P, 2008, ANN APPL STAT, V2, P777, DOI 10.1214/08-AOAS165; Group T.N.H.W., 2009, GENOME RES, V19, P2317; Hammesfahr Björn, 2011, BMC Res Notes, V4, P338, DOI 10.1186/1756-0500-4-338; Hubert M, 2004, BIOINFORMATICS, V20, P1728, DOI 10.1093/bioinformatics/bth158; Huson DH, 2014, BIOINFORMATICS, V30, P38, DOI 10.1093/bioinformatics/btt254; Klingenberg H, 2013, BIOINFORMATICS, V29, P973, DOI 10.1093/bioinformatics/btt077; Knights D, 2011, NAT METHODS, V8, P761, DOI 10.1038/nmeth.1650; Li WZ, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-359; Lingner T, 2011, NUCLEIC ACIDS RES, V39, pW518, DOI 10.1093/nar/gkr388; Liu ZQ, 2011, BIOINFORMATICS, V27, P3242, DOI 10.1093/bioinformatics/btr547; Maillet N, 2012, BMC BIOINFORMATICS, V13, DOI 10.1186/1471-2105-13-S19-S10; Meinicke P, 2005, IEEE T PATTERN ANAL, V27, P1379, DOI 10.1109/TPAMI.2005.183; Meinicke P, 2011, BIOINFORMATICS, V27, P1618, DOI 10.1093/bioinformatics/btr266; Meyer F, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-386; Mitra S, 2009, BIOINFORMATICS, V25, P1849, DOI 10.1093/bioinformatics/btp341; Mitra S, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-S1-S21; Ripley B.D., 1995, PATTERN RECOGNITION; SAMMON JW, 1969, IEEE T COMPUT, VC 18, P401, DOI 10.1109/T-C.1969.222678; Sanli K, 2013, BMC BIOINFORMATICS, V14, DOI 10.1186/1471-2105-14-38; Segata N, 2012, NAT METHODS, V9, P811, DOI [10.1038/nmeth.2066, 10.1038/NMETH.2066]; Su XQ, 2012, BIOINFORMATICS, V28, P2493, DOI 10.1093/bioinformatics/bts470; Teeling H, 2012, BRIEF BIOINFORM, V13, P728, DOI 10.1093/bib/bbs039; Yilmaz P, 2011, NAT BIOTECHNOL, V29, P415, DOI 10.1038/nbt.1823 28 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 1422-0067 INT J MOL SCI Int. J. Mol. Sci. JUL 2014 15 7 12364 12378 10.3390/ijms150712364 15 Chemistry, Multidisciplinary Chemistry AM7ID WOS:000340038500072 J Shin, YC; Jung, YH Shin, Yongchul; Jung, Younghun Development of Irrigation Water Management Model for Reducing Drought Severity Using Remotely Sensed Soil Moisture Footprints JOURNAL OF IRRIGATION AND DRAINAGE ENGINEERING English Article Irrigation water management model; Drought severity; Genetic algorithm (GA); Soil water atmosphere plant (SWAP); Soil hydraulic parameters; Remotely sensed soil moisture; Evapotranspiration; Soil moisture deficit index (SMDI) HYDRAULIC CONDUCTIVITY; SIRSA DISTRICT; AMSR-E; PERFORMANCE; SYSTEM; PRODUCTIVITY; RETRIEVAL; ALGORITHM; EVALUATE; MISSION With an increase of population, agriculture, and industry, the demand for water has increased gradually across the world. Currently, agricultural crops have been damaged by drought severity due to climate changes that contribute to water scarcity. Policy/decision makers need to be prepared for reducing damages to crops due to severe droughts. For this reason, a genetic algorithm (GA)-based irrigation water management model (IWMM) adapting a hydrological model [soil water atmosphere plant (SWAP)] was developed. This approach is linked with a noisy Monte Carlo genetic algorithm (NMCGA) that can estimate effective soil hydraulic properties from in situ/remotely sensed (RS) soil moisture data. Based on the estimated soil parameters, vegetation information, and historical weather forcings, long-term root zone soil moisture (SM) and evapotranspiration (ET) dynamics were reproduced at fields using SWAP in a forward mode. This approach incorporates a soil moisture deficit index (SMDI) that can estimate the weekly drought severity using the daily estimated soil moisture dynamics. The irrigation schedules, intervals, and amounts were determined by the degree of drought based on the SMDI values (below 0 indicating drought). The Lubbock and Walnut Creek (WC) 11/14 sites in Texas and Iowa were selected for testing the applicability of the studied approach using in situ (point scale) and RS (airborne sensing scale) soil moisture products. As this approach irrigates the appropriate/minimum water amounts (yearly average 65.5-136.1 mm) to the agricultural fields, one could prevent the drought-driven crop damages with the positive SMDI values. Thus, the newly developed model could be helpful for improving agricultural water management and reducing drought severity efficiently in irrigated agriculture. (C) 2014 American Society of Civil Engineers. [Shin, Yongchul] Texas A&M Univ, Dept Biol & Agr Engn, College Stn, TX 77843 USA; [Jung, Younghun] Inha Univ, Dept Civil Engn, Inchon 402751, South Korea Jung, YH (reprint author), Inha Univ, Dept Civil Engn, 100 Inha Ro, Inchon 402751, South Korea. jung.younghun@gmail.com Aswathanarayana U., 2001, WATER RESOURCES MANA; Bastiaanssen WGM, 2004, WAG UR FRON, V6, P263; BELMANS C, 1983, J HYDROL, V63, P271, DOI 10.1016/0022-1694(83)90045-8; Bindlish R, 2006, REMOTE SENS ENVIRON, V103, P127, DOI 10.1016/j.rse.2005.02.003; Bos M. G., 1990, ILRI PUBLICATION, V19, P141; Bos Marinus G., 1997, Irrigation and Drainage Systems, V11, P119, DOI 10.1023/A:1005826407118; Das NN, 2011, IEEE T GEOSCI REMOTE, V49, P1504, DOI 10.1109/TGRS.2010.2089526; DRACUP JA, 1980, WATER RESOUR RES, V16, P289, DOI 10.1029/WR016i002p00289; Droogers P, 2002, J IRRIG DRAIN E-ASCE, V128, P11, DOI 10.1061/(ASCE)0733-9437(2002)128:1(11); Droogers P, 2000, AGR WATER MANAGE, V43, P183, DOI 10.1016/S0378-3774(99)00055-4; Droogers P., 1999, Irrigation and Drainage Systems, V13, P275, DOI 10.1023/A:1006345724659; Efron B., 1982, SOC IND APPL MATH; Entekhabi D, 2010, P IEEE, V98, P704, DOI 10.1109/JPROC.2010.2043918; Feddes RA, 1978, SIMULATION FIELD WAT; Federal Emergency Management Agency (FEMA), 1995, NAT MIT STRAT PARTN; Goldberg D. E., 1989, GENETIC ALGORITHMS S; Holland J. H., 1975, ADAPTATION NATURAL A; Ines AVM, 2008, WATER RESOUR RES, V44, DOI 10.1029/2007WR006125; Ines AVM, 2008, WATER RESOUR RES, V44, DOI 10.1029/2007WR005990; Ines AVM, 2009, WATER RESOUR RES, V45, DOI 10.1029/2008WR007022; Ines AVM, 2002, AGR WATER MANAGE, V54, P205, DOI 10.1016/S0378-3774(01)00173-1; Ines AVM, 2006, AGR WATER MANAGE, V83, P221, DOI 10.1016/j.agwat.2005.12.006; Jackson T., 2004, SMEX02 CODIAC SOIL C; Jackson T., 2003, SMEX02 WATERSHED SOI; Kerr YH, 2001, IEEE T GEOSCI REMOTE, V39, P1729, DOI 10.1109/36.942551; Kroes J. G., 1999, 81 DLO WIN STAR CTR; Legislative Budget Board, 2011, FISC IMP DROUGHT STA; Leij F. J., 1999, CHARACTERIZATION MEA, P1269; Lettenmaier D. P., 1996, WATER RESOURCES HDB, P293; Marek T., 2010, ASSESSMENT TEXAS EVA; McKee T. B., 1993, 8 C APPL CLIM; Mishra A. K., 2005, INT J RIVER BASIN MA, V3, P31; Mishra AK, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010986; Molden D., 1997, ACCOUNTING WATER USE, V1, P16; MUALEM Y, 1976, WATER RESOUR RES, V12, P513, DOI 10.1029/WR012i003p00513; Narasimhan B, 2005, AGR FOREST METEOROL, V133, P69, DOI 10.1016/j.agrformet.2005.07.012; National Atlas of the United States, 2005, STAT BOUND US; Njoku E., 2008, AMSR E AQUA DAILY L3; Njoku EG, 2003, IEEE T GEOSCI REMOTE, V41, P215, DOI 10.1109/TGRS.2002.808243; Prueger J., 2009, SMEX02 SMACEX TOWER; Sarwar A., 2000, Irrigation and Drainage Systems, V14, P257, DOI 10.1023/A:1006468905194; Seckler D., 1999, International Journal of Water Resources Development, V15, P29, DOI 10.1080/07900629948916; Seckler D., 1996, NEW ERA WATER MANAGE, P17; Shin Y, 2013, WATER RESOUR RES, V49, P6208, DOI 10.1002/wrcr.20495; Shin YC, 2012, WATER RESOUR RES, V48, DOI 10.1029/2010WR009581; Singh R, 2006, J HYDROL, V329, P692, DOI 10.1016/j.jhydrol.2006.03.037; Singh R, 2006, J HYDROL, V329, P714, DOI 10.1016/j.jhydrol.2006.03.016; Svoboda M, 2002, B AM METEOROL SOC, V83, P1181; United States Geological Survey (USGS), 2006, WHAT IS NED; Van Dam J, 2000, THESIS WAGENINGEN U; van Dam J. C., 1997, 45 DLO WIN STAR CTR; VANGENUCHTEN MT, 1980, SOIL SCI SOC AM J, V44, P892; Wesseling J. G., 1998, 160 DLO WIN STAR CTR; Wilhite DA, 2000, ROUTLEDGE HAZARDS DI, P89; Yevjevich V. M., 1967, HYDROL PAP, V23, P18 55 0 0 ASCE-AMER SOC CIVIL ENGINEERS RESTON 1801 ALEXANDER BELL DR, RESTON, VA 20191-4400 USA 0733-9437 1943-4774 J IRRIG DRAIN ENG J. Irrig. Drainage Eng-ASCE JUL 2014 140 7 04014021 10.1061/(ASCE)IR.1943-4774.0000736 11 Agricultural Engineering; Engineering, Civil; Water Resources Agriculture; Engineering; Water Resources AM9CA WOS:000340176000010 J Xie, Y; Yu, HM; Hu, R Xie, Yi; Yu, Hui-min; Hu, Roland Probabilistic hypergraph based hash codes for social image search JOURNAL OF ZHEJIANG UNIVERSITY-SCIENCE C-COMPUTERS & ELECTRONICS English Article Hypergraph Laplacian; Probabilistic hypergraph; Hash codes; Image search APPROXIMATE NEAREST-NEIGHBOR; DIMENSIONS With the rapid development of the Internet, recent years have seen the explosive growth of social media. This brings great challenges in performing efficient and accurate image retrieval on a large scale. Recent work shows that using hashing methods to embed high-dimensional image features and tag information into Hamming space provides a powerful way to index large collections of social images. By learning hash codes through a spectral graph partitioning algorithm, spectral hashing (SH) has shown promising performance among various hashing approaches. However, it is incomplete to model the relations among images only by pairwise simple graphs which ignore the relationship in a higher order. In this paper, we utilize a probabilistic hypergraph model to learn hash codes for social image retrieval. A probabilistic hypergraph model offers a higher order representation among social images by connecting more than two images in one hyperedge. Unlike a normal hypergraph model, a probabilistic hypergraph model considers not only the grouping information, but also the similarities between vertices in hyperedges. Experiments on Flickr image datasets verify the performance of our proposed approach. [Xie, Yi; Yu, Hui-min; Hu, Roland] Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China Xie, Y (reprint author), Zhejiang Univ, Dept Informat Sci & Elect Engn, Hangzhou 310027, Peoples R China. yixie@zju.edu.cn; yhm2005@zju.edu.cn National Basic Research Program (973) of China [2012CB316400] Project supported by the National Basic Research Program (973) of China (No. 2012CB316400) Andoni A, 2006, ANN IEEE SYMP FOUND, P459; Arya S, 1998, J ACM, V45, P891, DOI 10.1145/293347.293348; Bengio Y, 2004, NEURAL COMPUT, V16, P2197, DOI 10.1162/0899766041732396; Chua T.S., 2009, P ACM INT C IM VID R, P48, DOI 10.1145/1646396.1646452; Gao Y, 2013, IEEE T IMAGE PROCESS, V22, P363, DOI 10.1109/TIP.2012.2202676; He J.R., 2004, P ACM INT C MULT, P9, DOI DOI 10.1145/1027527.1027531; He JR, 2006, IEEE T IMAGE PROCESS, V15, P3170, DOI 10.1109/TIP.2006.877491; Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647; Huang YC, 2010, PROC CVPR IEEE, P3376, DOI 10.1109/CVPR.2010.5540012; Jiang Y.G., 2008, P 31 INT ACM SIGIR C, P769, DOI 10.1145/1390334.1390495; Li P, 2013, IEEE T MULTIMEDIA, V15, P141, DOI 10.1109/TMM.2012.2199970; Liu H., 2011, ICDM, P398, DOI 10.1109/ICDM.2011.12; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Salakhutdinov R., 2007, INT C ART INT STAT, P412; Shakhnarovich G, 2003, NINTH IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION, VOLS I AND II, PROCEEDINGS, P750; Silpa-Anan C., 2008, IEEE C COMP VIS PATT, P1, DOI 10.1109/CVPR.2008.4587638; Torralba A., 2008, IEEE C COMP VIS PATT, P1, DOI [10.1109/CVPR.2008.4587633, DOI 10.1109/CVPR.2008.4587633]]; Weiss Y., 2008, 21 ADV NIPS, P1753; Xia SP, 2008, LECT NOTES COMPUT SC, V5342, P318; Yang J., 2007, P INT WORKSH MULT IN, P197, DOI 10.1145/1290082.1290111; Yu J, 2012, IEEE T IMAGE PROCESS, V21, P3262, DOI 10.1109/TIP.2012.2190083; Zass R., 2008, IEEE C COMP VIS PATT, P1, DOI 10.1109/CVPR.2008.4587500; Zhang D, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P18; Zhou D., 2006, 19 ADV NIPS, P1601; ZHOU DY, 2004, 17 ADV NIPS, V16, P321; Zhuang Y., 2011, P ACM MULT, P1457, DOI 10.1145/2072298.2072039 26 0 0 ZHEJIANG UNIV HANGZHOU EDITORIAL BOARD, 20 YUGU RD, HANGZHOU, 310027, PEOPLES R CHINA 1869-1951 1869-196X J ZHEJIANG U-SCI C J. Zhejiang Univ.-SCI. C. JUL 2014 15 7 537 550 10.1631/jzus.C1300268 14 Computer Science, Information Systems; Computer Science, Software Engineering; Engineering, Electrical & Electronic Computer Science; Engineering AM4YQ WOS:000339862500004 J Erickson, RL; Paul, LK; Brown, WS Erickson, Roger L.; Paul, Lynn K.; Brown, Warren S. Verbal learning and memory in agenesis of the corpus callosum NEUROPSYCHOLOGIA English Article Corpus callosum; Verbal learning; Verbal memory; Encoding POSITRON-EMISSION-TOMOGRAPHY; CONFIRMATORY FACTOR-ANALYSIS; EDITION CVLT-II; INTERHEMISPHERIC-TRANSFER; PARTIAL COMMISSUROTOMY; CEREBRAL HEMISPHERES; VISUAL INFORMATION; HERA MODEL; DEFICITS; LANGUAGE The role of interhemispheric interactions in the encoding, retention, and retrieval of verbal memory can be clarified by assessing individuals with complete or partial agenesis of the corpus callosum (AgCC), but who have normal intelligence. This study assessed verbal learning and memory in AgCC using the California Verbal Learning Test Second Edition (CVLT-II). Twenty-six individuals with AgCC were compared to 24 matched controls on CVLT-II measures, as well as Donders' four CVLT-II factors (i.e., Attention Span, Learning Efficiency, Delayed Memory, and Inaccurate Memory). Individuals with AgCC performed significantly below healthy controls on the Delayed Memory factor, confirmed by significant deficits in short and long delayed free recall and cued recall. They also performed less well in original learning. Deficient performance by individuals with AgCC during learning trials, as well as deficits in all forms of delayed memory, suggest that the corpus callosum facilitates interhemispheric elaboration and encoding of verbal information. (C) 2014 Elsevier Ltd. All rights reserved. [Erickson, Roger L.; Brown, Warren S.] Fuller Grad Sch Psychol, Travis Res Inst, Pasadena, CA 91101 USA; [Paul, Lynn K.] CALTECH, Div Humanities & Social Sci, Pasadena, CA 91125 USA Brown, WS (reprint author), Fuller Grad Sch Psychol, Travis Res Inst, 180 N Oakland Ave, Pasadena, CA 91101 USA. wsbrown@fuller.edu BENZING WC, 1989, BEHAV NEUROSCI, V103, P538, DOI 10.1037/0735-7044.103.3.538; Borod Joan C, 2002, Appl Neuropsychol, V9, P23, DOI 10.1207/S15324826AN0901_4; Brown W. S., 2000, COGNITIVE NEUROPSYCH, V5, P135, DOI DOI 10.1080/135468000395781; Brown WS, 2001, J INT NEUROPSYCH SOC, V7, P302, DOI 10.1017/S1355617701733048; Brown WS, 2005, NEUROPSYCHOLOGIA, V43, P906, DOI 10.1016/j.neuropsychologia.2004.09.008; Brown WS, 2005, BRAIN LANG, V93, P135, DOI 10.1016/j.bandl.2004.09.003; Brown WS, 1999, NEUROPSYCHOLOGIA, V37, P1165, DOI 10.1016/S0028-3932(99)00011-1; BUSCHKE H, 1973, J VERB LEARN VERB BE, V12, P543, DOI 10.1016/S0022-5371(73)80034-9; Callan DE, 2006, NEUROIMAGE, V31, P1327, DOI 10.1016/j.neuroimage.2006.01.036; CHIARELLO C, 1980, BRAIN LANG, V11, P128, DOI 10.1016/0093-934X(80)90116-9; CHIARELLO C, 1992, NEUROPSYCHOLOGIA, V30, P381, DOI 10.1016/0028-3932(92)90111-X; CHIARELLO C, 1990, BRAIN LANG, V38, P75, DOI 10.1016/0093-934X(90)90103-N; CLARK CR, 1989, BRAIN, V112, P165, DOI 10.1093/brain/112.1.165; Code C., 1987, LANGUAGE APHASIA RIG; DeJong J, 2009, ASSESSMENT, V16, P328, DOI 10.1177/1073191109336989; Delis D. C., 1994, CA VERBAL LEARNING T; Delis DC, 1987, CALIFORNIA VERBAL LE; Delis DCKJ, 2000, CALIFORNIA VERBAL LE; Donders J, 2008, ASSESSMENT, V15, P123, DOI 10.1177/1073191107310926; FISCHER M, 1992, ARCH NEUROL-CHICAGO, V49, P271; GAFFAN D, 1974, J COMP PHYSIOL PSYCH, V86, P1100, DOI 10.1037/h0037649; GEFFEN GM, 1994, ADV BEHAV BIOL, V42, P247; Glass H. C., 2008, AM J MED GENET, V146, P2495; GOTT PS, 1978, NEUROLOGY, V28, P1272; Habib R, 2003, TRENDS COGN SCI, V7, P241, DOI 10.1016/S1364-6613(03)00110-4; Hines RJ, 2002, NEUROPSYCHOLOGIA, V40, P1804, DOI 10.1016/S0028-3932(02)00032-5; Imamura T, 1994, Behav Neurol, V7, P43, DOI 10.3233/BEN-1994-7201; James W, 1890, PRINCIPLES PSYCHOL; Jeeves M. A., 1979, STRUCTURE FUNCTION C, P449; JEEVES MA, 1988, DEV NEUROPSYCHOL, V4, P305; JEEVES MA, 1988, CORTEX, V24, P601; JEEVES MA, 1988, NEUROPSYCHOLOGIA, V26, P833, DOI 10.1016/0028-3932(88)90053-X; Jeret J S, 1985, Pediatr Neurosci, V12, P101, DOI 10.1159/000120229; Joanette Y., 1990, RIGHT HEMISPHERE VER; KARNATH HO, 1991, CORTEX, V27, P345; Kaufman JA, 2008, ACTA NEUROPATHOL, V116, P479, DOI 10.1007/s00401-008-0434-7; KESSLER J, 1991, INT J NEUROSCI, V58, P275; Kompus K, 2011, BRAIN RES, V1419, P61, DOI 10.1016/j.brainres.2011.08.052; LEDOUX JE, 1977, BRAIN, V100, P87, DOI 10.1093/brain/100.1.87; Maine de Biran F. P. G, 1929, INFLUENCE HABIT FACU; Marco EJ, 2012, J INT NEUROPSYCH SOC, V18, P521, DOI 10.1017/S1355617712000045; Milner B, 1965, PHYSL HIPPOCAMPE, p[97, 257]; Mueller KLO, 2009, BEHAV NEUROSCI, V123, P1000, DOI 10.1037/a0016868; Nyberg L, 1996, PSYCHON B REV, V3, P135, DOI 10.3758/BF03212412; O'Keefe J., 1978, HIPPOCAMPUS COGNITIV; OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4; Panos PT, 2001, ARCH CLIN NEUROPSYCH, V16, P507, DOI 10.1016/S0887-6177(00)00061-5; Paul LK, 2007, NAT REV NEUROSCI, V8, P287, DOI 10.1038/nrn2107; Paul LK, 2003, BRAIN LANG, V85, P313, DOI 10.1016/S0093-934X(03)00062-2; Peck KK, 2009, NEUROSURGERY, V64, P644, DOI 10.1227/01.NEU.0000339122.01957.0A; Phelps EA, 1991, CEREB CORTEX, V1, P492, DOI 10.1093/cercor/1.6.492; Priozzolo F. J., 1979, CLIN NEUROPSYCHOLOGY, V1, P13; RAUCH RA, 1994, CALLOSAL AGENESIS, V42, P83; Rey A., 1958, EXAMEN CLIN PSYCHOL; SAUERWEIN H, 1983, NEUROPSYCHOLOGIA, V21, P167, DOI 10.1016/0028-3932(83)90084-2; SAUERWEIN HC, 1994, BEHAV BRAIN RES, V64, P229, DOI 10.1016/0166-4328(94)90135-X; Smith L. A., 1995, SYNDROME NONVERBAL L, P45; SOLURSH LP, 1965, J NERV MENT DIS, V141, P180, DOI 10.1097/00005053-196508000-00005; Sperry R. W., 1974, NEUROSCIENCES 3 STUD, P5; SPERRY RW, 1968, AM PSYCHOL, V23, P723, DOI 10.1037/h0026839; Strauss E., 2006, COMPENDIUM NEUROPSYC; Symington S. H., 2010, SOCIAL NEUROSCIENCE, V1, P1; TULVING E, 1994, P NATL ACAD SCI USA, V91, P2016, DOI 10.1073/pnas.91.6.2016; Turk AA, 2010, NEUROPSYCHOLOGIA, V48, P43, DOI 10.1016/j.neuropsychologia.2009.08.009; VANLANCKER D, 1991, BRAIN COGNITION, V17, P64, DOI 10.1016/0278-2626(91)90067-I; VanLancker D, 1997, BRAIN LANG, V57, P1, DOI 10.1006/brln.1997.1850; Wahl M, 2009, AM J NEURORADIOL, V30, P282, DOI 10.3174/ajnr.A1361; Wechsler D, 1997, WECHSLER ADULT INTEL; Wechsler D., 1999, WECHSLER ABBREVIATED; Wechsler D, 1945, J PSYCHOL, V19, P87; ZAIDEL D, 1974, BRAIN, V97, P263, DOI 10.1093/brain/97.1.263; Zaidel D. W., 1995, BROKEN MEMORIES CASE, P213; Zaidel D. W., 1990, BRAIN CIRCUITS FUNCT; ZAIDEL DW, 1995, INT J NEUROSCI, V82, P215 74 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0028-3932 1873-3514 NEUROPSYCHOLOGIA Neuropsychologia JUL 2014 60 121 130 10.1016/j.neuropsychologia.2014.06.003 10 Behavioral Sciences; Neurosciences; Psychology, Experimental Behavioral Sciences; Neurosciences & Neurology; Psychology AN0WR WOS:000340305200014 J Veikkolainen, T; Pesonen, LJ; Evans, DAD Veikkolainen, Toni; Pesonen, Lauri J.; Evans, David A. D. PALEOMAGIA: A PHP/MYSQL database of the Precambrian paleomagnetic data STUDIA GEOPHYSICA ET GEODAETICA English Article paleogeography; informatics; global; continent; craton; online; filtering; open-access EAST EUROPEAN CRATON; U-PB GEOCHRONOLOGY; DIABASE DYKE; MESOPROTEROZOIC SUPERCONTINENT; WESTERN-AUSTRALIA; POLAR WANDER; BALTICA; CANADA; LAURENTIA; POLES Most paleomagnetic applications require a precise, rationally organized and up-to-date catalogue or database of paleomagnetic results worldwide. These include reconstructions of continents, calculations of the Apparent Polar Wander Paths (APWPs) or paleolatitude drift curves, testing the Geocentric Axial Dipole (GAD) model, studies of geomagnetic paleosecular variation or reversal asymmetries, comparison of coeval results obtained from different types of rocks, estimation of inclination shallowing in sedimentary rocks and understanding the delay in remanence acquisition caused by slow cooling in large intrusions. For this purpose, various databases, such as the Global Paleomagnetic Database (GPMDB), and the Magnetics Information Consortium Database (MagIC) have been generated. This paper presents a new relational database (PALEOMAGIA) where 3278 entries of Precambrian data have been split geographically, sorted according to age and rock types and ranked using a revised version of the Van der Voo grading scheme. The latest geochronologic information is included wherever available. Significant effort has been put to the retrieval and archiving of data published in the last decade, which are virtually nonexistent in GPMDB. Here we present the database and its browser-based user interface from a scientific and a technical point of view. [Veikkolainen, Toni; Pesonen, Lauri J.] Univ Helsinki, Dept Phys, Div Geophys & Astron, FI-00014 Helsinki, Finland; [Evans, David A. D.] Yale Univ, Dept Geol & Geophys, New Haven, CT 06511 USA Veikkolainen, T (reprint author), Univ Helsinki, Dept Phys, Div Geophys & Astron, FI-00014 Helsinki, Finland. toni.veikkolainen@helsinki.fi Abrahamsen N., 2001, B GEOL SOC DEN, V48, P91; Bingen B, 2002, CAN J EARTH SCI, V39, P1425, DOI 10.1139/E02-054; BINGHAM DK, 1976, CAN J EARTH SCI, V13, P563, DOI 10.1139/e76-060; Bloxham J, 2000, NATURE, V405, P63, DOI 10.1038/35011045; Bogdanova S, 2006, GEOL SOC MEM, V32, P599, DOI 10.1144/GSL.MEM.2006.032.01.36; Buchan KL, 1998, CAN J EARTH SCI, V35, P1054, DOI 10.1139/e98-054; Buchan KL, 2000, TECTONOPHYSICS, V319, P167, DOI 10.1016/S0040-1951(00)00032-9; Buchan KL, 2009, CAN J EARTH SCI, V46, P361, DOI 10.1139/E09-026; Bylund G., 2002, GEOLOGISKA FORENINGE, V114, P143; Elston DP, 2002, GEOL SOC AM BULL, V114, P619, DOI 10.1130/0016-7606(2002)114<0619:TTBPSC>2.0.CO;2; Evans DAD, 2011, GEOLOGY, V39, P443, DOI 10.1130/G31654.1; EVANS ME, 1968, J GEOPHYS RES, V73, P3261, DOI 10.1029/JB073i010p03261; EVANS ME, 1976, NATURE, V262, P676, DOI 10.1038/262676a0; Glebovitsky VA, 2008, GEOTECTONICS+, V42, P8, DOI 10.1134/S0016852108010020; Gurevich E., 1993, PALEOMAGNETIC DIRECT; Irving E., 1964, PALAEOMAGNETISM ITS; Irving E., 1976, OTTAWA GEOMAGNETIC S, V6; Jarboe N.A., 2012, AM GEOPH UN FALL M S; Kent DV, 1998, EARTH PLANET SC LETT, V160, P391, DOI 10.1016/S0012-821X(98)00099-5; Khramov A., 1979, PALEOMAGNETIC DIRECT; Khramov A., 1971, PALEOMAGNETIC DIRECT; Komissarova R., 1971, PALEOMAGNETIC DIRECT; KORHONEN K, 2008, GEOCHEM GEOPHY GEOSY, V9, DOI DOI 10.1029/2007GC001893; Li ZX, 2011, GEOLOGY, V39, P39, DOI 10.1130/G31461.1; Lubnina NV, 2009, DOKL EARTH SCI, V428, P1174, DOI 10.1134/S1028334X09070307; MCELHINNY MW, 1977, GEOPHYS J ROY ASTR S, V49, P313, DOI 10.1111/j.1365-246X.1977.tb03712.x; McElhinny MW, 1996, SURV GEOPHYS, V17, P575, DOI 10.1007/BF01888979; Meert JG, 2014, GONDWANA RES, V25, P159, DOI 10.1016/j.gr.2013.02.003; Merrill R., 1998, MAGNETIC FIELD EARTH; Pavlov V., 1993, PALEOMAGNETIC DIRECT; Pesonen L. J., 2012, GEOPHYSICA, V48, P5; Pesonen L.J., 1981, PRECAMBRIAN PLATE TE, P623; Pesonen L.J., 1987, EOS T AM GEOPHYS UN, V68, P1157; Pesonen L.J., 2012, SUP S 2012 HELS FINL; Pesonen LJ, 2003, TECTONOPHYSICS, V375, P289, DOI 10.1016/S0040-1951(03)00343-3; PESONEN LJ, 1991, TECTONOPHYSICS, V195, P151, DOI 10.1016/0040-1951(91)90210-J; Pesonen L.J., 1989, P 6 WORKSH EUR GEOTR, P389; PIPER JDA, 1982, EARTH PLANET SC LETT, V59, P61, DOI 10.1016/0012-821X(82)90118-2; Pisarevsky SA, 2006, GEOPHYS J INT, V166, P1095, DOI 10.1111/j.1365-246X.2006.03076.x; Pisarevsky SA, 2005, EOS T, V86, P170, DOI DOI 10.1029/2005E0170004; Poorter R., 1981, PRECAMBRIAN PLATE TE, P599; Raub T., 2008, THESIS YALE U NEW HA; Roberts N., 1989, GEOMAGN AERON, V3, P163; ROEST WR, 1989, GEOLOGY, V17, P1000, DOI 10.1130/0091-7613(1989)017<1000:SFSITL>2.3.CO;2; Rogers JJW, 2002, GONDWANA RES, V5, P5, DOI 10.1016/S1342-937X(05)70883-2; Salminen J, 2009, GEOL SOC SPEC PUBL, V323, P199, DOI 10.1144/SP323.9; Sircombe K., 2001, GEOL SOC AUSTR ABSTR, V65; Smirnov AV, 2011, PHYS EARTH PLANET IN, V187, P225, DOI 10.1016/j.pepi.2011.05.003; Smirnov AV, 2013, PRECAMBRIAN RES, V224, P11, DOI 10.1016/j.precamres.2012.09.020; Tauxe L, 2009, PHYS EARTH PLANET IN, V177, P31, DOI 10.1016/j.pepi.2009.07.006; Teixeira W., 2000, TECTONIC EVOLUTION S, P101; Torsvik TH, 2012, EARTH-SCI REV, V114, P325, DOI 10.1016/j.earscirev.2012.06.007; Torsvik TH, 1999, COMPUT GEOSCI, V25, P395, DOI 10.1016/S0098-3004(98)00143-5; Torsvik TH, 2003, TECTONOPHYSICS, V362, P67, DOI 10.1016/S0040-1951(02)00631-5; Van der Voo R., 1993, PALEOMAGNETISM ATLAN; Veiklcolainen T., 2013, PRECAMBRIAN IN PRESS, DOI [10.1016/j.precamres.2013.09.004, DOI 10.1016/J.PRECAMRES.2013.09.004]; Walderhaug HJ, 1999, EARTH PLANET SC LETT, V169, P71, DOI 10.1016/S0012-821X(99)00066-7; Whitmeyer SJ, 2007, GEOSPHERE, V3, P220, DOI 10.1130/GES00055.1; Zhang Q.R., 1997, PRECAMBRIAN RES, V85, P173, DOI 10.1016/S0301-9268(97)00031-4; Zhang SH, 2012, EARTH PLANET SC LETT, V353, P145, DOI 10.1016/j.epsl.2012.07.034 60 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0039-3169 1573-1626 STUD GEOPHYS GEOD Stud. Geophys. Geod. JUL 2014 58 3 425 441 10.1007/s11200-013-0382-0 17 Geochemistry & Geophysics Geochemistry & Geophysics AM9QU WOS:000340216900005 J Wang, F; Wei, JC; Zhao, DT Wang, Fei; Wei, Jiuchang; Zhao, Dingtao A Quantifiable Risky Decision Model: Incorporating Individual Memory into Informational Cascade SYSTEMS RESEARCH AND BEHAVIORAL SCIENCE English Article memory based; risky decision-making; informational cascade; system CONTRASTING EXEMPLAR-RETRIEVAL; WORKING-MEMORY; SPEEDED CLASSIFICATION; PSYCHOSOCIAL STRESS; RECOGNITION MEMORY; STORAGE CAPACITY; WEAK TIES; STRENGTH; JUDGMENT; MANAGEMENT People memory system plays a key role in the decision-making process (DMP). In order to examine its influence on decision-making, an individual's memory-based decision-making system is developed, and then, we integrate the multi-agent decision systems and construct a sequential risky decision model on the basis of an informational cascade. This study aims at exploring the public's decisions on whether or not to take protective actions under risk. The findings indicate that people with different strength of ties to friends and relatives make huge differences on decision-making. In the group with weak ties, the agent's total size of decision information and the level of risk perception are reducing in the sequential decision process. We further prove that people with weak ties are more prone to take no protective action, and the probability is also decreasing with the decision turn. The sequence of people with strong ties makes decisions dynamically with the intensity of released information. Finally, the influences of forgetting rate and memory capacity on people's decision-making are examined. The model provides a new line of thought about building a multi-agent system of DMP, which is also very helpful for the design of an information management system during emergencies. Copyright (C) 2014 John Wiley & Sons, Ltd. [Wang, Fei; Wei, Jiuchang; Zhao, Dingtao] Univ Sci & Technol China, Sch Management, Hefei 230026, Anhui, Peoples R China Wei, JC (reprint author), Univ Sci & Technol China, Sch Management, 96 Jinzhai Rd, Hefei 230026, Anhui, Peoples R China. weijc@ustc.edu.cn Alter S., 2003, COMMUNICATIONS AIS, V2003, P365; Asano M, 2011, J THEOR BIOL, V281, P56, DOI 10.1016/j.jtbi.2011.04.022; ASHBY FG, 1986, PSYCHOL REV, V93, P154, DOI 10.1037//0033-295X.93.2.154; Atkinson R. C., 1968, PSYCHOL LEARN MOTIV, V2, P89, DOI DOI 10.1016/S0079-7421(08)60422-3; BADDELEY A, 1992, SCIENCE, V255, P556, DOI 10.1126/science.1736359; Baddeley A, 2001, J EXP PSYCHOL GEN, V130, P641, DOI 10.1037//0096-3445.130.4.641; Bannerjee AV, 1992, Q J ECON, V107, P797; Berg CA, 1999, INT J BEHAV DEV, V23, P615; Bergert FB, 2007, J EXP PSYCHOL LEARN, V33, P107, DOI 10.1037/0278-7393.33.1.107; BIKHCHANDANI S, 1992, J POLIT ECON, V100, P992, DOI 10.1086/261849; Bosch H, 1998, NEURAL NETWORKS, V11, P869, DOI 10.1016/S0893-6080(98)00035-5; Cavanagh JF, 2010, SOC COGN AFFECT NEUR, V6, P311; Celen B, 2004, AM ECON REV, V94, P484, DOI 10.1257/0002828041464461; Cokely ET, 2009, JUDGM DECIS MAK, V4, P20; Cokely ET, 2006, PSYCHON B REV, V13, P991, DOI 10.3758/BF03213914; Corbin J, 2010, JUDGM DECIS MAK, V5, P110; Cowan N, 2005, COGNITIVE PSYCHOL, V51, P42, DOI 10.1016/j.cogpsych.2004.12.001; Cowan N, 2001, BEHAV BRAIN SCI, V24, P87, DOI 10.1017/S0140525X01003922; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; DEGROOT MH, 1974, J AM STAT ASSOC, V69, P118, DOI 10.2307/2285509; Donkin C, 2012, PSYCHOL SCI, V23, P625, DOI 10.1177/0956797611430961; Dougherty MRP, 2003, ACTA PSYCHOL, V113, P263, DOI 10.1016/S0001-6918(03)00033-7; Gennaioli N, 2010, Q J ECON, V125, P1399, DOI 10.1162/qjec.2010.125.4.1399; Granovetter M. S., 1995, GETTING JOB STUDY CO; GRANOVET.MS, 1973, AM J SOCIOL, V78, P1360, DOI 10.1086/225469; HASTIE R, 1986, PSYCHOL REV, V93, P258, DOI 10.1037/0033-295X.93.3.258; Hevner AR, 2004, MIS QUART, V28, P75; HIRSHMAN E, 1995, J EXP PSYCHOL LEARN, V21, P302, DOI 10.1037/0278-7393.21.2.302; Hollingshead AB, 2001, J PERS SOC PSYCHOL, V81, P1080, DOI 10.1037//0022-3514.81.6.1080; JOHNSON MMS, 1993, PSYCHOL AGING, V8, P231, DOI 10.1037//0882-7974.8.2.231; KAHNEMAN D, 1972, COGNITIVE PSYCHOL, V3, P430, DOI 10.1016/0010-0285(72)90016-3; Keselman A, 2005, J BIOMED INFORM, V38, P331, DOI 10.1016/j.jbi.2005.05.001; Kuhlmann S, 2005, J NEUROSCI, V25, P2977, DOI 10.1523/JNEUROSCI.5139-04.2005; Lechuga MT, 2012, PSICOLOGICA, V33, P257; Lindell MK, 2006, INTRO EMERGENCY MANA; Lindell MK, 2004, COMMUNICATING ENV RI; Lindell MK, 2008, RISK ANAL, V28, P539, DOI 10.1111/j.1539-6924.2008.01032.x; Lindell MK, 2012, RISK ANAL, V32, P616, DOI 10.1111/j.1539-6924.2011.01647.x; Littlepage G, 1997, ORGAN BEHAV HUM DEC, V69, P133, DOI 10.1006/obhd.1997.2677; MADDOX WT, 1993, PERCEPT PSYCHOPHYS, V53, P49, DOI 10.3758/BF03211715; Marois R, 2005, TRENDS COGN SCI, V9, P296, DOI 10.1016/j.tics.2005.04.010; Mehta N, 2004, QUANTITATIVE MARKETI, V2, P107, DOI 10.1023/B:QMEC.0000027775.65062.50; MEYER BJF, 1995, PSYCHOL AGING, V10, P84, DOI 10.1037/0882-7974.10.1.84; Mullainathan S, 2002, Q J ECON, V117, P735, DOI 10.1162/003355302760193887; Nosofsky RA, 2005, J EXP PSYCHOL HUMAN, V31, P608, DOI 10.1037/0096-1523.31.3.608; Nosofsky RM, 2010, MEM COGNITION, V38, P916, DOI 10.3758/MC.38.7.916; Nosofsky RM, 1997, PSYCHOL REV, V104, P266, DOI 10.1037//0033-295X.104.2.266; Pashler H, 2002, STEVENS HDB EXPT PSY, P235; Pollard WE, 2003, J HEALTH COMMUN, V8, P93, DOI 10.1080/10810730390224893; Roedinger HL, 1996, MEMORY, P197, DOI 10.1016/B978-012102570-0/50009-4; Ruef M, 2002, IND CORP CHANGE, V11, P427, DOI 10.1093/icc/11.3.427; Sarafidis Y, 2007, ECON J, V117, P307, DOI 10.1111/j.1468-0297.2007.02019.x; Schwartz BL, 1997, CURR DIR PSYCHOL SCI, V6, P132, DOI 10.1111/1467-8721.ep10772899; SILVER MS, 1995, MIS QUART, V19, P361, DOI 10.2307/249600; SLOVIC P, 1987, SCIENCE, V236, P280, DOI 10.1126/science.3563507; Smeets T, 2007, BIOL PSYCHOL, V76, P116, DOI 10.1016/j.biopsycho.2007.07.001; Smith L, 2000, ECONOMETRICA, V68, P371, DOI 10.1111/1468-0262.00113; Smith PL, 2009, PSYCHOL REV, V116, P283, DOI 10.1037/a0015156; Tang O, 2011, INT J PROD ECON, V133, P25, DOI 10.1016/j.ijpe.2010.06.013; TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124; TVERSKY A, 1991, Q J ECON, V106, P1039, DOI 10.2307/2937956; Verde MF, 2007, MEM COGNITION, V35, P254, DOI 10.3758/BF03193446; Weber EU, 2006, CONSTRUCTION OF PREFERENCE, P397, DOI 10.1017/CBO9780511618031.022; Wegner D. M., 1985, COMPATIBLE INCOMPATI, P253, DOI [DOI 10.1007/978-1-4612-5044-9_12, 10.1007/978-1-4612-5044-9_12]; Wegner D. M., 1986, THEORIES GROUP BEHAV, P185; Wei J, 2012, INFORM RES, V17; WELCH I, 1992, J FINANC, V47, P695, DOI 10.2307/2329120; Wilson TD, 2006, J DOC, V62, P658, DOI 10.1108/00220410610714895; WINOCUR G, 1988, NEUROBIOL AGING, V9, P487, DOI 10.1016/S0197-4580(88)80102-7; Wu D, 2010, TECHNOL FORECAST SOC, V77, P837; Wu DD, 2010, INT J PROD RES, V48, P4919, DOI 10.1080/00207540903051684; Wu DD, 2010, TECHNOL FORECAST SOC, V77, P857, DOI 10.1016/j.techfore.2010.01.015; Wu DD, 2009, HUM ECOL RISK ASSESS, V15, P220, DOI 10.1080/10807030902760967; Wu DD, 2010, RISK ANAL, V30, P1440, DOI 10.1111/j.1539-6924.2010.01432.x 74 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1092-7026 1099-1743 SYST RES BEHAV SCI Syst. Res. Behav. Sci. JUL-AUG 2014 31 4 SI 537 553 10.1002/sres.2294 17 Management; Social Sciences, Interdisciplinary Business & Economics; Social Sciences - Other Topics AN0DS WOS:000340253500006 J Huttich, C; Korets, M; Bartalev, S; Zharko, V; Schepaschenko, D; Shvidenko, A; Schmullius, C Huettich, Christian; Korets, Mikhail; Bartalev, Sergey; Zharko, Vasily; Schepaschenko, Dmitry; Shvidenko, Anatoly; Schmullius, Christiane Exploiting Growing Stock Volume Maps for Large Scale Forest Resource Assessment: Cross-Comparisons of ASAR- and PALSAR-Based GSV Estimates with Forest Inventory in Central Siberia FORESTS English Article forest inventory; biomass; ALOS PALSAR; ENVISAT ASAR; land cover fragmentation; Siberia; boreal forest management BOREAL FOREST; BACKSCATTER DATA; STEM VOLUME; ABOVEGROUND BIOMASS; RADAR BACKSCATTER; ALOS PALSAR; CLASSIFICATION; AREA; UNCERTAINTY; RETRIEVAL Growing stock volume is an important biophysical parameter describing the state and dynamics of the Boreal zone. Validation of growing stock volume (GSV) maps based on satellite remote sensing is challenging due to the lack of consistent ground reference data. The monitoring and assessment of the remote Russian forest resources of Siberia can only be done by integrating remote sensing techniques and interdisciplinary collaboration. In this paper, we assess the information content of GSV estimates in Central Siberian forests obtained at 25 m from ALOS-PALSAR and 1 km from ENVISAT-ASAR backscatter data. The estimates have been cross-compared with respect to forest inventory data showing 34% relative RMSE for the ASAR-based GSV retrievals and 39.4% for the PALSAR-based estimates of GSV. Fragmentation analyses using a MODIS-based land cover dataset revealed an increase of retrieval error with increasing fragmentation of the landscape. Cross-comparisons of multiple SAR-based GSV estimates helped to detect inconsistencies in the forest inventory data and can support an update of outdated forest inventory stands. [Huettich, Christian; Schmullius, Christiane] Univ Jena, Dept Earth Observat, D-07743 Jena, Germany; [Korets, Mikhail] Russian Acad Sci, Siberian Branch, Sukachev Inst Forest, Krasnoyarsk 660036, Russia; [Bartalev, Sergey; Zharko, Vasily] Russian Acad Sci, Space Res Inst, Moscow 117997, Russia; [Schepaschenko, Dmitry; Shvidenko, Anatoly] Int Inst Adv Syst Anal, A-2361 Laxenburg, Austria Huttich, C (reprint author), Univ Jena, Dept Earth Observat, Lobdergraben 32, D-07743 Jena, Germany. Christian.huettich@uni-jena.de; mik@ksc.krasn.ru; bartalev@d902.iki.rssi.ru; zharko@d902.iki.rssi.ru; schepd@iiasa.ac.at; shvidenk@iiasa.ac.at; c.schmullius@uni-jena.de European Commission, Space, Cross-cutting Activities, International Cooperation, EU-Russia Cooperation in GMES (SICA) [SPA.2010.3.2-01] The authors express their thanks to Maurizio Santoro and Tim Robin van Doorn for helpful comments and improvements of the manuscript. This paper was realized in the framework of the FP 7 EU-Russia ZAPAS project on the assessment and monitoring of forest resources in Central Siberia. ZAPAS was funded by the European Commission, Space, Cross-cutting Activities, International Cooperation, Grant no. SPA.2010.3.2-01 EU-Russia Cooperation in GMES (SICA). The authors express their thanks to JAXA for providing the 25 m ALOS PALSAR backscatter mosaic data in the framework of the Kyoto and Carbon Science Initiative. Helpful comments and suggestions from the reviewers are highly appreciated. Bartalev SA, 2014, REMOTE SENS LETT, V5, P55, DOI 10.1080/2150704X.2013.870675; Bartalev S.A., 2011, CONT REMOTE SENS SPA, V8, P285; Bartalev SA, 2003, INT J REMOTE SENS, V24, P1977, DOI 10.1080/0143116031000066297; Blackard JA, 2008, REMOTE SENS ENVIRON, V112, P1658, DOI 10.1016/j.rse.2007.08.021; Breiman L., 1984, CLASSIFICATION REGRE, P368; Carreiras JMB, 2013, REMOTE SENS-BASEL, V5, P1524, DOI 10.3390/rs5041524; Cartus O, 2012, REMOTE SENS-BASEL, V4, P3320, DOI 10.3390/rs4113320; Corona P, 2010, IFOREST, V3, P59, DOI 10.3832/ifor0531-003; Cutler DR, 2007, ECOLOGY, V88, P2783, DOI 10.1890/07-0539.1; De Grandi GD, 2011, IEEE T GEOSCI REMOTE, V49, P3593, DOI 10.1109/TGRS.2011.2165288; Dolman J., 2012, BIOGEOSCIENCES, V9, P5323; FFSR, 1995, MAN FOR INV PLANN FO, P174; Gislason PO, 2006, PATTERN RECOGN LETT, V27, P294, DOI 10.1016/j.patrec.2005.08.011; Goetz Scott J, 2009, Carbon Balance Manag, V4, P2, DOI 10.1186/1750-0680-4-2; Gustafson EJ, 2010, ECOL APPL, V20, P700, DOI 10.1890/08-1693.1; Gusti M, 2010, CLIMATIC CHANGE, V103, P159, DOI 10.1007/s10584-010-9911-9; Hain H, 2005, INT FOREST REV, V7, P90, DOI 10.1505/ifor.2005.7.2.90; Houghton RA, 2007, ENVIRON RES LETT, V2, DOI 10.1088/1748-9326/2/4/045032; Huttich C., 2010, ENVIRON MONIT ASSESS, V176, P531; IMHOFF ML, 1995, IEEE T GEOSCI REMOTE, V33, P511, DOI 10.1109/36.377953; Jaeger JAG, 2000, LANDSCAPE ECOL, V15, P115, DOI 10.1023/A:1008129329289; JAXA, 2007, NEW GLOB 50 M RES PA; Kasischke ES, 2011, REMOTE SENS ENVIRON, V115, P227, DOI 10.1016/j.rse.2010.08.022; Kuemmerle T, 2009, REMOTE SENS ENVIRON, V113, P1194, DOI 10.1016/j.rse.2009.02.006; Le Toan T, 2011, REMOTE SENS ENVIRON, V115, P2850, DOI 10.1016/j.rse.2011.03.020; McGarigal K., 2002, SPATIAL PATTERN ANAL, P171; McRoberts RE, 2012, REMOTE SENS ENVIRON, V125, P157, DOI 10.1016/j.rse.2012.07.002; Minayeva L.Y., 1995, MIL TIPRG, V1, P274; Pereira HM, 2013, SCIENCE, V339, P277, DOI 10.1126/science.1229931; Powell SL, 2010, REMOTE SENS ENVIRON, V114, P1053, DOI 10.1016/j.rse.2009.12.018; Prasad M., 2006, ECOSYSTEMS, V9, P181; Rauste Y, 2005, REMOTE SENS ENVIRON, V97, P263, DOI 10.1016/j.rse.2005.05.002; Rosenqvist A, 2001, INT GEOSCI REMOTE SE, P546, DOI 10.1109/IGARSS.2001.976217; Ryan B., 2012, P UNFCCC COP 18 DOH; Santoro M, 2006, INT J REMOTE SENS, V27, P3425, DOI 10.1080/01431160600646037; Santoro M, 2012, INT GEOSCI REMOTE SE, P7204, DOI 10.1109/IGARSS.2012.6352000; Santoro M, 2011, REMOTE SENS ENVIRON, V115, P490, DOI 10.1016/j.rse.2010.09.018; Santoro M, 2007, REMOTE SENS ENVIRON, V106, P154, DOI 10.1016/j.rse.2006.08.004; Santoro M, 2013, REMOTE SENS-BASEL, V5, P4503, DOI 10.3390/rs5094503; Schepaschenko D., 2012, EARTHZINE; Seifert F.M., 2012, GLOBBIOMASS USER CON; Shimada M, 2010, IEEE J-STARS, V3, P637, DOI 10.1109/JSTARS.2010.2077619; Shvidenko A., 2011, OPTIONS, P18; Shvidenko A, 2007, ECOL MODEL, V204, P163, DOI 10.1016/j.ecolmodel.2006.12.040; Shvidenko A, 2010, CLIMATIC CHANGE, V103, P137, DOI 10.1007/s10584-010-9918-2; Simard M, 2011, J GEOPHYS RES-BIOGEO, V116, DOI 10.1029/2011JG001708; Thiel C, 2013, REMOTE SENS LETT, V4, P900, DOI 10.1080/2150704X.2013.810350; Tomppo E, 2002, REMOTE SENS ENVIRON, V82, P156, DOI 10.1016/S0034-4257(02)00031-7; Tomppo EO, 2009, REMOTE SENS ENVIRON, V113, P500, DOI 10.1016/j.rse.2008.05.021; Wagner W, 2003, REMOTE SENS ENVIRON, V85, P125, DOI 10.1016/S0034-4257(02)00198-0; Wulder MA, 2008, SENSORS-BASEL, V8, P529, DOI 10.3390/s8010529; Zharko V.O., 2012, P 10 ANN ALL RUSS OP, P386 52 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 1999-4907 FORESTS Forests JUL 2014 5 7 1753 1776 10.3390/f5071753 24 Forestry Forestry AM6QO WOS:000339989900013 J Jia, JY; Zhang, Q; Zeng, L; Liang, S Jia, Jin-Yuan; Zhang, Qian; Zeng, Long; Liang, Shuang Voxel-encoded descriptor for 3D model retrieval by exploring model's spatial information JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY English Article iGEOMEN; Voxel-encoded descriptor; Model retrieval; Visual similarity; Voxel representation; Shape descriptor VIEWS Retrieving similar products with a given one has attracted considerable attention. However, products are usually assembled by multiple components, frustrating the previous visual-based retrieval descriptors. We design a voxel-encoded descriptor (VED) by exploring models' spatial information, i.e., boundary data and internal data. This descriptor is computed in three steps. First, the posture of a polygonal model is normalized by improved voxel-based principal component analysis technique. Then, six color images are generated by projecting the voxels along its six local axes. The color value of each pixel encodes status of all voxels intersecting with the ray starting from the pixel and parallel to the axis. The status of all voxels along a ray embodies the spatial distribution of the model along this ray. Finally, the VED is computed by applying 2D Fourier transformation to the six color images. With VED, we can distinguish a hollow sphere from a solid sphere. To improve the retrieval efficiency, the database structure is optimized by an improved geometric manifold entropy (iGEOMEN) scheme. VED and iGEOMEN are integrated into a model retrieval system. Experimental results demonstrate that the VED descriptor outperforms the previous visual-based shape descriptors, especially for complex assembly models. [Jia, Jin-Yuan; Zhang, Qian; Liang, Shuang] Tongji Univ, Sch Software Engn, Shanghai 200092, Peoples R China; [Zeng, Long] Hong Kong Univ Sci & Technol, Dept Mech & Aerosp Engn, Hong Kong, Hong Kong, Peoples R China Liang, S (reprint author), Tongji Univ, Sch Software Engn, Shanghai 200092, Peoples R China. shuangliang@tongji.edu.cn National Science Foundation of China [61272276, 61305091]; National Twelfth Five-year Plan Major Science and Technology Project of China [2012BAC11B01-04-03]; Special Research Fund of Higher College's Doctorate [20130072110035]; Key Science and Technology Project of Jilin [20140204088GX]; Fundamental Research Funds for the Central Universities [2100219038]; Shanghai Pujiang Program [13PJ1408200]; Changbai Valley Talent Plan [3-2013006] This research work was supported by The National Science Foundation of China (No. 61272276, No. 61305091), the National Twelfth Five-year Plan Major Science and Technology Project of China (No. 2012BAC11B01-04-03), Special Research Fund of Higher College's Doctorate (No. 20130072110035), Key Science and Technology Project of Jilin (No. 20140204088GX), the Fundamental Research Funds for the Central Universities (No. 2100219038), Shanghai Pujiang Program (No. 13PJ1408200), and Changbai Valley Talent Plan (No. 3-2013006). Ankerst M., 1999, P 7 INT C INT SYST M; Ansary TF, 2005, LECT NOTES COMPUT SC, V3687, P473; Chaouch M., 2006, SMI 06, P36; Chaouch M., 2007, IM PROC 2007 ICIP 20, pVI; Chen DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669; Daras P, 2006, IEEE T MULTIMEDIA, V8, P101, DOI 10.1109/TMM.2005.861287; Daras P, 2010, INT J COMPUT VISION, V89, P229, DOI 10.1007/s11263-009-0277-2; Eisemann E., 2006, P 2006 S INT 3D GRAP; Kim SI, 2006, J MECH SCI TECHNOL, V20, P2034, DOI 10.1007/BF02916320; Liu Y, 2012, LANGMUIR, V28, P3, DOI 10.1021/la2032303; Liu YJ, 2013, IEEE T AUTOM SCI ENG, V10, P783, DOI 10.1109/TASE.2012.2228481; Liu YJ, 2011, IEEE T PATTERN ANAL, V33, P1502, DOI 10.1109/TPAMI.2010.221; Mahmoudi S, 2002, INT C PATT RECOG, P457; Nooruddin FS, 2003, IEEE T VIS COMPUT GR, V9, P191, DOI 10.1109/TVCG.2003.1196006; Papadakis P, 2010, INT J COMPUT VISION, V89, P177, DOI 10.1007/s11263-009-0281-6; Papadakis P, 2007, PATTERN RECOGN, V40, P2437, DOI 10.1016/j.patcog.2006.12.026; Shih JL, 2009, MULTIMED TOOLS APPL, V43, P45, DOI 10.1007/s11042-008-0256-6; Shih JL, 2007, PATTERN RECOGN, V40, P283, DOI 10.1016/j.patcog.2006.04.034; Shilane P, 2004, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON SHAPE MODELING AND APPLICATIONS, P167, DOI 10.1109/SMI.2004.1314504; Tangelder JH, 2008, MULTIMED TOOLS APPL, V39, P441, DOI 10.1007/s11042-007-0181-0; Vranic D. V., 2000, P SPRING C COMP GRAP, P89; Vranic D. V., 2005, 2005 IEEE INT C MULT; Zeng L, 2011, COMPUT AIDED DESIGN, V43, P1577, DOI 10.1016/j.cad.2011.06.007; Zhang C., 2009, P 21 INT JONT C ART; Zhang D., 2003, J CONSTRUCTION MANAG, P41; Zhang Y, 2013, J MECH SCI TECHNOL, V27, P3215, DOI 10.1007/s12206-013-0844-x 26 0 0 KOREAN SOC MECHANICAL ENGINEERS SEOUL KSTC NEW BLD. 7TH FLOOR, 635-4 YEOKSAM-DONG KANGNAM-KU, SEOUL 135-703, SOUTH KOREA 1738-494X 1976-3824 J MECH SCI TECHNOL J. Mech. Sci. Technol. JUL 2014 28 7 2459 2467 10.1007/s12206-014-0603-7 9 Engineering, Mechanical Engineering AM4MN WOS:000339828800003 J Cavalcanti, YC; Neto, PADS; Machado, ID; Vale, TF; de Almeida, ES; Meira, SRD Cavalcanti, Yguarata Cerqueira; da Mota Silveira Neto, Paulo Anselmo; Machado, Ivan do Carmo; Vale, Tassio Ferreira; de Almeida, Eduardo Santana; de Lemos Meira, Silvio Romero Challenges and opportunities for software change request repositories: a systematic mapping study JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS English Article software maintenance; software evolution; software quality assurance; change request repository; bug report; bug tracking BUG REPORTS; DEFECT REPORTS; TIME ANALYSIS; EVOLUTION; PRIORITIZATION; PREDICTION; SEVERITY; ACCURATE; TRIAGE Software maintenance starts as soon as the first artifacts are delivered and is essential for the success of the software. However, keeping maintenance activities and their related artifacts on track comes at a high cost. In this respect, change request (CR) repositories are fundamental in software maintenance. They facilitate the management of CRs and are also the central point to coordinate activities and communication among stakeholders. However, the benefits of CR repositories do not come without issues, and commonly occurring ones should be dealt with, such as the following: duplicate CRs, the large number of CRs to assign, or poorly described CRs. Such issues have led researchers to an increased interest in investigating CR repositories, by considering different aspects of software development and CR management. In this paper, we performed a systematic mapping study to characterize this research field. We analyzed 142 studies, which we classified in two ways. First, we classified the studies into different topics and grouped them into two dimensions: challenges and opportunities. Second, the challenge topics were classified in accordance with an existing taxonomy for information retrieval models. In addition, we investigated tools and services for CR management, to understand whether and how they addressed the topics identified. Copyright (C) 2013 John Wiley & Sons, Ltd. [Cavalcanti, Yguarata Cerqueira; da Mota Silveira Neto, Paulo Anselmo; de Lemos Meira, Silvio Romero] Fed Univ Pernambuco CIn UFPE, Ctr Informat, Recife, PE, Brazil; [Machado, Ivan do Carmo; Vale, Tassio Ferreira; de Almeida, Eduardo Santana] Fed Univ Bahia DCC UFBA, Dept Comp Sci, Salvador, BA, Brazil; [Cavalcanti, Yguarata Cerqueira; da Mota Silveira Neto, Paulo Anselmo; Machado, Ivan do Carmo; Vale, Tassio Ferreira; de Almeida, Eduardo Santana; de Lemos Meira, Silvio Romero] Reuse Software Engn Grp RiSE, Recife, PE, Brazil; [Cavalcanti, Yguarata Cerqueira] Brazilian Fed Org Data Proc SERPRO, Florianopolis, SC, Brazil Cavalcanti, YC (reprint author), Fed Univ Pernambuco CIn UFPE, Ctr Informat, Recife, PE, Brazil. yguarata@gmail.com; esa@dcc.ufba.br Brazilian Federal Organization for Data Processing (SERPRO); National Institute of Science and Technology for Software Engineering (INES) - CNPq [305968/2010-6, 559997/2010-8, 474766/2010-1]; FACEPE [573964/2008-4, APQ-1037-1.03/08]; FAPESB Thanks to the Brazilian Federal Organization for Data Processing (SERPROdagger) for the support on the execution of this research and to the JSEP's reviewers for the valuable feedback. This work was partially supported by the National Institute of Science and Technology for Software Engineering (INESdouble dagger), funded by CNPq grants 305968/2010-6, 559997/2010-8, and 474766/2010-1; FACEPE grants 573964/2008-4 and APQ-1037-1.03/08; and FAPESB. Abdelmoez W, 2012, 2012 22ND INTERNATIONAL CONFERENCE ON COMPUTER THEORY AND APPLICATIONS (ICCTA), P167, DOI 10.1109/ICCTA.2012.6523564; Ahsan SN, 2009, 2009 FOURTH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING ADVANCES (ICSEA 2009), P216, DOI 10.1109/ICSEA.2009.92; Ahsan SN, 2010, P 4 INT S EMP SOFTW; Aljarah I, 2011, P 7 INT C PRED MOD S; Anbalagan P, 2009, PROC IEEE INT CONF S, P523, DOI 10.1109/ICSM.2009.5306337; Antoniol G, 2008, P 2008 C CTR ADV STU; Antoniol G., 2000, Proceedings of the Fourth European Conference on Software Maintenance and Reengineering, DOI 10.1109/CSMR.2000.827331; Anvik J, 2007, P 4 INT WORKSH MIN S; Anvik J, 2006, P 28 INT C SOFTW ENG, V2006, P361; Anvik J, 2005, P OOPSLA WORKSH ECL, P35, DOI DOI 10.1145/1117696.1117704; Ayari K, 2007, P 2007 C CTR ADV STU, P215, DOI 10.1145/1321211.1321234; Bangcharoensap P, 2012, P 4 INT WORKSH EMP S, P10; Bertram D, 2010, 2010 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, P291; Bertram D., 2009, THESIS U CALGARY CAL; Bettenburg N, 2008, P 16 ACM SIGSOFT INT, V308-318, DOI 10.1145/1453101.1453146; Bettenburg N, 2008, PROC IEEE INT CONF S, P337, DOI 10.1109/ICSM.2008.4658082; Bettenburg N, 2008, P 2008 INT WORK C MI, V27-30, DOI DOI 10.1145/1370750.1370757; Bhattacharya P, 2012, J SYST SOFTWARE, V85, P2275, DOI 10.1016/j.jss.2012.04.053; Bhattacharya P, 2011, P 8 WORK C MIN SOFTW, P207; Bird C, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P121, DOI 10.1145/1595696.1595716; Bougie Gargi, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), DOI 10.1109/MSR.2010.5463291; Breu S, 2010, 2010 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, P301; Burch E., 1997, Proceedings International Conference on Software Maintenance (Cat. No.97CB36119), DOI 10.1109/ICSM.1997.624229; Caglayan B, 2012, ACM SIGSOFT SOFTWARE, V37, P1; Canfora G, 2005, P 11 IEEE INT SOFTW; Canfora G, 2006, P INT WORKSH MIN SOF, P105, DOI 10.1145/1137983.1138009; Canfora G., 2004, Journal of Computing and Information Technology - CIT, V12, DOI 10.2498/cit.2004.03.01; Canfora G, 2006, P ACM S APPL COMP SA, V2, P1767; Cavalcanti YC, 2013, P 17 INT C EV ASS SO; Cavalcanti YC, 2009, THESIS U FEDERAL PER; Cavalcanti YC, 2011, SOFTWARE QUALITY J; Chaturvedi K, 2012, P CSI 6 INT C SOFTW, P1; Chen IX, 2010, IEICE T INF SYST, VE93D, P1154, DOI 10.1587/transinf.E93.D.1154; Chen I-X, 2008, P 32 ANN IEEE INT C, P136; Chen L, 2011, JSW J SOFTWARE, V6, P421; Crowston K, 2004, P 1 INT WORKSH COMP, P21; Cubranic D, 2004, P 16 INT C SOFTW ENG, P92; da Cunha Carlos Eduardo Albuquerque, 2010, Proceedings 22nd International Conference on Software Engineering & Knowledge Engineering (SEKE 2010); Davidson JL, 2011, S VIS LANG HUM CEN C, P101, DOI 10.1109/VLHCC.2011.6070386; Davies Julius, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), DOI 10.1109/MSR.2010.5463288; Eastwood A, 1993, COMPUTING CANADA, V19, P17; Edwards HK, 2007, IAENG INT J COMPUTER, V33, P12; Ekanayake J, 2012, EMPIR SOFTW ENG, V17, P348, DOI 10.1007/s10664-011-9180-x; Elcock A, 2006, INNOVATIONS SYSTEMS, V2, P137, DOI 10.1007/s11334-006-0009-5; Erlikh L., 2000, IT Professional, V2, DOI 10.1109/6294.846201; Gegick Michael, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), DOI 10.1109/MSR.2010.5463340; Gethers M, 2012, P 34 IEEE ACM INT C, P2; Goulao M, 2012, EUR CON SFTWR MTNCE, P213, DOI 10.1109/CSMR.2012.30; Guo P, 2011, P ACM C COMP SUPP CO, P395; Guo PJ, 2010, P 32 ACM IEEE INT C, V1, P495, DOI 10.1145/1806799.1806871; Gupta A, 2006, P INT C SOFTW ENG AD; Hassouna A, 2010, INFORM SOFTWARE TECH, V52, P197, DOI 10.1016/j.infsof.2009.10.003; He Z, 2010, P 10 INT C QUAL SOFT, P220; Herraiz I, 2008, P 2008 INT WORK C MI, P145, DOI 10.1145/1370750.1370786; Herraiz I, 2009, PROC IEEE INT CONF S, P439, DOI 10.1109/ICSM.2009.5306299; Herzig K, 2012, TECHNICAL REPORT; Herzig K, 2012, ITS NOT BUG ITS FEAT; Hewett R, 2009, EMPIR SOFTW ENG, V14, P165, DOI 10.1007/s10664-008-9064-x; Hewett R, 2006, P 18 INT C SOFTW ENG, P499; Hiew L., 2006, THESIS U BRIT COLUMB; Hooimeijer P, 2007, P 22 IEEE ACM INT C, P34, DOI 10.1145/1321631.1321639; Hosseini H, 2012, EUR CON SFTWR MTNCE, P149, DOI 10.1109/CSMR.2012.25; Huff F, 1990, BUSINESS Q, V1, P30; IEEE Computer Society, 2004, SOFTW ENG BOD KNOWL; Ihara A, 2009, IWPSE-EVOL 09: ERCIM WORKSHOP ON SOFTWARE EVOLUTION (EVOL) AND INTERNATIONAL WORKSHOP ON PRINCIPLES OF SOFTWARE EVOLUTION (IWPSE), P135; Jain V, 2012, IEEE SYS MAN CYBERN, P2845; Jalbert N, 2008, I C DEPEND SYS NETWO, P52, DOI 10.1109/DSN.2008.4630070; Jeong G, 2009, 7TH JOINT MEETING OF THE EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND THE ACM SIGSOFT SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, P111, DOI 10.1145/1595696.1595715; Jung HW, 2004, J SYST SOFTWARE, V72, P235, DOI 10.1016/S0164-1212(03)00091-8; Kadar C, 2011, P 2011 ANN SRII GLOB, P430; Kagdi H, 2007, J SOFTW MAINT EVOL-R, V19, P77, DOI 10.1002/smr.344; Kagdi H, 2012, EMPIR SOFTW ENG, V18, P933; Kagdi H, 2012, J SOFTW-EVOL PROC, V24, P3, DOI 10.1002/smr.530; Kanwal J, 2012, J COMPUT SCI TECH-CH, V27, P397, DOI 10.1007/s11390-012-1230-3; Kaushik N, 2012, EUR CON SFTWR MTNCE, P159, DOI 10.1109/CSMR.2012.78; Kenmei B, 2008, CSMR 2008: 12TH EUROPEAN CONFERENCE ON SOFTWARE MAINTENANCE AND REENGINEERING, P73; Kim S, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P481; Kim S, 2006, P 2006 INT WORKSH MI, P173, DOI 10.1145/1137983.1138027; Knab P, 2009, P IEEE INT C SOFTW M; Ko AJ, 2006, P 2006 IEEE S VIS LA, P127, DOI DOI 10.1109/VLHCC.2006.3; Kumar Nagwani N., 2012, J SOFTWARE ENG APPL, V05, P436; Lamkanfi A, 2011, EUR CON SFTWR MTNCE, P249, DOI 10.1109/CSMR.2011.31; Lamkanfi Ahmed, 2010, Proceedings of the 2010 7th IEEE Working Conference on Mining Software Repositories (MSR 2010), DOI 10.1109/MSR.2010.5463284; Lamkanfi A, 2012, EUR CON SFTWR MTNCE, P379, DOI 10.1109/CSMR.2012.47; Laukkanen E. I., 2011, Proceedings of the 2011 5th International Symposium on Empirical Software Engineering and Measurement (ESEM 2011), DOI 10.1109/ESEM.2011.28; Lin Z, 2009, P 2009 3 INT S EMP S, P451; Linares-Vasquez M, 2012, P IEEE INT C SOFTW M; Linstead E, 2009, 2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, P99, DOI 10.1109/MSR.2009.5069486; Liu KJ, 2012, PROCEEDINGS OF THE ASME 10TH INTERNATIONAL CONFERENCE ON NANOCHANNELS, MICROCHANNELS AND MINICHANNELS 2012, P1; Lucca GAD, 2002, P 18 IEEE INT C SOFT; Matter D, 2009, P IEEE WORK C MIN SO, P131; Menzies T, 2008, PROC IEEE INT CONF S, P346, DOI 10.1109/ICSM.2008.4658083; Menzies T, 2012, PROMISE REPOSITORY E; Moad J, 1990, DATAMATION, V4, P61; Moin A, 2012, P 7 INT C SOFTW ENG; Moritz E, 2009, PROC INT CONF SOFTW, P123, DOI 10.1109/ICSE-COMPANION.2009.5070970; Nagwani N, 2012, P 2011 9 INT C ICT K, P113; Nagwani NK, 2009, P INT C ADV COMP COM, P202, DOI 10.1145/1523103.1523145; Nasim S., 2011, Proceedings of the 2011 Frontiers of Information Technology (FIT 2011), DOI 10.1109/FIT.2011.62; Nguyen A, 2011, P 26 IEEE ACM INT C, P263; Nguyen AT, 2012, P ACM SIGSOFT 20 INT; Nie C, 2010, P IEEE INT C SERV OP, P375, DOI 10.1109/SOLI.2010.5551550; Ohira M, 2012, P 28 IEEE INT C SOFT; Ostrand TJ, 2004, P INT WORKSH MIN SOF, V4, P85; Panjer LD, 2007, P INT WORKSH MIN SOF; Petersen K, 2008, EASE 08 P 12 INT C E; Pigoski TM, 1996, PRACTICAL SOFTWARE M; Prifti T, 2011, P 7 INT C PRED MOD S; Radlinski L., 2009, POLISH J ENV STUDIES, V18, P311; Rahman MM, 2009, P 2009 3 INT S EMP S, P439; Raja U, 2009, J SOFTW MAINT EVOL-R, V21, P49, DOI 10.1002/smr.398; Raja U, 2013, EMPIR SOFTW ENG, V18, P117, DOI 10.1007/s10664-012-9197-9; Rastkar S, 2010, P 32 ACM IEEE INT C, V505-514; Revell M., 2010, FEELING HEAT; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Rosso CD, 2009, J SOFTWARE MAINTENAN, V21, P189; Runeson P, 2007, PROC INT CONF SOFTW, P499; Rus Vasile, 2009, Proceedings 21st International Conference on Software Engineering & Knowledge Engineering (SEKE 2009); Sandusky RJ, 2005, P INT ACM SIGGROUP C, P187, DOI 10.1145/1099203.1099238; Sandusky RJS, 2004, P 1 INT WORKSH MIN S; Santana A, 2012, LECT NOTES COMPUTER, V7667, P592; Schugerl P, 2008, 2008 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE FOR MODELLING CONTROL & AUTOMATION, VOLS 1 AND 2, P1105, DOI 10.1109/CIMCA.2008.63; Schugerl P, 2008, P 4 INT WORKSH SEM W; Somasundaram K, 2012, PROCEEDINGS OF THE 5, P125; SOMMERVILLE I., 2007, SOFTWARE ENG; Song QB, 2006, IEEE T SOFTWARE ENG, V32, P69; Stojanov Z., 2011, Proceedings of the 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics (SISY 2011), DOI 10.1109/SISY.2011.6034369; Sun C, 2011, P ASE NOV, P253; Sun C, 2010, P 32 ACM IEEE INT C, V1, P45, DOI 10.1145/1806799.1806811; Sun J., 2011, P IEEE 4 INT C SOFTW, P407; Sureka A, 2010, P 17 AS PAC SOFTW EN; Sureka A, 2010, P IEEE INF THEOR WOR, P1, DOI 10.1145/1754288.1754297; Tamrawi A, 2011, P 19 ACM SIGSOFT S F, P365; Tan S, 2010, P 5 ANN CHINAGRID C, P281; Tian Y, 2012, EUR CON SFTWR MTNCE, P385, DOI 10.1109/CSMR.2012.48; Torchiano M, 2010, P 4 INT S EMP SOFTW; Valetto G, 2007, P INT WORKSH MIN SOF; Wang D, 2011, P INT S EMP SOFTW EN, P434; Wang Deqing, 2012, J SOFTW, V7, P1149; WANG XY, 2008, ICS P 30 INT C, P461; Wedel M, 2008, ESEM'08: PROCEEDINGS OF THE 2008 ACM-IEEE INTERNATIONAL SYMPOSIUM ON EMPIRICAL SOFTWARE ENGINEERING AND MEASUREMENT, P282; Weiss C, 2007, P INT WORKSH MIN SOF; Wu L, 2011, P 25 INT C SOFTW ENG, P95; Xuan J, 2012, INT J ADV COMPUTING, V4, P453; Xuan J, 2010, P 22 INT C SOFTW ENG; Xuan JF, 2012, PROC INT CONF SOFTW, P25; Yu L, 2010, P INT C COMP DES APP, V2, P93; Zhang CL, 2008, ADVANCES IN COMPUTER AND INFORMATIOM SCIENCES AND ENGINEERING, P108, DOI 10.1007/978-1-4020-8741-7_20; Zhang T, 2011, P 11 IEEE INT C COMP, P336; Zhang T, 2012, P 36 ANN INT COMP SO; Zhou J, 2012, PROC INT CONF SOFTW, P14; Zhou J, 2012, P 21 ACM INT C INF K; Zimmermann T, 2012, PROC INT CONF SOFTW, P1074, DOI 10.1109/ICSE.2012.6227112; Zimmermann T, 2010, IEEE T SOFTWARE ENG, V36, P618, DOI 10.1109/TSE.2010.63; Zou WQ, 2011, P INT COMP SOFTW APP, P576, DOI 10.1109/COMPSAC.2011.80 155 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2047-7473 2047-7481 J SOFTW-EVOL PROC J. Softw.-Evol. Proc. JUL 2014 26 7 620 653 10.1002/smr.1639 34 Computer Science, Software Engineering Computer Science AM2DW WOS:000339660300003 J Sulakhe, D; Taylor, A; Balasubramanian, S; Feng, B; Xie, BQ; Bornigen, D; Dave, UJ; Foster, IT; Gilliam, TC; Maltsev, N Sulakhe, Dinanath; Taylor, Andrew; Balasubramanian, Sandhya; Feng, Bo; Xie, Bingqing; Bornigen, Daniela; Dave, Utpal J.; Foster, Ian T.; Gilliam, T. Conrad; Maltsev, Natalia Lynx web services for annotations and systems analysis of multi-gene disorders NUCLEIC ACIDS RESEARCH English Article PROTEIN-INTERACTION NETWORKS; INTERACTION DATABASE; GENE PRIORITIZATION; KNOWLEDGE; NEUROGENETICS; TRANSCRIPTOME; INTEGRATION; INFORMATION; MEDICINE; RESOURCE Lynx is a web-based integrated systems biology platform that supports annotation and analysis of experimental data and generation of weighted hypotheses on molecular mechanisms contributing to human phenotypes and disorders of interest. Lynx has integrated multiple classes of biomedical data (genomic, proteomic, pathways, phenotypic, toxicogenomic, contextual and others) from various public databases as well as manually curated data from our group and collaborators (LynxKB). Lynx provides tools for gene list enrichment analysis using multiple functional annotations and network-based gene prioritization. Lynx provides access to the integrated database and the analytical tools via REST based Web Services (http://lynx.ci.uchicago.edu/webservices.html). This comprises data retrieval services for specific functional annotations, services to search across the complete LynxKB (powered by Lucene), and services to access the analytical tools built within the Lynx platform. [Sulakhe, Dinanath; Dave, Utpal J.; Foster, Ian T.; Gilliam, T. Conrad; Maltsev, Natalia] Univ Chicago, Computat Inst, Argonne Natl Lab, Chicago, IL 60637 USA; [Taylor, Andrew; Balasubramanian, Sandhya; Bornigen, Daniela; Gilliam, T. Conrad; Maltsev, Natalia] Univ Chicago, Dept Human Genet, Chicago, IL 60637 USA; [Feng, Bo; Xie, Bingqing] IIT, Dept Comp Sci, Chicago, IL 60616 USA; [Bornigen, Daniela] Toyota Technol Inst Chicago, Chicago, IL 60637 USA Sulakhe, D (reprint author), Univ Chicago, Computat Inst, Argonne Natl Lab, Chicago, IL 60637 USA. sulakhe@mcs.anl.gov Boler Family Foundation and National Institutes of Health/National Institute of Neurological Disorders and Stroke [NS050375]; Genetic Basis of Mid-Hindbrain Malformations; National Institute of Mental Health [1U24MH081810]; National Institutes of Health/National Institute of Neurological Disorders and Stroke [NS050375] Mr and Mrs Lawrence Hilibrand, Boler Family Foundation and National Institutes of Health/National Institute of Neurological Disorders and Stroke [NS050375]; Genetic Basis of Mid-Hindbrain Malformations; National Institute of Mental Health [1U24MH081810 to Clara M. Lajonchere ( PI)]. Funding for open access charge: National Institutes of Health/National Institute of Neurological Disorders and Stroke [NS050375]. [Anonymous], 2014, NUCL ACIDS RES, V42, pD7; Bastian F, 2008, LECT N BIOINFORMAT, V5109, P124, DOI 10.1007/978-3-540-69828-9_12; Basu SN, 2009, NUCLEIC ACIDS RES, V37, pD832, DOI 10.1093/nar/gkn835; Cerami EG, 2011, NUCLEIC ACIDS RES, V39, pD685, DOI 10.1093/nar/gkq1039; Chen J, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-73; Croft D, 2011, NUCLEIC ACIDS RES, V39, pD691, DOI 10.1093/nar/gkq1018; Flicek P, 2013, NUCLEIC ACIDS RES, V41, pD48, DOI 10.1093/nar/gks1236; Franceschini A, 2013, NUCLEIC ACIDS RES, V41, pD808, DOI 10.1093/nar/gks1094; Gotea V, 2010, GENOME RES, V20, P565, DOI 10.1101/gr.104471.109; Hawrylycz MJ, 2012, NATURE, V489, P391, DOI 10.1038/nature11405; Jia P, 2010, MOL PSYCHIATR, V15, P453, DOI 10.1038/mp.2009.93; Johnson R., 2005, PROFESSIONAL JAVA DE; Kanehisa M, 2014, NUCLEIC ACIDS RES, V42, pD199, DOI 10.1093/nar/gkt1076; Kawaguchi K., 2006, JAVA ARCHITECTURE XM; Kohler S, 2012, HUM MUTAT, V33, P1333, DOI 10.1002/humu.22112; Licata L, 2012, NUCLEIC ACIDS RES, V40, pD857, DOI 10.1093/nar/gkr930; Matys V, 2003, NUCLEIC ACIDS RES, V31, P374, DOI 10.1093/nar/gkg108; Mirzaa GM, 2014, AM J MED GENET A, V164, P1503, DOI 10.1002/ajmg.a.36517; Nitsch D, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-460; Nitsch D, 2011, NUCLEIC ACIDS RES, V39, pW334, DOI 10.1093/nar/gkr289; Page L., 1999, TECHNICAL REPORT; Potociar M., 2009, TECHNICAL REPORT; Pruitt KD, 2014, NUCLEIC ACIDS RES, V42, pD756, DOI 10.1093/nar/gkt1114; Rzhetsky A, 2004, J BIOMED INFORM, V37, P43, DOI 10.1016/j.jbi.2003.10.001; SAAD Y, 1992, SIAM J NUMER ANAL, V29, P209, DOI 10.1137/0729014; Schaefer CF, 2009, NUCLEIC ACIDS RES, V37, pD674, DOI 10.1093/nar/gkn653; Schriml LM, 2012, NUCLEIC ACIDS RES, V40, pD940, DOI 10.1093/nar/gkr972; Sulakhe D, 2014, ADV EXP MED BIOL, V799, P39, DOI 10.1007/978-1-4614-8778-4_3; Sulakhe D, 2014, NUCLEIC ACIDS RES, V42, pD1007, DOI 10.1093/nar/gkt1166; Visel A, 2007, NUCLEIC ACIDS RES, V35, pD88, DOI 10.1093/nar/gkl822; Xie B., 2012, P ACM C BIOINF COMP, P564 31 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0305-1048 1362-4962 NUCLEIC ACIDS RES Nucleic Acids Res. JUL 1 2014 42 W1 W473 W477 10.1093/nar/gku517 5 Biochemistry & Molecular Biology Biochemistry & Molecular Biology AM2XN WOS:000339715000077 J Poch, C; Campo, P; Barnes, GR Poch, Claudia; Campo, Pablo; Barnes, Gareth R. Modulation of alpha and gamma oscillations related to retrospectively orienting attention within working memory EUROPEAN JOURNAL OF NEUROSCIENCE English Article attention; magnetoencephalography; oscillatory activity; retro-cues; working memory SHORT-TERM-MEMORY; EVENT-RELATED POTENTIALS; SPATIAL SELECTIVE ATTENTION; NEURAL MEASURES REVEAL; TOP-DOWN MODULATION; VISUAL-ATTENTION; NEURONAL SYNCHRONIZATION; VISUOSPATIAL ATTENTION; MENTAL REPRESENTATIONS; BAND SYNCHRONIZATION Selective attention mechanisms allow us to focus on information that is relevant to the current behavior and, equally important, ignore irrelevant information. An influential model proposes that oscillatory neural activity in the alpha band serves as an active functional inhibitory mechanism. Recent studies have shown that, in the same way that attention can be selectively oriented to bias sensory processing in favor of relevant stimuli in perceptual tasks, it is also possible to retrospectively orient attention to internal representations held in working memory. However, these studies have not explored the associated oscillatory phenomena. In the current study, we analysed the patterns of neural oscillatory activity recorded with magnetoencephalography while participants performed a change detection task, in which a spatial retro-cue was presented during the maintenance period, indicating which item or items were relevant for subsequent retrieval. Participants benefited from retro-cues in terms of accuracy and reaction time. Retro-cues also modulated oscillatory activity in the alpha and gamma frequency bands. We observed greater alpha activity in a ventral visual region ipsilateral to the attended hemifield, thus supporting its suppressive role, i.e. a functional disengagement of task-irrelevant regions. Accompanying this modulation, we found an increase in gamma activity contralateral to the attended hemifield, which could reflect attentional orienting and selective processing. These findings suggest that the oscillatory mechanisms underlying attentional orienting to representations held in working memory are similar to those engaged when attention is oriented in the perceptual space. [Poch, Claudia] Univ Autonoma Madrid, Dept Psicol Biol & Salud, E-28049 Madrid, Spain; [Campo, Pablo] Univ Autonoma Madrid, Dept Basic Psychol, E-28049 Madrid, Spain; [Barnes, Gareth R.] UCL, Wellcome Trust Ctr Neuroimaging, London, England Poch, C (reprint author), Univ Autonoma Madrid, Dept Psicol Biol & Salud, Campus Cantoblanco, E-28049 Madrid, Spain. claudia.poch@uam.es Campo, Pablo/I-8156-2012 Spanish Ministry of Science and Innovation [PSI2010-16742]; Wellcome Trust [091593/Z/10/Z]; MRC UK MEG Partnership Grant [MR/K005464/1]; Ramon y Cajal Fellowship from the Spanish Ministry of Science and Innovation [RYC-2010-05748]; Spanish Ministry of Science and Education [AP2009-4131]; Wellcome Trust This work was supported by a research grant from the Spanish Ministry of Science and Innovation (PSI2010-16742) to P. Campo, by the Wellcome Trust (grant number 091593/Z/10/Z), and by an MRC UK MEG Partnership Grant (MR/K005464/1). P. Campo was supported by a Ramon y Cajal Fellowship from the Spanish Ministry of Science and Innovation (RYC-2010-05748). C. Poch was supported by the Spanish Ministry of Science and Education (AP2009-4131). G. R. Barnes is supported by the Wellcome Trust. Abdi H., 2007, ENCY MEASUREMENT STA, V99, P886; Andersen SK, 2011, J COGNITIVE NEUROSCI, V23, P238, DOI 10.1162/jocn.2009.21328; Anderson DE, 2013, J NEUROSCI, V33, P9273, DOI 10.1523/JNEUROSCI.0239-13.2013; Anton-Erxleben K, 2013, NAT REV NEUROSCI, V14, P188, DOI 10.1038/nrn3443; Asplund CL, 2010, NAT NEUROSCI, V13, P507, DOI 10.1038/nn.2509; Awh E, 2000, J EXP PSYCHOL HUMAN, V26, P834, DOI 10.1037/0096-1523.26.2.834; Awh E, 2000, J COGNITIVE NEUROSCI, V12, P840, DOI 10.1162/089892900562444; Bauer M, 2012, CURR BIOL, V22, P397, DOI 10.1016/j.cub.2012.01.022; Bauer M, 2012, J NEUROPHYSIOL, V107, P2342, DOI 10.1152/jn.00973.2011; Bays PM, 2008, SCIENCE, V321, P851, DOI 10.1126/science.1158023; Bonnefond Mathilde, 2013, Commun Integr Biol, V6, pe22702, DOI 10.4161/cib.22702; Bosman CA, 2012, NEURON, V75, P875, DOI 10.1016/j.neuron.2012.06.037; Brady TF, 2011, J VISION, V11, DOI 10.1167/11.5.4; Buffalo EA, 2011, P NATL ACAD SCI USA, V108, P11262, DOI 10.1073/pnas.1011284108; Capilla A, 2014, CEREB CORTEX, V24, P550, DOI 10.1093/cercor/bhs343; Capotosto P, 2009, J NEUROSCI, V29, P5863, DOI 10.1523/JNEUROSCI.0539-09.2009; Carrasco M, 2004, NAT NEUROSCI, V7, P308, DOI 10.1038/nn1194; Chun MM, 2007, CURR OPIN NEUROBIOL, V17, P177, DOI 10.1016/j.conb.2007.03.005; Corbetta M, 1998, P NATL ACAD SCI USA, V95, P831, DOI 10.1073/pnas.95.3.831; Cowan N, 2012, PSYCHOL REV, V119, P480, DOI 10.1037/a0027791; Dell'Acqua R, 2010, NEUROPSYCHOLOGIA, V48, P419, DOI 10.1016/j.neuropsychologia.2009.09.033; Doesburg SM, 2008, CEREB CORTEX, V18, P386, DOI 10.1093/cercor/bhm073; DUNCAN J, 1984, J EXP PSYCHOL GEN, V113, P501, DOI 10.1037/0096-3445.113.4.501; Engel AK, 2001, NAT REV NEUROSCI, V2, P704, DOI 10.1038/35094565; Ester EF, 2012, J NEUROSCI, V32, P7169, DOI 10.1523/JNEUROSCI.1218-12.2012; Fan J, 2007, J NEUROSCI, V27, P6197, DOI 10.1523/JNEUROSCI.1833-07.2007; Fougnie D, 2006, PSYCHOL SCI, V17, P526, DOI 10.1111/j.1467-9280.2006.01739.x; Fougnie D, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms2237; Foxe JJ, 1998, NEUROREPORT, V9, P3929, DOI 10.1097/00001756-199812010-00030; Freunberger R, 2008, EUR J NEUROSCI, V27, P2330, DOI 10.1111/j.1460-9568.2008.06190.x; Fries P, 2001, SCIENCE, V291, P1560, DOI 10.1126/science.1055465; Fries P, 2008, J NEUROSCI, V28, P4823, DOI 10.1523/JNEUROSCI.4499-07.2008; Fries P, 2009, ANNU REV NEUROSCI, V32, P209, DOI 10.1146/annurev.neuro.051508.135603; Fukuda K, 2009, J NEUROSCI, V29, P8726, DOI 10.1523/JNEUROSCI.2145-09.2009; Fukuda K, 2010, CURR OPIN NEUROBIOL, V20, P177, DOI 10.1016/j.conb.2010.03.005; Gazzaley A, 2012, TRENDS COGN SCI, V16, P129, DOI 10.1016/j.tics.2011.11.014; Giffrin I.C., 2003, J COGNITIVE NEUROSCI, p[15, 1176]; Gregoriou GG, 2009, SCIENCE, V324, P1207, DOI 10.1126/science.1171402; Grent-'t-Jong T, 2011, CEREB CORTEX, V21, P2204, DOI 10.1093/cercor/bhq279; Haegens S, 2010, HUM BRAIN MAPP, V31, P26, DOI 10.1002/hbm.20842; Handel BF, 2011, J COGNITIVE NEUROSCI, V23, P2494, DOI 10.1162/jocn.2010.21557; Hillyard SA, 1998, P NATL ACAD SCI USA, V95, P781, DOI 10.1073/pnas.95.3.781; Huang J, 2010, J NEUROSCI, V30, P13461, DOI 10.1523/JNEUROSCI.2560-10.2010; Jensen O, 2007, TRENDS NEUROSCI, V30, P317, DOI 10.1016/j.tins.2007.05.001; Jensen O, 2010, FRONT HUM NEUROSCI, V4, DOI 10.3389/fnhum.2010.00186; Jensen O, 2012, TRENDS COGN SCI, V16, P200, DOI 10.1016/j.tics.2012.03.002; Jiang YH, 2001, Q J EXP PSYCHOL-A, V54, P1105, DOI 10.1080/02724980042000516; Jokisch D, 2007, J NEUROSCI, V27, P3244, DOI 10.1523/JNEUROSCI.5399-06.2007; Jost K, 2011, CEREB CORTEX, V21, P1147, DOI 10.1093/cercor/bhq185; Kastner S, 2000, ANNU REV NEUROSCI, V23, P315; Kiebel SJ, 2004, NEUROIMAGE, V22, P492, DOI 10.1016/j.neuroimage.2004.02.013; Kiebel SJ, 2004, NEUROIMAGE, V22, P503, DOI 10.1016/j.neuroimage.2004.02.013; Klimesch W, 2007, BRAIN RES REV, V53, P63, DOI 10.1016/j.brainresrev.2006.06.003; Klimesch W, 2012, TRENDS COGN SCI, V16, P606, DOI 10.1016/j.tics.2012.10.007; Kuo B.C., 2011, J COGNITIVE NEUROSCI, V24, P51; Kuo BC, 2014, J COGNITIVE NEUROSCI, V26, P1377, DOI 10.1162/jocn_a_00577; Landman R, 2003, VISION RES, V43, P149, DOI 10.1016/S0042-6989(02)00402-9; Lepsien J, 2007, CEREB CORTEX, V17, P2072, DOI 10.1093/cercor/bhl116; Lepsien J, 2005, NEUROIMAGE, V26, P733, DOI 10.1016/j.neuroimage.2005.02.026; Lepsien J, 2006, BRAIN RES, V1105, P20, DOI 10.1016/j.brainres.2006.03.033; DASILVA FL, 1991, ELECTROEN CLIN NEURO, V79, P81; Lu ZL, 1998, VISION RES, V38, P1183, DOI 10.1016/S0042-6989(97)00273-3; Luck SJ, 1997, J NEUROPHYSIOL, V77, P24; Makovski T, 2008, J EXP PSYCHOL LEARN, V34, P369, DOI 10.1037/0278-7393.34.2.369; Makovski T, 2007, PSYCHON B REV, V14, P1072, DOI 10.3758/BF03193093; Mallat S., 1998, WAVELET TOUR SIGNAL; Matsukura M, 2007, PERCEPT PSYCHOPHYS, V69, P1422, DOI 10.3758/BF03192957; Mevorach C, 2010, J NEUROSCI, V30, P6072, DOI 10.1523/JNEUROSCI.0241-10.2010; MOTTER BC, 1993, J NEUROPHYSIOL, V70, P909; Munneke J, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0035528; Murray AM, 2013, PSYCHOL SCI, V24, P550, DOI 10.1177/0956797612457782; Nasr S, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0003282; Nobre A. C., 2008, FRONT HUM NEUROSCI, V1, P1, DOI [10.3389/neuro.09.004.2007, DOI 10.3389/NEURO.09.004.2007]; Nolte G, 2003, PHYS MED BIOL, V48, P3637, DOI 10.1088/0031-9155/48/22/002; Oberauer K, 2002, J EXP PSYCHOL LEARN, V28, P411, DOI 10.1037//0278-7393.28.3.411; OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4; Pashler H, 2002, STEVENS HDB EXPT PSY, P235; Peters JC, 2012, J NEUROSCI, V32, P17003, DOI 10.1523/JNEUROSCI.0591-12.2012; Poch C, 2010, NEUROPSYCHOLOGIA, V48, P3846, DOI 10.1016/j.neuropsychologia.2010.09.015; Posner M.I., 1980, Q J EXPT PSYCHOL, V32, P2, DOI DOI 10.1080/00335558008248231; Rerko L, 2014, MEM COGNITION, V42, P712, DOI 10.3758/s13421-013-0392-8; Rihs TA, 2009, NEUROIMAGE, V44, P190, DOI 10.1016/j.neuroimage.2008.08.022; Rutman AM, 2010, J COGNITIVE NEUROSCI, V22, P1224, DOI 10.1162/jocn.2009.21257; Sauseng P, 2009, CURR BIOL, V19, P1846, DOI 10.1016/j.cub.2009.08.062; Schroeder CE, 2009, TRENDS NEUROSCI, V32, P9, DOI 10.1016/j.tins.2008.09.012; Sekihara K, 2004, IEEE T BIO-MED ENG, V51, P1726, DOI 10.1109/TBME.2004.827926; Siegel M, 2008, NEURON, V60, P709, DOI 10.1016/j.neuron.2008.09.010; Sligte IG, 2009, J NEUROSCI, V29, P7432, DOI 10.1523/JNEUROSCI.0784-09.2009; Sligte IG, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0001699; Snyder AC, 2010, J NEUROSCI, V30, P4024, DOI 10.1523/JNEUROSCI.5684-09.2010; Tallon-Baudry C, 2009, FRONT BIOSCI, V14, P321, DOI 10.2741/3246; Taulu S, 2004, BRAIN TOPOGR, V16, P269; Taylor K, 2005, CEREB CORTEX, V15, P1424, DOI 10.1093/cercor/bhi023; Thut G, 2006, J NEUROSCI, V26, P9494, DOI 10.1523/JNEUROSCI.0875-06.2006; Tiesinga PH, 2009, NEURAL NETWORKS, V22, P1039, DOI 10.1016/j.neunet.2009.07.010; van Dijk H, 2010, P NATL ACAD SCI USA, V107, P900, DOI 10.1073/pnas.0908821107; van Ede F, 2011, J NEUROSCI, V31, P2016, DOI 10.1523/JNEUROSCI.5630-10.2011; Vogel EK, 2005, NATURE, V438, P500, DOI 10.1038/nature04171; Womelsdorf T, 2006, NATURE, V439, P733, DOI 10.1038/nature04258; Womelsdorf T, 2007, CURR OPIN NEUROBIOL, V17, P154, DOI 10.1016/j.conb.2007.02.002; Worden MS, 2000, J NEUROSCI, V20, part. no.; Worsley KJ, 1996, HUM BRAIN MAPP, V4, P58, DOI 10.1002/(SICI)1097-0193(1996)4:1<58::AID-HBM4>3.0.CO;2-O; Zanto TP, 2011, NAT NEUROSCI, V14, P656, DOI 10.1038/nn.2773 103 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0953-816X 1460-9568 EUR J NEUROSCI Eur. J. Neurosci. JUL 2014 40 2 2399 2405 10.1111/ejn.12589 7 Neurosciences Neurosciences & Neurology AM2YA WOS:000339716300008 J Grunenwald, A; Keyser, C; Sautereau, AM; Crubezy, E; Ludes, B; Drouet, C Grunenwald, A.; Keyser, C.; Sautereau, A. M.; Crubezy, E.; Ludes, B.; Drouet, C. Novel contribution on the diagenetic physicochemical features of bone and teeth minerals, as substrates for ancient DNA typing ANALYTICAL AND BIOANALYTICAL CHEMISTRY English Article Bone; Teeth; Diagenesis; Ancient DNA; Apatite; Carbonate content; FTIR; Crystallinity TRANSFORM INFRARED-SPECTROSCOPY; CALCIUM-PHOSPHATE; BIOMIMETIC APATITES; MITOCHONDRIAL-DNA; EARLY DEPOSITS; FOSSIL BONES; SOLID-PHASE; PO4 DOMAIN; PRESERVATION; EVOLUTION The extraction of DNA from skeletal remains is a major step in archeological or forensic contexts. However, diagenesis of mineralized tissues often compromises this task although bones and teeth may represent preservation niches allowing DNA to persist over a wide timescale. This exceptional persistence is not only explained on the basis of complex organo-mineral interactions through DNA adsorption on apatite crystals composing the mineral part of bones and teeth but is also linked to environmental factors such as low temperatures and/or a dry environment. The preservation of the apatite phase itself, as an adsorption substrate, is another crucial factor susceptible to significantly impact the retrieval of DNA. With the view to bring physicochemical evidence of the preservation or alteration of diagenetic biominerals, we developed here an analytical approach on various skeletal specimens (ranging from ancient archeological samples to recent forensic specimens), allowing us to highlight several diagenetic indices so as to better apprehend the complexity of bone diagenesis. Based on complementary techniques (X-ray diffraction (XRD), Fourier transform infrared (FTIR), calcium and phosphate titrations, SEM-EDX, and gravimetry), we have identified specific indices that allow differentiating 11 biological samples, primarily according to the crystallinity and maturation state of the apatite phase. A good correlation was found between FTIR results from the analysis of the v (3)(PO4) and v (4)(PO4) vibrational domains and XRD-based crystallinity features. A maximal amount of information has been sought from this analytical approach, by way of optimized posttreatment of the data (spectral subtraction and enhancement of curve-fitting parameters). The good overall agreement found between all techniques leads to a rather complete picture of the diagenetic changes undergone by these 11 skeletal specimens. Although the heterogeneity and scarcity of the studied samples did not allow us to seek direct correlations with DNA persistence, the physicochemical parameters described in this work permit a fine differentiation of key properties of apatite crystals among post mortem samples. As a perspective, this analytical approach could be extended to more numerous sets of specimens so as to draw statistical relationships between mineral and molecular conservation. [Grunenwald, A.; Sautereau, A. M.; Drouet, C.] Univ Toulouse, CIRIMAT Carnot Inst Phosphates, CNRS INPT UPS, ENSIACET, F-31030 Toulouse 4, France; [Grunenwald, A.; Keyser, C.] Univ Strasbourg, Inst Legal Med, AMIS Lab, CNRS UMR 5288, F-67085 Strasbourg, France; [Crubezy, E.] Univ Toulouse, Mol Anthropol & Image Synth Lab AMIS, CNRS UMR 5288, F-31000 Toulouse, France; [Ludes, B.] Paris Descartes Univ, Inst Legal Med, Paris Descartes Med Fac, F-75006 Paris, France Drouet, C (reprint author), Univ Toulouse, CIRIMAT Carnot Inst Phosphates, CNRS INPT UPS, ENSIACET, 4 Allee Emile Monso, F-31030 Toulouse 4, France. christophe.drouet@ensiacet.fr Institute of Ecology and Environment (INEE); Institute of Chemistry (INC) of the French National Center for Scientific Research (CNRS) This research was supported by the Institute of Ecology and Environment (INEE) and the Institute of Chemistry (INC) of the French National Center for Scientific Research (CNRS). Adler CJ, 2011, J ARCHAEOL SCI, V38, P956, DOI 10.1016/j.jas.2010.11.010; Amory S, 2012, FORENSIC SCI INT-GEN, V6, P398, DOI 10.1016/j.fsigen.2011.08.004; Boskey AL, 2005, VIB SPECTROSC, V38, P107, DOI 10.1016/j.vibspec.2005.02.015; Buckley M, 2008, J ARCHAEOL SCI, V35, P1756, DOI 10.1016/j.jas.2007.11.022; Campos PF, 2012, ANN ANAT, V194, P7, DOI 10.1016/j.aanat.2011.07.003; Cazalbou S, 2004, CR PALEVOL, V3, P563, DOI 10.1016/j.crpv.2004.07.003; Cazalbou S, 2005, J MATER SCI-MATER M, V16, P405, DOI 10.1007/s10856-005-6979-2; Cazalbou S, 2000, THESIS I NATL POLYTE; Charlot G, 1963, ANAL QUALITATIVE REA; Combes C, 2005, KEY ENG MATER, V284, P3; Drouet C, 2008, MAT SCI ENG C-BIO S, V28, P1544, DOI 10.1016/j.msec.2008.04.011; Drouet C, 2013, BIOMED RES INT, DOI 10.1155/2013/490946; Drouet C, 2005, GEOCHIM COSMOCHIM AC, V69, pA69; EANES ED, 1977, CALC TISS RES, V23, P259, DOI 10.1007/BF02012795; Elliott J.C., 1994, STRUCTURE CHEM APATI; Errassifi F, 2010, CERAM TRANS, V218, P159; Farlay D, 2010, J BONE MINER METAB, V28, P433, DOI 10.1007/s00774-009-0146-7; GEE A, 1953, ANAL CHEM, V25, P1320, DOI 10.1021/ac60081a006; Gilbert MTP, 2003, AM J HUM GENET, V72, P32, DOI 10.1086/345378; Gotherstrom A, 2002, ARCHAEOMETRY, V44, P395, DOI 10.1111/1475-4754.00072; Grunenwald A, 2014, APPL SURF SCI, V292, P867, DOI 10.1016/j.apsusc.2013.12.063; HAGELBERG E, 1991, PHILOS T ROY SOC B, V333, P399, DOI 10.1098/rstb.1991.0090; Higgins D, 2013, SCI JUSTICE, V53, P433, DOI 10.1016/j.scijus.2013.06.001; KAUPPINEN JK, 1981, APPL SPECTROSC, V35, P271, DOI 10.1366/0003702814732634; Keyser C, 2009, HUM GENET, V126, P395, DOI 10.1007/s00439-009-0683-0; Keyser-Tracqui C, 2003, AM J HUM GENET, V73, P247, DOI 10.1086/377005; Keyser-Tracqui Christine, 2005, V297, P253; Lebon M, 2012, ARCHEOSCIENCES, P179; Lee-Thorp JA, 2008, ARCHAEOMETRY, V50, P925, DOI 10.1111/j.1475-4754.2008.00441.x; LEFEVRE R, 1976, CALC TISS RES, V19, P251; LeGeros R Z, 1991, Monogr Oral Sci, V15, P1; LEGROS R, 1986, J CHEM RES-S, P8; LINDAHL T, 1993, NATURE, V362, P709, DOI 10.1038/362709a0; McElderry JDP, 2013, J SOLID STATE CHEM, V206, P192, DOI 10.1016/j.jssc.2013.08.011; Mendisco F, 2011, ELECTROPHORESIS, V32, P386, DOI 10.1002/elps.201000483; Mendisco F, 2011, APPORTS PALEOGENETIQ; Miller LM, 2001, BBA-GEN SUBJECTS, V1527, P11, DOI 10.1016/S0304-4165(01)00093-9; Orlando L, 2013, NATURE, V499, P74, DOI 10.1038/nature12323; Ostrom PH, 2000, GEOCHIM COSMOCHIM AC, V64, P1043, DOI 10.1016/S0016-7037(99)00381-6; Ouizat S, 1999, MAT RES B, V34, P2279, DOI 10.1016/S0025-5408(00)00167-7; Paabo S, 2004, ANNU REV GENET, V38, P645, DOI 10.1146/annurev.genet.37.110801.143214; Paschalis EP, 1997, CALCIFIED TISSUE INT, V61, P480, DOI 10.1007/s002239900371; PERSON A, 1995, J ARCHAEOL SCI, V22, P211, DOI 10.1006/jasc.1995.0023; POSNER AS, 1985, J BIOMED MATER RES, V19, P241, DOI 10.1002/jbm.820190307; PRICE TD, 1985, J HUM EVOL, V14, P419, DOI 10.1016/S0047-2484(85)80022-1; Puceat E, 2004, CHEM GEOL, V205, P83, DOI 10.1016/j.chemgeo.2003.12.014; Rey C, 1989, Connect Tissue Res, V21, P267, DOI 10.3109/03008208909050016; REY C, 1990, CALCIFIED TISSUE INT, V46, P384, DOI 10.1007/BF02554969; Rey C, 1995, CELL MATER, V5, P345; REY C, 1991, CALCIFIED TISSUE INT, V49, P383, DOI 10.1007/BF02555847; Rohland N, 2007, NAT PROTOC, V2, P1756, DOI 10.1038/nprot.2007.247; Rollin-Martinet S, 2013, AM MINERAL, V98, P2037, DOI 10.2138/am.2013.4537; Rowles S, 1965, TOOTH ENAMEL, p[23, 56]; Sader MS, 2013, MATER RES-IBERO-AM J, V16, P779, DOI 10.1590/S1516-14392013005000046; Scherrer P, 1918, NACHR GES WISS GOTT, V2, P96; Smith CI, 2003, J HUM EVOL, V45, P203, DOI 10.1016/S0047-2484(03)00106-4; Sosa C, 2013, AM J PHYS ANTHROPOL, V151, P102, DOI 10.1002/ajpa.22262; Thompson TJU, 2011, PALAEOGEOGR PALAEOCL, V299, P168, DOI 10.1016/j.palaeo.2010.10.044; Trueman CN, 2013, PALAEONTOLOGY, V56, P475, DOI 10.1111/pala.12041; Trueman CN, 2008, CR PALEVOL, V7, P145, DOI 10.1016/j.crpv.2008.02.006; Trueman CN, 2008, PALAEOGEOGR PALAEOCL, V266, P160, DOI 10.1016/j.palaeo.2008.03.038; Tutken T, 2011, PALAEOGEOGR PALAEOCL, V310, P1, DOI 10.1016/j.palaeo.2011.06.020; Vandecandelaere N, 2012, J MATER SCI-MATER M, V23, P2593, DOI 10.1007/s10856-012-4719-y; Vandecandelaere N, 2012, ELABORATION CARACTER; Yi HH, 2013, AM MINERAL, V98, P1066, DOI 10.2138/am.2013.4445 65 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1618-2642 1618-2650 ANAL BIOANAL CHEM Anal. Bioanal. Chem. JUL 2014 406 19 4691 4704 10.1007/s00216-014-7863-z 14 Biochemical Research Methods; Chemistry, Analytical Biochemistry & Molecular Biology; Chemistry AL5EE WOS:000339155500013 J Zhou, D; Liu, JX; Zhang, SR Zhou Dong; Liu Jianxun; Zhang Sanrong Utilizing Sub-topic Units for Patent Prior-Art Search CHINESE JOURNAL OF ELECTRONICS English Article Patent information retrieval; Sub-topic units; Multiple query representations; Collaborative filtering; Patent ranking TEXT One of the defining challenges in patent prior-art search is the problem of representing a long, technical document as a query. Previously work on this problem has concentrated on single query representations of the patent application. In the following paper, we describe an approach which uses multiple query representations generated from semantically coherent passages extracted from patent documents. We validate our technique in an experiment using the CLEF-IP 2011 patent search collection. Our system achieves statistically significant improvements over various state-of-art query generation techniques. [Zhou Dong] Hunan Univ Sci & Technol, Key Lab Knowledge Proc & Networked Mfg, Xiangtan 411201, Hunan, Peoples R China; Hunan Univ Sci & Technol, Sch Comp Sci & Engn, Xiangtan 411201, Hunan, Peoples R China Zhou, D (reprint author), Hunan Univ Sci & Technol, Key Lab Knowledge Proc & Networked Mfg, Xiangtan 411201, Hunan, Peoples R China. dongzhou1979@hotmail.com National Natural Science Foundation of China [61300129, 61272063, 61100054]; Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry [[2013] 1792]; Excellent Youth Foundation of Hunan Scientific Committee [11JJ1011]; Hunan Provincial Natural Science Foundation of China [12JJ6064, 12JJB009]; Scientific Research Fund of Hunan Provincial Education Department of China [11B048, 12K105] This work is supported by the National Natural Science Foundation of China (No.61300129, No.61272063 and No.61100054), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry (No.[2013] 1792), Excellent Youth Foundation of Hunan Scientific Committee (No.11JJ1011), and Hunan Provincial Natural Science Foundation of China (No.12JJ6064 and 12JJB009), Scientific Research Fund of Hunan Provincial Education Department of China (No.11B048 and No.12K105). Cacheda F., 2011, ACM T WEB, V5, P2; Cao J, 2012, CHINESE J ELECTRON, V21, P609; Choi F. Y. Y., 2000, P 1 N AM CHAPT ASS C, P26; Hearst MA, 1997, COMPUT LINGUIST, V23, P33; Lopez P., 2010, CLEF NOTEBOOK PAPERS, P1; Magdy W., 2010, CLEF NOTEVOOK PAPERS, P1; Magdy W, 2011, LECT NOTES COMPUT SC, V6611, P725, DOI 10.1007/978-3-642-20161-5_80; Mahdabi Parvaz, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348353; Piroi F., 2011, CLEF NOTEBOOK PAPERS, P1; Xue XB, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P808, DOI 10.1145/1571941.1572139; Zhong J, 2011, CHINESE J ELECTRON, V20, P228; Zhou D, 2013, COMM COM INF SC, V400, P310 12 0 0 TECHNOLOGY EXCHANGE LIMITED HONG KONG SHATIN 26-28 AU PUI WAN ST, STE 1102, FO TAN INDUSTRIAL CENTRE, FO TAN, SHATIN, 00000, PEOPLES R CHINA 1022-4653 2075-5597 CHINESE J ELECTRON Chin. J. Electron. JUL 2014 23 3 480 483 4 Engineering, Electrical & Electronic Engineering AL4GB WOS:000339089800009 J Rebholz-Schuhmann, D; Grabmuller, C; Kavaliauskas, S; Croset, S; Woollard, P; Backofen, R; Filsells, W; Clark, D Rebholz-Schuhmann, Dietrich; Grabmueller, Christoph; Kavaliauskas, Silvestras; Croset, Samuel; Woollard, Peter; Backofen, Rolf; Filsells, Wendy; Clark, Dominic A case study: semantic integration of gene-disease associations for type 2 diabetes mellitus from literature and biomedical data resources DRUG DISCOVERY TODAY English Review LIFE SCIENCES; TRANSLATIONAL RESEARCH; INFORMATION-RETRIEVAL; MOLECULAR-GENETICS; WEB; KNOWLEDGE; ONTOLOGY; BIOLOGY; DISCOVERY; SYSTEMS In the Semantic Enrichment of the Scientific Literature (SESL) project, researchers from academia and from life science and publishing companies collaborated in a pre-competitive way to integrate and share information for type 2 diabetes mellitus (T2DM) in adults. This case study exposes benefits from semantic interoperability after integrating the scientific literature with biomedical data resources, such as UniProt Knowledgebase (UniProtKB) and the Gene Expression Atlas (GXA). We annotated scientific documents in a standardized way, by applying public terminological resources for diseases and proteins, and other text-mining approaches. Eventually, we compared the genetic causes of T2DM across the data resources to demonstrate the benefits from the SESL triple store. Our solution enables publishers to distribute their content with little overhead into remote data infrastructures, such as into any Virtual Knowledge Broker. [Rebholz-Schuhmann, Dietrich; Grabmueller, Christoph; Kavaliauskas, Silvestras; Croset, Samuel; Clark, Dominic] European Bioinformat Inst, Cambridge CB10 1SD, England; [Rebholz-Schuhmann, Dietrich] Univ Zurich, Computerlinguist, CH-8050 Zurich, Switzerland; [Woollard, Peter] GlaxoSmithKline, Med Res Ctr, Stevenage SG1 2NY, Herts, England; [Backofen, Rolf] Univ Freiburg, D-79085 Freiburg, Germany; [Filsells, Wendy] Unilever R&D, Sharnbrook MK44 1LQ, Beds, England Rebholz-Schuhmann, D (reprint author), European Bioinformat Inst, Wellcome Trust Genome Campus, Cambridge CB10 1SD, England. rebholz@ebi.ac.uk Pistoia Alliance Special thanks to Samuel Croset for support in the transformation of literature content into a triple store representation. Misha Kapushesky is acknowledged for support in the integration of the gene expression atlas. Ian Harrow, Ian Stott, Nigel Wilkinson and Catherine Marshalls provided valuable project management and quality assurance support. Mike Westaway gave input on the optimization of the SESL triple store. The SESL project was funded by the Pistoia Alliance (http://www.pistoiaalliance.org). Particular thanks to the publishing houses, Reed Elsevier, Nature Publishing Group, Oxford University Press and the Royal Society for Chemistry, for providing literature content for the development of the SESL prototype. Altshuler D, 2008, SCIENCE, V322, P881, DOI 10.1126/science.1156409; Amberger J, 2009, NUCLEIC ACIDS RES, V37, pD793, DOI 10.1093/nar/gkn665; Antezana E, 2009, BRIEF BIOINFORM, V10, P392, DOI 10.1093/bib/bbp024; Apweiler R, 2011, NUCLEIC ACIDS RES, V39, pD214, DOI 10.1093/nar/gkq1020; Ashburner M, 2000, NAT GENET, V25, P25; Bell J, 2010, CLIN MED, V10, P584; Belleau F, 2008, J BIOMED INFORM, V41, P706, DOI 10.1016/j.jbi.2008.03.004; BIZER C, 2009, INT J SEMANT WEB INF, V5, P1; Burgun A, 2008, Yearb Med Inform, P91; Cannata N, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-S4-S1; Casher O, 2006, J CHEM INF MODEL, V46, P2396, DOI 10.1021/ci060139c; Chen HJ, 2009, BRIEF BIOINFORM, V10, P177, DOI 10.1093/bib/bbp002; Chen HJ, 2013, BRIEF BIOINFORM, V14, P109, DOI 10.1093/bib/bbs014; Cheung Kei-Hoi, 2010, Chin Med, V5, P2, DOI 10.1186/1749-8546-5-2; Cheung KH, 2010, MOL SYST BIOL, V6, DOI 10.1038/msb.2010.45; Cheung KH, 2009, BRIEF BIOINFORM, V10, P111, DOI 10.1093/bib/bbp015; Cheung KH, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S10-S10; Clee SM, 2007, ENDOCR REV, V28, P48, DOI 10.1210/er.2006-0035; Courtot M, 2011, MOL SYST BIOL, V7, DOI 10.1038/msb.2011.77; Cruz-Toledo Jose, 2010, J Biomed Semantics, V1 Suppl 1, pS2, DOI 10.1186/2041-1480-1-S1-S2; Degtyarenko K., 2009, CURR PROTOC BIOINFOR; Du P, 2009, BIOINFORMATICS, V25, pI63, DOI 10.1093/bioinformatics/btp193; Farley T, 2013, J AM MED INFORM ASSN, V20, P128, DOI 10.1136/amiajnl-2011-000646; Gessler DDG, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-309; Groth, 2010, INF SERVICES USE, V30, P51; Harrow I, 2013, DRUG DISCOV TODAY, V18, P428, DOI 10.1016/j.drudis.2012.11.012; Hassanzadeh O, 2012, PROC INT CONF DATA, P1204, DOI 10.1109/ICDE.2012.141; Herder C, 2011, EUR J CLIN INVEST, V41, P679, DOI 10.1111/j.1365-2362.2010.02454.x; Hettne Kristina M, 2010, J Cheminform, V2, P4, DOI 10.1186/1758-2946-2-4; Hoehndorf R, 2011, BIOINFORMATICS, V27, P1001, DOI 10.1093/bioinformatics/btr058; Hoehndorf R, 2011, NUCLEIC ACIDS RES, V39, DOI 10.1093/nar/gkr538; Joshi-Tope G, 2005, NUCLEIC ACIDS RES, V33, pD428, DOI 10.1093/nar/gki072; Karp PD, 2005, NUCLEIC ACIDS RES, V33, P6083, DOI 10.1093/nar/gki892; Kirsch H, 2006, INT J MED INFORM, V75, P496, DOI 10.1016/j.ijmedinf.2005.06.011; Kohler S, 2009, AM J HUM GENET, V85, P457, DOI 10.1016/j.ajhg.2009.09.003; Kota Sunil K, 2012, Diabetes Metab Syndr, V6, P54, DOI 10.1016/j.dsx.2012.05.014; Li S, 2011, DIABETOLOGIA, V54, P776, DOI 10.1007/s00125-011-2044-5; Machado C., 2013, BRIEF BIOINFORM; Maglott D, 2011, NUCLEIC ACIDS RES, V39, pD52, DOI 10.1093/nar/gkq1237; Makris K., 2012, P 15 INT C EXT DAT T, P610; Malandrino N, 2011, CLIN CHEM, V57, P231, DOI 10.1373/clinchem.2010.156901; Malone J., 2008, P ISMB 2008 SIG M BI; Malone J, 2010, BIOINFORMATICS, V26, P1112, DOI 10.1093/bioinformatics/btq099; McCarthy MI, 2008, DIABETES, V57, P2889, DOI 10.2337/db08-0343; McCarthy MI, 2009, GENOME MED, V1, DOI 10.1186/gm66; McCarthy M.I., 2004, HUM MOL GENET, V13, P33; McEntyre JR, 2011, NUCLEIC ACIDS RES, V39, pD58, DOI 10.1093/nar/gkq1063; Meigs JB, 2009, DIABETOLOGIA, V52, P568, DOI 10.1007/s00125-009-1296-9; Mlinar B, 2007, CLIN CHIM ACTA, V375, P20, DOI 10.1016/j.cca.2006.07.005; Mons B, 2011, NAT GENET, V43, P281, DOI 10.1038/ng0411-281; Mukherjea S, 2005, BRIEF BIOINFORM, V6, P252, DOI 10.1093/bib/6.3.252; Neumann E, 2007, BRIEF BIOINFORM, V8, P141, DOI 10.1093/bib/bbm013; Ogata H, 1999, NUCLEIC ACIDS RES, V27, P29, DOI 10.1093/nar/27.1.29; O'Rahilly S, 2009, NATURE, V462, P307, DOI 10.1038/nature08532; Parkinson H, 2011, NUCLEIC ACIDS RES, V39, pD1002, DOI 10.1093/nar/gkq1040; Peltonen L., 2006, HUM MOL GENET, V15, P67; Pezik P., 2008, STATIC DICT FEATURES; Rebholz-Schuhmann D, 2013, J BIOMED SEMANTICS, V4; Rebholz-Schuhmann D., 2008, J BIOINFORMATICS, V24, P296; Rebholz-Schuhmann D., 2008, ISMB SIG BIOLINK; Rebholz-Schuhmann Dietrich, 2010, J Biomed Semantics, V1, P1, DOI 10.1186/2041-1480-1-1; Rebholz-Schuhmann D, 2012, NAT REV GENET, V13, P829, DOI 10.1038/nrg3337; Rebholz-Schuhmann D., 2007, J BIOINFORMATICS, V23, pe237; Rebholz-Schuhmann Dietrich, 2010, Journal of Bioinformatics and Computational Biology, V8, P163, DOI 10.1142/S0219720010004562; Rebholz-Schuhmann D, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0075185; Reformat M.Z., 2012, ADV COMPUT INTELL LC, V7311, P191; Robinson PN, 2008, AM J HUM GENET, V83, P610, DOI 10.1016/j.ajhg.2008.09.017; Roos M, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S10-S9; Rosse C, 2003, J BIOMED INFORM, V36, P478, DOI 10.1016/j.jbi.2003.11.007; Ruttenberg A, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-S3-S2; Ruttenberg A, 2009, BRIEF BIOINFORM, V10, P193, DOI 10.1093/bib/bbp004; Sahoo SS, 2008, J BIOMED INFORM, V41, P752, DOI 10.1016/j.jbi.2008.02.006; Samwald Matthias, 2010, J Biomed Semantics, V1 Suppl 1, pS5, DOI 10.1186/2041-1480-1-S1-S5; Sansone SA, 2012, NAT GENET, V44, P121, DOI 10.1038/ng.1054; Shotton D, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000361; Smith A, 2007, BIOINFORMATICS, V23, P3073, DOI 10.1093/bioinformatics/btm452; Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346; Smith RJ, 2010, J CLIN ENDOCR METAB, V95, P1566, DOI 10.1210/jc.2009-1966; Splendiani A, 2011, BRIEF BIOINFORM, V12, P562, DOI 10.1093/bib/bbr051; Stoy J, 2010, REV ENDOCR METAB DIS, V11, P205, DOI 10.1007/s11154-010-9151-3; Thompson P, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-397; Verona G, 2006, ORGAN STUD, V27, P765, DOI 10.1177/0170840606061073; Wilkinson MD, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-S12-S7; Wilkinson M.D, 2011, J BIOMED SEMANTICS, V2; Williams AJ, 2012, DRUG DISCOV TODAY, V17, P1188, DOI 10.1016/j.drudis.2012.05.016; Wishart DS, 2008, NUCLEIC ACIDS RES, V36, pD901, DOI 10.1093/nar/gkm958; Witte Rene, 2007, Int J Bioinform Res Appl, V3, P389, DOI 10.1504/IJBRA.2007.015009 87 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1359-6446 1878-5832 DRUG DISCOV TODAY Drug Discov. Today JUL 2014 19 7 882 889 10.1016/j.drudis.2013.10.024 8 Pharmacology & Pharmacy Pharmacology & Pharmacy AL5BO WOS:000339148700011 J Gong, MG; Li, Y; Jiao, LC; Jia, M; Su, LZ Gong, Maoguo; Li, Yu; Jiao, Licheng; Jia, Meng; Su, Linzhi SAR change detection based on intensity and texture changes ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING English Article Change detection; Multivariate generalized Gaussian model; Robust principal component analysis; Graph cuts; Synthetic aperture radar UNSUPERVISED CHANGE DETECTION; OIL-SPILL SEGMENTATION; GRAPH CUTS; IMAGE CLASSIFICATION; ENERGY MINIMIZATION; DISCRIMINATION; RETRIEVAL; FEATURES; MODEL In this paper, a novel change detection approach is proposed for multitemporal synthetic aperture radar (SAR) images. The approach is based on two difference images, which are constructed through intensity and texture information, respectively. In the extraction of the texture differences, robust principal component analysis technique is used to separate irrelevant and noisy elements from Gabor responses. Then graph cuts are improved by a novel energy function based on multivariate generalized Gaussian model for more accurately fitting. The effectiveness of the proposed method is proved by the experiment results obtained on several real SAR images data sets. (C) 2014 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS) Published by Elsevier B.V. All rights reserved. [Gong, Maoguo; Li, Yu; Jiao, Licheng; Jia, Meng; Su, Linzhi] Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China Gong, MG (reprint author), Xidian Univ, Minist Educ, Key Lab Intelligent Percept & Image Understanding, Xian 710071, Peoples R China. gong@ieee.org National Natural Science Foundation of China [61273317]; National Top Youth Talents Program of China; Fundamental Research Fund for the Central Universities [K5051202053]; Specialized Research Fund for the Doctoral Program of Higher Education [20130203110011] The authors wish to thank the editors and anonymous reviewers for their valuable comments and helpful suggestions which greatly improved the paper's quality. This work was supported by the National Natural Science Foundation of China (Grant No. 61273317), the National Top Youth Talents Program of China, the Specialized Research Fund for the Doctoral Program of Higher Education (Grant No. 20130203110011) and the Fundamental Research Fund for the Central Universities (Grant No. K5051202053). Bazi Y, 2010, IEEE T GEOSCI REMOTE, V48, P3178, DOI 10.1109/TGRS.2010.2045506; Bazi Y, 2005, IEEE T GEOSCI REMOTE, V43, P874, DOI 10.1109/TGRS.2004.842441; BLASCHKE T, 2010, ISPRS J PHOTOGRAMM, V65, P2, DOI DOI 10.1016/J.ISPRSJPRS.2009.06.004; Bovolo F, 2005, IEEE T GEOSCI REMOTE, V43, P2963, DOI 10.1109/TGRS.2005.857987; Boykov Y, 2006, INT J COMPUT VISION, V70, P109, DOI 10.1007/s11263-006-7934-5; Boykov Y, 2001, IEEE T PATTERN ANAL, V23, P1222, DOI 10.1109/34.969114; Boykov Y, 2004, IEEE T PATTERN ANAL, V26, P1124, DOI 10.1109/TPAMI.2004.60; Bruzzone L, 2000, IEEE T GEOSCI REMOTE, V38, P1171, DOI 10.1109/36.843009; Bruzzone L, 2002, IEEE T IMAGE PROCESS, V11, P452, DOI 10.1109/TIP.2002.999678; Candes EJ, 2011, J ACM, V58, DOI 10.1145/1970392.1970395; Candes EJ, 2009, FOUND COMPUT MATH, V9, P717, DOI 10.1007/s10208-009-9045-5; Celik T, 2010, IEEE GEOSCI REMOTE S, V7, P386, DOI 10.1109/LGRS.2009.2037024; CONGALTON RG, 1991, REMOTE SENS ENVIRON, V37, P35, DOI 10.1016/0034-4257(91)90048-B; Delong Z., 2009, COMP SCI INF ENG 200, P169; Deng H, 2005, IEEE T GEOSCI REMOTE, V43, P528, DOI 10.1109/TGRS.2004.839589; Deng HW, 2004, IEEE T PATTERN ANAL, V26, P951, DOI 10.1109/TPAMI.2004.30; Do MN, 2002, IEEE T IMAGE PROCESS, V11, P146, DOI 10.1109/83.982822; Florindo JB, 2012, PHYSICA A, V391, P4909, DOI 10.1016/j.physa.2012.03.039; Gong MG, 2012, IEEE T IMAGE PROCESS, V21, P2141, DOI 10.1109/TIP.2011.2170702; Hao Y., 2005, J IMAGE GRAPH, V4; He C., 2010, INF SCI ENG ICISE 20, P3809; Hussain M, 2013, ISPRS J PHOTOGRAMM, V80, P91, DOI 10.1016/j.isprsjprs.2013.03.006; Jain AK, 1996, IEEE T PATTERN ANAL, V18, P195, DOI 10.1109/34.481543; JAIN AK, 1991, PATTERN RECOGN, V24, P1167, DOI 10.1016/0031-3203(91)90143-S; Kandaswamy U, 2005, IEEE T GEOSCI REMOTE, V43, P2075, DOI 10.1109/TGRS.2005.852768; Keshavan RH, 2010, J MACH LEARN RES, V11, P2057; Kim JS, 2009, PATTERN RECOGN, V42, P735, DOI 10.1016/j.patcog.2008.09.031; Kolmogorov V, 2004, IEEE T PATTERN ANAL, V26, P147, DOI 10.1109/TPAMI.2004.1262177; Kruizinga P, 1999, IEEE T IMAGE PROCESS, V8, P1395, DOI 10.1109/83.791965; Li Y., 2006, INF ACQ 2006 IEEE IN, P201; Ma WY, 2000, IEEE T IMAGE PROCESS, V9, P1375, DOI 10.1109/83.855433; Manian V, 1997, INT GEOSCI REMOTE SE, P335, DOI 10.1109/IGARSS.1997.615878; Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803; Min W., 2010, WIR COMM NETW MOB CO, P1; Moser G, 2006, IEEE T GEOSCI REMOTE, V44, P2972, DOI 10.1109/TGRS.2006.876288; Oliver C., 2004, UNDERSTANDING SYNTHE; Pelizzari S, 2007, INT GEOSCI REMOTE SE, P1318; Pelizzari S, 2007, LECT NOTES COMPUT SC, V4478, P637; RIGNOT EJM, 1993, IEEE T GEOSCI REMOTE, V31, P896, DOI 10.1109/36.239913; Schubert A, 2013, ISPRS J PHOTOGRAMM, V82, P49, DOI 10.1016/j.isprsjprs.2013.04.010; Torres-Torriti M., 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205), DOI 10.1109/ICIP.2001.958305; ULABY FT, 1986, IEEE T GEOSCI REMOTE, V24, P235, DOI 10.1109/TGRS.1986.289643; Verdoolaege G, 2011, INT J COMPUT VISION, V95, P265, DOI 10.1007/s11263-011-0448-9; VILLASENOR JD, 1993, IEEE T GEOSCI REMOTE, V31, P227, DOI 10.1109/36.210462; Wright J., 2009, P C NEUR INF PROC SY 45 1 1 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0924-2716 1872-8235 ISPRS J PHOTOGRAMM ISPRS-J. Photogramm. Remote Sens. JUL 2014 93 123 135 10.1016/j.isprsjprs.2014.04.010 13 Geography, Physical; Geosciences, Multidisciplinary; Remote Sensing; Imaging Science & Photographic Technology Physical Geography; Geology; Remote Sensing; Imaging Science & Photographic Technology AL4VW WOS:000339133900012 J Uchida, Y; Ueno, T; Miyamoto, Y Uchida, Yukiko; Ueno, Taiji; Miyamoto, Yuri You were always on my mind: The importance of "significant others" in the attenuation of retrieval-induced forgetting in Japan JAPANESE PSYCHOLOGICAL RESEARCH English Article interdependence; significant others; retrieval-induced forgetting LONG-TERM-MEMORY; CORRESPONDENCE BIAS; SELF; CULTURE; COGNITION; CONTEXT; AGENCY Research on memory has demonstrated that remembering material can cause forgetting of related information, which is known as retrieval-induced forgetting (RIF). Macrae and Roseveare identified "self" as one of the boundary conditions of this effect in the Western cultural context, showing that RIF was eliminated when material was encoded to be related to the self (known as self-referential effect), but not to significant others. In this study, we predicted and found that significant others could be another boundary condition in Japanese cultural contexts in which self and agency are more interdependent or conjoint; RIF was observed neither under best-friend-related encoding nor under family-related encoding in Japan. The effect of significant others is found uniquely in Japanese cultural contexts, suggesting that the cultural model of self has significant power in the spontaneous system of memory. [Uchida, Yukiko] Kyoto Univ, Kyoto 6068501, Japan; [Ueno, Taiji] Univ York, York YO10 5DD, N Yorkshire, England; [Miyamoto, Yuri] Univ Wisconsin Madison, Madison, WI 53706 USA Uchida, Y (reprint author), Kyoto Univ, Kokoro Res Ctr, Sakyo Ku, 46 Yoshida Shimoadachicho, Kyoto 6068501, Japan. yukikou@educ.kyoto-u.ac.jp Anderson MC, 2000, J EXP PSYCHOL LEARN, V26, P1141, DOI 10.1037/0278-7393.26.5.1141; ANDERSON MC, 1995, PSYCHOL REV, V102, P68, DOI 10.1037/0033-295X.102.1.68; Anderson MC, 1999, J EXP PSYCHOL LEARN, V25, P608, DOI 10.1037//0278-7393.25.3.608; ANDERSON MC, 1994, J EXP PSYCHOL LEARN, V20, P1063, DOI 10.1037/0278-7393.20.5.1063; Benson C., 2000, CULTURAL PSYCHOL SEL; Brewer W. F., 1986, AUTOBIOGRAPHICAL MEM, P25; Camp G, 2009, J EXP PSYCHOL LEARN, V35, P934, DOI 10.1037/a0015536; Chiao J., 2007, HDB CULTURAL PSYCHOL, P237; Ciranni MA, 1999, J EXP PSYCHOL LEARN, V25, P1403, DOI 10.1037/0278-7393.25.6.1403; Cohen D, 2002, PSYCHOL SCI, V13, P55, DOI 10.1111/1467-9280.00409; Cohen J, 1988, STAT POWER ANAL BEHA; Conway W. A., 2003, CORTEX, V39, P667; Han SH, 2008, NAT REV NEUROSCI, V9, P646, DOI 10.1038/nrn2456; Hartinger A., 1999, ABSTR PSYCH SOC, V4, P57; Hedden T, 2008, PSYCHOL SCI, V19, P12, DOI 10.1111/j.1467-9280.2008.02038.x; Kawaguchi J., 2004, JAPANESE J PSYCHOL, V75, P125; Kitayama S., 2012, SOCIAL COGNITIVE AFF, V9, P201; Kitayama S, 2003, J EXP SOC PSYCHOL, V39, P476, DOI 10.1016/S0022-1031(03)00026-X; Kitayama S, 2003, PSYCHOL SCI, V14, P201, DOI 10.1111/1467-9280.02432; Kitayama S, 2005, ONT SYMP P, V10, P137; KLEIN SB, 1988, J PERS SOC PSYCHOL, V55, P5, DOI 10.1037/0022-3514.55.1.5; Kobayashi C, 2003, J CROSS CULT PSYCHOL, V34, P567, DOI 10.1177/0022022103256479; Macleod M, 2002, APPL COGNITIVE PSYCH, V16, P135, DOI 10.1002/acp.782; Macrae CN, 2002, PSYCHON B REV, V9, P611; Macrae CN, 1999, J PERS SOC PSYCHOL, V77, P463, DOI 10.1037//0022-3514.77.3.463; Markus H. R., 2004, CROSS CULTURAL DIFFE, P1; Markus H. R., 2007, HDB CULTURAL PSYCHOL, P3; MARKUS HR, 1991, PSYCHOL REV, V98, P224, DOI 10.1037/0033-295X.98.2.224; Markus HR, 2010, PERSPECT PSYCHOL SCI, V5, P420, DOI 10.1177/1745691610375557; Markus HR, 2006, PSYCHOL SCI, V17, P103, DOI 10.1111/j.1467-9280.2006.01672.x; Masuda T, 2004, J EXP SOC PSYCHOL, V40, P409, DOI 10.1016/j.jesp.2003.08.004; Masuda T, 2001, J PERS SOC PSYCHOL, V81, P922, DOI 10.1037//0022-3514.81.5.922; MILLER JG, 1984, J PERS SOC PSYCHOL, V46, P961, DOI 10.1037/0022-3514.46.5.961; Miyamoto Y, 2002, J PERS SOC PSYCHOL, V83, P1239, DOI 10.1037//0022-3514.83.5.1239; Morling B, 2008, PERS SOC PSYCHOL REV, V12, P199, DOI 10.1177/1088868308318260; Ross M, 2010, PERSPECT PSYCHOL SCI, V5, P401, DOI 10.1177/1745691610375555; Tsukimoto T, 2006, JPN PSYCHOL RES, V48, P40, DOI 10.1111/j.1468-5884.2006.00304.x; Wagar BM, 2003, J EXP SOC PSYCHOL, V39, P468, DOI 10.1016/S0022-1031(03)00021-0; Wang Q, 2001, J PERS SOC PSYCHOL, V81, P220, DOI 10.1037//0022-3514.81.2.220; Yamada Y., 2012, THESIS NAGOYA U NAGO; Zhang M, 2011, EMOTION, V11, P866, DOI 10.1037/a0024025; Zhu Y, 2007, NEUROIMAGE, V34, P1310, DOI 10.1016/j.neuroimage.2006.07.047 42 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0021-5368 1468-5884 JPN PSYCHOL RES Jpn. Psychol. Res. JUL 2014 56 3 263 274 10.1111/jpr.12051 12 Psychology, Multidisciplinary Psychology AL4IE WOS:000339095500006 J Zhou, J; Lei, HC; Ji, L; Hou, TJ Zhou, Jun; Lei, Hengchi; Ji, Lei; Hou, Tuanjie A Fast Inverse Algorithm Based on the Multigrid Technique for Cloud Tomography JOURNAL OF ATMOSPHERIC AND OCEANIC TECHNOLOGY English Article DATA ASSIMILATION A fast inverse algorithm based on the half-V cycle scheme (HV) of the multigrid technique is developed for cloud tomography. Fourier analysis shows that the slow convergence problem caused by the smoothing property of the iterative algorithm can be effectively alleviated in HV by performing iterations from the coarsest to the finest grid. In this way, the resolvable scales of information contained in observations can be retrieved efficiently on the coarser grid level and the unresolvable scales are left as errors on the finer grid level. Numerical simulations indicate that, compared with the previous algorithm without HV (NHV), HV can significantly reduce the runtime by 89%-96.9% while retaining a similar level of retrieval accuracy. For the currently feasible two-level flight scheme for a 20-km-wide target area, convergence can be accelerated from 407s in NHV to 13s in HV. This reduction in time would be multiplied several times if the target area were much wider; but then segmental retrieval would be required to avoid exceeding the time limit of cloud tomography. This improvement represents an important saving in terms of computing resources and ensures the real-time application of cloud tomography in a much wider range of fields. [Zhou, Jun; Lei, Hengchi; Ji, Lei; Hou, Tuanjie] Chinese Acad Sci, Key Lab Cloud Precipitat Phys & Severe Storms, Inst Atmospher Phys, Beijing 100029, Peoples R China; [Ji, Lei] China Meteorol Adm, Inst Urban Meteorol, Beijing, Peoples R China; [Ji, Lei] Beijing Weather Modificat Off, Beijing, Peoples R China Zhou, J (reprint author), Chinese Acad Sci, Key Lab Cloud Precipitat Phys & Severe Storms, Inst Atmospher Phys, Beijing 100029, Peoples R China. zhoujun@mail.iap.ac.cn National Natural Science Foundation of China [41105016]; 973 Program of China [2013CB430105]; Chinese Academy of Sciences [KZCX2-EW-203, XDA05100300]; Central Public-Interest Scientific Institution Basal Research Foundation of China [IUMKY201313PP0403] The authors thank Professor Yuanfu Xie from NOAA for the instructive and helpful discussions on multigrid theory. The authors also appreciate the comments and suggestions from the anonymous reviewers, which greatly contributed to improving the original manuscript. This research was supported by the National Natural Science Foundation of China (Grant 41105016), the 973 Program of China (Grant 2013CB430105), the Knowledge Innovation Program of the Chinese Academy of Sciences (Grant KZCX2-EW-203), the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant XDA05100300), and the Central Public-Interest Scientific Institution Basal Research Foundation of China (Grant IUMKY201313PP0403). Briggs W. L., 2000, MULTIGRID TUTORIAL; Drake J. F., 1988, Journal of Atmospheric and Oceanic Technology, V5, DOI 10.1175/1520-0426(1988)005<0844:ATSOTA>2.0.CO;2; He ZJ, 2008, J ATMOS OCEAN TECH, V25, P1018, DOI 10.1175/2007JTECHO540.1; Huang D, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009133; Li W, 2008, J ATMOS OCEAN TECH, V25, P2106, DOI 10.1175/2008JTECHO510.1; Li W, 2010, J ATMOS OCEAN TECH, V27, P319, DOI 10.1175/2009JTECHA1271.1; MacDonald AE, 2002, MON WEATHER REV, V130, P386, DOI 10.1175/1520-0493(2002)130<0386:DOTDWV>2.0.CO;2; Steven W. S., 1997, SCI ENG GUIDE DIGITA; Wang Y. F., 2007, COMPUTATIONAL METHOD, P83; Warner J., 1988, Journal of Atmospheric and Oceanic Technology, V5, DOI 10.1175/1520-0426(1988)005<0833:FTOAAR>2.0.CO;2; Warner J., 1985, Journal of Atmospheric and Oceanic Technology, V2, DOI 10.1175/1520-0426(1985)002<0293:DOCLWD>2.0.CO;2; Xie Y, 2011, MON WEATHER REV, V139, P1224, DOI 10.1175/2010MWR3338.1; Xie Y. F., 2005, 21 C WEATH AN FOR 17, p15B7; Young KC, 1996, B AM METEOROL SOC, V77, P2701, DOI 10.1175/1520-0477(1996)077<2701:WMATV>2.0.CO;2; [周珺 ZHOU Jun], 2011, [高原气象, Plateau Meteorology], V30, P760; Zhou J, 2013, J ATMOS OCEAN TECH, V30, P301, DOI 10.1175/JTECH-D-12-00054.1; Zhu CY, 1997, ACM T MATH SOFTWARE, V23, P550, DOI 10.1145/279232.279236 17 0 0 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693 USA 0739-0572 1520-0426 J ATMOS OCEAN TECH J. Atmos. Ocean. Technol. JUL 2014 31 7 1653 1662 10.1175/JTECH-D-13-00184.1 10 Engineering, Ocean; Meteorology & Atmospheric Sciences Engineering; Meteorology & Atmospheric Sciences AL4KQ WOS:000339102200015 J Yeh, CH; Wei, ST; Chen, TW; Wang, CY; Tsai, MH; Lin, CD Yeh, Chung-Hui; Wei, Sung-Tai; Chen, Tsung-Wen; Wang, Ching-Yuang; Tsai, Ming-Hsui; Lin, Chia-Der A web-based audiometry database system JOURNAL OF THE FORMOSAN MEDICAL ASSOCIATION English Article audiometry; database system; pure tone audiometry To establish a real-time, web-based, customized audiometry database system, we worked in cooperation with the departments of medical records, information technology, and otorhinolaryngology at our hospital. This system includes an audiometry data entry system, retrieval and display system, patient information incorporation system, audiometry data transmission program, and audiometry data integration. Compared with commercial audiometry systems and traditional hand-drawn audiometry data, this web-based system saves time and money and is convenient for statistics research. Copyright (C) 2013, Elsevier Taiwan LLC & Formosan Medical Association. All rights reserved. [Yeh, Chung-Hui; Wang, Ching-Yuang; Tsai, Ming-Hsui; Lin, Chia-Der] China Med Univ & Hosp, Dept Otorhinolaryngol, Taichung 404, Taiwan; [Wei, Sung-Tai] China Med Univ & Hosp, Dept Neurosurg, Taichung 404, Taiwan; [Chen, Tsung-Wen] China Med Univ Hosp, Dept Informat Technol, Taichung, Taiwan; [Tsai, Ming-Hsui; Lin, Chia-Der] China Med Univ, Grad Inst Clin Med Sci, Taichung, Taiwan Lin, CD (reprint author), China Med Univ & Hosp, Dept Otolaryngol, 2 Yuh Der Rd, Taichung 404, Taiwan. chiader@seed.net.tw Department of Medical Research at China Medical University Hospital, Taiwan [CMU98-NTU-13, DMR-100-043]; Clinical Trial and Research Center of Excellence Funds from the Taiwanese Department of Health [DOH102-TD-B-111-004] This study was supported by a research grant (CMU98-NTU-13, DMR-100-043) from the Department of Medical Research at China Medical University Hospital, Taiwan, and Clinical Trial and Research Center of Excellence Funds (DOH102-TD-B-111-004) from the Taiwanese Department of Health. The authors would also like to thank Hsiu-Chen Lu for the illustrations. Gutierrez Martinez J, 2009, BIOMED INSTRUM TECHN, V43, P484; Lous J, 2005, COCHRANE DB SYST REV, DOI DOI 10.1002/14651858.CD001801.PUB2; MIZUKAMI C, 1994, ACTA OTO-LARYNGOL, P48; The World Health Organization, 2001, INT CLASS FUNCT DIS 4 0 0 ELSEVIER TAIWAN TAIPEI RM N-412, 4F, CHIA HSIN BUILDING 11, NO 96, ZHONG SHAN N ROAD SEC 2, TAIPEI, 10449, TAIWAN 0929-6646 J FORMOS MED ASSOC J. Formos. Med. Assoc. JUL 2014 113 7 477 480 10.1016/j.jfma.2013.10.006 4 Medicine, General & Internal General & Internal Medicine AL4FJ WOS:000339088000013 J Dai, CQ; Wang, XG; Zhou, GQ; Chen, JL Dai, Chaoqing; Wang, Xiaogang; Zhou, Guoquan; Chen, Junlang Optical image-hiding method with false information disclosure based on the interference principle and partial-phase-truncation in the fractional Fourier domain LASER PHYSICS LETTERS English Article image encryption; phase truncation; fractional Fourier domain; information disclosure GERCHBERG-SAXTON ALGORITHM; SILHOUETTE REMOVAL; FRESNEL DOMAIN; ASYMMETRIC CRYPTOSYSTEM; ENCRYPTION; TRANSFORM; SECURITY; RETRIEVAL; LIGHT An image-hiding method based on the optical interference principle and partial-phase-truncation in the fractional Fourier domain is proposed. The primary image is converted into three phase-only masks (POMs) using an analytical algorithm involved partial-phase-truncation and a fast random pixel exchange process. A procedure of a fake silhouette for a decryption key is suggested to reinforce the encryption and give a hint of the position of the key. The fractional orders of FrFT effectively enhance the security of the system. In the decryption process, the POM with false information and the other two POMs are, respectively, placed in the input and fractional Fourier planes to recover the primary image. There are no unintended information disclosures and iterative computations involved in the proposed method. Simulation results are presented to verify the validity of the proposed approach. [Dai, Chaoqing; Wang, Xiaogang; Zhou, Guoquan; Chen, Junlang] Zhejiang A&F Univ, Dept Phys, Coll Sci, Linan 311300, Peoples R China Dai, CQ (reprint author), Zhejiang A&F Univ, Dept Phys, Coll Sci, Linan 311300, Peoples R China. wxg1201@163.com National Natural Science Foundation of China [61205006, 11274273, 11074219, 11304287]; Zhejiang Provincial Natural Science Foundation of China [LY13F050006]; Scientific Research and Developed Fund of Zhejiang A F University [2013FR049] This work was supported by the National Natural Science Foundation of China (Grant Nos. 61205006, 11274273, 11074219 and 11304287), the Zhejiang Provincial Natural Science Foundation of China (Grant No. LY13F050006), and the Scientific Research and Developed Fund of Zhejiang A & F University (Grant No. 2013FR049). Abuturab MR, 2013, APPL OPTICS, V52, P1555, DOI 10.1364/AO.52.001555; Alfalou A, 2009, ADV OPT PHOTONICS, V1, P589, DOI 10.1364/AOP.1.000589; Chen W, 2009, OPT COMMUN, V282, P3680, DOI 10.1016/j.optcom.2009.06.014; Chen W, 2012, APPL OPTICS, V51, P6076, DOI 10.1364/AO.51.006076; Chen W, 2011, OPT COMMUN, V284, P3913, DOI 10.1016/j.optcom.2011.04.005; Chen W, 2013, OPT COMMUN, V286, P123, DOI 10.1016/j.optcom.2012.09.014; Hwang HE, 2009, OPT LETT, V34, P3917, DOI 10.1364/OL.34.003917; Hwang HE, 2009, OPT EXPRESS, V17, P13700; Jia W, 2012, APPL OPTICS, V51, P5253, DOI 10.1364/AO.51.005253; Kumar P, 2011, APPL OPTICS, V50, P1805, DOI 10.1364/AO.50.001805; Kumar P, 2010, J OPTICS-UK, V12, DOI 10.1088/2040-8978/12/9/095402; Liu ZJ, 2010, OPT EXPRESS, V18, P12033, DOI 10.1364/OE.18.012033; Ozaktas H.M., 2001, FRACTIONAL FOURIER T; Qin W, 2010, OPT LETT, V35, P118, DOI 10.1364/OL.35.000118; Qin Y, 2014, OPT COMMUN, V315, P220, DOI 10.1016/j.optcom.2013.11.018; Qin Y, 2014, OPT COMMUN, V310, P69, DOI 10.1016/j.optcom.2013.07.062; Qin Y, 2013, APPL OPTICS, V52, P3987, DOI 10.1364/AO.52.003987; Rajput SK, 2012, APPL OPTICS, V51, P1446, DOI 10.1364/AO.51.001446; REFREGIER P, 1995, OPT LETT, V20, P767; Rodrigo JA, 2010, OPT EXPRESS, V18, P1510, DOI 10.1364/OE.18.001510; Situ GH, 2004, OPT LETT, V29, P1584, DOI 10.1364/OL.29.001584; Tay CJ, 2010, OPT LASER TECHNOL, V42, P409, DOI 10.1016/j.optlastec.2009.08.016; Unnikrishnan G, 2000, OPT LETT, V25, P887, DOI 10.1364/OL.25.000887; Wang Q, 2012, OPT COMMUN, V285, P4294, DOI 10.1016/j.optcom.2012.06.071; Wang RK, 1996, OPT ENG, V35, P2464, DOI 10.1117/1.600849; Wang XG, 2012, OPT COMMUN, V285, P1078, DOI 10.1016/j.optcom.2011.12.017; Wang XG, 2012, APPL OPTICS, V51, P686, DOI 10.1364/AO.51.000686; Weng DD, 2011, OPT COMMUN, V284, P2485, DOI 10.1016/j.optcom.2011.01.039; Chen YY, 2013, APPL OPTICS, V52, P5247, DOI 10.1364/AO.52.005247; Zhang Y, 2009, J OPT A-PURE APPL OP, V11, DOI 10.1088/1464-4258/11/12/125406; Zhang Y, 2008, OPT LETT, V33, P2443, DOI 10.1364/OL.33.002443; Zhu N, 2009, OPT EXPRESS, V17, P13418; Zhu N, 2010, OPT COMMUN, V283, P4969, DOI 10.1016/j.optcom.2010.07.056 33 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 1612-2011 1612-202X LASER PHYS LETT Laser Phys. Lett. JUL 1 2014 11 7 075603 10.1088/1612-2011/11/7/075603 8 Optics; Physics, Applied Optics; Physics AL2ET WOS:000338939400014 J Rodriguez, AD; Clemente, P; Irles, E; Tajahuerce, E; Lancis, J Rodriguez, A. D.; Clemente, P.; Irles, E.; Tajahuerce, E.; Lancis, J. Resolution analysis in computational imaging with patterned illumination and bucket detection OPTICS LETTERS English Article MICROSCOPY In computational imaging by pattern projection, a sequence of microstructured light patterns codified onto a programmable spatial light modulator is used to sample an object. The patterns are used as generalized measurement modes where the object information is expressed. In this Letter, we show that the resolution of the recovered image is only limited by the numerical aperture of the projecting optics regardless of the quality of the collection optics. We provide proof-of-principle experiments where the single-pixel detection strategy outperforms the resolution achieved using a conventional optical array detector for optical imaging. It is advantageous in the presence of real-world conditions, such as optical aberrations and optical imperfections in between the sample and the sensor. We provide experimental verification of image retrieval even when an optical diffuser prevents imaging with a megapixel array camera. (C) 2014 Optical Society of America [Rodriguez, A. D.; Irles, E.; Tajahuerce, E.; Lancis, J.] Univ Jaume 1, Inst Noves Tecnol Imatge INIT, Castellon de La Plana 12071, Spain; [Clemente, P.] Univ Jaume 1, SCIC, Castellon de La Plana 12071, Spain Rodriguez, AD (reprint author), Univ Jaume 1, Inst Noves Tecnol Imatge INIT, Castellon de La Plana 12071, Spain. jimeneza@uji.es Generalitat Valenciana [PROMETEO/2012/021, ISIC/2012/013]; Universitat Jaume I [P1-1B2012-55, PREDOC/2012/41] This work was supported by the Generalitat Valenciana through projects PROMETEO/2012/021, ISIC/2012/013, and by the Universitat Jaume I through project P1-1B2012-55. A. D. Rodriguez acknowledges grant PREDOC/2012/41 from Universitat Jaume I. Candes E. J., 2008, IEEE SIGNAL PROCESS, V25, P682; Choi Y, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.023902; Davis BM, 2011, ANAL CHEM, V83, P5086, DOI 10.1021/ac103259v; Duarte MF, 2008, IEEE SIGNAL PROC MAG, V25, P83, DOI 10.1109/MSP.2007.914730; Duran V, 2012, OPT LETT, V37, P824, DOI 10.1364/OL.37.000824; Erkmen BI, 2010, ADV OPT PHOTONICS, V2, P405, DOI 10.1364/AOP.2.000405; Gong WL, 2013, APPL OPTICS, V52, P3510, DOI 10.1364/AO.52.003510; Howland GA, 2013, OPT EXPRESS, V21, P23822, DOI 10.1364/OE.21.023822; Lu RW, 2013, BIOMED OPT EXPRESS, V4, P1673, DOI 10.1364/BOE.4.001673; Mosk AP, 2012, NAT PHOTONICS, V6, P283, DOI [10.1038/nphoton.2012.88, 10.1038/NPHOTON.2012.88]; Oh JE, 2013, OPT LETT, V38, P682, DOI 10.1364/OL.38.000682; Shapiro JH, 2008, PHYS REV A, V78, DOI 10.1103/PhysRevA.78.061802; Shirai T, 2012, J OPT SOC AM A, V29, P1288, DOI 10.1364/JOSAA.29.001288; Soldevila F, 2013, APPL PHYS B-LASERS O, V113, P551, DOI 10.1007/s00340-013-5506-2; Studer V, 2012, P NATL ACAD SCI USA, V109, pE1679, DOI 10.1073/pnas.1119511109; Szameit A, 2012, NAT MATER, V11, P455, DOI [10.1038/nmat3289, 10.1038/NMAT3289]; Wilson T., 1984, THEORY PRACTICE SCAN; Zalevsky Z., 2003, OPTICAL SUPERRESOLUT 18 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 0146-9592 1539-4794 OPT LETT Opt. Lett. JUL 1 2014 39 13 3888 3891 10.1364/OL.39.003888 4 Optics Optics AL2CJ WOS:000338933200050 J Ma, ZW; Hu, XF; Huang, L; Bi, J; Liu, Y Ma, Zongwei; Hu, Xuefei; Huang, Lei; Bi, Jun; Liu, Yang Estimating Ground-Level PM2.5 in China Using Satellite Remote Sensing ENVIRONMENTAL SCIENCE & TECHNOLOGY English Article AEROSOL OPTICAL DEPTH; GEOGRAPHICALLY WEIGHTED REGRESSION; PARTICULATE AIR-POLLUTION; DUST STORMS; THICKNESS; STATES; MODEL; LAND; AREA; RETRIEVALS Estimating ground-level PM2.5 from satellite-derived aerosol optical depth (AOD) using a spatial statistical model is a promising new method to evaluate the spatial and temporal characteristics of PM2.5 exposure in a large geographic region. However, studies outside North America have been limited due to the lack of ground PM2.5 measurements to calibrate the model. Taking advantage of the newly established national monitoring network, we developed a national-scale geographically weighted regression (GWR) model to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor. The results showed that the meteorological and land use information can greatly improve model performance. The overall cross-validation (CV) R-2 is 0.64 and root mean squared prediction error (RMSE) is 32.98 mu g/m(3). The mean prediction error (MPE) of the predicted annual PM2.5 is 8.28 mu g/m(3). Our predicted annual PM2.5 concentrations indicated that over 96% of the Chinese population lives in areas that exceed the Chinese National Ambient Air Quality Standard (CNAAQS) Level 2 standard. Our results also confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM2.5 monitoring network in China. [Ma, Zongwei; Huang, Lei; Bi, Jun] Nanjing Univ, Sch Environm, State Key Lab Pollut Control & Resource Reuse, Nanjing 210023, Jiangsu, Peoples R China; [Ma, Zongwei; Hu, Xuefei; Liu, Yang] Emory Univ, Rollins Sch Publ Hlth, Dept Environm Hlth, Atlanta, GA 30322 USA Bi, J (reprint author), Nanjing Univ, Sch Environm, State Key Lab Pollut Control & Resource Reuse, Xianlin Campus,Box 624,163 Xianlin Ave, Nanjing 210023, Jiangsu, Peoples R China. jbi@nju.edu.cn; yang.liu@emory.edu NASA Applied Sciences Program [NNX11AI53G]; USEPA [R834799]; China Scholarship Council (CSC) This research was partially supported by NASA Applied Sciences Program (grant no. NNX11AI53G, PI: Yang Liu). In addition, this publication was made possible by USEPA grant R834799. Its contents are solely the responsibility of the grantee and do not necessarily represent the official views of the USEPA. Further, USEPA does not endorse the purchase of any commercial products or services mentioned in the publication. The work of Zongwei Ma was supported by the China Scholarship Council (CSC) under the State Scholarship Fund. BMI&CIESIN (Battelle Memorial Institute & Center for International Earth Science Information Network Columbia University), 2013, GLOB ANN AV PM2 5 GR; Breon FM, 2011, REMOTE SENS ENVIRON, V115, P3102, DOI 10.1016/j.rse.2011.06.017; Bright E. A., 2012, LANDSCAN 2011; Brunsdon C, 1996, GEOGR ANAL, V28, P281; Chang YH, 2012, ENVIRON SCI TECHNOL, V46, P7069, DOI 10.1021/es3022705; Chen RJ, 2013, AM J RESP CRIT CARE, V188, P1170, DOI 10.1164/rccm.201304-0678LE; Chen Y, 2010, ENV PROTECTION SCI, V36, P7; CRESSIE N, 1988, MATH GEOL, V20, P405, DOI 10.1007/BF00892986; Dominici F, 2006, JAMA-J AM MED ASSOC, V295, P1127, DOI 10.1001/jama.295.10.1127; Guo JP, 2009, ATMOS ENVIRON, V43, P5876, DOI 10.1016/j.atmosenv.2009.08.026; GUO JP, 2013, ENV SCI, V34, P817; Han B, 2010, WATER AIR SOIL POLL, V209, P15, DOI 10.1007/s11270-009-0176-8; Hoff RM, 2009, J AIR WASTE MANAGE, V59, P645, DOI 10.3155/1047-3289.59.6.645; Holben BN, 1998, REMOTE SENS ENVIRON, V66, P1, DOI 10.1016/S0034-4257(98)00031-5; Hsu NC, 2012, ATMOS CHEM PHYS, V12, P8037, DOI 10.5194/acp-12-8037-2012; Hu XF, 2014, REMOTE SENS ENVIRON, V140, P220, DOI 10.1016/j.rse.2013.08.032; Hu XF, 2013, ENVIRON RES, V121, P1, DOI 10.1016/j.envres.2012.11.003; Huang W, 2012, AM J EPIDEMIOL, V175, P556, DOI 10.1093/aje/kwr342; Huete A., 1999, MODIS VEGETATION IND; Jiang X, 2007, REMOTE SENS ENVIRON, V107, P45, DOI 10.1016/j.rse.2006.06.022; Kahn R, 1998, J GEOPHYS RES-ATMOS, V103, P32195, DOI 10.1029/98JD01752; Kahn R. A., 2005, Journal of Geophysical Research-Part D-Atmospheres, V110, DOI 10.1029/2004JD004706; Kloog I, 2012, ENVIRON SCI TECHNOL, V46, P11913, DOI 10.1021/es302673e; Liu Y, 2009, ENVIRON HEALTH PERSP, V117, P886, DOI 10.1289/ehp.0800123; Liu Y, 2007, REMOTE SENS ENVIRON, V107, P33, DOI 10.1016/j.rse.2006.05.022; Liu Y, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003981; Liu Y, 2004, J GEOPHYS RES ATMOS, V109; Liu Y, 2012, ENVIRON INT, V44, P100, DOI 10.1016/j.envint.2012.02.003; Liu Y, 2005, ENVIRON SCI TECHNOL, V39, P3269, DOI 10.1021/es049352rn; Lucchesi R., 2013, 4 GMAO OFF; Paciorek CJ, 2008, ENVIRON SCI TECHNOL, V42, P5800, DOI 10.1021/es703181j; Pope CA, 2002, JAMA-J AM MED ASSOC, V287, P1132, DOI 10.1001/jama.287.9.1132; Puttaswamy S. J., 2013, GEOCARTO INT, P1; Qian Y, 2000, GEOPHYS RES LETT, V27, P3521, DOI 10.1029/2000GL011942; Quan J, 2011, ATMOS CHEM PHYS, V11, P8205, DOI 10.5194/acp-11-8205-2011; Remer LA, 2005, J ATMOS SCI, V62, P947, DOI 10.1175/JAS3385.1; Rienecker M. M., 2008, TECHNICAL REPORT SER, V27; Rodriguez JD, 2010, IEEE T PATTERN ANAL, V32, P569, DOI 10.1109/TPAMI.2009.187; Song Y, 2006, ATMOS ENVIRON, V40, P1526, DOI 10.1016/j.atmosenv.2005.10.039; Sun JM, 2001, J GEOPHYS RES-ATMOS, V106, P10325, DOI 10.1029/2000JD900665; Sun Y., 2010, J GEOPHYS RES ATMOS, V115; Tao M., 2012, J GEOPHYS RES ATMOS, V117; van Donkelaar A, 2010, ENVIRON HEALTH PERSP, V118, P847, DOI 10.1289/ehp.0901623; [王静 WANG Jing], 2010, [中国科学院研究生院学报, Journal of the Graduate School of the Academy of Sciences], V27, P10; Wang J, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL018174; Wang XM, 2004, J ARID ENVIRON, V58, P559, DOI 10.1016/j.jaridenv.2003.11.009; Wei Y., 2009, ENV SCI MANAGE, V34, P29; WHO, 2005, AIR QUAL GUID GLOB U; Yu Y, 2011, J ENVIRON MONITOR, V13, P334, DOI 10.1039/c0em00467g; Yuan Y, 2012, ENVIRON SCI TECHNOL, V46, P3627, DOI 10.1021/es300984j; Zheng M, 2005, ATMOS ENVIRON, V39, P3967, DOI 10.1016/j.atmosenv.2005.03.036; Zhu S. J., 2012, ENV PROTECT XINJIANG, V34, P6 52 0 0 AMER CHEMICAL SOC WASHINGTON 1155 16TH ST, NW, WASHINGTON, DC 20036 USA 0013-936X 1520-5851 ENVIRON SCI TECHNOL Environ. Sci. Technol. JUL 1 2014 48 13 7436 7444 10.1021/es5009399 9 Engineering, Environmental; Environmental Sciences Engineering; Environmental Sciences & Ecology AK5TG WOS:000338488700031 J Borlund, P; Dreier, S Borlund, Pia; Dreier, Sabine An investigation of the search behaviour associated with Ingwersen's three types of information needs INFORMATION PROCESSING & MANAGEMENT English Article Information needs; User study; Interactive information retrieval; Internet searching; Ordinary users; Everyday-life information seeking RETRIEVAL; SYSTEMS; SEEKING We report a naturalistic interactive information retrieval (IIR) study of 18 ordinary users in the age of 20-25 who carry out everyday-life information seeking (ELIS) on the Internet with respect to the three types of information needs identified by Ingwersen (1986): the verificative information need (VIN), the conscious topical information need (CIN), and the muddled topical information need (MIN). The searches took place in the private homes of the users in order to ensure as realistic searching as possible. Ingwersen (1996) associates a given search behaviour to each of the three types of information needs, which are analytically deduced, but not yet empirically tested. Thus the objective of the study is to investigate whether empirical data does, or does not, conform to the predictions derived from the three types of information needs. The main conclusion is that the analytically deduced information search behaviour characteristics by Ingwersen are positively corroborated for this group of test participants who search the Internet as part of ELIS. (C) 2014 Elsevier Ltd. All rights reserved. [Borlund, Pia] Univ Copenhagen, Royal Sch Lib & Informat Sci, DK-9220 Aalborg, Denmark; [Dreier, Sabine] Aalborg Univ, Aalborg Univ Lib, DK-9220 Aalborg, Denmark Borlund, P (reprint author), Univ Copenhagen, Royal Sch Lib & Informat Sci, Fredrik Bajers Vej 7K, DK-9220 Aalborg, Denmark. sjc900@iva.ku.dk; sd@aub.aau.dk Danish Ministry of Culture [TAKT2011-072] We are grateful to Jesper W. Schneider for the many interesting discussions on the use of statistics, the power of sample techniques, effect sizes as well as the many other interesting topics we have come across in those discussions. We also thank the test participants for their time and effort, and the reviewers for constructive input that has improved the paper. This work has been supported in part by the Danish Ministry of Culture (REX project/TAKT2011-072). Aula A., 2005, P 14 INT C WORLD WID, P583, DOI 10.1145/1060745.1060831; BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320; BELKIN NJ, 1980, CAN J INFORM SCI, V5, P133; BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722; Bell J. D., 2004, P 26 EUR C INF RETR, P57; Borlund P., 2010, JANUS FACED SCHOLAR, P23; Borlund P., 2003, INFORM RES, V8; Borlund P., 2010, P 3 S INF INT CONT 2, P155, DOI 10.1145/1840784.1840808; Borlund P, 2000, J DOC, V56, P71, DOI 10.1108/EUM0000000007110; Bryman A., 2008, SOCIAL RES METHODS; CARVER RP, 1978, HARVARD EDUC REV, V48, P378; Cleverdon C. W., 1966, ASLIB CRANFIELD RES, V2; Cohen, 1994, AM PSYCHOL, V49, P997; Corbin J, 2008, BASICS QUALITATIVE R; Crystal A. J., 2009, APPL SOCIAL SCI RES; Ingwersen P., 1992, INFORM RETRIEVAL INT; Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960; Ingwersen P., 2005, TURN INTEGRATION INF; Ingwersen P., 1980, USER LIBRARIAN NEGOT; Ingwersen P., 2000, INF RETR 3 EUR SUMM, P157; Ingwersen P, 1986, INTELLIGENT INFORMAT, P206; Keen E. M., 1966, ASLIB CRANFIELD RES, V1; Kelly Diane, 2009, Foundations and Trends in Information Retrieval, V3, DOI 10.1561/1500000012; Keskustalo H., 2010, SIMULATION INTERACTI, P29; Keskustalo H, 2009, LECT NOTES COMPUT SC, V5839, P63, DOI 10.1007/978-3-642-04769-5_6; Kirk R. E., 1996, EDUC PSYCHOL MEAS, V61, P246; Kvale S., 2007, DOING INTERVIEWS; ROBERTSON SE, 1992, INFORM PROCESS MANAG, V28, P457, DOI 10.1016/0306-4573(92)90004-J; SAVOLAINEN R, 1995, LIBR INFORM SCI RES, V17, P259, DOI 10.1016/0740-8188(95)90048-9; SHAVER JP, 1993, J EXP EDUC, V61, P293; Tombros A, 2005, J AM SOC INF SCI TEC, V56, P327, DOI 10.1002/asi.20106; Toms E. G., 2007, P 6 INT WORKSH IN EV, P359; White R., 2008, ACM T WEB, V2, P30; White R. W., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; White R. W., 2001, P 24 ANN INT SIGIR C, P412, DOI 10.1145/383952.384062; White RW, 2003, INFORM PROCESS MANAG, V39, P707, DOI 10.1016/S0306-4573(02)00033-X; Wildemuth B., 2013, P 2 ASS INF SCI TECH, P131 37 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. JUL 2014 50 4 493 507 10.1016/j.ipm.2014.03.001 15 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AL0MX WOS:000338822100001 J Belem, FM; Martins, EF; Almeida, JM; Goncalves, MA Belem, Fabiano M.; Martins, Eder F.; Almeida, Jussara M.; Goncalves, Marcos A. Personalized and object-centered tag recommendation methods for Web 2.0 applications INFORMATION PROCESSING & MANAGEMENT English Article Tag recommendation; Relevance metrics; Personalization Several Web 2.0 applications allow users to assign keywords (or tags) to provide better organization and description of the shared content. Tag recommendation methods may assist users in this task, improving the quality of the available information and, thus, the effectiveness of various tag-based information retrieval services, such as searching, content recommendation and classification. This work addresses the tag recommendation problem from two perspectives. The first perspective, centered at the object, aims at suggesting relevant tags to a target object, jointly exploiting the following three dimensions: (i) tag cooccurrences, (ii) terms extracted from multiple textual features (e.g., title, description), and (iii) various metrics to estimate tag relevance. The second perspective, centered at both object and user, aims at performing personalized tag recommendation to a target objectuser pair, exploiting, in addition to the three aforementioned dimensions, a metric that captures user interests. In particular, we propose new heuristic methods that extend state-of-the-art strategies by including new metrics that estimate how accurately a candidate tag describes the target object. We also exploit three learning-to-rank (L2R) based techniques, namely, RankSVM, Genetic Programming (GP) and Random Forest (RF), for generating ranking functions that exploit multiple metrics as attributes to estimate the relevance of a tag to a given object or object-user pair. We evaluate the proposed methods using data from four popular Web 2.0 applications, namely, Bibsonomy, LastFM, YouTube and YahooVideo. Our new heuristics for object-centered tag recommendation provide improvements in precision over the best state-of-the-art alternative of 12% on average (up to 20% in any single dataset), while our new heuristics for personalized tag recommendation produce average gains in precision of 121% over the baseline. Similar performance gains are also achieved in terms of other metrics, notably recall, Normalized Discounted Cumulative Gain (NDCG) and Mean-Reciprocal Rank (MRR). Further improvements, for both object-centered (up to 23% in precision) and personalized tag recommendation (up to 13% in precision), can also be achieved with our new L2R-based strategies, which are flexible and can be easily extended to exploit other aspects of the tag recommendation problem. Finally, we also quantify the benefits of personalized tag recommendation to provide better descriptions of the target object when compared to object-centered recommendation by focusing only on the relevance of the suggested tags to the object. We find that our best personalized method outperforms the best object-centered strategy, with average gains in precision of 10%. (C) 2014 Elsevier Ltd. All rights reserved. [Belem, Fabiano M.; Martins, Eder F.; Almeida, Jussara M.; Goncalves, Marcos A.] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG, Brazil Belem, FM (reprint author), Univ Fed Minas Gerais, Dept Comp Sci, Av Antonio Carlos 6627, BR-31270010 Belo Horizonte, MG, Brazil. fmuniz@dcc.ufmg.br; ederfm@dcc.ufmg.br; jussara@dcc.ufmg.br; mgoncalv@dcc.ufmg.br Brazilian National Institute of Science and Technology for Web Research (MCT/CNPq/INCT Web) [573871/2008-6]; CNPq; CAPES; FAPEMIG This research is partially funded by the Brazilian National Institute of Science and Technology for Web Research (MCT/CNPq/INCT Web Grant Number 573871/2008-6), and by the authors individual grants from CNPq, CAPES and FAPEMIG. We also would like to thank the reviewers for their comments and suggestions, which greatly contributed for this work. Abdi H., 2007, BONFERRONI SIDAK COR; Adler J., 2012, P 35 INT ACM C RES D, P651; Agrawal R., 1994, P 20 INT C VER LARG, P487; Almeida J., 2010, IEEE INTERNET COMPUT, V14; Aslam JA, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P468, DOI 10.1145/1571941.1572022; Baeza-Yates R., 2011, MODERN INFORM RETRIE; Banzhaf W., 1998, GENETIC PROGRAMMING; Belem F., 2013, P ACM C REC SYST REC; Belem F., 2013, P 35 EUR C ADV INF R, P380; Belem F., 2011, P 34 INT ACM SIGIR C, P1033; Belem F., 2010, P 19 ACM C INF KNOWL, P1793, DOI 10.1145/1871437.1871731; Benz D, 2010, VLDB J, V19, P849, DOI 10.1007/s00778-010-0208-4; Bi Bin, 2013, P 22 ACM INT C INF K; Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324; Burke Robin, 2010, P 19 ACM INT C INF K; Cao H., 2009, P ECML PKDD DISC CHA; Faria F., 2010, P INT C MULT INF RET, P285, DOI 10.1145/1743384.1743434; Feng W., 2012, P 18 ACM SIGKDD INT, P1276; Figueiredo F, 2013, INFORM PROCESS MANAG, V49, P222, DOI 10.1016/j.ipm.2012.03.003; Freund Y., 2003, J MACHINE LEARNING R; Friedman J., 2000, ANN STAT, V29, P1189; Games G., 2013, J INFORM DATA MANAGE, V4, P57; Garg N, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P67; Guan Z., 2009, P 32 ANN INT ACM C R; GUY I, 2010, P 33 INT ACM SIGIR C, P194, DOI 10.1145/1835449.1835484; Heymann P., 2008, P 31 ANN INT ACM SIG, P531, DOI DOI 10.1145/1390334.1390425; Jaschke R, 2007, LECT NOTES ARTIF INT, V4702, P506; Joachims T., 2006, P 12 ACM SIGKDD INT, P217, DOI DOI 10.1145/1150402.1150429; Katakis I., 2008, P ECML PKDD DISC CHA; Koutrika G., 2008, ACM T WEB, V2; Krestel R, 2009, P 3 ACM C REC SYST, P61, DOI 10.1145/1639714.1639726; Li X., 2008, P 17 INT C WORLD WID, P675, DOI 10.1145/1367497.1367589; Liaw A., 2002, R NEWS, V2, P18, DOI DOI 10.1016/J.MEMSCI.2010.02.036; Lin Z., 2012, P 21 ACM INT C INF K, P1784; Lipczak M., 2011, ACM T INTELLIGENT SY, V3; Lipczak M., 2010, P 21 ACM C HYP HYP H, P179, DOI 10.1145/1810617.1810648; Lipczak M., 2009, P ECML PKDD DISC CHA; Liu T., 2009, FDN TRENDS INFORM RE, V3, P225, DOI DOI 10.1561/1500000016; Lu YT, 2009, 21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, P2064; Menezes G., 2010, P EUR C MACH LEARN P; Mohan A., 2011, JMLR WORKSH C P, V14, P77; Niu S., 2012, P 35 INT ACM SIGIR C, P751; Pedro J., 2011, ACM T INFORM SYSTEMS, V29, P13; Prokofyev Roman, 2012, P 11 INT C SEM WEB 2; Qin T, 2010, INFORM RETRIEVAL, V13, P375, DOI 10.1007/s10791-009-9124-x; Rader E, 2008, CSCW: 2008 ACM CONFERENCE ON COMPUTER SUPPORTED COOPERATIVE WORK, CONFERENCE PROCEEDINGS, P239; Rae A., 2010, RIAO 2010 9 INT C AD; Ramage D., 2009, P 2 ACM INT C WEB SE, P54, DOI 10.1145/1498759.1498809; Rendle S., 2010, P 3 ACM INT C WEB SE, P81, DOI 10.1145/1718487.1718498; Rendle Steffen, 2009, P 15 ACM SIGKDD INT; Siersdorfer S, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P395, DOI 10.1145/1571941.1572010; Sigurbjornsson B., 2008, P 17 INT C WORLD WID, P327, DOI 10.1145/1367497.1367542; Song Y, 2008, PROCEEDINGS OF THE ASME INTERNATIONAL DESIGN ENGINEERING TECHNICAL CONFERENCES AND COMPUTERS AND INFORMATION IN ENGINEERING CONFERENCE 2007, VOL 6, PTS A AND B, P515, DOI 10.1145/1390334.1390423; Song Y, 2011, ACM T WEB, V5, DOI 10.1145/1921591.1921595; Veloso A., 2008, P 31 ANN INT ACM SIG, P267, DOI 10.1145/1390334.1390381; Wang J., 2009, P ECML PKDD DISC CHA; Wu L., 2009, P 18 INT C WORLD WID, P361, DOI 10.1145/1526709.1526758; Yeh J., 2007, P ACM SIGIR WORKSH L; Yin De-suo, 2011, Zhongguo Shuidao Kexue, V25, P25, DOI 10.3969/j.issn.1001-7216.2011.01.004; Zhang H., 2012, P 5 ACM INT C WEB SE, P33; Zhang N., 2009, P ECML PKDD DISC CHA; Zhang Y. C., 2012, P 5 ACM INT C WEB SE, P13 62 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0306-4573 1873-5371 INFORM PROCESS MANAG Inf. Process. Manage. JUL 2014 50 4 524 553 10.1016/j.ipm.2014.03.002 30 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AL0MX WOS:000338822100003 J Chen, W; Chen, XD Chen, Wen; Chen, Xudong Optical color-image verification using multiple-pinhole phase retrieval JOURNAL OF OPTICS English Article phase retrieval; multiple-pinhole pattern; optical color-image verification ENCRYPTION; ALGORITHM; DOMAIN; AUTHENTICATION; INTERFERENCE; MICROSCOPY; ATTACK; PLANE; FIELD We propose a multiple-pinhole phase retrieval algorithm to generate an intermediate modulated wavefront for optical color-image verification. Since an incomplete but modulated wavefront is extracted in the intermediate optical path, the reconstructed objects will contain noise-like signals and do not visually render specimen information. Nonlinear optical correlation is further conducted to verify the recovered objects. The performance of the setup parameters and robustness of the proposed method are also illustrated. It is found that pinhole patterns placed in the intermediate optical path not only serve as the constraints for the developed phase retrieval algorithm, but also can provide a channel to modulate noise-like images obtained in the object plane during the recovery. [Chen, Wen; Chen, Xudong] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117583, Singapore Chen, W (reprint author), Natl Univ Singapore, Dept Elect & Comp Engn, 4 Engn Dr 3, Singapore 117583, Singapore. chenwen327@gmail.com Chen, Wen/F-5431-2012 Singapore Temasek Defence Systems Institute [TDSI/11-009/1A] This work was supported by the Singapore Temasek Defence Systems Institute under grant TDSI/11-009/1A. Abbey B, 2008, NAT PHYS, V4, P394, DOI 10.1038/nphys896; Anand A, 2007, OPT LETT, V32, P1584, DOI 10.1364/OL.32.001584; Chen W, 2010, OPT LETT, V35, P3817, DOI 10.1364/OL.35.003817; Chen W, 2013, J OPT SOC AM A, V30, P806, DOI 10.1364/JOSAA.30.000806; Chen W, 2014, ADV OPT PHOTONICS, V6, P120, DOI 10.1364/AOP.6.000120; Chen W, 2013, OPT EXPRESS, V21; Chen W, 2013, APPL PHYS LETT, V103, DOI 10.1063/1.4836995; Dierolf M, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/035017; Elser V, 2003, J OPT SOC AM A, V20, P40, DOI 10.1364/JOSAA.20.000040; Faulkner HML, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.023903; FIENUP JR, 1982, APPL OPTICS, V21, P2758, DOI 10.1364/AO.21.002758; GERCHBER.RW, 1972, OPTIK, V35, P237; Giacovazzo C., 2011, FUNDAMENTALS CRYSTAL; Gong Q, 2013, APPL OPTICS, V52, P7486, DOI 10.1364/AO.52.007486; Goodman J. W., 1996, INTRO FOURIER OPTICS; He WQ, 2012, APPL OPTICS, V51, P7750, DOI 10.1364/AO.51.007750; Hwang HE, 2009, OPT LETT, V34, P3917, DOI 10.1364/OL.34.003917; JAVIDI B, 1989, APPL OPTICS, V28, P2358, DOI 10.1364/AO.28.002358; Johnson EG, 1996, OPT LETT, V21, P1271, DOI 10.1364/OL.21.001271; Johnson I, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.155503; Liu ZJ, 2010, APPL OPTICS, V49, P5632, DOI 10.1364/AO.49.005632; Matsushima K, 2009, OPT EXPRESS, V17, P19662, DOI 10.1364/OE.17.019662; McBride W, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.233902; Meng XF, 2006, OPT LETT, V31, P1414, DOI 10.1364/OL.31.001414; Miao JW, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.215503; Miao JW, 1999, NATURE, V400, P342, DOI 10.1038/22498; Nugent KA, 2003, PHYS REV LETT, V91, DOI 10.1103/PhysRevLett.91.203902; Peng X, 2006, OPT LETT, V31, P1044, DOI 10.1364/OL.31.001044; Perez-Cabre E, 2011, OPT LETT, V36, P22, DOI 10.1364/OL.36.000022; Pfeifer MA, 2006, NATURE, V442, P63, DOI 10.1038/nature04867; REFREGIER P, 1995, OPT LETT, V20, P767; Robinson IK, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.195505; Rodenburg JM, 2004, APPL PHYS LETT, V85, P4795, DOI 10.1063/1.1823034; Sadjadi F., 2007, PHYS AUTOMATIC TARGE; Shao ZH, 2014, OPT EXPRESS, V22, P4932, DOI 10.1364/OE.22.004932; Shapiro D, 2005, P NATL ACAD SCI USA, V102, P15343, DOI 10.1073/pnas.0503305102; Shi YS, 2013, OPT LETT, V38, P1425, DOI 10.1364/OL.38.001425; Situ GH, 2005, OPT LETT, V30, P1306, DOI 10.1364/OL.30.001306; TEAGUE MR, 1983, J OPT SOC AM, V73, P1434; Unnikrishnan G, 2000, OPT LETT, V25, P887, DOI 10.1364/OL.25.000887; Vilardy JM, 2013, J OPTICS-UK, V15, DOI 10.1088/2040-8978/15/2/025401; Waller L, 2011, OPT EXPRESS, V19, P2805, DOI 10.1364/OE.19.002805; Wang XG, 2012, OPT EXPRESS, V20, P11994, DOI 10.1364/OE.20.011994; Williams GJ, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.025506; Wu C, 2010, APPL OPTICS, V49, P1831, DOI 10.1364/AO.49.001831; Zhang FC, 2007, PHYS REV A, V75, DOI 10.1103/PhysRevA.75.043805; Zhang Y, 2008, OPT LETT, V33, P2443, DOI 10.1364/OL.33.002443; Zheng GA, 2013, NAT PHOTONICS, V7, P739, DOI 10.1038/NPHOTON.2013.187 48 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 2040-8978 2040-8986 J OPTICS-UK J. Opt. JUL 2014 16 7 075403 10.1088/2040-8978/16/7/075403 12 Optics Optics AK9PO WOS:000338759200014 J Liu, Q; Wang, Y; He, JG; Ji, F; Wang, BR Liu, Qian; Wang, Yang; He, Jianguo; Ji, Fang; Wang, Baorui Tilt shift determinations with spatial-carrier phase-shift method in temporal phase-shift interferometry JOURNAL OF OPTICS English Article phase-shift interferometry; fringe analysis; phase retrieval; tilt-shift error; phase shifting interferometry ITERATIVE ALGORITHM; EXTRACTION; INTERFEROGRAMS; IMMUNE An algorithm is proposed to deal with tilt-shift errors in temporal phase-shift interferometry (PSI). In the algorithm, the tilt shifts are detected with the spatial-carrier phase-shift (SCPS) method and then the tilt shifts are applied as priori information to the least-squares fittings of phase retrieval. The algorithm combines the best features of the SCPS and the temporal PSI. The algorithm could be applied to interferograms of arbitrary aperture without data extrapolation for the Fourier transform is not involved. Simulations and experiments demonstrate the effectiveness of the algorithm. The statistics of simulation results show a satisfied accuracy in detecting tilt-shift errors. Comparisons of the measurements with and without environmental vibration show that the proposed algorithm could compensate tilt-shift errors and retrieve wavefront phase accurately. The algorithm provides an approach to retrieve wavefront phase for the temporal PSI in vibrating environment. [Liu, Qian; Wang, Yang; He, Jianguo; Ji, Fang; Wang, Baorui] China Acad Engn Phys, Inst Machinery Mfg Technol, Mianyang 621000, Sichuan, Peoples R China Liu, Q (reprint author), China Acad Engn Phys, Inst Machinery Mfg Technol, 64th Mianshan Rd, Mianyang 621000, Sichuan, Peoples R China. liuqianblue@126.com Chen MY, 2000, APPL OPTICS, V39, P3894, DOI 10.1364/AO.39.003894; Chen YC, 2013, APPL OPTICS, V52, P3381, DOI 10.1364/AO.52.003381; Dorrio B V, 1999, MEAS SCI TECHNOL, V10, P33; Gao P, 2009, OPT LETT, V34, P3553, DOI 10.1364/OL.34.003553; Gardner N, 2005, P SOC PHOTO-OPT INS, V5869; Groot P. d., 2000, APPL OPTICS, V39, P2658; Guo HW, 2013, APPL OPTICS, V52, P6572, DOI 10.1364/AO.52.006572; Hao Q, 2009, OPT LETT, V34, P1288; Harada Y, 2010, PROC SPIE, V7619, DOI 10.1117/12.842878; Liu Q, 2013, APPL OPTICS, V52, P7654, DOI 10.1364/AO.52.007654; Liu Q, 2013, OPT EXPRESS, V21, P29505, DOI 10.1364/OE.21.029505; RODDIER C, 1987, APPL OPTICS, V26, P1668, DOI 10.1364/AO.26.001668; Styk A, 2007, APPL OPTICS, V46, P4613, DOI 10.1364/AO.46.004613; Vannoni M, 2012, OPT COMMUN, V285, P517, DOI 10.1016/j.optcom.2011.11.016; Wang ZY, 2004, OPT LETT, V29, P1671, DOI 10.1364/OL.29.001671; Wingerden J, 1991, APPL OPTICS, V30, P2718; Xu JC, 2008, J OPT A-PURE APPL OP, V10, DOI 10.1088/1464-4258/10/7/075011; Xu JC, 2008, APPL OPTICS, V47, P480, DOI 10.1364/AO.47.000480; Xu XF, 2010, J OPTICS-UK, V12, DOI 10.1088/2040-8978/12/1/015301; Xu XF, 2006, OPT LETT, V31, P1966, DOI 10.1364/OL.31.001966 20 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 2040-8978 2040-8986 J OPTICS-UK J. Opt. JUL 2014 16 7 075404 10.1088/2040-8978/16/7/075404 8 Optics Optics AK9PO WOS:000338759200015 J Lee, CL; Mirman, D; Buxbaum, LJ Lee, Chia-lin; Mirman, Daniel; Buxbaum, Laurel J. Abnormal dynamics of activation of object use information in apraxia: Evidence from eyetracking NEUROPSYCHOLOGIA English Article Object use; Apraxia; Eye tracking; Parietal; Temporal SPOKEN WORD RECOGNITION; LEFT-HEMISPHERE STROKE; LIMB APRAXIA; IDEOMOTOR APRAXIA; ALZHEIMERS-DISEASE; BRAIN-REGIONS; TOOL USE; SEMANTIC COGNITION; NEURAL MECHANISMS; ACTION SIMULATION Action representations associated with object use may be incidentally activated during visual object processing, and the time course of such activations may be influenced by lexical-semantic context (e.g., Lee, Middleton, Mirman, Kalenine, & Buxbaum (2012). Journal of Experimental Psychology: Human Perception and Performance, 39(1), 257-270). In this study we used the "visual world" eye-tracking paradigm to examine whether a deficit in producing skilled object-use actions (apraxia) is associated with abnormalities in incidental activation of action information, and assessed the neuroanatomical substrates of any such deficits. Twenty left hemisphere stroke patients, ten of whom were apraxic, performed a task requiring identification of a named object in a visual display containing manipulation-related and unrelated distractor objects. Manipulation relationships among objects were not relevant to the identification task. Objects were cued with neutral ("S/he saw the...."), or action-relevant ("S/he used the....") sentences. Non-apraxic participants looked at use-related non-target objects significantly more than at unrelated non-target objects when cued both by neutral and action-relevant sentences, indicating that action information is incidentally activated. In contrast, apraxic participants showed delayed activation of manipulation-based action information during object identification when cued by neutral sentences. The magnitude of delayed activation in the neutral sentence condition was reliably predicted by lower scores on a test of gesture production to viewed objects, as well as by lesion loci in the inferior parietal and posterior temporal lobes. However, when cued by a sentence containing an action verb, apraxic participants showed fixation patterns that were statistically indistinguishable from non-apraxic controls. In support of grounded theories of cognition, these results suggest that apraxia and temporal-parietal lesions may be associated with abnormalities in incidental activation of action information from objects. Further, they suggest that the previously-observed facilitative role of action verbs in the retrieval of object-related action information extends to participants with apraxia. (C) 2014 Elsevier Ltd. All rights reserved. [Lee, Chia-lin] Natl Taiwan Univ, Grad Inst Brain & Mind Sci & Neurobiol, Grad Inst Linguist, Dept Psychol, Taipei 10764, Taiwan; [Lee, Chia-lin] Natl Taiwan Univ, Cognit Neurosci Ctr, Taipei 10764, Taiwan; [Mirman, Daniel; Buxbaum, Laurel J.] Moss Rehabil Res Inst, Philadelphia, PA USA; [Mirman, Daniel] Drexel Univ, Dept Psychol, Philadelphia, PA USA Lee, CL (reprint author), Natl Taiwan Univ, Grad Inst Brain & Mind Sci & Neurobiol, Grad Inst Linguist, Dept Psychol, Taipei 10764, Taiwan. chialinlee@ntu.edu.tw NIH [R01 NS065049, R01 DC010805]; James S. McDonnell Foundation [220020190]; Taiwan National Science Council [NSC102-2410-H-002-055] The authors wish to thank Solene Kalenine, Steven Jax, and Branch Coslett for their helpful suggestions during manuscript preparation, Allison Shapiro and Branch Coslett for their help with lesion segmentation and registration, and Allison Shapiro and Rachel German for assistance with data collection. This study was supported by a NIH grant R01 NS065049 and James S. McDonnell Foundation grant. 220020190 to Laurel J. Buxbaum, NIH grant R01 DC010805 to Daniel Mirman, and a Taiwan National Science Council grant NSC102-2410-H-002-055 to Chia-lin Lee. Allport D., 1985, DISTRIBUTED MEMOTY M, P207; Auchterlonie S, 2002, BRAIN COGNITION, V48, P264, DOI 10.1006/brcg.2001.1358; Avants BB, 2006, MED IMAGE ANAL, V10, P397, DOI 10.1016/j.media.2005.03.005; Bach P., 2010, CEREBRAL CORTEX NEW, V20, P2798; BARBIERI C, 1988, CORTEX, V24, P535; Barr DJ, 2013, J MEM LANG, V68, P255, DOI 10.1016/j.jml.2012.11.001; Barsalou LW, 2008, ANNU REV PSYCHOL, V59, P617, DOI 10.1146/annurev.psych.59.103006.093639; Blakemore SJ, 2003, CEREB CORTEX, V13, P837, DOI 10.1093/cercor/13.8.837; BLUMSTEIN SE, 1982, BRAIN LANG, V17, P301, DOI 10.1016/0093-934X(82)90023-2; Borghi AM, 2009, HUM MOVEMENT SCI, V28, P12, DOI 10.1016/j.humov.2008.07.002; Boronat CB, 2005, COGNITIVE BRAIN RES, V23, P361, DOI 10.1016/j.cogbrainres.2004.11.001; Bouillaud J.-B., 1825, MIGNERET; Brambati SM, 2012, CORTEX, V48, P414, DOI 10.1016/j.cortex.2011.04.001; Britt A. E., 2014, PHILOSOPHICAL TRANSA, V369, P1634; Buxbaum, BRAIN IN PRESS; Buxbaum LJ, 1998, COGNITIVE NEUROPSYCH, V15, P279; Buxbaum LJ, 2007, CORTEX, V43, P411, DOI 10.1016/S0010-9452(08)70466-0; Buxbaum LJ, 2000, NEUROCASE, V6, P83, DOI 10.1080/13554790008402763; Buxbaum LJ, 2005, NEUROPSYCHOLOGIA, V43, P917, DOI 10.1016/j.neuropsychologia.2004.09.006; Buxbaum LJ, 2001, NEUROCASE, V7, P445, DOI 10.1093/neucas/7.6.445; Buxbaum LJ, 2005, COGNITIVE BRAIN RES, V25, P226, DOI 10.1016/j.cogbrainres.2005.05.014; Buxbaum LJ, 2006, BRAIN RES, V1117, P175, DOI 10.1016/j.brainres.2006.08.010; Buxbaum LJ, 2003, NEUROPSYCHOLOGIA, V41, P1091, DOI 10.1016/S0028-3932(02)00314-7; Campanella F, 2009, BRAIN, V132, P87, DOI 10.1093/brain/awn302; Campanella F, 2011, EXP BRAIN RES, V208, P369, DOI 10.1007/s00221-010-2489-7; Campanella F, 2011, COGNITION, V118, P417, DOI 10.1016/j.cognition.2010.08.005; Caspers S, 2010, NEUROIMAGE, V50, P1148, DOI 10.1016/j.neuroimage.2009.12.112; Castelli F, 2000, NEUROIMAGE, V12, P314, DOI 10.1006/nimg.2000.0612; Chao LL, 2000, NEUROIMAGE, V12, P478, DOI 10.1006/nimg.2000.0635; Chatterjee Anjan, 2008, Seminars in Speech and Language, V29, P226, DOI 10.1055/s-0028-1082886; CLARK MA, 1994, BRAIN, V117, P1093, DOI 10.1093/brain/117.5.1093; Colangelo A, 2003, BRAIN COGNITION, V53, P166, DOI 10.1016/S0278-2626(03)00102-7; Connell L, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0033321; Corbett F, 2011, J COGNITIVE NEUROSCI, V23, P1125, DOI 10.1162/jocn.2010.21539; DAMASIO AR, 1990, TRENDS NEUROSCI, V13, P95, DOI 10.1016/0166-2236(90)90184-C; DERENZI E, 1994, CORTEX, V30, P3; di Pellegrino G, 2005, CURR BIOL, V15, P1469, DOI 10.1016/j.cub.2005.06.068; FAGLIONI P, 1990, ATTENTION PERFORM, V8, P837; Frak V, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0009728; Garcea Frank E., 2013, FRONTIERS IN HUMAN N, V7; GESCHWIND N, 1975, AM SCI, V63, P188; Giese MA, 2003, NAT REV NEUROSCI, V4, P179, DOI 10.1038/nrn1057; Goldenberg G, 2009, BRAIN, V132, P1645, DOI 10.1093/brain/awp080; Goldenberg G, 2009, NEUROPSYCHOLOGIA, V47, P1449, DOI 10.1016/j.neuropsychologia.2008.07.014; Grafton ST, 2009, ANN NY ACAD SCI, V1156, P97, DOI 10.1111/j.1749-6632.2009.04425.x; Green C, 2006, J EXP PSYCHOL HUMAN, V32, P1107, DOI 10.1037/0096-1523.32.5.1107; Griffiths D, 2012, Q J EXP PSYCHOL, V65, P1241, DOI 10.1080/17470218.2012.688978; HAALAND KY, 1984, BRAIN COGNITION, V3, P370, DOI 10.1016/0278-2626(84)90029-0; Haaland KY, 1999, BRAIN, V122, P1169, DOI 10.1093/brain/122.6.1169; Haaland KY, 2000, BRAIN, V123, P2306, DOI 10.1093/brain/123.11.2306; Hauk O, 2004, NEURON, V41, P301, DOI 10.1016/S0896-6273(03)00838-9; Heilman K. M., 1982, NEUROLOGY, V32; Heilman K. M., 1993, CLIN NEUROPSYCHOLOGY, P141; Holmes CJ, 1998, J COMPUT ASSIST TOMO, V22, P324, DOI 10.1097/00004728-199803000-00032; Huettig F, 2005, COGNITION, V96, pB23, DOI 10.1016/j.cognition.2004.10.003; Huettig F, 2007, VIS COGN, V15, P985, DOI 10.1080/13506280601130875; Ishibashi R, 2011, NEUROPSYCHOLOGIA, V49, P1128, DOI 10.1016/j.neuropsychologia.2011.01.004; James TW, 2011, NEUROPSYCHOLOGIA, V49, P108, DOI 10.1016/j.neuropsychologia.2010.10.030; Jarry C, 2013, CORTEX, V49, P2322, DOI 10.1016/j.cortex.2013.02.011; Jax SA, 2006, J COGNITIVE NEUROSCI, V18, P2063, DOI 10.1162/jocn.2006.18.12.2063; Jax SA, 2013, J NEUROPSYCHOL, V7, P12, DOI 10.1111/j.1748-6653.2012.02031.x; Jeannerod M, 2001, NEUROIMAGE, V14, pS103, DOI 10.1006/nimg.2001.0832; Jefferies E, 2006, BRAIN, V129, P2132, DOI 10.1093/brain/awl153; Jefferies E, 2008, NEUROPSYCHOLOGIA, V46, P649, DOI 10.1016/j.neuropsychologia.2007.09.007; Jefferies E, 2007, NEUROPSYCHOLOGIA, V45, P1065, DOI 10.1016/j.neuropsychologia.2006.09.009; Kable JW, 2005, J COGNITIVE NEUROSCI, V17, P1855, DOI 10.1162/089892905775008625; Kalenine S, 2010, BRAIN, V133, P3269, DOI 10.1093/brain/awq210; Kalenine S, 2012, J EXP PSYCHOL LEARN, V38, P1274, DOI 10.1037/a0027626; Kalenine S, 2012, FRONT HUM NEUROSCI, V6, DOI 10.3389/fnhum.2012.00106; Kellenbach ML, 2003, J COGNITIVE NEUROSCI, V15, P30, DOI 10.1162/089892903321107800; Kertesz A., 1982, WESTERN APHASIA BATT; Knutson KM, 2012, COGN NEUROSCI-UK, V3, P131, DOI 10.1080/17588928.2012.688018; Knutson KM, 2013, NEUROPSYCHOLOGIA, V51, P686, DOI 10.1016/j.neuropsychologia.2013.01.003; Lee C. L., 2012, JOURNAL OF EXPERIMEN, V39, P257; Lieberman MD, 2009, SOC COGN AFFECT NEUR, V4, P423, DOI 10.1093/scan/nsp052; Liepmann H., 1905, THE LEFT HEMISPHERE; Lordat J., 1843, L CASTEL; MARSHALL JC, 1988, NATURE, V336, P766, DOI 10.1038/336766a0; Martin A, 1996, NATURE, V379, P649, DOI 10.1038/379649a0; MCDONALD S, 1994, BRAIN COGNITION, V25, P250, DOI 10.1006/brcg.1994.1035; MILBERG W, 1981, BRAIN LANG, V14, P371, DOI 10.1016/0093-934X(81)90086-9; Mirman D., 2012, AGGREGATING FIXATION; Mirman D, 2012, NEUROPSYCHOLOGIA, V50, P1990, DOI 10.1016/j.neuropsychologia.2012.04.024; Mirman D, 2011, BRAIN LANG, V117, P53, DOI 10.1016/j.bandl.2011.01.004; Mirman D, 2009, MEM COGNITION, V37, P1026, DOI 10.3758/MC.37.7.1026; Mirman D., 2014, GROWTH CURVE ANALYSI; Mizelle JC, 2010, FRONT PSYCHOL, V1, DOI 10.3389/fpsyg.2010.00195; Morlaas J., 1928, CONTRIBUTION A LETUD; Myung JY, 2010, BRAIN LANG, V112, P101, DOI 10.1016/j.bandl.2009.12.003; Myung JY, 2006, COGNITION, V98, P223, DOI 10.1016/j.cognition.2004.11.010; Peeters RR, 2013, NEUROIMAGE, V78, P83, DOI 10.1016/j.neuroimage.2013.04.023; Pelgrims B, 2011, HUM BRAIN MAPP, V32, P1802, DOI 10.1002/hbm.21149; POIZNER H, 1995, BRAIN, V118, P227, DOI 10.1093/brain/118.1.227; POPPEL E, 1973, NATURE, V243, P295, DOI 10.1038/243295a0; Predovan D, 2014, NEUROCASE, V20, P263, DOI 10.1080/13554794.2013.770876; Puce A, 2003, PHILOS T R SOC B, V358, P435, DOI 10.1098/rstb.2002.1221; Pulvermuller F, 2005, NAT REV NEUROSCI, V6, P576, DOI 10.1038/nrn1706; Rafal R, 2006, BRAIN RES, V1080, P2, DOI 10.1016/j.brainres.2005.01.108; Randerath J, 2010, NEUROIMAGE, V53, P171, DOI 10.1016/j.neuroimage.2010.06.038; Randerath J, 2009, NEUROPSYCHOLOGIA, V47, P497, DOI 10.1016/j.neuropsychologia.2008.10.005; Reilly J, 2011, NEUROPSYCHOLOGY, V25, P413, DOI 10.1037/a0022738; Renzi E. D., 1988, BRAIN, V111, P1173; Riddoch MJ, 2011, NEUROCASE, V17, P1, DOI 10.1080/13554791003785919; Riggio L., 2005, BRAIN RESEARCH COGNI, V24, P355; Roberts KL, 2011, ATTEN PERCEPT PSYCHO, V73, P597, DOI 10.3758/s13414-010-0043-0; Rothi L. J. G., 1985, NEUROPSYCHOLOGICAL S, P65; Sakreida K, 2013, FRONT HUM NEUROSCI, V7, DOI 10.3389/fnhum.2013.00125; Schnur TT, 2009, P NATL ACAD SCI USA, V106, P322, DOI 10.1073/pnas.0805874106; Schuil KDI, 2013, FRONT HUM NEUROSCI, V7, DOI 10.3389/fnhum.2013.00100; Schultz J, 2005, NEURON, V45, P625, DOI 10.1016/j.neuron.2004.12.052; Schwartz MF, 2005, ARCH PHYS MED REHAB, V86, P1807, DOI 10.1016/j.apmr.2005.03.009; SHALLICE T, 1988, COGNITIVE NEUROPSYCH, V5, P133, DOI 10.1080/02643298808252929; Sirigu A, 1996, SCIENCE, V273, P1564, DOI 10.1126/science.273.5281.1564; SIRIGU A, 1995, CORTEX, V31, P41; Smania N, 2000, ARCH PHYS MED REHAB, V81, P379, DOI 10.1053/mr.2000.6921; Smania N, 2006, NEUROLOGY, V67, P2050, DOI 10.1212/01.wnl.0000247279.63483.1f; Springer A, 2012, PSYCHOL RES-PSYCH FO, V76, P456, DOI 10.1007/s00426-012-0411-6; Stamenova V, 2010, BRAIN COGNITION, V72, P483, DOI 10.1016/j.bandc.2010.01.004; Tettamanti M., 2011, JOURNAL OF COGNITIVE, V17, P273; Tidoni E, 2013, J NEUROSCI, V33, P611, DOI 10.1523/JNEUROSCI.2228-11.2013; Tipper SP, 2006, PSYCHON B REV, V13, P493, DOI 10.3758/BF03193875; TYLER LK, 1994, NEUROPSYCHOLOGIA, V32, P1001, DOI 10.1016/0028-3932(94)90049-3; Vanbellingen T, 2011, NEUROREHABILITATION, V28, P91, DOI 10.3233/NRE-2011-0637; Vingerhoets G, 2009, NEUROIMAGE, V47, P1832, DOI 10.1016/j.neuroimage.2009.05.100; WARRINGTON EK, 1984, BRAIN, V107, P829, DOI 10.1093/brain/107.3.829; WARRINGTON EK, 1979, BRAIN, V102, P43, DOI 10.1093/brain/102.1.43; WARRINGTON EK, 1987, BRAIN, V110, P1273, DOI 10.1093/brain/110.5.1273; Watson CE, 2013, J COGNITIVE NEUROSCI, V25, P1191, DOI 10.1162/jocn_a_00401; Witt JK, 2010, PSYCHOL SCI, V21, P1215, DOI 10.1177/0956797610378307; Wu DH, 2007, J COGNITIVE NEUROSCI, V19, P1542, DOI 10.1162/jocn.2007.19.9.1542; Yee E., 2007, JOURNAL OF COGNITIVE, V20, P592; Yee E, 2006, J EXP PSYCHOL LEARN, V32, P1, DOI 10.1037/0278-7393.32.1.1; Yee E, 2013, PSYCHOL SCI, V24, P909, DOI 10.1177/0956797612464658; Zwinkels A, 2004, CLIN REHABIL, V18, P819, DOI 10.1191/0269215504cr816oa 134 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0028-3932 1873-3514 NEUROPSYCHOLOGIA Neuropsychologia JUL 2014 59 13 26 10.1016/j.neuropsychologia.2014.04.004 14 Behavioral Sciences; Neurosciences; Psychology, Experimental Behavioral Sciences; Neurosciences & Neurology; Psychology AL0JR WOS:000338813700002 J Hurley, J; Teanby, NA; Irwin, PGJ; Calcutt, SB; Sefton-Nash, E Hurley, J.; Teanby, N. A.; Irwin, P. G. J.; Calcutt, S. B.; Sefton-Nash, E. Differentiability and retrievability of CO2 and H2O clouds on Mars from MRO/MCS measurements: A radiative-transfer study PLANETARY AND SPACE SCIENCE English Article Mars; Atmospheres; Clouds; Infrared; MRO/MCS; CO2 CARBON-DIOXIDE; MARTIAN MESOSPHERE; UPPER-ATMOSPHERE; ICE CLOUDS; EMISSION; ULTRAVIOLET; ABSORPTION; PHOBOS; VENUS Since the 1970s, it has been predicted that both CO2 and H2O clouds can form in the Martian atmosphere, and many remote-sounding instruments have directly observed layers of extinction asserted to be clouds composed of either CO2 or H2O ice on Mars. The Mars Climate Sounder, onboard the Mars Reconnaissance Orbiter (MRO/MCS), entered orbit around Mars in 2006, and has been providing near-continuous coverage of the full planet since, at wavelengths from visible through to the mid-infrared, primarily in limb-viewing geometry, making it a suitable candidate to study the parameters of these clouds. In this work, the multiple scattering radiative-transfer tool NemesisMCS has been used to create a large dataset of simulations of CO2 and H2O clouds on Mars as would be measured by MRO/MCS, using a range of atmospheric conditions as well as cloud parameters derived from literature suitable for upper atmospheric clouds, and building specifically on the work of Sefton-Nash et al. (2013). This ensemble of simulations has been used to characterise the spectral signature of plausible CO2 and H2O clouds, as well as to assess the suitability of MRO/MCS to observe, to differentiate between, and to derive properties of such clouds. It has been found, given the noise levels expected for MRO/MCS and the range of atmospheric and cloud parameters sampled in this study, that radiance signals introduced by upper atmospheric clouds having nadir optical depths greater than about 10(-5) should be distinguishable, with S/N >= 1. This corresponds to specific concentrations greater than about 105 particles/g, particle radii greater than around 0.5 mu m, and cloud depths greater than about 2 km. MRO/MCS measurements should be able to be used with confidence to differentiate between upper atmospheric cloud and dust in the lower atmosphere, and clear conditions, with high success (approximate to 100%). Lower reliability classification is accomplished for CO2 clouds, with only 60% being correctly identified as CO2, and the remainder classified instead as H2O cloud, in the case of optical depths in the expected range for upper atmospheric cloudswhich are detectable by MRO/MCS, although this result is highly dependent upon the sampled selection of optically thin and thick clouds and the atmospheric model employed. Although almost all the H2O clouds are correctly identified, the fact that such a large proportion of CO2 clouds are misclassified as H2O clouds shows that the spectral information alone from MRO/MCS is insufficient to differentiate between CO2 and H2O clouds when optically thin but detectable clouds are included in the analysis. Using a simple look-up table (LUT) scheme and simulated data, retrieval of properties of upper atmospheric clouds of sufficient opacity is possible, with preliminary estimates indicating that H2O cloud and dust parameters can be correctly reproduced between 48% and 100% of the time, and between 18% and 92% of the time for CO2 cloud test cases, although it must be noted that these values must be taken as a qualitative measure which does not capture the full range of atmospheric and cloud conditions on Mars which would be present in real MRO/MCS data. Furthermore, because of the optical properties of H2O and CO2, on a like-with-like selection, the H2O clouds always produce greater perturbations in radiance, thus biasing results to a higher success rate for H2O cloud retrievals. Application of the method to MRO/MCS data with a full-optimal estimation retrieval tool such as Nemesismcs will be the topic of a future study. (C) 2014 Elsevier Ltd. All rights reserved. [Hurley, J.] STFC Rutherford Appleton Lab, Didcot OX11 0QX, Oxon, England; [Hurley, J.; Irwin, P. G. J.; Calcutt, S. B.] Univ Oxford, Dept Phys, Clarendon Lab, Oxford OX1 3PU, England; [Teanby, N. A.] Univ Bristol, Sch Earth Sci, Bristol BS8 1RJ, Avon, England; [Sefton-Nash, E.] Univ Calif Los Angeles, Sch Earth & Space Sci, Los Angeles, CA 90095 USA Hurley, J (reprint author), STFC Rutherford Appleton Lab, Harwell Sci & Innovat Campus, Didcot OX11 0QX, Oxon, England. jane.hurley@stfc.ac.uk STFC (Science and Technology Facilities Council); Leverhulme Trust The authors would like to thank J.T. Schofield, DJ. McCleese, A. Kleinbohl, D.M. Kass and P.O. Hayne of NASA/JPL for many helpful comments, clarifications and input both on the analysis itself and on the written drafts of the paper, as well as for providing expertise on MRO/MCS. We acknowledge the STFC (Science and Technology Facilities Council) and the Leverhulme Trust for supporting the principal and co-authors. Clancy RT, 2007, J GEOPHYS RES-PLANET, V112, DOI 10.1029/2006JE002805; Clancy RT, 1998, GEOPHYS RES LETT, V25, P489, DOI 10.1029/98GL00114; Clancy R.T., 2004, B AM ASTRON SOC, V36, P1128; CLANCY RT, 1995, J GEOPHYS RES-PLANET, V100, P5251, DOI 10.1029/94JE01885; Colaprete, 2003, J GEOPHYS RES, V108, P5025; Colaprete A, 2003, J GEOPHYS RES-PLANET, V108, DOI 10.1029/2003JE002053; CONRATH BJ, 1975, ICARUS, V24, P36, DOI 10.1016/0019-1035(75)90156-6; DEMING D, 1983, ICARUS, V55, P347, DOI 10.1016/0019-1035(83)90107-0; Gonzalez-Galindo F, 2011, ICARUS, V216, P10, DOI 10.1016/j.icarus.2011.08.006; GOODY R, 1989, J QUANT SPECTROSC RA, V42, P539, DOI 10.1016/0022-4073(89)90044-7; Greeley R, 2003, J GEOPHYS RES-PLANET, V108, DOI 10.1029/2002JE001987; Hansen GB, 1997, J GEOPHYS RES-PLANET, V102, P21569, DOI 10.1029/97JE01875; Hayne PO, 2012, J GEOPHYS RES-PLANET, V117, DOI 10.1029/2011JE004040; HERR KC, 1970, SCIENCE, V167, P47, DOI 10.1126/science.167.3914.47; Irwin PGJ, 2008, J QUANT SPECTROSC RA, V109, P1136, DOI 10.1016/j.jqsrt.2007.11.006; JOHNSON MA, 1976, ASTROPHYS J, V208, pL145, DOI 10.1086/182252; Kleinbohl A, 2009, J GEOPHYS RES-PLANET, V114, DOI 10.1029/2009JE003358; KORABLEV OI, 1993, ICARUS, V102, P76, DOI 10.1006/icar.1993.1033; LACIS AA, 1991, J GEOPHYS RES-ATMOS, V96, P9027, DOI 10.1029/90JD01945; Lewis SR, 1999, J GEOPHYS RES-PLANET, V104, P24177, DOI 10.1029/1999JE001024; Lide D.R., 1995, CRC HDB CHEM PHYS; Maattanen A, 2010, ICARUS, V209, P452, DOI 10.1016/j.icarus.2010.05.017; McCleese DJ, 2007, J GEOPHYS RES-PLANET, V112, DOI 10.1029/2006JE002790; McConnochie TH, 2010, ICARUS, V210, P545, DOI 10.1016/j.icarus.2010.07.021; Montabone L, 2011, MARS ANAL CORRECTION; Montmessin F, 2006, ICARUS, V183, P403, DOI 10.1016/j.icarus.2006.03.015; Montmessin F., 2007, J GEOPHYS RES, V112, P1; Mulholland D., 2011, 4 INT WORKSH MARS AT; Schofield JT, 1997, SCIENCE, V278, P1752, DOI 10.1126/science.278.5344.1752; Scholten F, 2010, PLANET SPACE SCI, V58, P1207, DOI 10.1016/j.pss.2010.04.015; Sefton-Nash E, 2013, ICARUS, V222, P342, DOI 10.1016/j.icarus.2012.11.012; Seiff A., 1977, Journal of Geophysical Research, V82, DOI 10.1029/JS082i028p04364; Smith PH, 1997, SCIENCE, V278, P1758, DOI 10.1126/science.278.5344.1758; Thomas G.E., 2009, SATELLITE AEROSOL RE; Toon O., 1977, PHYS PROPERTIES PART; WARREN SG, 1984, APPL OPTICS, V23, P1206; Wolff M. J., 2006, Journal of Geophysical Research-Part E-Planets, V111, DOI 10.1029/2006JE002786 37 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0032-0633 PLANET SPACE SCI Planet Space Sci. JUL 2014 97 65 84 10.1016/j.pss.2014.03.015 20 Astronomy & Astrophysics Astronomy & Astrophysics AK7OS WOS:000338618200008 J Ma, PF; Chen, LF; Tao, JH; Su, L; Tao, MH; Wang, ZF; Zou, MM; Zhang, Y Ma Peng-fei; Chen Liang-fu; Tao Jin-hua; Su Lin; Tao Ming-hui; Wang Zi-feng; Zou Ming-min; Zhang Ying Simulation of Atmospheric Temperature and Moisture Profiles Retrieval from CrIS Observations SPECTROSCOPY AND SPECTRAL ANALYSIS Chinese Article CrIS; Atmospheric profile; Eigenvector; Nonlinear newton iteration; Jacobian VALIDATION In order to get higher vertical resolution atmosphere profile information, the present paper retrieves atmospheric temperature and moisture profiles from the Cross-track Infrared Sounder (CrIS) on the newly-launched Suomi National Polar-orbiting Partnership (Suomi NPP) and future Joint Polar Satellite System (JPSS) with a nonlinear Newton iteration method by using the profiles retrieved via statical regression method as the first guess, and the issue of channel selection is discussed. The retrieved profiles are compared with radiosonde observations, and National Centers for Environmental Prediction (NCEP) Global Data Assimilation System (GDAS) analyses show that the physical retrievals of temperature and moisture are in good agreement with the distributions from GDAS analysis fields and radiosonde observations, and have a notable improvements of the atmospheric profile retrieval accuracy as compared with the eigenvector regression algorithm. For pressures between 200 and 700 hPa the accuracy is of the order of 1 K for the temperature profile, and 20% for the relative humidity profile is consistent with the jacobian peaks of the selected channels. [Ma Peng-fei; Chen Liang-fu; Tao Jin-hua; Su Lin; Tao Ming-hui; Wang Zi-feng; Zou Ming-min; Zhang Ying] Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100101, Peoples R China; [Ma Peng-fei] Univ Chinese Acad Sci, Beijing 100049, Peoples R China Chen, LF (reprint author), Chinese Acad Sci, State Key Lab Remote Sensing Sci, Inst Remote Sensing & Digital Earth RADI, Beijing 100101, Peoples R China. mpf136@163.com; lfchen@irsa.ac.cn CHURNSIDE JH, 1994, J ATMOS OCEAN TECH, V11, P105, DOI 10.1175/1520-0426(1994)011<0105:TPWNNI>2.0.CO;2; Han Y P, 2006, NOAA TECH REP NESDIS; Li J, 2001, SCI CHINA SER D, V31, P70; Li J, 2000, J APPL METEOROL, V39, P1248, DOI 10.1175/1520-0450(2000)039<1248:GSOTAF>2.0.CO;2; LI Wan-biao, 2003, JOURNAL OF PEKING UN, V39, P656; Pougatchev N, 2009, ATMOS CHEM PHYS, V9, P6453; RODGERS CD, 1976, REV GEOPHYS, V14, P609, DOI 10.1029/RG014i004p00609; Smith W L, 2009, ATMOS CHEM PHYS DISC, V9, P6541, DOI DOI 10.5194/ACPD-9-6541-2009; SMITH WL, 1976, J ATMOS SCI, V33, P1127, DOI 10.1175/1520-0469(1976)033<1127:TUOEOS>2.0.CO;2; SMITH WL, 1970, APPL OPTICS, V9, P1993, DOI 10.1364/AO.9.001993; Weng F, 2005, TECH P 14 INT ATOVS, P217; ZENG Qing-cun, 1974, PRINCIPLES FOR ATMOS; 李俊, 1994, Advances in Atmospheric Sciences, V11, P128 13 0 0 OFFICE SPECTROSCOPY & SPECTRAL ANALYSIS BEIJING NO 76 COLLAGE SOUTH RD BEIJING, BEIJING 100081, PEOPLES R CHINA 1000-0593 SPECTROSC SPECT ANAL Spectrosc. Spectr. Anal. JUL 2014 34 7 1894 1897 10.3964/j.issn.1000-0593(2014)07-1894-04 4 Spectroscopy Spectroscopy AL0OQ WOS:000338826600034 J Forsati, R; Shamsfard, M Forsati, Rana; Shamsfard, Mehrnoush Hybrid PoS-tagging: A cooperation of evolutionary and statistical approaches APPLIED MATHEMATICAL MODELLING English Article Part-of-Speech tagging; Bee colony optimization; Natural language processing; Evolutionary algorithms; Optimization HARMONY SEARCH; GENETIC ALGORITHMS The assigning of syntactic categories to words in a sentence, which is referred to as part-of-speech (PoS) tagging problem, plays an essential role in many natural language processing and information retrieval applications. Despite the vast scope of methods, PoS-tagging brings an array of challenges that require novel solutions. To address these challenges in a principled way, one solution would be to formulate the tagging problem as an optimization problem with well-specified objectives and then apply the evolutionary methods to solve the optimization problem. This paper discusses the relative advantages of different evolutionary approaches to handle Part-of-Speech tagging problem and aims at presenting novel language-independent evolutionary algorithms to solve the PoS tagging problem. We show that by exploiting statistical measures to evaluate the solutions in tagging process, the proposed algorithms are able to generate more accurate solution in a reasonable amount of time. The experiments we have conducted on few well known corpus reveal that the proposed algorithms achieve better average accuracy in comparison to other evolutionary-based and classical Part-of-Speech tagging methods. (C) 2014 Elsevier Inc. All rights reserved. [Forsati, Rana; Shamsfard, Mehrnoush] Shahid Beheshti Univ, Fac Elect & Comp Engn, Nat Language Proc NLP Res Lab, GC, Tehran, Iran Forsati, R (reprint author), Shahid Beheshti Univ, Fac Elect & Comp Engn, Nat Language Proc NLP Res Lab, GC, Tehran, Iran. r_forsati@sbu.ac.ir; m_shams@sbu.ac.ir Al Shamsi F., 2006, P 8 INT C STAT AN TE, P31; Alba E, 2006, INFORM PROCESS LETT, V100, P173, DOI 10.1016/j.ipl.2006.07.002; Aone C., 1996, P COLING, V96, P53; Araujo L, 2004, IEEE T EVOLUT COMPUT, V8, P14, DOI 10.1109/TEVC.2003.818189; Araujo L., 2002, Computational Linguistics and Intelligent Text Processing. Third International Conference, CICLing 2002. Proceedings (Lecture Notes in Computer Science Vol.2276); Araujo L, 2003, LECT NOTES COMPUT SC, V2724, P1951; Brants T., 2000, P 6 C APPL NAT LANG, P224, DOI 10.3115/974147.974178; Brill E, 1995, COMPUT LINGUIST, V21, P543; Carberry S., 2001, NAT LANG ENG, V7, P99; Carlberger J, 1999, SOFTWARE PRACT EXPER, V29, P815, DOI 10.1002/(SICI)1097-024X(19990725)29:9<815::AID-SPE256>3.0.CO;2-F; Charniak E., 1996, STAT LANGUAGE LEARNI; Collins Michael, 2002, P C EMP METH NAT LAN, V10, P1, DOI 10.3115/1118693.1118694; Curran J.R., 2000, 23 AUSTR C SCI C 200, P51; Daelemans W., 1996, P 4 WORKSH VER LARG, P14; DeRose S. J., 1988, Computational Linguistics, V14; Ekbal A, 2008, ICIT 2008: PROCEEDINGS OF THE 11TH INTERNATIONAL CONFERENCE ON INFORMATION TECHNOLOGY, P106, DOI 10.1109/ICIT.2008.12; FORNEY GD, 1973, P IEEE, V61, P268, DOI 10.1109/PROC.1973.9030; Forsati R., 2012, 2012 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), DOI 10.1109/AISP.2012.6313789; Forsati R, 2008, COMPUT COMMUN, V31, P2505, DOI 10.1016/j.comcom.2008.03.019; Forsati R, 2010, STUD COMPUT INTELL, V270, P51; Forsati R, 2013, INFORM SCIENCES, V220, P269, DOI 10.1016/j.ins.2012.07.025; Francis W.N., 1979, MANUAL INFORM ACCOMP; Geem ZW, 2008, APPL MATH COMPUT, V199, P223, DOI 10.1016/j.amc.2007.09.049; Geem ZW, 2005, LECT NOTES COMPUT SC, V3612, P741; Gimenez J., 2004, P 4 INT C LANG RES E; Gungor T., 2010, HDB NATURAL LANGUAGE, V2, P10; Jelinek F., 1985, Impact of Processing Techniques on Communications. Proceedings of the NATO Advanced Study Institute; Jurafsky D., 2010, SPEECH LANGUAGE PROC; Jurafsky D., SPEECH LANGUAGE PROC; Kudo T., 2004, P 2004 C EMP METH NA, P230; Lee GG, 2002, COMPUT LINGUIST, V28, P53, DOI 10.1162/089120102317341774; Lee KS, 2005, COMPUT METHOD APPL M, V194, P3902, DOI 10.1016/j.cma.2004.09.007; Lee S.-Z., 2000, P 1 N AM ANN M ASS C, P263, DOI 10.3115/1075218.1075252; Losee RM, 1996, INFORM PROCESS MANAG, V32, P185, DOI 10.1016/S0306-4573(96)85005-9; Lua K., 1996, P ICCC96, P45; Lucic P., 2001, P TRIENN S TRANSP AN, P441; Mahdavi M, 2008, APPL MATH COMPUT, V201, P441, DOI 10.1016/j.amc.2007.12.058; Manning C., 1999, FDN STAT NATURAL LAN; Marquez L., AUT ACQ LANG MOD POS; Mirkhani M., ROBOTICS AUTONOMOUS; Pla F., 2004, Natural Language Engineering, DOI 10.1017/S1351324904003353; Ratnaparkhi A., 1996, P EMNLP, V1, P133; Reiser P.G., 1999, P 1999 C EV COMP 199, V2; Sarikaya R, 2008, IEEE T AUDIO SPEECH, V16, P1330, DOI 10.1109/TASL.2008.924591; Selmic M, 2010, TRANSPORT PLAN TECHN, V33, P481, DOI 10.1080/03081060.2010.505047; Smith L.H., 2006, NAT LANG ENG, V12, P335, DOI 10.1017/S1351324905003967; Tasharofi S., 2007, 9 INT S SIGN PROC IT, P1; Teodorovic D., 2011, ACM T COMPUT LOG, V1529, P3785; Van Halteren H., 1998, P 36 ANN M ASS COMP, V1, P491; Volk M., CS9811016 ARXIV 50 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0307-904X 1872-8480 APPL MATH MODEL Appl. Math. Model. JUL 1 2014 38 13 3193 3211 10.1016/j.apm.2013.11.047 19 Engineering, Multidisciplinary; Mathematics, Interdisciplinary Applications; Mechanics Engineering; Mathematics; Mechanics AK1MT WOS:000338179900011 J van Harten, G; Snik, F; Rietjens, JHH; Smit, JM; Keller, CU van Harten, Gerard; Snik, Frans; Rietjens, Jeroen H. H.; Smit, J. Martijn; Keller, Christoph U. Spectral line polarimetry with a channeled polarimeter APPLIED OPTICS English Article POLARIZATION GRATINGS; AEROSOL PROPERTIES; SKYLIGHT; COMPACT; BAND; RETRIEVAL; PRODUCTS; SPACE Channeled spectropolarimetry or spectral polarization modulation is an accurate technique for measuring the continuum polarization in one shot with no moving parts. We show how a dual-beam implementation also enables spectral line polarimetry at the intrinsic resolution, as in a classic beam-splitting polarimeter. Recording redundant polarization information in the two spectrally modulated beams of a polarizing beam-splitter even provides the possibility to perform a postfacto differential transmission correction that improves the accuracy of the spectral line polarimetry. We perform an error analysis to compare the accuracy of spectral line polarimetry to continuum polarimetry, degraded by a residual dark signal and differential transmission, as well as to quantify the impact of the transmission correction. We demonstrate the new techniques with a blue sky polarization measurement around the oxygen A absorption band using the groundSPEX instrument, yielding a polarization in the deepest part of the band of 0.160 +/- 0.010, significantly different from the polarization in the continuum of 0.2284 +/- 0.0004. The presented methods are applicable to any dual-beam channeled polarimeter, including implementations for snapshot imaging polarimetry. (C) 2014 Optical Society of America [van Harten, Gerard; Snik, Frans; Keller, Christoph U.] Leiden Univ, Leiden Observ, NL-2333 CA Leiden, Netherlands; [Rietjens, Jeroen H. H.; Smit, J. Martijn] SRON Netherlands Inst Space Res, NL-3584 CA Utrecht, Netherlands van Harten, G (reprint author), Leiden Univ, Leiden Observ, Niels Bohrweg 2, NL-2333 CA Leiden, Netherlands. harten@strw.leidenuniv.nl Utrecht University GvH acknowledges Utrecht University for funding his research. The groundSPEX instrument was developed for the National Institute for Public Health and the Environment (RIVM). The CESAR Observatory is operated by the Royal Netherlands Meteorological Institute (KNMI). We gratefully acknowledge Piet Stammes for stimulating discussions and valuable comments on an earlier version of the manuscript. We thank the anonymous reviewers for their helpful comments. Aben I, 1999, GEOPHYS RES LETT, V26, P591, DOI 10.1029/1999GL900025; Apituley A., 2009, P 8 INT S TROP PROF; Beelen R, 2014, LANCET, V383, P785, DOI 10.1016/S0140-6736(13)62158-3; Bermudo F., 2004, ESA SPECIAL PUBLICAT, V554, P139; Boesche E, 2006, APPL OPTICS, V45, P8790, DOI 10.1364/AO.45.008790; Boesche E, 2008, APPL OPTICS, V47, P3467, DOI 10.1364/AO.47.003467; Craven J, 2010, OPT ENG, V49, DOI 10.1117/1.3430565; de Graaf M, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2004JD005178; Hagen N, 2007, OPT LETT, V32, P2100, DOI 10.1364/OL.32.002100; Hasekamp OP, 2007, APPL OPTICS, V46, P3332, DOI 10.1364/AO.46.003332; KAWATA Y, 1978, ICARUS, V33, P217, DOI 10.1016/0019-1035(78)90035-0; Kudenov M. W., 2011, POLARIZED LIGHT, P401; Kudenov MW, 2011, APPL OPTICS, V50, P2283, DOI 10.1364/AO.50.002283; Kudenov MW, 2012, OPT LETT, V37, P1367, DOI 10.1364/OL.37.001367; Kudenov MW, 2012, OPT EXPRESS, V20, P17973, DOI 10.1364/OE.20.017973; Levelt PF, 2006, IEEE T GEOSCI REMOTE, V44, P1093, DOI 10.1109/TGRS.2006.872333; Lyot B., 1933, Comptes Rendus Hebdomadaires des Seances de l'Academie des Sciences, V197; Mishchenko MI, 2004, J QUANT SPECTROSC RA, V88, P149, DOI 10.1016/j.jqsrt.2004.03.030; NORDSIEC.KH, 1974, PUBL ASTRON SOC PAC, V86, P324, DOI 10.1086/129610; Oka K, 2003, OPT EXPRESS, V11, P1510; Oka K, 1999, OPT LETT, V24, P1475, DOI 10.1364/OL.24.001475; Oka K, 2006, P SOC PHOTO-OPT INS, V6295, P29508, DOI 10.1117/12.683284; Pust NJ, 2012, OPT EXPRESS, V20, P15559, DOI 10.1364/OE.20.015559; Pust NJ, 2006, APPL OPTICS, V45, P5470, DOI 10.1364/AO.45.005470; Remer LA, 2005, J ATMOS SCI, V62, P947, DOI 10.1175/JAS3385.1; Rietjens J. H. H., PERFORMANCE SP UNPUB; Snik F, 2009, APPL OPTICS, V48, P1337, DOI 10.1364/AO.48.001337; Snik F., 2013, PLANETS STARS STELLA, V2, P175; Sparks W, 2012, APPL OPTICS, V51, P5495, DOI 10.1364/AO.51.005495; Stam DL, 1999, J GEOPHYS RES-ATMOS, V104, P16843, DOI 10.1029/1999JD900159; Stammes P., 1994, P PROGR EL RES S; Stocker T. F., 2013, WORKING GROUP 1 CONT; Tyo JS, 2006, APPL OPTICS, V45, P5453, DOI 10.1364/AO.45.005453; van Harten G, 2011, PROC SPIE, V8160, DOI 10.1117/12.893741; van Harten G., ATMOS MEAS IN PRESS, V7; Vaughan M, 2004, BBA LIB, V5575, P16, DOI 10.1117/12.572024 36 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics JUL 1 2014 53 19 4187 4194 10.1364/AO.53.004187 8 Optics Optics AK5DF WOS:000338443900014 J Brown, TI; Stern, CE Brown, Thackery I.; Stern, Chantal E. Contributions of Medial Temporal Lobe and Striatal Memory Systems to Learning and Retrieving Overlapping Spatial Memories CEREBRAL CORTEX English Article caudate; fMRI; hippocampus; navigation; parahippocampal EPISODIC MEMORY; PARAHIPPOCAMPAL CORTEX; HIPPOCAMPAL REPLAY; BASAL GANGLIA; HUMAN BRAIN; CONTEXT; EVENTS; RECONSOLIDATION; INFORMATION; RECOGNITION Many life experiences share information with other memories. In order to make decisions based on overlapping memories, we need to distinguish between experiences to determine the appropriate behavior for the current situation. Previous work suggests that the medial temporal lobe (MTL) and medial caudate interact to support the retrieval of overlapping navigational memories in different contexts. The present study used functional magnetic resonance imaging (fMRI) in humans to test the prediction that the MTL and medial caudate play complementary roles in learning novel mazes that cross paths with, and must be distinguished from, previously learned routes. During fMRI scanning, participants navigated virtual routes that were well learned from prior training while also learning new mazes. Critically, some routes learned during scanning shared hallways with those learned during pre-scan training. Overlap between mazes required participants to use contextual cues to select between alternative behaviors. Results demonstrated parahippocampal cortex activity specific for novel spatial cues that distinguish between overlapping routes. The hippocampus and medial caudate were active for learning overlapping spatial memories, and increased their activity for previously learned routes when they became context dependent. Our findings provide novel evidence that the MTL and medial caudate play complementary roles in the learning, updating, and execution of context-dependent navigational behaviors. [Brown, Thackery I.; Stern, Chantal E.] Boston Univ, Dept Psychol, Boston, MA 02215 USA; [Brown, Thackery I.; Stern, Chantal E.] Boston Univ, Ctr Memory & Brain, Boston, MA 02215 USA; [Brown, Thackery I.; Stern, Chantal E.] Massachusetts Gen Hosp, Athinoula A Martinos Ctr Biomed Imaging, Charlestown, MA 02129 USA Brown, TI (reprint author), Boston Univ, Ctr Memory & Brain, 2 Cummington St, Boston, MA 02215 USA. thackery@bu.edu National Institutes of Health [P50 MH094263]; Office of Naval Research [MURI N00014-10-1-0936]; National Center for Research Resources Grant [P41RR14075] This work was supported by the National Institutes of Health grant P50 MH094263 and Office of Naval Research grant MURI N00014-10-1-0936 to the Cognitive Neuroimaging Lab, Center for Memory and Brain, Boston University (Boston, MA), and a National Center for Research Resources Grant P41RR14075 to the Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School (Charlestown, MA). Addis DR, 2007, NEUROPSYCHOLOGIA, V45, P1363, DOI 10.1016/j.neuropsychologia.2006.10.016; Agster KL, 2002, J NEUROSCI, V22, P5760; Andersson JLR, 2001, NEUROIMAGE, V13, P903, DOI 10.1006/nimg.2001.0746; Ashburner J, 2007, NEUROIMAGE, V38, P95, DOI 10.1016/j.neuroimage.2007.07.007; Bar M, 2003, NEURON, V38, P347, DOI 10.1016/S0896-6273(03)00167-3; Brown TI, 2010, J NEUROSCI, V30, P7414, DOI 10.1523/JNEUROSCI.6021-09.2010; Brown TI, 2012, NEUROIMAGE, V60, P1316, DOI 10.1016/j.neuroimage.2012.01.046; Burgess N, 2001, NEUROIMAGE, V14, P439, DOI 10.1006/nimg.2001.0806; Davachi L, 2003, P NATL ACAD SCI USA, V100, P2157, DOI 10.1073/pnas.0337195100; Davachi L, 2006, CURR OPIN NEUROBIOL, V16, P693, DOI 10.1016/j.conb.2006.10.012; Davidson TJ, 2009, NEURON, V63, P497, DOI 10.1016/j.neuron.2009.07.027; DeCoteau WE, 2007, P NATL ACAD SCI USA, V104, P5644, DOI 10.1073/pnas.0700818104; Devan BD, 1999, J NEUROSCI, V19, P2789; Eichenbaum H, 2011, NEUROSCI BIOBEHAV R, V36, P1597, DOI DOI 10.1016/J.NEUBI0REV.2011.07.006; Eichenbaum H, 2007, ANNU REV NEUROSCI, V30, P123, DOI 10.1146/annurev.neuro.30.051606.094328; Ekstrom AD, 2007, LEARN MEMORY, V14, P645, DOI 10.1101/lm.575107; Epstein R, 1998, NATURE, V392, P598, DOI 10.1038/33402; Epstein RA, 2007, J NEUROSCI, V27, P6141, DOI 10.1523/JNEUROSCI.0799-07.2007; Epstein RA, 2007, CEREB CORTEX, V17, P1680, DOI 10.1093/cercor/bhl079; Fanselow MS, 2010, NEURON, V65, P7, DOI 10.1016/j.neuron.2009.11.031; Ferbinteanu J, 2003, NEURON, V40, P1227, DOI 10.1016/S0896-6273(03)00752-9; Foster DJ, 2011, CURR OPIN NEUROBIOL, V22, P1; Ginther MR, 2011, J NEUROSCI, V31, P2706, DOI 10.1523/JNEUROSCI.3413-10.2011; Graham S, 2009, NEUROIMAGE, V45, P1359, DOI 10.1016/j.neuroimage.2008.12.040; Gupta AS, 2010, NEURON, V65, P695, DOI 10.1016/j.neuron.2010.01.034; Hartley T, 2003, NEURON, V37, P877, DOI 10.1016/S0896-6273(03)00095-3; Hassabis D, 2007, J NEUROSCI, V27, P14365, DOI 10.1523/JNEUROSCI.4549-07.2007; Hasselmo ME, 2009, NEUROBIOL LEARN MEM, V92, P559, DOI 10.1016/j.nlm.2009.07.005; Hasselmo ME, 2005, NEURAL NETWORKS, V18, P1172, DOI 10.1016/j.neunet.2005.08.007; Howard LR, 2011, J NEUROSCI, V31, P5253, DOI 10.1523/JNEUROSCI.6055-10.2011; Hupbach A, 2008, LEARN MEMORY, V15, P574, DOI 10.1101/lm.1022308; Hupbach A, 2007, LEARN MEMORY, V14, P47, DOI 10.1101/lm.365707; Janzen G, 2004, NAT NEUROSCI, V7, P673, DOI 10.1038/nn1257; Jenkins LJ, 2010, J NEUROSCI, V30, P15558, DOI 10.1523/JNEUROSCI.1337-10.2010; Johnson A, 2007, CURR OPIN NEUROBIOL, V17, P692, DOI 10.1016/j.conb.2008.01.003; Johnson A, 2007, J NEUROSCI, V27, P12176, DOI 10.1523/JNEUROSCI.3761-07.2007; Kuhl BA, 2010, NAT NEUROSCI, V13, P501, DOI 10.1038/nn.2498; Kumaran D, 2006, NEURON, V49, P617, DOI 10.1016/j.neuron.2005.12.024; Lee I, 2006, NEURON, V51, P639, DOI 10.1016/j.neuron.2006.06.033; MacDonald CJ, 2011, NEURON, V71, P737, DOI 10.1016/j.neuron.2011.07.012; Maldjian JA, 2004, NEUROIMAGE, V21, P450, DOI 10.1016/j.neuroimage.2003.09.032; Maldjian JA, 2003, NEUROIMAGE, V19, P1233, DOI 10.1016/S1053-8119(03)00169-1; Monchi O, 2001, J NEUROSCI, V21, P7733; Monchi O, 2006, ANN NEUROL, V59, P257, DOI 10.1002/ana.20742; Mullally SL, 2011, J NEUROSCI, V31, P7441, DOI 10.1523/JNEUROSCI.0267-11.2011; O'Craven KM, 2000, J COGNITIVE NEUROSCI, V12, P1013, DOI 10.1162/08989290051137549; Pasupathy A, 2005, NATURE, V433, P873, DOI 10.1038/nature03287; Ragozzino ME, 2002, BRAIN RES, V953, P205, DOI 10.1016/S0006-8993(02)03287-0; Ranganath C, 2003, NEUROPSYCHOLOGIA, V42, P2; Rosenbaum RS, 2004, HIPPOCAMPUS, V14, P826, DOI 10.1002/hipo.10218; Ross RS, 2009, HIPPOCAMPUS, V19, P790, DOI 10.1002/hipo.20558; Ross RS, 2008, J COGNITIVE NEUROSCI, V20, P432, DOI 10.1162/jocn.2008.20.3.432; Sederberg PB, 2011, PSYCHON B REV, V18, P455, DOI 10.3758/s13423-011-0086-9; Shohamy D, 2008, NEURON, V60, P378, DOI 10.1016/j.neuron.2008.09.023; Smith DM, 2006, J NEUROSCI, V26, P3154, DOI 10.1523/JNEUROSCI.3234-05.2006; Thorn CA, 2010, NEURON, V66, P781, DOI 10.1016/j.neuron.2010.04.036; Tronson NC, 2007, NAT REV NEUROSCI, V8, P262, DOI 10.1038/nrn2090; Turk-Browne NB, 2012, J NEUROSCI, V32, P7202, DOI 10.1523/JNEUROSCI.0942-12.2012; Turnock M, 2008, BRAIN RES, V1202, P87, DOI 10.1016/j.brainres.2007.06.078; Tzourio-Mazoyer N, 2002, NEUROIMAGE, V15, P273, DOI 10.1006/nimg.2001.0978; Wood ER, 2000, NEURON, V27, P623, DOI 10.1016/S0896-6273(00)00071-4; Xue G, 2008, J NEUROSCI, V28, P11196, DOI 10.1523/JNEUROSCI.4001-08.2008; Yin HH, 2006, NAT REV NEUROSCI, V7, P464, DOI 10.1038/nrn1919; Yin HH, 2004, LEARN MEMORY, V11, P459, DOI 10.1101/lm.81004; Zilli EA, 2008, HIPPOCAMPUS, V18, P193, DOI 10.1002/hipo.20382 65 0 0 OXFORD UNIV PRESS INC CARY JOURNALS DEPT, 2001 EVANS RD, CARY, NC 27513 USA 1047-3211 1460-2199 CEREB CORTEX Cereb. Cortex JUL 2014 24 7 1906 1922 10.1093/cercor/bht041 17 Neurosciences Neurosciences & Neurology AK0NS WOS:000338110900018 J Pezzella, A; Barbonetti, A; D'Andrea, S; Necozione, S; Micillo, A; Di Gregorio, A; Francavilla, F; Francavilla, S Pezzella, A.; Barbonetti, A.; D'Andrea, S.; Necozione, S.; Micillo, A.; Di Gregorio, A.; Francavilla, F.; Francavilla, S. Ultrasonographic caput epididymis diameter is reduced in non-obstructive azoospermia compared with normozoospermia but is not predictive for successful sperm retrieval after TESE HUMAN REPRODUCTION English Article epididymis; ultrasonography; azoospermia; infertility; sperm retrieval SERUM FSH; TESTICULAR FUNCTION; DUCTULI EFFERENTES; INFERTILE MEN; SPERMATOGENESIS; VOLUME; SPERMATOZOA; ULTRASOUND; SONOGRAPHY; TESTICLES Is the ultrasonographic determination of the caput epididymis diameter predictive for sperm retrieval after testicular sperm extraction (TESE) in non-obstructive azoospermia (NOA)? Ultrasonographic determination of the caput epididymis diameter did not give any relevant clinical information in NOA and was not predictive for positive sperm retrieval after TESE. The diameter of the caput epididymis in ultrasonography (US) has a diagnostic relevance in azoospermic men to correctly identify obstructive azoospermia; however, its clinical value in NOA is not yet determined. We performed a retrospective study of 100 azoospermic and 160 normozoospermic men attending a university infertility clinic. Participants were submitted to scrotal US to determine the mean value of bilateral testicular volumes (ml), the bilateral longitudinal caput diameter (mm) and the antero-posterior diameter of the corpus (mm) epididymis. The number of spermatozoa retrieved after TESE and the testicular histology of azoospermic men was obtained and the percentage of seminiferous tubules with elongated spermatids (%T) was used to classify cases with normal spermatogenesis (obstructive azoospermia) (OA) (n = 20; %T a parts per thousand yen 80) or with NOA (n = 80; %T < 70). The US testes volumes and caput diameters were reduced (P < 0.05) in NOA compared with OA and with normozoospermia, but the corpus values were not different. The caput diameter in the side submitted to biopsy was significantly reduced when germinal epithelium was absent (Sertoli cell only) (P < 0.05) and the lowest value of caput diameter was observed when the seminiferous epithelium and tubule lumen were absent (testicular hyalinosis). On the contrary, a total arrest of spermatogenesis at the first meiosis level, or a defect of spermiogenesis resulting in scattered elongated spermatids in each tubule, did not show a reduced diameter of caput epididymis compared with normozoospermia. The caput diameter did not show any difference between NOA patients with or without successful sperm retrieval at TESE. On the contrary testicular volume was significantly reduced in NOA patients with no sperm retrieval (P = 0.0037). The caput diameter was not correlated with the number of retrieved sperm, the serum level of follicle stimulating hormone, or with the percentage of tubules with elongated spermatids at histological analysis. The aetiology of NOA was not included in the statistical analysis due to the low rate of cases with a specific aetiology for a testicular failure. Larger studies should exclude the possibility that besides testicular histology, aetiology of NOA might influence the diameter of caput epididymis. Moreover, whether a reduced diameter of caput epididymis is only a result of a testicular pathologic phenotype or whether it may underscore a primitive dysfunction influencing the number of ejaculated spermatozoa is not yet determined. We reported that US diameter of the caput epididymis is reduced in cases of NOA but, in contrast with the testicular volume, it is independent of the completion of spermatogenesis and subsequent presence of spermatozoa in the epididymis. Therefore ultrasonographic determination of caput epididymis diameter is not predictive for positive sperm retrieval after TESE in cases of a primitive testicular failure. Our novel findings may help to define which reproducible parameters of scrotal US should be assessed in the work-up of male infertility. This work was supported by the Ministero dell'UniversitA e Ricerca (I) PRIN 2009. The authors declare no competing interest. [Pezzella, A.; Barbonetti, A.; D'Andrea, S.; Necozione, S.; Micillo, A.; Di Gregorio, A.; Francavilla, F.; Francavilla, S.] Univ Aquila, Dept Life Hlth & Environm Sci Androl & Epidemiol, I-67100 Laquila, Italy Francavilla, S (reprint author), Univ Aquila, Dept Internal Med, Androl Unit, Via Vetoio, I-67100 Laquila, Italy. sandro.francavilla@univaq.it Ministero dell'Universita e Ricerca (I), PRIN This work was supported by a grant from the Ministero dell'Universita e Ricerca (I), PRIN 2009 (attributed to S.F.). Behre HM, 1995, INT J ANDROL, V18, P27; BERGMANN M, 1994, CLIN ENDOCRINOL, V40, P133, DOI 10.1111/j.1365-2265.1994.tb02455.x; Clulow J, 1998, J REPROD FERTIL, P1; DEKRETSE.DM, 1972, J CLIN ENDOCR METAB, V35, P392; DEVROEY P, 1995, HUM REPROD, V10, P1457; Dogra VS, 2003, RADIOLOGY, V227, P18, DOI 10.1148/radiol.2271001744; Du J, 2010, RADIOLOGY, V256, P493, DOI 10.1148/radiol.10091578; Francavilla S, 2001, HUM REPROD, V16, P1440, DOI 10.1093/humrep/16.7.1440; FRANCAVILLA S, 1986, J EMBRYOL EXP MORPH, V96, P51; FRANCHIM.P, 1972, J CLIN ENDOCR METAB, V34, P1003; FUSE H, 1990, INT J ANDROL, V13, P267, DOI 10.1111/j.1365-2605.1990.tb01031.x; Hauser R, 1998, HUM REPROD, V13, P3081, DOI 10.1093/humrep/13.11.3081; Hess RA, 2000, REV REPROD, V5, P84; Holstein AF, 1983, ADV ANDROLOGY, V8, P109; ILIO KY, 1994, MICROSC RES TECHNIQ, V29, P432, DOI 10.1002/jemt.1070290604; LENZ S, 1993, EUR UROL, V24, P231; LENZ S, 1994, HUM REPROD, V9, P878; Lotti F, 2012, HUM REPROD, V27, P974, DOI 10.1093/humrep/des032; Moon MH, 2006, RADIOLOGY, V239, P168, DOI 10.1148/radiol.2391050272; Oyen RH, 2002, EUR RADIOL, V12, P19, DOI 10.1007/s00330-001-1224-y; Pezzella A, 2013, ANDROLOGY-US, V1, P133, DOI 10.1111/j.2047-2927.2012.00010.x; Puhse G, 2011, HUM REPROD, V26, P2606, DOI 10.1093/humrep/der257; Puttemans T, 2006, J CLIN ULTRASOUND, V34, P385, DOI 10.1002/jcu.20257; Sakamoto H, 2008, ASIAN J ANDROL, V10, P319, DOI 10.1111/j.1745-7262.2008.00340.x; Silber SJ, 1997, HUM REPROD, V12, P2422, DOI 10.1093/humrep/12.11.2422; Tournaye H, 1996, HUM REPROD, V11, P127; Tuttelmann F, 2011, INT J ANDROL, V34, P291, DOI 10.1111/j.1365-2605.2010.01087.x; von Eckardstein S, 1999, J CLIN ENDOCR METAB, V84, P2496, DOI 10.1210/jc.84.7.2496; World Health Organization Department of Reproductive Health and Research, 2010, WHO LAB MAN EX PROC; YEUNG CH, 1991, AM J ANAT, V191, P261, DOI 10.1002/aja.1001910306 30 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 0268-1161 1460-2350 HUM REPROD Hum. Reprod. JUL 2014 29 7 1368 1374 10.1093/humrep/deu092 7 Obstetrics & Gynecology; Reproductive Biology Obstetrics & Gynecology; Reproductive Biology AK0SV WOS:000338126500005 J Sumiya, K; Kitayama, D; Chandrasiri, NP Sumiya, Kazutoshi; Kitayama, Daisuke; Chandrasiri, Naiwala P. Inferred Information Retrieval with User Operations on Digital Maps IEEE INTERNET COMPUTING English Article Information retrieval; Educational institutions; Data mining; Geographic information systems; Estimation; Cities and towns; Internet; geographic information systems; mobile; Internet; Web technologies; user interfaces; information retrieval The authors' information retrieval approach automatically extracts users' intentions when they interact with a device to access information, obviating the need for keyword inputs. The approach extracts these intentions by analyzing basic operations such as zooming, centering, and panning on a map, and applying them as a basis for retrieving related information. [Sumiya, Kazutoshi] Univ Hyogo, Sch Human Sci & Environm, Kobe, Hyogo 6500044, Japan; [Kitayama, Daisuke] Kogakuin Univ, Fac Informat Studies, Tokyo 160, Japan; [Chandrasiri, Naiwala P.] Kogakuin Univ, Fac Informat, Tokyo 160, Japan Sumiya, K (reprint author), Univ Hyogo, Sch Human Sci & Environm, Kobe, Hyogo 6500044, Japan. sumiya@shse.u-hyogo.ac.jp; kitayama@cc.kogakuin.ac.jp; chandrasiri@cc.kogakuin.ac.jp Japan Society for the Promotion of Science KAKENHI [20300039, 26280042] This work was supported by the Japan Society for the Promotion of Science KAKENHI grant numbers 20300039 and 26280042. CHARNIAK E, 1993, ARTIF INTELL, V64, P53, DOI 10.1016/0004-3702(93)90060-O; Hiramoto R., 2006, P 14 ACM INT S ADV G, P99, DOI 10.1145/1183471.1183489; Kobayashi K, 2012, PROCEEDINGS OF THE INTERNATIONAL WORKING CONFERENCE ON ADVANCED VISUAL INTERFACES, P677, DOI 10.1145/2254556.2254683; Nakajima S., 2012, P 2012 IEEE INT C VE, P318; Petit M, 2007, LECT NOTES GEOINF CA, P121, DOI 10.1007/978-3-540-72385-1_7; Sumiya K., 2008, P 16 ACM SIGSPATIAL; Tezuka T, 2004, LECT NOTES COMPUT SC, V3306, P113; Weakliam J., 2005, P 13 ACM INT S ADV G, P285, DOI 10.1145/1097064.1097104 8 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1089-7801 1941-0131 IEEE INTERNET COMPUT IEEE Internet Comput. JUL-AUG 2014 18 4 70 73 10.1109/MIC.2014.72 4 Computer Science, Software Engineering Computer Science AK6BU WOS:000338513400012 J Park, SB; Bae, SJ Park, Se-Bum; Bae, Sung Joo Different routes to metacognitive judgments: The role of accuracy motivation JOURNAL OF CONSUMER PSYCHOLOGY English Article Metacognitive difficulty; Accuracy motivation; Flexible interpretation; Self-validation; Fit FLEXIBLE CORRECTION PROCESSES; SELF-VALIDATION HYPOTHESIS; EASE-OF-RETRIEVAL; SOCIAL JUDGMENT; ACCESSIBILITY EXPERIENCES; SUBJECTIVE EASE; INFORMATION; PERSUASION; DIFFICULTY; FAMILIARITY The current research proposes that metacognitive difficulty affects product evaluation through two different routes the feelings of ease-of-retrieval heuristic and the self-validation process. The findings across four laboratory experiments show that metacognitive difficulty can undermine product evaluation through the feelings of ease-of-retrieval heuristic among low-accuracy individuals, regardless of a perceived fit between expected and experienced difficulty. In contrast, the findings indicate that metacognitive difficulty can enhance (vs. undermine) product evaluation among high-accuracy individuals through the self-validation process when there is a perceived fit (vs. misfit) between expected and experienced difficulty. We suggest that individuals under high accuracy motivation are more likely than those under low accuracy motivation to draw less determined and more flexible interpretation of metacognitive difficulty in making their product evaluation. (C) 2013 Society for Consumer Psychology. Published by Elsevier Inc. All rights reserved. [Park, Se-Bum; Bae, Sung Joo] Yonsei Univ, Sch Business, Seoul 120749, South Korea Park, SB (reprint author), Yonsei Univ, Sch Business, Seoul 120749, South Korea. seapark@yonsei.ac.kr; sjbae@yonsei.ac.kr Aarts H, 1999, ACTA PSYCHOL, V103, P77, DOI 10.1016/S0001-6918(99)00035-9; Alter AL, 2007, J EXP PSYCHOL GEN, V136, P569, DOI 10.1037/0096-3445.136.4.569; Brinol P, 2009, ADV EXP SOC PSYCHOL, V41, P69, DOI 10.1016/S0065-2601(08)00402-4; Brinol P, 2006, PSYCHOL SCI, V17, P200, DOI 10.1111/j.1467-9280.2006.01686.x; Caruso EM, 2008, J EXP SOC PSYCHOL, V44, P148, DOI 10.1016/j.jesp.2006.11.003; Chen S, 1999, DUAL-PROCESS THEORIES IN SOCIAL PSYCHOLOGY, P73; Darke PR, 1998, PERS SOC PSYCHOL B, V24, P1205, DOI 10.1177/01461672982411007; Dijksterhuis A, 1999, PERS SOC PSYCHOL B, V25, P760; Grayson CE, 1999, SOC COGNITION, V17, P1, DOI 10.1521/soco.1999.17.1.1; Greifeneder R, 2007, SOC COGNITION, V25, P853, DOI 10.1521/soco.2007.25.6.853; Greifeneder R., 2010, PERSONALITY SOCIAL P, V15, P107; Haddock G, 2002, BRIT J PSYCHOL, V93, P257, DOI 10.1348/000712602162571; Hong JW, 2008, J CONSUM RES, V34, P682, DOI 10.1086/521902; Kim S, 2011, J CONSUM RES, V38, P712, DOI 10.1086/660806; Kuhnen U, 2010, PERS SOC PSYCHOL B, V36, P47, DOI 10.1177/0146167209346746; Labroo AA, 2006, J MARKETING RES, V43, P374, DOI 10.1509/jmkr.43.3.374; Labroo AA, 2009, PSYCHOL SCI, V20, P127, DOI 10.1111/j.1467-9280.2008.02264.x; Menon G, 2003, J CONSUM RES, V30, P230, DOI 10.1086/376804; Petty RE, 1999, DUAL-PROCESS THEORIES IN SOCIAL PSYCHOLOGY, P41; Petty RE, 2002, J PERS SOC PSYCHOL, V82, P722, DOI 10.1037//0022-3514.82.5.722; PETTY RE, 1993, J EXP SOC PSYCHOL, V29, P137, DOI 10.1006/jesp.1993.1007; Pocheptsova A, 2010, J MARKETING RES, V47, P1059, DOI 10.1509/jmkr.47.6.1059; Raghubir P, 2005, MEM COGNITION, V33, P821, DOI 10.3758/BF03193077; Rothman AJ, 1997, PERS SOC PSYCHOL B, V23, P123, DOI 10.1177/0146167297232002; Rothman AJ, 1998, PERS SOC PSYCHOL B, V24, P1053; Schwarz N, 1998, Pers Soc Psychol Rev, V2, P87, DOI 10.1207/s15327957pspr0202_2; Schwarz N, 2004, J CONSUM PSYCHOL, V14, P332, DOI 10.1207/s15327663jcp1404_2; Sela A, 2012, J CONSUM RES, V39, P360, DOI 10.1086/662997; Tormala ZL, 2007, J PERS SOC PSYCHOL, V93, P143, DOI 10.1037/0022-3514.93.2.143; Tormala ZL, 2008, J EXP SOC PSYCHOL, V44, P141, DOI 10.1016/j.jesp.2006.11.002; Tormala ZL, 2002, PERS SOC PSYCHOL B, V28, P1700, DOI 10.1177/014616702237651; Tybout AM, 2005, J CONSUM RES, V32, P76, DOI 10.1086/426617; Wang J, 2006, J MARKETING RES, V43, P28, DOI 10.1509/jmkr.43.1.28; Wanke M., 2000, MESSAGE ROLE SUBJECT, P143; WEGENER DT, 1995, J PERS SOC PSYCHOL, V68, P36, DOI 10.1037/0022-3514.68.1.36; Whittlesea BWA, 1998, ACTA PSYCHOL, V98, P141, DOI 10.1016/S0001-6918(97)00040-1; Whittlesea BWA, 2000, J EXP PSYCHOL LEARN, V26, P547, DOI 10.1037//0278-7393.26.3.547; WILSON TD, 1994, PSYCHOL BULL, V116, P117, DOI 10.1037/0033-2909.116.1.117 38 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 1057-7408 1532-7663 J CONSUM PSYCHOL J. Consum. Psychol. JUL 2014 24 3 307 319 10.1016/j.jcps.2013.09.002 13 Business; Psychology, Applied Business & Economics; Psychology AK4KD WOS:000338392700002 J Rose, NS; Buchsbaum, BR; Craik, FIM Rose, Nathan S.; Buchsbaum, Bradley R.; Craik, Fergus I. M. Short-term retention of a single word relies on retrieval from long-term memory when both rehearsal and refreshing are disrupted MEMORY & COGNITION English Article Short-term memory; Working memory; Long-term memory PROCESSING SPAN TASK; WORKING-MEMORY; INDIVIDUAL-DIFFERENCES; SECONDARY MEMORY; IMMEDIATE MEMORY; SERIAL ORDER; INFORMATION; CAPACITY; RECALL; MODEL Many working memory (WM) models propose that the focus of attention (or primary memory) has a capacity limit of one to four items, and therefore, that performance on WM tasks involves retrieving some items from long-term (or secondary) memory (LTM). In the present study, we present evidence suggesting that recall of even one item on a WM task can involve retrieving it from LTM. The WM task required participants to make a deep (living/nonliving) or shallow ("e"/no "e") level-of-processing (LOP) judgment on one word and to recall the word after a 10-s delay on each trial. During the delay, participants either rehearsed the word or performed an easy or a hard math task. When the to-be-remembered item could be rehearsed, recall was fast and accurate. When it was followed by a math task, recall was slower, error-prone, and benefited from a deeper LOP at encoding, especially for the hard math condition. The authors suggest that a covert-retrieval mechanism may have refreshed the item during easy math, and that the hard math condition shows that even a single item cannot be reliably held in WM during a sufficiently distracting task-therefore, recalling the item involved retrieving it from LTM. Additionally, performance on a final free recall (LTM) test was better for items recalled following math than following rehearsal, suggesting that initial recall following math involved elaborative retrieval from LTM, whereas rehearsal did not. The authors suggest that the extent to which performance on WM tasks involves retrieval from LTM depends on the amounts of disruption to both rehearsal and covert-retrieval/refreshing maintenance mechanisms. [Rose, Nathan S.; Buchsbaum, Bradley R.; Craik, Fergus I. M.] Univ Toronto, Rotman Res Inst Baycrest, Toronto, ON, Canada; [Rose, Nathan S.] Univ Wisconsin, Dept Psychiat, Madison, WI 53715 USA Rose, NS (reprint author), Univ Wisconsin, Dept Psychiat, 6001 Res Pk Blvd, Madison, WI 53715 USA. nsrose@wisc.edu Atkinson R. C., 1968, PSYCHOL LEARN MOTIV, V2, P89, DOI DOI 10.1016/S0079-7421(08)60422-3; ATKINSON RC, 1971, SCI AM, V225, P82; Baddeley A, 2000, TRENDS COGN SCI, V4, P417, DOI 10.1016/S1364-6613(00)01538-2; Baddeley A. D., 1986, WORKING MEMORY; Barrouillet P, 2004, J EXP PSYCHOL GEN, V133, P83, DOI 10.1037/0096-3445.133.1.83; Brown GDA, 2000, PSYCHOL REV, V107, P127, DOI 10.1037//0033-295X.107.1.127; BROWN J, 1958, Q J EXP PSYCHOL, V10, P12, DOI 10.1080/17470215808416249; Burgess N, 1999, PSYCHOL REV, V106, P551, DOI 10.1037/0033-295X.106.3.551; Camos V, 2009, J MEM LANG, V61, P457, DOI 10.1016/j.jml.2009.06.002; Camos V, 2011, MEM COGNITION, V39, P231, DOI 10.3758/s13421-010-0011-x; Carpenter SK, 2009, J EXP PSYCHOL LEARN, V35, P1563, DOI 10.1037/a0017021; COWAN N, 1992, J MEM LANG, V31, P668, DOI 10.1016/0749-596X(92)90034-U; Cowan N., 2005, HDB UNDERSTANDING ME, P469; Cowan N, 2008, PROG BRAIN RES, V169, P323, DOI 10.1016/S0079-6123(07)00020-9; Cowan N., 1995, ATTENTION MEMORY INT; Cowan N, 2001, BEHAV BRAIN SCI, V24, P87, DOI 10.1017/S0140525X01003922; Craik F. I. M., 1975, COGNITIVE THEORY, P173; CRAIK FIM, 1970, J VERB LEARN VERB BE, V9, P143, DOI 10.1016/S0022-5371(70)80042-1; CRAIK FIM, 1975, J EXP PSYCHOL GEN, V104, P268, DOI 10.1037//0096-3445.104.3.268; CRAIK FIM, 1973, J VERB LEARN VERB BE, V12, P599, DOI 10.1016/S0022-5371(73)80039-8; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; Davelaar E. J., 2002, P 7 NEUR COMP PSYCH; Davelaar EJ, 2005, PSYCHOL REV, V112, P3, DOI 10.1037/0033-295X.112.1.3; Delaney PF, 2010, PSYCHOL LEARN MOTIV, V53, P63, DOI 10.1016/S0079-7421(10)53003-2; ELMES DG, 1975, J VERB LEARN VERB BE, V14, P30, DOI 10.1016/S0022-5371(75)80004-1; Flegal KE, 2014, MEM COGNITION, V42, P701, DOI 10.3758/s13421-013-0391-9; Hudjetz A, 2007, MEM COGNITION, V35, P1675, DOI 10.3758/BF03193501; JACOBY LL, 1972, J VERB LEARN VERB BE, V11, P561, DOI 10.1016/S0022-5371(72)80039-2; JOHNSON MK, 1992, J COGNITIVE NEUROSCI, V4, P268, DOI 10.1162/jocn.1992.4.3.268; Jonides J, 2008, ANNU REV PSYCHOL, V59, P193, DOI 10.1146/annurev.psych.59.103006.093615; JUST MA, 1992, PSYCHOL REV, V99, P122, DOI 10.1037/0033-295X.99.1.122; LaRocque J. J., 2014, FRONTIERS NEUROSCIEN, V8, P1, DOI [10.3389/fnhum.2014.00005, DOI 10.3389/FNHUM.2014.00005]; Lehman M, 2013, PSYCHOL REV, V120, P155, DOI 10.1037/a0030851; Lewandowsky S, 2009, J EXP PSYCHOL LEARN, V35, P1545, DOI 10.1037/a0017010; Loaiza V., 2013, ANN M ASS PSYCH SCI; Loaiza VM, 2011, J EXP PSYCHOL LEARN, V37, P1258, DOI 10.1037/a0023923; Marsh RL, 1997, MEM COGNITION, V25, P173, DOI 10.3758/BF03201110; Mazuryk G. F., 1974, CAN J PSYCHOL, V23, P114; McCabe DP, 2008, J MEM LANG, V58, P480, DOI 10.1016/j.jml.2007.04.004; McElree B, 2006, PSYCHOL LEARN MOTIV, V46, P155, DOI 10.1016/S0079-7421(06)46005-9; MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037/0033-295X.101.2.343; NAIRNE JS, 1990, MEM COGNITION, V18, P251, DOI 10.3758/BF03213879; Oberauer K, 2012, CURR DIR PSYCHOL SCI, V21, P164, DOI 10.1177/0963721412444727; Oberauer K, 2002, J EXP PSYCHOL LEARN, V28, P411, DOI 10.1037//0278-7393.28.3.411; Oberauer K, 2009, PSYCHOL LEARN MOTIV, V51, P45, DOI 10.1016/S0079-7421(09)51002-X; PETERSON LR, 1959, J EXP PSYCHOL, V58, P193, DOI 10.1037/h0049234; Pyc MA, 2010, SCIENCE, V330, P335, DOI 10.1126/science.1191465; RAAIJMAKERS JGW, 1993, ATTENTION PERFORM, V14, P467; Ranganath C, 2005, TRENDS COGN SCI, V9, P374, DOI 10.1016/j.tics.2005.06.009; Roediger HL, 2011, TRENDS COGN SCI, V15, P20, DOI 10.1016/j.tics.2010.09.003; Rose N. S., 2010, THESIS WASH U, P300; Rose NS, 2012, J EXP PSYCHOL LEARN, V38, P1019, DOI 10.1037/a0026976; Rose NS, 2010, J EXP PSYCHOL LEARN, V36, P471, DOI 10.1037/a0018405; Rose NS, 2013, CAN J EXP PSYCHOL, V67, P260, DOI 10.1037/a0034351; Rose NS, 2012, NEUROPSYCHOLOGIA, V50, P11, DOI 10.1016/j.neuropsychologia.2011.10.016; Saito S, 2004, J MEM LANG, V50, P425, DOI 10.1016/j.jml.2003.12.003; Shivde G, 2011, J EXP PSYCHOL LEARN, V37, P1342, DOI 10.1037/a0024832; Speer NK, 2003, COGN AFFECT BEHAV NE, V3, P155, DOI 10.3758/CABN.3.3.155; Towse JN, 2000, MEM COGNITION, V28, P341, DOI 10.3758/BF03198549; Unsworth N, 2008, J EXP PSYCHOL LEARN, V34, P616, DOI 10.1037/0278-7393.34.3.616; Unsworth N, 2010, ACTA PSYCHOL, V134, P16, DOI 10.1016/j.actpsy.2009.11.010; Unsworth N, 2007, PSYCHOL REV, V114, P104, DOI 10.1037/0033-295X.114.1.104; WAUGH NC, 1965, PSYCHOL REV, V72, P89, DOI 10.1037/h0021797; WHITTEN WB, 1977, J VERB LEARN VERB BE, V16, P465, DOI 10.1016/S0022-5371(77)80040-6 64 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0090-502X 1532-5946 MEM COGNITION Mem. Cogn. JUL 2014 42 5 689 700 10.3758/s13421-014-0398-x 12 Psychology, Experimental Psychology AK1QZ WOS:000338191900001 J Yoon, CP; Moon, SJ; Hwang, CG Yoon, Chang-Pyo; Moon, Seok-Jae; Hwang, Chi-Gon MCSOSA: multimedia content share using ontology and secure access agent in mobile cloud MULTIMEDIA TOOLS AND APPLICATIONS English Article Mobile cloud; Intent; Multimedia content; Ontology; Vulnerability INTEGRATION Mobile cloud is not just a traditional cloud, but a concept of virtualization that has expanded into mobile technology. It provides access to the data created and used by a user and content service by cloud platform. A feature of mobile cloud is supported that is the convenience of multimedia content sharing by mobile devices. However, there is a problem of inaccuracy of information retrieval in the process of sharing as well as personal information leakage and service inability status due to the malicious access to the mobile terminal in the retrieval process. This paper suggests the model to which the protective technique of multimedia content retrieval & access in mobile cloud is applied. The model stores and manages the individually different forms of content, and constructs the multimedia ontology in order to enhance the reliability in mismatched problems occurring in the retrieval process, and also suggests the response technique to security vulnerability occurring in the content access. [Yoon, Chang-Pyo] Gyeonggi Collage Sci & Technol, Dept Mobile Informat Convergence Technol, Gyeonggi, South Korea; [Moon, Seok-Jae; Hwang, Chi-Gon] Kwangwoon Univ, Dept Comp Sci, Seoul, South Korea Moon, SJ (reprint author), Kwangwoon Univ, Dept Comp Sci, Seoul, South Korea. cpyoon@gtec.ac.kr; msj8086@kw.ac.kr; duck1052@kw.ac.kr Dinh HT, 2013, WIREL COMMUN MOB COM, V13, P1587, DOI 10.1002/wcm.1203; Enrique Ortiz C, 2010, UNDERSTANDING SECURI, P1; GRUBER TR, 1993, KNOWL ACQUIS, V5, P199, DOI 10.1006/knac.1993.1008; Guarino N., 1998, P 1 INT C FORM ONT I, P3; Hammiche S, 2004, 2 ACM INT WORKSH MUL, P36; Hunter J, 2003, IEEE T CIRC SYST VID, V13, P49, DOI 10.1109/TCSVT.2002.808088; Hwang C, 2011, COMM COM INF SC, V195, P36; Jung K-D, 2013, KOREA I INF COMMUN E, V17, P453; Lagoze C, 2001, ABC ONTOLOGY MODEL, P160; Lim JW, 2012, RESPONSE TECHNIQUE V, V12-6, P61; Mell P., 2011, SPECIAL PUBLICATION, V800-145; Mohiuddin K, 2012, P 3 INT C CLOUD COMP, P88; Paliouras G, 2011, LECT NOTES ARTIF INT, V6050, P1, DOI 10.1007/978-3-642-20795-2_1; Rmakrishnan R, 2007, SHERPA CLOUD COMPUTI, P33; Tsinaraki C, 2004, BIOMED SCI INSTRUM, V3084, P398; Youseff L, 2008, P GRID COMP ENV WORK, DOI [10.1109/GCE.2008.4738443, DOI 10.1109/GCE.2008.4738443] 16 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUL 2014 71 2 667 684 10.1007/s11042-013-1648-9 18 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AK3IS WOS:000338317400018 J Li, Q; Lee, S; Jung, H; Lee, YS; Cho, JH; Song, SK Li, Qing; Lee, Seungwoo; Jung, Hanmin; Lee, Yeong Su; Cho, Jae-Hyun; Song, Sa-kwang Term weighting for information retrieval based on term's discrimination power MULTIMEDIA TOOLS AND APPLICATIONS English Article Information retrieval; Term weighting; Discrimination power; Difference based DP; Evidential weight One of the most important research topics in Information Retrieval is term weighting for document ranking and retrieval, such as TFIDF, BM25, etc. We propose a term weighting method that utilizes past retrieval results consisting of the queries that contain a particular term, retrieval documents, and their relevance judgments. A term's Discrimination Power(DP) is based on the difference degree of the term's average weights obtained from between relevant and non-relevant retrieved document sets. The difference based DP performs better compared to ratio based DP introduced in the previous research. Our experimental result shows that a term weighting scheme based on the discrimination power method outperforms a TF*IDF based scheme. [Li, Qing] Southwestern Univ Finance & Econ, Chengdu, Peoples R China; [Lee, Seungwoo] Korea Inst Sci & Technol Informat, Platform Res Grp, Taejon, South Korea; [Jung, Hanmin] Korea Inst Sci & Technol Informat, Dept SW Res, Taejon, South Korea; [Song, Sa-kwang] Korea Inst Sci & Technol Informat, Taejon, South Korea; [Lee, Yeong Su] Univ Munich, Munich, Germany; [Cho, Jae-Hyun] Pusan Chatol Univ, Pusan, South Korea Song, SK (reprint author), Korea Inst Sci & Technol Informat, Taejon, South Korea. liq_t@swufe.edu.cn; swlee@kisti.re.kr; jhm@kisti.re.kr; yeong@cis.uni-muenchen.de; jhcho@cup.ac.kr; esmallj@kisti.re.kr Broglio J, 1994, P 3 TEXT RETRIEVAL C; Cao G, 2007, P ACM C RES DEV INF; Chun H-W, 2011, LNCS, V6890, P324; Chun H-W, 2011, UNESST 2011; Craswell N, 2005, P 14 TEXT RETRIEVAL; Cummins R, 2006, INFORM RETRIEVAL, V9, P311, DOI 10.1007/s10791-006-1682-6; Kleinberg JM, 1999, J ACM, V46, P667; Pahikkala T, 2007, P ACM C RES DEV INF; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582]; Robertson SE, 1996, P 4 TEXT RETRIEVAL C; SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H; Song SK, 2012, INFORM PROCESS MANAG, V48, P921, DOI 10.1016/j.ipm.2012.03.004; TURTLE H, 1991, ACM T INFORM SYST, V9, P187, DOI 10.1145/125187.125188; Yeh J-Y, 2007, P ACM C RES DEV INF 15 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUL 2014 71 2 769 781 10.1007/s11042-013-1420-1 13 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AK3IS WOS:000338317400023 J Durao, F; Bayyapu, K; Xu, GD; Dolog, P; Lage, R Durao, Frederico; Bayyapu, Karunakar; Xu, Guandong; Dolog, Peter; Lage, Ricardo Expanding user's query with tag-neighbors for effective medical information retrieval MULTIMEDIA TOOLS AND APPLICATIONS English Article Medical information retrieval; Query expansion; Tagging; Health information system EXPANSION; SYSTEMS; WEB Medical information is a natural human demand. Existing search engines on the Web often are unable to handle medical search well because they do not consider its special requirements. Often a medical information searcher is uncertain about his exact questions and unfamiliar with medical terminology. Under-specified queries often lead to undesirable search results that do not contain the information needed. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users' original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the qualified tag neighbors to expand an entry query. The proposed approach is evaluated by using MedWorm medical article collection and results show considerable precision improvements over state-of-the-art approaches. [Durao, Frederico; Dolog, Peter; Lage, Ricardo] Aalborg Univ, Dept Comp Sci, IWIS Intelligent Web & Informat Syst, Aalborg, Denmark; [Bayyapu, Karunakar] Tech Univ Denmark, Dept Syst Biol, CBS Ctr Biol Sequence Anal, DK-2800 Kongens Lyngby, Denmark; [Xu, Guandong] Victoria Univ, Ctr Appl Informat, Melbourne, Vic 8001, Australia Durao, F (reprint author), Aalborg Univ, Dept Comp Sci, IWIS Intelligent Web & Informat Syst, Room 2-2-05 Selma Lagerlofs Vej 300, Aalborg, Denmark. freddurao@gmail.com; karun@cbs.dtu.dk; guandong.xu@vu.edu.au; dolog@cs.aau.dk; ricardol@cs.aau.dk FP7 ICT project M-Eco: Medical Ecosystem Personalized Event-Based Surveillance [247829] This work has been partially supported by FP7 ICT project M-Eco: Medical Ecosystem Personalized Event-Based Surveillance under grant number 247829. This journal is a extended version of previously published paper at the International Conference on Information Science and Applications (ICISA 2011). Anderson T. W., 1984, INTRO MULTIVARIATE S; Andreou A, 2005, THESIS; Bianco CE, 2009, J MED LIBR ASSOC, V97, P136, DOI 10.3163/1536-5050.97.2.012; Carpineto C, 2001, ACM T INFORM SYST, V19, P1, DOI 10.1145/366836.366860; Clarke SJ, 1997, ASLIB PROC, V49, P184, DOI 10.1108/eb051463; Diaz-Galiano MC, 2009, COMPUT BIOL MED, V39, P396, DOI 10.1016/j.compbiomed.2009.01.012; Diem LT, 2007, RES INNOVATION VISIO, P242; Dozier C, 2007, P 11 INT C ART INT L, P253, DOI 10.1145/1276318.1276367; Durao F, 2011, INF SCI APP ICISA, V0, P1; Durao F, 2010, SAC 2010, P1723; Efthimiadis E. N., 1993, SIGIR Forum; Fu WT, 2010, ACM T COMPUT-HUM INT, V17; Gordon-Murnane L., 2006, Searcher, V14; Gruber T, 2008, J WEB SEMANT, V6, P4, DOI 10.1016/j.websem.2007.11.011; Hatcher E, 2004, LUCENE ACTION ACTION; Hersh WR, 1998, JAMA-J AM MED ASSOC, V280, P1347, DOI 10.1001/jama.280.15.1347; Jain H, 2010, ENHANCING ELECT MED, P1; Jang H, 2006, ENG MED BIOL SOC 200; Jansen B. J., 1998, SIGIR Forum, V32; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; JIN S, 2009, 2009 IEEE INT C, P300; Johnson SB, 1999, J AM MED INFORM ASSN, V6, P205; Kelly D, 2010, CHI2010: PROCEEDINGS OF THE 28TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P45; Liu Z, 2005, SAC 05 ACM S APPL CO, P1076; Lu ZY, 2009, INFORM RETRIEVAL, V12, P69, DOI 10.1007/s10791-008-9074-8; Luo G., 2008, P CIKM 08, P143, DOI 10.1145/1458082.1458104; Ma H., 2008, P 17 ACM C INF KNOWL, P709, DOI 10.1145/1458082.1458177; Matos S, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-212; Mei Q, 2008, P 17 ACM C INF KNOWL, P469, DOI 10.1145/1458082.1458145; Milicevic AK, 2010, ARTIF INTELL REV, V33, P187, DOI 10.1007/s10462-009-9153-2; Ravid G, 2007, J INF SCI, V33, P567, DOI 10.1177/0165551506076326; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Ruch P, 1999, J AM MED INFORM ASSN, V6, P205; Smith G, 2007, TAGGING PEOPLE POWER; Strohmaier M, 2008, P 2008 ACM WORKSH SE, P35, DOI 10.1145/1458583.1458603; West J, 2007, LIB MEDIA CONNECTION, V25, P58; Yuan MJ, 2009, SEAM FRAMEWORK EXPER 37 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUL 2014 71 2 905 929 10.1007/s11042-012-1316-5 25 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AK3IS WOS:000338317400032 J Hommel, B; Memelink, J; Zmigrod, S; Colzato, LS Hommel, Bernhard; Memelink, Jiska; Zmigrod, Sharon; Colzato, Lorenza S. Attentional control of the creation and retrieval of stimulus-response bindings PSYCHOLOGICAL RESEARCH-PSYCHOLOGISCHE FORSCHUNG English Article FEATURE-INTEGRATION; CONFLICT ADAPTATION; EVENT FILES; VISUAL-ATTENTION; CONTROL SETTINGS; SIMON TASK; INFORMATION; PERCEPTION; AUTOMATICITY; ACTIVATION Two experiments studied the degree to which the creation and retrieval of episodic feature bindings is modulated by attentional control. Experiment 1 showed that the impact of bindings between stimulus and response features varies as a function of the current attentional set: only bindings involving stimulus features that match the current set affect behavior. Experiment 2 varied the time point at which new attentional sets were implemented-either before or after the processing of the to-be-integrated stimuli and responses. The time point did not matter, suggesting that the attentional set has no impact on feature integration proper but controls which features get access to and can thus trigger the retrieval of bindings. [Hommel, Bernhard; Memelink, Jiska; Zmigrod, Sharon; Colzato, Lorenza S.] Leiden Univ, Dept Psychol, Cognit Psychol Unit, NL-2300 RB Leiden, Netherlands Hommel, B (reprint author), Leiden Univ, Dept Psychol, Cognit Psychol Unit, Postbus 9555, NL-2300 RB Leiden, Netherlands. hommel@fsw.leidenuniv.nl Bargh JA, 2000, PSYCHOL BULL, V126, P925, DOI 10.1037//0033-2909.126.6.925; BERTELSON P, 1963, J EXP PSYCHOL, V65, P478, DOI 10.1037/h0047742; Bogacz R, 2007, TRENDS COGN SCI, V11, P118, DOI 10.1016/j.tics.2006.12.006; Botvinick MM, 2001, PSYCHOL REV, V108, P624, DOI 10.1037//0033-295X.108.3.624; Cohen A, 1997, COGNITIVE PSYCHOL, V32, P128, DOI 10.1006/cogp.1997.0648; COHEN R, 1992, ANN NY ACAD SCI, V658, P163, DOI 10.1111/j.1749-6632.1992.tb22844.x; Colzato LS, 2006, J EXP PSYCHOL HUMAN, V32, P705, DOI 10.1037/0096-1523.32.3.705; Colzato LS, 2008, NEUROPSYCHOLOGIA, V46, P1570, DOI 10.1016/j.neuropsychologia.2007.12.014; Colzato LS, 2007, NEUROPSYCHOLOGIA, V45, P440, DOI 10.1016/j.neuropsychologia.2006.06.032; Colzato LS, 2005, EUR J NEUROSCI, V21, P591, DOI 10.1111/j.1460-9568.2005.03868.x; Cowey A., 1985, ATTENTION PERFORM, P41; DEYOE EA, 1988, TRENDS NEUROSCI, V11, P219, DOI 10.1016/0166-2236(88)90130-0; Duncan J., 1996, ATTENTION PERFORM, P549; ERIKSEN CW, 1979, PERCEPT PSYCHOPHYS, V25, P249, DOI 10.3758/BF03198804; FOLK CL, 1992, J EXP PSYCHOL HUMAN, V18, P1030, DOI 10.1037//0096-1523.18.4.1030; GRATTON G, 1992, J EXP PSYCHOL GEN, V121, P480, DOI 10.1037//0096-3445.121.4.480; Hommel B, 2004, PSYCHOL RES-PSYCH FO, V68, P1, DOI 10.1007/s00426-003-0132-y; Hommel B, 2005, J EXP PSYCHOL HUMAN, V31, P1067, DOI 10.1037/0096-1523.31.5.1067; Hommel B, 1998, VIS COGN, V5, P183, DOI 10.1080/713756773; Hommel B., 2009, OXFORD HDB HUMAN ACT, P371; Hommel B, 2007, PSYCHOL RES-PSYCH FO, V71, P42, DOI 10.1007/s00426-005-0035-1; Hommel B, 2004, TRENDS COGN SCI, V8, P494, DOI 10.1016/j.tics.2004.08.007; Hommel B, 2004, VIS COGN, V11, P483, DOI 10.1080/13506280344000400; Jolicoeur P., 2002, COMMON MECH PERCEPTI, P558; KAHNEMAN D, 1992, COGNITIVE PSYCHOL, V24, P175, DOI 10.1016/0010-0285(92)90007-O; KEELE SW, 1972, J EXP PSYCHOL, V93, P245, DOI 10.1037/h0032460; Logan GD, 1996, J EXP PSYCHOL LEARN, V22, P620, DOI 10.1037/0278-7393.22.3.620; LOGAN GD, 1988, PSYCHOL REV, V95, P492, DOI 10.1037//0033-295X.95.4.492; Maruff P, 1999, PSYCHOL SCI, V10, P522, DOI 10.1111/1467-9280.00199; Mayr U, 2003, NAT NEUROSCI, V6, P450, DOI 10.1038/nn1051; Meiran N, 1996, J EXP PSYCHOL LEARN, V22, P1423, DOI 10.1037//0278-7393.22.6.1423; Memelink J, 2013, PSYCHOL RES-PSYCH FO, V77, P249, DOI 10.1007/s00426-012-0435-y; MILLIKEN B, 1994, J EXP PSYCHOL HUMAN, V20, P624, DOI 10.1037//0096-1523.20.3.624; Park D. C., 1996, PROSPECTIVE MEMORY T, P369; Posse B, 2006, EUR J COGN PSYCHOL, V18, P640, DOI 10.1080/09541440500423285; Pratt J, 2003, J EXP PSYCHOL HUMAN, V29, P835, DOI 10.1037/0096-1523.29.5.835; Remington RW, 2001, PSYCHOL SCI, V12, P511, DOI 10.1111/1467-9280.00394; ROGERS RD, 1995, J EXP PSYCHOL GEN, V124, P207, DOI 10.1037//0096-3445.124.2.207; SINGER W, 1994, ATTENTION PERFORM, V15, P77; Spape MM, 2008, PSYCHON B REV, V15, P1117, DOI 10.3758/PBR.15.6.1117; Stoet G, 1999, J EXP PSYCHOL HUMAN, V25, P1625, DOI 10.1037/0096-1523.25.6.1625; Sturmer B, 2002, J EXP PSYCHOL HUMAN, V28, P1345, DOI 10.1037//0096-1523.28.6.1345; Treisman A, 1996, CURR OPIN NEUROBIOL, V6, P171, DOI 10.1016/S0959-4388(96)80070-5; Ullsperger M, 2005, COGN AFFECT BEHAV NE, V5, P467, DOI 10.3758/CABN.5.4.467; Wuhr P, 2005, Q J EXP PSYCHOL-A, V58, P705, DOI 10.1080/02724980443000269 45 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0340-0727 1430-2772 PSYCHOL RES-PSYCH FO Psychol. Res.-Psychol. Forsch. JUL 2014 78 4 520 538 10.1007/s00426-013-0503-y 19 Psychology, Experimental Psychology AK0VF WOS:000338133400006 J Wagers, MW; Phillips, C Wagers, Matthew W.; Phillips, Colin Going the distance: Memory and control processes in active dependency construction QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY English Article Parsing; Psycholinguistics; Syntax; Memory; Unbounded dependencies SHORT-TERM-MEMORY; FILLER-GAP DEPENDENCIES; WORKING-MEMORY; SENTENCE COMPREHENSION; UNBOUNDED DEPENDENCIES; TIME-COURSE; SYNTACTIC DEPENDENCIES; EMPTY CATEGORIES; BRAIN POTENTIALS; LEXICAL DECISION Filler-gap dependencies make strong demands on working memory in language comprehension because they cannot always be immediately resolved. In a series of three reading-time studies, we test the idea that these demands can be decomposed into active maintenance processes and retrieval events. Results indicate that the fact that a displaced phrase exists and the identity of its basic syntactic category both immediately impact comprehension at potential gap sites. In contrast, specific lexical details of the displaced phrase show an immediate effect only for short dependencies and a much later effect for longer dependencies. We argue that coarse-grained information about the filler is actively maintained and is used to make phrase structure parsing decisions, whereas finer grained information is more quickly released from active maintenance and consequently has to be retrieved at the gap site. [Wagers, Matthew W.] Univ Calif Santa Cruz, Dept Linguist, Santa Cruz, CA 95064 USA; [Phillips, Colin] Univ Maryland, Dept Linguist, Neurosci & Cognit Sci Program, College Pk, MD 20742 USA Wagers, MW (reprint author), Univ Calif Santa Cruz, Dept Linguist, 1156 High St, Santa Cruz, CA 95064 USA. mwagers@ucsc.edu Aoshima S, 2004, J MEM LANG, V51, P23, DOI 10.1016/j.jml.2004.03.001; Baayen H., 2008, ANAL LINGUISTIC DATA; Baayen R. H., 2010, INT J PSYCHOL RES, V3, P12; BEVER TG, 1969, PERCEPT PSYCHOPHYS, V5, P225, DOI 10.3758/BF03210545; BEVER TG, 1988, LINGUIST INQ, V19, P35; Boland JE, 1995, J MEM LANG, V34, P774, DOI 10.1006/jmla.1995.1034; Bourdages J. S., 1992, ISLAND CONSTRAINTS T, P61; Broadbent D. E., 1958, PERCEPTION COMMUNICA; Chomsky N., 1987, BARRIERS; Chomsky N., 1977, FORMAL SYNTAX, P71; Clahsen H, 1999, J PSYCHOLINGUIST RES, V28, P415, DOI 10.1023/A:1023293132656; Conklin K, 2004, BRAIN LANG, V90, P221, DOI 10.1016/S0093-934X(03)00435-8; Cowan N, 2001, BEHAV BRAIN SCI, V24, P87, DOI 10.1017/S0140525X01003922; Crain S., 1985, NATURAL LANGUAGE PAR, P94; Drummond A., 2010, IBEXFARM VERSION 0 3; Faul F, 2007, BEHAV RES METHODS, V39, P175, DOI 10.3758/BRM.41.4.1149; Fiebach CJ, 2002, J MEM LANG, V47, P250, DOI 10.1016/S0749-596X(02)00004-9; Fraser B., 1971, LINGUIST INQ, V2, P603; FRAZIER L, 1987, NAT LANG LINGUIST TH, V5, P519, DOI 10.1007/BF00138988; FRAZIER L, 1989, J MEM LANG, V28, P331, DOI 10.1016/0749-596X(89)90037-5; FRAZIER L, 1989, Language and Cognitive Processes, V4, P93, DOI 10.1080/01690968908406359; Frisson S., 2009, LANGUAGE LINGUISTICS, V3, P111, DOI [10.1111/j.1749-818X.2008.00104.x, DOI 10.1111/J.1749-818X.2008.00104.X]; Garavan H, 1998, MEM COGNITION, V26, P263, DOI 10.3758/BF03201138; GARNSEY SM, 1989, J PSYCHOLINGUIST RES, V18, P51, DOI 10.1007/BF01069046; Gazdar Gerald, 1985, GEN PHRASE STRUCTURE; Gibson E, 2000, IMAGE, LANGUAGE, BRAIN, P95; Gibson E, 1998, COGNITION, V68, P1, DOI 10.1016/S0010-0277(98)00034-1; Jackendoff R., 1977, XBAR SYNTAX STUDY PH; Jonides J., 2008, ANNU REV PSYCHOL, V59, P151; JUST MA, 1982, J EXP PSYCHOL GEN, V111, P228, DOI 10.1037/0096-3445.111.2.228; KING J, 1991, J MEM LANG, V30, P580, DOI 10.1016/0749-596X(91)90027-H; KING JW, 1995, J COGNITIVE NEUROSCI, V7, P376, DOI 10.1162/jocn.1995.7.3.376; KLUENDER R, 1993, LANG COGNITIVE PROC, V8, P573, DOI 10.1080/01690969308407588; Lee MW, 2004, J PSYCHOLINGUIST RES, V33, P51, DOI 10.1023/B:JOPR.0000010514.50468.30; Levin B., 1986, LEXICON PROJECT WORK, V11; Lewis R. L., 2000, ARCHITECTURES MECH L, P56; Lewis RL, 2005, COGNITIVE SCI, V29, P375, DOI 10.1207/s15516709cog0000_25; Macmillan N. A., 2004, DETECTION THEORY USE; Maechler M., 2011, LME4 LINEAR MIXED EF; Manzini M. R., 1992, LOCALITY THEORY SOME; McElree B, 2003, J MEM LANG, V48, P67, DOI 10.1016/S0749-596X(02)00515-6; McElree B, 2000, J PSYCHOLINGUIST RES, V29, P111, DOI 10.1023/A:1005184709695; McElree B, 2001, J EXP PSYCHOL LEARN, V27, P817, DOI 10.1037//0278-7393.27.3.817; McElree B, 1998, J EXP PSYCHOL LEARN, V24, P432, DOI 10.1037//0278-7393.24.2.432; MCELREE B, 1989, J EXP PSYCHOL GEN, V118, P346, DOI 10.1037//0096-3445.118.4.346; McElree B, 2006, PSYCHOL LEARN MOTIV, V46, P155, DOI 10.1016/S0079-7421(06)46005-9; McKinnon R, 1996, LANG COGNITIVE PROC, V11, P495, DOI 10.1080/016909696387132; MCKOON G, 1994, J EXP PSYCHOL LEARN, V20, P1239, DOI 10.1037//0278-7393.20.5.1239; Nairne J. S., 2006, DISTINCTIVENESS MEMO, P27; NEVILLE H, 1991, J COGNITIVE NEUROSCI, V3, P151, DOI 10.1162/jocn.1991.3.2.151; NICOL J, 1989, J PSYCHOLINGUIST RES, V18, P5, DOI 10.1007/BF01069043; NICOL JL, 1994, J EXP PSYCHOL LEARN, V20, P1229, DOI 10.1037//0278-7393.20.5.1229; Oberauer K, 2009, Q J EXP PSYCHOL, V62, P967, DOI 10.1080/17470210802372912; Oberauer K, 2006, J MEM LANG, V55, P601, DOI 10.1016/j.jml.2006.08.009; Omaki A, 2011, STUD SECOND LANG ACQ, V33, P563, DOI 10.1017/S0272263111000313; Phillips C., 2007, OXFORD HDB PSYCHOLIN, P739; Phillips C, 2005, COGNITIVE BRAIN RES, V22, P407, DOI 10.1016/j.cogbrainres.2004.09.012; Phillips C, 2006, LANGUAGE, V82, P795, DOI 10.1353/lan.2006.0217; Phillips C., 2010, HYPER ACTIVE GAP FIL; Phillips C, 2011, SYNTAX SEMANTICS, V37, P147, DOI 10.1108/S0092-4563(2011)0000037009; PICKERING M, 1994, PERSPECTIVES ON SENTENCE PROCESSING, P199; Pickering MJ, 2003, LANG COGNITIVE PROC, V18, P469, DOI 10.1080/01690960344000017; Pickering MJ, 2001, J EXP PSYCHOL LEARN, V27, P1401, DOI 10.1037//0278-7393.27.6.1401; R Development Core Team, 2011, R LANG ENV STAT COMP; Rizzi L., 1990, RELATIVIZED MINIMALI; Rohde D., 2003, LINGER VERSION 2 94; Ross J. R., 1967, THESIS MIT; RUCHKIN DS, 1990, ELECTROEN CLIN NEURO, V76, P419, DOI 10.1016/0013-4694(90)90096-3; Simmons JP, 2011, PSYCHOL SCI, V22, P1359, DOI 10.1177/0956797611417632; Sprouse J, 2012, LANGUAGE, V88, P82; Staub A, 2007, J EXP PSYCHOL LEARN, V33, P550, DOI 10.1037/0278-7393.33.3.550; Stowe L., 1986, LANG COGNITIVE PROC, V1, P227, DOI 10.1080/01690968608407062; STOWE LA, 1991, LANG SPEECH, V34, P319; Sturt P, 2004, PSYCHON B REV, V11, P882, DOI 10.3758/BF03196716; Sussman RS, 2003, LANG COGNITIVE PROC, V18, P143, DOI 10.1080/01690960143000498; Swinney D., 1988, TEMPORAL COURS UNPUB; Tanenhaus M. K., 1985, 7TH P ANN COGN SCI S, P361; Traxler MJ, 2002, J MEM LANG, V47, P69, DOI 10.1006/jmla.2001.2836; Traxler MJ, 1996, J MEM LANG, V35, P454, DOI 10.1006/jmla.1996.0025; TREISMAN AM, 1980, COGNITIVE PSYCHOL, V12, P97, DOI 10.1016/0010-0285(80)90005-5; van der Velde F, 2006, BEHAV BRAIN SCI, V29, P37; Van Dyke JA, 2006, J MEM LANG, V55, P157, DOI 10.1016/j.jml.2006.03.007; Verhaeghen P, 2004, J EXP PSYCHOL LEARN, V30, P1322, DOI 10.1037/0278-7393.30.6.1322; Wagers MW, 2009, J LINGUIST, V45, P395, DOI 10.1017/S0022226709005726; Wanner E., 1978, LINGUISTIC THEORY PS, P119; WEINBERG A, 1993, J PSYCHOLINGUIST RES, V22, P339, DOI 10.1007/BF01068016 86 0 0 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND 1747-0218 1747-0226 Q J EXP PSYCHOL Q. J. Exp. Psychol. JUL 2014 67 7 1274 1304 10.1080/17470218.2013.858363 31 Psychology, Biological; Physiology; Psychology; Psychology, Experimental Psychology; Physiology AJ9FP WOS:000338013400003 J Lee, JM; Irwin, PGJ; Fletcher, LN; Heng, K; Barstow, JK Lee, Jae-Min; Irwin, Patrick G. J.; Fletcher, Leigh N.; Heng, Kevin; Barstow, Joanna K. CONSTRAINING THE ATMOSPHERIC COMPOSITION OF THE DAY-NIGHT TERMINATORS OF HD 189733b: ATMOSPHERIC RETRIEVAL WITH AEROSOLS ASTROPHYSICAL JOURNAL English Article planets and satellites: atmospheres EXTRASOLAR GIANT PLANETS; HUBBLE-SPACE-TELESCOPE; HR 8799 PLANETS; TRANSMISSION SPECTROSCOPY; OPTICAL-CONSTANTS; DWARF ATMOSPHERES; MODEL ATMOSPHERES; BROWN DWARFS; SUPER-EARTHS; MU-M A number of observations have shown that Rayleigh scattering by aerosols dominates the transmission spectrum of HD 189733b at wavelengths shortward of 1 mu m. In this study, we retrieve a range of aerosol distributions consistent with transmission spectroscopy between 0.3-24 mu m that were recently re-analyzed by Pont et al. To constrain the particle size and the optical depth of the aerosol layer, we investigate the degeneracies between aerosol composition, temperature, planetary radius, and molecular abundances that prevent unique solutions for transit spectroscopy. Assuming that the aerosol is composed of MgSiO3, we suggest that a vertically uniform aerosol layer over all pressures with a monodisperse particle size smaller than about 0.1 mu m and an optical depth in the range 0.002-0.02 at 1 mu m provides statistically meaningful solutions for the day/night terminator regions of HD 189733b. Generally, we find that a uniform aerosol layer provide adequate fits to the data if the optical depth is less than 0.1 and the particle size is smaller than 0.1 mu m, irrespective of the atmospheric temperature, planetary radius, aerosol composition, and gaseous molecules. Strong constraints on the aerosol properties are provided by spectra at wavelengths shortward of 1 mu m as well as longward of 8 mu m, if the aerosol material has absorption features in this region. We show that these are the optimal wavelengths for quantifying the effects of aerosols, which may guide the design of future space observations. The present investigation indicates that the current data offer sufficient information to constrain some of the aerosol properties of HD189733b, but the chemistry in the terminator regions remains uncertain. [Lee, Jae-Min] Univ Zurich, Inst Computat Sci, CH-8057 Zurich, Switzerland; [Lee, Jae-Min; Heng, Kevin] Univ Bern, Ctr Space & Habitabil, CH-3012 Bern, Switzerland; [Irwin, Patrick G. J.; Fletcher, Leigh N.; Barstow, Joanna K.] Univ Oxford, Dept Atmospher Ocean & Planetary Phys, Oxford OX1 3PU, England; [Barstow, Joanna K.] Univ Oxford, Dept Astrophys, Oxford OX1 3RH, England Lee, JM (reprint author), Univ Zurich, Inst Computat Sci, Winterthurerstr 190, CH-8057 Zurich, Switzerland. lee@physik.uzh.ch Swiss-based MERAC Foundation; University of Bern; University of Zurich; United Kingdom Science and Technology Facilities Council; Royal Society Research Fellowship; John Fell fund by the University Oxford Press J.L. and K.H. acknowledge the support from the Swiss-based MERAC Foundation, the University of Bern, and the University of Zurich. P.G.J.I. acknowledges the support of the United Kingdom Science and Technology Facilities Council. L.N.F. is supported by a Royal Society Research Fellowship. J.K.B. is supported by the John Fell fund by the University Oxford Press. The calculations were performed using the zBox4 computing cluster at the University of Zurich due to support from Doug Potter, Simon Grimm and Joachim Stadel. We are grateful to David Sing, Frederic Pont, Suzanne Aigrain and Neale Gibson for very helpful discussions. Ackerman AS, 2001, ASTROPHYS J, V556, P872, DOI 10.1086/321540; Agol E, 2009, IAU SYMP P SERIES, V4, P209, DOI 10.1017/S1743921308026422; Barman TS, 2011, ASTROPHYS J, V733, DOI 10.1088/0004-637X/733/1/65; Barstow JK, 2014, ASTROPHYS J, V786, DOI 10.1088/0004-637X/786/2/154; Barstow JK, 2013, MON NOT R ASTRON SOC, V434, P2616, DOI 10.1093/mnras/stt1204; Beaulieu JP, 2008, ASTROPHYS J, V677, P1343, DOI 10.1086/527045; Benneke B, 2013, ASTROPHYS J, V778, DOI 10.1088/0004-637X/778/2/153; Benneke B, 2012, ASTROPHYS J, V753, DOI 10.1088/0004-637X/753/2/100; Burrows A, 1999, ASTROPHYS J, V512, P843, DOI 10.1086/306811; Desert JM, 2009, ASTROPHYS J, V699, P478, DOI 10.1088/0004-637X/699/1/478; DRAINE BT, 1984, ASTROPHYS J, V285, P89, DOI 10.1086/162480; Fortney JJ, 2010, ASTROPHYS J, V709, P1396, DOI 10.1088/0004-637X/709/2/1396; Gibson NP, 2011, MON NOT R ASTRON SOC, V411, P2199, DOI 10.1111/j.1365-2966.2010.17837.x; Gibson NP, 2012, MON NOT R ASTRON SOC, V422, P753, DOI 10.1111/j.1365-2966.2012.20655.x; Gibson NP, 2012, MON NOT R ASTRON SOC, V419, P2683, DOI 10.1111/j.1365-2966.2011.19915.x; Helling C, 2006, ASTRON ASTROPHYS, V455, P325, DOI 10.1051/0004-6361:20054598; Helling C, 2008, ASTRON ASTROPHYS, V485, P547, DOI 10.1051/0004-6361:20078220; Heng K, 2013, ASTROPHYS J, V777, DOI 10.1088/0004-637X/777/2/100; Huitson CM, 2012, MON NOT R ASTRON SOC, V422, P2477, DOI 10.1111/j.1365-2966.2012.20805.x; Irwin PGJ, 2008, J QUANT SPECTROSC RA, V109, P1136, DOI 10.1016/j.jqsrt.2007.11.006; Khachai H, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/9/095404; KHARE BN, 1984, ICARUS, V60, P127, DOI 10.1016/0019-1035(84)90142-8; Knutson HA, 2007, NATURE, V447, P183, DOI 10.1038/nature05782; Kupka F., 2000, BALT ASTRON, V9, P590; LAOR A, 1993, ASTROPHYS J, V402, P441, DOI 10.1086/172149; Etangs ALD, 2008, ASTRON ASTROPHYS, V481, pL83, DOI 10.1051/0004-6361:200809388; Lee JM, 2013, ASTROPHYS J, V778, DOI 10.1088/0004-637X/778/2/97; Lee JM, 2012, MON NOT R ASTRON SOC, V420, P170, DOI 10.1111/j.1365-2966.2011.20013.x; Line MR, 2013, ASTROPHYS J, V775, DOI 10.1088/0004-637X/775/2/137; Line M. R., 2014, APJ, V783, P13; Line MR, 2012, ASTROPHYS J, V749, DOI 10.1088/0004-637X/749/1/93; Line MR, 2010, ASTROPHYS J, V717, P496, DOI 10.1088/0004-637X/717/1/496; Lodders K, 1999, ASTROPHYS J, V519, P793, DOI 10.1086/307387; Madhusudhan N, 2011, ASTROPHYS J, V737, DOI 10.1088/0004-637X/737/1/34; Madhusudhan N, 2009, ASTROPHYS J, V707, P24, DOI 10.1088/0004-637X/707/1/24; Marley MS, 2012, ASTROPHYS J, V754, DOI 10.1088/0004-637X/754/2/135; Marley MS, 1999, ASTROPHYS J, V513, P879, DOI 10.1086/306881; MONTANER A, 1979, PHYS STATUS SOLIDI A, V52, P597, DOI 10.1002/pssa.2210520228; Morley CV, 2012, ASTROPHYS J, V756, DOI 10.1088/0004-637X/756/2/172; Moses JI, 2011, ASTROPHYS J, V737, DOI 10.1088/0004-637X/737/1/15; Pont F, 2008, MON NOT R ASTRON SOC, V385, P109, DOI 10.1111/j.1365-2966.2008.12852.x; Pont F, 2013, MON NOT R ASTRON SOC, V432, P2917, DOI 10.1093/mnras/stt651; Rodgers C. D., 2000, INVERSE METHODS ATMO; Rothman LS, 2010, J QUANT SPECTROSC RA, V111, P2139, DOI 10.1016/j.jqsrt.2010.05.001; Scott A, 1996, ASTROPHYS J SUPPL S, V105, P401, DOI 10.1086/192321; Shabram M, 2011, ASTROPHYS J, V727, DOI 10.1088/0004-637X/727/2/65; Sing DK, 2011, MON NOT R ASTRON SOC, V416, P1443, DOI 10.1111/j.1365-2966.2011.19142.x; Swain MR, 2008, NATURE, V452, P329, DOI 10.1038/nature06823; Tinetti G, 2007, NATURE, V448, P169, DOI 10.1038/nature06002; Tinetti G, 2007, ASTROPHYS J, V654, pL99, DOI 10.1086/510716; Torres G, 2008, ASTROPHYS J, V677, P1324, DOI 10.1086/529429; Wenger D., 1998, JQSRT, V59, P471 52 0 0 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. JUL 1 2014 789 1 14 10.1088/0004-637X/789/1/14 11 Astronomy & Astrophysics Astronomy & Astrophysics AK0LD WOS:000338103400014 J Xhafa, F; Barolli, L Xhafa, Fatos; Barolli, Leonard Semantics, intelligent processing and services for big data FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE English Editorial Material Big data; Semantics; Intelligent services; Security; Massive and distributed processing With the continuous increase of data, scaling up to unprecedented amounts, generated by Internet-based systems, Big Data has emerged as a new research field, coined as "Big Data Science". The core of Big Data Science is the extraction of knowledge from data as a basis for intelligent services and decision making systems, however, it encompasses many research topics and investigates a variety of techniques and theories from different fields, including data mining and machine learning, information retrieval, analytics, and indexing services, massive processing and high performance computing. Altogether the aim is the development of advanced data-aware knowledge based systems. This special issue presents advances in Semantics, Intelligent Processing and Services for Big Data and their applications to a variety of domains including mobile computing, smart cities, forensics and medicine. (C) 2014 Published by Elsevier B.V. [Xhafa, Fatos] Univ Politecn Cataluna, ES-08034 Barcelona, Spain; [Barolli, Leonard] Fukuoka Inst Technol, Higashi Ku, Fukuoka 8110295, Japan Xhafa, F (reprint author), Univ Politecn Cataluna, Campus Nord,Ed Omega,C Jordi Girona 1-3, ES-08034 Barcelona, Spain. fatos@lsi.upc.edu; barolli@fit.ac.jp Alamri S, 2014, FUTURE GENER COMP SY, V37, P232, DOI 10.1016/j.future.2014.02.007; Alghamdi NS, 2014, FUTURE GENER COMP SY, V37, P212, DOI 10.1016/j.future.2014.02.010; Beloglazov A, 2012, FUTURE GENER COMP SY, V28, P755, DOI 10.1016/j.future.2011.04.017; Buyya, 2009, FGCS, V25, P599; Castiglione A, 2014, FUTURE GENER COMP SY, V37, P203, DOI 10.1016/j.future.2013.07.016; Chen XF, 2014, FUTURE GENER COMP SY, V37, P252, DOI 10.1016/j.future.2013.07.015; Dobre C, 2014, FUTURE GENER COMP SY, V37, P267, DOI 10.1016/j.future.2013.07.014; Dodonov E, 2010, FUTURE GENER COMP SY, V26, P740, DOI 10.1016/j.future.2009.05.004; Farruggia A, 2014, FUTURE GENER COMP SY, V37, P243, DOI 10.1016/j.future.2014.02.008; Li J, 2014, FUTURE GENER COMP SY, V37, P259, DOI 10.1016/j.future.2013.10.006; Mian R, 2013, FUTURE GENER COMP SY, V29, P1452, DOI 10.1016/j.future.2012.01.008 11 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JUL 2014 37 201 202 10.1016/j.future.2014.02.004 2 Computer Science, Theory & Methods Computer Science AJ8CV WOS:000337931200019 J Farruggia, A; Magro, R; Vitabile, S Farruggia, Alfonso; Magro, Rosario; Vitabile, Salvatore A text based indexing system for mammographic image retrieval and classification FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE English Article Information retrieval; Medical documents indexing and classification; Medical images indexing and classification In modern medical systems huge amount of text, words, images and videos are produced and stored in ad hoc databases. Medical community needs to extract precise information from that large amount of data. Currently ICT approaches do not provide a methodology for content-based medical images retrieval and classification. On the other hand, from the Internet of Things (IoT) perspective, the ICT medical data can be produced by several devices. Produced data complies with all Big Data features and constraints. The IoT guidelines put at the center of the system a new smart software to manage and transform Big Data in a new understanding form. This paper describes a text based indexing system for mammographic images retrieval and classification. The system deals with text (structured reports) and images (mammograms) mining and classification in a typical Department of Radiology. DICOM structured reports, containing free text for medical diagnosis, have been analyzed and labeled in order to classify the corresponding mammographic images. Information Retrieval process is based on some text manipulation techniques, such as light semantic analysis, stop-word removing, and light medical natural language processing. The system includes also a Search Engine module, based on a Bayes Naive Classifier. The experimental results provide interesting performance in terms of Specificity and Sensibility. Two more indexes have been computed in order to assess the system robustness: the A(Z) (Area under ROC Curve) index and the sigma(Az) (A(z) standard error) index. The dataset is composed of healthy and pathological DICOM structured reports. Two use case scenarios are presented and described to prove the effectiveness of the proposed approach. (C) 2014 Elsevier B.V. All rights reserved. [Farruggia, Alfonso; Magro, Rosario; Vitabile, Salvatore] Univ Palermo, Dipartimento Biopatol & Biotecnol Med & Forensi, I-90127 Palermo, Italy Vitabile, S (reprint author), Univ Palermo, Dipartimento Biopatol & Biotecnol Med & Forensi, Viale Vespro, I-90127 Palermo, Italy. salvatore.vitabile@unipa.it Italian Ministero della Salute [RF-SIC-2007-646441]; Italian Ministero dell'Istruzione, dell'Universita e della Ricerca (PON Smart Cities PON04a2_C "SMART HEALTH - CLUSTEROSDH - SMART FSE - STAYWELL") This work was partially supported by the Italian Ministero della Salute (project code RF-SIC-2007-646441) and by the Italian Ministero dell'Istruzione, dell'Universita e della Ricerca (PON Smart Cities PON04a2_C "SMART HEALTH - CLUSTEROSDH - SMART FSE - STAYWELL"). Aizawa A, 2003, INFORM PROCESS MANAG, V39, P45, DOI 10.1016/S0306-4573(02)00021-3; Bishop C.M, 2006, PATTERN RECOGNITION, V4; Bradley A.P., USE AREA UNDER ROC C, V30, P1145; Cannella V, 2009, CISIS: 2009 INTERNATIONAL CONFERENCE ON COMPLEX, INTELLIGENT AND SOFTWARE INTENSIVE SYSTEMS, VOLS 1 AND 2, P778; Chang CC, 2009, 2009 9TH IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOENGINEERING, P415, DOI 10.1109/BIBE.2009.59; Ciolko E, 2010, IEEE ENG MED BIO, P6781, DOI 10.1109/IEMBS.2010.5625982; Clunie D. A., 2000, DICOM STRUCTURED REP; Das D., P 49 ANN M ASS COMP, V1, P600; Devasena C., 2012, ADV ENG SCI MAN ICAE, P594; Elevitch Franklin R, 2005, AANA J, V73, P361; Farruggia A., 2013, INT C COMP MED APPL, P1; Farruggia A, 2013, 2013 IEEE 27TH INTERNATIONAL CONFERENCE ON ADVANCED INFORMATION NETWORKING AND APPLICATIONS WORKSHOPS (WAINA), P225, DOI 10.1109/WAINA.2013.77; Goncalves A., 2010, BEGINNING JAVA EE 6; Guo H., AAAI KDD UAI02 JOINT; Hatcher E., 2004, ACTION SERIES; Jacquelinet C., 2005, CONNECTING MED INFOR, P1261; Liu B., 2003, DAT MIN 2003 ICDM 20, P179; LOWE HJ, 1994, JAMA-J AM MED ASSOC, V271, P1103, DOI 10.1001/jama.271.14.1103; LUHN HP, 1958, IBM J RES DEV, V2, P159; Metz C., SEMINARS NUCL MED, V8, P283; Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085; Ogescu C, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON AUTOMATION, QUALITY AND TESTING, ROBOTICS (AQTR 2008), THETA 16TH EDITION, VOL III, PROCEEDINGS, P257, DOI 10.1109/AQTR.2008.4588923; Ontrup J, 2003, P ANN INT IEEE EMBS, V25, P1303; Pianta E., P 1 INT C GLOB WORDN; Roventini A., LREC; SWETS JA, 1988, SCIENCE, V240, P1285, DOI 10.1126/science.3287615; Torres E., 2003, COMPUTING HIGH ENERG; Willett P, 2006, PROGRAM-ELECTRON LIB, V40, P219, DOI 10.1108/00330330610681295; Zelikovitz S., P 17 INT C MACH LEAR, P1183; Zipf G., 1949, HUMAN BEHAV PRINCIPL 30 1 1 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JUL 2014 37 243 251 10.1016/j.future.2014.02.008 9 Computer Science, Theory & Methods Computer Science AJ8CV WOS:000337931200023 J Scholl, SG; Greifeneder, R; Bless, H Scholl, Sabine G.; Greifeneder, Rainer; Bless, Herbert When Fluency Signals Truth: Prior Successful Reliance on Fluency Moderates the Impact of Fluency on Truth Judgments JOURNAL OF BEHAVIORAL DECISION MAKING English Article fluency; truth effect; feedback learning; subjective experiences PERCEPTUAL FLUENCY; PROCESSING FLUENCY; EXPERIENCED EASE; DECISION-MAKING; RETRIEVAL; FAMILIARITY; FREQUENCY; AVAILABILITY; INFORMATION; FEELINGS Repeated statements are more frequently judged to be true. One position relates this so-called truth effect to metacognitive experiences of fluency, suggesting that repeated statements are more frequently judged to be true because they are processed more fluently. Although most prior research focused on why repetition influences truth judgments, considerably less is known about when fluency is used as information. The present research addresses this question and investigates whether reliance on fluency is moderated by learning experiences. Specifically, we focus on changes in the reliance on fluency over the course of time. A series of experiments reveals that fluency is more likely to be used in truth judgments when previous reliance on fluency has resulted in valid judgments, compared with when previous reliance on fluency was misleading. These findings suggest that reliance on fluency in judgments is a finely tuned process that takes prior experiences with fluency-based judgments into account. Copyright (c) 2013 John Wiley & Sons, Ltd. [Scholl, Sabine G.] Univ Mannheim, Sch Social Sci, D-68131 Mannheim, Germany; [Bless, Herbert] Univ Mannheim, Sch Social Sci, Dept Microsociol & Social Psychol, D-68131 Mannheim, Germany; [Greifeneder, Rainer] Univ Basel, Ctr Social Psychol, CH-4003 Basel, Switzerland Scholl, SG (reprint author), Univ Mannheim, Sch Social Sci, D-68131 Mannheim, Germany. sabine.scholl@uni-mannheim.de Aarts H, 1999, ACTA PSYCHOL, V103, P77, DOI 10.1016/S0001-6918(99)00035-9; Alter AL, 2009, PERS SOC PSYCHOL REV, V13, P219, DOI 10.1177/1088868309341564; Arkes H. R., 1989, J BEHAVIORAL DECISIO, V2, P81, DOI DOI 10.1002/BDM.3960020203; Avnet T, 2012, J CONSUM RES, V39, P720, DOI 10.1086/664978; BEGG IM, 1992, J EXP PSYCHOL GEN, V121, P446, DOI 10.1037/0096-3445.121.4.446; Brinol P, 2006, PSYCHOL SCI, V17, P200, DOI 10.1111/j.1467-9280.2006.01686.x; Brown AS, 1996, J EXP PSYCHOL LEARN, V22, P1088, DOI 10.1037/0278-7393.22.5.1088; Dechene A, 2010, PERS SOC PSYCHOL REV, V14, P238, DOI 10.1177/1088868309352251; Elster J., 1999, ALCHEMIES MIND RATIO; Garcia-Marques T, 2001, SOC COGNITION, V19, P9, DOI 10.1521/soco.19.1.9.18959; GILBERT DT, 1991, AM PSYCHOL, V46, P107, DOI 10.1037//0003-066X.46.2.107; Greifeneder R, 2007, SOC COGNITION, V25, P853, DOI 10.1521/soco.2007.25.6.853; Greifeneder R, 2013, EXPERIENCE OF THINKING: HOW THE FLUENCY OF MENTAL PROCESSES INFLUENCES COGNITION AND BEHAVIOR, P220; Greifeneder R, 2011, PERS SOC PSYCHOL REV, V15, P107, DOI 10.1177/1088868310367640; Halberstadt J, 2008, SOC COGNITION, V26, P755; Hansen J, 2008, J EXP SOC PSYCHOL, V44, P687, DOI 10.1016/j.jesp.2007.04.005; HASHER L, 1977, J VERB LEARN VERB BE, V16, P107, DOI 10.1016/S0022-5371(77)80012-1; HAWKINS SA, 1992, J CONSUM RES, V19, P212, DOI 10.1086/209297; Hertwig R, 2008, J EXP PSYCHOL LEARN, V34, P1191, DOI 10.1037/a0013025; JACOBY LL, 1989, J PERS SOC PSYCHOL, V56, P326, DOI 10.1037//0022-3514.56.3.326; KELLEY CM, 1993, J MEM LANG, V32, P1, DOI 10.1006/jmla.1993.1001; Koch AS, 2012, J EXP SOC PSYCHOL, V48, P481, DOI 10.1016/j.jesp.2011.10.006; KORIAT A, 1993, PSYCHOL REV, V100, P609, DOI 10.1037/0033-295X.100.4.609; NISBETT RE, 1977, PSYCHOL REV, V84, P231, DOI 10.1037/0033-295X.84.3.231; Oppenheimer DM, 2004, PSYCHOL SCI, V15, P100, DOI 10.1111/j.0963-7214.2004.01502005.x; Ozubko JD, 2011, J EXP PSYCHOL LEARN, V37, P270, DOI 10.1037/a0021323; Reber R, 1998, PSYCHOL SCI, V9, P45, DOI 10.1111/1467-9280.00008; Reber R, 1999, CONSCIOUS COGN, V8, P338, DOI 10.1006/ccog.1999.0386; Reber R, 2004, CONSCIOUS COGN, V13, P47, DOI 10.1016/S1053-8100(03)00049-7; Roggeveen AL, 2002, J CONSUM PSYCHOL, V12, P81, DOI 10.1207/S15327663JCP1202_02; SCHWARTZ M, 1982, AM J PSYCHOL, V95, P393, DOI 10.2307/1422132; SCHWARZ N, 1991, J PERS SOC PSYCHOL, V61, P195, DOI 10.1037//0022-3514.61.2.195; Schwarz N, 2002, EMO SOC BEH, P144; Schwarz N, 1998, Pers Soc Psychol Rev, V2, P87, DOI 10.1207/s15327957pspr0202_2; Schwarz N, 2004, J CONSUM PSYCHOL, V14, P332, DOI 10.1207/s15327663jcp1404_2; Skurnik I, 2005, J CONSUM RES, V31, P713, DOI 10.1086/426605; Stanislaw H, 1999, BEHAV RES METH INS C, V31, P137, DOI 10.3758/BF03207704; TVERSKY A, 1973, COGNITIVE PSYCHOL, V5, P207, DOI 10.1016/0010-0285(73)90033-9; Unkelbach C, 2006, PSYCHOL SCI, V17, P339, DOI 10.1111/j.1467-9280.2006.01708.x; Unkelbach C, 2007, J EXP PSYCHOL LEARN, V33, P219, DOI 10.1037/0278-7393.33.1.219; Unkelbach C, 2009, CONSCIOUS COGN, V18, P22, DOI 10.1016/j.concog.2008.09.006; Unkelbach C, 2011, CONSCIOUS COGN, V20, P594, DOI 10.1016/j.concog.2010.09.015; WANKE M, 1995, ACTA PSYCHOL, V89, P83, DOI 10.1016/0001-6918(93)E0072-A; Whittlesea BWA, 2000, J EXP PSYCHOL LEARN, V26, P547, DOI 10.1037//0278-7393.26.3.547; Winkielman P, 2001, PSYCHOL SCI, V12, P176, DOI 10.1111/1467-9280.00330; Winkielman P, 1998, PSYCHOL SCI, V9, P124, DOI 10.1111/1467-9280.00022; Wurtz P, 2008, CONSCIOUS COGN, V17, P171, DOI 10.1016/j.concog.2007.07.001 47 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0894-3257 1099-0771 J BEHAV DECIS MAKING J. Behav. Decis. Mak. JUL 2014 27 3 268 280 10.1002/bdm.1805 13 Psychology, Applied Psychology AJ8PF WOS:000337967600008 J Wright, JM; Cottrell, DJ; Mir, G Wright, Judy M.; Cottrell, David J.; Mir, Ghazala Searching for religion and mental health studies required health, social science, and grey literature databases JOURNAL OF CLINICAL EPIDEMIOLOGY English Article Bibliographic databases; Information retrieval; Religion; Depression; Literature searching; Qualitative research SYSTEMATIC REVIEWS; MUSLIM PATIENTS; DEPRESSION; TRIALS; CARE Objective: To determine the optimal databases to search for studies of faith-sensitive interventions for treating depression. Study Design and Setting: We examined 23 health, social science, religious, and grey literature databases searched for an evidence synthesis. Databases were prioritized by yield of (1) search results, (2) potentially relevant references identified during screening, (3) included references contained in the synthesis, and (4) included references that were available in the database. We assessed the impact of databases beyond MEDLINE, EMBASE, and PsycINFO by their ability to supply studies identifying new themes and issues. We identified pragmatic workload factors that influence database selection. Results: PsycINFO was the best performing database within all priority lists. ArabPsyNet, CINAHL, Dissertations and Theses, EMBASE, Global Health, Health Management Information Consortium, MEDLINE, PsycINFO, and Sociological Abstracts were essential for our searches to retrieve the included references. Citation tracking activities and the personal library of one of the research teams made significant contributions of unique, relevant references. Religion studies databases (Am Theo Lib Assoc, FRANCIS) did not provide unique, relevant references. Conclusion: Literature searches for reviews and evidence syntheses of religion and health studies should include social science, grey literature, non-Western databases, personal libraries, and citation tracking activities. (C) 2014 Elsevier Inc. All rights reserved. [Wright, Judy M.; Cottrell, David J.; Mir, Ghazala] Univ Leeds, Leeds Inst Hlth Sci, Leeds LS2 9LJ, W Yorkshire, England Wright, JM (reprint author), Univ Leeds, Leeds Inst Hlth Sci, 101 Clarendon Rd, Leeds LS2 9LJ, W Yorkshire, England. j.m.wright@leeds.ac.uk National Institute for Health Research [PB-PG-1208-18107] This article presents independent research funded by the National Institute for Health Research under its Research for Patient Benefit Programme (grant reference number PB-PG-1208-18107). Badri M, 2007, CONTEMPLATION ISLAMI; Beyer FR, 2013, HEALTH INFO LIBR J, V30, P49, DOI 10.1111/hir.12009; Brunton G, 2012, INTRO SYSTEMATIC REV, P107; Centre for Reviews and Dissemination, 2011, SYST REV CRDS GUID U, P281; Department of Health, 1999, NAT SERV FRAM MENT H, P149; Department of Health, 2014, DEL RAC EQ MENT HLTH, V67, P800; Dobbs J, 2006, ETHNICITY RELIG 2006, P165; Efthimiadis EN, 1996, B MED LIBR ASSOC, V84, P386; Egger M, 2003, Health Technol Assess, V7, P1; Ekers D, 2008, PSYCHOL MED, V38, P611, DOI 10.1017/S0033291707001614; Geppert C, 2007, DRUG ALCOHOL REV, V26, P389, DOI 10.1080/09595230701373826; Glanville J, 2010, INT J TECHNOL ASSESS, V26, P436, DOI 10.1017/S0266462310000991; Lefebvre C, 2011, COCHRANE HDB SYSTEMA; Lukoff D, 1999, ALTERN THER HEALTH M, V5, P64; McDonald S, 1999, Health Libr Rev, V16, P151, DOI 10.1046/j.1365-2532.1999.00222.x; Mir G, 2010, ETHNIC HEALTH, V15, P327, DOI 10.1080/13557851003624273; Moffat J, 2009, INT REV PSYCHIATR, V21, P439, DOI 10.1080/09540260802204105; National Institute of Health and Clinical Excellence, 2009, DEPR TREATM MAN DEPR, P64; O'Connor Thomas St James, 2002, J Pastoral Care Counsel, V56, P227; Office for National Statistics, 2011, MIGR STAT Q REP; Pew Forum on Religion & Public Life, 2009, MAPP GLOB MUSL POP, P59; Roberts L, 2007, COCHRANE DB SYST REV, DOI 10.1002/14651858.CD000368.pub2; Royle P, 2003, INT J TECHNOL ASSESS, V19, P591; Royle P, 2003, HEALTH TECHNOL ASSES, V7, P1; Royle P, 2003, HEALTH TECHNOL ASSES, V7, piii; Royle P, 2003, HEALTH TECHNOL ASSES, V7, pix; Stevinson C, 2004, COMPLEMENT THER MED, V12, P228, DOI 10.1016/j.ctim.2004.09.003; Walpole SC, 2013, J AFFECT DISORDERS, V145, P11, DOI 10.1016/j.jad.2012.06.035; Whiting P, 2008, J CLIN EPIDEMIOL, V61, P357, DOI 10.1016/j.jclinepi.2007.05.013; Wolters Kluwer Health, 2006, ART CIT PUBMED NOT O 30 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0895-4356 1878-5921 J CLIN EPIDEMIOL J. Clin. Epidemiol. JUL 2014 67 7 800 810 10.1016/j.jclinepi.2014.02.017 11 Health Care Sciences & Services; Public, Environmental & Occupational Health Health Care Sciences & Services; Public, Environmental & Occupational Health AJ8UP WOS:000337983600011 J Ginsburg, V; van Dijck, JP; Previtali, P; Fias, W; Gevers, W Ginsburg, Veronique; van Dijck, Jean-Philippe; Previtali, Paola; Fias, Wim; Gevers, Wim The Impact of Verbal Working Memory on Number-Space Associations JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION English Article numbers; space; SNARC effect; working memory; ordinal coding SPATIAL-NUMERICAL ASSOCIATIONS; ATTENTION; INFORMATION; MAGNITUDE; TASK Spatial-numerical associations are observed when participants perform number categorization tasks. One such observation is the spatial numerical associations of response codes (SNARC) effect, showing an association between small numbers and the left-hand side and between large numbers and the right-hand side. It has long been argued that this spatial association is automatically activated by the long-term representation underlying numbers processing. Instead, van Dijck and Fias (2011) argued that this association is a short-term representation that is constructed during task execution. This argument was based on the observation of an association between the ordinal position of an item in working memory and response side (e. g., the ordinal position effect). Four different experiments were set up to systematically investigate this assumption. Our results indicate that the activation of the canonical order of numbers in working memory (e. g., 1, 2, 3, etc.) is indeed necessary to observe the SNARC effect. The activation of the standard sequence of numbers (e. g., from 1 to 9) can be overruled when a new random sequence is memorized. However, this is only observed when retrieval of the memorized sequence is required during the numbers classification task. [Ginsburg, Veronique; Gevers, Wim] Univ Libre Brussels, CRCN, UNI, B-1050 Brussels, Belgium; [van Dijck, Jean-Philippe; Fias, Wim] Univ Ghent, Dept Expt Psychol, B-9000 Ghent, Belgium; [van Dijck, Jean-Philippe] Univ Antwerp, Collaborat Antwerp Psychiat Res Inst, Antwerp, Belgium; [Previtali, Paola] Univ Milano Bicocca, Dept Psychol, Milan, Italy Ginsburg, V (reprint author), Univ Libre Brussels, Ave FD Roosevelt 50, B-1050 Brussels, Belgium. veronique.ginsburg@ulb.ac.be Abrahamse E., Q J EXPT PS IN PRESS; Bachot J., 2005, PSYCHOL SCI, V47, P172; Bachtold D, 1998, NEUROPSYCHOLOGIA, V36, P731, DOI 10.1016/S0028-3932(98)00002-5; Baddeley A, 2000, TRENDS COGN SCI, V4, P417, DOI 10.1016/S1364-6613(00)01538-2; Baddeley A. D., 1974, PSYCHOL LEARN MOTIV, V8, P47, DOI [DOI 10.1016/S0079-7421(08)60452-I, DOI 10.1016/S0079-7421(08)60452-1]; Baddeley A. D., 1986, WORKING MEMORY; Chen Q, 2010, COGNITIVE PSYCHOL, V60, P218, DOI 10.1016/j.cogpsych.2010.01.001; Cowan N., 1999, MODELS WORKING MEMOR, P62; Cowan N., 1995, ATTENTION MEMORY INT; DEHAENE S, 1990, J EXP PSYCHOL HUMAN, V16, P626, DOI 10.1037/0096-1523.16.3.626; DEHAENE S, 1993, J EXP PSYCHOL GEN, V122, P371, DOI 10.1037/0096-3445.122.3.371; Fias W, 1996, MATH COGNITION, V2, P95, DOI DOI 10.1080/135467996387552; Fias W, 2011, ATTENTION PERFORM, P133, DOI 10.1016/B978-0-12-385948-8.00010-4; Fischer MH, 2003, NAT NEUROSCI, V6, P555, DOI 10.1038/nn1066; Fischer MH, 2006, CORTEX, V42, P1066, DOI 10.1016/S0010-9452(08)70218-1; Gevers W, 2006, J EXP PSYCHOL HUMAN, V32, P32, DOI 10.1037/0096-1523.32.1.32; Gevers W, 2006, EXP PSYCHOL, V53, P58, DOI 10.1027/1618-3169.53.1.58; Gut M, 2012, INT J PSYCHOPHYSIOL, V85, P7, DOI 10.1016/j.ijpsycho.2012.02.007; Herrera A, 2008, ACTA PSYCHOL, V128, P225, DOI 10.1016/j.actpsy.2008.01.002; Lindemann O, 2008, Q J EXP PSYCHOL, V61, P515, DOI 10.1080/17470210701728677; LORCH RF, 1990, J EXP PSYCHOL LEARN, V16, P149, DOI 10.1037//0278-7393.16.1.149; Monsell S, 2003, TRENDS COGN SCI, V7, P134, DOI 10.1016/S1364-6613(03)00028-7; MOYER RS, 1967, NATURE, V215, P1519, DOI 10.1038/2151519a0; Nuerk HC, 2005, EXP PSYCHOL, V52, P187, DOI 10.1027/1618-3169.52.3.187; Oberauer K, 2012, CURR DIR PSYCHOL SCI, V21, P164, DOI 10.1177/0963721412444727; Oberauer K, 2002, J EXP PSYCHOL LEARN, V28, P411, DOI 10.1037//0278-7393.28.3.411; Shah P, 1996, J EXP PSYCHOL GEN, V125, P4, DOI 10.1037/0096-3445.125.1.4; Shaki S, 2008, COGNITION, V108, P590, DOI 10.1016/j.cognition.2008.04.001; Shaki S, 2009, PSYCHON B REV, V16, P328, DOI 10.3758/PBR.16.2.328; Smith EE, 1996, CEREB CORTEX, V6, P11, DOI 10.1093/cercor/6.1.11; van Dijck Jean-Philippe, 2011, Front Hum Neurosci, V5, P182, DOI 10.3389/fnhum.2011.00182; van Dijck JP, 2009, COGNITION, V113, P248, DOI 10.1016/j.cognition.2009.08.005; van Dijck JP, 2011, COGNITION, V119, P114, DOI 10.1016/j.cognition.2010.12.013; van Dijck JP, 2013, PSYCHOL SCI, V24, P1854, DOI 10.1177/0956797613479610; Zebian S., 2005, J COGNITION CULTURE, V5, P165, DOI [10.1163/1568537054068660, DOI 10.1163/1568537054068660] 35 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0278-7393 1939-1285 J EXP PSYCHOL LEARN J. Exp. Psychol.-Learn. Mem. Cogn. JUL 2014 40 4 976 986 10.1037/a0036378 11 Psychology; Psychology, Experimental Psychology AJ6KR WOS:000337803400005 J Spurgeon, J; Ward, G; Matthews, WJ Spurgeon, Jessica; Ward, Geoff; Matthews, William J. Examining the Relationship Between Immediate Serial Recall and Immediate Free Recall: Common Effects of Phonological Loop Variables but Only Limited Evidence for the Phonological Loop JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION English Article free recall; serial recall; phonological loop; phonological similarity effect; concurrent articulation SHORT-TERM-MEMORY; PREDICTING FREE RECALLS; RECENCY-BASED ACCOUNT; WORKING-MEMORY; ARTICULATORY SUPPRESSION; NETWORK MODEL; WORD-LENGTH; IRRELEVANT SPEECH; ORDER INFORMATION; RETRIEVAL MODEL We examined the contribution of the phonological loop to immediate free recall (IFR) and immediate serial recall (ISR) of lists of between one and 15 words. Following Baddeley (1986, 2000, 2007, 2012), we assumed that visual words could be recoded into the phonological store when presented silently but that recoding would be prevented by concurrent articulation (CA; Experiment 1). We further assumed that the use of the phonological loop would be evidenced by greater serial recall for lists of phonologically dissimilar words relative to lists of phonologically similar words (Experiments 2A and 2B). We found that in both tasks, (a) CA reduced recall; (b) participants recalled short lists from the start of the list, leading to enhanced forward-ordered recall; (c) participants were increasingly likely to recall longer lists from the end of the list, leading to extended recency effects; (d) there were significant phonological similarity effects in ISR and IFR when both were analyzed using serial recall scoring; (e) these were reduced by free recall scoring and eliminated by CA; and (f) CA but not phonological similarity affected the tendency to initiate recall with the first list item. We conclude that similar mechanisms underpin ISR and IFR. Critically, the phonological loop is not strictly necessary for the forward-ordered recall of short lists on both tasks but may augment recall by increasing the accessibility of the list items (relative to CA), and in so doing, the order of later items is preserved better in phonologically dissimilar than in phonologically similar lists. [Spurgeon, Jessica; Ward, Geoff; Matthews, William J.] Univ Essex, Dept Psychol, Colchester CO4 3SQ, Essex, England Ward, G (reprint author), Univ Essex, Dept Psychol, Wivenhoe Pk, Colchester CO4 3SQ, Essex, England. gdward@essex.ac.uk Allen R. J., 2008, INTERACTIONS SHORT T, P63; Allen RJ, 2006, J EXP PSYCHOL GEN, V135, P298, DOI 10.1037/0096-3445.135.2.298; Anderson JR, 1998, J MEM LANG, V38, P341, DOI 10.1006/jmla.1997.2553; ATKINSON RC, 1971, SCI AM, V225, P82; Baddeley A, 2012, ANNU REV PSYCHOL, V63, P1, DOI 10.1146/annurev-psych-120710-100422; BADDELEY A, 1984, B PSYCHONOMIC SOC, V22, P403; Baddeley A, 2003, Q J EXP PSYCHOL-A, V56, P1301, DOI 10.1080/02724980343000530; BADDELEY A, 1984, Q J EXP PSYCHOL-A, V36, P233; Baddeley A, 2000, TRENDS COGN SCI, V4, P417, DOI 10.1016/S1364-6613(00)01538-2; BADDELEY AD, 1966, Q J EXP PSYCHOL, V18, P362, DOI 10.1080/14640746608400055; BADDELEY AD, 1968, Q J EXP PSYCHOL, V20, P249, DOI 10.1080/14640746808400159; Baddeley A. D., 1986, WORKING MEMORY; Baddeley A. D., 1974, RECENT ADV LEARNING, V8, P47; Baddeley A. D., 1977, ATTENTION PERFORM, P647; Baddeley A. D., 2007, WORKING MEMORY THOUG, DOI [10.1093/acprof:oso/9780198528012.001.0001, DOI 10.1093/ACPROF:OSO/9780198528012.001.0001]; BADDELEY AD, 1975, J VERB LEARN VERB BE, V14, P575, DOI 10.1016/S0022-5371(75)80045-4; Baddeley AD, 2007, Q J EXP PSYCHOL, V60, P497, DOI 10.1080/17470210601147572; Beaman CP, 2000, COGNITION, V77, pB59, DOI 10.1016/S0010-0277(00)00107-4; Beaman CP, 1998, Q J EXP PSYCHOL-A, V51, P615; Beaman CP, 2008, J EXP PSYCHOL LEARN, V34, P219, DOI 10.1037/0278-7393.34.1.219; Bhatarah P, 2008, MEM COGNITION, V36, P20, DOI 10.3758/MC.36.1.20; Bhatarah P, 2009, MEM COGNITION, V37, P689, DOI 10.3758/MC.37.5.689; Bhatarah P, 2006, J EXP PSYCHOL LEARN, V32, P215, DOI 10.1037/0278-7393.32.1215; Botvinick MM, 2006, PSYCHOL REV, V113, P201, DOI 10.1037/0033-295X.113.2.201; Brown GDA, 2008, PSYCHOL REV, V115, P781, DOI 10.1037/a0012563; Brown GDA, 2000, PSYCHOL REV, V107, P127, DOI 10.1037//0033-295X.107.1.127; Brown GDA, 2007, PSYCHOL REV, V114, P539, DOI 10.1037/0033-295X.114.3.539; Burgess N, 2006, J MEM LANG, V55, P627, DOI 10.1016/j.jml.2006.08.005; BURGESS N, 1992, J MEM LANG, V31, P429, DOI 10.1016/0749-596X(92)90022-P; Burgess N, 1999, PSYCHOL REV, V106, P551, DOI 10.1037/0033-295X.106.3.551; Citroen R., 2012, DEV PSYCHOL; COLTHEART M, 1981, Q J EXP PSYCHOL-A, V33, P497; COLTHEART V, 1993, MEM COGNITION, V21, P539, DOI 10.3758/BF03197185; CONRAD R, 1964, BRIT J PSYCHOL, V55, P75; CONRAD R, 1964, BRIT J PSYCHOL, V55, P429; CORBALLI.MC, 1967, J EXP PSYCHOL, V74, P99, DOI 10.1037/h0024500; Davelaar EJ, 2005, PSYCHOL REV, V112, P3, DOI 10.1037/0033-295X.112.1.3; DREWNOWSKI A, 1980, J EXP PSYCHOL-HUM L, V6, P319, DOI 10.1037//0278-7393.6.3.319; Fallon AB, 1999, INT J PSYCHOL, V34, P301; Farrell S, 2010, J EXP PSYCHOL LEARN, V36, P324, DOI 10.1037/a0018042; Farrell S, 2002, PSYCHON B REV, V9, P59, DOI 10.3758/BF03196257; Farrell S, 2012, PSYCHOL REV, V119, P223, DOI 10.1037/a0027371; Farrell S, 2006, J MEM LANG, V55, P587, DOI 10.1016/j.jml.2006.06.002; Fournet N, 2003, INT J PSYCHOL, V38, P384, DOI 10.1080/00207590344000204; FRIENDLY M, 1982, BEHAV RES METH INSTR, V14, P375, DOI 10.3758/BF03203275; Golomb JD, 2008, MEM COGNITION, V36, P947, DOI 10.3758/MC.36.5.947; Grenfell-Essam R, 2012, J MEM LANG, V67, P106, DOI 10.1016/j.jml.2012.04.004; Grenfell-Essam R, 2013, J EXP PSYCHOL LEARN, V39, P317, DOI 10.1037/a0028974; Grossberg S, 2008, PSYCHOL REV, V115, P677, DOI 10.1037/a0012618; Gupta P, 2005, MEM COGNITION, V33, P1001, DOI 10.3758/BF03193208; Hanley JR, 2012, MEMORY, V20, P415, DOI 10.1080/09658211.2012.670249; Henson RNA, 1998, COGNITIVE PSYCHOL, V36, P73, DOI 10.1006/cogp.1998.0685; Howard MW, 1999, J EXP PSYCHOL LEARN, V25, P923, DOI 10.1037//0278-7393.25.4.923; Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388; Jalbert A, 2011, MEM COGNITION, V39, P1198, DOI 10.3758/s13421-011-0094-z; Jones D. M., 1993, ATTENTION SELECTION, P87; Jones DM, 2004, J EXP PSYCHOL LEARN, V30, P656, DOI 10.1037/0278-7393.30.3.656; Jones DM, 2007, Q J EXP PSYCHOL, V60, P505, DOI 10.1080/17470210601147598; Jones DM, 2006, J MEM LANG, V54, P265, DOI 10.1016/j.jml.2005.10.006; Kahana M. J., 2012, FDN HUMAN MEMORY; Kahana MJ, 1996, MEM COGNITION, V24, P103, DOI 10.3758/BF03197276; Klein KA, 2005, MEM COGNITION, V33, P833, DOI 10.3758/BF03193078; Laming D, 1999, INT J PSYCHOL, V34, P419, DOI 10.1080/002075999399774; Laming D, 2006, J EXP PSYCHOL LEARN, V32, P1146, DOI 10.1037/0278-7393.32.5.1146; Laming D, 2008, COGNITIVE PSYCHOL, V57, P179, DOI 10.1016/j.cogpsych.2008.01.001; Laming D, 2010, PSYCHOL REV, V117, P93, DOI 10.1037/a0017839; Larsen JD, 2003, Q J EXP PSYCHOL-A, V56, P1249, DOI 10.1080/02724980244000765; LEE CL, 1981, J EXP PSYCHOL-HUM L, V7, P149, DOI 10.1037/0278-7393.7.3.149; Lehman M, 2013, PSYCHOL REV, V120, P155, DOI 10.1037/a0030851; LEVY BA, 1971, J VERB LEARN VERB BE, V10, P123, DOI 10.1016/S0022-5371(71)80003-8; Lewandowsky S, 2008, PSYCHOL LEARN MOTIV, V49, P1, DOI 10.1016/SO079-7421(08)00001-7; LEWANDOWSKY S, 1989, PSYCHOL REV, V96, P25, DOI 10.1037/0033-295X.96.1.25; Lian A, 2004, MEMORY, V12, P314, DOI 10.1080/09658210344000026; Logie RH, 2000, Q J EXP PSYCHOL-A, V53, P626, DOI 10.1080/027249800410463; METCALFE J, 1981, J VERB LEARN VERB BE, V20, P161, DOI 10.1016/S0022-5371(81)90365-0; MURDOCK BB, 1962, J EXP PSYCHOL, V64, P482, DOI 10.1037/h0045106; Murdock B, 2008, PSYCHOL REV, V115, P779, DOI 10.1037/0033-295X.115.3.779; MURRAY DJ, 1968, J EXP PSYCHOL, V78, P679, DOI 10.1037/h0026641; MURRAY DJ, 1967, CAN J PSYCHOLOGY, V21, P263, DOI 10.1037/h0082978; NAIRNE JS, 1990, MEM COGNITION, V18, P251, DOI 10.3758/BF03213879; Neath I, 1996, MEMORY, V4, P225; Neath I, 2000, PSYCHON B REV, V7, P403, DOI 10.3758/BF03214356; Oberauer K, 2008, PSYCHOL REV, V115, P544, DOI 10.1037/0033-295X.115.3.544; Page MPA, 1998, PSYCHOL REV, V105, P761, DOI 10.1037/0033-295X.105.4.761-781; PETERSON LR, 1971, J VERB LEARN VERB BE, V10, P346, DOI 10.1016/S0022-5371(71)80033-6; Polyn SM, 2009, PSYCHOL REV, V116, P129, DOI 10.1037/a0014420; RAAIJMAKERS JGW, 1981, PSYCHOL REV, V88, P93, DOI 10.1037/0033-295X.88.2.93; Romani C, 2008, Q J EXP PSYCHOL, V61, P292, DOI 10.1080/17470210601147747; SAITO S, 1993, PSYCHOLOGIA, V36, P27; Saito S, 1997, BRIT J PSYCHOL, V88, P565; SAITO S, 1994, MEM COGNITION, V22, P181; SALAME P, 1982, J VERB LEARN VERB BE, V21, P150, DOI 10.1016/S0022-5371(82)90521-7; Sederberg PB, 2008, PSYCHOL REV, V115, P893, DOI 10.1037/a0013396; Tan L, 2000, J EXP PSYCHOL LEARN, V26, P1589, DOI 10.1037//0278-7393.26.6.1589; Tan L, 2008, PSYCHON B REV, V15, P535, DOI 10.3758/PBR.15.3.535; Treisman A, 2006, MEM COGNITION, V34, P1704, DOI 10.3758/BF03195932; Unsworth N, 2007, PSYCHOL REV, V114, P104, DOI 10.1037/0033-295X.114.1.104; Ward G, 2004, J EXP PSYCHOL LEARN, V30, P1196, DOI 10.1037/0278-7393-30.6.1196; Ward G, 2010, J EXP PSYCHOL LEARN, V36, P1207, DOI 10.1037/a0020122; Ward G, 2002, MEM COGNITION, V30, P885, DOI 10.3758/BF03195774; WATKINS MJ, 1974, J VERB LEARN VERB BE, V13, P430, DOI 10.1016/S0022-5371(74)80021-6 101 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0278-7393 1939-1285 J EXP PSYCHOL LEARN J. Exp. Psychol.-Learn. Mem. Cogn. JUL 2014 40 4 1110 1141 10.1037/a0035784 32 Psychology; Psychology, Experimental Psychology AJ6KR WOS:000337803400014 J Vendetti, MS; Wu, A; Rowshanshad, E; Knowlton, BJ; Holyoak, KJ Vendetti, Michael S.; Wu, Aaron; Rowshanshad, Ebi; Knowlton, Barbara J.; Holyoak, Keith J. When Reasoning Modifies Memory: Schematic Assimilation Triggered by Analogical Mapping JOURNAL OF EXPERIMENTAL PSYCHOLOGY-LEARNING MEMORY AND COGNITION English Article analogical reasoning; recognition memory; schema; abstraction CONSTRAINT SATISFACTION; RELATIONAL INTEGRATION; INHIBITORY ACCOUNT; RETRIEVAL; RECOGNITION; CONSISTENCY; MECHANISMS; INFERENCES; SIMILARITY; KNOWLEDGE Analogical mapping highlights shared relations that link 2 situations, potentially at the expense of information that does not fit the dominant pattern of correspondences. To investigate whether analogical mapping can alter subsequent recognition memory for features of a source analog, we performed 2 experiments with 4-term proportional analogies (A:B::C:D), using problems based on cartoon figures varying on 4 visual dimensions. The source analog (A:B) was encoded before the reasoner was told which dimension was relevant to the analogy. After encoding, the A:B pair disappeared, 1 randomly selected dimension was specified as the basis for an analogical decision, and the target (C:D) was presented. A decision about the validity of the analogy was then made, after which memory for the A: B pair was assessed by a recognition test. In Experiment 1, we found that participants' recognition memory was reduced for lures involving a feature change on a dimension initially inconsistent with the analogical decision relative to a change on a dimension that had been consistent with it. The results of Experiment 2 revealed that this memory decrement occurred only when the change in the initially inconsistent feature caused the lure to be coherent with the overall schematic pattern of relational correspondences. These findings suggest that analogical reasoning can trigger changes in the memory representation of a source analog stored in memory such that subsequent recognition is guided by a relational schema. [Vendetti, Michael S.] Univ Calif Berkeley, Helen Wills Neurosci Inst, Berkeley, CA 94720 USA; [Wu, Aaron; Rowshanshad, Ebi; Knowlton, Barbara J.; Holyoak, Keith J.] Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA 90024 USA Vendetti, MS (reprint author), Univ Calif Berkeley, Helen Wills Neurosci Inst, 134 Barker Hall, Berkeley, CA 94720 USA. m.vendetti@berkeley.edu Office of Naval Research [N000140810186] This study was part of a doctoral dissertation completed by Michael S. Vendetti under the direction of Barbara J. Knowlton and Keith J. Holyoak. The project was supported by Office of Naval Research Grant N000140810186. We thank Robert Bjork, Jesse Rissman, and Martin Monti for helpful advice at various points during this research. ANDERSON MC, 1995, PSYCHOL REV, V102, P68, DOI 10.1037/0033-295X.102.1.68; Bartlett F. C., 1932, REMEMBERING STUDY EX; Bauml KH, 2010, NEUROSCI BIOBEHAV R, V34, P1047, DOI 10.1016/j.neubiorev.2009.04.005; Blanchette I, 2002, J EXP PSYCHOL LEARN, V28, P672, DOI 10.1037//0278-7393.28.4.672; Brainard DH, 1997, SPATIAL VISION, V10, P433, DOI 10.1163/156856897X00357; Butler KM, 2001, J EXP PSYCHOL LEARN, V27, P1314, DOI 10.1037//0278-7393.27.5.1314; CATRAMBONE R, 1989, J EXP PSYCHOL LEARN, V15, P1147, DOI 10.1037//0278-7393.15.6.1147; Cho S, 2007, MEM COGNITION, V35, P1445, DOI 10.3758/BF03193614; Cho S, 2010, CEREB CORTEX, V20, P524, DOI 10.1093/cercor/bhp121; Day SB, 2007, MEM COGNITION, V35, P39, DOI 10.3758/BF03195940; Fischhoff B., 1982, JUDGMENT UNCERTAINTY, P335, DOI 10.1017/CBO9780511809477.024; GENTNER D, 1983, COGNITIVE SCI, V7, P155, DOI 10.1207/s15516709cog0702_3; Gentner D, 2011, WIRES COGN SCI, V2, P266, DOI 10.1002/wcs.105; Gentner D, 2009, COGNITIVE SCI, V33, P1343, DOI 10.1111/j.1551-6709.2009.01070.x; Gentner D., 1983, MENTAL MODELS, P99; GICK ML, 1980, COGNITIVE PSYCHOL, V12, P306, DOI 10.1016/0010-0285(80)90013-4; GICK ML, 1983, COGNITIVE PSYCHOL, V15, P1, DOI 10.1016/0010-0285(83)90002-6; GOETHALS GR, 1973, J EXP PSYCHOL, V9, P491, DOI 10.1016/0022-1031(73)90030-9; GOLDSTONE RL, 1991, COGNITIVE PSYCHOL, V23, P222, DOI 10.1016/0010-0285(91)90010-L; Gomez-Ariza CJ, 2005, MEM COGNITION, V33, P1431, DOI 10.3758/BF03193376; Green AE, 2006, MEM COGNITION, V34, P1414, DOI 10.3758/BF03195906; Hicks JL, 2004, PSYCHON B REV, V11, P125, DOI 10.3758/BF03206471; HIGGINS ET, 1994, COGNITIVE PSYCHOL, V27, P227, DOI 10.1006/cogp.1994.1017; Holyoak K. J., 1995, MENTAL LEAPS ANALOGY; Holyoak K. J., 2012, OXFORD HDB THINKING, P234, DOI 10.1093/oxfordhb/9780199734689.001.0001; HOLYOAK KJ, 1987, MEM COGNITION, V15, P332, DOI 10.3758/BF03197035; Holyoak KJ, 1999, J EXP PSYCHOL GEN, V128, P3, DOI 10.1037/0096-3445.128.1.3; HOLYOAK KJ, 1989, COGNITIVE SCI, V13, P295, DOI 10.1207/s15516709cog1303_1; Knowlton BJ, 2012, TRENDS COGN SCI, V16, P373, DOI 10.1016/j.tics.2012.06.002; Kroger J. K., 2004, Cognitive Science, V28, DOI 10.1016/j.cogsci.2003.06.003; Kurtz KJ, 2007, MEM COGNITION, V35, P334, DOI 10.3758/BF03193454; LOFTUS EF, 1974, J VERB LEARN VERB BE, V13, P585, DOI 10.1016/S0022-5371(74)80011-3; Love BC, 1999, COGNITIVE PSYCHOL, V38, P291, DOI 10.1006/cogp.1998.0697; Markman AB, 1997, PSYCHOL SCI, V8, P363, DOI 10.1111/j.1467-9280.1997.tb00426.x; Markman AB, 1997, COGNITIVE SCI, V21, P373, DOI 10.1207/s15516709cog2104_1; Morrison R. G., 2001, P 24 ANN C COGN SCI, P663; Perfect TJ, 2002, J EXP PSYCHOL LEARN, V28, P1111, DOI 10.1037//0278-7393.28.6.1111; Perrott DA, 2005, PSYCHON B REV, V12, P696, DOI 10.3758/BF03196760; Richland LE, 2006, J EXP CHILD PSYCHOL, V94, P249, DOI 10.1016/j.jecp.2006.02.002; SCHUSTACK MW, 1979, J VERB LEARN VERB BE, V18, P565, DOI 10.1016/S0022-5371(79)90314-1; Simon D, 2002, PERS SOC PSYCHOL REV, V6, P283, DOI 10.1207/S15327957PSPR0604_03; STERNBERG RJ, 1977, PSYCHOL REV, V84, P353, DOI 10.1037//0033-295X.84.4.353; Storm BC, 2012, MEM COGNITION, V40, P827, DOI 10.3758/s13421-012-0211-7; Verde MF, 2011, PSYCHON B REV, V18, P1166, DOI 10.3758/s13423-011-0143-4; Viskontas IV, 2004, PSYCHOL AGING, V19, P581, DOI 10.1037/0882-7974.19.4.581; WIXON DR, 1976, J PERS SOC PSYCHOL, V34, P376, DOI 10.1037//0022-3514.34.3.376 46 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0278-7393 1939-1285 J EXP PSYCHOL LEARN J. Exp. Psychol.-Learn. Mem. Cogn. JUL 2014 40 4 1172 1180 10.1037/a0036350 9 Psychology; Psychology, Experimental Psychology AJ6KR WOS:000337803400018 J Wang, JJY; Sun, YJ Wang, Jim Jing-Yan; Sun, Yijun From one graph to many: Ensemble transduction for content-based database retrieval KNOWLEDGE-BASED SYSTEMS English Article Content-based database retrieval; Contextual similarity; Graph transduction; Multi-Kernel Learning; Ensembel learning KULLBACK-LEIBLER DIVERGENCE; DEMENTED OLDER-ADULTS; OPEN ACCESS SERIES; CROSS-VALIDATION; IMAGE RETRIEVAL; CHI-SQUARE; ROC CURVE; MRI DATA; K-FOLD; DISTANCE Similarity learning plays a fundamental role in the problem of database retrieval and nearest classification problem. Traditional pairwise similarity measure ignores the contextual information, and the Graph Transduction (GT) has been proposed as a contextual similarity learning algorithm to utilize the contextual information, which is embedded in a nearest neighbor graph. On main shortage of this method is that it is difficult to choose the optimal graph since different graphs may focus on different aspects of the objects. Co-Transduction (CT) is lately proposed by fusing two different graphs. In this paper, we generalize this problem by using the ensemble of many candidate graphs with different models and parameters for transduction, by assuming that the optimal graph could be obtained by the weighted linear ensemble of these candidate graphs. The similarities and graph weights are modeled within one unified objective function, and optimized alternately in an iterative algorithm. The new proposed algorithm, named as Ensemble Transduction (ET), is tested on two challenging tasks and the experimental results show that it can outperform both the GT and CT. (C) 2014 Elsevier B.V. All rights reserved. [Wang, Jim Jing-Yan; Sun, Yijun] SUNY Buffalo, Buffalo, NY 14203 USA; [Wang, Jim Jing-Yan] Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China Sun, YJ (reprint author), SUNY Buffalo, Buffalo, NY 14203 USA. jimjywang@gmail.com; yijunsun@buffalo.edu Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China [KJS1324]; US National Science Foundation [DBI-1322212]; NIH [P50 AG05681, P01 AG03991, P20 MH071616, RR14075, RR 16594, BIRN002]; Alzheimers Association; James S. McDonnell Foundation; Mental Illness and Neuroscience Discovery Institute; Howard Hughes Medical Institute The study was supported by Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China (Grant No. KJS1324), and US National Science Foundation (Grant No. DBI-1322212). The acquisition of the MRI data were supported by NIH Grants P50 AG05681, P01 AG03991, P20 MH071616, RR14075, RR 16594, BIRN002, the Alzheimers Association, the James S. McDonnell Foundation, the Mental Illness and Neuroscience Discovery Institute, and the Howard Hughes Medical Institute. Ali H., 2013, FAR E J MATH SCI, V74, P249; Bai X, 2012, IEEE T IMAGE PROCESS, V21, P2747, DOI 10.1109/TIP.2011.2170082; Bai X, 2010, IEEE T PATTERN ANAL, V32, P861, DOI 10.1109/TPAMI.2009.85; Chakraborty J., 2013, SPIE MED IMAGING, V8670; Daliri MR, 2013, BIOMED SIGNAL PROCES, V8, P66, DOI 10.1016/j.bspc.2012.04.007; Emran SM, 2002, QUAL RELIAB ENG INT, V18, P19, DOI 10.1002/qre.441; Fober T, 2009, BIOINFORMATICS, V25, P2110, DOI 10.1093/bioinformatics/btp144; Fober T., 2010, GCB 2010 GERM C BIOI, P51; Fober T., 2008, P GERM C BIOINF GCB, P44; Fu K, 2013, ASIA PAC J OPERAT RE, V30; Fushiki T, 2011, STAT COMPUT, V21, P137, DOI 10.1007/s11222-009-9153-8; Goadrich M, 2006, MACH LEARN, V64, P231, DOI 10.1007/s10994-006-8958-3; Jia Z., 2008, PATTERN RECOGN, V41, P1479, DOI 10.1016/j.patcog.2007.06.034; Lee JA, 2013, NEUROCOMPUTING, V112, P92, DOI 10.1016/j.neucom.2012.12.036; Liang ZZ, 2013, PATTERN RECOGN, V46, P1209, DOI 10.1016/j.patcog.2012.10.017; Liao KY, 2013, KNOWL-BASED SYST, V49, P123, DOI 10.1016/j.knosys.2013.05.003; Liu ZG, 2013, PATTERN RECOGN, V46, P834, DOI 10.1016/j.patcog.2012.10.001; Mao W., 2012, NEUR COMPUT APPL, P1; Marcus DS, 2007, J COGNITIVE NEUROSCI, V19, P1498, DOI 10.1162/jocn.2007.19.9.1498; Marcus DS, 2010, J COGNITIVE NEUROSCI, V22, P2677, DOI 10.1162/jocn.2009.21407; Marukatat S, 2013, PATTERN RECOGN LETT, V34, P1101, DOI 10.1016/j.patrec.2013.03.006; Meijer RJ, 2013, BIOMETRICAL J, V55, P141, DOI 10.1002/bimj.201200088; Meiyappan Y., 2013, INT ARAB J INFORM TE, V10; Moreo A, 2013, KNOWL-BASED SYST, V53, P108, DOI 10.1016/j.knosys.2013.08.018; Murala S, 2013, NEUROCOMPUTING, V119, P399, DOI 10.1016/j.neucom.2013.03.018; Orabona F, 2012, J MACH LEARN RES, V13, P227; Pang B, 2012, BIOINFORMATICS, V28, P1345, DOI 10.1093/bioinformatics/bts138; Pedronette DCG, 2013, PATTERN RECOGN, V46, P2350, DOI 10.1016/j.patcog.2013.01.004; Rajakumar Muttan, 2013, J COMP SCI, V9, P285; Rashedi E, 2013, KNOWL-BASED SYST, V39, P85, DOI 10.1016/j.knosys.2012.10.011; Rodriguez-Garcia MA, 2014, KNOWL-BASED SYST, V56, P15, DOI 10.1016/j.knosys.2013.10.006; Seghouane AK, 2012, NEURAL COMPUT, V24, P1722, DOI 10.1162/NECO_a_00291; Srinivas K., 2013, LECT NOTES ELECT ENG, V150, P65; Sun HW, 2013, ACTA MATH SIN, V29, P1607, DOI 10.1007/s10114-013-0675-9; Xu WC, 2013, SIGNAL PROCESS, V93, P3111, DOI 10.1016/j.sigpro.2013.05.010; Yang GW, 2013, APPL MECH MATER, V321-324, P1055, DOI 10.4028/www.scientific.net/AMM.321-324.1055; Yousef WA, 2013, COMPUT STAT DATA AN, V64, P51, DOI 10.1016/j.csda.2013.02.032; Zheng Q., J DIG IMAG, V26; Zheng Qian, 2011, Chinese Journal of Biomedical Engineering, V30, DOI 10.3969/j.issn.0258-8021.2011.03.007 39 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-7051 1872-7409 KNOWL-BASED SYST Knowledge-Based Syst. JUL 2014 65 31 37 10.1016/j.knosys.2014.04.003 7 Computer Science, Artificial Intelligence Computer Science AJ7JB WOS:000337871900004 J Yilmaz, T; Yazici, A; Kitsuregawa, M Yilmaz, Turgay; Yazici, Adnan; Kitsuregawa, Masaru RELIEF-MM: effective modality weighting for multimedia information retrieval MULTIMEDIA SYSTEMS English Article RELIEF; Feature weighting; Multimodal fusion; Multimedia information retrieval FEATURE-SELECTION; FUSION; SYSTEMS Fusing multimodal information in multimedia data usually improves the retrieval performance. One of the major issues in multimodal fusion is how to determine the best modalities. To combine the modalities more effectively, we propose a RELIEF-based modality weighting approach, named as RELIEF-MM. The original RELIEF algorithm is extended for weaknesses in several major issues: class-specific feature selection, complexities with multi-labeled data and noise, handling unbalanced datasets, and using the algorithm with classifier predictions. RELIEF-MM employs an improved weight estimation function, which exploits the representation and reliability capabilities of modalities, as well as the discrimination capability, without any increase in the computational complexity. The comprehensive experiments conducted on TRECVID 2007, TRECVID 2008 and CCV datasets validate RELIEF-MM as an efficient, accurate and robust way of modality weighting for multimedia data. [Yilmaz, Turgay; Yazici, Adnan] Middle E Tech Univ, Dept Comp Engn, TR-06531 Ankara, Turkey; [Yilmaz, Turgay; Kitsuregawa, Masaru] Univ Tokyo, Inst Ind Sci, Tokyo 1538505, Japan; [Kitsuregawa, Masaru] Natl Inst Informat, Chiyoda Ku, Tokyo 1018430, Japan Yilmaz, T (reprint author), Middle E Tech Univ, Dept Comp Engn, TR-06531 Ankara, Turkey. turgay@ceng.metu.edu.tr; yazici@ceng.metu.edu.tr; kitsure@tkl.iis.u-tokyo.ac.jp Atrey PK, 2007, ACM T MULTIM COMPUT, V3, DOI 10.1145/1198302.1198304; Atrey PK, 2010, MULTIMEDIA SYST, V16, P345, DOI 10.1007/s00530-010-0182-0; Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199; Chawla N. V., 2004, ACM SIGKDD EXPLORATI, V6, P1, DOI DOI 10.1145/1007730.1007733; Chen YY, 2013, IEEE T MULTIMEDIA, V15, P1388, DOI 10.1109/TMM.2013.2250492; Dietterich TG, 1997, AI MAG, V18, P97; DOQUIRE G, 2011, P 11 INT C ART NEUR, P9; FERRI FJ, 1994, MACH INTELL PATT REC, V16, P403; Fumera G, 2005, IEEE T PATTERN ANAL, V27, P942, DOI 10.1109/TPAMI.2005.109; Guyon I., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753616; Hall M. A., 1999, THESIS U WAIKATO NZ; HUANG KC, 2013, MULT EXP ICME 2013 I, P1, DOI DOI 10.1109/ICME.2013.6607472; Hunt E. B., 1966, EXPT INDUCTION; INOUE N, 2011, NIST TRECVID WORKSH; Jain A, 2005, PATTERN RECOGN, V38, P2270, DOI 10.1016/j.patcog.2005.01.012; Jain AK, 2000, IEEE T PATTERN ANAL, V22, P4, DOI 10.1109/34.824819; JIANG YG, 2008, 22320081 ADVENT COL; JIANG YG, 2011, P 1 ACM INT C MULT R, DOI DOI 10.1145/1991996.1992025; JIANG YG, 2010, TRECVID; Jiang Y.-G., 2012, INT J PHOTOENERGY, P1, DOI DOI 10.1109/TPEL.2012.2220158; KALAMARAS I, 2013, ELECT LETT COMP VIS, V12; Kankanhalli MS, 2006, IEEE T MULTIMEDIA, V8, P947, DOI 10.1109/TMM.2006.879875; Kira K., 1992, P 9 INT C MACH LEARN, P249; Kittler J., 1978, Pattern Recognition and Signal Processing; Kittler J, 1998, IEEE T PATTERN ANAL, V20, P226, DOI 10.1109/34.667881; Kludas J, 2009, MULTIMED TOOLS APPL, V42, P57, DOI 10.1007/s11042-008-0251-y; Kong DG, 2012, PROC CVPR IEEE, P2352; Kononenko I., 1994, P EUR C MACH LEARN, P171; Liu H, 2004, ARTIF INTELL, V159, P49, DOI 10.1016/j.artini.2004.05.009; MATHIEU B, 2010, P 11 ISMIR C UTR NET; Moulin C, 2014, PATTERN RECOGN, V47, P260, DOI 10.1016/j.patcog.2013.06.003; NATARAJAN P, 2011, NIST TRECVID WORKSH; OVER P, 2008, TRECVID; OVER P, 2007, TRECVID; Poh N, 2010, MULTIMODAL SIGNAL PROCESSING: THEORY AND APPLICATIONS FOR HUMAN-COMPUTER INTERACTION, P153, DOI 10.1016/B978-0-12-374825-6.00017-4; Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877; RAHMAN M, 2013, INT J MULTIMEDIA INF, V2, P159, DOI DOI 10.1007/S13735-013-0038-4; Robnik-Sikonja M, 2003, MACH LEARN, V53, P23, DOI 10.1023/A:1025667309714; Kononenko I, 1997, ICML 97, P296; Saeys Y, 2007, BIOINFORMATICS, V23, P2507, DOI 10.1093/bioinformatics/btm344; SIKONJA MR, 1998, P EL COMP SCI C ERK, P137; Snidaro L, 2007, IEEE T SYST MAN CY B, V37, P1044, DOI 10.1109/TSMCB.2007.895331; Snoek CGM, 2005, MULTIMED TOOLS APPL, V25, P5, DOI 10.1023/B:MTAP.0000046380.27575.a5; Sun YJ, 2007, IEEE T PATTERN ANAL, V29, P1035, DOI 10.1109/TPAMI.2007.1093; Temko A, 2008, PATTERN RECOGN, V41, P1814, DOI 10.1016/j.patcog.2007.10.026; Tsoumakas G, 2010, DATA MINING AND KNOWLEDGE DISCOVERY HANDBOOK, SECOND EDITION, P667, DOI 10.1007/978-0-387-09823-4_34; TUMER K, 1999, CORR; Wang LP, 2008, IEEE T NEURAL NETWOR, V19, P1267, DOI 10.1109/TNN.2008.2000395; Wu QX, 2013, IEEE T SYST MAN CY-S, V43, P875, DOI 10.1109/TSMCA.2012.2226575; WU Y, 2004, P 12 ANN ACM INT C M, P572, DOI 10.1145/1027527.1027665; YAN R, 2003, P 11 ACM INT C MULT, P339; YILMAZ T, 2012, P 2 ACM INT C MULT R, DOI DOI 10.1145/2324796.2324858; Yilmaz T, 2011, LECT NOTES ARTIF INT, V7022, P149, DOI 10.1007/978-3-642-24764-4_14; 2003, MPEG MPEG 7 REFERENC 54 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0942-4962 1432-1882 MULTIMEDIA SYST Multimedia Syst. JUL 2014 20 4 389 413 10.1007/s00530-014-0360-6 25 Computer Science, Information Systems; Computer Science, Theory & Methods Computer Science AK2DO WOS:000338229500004 J Zhong, CC; Miao, ZJ Zhong, Cencen; Miao, Zhenjiang Graph regularized GM-pLSA and its applications to video content analysis MULTIMEDIA SYSTEMS English Article Probabilistic latent semantic analysis with Gaussian mixtures; Term correlation; Graph regularizer; Video categorization; Video concept detection AUTOMATIC IMAGE ANNOTATION; SEMANTIC ANALYSIS; CLASSIFICATION; FRAMEWORK; MODELS; RETRIEVAL As standard probabilistic latent semantic analysis (pLSA) is oriented to discrete quantity only, pLSA with Gaussian mixtures (GM-pLSA) succeeding in transferring it to continuous feature space is proposed, which uses Gaussian mixture model to describe the feature distribution under each latent aspect. However, inheriting from pLSA, GM-pLSA still overlooks the intrinsic interdependence between terms, which indeed is an important clue for performance improvement. In this paper, we present a graph regularized GM-pLSA (GRGM-pLSA) model as an extension of GM-pLSA to embed this term correlation information into the process of model learning. Specifically, grounded on the manifold regularization principle, a graph regularizer is introduced to characterize the correlation between terms; by imposing it on the objective function of GM-pLSA, model parameters of GRGM-pLSA are derived via corresponding expectation maximization algorithm. Furthermore, two applications to video content analysis are devised. One is video categorization where GRGM-pLSA serves for feature mapping with two kinds of sub-shot correlations, respectively, incorporated, while the other provides a new perspective on video concept detection, which transforms the detection task to a GRGM-pLSA-based visual-to-textual feature conversion problem. Extensive experiments and comparison with GM-pLSA and several state-of-the-art approaches in both applications demonstrate the effectiveness of GRGM-pLSA. [Zhong, Cencen; Miao, Zhenjiang] Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China Zhong, CC (reprint author), Beijing Jiaotong Univ, Inst Informat Sci, Beijing 100044, Peoples R China. 07112072@bjtu.edu.cn; zjmiao@bjtu.edu.cn National Science Foundation of China [61273274, 4123104]; National 973 Key Research Program of China [2011CB302203]; Ph.D. Programs Foundation of Ministry of Education of China [20100009110004]; National Key Technology R&D Program of China [2012BAH01F03]; Tsinghua-Tencent Joint Lab for IIT This work is supported by the National Science Foundation of China (61273274, 4123104), National 973 Key Research Program of China (2011CB302203), Ph.D. Programs Foundation of Ministry of Education of China (20100009110004), National Key Technology R&D Program of China (2012BAH01F03) and Tsinghua-Tencent Joint Lab for IIT. Ahrendt P, 2005, MACHINE LEARN SIGN P, P247, DOI 10.1109/MLSP.2005.1532908; Bekkerman R., 2004, IR408 CIIR; Belkin M, 2006, J MACH LEARN RES, V7, P2399; Belkin M, 2002, ADV NEUR IN, V14, P585; Blei D.M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460; Bosch A, 2006, LECT NOTES COMPUT SC, V3954, P517; Brezeale D, 2008, IEEE T SYST MAN CY C, V38, P416, DOI 10.1109/TSMCC.2008.919173; Cai D, 2011, IEEE T PATTERN ANAL, V33, P1548, DOI 10.1109/TPAMI.2010.231; Chen B., 2009, ACM T ASIAN LANG INF, V8, P1; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Ewerth R., 2007, P ACM INT C IM VID R, P154; Feng SL, 2004, PROC CVPR IEEE, P1002; Fergus R, 2005, IEEE I CONF COMP VIS, P1816; Grangier D, 2008, IEEE T PATTERN ANAL, V30, P1371, DOI 10.1109/TPAMI.2007.70791; Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950; Horster E., 2008, P CVIR 2008 NEW YORK, P319, DOI 10.1145/1386352.1386395; Hu WM, 2011, IEEE T SYST MAN CY C, V41, P797, DOI 10.1109/TSMCC.2011.2109710; Huang CL, 2006, IEEE T MULTIMEDIA, V8, P749, DOI 10.1109/TMM.2006.876289; Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459; Jianming Wu, 2009, Proceedings of the 2009 IEEE 70th Vehicular Technology Conference (VTC 2009 Fall), DOI [10.1109/VETECF.2009.5378695, 10.1109/ICMSS.2009.5302820, 10.1109/CCPR.2009.5343965, 10.1109/WICOM.2009.5301237, 10.1109/CISP.2009.5303686, 10.1109/CISP.2009.5303727, 10.1109/CHINACOM.2009.5339954]; Jiang YG, 2010, IEEE T MULTIMEDIA, V12, P42, DOI 10.1109/TMM.2009.2036235; Laptev I, 2005, INT J COMPUT VISION, V64, P107, DOI 10.1007/s11263-005-1838-7; Lee K, 2010, IEEE T AUDIO SPEECH, V18, P1406, DOI 10.1109/TASL.2009.2034776; Lehane B., 2004, P EUR WORKSH INT KNO; Li YN, 2010, IEEE T MULTIMEDIA, V12, P814, DOI 10.1109/TMM.2010.2066960; Li ZX, 2010, J VIS COMMUN IMAGE R, V21, P798, DOI 10.1016/j.jvcir.2010.06.004; Li ZX, 2011, PATTERN RECOGN LETT, V32, P516, DOI 10.1016/j.patrec.2010.11.015; Liu J., 2010, P 24 C ART INT, P512; Liu KH, 2008, IEEE T MULTIMEDIA, V10, P240, DOI 10.1109/TMM.2007.911826; Monay F., 2003, P 11 ACM INT C MULT, P275; Monay F, 2007, IEEE T PATTERN ANAL, V29, P1802, DOI 10.1109/TPAMI.2007.1097; Shi R, 2006, LECT NOTES COMPUT SC, V4071, P102; Tang J, 2007, ELECTRON LETT, V43, P448, DOI 10.1049/el:20073674; Truong B.T., 2000, P INT C PATT REC, P4230; Ulges A, 2008, LECT NOTES COMPUT SC, V5008, P415; Ulges A, 2010, COMPUT VIS IMAGE UND, V114, P429, DOI 10.1016/j.cviu.2009.08.002; Wang CH, 2009, PROC CVPR IEEE, P1643; Wong S., 2007, P IEEE INT C COMP VI, P1; Wright J, 2010, P IEEE, V98, P1031, DOI 10.1109/JPROC.2010.2044470; Xu CS, 2008, IEEE T MULTIMEDIA, V10, P421, DOI 10.1109/TMM.2008.917346; Xu G, 2005, IEEE T CIRC SYST VID, V15, P1422, DOI 10.1109/TCSVT.2005.856903; Yanagawa A., 2007, 22220068 ADVENT COL; Yanagawa A., 2006, 21920065 ADVENT COL; Yang J., 2006, P 8 ACM SIGMM INT WO, P33, DOI 10.1145/1178677.1178685; Yang J., 2007, P 15 INT C MULT, P188, DOI 10.1145/1291233.1291276; Yang L., 2007, P INT WORKSH MULT IN, P265, DOI 10.1145/1290082.1290119; Yuan X, 2006, IEEE IMAGE PROC, P2905, DOI 10.1109/ICIP.2006.313037; Zha ZJ, 2009, J VIS COMMUN IMAGE R, V20, P97, DOI 10.1016/j.jvcir.2008.11.009; Zhang JG, 2010, COMPUT VIS IMAGE UND, V114, P857, DOI 10.1016/j.cviu.2010.04.006; Zhou DY, 2004, ADV NEUR IN, V16, P321 50 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0942-4962 1432-1882 MULTIMEDIA SYST Multimedia Syst. JUL 2014 20 4 429 445 10.1007/s00530-014-0378-9 17 Computer Science, Information Systems; Computer Science, Theory & Methods Computer Science AK2DO WOS:000338229500006 J Zhao, MB; Zhang, Z; Chow, TWS; Li, B Zhao, Mingbo; Zhang, Zhao; Chow, Tommy W. S.; Li, Bing A general soft label based Linear Discriminant Analysis for semi-supervised dimensionality reduction NEURAL NETWORKS English Article Linear Discriminant Analysis; Semi-supervised dimension reduction; Soft label; Label propagation IMAGE RETRIEVAL; GEOMETRIC FRAMEWORK; RECOGNITION; SUBSPACE; LDA Dealing with high-dimensional data has always been a major problem in research of pattern recognition and machine learning, and Linear Discriminant Analysis (LDA) is one of the most popular methods for dimension reduction. However, it only uses labeled samples while neglecting unlabeled samples, which are abundant and can be easily obtained in the real world. In this paper, we propose a new dimension reduction method, called "SL-LDA", by using unlabeled samples to enhance the performance of LDA. The new method first propagates label information from the labeled set to the unlabeled set via a label propagation process, where the predicted labels of unlabeled samples, called "soft labels", can be obtained. It then incorporates the soft labels into the construction of scatter matrixes to find a transformed matrix for dimension reduction. In this way, the proposed method can preserve more discriminative information, which is preferable when solving the classification problem. We further propose an efficient approach for solving SL-LDA under a least squares framework, and a flexible method of SL-LDA (FSL-LDA) to better cope with datasets sampled from a nonlinear manifold. Extensive simulations are carried out on several datasets, and the results show the effectiveness of the proposed method. (C) 2014 Elsevier Ltd. All rights reserved. [Zhao, Mingbo; Chow, Tommy W. S.] City Univ Hong Kong, Dept Elect Engn, Kowloon, Hong Kong, Peoples R China; [Zhang, Zhao] Soochow Univ, Sch Compute Sci & Technol, Suzhou 215006, Peoples R China; [Li, Bing] Wuhan Univ Technol, Sch Econ, Wuhan 430070, Peoples R China Zhang, Z (reprint author), Soochow Univ, Sch Compute Sci & Technol, Suzhou 215006, Peoples R China. mbzhao4@gmail.com; cszzhang@gmail.com; eetchow@cityu.edu.hk; lib675@163.com National Natural Science Foundation of China [61300209] This work was partly supported by the National Natural Science Foundation of China under Grant no. 61300209. [Anonymous], 1990, SCIENCES; Belhumeur PN, 1997, IEEE T PATTERN ANAL, V19, P711, DOI 10.1109/34.598228; Belkin M, 2006, J MACH LEARN RES, V7, P2399; Cai D., 2007, P ACM MM; Cai D., 2007, P ICCV; Cai D, 2008, IEEE T KNOWL DATA EN, V20, P1, DOI 10.1109/TKDE.2007.190669; Chen J., 2007, P CVPR; Chen LF, 2000, PATTERN RECOGN, V33, P1713, DOI 10.1016/S0031-3203(99)00139-9; Chow TWS, 2006, PATTERN ANAL APPL, V9, P1, DOI 10.1007/s10044-005-0019-1; CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411; Cun Y. L., 1998, P IEEE, V86, P2278, DOI DOI 10.1109/5.726791; FRIEDMAN JH, 1989, J AM STAT ASSOC, V84, P165, DOI 10.2307/2289860; Hastie T., 2001, ELEMENTS STAT LEARNI; He X, 2008, IEEE T KNOWL DATA EN, V20, P189, DOI 10.1109/TKDE.2007.190692; He XF, 2005, IEEE T PATTERN ANAL, V27, P328; Huang Y, 2012, IEEE T NEUR NET LEAR, V23, P519, DOI 10.1109/TNNLS.2011.2178037; HULL JJ, 1994, IEEE T PATTERN ANAL, V16, P550, DOI 10.1109/34.291440; Jia YQ, 2009, IEEE T NEURAL NETWOR, V20, P729, DOI 10.1109/TNN.2009.2015760; Leibe B., 2003, P CVPR; Li J, 2006, IEEE T IMAGE PROCESS, V15, P3597, DOI 10.1109/TIP.2006.881938; Nene S., 1996, CUCS00596; Nie FP, 2010, IEEE T IMAGE PROCESS, V19, P1921, DOI 10.1109/TIP.2010.2044958; Nie FP, 2010, NEURAL COMPUT APPL, V19, P549, DOI 10.1007/s00521-009-0305-8; Nie FP, 2009, PATTERN RECOGN, V42, P2615, DOI 10.1016/j.patcog.2009.04.001; PAIGE CC, 1982, ACM T MATH SOFTWARE, V8, P195, DOI 10.1145/355993.356000; Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323; Scholkopf B, 1998, NEURAL COMPUT, V10, P1299, DOI 10.1162/089976698300017467; Sim T., 2001, IEEE T PATTERN ANAL, V23, P643; Sun L., 2010, P KDD; Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319; Turk M., 1991, P CVPR; Vapnik V, 1995, NATURE STAT LEARNING; Vapnik VN, 1998, STAT LEARNING THEORY; Wang F, 2008, IEEE T KNOWL DATA EN, V20, P55, DOI 10.1109/TKDE.2007.190672; Wang JD, 2009, IEEE T PATTERN ANAL, V31, P1600, DOI 10.1109/TPAMI.2008.216; Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109; Wang M, 2009, COMPUT VIS IMAGE UND, V113, P384, DOI 10.1016/j.cviu.2008.08.003; Yan SC, 2007, IEEE T PATTERN ANAL, V29, P40, DOI 10.1109/TPAMI.2007.250598; Yang J, 2005, IEEE T PATTERN ANAL, V27, P230; Ye J., 2007, P ICML; Ye JP, 2006, IEEE T KNOWL DATA EN, V18, P1312, DOI 10.1109/TKDE.2006.160; Yu H., 2002, P ICIP; Zhang CS, 2010, NEUROCOMPUTING, V73, P959, DOI 10.1016/j.neucom.2009.08.014; Zhang L., 2001, P ICIP; Zhang TH, 2009, IEEE T KNOWL DATA EN, V21, P1299, DOI 10.1109/TKDE.2008.212; Zhang Z, 2014, NEURAL NETWORKS, V53, P81, DOI 10.1016/j.neunet.2014.01.001; Zhang Z, 2012, NEURAL NETWORKS, V36, P97, DOI 10.1016/j.neunet.2012.09.010; Zhang Z, 2013, IEEE T KNOWL DATA EN, V25, P1148, DOI 10.1109/TKDE.2012.47; Zhang Z., 2013, IEEE T KNOWLEDGE DAT; Zhang ZH, 2010, J MACH LEARN RES, V11, P2199; Zhao MB, 2014, COMPUT VIS IMAGE UND, V121, P86, DOI 10.1016/j.cviu.2014.01.008; Zhao MB, 2012, PATTERN RECOGN, V45, P1482, DOI 10.1016/j.patcog.2011.10.008; Zhou D., 2004, P NIPS; Zhu X., 2003, P ICML 54 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0893-6080 1879-2782 NEURAL NETWORKS Neural Netw. JUL 2014 55 83 97 10.1016/j.neunet.2014.03.005 15 Computer Science, Artificial Intelligence; Neurosciences Computer Science; Neurosciences & Neurology AJ7ES WOS:000337860600008 J Lin, ZJ; Ding, GG; Hu, MQ; Lin, YZ; Ge, SS Lin, Zijia; Ding, Guiguang; Hu, Mingqing; Lin, Yunzhen; Ge, Shuzhi Sam Image tag completion via dual-view linear sparse reconstructions COMPUTER VISION AND IMAGE UNDERSTANDING English Article Image tag completion; Linear sparse reconstruction; DLSR; Image tagging; Tag refinement ANNOTATION User-provided textual tags of web images are widely utilized for facilitating image management and retrieval. Yet they are usually incomplete and insufficient to describe the whole semantic content of the corresponding images, resulting in performance degradations of various tag-dependent applications. In this paper, we propose a novel method denoted as DLSR for automatic image tag completion via Dual-view Linear Sparse Reconstructions. Given an incomplete initial tagging matrix with each row representing an image and each column representing a tag, DLSR performs tag completion from both views of image and tag, exploiting various available contextual information. Specifically, for a to-be-completed image, DLSR exploits image-image correlations by linearly reconstructing its low-level image features and initial tagging vector with those of others, and then utilizes them to obtain an image-view reconstructed tagging vector. Meanwhile, by linearly reconstructing the tagging column vector of each tag with those of others, DLSR exploits tag-tag correlations to get a tag-view reconstructed tagging vector with the initially labeled tags. Then both image-view and tag-view reconstructed tagging vectors are combined for better predicting missing related tags. Extensive experiments conducted on benchmark datasets and real-world web images well demonstrate the reasonableness and effectiveness of the proposed DLSR. And it can be utilized to enhance a variety of tag-dependent applications such as image auto-annotation. (C) 2014 Elsevier Inc. All rights reserved. [Lin, Zijia] Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China; [Ding, Guiguang] Tsinghua Univ, Sch Software, Beijing 100084, Peoples R China; [Hu, Mingqing] Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China; [Lin, Yunzhen; Ge, Shuzhi Sam] Natl Univ Singapore, Dept Elect & Comp Engn, Singapore 117576, Singapore Lin, ZJ (reprint author), Tsinghua Univ, Dept Comp Sci & Technol, Beijing 100084, Peoples R China. linzijia07@tsinghua.org.cn; dinggg@tsinghua.edu.cn; humingqing@ict.ac.cn; lin-yz12@mails.tsinghua.edu.cn; samge@nus.edu.sg Ames M., 2007, SIGCHI 07; Andoni A., 2006, FOCS 06; Aslam J.A., 2000, SIGIR 00; Aslam J.A., 2001, SIGIR 01; Binder A, 2013, COMPUT VIS IMAGE UND, V117, P466, DOI 10.1016/j.cviu.2012.09.006; Chua T.-S., 2009, CIVR 09; Eom W., 2011, ICIP 11; Feng S., 2004, CVPR 04; Garg N., 2008, RECSYS 08; Gong Y., 2011, CVPR 11; Guillaumin M., 2009, ICCV 09; H Xu, 2009, MM 09; He K, 2013, CVPR 13; Jin Y., 2005, MM 05; Lee S., 2010, PATTERN RECOGNIT LET, V31; Lee S, 2010, SIGNAL PROCESS-IMAGE, V25, P761, DOI 10.1016/j.image.2010.10.002; Lee S., 2010, ICME 10; Lin Y., 2013, CVPR 13; Lin Z., 2013, CVPR 13; Liu D., 2009, WWW 09; Liu D, 2011, IEEE T MULTIMEDIA, V13, P702, DOI 10.1109/TMM.2011.2134078; Liu D., 2010, MM 10; Liu J., 2009, SLEP SPARSE LEARNING; Liu J., 2009, PATTERN RECOGNIT, V42; Liu X., 2012, ACM T MULTIMED COMPU, V8; Liu Y., 2011, ICDM 11; Lux M., 2008, MM 08; Ma Z., 2011, MM 11; Ma ZG, 2012, IEEE T MULTIMEDIA, V14, P1021, DOI 10.1109/TMM.2012.2187179; Makadia A., 2008, ECCV 08; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; Montague M., 2001, CIKM 01; Qi Z., 2011, SIGKDD 11; Renda M.E., 2003, SAC 03; Schmidt M., 2009, TR200919 U BRIT COL; Sigurbjornsson B., 2008, WWW 08; Sun A., 2011, MM 11; Vogt C. C., 1999, Information Retrieval, V1, DOI 10.1023/A:1009980820262; Wang C., 2007, CVPR 07; Wu L, 2013, IEEE T PATTERN ANAL, V35, P716, DOI 10.1109/TPAMI.2012.124; Wu L., 2009, WWW 09; Zhang S., 2010, CVPR 10; Zhu G., 2010, MM 10 43 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1077-3142 1090-235X COMPUT VIS IMAGE UND Comput. Vis. Image Underst. JUL 2014 124 SI 42 60 10.1016/j.cviu.2014.03.012 19 AJ4RN WOS:000337663600006 J Brown, TI; Hasselmo, ME; Stern, CE Brown, Thackery I.; Hasselmo, Michael E.; Stern, Chantal E. A High-resolution study of hippocampal and medial temporal lobe correlates of spatial context and prospective overlapping route memory HIPPOCAMPUS English Review dentate gyrus; CA3; CA1; fMRI; navigation PATTERN SEPARATION; PARAHIPPOCAMPAL CORTEX; ENTORHINAL CORTEX; RHESUS-MONKEY; DENTATE GYRUS; CA3 SUBREGION; COMPUTATIONAL CONSTRAINTS; ANTERIOR HIPPOCAMPUS; ORBITOFRONTAL CORTEX; DOUBLE DISSOCIATION When navigating our world we often first plan or retrieve an ideal route to our goal, avoiding alternative paths that lead to other destinations. The medial temporal lobe (MTL) has been implicated in processing contextual information, sequence memory, and uniquely retrieving routes that overlap or cross paths. However, the identity of subregions of the hippocampus and neighboring cortex that support these functions in humans remains unclear. The present study used high-resolution functional magnetic resonance imaging (hr-fMRI) in humans to test whether the CA3/DG hippocampal subfield and parahippocampal cortex are important for processing spatial context and route retrieval, and whether the CA1 subfield facilitates prospective planning of mazes that must be distinguished from alternative overlapping routes. During hr-fMRI scanning, participants navigated virtual mazes that were well-learned from prior training while also learning new mazes. Some routes learned during scanning shared hallways with those learned during pre-scan training, requiring participants to select between alternative paths. Critically, each maze began with a distinct spatial contextual Cue period. Our analysis targeted activity from the Cue period, during which participants identified the current navigational episode, facilitating retrieval of upcoming route components and distinguishing mazes that overlap. Results demonstrated that multiple MTL regions were predominantly active for the contextual Cue period of the task, with specific regions of CA3/DG, parahippocampal cortex, and perirhinal cortex being consistently recruited across trials for Cue periods of both novel and familiar mazes. During early trials of the task, both CA3/DG and CA1 were more active for overlapping than non-overlapping Cue periods. Trial-by-trial Cue period responses in CA1 tracked subsequent overlapping maze performance across runs. Together, our findings provide novel insight into the contributions of MTL subfields to processing spatial context and route retrieval, and support a prominent role for CA1 in distinguishing overlapping episodes during navigational look-ahead periods. (c) 2014 Wiley Periodicals, Inc. [Brown, Thackery I.; Hasselmo, Michael E.; Stern, Chantal E.] Boston Univ, Dept Psychol & Brain Sci, Boston, MA 02215 USA; [Brown, Thackery I.; Hasselmo, Michael E.; Stern, Chantal E.] Boston Univ, Ctr Memory & Brain, Boston, MA 02215 USA; [Brown, Thackery I.; Stern, Chantal E.] Massachusetts Gen Hosp, Athinoula A Martinos Ctr Biomed Imaging, Charlestown, MA USA Stern, CE (reprint author), Boston Univ, Ctr Memory & Brain, 617-353-1396,2 Cummington Mall, Boston, MA 02215 USA. chantal@bu.edu National Institutes of Health; Office of Naval Research Multidisciplinary University Research Initiative [P50 MH094263, ONR MURI N00014-10-1-0936]; NIH NCRR Shared Instrumentation Grant Program and/or High-End Instrumentation Grant Program; NCRR Biomedical Technology Program of the National Center for Research Resources [NIH S10RR021110, P41RR14075] Grant sponsors: National Institutes of Health grant and Office of Naval Research Multidisciplinary University Research Initiative grant to the Cognitive Neuroimaging Lab, Center for Memory and Brain, Boston University (Boston, MA); Grant numbers: P50 MH094263 and ONR MURI N00014-10-1-0936; Grant sponsors: NIH NCRR Shared Instrumentation Grant Program and/or High-End Instrumentation Grant Program and NCRR Biomedical Technology Program of the National Center for Research Resources; Grant numbers: NIH S10RR021110 and P41RR14075. AMARAL DG, 1989, NEUROSCIENCE, V31, P571, DOI 10.1016/0306-4522(89)90424-7; Aminoff EM, 2013, TRENDS COGN SCI, V17, P379, DOI 10.1016/j.tics.2013.06.009; Andersson JLR, 2001, NEUROIMAGE, V13, P903, DOI 10.1006/nimg.2001.0746; Avants BB, 2008, MED IMAGE ANAL, V12, P26, DOI 10.1016/j.media.2007.06.004; Bakker A, 2008, SCIENCE, V319, P1640, DOI 10.1126/science.1152882; Barbas H, 1995, HIPPOCAMPUS, V5, P511, DOI 10.1002/hipo.450050604; Barbas H, 2007, ANN NY ACAD SCI, V1121, P10, DOI 10.1196/annals.1401.015; Blatt GJ, 1998, J COMP NEUROL, V392, P92, DOI 10.1002/(SICI)1096-9861(19980302)392:1<92::AID-CNE7>3.0.CO;2-K; Bonnici HM, 2012, HIPPOCAMPUS, V22, P1143, DOI 10.1002/hipo.20960; Brown TI, 2010, J NEUROSCI, V30, P7414, DOI 10.1523/JNEUROSCI.6021-09.2010; Brown TI, 2012, NEUROIMAGE, V60, P1316, DOI 10.1016/j.neuroimage.2012.01.046; Brown TI, 2013, CEREB CORTEX, DOI [10.1093/cercor/bht041, DOI 10.1093/CERCOR/BHT041]; Burgess N, 2001, NEUROIMAGE, V14, P439, DOI 10.1006/nimg.2001.0806; Carr MF, 2011, NAT NEUROSCI, V14, P147, DOI 10.1038/nn.2732; Cavada C, 2000, CEREB CORTEX, V10, P220, DOI 10.1093/cercor/10.3.220; Chen J, 2011, LEARN MEMORY, V18, P523, DOI 10.1101/lm.2135211; Davachi L, 2006, CURR OPIN NEUROBIOL, V16, P693, DOI 10.1016/j.conb.2006.10.012; Davidson TJ, 2009, NEURON, V63, P497, DOI 10.1016/j.neuron.2009.07.027; Day M, 2003, NATURE, V424, P205, DOI 10.1038/nature01769; Duvernoy H.M., 2005, HUMAN HIPPOCAMPUS FU; Eichenbaum H, 2007, ANNU REV NEUROSCI, V30, P123, DOI 10.1146/annurev.neuro.30.051606.094328; Eichenbaum H, 2012, NEUROSCI BIOBEHAV R, V36, P1597, DOI 10.1016/j.neubiorev.2011.07.006; Epstein R, 1998, NATURE, V392, P598, DOI 10.1038/33402; Epstein RA, 2007, J NEUROSCI, V27, P6141, DOI 10.1523/JNEUROSCI.0799-07.2007; Epstein RA, 2007, CEREB CORTEX, V17, P1680, DOI 10.1093/cercor/bhl079; Evensmoen HR, 2013, J COGNITIVE NEUROSCI, V25, P1908, DOI 10.1162/jocn_a_00436; Ferbinteanu J, 2003, NEURON, V40, P1227, DOI 10.1016/S0896-6273(03)00752-9; Fortin NJ, 2002, NAT NEUROSCI, V5, P458, DOI 10.1038/nn834; Gilbert PE, 2001, HIPPOCAMPUS, V11, P626, DOI 10.1002/hipo.1077; Gold AE, 2005, HIPPOCAMPUS, V15, P808, DOI 10.1002/hipo.20103; Gupta K, 2014, CEREB CORTEX, V24, P1630, DOI 10.1093/cercor/bht020; Gupta K, 2013, NEUROSCIENCE, V247, P395, DOI 10.1016/j.neuroscience.2013.04.056; Hafting T, 2005, NATURE, V436, P801, DOI 10.1038/nature03721; Hartley T, 2003, NEURON, V37, P877, DOI 10.1016/S0896-6273(03)00095-3; Hasselmo ME, 1997, BEHAV BRAIN RES, V89, P1, DOI 10.1016/S0166-4328(97)00048-X; Hasselmo ME, 2009, NEUROBIOL LEARN MEM, V92, P559, DOI 10.1016/j.nlm.2009.07.005; Hasselmo ME, 2014, NEUROIMAGE, V85, P656, DOI 10.1016/j.neuroimage.2013.06.022; Hasselmo ME, 2005, NEURAL NETWORKS, V18, P1172, DOI 10.1016/j.neunet.2005.08.007; Hasselmo ME, 2005, BEHAV NEUROSCI, V119, P342, DOI 10.1037/0735-7044.119.1.342; HASSELMO ME, 1995, J NEUROSCI, V15, P5249; Howard LR, 2011, J NEUROSCI, V31, P5253, DOI 10.1523/JNEUROSCI.6055-10.2011; Insausti R, 1998, AM J NEURORADIOL, V19, P659; Janzen G, 2004, NAT NEUROSCI, V7, P673, DOI 10.1038/nn1257; Johnson A, 2007, J NEUROSCI, V27, P12176, DOI 10.1523/JNEUROSCI.3761-07.2007; Kesner RP, 2007, LEARN MEMORY, V14, P771, DOI 10.1101/lm.688207; Kesner RP, 2008, BEHAV NEUROSCI, V122, P1217, DOI 10.1037/a0013592; Kirwan CB, 2007, LEARN MEMORY, V14, P625, DOI 10.1101/lm.663507; Kirwan CB, 2007, HUM BRAIN MAPP, V28, P959, DOI 10.1002/hbm.20331; Klein A, 2009, NEUROIMAGE, V46, P786, DOI 10.1016/j.neuroimage.2008.12.037; Kuhl BA, 2010, NAT NEUROSCI, V13, P501, DOI 10.1038/nn.2498; Kumaran D, 2006, NEURON, V49, P617, DOI 10.1016/j.neuron.2005.12.024; Lacy JW, 2011, LEARN MEMORY, V18, P15, DOI [10.1101/lm.1971111, 10.1101/lm.1971110]; LaRocque KF, 2013, J NEUROSCI, V33, P5466, DOI 10.1523/JNEUROSCI.4293-12.2013; Lee I, 2005, BEHAV NEUROSCI, V119, P145, DOI 10.1037/0735-7044.119.1.145; Lee I, 2006, NEURON, V51, P639, DOI 10.1016/j.neuron.2006.06.033; Lehn H, 2009, J NEUROSCI, V29, P3475, DOI 10.1523/JNEUROSCI.5370-08.2009; Leutgeb JK, 2007, SCIENCE, V315, P961, DOI 10.1126/science.1135801; Levy WB, 1996, HIPPOCAMPUS, V6, P579, DOI 10.1002/(SICI)1098-1063(1996)6:6<579::AID-HIPO3>3.0.CO;2-C; Lipton PA, 2007, J NEUROSCI, V27, P5787, DOI 10.1523/JNEUROSCI.1063-07.2007; MCNAUGHTON BL, 1987, TRENDS NEUROSCI, V10, P408, DOI 10.1016/0166-2236(87)90011-7; Mizumori SJY, 1999, HIPPOCAMPUS, V9, P444, DOI 10.1002/(SICI)1098-1063(1999)9:4<444::AID-HIPO10>3.0.CO;2-Z; Moser EI, 2008, ANNU REV NEUROSCI, V31, P69, DOI 10.1146/annurev.neuro.31.061307.090723; Moser EI, 2008, HIPPOCAMPUS, V18, P1142, DOI 10.1002/hipo.20483; Mullally SL, 2011, J NEUROSCI, V31, P7441, DOI 10.1523/JNEUROSCI.0267-11.2011; Newmark RE, 2013, HIPPOCAMPUS, V23, P467, DOI 10.1002/hipo.22106; O'Craven KM, 2000, J COGNITIVE NEUROSCI, V12, P1013, DOI 10.1162/08989290051137549; OREILLY RC, 1994, HIPPOCAMPUS, V4, P661, DOI 10.1002/hipo.450040605; Poppenk J, 2013, TRENDS COGN SCI, V17, P230, DOI 10.1016/j.tics.2013.03.005; Preston AR, 2010, J COGNITIVE NEUROSCI, V22, P156, DOI 10.1162/jocn.2009.21195; Pruessner JC, 2002, CEREB CORTEX, V12, P1342, DOI 10.1093/cercor/12.12.1342; Pruessner JC, 2000, CEREB CORTEX, V10, P433, DOI 10.1093/cercor/10.4.433; Rempel-Clower NL, 2000, CEREB CORTEX, V10, P851, DOI 10.1093/cercor/10.9.851; Roberts AC, 2007, J COMP NEUROL, V502, P86, DOI 10.1002/cne.21300; Rolls ET, 2005, J NEUROPHYSIOL, V94, P833, DOI 10.1152/jn.01063.2004; Rosenbaum RS, 2004, HIPPOCAMPUS, V14, P826, DOI 10.1002/hipo.10218; Ross RS, 2009, HIPPOCAMPUS, V19, P790, DOI 10.1002/hipo.20558; Schon K, 2004, J NEUROSCI, V24, P11088, DOI 10.1523/JNEUROSCI.3807-04.2004; Sherrill KR, 2013, J NEUROSCI, V33, P19304, DOI 10.1523/JNEUROSCI.1825-13.2013; Shohamy D, 2008, NEURON, V60, P378, DOI 10.1016/j.neuron.2008.09.023; Smith DM, 2006, J NEUROSCI, V26, P3154, DOI 10.1523/JNEUROSCI.3234-05.2006; Spiers HJ, 2006, NEUROIMAGE, V31, P1826, DOI 10.1016/j.neuroimage.2006.01.037; Sreenivasan S, 2011, NAT NEUROSCI, V14, P1330, DOI 10.1038/nn.2901; Staresina BP, 2008, J COGNITIVE NEUROSCI, V20, P1478, DOI 10.1162/jocn.2008.20104; Stern CE, 1996, P NATL ACAD SCI USA, V93, P8660, DOI 10.1073/pnas.93.16.8660; Suthana N, 2012, NEW ENGL J MED, V366, P502, DOI 10.1056/NEJMoa1107212; SUZUKI WA, 1994, J NEUROSCI, V14, P1856; Tamminga CA, 2010, AM J PSYCHIAT, V167, P1178, DOI 10.1176/appi.ajp.2010.09081187; TREVES A, 1992, HIPPOCAMPUS, V2, P189, DOI 10.1002/hipo.450020209; Treves A, 2004, HIPPOCAMPUS, V14, P539, DOI 10.1002/hipo.10187; Turk-Browne NB, 2012, J NEUROSCI, V32, P7202, DOI 10.1523/JNEUROSCI.0942-12.2012; van Strien NM, 2009, NAT REV NEUROSCI, V10, P272, DOI 10.1038/nrn2614; Viard A, 2011, J NEUROSCI, V31, P4613, DOI 10.1523/JNEUROSCI.4640-10.2011; Watson HC, 2012, J NEUROSCI, V32, P4473, DOI 10.1523/JNEUROSCI.5751-11.2012; Wood ER, 2000, NEURON, V27, P623, DOI 10.1016/S0896-6273(00)00071-4; Yassa MA, 2009, NEUROIMAGE, V44, P319, DOI 10.1016/j.neuroimage.2008.09.016; Yassa MA, 2010, NEUROIMAGE, V51, P1242, DOI 10.1016/j.neuroimage.2010.03.040; Yassa MA, 2011, P NATL ACAD SCI USA, V108, P8873, DOI 10.1073/pnas.1101567108; Yassa MA, 2011, TRENDS NEUROSCI, V34, P515, DOI 10.1016/j.tins.2011.06.006; Yushkevich PA, 2006, NEUROIMAGE, V31, P1116, DOI 10.1016/j.neuroimage.2006.01.015; Zeineh MM, 2000, NEUROIMAGE, V11, P668, DOI 10.1006/nimg.2000.0561; Zilli EA, 2008, HIPPOCAMPUS, V18, P193, DOI 10.1002/hipo.20382 101 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1050-9631 1098-1063 HIPPOCAMPUS Hippocampus JUL 2014 24 7 819 839 10.1002/hipo.22273 21 AJ4TD WOS:000337668200010 J Dew, ITZ; Ritchey, M; LaBar, KS; Cabeza, R Dew, Ilana T. Z.; Ritchey, Maureen; LaBar, Kevin S.; Cabeza, Roberto Prior perceptual processing enhances the effect of emotional arousal on the neural correlates of memory retrieval NEUROBIOLOGY OF LEARNING AND MEMORY English Review Emotion; Memory; Amygdala; fMRI MEDIAL TEMPORAL-LOBE; PARIETAL CORTEX; VISUAL-CORTEX; RECOLLECTIVE EXPERIENCE; COGNITIVE NEUROSCIENCE; AMYGDALA ACTIVITY; EPISODIC MEMORY; STIMULI; WORDS; RECOGNITION A fundamental idea in memory research is that items are more likely to be remembered if encoded with a semantic, rather than perceptual, processing strategy. Interestingly, this effect has been shown to reverse for emotionally arousing materials, such that perceptual processing enhances memory for emotional information or events. The current fMRI study investigated the neural mechanisms of this effect by testing how neural activations during emotional memory retrieval are influenced by the prior encoding strategy. Participants incidentally encoded emotional and neutral pictures under instructions to attend to either semantic or perceptual properties of each picture. Recognition memory was tested 2 days later. fMRI analyses yielded three main findings. First, right amygdalar activity associated with emotional memory strength was enhanced by prior perceptual processing. Second, prior perceptual processing of emotional pictures produced a stronger effect on recollection- than familiarity-related activations in the right amygdala and left hippocampus. Finally, prior perceptual processing enhanced amygdalar connectivity with regions strongly associated with retrieval success, including hippocampal/parahippocampal regions, visual cortex, and ventral parietal cortex. Taken together, the results specify how encoding orientations yield alterations in brain systems that retrieve emotional memories. (C) 2013 Elsevier Inc. All rights reserved. [Dew, Ilana T. Z.; LaBar, Kevin S.; Cabeza, Roberto] Duke Univ, Ctr Cognit Neurosci, Durham, NC 27708 USA; [Ritchey, Maureen] Univ Calif Davis, Ctr Neurosci, Davis, CA USA Dew, ITZ (reprint author), Duke Univ, Ctr Cognit Neurosci, LSRC Box 90999, Durham, NC 27708 USA. ilana.dew@duke.edu National Institutes of Health [NS41328, AG23770, AG19731]; National Research Service Award [F31MH085384, F32AG038298] This work was supported by the National Institutes of Health Grant Nos. NS41328, AG23770, and AG19731. M.R. was supported by National Research Service Award Grant no. F31MH085384 and I.D. was supported by National Research Service Award no. F32AG038298. Amaral DG, 2003, NEUROSCIENCE, V118, P1099, DOI 10.1016/S0306-4522(02)01001-1; Anders S, 2004, HUM BRAIN MAPP, V23, P200, DOI 10.1002/hbm.20048; Anderson AK, 2001, NATURE, V411, P305, DOI 10.1038/35077083; Bradley MA, 2003, BEHAV NEUROSCI, V117, P369, DOI 10.1037/0735-7044.117.2.369; Buchanan TW, 2007, PSYCHOL BULL, V133, P761, DOI 10.1037/0033-2909.133.5.761; Buckner RL, 2001, NAT REV NEUROSCI, V2, P624, DOI 10.1038/35090048; Cabeza R, 2012, TRENDS COGN SCI, V16, P338, DOI 10.1016/j.tics.2012.04.008; Cabeza R, 2008, NAT REV NEUROSCI, V9, P613, DOI 10.1038/nrn2459; Cabeza R, 2008, NEUROPSYCHOLOGIA, V46, P1813, DOI 10.1016/j.neuropsychologia.2008.03.019; Ciaramelli E, 2008, NEUROPSYCHOLOGIA, V46, P1828, DOI 10.1016/j.neuropsychologia.2008.03.022; CRAIK FIM, 1983, PHILOS T ROY SOC B, V302, P341, DOI 10.1098/rstb.1983.0059; Craik FIM, 2002, MEMORY, V10, P305, DOI 10.1080/09658210244000135; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; Dobbins IG, 2003, NEUROPSYCHOLOGIA, V41, P318, DOI 10.1016/S0028-3932(02)00164-1; Doerksen S, 2001, EMOTION, V1, P5, DOI 10.1037/1528-3542.1.1.5; Dolcos F, 2005, P NATL ACAD SCI USA, V102, P2626, DOI 10.1073/pnas.0409848102; Dolcos F, 2004, NEURON, V42, P855, DOI 10.1016/S0896-6273(04)00289-2; Dougal S, 2007, COGN AFFECT BEHAV NE, V7, P233, DOI 10.3758/CABN.7.3.233; Gallo DA, 2008, J MEM LANG, V58, P1095, DOI 10.1016/j.jml.2007.12.001; Garavan H, 2001, NEUROREPORT, V12, P2779, DOI 10.1097/00001756-200108280-00036; GARDINER JM, 1988, MEM COGNITION, V16, P309, DOI 10.3758/BF03197041; Hackmann A, 2004, MEMORY, V12, P389, DOI 10.1080/09658210444000133; Hamann S, 2002, NEUROREPORT, V13, P15, DOI 10.1097/00001756-200201210-00008; Hamann SB, 1999, NAT NEUROSCI, V2, P289, DOI 10.1038/6404; Jay T, 2008, AM J PSYCHOL, V121, P83; Kensinger EA, 2003, MEM COGNITION, V31, P1169, DOI 10.3758/BF03195800; Kensinger EA, 2007, J COGNITIVE NEUROSCI, V19, P1872, DOI 10.1162/jocn.2007.19.11.1872; Kensinger EA, 2009, EMOT REV, V1, P99, DOI 10.1177/1754073908100432; KOLERS PA, 1973, MEM COGNITION, V1, P347, DOI 10.3758/BF03198119; LaBar KS, 2005, NEUROPSYCHOLOGIA, V43, P1824, DOI 10.1016/j.neuropsychologia.2005.01.018; LaBar KS, 2006, NAT REV NEUROSCI, V7, P54, DOI 10.1038/nrn1825; Lang P. J., 2008, A8 U FLOR GAIN FL; Lang PJ, 1998, PSYCHOPHYSIOLOGY, V35, P199, DOI 10.1017/S0048577298001991; Lieberman MD, 2007, PSYCHOL SCI, V18, P421, DOI 10.1111/j.1467-9280.2007.01916.x; Macmillan N. A., 2005, DETECTION THEORY; Mather M, 2007, PERSPECT PSYCHOL SCI, V2, P33, DOI 10.1111/j.1745-6916.2007.00028.x; McGaugh JL, 2004, ANNU REV NEUROSCI, V27, P1, DOI 10.1146/annurev.neuro.27.070203.144157; MORRIS CD, 1977, J VERB LEARN VERB BE, V16, P519, DOI 10.1016/S0022-5371(77)80016-9; Ochsner KN, 2000, J EXP PSYCHOL GEN, V129, P242, DOI 10.1037//0096-3445.129.2.242; Prince SE, 2005, J NEUROSCI, V25, P1203, DOI 10.1523/JNEUROSCI.2540-04.2005; RAJARAM S, 1993, MEM COGNITION, V21, P89, DOI 10.3758/BF03211168; Ramponi C, 2010, EMOTION, V10, P294, DOI 10.1037/a0018491; REBER R, 1994, SCHWEIZ Z PSYCHOL, V53, P78; Rimmele U, 2011, EMOTION, V11, P553, DOI 10.1037/a0024246; Rissman J, 2004, NEUROIMAGE, V23, P752, DOI 10.1016/j.neuroimage.2004.06.035; Ritchey M, 2011, J COGNITIVE NEUROSCI, V23, P757, DOI 10.1162/jocn.2010.21487; Sabatinelli D, 2007, CEREB CORTEX, V17, P1085, DOI 10.1093/cercor/bhl017; Sakaki M, 2011, EMOTION, V11, P1263, DOI 10.1037/a0026329; Sharot T, 2007, PLOS ONE, V2, DOI 10.1371/journal.pone.0001068; Sheridan H, 2012, CONSCIOUS COGN, V21, P438, DOI 10.1016/j.concog.2011.09.022; Simpson JR, 2000, J COGNITIVE NEUROSCI, V12, P157, DOI 10.1162/089892900564019; Slotnick SD, 2003, COGNITIVE BRAIN RES, V17, P75, DOI 10.1016/S0926-6410(03)00082-X; Todd RM, 2012, J NEUROSCI, V32, P11201, DOI 10.1523/JNEUROSCI.0155-12.2012; Todd RM, 2013, FRONT BEHAV NEUROSCI, V7, DOI 10.3389/fnbeh.2013.00040; Vilberg KL, 2008, NEUROPSYCHOLOGIA, V46, P1787, DOI 10.1016/j.neuropsychologia.2008.01.004; Wolf OT, 2008, ACTA PSYCHOL, V127, P513, DOI 10.1016/j.actpsy.2007.08.002; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864; Yonelinas AP, 2005, J NEUROSCI, V25, P3002, DOI 10.1523/JNEUROSCI.5295-04.2005 58 1 1 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1074-7427 1095-9564 NEUROBIOL LEARN MEM Neurobiol. Learn. Mem. JUL 2014 112 104 113 10.1016/j.nlm.2013.12.012 10 AJ3FR WOS:000337552400012 J Mac Callum, PE; Hebert, M; Adamec, RE; Blundell, J Mac Callum, Phillip E.; Hebert, Mark; Adamec, Robert E.; Blundell, Jacqueline Systemic inhibition of mTOR kinase via rapamycin disrupts consolidation and reconsolidation of auditory fear memory NEUROBIOLOGY OF LEARNING AND MEMORY English Article mTOR; Reconsolidation; Consolidation; Auditory fear memory; Posttraumatic stress disorder; Rapamycin PROTEIN-SYNTHESIS INHIBITOR; LONG-TERM-MEMORY; ANISOMYCIN-INDUCED AMNESIA; MESSENGER-RNA SYNTHESIS; 2 TIME WINDOWS; MAMMALIAN TARGET; SYNAPTIC PLASTICITY; RECOGNITION MEMORY; DORSAL HIPPOCAMPUS; SIGNALING PATHWAY The mammalian target of rapamycin (mTOR) kinase is a critical regulator of mRNA translation and is known to be involved in various long lasting forms of synaptic and behavioural plasticity. However, information concerning the temporal pattern of mTOR activation and susceptibility to pharmacological intervention during both consolidation and reconsolidation of long-term memory (LTM) remains scant. Male C57BL/6 mice were injected systemically with rapamycin at various time points following conditioning or retrieval in an auditory fear conditioning paradigm, and compared to vehicle (and/or anisomycin) controls for subsequent memory recall. Systemic blockade of mTOR with rapamycin immediately or 12 h after training or reactivation impairs both consolidation and reconsolidation of an auditory fear memory. Further behavioural analysis revealed that the enduring effects of rapamycin on reconsolidation are dependent upon reactivation of the memory trace. Rapamycin, however, has no effect on short-term memory or the ability to retrieve an established fear memory. Collectively, our data suggest that biphasic mTOR signalling is essential for both consolidation and reconsolidation-like activities that contribute to the formation, re-stabilization, and persistence of long term auditory-fear memories, while not influencing other aspects of the memory trace. These findings also provide evidence for a cogent treatment model for reducing the emotional strength of established, traumatic memories analogous to those observed in acquired anxiety disorders such as posttraumatic stress disorder (PTSD) and specific phobias, through pharmacologic blockade of mTOR using systemic rapamycin following reactivation. (C) 2013 Elsevier Inc. All rights reserved. [Mac Callum, Phillip E.; Hebert, Mark; Adamec, Robert E.; Blundell, Jacqueline] Mem Univ Newfoundland, Dept Psychol, St John, NF A1B 3X9, Canada Blundell, J (reprint author), Mem Univ Newfoundland, Dept Psychol, 232 Elizabeth Ave, St John, NF A1B 3X9, Canada. jblundell@mun.ca National Alliance for Research on Schizophrenia and Depression Young Investigator Award; Natural Science and Engineering Research Council Discovery Grant; Canadian Institute for Health Research [ROP 91548] This work was supported by the National Alliance for Research on Schizophrenia and Depression Young Investigator Award the (J.B.), Natural Science and Engineering Research Council Discovery Grant (J.B), and Canadian Institute for Health Research (Grant ROP 91548, R.E.A.). J.B. and PM. conceived and designed the experiments; P.M., with assistance from M.H., carried them out. P.M. and J.B. performed statistical analysis and created figures; P.M. wrote the manuscript with input from J.B. Adamec R, 2008, BEHAV BRAIN RES, V189, P180, DOI 10.1016/j.bbr.2007.12.023; BAKER H, 1978, J ANTIBIOT, V31, P539; Bekinschtein P, 2010, NEUROTOX RES, V18, P377, DOI 10.1007/s12640-010-9155-5; Bekinschtein P, 2007, NEUROBIOL LEARN MEM, V87, P303, DOI 10.1016/j.nlm.2006.08.007; Bekinschtein P, 2008, P NATL ACAD SCI USA, V105, P2711, DOI 10.1073/pnas.0711863105; Bekinschtein P, 2007, NEURON, V53, P261, DOI 10.1016/j.neuron.2006.11.025; Bernabeu R, 1997, P NATL ACAD SCI USA, V94, P7041, DOI 10.1073/pnas.94.13.7041; Blundell J, 2008, NEUROBIOL LEARN MEM, V90, P28, DOI 10.1016/j.nlm.2007.12.004; Bourtchouladze R, 1998, LEARN MEMORY, V5, P365; Cai WH, 2006, J NEUROSCI, V26, P9560, DOI 10.1523/JNEUROSCI.2397-06.2006; Casadio A, 1999, CELL, V99, P221, DOI 10.1016/S0092-8674(00)81653-0; Cohen H, 2006, BIOL PSYCHIAT, V60, P767, DOI 10.1016/j.biopsych.2006.03.013; Dash PK, 2006, J NEUROSCI, V26, P8048, DOI 10.1523/JNEUROSCI.0671-06.2006; DAVIS HP, 1984, PSYCHOL BULL, V96, P518, DOI 10.1037/0033-2909.96.3.518; Debiec J, 2004, NEUROSCIENCE, V129, P267, DOI 10.1016/j.neuroscience.2004.08.018; Desgranges B, 2008, BRAIN RES, V1236, P166, DOI 10.1016/j.brainres.2008.07.123; Duvarci S, 2008, LEARN MEMORY, V15, P747, DOI 10.1101/lm.1027208; Fortin DA, 2012, J NEUROSCI, V32, P8127, DOI 10.1523/JNEUROSCI.6034-11.2012; FREEMAN FM, 1995, NEUROBIOL LEARN MEM, V63, P291, DOI 10.1006/nlme.1995.1034; Gafford GM, 2011, NEUROSCIENCE, V182, P98, DOI 10.1016/j.neuroscience.2011.03.023; Glover EM, 2010, LEARN MEMORY, V17, P577, DOI 10.1101/lm.1908310; Gong R, 2006, J BIOL CHEM, V281, P18802, DOI 10.1074/jbc.M512524200; GRECKSCH G, 1980, PHARMACOL BIOCHEM BE, V12, P663, DOI 10.1016/0091-3057(80)90145-8; Hay N, 2004, GENE DEV, V18, P1926, DOI 10.1101/gad.1212704; Igaz LM, 2002, J NEUROSCI, V22, P6781; Jobim PFC, 2012, BEHAV BRAIN RES, V228, P151, DOI 10.1016/j.bbr.2011.12.004; Jobim PFC, 2012, NEUROBIOL LEARN MEM, V97, P105, DOI 10.1016/j.nlm.2011.10.002; Lattal KM, 2004, P NATL ACAD SCI USA, V101, P4667, DOI 10.1073/pnas.0306546101; Lee JLC, 2004, SCIENCE, V304, P839, DOI 10.1126/science.1095760; Matus-Amat P, 2004, J NEUROSCI, V24, P2431, DOI 10.1523/JNEUROSCI.1598-03.2004; McGaugh JL, 2000, SCIENCE, V287, P248, DOI 10.1126/science.287.5451.248; Meiri N, 1998, BRAIN RES, V789, P48, DOI 10.1016/S0006-8993(97)01528-X; Milekic MH, 2007, LEARN MEMORY, V14, P504, DOI 10.1101/lm.598307; Miller S, 2002, NEURON, V36, P507, DOI 10.1016/S0896-6273(02)00978-9; Myskiw JC, 2008, NEUROBIOL LEARN MEM, V89, P338, DOI 10.1016/j.nlm.2007.10.002; Nader K, 2000, NATURE, V406, P722, DOI 10.1038/35021052; Nakayama D, 2013, LEARN MEMORY, V20, P307, DOI 10.1101/lm.029660.112; Parsons RG, 2006, J NEUROSCI, V26, P12977, DOI 10.1523/JNEUROSCI.4209-06.2006; Patterson SL, 1996, NEURON, V16, P1137, DOI 10.1016/S0896-6273(00)80140-3; Quevedo J, 1999, LEARN MEMORY, V6, P600, DOI 10.1101/lm.6.6.600; Raught B, 2001, P NATL ACAD SCI USA, V98, P7037, DOI 10.1073/pnas.121145898; Schratt GM, 2004, J NEUROSCI, V24, P7366, DOI 10.1523/JNEUROSCI.1739-04.2004; Slipczuk L., 2009, PLOS ONE, V4, P1, DOI DOI 10.1371/J0URNAL.P0NE.0006007.E6007; Takei N, 2004, J NEUROSCI, V24, P9760, DOI 10.1523/JNEUROSCI.1427-04.2004; Tang SJ, 2002, P NATL ACAD SCI USA, V99, P467, DOI 10.1073/pnas.012605299; Trifilieff P, 2006, LEARN MEMORY, V13, P349, DOI 10.1101/lm.80206; Trifilieff P, 2007, NEUROBIOL LEARN MEM, V88, P424, DOI 10.1016/j.nlm.2007.05.004; Tyler WJ, 2002, LEARN MEMORY, V9, P224, DOI 10.1101/lm.51202; Vickers CA, 2005, J PHYSIOL-LONDON, V568, P803, DOI 10.1113/jphysiol.2005.092924; Wanisch K, 2008, NEUROBIOL LEARN MEM, V90, P485, DOI 10.1016/j.nlm.2008.02.007; Zovkic IB, 2013, NEUROPSYCHOPHARMACOL, V38, P77, DOI 10.1038/npp.2012.79 51 1 1 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1074-7427 1095-9564 NEUROBIOL LEARN MEM Neurobiol. Learn. Mem. JUL 2014 112 176 185 10.1016/j.nlm.2013.08.014 10 AJ3FR WOS:000337552400020 J Wiemers, US; Sauvage, MM; Wolf, OT Wiemers, Uta S.; Sauvage, Magdalena M.; Wolf, Oliver T. Odors as effective retrieval cues for stressful episodes NEUROBIOLOGY OF LEARNING AND MEMORY English Article Stress; HPA axis; Odor; Memory; Context-effect CONTEXT-DEPENDENT MEMORY; SALIVARY ALPHA-AMYLASE; MULTIPLE TRACE THEORY; HEALTHY-YOUNG MEN; PSYCHOSOCIAL STRESS; RECOGNITION MEMORY; AUTOBIOGRAPHICAL MEMORY; SYMPATHETIC ACTIVITY; DECLARATIVE MEMORY; EMOTIONAL AROUSAL Olfactory information seems to play a special role in memory due to the fast and direct processing of olfactory information in limbic areas like the amygdala and the hippocampus. This has led to the assumption that odors can serve as effective retrieval cues for autobiographic memories, especially emotional memories. The current study sought to investigate whether an olfactory cue can serve as an effective retrieval cue for memories of a stressful episode. A total of 95 participants were exposed to a psychosocial stressor or a well matching but not stressful control condition. During both conditions were visual objects present, either bound to the situation (central objects) or not (peripheral objects). Additionally, an ambient odor was present during both conditions. The next day, participants engaged in an unexpected object recognition task either under the influence of the same odor as was present during encoding (congruent odor) or another odor (non-congruent odor). Results show that stressed participants show a better memory for all objects and especially for central visual objects if recognition took place under influence of the congruent odor. An olfactory cue thus indeed seems to be an effective retrieval cue for stressful memories. (C) 2013 Elsevier Inc. All rights reserved. [Wiemers, Uta S.; Wolf, Oliver T.] Ruhr Univ Bochum, Dept Cognit Psychol, Inst Cognit Neurosci, Fac Psychol, D-44780 Bochum, Germany; [Wiemers, Uta S.; Sauvage, Magdalena M.; Wolf, Oliver T.] Ruhr Univ Bochum, Int Grad Sch Neurosci, D-44780 Bochum, Germany; [Sauvage, Magdalena M.] Ruhr Univ Bochum, Fac Med, Mercator Res Grp, Funct Architecture Memory Unit, D-44780 Bochum, Germany Wolf, OT (reprint author), Ruhr Univ Bochum, Dept Cognit Psychol, Univ Str 150, D-44780 Bochum, Germany. uta.wiemers@rub.de; magdalena.sauvage@g-mail.com; oliver.t.wolf@rub.de German Research Foundation (DFG) [(SFB) 874] This study was funded by the German Research Foundation (DFG) project B4 of Collaborative Research Center (SFB) 874 "Integration and Representation of Sensory Processes". Aggleton JP, 1999, BRIT J PSYCHOL, V90, P1, DOI 10.1348/000712699161170; Baddeley A, 2001, PHILOS T ROY SOC B, V356, P1345, DOI 10.1098/rstb.2001.0957; Buchanan TW, 2008, NEUROBIOL LEARN MEM, V89, P134, DOI 10.1016/j.nlm.2007.07.003; Buck L. B., 2000, PRINCIPLES NEURAL SC, P625; Cahill L, 2003, LEARN MEMORY, V10, P270, DOI 10.1101/lm.62403; Cahill L, 1998, TRENDS NEUROSCI, V21, P294, DOI 10.1016/S0166-2236(97)01214-9; Chu S, 2002, MEM COGNITION, V30, P511, DOI 10.3758/BF03194952; de Kloet ER, 2005, NAT REV NEUROSCI, V6, P463, DOI 10.1038/nrn1683; de Quervain DJF, 2000, NAT NEUROSCI, V3, P313, DOI 10.1038/73873; Diamond DM, 2005, HIPPOCAMPUS, V15, P1006, DOI 10.1002/hipo.20107; EASTERBROOK JA, 1959, PSYCHOL REV, V66, P183, DOI 10.1037/h0047707; ENGEN T, 1987, AM SCI, V75, P497; GODDEN DR, 1975, BRIT J PSYCHOL, V66, P325; HERZ RS, 1995, CHEM SENSES, V20, P517, DOI 10.1093/chemse/20.5.517; Herz RS, 1997, AM J PSYCHOL, V110, P489, DOI 10.2307/1423407; Joels M, 2006, TRENDS COGN SCI, V10, P152, DOI 10.1016/j.tics.2006.02.002; Joels M, 2008, TRENDS NEUROSCI, V31, P1, DOI 10.1016/j.tins.2007.10.005; Kensinger EA, 2009, EMOT REV, V1, P99, DOI 10.1177/1754073908100432; KIRSCHBAUM C, 1993, NEUROPSYCHOBIOLOGY, V28, P76, DOI 10.1159/000119004; Kirschbaum C, 1999, PSYCHOSOM MED, V61, P154; Kuhlmann S, 2005, NEUROBIOL LEARN MEM, V83, P158, DOI 10.1016/j.nlm.2004.09.001; Kuhlmann S, 2005, J NEUROSCI, V25, P2977, DOI 10.1523/JNEUROSCI.5139-04.2005; Kuhlmann S, 2006, BEHAV NEUROSCI, V120, P217, DOI 10.1037/0735-7044.120.1.217; Mather M, 2007, PERSPECT PSYCHOL SCI, V2, P33, DOI 10.1111/j.1745-6916.2007.00028.x; Moscovitch M, 2005, J ANAT, V207, P35, DOI 10.1111/j.1469-7580.2005.00421.x; Moscovitch M, 2006, CURR OPIN NEUROBIOL, V16, P179, DOI 10.1016/j.conb.2006.03.013; Mouly A.-M., 2010, FRONTIERS NEUROSCIEN, P367; Nadel L, 2000, HIPPOCAMPUS, V10, P352, DOI 10.1002/1098-1063(2000)10:4<352::AID-HIPO2>3.0.CO;2-D; Pointer SC, 1998, CHEM SENSES, V23, P359; Preuss D, 2009, NEUROBIOL LEARN MEM, V92, P318, DOI 10.1016/j.nlm.2009.03.009; Pruessner JC, 2003, PSYCHONEUROENDOCRINO, V28, P916, DOI 10.1016/S0306-4530(02)00108-7; Rohleder N, 2009, PSYCHONEUROENDOCRINO, V34, P469, DOI 10.1016/j.psyneuen.2008.12.004; Rohleder N, 2004, ANN NY ACAD SCI, V1032, P258, DOI 10.1196/annals.1314.033; Roozendaal B, 2011, BEHAV NEUROSCI, V125, P797, DOI 10.1037/a0026187; Sandi Carmen, 2007, Neural Plast, V2007, P78970; Sauvage MM, 2008, NAT NEUROSCI, V11, P16, DOI 10.1038/nn2016; Schoofs D, 2008, PSYCHONEUROENDOCRINO, V33, P643, DOI 10.1016/j.psyneuen.2008.02.004; Schwabe L, 2009, LEARN MEMORY, V16, P110, DOI 10.1101/lm.1257509; Schwabe L, 2008, NEUROBIOL LEARN MEM, V90, P44, DOI 10.1016/j.nlm.2008.02.002; Smeets T, 2009, PSYCHONEUROENDOCRINO, V34, P1152, DOI 10.1016/j.psyneuen.2009.03.001; Smeets T, 2008, PSYCHONEUROENDOCRINO, V33, P1378, DOI 10.1016/j.psyneuen.2008.07.009; Smith SM, 2001, PSYCHON B REV, V8, P203, DOI 10.3758/BF03196157; SNODGRASS JG, 1988, J EXP PSYCHOL GEN, V117, P34, DOI 10.1037//0096-3445.117.1.34; Sulmont C, 2002, CHEM SENSES, V27, P307, DOI 10.1093/chemse/27.4.307; Thompson LA, 2001, HUM FACTORS, V43, P611, DOI 10.1518/001872001775870377; Toffolo MBJ, 2012, COGNITION EMOTION, V26, P83, DOI 10.1080/02699931.2011.555475; Tollenaar MS, 2008, ACTA PSYCHOL, V127, P542, DOI 10.1016/j.actpsy.2007.10.007; TULVING E, 1973, PSYCHOL REV, V80, P352, DOI 10.1037/h0020071; Ulrich-Lai YM, 2009, NAT REV NEUROSCI, V10, P397, DOI 10.1038/nrn2647; Vermetten E, 2007, PSYCHOPHARMACOL BULL, V40, P8; Waring JD, 2011, NEUROPSYCHOLOGIA, V49, P1831, DOI 10.1016/j.neuropsychologia.2011.03.007; WATSON D, 1988, J PERS SOC PSYCHOL, V54, P1063, DOI 10.1037/0022-3514.54.6.1063; Wiemers US, 2013, PSYCHONEUROENDOCRINO, V38, P2268, DOI 10.1016/j.psyneuen.2013.04.015; Wiemers US, 2013, STRESS, V16, P254, DOI 10.3109/10253890.2012.714427; Willander J, 2007, MEM COGNITION, V35, P1659, DOI 10.3758/BF03193499; Wilson DA, 2004, NEUROSCIENTIST, V10, P513, DOI 10.1177/1073858404267048; Wolf OT, 2002, PSYCHONEUROENDOCRINO, V27, P777, DOI 10.1016/S0306-4530(01)00079-8; Wolf OT, 2009, BRAIN RES, V1293, P142, DOI 10.1016/j.brainres.2009.04.013; Yonelinas AP, 1997, MEM COGNITION, V25, P747, DOI 10.3758/BF03211318; Yonelinas AP, 2007, PSYCHOL BULL, V133, P800, DOI 10.1037/0033-2909.133.5.800; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864 61 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1074-7427 1095-9564 NEUROBIOL LEARN MEM Neurobiol. Learn. Mem. JUL 2014 112 230 236 10.1016/j.nlm.2013.10.004 7 AJ3FR WOS:000337552400026 J Gao, Y; Ji, RR; Cui, P; Dai, QH; Hua, G Gao, Yue; Ji, Rongrong; Cui, Peng; Dai, Qionghai; Hua, Gang Hyperspectral Image Classification Through Bilayer Graph-Based Learning IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Hyperspectral imaging; image classification; graph based learning; hypergraph learning REMOTE-SENSING IMAGES; COMPONENT ANALYSIS; SEGMENTATION; HYPERGRAPH; RECOGNITION; INFORMATION; RETRIEVAL Hyperspectral image classification with limited number of labeled pixels is a challenging task. In this paper, we propose a bilayer graph-based learning framework to address this problem. For graph-based classification, how to establish the neighboring relationship among the pixels from the high dimensional features is the key toward a successful classification. Our graph learning algorithm contains two layers. The first-layer constructs a simple graph, where each vertex denotes one pixel and the edge weight encodes the similarity between two pixels. Unsupervised learning is then conducted to estimate the grouping relations among different pixels. These relations are subsequently fed into the second layer to form a hypergraph structure, on top of which, semisupervised transductive learning is conducted to obtain the final classification results. Our experiments on three data sets demonstrate the merits of our proposed approach, which compares favorably with state of the art. [Gao, Yue; Dai, Qionghai] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Dept Automat, Beijing 100084, Peoples R China; [Ji, Rongrong] Xiamen Univ, Sch Informat Sci & Engn, Dept Cognit Sci, Xiamen 361005, Peoples R China; [Cui, Peng] Tsinghua Univ, Tsinghua Natl Lab Informat Sci & Technol, Beijing 100084, Peoples R China; [Hua, Gang] Stevens Inst Technol, Dept Comp Sci, Hoboken, NJ 07030 USA Ji, RR (reprint author), Xiamen Univ, Sch Informat Sci & Engn, Dept Cognit Sci, Xiamen 361005, Peoples R China. jirongrong@gmail.com Natural Science Foundation of China [61327902, 61120106003, 61035002, 61373076]; Fundamental Research Funds for the Central Universities [2013121026]; 985 Project, Xiamen University; National Key Technology Research and Development Program, Ministry of Science and Technology of China [2013BAB06B04]; U.S. National Science Foundation [IIS 1350763]; China National Natural Science Foundation [61228303]; Stevens Institute of Technology; Google Research Faculty Award; Microsoft Research; NEC Labs American This work was supported in part by the Natural Science Foundation of China under Grant 61327902, Grant 61120106003, Grant 61035002, and Grant 61373076, in part by the Fundamental Research Funds for the Central Universities under Grant 2013121026, in part by the 985 Project, Xiamen University, and in part by the National Key Technology Research and Development Program, Ministry of Science and Technology of China, under Grant 2013BAB06B04. The work of G. Hua was supported in part by the U.S. National Science Foundation under Grant IIS 1350763, in part by the China National Natural Science Foundation Grant 61228303, in part by the Stevens Institute of Technology, in part by a Google Research Faculty Award, in part by Microsoft Research, and in part by the NEC Labs American. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Farhan A. Baqai. (Corresponding author: R. Ji.) Bali N, 2008, IEEE T IMAGE PROCESS, V17, P217, DOI 10.1109/TIP.2007.914227; Bernard K, 2012, IEEE T IMAGE PROCESS, V21, P2008, DOI 10.1109/TIP.2011.2175741; Bilgin G, 2008, IEEE GEOSCI REMOTE S, V5, P673, DOI 10.1109/LGRS.2008.2002319; Bruzzone L, 2006, IEEE T GEOSCI REMOTE, V44, P3363, DOI 10.1109/TGRS.2006.877950; Bu J., 2010, P INT C MULT, P391, DOI 10.1145/1873951.1874005; Camps-Valls G, 2007, IEEE T GEOSCI REMOTE, V45, P3044, DOI 10.1109/TGRS.2007.895416; COVER T., 1991, ELEMENTS INFORM THEO; Du H., 2003, P 32 APPL IM PATT RE, P93; Eches O, 2010, IEEE T IMAGE PROCESS, V19, P1403, DOI 10.1109/TIP.2010.2042993; Eches O, 2013, IEEE T IMAGE PROCESS, V22, P5, DOI 10.1109/TIP.2012.2204270; Fauvel M, 2013, P IEEE, V101, P652, DOI 10.1109/JPROC.2012.2197589; Gao Y, 2013, IEEE T IMAGE PROCESS, V22, P363, DOI 10.1109/TIP.2012.2202676; Gao Y, 2012, IEEE T IMAGE PROCESS, V21, P4290, DOI 10.1109/TIP.2012.2199502; Gomez-Chova L, 2008, IEEE GEOSCI REMOTE S, V5, P336, DOI 10.1109/LGRS.2008.916070; Gu YF, 2012, IEEE T GEOSCI REMOTE, V50, P2852, DOI 10.1109/TGRS.2011.2176341; Guo BF, 2006, IEEE GEOSCI REMOTE S, V3, P522, DOI 10.1109/LGRS.2006.878240; Huang YC, 2010, PROC CVPR IEEE, P3376, DOI 10.1109/CVPR.2010.5540012; Huang YC, 2009, PROC CVPR IEEE, P1738; Huang YC, 2011, IEEE T PATTERN ANAL, V33, P1266, DOI 10.1109/TPAMI.2011.25; Hyvarinen A, 2000, NEURAL NETWORKS, V13, P411, DOI 10.1016/S0893-6080(00)00026-5; Ji RR, 2014, IEEE T GEOSCI REMOTE, V52, P1811, DOI 10.1109/TGRS.2013.2255297; Kuo BC, 2009, IEEE T GEOSCI REMOTE, V47, P1139, DOI 10.1109/TGRS.2008.2008308; Li CB, 2012, IEEE T IMAGE PROCESS, V21, P1200, DOI 10.1109/TIP.2011.2167626; Li J, 2010, IEEE T GEOSCI REMOTE, V48, P4085, DOI 10.1109/TGRS.2010.2060550; Liu QS, 2011, PATTERN RECOGN, V44, P2255, DOI 10.1016/j.patcog.2010.07.014; Ma L, 2010, IEEE T GEOSCI REMOTE, V48, P4099, DOI 10.1109/TGRS.2010.2055876; MacQueen J.B., 1967, P 5 BERK S MATH STAT, P281, DOI DOI 10.1234/12345678; Moser G, 2013, P IEEE, V101, P631, DOI 10.1109/JPROC.2012.2211551; Ratle F, 2010, IEEE T GEOSCI REMOTE, V48, P2271, DOI 10.1109/TGRS.2009.2037898; Sami ul Haq Q., 2012, IEEE T GEOSCI REMOTE, V50, P2287; Schweizer SM, 2001, IEEE T IMAGE PROCESS, V10, P584, DOI 10.1109/83.913593; Villa A, 2011, IEEE T GEOSCI REMOTE, V49, P4865, DOI 10.1109/TGRS.2011.2153861; WOLD S, 1987, CHEMOMETR INTELL LAB, V2, P37, DOI 10.1016/0169-7439(87)80084-9; Xia SP, 2008, LECT NOTES COMPUT SC, V5342, P117; Zhang LF, 2012, IEEE T GEOSCI REMOTE, V50, P879, DOI 10.1109/TGRS.2011.2162339; Zhang LF, 2011, IEEE GEOSCI REMOTE S, V8, P374, DOI 10.1109/LGRS.2010.2077272; Zhong P, 2010, IEEE T IMAGE PROCESS, V19, P1890, DOI 10.1109/TIP.2010.2045034; Zhong P, 2011, IEEE T GEOSCI REMOTE, V49, P688, DOI 10.1109/TGRS.2010.2059706; Zhou D, 2007, ADV NEURAL INFORM PR, V19, P1601; Zhou DY, 2004, ADV NEUR IN, V16, P321 40 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. JUL 2014 23 7 2769 2778 10.1109/TIP.2014.2319735 10 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AI8EU WOS:000337141400001 J Tibon, R; Vakil, E; Levy, DA; Goldstein, A Tibon, Roni; Vakil, Eli; Levy, Daniel A.; Goldstein, Abraham Episodic temporal structure modulates associative recognition processes: An MEG study PSYCHOPHYSIOLOGY English Article Episodic memory; Familiarity; Recollection; Intertemporal associations; MEG EVENT-RELATED POTENTIALS; ELECTROPHYSIOLOGICAL EVIDENCE; BRAIN POTENTIALS; SOURCE MEMORY; ITEM MEMORY; FAMILIARITY; CONTEXT; RETRIEVAL; RECOLLECTION; HIPPOCAMPAL The formation of mnemonic associations can occur between items processed in temporal proximity. It has been proposed that such intertemporal associations are not unitizable, and may therefore be retrieved only via recollective processes. To examine this claim, we conducted a magnetoencephalograph study of recognition memory for items encoded and retrieved sequentially. Participants studied successively presented pairs of object pictures, and subsequently made old-new item judgments under several retrieval conditions, differing in degree of reinstatement of associative information. Correct recognition was accompanied by an early event-related field (ERF) component, seemingly corresponding to the FN400 event-related potential component asserted to reflect familiarity; this retrieval success effect was not modulated by degree of associative binding. A later ERF component, corresponding to the late positive component asserted to reflect recollection, was modulated by degree of associative reinstatement. These results suggest that memory of intertemporal associations, which are not amenable to unitization, is accessed via recollection. [Tibon, Roni; Vakil, Eli; Goldstein, Abraham] Bar Ilan Univ, Dept Psychol, IL-52900 Ramat Gan, Israel; [Vakil, Eli; Goldstein, Abraham] Bar Ilan Univ, Leslie & Susan Gonda Goldschmied Multidisciplinar, IL-52900 Ramat Gan, Israel; [Levy, Daniel A.] Interdisciplinary Ctr, Sch Psychol, Herzliyya, Israel Tibon, R (reprint author), Bar Ilan Univ, Dept Psychol, IL-52900 Ramat Gan, Israel. ronitibon@gmail.com I-CORE Program of the Planning and Budgeting Committee; Israel Science Foundation [51/11] This paper is based on a thesis written by the first author and supervised by two of the authors (Eli Vakil and Abraham Goldstein), submitted to Bar-Ilan University in partial fulfillment of the requirements toward a Ph.D. degree. The work was supported by the I-CORE Program of the Planning and Budgeting Committee and The Israel Science Foundation (grant No. 51/11). We thank Roni Suchowski for assistance with data analysis. Aggleton JP, 1999, BEHAV BRAIN SCI, V22, P425; Aggleton JP, 2006, TRENDS COGN SCI, V10, P455, DOI 10.1016/j.tics.2006.08.003; Allan K, 1998, ACTA PSYCHOL, V98, P231, DOI 10.1016/S0001-6918(97)00044-9; Bader R, 2010, NEUROIMAGE, V50, P772, DOI 10.1016/j.neuroimage.2009.12.100; Bastin C, 2010, EUR J COGN PSYCHOL, V22, P922, DOI 10.1080/09541440903007834; Bridson NC, 2009, BRAIN RES, V1283, P73, DOI 10.1016/j.brainres.2009.05.093; Cycowicz YM, 2003, PSYCHOPHYSIOLOGY, V40, P455, DOI 10.1111/1469-8986.00047; Duzel E, 2003, NEUROIMAGE, V18, P185, DOI 10.1016/S1053-8119(02)0031-9; Ecker UKH, 2007, INT J PSYCHOPHYSIOL, V64, P146, DOI 10.1016/j.ijpsycho.2007.01.005; Eichenbaum H, 2007, ANNU REV NEUROSCI, V30, P123, DOI 10.1146/annurev.neuro.30.051606.094328; Friedman D, 2000, MICROSC RES TECHNIQ, V51, P6, DOI 10.1002/1097-0029(20001001)51:1<6::AID-JEMT2>3.0.CO;2-R; Friedman D, 2005, COGNITIVE BRAIN RES, V23, P185, DOI 10.1016/j.cogbrainres.2004.10.005; Graham R, 2001, CAN J EXP PSYCHOL, V55, P154, DOI 10.1037/h0087362; Gross J., 2012, NEUROIMAGE C, V65, P349, DOI [10.1016/j.neuroimage.2012.10.001, DOI 10.1016/J.NEUROIMAGE.2012.10.001]; Guo CY, 2006, BRAIN RES, V1118, P142, DOI 10.1016/j.brainres.2006.08.034; HAMALAINEN M, 1993, REV MOD PHYS, V65, P413, DOI 10.1103/RevModPhys.65.413; Jäger Theodor, 2006, Neuron, V52, P535, DOI 10.1016/j.neuron.2006.09.013; Keil A., 2013, PSYCHOPHYSIOLOGY, V51, P1, DOI [10.1111/psyp.12147, DOI 10.1111/PSYP.12147]; Kriukova O, 2013, BRAIN COGNITION, V83, P93, DOI 10.1016/j.bandc.2013.07.006; Levy DA, 2008, Q J EXP PSYCHOL, V61, P1620, DOI 10.1080/17470210802134767; Lezak M. D., 2004, NEUROPSYCHOLOGICAL A; MacDonald CJ, 2011, NEURON, V71, P737, DOI 10.1016/j.neuron.2011.07.012; Makeig S, 1999, J NEUROSCI, V19, P2665; Mayes A, 2007, TRENDS COGN SCI, V11, P126, DOI 10.1016/j.tics.2006.12.003; Mecklinger A, 2000, PSYCHOPHYSIOLOGY, V37, P565, DOI 10.1111/1469-8986.3750565; MITCHELL PF, 1993, PSYCHOPHYSIOLOGY, V30, P496, DOI 10.1111/j.1469-8986.1993.tb02073.x; Norman KA, 2003, PSYCHOL REV, V110, P611, DOI 10.1037/0033-295X.110.4.611; Oostenveld R, 2011, COMPUT INTEL NEUROSC, DOI 10.1155/2011/156869; Quamme JR, 2007, HIPPOCAMPUS, V17, P192, DOI 10.1002/hipo.20257; Rhodes SM, 2007, NEUROPSYCHOLOGIA, V45, P412, DOI 10.1016/j.neuropsychologia.2006.06.022; RUGG MD, 1990, MEM COGNITION, V18, P367, DOI 10.3758/BF03197126; Rugg MD, 2007, TRENDS COGN SCI, V11, P251, DOI 10.1016/j.tics.2007.04.004; Senkfor AJ, 1998, J EXP PSYCHOL LEARN, V24, P1005, DOI 10.1037/0278-7393.24.4.1005; SMITH ME, 1989, J EXP PSYCHOL LEARN, V15, P50; Speer NK, 2007, BRAIN RES, V1174, P97, DOI 10.1016/j.brainres.2007.08.024; Staresina BP, 2005, NEUROIMAGE, V27, P83, DOI 10.1016/j.neuroimage.2005.02.051; STROBACH P, 1994, IEEE T BIO-MED ENG, V41, P343, DOI 10.1109/10.284962; Tal I, 2013, J NEUROSCI METH, V217, P31, DOI 10.1016/j.jneumeth.2013.04.002; Tendolkar I, 2000, NEUROSCI LETT, V280, P69, DOI 10.1016/S0304-3940(99)01001-0; Tibon R, 2014, BRAIN COGNITION, V84, P1, DOI 10.1016/j.bandc.2013.10.003; Tibon R, 2012, J MEM LANG, V67, P93, DOI 10.1016/j.jml.2012.02.003; Toga A. W., 2002, BRAIN MAPPING SYSTEM; Tsivilis D, 2001, NEURON, V31, P497, DOI 10.1016/S0896-6273(01)00376-2; Vakil E, 2007, Q J EXP PSYCHOL, V60, P916, DOI 10.1080/17470210701357568; Wechsler D., 1997, WAIS 3 WECHSLER ADUL; Wilding E. L., 2011, OXFORD HDB ERP COMPO, P373, DOI [DOI 10.1093/OXFORDHB/9780195374148.013.0187, 10.1093/oxfordhb/9780195374148.013.0187, DOI 10.1093/0XFORDHB/9780195374148.013.0187]; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864; Yonelinas AP, 1999, PSYCHON B REV, V6, P654, DOI 10.3758/BF03212975 48 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0048-5772 1469-8986 PSYCHOPHYSIOLOGY Psychophysiology JUL 2014 51 7 634 644 10.1111/psyp.12207 11 Psychology, Biological; Neurosciences; Physiology; Psychology; Psychology, Experimental Psychology; Neurosciences & Neurology; Physiology AJ4AO WOS:000337611600006 J Cao, WM; Liu, N; Kong, QC; Feng, H Cao WenMing; Liu Ning; Kong QiCong; Feng Hao Content-based image retrieval using high-dimensional information geometry SCIENCE CHINA-INFORMATION SCIENCES English Article image retrieval; angel cosine; high-dimensional information; feature extraction; information subspace In this paper, a new content-based image retrieval approach is proposed based on high- dimensional information theory. The proposed approach overcomes the disadvantages of the current content-based image retrieval algorithms that suffer from the semantic gap. First, we present a new multidimensional information space's vector angle cosine algorithm of high-dimensional geometry, then, we provide a detailed description of our images retrieval method including proposal of an overlapping image block method and definition of a similarity degree between images on the non-dimensional information subspaces. Finally, experimental results show the higher retrieval efficiency of the proposed algorithm. [Cao WenMing; Liu Ning; Kong QiCong] Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China; [Feng Hao] Hangzhou Dianzi Univ, Coll Automat, Hangzhou 310018, Zhejiang, Peoples R China Cao, WM (reprint author), Shenzhen Univ, Coll Informat Engn, Shenzhen 518060, Peoples R China. wmcao@szu.edu.cn National Natural Science Foundation of China [61375015, 61070087, 61373084]; Foundational Investigated Funds of Shenzhen City [JC201005280570A, JC2011051706-47A] This work was supported by National Natural Science Foundation of China (Grant Nos. 61375015, 61070087, 61373084) and Foundational Investigated Funds of Shenzhen City (Grant Nos. JC201005280570A, JC2011051706-47A). Cao WM, 2011, PROCEDIA ENGINEER, V15, DOI 10.1016/j.proeng.2011.08.854; Cao W M, 2011, HIGH DIMENSIONAL INF; Cao WM, 2009, CHINESE J ELECTRON, V18, P650; Diminnie C, 1973, DEMONSTRATIO MATH, V6, P525; Dinakaran B, 2010, CYBERNETCS INFORM TE, V10, P20; Gunawan H, 2005, CONTRIBUTIONS ALGEBR, V46, P311; HIRATA K, 1993, NEC RES DEV, V34, P263; Jain AK, 1996, IEEE T PATTERN ANAL, V18, P267, DOI 10.1109/34.485555; Jhanwar N, 2004, IMAGE VISION COMPUT, V22, P1211, DOI 10.1016/j.imavis.2004.03.026; Liu YN, 2011, SCI CHINA INFORM SCI, V54, P2051, DOI 10.1007/s11432-011-4344-2; Nilback W, 1993, P SOC PHOTO-OPT INS, V1908, P173; Pentland A, 1996, INT J COMPUT VISION, V18, P233, DOI 10.1007/BF00123143; Shao ZF, 2011, SCI CHINA INFORM SCI, V54, P732, DOI 10.1007/s11432-011-4207-x; Smith J. R., 1996, Proceedings ACM Multimedia 96, DOI 10.1145/244130.244151; Wang S J, 2008, 1 STEP MULTIDIMENSIO; Xu C, 2012, SCI CHINA INFORM SCI, V55, P260, DOI 10.1007/s11432-011-4540-0 16 0 0 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 1674-733X 1869-1919 SCI CHINA INFORM SCI Sci. China-Inf. Sci. JUL 2014 57 7 072116 10.1007/s11432-014-5086-8 11 Computer Science, Information Systems Computer Science AI9NM WOS:000337259300016 J Shin, D; Kim, T; Choi, J; Kim, J Shin, Dongwook; Kim, Taehwan; Choi, Joongmin; Kim, Jungsun Author name disambiguation using a graph model with node splitting and merging based on bibliographic information SCIENTOMETRICS English Article Author name disambiguation; Graph model; Namesake resolution; Heteronymous name resolution; Digital library CITATIONS; WEB Author ambiguity mainly arises when several different authors express their names in the same way, generally known as the namesake problem, and also when the name of an author is expressed in many different ways, referred to as the heteronymous name problem. These author ambiguity problems have long been an obstacle to efficient information retrieval in digital libraries, causing incorrect identification of authors and impeding correct classification of their publications. It is a nontrivial task to distinguish those authors, especially when there is very limited information about them. In this paper, we propose a graph based approach to author name disambiguation, where a graph model is constructed using the co-author relations, and author ambiguity is resolved by graph operations such as vertex (or node) splitting and merging based on the co-authorship. In our framework, called a Graph Framework for Author Disambiguation (GFAD), the namesake problem is solved by splitting an author vertex involved in multiple cycles of coauthorship, and the heteronymous name problem is handled by merging multiple author vertices having similar names if those vertices are connected to a common vertex. Experiments were carried out with the real DBLP and Arnetminer collections and the performance of GFAD is compared with three representative unsupervised author name disambiguation systems. We confirm that GFAD shows better overall performance from the perspective of representative evaluation metrics. An additional contribution is that we released the refined DBLP collection to the public to facilitate organizing a performance benchmark for future systems on author disambiguation. [Shin, Dongwook; Kim, Taehwan; Choi, Joongmin; Kim, Jungsun] Hanyang Univ, Dept Comp Sci & Engn, Ansan 426791, Gyeonggi Do, South Korea Kim, J (reprint author), Hanyang Univ, Dept Comp Sci & Engn, 55 Hanyangdaehak Ro, Ansan 426791, Gyeonggi Do, South Korea. foremostdw@gmail.com; kimth@islab.hanyang.ac.kr; jmchoi@hanyang.ac.kr; kimjs@hanyang.ac.kr Benjelloun O, 2009, VLDB J, V18, P255, DOI 10.1007/s00778-008-0098-x; Bhattacharya I., 2006, P 6 SIAM INT C DAT M; Bhattacharya I., 2007, ACM T KNOWL DISCOV D, V1, P1, DOI 10.1145/1217299.1217304; Borgman CL, 1999, INFORM PROCESS MANAG, V35, P227; Carvalho A., 2011, J INFORM DATA MANAGE, V2, P289; Cherednichenko S., 2005, THESIS U JOENSUU; Cota RG, 2010, J AM SOC INF SCI TEC, V61, P1853, DOI 10.1002/asi.21363; Fan X., 2011, ACM J DATA INFORM QU, V2, P10; Ferreira A., 2010, P 2010 ACM IEEE JOIN, P39, DOI 10.1145/1816123.1816130; Ferreira AA, 2012, SIGMOD REC, V41, P15, DOI 10.1145/2350036.2350040; Han H., 2004, Proceedings of the Fourth ACM/IEEE Joint Conference on Digital Libraries (IEEE Cat. No.04TH8766), DOI 10.1145/996350.996419; Han H, 2005, PROCEEDINGS OF THE 5TH ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, PROCEEDINGS, P334, DOI 10.1145/1065385.1065462; Johnson D. B., 1975, SIAM Journal on Computing, V4, DOI 10.1137/0204007; Kang IS, 2009, INFORM PROCESS MANAG, V45, P84, DOI 10.1016/j.ipm.2008.06.006; Klass V., 2007, THESIS LMU MUNICH; Levin F., 2010, J INFORM DATA MANAGE, V1, P183; Ley M, 2002, P 9 INT S STRING PRO, V2476, P1; Masada T., 2007, P 2 INT C SCAL INF S; Pasula H., 2003, ADV NEURAL INFORMATI, V15, P1401; Peng HT, 2012, EXPERT SYST APPL, V39, P10521, DOI 10.1016/j.eswa.2012.02.121; Pereira DA, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P49; Pereira DA, 2011, J AM SOC INF SCI TEC, V62, P919, DOI 10.1002/asi.21518; Scoville Caryn L, 2003, Med Ref Serv Q, V22, P1, DOI 10.1300/J115v22n04_01; Soler JM, 2007, SCIENTOMETRICS, V72, P281, DOI 10.1007/s11192-007-1730-z; Tan Y., 2006, P ACM IEEE JOINT C D, P314, DOI 10.1145/1141753.1141826; Tang J., 2008, P 14 ACM SIGKDD INT, P990, DOI 10.1145/1401890.1402008; Tang J., 2011, P 34 INT ACM SIGIR C, P1233; Veloso A, 2012, INFORM PROCESS MANAG, V48, P680, DOI 10.1016/j.ipm.2011.08.005; Wang X., 2011, P IEEE 11 INT C DAT, P794; Wooding S, 2006, SCIENTOMETRICS, V66, P11, DOI 10.1007/s11192-006-0002-7; Wu J, 2013, SCIENTOMETRICS, V96, P683, DOI 10.1007/s11192-013-0978-8; Yang KH, 2008, LECT NOTES COMPUT SC, V5173, P185; Yin X., 2007, P IEEE 23 INT C DAT, P1242 33 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0138-9130 1588-2861 SCIENTOMETRICS Scientometrics JUL 2014 100 1 15 50 10.1007/s11192-014-1289-4 36 Computer Science, Interdisciplinary Applications; Information Science & Library Science Computer Science; Information Science & Library Science AI8MQ WOS:000337171300002 J Akritidis, L; Bozanis, P Akritidis, Leonidas; Bozanis, Panayiotis Improving opinionated blog retrieval effectiveness with quality measures and temporal features WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS English Article Information retrieval; Opinionated retrieval; Search; Blog; Post; Blogger; Influence; Impact; Ranking The massive acceptance and usage of the blog communities by a significant portion of the Web users has rendered knowledge extraction from blogs a particularly important research field. One of the most interesting related problems is the issue of the opinionated retrieval, that is, the retrieval of blog entries which contain opinions about a topic. There has been a remarkable amount of work towards the improvement of the effectiveness of the opinion retrieval systems. The primary objective of these systems is to retrieve blog posts which are both relevant to a given query and contain opinions, and generate a ranked list of the retrieved documents according to the relevance and opinion scores. Although a wide variety of effective opinion retrieval methods have been proposed, to the best of our knowledge, none of them takes into consideration the issue of the importance of the retrieved opinions. In this work we introduce a ranking model which combines the existing retrieval strategies with query-independent information to enhance the ranking of the opinionated documents. More specifically, our model accounts for the influence of the blogger who authored an opinion, the reputation of the blog site which published a specific blog post, and the impact of the post itself. Furthermore, we expand the current proximity-based opinion scoring strategies by considering the physical locations of the query and opinion terms within a document. We conduct extensive experiments with the TREC Blogs08 dataset which demonstrate that the application of our methods enhances retrieval precision by a significant margin. [Akritidis, Leonidas; Bozanis, Panayiotis] Univ Thessaly, Dept Comp & Commun Engn, Volos, Greece Akritidis, L (reprint author), Univ Thessaly, Dept Comp & Commun Engn, Volos, Greece. leoakr@inf.uth.gr; pbozanis@inf.uth.gr Agarwal N., 2008, ACM SIGKDD EXPLORATI, V10, P18, DOI 10.1145/1412734.1412737; Agarwal N., 2008, P INT C WEB SEARCH W, P207, DOI DOI 10.1145/1341531.134155; Akritidis L, 2009, 2009 IEEE/WIC/ACM INTERNATIONAL JOINT CONFERENCES ON WEB INTELLIGENCE (WI) AND INTELLIGENT AGENT TECHNOLOGIES (IAT), VOL 1, P76; Akritidis L, 2011, IEEE T SYST MAN CY C, V41, P759, DOI 10.1109/TSMCC.2010.2099216; Akritidis L, 2012, SIMUL MODEL PRACT TH, V22, P74, DOI 10.1016/j.simpat.2011.12.002; Buttcher S., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148285; Dave K., 2003, P 12 INT C WORLD WID, P519, DOI DOI 10.1145/775152.775226; Esuli A., 2006, P LREC, V6, P417; Garfield E., 1994, APPL CITATION INDEXI; Gerani S, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P403; Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102; Kritikopoulos A., 2006, P 2 INT WORKSH ADV A, P8, DOI 10.1145/1190183.1190193; Langville A., 2006, GOOGLE PAGE RANK SCI; Lee Y., 2008, P TREC 2008; Macdonald C., 2007, P TREC 2007; Mullen T., 2004, P C EMP METH NAT LAN, V4, P412; Na SH, 2009, LECT NOTES COMPUT SC, V5478, P734; Ounis I., 2008, P TREC 2008; Ounis I., 2006, P TREC 2006; Pang B, 2002, PROCEEDINGS OF THE 2002 CONFERENCE ON EMPIRICAL METHODS IN NATURAL LANGUAGE PROCESSING, P79; Tayebi MA, 2007, PROCEEDINGS OF THE IEEE/WIC/ACM INTERNATIONAL CONFERENCE ON WEB INTELLIGENCE, P104; Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153; Turney PD, 2003, ACM T INFORM SYST, V21, P315, DOI 10.1145/944012.944013; Vechtomova O, 2010, INFORM PROCESS MANAG, V46, P71, DOI 10.1016/j.ipm.2009.06.005; Zhang M., 2008, P 31 ANN INT ACM SIG, P411, DOI 10.1145/1390334.1390405; Zhang WS, 2007, PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON AGRICULTURE ENGINEERING, P831, DOI 10.1145/1321440.1321555 26 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1386-145X 1573-1413 WORLD WIDE WEB World Wide Web JUL 2014 17 4 777 798 10.1007/s11280-013-0237-1 22 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AI8KT WOS:000337163500016 J Yi, CC; Tian, YL Yi, Chucai; Tian, Yingli Scene Text Recognition in Mobile Applications by Character Descriptor and Structure Configuration IEEE TRANSACTIONS ON IMAGE PROCESSING English Article Scene text detection; scene text recognition; mobile application; character descriptor; stroke configuration; text understanding; text retrieval; mobile application IMAGES; SEGMENTATION; EVOLUTION Text characters and strings in natural scene can provide valuable information for many applications. Extracting text directly from natural scene images or videos is a challenging task because of diverse text patterns and variant background interferences. This paper proposes a method of scene text recognition from detected text regions. In text detection, our previously proposed algorithms are applied to obtain text regions from scene image. First, we design a discriminative character descriptor by combining several state-of-the-art feature detectors and descriptors. Second, we model character structure at each character class by designing stroke configuration maps. Our algorithm design is compatible with the application of scene text extraction in smart mobile devices. An Android-based demo system is developed to show the effectiveness of our proposed method on scene text information extraction from nearby objects. The demo system also provides us some insight into algorithm design and performance improvement of scene text extraction. The evaluation results on benchmark data sets demonstrate that our proposed scheme of text recognition is comparable with the best existing methods. [Yi, Chucai; Tian, Yingli] CUNY, Grad Ctr, New York, NY 10016 USA Yi, CC (reprint author), CUNY, Grad Ctr, New York, NY 10016 USA. cyi@gc.cuny.edu; ytian@ccny.cuny.edu National Science Foundation [EFRI-1137172, IIP-1343402]; Federal Highway Administration [DTFH61-12-H-00002]; Army Research Office [W911NF-09-1-0565] This work was supported in part by the National Science Foundation under Grant EFRI-1137172 and Grant IIP-1343402, in part by the Federal Highway Administration under Grant DTFH61-12-H-00002, and in part by the Army Research Office under Grant W911NF-09-1-0565. The associate editor coordinating the review of this manuscript and approving it for publication was Dr. Gang Hua. Bai X, 2007, IEEE T PATTERN ANAL, V29, P449, DOI 10.1109/TPAMI.2007.59; Beaufort R., 2007, P 9 INT C DOC AN REC, P889; Chen XL, 2004, IEEE T IMAGE PROCESS, V13, P87, DOI 10.1109/TIP.2003.819223; Coates A., 2011, Proceedings of the 2011 11th International Conference on Document Analysis and Recognition (ICDAR 2011), DOI 10.1109/ICDAR.2011.95; Dalal N, 2005, PROC CVPR IEEE, P886; de Campos T., 2009, P VISAPP; Epshtein B, 2010, PROC CVPR IEEE, P2963, DOI 10.1109/CVPR.2010.5540041; Felzenszwalb PF, 2010, IEEE T PATTERN ANAL, V32, P1627, DOI 10.1109/TPAMI.2009.167; Jiang TT, 2009, PROC CVPR IEEE, P848; Kumar S, 2007, IEEE T IMAGE PROCESS, V16, P2117, DOI 10.1109/TIP.2007.900098; Latecki LJ, 1999, COMPUT VIS IMAGE UND, V73, P441, DOI 10.1006/cviu.1998.0738; Liu Y, 2008, 2008 CHINESE CONTROL AND DECISION CONFERENCE, VOLS 1-11, P203; Lu SJ, 2008, IEEE T PATTERN ANAL, V30, P1913, DOI 10.1109/TPAMI.2008.89; Lucas S., 2003, P 7 INT C DOC AN REC, V2, P682; Mishra A., 2012, P IEEE C COMP VIS PA, P1063; Neumann L, 2012, PROC CVPR IEEE, P3538, DOI 10.1109/CVPR.2012.6248097; Nikolaou N, 2009, INT J IMAG SYST TECH, V19, P14, DOI 10.1002/ima.20174; Ohbuchi E., 2004, P 2004 INT C CYB CW, P260, DOI DOI 10.1109/CW.2004.23; Shahab A., 2011, Proceedings of the 2011 11th International Conference on Document Analysis and Recognition (ICDAR 2011), DOI 10.1109/ICDAR.2011.296; Shi CZ, 2013, PROC CVPR IEEE, P2961, DOI 10.1109/CVPR.2013.381; Shivakumara P, 2008, PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, P307, DOI 10.1109/DAS.2008.17; Smith DL, 2011, PROC CVPR IEEE, P73, DOI 10.1109/CVPR.2011.5995700; Smith R., 2007, ICDAR 07, V2, P629; Viola P, 2004, INT J COMPUT VISION, V57, P137, DOI 10.1023/B:VISI.0000013087.49260.fb; Wang K, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P1457; Wang K., 2010, P EUR C COMP VIS; Weinman JJ, 2009, IEEE T PATTERN ANAL, V31, P1733, DOI 10.1109/TPAMI.2009.38; Yao C, 2012, PROC CVPR IEEE, P1083; Yi C., 2013, P 12TH ICDAR AUG, P907; Yi CC, 2011, IEEE T IMAGE PROCESS, V20, P2594, DOI 10.1109/TIP.2011.2126586; Yi CC, 2012, IEEE T IMAGE PROCESS, V21, P4256, DOI 10.1109/TIP.2012.2199327; Zhang J, 2008, PROCEEDINGS OF THE 8TH IAPR INTERNATIONAL WORKSHOP ON DOCUMENT ANALYSIS SYSTEMS, P5, DOI 10.1109/DAS.2008.49; Zheng Q., 2010, LNCS, V6494, P121 33 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1057-7149 1941-0042 IEEE T IMAGE PROCESS IEEE Trans. Image Process. JUL 2014 23 7 2972 2982 10.1109/TIP.2014.2317980 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AI7VJ WOS:000337108600003 J Wang, RJ; Yang, YT; Chang, PC Wang, Ren-Jie; Yang, Ya-Ting; Chang, Pao-Chi Content-based image retrieval using H.264 intra coding features JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION English Article Content-based image/video retrieval; H.264; Intra prediction; Video coding; Compression domain; Geometrical verification; Image search; Texture features SHOT CHANGE DETECTION; COMPRESSED VIDEO; STANDARD; SEARCH Efficient multimedia retrieval has become a vital issue because more audio and video data are now available. This paper focuses on content-based image retrieval (CBIR) in the compression domain (CPD). The retrieval features are extracted based on I-frame coding information in H.264. This paper proposes using a local mode histogram as the texture feature to match images and applying the residual coefficients to filter non-confident modes. The geometrical correspondence between two images is also considered. The experimental results show that the proposed method can substantially reduce computational and memory resource consumption, and provides similar performance compared with methods that extract features from decompressed images. (C) 2014 Elsevier Inc. All rights reserved. [Wang, Ren-Jie; Yang, Ya-Ting; Chang, Pao-Chi] Natl Cent Univ, Dept Commun Engn, Jhongli 320, Taiwan Chang, PC (reprint author), Natl Cent Univ, Dept Commun Engn, Jhongli 320, Taiwan. pcchang@ce.ncu.edu.tw Baharudin B., 2012, P INT C COMP INF SCI, P425; Bescos J, 2004, IEEE T CIRC SYST VID, V14, P475, DOI 10.1109/TCSVT.2004.825546; Ferman M., 2002, IEEE T IMAGE PROCESS, V11, P497; Girod B, 2011, IEEE MULTIMEDIA, V18, P86, DOI 10.1109/MMUL.2011.48; Jegou H, 2008, LECT NOTES COMPUT SC, V5302, P304, DOI 10.1007/978-3-540-88682-2_24; Junding S., 2009, P INT C COMP INT SEC, P349; Nya P. N., 2000, ISO WG11 MPEG M GEN; Phillbin J., 2007, P C COMP VIS PATT RE, P18; Richardson I. E., 2010, H 264 ADV VIDEO COMP, P3; Schaefer G., 2012, LECT NOTES COMPUT SC, V7669, P318; Schaefer G., 2012, PROC IEEE INT C SIGN, P587; Simonea F.D., 2007, P SPIE OPTICS PHOTON, V6696; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487; Wang FP, 2012, 2012 IEEE NINTH INTERNATIONAL CONFERENCE ON ADVANCED VIDEO AND SIGNAL-BASED SURVEILLANCE (AVSS), P258, DOI 10.1109/AVSS.2012.46; Wang HL, 2003, J VIS COMMUN IMAGE R, V14, P150, DOI 10.1016/S1047-3203(03)00019-1; Wang Y., 2011, P INT C INT SCI INF, P450; Wiegand T, 2003, IEEE T CIRC SYST VID, V13, P560, DOI 10.1109/TCSVT.2003.815165; Xe Q, 2005, P INT C WIR COMM NET, P1253; Yeo C., 2008, COMPRESSED DOMAIN VI; Zargari F, 2010, IEEE T CONSUM ELECTR, V56, P728, DOI 10.1109/TCE.2010.5505994; Zeng W, 2005, IEEE INT SYMP CIRC S, P3459; Zhang X.H., 2005, P 5 INT C COMP INF T, P629; Zhong D, 2005, PATTERN RECOGN LETT, V26, P2272, DOI 10.1016/j.patrec.2005.04.012; Zhong Y, 2000, IEEE T PATTERN ANAL, V22, P385; Zhou XS, 2001, PATTERN RECOGN LETT, V22, P457, DOI 10.1016/S0167-8655(00)00124-0; Zhu M., 2004, 9 U WAT DEP STAT ACT; Zhuo L., 2012, SIGNAL PROCESS, V93, P2126 28 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1047-3203 1095-9076 J VIS COMMUN IMAGE R J. Vis. Commun. Image Represent. JUL 2014 25 5 963 969 10.1016/j.jvcir.2014.02.016 7 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AI5FQ WOS:000336891200024 J Stoilos, G; Stamou, G Stoilos, Giorgos; Stamou, Giorgos Reasoning with fuzzy extensions of OWL and OWL 2 KNOWLEDGE AND INFORMATION SYSTEMS English Article Fuzzy description logics; Reasoning; Fuzzy nominals; Fuzzy-SHOIQ; Fuzzy-SROIQ DESCRIPTION LOGICS; INCLUSION AXIOMS; RETRIEVAL; MODEL Fuzzy Description Logics (f-DLs) have been proposed as logical formalisms capable of representing and reasoning with vague/fuzzy information. They are envisioned to be helpful for many applications that need to cope with such type of information such as multimedia processing, decision making, automatic negotiation and more. Recent results have provided with many tableaux algorithms for supporting reasoning over quite expressive f-DLs. However, no (direct) tableaux algorithm for reasoning with fuzzy extensions of DLs such as and exists today. and are particularly interesting formalisms as they constitute the logical underpinnings of the Web ontology languages OWL DL and OWL 2 DL. In the current paper, we present an algorithm for reasoning with the fuzzy DLs f- and f-. In addition, we also provide a tableaux algorithm for fuzzy nominals, thus providing reasoning support for the fuzzy DL language (we call) f-SHO(f)IQ. [Stoilos, Giorgos; Stamou, Giorgos] Natl Tech Univ Athens, Sch Elect & Comp Engn, Zografos, Greece Stoilos, G (reprint author), Natl Tech Univ Athens, Sch Elect & Comp Engn, Zografos, Greece. gstoil@image.ece.ntua.gr; gstam@softlab.ece.ntua.gr Baader F., 2002, DESCRIPTION LOGIC HD; Baader F, 2011, IEEE INT CONF FUZZY, P1735; Baader F, 2011, P 24 INT WORKSH DESC; Bobillo F, 2009, INT J UNCERTAIN FUZZ, V17, P501, DOI 10.1142/S0218488509006121; Bobillo F, 2006, P 2 INT WORKSH UNC R; Bobillo F, 2012, INT J UNCERTAIN FUZZ, V20, P475, DOI 10.1142/S0218488512500249; Bobillo F, 2011, INFORM SCIENCES, V181, P758, DOI 10.1016/j.ins.2010.10.020; Bobillo F, 2008, P 12 INT C INF PROC, P1008; Bobillo F, 2007, P IEEE INT C FUZZ SY; Bobillo F, 2011, INT J APPROX REASON, V52, P1073, DOI [10.1016/j.ijar.2011.05.003, 10.1016/j.jaac.2011.05.003]; Bobillo F, 2008, IEEE INT CONF FUZZY, P923, DOI 10.1109/FUZZY.2008.4630480; Bobillo F, 2011, FUZZY SET SYST, V172, P1, DOI 10.1016/j.fss.2011.02.012; Borgwardt S., 2012, LECT NOTES COMPUTER, V7497, P9; Borgwardt S, 2012, P 13 INT C PRINC KNO; Cerami M, 2013, INFORM SCIENCES, V227, P1, DOI 10.1016/j.ins.2012.11.019; Cimiano P, 2008, WORKSH ADV REAS WEB; Dasiopoulou S., 2008, P 3 INT C SEM DIG ME; Demri S., 2005, Journal of Logic, Language and Information, V14, DOI 10.1007/s10849-005-5788-9; Derriere S, 2006, P 26 M IAU VIRT OBS, P17; Dragoni M, 2007, P GEN EV COMP C GECC; Ferrara A., 2008, P 3 INT WORKSH ONT M; Glimm B, 2012, J WEB SEMANT, V14, P84, DOI 10.1016/j.websem.2011.12.007; Golbreich C, 2006, J WEB SEMANT, V4, P181, DOI 10.1016/j.websem.2006.05.007; Hahnle R, 2001, HDB AUTOMATED REASON, P103; Hajek P, 2005, FUZZY SET SYST, V154, P1, DOI 10.1016/j.fss.2005.03.005; Holi M, 2006, P AS SEM WEB C P AS; Hollunder B, 1990, P 9 EUR C ART INT EC, P348; Horrocks I., 2003, J WEB SEMANT, V1, P7; Horrocks I, 1999, J LOGIC COMPUT, V9, P385, DOI 10.1093/logcom/9.3.385; Horrocks I, 2004, ARTIF INTELL, V160, P79, DOI 10.1016/j.artint.2004.06.002; Horrocks I., 2006, P 10 INT C PRINC KNO, P57; Horrocks I, 2005, P 19 INT JOINT C ART; Klir G., 1995, FUZZY SETS FUZZY LOG; Lacy L, 2005, P OWL EXP DIR WORKSH; Li Y, 2006, P INT WORKSH DESCR L; Lukasiewicz T, 2008, J WEB SEMANT, V6, P291, DOI 10.1016/j.websem.2008.04.001; Meghini C, 2001, J ACM, V48, P909, DOI 10.1145/502102.502103; Motik B., 2009, OWL 2 WEB ONTOLOGY L; Motik B, 2009, J ARTIF INTELL RES, V36, P165; RECTOR A, 2006, 2 INT SUMMER SCH REA, V4126, P197; SANCHEZ D, 2006, CAPTURING INTELLIGEN; Sidhu A, 2005, P OWL EXP DIR WORKSH; Simou N, 2010, P 23 INT WORKSH DESC; Simou N, 2008, SIGNAL IMAGE VIDEO P, V2, P321, DOI 10.1007/s11760-008-0084-1; Stoilos G, 2006, FR ART INT, V141, P457; Stoilos G, 2007, J ARTIF INTELL RES, V30, P273; Stoilos G, 2006, P INT WORKSH DESCR L; Stoilos G, 2005, P INT WORKSH OWL EXP; Stoilos G, 2008, P INT C FUZZ SYST FU; Stoilos Giorgos, 2010, INT J APPROX REASON, V21, P656; Straccia U, 2001, J ARTIF INTELL RES, V14, P137; Straccia U, 2009, LECT NOTES ARTIF INT, P79; Straccia U, 2005, P 2 EUR SEM WEB C; Straccia U, 2004, LECT NOTES COMPUT SC, V3229, P385, DOI 10.1007/978-3-540-30227-8_33; ZADEH LA, 1965, INFORM CONTROL, V8, P338, DOI 10.1016/S0019-9958(65)90241-X 55 0 0 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0219-1377 0219-3116 KNOWL INF SYST Knowl. Inf. Syst. JUL 2014 40 1 205 242 10.1007/s10115-013-0641-y 38 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AI7BD WOS:000337033900007 J Perl, H; Mohammed, Y; Brenner, M; Smith, M Perl, H.; Mohammed, Y.; Brenner, M.; Smith, M. Privacy/performance trade-off in private search on bio-medical data FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF GRID COMPUTING AND ESCIENCE English Article Privacy; Homomorphic Cryptography; Bloom filters; Secure search INFORMATION-RETRIEVAL; COMMUNICATION Outsourcing of biomedical data, especially human patient data, for processing is heavily constrained by legal issues. For instance searching for a biological sequence of amino acids or DNA nucleotides in a library or database of sequences of interest to identify similarities is not something which can easily be outsourced due to the data protection and privacy laws. However, DNA sequencing is becoming a main stream technology, thus it would be desirable to be able to offer computational services without endangering the patient privacy. While data in transit can easily be protected by transport layer security, the data must be stored in the clear during processing. Most algorithms and schemes are either optimized for speed with no consideration for data protection and thus cannot be used to offer services. On the other hand the theoretical Private Information Retrieval (PIR) schemes that protect the privacy of patient data are so slow that they are not feasible for the real world use. Since the search spaces represented for instance by the genome or proteome of complex organisms are immense, fast privacy preserving search algorithms are needed. In the previous work we introduced the foundation for such a privacy preserving genome search engine. In this work, we improve and elaborate on this and present an extensive evaluation and comparison showing that this scheme is both secure and practical. Our approach is based on Bloom filters with a configurable security property that performs more than 2000 times faster than PIR equivalents for large datasets, making it suitable for applications in bioinformatics. The results can then be further aggregated using Homomorphic Cryptography to allow an exact-match searching. In performance tests a search of a 50-nucleotides-long sequence against human chromosomes can be securely executed in less than 0.1 s on a 2.8 GHz Intel Core i7. We offer the entire system as an open source service for the community and offer ready-to-use REST as well as SOAP Web services. (C) 2013 Elsevier B.V. All rights reserved. [Perl, H.; Brenner, M.; Smith, M.] Leibniz Univ Hannover, Distributed Comp & Secur Grp, D-30159 Hannover, Germany; [Mohammed, Y.] Leiden Univ, Med Ctr, Biomol Mass Spectrometry Unit, NL-2333 ZC Leiden, Netherlands Perl, H (reprint author), Leibniz Univ Hannover, Distributed Comp & Secur Grp, Schlosswender Str 5, D-30159 Hannover, Germany. perl@dcsec.uni-hannover.de; y.mohammed@lumc.nl; brenner@dcsec.uni-hannover.de; smith@dcsec.uni-hannover.de BLOOM BH, 1970, COMMUN ACM, V13, P422, DOI 10.1145/362686.362692; Boneh D., 2007, P 27 ANN INT CRYPT C; Boneh D., 2005, LECT NOTES COMPUTER, V3378; Brakerski Z., 2012, P 3 INN THEOR COMP S; Brenner M., 2011, 2011 P 5 IEEE INT C; Cachin C, 1999, LECT NOTES COMPUT SC, V1592, P402; Camenisch J., 2009, P 16 ACM C COMP COMM; Canetti R., 2011, P 18 ACM C COMP COMM; Coron J.-S., 2012, EUROCRYPT 12; Costea S., 2012, 4 INT C INT NETW COL; Gentry C., 2009, P 41 ANN ACM S THEOR; Gentry C, 2005, LECT NOTES COMPUT SC, V3580, P803; Gentry C., 2009, THESIS STANFORD U; Goel A., 2010, P ACM SIGMETRICS INT; Gymrek M, 2013, SCIENCE, V339, P321, DOI 10.1126/science.1229566; Kushilevitz E., 1997, P 38 ANN S FDN COMP, P364; Malka L., 2011, P 18 ACM C COMP COMM; Malkhi D., USENIX SEC S; Mitchell C., 2005, TRUSTED COMPUTING; Perl H., 2012, P 8 IEEE INT C ESCIE; Smart NP, 2010, LECT NOTES COMPUT SC, V6056, P420; Yao A.C.-C., 1986, 27 ANN S FDN COMP SC, P162 22 1 1 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-739X 1872-7115 FUTURE GENER COMP SY Futur. Gener. Comp. Syst. JUL 2014 36 441 452 10.1016/j.future.2013.12.006 12 Computer Science, Theory & Methods Computer Science AI3OB WOS:000336770700038 J Wang, XL; Hong, ZJ; Xu, YJ; Zhang, CH; Ling, H Wang, Xiaolun; Hong, Zhijuan; Xu, Yunjie (Calvin); Zhang, Chenghong; Ling, Hong Relevance Judgments of Mobile Commercial Information JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article PRIVACY CALCULUS; CRITERIA; USERS; TOPICALITY; RETRIEVAL; BEHAVIOR; CONTEXT; SEEKING; FRAMEWORK; ATTITUDES In the age of mobile commerce, users receive floods of commercial messages. How do users judge the relevance of such information? Is their relevance judgment affected by contextual factors, such as location and time? How do message content and contextual factors affect users' privacy concerns? With a focus on mobile ads, we propose a research model based on theories of relevance judgment and mobile marketing research. We suggest topicality, reliability, and economic value as key content factors and location and time as key contextual factors. We found mobile relevance judgment is affected mainly by content factors, whereas privacy concerns are affected by both content and contextual factors. Moreover, topicality and economic value have a synergetic effect that makes a message more relevant. Higher topicality and location precision exacerbate privacy concerns, whereas message reliability alleviates privacy concerns caused by location precision. These findings reveal an interesting intricacy in user relevance judgment and privacy concerns and provide nuanced guidance for the design and delivery of mobile commercial information. [Wang, Xiaolun; Hong, Zhijuan; Xu, Yunjie (Calvin); Zhang, Chenghong; Ling, Hong] Fudan Univ, Sch Management, Shanghai 200433, Peoples R China Wang, XL (reprint author), Fudan Univ, Sch Management, 670 Guoshun Rd, Shanghai 200433, Peoples R China. 11210690029@fudan.edu.cn; 11210690023@fudan.edu.cn; yunjiexu@fudan.edu.cn; chzhang@fudan.edu.cn; hling@fudan.edu.cn National Science Foundation of China [71172038, 71229101]; Shanghai Pujiang Program; Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, Shanghai Social Science Fund [2012BGL009]; Program of the National "985" Project [2012SHKXYB005] This research is supported by the National Science Foundation of China (grant #71172038 and 71229101), Shanghai Pujiang Program, the Program for Professor of Special Appointment (Eastern Scholar) at Shanghai Institutions of Higher Learning, Shanghai Social Science Fund (grant # 2012BGL009) and the Program of the National "985" Project (grant # 2012SHKXYB005). Yunjie Xu (yunjiexu@fudan.edu.cn) is the corresponding author of the paper. Abowd GD, 1999, LECT NOTES COMPUT SC, V1707, P304; Ackerman M.S., 1999, EC 99, P1; Agarwal NK, 2011, J AM SOC INF SCI TEC, V62, P1087, DOI 10.1002/asi.21513; ALEXANDER CS, 1978, PUBLIC OPIN QUART, V42, P93, DOI 10.1086/268432; Balasubramanian S, 2002, J ACAD MARKET SCI, V30, P348, DOI 10.1177/009207002236910; Barkhuus L., 2003, P INT CIT, V2003, P709; Barnes SJ, 2002, INT J INFORM MANAGE, V22, P91, DOI 10.1016/S0268-4012(01)00047-0; BARON RM, 1986, J PERS SOC PSYCHOL, V51, P1173, DOI 10.1037/0022-3514.51.6.1173; Barry C.L., 1993, THESIS SYRACUSE U SY; BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J; Barutcu S., 2007, J TARGETING MEASUREM, V16, P26, DOI 10.1057/palgrave.jt.5750061; Barwise P., 2002, J INTERACT MARK, V16, P14, DOI DOI 10.1002/DIR.10000; Bennett CJ, 1997, INFORM SOC, V13, P245; Berendt B, 2005, COMMUN ACM, V48, P101, DOI 10.1145/1053291.1053295; Blum L., 2006, MOBILE USERS WELCOME, V11; Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286; BOYCE B, 1982, INFORM PROCESS MANAG, V18, P105, DOI 10.1016/0306-4573(82)90033-4; Brackett LK, 2001, J ADVERTISING RES, V41, P23; Bruner II G. C., 2007, J INTERACTIVE ADVERT, V7, P3; Chang CH, 2011, LECT NOTES COMPUT SC, V7097, P127; Chellappa R. K., 2005, Information Technology & Management, V6, DOI 10.1007/s10799-005-5879-y; Cohen J.E., 2000, GEORGETOWN LAW J, V89; Consolvo S., 2005, CHI 05, P81; Cool C., 1993, P 14 NAT ONL M, V14, P77; Coppola P, 2004, LECT NOTES COMPUT SC, V2954, P1; Cosijn E, 2000, INFORM PROCESS MANAG, V36, P533, DOI 10.1016/S0306-4573(99)00072-2; Culnan MJ, 2003, J SOC ISSUES, V59, P323, DOI 10.1111/1540-4560.00067; De Sabbata S., 2010, GISCIENCE 2010 6 INT; Ducoffe RH, 1996, J ADVERTISING RES, V36, P21; FORNELL C, 1981, J MARKETING RES, V18, P382, DOI 10.2307/3150980; Fuller P., 2005, WHY SPAM DOESNT HAVE; Greisdorf H, 2003, INFORM PROCESS MANAG, V39, P403, DOI 10.1016/S0306-4573(02)00032-8; Grice H.P., 1975, SYNTAX SEMANTICS, P41; GRICE HP, 1989, STUDIES WAY WORDS; Hagen P., 1999, SMART PERSONALIZATIO; Hair Jr J.F., 1995, MULTIVARIATE DATA AN; Hann IH, 2007, J MANAGE INFORM SYST, V24, P13, DOI 10.2753/MIS0742-1222240202; Hjorland B, 2010, J AM SOC INF SCI TEC, V61, P217, DOI 10.1002/asi.21261; JUNGLAS I., 2006, COMMUNICATIONS INFOR, V17, P26; Kim H. W., 2004, J ASSOC INF SYST, V5, P392; Kim S, 2009, J AM SOC INF SCI TEC, V60, P716, DOI 10.1002/asi.21026; Leppaniemi M., 2005, International Journal of Mobile Communications, V3, DOI 10.1504/IJMC.2005.006580; Lewis S., 2001, ASIAN BUSINESS, V37, P31; Li H, 2010, J COMPUT INFORM SYST, V51, P62; Liu D., 2009, NETWORK INTEGRATED M; Maglaughlin KL, 2002, J AM SOC INF SCI TEC, V53, P327, DOI 10.1002/asi.10049; McKenzie S.B., 1989, J MARKETING, V53, P48; Merisavo M., 2007, J INTERACTIVE ADVERT, V7, P41; Nivala A.M., 2003, P 9 SCANGIS C, P15; Nunnally J.C., 1994, PSYCHOMETRIC THEORY; Okazaki S., 2004, INT J ADVERT, V23, P429; PARK TK, 1993, LIBR QUART, V63, P318; Pura M., 2003, MOBILE COMMERCE TECH; Raper J, 2007, J DOC, V63, P836, DOI 10.1108/00220410710836385; Reichenbacher T., 2007, LOCATION BASED SERVI, P231, DOI 10.1007/978-3-540-36728-4_18; Reichenbacher T., 2004, MOBILE CARTOGRAPHY A; Reichenbacher T., 2009, P 6 INT S LBS TELECA; Saracevic T., 1996, P 2 C CONC LIB INF S, P201; SARACEVIC T, 1975, J AM SOC INFORM SCI, V26, P321, DOI 10.1002/asi.4630260604; Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P2126, DOI 10.1002/asi.20681; SCHAMBER L, 1991, P ASIS ANNU MEET, V28, P126; SCHAMBER L, 1994, ANNU REV INFORM SCI, V29, P3; SCHAMBER L, 1990, INFORM PROCESS MANAG, V26, P755, DOI 10.1016/0306-4573(90)90050-C; Schmidt A, 1999, COMPUT GRAPH-UK, V23, P893, DOI 10.1016/S0097-8493(99)00120-X; Shankar V., 2007, ONLINE MOBILE ADVERT, P7; Smith HJ, 2011, MIS QUART, V35, P989; Snider M., 2012, US TODAY; STONE EF, 1983, J APPL PSYCHOL, V68, P459, DOI 10.1037/0021-9010.68.3.459; Stone Eugene F., 1990, RES PERSONNEL HUMAN, V8, P349; Sun Z., 2011, THESIS JILIN U CHANG; Toms EG, 2005, LECT NOTES COMPUT SC, V3507, P59; Wang PL, 1998, J AM SOC INFORM SCI, V49, P115, DOI 10.1002/(SICI)1097-4571(1998)49:2<115::AID-ASI3>3.0.CO;2-1; Wang T., 2009, 9 INT C EL BUS MAC C; Wason K. D., 2002, AUSTRALASIAN MARKETI, V10, P41, DOI DOI 10.1016/S1441-3582(02)70157-2; WILSON P, 1973, INFORM STORAGE RET, V9, P457, DOI 10.1016/0020-0271(73)90096-X; Xu H., 2012, 33 INT C INF SYST; Xu H, 2011, DECIS SUPPORT SYST, V51, P42, DOI 10.1016/j.dss.2010.11.017; Xu H, 2009, J MANAGE INFORM SYST, V26, P135, DOI 10.2753/MIS0742-1222260305; Xu YJ, 2006, J AM SOC INF SCI TEC, V57, P1666, DOI 10.1002/asi.20339; Xu YJ, 2010, IEEE T PROF COMMUN, V53, P370, DOI 10.1109/TPC.2010.2044620; Xu YJ, 2010, J MANAGE INFORM SYST, V27, P211, DOI 10.2753/MIS0742-1222270308; Xu YJ, 2008, J AM SOC INF SCI TEC, V59, P201, DOI 10.1002/asi.20709; Xu YJ, 2007, J AM SOC INF SCI TEC, V58, P179, DOI 10.1002/asi.20461; Xu YJ, 2006, J AM SOC INF SCI TEC, V57, P961, DOI 10.1002/asi.20361; Yang B., 2012, J BUSINESS RES; Soroa-Koury Sandra, 2010, Telematics and Informatics, V27, DOI 10.1016/j.tele.2009.06.001; Zeng X., 2008, CONSUMER EC, V24, P81 87 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUL 2014 65 7 1335 1348 10.1002/asi.23060 14 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AI7AH WOS:000337030700003 J Tuomaala, O; Jarvelin, K; Vakkari, P Tuomaala, Otto; Jarvelin, Kalervo; Vakkari, Pertti Evolution of Library and Information Science, 1965-2005: Content Analysis of Journal Articles JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article AUTHOR COCITATION ANALYSIS; COGNITIVE VIEWPOINT; LIS This article first analyzes library and information science (LIS) research articles published in core LIS journals in 2005. It also examines the development of LIS from 1965 to 2005 in light of comparable data sets for 1965, 1985, and 2005. In both cases, the authors report (a) how the research articles are distributed by topic and (b) what approaches, research strategies, and methods were applied in the articles. In 2005, the largest research areas in LIS by this measure were information storage and retrieval, scientific communication, library and information-service activities, and information seeking. The same research areas constituted the quantitative core of LIS in the previous years since 1965. Information retrieval has been the most popular area of research over the years. The proportion of research on library and information-service activities decreased after 1985, but the popularity of information seeking and of scientific communication grew during the period studied. The viewpoint of research has shifted from library and information organizations to end users and development of systems for the latter. The proportion of empirical research strategies was high and rose over time, with the survey method being the single most important method. However, attention to evaluation and experiments increased considerably after 1985. Conceptual research strategies and system analysis, description, and design were quite popular, but declining. The most significant changes from 1965 to 2005 are the decreasing interest in library and information-service activities and the growth of research into information seeking and scientific communication. [Tuomaala, Otto; Jarvelin, Kalervo; Vakkari, Pertti] Univ Tampere, Sch Informat Sci, FI-33014 Tampere, Finland Tuomaala, O (reprint author), Univ Tampere, Sch Informat Sci, Kanslerinrinne 1, FI-33014 Tampere, Finland. otto.tuomaala@gmail.com; kalervo.jarvelin@uta.fi; pertti.vakkari@uta.fi Astrom F., 2006, SOCIAL INTELLECTUAL; Astrom F., 2002, EMERGING FRAMEWORKS, P185; Astrom F, 2010, LIBR QUART, V80, P143; Astrom F., 2007, J AM SOC INFORM SCI, V58, P47; Atkins Stephen E., 1988, LIBR TRENDS, V36, P633; BELKIN NJ, 1990, J INFORM SCI, V16, P11, DOI 10.1177/016555159001600104; BUTTLAR L, 1991, COLL RES LIBR, V52, P38; Cronin B, 2008, J AM SOC INF SCI TEC, V59, P551, DOI 10.1002/asi.20764; Davarpanah MR, 2008, SCIENTOMETRICS, V77, P21, DOI 10.1007/s11192-007-1803-z; DERVIN B, 1986, ANNU REV INFORM SCI, V21, P3; FEEHAN PE, 1987, LIBR INFORM SCI RES, V9, P173; FROHMANN B, 1992, J DOC, V48, P365, DOI 10.1108/eb026904; Gonzalez-Alcaide G, 2008, J AM SOC INF SCI TEC, V59, P150, DOI 10.1002/asi.20720; Hayes A. F., 2007, COMMUNICATION METHOD, V1, P77, DOI DOI 10.1080/19312450709336664; Hider P, 2008, LIBR INFORM SCI RES, V30, P108, DOI 10.1016/j.lisr.2007.11.007; Hjorland B, 2002, J AM SOC INF SCI TEC, V53, P257, DOI 10.1002/asi.10042; Hjorland B, 2010, J AM SOC INF SCI TEC, V61, P217, DOI 10.1002/asi.21261; Huang MH, 2012, SCIENTOMETRICS, V91, P789, DOI 10.1007/s11192-012-0619-7; INGWERSEN P, 1992, LIBRI, V42, P99, DOI 10.1515/libr.1992.42.2.99; Jarvelin K., 1993, INFORM PROCESSING MA, V29, P129; JARVELIN K, 1990, LIBR INFORM SCI RES, V12, P395; Julien H., 2011, LIB INFORM SCI RES, V33, P19; Kim SJ, 2006, LIBR INFORM SCI RES, V28, P548, DOI 10.1016/j.lisr.2006.03.018; Koufogiannakis D, 2004, J INFORM SCI, V30, P227, DOI 10.1177/0165551504044668; Krippendorff K, 2004, CONTENT ANAL INTRO I; KUMPULAINEN S, 1991, LIBRI, V41, P59, DOI 10.1515/libr.1991.41.1.59; Lariviere V, 2012, J AM SOC INF SCI TEC, V63, P997, DOI 10.1002/asi.22645; Milojevic S, 2011, J AM SOC INF SCI TEC, V62, P1933, DOI 10.1002/asi.21602; Nolin J., 2007, INFORM RES, V12; Nolin J, 2010, J DOC, V66, P7, DOI 10.1108/00220411011016344; NOUR MM, 1985, LIBR INFORM SCI RES, V7, P261; Peritz B. C., 1980, LIBR RES, V2, P251; Pettigrew KE, 2001, J AM SOC INF SCI TEC, V52, P62, DOI 10.1002/1532-2890(2000)52:1<62::AID-ASI1061>3.3.CO;2-A; Powell R., 1985, BASIC RES METHODS LI; Rochester M. K., 2003, IFLA PROFESSIONAL RE, V82, P1; Rokkan Stein, 1969, QUANTITATIVE ECOLOGI; Saracevic T, 1999, J AM SOC INFORM SCI, V50, P1051, DOI 10.1002/(SICI)1097-4571(1999)50:12<1051::AID-ASI2>3.0.CO;2-Z; Sugimoto CR, 2011, J AM SOC INF SCI TEC, V62, P185, DOI 10.1002/asi.21435; Sugimoto CR, 2011, SCIENTOMETRICS, V86, P449, DOI 10.1007/s11192-010-0275-8; Sugimoto CR, 2010, J INF SCI, V36, P481, DOI 10.1177/0165551510369992; Vakkari P., 1994, ADV LIBRARIANSHIP, V18, P1, DOI 10.1108/S0065-2830(1994)0000018003; White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-W; Wilson T. D., 2000, INFORM RES, V5; Zhao DZ, 2008, J AM SOC INF SCI TEC, V59, P916, DOI 10.1002/asi.20799 44 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUL 2014 65 7 1446 1462 10.1002/asi.23034 17 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AI7AH WOS:000337030700010 J van der Sluis, F; van den Broek, EL; Glassey, RJ; van Dijk, EMAG; de Jong, FMG van der Sluis, Frans; van den Broek, Egon L.; Glassey, Richard J.; van Dijk, Elisabeth M. A. G.; de Jong, Franciska M. G. When Complexity Becomes Interesting JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article INFORMATION-RETRIEVAL; SITUATIONAL INTEREST; RECOMMENDER SYSTEMS; TOPIC INTEREST; COH-METRIX; RELEVANCE; TEXT; EMOTION; READABILITY; MODEL How to provide users a positive experience during interaction with information (i.e., the "Information eXperience" (IX)) is still an open question. As a starting point, this work investigates how the emotion of interest can be influenced by modifying the complexity of the information presented to users. The appraisal theory of interest suggests a "sweet spot" where interest will be at its peak: information that is novel and complex yet still comprehensible. This "sweet spot" is approximated using two studies. Study One develops a computational model of textual complexity founded on psycholinguistic theory on processing difficulty. The model was trained and tested on 12,420 articles, achieving a classification performance of 90.87% on two classes of complexity. Study Two puts the model to its ultimate test: Its application to change the user's IX. Using 18 news articles the influence of complexity on interest and its appraisals is unveiled. A structural equation model shows a positive influence of complexity on interest, yet a negative influence of comprehensibility, confirming a seemingly paradoxical relationship between complexity and interest. By showing when complexity becomes interesting, this paper shows how information systems can use the model of textual complexity to construct an interesting IX. [van der Sluis, Frans; van den Broek, Egon L.; van Dijk, Elisabeth M. A. G.; de Jong, Franciska M. G.] Univ Twente, Human Media Interact Grp, Fac Elect Engn Math & Comp Sci, NL-7500 AE Enschede, Netherlands; [Glassey, Richard J.] Robert Gordon Univ, Sch Comp, Aberdeen AB10 7QJ, Scotland van der Sluis, F (reprint author), Univ Twente, Human Media Interact Grp, Fac Elect Engn Math & Comp Sci, POB 217, NL-7500 AE Enschede, Netherlands. f.vandersluis@acm.org; vandenbroek@acm.org; r.j.glassey@rgu.ac.uk; e.m.a.g.vandijk@utwente.nl; f.m.g.dejong@utwente.nl European Union The authors thank the anonymous reviewers for their valuable comments and suggestions to improve the quality of this paper. Furthermore, the authors thank Anton Nijholt for his valuable input. This work was part of the PuppyIR project, which is supported by a grant of the 7th Framework ICT Programme (FP7-ICT-2007-3) of the European Union. Arapakis I., 2008, P 31 ANN INT ACM SIG, P395, DOI 10.1145/1390334.1390403; Balota DA, 2004, J EXP PSYCHOL GEN, V133, P283, DOI 10.1037/0096-3445.133.2.283; Banse R, 1996, J PERS SOC PSYCHOL, V70, P614, DOI 10.1037/0022-3514.70.3.614; Barry CL, 1998, INFORM PROCESS MANAG, V34, P219, DOI 10.1016/S0306-4573(97)00078-2; BARRY CL, 1994, J AM SOC INFORM SCI, V45, P149, DOI 10.1002/(SICI)1097-4571(199404)45:3<149::AID-ASI5>3.0.CO;2-J; Belkin Nicholas J, 2008, SIGIR Forum, V42, DOI 10.1145/1394251.1394261; BELKIN NJ, 1992, COMMUN ACM, V35, P29, DOI 10.1145/138859.138861; Benjamin RG, 2012, EDUC PSYCHOL REV, V24, P63, DOI 10.1007/s10648-011-9181-8; Berlyne D. E., 1960, CONFLICT AROUSAL CUR; BERLYNE DE, 1975, CAN PSYCHOL REV, V16, P69, DOI 10.1037/h0081798; Borlund P, 2003, INFORM RES, V8; Borlund P., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291019; Bowler L, 2010, J AM SOC INF SCI TEC, V61, P1332, DOI 10.1002/asi.21334; Breiman L, 2001, MACH LEARN, V45, P5, DOI 10.1023/A:1010933404324; Carbonell J., 1998, P 21 ANN INT ACM SIG, V99, P335, DOI DOI 10.1145/290941.291025; Cer D., 2010, P 7 INT C LANG RES E; Chall J.S., 1995, T1 READABILITY REVIS; Collins-Thompson K, 2005, J AM SOC INF SCI TEC, V56, P1448, DOI 10.1002/asi.20243; Collins-Thompson K., 2011, P 20 ACM INT C INF K, P403, DOI 10.1145/2063576.2063639; Connelly DA, 2011, LEARN INDIVID DIFFER, V21, P624, DOI 10.1016/j.lindif.2011.04.007; Cosijn E, 2000, INFORM PROCESS MANAG, V36, P533, DOI 10.1016/S0306-4573(99)00072-2; Cover T.M., 2006, ELEMENTS INFORM THEO, P13; CRONBACH LJ, 1955, PSYCHOL BULL, V52, P281, DOI 10.1037/h0040957; Crossley SA, 2008, TESOL QUART, V42, P475; CSIKSZENTMIHALY.M, 1991, PSYCHOL OPTIMAL EXPE; Demartini G, 2006, LECT NOTES COMPUT SC, V3936, P488; Descartes R, 1989, PASSIONS SOUL; DuBay W., 2007, TECHNICAL REPORT; Ellsworth PC, 2003, SER AFFECTIVE SCI, P572; Feng L., 2010, P 23 INT C COMP LING, P276; Flesch R, 1948, J APPL PSYCHOL, V32, P221, DOI 10.1037/h0057532; Fry E, 2002, READ TEACH, V56, P286; Gernsbacher M. A., 2006, HDB PSYCHOLINGUISTIC, P285, DOI DOI 10.1016/B978-012369374-7/50010-9; Gibson E, 2000, IMAGE, LANGUAGE, BRAIN, P95; Gibson E, 1998, COGNITION, V68, P1, DOI 10.1016/S0010-0277(98)00034-1; Glassey R., 2011, P AM SOC INFORM SCI, V48, P1; Gluck M, 1996, INFORM PROCESS MANAG, V32, P89, DOI 10.1016/0306-4573(95)00031-B; Graesser AC, 2004, BEHAV RES METH INS C, V36, P193, DOI 10.3758/BF03195564; Hanani U, 2001, USER MODEL USER-ADAP, V11, P203, DOI 10.1023/A:1011196000674; Hassenzahl M., 2013, ENCY HUMAN COMPUTER; Herlocker JL, 2004, ACM T INFORM SYST, V22, P5, DOI 10.1145/963770.963772; HESS EH, 1960, SCIENCE, V132, P349, DOI 10.1126/science.132.3423.349; Hidi S, 2006, EDUC PSYCHOL, V41, P111, DOI 10.1207/s15326985ep4102_4; HIDI S, 1990, REV EDUC RES, V60, P549, DOI 10.3102/00346543060004549; Hill W., 1995, P ACM CHI 95 C HUM F, P194, DOI 10.1145/223904.223929; Huffman S.B., 2007, P ACM C RES DEV INF, P567, DOI 10.1145/1277741.1277839; Iacobucci D, 2010, J CONSUM PSYCHOL, V20, P90, DOI 10.1016/j.jcps.2009.09.003; Ihaka R., 1996, J COMPUTATIONAL GRAP, V5, P299, DOI DOI 10.2307/1390807; INHOFF AW, 1986, PERCEPT PSYCHOPHYS, V40, P431, DOI 10.3758/BF03208203; Jaeger TF, 2011, WIRES COGN SCI, V2, P323, DOI 10.1002/wcs.126; Jonassen DH, 2000, ETR&D-EDUC TECH RES, V48, P63, DOI 10.1007/BF02300500; Kincaid Jr J. P., 1975, TECHNICAL REPORT; KINTSCH W, 1978, PSYCHOL REV, V85, P363, DOI 10.1037//0033-295X.85.5.363; Klein D., 2003, P 41 ANN M ASS COMP, V1, P423; Knijnenburg BP, 2012, USER MODEL USER-ADAP, V22, P441, DOI 10.1007/s11257-011-9118-4; Konstan JA, 2012, USER MODEL USER-ADAP, V22, P101, DOI 10.1007/s11257-011-9112-x; Kuhlthau C. C., 2004, SEEKING MEANING PROC; Lapata M, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1085; LAZARUS RS, 1991, AM PSYCHOL, V46, P819, DOI 10.1037//0003-066X.46.8.819; Ledoux Kerry, 2006, Behav Cogn Neurosci Rev, V5, P107, DOI 10.1177/1534582306289573; Lewis RL, 2006, TRENDS COGN SCI, V10, P447, DOI 10.1016/j.tics.2006.08.007; Lively B.A., 1923, ED ADM SUPERVISION, V9, P389; Long D.L., 2006, HDB PSYCHOLINGUISTIC, P801, DOI 10.1016/B978-012369374-7/50021-3; Long DL, 2006, J EXP PSYCHOL LEARN, V32, P816, DOI 10.1037/0278-7393.32.4.816; McNamara DS, 2010, DISCOURSE PROCESS, V47, P292, DOI 10.1080/01638530902959943; Meyer D, 2003, NEUROCOMPUTING, V55, P169, DOI 10.1016/S0925-2312(03)00431-4; Michel JB, 2011, SCIENCE, V331, P176, DOI 10.1126/science.1199644; Morris J., 1991, Computational Linguistics, V17; O'Brien HL, 2008, J AM SOC INF SCI TEC, V59, P938, DOI 10.1002/asi.20801; Peng F., 2003, ADV INFORM RETRIEVAL, V2633, P547; Perfetti C. A., 1988, READING RES ADV THEO, V6, P109; Porter M. F., 2001, SNOWBALL LANGUAGE ST; Powers D. M. W., 2011, J MACHINE LEARNING T, V2, P37, DOI DOI 10.9735/2229-3981; Rayner K, 2010, WIRES COGN SCI, V1, P787, DOI 10.1002/wcs.68; REEVE J, 1989, MOTIV EMOTION, V13, P83, DOI 10.1007/BF00992956; Reichle ED, 1998, PSYCHOL REV, V105, P125, DOI 10.1037/0033-295X.105.1.125; Ricci F., 2009, RECOMMENDER SYSTEMS; Rice ME, 2005, LAW HUMAN BEHAV, V29, P615, DOI 10.1007/s10979-005-6832-7; Rosseel Y, 2012, J STAT SOFTW, V48, P1; Ruthven I, 2007, J DOC, V63, P482, DOI 10.1108/00220410710758986; Ryan K., 2012, FATHOM MEASURE READA; Sadoski M, 2001, EDUC PSYCHOL REV, V13, P263, DOI 10.1023/A:1016675822931; Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P1915, DOI 10.1002/asi.20682; Schiefele U, 1996, CONTEMP EDUC PSYCHOL, V21, P3, DOI 10.1006/ceps.1996.0002; Schiefele U, 1996, LEARN INDIVID DIFFER, V8, P141, DOI 10.1016/S1041-6080(96)90030-8; Schraw G, 2001, EDUC PSYCHOL REV, V13, P23, DOI 10.1023/A:1009004801455; Schraw G, 1997, CONTEMP EDUC PSYCHOL, V22, P436, DOI 10.1006/ceps.1997.0944; SCHRAW G, 1995, APPL COGNITIVE PSYCH, V9, P523, DOI 10.1002/acp.2350090605; Schumacker R. E, 2010, BEGINNERS GUIDE STRU; Shannon C.E., 1948, BELL SYST TECH J, V27, P625; SHANNON CE, 1948, AT&T TECH J, V27, P623; Silvia PJ, 2005, EMOTION, V5, P89, DOI 10.1037/1528-3542.5.1.89; Silvia P., 2001, REV GEN PSYCHOL, V5, P270, DOI 10.1037//1089-2680.5.3.270; Silvia P. J., 2006, EXPLORING PSYCHOL IN; Silvia PJ, 2008, COGNITION EMOTION, V22, P94, DOI 10.1080/02699930701298481; Silvia PJ, 2008, CURR DIR PSYCHOL SCI, V17, P57, DOI 10.1111/j.1467-8721.2008.00548.x; Soboroff I., 2005, P C HUM LANG TECHN E, P105, DOI 10.3115/1220575.1220589; Spink A, 2001, J AM SOC INF SCI TEC, V52, P161, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1564>3.3.CO;2-C; SU LT, 1994, J AM SOC INFORM SCI, V45, P207, DOI 10.1002/(SICI)1097-4571(199404)45:3<207::AID-ASI10>3.0.CO;2-1; Toutanova K., 2003, P 2003 C N AM CHAPT, V1, P173, DOI DOI 10.3115/1073445.107347; Van der Sluis F., 2012, ACM P 4 S INF INT CO, P314; Voorhees E., 2002, LECT NOTES COMPUTER, V2406, P143; vor der Bruck Tim, 2008, Informatica, V32; Xu YJ, 2006, J AM SOC INF SCI TEC, V57, P961, DOI 10.1002/asi.20361; Zipf G., 1935, PSYCHOBIOLOGY LANGUA 105 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUL 2014 65 7 1478 1500 10.1002/asi.23095 23 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AI7AH WOS:000337030700012 J Wang, L; Li, CC; Yao, ZG; Zhao, ZL; Han, ZG; Wei, Q Wang Lei; Li Chengcai; Yao Zhigang; Zhao Zengliang; Han Zhigang; Wei Qiang Application of aircraft observations over Beijing in cloud microphysical property retrievals from CloudSat ADVANCES IN ATMOSPHERIC SCIENCES English Article CloudSat; liquid water content; a priori data; aircraft observations WATER-CONTENT; STRATIFORM; REFLECTIVITY; RADIATION; CLIMATE; RADAR; MODEL Cloud microphysical property retrievals from the active microwave instrument on a satellite require the cloud droplet size distribution obtained from aircraft observations as a priori data in the iteration procedure. The cloud lognormal size distributions derived from 12 flights over Beijing, China, in 2008-09 were characterized to evaluate and improve regional CloudSat cloud water content retrievals. We present the distribution parameters of stratiform cloud droplet (diameter < 500 mu m and < 1500 mu m) and discuss the effect of large particles on distribution parameter fitting. Based on three retrieval schemes with different lognormal size distribution parameters, the vertical distribution of cloud liquid and ice water content were derived and then compared with the aircraft observations. The results showed that the liquid water content (LWC) retrievals from large particle size distributions were more consistent with the vertical distribution of cloud water content profiles derived from in situ data on 25 September 2006. We then applied two schemes with different a priori data derived from flight data to CloudSat overpasses in northern China during April-October in 2008 and 2009. The CloudSat cloud water path (CWP) retrievals were compared with Moderate Resolution Imaging Spectroradiometer (MODIS) liquid water path (LWP) data. The results indicated that considering a priori data including large particle size information can significantly improve the consistency between the CloudSat CWP and MODIS CWP. These results strongly suggest that it is necessary to consider particles with diameters greater than 50 mu m in CloudSat LWC retrievals. [Wang Lei; Li Chengcai] Peking Univ, Sch Phys, Dept Atmospher & Ocean Sci, Beijing 100871, Peoples R China; [Yao Zhigang; Zhao Zengliang; Han Zhigang; Wei Qiang] Beijing Inst Appl Meteorol, Beijing 100029, Peoples R China Yao, ZG (reprint author), Beijing Inst Appl Meteorol, Beijing 100029, Peoples R China. yzg_biam@163.com LI, Chengcai/B-4654-2012 LI, Chengcai/0000-0001-8860-1916 China public science and technology research funds projects of meteorology [GYHY201406015]; Chinese Academy of Sciences [XDA05040000]; National High-Tech R&D Program of China [SQ2010AA1221583001]; National Science Foundation program [41375024, 40775002, 41175020, 41375008]; [2010CB950802] This work is supported by China public science and technology research funds projects of meteorology (Grant No. GYHY201406015), the Chinese Academy of Sciences (Grant No. XDA05040000), the National High-Tech R&D Program of China (Grant No. SQ2010AA1221583001), National Science Foundation program (Grant Nos. 41375024, 40775002, 41175020, and 41375008), and the basic research program (Grant No. 2010CB950802). We would like to acknowledge the NASA CloudSat project for making CloudSat data available to the scientific community. We are also grateful to NASA/GSFC for the use of their MODIS Level 2 cloud products. Austin R. T., 2007, LEVEL 2B RADAR ONLY, P24; Austin RT, 2001, J GEOPHYS RES-ATMOS, V106, P28233, DOI 10.1029/2000JD000293; Austin RT, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010049; Barker HW, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD009971; Brunke MA, 2010, ATMOS CHEM PHYS, V10, P6527, DOI 10.5194/acp-10-6527-2010; Cober SG, 2001, J APPL METEOROL, V40, P1984, DOI 10.1175/1520-0450(2001)040<1984:COAIET>2.0.CO;2; Comstock KK, 2004, Q J ROY METEOR SOC, V130, P2891, DOI 10.1256/qj.03.187; de La Torre Juarez M., 2009, ATMOS CHEM PHYS DISC, V9, P3367, DOI DOI 10.5194/ACPD-9-3367-2009; Dong XQ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008438; Feng W. W., 2009, J PLA U SCI TECHNOLO, V10, P95; [郭学良 Guo Xueliang], 2013, [大气科学, Chinese Journal of Atmospheric Sciences], V37, P351; Kahn BH, 2008, ATMOS CHEM PHYS, V8, P1231; Knollenberg R. G., 1976, INT C CLOUD PHYS BOU, P554; Li JLF, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL035427; Matrosov SY, 2004, J APPL METEOROL, V43, P405, DOI 10.1175/1520-0450(2004)043<0405:EORREO>2.0.CO;2; Miles NL, 2000, J ATMOS SCI, V57, P295, DOI 10.1175/1520-0469(2000)057<0295:CDSDIL>2.0.CO;2; Morrison H, 2008, J CLIMATE, V21, P3642, DOI 10.1175/2008JCLI2105.1; Noh YJ, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015420; Partain P., 2007, CLOUDSAT MODIS AUX A, P23; [彭杰 Peng Jie], 2013, [大气科学, Chinese Journal of Atmospheric Sciences], V37, P91; Platnick S, 2003, IEEE T GEOSCI REMOTE, V41, P459, DOI 10.1109/TGRS.2002.808301; RAMANATHAN V, 1989, SCIENCE, V243, P57, DOI 10.1126/science.243.4887.57; RODGERS CD, 1976, REV GEOPHYS, V14, P609, DOI 10.1029/RG014i004p00609; SEETHALA C, 2010, J GEOPHYS RES-ATMOS, V115, P18702, DOI DOI 10.1029/2009JD; Stephens GL, 2002, B AM METEOROL SOC, V83, P1771, DOI 10.1175/BAMS-83-12-1771; Vidaurre G, 2009, Q J ROY METEOR SOC, V135, P1292, DOI 10.1002/qj.440; [王磊 Wang Lei], 2014, [大气科学, Chinese Journal of Atmospheric Sciences], V38, P201; [王帅辉 Wang Shuaihui], 2011, [高原气象, Plateau Meteorology], V30, P38; [王扬锋 WANG Yangfeng], 2005, [南京气象学院学报, Journal of Nanjing Institute of Meteorology], V28, P787; Yan C. F., 1990, J APPL METEOR, V1, P352; [杨大生 Yang Dasheng], 2012, [气候与环境研究, Climatic and Environmental Research], V17, P433; [赵增亮 Zhao Zengliang], 2010, [气象, Meteorological Monthly], V36, P71 32 0 0 SCIENCE PRESS BEIJING 16 DONGHUANGCHENGGEN NORTH ST, BEIJING 100717, PEOPLES R CHINA 0256-1530 1861-9533 ADV ATMOS SCI Adv. Atmos. Sci. JUL 2014 31 4 926 937 10.1007/s00376-013-3156-2 12 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AH6SY WOS:000336262200017 J Calheiros, AJP; Machado, LAT Calheiros, Alan J. P.; Machado, Luiz A. T. Cloud and rain liquid water statistics in the CHUVA campaign ATMOSPHERIC RESEARCH English Article Cloud liquid water; Cloud types; Droplet size distribution; Radar; Radiometer DROP SIZE DISTRIBUTION; MICROWAVE RADIOMETER; WARM-RAIN; RADAR; TEMPERATURE; VAPOR; PATH; PRECIPITATION; RETRIEVAL; ACCURACY The purpose of this study is to present statistics related to the integration of cloud and rain liquid water and the profiles for different cloud types and regimes. From 2010 to 2012, the CHUVA project collected information regarding cloud and rain characteristics in different precipitation regimes in Brazil. CHINA had four field campaigns between 2010 and 2011, located in the North, Northeast and Southeast regions of Brazil, covering the semi-arid, Amazon, coastal and mountain regions. The synergy of several instruments allowed us to classify rain events and describe the cloud processes regionally. Microwave radiometers, LiDAR, radar, and disdrometers were employed in this study. The rain type classification was made using vertical profiles of reflectivity (VPR) and polarimetric variables from dual polarization radar (XPOL). The integrated liquid water (ILWC) for non-precipitating clouds was retrieved with a microwave ground-based radiometer using a neural network. For rainy conditions, the profiles from the rain liquid water content (LWCR) and their integrated (ILWR) properties were estimated by Micro Rain Radar (MRR) and XPOL VPRs. For non-precipitating clouds, the ILWC values were larger for the sites in tropical regions, in particular near the coast, than for Southeast Brazil. For rainy cases, distinct LWCR profiles were observed for different rain classifications and regions. The differences are small for low rain rates and a distinction between different rainfall regimes is more evident for high rain rates. Vale and Belem clouds present the deepest layers and largest convective rain rates. The clouds in the Southeast region of Brazil (Vale do Paraiba) and North region (Belem) showed the largest reflectivity in the mixed and glaciated layers, respectively. In contrast, the Northeast coastal site (e.g. Fortaleza) showed larger values in the warm part of the clouds. Several analyses are presented, describing the cloud processes and the differences among the cloud types, rain rates and regions. (C) 2014 Elsevier B.V. All rights reserved. [Calheiros, Alan J. P.; Machado, Luiz A. T.] Ctr Previsao Tempo & Etud Climat, Inst Nacl Pesquisas Espaciais, Cachoeira Pautista, SP, Brazil Calheiros, AJP (reprint author), Ctr Previsao Tempo & Etud Climat, Inst Nacl Pesquisas Espaciais, Cachoeira Pautista, SP, Brazil. alan.calheiros@cptec.inpe.br; luiz.machado@cptec.inpe.br FAPESP [2009/15235-8]; CNPQ [140818/2011-1] This work was supported by FAPESP grant No. 2009/15235-8 and CNPQ No. 140818/2011-1. We also thank the CHUVA campaign team for their efforts in helping with instrument function, in particular Riad Bourayou, Marc Schneebeli and Ali Tokay for helping us to define the LiDAR, Radiosonde, and Disdrometer retrievals, respectively. We are particularly grateful for the valuable and constructive suggestions and review given by the anonymous reviewers. ATLAS D, 1954, J METEOROL, V11, P309, DOI 10.1175/1520-0469(1954)011<0309:TEOCPB>2.0.CO;2; Awaka J, 2007, ADV GLOB CHANGE RES, V28, P213, DOI 10.1007/978-1-4020-5835-6_17; Battaglia A., 2011, J GEOPHYS RES, V116, P1; Battaglia A, 2003, J ATMOS OCEAN TECH, V20, P856, DOI 10.1175/1520-0426(2003)020<0856:CMBTBR>2.0.CO;2; Battan L. J., 1973, RADAR OBSERVATION AT; BEARD KV, 1993, J APPL METEOROL, V32, P608, DOI 10.1175/1520-0450(1993)032<0608:WRIAOO>2.0.CO;2; Bourayou R., 2011, WORKSH LIDAR MEAS LA; Cadeddu MP, 2013, ATMOS MEAS TECH, V6, P2359, DOI 10.5194/amt-6-2359-2013; Campos E., 2014, ATMOS RES IN PRESS; Caracciolo C, 2006, ATMOS RES, V82, P137, DOI 10.1016/j.atmosres.2005.09.007; Cimini D, 2011, IEEE T GEOSCI REMOTE, V49, P4959, DOI 10.1109/TGRS.2011.2154337; Cimini D, 2003, IEEE T GEOSCI REMOTE, V41, P2605, DOI [10.1109/TGRS.2003.815673, 10.1009/TGRS.2003.815673]; Cotton W.R., 2010, STORM CLOUD DYNAMICS, P99; Crewell S, 2003, RADIO SCI, V38, DOI 10.1029/2002RS002634; Czekala H, 2001, GEOPHYS RES LETT, V28, P267, DOI 10.1029/2000GL012247; Czekala H, 1998, J QUANT SPECTROSC RA, V60, P365, DOI 10.1016/S0022-4073(98)00012-0; DONALDSON RJ, 1955, J METEOROL, V12, P238, DOI 10.1175/1520-0469(1955)012<0238:TMOCLW>2.0.CO;2; Doviak R., 1993, DOPPLER RADAR WEATHE; Ebell K, 2010, ATMOS RES, V98, P57, DOI 10.1016/j.atmosres.2010.06.002; Eccles P. J., 1971, J APPL METEOROL, V10, P1252, DOI 10.1175/1520-0450(1971)010<1252:XBAALW>2.0.CO;2; FABRY F, 1995, J ATMOS SCI, V52, P838, DOI 10.1175/1520-0469(1995)052<0838:LTROOT>2.0.CO;2; Gonzalez R.C., 2009, DIGITAL IMAGE PROCES, P2; GREENE DR, 1972, MON WEATHER REV, V100, P548, DOI 10.1175/1520-0493(1972)100<0548:VILWNA>2.3.CO;2; Hagen M, 2003, Q J ROY METEOR SOC, V129, P477, DOI 10.1256/qj.02.23; Han Y, 2000, IEEE T GEOSCI REMOTE, V38, P1260; HARDY WN, 1973, IEEE T MICROW THEORY, VMT21, P149, DOI 10.1109/TMTT.1973.1127954; Hewison T.J., 2006, THESIS U READING; Hogan RJ, 2005, J ATMOS OCEAN TECH, V22, P1207, DOI 10.1175/JTECH1768.1; Houze Jr R. A, 1993, CLOUD DYNAMICS; HU ZL, 1995, J ATMOS SCI, V52, P1761, DOI 10.1175/1520-0469(1995)052<1761:EORSDB>2.0.CO;2; Ingold T, 1998, RADIO SCI, V33, P905, DOI 10.1029/98RS01000; Islam T, 2012, ATMOS RES, V108, P57, DOI 10.1016/j.atmosres.2012.01.013; Jaffrain J, 2011, J HYDROMETEOROL, V12, P352, DOI 10.1175/2010JHM1244.1; Jonas PR, 1996, ATMOS RES, V40, P283, DOI 10.1016/0169-8095(95)00035-6; JOSS J, 1967, PURE APPL GEOPHYS, V68, P240, DOI 10.1007/BF00874898; Karmakar PK, 2011, ADV SPACE RES, V48, P1506, DOI 10.1016/j.asr.2011.06.032; KOUSKY VE, 1981, TELLUS, V33, P538; Lawson R. P., 1998, Atmospheric Research, V47-48, DOI 10.1016/S0169-8095(98)00063-5; Liljegren JC, 2001, J GEOPHYS RES-ATMOS, V106, P14485, DOI 10.1029/2000JD900817; Liu CT, 2009, J CLIMATE, V22, P767, DOI 10.1175/2008JCLI2641.1; Loffler-Mang M, 2000, J ATMOS OCEAN TECH, V17, P130, DOI 10.1175/1520-0426(2000)017<0130:AODFMS>2.0.CO;2; Lohnert U, 2001, J ATMOS OCEAN TECH, V18, P1354, DOI 10.1175/1520-0426(2001)018<1354:PCLWBC>2.0.CO;2; Lohnert U, 2003, RADIO SCI, V38, DOI 10.1029/2002RS002654; Machado L.A.T., 2014, B AM METEOR IN PRESS; Martins C.R., 2010, ATMOS RES, V96, P388; Matzler C, 2009, IEEE T GEOSCI REMOTE, V47, P1585, DOI 10.1109/TGRS.2008.2006984; Meywerk J, 2005, ATMOS RES, V75, P167, DOI 10.1016/j.atmosres.2004.12.009; Michaelides S, 2009, ATMOS RES, V94, P512, DOI 10.1016/j.atmosres.2009.08.017; PETER R, 1992, J GEOPHYS RES-ATMOS, V97, P18173; Peters G, 2010, J ATMOS OCEAN TECH, V27, P829, DOI 10.1175/2009JTECHA1342.1; Peters G, 2005, J APPL METEOROL, V44, P1930, DOI 10.1175/JAM2316.1; Pruppacher H. R., 1997, MICROPHYSICS CLOUDS; Rose T, 2005, ATMOS RES, V75, P183, DOI 10.1016/j.atmosres.2004.12.005; Rossow WB, 1999, B AM METEOROL SOC, V80, P2261, DOI 10.1175/1520-0477(1999)080<2261:AIUCFI>2.0.CO;2; Saavedra P., 2012, J GEOPHYS RES, V117, P1984; Satyamurty P., 1998, METEOROL MONOGR, V27, P119; Schneebeli M, 2012, ATMOS MEAS TECH, V5, P2183, DOI 10.5194/amt-5-2183-2012; Skou N., 2006, MICROWAVE RADIOMETER; Smith EA, 2007, ADV GLOB CHANGE RES, V28, P611, DOI 10.1007/978-1-4020-5835-6_48; Solheim F, 1998, RADIO SCI, V33, P393, DOI 10.1029/97RS03656; Stephens GL, 2007, J ATMOS SCI, V64, P3742, DOI 10.1175/2006JAS2375.1; Testud J., 2000, J ATMOS OCEAN TECH, V17, P322; Tokay A, 2013, J ATMOS OCEAN TECH, V30, P1672, DOI 10.1175/JTECH-D-12-00163.1; Tokay A, 2001, J APPL METEOROL, V40, P2083, DOI 10.1175/1520-0450(2001)040<2083:CODSDM>2.0.CO;2; Tokay A, 1996, J APPL METEOROL, V35, P355, DOI 10.1175/1520-0450(1996)035<0355:EFTRSO>2.0.CO;2; Tridon F, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2010GL046018; Ware R, 2003, RADIO SCI, V38, DOI 10.1029/2002RS002856; Ware R, 2013, ATMOS RES, V132, P278, DOI 10.1016/j.atmosres.2013.05.019; Westwater E.R., 2005, QUAT SOC ITAL ELETTR, V1, P8041; Westwater E.R., 1993, ATMOSPHERIC REMOTE S, P145; Won HY, 2009, ASIA-PAC J ATMOS SCI, V45, P55; Xu G., 2013, ATMOS RES, P2013; Zhao G, 2013, ATMOS RES, V120, P155, DOI 10.1016/j.atmosres.2012.08.011; Zhao LM, 2002, J APPL METEOROL, V41, P384, DOI 10.1175/1520-0450(2002)041<0384:ROICPU>2.0.CO;2; Zhao QY, 1997, MON WEATHER REV, V125, P1931, DOI 10.1175/1520-0493(1997)125<1931:APCSFO>2.0.CO;2; ZRNIC DS, 1994, J APPL METEOROL, V33, P45, DOI 10.1175/1520-0450(1994)033<0045:OOCCCT>2.0.CO;2 76 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0169-8095 1873-2895 ATMOS RES Atmos. Res. JUL 1 2014 144 SI 126 140 10.1016/j.atmosres.2014.03.006 15 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AH9NU WOS:000336469900010 J Kolbe, D; Zhu, Q; Pramanik, S Kolbe, Dashiell; Zhu, Qiang; Pramanik, Sakti k-Nearest neighbor searching in hybrid spaces INFORMATION SYSTEMS English Article Hybrid data space; Nearest neighbor search; Similarity search; Spatial indexing; Information retrieval DISCRETE-DATA SPACES; METRIC-SPACES; TREES Little work has been reported in the literature to support k-nearest neighbor (k-NN) searches/queries in hybrid data spaces (HDS). An HDS is composed of a combination of continuous and non-ordered discrete dimensions. This combination presents new challenges in data organization and search ordering. In this paper, we present an algorithm for k-NN searches using a multidimensional index structure in hybrid data spaces. We examine the concept of search stages and use the properties of an HDS to derive a new search heuristic that greatly reduces the number of disk accesses in the initial stage of searching. Further, we present a performance model for our algorithm that estimates the cost of performing such searches. Our experimental results demonstrate the effectiveness of our algorithm and the accuracy of our performance estimation model. (c) 2014 Elsevier Ltd. All rights reserved. [Kolbe, Dashiell; Pramanik, Sakti] Michigan State Univ, E Lansing, MI 48824 USA; [Zhu, Qiang] Univ Michigan, Dearborn, MI 48128 USA Kolbe, D (reprint author), Michigan State Univ, E Lansing, MI 48824 USA. kolbedas@msu.edu; qzhu@umich.edu; pramanik@cse.msu.edu US National Science Foundation (NSF) [IIS-1319909, IIS-1320078, IIS-0414576, IIS-0414594]; Michigan State University; University of Michigan This work was supported by the US National Science Foundation (NSF) (under Grants #IIS-1319909, #IIS-1320078, #IIS-0414576 and #IIS-0414594), the Michigan State University and the University of Michigan. The authors would also like to thank the anonymous reviewers for their valuable comments and constructive suggestions to improve the paper. BECKMANN N, 1990, SIGMOD REC, V19, P322, DOI 10.1145/93597.98741; Cantone D, 2005, IEEE T KNOWL DATA EN, V17, P535, DOI 10.1109/TKDE.2005.53; Catlett J., 1991, P EUR WORK SESS LEAR, P164; Chavez E, 2001, ACM COMPUT SURV, V33, P273, DOI 10.1145/502807.502808; Chen C., 2009, P EDBT, P462, DOI 10.1145/1516360.1516414; Ciaccia P, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P426; FREITAS AA, 2003, NAT COMP SER, P819; Hjaltason GR, 2003, ACM T DATABASE SYST, V28, P517, DOI 10.1145/958942.958948; Kolbe D, 2010, ACM T INFORM SYST, V28, DOI 10.1145/1740592.1740595; Kolbe D., 2007, P 23 INT C DAT ENG I, P426; Macskassy A., 2003, ARTIF INTELL, V143, P51; Navarro G., 2008, ACM J EXP ALGORITHMI, V12, P1; Qian G, 2006, ACM T DATABASE SYST, V31, P439, DOI 10.1145/1138394.1138395; Qian G, 2006, ACM T INFORM SYST, V24, P79, DOI 10.1145/1125857.1125860; Traina C, 2002, IEEE T KNOWL DATA EN, V14, P244, DOI 10.1109/69.991715; UHLMANN JK, 1991, INFORM PROCESS LETT, V40, P175, DOI 10.1016/0020-0190(91)90074-R 16 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0306-4379 1873-6076 INFORM SYST Inf. Syst. JUL 2014 43 SI 55 64 10.1016/j.is.2014.02.004 10 Computer Science, Information Systems Computer Science AH4PQ WOS:000336110900003 J Sellitto, P; Del Frate, F Sellitto, P.; Del Frate, F. The feasibility of retrieving vertical temperature profiles from satellite nadir UV observations: A sensitivity analysis and an inversion experiment with neural network algorithms JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER English Article Temperature profiles retrieval; Remote sensing; Neural networks; Ultraviolet radiation ABSORPTION CROSS-SECTIONS; OZONE PROFILES; WATER-VAPOR; GOME; VALIDATION; SCIAMACHY; O-3 Atmospheric temperature profiles are inferred from passive satellite instruments, using thermal infrared or microwave observations. Here we investigate on the feasibility of the retrieval of height resolved temperature information in the ultraviolet spectral region. The temperature dependence of the absorption cross sections of ozone in the Huggins band, in particular in the interval 320-325 nm, is exploited. We carried out a sensitivity analysis and demonstrated that a non-negligible information on the temperature profile can be extracted from this small band. Starting from these results, we developed a neural network inversion algorithm, trained and tested with simulated nadir EnviSat-SCIAMACHY ultraviolet observations. The algorithm is able to retrieve the temperature profile with root mean square errors and biases comparable to existing retrieval schemes that use thermal infrared or microwave observations. This demonstrates, for the first time, the feasibility of temperature profiles retrieval from space-borne instruments operating in the ultraviolet. (c) 2014 Elsevier Ltd. All rights reserved. [Sellitto, P.] Ecole Normale Super, UPMC ENS CNRS, Inst Pierre Simon Laplace, Lab Meteorol Dynam,UMR8539, F-75231 Paris, France; [Del Frate, F.] Univ Roma Tor Vergata, Earth Observat Lab, I-00133 Rome, Italy Sellitto, P (reprint author), Ecole Normale Super, UPMC ENS CNRS, Inst Pierre Simon Laplace, Lab Meteorol Dynam,UMR8539, 24 Rue Lhomond, F-75231 Paris, France. psellitto@lmd.ens.fr Aires F, 2001, J GEOPHYS RES-ATMOS, V106, P14887, DOI 10.1029/2001JD900085; BASS AM, 1984, P QUADR OZ S HALK GR, P606; Bishop C, 1995, NEURAL NETWORKS PATT; Blackwell WJ, 2012, EURASIP J ADV SIG PR, P1, DOI 10.1186/1687-6180-2012-71; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; BRION J, 1993, CHEM PHYS LETT, V213, P610, DOI 10.1016/0009-2614(93)89169-I; Burrows JP, 1999, J QUANT SPECTROSC RA, V61, P509, DOI 10.1016/S0022-4073(98)00037-5; CARUANA R, 2000, ADV NEURAL INFORM PR, V13; Chehade W, 2013, ATMOS MEAS TECH, V6, P1623, DOI 10.5194/amt-6-1623-2013; Del Frate F, 2005, J QUANT SPECTROSC RA, V92, P275, DOI 10.1016/j.jqsrt.2004.07.028; Del Frate F, 2002, IEEE T GEOSCI REMOTE, V40, P2263, DOI 10.1109/TGRS.2002.803622; Di Noia A, 2013, ATMOS MEAS TECH, V6, P895, DOI 10.5194/amt-6-895-2013; Froidevaux L, 2006, IEEE T GEOSCI REMOTE, V44, P1106, DOI 10.1109/TGRS.2006.864366; Haykin S. S, 1999, NEURAL NETWORKS COMP; HORNIK K, 1989, NEURAL NETWORKS, V2, P359, DOI 10.1016/0893-6080(89)90020-8; Kecman V, 2001, LEARNING SOFT COMPUT; Lamsal LN, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004680; Loyola DG, 2012, EURASIP J ADV SIG PR, DOI 10.1186/1687-6180-2012-91; Mayer B, 2005, ATMOS CHEM PHYS, V5, P1855; Moller A.F., 1993, NEURAL NETWORKS, V6, P525; Orphal J, 2003, J PHOTOCH PHOTOBIO A, V157, P185, DOI 10.1016/S1010-6030(03)00061-3; Pougatchev N, 2009, ATMOS CHEM PHYS, V9, P6453; Schwartz MJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008783; Sellitto P, 2012, J QUANT SPECTROSC RA, V113, P1429, DOI 10.1016/j.jqsrt.2012.04.007; Sellitto P, 2011, ATMOS MEAS TECH, V4, P2375, DOI 10.5194/amt-4-2375-2011; Sellitto P, 2012, IEEE T GEOSCI REMOTE, V50, P998, DOI 10.1109/TGRS.2011.2163198; STAHLI O, 2013, ATMOS MEAS TECH DISC, V6, P2857; TIAN B, 1999, IEEE T GEOSCI REMOTE, V25; Tilstra LG, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005853; Twomey S., 1996, INTRO MATH INVERSION; WESTWATER E, 1972, MON WEATHER REV, V100, P315 31 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0022-4073 1879-1352 J QUANT SPECTROSC RA J. Quant. Spectrosc. Radiat. Transf. JUL 2014 141 1 8 10.1016/j.jqsrt.2014.02.023 8 Spectroscopy Spectroscopy AH4QN WOS:000336113200001 J Maestri, T; Rizzi, R; Tosi, E; Veglio, P; Palchetti, L; Bianchini, G; Di Girolamo, P; Masiello, G; Serio, C; Summa, D Maestri, T.; Rizzi, R.; Tosi, E.; Veglio, P.; Palchetti, L.; Bianchini, G.; Di Girolamo, P.; Masiello, G.; Serio, C.; Summa, D. Analysis of cirrus cloud spectral signatures in the far infrared JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER English Article Far infrared; Cirrus clouds; Remote sensing; Ground-based measurements; Radiative transfer WATER-VAPOR CONTINUUM; RAMAN LIDAR MEASUREMENTS; RELATIVE-HUMIDITY; RADIANCE; BAND; SIMULATIONS; VARIABILITY; PERFORMANCE; PARAMETERS; RETRIEVAL This paper analyses high spectral resolution downwelling radiance measurements in the far infrared in the presence of cirrus clouds taken by the REFIR-PAD interferometer, deployed at 3500 m above the sea level at the Testa Grigia station (Italy), during the Earth COoling by WAter vapouR emission (ECOWAR) campaign. Atmospheric state and cloud geometry are characterised by the co-located millimeter-wave spectrometer GBMS and by radiosonde profile data, an interferometer (I-BEST) and a Raman lidar system deployed at a nearby location (Cervinia). Cloud optical depth and effective diameter are retrieved from REFIR-PAD data using a limited number of channels in the 820-960 cm(-1) interval. The retrieved cloud parameters are the input data for simulations covering the 250-1100 cm(-1) band in order to test our ability to reproduce the REFIR-PAD spectra in the presence of ice clouds. Inverse and forward simulations are based on the same radiative transfer code. A priori information concerning cloud ice vertical distribution is used to better constrain the simulation scheme and an analysis of the degree of approximation of the phase function within the radiative transfer codes is performed to define the accuracy of computations. Simulation-data residuals over the REFIR-PAD spectral interval show an excellent agreement in the window region, but values are larger than total measurement uncertainties in the far infrared. Possible causes are investigated. It is shown that the uncertainties related to the water vapour and temperature profiles are of the same order as the sensitivity to the a priori assumption on particle habits for an up-looking configuration. In case of a down-looking configuration, errors due to possible incorrect description of the water vapour profile would be drastically reduced. (C) 2014 Elsevier Ltd. All rights reserved. [Maestri, T.; Rizzi, R.; Tosi, E.] Univ Bologna, Dipartimento Fis & Astron, I-40127 Bologna, Italy; [Palchetti, L.; Bianchini, G.] CNR, Ist Nazl Ott, I-50125 Florence, Italy; [Di Girolamo, P.; Masiello, G.; Serio, C.; Summa, D.] Univ Basilicata, Scuola Ingn, I-85100 Potenza, Italy; [Veglio, P.] Univ Wisconsin, Space Sci & Engn Ctr, Madison, WI 53706 USA Maestri, T (reprint author), Univ Bologna, Dipartimento Fis & Astron, Viale Berti Pichat 6-2, I-40127 Bologna, Italy. tiziano.maestri@unibo.it Anderson GP, 1986, TECHNICAL REPORT, V01731; Baran AJ, 2009, J QUANT SPECTROSC RA, V110, P1239, DOI 10.1016/j.jqsrt.2009.02.026; Baum BA, 2010, J QUANT SPECTROSC RA, V111, P2534, DOI 10.1016/j.jqsrt.2010.07.008; Bhawar R, 2008, GEOPHYS RES LETT, P35; Bhawar R, 2011, Q J ROY METEOR SOC, V137, P325, DOI 10.1002/qj.697; Bianchini G, 2008, ATMOS CHEM PHYS, V8; Bianchini G, 2009, INFRARED PHYS TECHNO, V52; Bianchini G, 2011, J GEOPHYS RES, V116; Bianchini G, 2007, P SPIE, V6745; Bianchini G, 2006, P SPIE, V6361; Bozzo A, 2008, GEOPHYS RES LETT, V35; Bozzo A, 2010, ATMOS CHEM PHYS, V10, P7369, DOI 10.5194/acp-10-7369-2010; Clough SA, 2005, J QUANT SPECTROSC RA, V91, P233, DOI 10.1016/j.jqsrt.2004.05.058; Cox CV, 2010, Q J ROY METEOR SOC, V136, P718, DOI 10.1002/qj.596; Delamere JS, 2010, J GEOPHYS RES, V115; Di Girolamo P, 2004, GEOPHYS RES LETT, V31; Di Girolamo P, 2009, J ATMOS OCEAN TECH, V26, P1742, DOI 10.1175/2009JTECHA1253.1; Di Girolamo P, 2012, ATMOS ENVIRON, V50, P66, DOI 10.1016/j.atmosenv.2011.12.061; Di Girolamo P, 2009, ATMOS CHEM PHYS, V9, P8799; DIGIROLAMO P, 1994, GEOPHYS RES LETT, V21, P1295, DOI 10.1029/93GL02892; Di Girolamo P, 2006, APPL OPTICS, V45, P2474, DOI 10.1364/AO.45.002474; Di Girolamo P, 2012, ATMOS CHEM PHYS, V12, P4143, DOI 10.5194/acp-12-4143-2012; Di Giuseppe F, 1999, PHYS CHEM EARTH PT B, V24, P243, DOI 10.1016/S1464-1909(98)00045-8; Esposito F, 2007, Q J ROY METEOR SOC, V133, P191, DOI 10.1002/qj.131; Evans KF, 1991, J QUANT SPECTROSC RA, V46; Fiorucci I, 2008, J GEOPHYS RES; Green PD, 2012, PHILOS T R SOC A, V370, P2637, DOI 10.1098/rsta.2011.0263; Griaznov V, 2007, APPL OPTICS, V46, P6821, DOI 10.1364/AO.46.006821; Ham SH, 2009, J APPL METEOROL CLIM, V48, P1591, DOI 10.1175/2009JAMC2121.1; Harries J, 2008, REV GEOPHYS, V46; King MD, 1997, TECHNICAL REPORT; King MD, 1996, J ATMOS OCEAN TECH, V13, P777, DOI 10.1175/1520-0426(1996)013<0777:ASSFRS>2.0.CO;2; Liou KN, 1992, RAD CLOUD PROCESSES; Maestri T, 2009, IEEE T GEOSCI REMOTE, P47; Maestri T, 2010, ATMOS RES, V97, P157, DOI DOI 10.1016/J.ATM0SRES.2010.03.020; Maestri T, 2003, J GEOPHYS RES, P108; Masiello G, 2012, J QUANT SPECTROSC RA, V113, P1286, DOI 10.1016/j.jqsrt.2012.01.019; Merrelli A, 2012, J ATMOS OCEAN TECH, V29, P510, DOI 10.1175/JTECH-D-11-00113.1; Mona L, 2007, Q J ROY METEOR SOC, V133, P257, DOI 10.1002/qj.160; Naud C, 2001, P SPIE, V4168; Palchetti L, 2005, APPL OPT, V44; Palchetti L, 2006, ATMOS CHEM PHYS, V6; Palchetti L, 1999, INFRARED PHYS TECHNO, P40; Palchetti L, 2007, ATMOS CHEM PHYS, V7; Potter JF, 1970, J ATMOS SCI, V27; Revercomb HE, 1998, ASSFTS C TOUL FRANC; Rizzi R, 2001, P SOC PHOTO-OPT INS, V4485, P181; Rizzi R, 2000, REFIR RAD EXPLORER F, V1, P77; Rizzi R, 2001, STUD GEO OP, P567; Serio C, 2008, OPT EXPRESS, V16, P15816, DOI 10.1364/OE.16.015816; Serio C, 2008, APPL OPTICS, V47, P3909, DOI 10.1364/AO.47.003909; Stackhouse PW, 1991, J ATMOS SCI, V48, P20; Tobin DC, 1999, J GEOPHYS RES-ATMOS, V104, P2081, DOI 10.1029/1998JD200057; Tobin DC, 1996, APPL OPTICS, V35, P4724, DOI 10.1364/AO.35.004724; Turner DD, 2003, J ATMOS OCEAN TECH, V20, P117, DOI 10.1175/1520-0426(2003)020<0117:DBAVIV>2.0.CO;2; Turner DD, 2010, B AM METEOROL SOC, V91, P911, DOI 10.1175/2010BAMS2904.1; Veglio P, 2011, ATMOS CHEM PHYS, V11, P12925, DOI 10.5194/acp-11-12925-2011; Wiscombe WJ, 1977, J ATMOS SCI, V34; Yang P, 2003, J GEOPHYS RES, V108 59 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0022-4073 1879-1352 J QUANT SPECTROSC RA J. Quant. Spectrosc. Radiat. Transf. JUL 2014 141 49 64 10.1016/j.jqsrt.2014.02.030 16 Spectroscopy Spectroscopy AH4QN WOS:000336113200007 J Greer, J; Hamiliton, C; Riby, DM; Riby, LM Greer, Joanna; Hamiliton, Colin; Riby, Deborah M.; Riby, Leigh M. Deeper processing is beneficial during episodic memory encoding for adults with Williams syndrome RESEARCH IN DEVELOPMENTAL DISABILITIES English Article Williams Syndrome; Ageing; Cognition; Episodic memory; Semantic memory; Depths of processing; Encoding LONG-TERM-MEMORY; AGE-DIFFERENCES; DOWN-SYNDROME; OLDER-ADULTS; RECOLLECTION; FAMILIARITY; TASK; INDIVIDUALS; RECOGNITION; PERFORMANCE Previous research exploring declarative memory in Williams syndrome (WS) has revealed impairment in the processing of episodic information accompanied by a relative strength in semantic ability. The aim of the current study was to extend this literature by examining how relatively spared semantic memory may support episodic remembering. Using a level of processing paradigm, older adults with WS (aged 35-61 years) were compared to typical adults of the same chronological age and typically developing children matched for verbal ability. In the study phase, pictures were encoded using either a deep (decide if a picture belongs to a particular category) or shallow (perceptual based processing) memory strategy. Behavioural indices (reaction time and accuracy) at retrieval were suggestive of an overall difficulty in episodic memory for WS adults. Interestingly, however, semantic support was evident with a greater recall of items encoded with deep compared to shallow processing, indicative of an ability to employ semantic encoding strategies to maximise the strength of the memory trace created. Unlike individuals with autism who find semantic elaboration strategies problematic, the pattern of findings reported here suggests in those domains that are relatively impaired in WS, support can be recruited from relatively spared cognitive processes. (C) 2014 Elsevier Ltd. All rights reserved. [Greer, Joanna; Hamiliton, Colin; Riby, Leigh M.] Northumbria Univ, Dept Psychol, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England; [Riby, Deborah M.] Univ Durham, Dept Psychol, Durham DH1 3HP, England Riby, LM (reprint author), Northumbria Univ, Dept Psychol, Northumberland Bldg, Newcastle Upon Tyne NE1 8ST, Tyne & Wear, England. leigh.riby@northumbria.ac.uk BELLEZZA FS, 1981, REV EDUC RES, V51, P247, DOI 10.3102/00346543051002247; Brock J, 2007, DEV PSYCHOPATHOL, V19, P97, DOI 10.1017/S095457940707006X; Broman S., 1994, ATYPICAL COGNITIVE D; Costanzo F, 2013, CORTEX, V49, P232, DOI 10.1016/j.cortex.2011.06.007; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; Deruelle C, 2006, RES DEV DISABIL, V27, P243, DOI 10.1016/j.ridd.2005.03.002; Devenny DA, 2004, DEV NEUROPSYCHOL, V26, P691, DOI 10.1207/s15326942dn2603_3; Dunn L., 1997, BRIT PICTURE VOCABUL; Gallo DA, 2004, J EXP PSYCHOL LEARN, V30, P120, DOI 10.1037/0278-7393.30.1.120; Gardiner JM, 2006, COGN NEUROPSYCHOL, V23, P930, DOI 10.1080/02643290600588442; Grady CL, 2000, CURR OPIN NEUROBIOL, V10, P224, DOI 10.1016/S0959-4388(00)00073-8; Greer J, 2013, RES DEV DISABIL, V34, P4170, DOI 10.1016/j.ridd.2013.08.041; Jarrold C, 2007, CORTEX, V43, P233, DOI 10.1016/S0010-9452(08)70478-7; Jarrold C., 2000, COGNITIVE NEUROPSYCH, V5, P293; Luo L, 2007, PSYCHOL AGING, V22, P269, DOI 10.1037/0882-7974.22.2.269; Meyer-Lindenberg A, 2005, J CLIN INVEST, V115, P1888, DOI 10.1172/JCI24892; Meyer-Lindenberg A., 2006, NATURE REV NEUROSCIE, V7, P389; Morris CA, 2000, ANNU REV GENOM HUM G, V1, P461; Naveh-Benjamin M, 2000, J EXP PSYCHOL LEARN, V26, P1170, DOI 10.1037//0278-7393.26.2.1170; Pennington BF, 2003, CHILD DEV, V74, P75, DOI 10.1111/1467-8624.00522; Rhodes SM, 2011, J CLIN EXP NEUROPSYC, V33, P147, DOI 10.1080/13803395.2010.495057; Rhodes SM, 2010, NEUROPSYCHOLOGIA, V48, P1216, DOI 10.1016/j.neuropsychologia.2009.12.021; Riby LA, 2004, Q J EXP PSYCHOL-A, V57, P241, DOI 10.1080/02724980343000206; Searcy YM, 2004, AM J MENT RETARD, V109, P231, DOI 10.1352/0895-8017(2004)109<231:TRBAAI>2.0.CO;2; Smith AD, 2009, PERCEPTION, V38, P694, DOI 10.1068/p6050; SNODGRASS JG, 1980, J EXP PSYCHOL-HUM L, V6, P174, DOI 10.1037/0278-7393.6.2.174; Thomas MSC, 2006, LANG COGNITIVE PROC, V21, P721, DOI 10.1080/01690960500258528; Toichi M, 2002, NEUROPSYCHOLOGIA, V40, P964, DOI 10.1016/S0028-3932(01)00163-4; Troyer AK, 2006, J GERONTOL B-PSYCHOL, V61, pP67; Tyler LK, 1997, CORTEX, V33, P515, DOI 10.1016/S0010-9452(08)70233-8; UDWIN O, 1987, J CHILD PSYCHOL PSYC, V28, P297, DOI 10.1111/j.1469-7610.1987.tb00212.x; Vicari S, 2005, DEV MED CHILD NEUROL, V47, P305, DOI 10.1017/S0012162205000599; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864 33 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0891-4222 RES DEV DISABIL Res. Dev. Disabil. JUL 2014 35 7 1720 1726 10.1016/j.ridd.2014.03.004 7 Education, Special; Rehabilitation Education & Educational Research; Rehabilitation AH3GK WOS:000336011500030 J Yang, Y; Wiliem, A; Alavi, A; Lovell, BC; Hobson, P Yang, Yan; Wiliem, Arnold; Alavi, Azadeh; Lovell, Brian C.; Hobson, Peter Visual learning and classification of human epithelial type 2 cell images through spontaneous activity patterns PATTERN RECOGNITION English Article HEp-2 cells classification; Independent component analysis; Indirect immunofluorescence; Anti-nuclear antibody test; Computer aided diagnostic; Biologically inspired computer vision CORTICAL ORIENTATION SELECTIVITY; INDEPENDENT COMPONENT ANALYSIS; CAT STRIATE CORTEX; LATERAL INHIBITION; EXPERIENCE; DETECTORS; RETRIEVAL; ALGORITHM; MODEL Identifying the presence of anti-nuclear antibody (ANA) in human epithelial type 2 (HEp-2) cells via the indirect immunofluorescence (HE) protocol is commonly used to diagnose various connective tissue diseases in clinical pathology tests. As it is a labour and time intensive diagnostic process, several computer aided diagnostic (CAD) systems have been proposed. However, the existing CAD systems suffer from numerous shortcomings due to the selection of features, which is commonly based on expert experience. Such a choice of features may not work well when the CAD systems are retasked to another dataset To address this, in our previous work, we proposed a novel approach that learns a set of filters from HEp-2 cell images. It is inspired by the receptive fields in the mammalian's vision system, since the receptive fields can be thought as a set of filters for similar shapes. We obtain robust filters for HEp-2 cell classification by employing the independent component analysis (ICA) framework. Although, this approach may be held back due to one particular problem; ICA learning requires a sufficiently large volume of training data which is not always available. In this paper, we demonstrate a biologically inspired solution to address this issue via the use of spontaneous activity patterns (SAP). The spontaneous activity patterns, which are related to the spontaneous neural activities initialised by the chemical release in the brain, are found as the typical stimuli for the visual cell development of newborn animals. In the classification system for HEp-2 cells, we propose to model SAP as a set of small image patches containing randomly positioned Gaussian spots. The SAP image patches are generated and mixed with the training images in order to learn filters via the ICA framework. The obtained filters are adopted to extract the set of responses from a HEp-2 cell image. We then employ regions from this set of responses and stack them into "cubic regions", and apply a classification based on the correlation information of the features. We show that applying the additional SAP leads to a better classification performance on HEp-2 cell images compared to using only the existing patterns for training ICA filters. The improvement on classification is particularly significant when there are not enough specimen images available in the training set, as SAP adds more variations to the existing data that makes the learned ICA model more robust. We show that the proposed approach consistently outperforms three recently proposed CAD systems on two publicly available datasets: ICPR HEp-2 contest and SNPHEp-2. (C) 2013 Elsevier Ltd. All rights reserved. [Yang, Yan; Wiliem, Arnold; Alavi, Azadeh; Lovell, Brian C.] Univ Queensland, Sch ITEE, Brisbane, Qld 4072, Australia; [Yang, Yan; Alavi, Azadeh] NICTA, Queensland Res Lab, Sydney, NSW, Australia; [Hobson, Peter] Sullivan Nicolaides Pathol, Brisbane, Qld, Australia Yang, Y (reprint author), Univ Queensland, Sch ITEE, Brisbane, Qld 4072, Australia. yanyang@itee.uq.edu.au; a.wiliem@uq.edu.au; a.alavi@uq.edu.au; lovell@itee.uq.edu.au; Peter_Hobson@snp.com.au Sullivan Nicolaides Pathology, Australia; Australian Research Council (ARC) Linkage Projects Grant [LP130100230] This research was partly funded by Sullivan Nicolaides Pathology, Australia and the Australian Research Council (ARC) Linkage Projects Grant LP130100230. ADELSON EH, 1985, J OPT SOC AM A, V2, P284, DOI 10.1364/JOSAA.2.000284; Albert MV, 2008, PLOS COMPUT BIOL, V4, DOI 10.1371/journal.pcbi.1000137; Atick JJ, 1990, NEURAL COMPUT, V2, P308, DOI 10.1162/neco.1990.2.3.308; BARLOW HB, 1969, ANN NY ACAD SCI, V156, P872, DOI 10.1111/j.1749-6632.1969.tb14019.x; Bell AJ, 1997, VISION RES, V37, P3327, DOI 10.1016/S0042-6989(97)00121-1; BLAKEMOR.C, 1970, NATURE, V228, P37, DOI 10.1038/228037a0; BLAKEMOR.C, 1972, EXP BRAIN RES, V15, P439; BURGI PY, 1994, J NEUROSCI, V14, P7426; CHAPMAN B, 1993, J NEUROSCI, V13, P5251; Cordelli E., 2011, INT S COMP BAS MED S, P1; EGldibk P., 1995, SPARSE CODING PRIMAT; Elbischger P, 2009, 2009 IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING: FROM NANO TO MACRO, VOLS 1 AND 2, P562, DOI 10.1109/ISBI.2009.5193109; FIELD DJ, 1994, NEURAL COMPUT, V6, P559, DOI 10.1162/neco.1994.6.4.559; Figueiredo MAT, 2003, IEEE T IMAGE PROCESS, V12, P906, DOI 10.1109/TIP.2003.814255; Foggia P, 2013, IEEE T MED IMAGING, V32, P1878, DOI 10.1109/TMI.2013.2268163; Godfrey KB, 2007, PLOS COMPUT BIOL, V3, P2408, DOI 10.1371/journal.pcbi.0030245; Hagan MT, 1996, NEURAL NETWORK DESIG; Hiemann R, 2009, AUTOIMMUN REV, V9, P17, DOI 10.1016/j.autrev.2009.02.033; Hornillo-Mellado S, 2005, LECT NOTES COMPUT SC, V3512, P1035; Hou X., 2008, ADV NEURAL INFORM PR, V21, P681; Hsieh T., 2009, INT C INF COMM SIGN; Hunt JJ, 2012, NEURAL COMPUT, V24, P2422, DOI 10.1162/NECO_a_00333; Hyvarinen A, 2009, COMPUT IMAGING VIS, V39, P1; Hyvarinen A., 1984, PHILOS T ROY SOC A, V371; Hyvarinen A, 2000, NEURAL NETWORKS, V13, P411, DOI 10.1016/S0893-6080(00)00026-5; Hyvarinen A, 1999, NEURAL PROCESS LETT, V10, P1, DOI 10.1023/A:1018647011077; Jain AK, 1996, PATTERN RECOGN, V29, P1233, DOI 10.1016/0031-3203(95)00160-3; Jiang H., 2013, COMPUT MATH METHOD M, V2013, P1; JONES JP, 1987, J NEUROPHYSIOL, V58, P1233; Kanan C, 2010, PROC CVPR IEEE, P2472, DOI 10.1109/CVPR.2010.5539947; KUFFLER SW, 1953, J NEUROPHYSIOL, V16, P37; Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Lee H, 2007, P NIPS, V20, P801; Lee H., 2009, P 26 ANN INT C MACH, P609, DOI DOI 10.1145/1553374.1553453; Li S., 2001, P C COMP VIS PATT RE, V1, P1; Ngiam J., 2011, NEURAL INF PROCESS S, V24, P1125; Ohshiro T, 2011, J NEUROPHYSIOL, V106, P1923, DOI 10.1152/jn.00095.2011; Olshausen BA, 1996, NATURE, V381, P607, DOI 10.1038/381607a0; REID RC, 1987, P NATL ACAD SCI USA, V84, P8740, DOI 10.1073/pnas.84.23.8740; RODIECK R. W., 1965, VISION RES, V5, P583, DOI 10.1016/0042-6989(65)90033-7; Rui Y, 1999, J VIS COMMUN IMAGE R, V10, P39, DOI 10.1006/jvci.1999.0413; SHERK H, 1976, J NEUROPHYSIOL, V39, P63; Sherrington C., 1966, INTEGRATIVE ACTION N; Sherrington CS, 1906, J PHYSIOL-LONDON, V34, P1; Soda P, 2009, IEEE T INF TECHNOL B, V13, P322, DOI 10.1109/TITB.2008.2010855; Stauffer D., 1994, INTRO PERCOLATION TH; Strandmark P., 2012, INT C PATT REC, P562; STRYKER MP, 1978, J NEUROPHYSIOL, V41, P896; Tanaka S, 2006, NEUROIMAGE, V30, P462, DOI 10.1016/j.neuroimage.2005.09.056; Theodorakopoulos I, 2012, IEEE 12TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS & BIOENGINEERING, P689; White LE, 2001, NATURE, V411, P1049, DOI 10.1038/35082568; Wiik A.S., J AUTOIMMUN, V35; Wiliem A., 2013, IEEE WORKSH APPL COM; Yang Y., 2013, IEEE INT C IM PROC, P733; Zhou XS, 2000, IEEE WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO LIBRARIES, PROCEEDINGS, P10 55 1 1 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUL 2014 47 7 SI 2325 2337 10.1016/j.patcog.2013.10.013 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AF8PM WOS:000334978100004 J Ni, WJ; Sun, GQ; Ranson, KJ; Zhang, ZY; He, YT; Huang, WL; Guo, ZF Ni, Wenjian; Sun, Guoqing; Ranson, Kenneth Jon; Zhang, Zhiyu; He, Yating; Huang, Wenli; Guo, Zhifeng Model-Based Analysis of the Influence of Forest Structures on the Scattering Phase Center at L-Band IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Canopy height; depth of scattering phase center (DSPC); forest structure; height of scattering phase center (HSPC); interferometric synthetic aperture radar (InSAR) INTERFEROMETRIC SAR; RADAR BACKSCATTER; BOREAL FOREST; P-BAND; HEIGHT; INVERSION; CANOPIES The estimation of forest biomass from synthetic aperture radar (SAR) data is limited by the lack of forest structure information. Interferometric synthetic aperture radar (InSAR) provides a means for the extraction of forest structure. The crucial issue in InSAR application is to parameterize forest structure and to link the parameter with InSAR observations. Model-based analysis enables exploring the theoretical linkages between InSAR observations and forest structure free from temporal decorrelation effects. In this paper, a semicoherent model (SCSR) was first developed and verified. A series of simulations at L-band was then made for both homogeneous and heterogeneous forests generated from a forest growth model. The forest structure was parameterized by four height indices. Aside from the height of scattering phase center (HSPC), the depth of scattering phase center (DSPC) was also proposed to characterize the scattering phase center of InSAR. The results showed that the behavior of homogeneous forest on InSAR data was quite different from that of heterogeneous forest. Special care was needed when the retrieval algorithms of forest biomass developed on a homogeneous forest were applied to a heterogeneous forest. Crown size-weighted height (CWH) and Lorey's height were correlated with the HSPC at all polarizations and with the DSPC at copolarization in both cases of homogeneous and heterogeneous forests. These findings indicated that CWH could be an alternative biomass indicator of the Lorey's height for biomass estimation, which can be derived from the combination of InSAR data and the elevation of the forest canopy top from lidar or high-resolution stereo images. [Ni, Wenjian; Zhang, Zhiyu; Guo, Zhifeng] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China; [Ni, Wenjian; Zhang, Zhiyu; Guo, Zhifeng] Beijing Normal Univ, Beijing 100101, Peoples R China; [Sun, Guoqing; Huang, Wenli] Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA; [Ranson, Kenneth Jon] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [He, Yating] Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, Beijing 100081, Peoples R China Ni, WJ (reprint author), Univ Maryland, Dept Geog Sci, College Pk, MD 20742 USA. zhangzy@irsa.ac.cn National Basic Research Program of China [2013CB733404]; National Natural Science Foundation of China [41001208, 40971203, 41171283, 91125003]; National High-Tech R&D Program of China [2012AA12A306]; Strategic Priority Research Program-Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences [XDA05050100]; Terrestrial Ecology Program [NNX09AG66G] This work was supported in part by the National Basic Research Program of China under Grant 2013CB733404, by the National Natural Science Foundation of China under Grants 41001208, 40971203, 41171283, and 91125003, by the National High-Tech R&D Program of China under Grant 2012AA12A306, by the Strategic Priority Research Program-Climate Change: Carbon Budget and Related Issues of the Chinese Academy of Sciences under Grant XDA05050100, and by the Terrestrial Ecology Program under Grant NNX09AG66G. Balzter H, 2007, INT J REMOTE SENS, V28, P1173, DOI 10.1080/01431160600904998; CHAUHAN NS, 1991, IEEE T GEOSCI REMOTE, V29, P627, DOI 10.1109/36.135825; Du JY, 2006, IEEE T GEOSCI REMOTE, V44, P815, DOI 10.1109/TGRS.2006.872289; Fung A., 1994, MICROWAVE SCATTERING; Garestier F, 2010, IEEE T GEOSCI REMOTE, V48, P1528, DOI 10.1109/TGRS.2009.2032538; Imbriale W. A., 2006, SPACEBORNE ANTENNAS; Ishimaru A., 1978, WAVE PROPAGATION SCA; KARAM MA, 1988, IEEE T GEOSCI REMOTE, V26, P799, DOI 10.1109/36.7711; Lefsky M. A., 2005, GEOPHYS RES LETT, V32, P1; Lin YC, 1999, IEEE T GEOSCI REMOTE, V37, P440; Liu DW, 2008, PROG ELECTROMAGN RES, V84, P149, DOI 10.2528/PIER08071802; Liu DW, 2010, IEEE T GEOSCI REMOTE, V48, P349, DOI 10.1109/TGRS.2009.2024301; Neeff T, 2005, FOREST SCI, V51, P585; Pang Y., 2008, CANADIAN J REMOTE SE, V34, P471; Praks J, 2007, IEEE GEOSCI REMOTE S, V4, P466, DOI 10.1109/LGRS.2007.898083; Ranson K. J., 1990, P IGARSS 90, P861; Ranson KJ, 2001, REMOTE SENS ENVIRON, V75, P291, DOI 10.1016/S0034-4257(00)00174-7; Saatchi SS, 2011, P NATL ACAD SCI USA, V108, P9899, DOI 10.1073/pnas.1019576108; SUN GQ, 1995, IEEE T GEOSCI REMOTE, V33, P372; Thirion L, 2006, IEEE T GEOSCI REMOTE, V44, P849, DOI 10.1109/TGRS.2005.862523; Thirion-Lefevre L, 2007, IEEE T GEOSCI REMOTE, V45, P3172, DOI 10.1109/TGRS.2007.904921; Tsang L., 1985, THEORY MICROWAVE REM; Ulaby F. T., 1988, P IGARSS REM SENS MO, V2, P1009 23 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JUL 2014 52 7 3937 3946 10.1109/TGRS.2013.2278171 10 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5YO WOS:000332597100016 J Rivas, MB; Stoffelen, A; van Zadelhoff, GJ Rivas, Maria Belmonte; Stoffelen, Ad; van Zadelhoff, Gerd-Jan The Benefit of HH and VV Polarizations in Retrieving Extreme Wind Speeds for an ASCAT-Type Scatterometer IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Antennas; polarization; satellites; sea measurements; sea surface electromagnetic scattering; wind OCEAN; BAND The wind retrieval performance of a fixed fan-beam scatterometer operating at C-band in VV polarization [Advanced SCATterometer (ASCAT) type] is well established in the range of 0-25 m/s. This work evaluates prospective extensions with HH- and VH-polarized beams aimed at improving retrievals at extreme wind speeds, from 25 to 65 m/s. The geophysical model functions for C-band VV-, HH-, and VH-polarized backscatter used in the wind retrieval simulations are defined, along with an objective assessment of the quality scores ascribed to the optional beam configurations. Our study finds that the introduction of a VH capability in the midbeam improves the determination of wind speed over the entire scatterometer swath, with wind speed root-mean-square (rms) errors of about 0.5 m/s. The introduction of HH beams in the fore/aft antennas improves the determination of the ocean surface wind vector (wind speed and direction) but over a more limited portion of the outer swath, with wind vector rms errors as low as 6 m/s, but conditioned to a priori information to resolve the directional ambiguity. If the determination of wind speed primes over the determination of wind vectors at high wind speeds, then the introduction of a single VH capability in the midbeam antenna comes forth as the most simple and cost-effective manner to extend the wind retrieval capabilities of an ASCAT-type scatterometer into the domain of extreme winds. [Rivas, Maria Belmonte] Natl Ctr Atmospher Res, Boulder, CO 80301 USA; [Stoffelen, Ad; van Zadelhoff, Gerd-Jan] Royal Netherlands Meteorol Inst KNMI, NL-3730 AE De Bilt, Netherlands Rivas, MB (reprint author), Delft Univ Technol, NL-2628 CN Delft, Netherlands. m.belmonterivas@tudelft.nl; ad.stoffelen@knmi.nl; zadelhof@knmi.nl European Organisation for the Exploitation of Meteorological Satellites Numerical Weather Prediction Satellite Application Facility through its Associate Scientist funds This work was supported by the European Organisation for the Exploitation of Meteorological Satellites Numerical Weather Prediction Satellite Application Facility through its Associate Scientist funds. Belmonte M. Rivas, 2010, STUDY OBJECTIVE PERF; Belmonte Rivas M., 2012, NWPSAFKNVS009; Donnelly WJ, 1999, J GEOPHYS RES-OCEANS, V104, P11485, DOI 10.1029/1998JC900030; Esteban-Fernandez D., 2006, J GEOPHYS RES, V111; Hersbach H, 2007, J GEOPHYS RES, V112, DOI DOI 10.1029/2006JC003743; Hwang P. A., 2010, J GEOPHYS RES, V115; Lin CC, 2012, IEEE T GEOSCI REMOTE, V50, P2458, DOI 10.1109/TGRS.2011.2180393; Mouche AA, 2005, IEEE T GEOSCI REMOTE, V43, P753, DOI 10.1109/TGRS.2005.843951; Portabella M, 2009, J ATMOS OCEAN TECH, V26, P368, DOI 10.1175/2008JTECHO578.1; Portabella M, 2006, IEEE T GEOSCI REMOTE, V44, P3356, DOI 10.1109/TGRS.2006.877952; Stoffelen A, 2006, IEEE T GEOSCI REMOTE, V44, P1523, DOI 10.1109/TGRS.2005.862502; Ulhorn E. W., 2007, MON WEA REV, V135, P3070; Vachon P. W., 2011, IEEE GEOSCI REMOTE S, V8; Zadelhoff G. J., 2013, ATMOSPHERIC ME UNPUB 14 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JUL 2014 52 7 4273 4280 10.1109/TGRS.2013.2280876 8 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC5YO WOS:000332597100046 J Kozoderov, VV; Kondranin, TV; Dmitriev, EV; Sokolov, AA Kozoderov, Vladimir V.; Kondranin, Timofei V.; Dmitriev, Egor V.; Sokolov, Anton A. Retrieval of forest stand attributes using optical airborne remote sensing data OPTICS EXPRESS English Article CLASSIFICATION Optical remote sensing data processing is proposed for the airborne images of high spectral and spatial resolution. Optimization techniques are undertaken to gain information about spatial distribution of the pixels on the hyperspectral images and the texture of the forest stands of different species and ages together with reducing redundancy of the spectral channels used. The category of neighborhood of pixels for particular forest classes and the step up method of selecting optimal spectral channels are employed in the relevant processing procedures. We present examples of pattern recognition for the forests as a result of separating pixels, which characterize the sunlit tops, shaded space and intermediate cases of the Sun illumination conditions on the hyperspectral images. (C)2014 Optical Society of America [Kozoderov, Vladimir V.] Moscow MV Lomonosov State Univ, Moscow 119991, Russia; [Kondranin, Timofei V.; Dmitriev, Egor V.] Moscow Inst Phys & Technol, Dolgoprudnyi 141700, Moscow Region, Russia; [Dmitriev, Egor V.] Russian Acad Sci, Inst Numer Math, Moscow 119333, Russia; [Sokolov, Anton A.] Univ Littoral Cote dOpale, Lab PhysicoChim Atmosphere, F-59140 Dunkerque, France; [Sokolov, Anton A.] Univ Lille Nord France, F-59140 Dunkerque, France Kozoderov, VV (reprint author), Moscow MV Lomonosov State Univ, GSP 1, Moscow 119991, Russia. vkozod@mail.ru Russian Fund for Basic Research [13-01-00185, 14-0500598, 14-07-00141] This work is supported by the Russian Fund for Basic Research (# 13-01-00185, 14-0500598, 14-07-00141). BERTERO M, 1988, P IEEE, V76, P869, DOI 10.1109/5.5962; BESAG J, 1993, J ROY STAT SOC B MET, V55, P25; Bolton J, 2009, IEEE T GEOSCI REMOTE, V47, P3810, DOI 10.1109/TGRS.2009.2025497; Dalponte M., 2009, IEEE T GEOSCI REMOTE, V113, P1416; Dalponte M, 2009, REMOTE SENS ENVIRON, V113, P2345, DOI 10.1016/j.rse.2009.06.013; Dmitriev EV, 2013, PROC SPIE, V8887, DOI 10.1117/12.2028351; FRIEDLAND NS, 1992, IEEE T PATTERN ANAL, V14, P770, DOI 10.1109/34.142912; Hakala T, 2012, OPT EXPRESS, V20, P7119, DOI 10.1364/OE.20.007119; Hall FG, 1997, J GEOPHYS RES-ATMOS, V102, P29567, DOI 10.1029/97JD02578; Hastie T., 2001, ELEMENTS STAT LEARNI; JAIN AK, 1981, P IEEE, V69, P502, DOI 10.1109/PROC.1981.12021; Kohavi R., 1995, INT JOINT C ART INT, P528; Kozoderov V. V., 2013, P INT S ATM RAD DYNA, V41; Kozoderov V. V., 2013, THEAMTIC PROCESSING; Kozoderov V. V., 2012, CLIMATE NATURE, V2, P3; Kozoderov V. V., 2013, ISSLEDOVANIE ZEMLI K, V6, P57; Kozoderov VV, 2012, IZV ATMOS OCEAN PHY+, V48, P941, DOI 10.1134/S0001433812090083; Kozoderov VV, 2011, INT J REMOTE SENS, V32, P5699, DOI 10.1080/01431161.2010.507262; Kozoderov VV, 2008, INT J REMOTE SENS, V29, P2733, DOI 10.1080/01431160701767476; Li S. Z., IMAGE VIS COMPUT, V10, P566; Li S.Z., 1995, MARKOV RANDOM FIELD; Suomalainen J, 2011, ISPRS J PHOTOGRAMM, V66, P637, DOI 10.1016/j.isprsjprs.2011.04.002; Tso B, 2005, REMOTE SENS ENVIRON, V97, P127, DOI 10.1016/j.rse.2005.04.021 23 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUN 30 2014 22 13 15410 15423 10.1364/OE.22.015410 14 Optics Optics AJ9TN WOS:000338055900028 J Schafer, JS; Eck, TF; Holben, BN; Thornhill, KL; Anderson, BE; Sinyuk, A; Giles, DM; Winstead, EL; Ziemba, LD; Beyersdorf, AJ; Kenny, PR; Smirnov, A; Slutsker, I Schafer, J. S.; Eck, T. F.; Holben, B. N.; Thornhill, K. L.; Anderson, B. E.; Sinyuk, A.; Giles, D. M.; Winstead, E. L.; Ziemba, L. D.; Beyersdorf, A. J.; Kenny, P. R.; Smirnov, A.; Slutsker, I. Intercomparison of aerosol single-scattering albedo derived from AERONET surface radiometers and LARGE in situ aircraft profiles during the 2011 DRAGON-MD and DISCOVER-AQ experiments JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article SKY RADIANCE MEASUREMENTS; OPTICAL-PROPERTIES; LIGHT-ABSORPTION; WAVELENGTH DEPENDENCE; ATMOSPHERIC AEROSOLS; UNITED-STATES; RETRIEVAL; NETWORK; CARBON; DUST Single-scattering albedo (SSA) retrievals obtained with CIMEL Sun-sky radiometers from the Aerosol Robotic Network (AERONET) aerosol monitoring network were used to make comparisons with simultaneous in situ sampling from aircraft profiles carried out by the NASA Langley Aerosol Group Experiment (LARGE) team in the summer of 2011 during the coincident DRAGON-MD (Distributed Regional Aerosol Gridded Observational Network-Maryland) and DISCOVER-AQ (Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality) experiments. The single-scattering albedos (interpolated to 550 nm) derived from AERONET measurements for aerosol optical depth (AOD) at 440 nm >= 0.4 (mean SSA: 0.979) were on average 0.011 lower than the values derived from the LARGE profile measurements (mean SSA: 0.99). The maximum difference observed was 0.023 with all the observed differences within the combined uncertainty for the stated SSA accuracy (0.03 for AERONET; 0.02 for LARGE). Single-scattering albedo averages were also analyzed for lower aerosol loading conditions (AOD >= 0.2) and a dependence on aerosol optical depth was noted with significantly lower single-scattering albedos observed for lower AOD in both AERONET and LARGE data sets. Various explanations for the SSA trend were explored based on other retrieval products including volume median radius and imaginary refractive index as well as column water vapor measurements. Additionally, these SSA trends with AOD were evaluated for one of the DRAGON-MD study sites, Goddard Space Flight Center, and two other Mid-Atlantic AERONET sites over the long-term record dating to 1999. [Schafer, J. S.; Eck, T. F.; Holben, B. N.; Sinyuk, A.; Giles, D. M.; Kenny, P. R.; Smirnov, A.; Slutsker, I.] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Schafer, J. S.; Sinyuk, A.; Giles, D. M.; Kenny, P. R.; Smirnov, A.; Slutsker, I.] Sigma Space Corp, Lanham, MD USA; [Eck, T. F.] Univ Space Res Assoc, Columbia, MD USA; [Thornhill, K. L.; Anderson, B. E.; Winstead, E. L.; Ziemba, L. D.; Beyersdorf, A. J.] NASA, Langley Res Ctr, Hampton, VA 23665 USA Schafer, JS (reprint author), NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA. joel.schafer@nasa.gov Radiation Sciences Program (NASA); EOS project office (NASA); Joint Polar Satellite System program (NOAA) The AERONET project is supported by the Radiation Sciences Program (NASA), the EOS project office (NASA), and the Joint Polar Satellite System program (NOAA). We would like to thank the summer interns who installed and maintained the 40+ CIMEL Sun photometers; Anastasia Sorokine, Chris McPartland, Christopher Blackwell, Sarah Dickerson, and Christina Justice as well as the many local elementary schools and high schools that hosted our instrumentation for several months. Additionally, we would like to recognize the contributions of Jennifer Hains, Ryan Auvil, and others at the Maryland Department of the Environment who allowed us to operate our equipment at their facilities. The data used in this paper are available at 'http://aeronet.gsfc.nasa.gov/new_web/DRAGON-USA_2011_DC_Maryland.html' Bergstrom RW, 2002, J ATMOS SCI, V59, P567, DOI 10.1175/1520-0469(2002)059<0567:WDOTAO>2.0.CO;2; Bond TC, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007315; Bond TC, 1999, AEROSOL SCI TECH, V30, P582, DOI 10.1080/027868299304435; Chaudhry Z, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD009055; Chen Y, 2010, ATMOS CHEM PHYS, V10, P1773; Dubovik O, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006619; Dubovik O, 2000, J GEOPHYS RES-ATMOS, V105, P20673, DOI 10.1029/2000JD900282; Dubovik O, 2000, J GEOPHYS RES-ATMOS, V105, P9791, DOI 10.1029/2000JD900040; Dubovik O, 2002, J ATMOS SCI, V59, P590, DOI 10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2; Eck TF, 1999, J GEOPHYS RES-ATMOS, V104, P31333, DOI 10.1029/1999JD900923; Eck TF, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010870; Eck TF, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016839; Giles DM, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD018127; Hansen J, 2013, ENVIRON RES LETT, V8, DOI 10.1088/1748-9326/8/1/011006; HAYWOOD JM, 1995, GEOPHYS RES LETT, V22, P603, DOI 10.1029/95GL00075; Holben BN, 1998, REMOTE SENS ENVIRON, V66, P1, DOI 10.1016/S0034-4257(98)00031-5; Holben BN, 2006, P SOC PHOTO-OPT INS, V6408, pQ4080, DOI 10.1117/12.706524; Jacobson MC, 2000, REV GEOPHYS, V38, P267, DOI 10.1029/1998RG000045; Johnson BT, 2009, Q J ROY METEOR SOC, V135, P922, DOI 10.1002/qj.420; Kotchenruther RA, 1999, J GEOPHYS RES-ATMOS, V104, P2239, DOI 10.1029/98JD01751; Leahy LV, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2007GL029697; Lee KH, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD009077; Moody EG, 2005, IEEE T GEOSCI REMOTE, V43, P144, DOI 10.1109/TGRS.2004.838359; Ocko IB, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD018019; Schafer JS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009319; Schmid O, 2006, ATMOS CHEM PHYS, V6, P3443; Sheridan PJ, 2005, AEROSOL SCI TECH, V39, P1, DOI 10.1080/027868290901891; Smirnov A, 2000, REMOTE SENS ENVIRON, V73, P337, DOI 10.1016/S0034-4257(00)00109-7; Taubman BF, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006196; Torres B, 2014, ATMOS CHEM PHYS, V14, P847, DOI 10.5194/acp-14-847-2014; Virkkula A, 2005, AEROSOL SCI TECH, V39, P52, DOI 10.1080/027868290901918; Ziemba LD, 2013, GEOPHYS RES LETT, V40, P417, DOI 10.1029/2012GL054428 32 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUN 27 2014 119 12 7439 7452 10.1002/2013JD021166 14 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN0BP WOS:000340247000026 J Ishimoto, H; Okamoto, K; Okamoto, H; Sato, K Ishimoto, Hiroshi; Okamoto, Kozo; Okamoto, Hajime; Sato, Kaori One-dimensional variational (1D-Var) retrieval of middle to upper tropospheric humidity using AIRS radiance data JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article ATMOSPHERIC INFRARED SOUNDER; ICE SUPERSATURATION; RELATIVE-HUMIDITY; CIRRUS CLOUDS; INCA EXPERIMENT; SATELLITE; CALIPSO; CLIMATOLOGY; PARAMETERS; ACCURACY A one-dimensional variational analysis (1D-Var retrieval) of tropospheric humidity was conducted using hyper-spectral radiance data from the Atmospheric Infrared Sounder (AIRS). For the vertical range of the atmosphere between 200 and 600 hPa, the same high-resolution retrieval of humidity profiles as for clear-sky conditions was possible over low clouds if the cloud height was lower than 800 hPa. Global analyses from a global data assimilation system were used for initial profiles, and clear conditions over 800 hPa height were determined from AIRS radiance data. Results of analyses for 50 days of global radiosonde matchup data from 21 December 2008 to 8 February 2009 revealed that our 1D-Var calculations derived humidity profiles were closer to the sonde profiles than those of a global analysis at a height over 600 hPa. Furthermore, the results of 1D-Var retrieval often represented high and supersaturated relative humidity around the supposed ice clouds. The altitudes of the high humidity region agreed with the height of ice clouds that had been detected by CloudSat/CALIPSO. As well as possibly improving the humidity profiles in a numerical model by data assimilation, it is expected that these humidity analyses using AIRS radiance data will provide additional information for the study of ice clouds in the middle to upper troposphere. [Ishimoto, Hiroshi; Okamoto, Kozo] Meteorol Res Inst, Tsukuba, Ibaraki 305, Japan; [Okamoto, Hajime; Sato, Kaori] Kyushu Univ, Appl Mech Res Inst, Kasuga, Fukuoka 816, Japan Ishimoto, H (reprint author), Meteorol Res Inst, 1-1 Nagamine, Tsukuba, Ibaraki 305, Japan. hiroishi@mri-jma.go.jp JSPS KAKENHI [25247078] The data for this paper cannot be released without an approval from MRI-JMA. This work was supported by JSPS KAKENHI Grant 25247078. Barnet C., 2007, AIRS TEAM RETRIEVAL; Chahine MT, 2006, B AM METEOROL SOC, V87, P911, DOI 10.1175/BAMS-87-7-911; Clough SA, 2005, J QUANT SPECTROSC RA, V91, P233, DOI 10.1016/j.jqsrt.2004.05.058; Comstock JM, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL019539; DOWLING DR, 1990, J APPL METEOROL, V29, P970, DOI 10.1175/1520-0450(1990)029<0970:ASOTPP>2.0.CO;2; Fetzer EJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010000; Gayet JF, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD004803; Gettelman A., 2004, Geophysical Research Letters, V31, DOI 10.1029/2004GL020730; Haag W, 2003, ATMOS CHEM PHYS, V3, P1791; Hagihara Y, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012344; Hagihara Y, 2014, J GEOPHYS RES-ATMOS, V119, P4087; Ishimoto H, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL037665; Kahn BH, 2008, ATMOS CHEM PHYS, V8, P1501; Koop T, 2000, NATURE, V406, P611, DOI 10.1038/35020537; Kramer M, 2009, ATMOS CHEM PHYS, V9, P3505; Lamquin N, 2012, ATMOS CHEM PHYS, V12, P381, DOI 10.5194/acp-12-381-2012; Lamquin N, 2009, ATMOS CHEM PHYS, V9, P1779; Liang C. K., 2010, ATMOS MEAS TECH DISC, V3, P2834; Maddy ES, 2008, IEEE T GEOSCI REMOTE, V46, P2375, DOI 10.1109/TGRS.2008.917498; Mano Y, 2004, APPL OPTICS, V43, P6304, DOI 10.1364/AO.43.006304; Marchand R, 2008, J ATMOS OCEAN TECH, V25, P519, DOI 10.1175/2007JTECHA1006.1; Matricardi M, 2009, ATMOS CHEM PHYS, V9, P6899; McNally AP, 2003, Q J ROY METEOR SOC, V129, P3411, DOI 10.1256/qj.02.208; Okamoto H, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD009812; Okamoto H, 2007, SEIKAGAKU, V79, P1; Okamoto H, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013383; Ovarlez J, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL014440; Read WG, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008752; Rodgers C. D., 2000, INVERSE METHODS ATMO; Saunders R., 2012, NWPSAFMOTV023; Spichtinger P, 2003, METEOROL Z, V12, P143, DOI 10.1127/0941-2948/2003/0012-0143; Strom J, 2003, ATMOS CHEM PHYS, V3, P1807; Stubenrauch CJ, 2010, ATMOS CHEM PHYS, V10, P7197, DOI 10.5194/acp-10-7197-2010; Susskind J, 2003, IEEE T GEOSCI REMOTE, V41, P390, DOI 10.1109/TGRS.2002.808236; SUSSKIND J, 2006, J GEOPHYS RES, V111, DOI DOI 10.1029/2005JD006272; Takeuchi Y., 2013, OUTLINE OPERATIONAL; Vaughan M. A., 2005, 20201 PC SCI NASA LA; Wang F, 2007, APPL OPTICS, V46, P200, DOI 10.1364/AO.46.000200; Yue Q, 2013, J CLIMATE, V26, P8357, DOI 10.1175/JCLI-D-13-00065.1 39 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUN 27 2014 119 12 7633 7654 10.1002/2014JD021706 22 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN0BP WOS:000340247000036 J Vergados, P; Mannucci, AJ; Ao, CO Vergados, Panagiotis; Mannucci, Anthony J.; Ao, Chi O. Assessing the performance of GPS radio occultation measurements in retrieving tropospheric humidity in cloudiness: A comparison study with radiosondes, ERA-Interim, and AIRS data sets JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article NUMERICAL WEATHER-PREDICTION; PRECIPITABLE WATER-VAPOR; RADIATION DRY BIAS; TEMPERATURE; ECMWF; ASSIMILATION; REFRACTIVITY; VALIDATION; SIGNALS; AIRS/AMSU/HSB We assess the impact that the Global Positioning System radio occultations (GPSRO) measurements have on complementing different data sets in characterizing the lower-to-middle tropospheric humidity in cloudy conditions over both land and oceans using data from 1 August 2006 to 31 October 2006. We use observations from rawinsondes, Global Positioning System radio occultations (GPSRO), Atmospheric Infrared Sounder (AIRS), and the European Center for Medium-Range Weather Forecasts Reanalysis Interim (ERA-Interim). During the selected time period, Constellation Observing System for Meteorology, Ionosphere, and Climate data were not assimilated in ERA-Interim. From each data set, we estimate a zonally averaged tropospheric specific humidity profile at tropical, middle, and high latitudes. Over land, we use rawinsondes as the ground truth and quantify the specific humidity differences and root-mean-square-errors (RMSEs) of the GPSRO, AIRS, and ERA-Interim profiles. GPSRO are beneficial in retrieving lower tropospheric humidity than upper tropospheric profiles, due to their loss of sensitivity at high altitudes. Blending GPSRO with ERA-Interim produces profiles with smaller humidity biases outside the tropics, but GPSRO data do not improve the humidity RMSE when compared to rawinsondes. Combining GPSRO with AIRS leads to smaller humidity bias at the tropics and high latitudes, while reducing humidity's RMSEs. Over oceans, no rawinsonde information is available, and we use ERA-Interim as a reference. Combining GPSRO with AIRS leads to smaller humidity RMSEs than AIRS. We conclude that cross-comparisons and synergies among multi-instrument observations are promising in advancing our knowledge of the tropospheric humidity in cloudy conditions. GPSRO data can contribute to improving humidity retrievals over cloud-covered regions, especially over land and within the boundary layer. [Vergados, Panagiotis; Mannucci, Anthony J.; Ao, Chi O.] CALTECH, NASA Jet Prop Lab, Pasadena, CA 91125 USA Vergados, P (reprint author), CALTECH, NASA Jet Prop Lab, Pasadena, CA 91125 USA. Panagiotis.Vergados@jpl.nasa.gov National Aeronautics and Space Administration This research was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. P. Vergados acknowledges the NPP program administered by the Oak Ridge Associated Universities (ORAU). We would like to thank all reviewers for their extensive comments, which greatly helped us on the presentation of this research paper and for emphasizing upon the significance of our results. Agusti-Panareda A, 2009, Q J ROY METEOR SOC, V135, P595, DOI 10.1002/qj.396; Ao CO, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD003216; Aumann HH, 2003, IEEE T GEOSCI REMOTE, V41, P253, DOI 10.1109/TGRS.2002.808356; Borbas EE, 2008, J APPL METEOROL CLIM, V47, P2300, DOI 10.1175/2008JAMC1687.1; Cady-Pereira KE, 2008, J ATMOS OCEAN TECH, V25, P873, DOI 10.1175/2007JTECHA1027.1; Carlowicz M., 1996, EOS T AGU, V77, P11, DOI [10.1029/95EO00011, DOI 10.1029/95E000011]; Ciesielski PE, 2003, J CLIMATE, V16, P2370, DOI 10.1175/2790.1; Collard AD, 2009, Q J ROY METEOR SOC, V135, P1044, DOI 10.1002/qj.410; Collard AD, 2003, Q J ROY METEOR SOC, V129, P2741, DOI 10.1256/qj.02.124; Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828; Fetzer E, 2003, IEEE T GEOSCI REMOTE, V41, P418, DOI 10.1109/TGRS.2002.808293; Fetzer EJ, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010000; FETZER EJ, 2006, J GEOPHYS RES, V111, DOI DOI 10.1029/2005JD006598; Gettelman A, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006636; Gorbunov ME, 2011, J ATMOS OCEAN TECH, V28, P737, DOI 10.1175/2011JTECHA1489.1; Healy SB, 2006, Q J ROY METEOR SOC, V132, P605, DOI 10.1256/qj.04.182; Ho SP, 2007, J ATMOS OCEAN TECH, V24, P1726, DOI 10.1175/JTECH2071.1; Ho SP, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011969; Ho SP, 2010, REMOTE SENS-BASEL, V2, P1320, DOI 10.3390/rs2051320; Jensen AS, 2003, RADIO SCI, V38, DOI 10.1029/2002RS002763; KLEESPIES TJ, 1990, J APPL METEOROL, V29, P851, DOI 10.1175/1520-0450(1990)029<0851:ROPWFO>2.0.CO;2; Kursinski ER, 1997, J GEOPHYS RES-ATMOS, V102, P23429, DOI 10.1029/97JD01569; Kursinski ER, 2001, J GEOPHYS RES-ATMOS, V106, P1113, DOI 10.1029/2000JD900421; Kursinski ER, 2000, EARTH PLANETS SPACE, V52, P885; LENHARD RW, 1970, B AM METEOROL SOC, V51, P842, DOI 10.1175/1520-0477(1970)051<0842:AORTAP>2.0.CO;2; Miloshevich LM, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011565; Miloshevich LM, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006083; Milz M, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL037068; Poli P, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000935; Rocken C, 1997, J GEOPHYS RES-ATMOS, V102, P29849, DOI 10.1029/97JD02400; Schreiner W, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL027557; Seidel DJ, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014891; Simmons AJ, 2002, Q J ROY METEOR SOC, V128, P647, DOI 10.1256/003590002321042135; Sokolovskiy SV, 2001, RADIO SCI, V36, P441, DOI 10.1029/1999RS002273; Solheim FS, 1999, J GEOPHYS RES-ATMOS, V104, P9663, DOI 10.1029/1999JD900095; Susskind J, 2011, IEEE T GEOSCI REMOTE, V49, P883, DOI 10.1109/TGRS.2010.2070508; Turner DD, 2003, J ATMOS OCEAN TECH, V20, P117, DOI 10.1175/1520-0426(2003)020<0117:DBAVIV>2.0.CO;2; Vomel H, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007224; Vomel H, 2007, J ATMOS OCEAN TECH, V24, P953, DOI 10.1175/JTECH2019.1; von Engeln A, 2001, GEOPHYS RES LETT, V28, P775, DOI 10.1029/2000GL011718; Wang BR, 2013, ATMOS MEAS TECH, V6, P1073, DOI 10.5194/amt-6-1073-2013; Wang JH, 2000, J CLIMATE, V13, P3041, DOI 10.1175/1520-0442(2000)013<3041:CVSAIV>2.0.CO;2; Wang JH, 2013, J ATMOS OCEAN TECH, V30, P197, DOI 10.1175/JTECH-D-12-00113.1; Wilson B. D., 2005, 17 INT SCI STAT DATA; Wilson B. G., 2006, 18 INT C SCI STAT DA; Xie F, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL043299; Yang S, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016452; Zou X, 2012, J ATMOS SCI, V69, P3670, DOI 10.1175/JAS-D-11-0199.1 48 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUN 27 2014 119 12 7718 7731 10.1002/2013JD021398 14 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN0BP WOS:000340247000041 J Wecht, KJ; Jacob, DJ; Frankenberg, C; Jiang, Z; Blake, DR Wecht, Kevin J.; Jacob, Daniel J.; Frankenberg, Christian; Jiang, Zhe; Blake, Donald R. Mapping of North American methane emissions with high spatial resolution by inversion of SCIAMACHY satellite data JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article UNITED-STATES; SENTINEL-5 PRECURSOR; SURFACE MEASUREMENTS; CONTROL SPACE; GEOS-CHEM; CH4; OZONE; MODEL; ASSIMILATION; SEASONALITY We estimate methane emissions from North America with high spatial resolution by inversion of Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite observations using the Goddard Earth Observing System Chemistry (GEOS-Chem) chemical transport model and its adjoint. The inversion focuses on summer 2004 when data from the Intercontinental Chemical Transport Experiment-North America (INTEX-A) aircraft campaign over the eastern U. S. are available to validate the SCIAMACHY retrievals and evaluate the inversion. From the INTEX-A data we identify and correct a water vapor-dependent bias in the SCIAMACHY data. We conduct an initial inversion of emissions on the horizontal grid of GEOS-Chem (1/2 degrees x 2/3 degrees) to identify correction tendencies relative to the Emission Database for Global Atmospheric Research (EDGAR) v4.2 emission inventory used as a priori. We then cluster these grid cells with a hierarchical algorithm to extract the maximum information from the SCIAMACHY observations. A 1000 cluster ensemble can be adequately constrained, providing similar to 100 km resolution across North America. Analysis of results indicates that the Hudson Bay Lowland wetlands source is 2.1 Tg a(-1), lower than the a priori but consistent with other recent estimates. Anthropogenic U. S. emissions are 30.1 +/- 1.3 Tg a(-1), compared to 25.8 Tg a(-1) and 28.3 Tg a(-1) in the EDGAR v4.2 and Environmental Protection Agency (EPA) inventories, respectively. We find that U. S. livestock emissions are 40% greater than in these two inventories. No such discrepancy is apparent for overall U. S. oil and gas emissions, although this may reflect some compensation between overestimate of emissions from storage/distribution and underestimate from production. We find that U. S. livestock emissions are 70% greater than the oil and gas emissions, in contrast to the EDGAR v4.2 and EPA inventories where these two sources are of comparable magnitude. [Wecht, Kevin J.; Jacob, Daniel J.] Harvard Univ, Dept Earth & Planetary Sci, Cambridge, MA 02138 USA; [Frankenberg, Christian; Jiang, Zhe] CALTECH, Jet Prop Lab, Pasadena, CA USA; [Blake, Donald R.] Univ Calif Irvine, Dept Chem, Irvine, CA 92717 USA Wecht, KJ (reprint author), Harvard Univ, Dept Earth & Planetary Sci, 20 Oxford St, Cambridge, MA 02138 USA. wecht@fas.harvard.edu NASA Carbon Monitoring System (CMS); NASA Atmospheric Composition Modeling and Analysis Program (ACMAP); NASA This work was supported by the NASA Carbon Monitoring System (CMS), the NASA Atmospheric Composition Modeling and Analysis Program (ACMAP), and by a NASA Earth System Science Fellowship to K.J.W. We thank Tom Wirth (U.S. EPA) for providing information on seasonality in the EPA emission inventory. The INTEX-A data are available through NASA's LaRC Airborne Science Data for Atmospheric Composition: ftp://ftp-air.larc.nasa.gov/pub/INTEXA/DC8_AIRCRAFT/. The SCIAMACHY data are available upon request through the SCIAMACHY website: http://www.sciamachy.org/products/index.php. Instructions for downloading and running the GEOS-Chem CTM are available at http://geos-chem.org/ and for the GEOS-Chem adjoint at http://wiki.seas.harvard.edu/geos-chem/index.php/GEOS-Chem_Adjoint. Allen D, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014062; Bergamaschi P, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012287; Bergamaschi P, 2013, J GEOPHYS RES-ATMOS, V118, P7350, DOI 10.1002/jgrd.50480; Bergamaschi P, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007268; Bocquet M, 2009, MON WEATHER REV, V137, P2331, DOI 10.1175/2009MWR2789.1; Bocquet M, 2005, NONLINEAR PROC GEOPH, V12, P219; Bocquet M, 2011, Q J ROY METEOR SOC, V137, P1340, DOI 10.1002/qj.837; Brandt AR, 2014, SCIENCE, V343, P733, DOI 10.1126/science.1247045; Butz A, 2012, REMOTE SENS ENVIRON, V120, P267, DOI 10.1016/j.rse.2011.05.030; Colman JJ, 2001, ANAL CHEM, V73, P3723, DOI 10.1021/ac010027g; Considine DB, 2008, ATMOS CHEM PHYS, V8, P2365; Cressot C, 2014, ATMOS CHEM PHYS, V14, P577, DOI 10.5194/acp-14-577-2014; Crevoisier C, 2013, ATMOS CHEM PHYS, V13, P4279, DOI 10.5194/acp-13-4279-2013; Dils B, 2006, ATMOS CHEM PHYS, V6, P1953; Frankenberg C, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006235; Frankenberg C, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034300; Frankenberg C, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014849; FUNG I, 1991, J GEOPHYS RES-ATMOS, V96, P13033, DOI 10.1029/91JD01247; Hartmann D. L., 2013, CLIMATE CHANGE 2013, P159; Heald CL, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2004JD005185; Henze DK, 2007, ATMOS CHEM PHYS, V7, P2413; Houweling S., 2013, ATMOS CHEM PHYS DISC, V13, P28, DOI [10.5194/acpd-13-28117-2013, DOI 10.5194/ACPD-13-28117-2013]; International Energy Agency, 2013, WORLD EN OUTL SPEC R; JOHNSON SC, 1967, PSYCHOMETRIKA, V32, P241, DOI 10.1007/BF02289588; Kaplan JO, 2002, GEOPHYS RES LETT, V29, DOI 10.1029/2001GL013366; Karion A, 2013, GEOPHYS RES LETT, V40, P4393, DOI 10.1002/grl.50811; Katzenstein AS, 2003, P NATL ACAD SCI USA, V100, P11975, DOI 10.1073/pnas.1635258100; Kirschke S, 2013, NAT GEOSCI, V6, P813, DOI [10.1038/ngeo1955, 10.1038/NGEO1955]; Kort EA, 2008, GEOPHYS RES LETT, V35, DOI 10.1029/2008GL034031; Meirink JF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009740; Michalak AM, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004422; Miller SM, 2013, P NATL ACAD SCI USA, V110, P20018, DOI 10.1073/pnas.1314392110; Miller SM, 2014, GLOBAL BIOGEOCHEM CY, V28, P146, DOI 10.1002/2013GB004580; Monteil G, 2013, J GEOPHYS RES-ATMOS, V118, DOI 10.1002/2013JD019760; Mu M, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016245; Murray LT, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017934; Myhre G., 2013, CLIMATE CHANGE 2013, P659; Park RJ, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2002JD003190; Park RJ, 2006, ATMOS ENVIRON, V40, P5405, DOI 10.1016/j.atmosenv.2006.04.059; Parker R, 2011, GEOPHYS RES LETT, V38, DOI 10.1029/2011GL047871; Petron G, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016360; Pickett-Heaps CA, 2011, ATMOS CHEM PHYS, V11, P3773, DOI 10.5194/acp-11-3773-2011; Prather MJ, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051440; Rodgers C. D., 2000, INVERSE METHODS ATMO; Schepers D, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017549; Singh HB, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007905; Turner A. J., 2013, 2013 FALL M; United States Government Accountability Office, 2010, FED OIL GAS LEAS OPP; U.S. EPA, 2013, INV US GREENH GAS EM; van der Werf GR, 2010, ATMOS CHEM PHYS, V10, P11707, DOI 10.5194/acp-10-11707-2010; van Donkelaar A, 2012, ENVIRON SCI TECHNOL, V46, P11971, DOI 10.1021/es3025319; van Vuuren DP, 2010, ENERG ECON, V32, P1105, DOI 10.1016/j.eneco.2010.03.001; Veefkind JP, 2012, REMOTE SENS ENVIRON, V120, P70, DOI 10.1016/j.rse.2011.09.027; Voulgarakis A, 2013, ATMOS CHEM PHYS, V13, P2563, DOI 10.5194/acp-13-2563-2013; Wang JS, 2004, GLOBAL BIOGEOCHEM CY, V18, DOI 10.1029/2003GB002180; Wecht K. J., 2014, ATMOS CHEM PHYS DISC, V14, P4119, DOI [10.5194/acpd-14-4119-2014, DOI 10.5194/ACPD-14-4119-2014]; Wecht KJ, 2012, ATMOS CHEM PHYS, V12, P1823, DOI 10.5194/acp-12-1823-2012; Weyant J. P., 2006, ENERGY J, V27, P1; Wofsy SC, 2011, PHILOS T R SOC A, V369, P2073, DOI 10.1098/rsta.2010.0313; Worden J, 2012, ATMOS MEAS TECH, V5, P397, DOI 10.5194/amt-5-397-2012; Wu L, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016198; Xiao YP, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009415; Xiong X, 2013, ATMOS MEAS TECH, V6, P2255, DOI 10.5194/amt-6-2255-2013; Xiong XZ, 2008, J GEOPHYS RES-BIOGEO, V113, DOI 10.1029/2007JG000500; Zhang L, 2012, ATMOS CHEM PHYS, V12, P4539, DOI 10.5194/acp-12-4539-2012; Zhang L, 2011, ATMOS ENVIRON, V45, P6769, DOI 10.1016/j.atmosenv.2011.07.054; Zhang Y, 2012, ATMOS CHEM PHYS, V12, P6095, DOI 10.5194/acp-12-6095-2012; Zhu XD, 2013, GLOBAL BIOGEOCHEM CY, V27, P592, DOI 10.1002/gbc.20052 68 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. JUN 27 2014 119 12 7741 7756 10.1002/2014JD021551 16 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AN0BP WOS:000340247000043 J Chow, WY; Lewis, S; Phillips, C Chow, Wing-Yee; Lewis, Shevaun; Phillips, Colin Immediate sensitivity to structural constraints in pronoun resolution FRONTIERS IN PSYCHOLOGY English Article pronoun resolution; Principle B; memory retrieval; self-paced reading; eye-tracking EYE-MOVEMENTS; GENDER INFORMATION; BRAIN POTENTIALS; TIME-COURSE; COMPREHENSION; COREFERENCE; INHIBITION; ASSIGNMENT; REFLEXIVES; FREQUENCY Real-time interpretation of pronouns is sometimes sensitive to the presence of grammatically-illicit antecedents and sometimes not. This occasional sensitivity has been taken as evidence that structural constraints do not immediately impact the initial antecedent retrieval for pronoun interpretation. We argue that it is important to separate effects that reflect the initial antecedent retrieval process from those that reflect later processes. We present results from five reading comprehension experiments. Both the current results and previous evidence support the hypothesis that agreement features and structural constraints immediately constrain the antecedent retrieval process for pronoun interpretation. Occasional sensitivity to grammatically-antecedent. [Chow, Wing-Yee; Lewis, Shevaun; Phillips, Colin] Univ Maryland, Dept Linguist, College Pk, MD USA; [Chow, Wing-Yee] Basque Ctr Cognit Brain & Language, Donostia San Sebastian, San Sebastian, Spain; [Lewis, Shevaun] Johns Hopkins Univ, Dept Cognit Sci, Baltimore, MD 21218 USA; [Phillips, Colin] Univ Maryland, Program Neurosci & Cognit Sci, College Pk, MD 20742 USA Chow, WY (reprint author), Basque Ctr Cognit Brain & Language, Paseo Mikeletegi 69,2nd Floor, Donostia San Sebastian, San Sebastian, Spain. wingyeechow.zoey@gmail.com Arnold JE, 2000, COGNITION, V76, pB13, DOI 10.1016/S0010-0277(00)00073-1; Badecker W, 2002, J EXP PSYCHOL LEARN, V28, P748, DOI 10.1037//0278-7393.28.4.748; BOCK K, 1991, COGNITIVE PSYCHOL, V23, P45, DOI 10.1016/0010-0285(91)90003-7; Brysbaert M, 1996, Q J EXP PSYCHOL-A, V49, P664, DOI 10.1080/027249896392540; CARAMAZZA A, 1977, J VERB LEARN VERB BE, V16, P601, DOI 10.1016/S0022-5371(77)80022-4; Carreiras M, 1996, Q J EXP PSYCHOL-A, V49, P639, DOI 10.1080/027249896392531; Chomsky N., 1981, LECT GOVT BINDING; Clackson K, 2011, J MEM LANG, V65, P128, DOI 10.1016/j.jml.2011.04.007; Clifton C, 1997, J MEM LANG, V36, P276, DOI 10.1006/jmla.1996.2499; Clifton C., 1999, RIV LINGUISTICA, V11, P11; CORBETT AT, 1983, MEM COGNITION, V11, P283, DOI 10.3758/BF03196975; Dillon B, 2013, J MEM LANG, V69, P85, DOI 10.1016/j.jml.2013.04.003; EHRLICH K, 1983, J VERB LEARN VERB BE, V22, P75, DOI 10.1016/S0022-5371(83)80007-3; EVANS G, 1980, LINGUIST INQ, V11, P337; GARNHAM A, 1995, J MEM LANG, V34, P41, DOI 10.1006/jmla.1995.1003; Gordon PC, 2001, J EXP PSYCHOL LEARN, V27, P1411, DOI 10.1037//0278-7393.27.6.1411; GRODZINSKY Y, 1993, LINGUIST INQ, V24, P69; Heller D., 2012, ANN M PSYCH SOC MINN; JUST MA, 1982, J EXP PSYCHOL GEN, V111, P228, DOI 10.1037/0096-3445.111.2.228; Kennison SM, 2003, J MEM LANG, V49, P335, DOI 10.1016/S0749-596X(03)00071-8; Kennison SM, 2003, J PSYCHOLINGUIST RES, V32, P355, DOI 10.1023/A:1023599719948; KEPPEL G, 1962, J VERB LEARN VERB BE, V1, P153, DOI 10.1016/S0022-5371(62)80023-1; Lee M.-W., 2008, ROLE GRAMMATICAL CON; Lewis RL, 2005, COGNITIVE SCI, V29, P375, DOI 10.1207/s15516709cog0000_25; MACDONALD MC, 1990, J MEM LANG, V29, P469, DOI 10.1016/0749-596X(90)90067-A; Martin AE, 2008, J MEM LANG, V58, P879, DOI 10.1016/j.jml.2007.06.010; NICOL J, 1989, J PSYCHOLINGUIST RES, V18, P5, DOI 10.1007/BF01069043; Nicol J., 1997, PROCESSING PRONOUNS; Nieuwland MS, 2006, BRAIN RES, V1118, P155, DOI 10.1016/j.brainres.2006.08.022; Nieuwland MS, 2007, J COGNITIVE NEUROSCI, V19, P228, DOI 10.1162/jocn.2007.19.2.228; Osterhout L, 1995, J MEM LANG, V34, P739, DOI 10.1006/jmla.1995.1033; Parker D., 2012, TIM GRAMM WORKSH 35; Patterson C, 2014, FRONT PSYCHOL, V5, DOI 10.3389/fpsyg.2014.00147; RAYNER K, 1986, MEM COGNITION, V14, P191, DOI 10.3758/BF03197692; Rayner K, 1998, PSYCHOL BULL, V124, P372, DOI 10.1037/0033-2909.124.3.372; Rayner K., 1989, PSYCHOL READING; Reinhart T., 1983, ANAPHORA SEMANTIC IN; Rohde D., 2003, LINGER VERSION 2 94; Runner JT, 2006, COGNITIVE SCI, V30, P193, DOI 10.1207/s15516709cog0000_58; Staub A, 2010, COGNITION, V114, P447, DOI 10.1016/j.cognition.2009.11.003; Staub A, 2009, J MEM LANG, V60, P308, DOI 10.1016/j.jml.2008.11.002; Stewart AJ, 2007, Q J EXP PSYCHOL, V60, P1680, DOI 10.1080/17470210601160807; Sturt P., 2012, 25 ANN CUNY HUM SENT; Sturt P, 2003, J MEM LANG, V48, P542, DOI 10.1016/S0749-596X(02)00536-3; Sturt P., 2013, SENTENCE PROCESSING, P136; Swinney D., 2003, ANAPHORA REFERENCE G, P72, DOI 10.1002/9780470755594.ch3; Van Berkum J. J. A., 2004, ANN M COGN NEUR SOC; Wagers MW, 2009, J MEM LANG, V61, P206, DOI 10.1016/j.jml.2009.04.002; WALKER N, 1983, MEM COGNITION, V11, P275, DOI 10.3758/BF03196974 49 1 1 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUN 27 2014 5 630 10.3389/fpsyg.2014.00630 16 Psychology, Multidisciplinary Psychology AK9DG WOS:000338727200001 J Zanette, I; Zhou, T; Burvall, A; Lundstrom, U; Larsson, DH; Zdora, M; Thibault, P; Pfeiffer, F; Hertz, HM Zanette, I.; Zhou, T.; Burvall, A.; Lundstrom, U.; Larsson, D. H.; Zdora, M.; Thibault, P.; Pfeiffer, F.; Hertz, H. M. Speckle-Based X-Ray Phase-Contrast and Dark-Field Imaging with a Laboratory Source PHYSICAL REVIEW LETTERS English Article SCATTERING; INFORMATION; TOMOGRAPHY; RETRIEVAL We report on the observation and application of near-field speckles with a laboratory x-ray source. The detection of speckles is possible thanks to the enhanced brilliance properties of the used liquid-metal-jet source, and opens the way to a range of new applications in laboratory-based coherent x-ray imaging. Here, we use the speckle pattern for multimodal imaging of demonstrator objects. Moreover, we introduce algorithms for phase and dark-field imaging using speckle tracking, and we show that they yield superior results with respect to existing methods. [Zanette, I.; Zdora, M.; Pfeiffer, F.] Tech Univ Munich, Dept Phys, D-85748 Garching, Germany; [Zhou, T.; Burvall, A.; Lundstrom, U.; Larsson, D. H.; Hertz, H. M.] KTH Royal Inst Technol, Dept Appl Phys, S-10691 Stockholm, Sweden; [Thibault, P.] UCL, Dept Phys & Astron, London WC1E 6BT, England Zanette, I (reprint author), Tech Univ Munich, Dept Phys, D-85748 Garching, Germany. irene.zanette@tum.de DFG Cluster of Excellence Munich-Centre for Advanced Photonics (MAP); DFG Gottfried Wilhelm Leibniz program; European Research Council (ERC) [StG 240142, 279753]; Swedish Research council; Wallenberg foundation We acknowledge financial support from the DFG Cluster of Excellence Munich-Centre for Advanced Photonics (MAP), the DFG Gottfried Wilhelm Leibniz program, and the European Research Council (ERC, FP7, StG 240142 & 279753), the Swedish Research council, and the Wallenberg foundation. We acknowledge Hongchang Wang for fruitful discussions. Alaimo MD, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.194805; Axelsson O, 1996, ITERATIVE SOLUTION M; Berujon S, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.158102; Berujon S, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.063813; Cerbino R, 2008, NAT PHYS, V4, P238, DOI 10.1038/nphys837; Cloetens P, 1999, APPL PHYS LETT, V75, P2912, DOI 10.1063/1.125225; Diemoz PC, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.138105; Ferri F, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.041405; Fitzgerald R, 2000, PHYS TODAY, V53, P23, DOI 10.1063/1.1292471; Hemberg O, 2003, APPL PHYS LETT, V83, P1483, DOI 10.1063/1.1602157; HENKE BL, 1993, ATOM DATA NUCL DATA, V54, P181, DOI 10.1006/adnd.1993.1013; INGAL VN, 1995, J PHYS D APPL PHYS, V28, P2314, DOI 10.1088/0022-3727/28/11/012; Kottler C, 2007, OPT EXPRESS, V15, P1175, DOI 10.1364/OE.15.001175; Lundstrom U, 2012, PHYS MED BIOL, V57, P7431, DOI 10.1088/0031-9155/57/22/7431; Momose A, 1996, NAT MED, V2, P473, DOI 10.1038/nm0496-473; Momose A, 2003, JPN J APPL PHYS 2, V42, pL866, DOI [10.1143/JJAP.42.L866, 10.1143/JJAP.L866]; Morgan KS, 2012, APPL PHYS LETT, V100, DOI 10.1063/1.3694918; Pfeiffer F, 2006, NAT PHYS, V2, P258, DOI 10.1038/nphys265; Pfeiffer F, 2005, PHYS REV LETT, V94, DOI 10.1103/PhysRevLett.94.164801; Pfeiffer F, 2008, NAT MATER, V7, P134, DOI 10.1038/nmat2096; Stampanoni M, 2011, INVEST RADIOL, V46, P801, DOI 10.1097/RLI.0b013e31822a585f; Stockmar M, 2013, SCI REP-UK, V3, DOI 10.1038/srep01927; Tuohimaa T, 2007, APPL PHYS LETT, V91, DOI 10.1063/1.2769760; Wen H, 2009, RADIOLOGY, V251, P910, DOI 10.1148/radiol.2521081903; Wilkins SW, 1996, NATURE, V384, P335, DOI 10.1038/384335a0 25 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 0031-9007 1079-7114 PHYS REV LETT Phys. Rev. Lett. JUN 26 2014 112 25 253903 10.1103/PhysRevLett.112.253903 5 Physics, Multidisciplinary Physics AK2XY WOS:000338284900009 J Medeiros, MFT; Albuquerque, UP Trindade Medeiros, Maria Franco; Albuquerque, Ulysses Paulino Food flora in 17th century northeast region of Brazil in Historia Naturalis Brasiliae JOURNAL OF ETHNOBIOLOGY AND ETHNOMEDICINE English Article Food species; Marcgrave; 17th century; Brazil; Historical ethnobotany; Ethnobiology UMBU SPONDIAS-TUBEROSA; NORTHWESTERN PATAGONIA; MAPUCHE COMMUNITY; MEDICINAL-PLANTS; NE BRAZIL; ETHNOBOTANICAL KNOWLEDGE; ABORIGINAL COMMUNITIES; SEMIARID REGION; CONSERVATION; ARGENTINA Background: This article reports historical ethnobotany research conducted from a study of the work Historia Naturalis Brasiliae (Natural History of Brazil), authored by Piso and Marcgrave and published in 1648, with main focus on Caatinga of northeast region of Brazil. Methods: Focusing the content analysis on the section dedicated to plant species with multiple uses, Marcgrave's contribution to the aforementioned work, this research had the following objectives: the retrieval of 17th century knowledge about the food uses of the flora in the northeast region of Brazil, including the taxonomic classifications; the identification of plant parts, their modes of consumption and the ethnic group of consumers; and the verification of the use of these species over time. Results: The use of 80 food species at the time of the publication of the work is indicated, some of which are endemic to the Caatinga, such as "umbu" (Spondias tuberosa Arruda), "mandacaru" (Cereus jamacaru DC.) and "carnauba" (Copernicia cerifera Mart.). It is noticeable that among the species listed by Marcgrave, some species still lack current studies indicating their real nutritional value. The present study is an unprecedented work because it introduces, in a systematic way, the food plants described in a study of 17th century Brazil. Conclusions: Finally, this study makes information about plants consumed in the past accessible, aiming to provide material for studies that could develop new food products today. [Trindade Medeiros, Maria Franco] Univ Fed Campina Grande, Ctr Educ & Saude, Unidade Acad Educ, Dept Ciencias Biol, BR-58175000 Cuite, Paraiba, Brazil; [Albuquerque, Ulysses Paulino] Univ Fed Rural Pernambuco, Lab Appl & Theoret Ethnobiol LEA, Dept Biol, Area Bot, BR-52171900 Recife, PE, Brazil Medeiros, MFT (reprint author), Univ Fed Campina Grande, Ctr Educ & Saude, Unidade Acad Educ, Dept Ciencias Biol, Olho DAgua da Bica S-N, BR-58175000 Cuite, Paraiba, Brazil. mariaftm@hotmail.com CNPq (National Council for Scientific and Technological Development); FACEPE (Foundation for Support of Science and Technology); Potential Use of Biological Resources in the Semi-Arid Region of Northeastern Brazil [APQ-1264-2.05/10] To CNPq (National Council for Scientific and Technological Development) for the research support and postdoctoral scholarships granted to U.P. Albuquerque and M.F.T. Medeiros, respectively. This paper is contribution P016 of the Rede de Investigacao em Biodiversidade e Saberes Locais (REBISA-Network of Research in Biodiversity and Local Knowledge), with financial support from FACEPE (Foundation for Support of Science and Technology) to the project Nucleo de Pesquisa em Ecologia, conservacao e Potencial de Uso de Recursos Biologicos no Semiarido do Nordeste do Brasil (Center for Research in Ecology, Conservation and Potential Use of Biological Resources in the Semi-Arid Region of Northeastern Brazil-APQ-1264-2.05/10). Aguirre P, 2005, ESTRATEGIAS CONSUMO; Albuquerque UP, 2007, J ETHNOPHARMACOL, V114, P325, DOI DOI 10.1016/J.JEP.2007.08.017; Albuquerque UP, 2008, B LATINOAM CARIBE PL, V7, P156; Albuquerque UP, 2007, J ETHNOPHARMACOL, V113, P156, DOI 10.1016/j.jep.2007.05.025; Albuquerque UP, 2009, BIODIVERS CONSERV, V18, P127, DOI 10.1007/s10531-008-9463-8; Alencar NL, 2010, ECON BOT, V64, P68, DOI 10.1007/s12231-009-9104-5; Almeida ALS, 2008, FUNCTIONAL ECOSYSTEM, V2, P32; Almeida MM, 2007, REV CIENC AGRON, V38, P440; Amorozo MCM, 2008, FUNCTIONAL ECOSYSTEM, V2, P11; Andrade-Lima D, 1954, CONTRIBUTION STUDY F, VI; Anonimo, ATLAS COSTAS POSESIO, VXVII; Araujo TAD, 2008, J ETHNOPHARMACOL, V120, P72, DOI 10.1016/j.jep.2008.07.032; Aublet MF, 1775, HIST PLANTES GUIANE; Avancini EG, 1991, DOCE INFERNO ACUCAR; Bennett BC, 2000, ECON BOT, V54, P90, DOI 10.1007/BF02866603; Boxer CR, 2004, HOLANDESES BRASIL 16; Braga R, 1976, PLANTAS NORDESTE ESP; Bruce JW, SEGURIDAD ALIMENTARI; Camara MA, 1982, MANUEL ARRUDA CAMARA; Cartaxo SL, 2010, J ETHNOPHARMACOL, V131, P326, DOI 10.1016/j.jep.2010.07.003; Castro J, 1946, ARQUIVOS BRASILEIROS, V3, P5; Castro J, 1967, GEOGRAFIA FOME; Cavalcanti MLM, 2010, HIST SABORES PERNAMB; Cavalcanti NB, 1999, CIENC AGROTEC, V23, P212; Cavalcanti NB, 1999, 37 C BRAS EC SOC RUR; Cesar G, 1956, CURIOSIDADES NOSSA F; Chaves EMF, 2009, SUSTENTABILIDADE SEM; Cunha E, 1902, SERTOES COMPANHA CAN; Ellen R, 2007, MODERN CRISES TRADIT, P1; Eyssartier Cecilia, 2008, J Ethnobiol Ethnomed, V4, P25, DOI 10.1186/1746-4269-4-25; Felippe G, 1998, SABER SABOR PLANTAS; Ferraz JSF, 2005, ZONAS ARIDAS, V9, P27; Florentino A. T. N., 2007, Acta Botanica Brasilica, V21, P37, DOI 10.1590/S0102-33062007000100005; Francoso MC, 2009, THESIS U ESTADUAL CA; Gardner G, 1846, LOND J BOT, V1846, P455; Gesteira HM, 2004, REV SOC BRASILEIRA H, V2, P6; Gesteira HM, 2001, THESIS U FEDERAL FLU, P169; Guinand Y, 2001, POTENTIAL INDIGENOUS, P31; Henriquez FF, 2004, ANCORA MED CONSERVAR; Hoehne FC, 1937, BOT AGR BRASIL SECUL; Hughes J, 2009, ACTA HORTIC, V806, P39; Judd W.S., 2009, SISTEMATICA VEGETAL; Ladio A, 2007, J ARID ENVIRON, V69, P695, DOI 10.1016/j.jaridenv.2006.11.008; Ladio AH, 2004, BIODIVERS CONSERV, V13, P1153, DOI 10.1023/B:BIOC.0000018150.79156.50; Ladio AH, 2000, HUM ECOL, V28, P53, DOI 10.1023/A:1007027705077; Ladio AH, 2003, BIODIVERS CONSERV, V12, P937, DOI 10.1023/A:1022873725432; Ladio AH, 2001, ECON BOT, V55, P243, DOI 10.1007/BF02864562; Laet J, 2007, ROTEIRO BRASIL DESCO; Leite JRT, 1967, PINTURA BRASIL HOLAN; Lichtesnstein H, 1961, ESTUDO CRITICO TRABA; Lineu C, 1840, SPECIES PLANTARUM VO; Lozada M, 2006, ECON BOT, V60, P374, DOI 10.1663/0013-0001(2006)60[374:CTOEKI]2.0.CO;2; Lucena RFP, 2007, ENVIRON MONIT ASSESS, V125, P281, DOI 10.1007/s10661-006-9521-1; MAIA G. N, 2004, CAATINGA ARVORES ARB; Marcgrave G, 1942, HIST NATURAL BRASIL; Martius CF V, 1855, ABNANDL KGL AKAD MP, V7, P179; Martius CFP V, 1906, FLORA BRASILIENSIS, V1840, P1906; Medeiros MFT, 2010, METHODS TECHNIQUES E, P419; Medeiros PM, 2011, ENVIRON MANAGE, V47, P410, DOI 10.1007/s00267-011-9618-3; Mello EC, 2010, BRASIL HOLANDES 1630; Melo ED, 2008, REV BRAS CIENC FARM, V44, P193, DOI 10.1590/S1516-93322008000200005; Monteiro JM, 2011, ENVIRON MONIT ASSESS, V178, P179, DOI 10.1007/s10661-010-1681-3; Moreira J, 1917, INICIADORES ESTUDO M; Nascimento VT, 2010, THESIS U FEDERAL RUR; Nascimento VT, 2012, ECON BOT, V66, P22; Nascimento VT, 2011, FOOD RES INT, V44, P2112; Nascimento V. T., 2009, Environment Development and Sustainability, V11, P1005, DOI 10.1007/s10668-008-9164-1; Neto EMDL, 2010, ECON BOT, V64, P11, DOI 10.1007/s12231-009-9106-3; Neto EMDL, 2012, ENVIRON MONIT ASSESS, V184, P4489, DOI 10.1007/s10661-011-2280-7; Oliveira FMN, 2007, REV BRASILEIRA PRODU, V11, P15; Pelto G., 1989, RES METHODS NUTR ANT; Pickel Dom BJ, 1949, REV FLORA MED, V16, P155; Pickel Dom BJ, 2008, FLORA NORDESTE BRASI; Pilla M. A. C., 2009, Acta Botanica Brasilica, V23, P1190, DOI 10.1590/S0102-33062009000400030; Pio Correa P, 1926, DICIONARIO PLANTAS U; Piso W, 1648, HIST NATURALIS BRASI; Piso W, 1658, INDIAE UTRIUSQUE RE; Ramosa MA, 2008, BIOMASS BIOENERG, V32, P510, DOI 10.1016/j.biombioe.2007.11.015; Rochow M, 2009, J ETHNOBIOL ETHNOMED, V5, P18; Saint- Hilaire A, 1824, PLANTES USUELLES BRE; Salgado CL, 2008, 4 ENANPPAS ENC NAC A; Santos LL, 2009, ECON BOT, V63, P363; Silva FS, 2010, REV BRASILEIRA FARMA, V21, P382; Sa e Silva I. M. M., 2009, Environment Development and Sustainability, V11, P833, DOI 10.1007/s10668-008-9146-3; Silva S, 2001, FRUTAS BRASIL; Souza GS, 1971, TRATADO DESCRITIVO B, V117; Spix JB, 1981, VIAGEM PELO BRASIL 1; Stuessy T. F., 2009, PLANT TAXONOMY SYSTE; Toledo BA, 2009, J ETHNOBIOL ETHNOMED, V5, DOI 10.1186/1746-4269-5-40; von Martius CFP, 1979, NATUREZA DOENCAS MED 90 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1746-4269 J ETHNOBIOL ETHNOMED J. Ethnobiol. Ethnomed. JUN 25 2014 10 50 10.1186/1746-4269-10-50 20 Pharmacology & Pharmacy Pharmacology & Pharmacy AK8OH WOS:000338686800001 J Hattori, M Hattori, Motonobu A biologically inspired dual-network memory model for reduction of catastrophic forgetting NEUROCOMPUTING English Article Catastrophic forgetting; Chaotic neural network; Complementary learning systems; Dual-network; Hippocampus; Neuronal turnover COMPLEMENTARY-LEARNING-SYSTEMS; CONNECTIONIST MODELS; RECOGNITION MEMORY; HIPPOCAMPAL CA3; NEURAL-NETWORKS Neural networks encounter serious catastrophic forgetting when information is learned sequentially, which is unacceptable for both a model of human memory and practical engineering applications. In this study, we propose a novel biologically inspired dual-network memory model that can significantly reduce catastrophic forgetting. The proposed model consists of two distinct neural networks: hippocampal and neocortical networks. Information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network. In the hippocampal network, chaotic behavior of neurons in the CA3 region of the hippocampus and neuronal turnover in the dentate gyrus region are introduced. Chaotic recall by CA3 enables retrieval of stored information in the hippocampal network. Thereafter; information retrieved from the hippocampal network is interleaved with previously stored information and consolidated by using pseudopatterns in the neocortical network. The computer simulation results show the effectiveness of the proposed dual-network memory model. (C) 2014 Elsevier B.V. All rights reserved. [Hattori, Motonobu] Univ Yamanashi, Kofu, Yamanashi, Japan; [Hattori, Motonobu] Carnegie Mellon Univ, CNBC, Pittsburgh, PA 15213 USA Hattori, M (reprint author), 4-3-11 Takeda, Kofu, Yamanashi 4008511, Japan. m-hattori@yamanashi.ac.jp JSPS KAKENHI [25330286] The author would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported by JSPS KAKENHI (Grant no. 25330286). AIHARA K, 1990, PHYS LETT A, V144, P333, DOI 10.1016/0375-9601(90)90136-C; Akimoto H., 2006, WSEAS Transactions on Systems, V5; Ans B, 1997, CR ACAD SCI III-VIE, V320, P989, DOI 10.1016/S0764-4469(97)82472-9; Becker S, 2005, HIPPOCAMPUS, V15, P722, DOI 10.1002/hipo.20095; Eichenbaum H, 2002, COGNITIVE NEUROSCIEN; French R. M., 1997, Connection Science, V9, DOI 10.1080/095400997116595; Hattori M., 2011, RECENT RES COMPUTATI, P27; Hattori M., 2010, P IEEE INNS INT JOIN, P1678; Hattori M., 2012, LECT NOTES COMPUTE 2, V7664, P392; HAYASHI H, 1995, BRAIN RES, V686, P194, DOI 10.1016/0006-8993(95)00485-9; Hertz J, 1991, INTRO THEORY NEURAL; MCCLELLAND JL, 1995, PSYCHOL REV, V102, P419, DOI 10.1037/0033-295X.102.3.419; Norman KA, 2003, PSYCHOL REV, V110, P611, DOI 10.1037/0033-295X.110.4.611; Osana Y, 1996, IEEE IJCNN, P816, DOI 10.1109/ICNN.1996.549002; RATCLIFF R, 1990, PSYCHOL REV, V97, P285, DOI 10.1037//0033-295X.97.2.285; Robins A., 1995, Connection Science, V7, DOI 10.1080/09540099550039318; Samura T, 2008, NEUROCOMPUTING, V71, P3176, DOI 10.1016/j.neucom.2008.04.026; Wakagi Y., 2008, INT J MATH COMPUTERS, V2, P215; YECKEL MF, 1990, P NATL ACAD SCI USA, V87, P5832, DOI 10.1073/pnas.87.15.5832 19 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0925-2312 1872-8286 NEUROCOMPUTING Neurocomputing JUN 25 2014 134 SI 262 268 10.1016/j.neucom.2013.08.044 7 Computer Science, Artificial Intelligence Computer Science AG5VE WOS:000335486000034 J Fertonani, A; Brambilla, M; Cotelli, M; Miniussi, C Fertonani, Anna; Brambilla, Michela; Cotelli, Maria; Miniussi, Carlo The timing of cognitive plasticity in physiological aging: a tDCS study of naming FRONTIERS IN AGING NEUROSCIENCE English Article aging; language; transcranial direct current stimulation; facilitation; neuroplasticity; NIBS DIRECT-CURRENT STIMULATION; HUMAN MOTOR CORTEX; TRANSCRANIAL MAGNETIC STIMULATION; NONINVASIVE BRAIN-STIMULATION; PREFRONTAL CORTEX; HOMEOSTATIC PLASTICITY; LANGUAGE PRODUCTION; ALZHEIMER-DISEASE; WORKING-MEMORY; OLDER ADULTS This study aimed to explore the effects of transcranial direct current stimulation (tDCS) on physiologically aging adults performing a naming task. tDCS is a method that modulates human cortical excitability. Neuroplasticity is considered to have its foundation in cortical excitability as a property that adjusts the connection strength between neurons in the brain. Language efficiency, as all functions, relies on integration of information (i.e., effectiveness of connectivity) through neurons in the brain. So the use of tDCS, to modulate cortical excitability, can help to define the state of cognitive plasticity in the aging brain. Based on Hebb's rule, an increase in synaptic efficacy does not rely only on the increase of excitability but also on the timing of activation. Therefore, a key issue in this study is the timing of tDCS application in relation to a task: When to deliver tDCS to induce modulatory effects on task execution to facilitate naming. Anodal tDCS was applied to the left dorsolateral prefrontal cortex of older and young adults before and during a naming task.ln older adults, tDCS improved naming performance and decreased the verbal reaction times only if it was applied during the task execution, whereas in young subjects both stimulation conditions improved naming performance.These findings highlight that in healthy aging adults, the cerebral network dedicated to lexical retrieval processing may be facilitated only if stimulation is applied to an "active" neural network. We hypothesize that this change is due to the neuronal synaptic changes, in the aging brain, which reduce the window of when cortical excitability can facilitate synaptic efficacy and therefore plasticity. [Fertonani, Anna; Brambilla, Michela; Cotelli, Maria; Miniussi, Carlo] Ist Ctr San Giovanni di Dio Fatebenefratelli, IRCCS, Cognit Neurosci Sect, Brescia, Italy; [Miniussi, Carlo] Univ Brescia, Dept Clin & Expt Sci, Neurosci Sect, I-25123 Brescia, Italy Miniussi, C (reprint author), Univ Brescia, Dept Clin & Expt Sci, Viale Europa 11, I-25123 Brescia, Italy. carlo.miniussi@cognitiveneuroscience.it ARDILA A, 1989, DEV NEUROPSYCHOL, V5, P307; Bates E., 2000, INTRO CRL INT PICTUR, V12; BIENENSTOCK EL, 1982, J NEUROSCI, V2, P32; Boggio PS, 2009, J NEUROL NEUROSUR PS, V80, P444, DOI 10.1136/jnnp.2007.141853; Buchhold B, 2007, RESTOR NEUROL NEUROS, V25, P467; Burke D. M., 2008, HDB AGING COGNITION, P373; Burke DM, 1997, PHILOS T ROY SOC B, V352, P1845; Burke DM, 2004, CURR DIR PSYCHOL SCI, V13, P21, DOI 10.1111/j.0963-7214.2004.01301006.x; Cabeza R, 2002, PSYCHOL AGING, V17, P85, DOI 10.1037//0882-7974.17.1.85; Cappa SF, 2002, NEUROLOGY, V59, P720; Caserta MT, 2009, INT REV NEUROBIOL, V84, P1, DOI 10.1016/S0074-7742(09)00401-2; Chi RP, 2010, BRAIN RES, V1353, P168, DOI 10.1016/j.brainres.2010.07.062; Cotelli M, 2008, EUR J NEUROL, V15, P1286, DOI 10.1111/j.1468-1331.2008.02202.x; Cotelli M, 2006, ARCH NEUROL-CHICAGO, V63, P1602, DOI 10.1001/archneur.63.11.1602; Cotelli M, 2010, FRONT AGING NEUROSCI, V2, DOI 10.3389/fnagi.2010.00151; Cotelli Maria, 2012, Front Neurosci, V6, P120, DOI 10.3389/fnins.2012.00120; Davis SW, 2008, CEREB CORTEX, V18, P1201, DOI 10.1093/cercor/bhm155; Dumitriu D, 2010, J NEUROSCI, V30, P7507, DOI 10.1523/JNEUROSCI.6410-09.2010; Fertonani A, 2010, BEHAV BRAIN RES, V208, P311, DOI 10.1016/j.bbr.2009.10.030; Feyereisen P, 1997, J SPEECH LANG HEAR R, V40, P1328; Fitzjohn SM, 2002, CELL CALCIUM, V32, P405, DOI 10.1016/S0143-4160(02)00199-9; Fregni F, 2005, EXP BRAIN RES, V166, P23, DOI 10.1007/s00221-005-2334-6; Goodglass H., 1980, LANG COMMUN, P37; Goral M., 2007, MENTAL LEXICON, V2, P215, DOI [10.1075/ml.2.2.05gor, DOI 10.1075/ML.2.2.05GOR]; Hebb D.O., 1949, ORG BEHAV NEUROPSYCH; Holland R, 2011, CURR BIOL, V21, P1403, DOI 10.1016/j.cub.2011.07.021; Holland R, 2012, APHASIOLOGY, V26, P1169, DOI 10.1080/02687038.2011.616925; Hummert M., 2004, HDB COMMUNICATION AG, P91; Jacobson L, 2012, EXP BRAIN RES, V216, P1, DOI 10.1007/s00221-011-2891-9; Kemper S, 2001, PSYCHOL AGING, V16, P312, DOI 10.1037//0882-7974.16.2.312; Kemper S., 2006, LIFESPAN COGNITION M, P223, DOI [10.1093/acprof:oso/9780195169539.003.0015, DOI 10.1093/ACPROF:OSO/9780195169539.003.0015]; Kirischuk S, 1996, LIFE SCI, V59, P451, DOI 10.1016/0024-3205(96)00324-4; Kuo MF, 2008, NEUROPSYCHOLOGIA, V46, P2122, DOI 10.1016/j.neuropsychologia.2008.02.023; LABARGE E, 1986, BRAIN LANG, V27, P380, DOI 10.1016/0093-934X(86)90026-X; Liebetanz D, 2002, BRAIN, V125, P2238, DOI 10.1093/brain/awf238; Miceli G., 1994, BATTERIA ANALISI DEF; Miniussi C, 2013, NEUROSCI BIOBEHAV R, V37, P1702, DOI 10.1016/j.neubiorev.2013.06.014; Monti A., 2012, J NEUROL NEUROSUR PS, V84, P832, DOI 10.1136/jnnp-2012-302825; Morrison JH, 2012, NAT REV NEUROSCI, V13, P240, DOI 10.1038/nrn3200; Nakata H, 2005, EXP BRAIN RES, V162, P293, DOI 10.1007/s00221-004-2195-4; NICHOLAS M, 1985, CORTEX, V21, P595; Nitsche MA, 2003, J PHYSIOL-LONDON, V553, P293, DOI 10.1113/jphysiol.2003.049916; Nitsche MA, 2003, CLIN NEUROPHYSIOL, V114, P600, DOI 10.1016/S1388-2457(02)00412-1; Nitsche MA, 2001, NEUROLOGY, V57, P1899; Nitsche MA, 2008, BRAIN STIMUL, V1, P206, DOI 10.1016/j.brs.2008.06.004; Ohn SH, 2008, NEUROREPORT, V19, P43, DOI 10.1097/WNR.0b013e3282f2adfd; Pakkenberg B, 2003, EXP GERONTOL, V38, P95, DOI 10.1016/S0531-5565(02)00151-1; PALMER RM, 1990, GERIATRICS, V45, P47; Paulus W, 2011, NEUROPSYCHOL REHABIL, V21, P602, DOI 10.1080/09602011.2011.557292; Pena-Gomez C, 2012, BRAIN STIMUL, V5, P252, DOI 10.1016/j.brs.2011.08.006; Peters A, 2008, NEUROSCIENCE, V152, P970, DOI 10.1016/j.neuroscience.2007.07.014; Pirulli C, 2013, BRAIN STIMUL, V6, P683, DOI 10.1016/j.brs.2012.12.005; Pirulli C, 2014, FRONT BEHAV NEUROSCI, V8, DOI 10.3389/fnbeh.2014.00226; Poreisz C, 2007, BRAIN RES BULL, V72, P208, DOI 10.1016/j.brainresbull.2007.01.004; Priori A, 2003, CLIN NEUROPHYSIOL, V114, P589, DOI 10.1016/S1388-2457(02)00437-6; Ross LA, 2011, FRONT AGING NEUROSCI, V3, DOI 10.3389/fnagi.2011.00016; Sala-Llonch R., 2014, NEUROBIOL AGING, DOI [10.1016/j.neurobiolaging.2014.04.007, DOI 10.1016/J.NEUROBIOLAGING.2014.04.007]; Siebner HR, 2004, J NEUROSCI, V24, P3379, DOI 10.1523/JNEUROSCI.5316-03.2004; Siebner HR, 2010, CLIN NEUROPHYSIOL, V121, P461, DOI 10.1016/j.clinph.2009.12.009; Sparing R, 2008, NEUROPSYCHOLOGIA, V46, P261, DOI 10.1016/j.neuropsychologia.2007.07.009; Speisman RB, 2013, NEUROBIOL AGING, V34, P263, DOI 10.1016/j.neurobiolaging.2012.05.023; Stagg CJ, 2011, NEUROSCIENTIST, V17, P37, DOI 10.1177/1073858410386614; Stagg CJ, 2011, NEUROPSYCHOLOGIA, V49, P800, DOI 10.1016/j.neuropsychologia.2011.02.009; Thornton R., 2006, HDB PSYCHOL AGING, P262, DOI DOI 10.1016/B978-012101264-9/50015-X; Vidal-Pineiro D, 2014, BRAIN STIMUL, V7, P287, DOI 10.1016/j.brs.2013.12.016; Wirth M, 2011, NEUROPSYCHOLOGIA, V49, P3989, DOI 10.1016/j.neuropsychologia.2011.10.015; Ziemann U, 2008, BRAIN STIMUL, V1, P60, DOI 10.1016/j.brs.2007.08.003 67 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1663-4365 FRONT AGING NEUROSCI Front. Aging Neurosci. JUN 24 2014 6 131 10.3389/fnagi.2014.00131 9 Geriatrics & Gerontology; Neurosciences Geriatrics & Gerontology; Neurosciences & Neurology AM0TN WOS:000339559800001 J Fukushima, H; Zhang, Y; Archbold, G; Ishikawa, R; Nader, K; Kida, S Fukushima, Hotaka; Zhang, Yue; Archbold, Georgia; Ishikawa, Rie; Nader, Karim; Kida, Satoshi Enhancement of fear memory by retrieval through reconsolidation ELIFE English Article PROTEIN-SYNTHESIS; REACTIVATED MEMORY; RETROGRADE-AMNESIA; UP-REGULATION; C/EBP-BETA; EXTINCTION; TRANSCRIPTION; PLASTICITY; STABILITY; AMYGDALA Memory retrieval is considered to have roles in memory enhancement. Recently, memory reconsolidation was suggested to reinforce or integrate new information into reactivated memory. Here, we show that reactivated inhibitory avoidance (IA) memory is enhanced through reconsolidation under conditions in which memory extinction is not induced. This memory enhancement is mediated by neurons in the amygdala, hippocampus, and medial prefrontal cortex (mPFC) through the simultaneous activation of calcineurin-induced proteasome-dependent protein degradation and cAMP responsive element binding protein-mediated gene expression. Interestingly, the amygdala is required for memory reconsolidation and enhancement, whereas the hippocampus and mPFC are required for only memory enhancement. Furthermore, memory enhancement triggered by retrieval utilizes distinct mechanisms to strengthen IA memory by additional learning that depends only on the amygdala. Our findings indicate that reconsolidation functions to strengthen the original memory and show the dynamic nature of reactivated memory through protein degradation and gene expression in multiple brain regions. [Fukushima, Hotaka; Zhang, Yue; Ishikawa, Rie; Kida, Satoshi] Tokyo Univ Agr, Fac Appl Biosci, Dept Biosci, Tokyo, Japan; [Fukushima, Hotaka; Zhang, Yue; Kida, Satoshi] Japan Sci & Technol Agcy, CREST, Saitama, Japan; [Archbold, Georgia; Nader, Karim] McGill Univ, Dept Psychol, Montreal, PQ, Canada Kida, S (reprint author), Tokyo Univ Agr, Fac Appl Biosci, Dept Biosci, Tokyo, Japan. kida@nodai.ac.jp Japan Society for the Promotion of Science (JSPS) [23300120, 20380078, 24650172, 18022038, 22022039, 24116008, 24116001, 23115716]; Core Research for Evolutional Science and Technology, Japan Science and Technology Agency (CREST, JST); Sumitomo Foundation, Japan; Takeda Science Foundation, Japan Japan Society for the Promotion of Science (JSPS) Grant-in-Aids for Scientific Research (B), Japan (23300120, 20380078) Satoshi KidaJapan Society for the Promotion of Science (JSPS) Grant-in-Aids for Challenging Exploratory Research, Japan (24650172) Satoshi KidaJapan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research on Priority Areas, Molecular Brain Science (18022038, 22022039) Satoshi KidaJapan Society for the Promotion of Science (JSPS) Grant-in-Aid for Scientific Research on Innovative Areas, Japan (24116008, 24116001, 23115716) Satoshi KidaCore Research for Evolutional Science and Technology, Japan Science and Technology Agency (CREST, JST) Satoshi KidaThe Sumitomo Foundation, Japan Satoshi KidaThe Takeda Science Foundation, Japan Satoshi Kida Counotte DS, 2011, NAT NEUROSCI, V14, P417, DOI 10.1038/nn.2770; Debiec J, 2002, NEURON, V36, P527, DOI 10.1016/S0896-6273(02)01001-2; de la Fuente V, 2011, J NEUROSCI, V31, P5562, DOI 10.1523/JNEUROSCI.6066-10.2011; Dudai Y, 2012, ANNU REV NEUROSCI, V35, P227, DOI 10.1146/annurev-neuro-062111-150500; Dudai Y, 2002, CURR OPIN NEUROBIOL, V12, P211, DOI 10.1016/S0959-4388(02)00305-7; Eisenberg M, 2003, SCIENCE, V301, P1102, DOI 10.1126/science.1086881; FLOOD JF, 1973, PHYSIOL BEHAV, V10, P555, DOI 10.1016/0031-9384(73)90221-7; Frankland PW, 2006, LEARN MEMORY, V13, P451, DOI 10.1101/lm.183406; Franklin K.B.J., 1997, MOUSE BRAIN STEREOTA; Fukushima H, 2008, J NEUROSCI, V28, P9910, DOI 10.1523/JNEUROSCI.2625-08.2008; Gordon W.C., 1981, P319; Hosoda H, 2004, GENE, V338, P235, DOI 10.1016/j.gene.2004.05.022; Inda MC, 2011, J NEUROSCI, V31, P1635, DOI 10.1523/JNEUROSCI.4736-10.2011; Kaczmarek L, 2002, HDB CHEM NEUROANATOM; Kawashima T, 2009, P NATL ACAD SCI USA, V106, P316, DOI 10.1073/pnas.0806518106; Kida S, 2002, NAT NEUROSCI, V5, P348, DOI 10.1038/nn819; Kim R, 2011, MOL BRAIN, V4, DOI 10.1186/1756-6606-4-9; Lee HK, 2000, NATURE, V405, P955; Lee JLC, 2008, NAT NEUROSCI, V11, P1264, DOI 10.1038/nn.2205; Lee SH, 2008, SCIENCE, V319, P1253, DOI 10.1126/science.1150541; LEWIS DJ, 1979, PSYCHOL BULL, V86, P1054, DOI 10.1037//0033-2909.86.5.1054; MACTUTUS CF, 1979, SCIENCE, V204, P1319, DOI 10.1126/science.572083; Mamiya N, 2009, J NEUROSCI, V29, P402, DOI 10.1523/JNEUROSCI.4639-08.2009; Milekic MH, 2002, NEURON, V36, P521, DOI 10.1016/S0896-6273(02)00976-5; Milekic MH, 2007, LEARN MEMORY, V14, P504, DOI 10.1101/lm.598307; MISANIN JR, 1968, SCIENCE, V160, P554, DOI 10.1126/science.160.3827.554; Myers KM, 2002, NEURON, V36, P567, DOI 10.1016/S0896-6273(02)01064-4; Nader K, 2000, NATURE, V406, P722, DOI 10.1038/35021052; Nader K, 2009, NAT REV NEUROSCI, V10, P224, DOI 10.1038/nrn2590; Nomoto M, 2012, MOL BRAIN, V5, DOI 10.1186/1756-6606-5-8; Pavlov IP, 1927, CONDITIONED REFLEXES; Pedreira ME, 2003, NEURON, V38, P863, DOI 10.1016/S0896-6273(03)00352-0; Pedroso TR, 2013, J NEURAL TRANSM, V120, P1525, DOI 10.1007/s00702-013-1032-y; Rescorla RA, 2001, HANDBOOK OF CONTEMPORARY LEARNING THEORIES, P119; ROSENBLUM K, 1993, BEHAV NEURAL BIOL, V59, P49, DOI 10.1016/0163-1047(93)91145-D; SCHNEIDE.AM, 1968, SCIENCE, V159, P219, DOI 10.1126/science.159.3811.219; SHENG M, 1990, NEURON, V4, P571, DOI 10.1016/0896-6273(90)90115-V; Suzuki A, 2004, J NEUROSCI, V24, P4787, DOI 10.1523/JNEUROSCI.5491-03.2004; Suzuki A, 2008, LEARN MEMORY, V15, P426, DOI 10.1101/lm.888808; Suzuki A, 2011, J NEUROSCI, V31, P8786, DOI 10.1523/JNEUROSCI.3257-10.2011; Taubenfeld SM, 2001, NAT NEUROSCI, V4, P813, DOI 10.1038/90520; Tronel S, 2005, PLOS BIOL, V3, P1630, DOI 10.1371/journal.pbio.0030293; Tronson NC, 2006, NAT NEUROSCI, V9, P167, DOI 10.1038/nn1628; Van den Oever MC, 2008, NAT NEUROSCI, V11, P1053, DOI 10.1038/nn.2165; Zhang Y, 2011, MOL BRAIN, V4, DOI 10.1186/1756-6606-4-4 45 0 0 ELIFE SCIENCES PUBLICATIONS LTD CAMBRIDGE SHERATON HOUSE, CASTLE PARK, CAMBRIDGE, CB3 0AX, ENGLAND 2050-084X ELIFE eLife JUN 24 2014 3 e02736 10.7554/eLife.02736 19 Biology Life Sciences & Biomedicine - Other Topics AK9DE WOS:000338726900005 J Lloyd-Jones, TJ; Nakabayashi, K Lloyd-Jones, Toby J.; Nakabayashi, Kazuyo Long-term repetition priming and semantic interference in a lexical-semantic matching task: object names and colors FRONTIERS IN PSYCHOLOGY English Article color; object; name; shape; memory; repetition priming; modality-specific; semantic interference SPOKEN WORD PRODUCTION; LANGUAGE PRODUCTION; MEMORY-SYSTEMS; OPTIC APHASIA; RECOGNITION; ACTIVATION; FREQUENCY; PICTURES; ACCESS; REPRESENTATIONS Using a novel paradigm to engage the long-term mappings between object names and the prototypical colors for objects, we investigated the retrieval of object-color knowledge as indexed by long-term priming (the benefit in performance from a prior encounter with the same or a similar stimulus); a process about which little is known. We examined priming from object naming on alexical semantic matching task. In the matching task participants encountered a visually presented object name (Experiment 1) or object shape ( Experiment 2) paired with either a color patch or color name. The pairings could either match where by both were consistent with a familiar object (e.g., strawberry and red) or mismatch (strawberry and blue). We used the matching task to probe knowledge about familiar objects and their colors pre-activated during object naming. In particular, we examined whether the retrieval of object-color information was modality-specific and whether this influenced priming. Priming varied with the nature of the retrieval process: object-color priming a rose for object names but not object shapes and beneficial effects of priming were observed for color patches whereas inhibitory priming a rose with color names. These findings have implications for understanding how object knowledge is retrieved from memory and modified by learning. [Lloyd-Jones, Toby J.] Swansea Univ, Wales Inst Cognit Neurosci, Dept Psychol, Swansea SA2 8PP, W Glam, Wales; [Nakabayashi, Kazuyo] Univ Hull, Dept Psychol, Kingston Upon Hull HU6 7RX, N Humberside, England Lloyd-Jones, TJ (reprint author), Swansea Univ, Wales Inst Cognit Neurosci, Dept Psychol, Singleton Pk, Swansea SA2 8PP, W Glam, Wales. t.j.lloyd-jones@swansea.ac.uk Altmann G. T. M., 2010, Q J EXPT PSYCHOL, V64, P122, DOI [10.1080/17470218.2010.481474, DOI 10.1080/17470218.2010.481474]; ANDREWS S, 1992, J EXP PSYCHOL LEARN, V18, P234, DOI 10.1037//0278-7393.18.2.234; Barsalou LW, 1999, BEHAV BRAIN SCI, V22, P577; BEAUVOIS MF, 1985, COGNITIVE NEUROPSYCH, V2, P1, DOI 10.1080/02643298508252860; BEAUVOIS MF, 1982, PHILOS T ROY SOC B, V298, P35, DOI 10.1098/rstb.1982.0070; Belke E, 2005, Q J EXP PSYCHOL-A, V58, P667, DOI 10.1080/02724980443000142; Braisby N, 1999, J CHILD LANG, V26, P23, DOI 10.1017/S0305000998003638; Bramao I, 2011, ACTA PSYCHOL, V138, P244, DOI 10.1016/j.actpsy.2011.06.010; Bramao I, 2012, BRAIN COGNITION, V78, P28, DOI 10.1016/j.bandc.2011.10.004; Cabeza R, 2013, PERSPECT PSYCHOL SCI, V8, P49, DOI 10.1177/1745691612469033; CARR TH, 1982, J EXP PSYCHOL HUMAN, V8, P757, DOI 10.1037//0096-1523.8.6.757; Cohen J, 1988, STAT POWER ANAL BEHA; Coltheart M, 2001, PSYCHOL REV, V108, P204, DOI 10.1037//0033-295X.108.1.204; Damian MF, 2005, J EXP PSYCHOL LEARN, V31, P1372, DOI 10.1037/0278-7393.31.6.1372; DAVIDOFF J, 1993, COGNITION, V48, P121, DOI 10.1016/0010-0277(93)90027-S; Davidoff J, 1997, ACTA PSYCHOL, V97, P1, DOI 10.1016/S0001-6918(97)00020-6; DAVIDOFF J, 1993, APHASIOLOGY, V7, P135, DOI 10.1080/02687039308249502; Davidoff J., 1991, COGNITION COLOUR; DURSO FT, 1979, J EXP PSYCHOL-HUM L, V5, P449, DOI 10.1037/0278-7393.5.5.449; Frost R, 1998, PSYCHOL BULL, V123, P71, DOI 10.1037//0033-2909.123.1.71; Gleason TR, 2004, COGNITIVE DEV, V19, P1, DOI 10.1016/S0885-2014(03)00044-3; GRAF P, 1989, J EXP PSYCHOL LEARN, V15, P930, DOI 10.1037/0278-7393.15.5.930; Howard D, 2006, COGNITION, V100, P464, DOI 10.1016/j.cognition.2005.02.006; HUMPHREYS GW, 1995, MEMORY, V3, P535, DOI 10.1080/09658219508253164; Indefrey P, 2004, COGNITION, V92, P101, DOI 10.1016/j.cognition.2002.06.001; Joseph JE, 1997, ACTA PSYCHOL, V97, P95, DOI 10.1016/S0001-6918(97)00026-7; KROLL JF, 1994, J MEM LANG, V33, P149, DOI 10.1006/jmla.1994.1008; Levelt WJM, 1999, BEHAV BRAIN SCI, V22, P1; LloydJones T, 1997, MEM COGNITION, V25, P606, DOI 10.3758/BF03211303; Lloyd-Jones TJ, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0048550; Lloyd-Jones TJ, 2007, MEM COGNITION, V35, P816, DOI 10.3758/BF03193317; Vernon David, 2003, Quarterly Journal of Experimental Psychology Section A Human Experimental Psychology, V56A, P779, DOI 10.1080/02724980244000684; LloydJones TJ, 1997, MEM COGNITION, V25, P18, DOI 10.3758/BF03197282; Lloyd-Jones TJ, 2009, Q J EXP PSYCHOL, V62, P310, DOI 10.1080/17470210801954827; Lupyan G, 2012, J EXP PSYCHOL GEN, V141, P170, DOI 10.1037/a0024904; Mahon BZ, 2008, J PHYSIOL-PARIS, V102, P59, DOI 10.1016/j.jphysparis.2008.03.004; MCCANN RS, 1987, J EXP PSYCHOL HUMAN, V13, P14, DOI 10.1037/0096-1523.13.1.14; Miceli G, 2001, NAT NEUROSCI, V4, P662, DOI 10.1038/88497; Musen G, 2000, PSYCHON B REV, V7, P646, DOI 10.3758/BF03213002; Naor-Raz G, 2003, PERCEPTION, V32, P667, DOI 10.1068/p5050; Navarrete E, 2010, ACTA PSYCHOL, V134, P279, DOI 10.1016/j.actpsy.2010.02.009; Oppenheim GM, 2010, COGNITION, V114, P227, DOI 10.1016/j.cognition.2009.09.007; OSTERGAARD AL, 1994, Q J EXP PSYCHOL-A, V47, P331; POLLATSEK A, 1995, J EXP PSYCHOL LEARN, V21, P785, DOI 10.1037/0278-7393.21.3.785; PRICE CJ, 1989, Q J EXP PSYCHOL-A, V41, P797; Raaijmakers JGW, 1999, J MEM LANG, V41, P416, DOI 10.1006/jmla.1999.2650; Rahman RA, 2009, LANG COGNITIVE PROC, V24, P713, DOI 10.1080/01690960802597250; RAJARAM S, 1993, J EXP PSYCHOL LEARN, V19, P765, DOI 10.1037/0278-7393.19.4.765; Rapp B, 2000, PSYCHOL REV, V107, P460, DOI 10.1037/0033-295X.107.3.460; Roediger Henry L. Iii, 1993, P17; Rommers J, 2013, PSYCHOL SCI, V24, P2218, DOI 10.1177/0956797613490746; SCHRIEFERS H, 1990, J MEM LANG, V29, P86, DOI 10.1016/0749-596X(90)90011-N; Srinivas K, 1996, MEM COGNITION, V24, P441, DOI 10.3758/BF03200933; Stroop JR, 1935, J EXP PSYCHOL, V18, P643, DOI 10.1037/0096-3445.121.1.15; Tanaka J, 2001, TRENDS COGN SCI, V5, P211, DOI 10.1016/S1364-6613(00)01626-0; Tanaka JW, 1999, PERCEPT PSYCHOPHYS, V61, P1140, DOI 10.3758/BF03207619; VITKOVITCH M, 1993, J EXP PSYCHOL LEARN, V19, P243; VITKOVITCH M, 1991, J EXP PSYCHOL LEARN, V17, P664, DOI 10.1037/0278-7393.17.4.664; Vitkovitch M, 2001, BRIT J PSYCHOL, V92, P483, DOI 10.1348/000712601162301; Wang G, 2010, CLIN NEUROPHYSIOL, V121, P1473, DOI 10.1016/j.clinph.2010.03.032; WHEELDON LR, 1994, J MEM LANG, V33, P332, DOI 10.1006/jmla.1994.1016; WHEELDON LR, 1992, Q J EXP PSYCHOL-A, V44, P723; Yee E, 2012, PSYCHOL SCI, V23, P364, DOI 10.1177/0956797611430691; Zwaan RA, 2004, PSYCHOL LEARN MOTIV, V44, P35 64 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUN 24 2014 5 644 10.3389/fpsyg.2014.00644 10 Psychology, Multidisciplinary Psychology AK9BZ WOS:000338723800001 J Schubotz, RI; Wurm, MF; Wittmann, MK; von Cramon, DY Schubotz, Ricarda I.; Wurm, Moritz F.; Wittmann, Marco K.; von Cramon, D. Yves Objects tell us what action we can expect: dissociating brain areas for retrieval and exploitation of action knowledge during action observation in fMRI FRONTIERS IN PSYCHOLOGY English Article fMRI; object perception; action observation; apraxia; affordance; pantomime POSTERIOR PARIETAL CORTEX; EXTRASTRIATE BODY AREA; HUMAN VISUAL-CORTEX; PREMOTOR CORTEX; NEURAL BASIS; ACTION REPRESENTATIONS; INTRAPARIETAL SULCUS; MENTAL SIMULATION; PRESENTED OBJECTS; SOCIAL COGNITION Objects are reminiscent of actions often performed with them: knife and apple remind us on peeling the apple or cutting it. Mnemonic representations of object-related actions( action codes) evoked by the sight of an object may constrain and hence facilitate recognition of unrolling actions. The present fMRI study investigated if and how action codes influence brain activation during action observation. The average number of action codes (NAC) of 51 sets of objects was rated by a group of n= 24 participants. In an fMRI study, different volunteers were asked to recognize actions performed with the same objects presented in short videos. To disentangle areas reflecting the storage of action codes from those exploiting them, we showed object-compatible and object-incompatible ( pantomime) actions. Areas storing action codes were considered to positively co-vary with NAC in both object-compatible and object-incompatible action; due to its role in tool-related tasks, we here hypothesized left anterior inferior parietal cortex (aIPL). Incontrast, areas exploiting action codes were expected to show this correlation only in object-compatible but not incompatible action, as only object-compatible actions match one of the active action codes. For this interaction, we hypothesized ventrolateral premotorcortex (PMv) to join aIPL due to its role in biasing competition in IPL. We found left anterior intraparietal sulcus (IPS) and left posterior middle temporal gyrus (pMTG) to co-vary with NAC. In addition to these areas, action codes increased activity in object-compatible action in bilateral PMv, right IPS, and lateral occipital cortex (LO). Findings suggest that during action observation, the brain derives possible actions from perceived objects, and uses this information to shape action recognition. In particular, the number of expectable actions quantifies the activity level at PMv, IPL, [Schubotz, Ricarda I.] Univ Munster, Inst Psychol, D-48149 Munster, Germany; [Schubotz, Ricarda I.; von Cramon, D. Yves] Max Planck Inst Neurol Res, Cologne, Germany; [Schubotz, Ricarda I.] Univ Hosp, Dept Neurol, Cologne, Germany; [Wurm, Moritz F.] Univ Trento, Ctr Mind BrainSci CIMeC, Mattarello, Italy; [Wittmann, Marco K.] Univ Oxford, Dept Expt Psychol, Oxford OX1 3UD, England Schubotz, RI (reprint author), Univ Munster, Inst Psychol, Fliednerstr 21, D-48149 Munster, Germany. rschubotz@uni-muenster.de Schubotz, Ricarda/B-2871-2013 Andersen RA, 2009, NEURON, V63, P568, DOI 10.1016/j.neuron.2009.08.028; Bar M, 2003, J COGNITIVE NEUROSCI, V15, P600, DOI 10.1162/089892903321662976; Baumann MA, 2009, J NEUROSCI, V29, P6436, DOI 10.1523/JNEUROSCI.5479-08.2009; Beauchamp MS, 2002, NEURON, V34, P149, DOI 10.1016/S0896-6273(02)00642-6; Begliomini C, 2007, EUR J NEUROSCI, V25, P1245, DOI 10.1111/j.1460-9568.2007.05365.x; Binkofski F, 2013, BRAIN LANG, V127, P222, DOI 10.1016/j.bandl.2012.07.007; Blanke O, 2004, BRAIN, V127, P243, DOI 10.1093/brain/awh040; Boronat CB, 2005, COGNITIVE BRAIN RES, V23, P361, DOI 10.1016/j.cogbrainres.2004.11.001; Burton H, 2000, J CLIN NEUROPHYSIOL, V17, P575, DOI 10.1097/00004691-200011000-00004; Buxbaum LJ, 2005, NEUROPSYCHOLOGIA, V43, P917, DOI 10.1016/j.neuropsychologia.2004.09.006; Buxbaum LJ, 2002, BRAIN LANG, V82, P179, DOI 10.1016/S0093-934X(02)00014-7; Buxbaum LJ, 2003, NEUROPSYCHOLOGIA, V41, P1091, DOI 10.1016/S0028-3932(02)00314-7; Campanella F, 2011, EXP BRAIN RES, V208, P369, DOI 10.1007/s00221-010-2489-7; Caspers S, 2010, NEUROIMAGE, V50, P1148, DOI 10.1016/j.neuroimage.2009.12.112; Chao LL, 2000, NEUROIMAGE, V12, P478, DOI 10.1006/nimg.2000.0635; Cho D, 2010, J EXP PSYCHOL HUMAN, V36, P853, DOI 10.1037/a0019328; Cisek P, 2005, NEURON, V45, P801, DOI 10.1016/j.neuron.2005.01.027; Cohen YE, 2002, NAT REV NEUROSCI, V3, P553, DOI 10.1038/nrn873; Committeri G, 2007, BRAIN, V130, P431, DOI 10.1093/brain/awl265; Craighero L, 1999, J EXP PSYCHOL HUMAN, V25, P1673, DOI 10.1037/0096-1523.25.6.1673; Creem-Regehr SH, 2009, NEUROBIOL LEARN MEM, V91, P166, DOI 10.1016/j.nlm.2008.10.004; Culham JC, 2006, CURR OPIN NEUROBIOL, V16, P205, DOI 10.1016/j.conb.2006.03.005; Decety J, 2007, NEUROSCIENTIST, V13, P580, DOI 10.1177/1073858407304654; DECETY J, 1990, ACTA PSYCHOL, V73, P13, DOI 10.1016/0001-6918(90)90056-L; Derbyshire N, 2006, ACTA PSYCHOL, V122, P74, DOI 10.1016/j.actpsy.2005.10.004; Downing P E, 2006, Cereb Cortex, V16, P1453, DOI 10.1093/cercor/bhj086; Downing PE, 2006, SOC NEUROSCI, V1, P52, DOI 10.1080/17470910600668854; Downing PE, 2007, J NEUROSCI, V27, P226, DOI 10.1523/JNEUROSCI.3619-06.2007; Eskandar EN, 2002, J NEUROPHYSIOL, V88, P1777, DOI 10.1152/jn.00095.2002; Fagg AH, 1998, NEURAL NETWORKS, V11, P1277, DOI 10.1016/S0893-6080(98)00047-1; Fairhall SL, 2013, J NEUROSCI, V33, P10552, DOI 10.1523/JNEUROSCI.0051-13.2013; FEIN GG, 1981, CHILD DEV, V52, P1095, DOI 10.1111/j.1467-8624.1981.tb03157.x; Friston K. J., 1995, HUMAN BRAIN MAPPING, V2, P189, DOI DOI 10.1002/HBM.460020402; Frith C, 2001, NEUROPSYCHOLOGIA, V39, P1367, DOI 10.1016/S0028-3932(01)00124-5; GALLESE V, 1994, NEUROREPORT, V5, P1525, DOI 10.1097/00001756-199407000-00029; Gallese V, 2002, COMMON MECH PERCEPTI, P334; Gardner EP, 2007, J NEUROPHYSIOL, V98, P3708, DOI 10.1152/jn.00609.2007; Gardner EP, 2007, J NEUROPHYSIOL, V97, P387, DOI 10.1152/jn.00558.2006; Gibson JJ, 1977, PERCEIVING ACTING KN, P67; Glover GH, 1999, NEUROIMAGE, V9, P416, DOI 10.1006/nimg.1998.0419; Greenlee M W, 2000, Int Rev Neurobiol, V44, P269; Grefkes C, 2002, NEURON, V35, P173, DOI 10.1016/S0896-6273(02)00741-9; Grefkes C, 2005, J ANAT, V207, P3, DOI 10.1111/j.1469-7580.2005.00426.x; Grezes J, 2001, HUM BRAIN MAPP, V12, P1, DOI 10.1002/1097-0193(200101)12:1<1::AID-HBM10>3.0.CO;2-V; Guenot M, 2004, ADV TECH STAND NEUR, V29, P265; Helbig HB, 2006, EXP BRAIN RES, V174, P221, DOI 10.1007/s00221-006-0443-5; Iani C, 2011, J COGN PSYCHOL, V23, P121, DOI 10.1080/20445911.2011.467251; Ishibashi R, 2011, NEUROPSYCHOLOGIA, V49, P1128, DOI 10.1016/j.neuropsychologia.2011.01.004; Jeannerod M, 1999, Q J EXP PSYCHOL-A, V52, P1, DOI 10.1080/027249899391205; Johnson PB, 1996, CEREB CORTEX, V6, P102, DOI 10.1093/cercor/6.2.102; Johnson-Frey SH, 2004, TRENDS COGN SCI, V8, P71, DOI 10.1016/j.tics.2003.12.002; Kable JW, 2006, J COGNITIVE NEUROSCI, V18, P1498, DOI 10.1162/jocn.2006.18.9.1498; Kalaska John F, 2003, Adv Neurol, V93, P97; Kastner S, 2001, NEUROPSYCHOLOGIA, V39, P1263, DOI 10.1016/S0028-3932(01)00116-6; Kellenbach M. L., 2003, J COGNITIVE NEUROSCI, V15, P20, DOI 10.1162/089892903321107800; Keysers C, 2004, TRENDS COGN SCI, V8, P501, DOI 10.1016/j.tics.2004.09.005; Kourtzi Z, 2000, TRENDS COGN SCI, V4, P295, DOI 10.1016/S1364-6613(00)01512-6; Liu HS, 2009, NEURON, V62, P281, DOI 10.1016/j.neuron.2009.02.025; Lohmann G, 2001, COMPUT MED IMAG GRAP, V25, P449, DOI 10.1016/S0895-6111(01)00008-8; Luppino G, 2000, NEWS PHYSIOL SCI, V15, P219; Machner B, 2009, ANN NY ACAD SCI, V1164, P419, DOI 10.1111/j.1749-6632.2009.03769.x; Mahon BZ, 2007, NEURON, V55, P507, DOI 10.1016/j.neuron.2007.07.011; Martin A, 2007, ANNU REV PSYCHOL, V58, P25, DOI 10.1146/annurev.psych.57.102904.190143; McBride J, 2012, Q J EXP PSYCHOL, V65, P13, DOI 10.1080/17470218.2011.588336; McGeoch PD, 2007, MED HYPOTHESES, V69, P1165, DOI 10.1010/j.mehy.2007.05.017; McGrenere J, 2000, PROC GRAPH INTERF, P179; Milner TE, 2007, NEUROIMAGE, V36, P388, DOI 10.1016/j.neuroimage.2007.01.057; Mitchell JP, 2008, CEREB CORTEX, V18, P262, DOI 10.1093/cercor/bhm051; Mruczek REB, 2013, J NEUROPHYSIOL, V109, P2883, DOI 10.1152/jn.00658.2012; Murata A, 1996, J NEUROPHYSIOL, V75, P2180; Murata A, 1997, J NEUROPHYSIOL, V78, P2226; Myers A, 2008, NEUROIMAGE, V42, P1669, DOI 10.1016/j.neuroimage.2008.05.045; Myung JY, 2006, COGNITION, V98, P223, DOI 10.1016/j.cognition.2004.11.010; Nieuwenhuis S, 2011, NAT NEUROSCI, V14, P1105, DOI 10.1038/nn.2886; Norris DG, 2000, JMRI-J MAGN RESON IM, V11, P445, DOI 10.1002/(SICI)1522-2586(200004)11:4<445::AID-JMRI13>3.0.CO;2-T; OCHIPA C, 1989, ANN NEUROL, V25, P190, DOI 10.1002/ana.410250214; Pavese A, 2002, VIS COGN, V9, P559, DOI 10.1080/13506280143000584; Pellicano A, 2010, Q J EXP PSYCHOL, V63, P2190, DOI 10.1080/17470218.2010.486903; Pessoa L, 2003, J NEUROSCI, V23, P3990; Peuskens H, 2005, EUR J NEUROSCI, V21, P2864, DOI 10.1111/j.1460-9568.2005.04106.x; Phillips JC, 2002, VIS COGN, V9, P540, DOI 10.1080/13506280143000575; Proverbio AM, 2011, NEUROPSYCHOLOGIA, V49, P2711, DOI 10.1016/j.neuropsychologia.2011.05.019; Ramsey R, 2013, J COGNITIVE NEUROSCI, V25, P670, DOI 10.1162/jocn_a_00345; Reed CL, 2004, HUM BRAIN MAPP, V21, P236, DOI 10.1002/hbm.10162; RIZZOLATTI G, 1987, EXP BRAIN RES, V67, P220, DOI 10.1007/BF00269468; Rizzolatti G, 1998, NOVART FDN SYMP, V218, P81; Rothi L. J., 1997, APRAXIA NEUROPSYCHOL; Rowe JB, 2010, NEUROIMAGE, V51, P888, DOI 10.1016/j.neuroimage.2010.02.045; Rumiati RI, 2004, NEUROIMAGE, V21, P1224, DOI 10.1016/j.neuroimage.2003.11.017; Rushworth MFS, 2003, NEUROIMAGE, V20, pS89, DOI 10.1016/j.neuroimage.2003.09.011; Schubotz RI, 2007, TRENDS COGN SCI, V11, P211, DOI 10.1016/j.tics.2007.02.006; Schubotz RI, 2009, J COGNITIVE NEUROSCI, V21, P642, DOI 10.1162/jocn.2009.21049; Symes E, 2007, ACTA PSYCHOL, V124, P238, DOI 10.1016/j.actpsy.2006.03.005; Talairach J, 1988, COPLANAR STEREOTAXIC; Taylor JC, 2007, J NEUROPHYSIOL, V98, P1626, DOI 10.1152/jn.00012.2007; Tucker M, 2004, ACTA PSYCHOL, V116, P185, DOI 10.1016/j.actpsy.2004.01.004; Tucker M, 2001, VIS COGN, V8, P769; Tucker M, 1998, J EXP PSYCHOL HUMAN, V24, P830, DOI 10.1037//0096-1523.24.3.830; UGURBIL K, 1993, MAGN RESON QUART, V9, P259; Van Overwalle F, 2009, NEUROIMAGE, V48, P564, DOI 10.1016/j.neuroimage.2009.06.009; Van Overwalle F, 2009, HUM BRAIN MAPP, V30, P829, DOI 10.1002/hbm.20547; Volz KG, 2005, BRAIN RES BULL, V67, P403, DOI 10.1016/j.brainresbull.2005.06.011; WORSLEY KJ, 1995, NEUROIMAGE, V2, P173, DOI 10.1006/nimg.1995.1023 103 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUN 24 2014 5 636 10.3389/fpsyg.2014.00636 15 Psychology, Multidisciplinary Psychology AK9BU WOS:000338723300001 J Arend, D; Lange, M; Chen, JB; Colmsee, C; Flemming, S; Hecht, D; Scholz, U Arend, Daniel; Lange, Matthias; Chen, Jinbo; Colmsee, Christian; Flemming, Steffen; Hecht, Denny; Scholz, Uwe e!DAL - a framework to store, share and publish research data BMC BIOINFORMATICS English Article Research data management; Data publication; Persistent identifier; Metadata annotation; Shared repositories; JAVA API SYSTEMS BIOLOGY; BIOINFORMATICS; SOFTWARE; RESOURCES; STANDARDS Background: The life-science community faces a major challenge in handling "big data", highlighting the need for high quality infrastructures capable of sharing and publishing research data. Data preservation, analysis, and publication are the three pillars in the "big data life cycle". The infrastructures currently available for managing and publishing data are often designed to meet domain-specific or project-specific requirements, resulting in the repeated development of proprietary solutions and lower quality data publication and preservation overall. Results: e!DAL is a lightweight software framework for publishing and sharing research data. Its main features are version tracking, metadata management, information retrieval, registration of persistent identifiers (DOI), an embedded HTTP(S) server for public data access, access as a network file system, and a scalable storage backend. e!DAL is available as an API for local non-shared storage and as a remote API featuring distributed applications. It can be deployed "out-of-the-box" as an on-site repository. Conclusions: e!DAL was developed based on experiences coming from decades of research data management at the Leibniz Institute of Plant Genetics and Crop Plant Research (IPK). Initially developed as a data publication and documentation infrastructure for the IPK's role as a data center in the DataCite consortium, e! DAL has grown towards being a general data archiving and publication infrastructure. The e!DAL software has been deployed into the Maven Central Repository. [Arend, Daniel; Lange, Matthias; Chen, Jinbo; Colmsee, Christian; Flemming, Steffen; Hecht, Denny; Scholz, Uwe] OT Gatersleben, Leibniz Inst Plant Genet & Crop Plant Res IPK, D-06466 Stadt Seeland, Germany Arend, D (reprint author), OT Gatersleben, Leibniz Inst Plant Genet & Crop Plant Res IPK, Corrensstr 3, D-06466 Stadt Seeland, Germany. arendd@ipk-gatersleben.de European Commission within its 7th Framework Program, under the thematic area "Infrastructures" [283496]; Leibniz Association in the framework "Pakt fur Forschung"; German Federal Ministry of Education and Research [031A053] We thank Joscha Joel Benz for the initial WebDAV code and Thomas Munch, Heiko Miehe as administrator of the project website, code, and artifact repositories. This work was supported by the European Commission within its 7th Framework Program, under the thematic area "Infrastructures", contract number 283496, by the Leibniz Association in the framework "Pakt fur Forschung". Part of this work was performed within the German-Plant-Phenotyping Network, which is funded by the German Federal Ministry of Education and Research (project identification number: 031A053). Anderson NR, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-260; Arend D, 2012, P IEEE INT C BIOINF, P511, DOI [10.1109/BIBM.2012.6392737, DOI 10.1109/BIBM.2012.6392737]; Branschofsky M., 2002, JCDL 2002. Proceedings of the Second ACM/IEEE-CS Joint Conference on Digital Libraries; Brazma A, 2006, NAT REV GENET, V7, P593, DOI 10.1038/nrg1922; Brooksbank C, 2014, NUCLEIC ACIDS RES, V42, pD18, DOI 10.1093/nar/gkt1206; Chavan V, 2011, BMC BIOINFORMATI S15, V12, P2, DOI DOI 10.1186/1471-2105-12-S15-S2; Clark T, 2004, BRIEF BIOINFORM, V5, P59, DOI 10.1093/bib/5.1.59; Consultative Committee for Space Data Systems: Reference Model for an Open Archival Information System (OAIS), 2002, 6500B1 OAIS CCSDS; Craddock T, 2008, NAT REV MICROBIOL, V6, P248; Fernandez-Suarez XM, 2013, NUCLEIC ACIDS RES, V41, P1; Gray J, J GRAY E SCI TRANSFO; Hucka M, 2003, BIOINFORMATICS, V19, P524, DOI 10.1093/bioinformatics/btg015; Jameson D, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-183; Kane DW, 2006, BMC BIOINFORMATICS, V7, DOI 10.1186/1471-2105-7-273; Kiczales G, 2001, P 15 EUR C OBJ ORIEN; Kodama Y, 2012, NUCLEIC ACIDS RES, V40, pD54, DOI 10.1093/nar/gkr854; Kohl KI, 2008, PLANT METHODS, V4, DOI 10.1186/1746-4811-4-11; Lagoze C, 2006, INT J DIGITAL LIB, V6, P124, DOI DOI 10.1007/S00799-005-0130-3; Lange M, 2014, APPROACHES INTEGRATI, P73, DOI [10.1007/978-3-642-41281-3_3, DOI 10.1007/978-3-642-41281-3_3]; Le Novere N, 2006, NUCLEIC ACIDS RES, V34, P689; LU Z, 2011, DATABASE-OXFORD, DOI DOI 10.1093/DATABASE/BAQ036; Meckel H, 2014, BBA-PROTEINS PROTEOM, V1844, P2, DOI 10.1016/j.bbapap.2013.05.018; Nelson B, 2009, NATURE, V461, P160, DOI 10.1038/461160a; Nelson EK, 2011, BMC BIOINFORMATICS, V12, DOI 10.1186/1471-2105-12-71; Neuroth H, NESTOR HDB KLEINE EN; Pico AR, 2008, PLOS BIOL, V6, P184; Rocca-Serra P, 2010, BIOINFORMATICS, V26, P2354, DOI 10.1093/bioinformatics/btq415; Rohn H, 2012, BMC SYST BIOL, V6, DOI 10.1186/1752-0509-6-139; Roos DS, 2001, SCIENCE, V291, P1260, DOI 10.1126/science.291.5507.1260; Rother K, 2012, BRIEF BIOINFORM, V13, P244, DOI 10.1093/bib/bbr035; Sansone SA, 2012, NAT GENET, V44, P121, DOI 10.1038/ng.1054; Smith B, 2007, NAT BIOTECHNOL; Smith BE, 2011, METHODS MOL BIOL, V696, P123, DOI 10.1007/978-1-60761-987-1_8; Smith Vincent S, 2009, BMC Res Notes, V2, P113, DOI 10.1186/1756-0500-2-113; Stephan C, 2010, PROTEOMICS, V10, P1230, DOI 10.1002/pmic.200900420; Van Noorden Richard, 2013, Nature, V500, P243; Wallis JC, 2013, PLOS ONE, V8, P67332; Zhang J, 2011, DATABASE, V2011 38 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1471-2105 BMC BIOINFORMATICS BMC Bioinformatics JUN 24 2014 15 214 10.1186/1471-2105-15-214 13 Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology AK3CR WOS:000338301000001 J Korshkari, P; Vaiwsri, S; Flegel, TW; Ngamsuriyaroj, S; Sonthayanon, B; Prachumwat, A Korshkari, Parpakron; Vaiwsri, Sirintra; Flegel, Timothy W.; Ngamsuriyaroj, Sudsanguan; Sonthayanon, Burachai; Prachumwat, Anuphap ShrimpGPAT: a gene and protein annotation tool for knowledge sharing and gene discovery in shrimp BMC GENOMICS English Article Penaeid shrimp; Decapoda; EST; Transcriptomes; Knowledge base; Community-based functional annotation MULTIPLE SEQUENCE ALIGNMENT; PENAEUS-MONODON; DATABASE Background: Although captured and cultivated marine shrimp constitute highly important seafood in terms of both economic value and production quantity, biologists have little knowledge of the shrimp genome and this partly hinders their ability to improve shrimp aquaculture. To help improve this situation, the Shrimp Gene and Protein Annotation Tool (ShrimpGPAT) was conceived as a community-based annotation platform for the acquisition and updating of full-length complementary DNAs (cDNAs), Expressed Sequence Tags (ESTs), transcript contigs and protein sequences of penaeid shrimp and their decapod relatives and for in-silico functional annotation and sequence analysis. Description: ShrimpGPAT currently holds quality-filtered, molecular sequences of 14 decapod species (similar to 500,000 records for six penaeid shrimp and eight other decapods). The database predominantly comprises transcript sequences derived by both traditional EST Sanger sequencing and more recently by massive-parallel sequencing technologies. The analysis pipeline provides putative functions in terms of sequence homologs, gene ontologies and protein-protein interactions. Data retrieval can be conducted easily either by a keyword text search or by a sequence query via BLAST, and users can save records of interest for later investigation using tools such as multiple sequence alignment and BLAST searches against pre-defined databases. In addition, ShrimpGPAT provides space for community insights by allowing functional annotation with tags and comments on sequences. Community-contributed information will allow for continuous database enrichment, for improvement of functions and for other aspects of sequence analysis. Conclusions: ShrimpGPAT is a new, free and easily accessed service for the shrimp research community that provides a comprehensive and up-to-date database of quality-filtered decapod gene and protein sequences together with putative functional prediction and sequence analysis tools. An important feature is its community-based functional annotation capability that allows the research community to contribute knowledge and insights about the properties of molecular sequences for better, shared, functional characterization of shrimp genes. Regularly updated and expanded with data on more decapods, ShrimpGPAT is publicly available at http://shrimpgpat.sc.mahidol.ac.th/. [Korshkari, Parpakron; Vaiwsri, Sirintra; Flegel, Timothy W.; Sonthayanon, Burachai; Prachumwat, Anuphap] Mahidol Univ, Fac Sci, CENTEX Shrimp, Ctr Excellence Shrimp Mol Biol & Biotechnol, Bangkok 10400, Thailand; [Korshkari, Parpakron; Vaiwsri, Sirintra; Ngamsuriyaroj, Sudsanguan] Mahidol Univ, Fac Informat & Commun Technol, Nakhon Pathom 73170, Thailand; [Flegel, Timothy W.; Sonthayanon, Burachai; Prachumwat, Anuphap] Natl Sci & Technol Dev Agcy, Natl Ctr Genet Engn & Biotechnol BIOTEC, Amphoe Khlong Luang 12120, Pathum Thani, Thailand; [Prachumwat, Anuphap] Natl Sci & Technol Dev Agcy, Natl Ctr Genet Engn & Biotechnol BIOTEC, Agr Biotechnol Res Unit, Shrimp Virus Interact Lab, Amphoe Khlong Luang 12120, Pathum Thani, Thailand Sonthayanon, B (reprint author), Mahidol Univ, Fac Sci, CENTEX Shrimp, Ctr Excellence Shrimp Mol Biol & Biotechnol, Rama 6 Rd, Bangkok 10400, Thailand. burachais@gmail.com; anuphap.pra@biotec.or.th Higher Education Research Promotion and National University Development, Office of the Thailand Higher Education Commission; Mahidol University; Thailand Research Fund (TRF); National Center for Genetic Engineering and Biotechnology (BIOTEC) of the Thai National Science and Technology Development Agency (NSTDA); TRF/BIOTEC [TRG5680001/P-13-00608] This work was supported by a National Research Universities Initiative grant from the Higher Education Research Promotion and National University Development, Office of the Thailand Higher Education Commission, by Mahidol University, by the Thailand Research Fund (TRF) and by the National Center for Genetic Engineering and Biotechnology (BIOTEC) of the Thai National Science and Technology Development Agency (NSTDA). AP also acknowledges the support from TRF/BIOTEC Grant No. TRG5680001/P-13-00608. We thank P. Leekitcharoenphon for her help with the initial dataset, A. Tassanakajon for her EST collection of the black tiger shrimp and S. Lerthivaporn, Aung Thu Rha Hein, M. Samseng and P. Leerungnavarat for their help with data retrieval and database configuration. We thank the two anonymous BMC Genomics reviewers and V. Charoensawan for their critical reading and useful comments to improve ShrimpGPAT features and the manuscript. Access to the high-performance computing facilities in the Biostatistics & Informatics Laboratory at the Genome Institute, BIOTEC is greatly appreciated. Camacho C, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-421; Edgar RC, 2004, NUCLEIC ACIDS RES, V32, P1792, DOI 10.1093/nar/gkh340; Ewing B, 1998, GENOME RES, V8, P186; Flegel TW, 2012, J INVERTEBR PATHOL, V110, P166, DOI 10.1016/j.jip.2012.03.004; Huang HZ, 2011, BIOINFORMATICS, V27, P1190, DOI 10.1093/bioinformatics/btr101; Huang XQ, 1999, GENOME RES, V9, P868, DOI 10.1101/gr.9.9.868; Jung H, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0027938; Katoh K, 2013, MOL BIOL EVOL, V30, P772, DOI 10.1093/molbev/mst010; Kerrien S, 2012, NUCLEIC ACIDS RES, V40, pD841, DOI 10.1093/nar/gkr1088; Leekitcharoenphon Pimlapas, 2010, BMC Research Notes, V3, DOI 10.1186/1756-0500-3-295; Lehnert SA, 1999, MAR BIOTECHNOL, V1, P465, DOI 10.1007/PL00011803; Leu JH, 2011, MAR BIOTECHNOL, V13, P608, DOI 10.1007/s10126-010-9286-y; Leu JH, 2007, BMC GENOMICS, V8, DOI 10.1186/1471-2164-8-120; Lohse M, 2012, NUCLEIC ACIDS RES, V40, pW622, DOI 10.1093/nar/gks540; Maglott D, 2011, NUCLEIC ACIDS RES, V39, pD52, DOI 10.1093/nar/gkq1237; McKillen DJ, 2005, BMC GENOMICS, V6, DOI 10.1186/1471-2164-6-34; Robalino J, 2007, PHYSIOL GENOMICS, V29, P44, DOI 10.1152/physiolgenomics.00165.2006; Saito R, 2012, NAT METHODS; Stentiford GD, 2012, J INVERTEBR PATHOL, V110, P141, DOI 10.1016/j.jip.2012.03.013; Tassanakajon A, 2006, GENE, V384, P104, DOI 10.1016/j.gene.2006.07.012; Yu JK, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-461 21 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1471-2164 BMC GENOMICS BMC Genomics JUN 21 2014 15 506 10.1186/1471-2164-15-506 9 Biotechnology & Applied Microbiology; Genetics & Heredity Biotechnology & Applied Microbiology; Genetics & Heredity AK8NX WOS:000338685700001 J Lin, C; Shen, XJ; Wang, ZS; Zhao, C Lin, Chao; Shen, Xueju; Wang, Zhisong; Zhao, Cheng Optical asymmetric cryptography based on elliptical polarized light linear truncation and a numerical reconstruction technique APPLIED OPTICS English Article MIXTURE RETRIEVAL TYPE; YANG-GU ALGORITHM; FRACTIONAL FOURIER DOMAIN; ENCRYPTION; CRYPTOSYSTEM; INTERFERENCE; INFORMATION; TRANSFORMS; ATTACK We demonstrate a novel optical asymmetric cryptosystem based on the principle of elliptical polarized light linear truncation and a numerical reconstruction technique. The device of an array of linear polarizers is introduced to achieve linear truncation on the spatially resolved elliptical polarization distribution during image encryption. This encoding process can be characterized as confusion-based optical cryptography that involves no Fourier lens and diffusion operation. Based on the Jones matrix formalism, the intensity transmittance for this truncation is deduced to perform elliptical polarized light reconstruction based on two intensity measurements. Use of a quick response code makes the proposed cryptosystem practical, with versatile key sensitivity and fault tolerance. Both simulation and preliminary experimental results that support theoretical analysis are presented. An analysis of the resistance of the proposed method on a known public key attack is also provided. (C) 2014 Optical Society of America [Lin, Chao; Shen, Xueju; Zhao, Cheng] Shijiazhuang Mech Engn Coll, Dept Optoelect Engn, Shijiazhuang 050003, Peoples R China; [Wang, Zhisong] Baicheng Ordnance Test Ctr China, Dist Taobei 137001, Baicheng, Peoples R China Lin, C (reprint author), Shijiazhuang Mech Engn Coll, Dept Optoelect Engn, Heping West Rd 97, Shijiazhuang 050003, Peoples R China. vestigelinchao@163.com Abuturab MR, 2013, APPL OPTICS, V52, P1555, DOI 10.1364/AO.52.001555; Alfalou A, 2010, OPT LETT, V35, P2185, DOI 10.1364/OL.35.002185; Alfalou A, 2009, ADV OPT PHOTONICS, V1, P589, DOI 10.1364/AOP.1.000589; Chen W, 2012, APPL OPTICS, V51, P6076, DOI 10.1364/AO.51.006076; Eriksen RL, 2001, OPT COMMUN, V187, P325, DOI 10.1016/S0030-4018(00)01127-5; He WQ, 2013, OPT LETT, V38, P4044, DOI 10.1364/OL.38.004044; Lin C, 2012, OPT COMMUN, V285, P1023, DOI 10.1016/j.optcom.2011.10.046; Liu W, 2013, OPT LETT, V38, P4045, DOI 10.1364/OL.38.004045; Liu W, 2013, OPT LETT, V38, P1651, DOI 10.1364/OL.38.001651; Peng X, 2006, OPT LETT, V31, P3579, DOI 10.1364/OL.31.003579; Qin W, 2010, OPT LETT, V35, P118, DOI 10.1364/OL.35.000118; Rajput SK, 2013, APPL OPTICS, V52, P4343, DOI 10.1364/AO.52.004343; Rajput SK, 2012, APPL OPTICS, V51, P1446, DOI 10.1364/AO.51.001446; Rajput SK, 2012, APPL OPTICS, V51, P5377, DOI 10.1364/AO.51.005377; REFREGIER P, 1995, OPT LETT, V20, P767; Safrani A, 2009, OPT LETT, V34, P1801, DOI 10.1364/OL.34.001801; Tajahuerce E, 2000, APPL OPTICS, V39, P6595, DOI 10.1364/AO.39.006595; Unnikrishnan G, 2001, OPT COMMUN, V193, P51, DOI 10.1016/S0030-4018(01)01224-X; Unnikrishnan G, 2000, OPT LETT, V25, P887, DOI 10.1364/OL.25.000887; Unnikrishnan G., 2006, APPL OPTICS, V45, P5693; Wang XG, 2012, OPT COMMUN, V285, P1078, DOI 10.1016/j.optcom.2011.12.017; Wang XG, 2014, APPL OPTICS, V53, P208, DOI 10.1364/AO.53.000208; Yuan S, 2009, OPT EXPRESS, V17, P3270, DOI 10.1364/OE.17.003270 23 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1559-128X 2155-3165 APPL OPTICS Appl. Optics JUN 20 2014 53 18 3920 3928 10.1364/AO.53.003920 9 Optics Optics AK5CV WOS:000338442800014 J Mittner, M; Behrendt, J; Menge, U; Titz, C; Hasselhorn, M Mittner, Matthias; Behrendt, Joerg; Menge, Uwe; Titz, Cora; Hasselhorn, Marcus Response-retrieval in identity negative priming is modulated by temporal discriminability FRONTIERS IN PSYCHOLOGY English Article negative priming; selective attention; response retrieval; episodic memory; visual attention SELECTIVE ATTENTION; STIMULUS REPETITION; EVENT FILES; INHIBITION; PERSISTENCE; INTEGRATION; INFORMATION; MECHANISMS; OBJECTS; MEMORY Reaction times to previously ignored information are often delayed, a phenomenon referred to as negative priming (NP). Rotherrnund et al. (2005) proposed that NP is caused by the retrieval of incidental stimulus-response associations when consecutive displays share visual features but require different responses. In two experiments we examined whether the features (color, shape) that reappear in consecutive displays, or their level of processing (early-perceptual, late-semantic) moderate the likelihood that stimulus-response associations are retrieved. Using a perceptual matching task (Experiment 1), NP occurred independently of whether responses were repeated or switched. Only when implementing a semantic-matching task (Experiment 2), negative priming was determined by response-repetition as predicted by response-retrieval theory. The results can be explained in terms of a task-dependent temporal discrimination process (Milliken et al., 1998): Response-relevant features are encoded more strongly and/or are more likely to be retrieved than irrelevant features. [Mittner, Matthias] Univ Tromso, Dept Psychol, N-9037 Tromso, Norway; [Behrendt, Joerg] Univ Gottingen, Georg Elias Muller Inst Psychol, D-37073 Gottingen, Germany; [Menge, Uwe; Titz, Cora; Hasselhorn, Marcus] German Inst Int Educ Res DIPF, Frankfurt, Germany Mittner, M (reprint author), Univ Tromso, Dept Psychol, Huginbakken 32, N-9037 Tromso, Norway. matthias.mittner@uit.no Bakeman R, 2005, BEHAV RES METHODS, V37, P379, DOI 10.3758/BF03192707; Christie J, 2001, CAN J EXP PSYCHOL, V55, P24, DOI 10.1037/h0087350; DeSchepper B, 1996, J EXP PSYCHOL LEARN, V22, P27, DOI 10.1037/0278-7393.22.1.27; Frings C, 2007, Q J EXP PSYCHOL, V60, P1367, DOI 10.1080/17470210600955645; Frings C, 2006, Q J EXP PSYCHOL, V59, P683, DOI 10.1080/02724980443000872; Frings C, 2011, J EXP PSYCHOL LEARN, V37, P1209, DOI 10.1037/a0023915; Grison S, 2001, PERCEPT PSYCHOPHYS, V63, P1063, DOI 10.3758/BF03194524; Henson R. N., 2014, TRENDS COGN SCI, DOI [10.1016/j.tics.2014.03.004, DOI 10.1016/J.TICS.2014.03.004.]; HOLM S, 1979, SCAND J STAT, V6, P65; Hommel B, 2005, J EXP PSYCHOL HUMAN, V31, P1067, DOI 10.1037/0096-1523.31.5.1067; Hommel B, 1998, VIS COGN, V5, P183, DOI 10.1080/713756773; Hommel B, 2004, TRENDS COGN SCI, V8, P494, DOI 10.1016/j.tics.2004.08.007; Ihrke M, 2011, EXP PSYCHOL, V58, P154, DOI 10.1027/1618-3169/a000081; Ihrke M, 2013, EXP PSYCHOL, V60, P12, DOI 10.1027/1618-3169/a000169; Ihrke M, 2011, FRONT PSYCHOL, V2, DOI 10.3389/fpsyg.2011.00225; KAHNEMAN D, 1992, COGNITIVE PSYCHOL, V24, P175, DOI 10.1016/0010-0285(92)90007-O; Kane MJ, 1997, J EXP PSYCHOL HUMAN, V23, P632, DOI 10.1037/0096-1523.23.3.632; Kleinsorge T, 1999, ACTA PSYCHOL, V103, P295, DOI 10.1016/S0001-6918(99)00047-5; Kramer AF, 2001, PSYCHOL AGING, V16, P580, DOI 10.1037//0882-7974.16.4.580; Lammertyn J, 2005, Q J EXP PSYCHOL-A, V58, P1153, DOI 10.1080/02724980443000520; Lavie N, 2000, J EXP PSYCHOL HUMAN, V26, P1038, DOI 10.1037//0096-1523.26.3.1038; LOGAN GD, 1988, PSYCHOL REV, V95, P492, DOI 10.1037//0033-295X.95.4.492; LOWE DG, 1979, MEM COGNITION, V7, P382, DOI 10.3758/BF03196943; MALLEY GB, 1995, PERCEPT PSYCHOPHYS, V57, P657, DOI 10.3758/BF03213271; Marczinski CA, 2003, PSYCHOL AGING, V18, P780, DOI 10.1037/0882-7974.18.4.780; Mayr S, 2011, EXP PSYCHOL, V58, P353, DOI 10.1027/1618-3169/a000102; Milliken B, 1998, PSYCHOL REV, V105, P203, DOI 10.1037/0033-295X.105.2.203; Moeller B, 2014, ATTEN PERCEPT PSYCHO, V76, P959, DOI 10.3758/s13414-014-0648-9; MOORE CM, 1994, PERCEPT PSYCHOPHYS, V56, P133, DOI 10.3758/BF03213892; Nagai J, 2003, MEM COGNITION, V31, P369, DOI 10.3758/BF03194395; NEILL WT, 1994, PSYCHON B REV, V1, P119, DOI 10.3758/BF03200767; Neill W. T., 1998, PSYCHOL LEARN MOTIV, V38, P1, DOI DOI 10.1016/S0079-7421(08)60182-6; Neill W. T., 2007, INHIBITION COGNITION, P63, DOI [10.1037/11587-004, DOI 10.1037/11587-004]; Neill WT, 1997, J EXP PSYCHOL LEARN, V23, P1291, DOI 10.1037/0278-7393.23.6.1291; NEILL WT, 1992, J EXP PSYCHOL LEARN, V18, P993, DOI 10.1037/0278-7393.18.5.993; NEILL WT, 1992, J EXP PSYCHOL LEARN, V18, P565, DOI 10.1037//0278-7393.18.3.565; Neumann E, 1999, MEM COGNITION, V27, P1051, DOI 10.3758/BF03201234; Rothermund K, 2005, J EXP PSYCHOL LEARN, V31, P482, DOI 10.1037/0278-7393.31.3.482; Schmidt K, 1992, WORTSCHATZTEST WST; Schrobsdorff H, 2007, CONNECT SCI, V19, P203, DOI 10.1080/09540090701507823; SMITH MC, 1968, J EXP PSYCHOL, V77, P435, DOI 10.1037/h0021293; Strayer DL, 1999, J EXP PSYCHOL HUMAN, V25, P24, DOI 10.1037//0096-1523.25.1.24; TIPPER SP, 1985, Q J EXP PSYCHOL-A, V37, P591; TIPPER SP, 1990, J EXP PSYCHOL HUMAN, V16, P492, DOI 10.1037//0096-1523.16.3.492; TIPPER SP, 1985, Q J EXP PSYCHOL-A, V37, P571; TIPPER SP, 1994, Q J EXP PSYCHOL-A, V47, P809; TIPPER SP, 1987, PERS INDIV DIFFER, V8, P667, DOI 10.1016/0191-8869(87)90064-X; Titz C, 2008, EXP AGING RES, V34, P340, DOI 10.1080/03610730802273936; Treisman A., 1996, ATTENTION PERFORM, P15; Wechsler D., 1958, MEASUREMENT APPRAISA, DOI [10.1037/11167-000, DOI 10.1037/11167-000]; Zmigrod S, 2009, PSYCHOL RES-PSYCH FO, V73, P674, DOI 10.1007/s00426-008-0163-5 51 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUN 20 2014 5 621 10.3389/fpsyg.2014.00621 13 Psychology, Multidisciplinary Psychology AK8NZ WOS:000338685900001 J Carmagnola, F; Osborne, F; Torre, I Carmagnola, Francesca; Osborne, Francesco; Torre, Ilaria User data discovery and aggregation: The CS-UDD algorithm INFORMATION SCIENCES English Article Social web; User profiling; User data discovery; Information retrieval; Entity matching; Entity linkage CROSS-SYSTEM PERSONALIZATION; INTEROPERABILITY; ADAPTATION; PROFILES; NETWORKS In the social web, people use social systems for sharing content and opinions, for communicating with friends, for tagging, etc. People usually have different accounts and different profiles on all of these systems. Several tools for user data aggregation and people search have been developed and protocols and standards for data portability have been defined. This paper presents an approach and an algorithm, named Cross-System User Data Discovery (CS-UDD), to retrieve and aggregate user data distributed on social websites. It is designed to crawl websites, retrieve profiles that may belong to the searched user, correlate them, aggregate the discovered data and return them to the searcher which may, for example, be an adaptive system. The user attributes retrieved, namely attribute-value pairs, are associated with a certainty factor that expresses the confidence that they are true for the searched user. To test the algorithm, we ran it on two popular social networks, MySpace and Flickr. The evaluation has demonstrated the ability of the CS-UDD algorithm to discover unknown user attributes and has revealed high precision of the discovered attributes. (c) 2014 Elsevier Inc. All rights reserved. [Carmagnola, Francesca; Osborne, Francesco] Univ Turin, Dept Comp Sci, I-10124 Turin, Italy; [Torre, Ilaria] Univ Genoa, Dept Comp Sci Bioengn Robot & Syst Engn, I-16126 Genoa, Italy; [Osborne, Francesco] Open Univ, Knowledge Media Inst, London, England Torre, I (reprint author), Univ Genoa, Dept Comp Sci Bioengn Robot & Syst Engn, I-16126 Genoa, Italy. francesca.carmagnola@di.unito.it; francesco.osborne@di.unito.it; ilaria.torre@unige.it Abel F, 2010, LECT NOTES COMPUT SC, V6075, P16, DOI 10.1007/978-3-642-13470-8_4; Aroyo L, 2006, EDUC TECHNOL SOC, V9, P4; Batini Carlo, 2006, DATA QUALITY CONCEPT; Berkovsky S, 2008, USER MODEL USER-ADAP, V18, P245, DOI 10.1007/s11257-007-9042-9; Bischoff K., 2008, P 17 ACM C INF KNOWL, P193, DOI 10.1145/1458082.1458112; Bleiholder J, 2008, ACM COMPUT SURV, V41, DOI 10.1145/1456650.1456651; Brown DE, 2003, DECIS SUPPORT SYST, V34, P369, DOI 10.1016/S0167-9236(02)00064-7; Bundy A., 1985, Journal of Automated Reasoning, V1; Carmagnola F., 2009, P 8 IADIS INT C WWW; Carmagnola F., 2010, P 1 INT WORKSH INF H, P9, DOI 10.1145/1869446.1869448; Carmagnola F, 2009, INFORM SCIENCES, V179, P16, DOI 10.1016/j.ins.2008.08.022; Carmagnola F, 2008, USER MODEL USER-ADAP, V18, P497, DOI 10.1007/s11257-008-9052-2; Castillo Julio Javier, 2011, International Journal of Machine Learning and Cybernetics, V2, DOI 10.1007/s13042-011-0026-z; Cena F, 2006, AI COMMUN, V19, P369; Console L., 2011, P WORKSH INT SMART O, P1; DEMPSTER AP, 1967, ANN MATH STAT, V38, P325, DOI 10.1214/aoms/1177698950; Dolog P, 2005, LECT NOTES ARTIF INT, V3538, P397; DUBOIS D, 1987, IEEE T SYST MAN CYB, V17, P474, DOI 10.1109/TSMC.1987.4309063; Elmagarmid AK, 2007, IEEE T KNOWL DATA EN, V19, P1, DOI 10.1109/TKDE.2007.250581; FELLEGI IP, 1969, J AM STAT ASSOC, V64, P1183, DOI 10.2307/2286061; Finch E., 2003, DOT CONS CRIME DEVIA, P86; Gae-Won Y., 2011, P 14 INT C EXT DAT T, P515; Ghazizadeh M.A., 2012, INT J MACH LEARN CYB; Guy I, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P1017; Hay M, 2010, VLDB J, V19, P797, DOI 10.1007/s00778-010-0210-x; Heckerman D. E., 1992, Artificial Intelligence in Medicine, V4, DOI 10.1016/0933-3657(92)90036-O; Heckmann D., 2006, UBIQUITOUS USER MODE; Hong Xu, 1997, Decision Support Systems, V20, DOI 10.1016/S0167-9236(96)00062-0; Ioannou E., 2008, LNCS, V5074, P556; Iofciu T., 2011, P 5 INT C WEBL SOC M; Irani D, 2011, IEEE INTERNET COMPUT, V15, P13, DOI 10.1109/MIC.2011.25; Jiexun Li, 2011, Information Systems Frontiers, V13, DOI 10.1007/s10796-010-9270-0; Kay J, 2006, LECT NOTES COMPUT SC, V4018, P11, DOI 10.1007/11768012_2; Kobsa A, 2001, KNOWL ENG REV, V16, P111, DOI 10.1017/S0269888901000108; Kopcke H, 2010, DATA KNOWL ENG, V69, P197, DOI 10.1016/j.datak.2009.10.003; Kruppa M., 2005, KI J, V1, P56; Labitzke S., 2011, P 5 INT ACM WORKSH S, P51; Lee H, 2010, IEICE T ELECTRON, VE93C, P1525, DOI 10.1587/transele.E93.C.1525; Leonardi E., 2009, P INT WORKSH LIGHTW, P18; Levenshtein VI, 1966, SOV PHYS DOKL, V10, P707; Liu K., 2008, P ACM SIGMOD INT C M, P93, DOI 10.1145/1376616.1376629; MCCARTHY J, 1980, ARTIF INTELL, V13, P27, DOI 10.1016/0004-3702(80)90011-9; Mehta B, 2005, LECT NOTES ARTIF INT, V3538, P119; Mislove A., 2010, P 3 ACM INT C WEB SE, P251, DOI 10.1145/1718487.1718519; Motoyama M., 2009, P 11 INT WORKSH WEB, P67, DOI 10.1145/1651587.1651604; O'Reilly T, 2005, WHAT IS WEB 2 0 DESI; Perito D, 2011, LECT NOTES COMPUT SC, V6794, P1, DOI 10.1007/978-3-642-22263-4_1; REITER R, 1980, ARTIF INTELL, V13, P81, DOI 10.1016/0004-3702(80)90014-4; Salton G, 1984, INTRO MODERN INFORM; Shafer G., 1976, MATH THEORY EVIDENCE, V1; Shahbazian E., 2005, NATO SCI SERIES; Shen W., P 20 NAT C ART INT P, P862; Szomszor M, 2008, LECT NOTES COMPUT SC, V5318, P632, DOI 10.1007/978-3-540-88564-1_40; Vassileva J., 2001, P 9 INT C HUM COMP I, P122; VOSECKY J, 2009, P 1 INT C NETW DIG T, P360; Wang GA, 2006, IEEE T SYST MAN CY A, V36, P988, DOI 10.1109/TSMCA.2006.871799; Wang Y., 2006, P CHI2006 WORKSH PRI, P44; Wang YW, 2008, LECT NOTES COMPUT SC, V5149, P353; Windley P., 2005, DIGITAL IDENTITY; Zadeh L. A., 1978, FUZZY SETS SYSTEMS, V1, P1, DOI DOI 10.1016/0165-0114(78)90029-5; Zafarani R., 2009, P INT C WEBL SOC MED, P354; Zheleva E, 2009, P 18 INT C WORLD WID, P531, DOI DOI 10.1145/1526709.1526781 62 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0020-0255 1872-6291 INFORM SCIENCES Inf. Sci. JUN 20 2014 270 41 72 10.1016/j.ins.2014.02.111 32 Computer Science, Information Systems Computer Science AG7YV WOS:000335635600003 J Zelikowsky, M; Hersman, S; Chawla, MK; Barnes, CA; Fanselow, MS Zelikowsky, Moriel; Hersman, Sarah; Chawla, Monica K.; Barnes, Carol A.; Fanselow, Michael S. Neuronal Ensembles in Amygdala, Hippocampus, and Prefrontal Cortex Track Differential Components of Contextual Fear JOURNAL OF NEUROSCIENCE English Article amygdala; Arc; catFISH; contextual fear; hippocampus; prefrontal cortex MEMORY CONSOLIDATION; CONDITIONED FEAR; DORSAL HIPPOCAMPUS; EXPRESSION; ARC; ACQUISITION; RETRIEVAL; RESPONSES; STIMULUS; DISTINCT Although the circuit mediating contextual fear conditioning has been extensively described, the precise contribution that specific anatomical nodes make to behavior has not been fully elucidated. To clarify the roles of the dorsal hippocampus (DH), basolateral amygdala (BLA), and medial prefrontal cortex (mPFC) in contextual fear conditioning, activity within these regions was mapped using cellular compartment analysis of temporal activity using fluorescence in situ hybridization (catFISH) for Arc mRNA. Rats were delay-fear conditioned or immediately shocked (controls) and thereafter reexposed to the shocked context to test for fear memory recall. Subsequent catFISH analyses revealed that in the DH, cells were preferentially reactivated during the context test, regardless of whether animals had been fear conditioned or immediately shocked, suggesting that the DH encodes spatial information specifically, rather then the emotional valence of an environment. In direct contrast, neuronal ensembles in the BLA were only reactivated at test if animals had been fear conditioned, suggesting that the amygdala specifically tracks the emotional properties of a context. Interestingly, Arc expression in the mPFC was consistent with both amygdala-and hippocampus-like patterns, supporting a role for the mPFC in both fear and contextual processing. Collectively, these data provide crucial insight into the region-specific behavior of neuronal ensembles during contextual fear conditioning and demonstrate a dissociable role for the hippocampus and amygdala in processing the contextual and emotional properties of a fear memory. [Zelikowsky, Moriel; Hersman, Sarah; Fanselow, Michael S.] Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA 90095 USA; [Zelikowsky, Moriel; Hersman, Sarah; Fanselow, Michael S.] Univ Calif Los Angeles, Dept Psychiat, Los Angeles, CA 90095 USA; [Zelikowsky, Moriel; Hersman, Sarah; Fanselow, Michael S.] Univ Calif Los Angeles, Dept Behav Sci, Los Angeles, CA 90095 USA; [Zelikowsky, Moriel; Hersman, Sarah; Fanselow, Michael S.] Univ Calif Los Angeles, Integrat Ctr Learning & Memory, Los Angeles, CA 90095 USA; [Chawla, Monica K.; Barnes, Carol A.] Univ Arizona, Evelyn McKnight Brain Inst, Tucson, AZ 85724 USA; [Barnes, Carol A.] Univ Arizona, Dept Psychol, Tucson, AZ 85724 USA; [Barnes, Carol A.] Univ Arizona, Dept Neurol, Tucson, AZ 85724 USA; [Barnes, Carol A.] Univ Arizona, Dept Neurosci, Tucson, AZ 85724 USA Fanselow, MS (reprint author), Univ Calif Los Angeles, 405 Hilgard Ave, Los Angeles, CA 90095 USA. mfanselow@gmail.com National Institutes of Health [R01 MH62122]; National Alliance for Research in Schizophrenia and Affective Disorders Distinguished Investigator Award [18667]; American Psychological Association Dissertation Research Award; McKnight Brain Research Foundation This work was supported by National Institutes of Health Grant R01 MH62122 (M. S. F.), a National Alliance for Research in Schizophrenia and Affective Disorders Distinguished Investigator Award # 18667 (M. S. F.), an American Psychological Association Dissertation Research Award (M.Z.), and the McKnight Brain Research Foundation (C. A. B.). We thank James Lister, Lan Hoang, and Timothy Hast for technical assistance. Barot SK, 2009, PLOS ONE, V4, DOI 10.1371/journal.pone.0006156; Cahill L, 1999, NEURON, V23, P227, DOI 10.1016/S0896-6273(00)80774-6; Chawla MK, 2005, HIPPOCAMPUS, V15, P579, DOI 10.1002/hipo.20091; FANSELOW MS, 1990, ANIM LEARN BEHAV, V18, P264, DOI 10.3758/BF03205285; FANSELOW MS, 1986, ANN NY ACAD SCI, V467, P40, DOI 10.1111/j.1749-6632.1986.tb14617.x; FANSELOW MS, 1993, J EXP PSYCHOL ANIM B, V19, P121, DOI 10.1037/0097-7403.19.2.121; Fanselow MS, 2010, NEURON, V65, P7, DOI 10.1016/j.neuron.2009.11.031; Fanselow MS, 2000, BEHAV BRAIN RES, V110, P73, DOI 10.1016/S0166-4328(99)00186-2; Fanselow MS, 1999, NEURON, V23, P229, DOI 10.1016/S0896-6273(00)80775-8; Frankland PW, 2004, SCIENCE, V304, P881, DOI 10.1126/science.1094804; Frankland PW, 2006, P NATL ACAD SCI USA, V103, P509, DOI 10.1073/pnas.0510133103; Guzowski JF, 1999, NAT NEUROSCI, V2, P1120, DOI 10.1038/16046; Han JH, 2009, SCIENCE, V323, P1492, DOI 10.1126/science.1164139; Han JH, 2007, SCIENCE, V316, P457, DOI 10.1126/science.1139438; Josselyn SA, 2004, NEUROBIOL LEARN MEM, V82, P159, DOI 10.1016/j.nlm.2004.05.008; Kida S, 2002, NAT NEUROSCI, V5, P348, DOI 10.1038/nn819; KIM JJ, 1992, SCIENCE, V256, P675, DOI 10.1126/science.1585183; Lepicard EM, 2006, EUR J NEUROSCI, V23, P3063, DOI 10.1111/j.1460-9568.2006.04830.x; LYFORD GL, 1995, NEURON, V14, P433, DOI 10.1016/0896-6273(95)90299-6; Maren S, 1996, NEURON, V16, P237, DOI 10.1016/S0896-6273(00)80041-0; Matus-Amat P, 2004, J NEUROSCI, V24, P2431, DOI 10.1523/JNEUROSCI.1598-03.2004; Moita MAP, 2003, NEURON, V37, P485, DOI 10.1016/S0896-6273(03)00033-3; Morgan MA, 1999, NEUROBIOL LEARN MEM, V72, P244, DOI 10.1006/nlme.1999.3907; Nalloor R, 2012, FRONT BEHAV NEUROSCI, V6, DOI 10.3389/fnbeh.2012.00027; Quinn JJ, 2008, LEARN MEMORY, V15, P368, DOI 10.1101/lm.813608; Quirk GJ, 2010, NATURE, V463, P36, DOI 10.1038/463036a; Reijmers LG, 2007, SCIENCE, V317, P1230, DOI 10.1126/science.1143839; Schafe GE, 1999, LEARN MEMORY, V6, P97; Schafe GE, 2001, TRENDS NEUROSCI, V24, P540, DOI 10.1016/S0166-2236(00)01969-X; Shimizu E, 2000, SCIENCE, V290, P1170, DOI 10.1126/science.290.5494.1170; Strekalova T, 2003, GENES BRAIN BEHAV, V2, P3, DOI 10.1034/j.1601-183X.2003.00001.x; Vazdarjanova A, 2004, J NEUROSCI, V24, P6489, DOI 10.1523/JNEUROSCI.0350-04.2004; Zelikowsky M, 2013, P NATL ACAD SCI USA, V110, P9938, DOI 10.1073/pnas.1301691110; Zhou Y, 2009, NAT NEUROSCI, V12, P1438, DOI 10.1038/nn.2405 34 0 0 SOC NEUROSCIENCE WASHINGTON 11 DUPONT CIRCLE, NW, STE 500, WASHINGTON, DC 20036 USA 0270-6474 J NEUROSCI J. Neurosci. JUN 18 2014 34 25 8462 8466 10.1523/JNEUROSCI.3624-13.2014 5 Neurosciences Neurosciences & Neurology AK5FD WOS:000338449200008 J Kim, G; Lewis-Peacock, JA; Norman, KA; Turk-Browne, NB Kim, Ghootae; Lewis-Peacock, Jarrod A.; Norman, Kenneth A.; Turk-Browne, Nicholas B. Pruning of memories by context-based prediction error PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA English Article forgetting; learning; multivariate pattern analysis; perception; temporal context TEMPORAL CONTEXT; BRAIN ACTIVITY; VISUAL-CORTEX; REPRESENTATIONS; REPETITION; RETRIEVAL; STRIATUM The capacity of long-term memory is thought to be virtually unlimited. However, our memory bank may need to be pruned regularly to ensure that the information most important for behavior can be stored and accessed efficiently. Using functional magnetic resonance imaging of the human brain, we report the discovery of a context-based mechanism for determining which memories to prune. Specifically, when a previously experienced context is reencountered, the brain automatically generates predictions about which items should appear in that context. If an item fails to appear when strongly expected, its representation in memory is weakened, and it is more likely to be forgotten. We find robust support for this mechanism using multivariate pattern classification and pattern similarity analyses. The results are explained by a model in which context-based predictions activate item representations just enough for them to be weakened during a misprediction. These findings reveal an ongoing and adaptive process for pruning unreliable memories. [Kim, Ghootae; Norman, Kenneth A.; Turk-Browne, Nicholas B.] Princeton Univ, Dept Psychol, Princeton, NJ 08544 USA; [Kim, Ghootae; Norman, Kenneth A.; Turk-Browne, Nicholas B.] Princeton Univ, Princeton Neurosci Inst, Princeton, NJ 08544 USA; [Lewis-Peacock, Jarrod A.] Univ Texas Austin, Dept Psychol, Austin, TX 78712 USA; [Lewis-Peacock, Jarrod A.] Univ Texas Austin, Inst Neurosci, Austin, TX 78712 USA Turk-Browne, NB (reprint author), Princeton Univ, Dept Psychol, Princeton, NJ 08544 USA. ntb@princeton.edu National Institutes of Health [R01 EY021755, R01 MH069456] We thank Lila Davachi and Per Sederberg for helpful conversations.This work was supported by National Institutes of Health Grants R01 EY021755 (to N.B.T-B.) and R01 MH069456 (to K.A.N.). Anderson MC, 2003, J MEM LANG, V49, P415, DOI 10.1016/j.jml.2003.08.006; ARTOLA A, 1990, NATURE, V347, P69, DOI 10.1038/347069a0; Black A. H., 1972, CLASSICAL CONDITION, P64, DOI DOI 10.1016/J.COGPSYCH.2004.11.001; Brewer JB, 1998, SCIENCE, V281, P1185, DOI 10.1126/science.281.5380.1185; Cohen N. J., 1993, MEMORY AMNESIA HIPPO; Detre GJ, 2013, NEUROPSYCHOLOGIA, V51, P2371, DOI 10.1016/j.neuropsychologia.2013.02.017; EFRON B, 1979, ANN STAT, V7, P1, DOI 10.1214/aos/1176344552; Grill-Spector K, 2006, TRENDS COGN SCI, V10, P14, DOI 10.1016/j.tics.2005.11.006; Hansel C, 1996, J PHYSIOLOGY-PARIS, V90, P317, DOI 10.1016/S0928-4257(97)87906-5; HIRSH R, 1974, BEHAV BIOL, V12, P421, DOI 10.1016/S0091-6773(74)92231-7; Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388; Johnson MR, 2007, NEUROIMAGE, V37, P290, DOI 10.1016/j.neuroimage.2007.05.017; Kok P, 2012, NEURON, V75, P265, DOI 10.1016/j.neuron.2012.04.034; Kriegeskorte Nikolaus, 2008, Front Syst Neurosci, V2, P4, DOI 10.3389/neuro.06.004.2008; Kuhl BA, 2007, NAT NEUROSCI, V10, P908, DOI 10.1038/nn1918; Kuhl BA, 2012, NEUROPSYCHOLOGIA, V50, P458, DOI 10.1016/j.neuropsychologia.2011.09.002; MIYASHITA Y, 1993, ANNU REV NEUROSCI, V16, P245, DOI 10.1146/annurev.ne.16.030193.001333; Newman EL, 2010, CEREB CORTEX, V20, P2760, DOI 10.1093/cercor/bhq021; NISSEN MJ, 1987, COGNITIVE PSYCHOL, V19, P1, DOI 10.1016/0010-0285(87)90002-8; Niv Y, 2008, TRENDS COGN SCI, V12, P265, DOI 10.1016/j.tics.2008.03.006; Norman KA, 2007, PSYCHOL REV, V114, P887, DOI 10.1037/0033-295X.114.4.887; Norman KA, 2006, TRENDS COGN SCI, V10, P424, DOI 10.1016/j.tics.2006.07.005; Norman KA, 2006, NEURAL COMPUT, V18, P1577, DOI 10.1162/neco.2006.18.7.1577; O'Doherty J, 2004, SCIENCE, V304, P452, DOI 10.1126/science.1094285; Olson IR, 2001, J EXP PSYCHOL LEARN, V27, P1299, DOI 10.1037/0278-7393.27.5.1299; Pagnoni G, 2002, NAT NEUROSCI, V5, P97, DOI 10.1038/nn802; Schapiro AC, 2012, CURR BIOL, V22, P1622, DOI 10.1016/j.cub.2012.06.056; Schultz W, 2000, ANNU REV NEUROSCI, V23, P473, DOI 10.1146/annurev.neuro.23.1.473; Smith TA, 2013, J EXP PSYCHOL GEN, V142, P1298, DOI 10.1037/a0034067; Turk-Browne NB, 2012, J NEUROSCI, V32, P7202, DOI 10.1523/JNEUROSCI.0942-12.2012; Turk-Browne NB, 2006, NEURON, V49, P917, DOI 10.1016/j.neuron.2006.01.030; Wagner AD, 1998, SCIENCE, V281, P1188, DOI 10.1126/science.281.5380.1188 32 0 0 NATL ACAD SCIENCES WASHINGTON 2101 CONSTITUTION AVE NW, WASHINGTON, DC 20418 USA 0027-8424 P NATL ACAD SCI USA Proc. Natl. Acad. Sci. U. S. A. JUN 17 2014 111 24 8997 9002 10.1073/pnas.1319438111 6 Multidisciplinary Sciences Science & Technology - Other Topics AI9XO WOS:000337300100066 J Guizar-Sicairos, M; Johnson, I; Diaz, A; Holler, M; Karvinen, P; Stadler, HC; Dinapoli, R; Bunk, O; Menzel, A Guizar-Sicairos, Manuel; Johnson, Ian; Diaz, Ana; Holler, Mirko; Karvinen, Petri; Stadler, Hans-Christian; Dinapoli, Roberto; Bunk, Oliver; Menzel, Andreas High-throughput ptychography using Eiger: scanning X-ray nano-imaging of extended regions OPTICS EXPRESS English Article TRANSVERSE TRANSLATION DIVERSITY; COMPUTED-TOMOGRAPHY; PHASE RETRIEVAL; DIFFRACTION MICROSCOPY; RESOLUTION; DETECTOR; SPECTROMICROSCOPY; RECONSTRUCTION; ALGORITHMS; NANOSCALE The smaller pixel size and high frame rate of next-generation photon counting pixel detectors opens new opportunities for the application of X-ray coherent diffractive imaging (CDI). In this manuscript we demonstrate fast image acquisition for ptychography using an Eiger detector. We achieve above 25,000 resolution elements per second, or an effective dwell time of 40 mu s per resolution element, when imaging a 500 mu m x 290 mu m region of an integrated electronic circuit with 41 nm resolution. We further present the application of a scheme of sharing information between image parts that allows the field of view to exceed the range of the piezoelectric scanning system and requirements on the stability of the illumination to be relaxed. (C) 2014 Optical Society of America [Guizar-Sicairos, Manuel; Johnson, Ian; Diaz, Ana; Holler, Mirko; Karvinen, Petri; Stadler, Hans-Christian; Dinapoli, Roberto; Bunk, Oliver; Menzel, Andreas] Paul Scherrer Inst, Swiss Light Source, CH-5232 Villigen, Switzerland Guizar-Sicairos, M (reprint author), Paul Scherrer Inst, Swiss Light Source, CH-5232 Villigen, Switzerland. manuel.guizar-sicairos@psi.ch Bunk, Oliver/B-7602-2013; Menzel, Andreas/C-4388-2012; Guizar-Sicairos, Manuel/I-4899-2013; Diaz, Ana/I-4139-2013; Holler, Mirko/I-3962-2014 Bunk, Oliver/0000-0001-6563-4053; Menzel, Andreas/0000-0002-0489-609X; Diaz, Ana/0000-0003-0479-4752; Abbey B, 2008, APPL PHYS LETT, V93, DOI 10.1063/1.3025819; Abbey B, 2008, NAT PHYS, V4, P394, DOI 10.1038/nphys896; Ballabriga R, 2011, NUCL INSTRUM METH A, V633, pS15, DOI 10.1016/j.nima.2010.06.108; Braun P., 2013, THESIS U MONTPELLIER, V2; Chen B, 2013, SCI REP-UK, V3, DOI 10.1038/srep01177; Diaz A, 2012, PHYS REV B, V85, DOI 10.1103/PhysRevB.85.020104; Diaz A, 2014, CARBON, V67, P98, DOI 10.1016/j.carbon.2013.09.066; Dierolf M, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/035017; Dierolf M, 2010, NATURE, V467, P436, DOI 10.1038/nature09419; Dierolf M., 2014, PTYCHOGRAPHIC UNPUB; Dierolf M., 2012, COH 2012 INT WORKSH; Dilanian RA, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/9/093042; Dinapoli R, 2011, NUCL INSTRUM METH A, V650, P79, DOI 10.1016/j.nima.2010.12.005; Edo TB, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.053850; Esmaeili M, 2013, MACROMOLECULES, V46, P434, DOI 10.1021/ma3021163; Faulkner HML, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.023903; FIENUP JR, 1982, APPL OPTICS, V21, P2758, DOI 10.1364/AO.21.002758; GERCHBER.RW, 1972, OPTIK, V35, P237; Godard P, 2011, NAT COMMUN, V2, DOI 10.1038/ncomms1569; Guizar-Sicairos M, 2009, OPT EXPRESS, V17, P2670, DOI 10.1364/OE.17.002670; Guizar-Sicairos M, 2008, OPT LETT, V33, P156, DOI 10.1364/OL.33.000156; Guizar-Sicairos M, 2008, OPT EXPRESS, V16, P7264, DOI 10.1364/OE.16.007264; Guizar-Sicairos M, 2011, OPT EXPRESS, V19, P21345, DOI 10.1364/OE.19.021345; Henrich B, 2009, NUCL INSTRUM METH A, V607, P247, DOI 10.1016/j.nima.2009.03.200; Holler M, 2012, REV SCI INSTRUM, V83, DOI 10.1063/1.4737624; Holler M, 2014, SCI REP-UK, V4, DOI 10.1038/srep03857; Johnson I., 2014, J INSTRUM IN PRESS; Johnson I, 2012, J SYNCHROTRON RADIAT, V19, P1001, DOI 10.1107/S0909049512035972; Jones MWM, 2013, SCI REP-UK, V3, DOI 10.1038/srep02288; Kelly ST, 2013, REV SCI INSTRUM, V84, DOI 10.1063/1.4816649; Kewish CM, 2010, OPT EXPRESS, V18, P23420, DOI 10.1364/OE.18.023420; Kraft P, 2009, J SYNCHROTRON RADIAT, V16, P368, DOI 10.1107/S0909049509009911; Lima E, 2013, J MICROSC-OXFORD, V249, P1, DOI 10.1111/j.1365-2818.2012.03682.x; Llopart X, 2002, IEEE T NUCL SCI, V49, P2279, DOI 10.1109/TNS.2002.803788; Maiden AM, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms2640; Maiden AM, 2011, J OPT SOC AM A, V28, P604, DOI 10.1364/JOSAA.28.000604; Maiden AM, 2012, J OPT SOC AM A, V29, P1606, DOI 10.1364/JOSAA.29.001606; Maiden AM, 2009, ULTRAMICROSCOPY, V109, P1256, DOI 10.1016/j.ultramic.2009.05.012; Marchal J, 2013, J PHYS CONF SER, V425, DOI 10.1088/1742-6596/425/6/062003; Medjoubi K, 2013, J SYNCHROTRON RADIAT, V20, P293, DOI 10.1107/S0909049512052119; Miao JW, 1999, NATURE, V400, P342, DOI 10.1038/22498; Ponchut C, 2011, J INSTRUM, V6, DOI 10.1088/1748-0221/6/01/C01069; Schropp A, 2010, APPL PHYS LETT, V96, DOI 10.1063/1.3332591; Schropp A, 2011, J MICROSC-OXFORD, V241, P9, DOI 10.1111/j.1365-2818.2010.03453.x; Schropp A, 2012, APPL PHYS LETT, V100, DOI 10.1063/1.4729942; Takahashi Y, 2013, APPL PHYS LETT, V102, DOI 10.1063/1.4794063; Takahashi Y, 2011, PHYS REV B, V83, DOI 10.1103/PhysRevB.83.214109; Thibault P, 2013, NATURE, V494, P68, DOI 10.1038/nature11806; Thibault P, 2012, NEW J PHYS, V14, DOI 10.1088/1367-2630/14/6/063004; Thibault P, 2008, SCIENCE, V321, P379, DOI 10.1126/science.1158573; Thibault P, 2009, ULTRAMICROSCOPY, V109, P338, DOI 10.1016/j.ultramic.2008.12.011; Thieme J, 2010, J SYNCHROTRON RADIAT, V17, P149, DOI 10.1107/S0909049509049905; Trtik P, 2013, CEMENT CONCRETE COMP, V36, P71, DOI 10.1016/j.cemconcomp.2012.06.001; van Heel M, 2005, J STRUCT BIOL, V151, P250, DOI 10.1016/j.jsb.2005.05.009; Vila-Comamala J, 2011, OPT EXPRESS, V19, P21333, DOI 10.1364/OE.19.021333; Vine DJ, 2012, OPT EXPRESS, V20, P18287, DOI 10.1364/OE.20.018287; Wilke RN, 2012, OPT EXPRESS, V20, P19232, DOI 10.1364/OE.20.019232; Wilke RN, 2013, ACTA CRYSTALLOGR A, V69, P490, DOI 10.1107/S0108767313019612 58 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUN 16 2014 22 12 14859 14870 10.1364/OE.22.014859 12 Optics Optics AJ9PH WOS:000338044300079 J Shir, OM; Roslund, J; Whitley, D; Rabitz, H Shir, Ofer M.; Roslund, Jonathan; Whitley, Darrell; Rabitz, Herschel Efficient retrieval of landscape Hessian: Forced optimal covariance adaptive learning PHYSICAL REVIEW E English Article EVOLUTION STRATEGIES; QUANTUM CONTROL; MATRIX ADAPTATION; COHERENT CONTROL; SELF-ADAPTATION; CMA-ES; ALGORITHMS; OPTIMIZATION; DYNAMICS; FEEDBACK Knowledge of the Hessian matrix at the landscape optimum of a controlled physical observable offers valuable information about the system robustness to control noise. The Hessian can also assist in physical landscape characterization, which is of particular interest in quantum system control experiments. The recently developed landscape theoretical analysis motivated the compilation of an automated method to learn the Hessian matrix about the global optimum without derivative measurements from noisy data. The current study introduces the forced optimal covariance adaptive learning (FOCAL) technique for this purpose. FOCAL relies on the covariance matrix adaptation evolution strategy (CMA-ES) that exploits covariance information amongst the control variables by means of principal component analysis. The FOCAL technique is designed to operate with experimental optimization, generally involving continuous high-dimensional search landscapes (greater than or similar to 30) with large Hessian condition numbers (greater than or similar to 10(4)). This paper introduces the theoretical foundations of the inverse relationship between the covariance learned by the evolution strategy and the actual Hessian matrix of the landscape. FOCAL is presented and demonstrated to retrieve the Hessian matrix with high fidelity on both model landscapes and quantum control experiments, which are observed to possess nonseparable, nonquadratic search landscapes. The recovered Hessian forms were corroborated by physical knowledge of the systems. The implications of FOCAL extend beyond the investigated studies to potentially cover other physically motivated multivariate landscapes. [Shir, Ofer M.; Roslund, Jonathan; Rabitz, Herschel] Princeton Univ, Dept Chem, Princeton, NJ 08544 USA; [Whitley, Darrell] Colorado State Univ, Dept Comp Sci, Ft Collins, CO 80523 USA Shir, OM (reprint author), Princeton Univ, Dept Chem, Princeton, NJ 08544 USA. oshir@Princeton.EDU NSF; ARO The authors would like to thank Monte Lunacek and Tak-San Ho for the valuable discussions. The authors acknowledge support from NSF and ARO. Akimoto Y., 2008, GECCO 08, P479; Auger A., 2005, P IEEE C EV COMP, P1777; Back T., 1996, EVOLUTIONARY ALGORIT; Baumert T, 1997, APPL PHYS B-LASERS O, V65, P779, DOI 10.1007/s003400050346; Beltrani V, 2011, J CHEM PHYS, V134, DOI 10.1063/1.3589404; Beyer H.-G., 2002, Natural Computing, V1, DOI 10.1023/A:1015059928466; Beyer HG, 2008, LECT NOTES COMPUT SC, V5199, P123, DOI 10.1007/978-3-540-87700-4_13; Beyer H.-G., 2012, LECT NOTES COMPUTER, V7491, P367; Chakrabarti R, 2007, INT REV PHYS CHEM, V26, P671, DOI 10.1080/01442350701633300; Dudovich N, 2001, PHYS REV LETT, V86, P47, DOI 10.1103/PhysRevLett.86.47; Fanciulli R, 2008, LECT NOTES COMPUT SC, V4926, P219; Flannery B. P., 1992, NUMERICAL RECIPES; Fyodorov YV, 2004, PHYS REV LETT, V92, DOI 10.1103/PhysRevLett.92.240601; Fyodorov YV, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.167203; Hansen N., CMA EVOLUTION STRATE; Hansen N., 1995, P 6 INT C GEN ALG, P57; Hansen N., 2009, GECCO COMP, P2397; Hansen N, 2001, EVOL COMPUT, V9, P159, DOI 10.1162/106365601750190398; Hanson N., 1996, Proceedings of 1996 IEEE International Conference on Evolutionary Computation (ICEC'96) (Cat. No.96TH8114), DOI 10.1109/ICEC.1996.542381; Hansen N., 1998, LECT NOTES COMPUTER, V1498, P282; Hansen N., 2009, REFERENCES CMA ES AP; Hansen N, 2009, IEEE T EVOLUT COMPUT, V13, P180, DOI 10.1109/TEVC.2008.924423; Ho TS, 2006, J PHOTOCH PHOTOBIO A, V180, P226, DOI 10.1016/j.jphotochem.2006.03.038; Igel C, 2007, EVOL COMPUT, V15, P1, DOI 10.1162/evco.2007.15.1.1; JUDSON RS, 1992, PHYS REV LETT, V68, P1500, DOI 10.1103/PhysRevLett.68.1500; Klein A, 2012, J PHYS A-MATH THEOR, V45, DOI 10.1088/1751-8113/45/2/025001; Laforge FO, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.013401; Mehta D, 2013, PHYS REV E, V87, DOI 10.1103/PhysRevE.87.052143; Neumaier A, 1998, SIAM REV, V40, P636, DOI 10.1137/S0036144597321909; Nuernberger P, 2007, PHYS CHEM CHEM PHYS, V9, P2470, DOI 10.1039/b618760a; Ostermeier A, 1994, EVOL COMPUT, V2, P369, DOI 10.1162/evco.1994.2.4.369; Ostermeier A., 1993, TECHNICAL REPORT; Ostermeier A, 1994, LECT NOTES COMPUT SC, V866, P189; Pearson BJ, 2001, PHYS REV A, V63, DOI 10.1103/PhysRevA.63.063412; PEIRCE AP, 1988, PHYS REV A, V37, P4950, DOI 10.1103/PhysRevA.37.4950; Rabitz H, 2000, SCIENCE, V288, P824, DOI 10.1126/science.288.5467.824; Rabitz H, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.012721; Rabitz HA, 2004, SCIENCE, V303, P1998, DOI 10.1126/science.1093384; Ros R, 2008, LECT NOTES COMPUT SC, V5199, P296, DOI 10.1007/978-3-540-87700-4_30; Roslund J, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.043415; Roslund J, 2009, PHYS REV A, V80, DOI 10.1103/PhysRevA.80.013408; Roslund J, 2006, PHYS REV A, V74, DOI 10.1103/PhysRevA.74.043414; Rudolph G., 1992, PARALLEL PROBLEM SOL, P105; Shen ZW, 2006, J CHEM PHYS, V124, DOI 10.1063/1.2198836; Shir O. M., 2006, P 2006 IEEE WORLD C, P9817; Shir O. M., 2010, P 12 GEN EV COMP C G, P421, DOI 10.1145/1830483.1830563; Shir O. M., 2008, P GEN EV COMP C GECC, P519, DOI 10.1145/1389095.1389193; Shir OM, 2010, EVOL COMPUT, V18, P97, DOI 10.1162/evco.2010.18.1.18104; Shir Ofer M., 2009, Natural Computing, V8, P171, DOI 10.1007/s11047-007-9065-5; Shir OM, 2008, J PHYS B-AT MOL OPT, V41, DOI 10.1088/0953-4075/41/7/074021; Shir OM, 2012, GENET PROGRAM EVOL M, V13, P445, DOI 10.1007/s10710-012-9164-7; Siedschlag C, 2006, OPT COMMUN, V264, P511, DOI 10.1016/j.optcom.2006.01.065; Suganthan P., 2005, TECHNICAL REPORT; WARREN WS, 1993, SCIENCE, V259, P1581, DOI 10.1126/science.259.5101.1581; Wilson JW, 2008, REV SCI INSTRUM, V79, DOI 10.1063/1.2839919; Zeidler D, 2001, PHYS REV A, V64, part. no., DOI 10.1103/PhysRevA.64.023420 56 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 1539-3755 1550-2376 PHYS REV E Phys. Rev. E JUN 16 2014 89 6 063306 10.1103/PhysRevE.89.063306 16 AJ5OJ WOS:000337733900010 J Grezes, C; Julsgaard, B; Kubo, Y; Stern, M; Umeda, T; Isoya, J; Sumiya, H; Abe, H; Onoda, S; Ohshima, T; Jacques, V; Esteve, J; Vion, D; Esteve, D; Molmer, K; Bertet, P Grezes, C.; Julsgaard, B.; Kubo, Y.; Stern, M.; Umeda, T.; Isoya, J.; Sumiya, H.; Abe, H.; Onoda, S.; Ohshima, T.; Jacques, V.; Esteve, J.; Vion, D.; Esteve, D.; Molmer, K.; Bertet, P. Multimode Storage and Retrieval of Microwave Fields in a Spin Ensemble PHYSICAL REVIEW X English Article QUANTUM MEMORY; DIAMOND A quantum memory at microwave frequencies, able to store the state of multiple superconducting qubits for long times, is a key element for quantum information processing. Electronic and nuclear spins are natural candidates for the storage medium as their coherence time can be well above 1 s. Benefiting from these long coherence times requires one to apply the refocusing techniques used in magnetic resonance, a major challenge in the context of hybrid quantum circuits. Here, we report the first implementation of such a scheme, using ensembles of nitrogen-vacancy centers in diamond coupled to a superconducting resonator, in a setup compatible with superconducting qubit technology. We implement the active reset of the nitrogen-vacancy spins into their ground state by optical pumping and their refocusing by Hahn-echo sequences. This enables the storage of multiple microwave pulses at the picowatt level and their retrieval after up to 35 mu s, a 3 orders of magnitude improvement compared to previous experiments. [Grezes, C.; Kubo, Y.; Stern, M.; Vion, D.; Esteve, D.; Bertet, P.] CEA Saclay, DSM, IRAMIS, CNRS URA 2464,Quantron Grp,SPEC, F-91191 Gif Sur Yvette, France; [Julsgaard, B.; Molmer, K.] Aarhus Univ, Dept Phys & Astron, DK-8000 Aarhus C, Denmark; [Umeda, T.] Univ Tsukuba, Inst Appl Phys, Tsukuba, Ibaraki 3058573, Japan; [Isoya, J.] Univ Tsukuba, Res Ctr Knowledge Communities, Tsukuba, Ibaraki 3058550, Japan; [Sumiya, H.] Sumitomo Elect Ind Ltd, Itami, Hyogo 664001, Japan; [Abe, H.; Onoda, S.; Ohshima, T.] Japan Atom Energy Agcy, Takasaki, Gumma 3701292, Japan; [Kubo, Y.; Jacques, V.] ENS Cachan, Lab Photon Quant & Mol, F-94235 Cachan, France; [Kubo, Y.; Jacques, V.] CNRS, UMR 8537, F-94235 Cachan, France; [Jacques, V.] Univ Paris 11, CNRS, Aime Cotton Lab, F-91405 Orsay, France; [Jacques, V.] ENS Cachan, F-91405 Orsay, France; [Esteve, D.] Univ Paris 06, ENS, Lab Kastler Brossel, CNRS, F-75005 Paris, France Grezes, C (reprint author), CEA Saclay, DSM, IRAMIS, CNRS URA 2464,Quantron Grp,SPEC, F-91191 Gif Sur Yvette, France. Jacques, Vincent/D-3881-2014 French National Research Agency (ANR); European project SCALEQIT; Japanese Society for the Promotion of Science (JSPS); Villum Foundation We acknowledge technical support from P. Senat, D. Duet, J.-C. Tack, P. Pari, and P. Forget, as well as useful discussions within the Quantronics group and with A. Dreau, J.-F. Roch, T. Chaneliere, and J. Morton. We acknowledge support from the French National Research Agency (ANR) with the QINVC project from CHISTERA program, the European project SCALEQIT, and the C'Nano IdF project QUANTROCRYO. Y.K. is supported by the Japanese Society for the Promotion of Science (JSPS). B. J. and K. M. acknowledge support from the Villum Foundation. Afzelius M, 2013, NEW J PHYS, V15, DOI 10.1088/1367-2630/15/6/065008; Amsuss R, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.060502; ANDERSON AG, 1955, J APPL PHYS, V26, P1324, DOI 10.1063/1.1721903; Bar-Gill N, 2013, NAT COMMUN, V4, DOI 10.1038/ncomms2771; Benningshof OWB, 2013, J MAGN RESON, V230, P84, DOI 10.1016/j.jmr.2013.01.010; Clausen C, 2011, NATURE, V469, P508, DOI 10.1038/nature09662; Damon V, 2011, NEW J PHYS, V13, DOI 10.1088/1367-2630/13/9/093031; Dolde F, 2011, NAT PHYS, V7, P459, DOI [10.1038/nphys1969, 10.1038/NPHYS1969]; Felton S, 2009, PHYS REV B, V79, DOI 10.1103/PhysRevB.79.075203; HAHN EL, 1950, PHYS REV, V80, P580, DOI 10.1103/PhysRev.80.580; Hanson R, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.161203; Jacques V, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.057403; Julsgaard B, 2004, NATURE, V432, P482, DOI 10.1038/nature03064; Julsgaard B, 2012, PHYS REV A, V86, DOI 10.1103/PhysRevA.86.063810; Julsgaard B, 2013, PHYS REV A, V88, DOI 10.1103/PhysRevA.88.062324; Julsgaard B, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.250503; Kubo Y, 2011, PHYS REV LETT, V107, DOI 10.1103/PhysRevLett.107.220501; Kubo Y, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.140502; Kubo Y, 2012, PHYS REV A, V85, DOI 10.1103/PhysRevA.85.012333; Lvovsky AI, 2009, NAT PHOTONICS, V3, P706, DOI 10.1038/nphoton.2009.231; Malissa H, 2013, REV SCI INSTRUM, V84, DOI 10.1063/1.4792205; Manson NB, 2006, PHYS REV B, V74, DOI 10.1103/PhysRevB.74.104303; McAuslan DL, 2011, PHYS REV A, V84, DOI 10.1103/PhysRevA.84.022309; Muhonen J.T., ARXIV14027140; Neumann P, 2009, NEW J PHYS, V11, DOI 10.1088/1367-2630/11/1/013017; Probst S, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.157001; Ranjan V, 2013, PHYS REV LETT, V110, DOI 10.1103/PhysRevLett.110.067004; Robledo L, 2011, NATURE, V477, P574, DOI 10.1038/nature10401; Schuster DI, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.140501; Sigillito A. J., ARXIV14030018; Steger M, 2012, SCIENCE, V336, P1280, DOI 10.1126/science.1217635; Tordrup K, 2008, PHYS REV A, V77, DOI 10.1103/PhysRevA.77.020301; Tordrup K, 2008, PHYS REV LETT, V101, DOI 10.1103/PhysRevLett.101.040501; Tyryshkin AM, 2012, NAT MATER, V11, P143, DOI [10.1038/nmat3182, 10.1038/NMAT3182]; Wesenberg JH, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.070502; Wu H, 2010, PHYS REV LETT, V105, DOI 10.1103/PhysRevLett.105.140503; Zhu XB, 2011, NATURE, V478, P221, DOI 10.1038/nature10462 37 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 2160-3308 PHYS REV X Phys. Rev. X JUN 16 2014 4 2 021049 10.1103/PhysRevX.4.021049 9 AJ5OL WOS:000337734100001 J Tao, WK; Lang, S; Zeng, X; Li, X; Matsui, T; Mohr, K; Posselt, D; Chern, J; Peters-Lidard, C; Norris, PM; Kang, IS; Choi, I; Hou, A; Lau, KM; Yang, YM Tao, Wei-Kuo; Lang, Stephen; Zeng, Xiping; Li, Xiaowen; Matsui, Toshi; Mohr, Karen; Posselt, Derek; Chern, Jiundar; Peters-Lidard, Christa; Norris, Peter M.; Kang, In-Sik; Choi, Ildae; Hou, Arthur; Lau, K. -M.; Yang, Young-Min The Goddard Cumulus Ensemble model (GCE): Improvements and applications for studying precipitation processes ATMOSPHERIC RESEARCH English Article Cloud-resolving model; Cloud processes; Microphysics; Diurnal rain; Cloud aerosols; Multiscale modeling framework CLOUD-RESOLVING MODEL; TROPICAL OCEANIC CONVECTION; GENERAL-CIRCULATION MODEL; LAND INFORMATION-SYSTEM; MEASURING MISSION TRMM; PART II; WATER BUDGETS; MICROPHYSICS PARAMETERIZATION; SATELLITE-OBSERVATIONS; RADIATION INTERACTION Convection is the primary transport process in the Earth's atmosphere. About two-thirds of the Earth's rainfall and severe floods derive from convection. In addition, two-thirds of the global rain falls in the tropics, while the associated latent heat release accounts for three-fourths of the total heat energy for the Earth's atmosphere. Cloud-resolving models (CRMs) have been used to improve our understanding of cloud and precipitation processes and phenomena from micro-scale to cloud-scale and mesoscale as well as their interactions with radiation and surface processes. CRMs use sophisticated and realistic representations of cloud microphysical processes and can reasonably well resolve the time evolution, structure, and life cycles of clouds and cloud systems. CRMs also allow for explicit interaction between clouds, outgoing longwave (cooling) and incoming solar (heating) radiation, and ocean and land surface processes. Observations are required to initialize CRMs and to validate their results. The Goddard Cumulus Ensemble model (GCE) has been developed and improved at NASA/Goddard Space Flight Center over the past three decades. It is a multi-dimensional non-hydrostatic CRM that can simulate clouds and cloud systems in different environments. Early improvements and testing were presented in Tao and Simpson (1993) and Tao et al. (2003a). A review on the application of the GCE to the understanding of precipitation processes can be found in Simpson and Tao (1993) and Tao (2003). In this paper, recent model improvements (microphysics, radiation and land surface processes) are described along with their impact and performance on cloud and precipitation events in different geographic locations via comparisons with observations. In addition, recent advanced applications of the GCE are presented that include understanding the physical processes responsible for diurnal variation, examining the impact of aerosols (cloud condensation nuclei or CCN and ice nuclei or IN) on precipitation processes, utilizing a satellite simulator to improve the microphysics, providing better simulations for satellite-derived latent heating retrieval, and coupling with a general circulation model to improve the representation of precipitation processes. Future research is also discussed. Published by Elsevier B.V. [Tao, Wei-Kuo; Lang, Stephen; Zeng, Xiping; Li, Xiaowen; Matsui, Toshi; Mohr, Karen; Chern, Jiundar] NASA, GSFC, Mesoscale Atmospher Proc Lab, Greenbelt, MD 20771 USA; [Lang, Stephen] Sci Syst & Applicat Inc, Lanham, MD 20706 USA; [Zeng, Xiping; Li, Xiaowen; Chern, Jiundar] Morgan State Univ, Goddard Earth Sci Technol & Res, Baltimore, MD 21250 USA; [Matsui, Toshi] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA; [Posselt, Derek] Univ Michigan, Ann Arbor, MI 48109 USA; [Peters-Lidard, Christa] NASA, Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD 20771 USA; [Norris, Peter M.] NASA, Goddard Space Flight Ctr, Global Modeling & Assimilat Off, Greenbelt, MD 20771 USA; [Norris, Peter M.] Univ Space Res Assoc, Goddard Earth Sci Technol & Res, Columbia, MD 21044 USA; [Kang, In-Sik; Choi, Ildae; Yang, Young-Min] Seoul Natl Univ, Sch Earth & Environm Sci, Seoul, South Korea; [Hou, Arthur; Lau, K. -M.] NASA, Goddard Space Flight Ctr, Div Earth Sci, Greenbelt, MD 20771 USA Tao, WK (reprint author), NASA, GSFC, Mesoscale Atmospher Proc Lab, Greenbelt, MD 20771 USA. Wei-Kuo.Tao-1@nasa.gov Peters-Lidard, Christa/E-1429-2012 Peters-Lidard, Christa/0000-0003-1255-2876 NASA Precipitation Measurement Mission (PMM); NASA Modeling, Analysis, and Prediction (MAP) Program; NASA Advanced Information Systems Technology (AIST) Program The first author appreciates the inspiring and enthusiastic support of his mentor, Dr. Joanne Simpson, over a period of 25 years. The author is grateful to Dr. R. Kakar at NASA headquarters for his continuous support of Goddard Cumulus Ensemble model (GCE) improvements and applications. The GCE modeling is mainly supported by the NASA Precipitation Measurement Mission (PMM). The Goddard MMF, NU-WRF, and GPU work are supported by the NASA Modeling, Analysis, and Prediction (MAP) Program and the NASA Advanced Information Systems Technology (AIST) Program. We would also like to thank one anonymous reviewer for helping to improve the quality of the manuscript. Acknowledgment is also made to the NASA Ames Research Center and NASA Goddard Space Flight Center for computer time used in this research. Abdul-Razzak H, 2000, J GEOPHYS RES-ATMOS, V105, P6837, DOI 10.1029/1999JD901161; Ackerman AS, 2000, SCIENCE, V288, P1042, DOI 10.1126/science.288.5468.1042; Aires F, 2011, Q J ROY METEOR SOC, V137, P690, DOI 10.1002/qj.803; Alonge CJ, 2007, J HYDROMETEOROL, V8, P102, DOI 10.1175/JHM559.1; Baker RD, 2001, J HYDROMETEOROL, V2, P193, DOI 10.1175/1525-7541(2001)002<0193:TIOSMC>2.0.CO;2; Cheng CT, 2010, ATMOS RES, V96, P461, DOI 10.1016/j.atmosres.2010.02.005; Choi YS, 2010, P NATL ACAD SCI USA, V107, P11211, DOI 10.1073/pnas.1006241107; Chou M.-D., 2001, NASATM2001104606, V19; Chou M.-D., 1999, NASATM199910460, V15; Cotton WR, 2003, METEOROL ATMOS PHYS, V82, P5, DOI 10.1007/s00703-001-0584-9; DeMott PJ, 2010, P NATL ACAD SCI USA, V107, P11217, DOI 10.1073/pnas.0910818107; Ek MB, 2004, J HYDROMETEOROL, V5, P86, DOI 10.1175/1525-7541(2004)005<0086:IOSMOB>2.0.CO;2; Fan JW, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008136; Fan JW, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD007688; Fletcher N. H., 1962, PHYS RAIN CLOUDS; Garcia-Ortega E, 2012, ATMOS RES, V114, P91, DOI 10.1016/j.atmosres.2012.05.017; Grabowski WW, 1999, PHYSICA D, V133, P171, DOI 10.1016/S0167-2789(99)00104-9; GRAY WM, 1977, MON WEATHER REV, V105, P1171, DOI 10.1175/1520-0493(1977)105<1171:DVODCC>2.0.CO;2; Guy N, 2013, MON WEATHER REV, V141, P582, DOI 10.1175/MWR-D-12-00053.1; HALL WD, 1980, J ATMOS SCI, V37, P2486, DOI 10.1175/1520-0469(1980)037<2486:ADMMWA>2.0.CO;2; HALLETT J, 1974, NATURE, V249, P26, DOI 10.1038/249026a0; Hansen J, 1997, J GEOPHYS RES-ATMOS, V102, P6831, DOI 10.1029/96JD03436; Iguchi T, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD018101; Iguchi T, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL053329; IPCC, 2007, CLIM CHANG 2007 PHYS; Johnson DE, 2007, J ATMOS SCI, V64, P869, DOI 10.1175/JAS3846.1; Juang HMH, 2007, TERR ATMOS OCEAN SCI, V18, P593, DOI 10.3319/TAO.2007.18.3.593; Khain A, 2004, J ATMOS SCI, V61, P2983, DOI 10.1175/JAS-3281.1; Khain A, 2000, ATMOS RES, V55, P159, DOI 10.1016/S0169-8095(00)00064-8; Khairoutdinov M, 2005, J ATMOS SCI, V62, P2136, DOI 10.1175/JAS3453.1; Khairoutdinov M, 2000, MON WEATHER REV, V128, P229, DOI 10.1175/1520-0493(2000)128<0229:ANCPPI>2.0.CO;2; Khairoutdinov M, 2008, J CLIMATE, V21, P413, DOI 10.1175/2007JCLI1630.1; Khairoutdinov MF, 2001, GEOPHYS RES LETT, V28, P3617, DOI 10.1029/2001GL013552; Kim D, 2012, CLIM DYNAM, V38, P411, DOI 10.1007/s00382-010-0972-2; Kraus E.B, 1963, J ATMOS SCI, V20, P546; Kumar SV, 2006, ENVIRON MODELL SOFTW, V21, P1402, DOI 10.1016/j.envsoft.2005.07.004; Lang S, 2003, J APPL METEOROL, V42, P505, DOI 10.1175/1520-0450(2003)042<0505:MOCSPP>2.0.CO;2; Lang S., 2014, J ATMOS SCI IN PRESS; Lang S, 2007, J ATMOS SCI, V64, P1141, DOI 10.1175/JAS3879.1; Lang SE, 2011, J ATMOS SCI, V68, P2306, DOI 10.1175/JAS-D-10-05000.1; Lee MI, 2003, J METEOROL SOC JPN, V81, P963, DOI 10.2151/jmsj.81.963; Lee MI, 2001, J GEOPHYS RES-ATMOS, V106, P14219, DOI 10.1029/2001JD900143; Lee MI, 2010, CLIM DYNAM, V34, P419, DOI 10.1007/s00382-009-0531-x; Lee SS, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010513; Li XW, 2010, Q J ROY METEOR SOC, V136, P382, DOI 10.1002/qj.569; Li XW, 2009, J ATMOS SCI, V66, P3, DOI 10.1175/2008JAS2646.1; Li XW, 2009, J ATMOS SCI, V66, P22, DOI 10.1175/2008JAS2647.1; Lin X, 2000, J CLIMATE, V13, P4159, DOI 10.1175/1520-0442(2000)013<4159:DVOTHC>2.0.CO;2; LIN YL, 1983, J CLIM APPL METEOROL, V22, P1065, DOI 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2; Liu CH, 1998, J ATMOS SCI, V55, P2329, DOI 10.1175/1520-0469(1998)055<2329:ANSOTD>2.0.CO;2; Liu YG, 2004, J ATMOS SCI, V61, P1539, DOI 10.1175/1520-0469(2004)061<1539:POTAPI>2.0.CO;2; LONG AB, 1974, J ATMOS SCI, V31, P1040, DOI 10.1175/1520-0469(1974)031<1040:STTDCE>2.0.CO;2; Lynn BH, 1998, MON WEATHER REV, V126, P928, DOI 10.1175/1520-0493(1998)126<0928:ASOLGD>2.0.CO;2; Lynn BH, 2001, J ATMOS SCI, V58, P593, DOI 10.1175/1520-0469(2001)058<0593:APFTTO>2.0.CO;2; Masunaga H, 2010, B AM METEOROL SOC, V91, P1625, DOI 10.1175/2010BAMS2809.1; Matsui T., 2013, MESOSCALE MODELING S, P760; Matsui T, 2013, B AM METEOROL SOC, V94, P1653, DOI 10.1175/BAMS-D-12-00160.1; Matsui T, 2009, J ATMOS OCEAN TECH, V26, P1261, DOI 10.1175/2008JTECHA1168.1; Matsui T., 2014, J GEOPHYS RES UNPUB; Merino A, 2014, ATMOS RES, V140, P61, DOI 10.1016/j.atmosres.2014.01.015; Meyers MP, 1997, ATMOS RES, V45, P3, DOI 10.1016/S0169-8095(97)00018-5; MEYERS MP, 1992, J APPL METEOROL, V31, P708, DOI 10.1175/1520-0450(1992)031<0708:NPINPI>2.0.CO;2; Miura H, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL023672; Mohr KI, 2013, ENVIRON MODELL SOFTW, V39, P103, DOI 10.1016/j.envsoft.2012.02.023; Mohr KI, 2003, J HYDROMETEOROL, V4, P62, DOI 10.1175/1525-7541(2003)004<0062:TSOWAC>2.0.CO;2; Moncrieff MW, 2006, J ATMOS SCI, V63, P3404, DOI 10.1175/JAS3812.1; Morrison H, 2005, J ATMOS SCI, V62, P1678, DOI 10.1175/JAS3447.1; MOSSOP SC, 1974, SCIENCE, V186, P632, DOI 10.1126/science.186.4164.632; Nasuno T, 2008, J ATMOS SCI, V65, P1246, DOI 10.1175/2007JAS2395.1; Norris PM, 2008, Q J ROY METEOR SOC, V134, P1843, DOI 10.1002/qj.321; NRC, 2001, CLIM CHANG SCI AN SO, V29; Peters-Lidard C. D., 2007, Innovations in Systems and Software Engineering, V3, DOI 10.1007/s11334-007-0028-x; Pielke RA, 2001, REV GEOPHYS, V39, P151, DOI 10.1029/1999RG000072; Ping F, 2013, ATMOS RES, V120, P325, DOI 10.1016/j.atmosres.2012.09.019; Pinsky M, 2000, ATMOS RES, V53, P131, DOI 10.1016/S0169-8095(99)00048-4; Pinsky M, 2001, J ATMOS SCI, V58, P742, DOI 10.1175/1520-0469(2001)058<0742:CEODIA>2.0.CO;2; Pruppacher H. R., 1997, MICROPHYSICS CLOUDS; Raisanen P, 2004, Q J ROY METEOR SOC, V130, P2047, DOI 10.1256/qj.03.99; Randall D, 2003, B AM METEOROL SOC, V84, P1547, DOI 10.1175/BAMS-84-11-1547; RANDALL DA, 1991, J ATMOS SCI, V48, P40, DOI 10.1175/1520-0469(1991)048<0040:DVOTHC>2.0.CO;2; Rosenfeld D, 1999, GEOPHYS RES LETT, V26, P3105, DOI 10.1029/1999GL006066; Rosenfeld D, 2000, SCIENCE, V287, P1793, DOI 10.1126/science.287.5459.1793; Rosenfeld D, 2000, NATURE, V405, P440, DOI 10.1038/35013030; ROTUNNO R, 1988, J ATMOS SCI, V45, P463, DOI 10.1175/1520-0469(1988)045<0463:ATFSLL>2.0.CO;2; RUTLEDGE SA, 1984, J ATMOS SCI, V41, P2949, DOI 10.1175/1520-0469(1984)041<2949:TMAMSA>2.0.CO;2; Saleeby SM, 2004, J APPL METEOROL, V43, P182, DOI 10.1175/1520-0450(2004)043<0182:ALMAPN>2.0.CO;2; Saleeby SM, 2008, J APPL METEOROL CLIM, V47, P694, DOI 10.1175/2007JAMC1664.1; Santanello JA, 2009, J HYDROMETEOROL, V10, P577, DOI 10.1175/2009JHM1066.1; Satoh M, 2008, J COMPUT PHYS, V227, P3486, DOI 10.1016/j.jcp.2007.02.006; Satoh M., 2005, J EARTH SIMUL, V3, P1; Seifert A, 2005, J ATMOS SCI, V62, P1917, DOI 10.1175/JAS3432.1; Seifert A, 2001, ATMOS RES, V59, P265, DOI 10.1016/S0169-8095(01)00126-0; Shen XY, 2011, ATMOS RES, V101, P155, DOI 10.1016/j.atmosres.2011.02.001; Shen XY, 2014, ATMOS RES, V138, P293, DOI 10.1016/j.atmosres.2013.11.020; Shi J.J., 2013, Q J ROYAL M IN PRESS; Shi JJ, 2010, J APPL METEOROL CLIM, V49, P2246, DOI 10.1175/2010JAMC2282.1; SIMPSON J, 1988, B AM METEOROL SOC, V69, P278, DOI 10.1175/1520-0477(1988)069<0278:APTRMM>2.0.CO;2; Simpson J., 1993, TERR ATMOS OCEANIC S, V4, P73; Simpson J, 1996, METEOROL ATMOS PHYS, V60, P19, DOI 10.1007/BF01029783; SLINGO A, 1987, MON WEATHER REV, V115, P1451, DOI 10.1175/1520-0493(1987)115<1451:SOTDCO>2.0.CO;2; SOONG ST, 1980, J ATMOS SCI, V37, P2016, DOI 10.1175/1520-0469(1980)037<2016:RODTCC>2.0.CO;2; SOONG ST, 1980, J ATMOS SCI, V37, P2035, DOI 10.1175/1520-0469(1980)037<2035:ROTCTL>2.0.CO;2; Sui CH, 1998, J ATMOS SCI, V55, P2345, DOI 10.1175/1520-0469(1998)055<2345:RCPISD>2.0.CO;2; Sui CH, 1997, J ATMOS SCI, V54, P639, DOI 10.1175/1520-0469(1997)054<0639:DVITOC>2.0.CO;2; Tao WK, 2011, TERR ATMOS OCEAN SCI, V22, P673, DOI 10.3319/TAO.2011.08.26.01(TM); Tao WK, 2009, ANN GEOPHYS-GERMANY, V27, P3055; Tao WK, 2001, J APPL METEOROL, V40, P957, DOI 10.1175/1520-0450(2001)040<0957:RVPOLH>2.0.CO;2; TAO WK, 1986, J ATMOS SCI, V43, P2653, DOI 10.1175/1520-0469(1986)043<2653:ASOTRO>2.0.CO;2; Tao WK, 2009, B AM METEOROL SOC, V90, P515, DOI 10.1175/2008BAMS2542.1; Tao WK, 2013, J GEOPHYS RES-ATMOS, V118, P7199, DOI 10.1002/jgrd.50410; Tao W.-K., 1993, TERR ATMOS OCEANIC S, V4, P35; TAO WK, 1989, J ATMOS SCI, V46, P177, DOI 10.1175/1520-0469(1989)046<0177:MSOATS>2.0.CO;2; Tao WK, 2007, J METEOROL SOC JPN, V85B, P305, DOI 10.2151/jmsj.85B.305; Tao WK, 2009, REV GEOPHYS, V47, DOI 10.1029/2008RG000276; TAO WK, 1991, MON WEATHER REV, V119, P2699, DOI 10.1175/1520-0493(1991)119<2699:NSOASS>2.0.CO;2; Tao WK, 2003, METEOROL ATMOS PHYS, V82, P97, DOI 10.1007/s00703-001-0594-7; Tao WK, 2010, J CLIMATE, V23, P1874, DOI 10.1175/2009JCLI3278.1; Tao WK, 2011, ASIA-PAC J ATMOS SCI, V47, P1, DOI 10.1007/s13143-011-1001-z; Tao W.-K., 2014, AMS METEORO IN PRESS; TAO WK, 1987, J ATMOS SCI, V44, P3175, DOI 10.1175/1520-0469(1987)044<3175:SPOACE>2.0.CO;2; Tao WK, 2003, J ATMOS SCI, V60, P2929, DOI 10.1175/1520-0469(2003)060<2929:CSOTSC>2.0.CO;2; Tao WK, 2004, J ATMOS SCI, V61, P2405, DOI 10.1175/1520-0469(2004)061<2405:TAEBAL>2.0.CO;2; Tao W.-K., 2003, AMS METEOROLOGICAL M, P107; Tao WK, 2012, REV GEOPHYS, V50, DOI 10.1029/2011RG000369; TAO WK, 1993, J ATMOS SCI, V50, P673, DOI 10.1175/1520-0469(1993)050<0673:HMAWBO>2.0.CO;2; TAO WK, 1984, J ATMOS SCI, V41, P2901, DOI 10.1175/1520-0469(1984)041<2901:CIAMNS>2.0.CO;2; Tao WK, 2006, B AM METEOROL SOC, V87, P1555, DOI 10.1175/BAMS-87-11-1555; Tao WK, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008728; Tao WK, 1996, J ATMOS SCI, V53, P2624, DOI 10.1175/1520-0469(1996)053<2624:MOCRII>2.0.CO;2; Tiedtke M., 1984, BEITR PHYS ATMOS, V57, P216; Tomita H, 2005, GEOPHYS RES LETT, V32, DOI 10.1029/2005GL022459; TWOMEY S, 1977, J ATMOS SCI, V34, P1149, DOI 10.1175/1520-0469(1977)034<1149:TIOPOT>2.0.CO;2; TWOMEY SA, 1984, TELLUS B, V36, P356; VALI G, 1994, J ATMOS SCI, V51, P1843, DOI 10.1175/1520-0469(1994)051<1843:FRDTHN>2.0.CO;2; Van Weverberg K, 2011, ATMOS RES, V99, P15, DOI 10.1016/j.atmosres.2010.08.017; Vich M, 2011, ATMOS RES, V102, P227, DOI 10.1016/j.atmosres.2011.07.017; Vonnegut B., 1950, Bulletin of the American Meteorological Society, V31; Weng FZ, 2001, J GEOPHYS RES-ATMOS, V106, P20115, DOI 10.1029/2001JD900019; WETZEL PJ, 1995, J CLIMATE, V8, P1810, DOI 10.1175/1520-0442(1995)008<1810:APFLCE>2.0.CO;2; XU KM, 1995, J ATMOS SCI, V52, P785, DOI 10.1175/1520-0469(1995)052<0785:IOIRTO>2.0.CO;2; Zeng XP, 2013, J ATMOS SCI, V70, P487, DOI 10.1175/JAS-D-12-050.1; Zeng X, 2007, J ATMOS SCI, V64, P4153, DOI 10.1175/2007JAS2170.1; Zeng XP, 2009, J ATMOS SCI, V66, P41, DOI 10.1175/2008JAS2778.1; Zeng XP, 2008, J METEOROL SOC JPN, V86A, P45; Zeng XP, 2011, J ATMOS SCI, V68, P1424, DOI 10.1175/2011JAS3550.1; Zeng XP, 2009, Q J ROY METEOR SOC, V135, P1614, DOI 10.1002/qj.449 146 1 1 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0169-8095 1873-2895 ATMOS RES Atmos. Res. JUN 15 2014 143 392 424 10.1016/j.atmosres.2014.03.005 33 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AH4OY WOS:000336109100030 J Schult, J; von Stulpnagel, R; Steffens, MC Schult, Janette; von Stuelpnagel, Rul; Steffens, Melanie C. Enactment versus Observation: Item-Specific and Relational Processing in Goal-Directed Action Sequences (and Lists of Single Actions) PLOS ONE English Article SUBJECT-PERFORMED TASKS; ACTION EVENTS; FREE-RECALL; ACTION MEMORY; HYPERMNESIC RECALL; ORDER INFORMATION; INTEGRATION; EXPERIMENTER; RETRIEVAL; ADULTS What are the memory-related consequences of learning actions (such as "apply the patch'') by enactment during study, as compared to action observation? Theories converge in postulating that enactment encoding increases item-specific processing, but not the processing of relational information. Typically, in the laboratory enactment encoding is studied for lists of unrelated single actions in which one action execution has no overarching purpose or relation with other actions. In contrast, real-life actions are usually carried out with the intention to achieve such a purpose. When actions are embedded in action sequences, relational information provides efficient retrieval cues. We contrasted memory for single actions with memory for action sequences in three experiments. We found more reliance on relational processing for action-sequences than single actions. To what degree can this relational information be used after enactment versus after the observation of an actor? We found indicators of superior relational processing after observation than enactment in ordered pair recall (Experiment 1A) and in emerging subjective organization of repeated recall protocols (recall runs 2-3, Experiment 2). An indicator of superior item-specific processing after enactment compared to observation was recognition (Experiment 1B, Experiment 2). Similar net recall suggests that observation can be as good a learning strategy as enactment. We discuss possible reasons why these findings only partly converge with previous research and theorizing. [Schult, Janette] Univ Jena, Inst Psychol, Jena, Germany; [von Stuelpnagel, Rul] Univ Freiburg, Inst Informat & Gesell, D-79106 Freiburg, Germany; [Steffens, Melanie C.] Univ Koblenz Landau, Fachbereich Psychol, Landau, Germany Steffens, MC (reprint author), Univ Koblenz Landau, Fachbereich Psychol, Landau, Germany. steffens@uni-landau.de Deutsche Forschungsgemeinschaft (German Science Foundation) [938/7-1, 938/7-2] The research was supported by a grant from the Deutsche Forschungsgemeinschaft to the senior author (German Science Foundation, Ste 938/7-1,-2). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. BURNS DJ, 1993, J EXP PSYCHOL LEARN, V19, P163, DOI 10.1037//0278-7393.19.1.163; Cohen J., 1977, STAT POWER ANAL BEHA; COHEN J, 1993, BEHAV RES METH INSTR, V25, P257, DOI 10.3758/BF03204507; COHEN RL, 1983, MEM COGNITION, V11, P575, DOI 10.3758/BF03198282; COHEN RL, 1989, PSYCHOL RES-PSYCH FO, V51, P176, DOI 10.1007/BF00309145; COHEN RL, 1983, INTELLIGENCE, V7, P287, DOI 10.1016/0160-2896(83)90019-3; COHEN RL, 1987, MEM COGNITION, V15, P109, DOI 10.3758/BF03197022; COHEN RL, 1981, SCAND J PSYCHOL, V22, P267, DOI 10.1111/j.1467-9450.1981.tb00402.x; Engelkamp J, 2000, J EXP PSYCHOL LEARN, V26, P671, DOI 10.1037/0278-7393.26.3.671; Engelkamp J, 2002, PSYCHOL RES-PSYCH FO, V66, P91, DOI 10.1007/s00426-001-0082-1; Engelkamp J, 2003, Q J EXP PSYCHOL-A, V56, P829, DOI 10.1080/02724980244000648; Engelkamp J, 1998, MEMORY ACTIONS; Engelkamp J, 2004, PSYCHOL RES-PSYCH FO, V69, P1, DOI 10.1007/s00426-003-0160-7; Engelkamp J, 1983, SPRACHE KOGNIT, V2, P117; ENGELKAMP J, 1991, PSYCHOL RES-PSYCH FO, V53, P175, DOI 10.1007/BF00941384; ENGELKAMP J, 1980, Z EXP ANGEW PSYCHOL, V27, P511; Engelkamp J, 2003, PSYCHOL RES-PSYCH FO, V67, P280, DOI 10.1007/s00426-002-0118-1; Engelkamp J, 2005, MEM COGNITION, V33, P371, DOI 10.3758/BF03193055; Engelkamp J, 1997, ACTA PSYCHOL, V96, P43, DOI 10.1016/S0001-6918(97)00005-X; Faul F, 2007, BEHAV RES METHODS, V39, P175, DOI 10.3758/BRM.41.4.1149; Feyereisen P, 2009, MEMORY, V17, P374, DOI 10.1080/09658210902731851; Foley MA, 2001, MEMORY ACTION DISTIN; GLOVER JA, 1987, J EDUC PSYCHOL, V79, P445, DOI 10.1037//0022-0663.79.4.445; Gold DA, 2009, PSYCHOL RES-PSYCH FO, V73, P336, DOI 10.1007/s00426-008-0148-4; Golly-Haring C, 2003, J EXP PSYCHOL LEARN, V29, P965, DOI 10.1037/0278-7393.29.5.965; Hornstein SL, 2004, PSYCHON B REV, V11, P367, DOI 10.3758/BF03196584; KLEIN SB, 1989, J EXP PSYCHOL LEARN, V15, P1192, DOI 10.1037/0278-7393.15.6.1192; Knopf M, 1995, MEMORY PERFORMANCE AND COMPETENCIES, P127; Knopf M, 2005, SCAND J PSYCHOL, V46, P11, DOI 10.1111/j.1467-9450.2005.00430.x; KORIAT A, 1991, PSYCHOL RES-PSYCH FO, V53, P260, DOI 10.1007/BF00941396; KormiNouri R, 1995, EUR J COGN PSYCHOL, V7, P337, DOI 10.1080/09541449508403103; LICHTENSTEIN EH, 1980, COGNITIVE PSYCHOL, V12, P412, DOI 10.1016/0010-0285(80)90015-8; Manzi A, 2008, MEMORY, V16, P595, DOI 10.1080/09658210802070749; McDaniel MA, 1998, J EXP PSYCHOL LEARN, V24, P173, DOI 10.1037/0278-7393.24.1.173; Mulligan NW, 2003, MEM COGNITION, V31, P412, DOI 10.3758/BF03194399; Nadar MS, 2008, CLIN REHABIL, V22, P847, DOI 10.1177/0269215508091874; PAYNE DG, 1987, PSYCHOL BULL, V101, P5, DOI 10.1037//0033-2909.101.1.5; Prinz W, 1998, PSYCHOL RUNDSCH, V49, P10; RATNER HH, 1988, DEV PSYCHOL, V24, P664, DOI 10.1037//0012-1649.24.5.664; Ratner HH, 2001, J EXP CHILD PSYCHOL, V79, P162, DOI 10.1006/jecp.2000.2585; RATNER HH, 1991, PSYCHOL RES-PSYCH FO, V53, P195, DOI 10.1007/BF00941387; ROENKER DL, 1971, PSYCHOL BULL, V76, P45, DOI 10.1037/h0031355; Schult JC, 2011, MEM COGNITION, V39, P1487, DOI 10.3758/s13421-011-0110-3; SNODGRASS JG, 1988, J EXP PSYCHOL GEN, V117, P34, DOI 10.1037//0096-3445.117.1.34; Steffens MC, 2007, MEM COGNITION, V35, P1841, DOI 10.3758/BF03192919; Steffens MC, 2009, EUR J COGN PSYCHOL, V21, P61, DOI 10.1080/09541440701868668; Steffens MC, 2007, PSYCHON B REV, V14, P1194, DOI 10.3758/BF03193112; Steffens MC, 2006, Q J EXP PSYCHOL, V59, P557, DOI 10.1080/02724980443000764; TULVING E, 1962, PSYCHOL REV, V69, P344, DOI 10.1037/h0043150; Von Essen JD, 2005, SCAND J PSYCHOL, V46, P315 50 0 0 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One JUN 13 2014 9 6 e99985 10.1371/journal.pone.0099985 10 Multidisciplinary Sciences Science & Technology - Other Topics AK2VQ WOS:000338278100102 J Ono, T; Kuhara, S Ono, Toshihide; Kuhara, Satoru A novel method for gathering and prioritizing disease candidate genes based on construction of a set of disease-related MeSH (R) terms BMC BIOINFORMATICS English Article INFORMATION; EXPRESSION; ONTOLOGY; PROTEIN; PAIN; TOOL; REPRESENTATION; NORMALIZATION; HYPERALGESIA; ANNOTATION Background: Understanding the molecular mechanisms involved in disease is critical for the development of more effective and individualized strategies for prevention and treatment. The amount of disease-related literature, including new genetic information on the molecular mechanisms of disease, is rapidly increasing. Extracting beneficial information from literature can be facilitated by computational methods such as the knowledge-discovery approach. Several methods for mining gene-disease relationships using computational methods have been developed, however, there has been a lack of research evaluating specific disease candidate genes. Results: We present a novel method for gathering and prioritizing specific disease candidate genes. Our approach involved the construction of a set of Medical Subject Headings (MeSH) terms for the effective retrieval of publications related to a disease candidate gene. Information regarding the relationships between genes and publications was obtained from the gene2pubmed database. The set of genes was prioritized using a "weighted literature score" based on the number of publications and weighted by the number of genes occurring in a publication. Using our method for the disease states of pain and Alzheimer's disease, a total of 1101 pain candidate genes and 2810 Alzheimer's disease candidate genes were gathered and prioritized. The precision was 0.30 and the recall was 0.89 in the case study of pain. The precision was 0.04 and the recall was 0.6 in the case study of Alzheimer's disease. The precision-recall curve indicated that the performance of our method was superior to that of other publicly available tools. Conclusions: Our method, which involved the use of a set of MeSH terms related to disease candidate genes and a novel weighted literature score, improved the accuracy of gathering and prioritizing candidate genes by focusing on a specific disease. [Ono, Toshihide; Kuhara, Satoru] Kyushu Univ, Fac Agr, Dept Genet Resources Technol, Higashi Ku, Fukuoka 8128581, Japan; [Ono, Toshihide] Otsuka Pharmaceut Co Ltd, Inst Biomed Innovat, Tokushima 7710192, Japan Kuhara, S (reprint author), Kyushu Univ, Fac Agr, Dept Genet Resources Technol, Higashi Ku, 6-10-1 Hakozaki, Fukuoka 8128581, Japan. kuhara@grt.kyushu-u.ac.jp Department of Genetic Resources Technology, Faculty of Agriculture, Kyushu University; Otsuka Pharmaceutical Co. Ltd. We thank Haretsugu Hishigaki for comments and discussion on the research. Our research was supported by both the Department of Genetic Resources Technology, Faculty of Agriculture, Kyushu University and Otsuka Pharmaceutical Co. Ltd. Arias CR, 2012, SCI WORLD J, DOI 10.1100/2012/842727; Ashburner M, 2000, NAT GENET, V25, P25; BENJAMINI Y, 1995, J ROY STAT SOC B MET, V57, P289; Carbon S, 2009, BIOINFORMATICS, V25, P288, DOI 10.1093/bioinformatics/btn615; Cheng D, 2008, NUCLEIC ACIDS RES, V36, pW399, DOI 10.1093/nar/gkn296; Cheung WA, 2012, GENOME MED, V4, DOI 10.1186/gm376; Cho SH, 2011, J BIOL CHEM, V286, P32713, DOI 10.1074/jbc.M111.254268; Daniel GJ, 2013, DATABASE; Fontaine JF, 2011, NUCLEIC ACIDS RES, V39, pW455, DOI 10.1093/nar/gkr246; Hamosh A, 2005, NUCLEIC ACIDS RES, V33, pD514; Hoschouer EL, 2009, EXP NEUROL, V216, P22, DOI 10.1016/j.expneurol.2008.10.025; HUNT SP, 1987, NATURE, V328, P632, DOI 10.1038/328632a0; Ikeuchi M, 2008, PAIN, V137, P662, DOI 10.1016/j.pain.2008.01.020; Julius D, 2001, NATURE, V413, P203, DOI 10.1038/35093019; Kanehisa M, 2010, NUCLEIC ACIDS RES, V38, pD355, DOI 10.1093/nar/gkp896; Kudo W, 2012, CELL DEATH DIS, V3, DOI 10.1038/cddis.2012.43; LaCroix-Fralish ML, 2007, PAIN, V131, DOI 10.1016/j.pain.2007.04.041; Li LC, 2003, NUCLEIC ACIDS RES, V31, P291, DOI 10.1093/nar/gkg008; Li LH, 2007, NEUROSCI LETT, V424, P145, DOI 10.1016/j.neulet.2007.05.069; Lill CM, 2012, PLOS GENET, V8, DOI 10.1371/journal.pgen.1002548; Lotsch J, 2013, PHARMACOL THERAPEUT, V139, P60, DOI 10.1016/j.pharmthera.2013.04.004; Ma XT, 2007, BIOINFORMATICS, V23, P215, DOI 10.1093/bioinformatics/btl569; Mair N, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0017268; Masoudi-Nejad A, 2012, MOL GENET GENOMICS, V287, P679, DOI 10.1007/s00438-012-0710-z; Mitchell Joyce A, 2003, AMIA Annu Symp Proc, P460; Moreau Y, 2012, NAT REV GENET, V13, P523, DOI 10.1038/nrg3253; Morgan AA, 2008, GENOME BIOL, V9, DOI [10.1186/gb-2008-9-s2-s3, 10.1186/gb-2008-9-S2-S3]; Perkins JR, 2013, PAIN, V154; Smith CL, 2005, GENOME BIOL, V6, DOI 10.1186/gb-2004-6-1-r7; Tsai RTH, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S15-S9; Wall DP, 2010, BMC MED GENOMICS, V3, DOI 10.1186/1755-8794-3-50; Wermter J, 2009, BIOINFORMATICS, V25, P815, DOI 10.1093/bioinformatics/btp071; Wheeler DL, 2005, NUCLEIC ACIDS RES, V33, pD39, DOI 10.1093/nar/gki062; Yu W, 2008, BMC BIOINFORMATICS, V9, DOI 10.1186/1471-2105-9-528; Zhang GH, 2004, ANESTH ANALG, V99, P152, DOI 10.1213/01.ANE.0000117141.76392.65; Zhang SW, 2014, MOL BIOSYST, V10, P1400, DOI 10.1039/c3mb70588a 36 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1471-2105 BMC BIOINFORMATICS BMC Bioinformatics JUN 10 2014 15 179 10.1186/1471-2105-15-179 12 Biochemical Research Methods; Biotechnology & Applied Microbiology; Mathematical & Computational Biology Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Mathematical & Computational Biology AK2OA WOS:000338257700001 J Guida, A; Lavielle-Guida, M Guida, Alessandro; Lavielle-Guida, Magali 2011 space odyssey: spatialization as a mechanism to code order allows a close encounter between memory expertise and classic immediate memory studies FRONTIERS IN PSYCHOLOGY English Editorial Material spatialization; immediate memory; expertise; long-term working memory; retrieval structures SHORT-TERM-MEMORY; WORKING-MEMORY; SERIAL-ORDER; MENTAL REPRESENTATION; INFORMATION; RECALL; MODEL; ITEM; DISTINCTIVENESS; SHORTCOMINGS [Guida, Alessandro] Univ Rennes 2, Dept Psychol, CRPCC, F-35043 Rennes, France; [Lavielle-Guida, Magali] Cabinet Psychol & Orthophonie, St Malo, France Guida, A (reprint author), Univ Rennes 2, Dept Psychol, CRPCC, F-35043 Rennes, France. alessandro.guida@univ-rennes2.fr Anderson JR, 1997, PSYCHOL REV, V104, P728, DOI 10.1037/0033-295X.104.4.728; Baddeley A, 1990, HUMAN MEMORY THEORY; Brown GDA, 2000, PSYCHOL REV, V107, P127, DOI 10.1037//0033-295X.107.1.127; Brown GDA, 2007, PSYCHOL REV, V114, P539, DOI 10.1037/0033-295X.114.3.539; Burgess N, 1999, PSYCHOL REV, V106, P551, DOI 10.1037/0033-295X.106.3.551; Carpenter P. A., 1989, COMPLEX INFORMATION, P31; Chase W. G., 1981, COGNITIVE SKILLS THE, P141; CHASE WG, 1973, COGNITIVE PSYCHOL, V4, P55, DOI 10.1016/0010-0285(73)90004-2; Chatwin Bruce, 1987, SONGLINES; DALE RHI, 1987, ANIM LEARN BEHAV, V15, P293, DOI 10.3758/BF03205022; DEHAENE S, 1993, J EXP PSYCHOL GEN, V122, P371, DOI 10.1037/0096-3445.122.3.371; Dobel C, 2007, PSYCHOL SCI, V18, P487, DOI 10.1111/j.1467-9280.2007.01926.x; Ebbinghaus H., 1885, MEMOIRE RECHERCHES P; Engelkamp J, 2000, J EXP PSYCHOL LEARN, V26, P671, DOI 10.1037/0278-7393.26.3.671; Ericsson KA, 2000, BRIT J PSYCHOL, V91, P571, DOI 10.1348/000712600161998; ERICSSON KA, 1995, PSYCHOL REV, V102, P211, DOI 10.1037//0033-295X.102.2.211; Estes W. K., 1991, RELATING THEORY DATA, P175; Gobel SM, 2011, J CROSS CULT PSYCHOL, V42, P543, DOI 10.1177/0022022111406251; Gobet F., 2000, BRIT J PSYCHOL, V91, P591, DOI 10.1348/000712600162005; Gobet F, 2000, BRIT J PSYCHOL, V91, P551, DOI 10.1348/000712600161989; Guida A, 2009, EUR J COGN PSYCHOL, V21, P862, DOI 10.1080/09541440802236369; Guida A, 2013, MEM COGNITION, V41, P571, DOI 10.3758/s13421-012-0284-3; HACKER MJ, 1980, J EXP PSYCHOL-HUM L, V6, P651; HARWOOD F, 1976, AM ANTHROPOL, V78, P783, DOI 10.1525/aa.1976.78.4.02a00040; Henson R, 2003, Q J EXP PSYCHOL-A, V56, P1307, DOI 10.1080/02724980244000747; Henson R. N. A., 1996, THESIS U CAMBRIDGE C; Henson RNA, 1998, COGNITIVE PSYCHOL, V36, P73, DOI 10.1006/cogp.1998.0685; Henson RNA, 1999, INT J PSYCHOL, V34, P403, DOI 10.1080/002075999399756; Jordan M. I., 1986, 8604 ICS U CAL; Lewandowsky S, 2005, MEMORY, V13, P283, DOI 10.1080/09658210344000251; Lewandowsky S, 2008, PSYCHOL LEARN MOTIV, V49, P1, DOI 10.1016/SO079-7421(08)00001-7; LEWANDOWSKY S, 1989, PSYCHOL REV, V96, P25, DOI 10.1037/0033-295X.96.1.25; Lewandowsky S, 2004, PSYCHON B REV, V11, P771, DOI 10.3758/BF03196705; Lewandowsky S, 2006, J MEM LANG, V54, P20, DOI 10.1016/j.jml.2005.08.004; Lewandowsky S, 2008, J MEM LANG, V58, P429, DOI 10.1016/j.jml.2007.01.005; Maass A, 2003, PSYCHOL SCI, V14, P296, DOI 10.1111/1467-9280.14421; Marshuetz C, 2000, J COGNITIVE NEUROSCI, V12, P130, DOI 10.1162/08989290051137459; Marshuetz C, 2005, PSYCHOL BULL, V131, P323, DOI 10.1037/0033-2909.131.3.323; Mulligan NW, 1999, J EXP PSYCHOL LEARN, V25, P54, DOI 10.1037/0278-7393.25.1.54; O'Reilly R. C., 2001, ADV NEURAL INFORM PR, P83; Oberauer K, 2011, PSYCHON B REV, V18, P10, DOI 10.3758/s13423-010-0020-6; Ong WJ, 2012, NEW ACCENT, P1; Poirier M, 1996, CAN J EXP PSYCHOL, V50, P408, DOI 10.1037/1196-1961.50.4.408; Pridmore B., 2013, BE CLEVER; Rubin D. C., 1997, MEMORY ORAL TRADITIO; Schneider W., 1987, PSYCHOL LEARN MOTIV, P54; Shaki S, 2009, PSYCHON B REV, V16, P328, DOI 10.3758/PBR.16.2.328; van Dijck JP, 2011, COGNITION, V119, P114, DOI 10.1016/j.cognition.2010.12.013; van Dijck JP, 2013, PSYCHOL SCI, V24, P1854, DOI 10.1177/0956797613479610; WICKELGREN WA, 1965, AM J PSYCHOL, V78, P567, DOI 10.2307/1420917; Worthen JB, 2011, ESSAYS COGN PSYCHOL, P1; Yates Frances A., 1966, ART MEMORY 52 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. JUN 10 2014 5 573 10.3389/fpsyg.2014.00573 5 Psychology, Multidisciplinary Psychology AJ7RP WOS:000337894900002 J Igarashi, KM; Lu, L; Colgin, LL; Moser, MB; Moser, EI Igarashi, Kei M.; Lu, Li; Colgin, Laura L.; Moser, May-Britt; Moser, Edvard I. Coordination of entorhinal-hippocampal ensemble activity during associative learning NATURE English Article GAMMA-OSCILLATIONS; SPATIAL REPRESENTATION; OLFACTORY-BULB; BEHAVING RAT; CORTEX; SYNCHRONIZATION; DISCRIMINATION; INFORMATION; MEMORY; FREQUENCY Accumulating evidence points to cortical oscillations as a mechanism for mediating interactions among functionally specialized neurons in distributed brain circuits(1-6). A brain function that may use such interactions is declarative memory-that is, memory that can be consciously recalled, such as episodes and facts. Declarative memory is enabled by circuits in the entorhinal cortex that interface the hippocampus with the neocortex(7,8). During encoding and retrieval of declarative memories, entorhinal and hippocampal circuits are thought to interact via theta and gamma oscillations(4,6,8), which in awake rodents predominate frequency spectra in both regions(9-12). In favour of this idea, theta-gamma coupling has been observed between entorhinal cortex and hippocampus under steady-state conditions inwell-trained rats(12); however, the relationship between interregional coupling and memory formation remains poorly understood. Here we show, by multisite recording at successive stages of associative learning, that the coherence of firing patterns in directly connected entorhinal-hippocampus circuits evolves as rats learn to use an odour cue to guide navigational behaviour, and that such coherence is invariably linked to the development of ensemble representations for unique trial outcomes in each area. Entorhinal-hippocampal coupling was observed specifically in the 20-40-hertz frequency band and specifically between the distal part of hippocampal area CA1 and the lateral part of entorhinal cortex, the subfields that receive the predominant olfactory input to the hippocampal region(13). Collectively, the results identify 20-40-hertz oscillations as a mechanism for synchronizing evolving representations in dispersed neural circuits during encoding and retrieval of olfactory-spatial associative memory. [Igarashi, Kei M.; Lu, Li; Moser, May-Britt; Moser, Edvard I.] Norwegian Univ Sci & Technol, Kavli Inst Syst Neurosci, N-7491 Trondheim, Norway; [Igarashi, Kei M.; Lu, Li; Moser, May-Britt; Moser, Edvard I.] Norwegian Univ Sci & Technol, Ctr Neural Computat, N-7491 Trondheim, Norway; [Colgin, Laura L.] Univ Texas Austin, Ctr Learning & Memory, Austin, TX 78712 USA Moser, EI (reprint author), Norwegian Univ Sci & Technol, Kavli Inst Syst Neurosci, Olav Kyrres Gate 9,MTFS,7491, N-7491 Trondheim, Norway. kei.igarashi@ntnu.no; edvard.moser@ntnu.no European Research Council [232608, 268598]; Kavli Foundation; Centre of Excellence scheme of the Research Council of Norway (Centre for the Biology of Memory and Centre for Neural Computation); Mishima Kaiun Memorial Foundation; Japan Society for the Promotion of Science We thank A. M. Amundsgard, K. Haugen, K. Jenssen, E. Krakvik, R. Skjerpeng and H. Waade for technical assistance, and M. Witter and members of the Moser laboratory for discussions. This work was supported by two Advanced Investigator grants from the European Research Council ('CIRCUIT', Grant Agreement no. 232608; 'ENSEMBLE', Grant Agreement no. 268598), the Kavli Foundation, the Centre of Excellence scheme of the Research Council of Norway (Centre for the Biology of Memory and Centre for Neural Computation), the Mishima Kaiun Memorial Foundation, and the Japan Society for the Promotion of Science. Ahmed OJ, 2012, J NEUROSCI, V32, P7373, DOI 10.1523/JNEUROSCI.5110-11.2012; Berke JD, 2008, HIPPOCAMPUS, V18, P519, DOI 10.1002/hipo.20435; Bi GQ, 1998, J NEUROSCI, V18, P10464; BRAGIN A, 1995, J NEUROSCI, V15, P47; Buschman TJ, 2007, SCIENCE, V315, P1860, DOI 10.1126/science.1138071; Buzsaki G., 2006, RHYTHMS BRAIN; Buzsáki G, 1983, Brain Res, V287, P139; Buzsaki G, 2013, NAT NEUROSCI, V16, P130, DOI 10.1038/nn.3304; Chrobak JJ, 1998, J NEUROSCI, V18, P388; Colgin LL, 2013, ANNU REV NEUROSCI, V36, P295, DOI 10.1146/annurev-neuro-062012-170330; Colgin LL, 2009, NATURE, V462, P353, DOI 10.1038/nature08573; Day M, 2003, NATURE, V424, P205, DOI 10.1038/nature01769; EICHENBAUM H, 1987, J NEUROSCI, V7, P716; Engel AK, 2010, CURR OPIN NEUROBIOL, V20, P156, DOI 10.1016/j.conb.2010.02.015; Fanselow MS, 2010, NEURON, V65, P7, DOI 10.1016/j.neuron.2009.11.031; FREEMAN WJ, 1978, ELECTROEN CLIN NEURO, V44, P586, DOI 10.1016/0013-4694(78)90126-8; Fries P, 2001, SCIENCE, V291, P1560, DOI 10.1126/science.1055465; Fries P, 2009, ANNU REV NEUROSCI, V32, P209, DOI 10.1146/annurev.neuro.051508.135603; Fyhn M, 2004, SCIENCE, V305, P1258, DOI 10.1126/science.1099901; Fyhn M, 2007, NATURE, V446, P190, DOI 10.1038/nature05601; GRAY CM, 1989, NATURE, V338, P334, DOI 10.1038/338334a0; Hafting T, 2005, NATURE, V436, P801, DOI 10.1038/nature03721; Henriksen EJ, 2010, NEURON, V68, P127, DOI 10.1016/j.neuron.2010.08.042; Howe MW, 2011, P NATL ACAD SCI USA, V108, P16801, DOI 10.1073/pnas.1113158108; Igarashi KM, 2012, J NEUROSCI, V32, P7970, DOI 10.1523/JNEUROSCI.0154-12.2012; Jezek K, 2011, NATURE, V478, P246, DOI 10.1038/nature10439; Kepecs A, 2007, J NEUROPHYSIOL, V98, P205, DOI 10.1152/jn.00071.2007; Kopell N, 2000, P NATL ACAD SCI USA, V97, P1867, DOI 10.1073/pnas.97.4.1867; Langston RF, 2010, SCIENCE, V328, P1576, DOI 10.1126/science.1188210; MACMILLAN N.A., 1991, DETECTION THEORY USE; Martin C, 2007, J NEUROPHYSIOL, V98, P2196, DOI 10.1152/jn.00524.2007; MCNAUGHTON BL, 1978, BRAIN RES, V157, P277, DOI 10.1016/0006-8993(78)90030-6; Mitra PP, 1999, BIOPHYS J, V76, P691, DOI 10.1016/S0006-3495(99)77236-X; Montgomery SM, 2007, P NATL ACAD SCI USA, V104, P14495, DOI 10.1073/pnas.0701826104; Naya Y, 2011, SCIENCE, V333, P773, DOI 10.1126/science.1206773; Ravel N, 2003, EUR J NEUROSCI, V17, P350, DOI 10.1046/j.1460-9568.2003.02445.x; Sargolini F, 2006, SCIENCE, V312, P758, DOI 10.1126/science.1125572; SINGER W, 1993, ANNU REV PHYSIOL, V55, P349, DOI 10.1146/annurev.physiol.55.1.349; Skaggs WE., 1993, ADV NEURAL INFORMATI, V5, P1030; Skaggs WE, 1996, HIPPOCAMPUS, V6, P149, DOI 10.1002/(SICI)1098-1063(1996)6:2<149::AID-HIPO6>3.0.CO;2-K; Solstad T, 2008, SCIENCE, V322, P1865, DOI 10.1126/science.1166466; SQUIRE LR, 1992, PSYCHOL REV, V99, P195, DOI 10.1037//0033-295X.99.2.195; STEWARD O, 1976, J COMP NEUROL, V167, P285, DOI 10.1002/cne.901670303; Tort ABL, 2009, P NATL ACAD SCI USA, V106, P20942, DOI 10.1073/pnas.0911331106; Witter MP, 2004, RAT NERVOUS SYSTEM, P635 45 2 2 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 0028-0836 1476-4687 NATURE Nature JUN 5 2014 510 7503 143 + 10.1038/nature13162 20 Multidisciplinary Sciences Science & Technology - Other Topics AI3NR WOS:000336768900045 J Taboada, M; Rodriguez, H; Martinez, D; Pardo, M; Sobrido, MJ Taboada, Maria; Rodriguez, Hadriana; Martinez, Diego; Pardo, Maria; Jesus Sobrido, Maria Automated semantic annotation of rare disease cases: a case study DATABASE-THE JOURNAL OF BIOLOGICAL DATABASES AND CURATION English Article GENE ONTOLOGY; LINKING Motivation: As the number of clinical reports in the peer-reviewed medical literature keeps growing, there is an increasing need for online search tools to find and analyze publications on patients with similar clinical characteristics. This problem is especially critical and challenging for rare diseases, where publications of large series are scarce. Through an applied example, we illustrate how to automatically identify new relevant cases and semantically annotate the relevant literature about patient case reports to capture the phenotype of a rare disease named cerebrotendinous xanthomatosis. Results: Our results confirm that it is possible to automatically identify new relevant case reports with a high precision and to annotate them with a satisfactory quality (74% F-measure). Automated annotation with an emphasis to entirely describe all phenotypic abnormalities found in a disease may facilitate curation efforts by supplying phenotype retrieval and assessment of their frequency. Availability and Supplementary information: http://www.usc.es/keam/PhenotypeAnnotation/. Database URL: http://www.usc.es/keam/PhenotypeAnnotation/ [Taboada, Maria; Rodriguez, Hadriana] Univ Santiago de Compostela, Dept Elect & Comp Sci, Santiago De Compostela, Spain; [Martinez, Diego] Univ Santiago de Compostela, Dept Appl Phys, Santiago De Compostela, Spain; [Pardo, Maria] Univ Hosp Clin Santiago de Compostela, Dept Neurol, Santiago De Compostela, Spain; [Jesus Sobrido, Maria] Inst Invest Sanitaria Santiago IDIS, Fdn Publ Galega Med Xenom, Santiago De Compostela, Spain; [Jesus Sobrido, Maria] Ctr Invest Biomed Red Enfermedades Raras CIBERER, Santiago De Compostela, Spain Taboada, M (reprint author), Univ Santiago de Compostela, Dept Elect & Comp Sci, Campus Vida, Santiago De Compostela, Spain. maria.taboada@usc.es National Institute of Health Carlos III [FIS2012-PI12/00373] National Institute of Health Carlos III (grant no. FIS2012-PI12/00373: OntoNeurophen). Funding for open access charge: National Institute of Health Carlos III (grant no. FIS2012-PI12/00373). Balakrishnan R., 2013, DATABASE, V2013; Benabderrahmane S, 2010, BMC BIOINFORMATICS, V11, DOI 10.1186/1471-2105-11-588; Bettembourg Charles, 2012, J Biomed Semantics, V3, P7, DOI 10.1186/2041-1480-3-7; Buza TJ, 2008, NUCLEIC ACIDS RES, V36, DOI 10.1093/nar/gkm1167; Cunningham H, 2013, PLOS COMPUT BIOL, V9, DOI 10.1371/journal.pcbi.1002854; Dill S., 2003, J WEB SEMANT, V1, P115; Dolan ME, 2005, BIOINFORMATICS, V21, pI136, DOI 10.1093/bioinformatics/bti1019; Doms A, 2005, NUCLEIC ACIDS RES, V33, pW783, DOI 10.1093/nar/gki470; Federico A., 1993, GENEREVIEWS, P1993; Harris MA, 2004, NUCLEIC ACIDS RES, V32, pD258, DOI 10.1093/nar/gkh036; Kiryakov A., 2005, J WEB SEMANT, V2, P49; Kiyavitskaya N, 2009, DATA KNOWL ENG, V68, P1470, DOI 10.1016/j.datak.2009.07.012; Kohler S, 2014, NUCLEIC ACIDS RES, V42, pD966, DOI 10.1093/nar/gkt1026; Lu ZY, 2009, INFORM RETRIEVAL, V12, P69, DOI 10.1007/s10791-008-9074-8; Musen MA, 2012, J AM MED INFORM ASSN, V19, P190, DOI 10.1136/amiajnl-2011-000523; Rodriguez-Garcia MA, 2014, KNOWL-BASED SYST, V56, P15, DOI 10.1016/j.knosys.2013.10.006; Shah NH, 2009, BMC BIOINFORMATICS, V10, DOI 10.1186/1471-2105-10-S9-S14; Skunca S N., 2012, PLOS COMPUT BIOL, V8; Smith B, 2007, NAT BIOTECHNOL, V25, P1251, DOI 10.1038/nbt1346; Tripathi S., 2013, DATABASE, V2013; Tsatsaronis G., 2012, AAAI FALL S INF RETR, P92; Vanteru BC, 2008, BMC GENOMICS, V9, DOI 10.1186/1471-2164-9-S1-S10; Vidal JC, 2014, KNOWL-BASED SYST, V55, P29, DOI 10.1016/j.knosys.2013.10.007; Wessman A., 2005, 4 INT C INF SYST TEC, P239; Whetzel Patricia L, 2013, J Biomed Semantics, V4 Suppl 1, pS8, DOI 10.1186/2041-1480-4-S1-S8 25 0 0 OXFORD UNIV PRESS OXFORD GREAT CLARENDON ST, OXFORD OX2 6DP, ENGLAND 1758-0463 DATABASE-OXFORD Database JUN 4 2014 bau045 10.1093/database/bau045 13 Mathematical & Computational Biology Mathematical & Computational Biology AK6VX WOS:000338566500001 J Kuhl, BA; Chun, MM Kuhl, Brice A.; Chun, Marvin M. Successful Remembering Elicits Event-Specific Activity Patterns in Lateral Parietal Cortex JOURNAL OF NEUROSCIENCE English Article angular gyrus; decoding; memory reactivation; MVPA; parietal cortex; recall EPISODIC MEMORY RETRIEVAL; MEDIAL TEMPORAL-LOBE; HIGH-RESOLUTION FMRI; COGNITIVE NEUROSCIENCE; SUBSEQUENT MEMORY; NEURAL BASIS; MECHANISMS; SIMILARITY; REPRESENTATION; REACTIVATION Remembering a past event involves reactivation of content-specific patterns of neural activity in high-level perceptual regions (e.g., ventral temporal cortex, VTC). In contrast, the subjective experience of vivid remembering is typically associated with increased activity in lateral parietal cortex (LPC)-"retrieval success effects" that are thought to generalize across content types. However, the functional significance of LPC activation during memory retrieval remains a subject of active debate. In particular, theories are divided with respect to whether LPC actively represents retrieved content or if LPC activity only scales with content reactivation elsewhere (e.g., VTC). Here, we report a human fMRI study of visual memory recall (faces vs scenes) in which complementary forms of multivoxel pattern analysis were used to test for and compare content reactivation within LPC and VTC. During recall of visual images, we observed robust reactivation of broad category information (face vs scene) in both VTC and LPC. Moreover, recall-related activity patterns in LPC, but not VTC, differentiated between individual events. Importantly, these content effects were particularly evident in areas of LPC (namely, angular gyrus) in which activity scaled with subjective reports of recall vividness. These findings provide striking evidence that LPC not only signals that memories have been successfully recalled, but actively represents what is being remembered. [Kuhl, Brice A.] NYU, Dept Psychol, New York, NY 10003 USA; [Kuhl, Brice A.] NYU, Ctr Neural Sci, New York, NY 10003 USA; [Chun, Marvin M.] Yale Univ, Dept Psychol, New Haven, CT 06520 USA Kuhl, BA (reprint author), NYU, Dept Psychol, 6 Washington Pl, New York, NY 10003 USA. brice.kuhl@nyu.edu National Institutes of Health [R01-EY014193, EY019624-02] This work was supported by the National Institutes of Health (Grant R01-EY014193 to M.M.C. and Grant EY019624-02 to B.A.K.). Baddeley A, 2000, TRENDS COGN SCI, V4, P417, DOI 10.1016/S1364-6613(00)01538-2; Buchsbaum BR, 2012, J COGNITIVE NEUROSCI, V24, P1867, DOI 10.1162/jocn_a_00253; Buckner RL, 2001, NAT REV NEUROSCI, V2, P624, DOI 10.1038/35090048; Cabeza R, 2008, NAT REV NEUROSCI, V9, P613, DOI 10.1038/nrn2459; Chadwick MJ, 2011, LEARN MEMORY, V18, P742, DOI 10.1101/lm.023671.111; Christophel TB, 2012, J NEUROSCI, V32, P12983, DOI 10.1523/JNEUROSCI.0184-12.2012; Dobbins IG, 2005, CEREB CORTEX, V15, P1768, DOI 10.1093/cercor/bhi054; Freedman DJ, 2006, NATURE, V443, P85, DOI 10.1038/nature05078; Gordon AM, 2013, CEREB CORTE IN PRESS; Guerin SA, 2012, NEURON, V75, P1122, DOI 10.1016/j.neuron.2012.08.020; Hutchinson J Benjamin, 2014, Cereb Cortex, V24, P49, DOI 10.1093/cercor/bhs278; Hutchinson JB, 2009, LEARN MEMORY, V16, P343, DOI 10.1101/lm.919109; Johnson JD, 2013, FRONT HUM NEUROSCI, V7, DOI 10.3389/fnhum.2013.00219; JOHNSON MK, 1988, J EXP PSYCHOL GEN, V117, P371, DOI 10.1037//0096-3445.117.4.371; Johnson MK, 1998, TRENDS COGN SCI, V2, P137, DOI 10.1016/S1364-6613(98)01152-8; Kamitani Y, 2010, NEUROIMAGE, V49, P1949, DOI 10.1016/j.neuroimage.2009.06.040; Kriegeskorte Nikolaus, 2008, Front Syst Neurosci, V2, P4, DOI 10.3389/neuro.06.004.2008; Kuhl BA, 2009, HDB NEUROSCIENCES BE, P586; Kuhl BA, 2011, P NATL ACAD SCI USA, V108, P5903, DOI 10.1073/pnas.1016939108; Kuhl BA, 2012, J NEUROSCI, V32, P3453, DOI 10.1523/JNEUROSCI.5846-11.2012; Kuhl BA, 2013, J NEUROSCI, V33, P16099, DOI 10.1523/JNEUROSCI.0207-13.2013; Kuhl BA, 2012, NEUROPSYCHOLOGIA, V50, P458, DOI 10.1016/j.neuropsychologia.2011.09.002; LaRocque KF, 2013, J NEUROSCI, V33, P5466, DOI 10.1523/JNEUROSCI.4293-12.2013; Olson IR, 2009, NEUROBIOL LEARN MEM, V91, P155, DOI 10.1016/j.nlm.2008.09.006; Op de Beeck HP, 2010, NEUROIMAGE, V49, P1943, DOI 10.1016/j.neuroimage.2009.02.047; Polyn SM, 2005, SCIENCE, V310, P1963, DOI 10.1126/science.1117645; Rissman J, 2010, P NATL ACAD SCI USA, V107, P9849, DOI 10.1073/pnas.1001028107; Ritchey M, 2013, CEREB CORTEX, V23, P2818, DOI 10.1093/cercor/bhs258; Schacter DL, 2011, TRENDS COGN SCI, V15, P467, DOI 10.1016/j.tics.2011.08.004; Schacter DL, 2004, NEURON, V44, P149, DOI 10.1016/j.neuron.2004.08.017; Sestieri C, 2011, J NEUROSCI, V31, P4407, DOI 10.1523/JNEUROSCI.3335-10.2011; Sestieri C, 2013, NEUROPSYCHOLOGIA, V51, P900, DOI 10.1016/j.neuropsychologia.2013.01.023; Shadlen MN, 2001, J NEUROPHYSIOL, V86, P1916; Shimamura AP, 2011, COGN AFFECT BEHAV NE, V11, P277, DOI 10.3758/s13415-011-0031-4; Spaniol J, 2009, NEUROPSYCHOLOGIA, V47, P1765, DOI 10.1016/j.neuropsychologia.2009.02.028; Staresina BP, 2012, J NEUROSCI, V32, P18150, DOI 10.1523/JNEUROSCI.4156-12.2012; Todd MT, 2013, NEUROIMAGE, V77, P157, DOI 10.1016/j.neuroimage.2013.03.039; Tosoni A, 2008, NAT NEUROSCI, V11, P1446, DOI 10.1038/nn.2221; Toth LJ, 2002, NATURE, V415, P165, DOI 10.1038/415165a; Tzourio-Mazoyer N, 2002, NEUROIMAGE, V15, P273, DOI 10.1006/nimg.2001.0978; Vilberg KL, 2012, J NEUROSCI, V32, P15679, DOI 10.1523/JNEUROSCI.3065-12.2012; Vilberg KL, 2009, HUM BRAIN MAPP, V30, P1490, DOI 10.1002/hbm.20618; Vilberg KL, 2008, NEUROPSYCHOLOGIA, V46, P1787, DOI 10.1016/j.neuropsychologia.2008.01.004; Wagner AD, 2005, TRENDS COGN SCI, V9, P445, DOI 10.1016/j.tics.2005.07.001; Ward EJ, 2013, J NEUROSCI, V33, P14749, DOI 10.1523/JNEUROSCI.4889-12.2013; Wheeler ME, 2003, J NEUROSCI, V23, P3869; WILSON M, 1988, BEHAV RES METH INSTR, V20, P6, DOI 10.3758/BF03202594; Xue G, 2013, CEREB CORTEX, V23, P1562, DOI 10.1093/cercor/bhs143; Xue G, 2010, SCIENCE, V330, P97, DOI 10.1126/science.1193125 49 0 0 SOC NEUROSCIENCE WASHINGTON 11 DUPONT CIRCLE, NW, STE 500, WASHINGTON, DC 20036 USA 0270-6474 J NEUROSCI J. Neurosci. JUN 4 2014 34 23 8051 8060 10.1523/JNEUROSCI.4328-13.2014 10 AJ4GH WOS:000337630700031 J Taniguchi, K; Obata, K; Yoshioka, H Taniguchi, Kenta; Obata, Kenta; Yoshioka, Hiroki Derivation and approximation of soil isoline equations in the red-near-infrared reflectance subspace JOURNAL OF APPLIED REMOTE SENSING English Article LEAF-AREA INDEX; ADJUSTED VEGETATION INDEX; SPECTRAL RESPONSE; PLANT CANOPY; INVERSION; SENSORS; MODEL; SPACE; OPTIMIZATION; INFORMATION This study describes the derivation of an expression for the relationship between red and near-infrared reflectances, called soil isolines, as an orthogonal concept for the vegetation isoline. An analytical representation of soil isoline would be useful for estimating soil optical properties. Soil isolines often contain a singular point on a dark soil background. Singularities are difficult to model using simple polynomial forms. This difficulty was circumvented in this work by rotating the original axis and employing a vegetation index-like parasite parameter. This approach produced a soil isoline model that could yield any desired level of accuracy based on the use of an index-like parameter. A technique is further introduced for approximating the removal of the parasite parameter from the relationship by truncating the higher-order terms during the derivation steps. Numerical experiments by PROSAIL were conducted to investigate the influence of the truncation errors on the accuracy of the approximated soil isoline equation. The numerical results showed that truncating terms of order greater than two in both bands, yielded negligible truncation errors. These results suggest that the derived and approximated soil isoline equations may be useful in other applications, such as the analysis and retrieval of soil optical properties. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI. [Taniguchi, Kenta; Yoshioka, Hiroki] Aichi Prefectural Univ, Dept Informat Sci & Technol, Nagakute, Aichi 4801198, Japan; [Obata, Kenta] Natl Inst Adv Ind Sci & Technol, Inst Geol & Geoinformat, Tsukuba, Ibaraki 3058567, Japan Yoshioka, H (reprint author), Aichi Prefectural Univ, Dept Informat Sci & Technol, Nagakute, Aichi 4801198, Japan. yoshioka@ist.aichi-pu.ac.jp Atzberger C, 2012, REMOTE SENS ENVIRON, V120, P208, DOI 10.1016/j.rse.2011.10.035; Atzberger C, 2011, PROC SPIE, V8174, DOI 10.1117/12.898479; Atzberger C, 2009, PROC SPIE, V7478, DOI 10.1117/12.830009; BARET F, 1991, REMOTE SENS ENVIRON, V35, P161, DOI 10.1016/0034-4257(91)90009-U; BARET F, 1993, ADV SPACE RES, V13, P281, DOI 10.1016/0273-1177(93)90560-X; Baret F., 1989, IGARSS 89, V3, P1355; CLEVERS JGPW, 1988, REMOTE SENS ENVIRON, V25, P53, DOI 10.1016/0034-4257(88)90041-7; Gilabert MA, 2002, REMOTE SENS ENVIRON, V82, P303, DOI 10.1016/S0034-4257(02)00048-2; Huete A, 2002, REMOTE SENS ENVIRON, V83, P195, DOI 10.1016/S0034-4257(02)00096-2; HUETE AR, 1988, REMOTE SENS ENVIRON, V25, P295, DOI 10.1016/0034-4257(88)90106-X; HUETE AR, 1987, AGRON J, V79, P61; HUETE AR, 1985, REMOTE SENS ENVIRON, V17, P37, DOI 10.1016/0034-4257(85)90111-7; HUETE AR, 1987, REMOTE SENS ENVIRON, V23, P213, DOI 10.1016/0034-4257(87)90038-1; Huete A. R., 1989, Theory and applications of optical remote sensing., P107; JACKSON RD, 1991, PREV VET MED, V11, P185, DOI 10.1016/S0167-5877(05)80004-2; JACQUEMOUD S, 1995, REMOTE SENS ENVIRON, V52, P163, DOI 10.1016/0034-4257(95)00018-V; Jacquemoud S., 2009, REMOTE SENS ENVIRON, V113, P556; JACQUEMOUD S, 1990, REMOTE SENS ENVIRON, V34, P75, DOI 10.1016/0034-4257(90)90100-Z; Jiang ZY, 2007, J APPL REMOTE SENS, V1, DOI 10.1117/1.2709702; Jiang ZY, 2008, REMOTE SENS ENVIRON, V112, P3833, DOI 10.1016/j.rse.2008.06.006; JORDAN CF, 1969, ECOLOGY, V50, P663, DOI 10.2307/1936256; Kallel A, 2007, REMOTE SENS ENVIRON, V111, P553, DOI 10.1016/j.rse.2007.04.006; Kauth R. J., 1976, S MACH PROC REM SENS; Kim Y, 2010, J APPL REMOTE SENS, V4, DOI 10.1117/1.3400635; Li ZQ, 2013, J APPL REMOTE SENS, V7, DOI 10.1117/1.JRS.7.073567; Liu F, 2011, INT GEOSCI REMOTE SE, P3074; Obata K, 2013, J APPL REMOTE SENS, V7, DOI 10.1117/1.JRS.7.073467; PRICE JC, 1995, REMOTE SENS ENVIRON, V52, P55, DOI 10.1016/0034-4257(94)00111-Y; QI J, 1994, REMOTE SENS ENVIRON, V48, P119, DOI 10.1016/0034-4257(94)90134-1; Richardson A. J., 1992, GEOCARTO INT, V7, P63; RICHARDSON AJ, 1977, PHOTOGRAMM ENG REM S, V43, P1541; Rondeaux G, 1996, REMOTE SENS ENVIRON, V55, P95, DOI 10.1016/0034-4257(95)00186-7; Rouse J. W., 1973, 3 ERTS S, V1, P48; Shabanov NV, 2005, IEEE T GEOSCI REMOTE, V43, P1855, DOI 10.1109/TGRS.2005.852477; Shabanov NV, 2002, IEEE T GEOSCI REMOTE, V40, P115, DOI 10.1109/36.981354; Shen L, 2013, J APPL REMOTE SENS, V7, DOI 10.1117/1.JRS.7.073574; Taniguchi K, 2012, PROC SPIE, V8524, DOI 10.1117/12.977322; Taniguchi K., 2013, IEEE INT GEOSC REM S, P2613; Tong A, 2013, J APPL REMOTE SENS, V7, DOI 10.1117/1.JRS.7.073599; Trishchenko AP, 2002, REMOTE SENS ENVIRON, V81, P1, DOI 10.1016/S0034-4257(01)00328-5; TUCKER CJ, 1979, REMOTE SENS ENVIRON, V8, P127, DOI 10.1016/0034-4257(79)90013-0; VERHOEF W, 1984, REMOTE SENS ENVIRON, V16, P125, DOI 10.1016/0034-4257(84)90057-9; Verstraete MM, 1996, IEEE T GEOSCI REMOTE, V34, P1254, DOI 10.1109/36.536541; Verstraete MM, 1996, REMOTE SENS ENVIRON, V58, P201, DOI 10.1016/S0034-4257(96)00069-7; Yoshioka H, 2009, REMOTE SENS-BASEL, V1, P842, DOI 10.3390/rs1040842; Yoshioka H, 2012, REMOTE SENS-BASEL, V4, P583, DOI 10.3390/rs4030583; Yoshioka H, 2000, REMOTE SENS ENVIRON, V74, P313, DOI 10.1016/S0034-4257(00)00130-9; Yoshioka H, 2011, INT GEOSCI REMOTE SE, P3082; Yoshioka H, 2004, IEEE T GEOSCI REMOTE, V42, P166, DOI 10.1109/TGRS.2003.817793; Yoshioka H, 2000, IEEE T GEOSCI REMOTE, V38, P838, DOI 10.1109/36.842012; Yoshioka H, 2003, IEEE T GEOSCI REMOTE, V41, P1363, DOI 10.1109/TGRS.2003.813212; Yoshioka H, 2002, INT GEOSCI REMOTE SE, P1639 52 0 0 SPIE-SOC PHOTO-OPTICAL INSTRUMENTATION ENGINEERS BELLINGHAM 1000 20TH ST, PO BOX 10, BELLINGHAM, WA 98225 USA 1931-3195 J APPL REMOTE SENS J. Appl. Remote Sens. JUN 3 2014 8 083621 10.1117/1.JRS.8.083621 19 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AK5VH WOS:000338494100001 J Ferrand, P; Gasecka, P; Kress, A; Wang, X; Bioud, FZ; Duboisset, J; Brasselet, S Ferrand, Patrick; Gasecka, Paulina; Kress, Alla; Wang, Xiao; Bioud, Fatma-Zohra; Duboisset, Julien; Brasselet, Sophie Ultimate Use of Two-Photon Fluorescence Microscopy to Map Orientational Behavior of Fluorophores BIOPHYSICAL JOURNAL English Article CELL-MEMBRANES; POLARIZATION MICROSCOPY; MOLECULAR ORDER; CHOLESTEROL; DEPOLARIZATION; ORGANIZATION; ANISOTROPY; DYNAMICS; SYSTEMS; PROTEIN The orientational distribution of fluorophores is an important reporter of the structure and function of their molecular environment. Although this distribution affects the fluorescence signal under polarized-light excitation, its retrieval is limited to a small number of parameters. Because of this limitation, the need for a geometrical model (cone, Gaussian, etc.) to effect such retrieval is often invoked. In this work, using a symmetry decomposition of the distribution function of the fluorescent molecules, we show that polarized two-photon fluorescence based on tunable linear dichroisnn allows for the retrieval of this distribution with reasonable fidelity and without invoking either an a priori knowledge of the system to be investigated or a geometrical model. We establish the optimal level of.detail to which any distribution can be retrieved using this technique. As applied to artificial lipid vesicles and cell membranes, the ability of this method to identify and quantify specific structural properties that complement the more traditional molecular-order information is demonstrated. In particular, we analyze situations that give access to the sharpness of the angular constraint, and to the evidence of an isotropic population of fluorophores within the focal volume encompassing the membrane. Moreover, this technique has the potential to address complex situations such as the distribution of a tethered membrane protein label in an ordered environment. [Ferrand, Patrick; Gasecka, Paulina; Kress, Alla; Wang, Xiao; Bioud, Fatma-Zohra; Duboisset, Julien; Brasselet, Sophie] Aix Mrseille Univ, CNRS, Cent Marseille, Inst Fresnel UMR 7249, Marseille, France Ferrand, P (reprint author), Aix Mrseille Univ, CNRS, Cent Marseille, Inst Fresnel UMR 7249, Marseille, France. patrick.ferrand@fresnel.fr Ferrand, Patrick/B-4318-2008 ReceptORlENT [ANR-2010-BLAN-150902]; France-BioImaging [ANR-10-INBS-04-01]; France Life Imaging [ANA-11')NSB-0006]; A-Midex Foundation [ANR-11-EDEX-0001-02]; Erasmus Mundus Doctorate Program Europhotonics [159224-1-2009-1-FR-ERA MUNDUS-EMJD]; China Scholarship Council This work was supported by contracts ANR-2010-BLAN-150902 (ReceptORlENT), ANR-10-INBS-04-01 (France-BioImaging), ANR-11-NSB-0006 (France Life Imaging), ANR-11-EDEX-0001-02 (A-Midex Foundation) and Conseil Regional Provence Alpes Cote d'Azur. X.W. received a scholarship from the China Scholarship Council. F.-Z.B. received a scholarship from the Erasmus Mundus Doctorate Program Europhotonics (grant no. 159224-1-2009-1-FR-ERA MUNDUS-EMJD). ADLER M, 1988, BIOPHYS J, V53, P989; AXELROD D, 1979, BIOPHYS J, V26, P557; Bangham A., 1974, METHODS MEMBRANE BIO, P1; Benninger RKP, 2005, BIOPHYS J, V88, P609, DOI 10.1529/biophysj.104.050096; Bidault S, 2004, OPT LETT, V29, P1242, DOI 10.1364/OL.29.001242; BOREJDO J, 1993, BIOPHYS J, V65, P300; Brasselet S., 2013, FLUORESCENT METHODS, P311; Ait-Belkacem D, 2010, OPT EXPRESS, V18, P14859, DOI 10.1364/OE.18.014859; Callis P. R., 2002, TOPICS FLUORESCENCE, P1; CHEN SY, 1993, BIOPHYS J, V64, P1567; Dale RE, 1999, BIOPHYS J, V76, P1606; DeMay BS, 2011, BIOPHYS J, V101, P985, DOI 10.1016/j.bpj.2011.07.008; DIX JA, 1990, BIOPHYS J, V57, P231; Duboisset J, 2013, J PHYS CHEM B, V117, P784, DOI 10.1021/jp309528f; Gasecka A, 2012, PHYS REV LETT, V108, DOI 10.1103/PhysRevLett.108.263901; Gasecka A, 2009, BIOPHYS J, V97, P2854, DOI 10.1016/j.bpj.2009.08.052; Haluska CK, 2008, BIOPHYS J, V95, P5737, DOI 10.1529/biophysj.108.131490; Kress A, 2011, BIOPHYS J, V101, P468, DOI 10.1016/j.bpj.2011.05.021; Kress A, 2013, BIOPHYS J, V105, P127, DOI 10.1016/j.bpj.2013.05.043; Lazar J, 2011, NAT METHODS, V8, P684, DOI [10.1038/nmeth.1643, 10.1038/NMETH.1643]; LENTZ BR, 1979, BIOPHYS J, V25, P489; Mitchell DC, 1998, BIOPHYS J, V75, P896; POTTEL H, 1986, CHEM PHYS, V102, P37, DOI 10.1016/0301-0104(86)85115-1; Reeve JE, 2012, BIOPHYS J, V103, P907, DOI 10.1016/j.bpj.2012.08.003; RICHARDS B, 1959, PROC R SOC LON SER-A, V253, P358, DOI 10.1098/rspa.1959.0200; Schon P, 2008, OPT EXPRESS, V16, P20891, DOI 10.1364/OE.16.020891; Solanko LM, 2013, BIOPHYS J, V105, P2082, DOI 10.1016/j.bpj.2013.09.031; Steinbach Gábor, 2011, J Fluoresc, V21, P983, DOI 10.1007/s10895-010-0684-3; VANDERMEER BW, 1982, CHEM PHYS, V66, P39, DOI 10.1016/0301-0104(82)88004-X; Vrabioiu AM, 2006, NATURE, V443, P466, DOI 10.1038/nature05109; Wang X, 2013, REV SCI INSTRUM, V84, DOI 10.1063/1.4807318; WEBER G, 1954, T FARADAY SOC, V50, P552, DOI 10.1039/tf9545000552 32 0 0 CELL PRESS CAMBRIDGE 600 TECHNOLOGY SQUARE, 5TH FLOOR, CAMBRIDGE, MA 02139 USA 0006-3495 1542-0086 BIOPHYS J Biophys. J. JUN 3 2014 106 11 2330 2339 10.1016/j.bpj.2014.04.011 10 Biophysics Biophysics AI6UK WOS:000337012300007 J Wright, K; Golder, S; Rodriguez-Lopez, R Wright, Kath; Golder, Su; Rodriguez-Lopez, Rocio Citation searching: a systematic review case study of multiple risk behaviour interventions BMC MEDICAL RESEARCH METHODOLOGY English Article Systematic reviews; Information retrieval; Citation searching RANDOMIZED-CONTROLLED-TRIAL; LIFE-STYLE INTERVENTION; PHYSICAL-ACTIVITY; COLLEGE-STUDENTS; HEALTH BEHAVIORS; SMOKING-CESSATION; CANCER PREVENTION; WISEWOMAN PROJECT; PREGNANT-WOMEN; WEIGHT-GAIN Background: The value of citation searches as part of the systematic review process is currently unknown. While the major guides to conducting systematic reviews state that citation searching should be carried out in addition to searching bibliographic databases there are still few studies in the literature that support this view. Rather than using a predefined search strategy to retrieve studies, citation searching uses known relevant papers to identify further papers. Methods: We describe a case study about the effectiveness of using the citation sources Google Scholar, Scopus, Web of Science and OVIDSP MEDLINE to identify records for inclusion in a systematic review. We used the 40 included studies identified by traditional database searches from one systematic review of interventions for multiple risk behaviours. We searched for each of the included studies in the four citation sources to retrieve the details of all papers that have cited these studies. We carried out two analyses; the first was to examine the overlap between the four citation sources to identify which citation tool was the most useful; the second was to investigate whether the citation searches identified any relevant records in addition to those retrieved by the original database searches. Results: The highest number of citations was retrieved from Google Scholar (1680), followed by Scopus (1173), then Web of Science (1095) and lastly OVIDSP (213). To retrieve all the records identified by the citation tracking searching all four resources was required. Google Scholar identified the highest number of unique citations. The citation tracking identified 9 studies that met the review's inclusion criteria. Eight of these had already been identified by the traditional databases searches and identified in the screening process while the ninth was not available in any of the databases when the original searches were carried out. It would, however, have been identified by two of the database search strategies if searches had been carried out later. Conclusions: Based on the results from this investigation, citation searching as a supplementary search method for systematic reviews may not be the best use of valuable time and resources. It would be useful to verify these findings in other reviews. [Wright, Kath; Golder, Su; Rodriguez-Lopez, Rocio] Univ York, Alcuin Coll, Ctr Reviews & Disseminat, York YO10 5DD, N Yorkshire, England Wright, K (reprint author), Univ York, Alcuin Coll, Ctr Reviews & Disseminat, A-B Block, York YO10 5DD, N Yorkshire, England. kath.wright@york.ac.uk Department of Health Policy Research Programme The work presented here was undertaken independently by the authors. The scoping review used in the case study was undertaken by the Centre for Reviews and Dissemination as part of the Public Health Research Consortium. The Public Health Research Consortium is funded by the Department of Health Policy Research Programme. The views expressed in this poster are those of the authors alone and not those of CRD, the PHRC or the DH. Aldana Steven G, 2006, Prev Chronic Dis, V3, pA05; Bakkalbasi Nisa, 2006, Biomed Digit Libr, V3, P7, DOI 10.1186/1742-5581-3-7; Braithwaite R, 2005, J HEALTH CARE POOR U, V16, P130, DOI 10.1353/hpu.2005.0111; Burke L, 2013, INT J BEHAV NUTR PHY, V10; BURTON LC, 1995, PREV MED, V24, P492, DOI 10.1006/pmed.1995.1078; Campbell MK, 2004, HEALTH PSYCHOL, V23, P492, DOI 10.1037/0278-6133.23.5.492; Campo Osaba MA, 2013, EDICIONES MAYO, P54; Centre for Reviews and Dissemination, 2009, SYST REV CRDS GUID U; De Groote SL, 2012, NURS OUTLOOK, V60, P391, DOI 10.1016/j.outlook.2012.04.007; de Vries H, 2008, AM J HEALTH PROMOT, V22, P417, DOI 10.4278/ajhp.22.6.417; Emmons KM, 2005, AM J PUBLIC HEALTH, V95, P1200, DOI 10.2105/AJPH.2004.038695; Franko DL, 2008, PREV MED, V47, P369, DOI 10.1016/j.ypmed.2008.06.013; Greene GW, 2012, AM J HEALTH PROMOT, V27, pE47, DOI 10.4278/ajhp.110606-QUAN-239; Greenhalgh T, 2005, BRIT MED J, V331, P1064, DOI 10.1136/bmj.38636.593461.68; Hammerstrom K, 2010, SEARCHING STUDIES GU; Higgins JPT, 2011, COCHRANE COLLABORATI, DOI DOI 10.1136/BMJ.D5928; Hillier FC, 2012, PUBLIC HEALTH NUTR, V15, P1446, DOI 10.1017/S1368980011002862; Hui A, 2012, BJOG-INT J OBSTET GY, V119, P70, DOI 10.1111/j.1471-0528.2011.03184.x; Imperial Cancer Research Fund Oxcheck Study Group, 1995, BMJ-BRIT MED J, V310, P1099; Institute of Medicine, 2011, FIND WHAT WORKS HLTH; Jacobs N, 2011, HEALTH EDUC RES, V26, P886, DOI 10.1093/her/cyr046; Keyserling TC, 2008, PREV MED, V46, P499, DOI 10.1016/j.ypmed.2008.02.011; Kreuter MW, 1996, HEALTH EDUC RES, V11, P97, DOI 10.1093/her/11.1.97; Kypri K, 2005, PREV MED, V41, P761, DOI 10.1016/j.ypmed.2005.07.010; LaChausse RG, 2012, J AM COLL HEALTH, V60, P324, DOI 10.1080/07448481.2011.623333; LEIGH JP, 1992, ARCH INTERN MED, V152, P1201, DOI 10.1001/archinte.152.6.1201; Lombard CA, 2009, BMC PUBLIC HLTH, V9; McCambridge J, 2011, DRUG ALCOHOL DEPEN, V114, P177, DOI 10.1016/j.drugalcdep.2010.07.028; Oenema A, 2008, ANN BEHAV MED, V35, P125, DOI 10.1007/s12160-008-9023-1; Papaioannou D, 2010, HEALTH INFO LIBR J, V27, P114, DOI [10.1111/j.1471-1842.2009.00863.x, 10.1111/J.1471-1842.2009.00863.x]; Parekh S, 2012, INT J BEHAV NUTR PHY, V9, DOI 10.1186/1479-5868-9-108; Peragallo N, 2012, AIDS BEHAV, V16, P1316, DOI 10.1007/s10461-011-0052-6; Rauh K, 2013, BMC PREGNANCY CHILDB, V13, DOI 10.1186/1471-2393-13-151; Ruffin MT, 2011, ANN FAM MED, V9, P3, DOI 10.1370/afm.1197; Salamone LM, 1999, AM J CLIN NUTR, V70, P97; SIKKEMA KJ, 1995, AIDS EDUC PREV, V7, P145; Simkin-Silverman L R, 1998, Womens Health, V4, P255; Spring B, 2012, ARCH INTERN MED, V172, P789, DOI 10.1001/archinternmed.2012.1044; Staten LK, 2004, J WOMENS HEALTH, V13, P547, DOI 10.1089/1540999041281133; Ulla Diez SM, 2012, NURS RES, V61, P121; Ussher M, 2003, ADDICTION, V98, P523, DOI 10.1046/j.1360-0443.2003.00346.x; VANASSEMA P, 1994, PREV MED, V23, P394, DOI 10.1006/pmed.1994.1054; Vandelanotte C, 2008, PREV MED, V46, P232, DOI 10.1016/j.ypmed.2007.07.008; van Keulen HM, 2011, ANN BEHAV MED, V41, P104, DOI 10.1007/s12160-010-9231-3; Weisman CS, 2011, WOMEN HEALTH ISS, V21, P265, DOI 10.1016/j.whi.2011.03.007; Werch CE, 2010, PREV MED, V50, P30, DOI 10.1016/j.ypmed.2009.12.010; Wilcox S, 2013, AM J PREV MED, V44, P122, DOI 10.1016/j.amepre.2012.09.062; Wilkinson SA, 2012, BMC PREGNANCY CHILDB, V12, DOI 10.1186/1471-2393-12-131; Zhou BA, 2010, EDUC GERONTOL, V36, P919, DOI 10.1080/03601271003689514 49 0 0 BIOMED CENTRAL LTD LONDON 236 GRAYS INN RD, FLOOR 6, LONDON WC1X 8HL, ENGLAND 1471-2288 BMC MED RES METHODOL BMC Med. Res. Methodol. JUN 3 2014 14 73 10.1186/1471-2288-14-73 8 Health Care Sciences & Services Health Care Sciences & Services AI4SR WOS:000336855600001 J Dong, SY; Horstmeyer, R; Shiradkar, R; Guo, KK; Ou, XZ; Bian, ZC; Xin, HL; Zheng, GA Dong, Siyuan; Horstmeyer, Roarke; Shiradkar, Radhika; Guo, Kaikai; Ou, Xiaoze; Bian, Zichao; Xin, Huolin; Zheng, Guoan Aperture-scanning Fourier ptychography for 3D refocusing and super-resolution macroscopic imaging OPTICS EXPRESS English Article PHASE RETRIEVAL; WIDE-FIELD; HIGH-RESOLUTION; HOLOGRAPHIC MICROSCOPY; ELECTRON-MICROSCOPY; LED ARRAY; RECONSTRUCTION; DIFFRACTION; DIVERSITY; ABERRATIONS We report an imaging scheme, termed aperture-scanning Fourier ptychography, for 3D refocusing and super-resolution macroscopic imaging. The reported scheme scans an aperture at the Fourier plane of an optical system and acquires the corresponding intensity images of the object. The acquired images are then synthesized in the frequency domain to recover a high-resolution complex sample wavefront; no phase information is needed in the recovery process. We demonstrate two applications of the reported scheme. In the first example, we use an aperture-scanning Fourier ptychography platform to recover the complex hologram of extended objects. The recovered hologram is then digitally propagated into different planes along the optical axis to examine the 3D structure of the object. We also demonstrate a reconstruction resolution better than the detector pixel limit (i.e., pixel super-resolution). In the second example, we develop a camera-scanning Fourier ptychography platform for super-resolution macroscopic imaging. By simply scanning the camera over different positions, we bypass the diffraction limit of the photographic lens and recover a super-resolution image of an object placed at the far field. This platform's maximum achievable resolution is ultimately determined by the camera's traveling range, not the aperture size of the lens. The FP scheme reported in this work may find applications in 3D object tracking, synthetic aperture imaging, remote sensing, and optical/electron/X-ray microscopy. (C) 2014 Optical Society of America [Dong, Siyuan; Shiradkar, Radhika; Guo, Kaikai; Bian, Zichao; Zheng, Guoan] Univ Connecticut, Storrs, CT 06269 USA; [Horstmeyer, Roarke; Ou, Xiaoze] CALTECH, Pasadena, CA 91125 USA; [Xin, Huolin] Brookhaven Natl Lab, Elect Microscopy Grp, Upton, NY 11973 USA Zheng, GA (reprint author), Univ Connecticut, Storrs, CT 06269 USA. guoan.zheng@uconn.edu Center for Functional Nanomaterials, Brookhaven National Laboratory - U.S. Department of Energy, Office of Basic Energy Sciences [DE-AC02-98CH10886] We thank Prof. Changhuei Yang for helpful discussion. We also thank him for letting us use his motion controller. Huolin Xin acknowledges support from the Center for Functional Nanomaterials, Brookhaven National Laboratory, which is supported by the U.S. Department of Energy, Office of Basic Energy Sciences, under Contract No. DE-AC02-98CH10886. For more information on Fourier ptychography, please visit us at 'Smart Imaging Lab at UConn': https://sites.google.com/site/gazheng/. Alexandrov SA, 2006, PHYS REV LETT, V97, DOI 10.1103/PhysRevLett.97.168102; Allen LJ, 2001, OPT COMMUN, V199, P65, DOI 10.1016/S0030-4018(01)01556-5; Ben-Ezra M, 2011, IEEE COMPUT GRAPH, V31, P49, DOI 10.1109/MCG.2011.1; Bian ZC, 2013, OPT EXPRESS, V21, P32400, DOI 10.1364/OE.21.032400; Bishara W, 2010, OPT EXPRESS, V18, P11181, DOI 10.1364/OE.18.011181; Brady D.J., 2009, OPTICAL IMAGING SPEC; Cossairt O. S., 2011, COMPUTATIONAL PHOLOG, P1; DaneshPanah M, 2007, OPT EXPRESS, V15, P10761, DOI 10.1364/OE.15.010761; DaneshPanah M, 2010, J DISP TECHNOL, V6, P490, DOI 10.1109/JDT.2010.2043499; Dean BH, 2003, J OPT SOC AM A, V20, P1490, DOI 10.1364/JOSAA.20.001490; Di JL, 2008, APPL OPTICS, V47, P5654, DOI 10.1364/AO.47.005654; Dierolf M, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/035017; Dong SY, 2014, BIOMED OPT EXPRESS, V5, P1757, DOI 10.1364/BOE.5.001757; Edo TB, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.053850; Elser V, 2003, J OPT SOC AM A, V20, P40, DOI 10.1364/JOSAA.20.000040; Faulkner HML, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.023903; FIENUP JR, 1982, APPL OPTICS, V21, P2758, DOI 10.1364/AO.21.002758; FIENUP JR, 1978, OPT LETT, V3, P27, DOI 10.1364/OL.3.000027; Garcia J, 2005, OPT EXPRESS, V13, P6073, DOI 10.1364/OPEX.13.006073; GERCHBER.RW, 1972, OPTIK, V35, P237; GILLETT JC, 1995, OPT ENG, V34, P3130, DOI 10.1117/12.213590; Gonsalves R. A., 1982, OPT ENG, V21; GONSALVES RA, 1987, J OPT SOC AM A, V4, P166, DOI 10.1364/JOSAA.4.000166; Guizar-Sicairos M, 2008, OPT EXPRESS, V16, P7264, DOI 10.1364/OE.16.007264; Hillman TR, 2009, OPT EXPRESS, V17, P7873, DOI 10.1364/OE.17.007873; HOPPE W, 1969, ACTA CRYSTALL A-CRYS, VA 25, P502, DOI 10.1107/S0567739469001057; Hue F, 2011, ULTRAMICROSCOPY, V111, P1117, DOI 10.1016/j.ultramic.2011.02.005; Humphry MJ, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1733; Iglesias I, 2011, OPT LETT, V36, P3636, DOI 10.1364/OL.36.003636; Jang JS, 2002, OPT LETT, V27, P1144, DOI 10.1364/OL.27.001144; Liang C.-K., 2008, ACM T GRAPHIC, V27, P1, DOI 10.1145/1360612.1360654; Lohmann AW, 1996, J OPT SOC AM A, V13, P470, DOI 10.1364/JOSAA.13.000470; Lu CH, 2013, APPL OPTICS, V52, pD92, DOI 10.1364/AO.52.000D92; Maiden AM, 2010, OPT LETT, V35, P2585, DOI 10.1364/OL.35.002585; Marchesini S, 2013, INVERSE PROBL, V29, DOI 10.1088/0266-5611/29/11/115009; MEINEL AB, 1970, APPL OPTICS, V9, P2501, DOI 10.1364/AO.9.002501; Mico V, 2006, J OPT SOC AM A, V23, P3162, DOI 10.1364/JOSAA.23.003162; Mico V, 2006, OPT EXPRESS, V14, P5168, DOI 10.1364/OE.14.005168; Mico V, 2004, OPT EXPRESS, V12, P2589, DOI 10.1364/OPEX.12.002589; Ou XZ, 2014, OPT EXPRESS, V22, P4960, DOI 10.1364/OE.22.004960; Ou XZ, 2013, OPT LETT, V38, P4845, DOI 10.1364/OL.38.004845; Park SC, 2003, IEEE SIGNAL PROC MAG, V20, P21; Parthasarathy AB, 2012, OPT LETT, V37, P4062, DOI 10.1364/OL.37.004062; RODENBURG JM, 1992, PHILOS T ROY SOC A, V339, P521, DOI 10.1098/rsta.1992.0050; RYLE M, 1960, MON NOT R ASTRON SOC, V120, P220; Shenfield A, 2011, J APPL PHYS, V109, DOI 10.1063/1.3600235; Stern A, 2003, OPT EXPRESS, V11, P2446, DOI 10.1364/OE.11.002446; TAYLOR LS, 1981, IEEE T ANTENN PROPAG, V29, P386, DOI 10.1109/TAP.1981.1142559; Thibault P, 2008, SCIENCE, V321, P379, DOI 10.1126/science.1158573; Thibault P, 2009, ULTRAMICROSCOPY, V109, P338, DOI 10.1016/j.ultramic.2008.12.011; Tian L, 2014, OPT LETT, V39, P1326, DOI 10.1364/OL.39.001326; Vaish V., 2006, P IEEE C COMP VIS PA, V2, P2331; Waller L, 2010, OPT EXPRESS, V18, P22817, DOI 10.1364/OE.18.022817; Shaked NT, 2011, SPRINGER SER SURF SC, V46, P169, DOI 10.1007/978-3-642-15813-1_7; Wilburn B, 2005, ACM T GRAPHIC, V24, P765, DOI 10.1145/1073204.1073259; Wilburn B. S., 2001, ELECT IMAGING 2002, P29; Williams D.B., 1996, TRANSMISSION ELECT M; Xiao X, 2013, APPL OPTICS, V52, P546, DOI 10.1364/AO.52.000546; Zheng G., 2014, IEEE PHOTONICS J, V6; Zheng GA, 2014, OPT PHOTONICS NEWS, V25, P26; Zheng GA, 2013, NAT PHOTONICS, V7, P739, DOI 10.1038/NPHOTON.2013.187; Zheng GA, 2010, LAB CHIP, V10, P3125, DOI 10.1039/c0lc00213e; Zheng GA, 2014, BIOMED OPT EXPRESS, V5, P1, DOI 10.1364/BOE.5.000001; Zheng GA, 2011, OPT LETT, V36, P3987, DOI 10.1364/OL.36.003987; Zheng GA, 2013, OPT EXPRESS, V21, P15131, DOI 10.1364/OE.21.015131 65 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express JUN 2 2014 22 11 13586 13599 10.1364/OE.22.013586 14 AJ2PW WOS:000337501600086 J Eden, AS; Zwitserlood, P; Keuper, K; Junghofer, M; Laeger, I; Zwanzger, P; Dobel, C Eden, Annuschka Salima; Zwitserlood, Pienie; Keuper, Katharina; Junghoefer, Markus; Laeger, Inga; Zwanzger, Peter; Dobel, Christian All in Its Proper Time: Monitoring the Emergence of a Memory Bias for Novel, Arousing-Negative Words in Individuals with High and Low Trait Anxiety PLOS ONE English Article POSTTRAUMATIC-STRESS-DISORDER; IMPLICIT ATTITUDE FORMATION; EVENT-RELATED POTENTIALS; EXPLICIT MEMORY; EMOTIONAL INFORMATION; DECLARATIVE MEMORY; AUDITORY-CORTEX; RESPONSE BIAS; ERP ANALYSIS; FREE-RECALL The well-established memory bias for arousing-negative stimuli seems to be enhanced in high trait-anxious persons and persons suffering from anxiety disorders. We monitored the emergence and development of such a bias during and after learning, in high and low trait anxious participants. A word-learning paradigm was applied, consisting of spoken pseudowords paired either with arousing-negative or neutral pictures. Learning performance during training evidenced a short-lived advantage for arousing-negative associated words, which was not present at the end of training. Cued recall and valence ratings revealed a memory bias for pseudowords that had been paired with arousing-negative pictures, immediately after learning and two weeks later. This held even for items that were not explicitly remembered. High anxious individuals evidenced a stronger memory bias in the cued-recall test, and their ratings were also more negative overall compared to low anxious persons. Both effects were evident, even when explicit recall was controlled for. Regarding the memory bias in anxiety prone persons, explicit memory seems to play a more crucial role than implicit memory. The study stresses the need for several time points of bias measurement during the course of learning and retrieval, as well as the employment of different measures for learning success. [Eden, Annuschka Salima; Keuper, Katharina; Junghoefer, Markus; Dobel, Christian] Univ Hosp Munster, Inst Biomagnetism & Biosignalanal, Munster, Germany; [Zwitserlood, Pienie] Univ Munster, Inst Psychol, D-48149 Munster, Germany; [Laeger, Inga; Zwanzger, Peter] Univ Hosp Munster, Dept Psychiat, Munster, Germany Eden, AS (reprint author), Univ Hosp Munster, Inst Biomagnetism & Biosignalanal, Munster, Germany. Annuschka.Eden@uni-muenster.de Interdisziplinares Zentrums fur Klinische Forschung Munster, IZKF (Interdisciplinary Center for Clinical Research of Muenster) [Do3/021/10] This work was supported by Interdisziplinares Zentrums fur Klinische Forschung Munster, IZKF (Interdisciplinary Center for Clinical Research of Muenster) (http://campus.uni-muenster.de/3855.html) project number: Do3/021/10. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Alger SE, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0012131; Baayen RH, 1995, CELEX DATABASE; BAEYENS F, 1988, ADV BEHAV RES THER, V10, P179, DOI 10.1016/0146-6402(88)90014-8; BAEYENS F, 1990, COGNITION EMOTION, V4, P3, DOI 10.1080/02699939008406760; Barlow D. H., 2002, ANXIETY ITS DISORDER; Betsch T, 2001, PERS SOC PSYCHOL B, V27, P242, DOI 10.1177/0146167201272009; Friedman Bruce H., 2000, Behavior Therapy, V31, P745, DOI 10.1016/S0005-7894(00)80042-8; Born J, 2006, NEUROSCIENTIST, V12, P410, DOI 10.1177/1073858406292647; BRADLEY MM, 1992, J EXP PSYCHOL LEARN, V18, P379, DOI 10.1037//0278-7393.18.2.379; BRADLEY MM, 1994, J BEHAV THER EXP PSY, V25, P49, DOI 10.1016/0005-7916(94)90063-9; Breitenstein C, 2007, RESTOR NEUROL NEUROS, V25, P493; Breitenstein C, 2002, J NEUROSCI METH, V114, P173, DOI 10.1016/S0165-0270(01)00525-8; Buzsaki G, 1998, J SLEEP RES, V7, P17, DOI 10.1046/j.1365-2869.7.s1.3.x; Cahill L, 1996, P NATL ACAD SCI USA, V93, P8016, DOI 10.1073/pnas.93.15.8016; CALVO MG, 1994, COGNITION EMOTION, V8, P127, DOI 10.1080/02699939408408932; COHEN NJ, 1980, SCIENCE, V210, P207, DOI 10.1126/science.7414331; Dalgleish T, 2003, J CLIN CHILD ADOLESC, V32, P10, DOI 10.1207/15374420360533022; Davis MH, 2009, J COGNITIVE NEUROSCI, V21, P803, DOI 10.1162/jocn.2009.21059; De Houwer J, 2001, PSYCHOL BULL, V127, P853, DOI 10.1037//0033-2909.127.6.853; DEUTSCH JA, 1971, SCIENCE, V174, P788, DOI 10.1126/science.174.4011.788; Dienes Z, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0033400; Dobel C, 2009, PHILOS T R SOC B, V364, P3697, DOI 10.1098/rstb.2009.0158; Dobel C, 2010, J COGNITIVE NEUROSCI, V22, P1251, DOI 10.1162/jocn.2009.21297; Dowens MG, 2003, COGNITION EMOTION, V17, P843, DOI 10.1080/02699930244000381; EYSENCK MW, 1994, COGNITION EMOTION, V8, P415, DOI 10.1080/02699939408408950; Eysenck MW, 2007, EMOTION, V7, P336, DOI 10.1037/1528-3542.7.2.336; Flaisch T, 2011, SOC COGN AFFECT NEUR, V6, P109, DOI 10.1093/scan/nsq022; Frankland PW, 2005, NAT REV NEUROSCI, V6, P119, DOI 10.1038/nrn1607; Fritsch N, 2013, BRAIN LANG, V124, P75, DOI 10.1016/j.bandl.2012.12.001; GABRIELI JDE, 1995, PSYCHOL SCI, V6, P76, DOI 10.1111/j.1467-9280.1995.tb00310.x; Galvan VV, 2002, NEUROBIOL LEARN MEM, V77, P78, DOI 10.1006/nlme.2001.4044; Graves L, 2001, TRENDS NEUROSCI, V24, P237, DOI 10.1016/S0166-2236(00)01744-6; Grillon C, 1999, J ABNORM PSYCHOL, V108, P134, DOI 10.1037/0021-843X.108.1.134; Herbert C, 2008, PSYCHOPHYSIOLOGY, V45, P487, DOI 10.1111/j.1469-8986.2007.00638.x; HEUER F, 1990, MEM COGNITION, V18, P496, DOI 10.3758/BF03198482; Hu P, 2006, PSYCHOL SCI, V17, P891, DOI 10.1111/j.1467-9280.2006.01799.x; Junghofer M, 2001, PSYCHOPHYSIOLOGY, V38, P175, DOI 10.1017/S0048577201000762; Kaestner EJ, 2013, J COGNITIVE NEUROSCI, V25, P1597, DOI 10.1162/jocn_a_00433; Keuper K, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0070788; Keuper K, 2012, HUM BRAIN MAPP; Kissler J, 2006, PROG BRAIN RES, V156, P147, DOI 10.1016/S0079-6123(06)56008-X; Kissler J, 2009, BIOL PSYCHOL, V80, P75, DOI 10.1016/j.biopsycho.2008.03.004; Kissler J, 2007, PSYCHOL SCI, V18, P475, DOI 10.1111/j.1467-9280.2007.01924.x; LaBar KS, 1998, PSYCHOL SCI, V9, P490, DOI 10.1111/1467-9280.00090; Laeger I, 2012, BEHAV BRAIN RES, V233, P508, DOI 10.1016/j.bbr.2012.05.036; Lang P. J., 1999, INT AFFECTIVE PICTUR; LeDoux J, 2007, CURR BIOL, V17, P868, DOI DOI 10.1016/J.CUB.2007.08.005; LeDoux JE, 2000, ANNU REV NEUROSCI, V23, P155, DOI 10.1146/annurev.neuro.23.1.155; LeDoux JE, 2011, MEMORY PROCESS NEURO, P153; Lissek S, 2010, Z PSYCHOL, V218, P146, DOI 10.1027/0044-3409/a000022; Lissek S, 2005, BEHAV RES THER, V43, P1391, DOI 10.1016/j.brat.2004.10.007; Lissek S, 2010, CORD C P, V167, P47; Mac Macmillan NA, 1991, DETECTION THEORY USE; McCabe RE, 1999, COGNITIVE THER RES, V23, P21, DOI 10.1023/A:1018706607051; Mineka S, 1996, Nebr Symp Motiv, V43, P135; Mitte K, 2008, PSYCHOL BULL, V134, P886, DOI 10.1037/a0013343; Muhlberger A, 2009, J NEURAL TRANSM, V116, P735, DOI 10.1007/s00702-008-0108-6; NISSEN MJ, 1987, COGNITIVE PSYCHOL, V19, P1, DOI 10.1016/0010-0285(87)90002-8; Norton GR, 1988, J ANXIETY DIORD, V2, P169, DOI 10.1016/0887-6185(88)90023-0; Olson MA, 2001, PSYCHOL SCI, V12, P413, DOI 10.1111/1467-9280.00376; Payne JD, 2011, J COGNITIVE NEUROSCI, V23, P1285, DOI 10.1162/jocn.2010.21526; Perruchet P, 2006, TRENDS COGN SCI, V10, P233, DOI 10.1016/j.tics.2006.03.006; Pulvermuller F., 1999, BEHAV BRAIN SCI, V22, P252; Rugg MD, 1998, NATURE, V392, P595, DOI 10.1038/33396; Russo R, 1999, COGNITION EMOTION, V13, P435; Russo R, 2006, MEMORY, V14, P393, DOI 10.1080/09658210500343166; SCHEICH H, 1992, NATO ADV SCI I D-BEH, V68, P447; Schupp HT, 2004, EMOTION, V4, P189, DOI 10.1037/1528-3542.4.2.189; Schupp HT, 2004, PSYCHOPHYSIOLOGY, V41, P441, DOI 10.1111/j.1469-8986.2004.00174.x; Scott GG, 2009, BIOL PSYCHOL, V80, P95, DOI 10.1016/j.biopsycho.2008.03.010; SEGER CA, 1994, PSYCHOL BULL, V115, P163, DOI 10.1037/0033-2909.115.2.163; Sejnowski TJ, 2000, BRAIN RES, V886, P208, DOI 10.1016/S0006-8993(00)03007-9; Sharot T, 2004, COGN AFFECT BEHAV NE, V4, P294, DOI 10.3758/CABN.4.3.294; Sheehan DV, 1998, J CLIN PSYCHIAT, V59, P22, DOI 10.4088/JCP.09m05305whi; Smith C, 2001, SLEEP MED REV, V5, P491, DOI 10.1053/smrv.2001.0164; Spielberger C. D., 1983, MANUAL STATE TRAIT A; STARR A, 1970, NEUROPSYCHOLOGIA, V8, P75, DOI 10.1016/0028-3932(70)90027-8; Steinberg C, 2012, J COGNITIVE NEUROSCI, V24, P17, DOI 10.1162/jocn_a_00067; Steriade M, 1999, TRENDS NEUROSCI, V22, P337, DOI 10.1016/S0166-2236(99)01407-1; Stickgold R, 2005, NATURE, V437, P1272, DOI 10.1038/nature04286; Touryan SR, 2007, MEMORY, V15, P154, DOI 10.1080/09658210601151310; Waldhauser GT, 2011, SCAND J PSYCHOL, V52, P21, DOI 10.1111/j.1467-9450.2010.00845.x; Weinberger NM, 2007, LEARN MEMORY, V14, P1, DOI 10.1101/lm.421807; Williams J. M. G., 1997, COGNITIVE PSYCHOL EM; Williams JMG, 1996, PSYCHOL BULL, V120, P3, DOI 10.1037/0033-2909.120.1.3; Williams J.M.G., 1988, COGNITIVE PSYCHOL EM; Yegiyan NS, 2011, COGNITION EMOTION, V25, P1255, DOI 10.1080/02699931.2010.540821 87 0 0 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One JUN 2 2014 9 6 e98339 10.1371/journal.pone.0098339 9 Multidisciplinary Sciences Science & Technology - Other Topics AI6CB WOS:000336956300056 J Sales, A; Melendez, JC; Algarabel, S; Pitarque, A Sales, Alicia; Melendez, Juan-Carlos; Algarabel, Salvador; Pitarque, Alfonso Differences in familiarity according to the cognitive reserve of healthy elderly people ESTUDIOS DE PSICOLOGIA English Article episodic memory; recognition; familiarity; cognitive reserve ALZHEIMERS-DISEASE; RECOGNITION MEMORY; ASSOCIATIVE RECOGNITION; RESPONSE BIAS; OLDER-ADULTS; RECOLLECTION; OCCUPATION; EDUCATION; YOUNG; AGE This study examines the relationship between cognitive reserve and familiarity processes in recognition memory. We hypothesize that people with high cognitive reserve are able to better compensate in alternative information retrieval processes. Forty-five participants, divided into high and low cognitive reserve groups, conducted a recognition experiment where they were asked to discriminate between studied and non-studied words that varied in perceptual familiarity. The results indicated that participants were able to use perceptual familiarity to improve their level of recognition. More importantly, people with high cognitive reserve used familiarity better than those with low cognitive reserve. The results provide the first empirical evidence indicating that people with high cognitive reserve are more efficient at balancing recollection and familiarity processes, and thus maintain a better performance level than those with low cognitive reserve. [Sales, Alicia; Melendez, Juan-Carlos; Algarabel, Salvador; Pitarque, Alfonso] Univ Valencia, Valencia 46010, Spain Algarabel, S (reprint author), Univ Valencia, Dept Metodol Ciencias Comportamiento, Av Blasco Ibanez 21, Valencia 46010, Spain. salvador.algarabel@uv.es Alameda J. R., 1995, DICCIONARIO FRECUENC; Albert SM, 1999, AM J PUBLIC HEALTH, V89, P95; Alexander GE, 1997, AM J PSYCHIAT, V154, P165; Algarabel S, 2010, NEUROPSYCHOLOGY, V24, P599, DOI 10.1037/a0019221; Algarabel S, 2009, NEUROPSYCHOLOGIA, V47, P2056, DOI 10.1016/j.neuropsychologia.2009.03.016; Algarabel S, 2012, AGING NEUROPSYCHOL C, V19, P608, DOI 10.1080/13825585.2011.640657; Ally BA, 2009, BRAIN COGNITION, V69, P504, DOI 10.1016/j.bandc.2008.11.003; Ballesteros S, 2007, PSICOTHEMA, V19, P239; Bastin C, 2003, NEUROPSYCHOLOGY, V17, P14, DOI 10.1037/0894-4105.17.1.14; Budson AE, 2006, NEUROPSYCHOLOGIA, V44, P2222, DOI 10.1016/j.neuropsychologia.2006.05.024; Cabeza R, 2004, CEREB CORTEX, V14, P364, DOI 10.1093/cercor/bhg133; Edmonds EC, 2012, PSYCHOL AGING, V27, P54, DOI 10.1037/a0024582; Evans D A, 1993, Ann Epidemiol, V3, P71; FOLSTEIN MF, 1975, J PSYCHIAT RES, V12, P189, DOI 10.1016/0022-3956(75)90026-6; Gallo DA, 2006, NEUROPSYCHOLOGY, V20, P625, DOI 10.1037/0894-4105.20.6.625; Gold CA, 2007, NEUROPSYCHOLOGIA, V45, P2791, DOI 10.1016/j.neuropsychologia.2007.04.021; Gutchess AH, 2009, EUR J COGN PSYCHOL, V21, P235, DOI 10.1080/09541440802257274; Hockley WE, 1999, MEM COGNITION, V27, P657, DOI 10.3758/BF03211559; Lobo A., 2002, ADAPTACION EXAMEN CO; Meléndez Moral Juan Carlos, 2013, Univ. Psychol., V12, P73; MORTEL KF, 1995, DEMENTIA, V6, P55, DOI 10.1159/000106922; Old SR, 2008, PSYCHOL AGING, V23, P104, DOI 10.1037/0882-7974.23.1.104; Parkin AJ, 2001, PSYCHON B REV, V8, P812, DOI 10.3758/BF03196222; Quamme JR, 2007, HIPPOCAMPUS, V17, P192, DOI 10.1002/hipo.20257; Rentz DM, 2010, ANN NEUROL, V67, P353, DOI 10.1002/ana.21904; ROCCA WA, 1990, NEUROLOGY, V40, P626; Scarmeas N, 2003, NEUROIMAGE, V19, P1215, DOI 10.1016/S1053-8119(03)00074-0; Stern Y, 2005, CEREB CORTEX, V15, P394, DOI 10.1093/cercor/bhh142; Stern Y, 2002, J INT NEUROPSYCH SOC, V8, P448, DOI 10.1017/S1355617702813248; STERN Y, 1994, JAMA-J AM MED ASSOC, V271, P1004, DOI 10.1001/jama.271.13.1004; Stern Y, 2009, NEUROPSYCHOLOGIA, V47, P2015, DOI 10.1016/j.neuropsychologia.2009.03.004; Stern Y, 2003, J CLIN EXP NEUROPSYC, V25, P589, DOI 10.1076/jcen.25.5.589.14571; STERN Y, 1995, NEUROLOGY, V45, P55; STERN Y, 1995, ANN NEUROL, V37, P590, DOI 10.1002/ana.410370508; Vakil E., 2008, J GERONTOL B-PSYCHOL, V63, P171, DOI DOI 10.1093/GER0NB/63.3.P171; Valenzuela MJ, 2006, PSYCHOL MED, V36, P441, DOI 10.1017/S0033291705006264; Wechsler D., 2001, WECHSLER INTELLIGENC; Westerberg CE, 2006, NEUROPSYCHOLOGY, V20, P193, DOI 10.1037/0894-4105.20.2.193; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864; Yonelinas AP, 2005, J NEUROSCI, V25, P3002, DOI 10.1523/JNEUROSCI.5295-04.2005 40 0 0 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND 0210-9395 1579-3699 ESTUD PSICOL-MADRID Estud. Psicol. JUN 2014 35 2 341 358 10.1080/02109395.2014.922262 18 Psychology, Multidisciplinary Psychology AN0GC WOS:000340261200007 J Berketa, JW Berketa, John W. Maximizing postmortem oral-facial data to assist identification following severe incineration FORENSIC SCIENCE MEDICINE AND PATHOLOGY English Review Identification; Incineration; Postmortem data; Forensic odontology FRONTAL-SINUS PATTERNS; DENTAL AGE ESTIMATION; FORENSIC ODONTOLOGY; HUMAN REMAINS; POSITIVE IDENTIFICATION; VICTIM IDENTIFICATION; HIGH-TEMPERATURES; SKELETAL REMAINS; FIRE VICTIMS; TEETH Purpose This paper reviews the literature for methods of maximizing the postmortem oral-facial information available for a comparison to be made for identification following an incident resulting in incineration. Method A search was initially instigated utilizing PubMed, Scopus, and Google Scholar, with further library searches and correspondences among peers around the world leading to a comprehensive review of the literature. Conclusion Maximizing postmortem dental evidence in a severe incineration event requires correct recognition and recording of dental data. Odontologists should attend the scene to facilitate this recognition. The information should be documented, photographed, and stabilized before retrieval. Wrapping, padding, and further support of the remains during transportation to the examination mortuary will aid this process. Examination at the mortuary requires further photography, complete charting, and radiographic examination of any dental material available, as well as awareness of other possible medical evidence, to enable identification of the human remains. Univ Adelaide, Forens Odontol Unit, Adelaide, SA 5005, Australia Berketa, JW (reprint author), Univ Adelaide, Forens Odontol Unit, Adelaide, SA 5005, Australia. johnberketa@hotmail.com Acton C, 1999, AUST DENT J, V44, P20, DOI 10.1111/j.1834-7819.1999.tb00531.x; Al-Amad S, 2006, J Forensic Odontostomatol, V24, P47; ANDERSEN L, 1995, INT J LEGAL MED, V107, P229, DOI 10.1007/BF01245479; Angyal M, 1998, J FORENSIC SCI, V43, P1089; [Anonymous], 2009, AUSTRALIAN; [Anonymous], 2009, NTR; Asamura H, 2004, J FORENSIC SCI, V49, P592; AYTON FD, 1985, BRIT DENT J, V159, P262, DOI 10.1038/sj.bdj.4805699; Azcentral.com, 2013, NTR; Balato N, 2013, GIORN ITAL DERMAT V, V148, P135; Bassed R, 2011, FORENSIC SCI INT, V205, P73, DOI 10.1016/j.forsciint.2010.06.026; BASTIAAN RJ, 1984, AUST DENT J, V29, P105; Beckett S, 2011, J FORENSIC SCI, V56, P571, DOI 10.1111/j.1556-4029.2010.01690.x; Berketa J, 2013, FORENSIC SCI MED PAT, V9, P422, DOI 10.1007/s12024-013-9439-1; Berketa J, 2010, J Forensic Odontostomatol, V28, P1; Berketa JW, 2010, J FORENSIC SCI, V55, P66, DOI 10.1111/j.1556-4029.2009.01226.x; Berketa JW, 2012, FORENSIC SCI MED PAT, V8, P148, DOI 10.1007/s12024-011-9279-9; Bertoluzza A, 1997, J RAMAN SPECTROSC, V28, P185; Blenkin MRB, 2010, J FORENSIC SCI, V55, P1504, DOI 10.1111/j.1556-4029.2010.01491.x; Bohnert M, 1998, FORENSIC SCI INT, V95, P11, DOI 10.1016/S0379-0738(98)00076-0; Bonavilla JD, 2008, J FORENSIC SCI, V53, P412, DOI 10.1111/j.1556-4029.2007.00653.x; Botha C T, 1986, J Forensic Odontostomatol, V4, P67; Bush MA, 2008, J FORENSIC SCI, V53, P419, DOI 10.1111/j.1556-4029.2007.00654.x; Bush MA, 2006, J FORENSIC SCI, V51, P636, DOI 10.1111/j.1556-4029.2006.00121.x; Bush MA, 2007, J FORENSIC SCI, V52, P157, DOI 10.1111/j.1556-4029.2006.00308.x; Byard RW, 2012, J FORENSIC SCI, V57, P969, DOI 10.1111/j.1556-4029.2012.02083.x; Cameriere R, 2005, J FORENSIC SCI, V50, P770; Campobasso CP, 2007, AM J FOREN MED PATH, V28, P182, DOI 10.1097/PAF.0b013e31806195cb; Cardoza Anthony R, 2004, J Calif Dent Assoc, V32, P689; Chapenoire S, 1998, AM J FOREN MED PATH, V19, P352, DOI 10.1097/00000433-199812000-00011; Christensen AM, 2005, J FORENSIC SCI, V50, P18; Claes P, 2010, FORENSIC SCI INT, V201, P138, DOI 10.1016/j.forsciint.2010.03.008; Cordner SM, 2011, FORENSIC SCI INT, V205, P2, DOI 10.1016/j.forsciint.2010.08.008; Correia PM, 2002, ADV FORENSIC TAPHONO; Cox M, 2009, J FORENSIC SCI, V54, P761, DOI 10.1111/j.1556-4029.2009.01075.x; Culbert WL, 1927, J AMER MED ASSOC, V88, P1634; Datta Pankaj, 2012, J Forensic Dent Sci, V4, P42, DOI 10.4103/0975-1475.99165; Delattre VF, 2000, J FORENSIC SCI, V45, P589; Dumancic J, 2001, CROAT MED J, V42, P657; ECKERT WG, 1988, AM J FOREN MED PATH, V9, P188, DOI 10.1097/00000433-198809000-00002; Fairgrieve SI, 2008, FORENSIC CREMATION R; FAIRGRIEVE SI, 1994, J FORENSIC SCI, V39, P557; Fauzi A, 2011, STABILIZATION INCINE; Fernandes CL, 2004, AM J FOREN MED PATH, V25, P302, DOI 10.1097/01.paf.0000146379.85804.da; Gaytmenn R, 2003, J FORENSIC SCI, V48, P622; Glassman DM, 1996, J FORENSIC SCI, V41, P152; GRIFFITHS CJ, 1993, FORENSIC SCI INT, V60, P57, DOI 10.1016/0379-0738(93)90092-O; HAGLUND WD, 1993, J FORENSIC SCI, V38, P708; Hardy JH, 2007, FORENSIC HUMAN IDENT; Harris AMP, 1987, FRONTAL SINUS FORENS; Herschaft EE, 2006, MANUAL FORENSIC ODON; Higgins D, 2013, SCI JUSTICE, V53, P433, DOI 10.1016/j.scijus.2013.06.001; Higgins D, 2011, AUST J FORENSIC SCI, V43, P287, DOI 10.1080/00450618.2011.583278; Hill AJ, 2011, FORENSIC SCI INT, V205, P40, DOI 10.1016/j.forsciint.2010.08.011; Hill AJ, 2011, FORENSIC SCI INT, V205, P44, DOI 10.1016/j.forsciint.2010.08.013; Hill IR, 1984, FORENSIC ODONTOLOGY; Hinchliffe J, 2011, BRIT DENT J, V210, P317, DOI 10.1038/sj.bdj.2011.239; HOLDEN JL, 1995, J BONE MINER RES, V10, P1400; HOLLAND TD, 1989, J FORENSIC SCI, V34, P458; Jakobsen J, 1991, Tandlaegebladet, V95, P325; James H, 2010, FORENSIC DENT EVIDEN, P273; Jayaraman J, 2013, J FORENSIC LEG MED, V20, P373, DOI 10.1016/j.jflm.2013.03.015; Johnston FH, 2009, AUST FAM PHYSICIAN, V38, P720; Kalsbeek N, 2006, STUD CONSERV, V51, P123; Karkhanis S, 2009, J Forensic Odontostomatol, V27, P9; Kirk NJ, 2002, J FORENSIC SCI, V47, P318; Kramer A, 1989, POP PHOTOGR, V96, P53; Krompecher T, 2000, FORENSIC SCI INT, V110, P215, DOI 10.1016/S0379-0738(00)00176-6; Kullman L, 1990, J Forensic Odontostomatol, V8, P3; Labovich MH, 2003, MIL MED, V168, P19; Lain R, 2011, FORENSIC SCI INT, V205, P36, DOI 10.1016/j.forsciint.2010.06.008; Marella G L, 1999, J Forensic Odontostomatol, V17, P16; MARLIN DC, 1991, J FORENSIC SCI, V36, P1765; McCarroll JE, 1996, AM J PSYCHIAT, V153, P778; MERBS CF, 1967, AM ANTIQUITY, V32, P498, DOI 10.2307/2694077; Merlati G, 2002, J Forensic Odontostomatol, V20, P17; Merlati G, 2004, J Forensic Odontostomatol, V22, P34; MINCER HH, 1990, J FORENSIC SCI, V35, P971; Muller M, 1998, J Forensic Odontostomatol, V16, P1; Muthusubramanian M, 2005, J Forensic Odontostomatol, V23, P26; Nambiar P, 1999, CLIN ANAT, V12, P16, DOI 10.1002/(SICI)1098-2353(1999)12:1<16::AID-CA3>3.0.CO;2-D; Nambiar P, 1997, Int Dent J, V47, P9; O'Donnell C, 2011, FORENSIC SCI INT, V205, P15, DOI 10.1016/j.forsciint.2010.05.026; OWSLEY DW, 1993, J FORENSIC SCI, V38, P1372; Park DK, 2009, J FORENSIC SCI, V54, P513, DOI 10.1111/j.1556-4029.2009.01027.x; Pechony O, 2010, P NATL ACAD SCI USA, V107, P19167, DOI 10.1073/pnas.1003669107; Piga G, 2013, J ARCHAEOL SCI, V40, P778, DOI 10.1016/j.jas.2012.07.004; Post H., 2013, LAC MEGNATIC DISASTE; Purves J D, 1975, Forensic Sci, V6, P217, DOI 10.1016/0300-9432(75)90012-6; Quartrehomme G, 1996, FORENSIC SCI INT, V83, P147; REICHS KJ, 1993, FORENSIC SCI INT, V61, P141, DOI 10.1016/0379-0738(93)90222-V; Reichs KJ, 1998, FORENSIC OSTEOLOGY A; Rossouw R J, 1999, J Forensic Odontostomatol, V17, P1; Sandholzer MA, 2013, J FORENSIC SCI; Sandholzer MA, 2013, J FORENSIC RADIOL IM, V1, P107; Savio C, 2006, FORENSIC SCI INT, V158, P108, DOI 10.1016/j.forsciint.2005.05.003; Senn DR, 2013, MANUAL FORENSIC ODON; SOLHEIM T, 1992, INT J LEGAL MED, V104, P339, DOI 10.1007/BF01369554; Stene-Johansen W, 1992, J FORENSIC ODONTOSTO, V10, P15; STEPHENS BG, 1989, J FORENSIC SCI, V34, P454; SWEET DJ, 1995, J FORENSIC SCI, V40, P310; Taylor PTG, 2002, FORENSIC SCI INT, V130, P174, DOI 10.1016/S0379-0738(02)00372-9; Teke HY, 2007, SURG RADIOL ANAT, V29, P9, DOI 10.1007/s00276-006-0157-1; Thali MJ, 2006, J FORENSIC SCI, V51, P113, DOI 10.1111/j.1556-4029.2005.00019.x; Thompson TJU, 2004, FORENSIC SCI INT, V146, pS203, DOI 10.1016/j.forsciint.2004.09.063; Uthman AT, 2011, J FORENSIC SCI, V56, P403, DOI 10.1111/j.1556-4029.2010.01642.x; Valenzuela A, 2000, INT J LEGAL MED, V113, P236, DOI 10.1007/s004149900099; von Wurmb-Schwark N, 2004, INT CONGR SER, V1261, P50, DOI 10.1016/s0531-5131(03)01830-2; White TD., 1991, HUMAN OSTEOLOGY; Woisetschlager M, 2011, EUR J RADIOL, V80, P432, DOI 10.1016/j.ejrad.2010.06.012; Wood RE, 2010, FORENSIC SCI INT, V201, P27, DOI 10.1016/j.forsciint.2010.04.018; Yang F, 2006, FORENSIC SCI INT, V159, pS78, DOI 10.1016/j.forsciint.2006.02.031; YOSHINO M, 1987, FORENSIC SCI INT, V34, P289, DOI 10.1016/0379-0738(87)90041-7 113 0 0 HUMANA PRESS INC TOTOWA 999 RIVERVIEW DRIVE SUITE 208, TOTOWA, NJ 07512 USA 1547-769X 1556-2891 FORENSIC SCI MED PAT Forensic Sci. Med. Pathol. JUN 2014 10 2 208 216 10.1007/s12024-013-9497-4 9 Medicine, Legal; Pathology Legal Medicine; Pathology AN3CD WOS:000340461600010 J Law, KH; Lau, G; Kerrigan, S; Ekstrom, JA Law, Kincho H.; Lau, Gloria; Kerrigan, Shawn; Ekstrom, Julia A. REGNET: Regulatory information management, compliance and analysis GOVERNMENT INFORMATION QUARTERLY English Article Regulations; Compliance assistance; Relatedness analysis; Information retrieval; e-Government; Eco-system models ECOSYSTEMS; GOVERNANCE This paper provides an overview of a research effort that aims to investigate methodologies and tools to facilitate access, compliance and analysis of government regulations. The complexity, diversity, and volume of government regulations are detrimental to business and hinder public understanding of government. The burden of complying with regulations can fall disproportionately on small businesses since these businesses may not have the expertise or resources to keep track of the regulations and the requirements. Regulations emanating from different agencies, each has its own objectives and scopes of concerns, may overlap on similar and related issues and may have inconsistency. The situation can potentially be improved by developing appropriate methodologies and tools that can help facilitate the development and analysis of regulatory documents as well as compliance process. To illustrate, this paper discusses the applications of information technology for selected services related to regulations, such as compliance assistance and comparison of regulations. (C) 2014 Elsevier Inc. All rights reserved. [Law, Kincho H.; Lau, Gloria; Kerrigan, Shawn; Ekstrom, Julia A.] Stanford Univ, Dept Civil & Environm Engn, Engn Informat Grp, Stanford, CA 95014 USA Law, KH (reprint author), 473 Via Ortega,Room 277A,Y2E2 Bldg, Stanford, CA 94305 USA. law@stanford.edu; glau@stanford.edu; kerrigan@stanfordalumni.org; jaekstrom@gmail.com [Anonymous], 2001, 8300 BSI; [Anonymous], 2002, DRAFT GUID ACC PUBL; [Anonymous], 2002, BUS COMPL ON STOP WO; Bench-Capon T., 1991, APIC SERIES, V36; Botkin A., 2002, NAT COMPL ASS PROV F; Cheng C. P., 2008, ARTIF INTELL, V16, P277, DOI 10.1007/s10506-008-9065-5; Cheng C. P., 2008, P 9 ANN INT C DIG GO; Coglianese C, 2009, GEORGE WASH LAW REV, V77, P924; Coglianese C, 2004, SOC SCI COMPUT REV, V22, P85, DOI 10.1177/0894439303259890; Coglianese C., 2004, FACULTY RES WORKING; Cormier R., 2010, P ICES ANN SCI C NAN; Cortner HJ, 1998, LANDSCAPE URBAN PLAN, V40, P159, DOI 10.1016/S0169-2046(97)00108-4; Crowder LB, 2006, SCIENCE, V313, P617, DOI 10.1126/science.1129706; Ekstrom J., 2010, I S J LAW POLICY INF, V6, P189; Ekstrom J., 2011, GULF REGION OCEAN MO; Ekstrom J., 2011, P 9 ANN INT C DIG GO; Ekstrom JA, 2010, COAST MANAGE, V38, P457, DOI 10.1080/08920753.2010.498400; Fontane J. E., 2003, COMMUN ACM, V46, P63; Heffron F. A., 1983, ADM REGULATORY PROCE; Joint Ocean Commission Initiative, 2009, ON COAST ON FUT SEC; Juda L, 2001, OCEAN DEV INT LAW, V32, P43; Kerrigan S., 2003, THESIS STANFORD U; Kerrigan SL, 2005, J COMPUT CIVIL ENG, V19, P1, DOI 10.1016/(ASCE)0887-3801(2005)19:1(1); Klein D., 2003, P 41 ANN M ASS COMP, V1, P423, DOI DOI 10.3115/1075096.1075150; Lau G. T., 2004, THESIS STANFORD U ST; Lau G. T., 2005, J LAW POLICY INFORM, V1, P95; Lau G. T., 2006, P NAT C DIG GOV RES; Lau G. T., 2007, ENCY DIGITAL GOVT; Lau GT, 2006, INFORM RETRIEVAL, V9, P657, DOI 10.1007/s10791-006-9010-8; Lau GT, 2005, COMPUTER, V38, P70, DOI 10.1109/MC.2005.397; Manning C, 2008, INTRO INFORM RETRIEV; McCune W. W., 1993, ANL946 MATH COMP SCI; McLeod K. L., 2005, SCI CONSENSUS STATEM; New York State Department of Environmental Conservation Pollution Prevention Unit, 2002, ENV COMPL POLL PREV; O'Hare S., 1997, ANNU REV INFORM SCI, VASIS, P32; Oskamp A., 2006, INFORM TECHNOLOGY LA; Ouellette M., 2010, P ICES ANN SCI C NAN; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Romine M., 1999, ENVIRON LAW, V6, P1; Rosenberg AA, 2005, MAR ECOL PROG SER, V300, P270; Sherman K, 2005, MAR ECOL PROG SER, V300, P275; Skrzycki C., 2000, WASHINGTON POST; Taduri Siddharth, 2011, Journal of Theoretical and Applied Electronic Commerce Research, V6, DOI 10.4067/S0718-18762011000200002; Uniform Federal Accessibility Standards (UFAS), 1997, US ARCH TRANSP BARR; United States Commission on Ocean Policy (USCOP), 2004, OC BLUEPR 21 CENT FI; Van Wert J.M., 2002, BUSINESS COMPLIANCE; Wahlgren P., 1992, AUTOMATION LEGAL REA; Yu H, 2012, GOV INFORM Q, V29, pS11, DOI 10.1016/j.giq.2011.08.013; Zeleznikow J., 1994, BUILDING INTELLIGENT 49 0 0 ELSEVIER INC SAN DIEGO 525 B STREET, STE 1900, SAN DIEGO, CA 92101-4495 USA 0740-624X 1872-9517 GOV INFORM Q Gov. Inf. Q. JUN 2014 31 1 S37 S48 10.1016/j.giq.2014.01.006 12 Information Science & Library Science Information Science & Library Science AN1DV WOS:000340323800005 J Phillips, J; Withrow, K Phillips, James; Withrow, Kirk Outcomes of Holmium Laser-Assisted Lithotripsy with Sialendoscopy in Treatment of Sialolithiasis OTOLARYNGOLOGY-HEAD AND NECK SURGERY English Article sialolithiasis; salivary endoscopy; obstructive salivary disease; laser lithotripsy sialoendoscopy; sialoadenitis; salivary gland disorders INTRACORPOREAL LITHOTRIPSY; SALIVARY STONES; MANAGEMENT; EXPERIENCE Objectives. The purpose of the current study was to compare outcomes and complication rates of sialolithiasis treated with intracorporeal holmium laser lithotripsy in conjunction with salivary endoscopy with those treated with simple basket retrieval or a combined endoscopic/open procedure. Study Design. Case-comparison study. Setting. Tertiary hospital. Methods. Review of prospectively collected data of patients who underwent treatment for sialolithiasis by the senior author during 2011 to 2013. Patient demographics, operative techniques, surgical findings, clinical outcomes, and complications were recorded. Additional information regarding symptoms and satisfaction with treatment was obtained via standardized telephone questionnaire at the time of the data analysis. Results. Thirty-one patients were treated for sialolithiasis. Sialoliths averaged 5.9 mm in size (range, 2-20 mm) and were comparable between both groups. Sixty-eight percent were in the submandibular gland (n = 21), with the remaining 32% in the parotid gland (n = 10). Fifty-two percent of patients (n = 16) were treated endoscopically with intracorporeal holmium laser lithotripsy, while the remaining 48% (n = 15) were treated with salivary endoscopy techniques other than laser lithotripsy. Successful stone removal without additional maneuvers occurred in 81% of the laser cases and 93% of the nonlaser group. Patients in the laser group reported an average improvement of symptoms of 95% compared with 90% of the nonlaser group when adjusted for outliers. Complications in all patients included ductal stenosis (n = 2) and salivary fistula (n = 1). Conclusion. The results of our series show favorable results with the use of intracorporeal holmium laser lithotripsy for the endoscopic management of sialolithiasis with minimal adverse events. [Phillips, James; Withrow, Kirk] Univ Alabama Birmingham, Dept Surg, Div Otolaryngol HNS, Birmingham, AL 35294 USA Withrow, K (reprint author), Univ Alabama Birmingham, Dept Surg, Div Otolaryngol HNS, BDB 563,1720 2nd Ave S, Birmingham, AL 35294 USA. kwithrow@uabmc.edu Arzoz E, 1996, J ORAL MAXIL SURG, V54, P847, DOI 10.1016/S0278-2391(96)90533-9; Chu DW, 2003, SURG ENDOSC, V17, P876, DOI 10.1007/s00464-002-8563-x; Goodhew Mark, 2001, New Zealand Dental Journal, V97, P19; Harrison John D, 2009, Otolaryngol Clin North Am, V42, P927, DOI 10.1016/j.otc.2009.08.012; IRO H, 1995, HNO, V43, P172; Luers JC, 2010, ARCH OTOLARYNGOL, V136, P762, DOI 10.1001/archoto.2010.109; Marchal F, 2003, ARCH OTOLARYNGOL, V129, P951, DOI 10.1001/archotol.129.9.951; McGurk M, 2005, BRIT J SURG, V92, P107, DOI 10.1002/bjs.4789; Papadaki ME, 2008, J ORAL MAXIL SURG, V66, P954, DOI 10.1016/j.joms.2008.01.017; Raif J, 2006, LASER SURG MED, V38, P580, DOI 10.1002/lsm.20344; Serbetci E, 2010, ANN OTO RHINOL LARYN, V119, P155; Walvekar RR, 2009, AM J OTOLARYNG, V30, P153, DOI 10.1016/j.amjoto.2008.03.007; Yu CQ, 2010, J ORAL MAXIL SURG, V68, P1770, DOI 10.1016/j.joms.2009.09.118 13 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0194-5998 1097-6817 OTOLARYNG HEAD NECK Otolaryngol. Head Neck Surg. JUN 2014 150 6 962 967 10.1177/0194599814524716 6 Otorhinolaryngology; Surgery Otorhinolaryngology; Surgery AN2YC WOS:000340451000012 J Pilet, J; Saito, H Pilet, Julien; Saito, Hideo Dynamic learning, retrieval, and tracking to augment hundreds of photographs VIRTUAL REALITY English Article Augmented reality; Multiple object tracking; Image retrieval Tracking is a major issue of virtual and augmented reality applications. Single object tracking on monocular video streams is fairly well understood. However, when it comes to multiple objects, existing methods lack scalability and can recognize only a limited number of objects. Thanks to recent progress in feature matching, state-of-the-art image retrieval techniques can deal with millions of images. However, these methods do not focus on real-time video processing and cannot track retrieved objects. In this paper, we present a method that combines the speed and accuracy of tracking with the scalability of image retrieval. At the heart of our approach is a bi-layer clustering process that allows our system to index and retrieve objects based on tracks of features, thereby effectively summarizing the information available on multiple video frames. Dynamic learning of new viewpoints as the camera moves naturally yields the kind of robustness and reliability expected from an augmented reality engine. As a result, our system is able to track in real-time multiple objects, recognized with low delay from a database of more than 300 entries. We released the source code of our system in a package called Polyora. [Pilet, Julien; Saito, Hideo] Keio Univ, Kohoku Ku, Yokohama, Kanagawa 2238522, Japan Pilet, J (reprint author), Keio Univ, Kohoku Ku, 3-14-1 Hiyoshi, Yokohama, Kanagawa 2238522, Japan. julien.pilet@gmail.com; saito@hvrl.ics.keio.ac.jp Baker S, 2004, INT J COMPUT VISION, V56, P221, DOI 10.1023/B:VISI.0000011205.11775.fd; Bay H, 2006, EUR C COMP VIS; C Wu, 2008, GPU IMPLEMENTATION D; Fiala M, 2005, PROC CVPR IEEE, P590; FISCHLER MA, 1981, COMMUN ACM, V24, P381, DOI 10.1145/358669.358692; Harris C, 1988, 4 ALV VIS C MANCH; Jegou H, 2008, LECT NOTES COMPUT SC, V5302, P304, DOI 10.1007/978-3-540-88682-2_24; Kato H, 2000, IEEE AND ACM INTERNATIONAL SYMPOSIUM ON AUGMENTED REALITY, PROCEEDING, P111; Lepetit Vincent, 2005, Foundations and Trends in Computer Graphics and Vision, V1, DOI 10.1561/0600000001; Lepetit V, 2004, C COMP VIS PATT REC; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Lucas B. D., 1981, P 7 INT JOINT C ART, V2, P674; Matas J., 2002, BRIT MACH VIS C, V1, P384; Nister D, 2006, EUR C COMP VIS GRAZ; Obdrzalek S., 2005, BRIT MACH VIS C; Ozuysal M, 2006, EUR C COMP VIS GRAZ; Ozuysal M, 2007, C COMP VIS PATT REC; Park Y, 2008, 7TH IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY 2008, PROCEEDINGS, P117; Philbin J, 2008, C COMP VIS PATT REC; Philbin J, 2007, C COMP VIS PATT REC; Rosten E, 2006, EUR C COMP VIS; Shi J, 1994, C COMP VIS PATT REC; Sivic J., 2003, P ICCV, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663]; Uchiyama H, 2009, 2009 8TH IEEE INTERNATIONAL SYMPOSIUM ON MIXED AND AUGMENTED REALITY, P95, DOI 10.1109/ISMAR.2009.5336491; Wagner D, 2009, INT S MIX AUGM REAL; Wagner D, 2008, INT S MIX AUGM REAL 26 0 0 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 1359-4338 1434-9957 VIRTUAL REAL-LONDON Virtual Real. JUN 2014 18 2 89 100 10.1007/s10055-013-0228-7 12 Computer Science, Interdisciplinary Applications; Computer Science, Software Engineering; Imaging Science & Photographic Technology Computer Science; Imaging Science & Photographic Technology AN5HV WOS:000340622300001 J Behrangi, A; Tian, YD; Lambrigtsen, BH; Stephens, GL Behrangi, Ali; Tian, Yudong; Lambrigtsen, Bjorn H.; Stephens, Graeme L. What does CloudSat reveal about global land precipitation detection by other spaceborne sensors? WATER RESOURCES RESEARCH English Article MICROWAVE SOUNDING UNIT; PASSIVE MICROWAVE; CLASSIFICATION-SYSTEM; TROPICAL RAINFALL; SATELLITE; RADAR; ALGORITHM; STATES; WATER; INFORMATION Current orbital land precipitation products have serious shortcomings in detecting light rain and snowfall, the most frequent types of global precipitation. The missed precipitation is then propagated into the merged precipitation products that are widely used. Precipitation characteristics such as frequency and intensity and their regional distribution are expected to change in a warming climate. It is important to accurately capture those characteristics to understand and model the current state of the Earth's climate and predict future changes. In this work, the precipitation detection performance of a suite of precipitation sensors, commonly used in generating the merged precipitation products, are investigated. The high sensitivity of CloudSat Cloud Profiling Radar (CPR) to liquid and frozen hydrometeors enables superior estimates of light rainfall and snowfall within 80 degrees S-80 degrees N. Three years (2007-2009) of CloudSat precipitation data were collected to construct a climatology reference for guiding our analysis. In addition, auxiliary data such as infrared brightness temperature, surface air temperature, and cloud types were used for a more detailed assessment. The analysis shows that no more than 50% of the tropical (40 degrees S-40 degrees N) precipitation occurrence is captured by the current suite of precipitation measuring sensors. Poleward of 50 degrees latitude, a combination of various factors such as an abundance of light rainfall, snowfall, shallow precipitation-bearing clouds, and frozen surfaces reduces the space-based precipitation detection rate to less than 20%. This shows that for a better understanding of precipitation from space, especially at higher latitudes, there is a critical need to improve current precipitation retrieval techniques and sensors. [Behrangi, Ali; Lambrigtsen, Bjorn H.; Stephens, Graeme L.] CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA; [Tian, Yudong] Univ Maryland, Earth Syst Sci Interdisciplinary Ctr, College Pk, MD 20742 USA; [Tian, Yudong] NASA, Goddard Space Flight Ctr, Hydrol Sci Lab, Greenbelt, MD 20771 USA Behrangi, A (reprint author), CALTECH, Jet Prop Lab, Pasadena, CA 91125 USA. Ali.Behrangi@jpl.nasa.gov NASA's Weather program We acknowledge support from NASA's Weather program through Dr. Ramesh Kakar. The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship is acknowledged. Adler R., 2004, AMSR E AQUA L2B GLOB; Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2; AghaKouchak A, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD014741; Behrangi A, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2012JD017979; Behrangi A, 2010, J APPL METEOROL CLIM, V49, P1015, DOI 10.1175/2009JAMC2291.1; Behrangi A, 2011, J HYDROL, V397, P225, DOI 10.1016/j.jhydrol.2010.11.043; Behrangi A, 2009, J HYDROMETEOROL, V10, P684, DOI 10.1175/2009JHM1077.1; Behrangi A, 2009, J HYDROMETEOROL, V10, P1414, DOI 10.1175/2009JHM1139.1; Behrangi A, 2010, J HYDROMETEOROL, V11, P1305, DOI 10.1175/2010JHM1248.1; Berg W, 2006, J APPL METEOROL CLIM, V45, P434, DOI 10.1175/JAM2331.1; Berg W, 2010, J APPL METEOROL CLIM, V49, P535, DOI 10.1175/2009JAMC2330.1; Bitew MM, 2011, WATER RESOUR RES, V47, DOI 10.1029/2010WR009917; Boushaki FI, 2009, J HYDROMETEOROL, V10, P1231, DOI 10.1175/2009JHM1099.1; Cao Q., 2013, 36 C RAD MET 16 20 S; Dinku T, 2010, J APPL METEOROL CLIM, V49, P1322, DOI 10.1175/2010JAMC2281.1; Ebert EE, 2007, B AM METEOROL SOC, V88, P47, DOI 10.1175/BAMS-88-1-47; Ellis TD, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2008GL036728; Ferraro R., 1994, REMOTE SENS REV, V11, P195; Ferraro RR, 1997, J GEOPHYS RES-ATMOS, V102, P16715, DOI 10.1029/97JD01210; Ferraro RR, 2000, GEOPHYS RES LETT, V27, P2669, DOI 10.1029/2000GL011665; Gopalan K, 2010, J ATMOS OCEAN TECH, V27, P1343, DOI 10.1175/2010JTECHA1454.l; Gourley J. J., 2009, J APPL METEOROL CLIM, V49, P437; Grody N, 2001, J GEOPHYS RES-ATMOS, V106, P2943, DOI 10.1029/2000JD900616; Haynes JM, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD009973; Held IM, 2000, ANNU REV ENERG ENV, V25, P441, DOI 10.1146/annurev.energy.25.1.441; Hong Y, 2004, J APPL METEOROL, V43, P1834, DOI 10.1175/JAM2173.1; Hong Y, 2007, WATER RESOUR RES, V43, DOI 10.1029/2006WR005739; Hsu KL, 1997, J APPL METEOROL, V36, P1176, DOI 10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2; Huffman GJ, 2007, J HYDROMETEOROL, V8, P38, DOI 10.1175/JHM560.1; Iguchi T., 2011, INT GEOSC REM SENS S; Joyce RJ, 2011, J HYDROMETEOROL, V12, P1547, DOI 10.1175/JHM-D-11-022.1; Joyce RJ, 2004, J HYDROMETEOROL, V5, P487, DOI 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2; Kidd C, 2003, J HYDROMETEOROL, V4, P1088, DOI 10.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2; Kirstetter PE, 2012, J HYDROMETEOROL, V13, P1285, DOI 10.1175/JHM-D-11-0139.1; Kongoli C, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2003GL017177; Kucera PA, 2013, B AM METEOROL SOC, V94, P365, DOI 10.1175/BAMS-D-11-00171.1; Kuligowski RJ, 2002, J HYDROMETEOROL, V3, P112, DOI 10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2; Kummerow C, 1998, J ATMOS OCEAN TECH, V15, P809, DOI 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2; Kursinski AL, 2008, J HYDROMETEOROL, V9, P3, DOI 10.1175/2007JHM856.1; Lebsock MD, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD016076; Lin X, 2012, J CLIMATE, V25, P1901, DOI 10.1175/JCLI-D-11-00151.1; Liu CT, 2009, J CLIMATE, V22, P767, DOI 10.1175/2008JCLI2641.1; Liu GS, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009766; Nikolopoulos EI, 2010, J HYDROMETEOROL, V11, P520, DOI 10.1175/2009JHM1169.1; SCHAEFER JT, 1990, WEATHER FORECAST, V5, P570, DOI 10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2; Short DA, 2000, J CLIMATE, V13, P4107, DOI 10.1175/1520-0442(2000)013<4107:TROOSP>2.0.CO;2; Smalley M., 2013, J HYDROMETEOROL, V15, P444; Sorooshian S, 2000, B AM METEOROL SOC, V81, P2035, DOI 10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2; Stephens GL, 2008, J CLIMATE, V21, P6141, DOI 10.1175/2008JCLI2144.1; Stephens GL, 2012, NAT GEOSCI, V5, P691, DOI [10.1038/ngeo1580, 10.1038/NGEO1580]; Stephens GL, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD014532; Stephens GL, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD009982; Surussavadee C., 2010, INT GEOSC REM SENS S; Tanelli S, 2008, IEEE T GEOSCI REMOTE, V46, P3560, DOI 10.1109/TGRS.2008.2002030; Tang L, 2012, ATMOS RES, V104, P182, DOI 10.1016/j.atmosres.2011.10.006; Tesfagiorgis K, 2011, HYDROL EARTH SYST SC, V15, P2631, DOI 10.5194/hess-15-2631-2011; Thiemig V, 2013, J HYDROL, V499, P324, DOI 10.1016/j.jhydrol.2013.07.012; Tian Y, 2007, J HYDROMETEOROL, V8, P1165, DOI 10.1175/2007JHM859.1; Tian YD, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD011949; Tian YD, 2013, GEOPHYS RES LETT, V40, P2060, DOI 10.1002/grl.50320; Tian YD, 2010, J HYDROMETEOROL, V11, P1275, DOI 10.1175/2010JHM1246.1; Trenberth KE, 2007, J HYDROMETEOROL, V8, P758, DOI 10.1175/JHM600.1; Trenberth KE, 2003, B AM METEOROL SOC, V84, P1205, DOI 10.1175/BAMS-84-9-1205; Turk F. J., 2000, P 2000 EUMETSAT MET, V2, P705; Vila D, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008617; WILHEIT TT, 1986, B AM METEOROL SOC, V67, P1226, DOI 10.1175/1520-0477(1986)067<1226:SCOPMM>2.0.CO;2; Wilks D. S., 2011, STAT METHODS ATMOSPH 67 1 1 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 0043-1397 1944-7973 WATER RESOUR RES Water Resour. Res. JUN 2014 50 6 4893 4905 10.1002/2013WR014566 13 Environmental Sciences; Limnology; Water Resources Environmental Sciences & Ecology; Marine & Freshwater Biology; Water Resources AN2QM WOS:000340430400020 J Gavel, Y; Andersson, PO Gavel, Ylva; Andersson, Per-Olov Multilingual query expansion in the SveMed plus bibliographic database: A case study JOURNAL OF INFORMATION SCIENCE English Article Bibliographic databases; multilingual retrieval; query expansion; thesauri MEDICAL SUBJECT-HEADINGS; NATIONAL-LIBRARY; MESH; MEDLINE; RETRIEVAL; SYSTEM; THESAURUS; LANGUAGE; SEARCH; IMPLEMENTATION SveMed+ is a bibliographic database covering Scandinavian medical journals. It is produced by the University Library of Karolinska Institutet in Sweden. The bibliographic references are indexed with terms from the Medical Subject Headings (MeSH) thesaurus. The MeSH has been translated into several languages, including Swedish, making it suitable as the basis for multilingual tools in the medical field. The data structure of SveMed+ closely mimics that of PubMed/MEDLINE. Users of PubMed/MEDLINE and similar databases typically expect retrieval features that are not readily available off-the-shelf. The SveMed+ interface is based on a free text search engine (Solr) and a relational database management system (Microsoft SQL Server) containing the bibliographic database and a multilingual thesaurus database. The thesaurus database contains medical terms in three different languages and information about relationships between the terms. A combined approach involving the Solr free text index, the bibliographic database and the thesaurus database allowed the implementation of functionality such as automatic multilingual query expansion, faceting and hierarchical explode searches. The present paper describes how this was done in practice. [Gavel, Ylva; Andersson, Per-Olov] Karolinska Inst, Univ Lib, S-17177 Stockholm, Sweden Gavel, Y (reprint author), Karolinska Inst, Univ Lib, Fe 200, S-17177 Stockholm, Sweden. Ylva.Gavel@ki.se Almling M, 1992, MIC GLIMPSE; [Anonymous], 1973, MEDLARS 2 STAFF OP M; BECKELHIMER MA, 1978, MED INFORM, V3, P197; Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061; CAIN AM, 1969, B MED LIBR ASSOC, V57, P250; Canese K, 2002, NCBI HDB; Coletti MH, 2001, J AM MED INFORM ASSN, V8, P317; CORNING ME, 1972, INFORM STORAGE RET, V8, P255, DOI 10.1016/0020-0271(72)90016-2; Date CJ, 1995, INTRO DATABASE SYSTE; Dee CR, 2007, J MED LIBR ASSOC, V95, P416, DOI 10.3163/1536-5050.95.4.416; Doszkocs TE., 1983, RES DEV INFORM RETRI, P251; Douyère Magaly, 2004, Health Info Libr J, V21, P253, DOI 10.1111/j.1471-1842.2004.00526.x; Gault LV, 2002, J MED LIBR ASSOC, V90, P173; Gavel Y, 2006, HEALTH INFO LIBR J, V23, P169, DOI 10.1111/j.1471-1842.2006.00669.x; Griffon N, 2012, BMC MED INFORM DECIS, V12, DOI 10.1186/1472-6947-12-12; Haglund L., 2010, J EUROPEAN ASS HLTH, V6, P21; Harman D, 1992, INFORMATION RETRIEVA, P28; Hersh WR, 2009, INFORM RETRIEVAL HLT, P199; Hersh WR, 2009, INFORM RETRIEVAL HLT, P159; HUMPHREY SM, 1984, J AM SOC INFORM SCI, V35, P34, DOI 10.1002/asi.4630350106; Kellerman FR, 1997, INTRO HLTH SCI LIBRA, P57; KENTON C, 1978, MED INFORM, V3, P225; Knecht LWS, 2002, J MED LIBR ASSOC, V90, P475; Knutsson G, 2010, J EUROPEAN ASS HLTH, V6, P48; Lin J, 2007, BMC BIOINFORMATICS, V8, DOI 10.1186/1471-2105-8-423; Lipscomb CE, 2000, B MED LIBR ASSOC, V88, P265; Liu Fang, 2006, AMIA Annu Symp Proc, P1012; LOWE HJ, 1994, JAMA-J AM MED ASSOC, V271, P1103, DOI 10.1001/jama.271.14.1103; Lu ZY, 2009, INFORM RETRIEVAL, V12, P69, DOI 10.1007/s10791-008-9074-8; MCCARN DB, 1980, J AM SOC INFORM SCI, V31, P181, DOI 10.1002/asi.4630310310; McGregor B, 2002, J MED LIBR ASSOC, V90, P339; Nahin AM, 2008, NLM TECHNICAL B, pe10; Nelson SJ, 2001, INFO SCI KNOW MANAGE, V2, P171; Nelson SJ, 2004, ST HEAL T, V107, P67; NLM, 2013, PUBMED TUT AUT TERM; NLM, 2013, PUBMED WORKS AUT TER; Oliver DE, 2004, BMC BIOINFORMATICS, V5, DOI 10.1186/1471-2105-5-146; Ostell J., 2005, ACM Queue, V3, DOI 10.1145/1059791.1059806; Othman R, 2004, ONLINE INFORM REV, V28, P200, DOI 10.1108/14684520410543643; Schoonbaert D, 1997, B MED LIBR ASSOC, V85, P439; SCHULTHEISZ RJ, 1978, J AM SOC INFORM SCI, V29, P173, DOI 10.1002/asi.4630290404; SEWELL W, 1964, B MED LIBR ASSOC, V52, P164; Shiri A, 2006, J AM SOC INF SCI TEC, V57, P462, DOI 10.1002/asi.20319; Shiri AA, 2002, J INFORM SCI, V28, P111, DOI 10.1177/0165551024234011; Smiley D, 2009, SOLR 1 4 ENTERPRISE; Srinivasan P, 1996, INFORM PROCESS MANAG, V32, P431, DOI 10.1016/0306-4573(95)00076-3; Thirion B, 2009, MED INFORM UNITED HL; Wiesman F, 1997, INT J MED INFORM, V47, P5, DOI 10.1016/S1386-5056(97)00094-4; Williams S, 2013, CODE4LIB, P20; Zobel J, 2006, ACM COMPUTING SURVEY, V38 50 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. JUN 2014 40 3 269 280 10.1177/0165551514524685 12 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UG WOS:000340152500001 J Ren, FL Ren, Feiliang Learning time-sensitive domain ontology from scientific papers with a hybrid learning method JOURNAL OF INFORMATION SCIENCE English Article domain ontology; life cycle division; relation assignment; time sensitive SEMANTIC WEB; CONSTRUCTION; DOCUMENTS; TEXT; KNOWLEDGE; FRAMEWORK; EXTRACTION; RETRIEVAL Large numbers of available scientific papers makes the research of ontology construction an attractive application area. However, there are two shortcomings for most current ontology construction approaches. First, implicit time properties of domain concepts are rarely taken into account in current approaches. Second, current automatic concept relation extraction methods mainly rely on the local context information that surrounds current considered concepts. These two problems prevent most current ontology construction methods from being employed to their full potential. To tackle these problems, we propose a hybrid learning method to integrate concepts' global information and human experts' knowledge together into ontology construction, among which concepts' temporal attributes are taken into account. Our method first divides each concept into four time periods according to their attribution distribution on a time axis. Then global time-related attributions are collected for each concept. Finally, concept relations are extracted with a hybrid learning method. We evaluated our method by testing it on Chinese academic papers. It outperformed a baseline system based on only hierarchical concept relations, showing the effectiveness of our approach. Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China Ren, FL (reprint author), Northeastern Univ, Shenyang 110819, Liaoning, Peoples R China. renfeiliang@ise.neu.edu.cn Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China [61003159, 61100089, 61073140, 61272376] This paper is supported by the 'Fundamental Research Funds for the Central Universities' and the National Natural Science Foundation of China (grant nos 61003159, 61100089, 61073140 and 61272376). Boonchom VS, 2012, J INF SCI, V38, P37, DOI 10.1177/0165551511426249; Brijesh B, 2012, P 10 WORKSH AS LANG, P75; Brusa G, 2008, EXPERT SYST, V25, P484, DOI 10.1111/j.1468-0394.2008.00471.x; Buitelaar P, 2004, LECT NOTES COMPUT SC, V3053, P31; Chen RC, 2008, EXPERT SYST APPL, V34, P488, DOI 10.1016/j.eswa.2006.09.012; Cimiano P, 2005, LECT NOTES COMPUT SC, V3513, P227; CORTES C, 1995, MACH LEARN, V20, P273, DOI 10.1023/A:1022627411411; Dahab MY, 2008, EXPERT SYST APPL, V34, P1474, DOI 10.1016/j.eswa.2007.01.043; Estival D, 2004, P 4 WORKSH NLP XML N, P59, DOI 10.3115/1621066.1621075; He TT, 2006, PACLIC 20: Proceedings of the 20th Pacific Asia Conference on Language, Information and Computation, P150; Hou X, 2011, EXPERT SYST APPL, V38, P11958, DOI 10.1016/j.eswa.2011.03.090; Hsieh SH, 2011, ADV ENG INFORM, V25, P288, DOI 10.1016/j.aei.2010.08.004; Huang CR, 2008, PACLIC 22: PROCEEDINGS OF THE 22ND PACIFIC ASIA CONFERENCE ON LANGUAGE, INFORMATION AND COMPUTATION, P189; Huang CR, 2004, PACLIC 18: Proceedings of the 18th Pacific Asia Conference on Language, Information and Computation, P17; Jiang X, 2010, J AM SOC INF SCI TEC, V61, P150, DOI 10.1002/asi.21231; Kang XP, 2012, KNOWL-BASED SYST, V27, P152, DOI 10.1016/j.knosys.2011.09.016; Khoo CSG, 2006, ANNU REV INFORM SCI, V40, P157, DOI 10.1002/aris.1440400112; Kietz JU, 2000, P CONLL 2000 LLL 200, P167; Lee CS, 2007, DATA KNOWL ENG, V60, P547, DOI 10.1016/j.datak.2006.04.001; Legg C, 2007, ANNU REV INFORM SCI, V41, P407, DOI 10.1002/aris.2007.1440410116; Lumsden J, 2011, J INF SCI, V37, P246, DOI 10.1177/0165551511401804; Maedche A, 2001, IEEE INTELL SYST APP, V16, P72, DOI 10.1109/5254.920602; Meng XX, 2012, J INTEGR AGR, V11, P808, DOI 10.1016/S2095-3119(12)60071-9; Navigli R, 2004, COMPUT LINGUIST, V30, P151, DOI 10.1162/089120104323093276; Navigli R, 2003, IEEE INTELL SYST, V18, P22, DOI 10.1109/MIS.2003.1179190; Ochoa JL, 2013, EXPERT SYST APPL, V40, P2058, DOI 10.1016/j.eswa.2012.10.017; Ren FL, 2012, J INFORM COMPUTATION, V9, P5823; Ren FL, 2012, P COLING 2012 DEM, P369; Ruiz-Martinez JM, 2011, EXPERT SYST APPL, V38, P12365, DOI 10.1016/j.eswa.2011.04.016; Sabou M, 2005, J WEB SEMANT, V3, P340, DOI 10.1016/j.websem.2005.09.008; Salem S, 2010, P 23 INT C COMP LING, P967; Sanchez D, 2008, DATA KNOWL ENG, V64, P600, DOI 10.1016/j.datak.2007.10.001; Sanchez D, 2012, EXPERT SYST APPL, V39, P5792, DOI 10.1016/j.eswa.2011.11.088; Shamsfard M, 2004, INT J HUM-COMPUT ST, V60, P17, DOI [10.1016/j.ijhcs.2003.08.001, 10.1016/jijhcs.2003.08.001]; Shih CW, 2011, EXPERT SYST APPL, V38, P7544, DOI 10.1016/j.eswa.2010.12.112; Tan H, 2009, P WORKSH BIONLP, P55, DOI 10.3115/1572364.1572372; Tian F, 2010, IEEJ T ELECTR ELECTR, V5, P188, DOI 10.1002/tee.20516; Trapman J, 2009, P INT C REC ADV NAT, P455; Valencia-Garcia R, 2008, EXPERT SYST, V25, P314, DOI 10.1111/j.1468-0394.2008.00464.x; Vela M, 2009, P 8 INT C COMP SEM, P346, DOI 10.3115/1693756.1693801; Villaverde J, 2009, EXPERT SYST APPL, V36, P10288, DOI 10.1016/j.eswa.2009.01.048; Wei YY, 2012, J INTEGR AGR, V11, P775, DOI 10.1016/S2095-3119(12)60067-7; Yang Y, 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026; Zou Q, 2011, J INF SCI, V37, P332, DOI 10.1177/0165551511406063; Zouaq A, 2011, INFORM SYST, V36, P1064, DOI 10.1016/j.is.2011.03.005 45 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. JUN 2014 40 3 329 345 10.1177/0165551514521927 17 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UG WOS:000340152500005 J Yaseen, Q; Hmeidi, I Yaseen, Qussai; Hmeidi, Ismail Extracting the roots of Arabic words without removing affixes JOURNAL OF INFORMATION SCIENCE English Article Arabic roots; information retrieval; search engines; stemming Most research in Arabic roots extraction focuses on removing affixes from Arabic words. This process adds processing overhead and may remove non-affix letters, which leads to the extraction of incorrect roots. This paper advises a new approach to dealing with this issue by introducing a new algorithm for extracting Arabic words' roots. The proposed algorithm, which is called the Word Substring Stemming Algorithm, does not remove affixes during the extraction process. Rather, it is based on producing the set of all substrings of an Arabic word, and uses the Arabic roots file, the Arabic patterns file and a concrete set of rules to extract correct roots from substrings. The experiments have shown that the proposed approach is competitive and its accuracy is 83.9%, Furthermore, its accuracy can be enhanced more in the sense that, for about 9.9% of the tested words, the WSS algorithm retrieves two candidates (in most cases) for the correct root. [Yaseen, Qussai] Yarmouk Univ, Irbid, Jordan; [Hmeidi, Ismail] Jordan Univ Sci & Technol, Irbid, Jordan Yaseen, Q (reprint author), Yarmouk Univ, Dept Comp Sci, Irbid, Jordan. qyaseen@yu.edu.jo Al-Ameed H., 2006, THESIS; Al-Fedaghi S, 1989, THE 11TH NATIONAL CO; Al-Kabi M, 2006, THE INTERNATIONAL AR; Al-Kabi MN, 2011, J INF SCI, V37, P111, DOI 10.1177/0165551510392305; Al-Nashashibi M, 2010, THE 2ND INTERNATIONA; Al-Sarhan H, 2003, THE 2003 ARAB CONFER; Al-Shammari E, 2008, THE 2ND ACM WORKSHOP; Al-Sughaiyer IA, 2004, J AM SOC INF SCI TEC, V55, P189, DOI 10.1002/asi.10368; Beesley K., 1998, THE 6TH INTERNATIONA; Boudlal A, 2011, INT ARAB J INF TECHN, V8, P91; Chen A, 2002, TREC 2002; Chowdhury A, 2002, IIT AT TREC 2002; De Roeck A, 2000, THE 38TH ANNUAL MEET; Duwairi R., 2007, THE INTERNATIONAL AR, V4, P125; Ghawanmeh S, 2005, THE 5TH INTERNATIONA; Harmanani H, 2006, THE INTERNATIONAL AR, V3, P265; Hmeidi II, 2010, J AM SOC INF SCI TEC, V61, P583, DOI 10.1002/asi.21247; Kadri Y, 2006, THE CHALLENGE OF ARA; Khoja S, 2008, STEMMING ARABIC TEXT; Mayfield J, 2001, TREC 2001; Momani M, 2007, THE IEEE ACS INTERNA; Rogati M, 2003, THE 41ST ANNUAL MEET; Taghva K, 2005, THE INTERNATIONAL CO 23 0 0 SAGE PUBLICATIONS LTD LONDON 1 OLIVERS YARD, 55 CITY ROAD, LONDON EC1Y 1SP, ENGLAND 0165-5515 1741-6485 J INF SCI J. Inf. Sci. JUN 2014 40 3 376 385 10.1177/0165551514526348 10 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AM8UG WOS:000340152500009 J Rowbottom, DP Rowbottom, Darrell P. INFORMATION VERSUS KNOWLEDGE IN CONFIRMATION THEORY LOGIQUE ET ANALYSE English Article SCIENTIFIC PROGRESS; SEMANTIC INFORMATION; N-RAYS; PROBABILITY; RETRIEVAL; VIEW I argue that so-called 'background knowledge' in confirmation theory has little, if anything, to do with 'knowledge' in the sense of mainstream epistemology. I argue that it is better construed as 'background information', which need not be believed in, justified, or true. Lingnan Univ, Dept Philosophy, Hong Kong, Hong Kong, Peoples R China Rowbottom, DP (reprint author), Lingnan Univ, Dept Philosophy, Hong Kong, Hong Kong, Peoples R China. darrellrowbottom@ln.edu.hk Allo P, 2011, PHILOS STUD, V153, P417, DOI 10.1007/s11098-010-9516-1; Allo P, 2010, METAPHILOSOPHY, V41, P247; AUDI R, 1994, NOUS, V28, P419, DOI 10.2307/2215473; BARTLEY W. W., 1984, RETREAT COMMITMENT; BAUER H. H., 2002, HYLE, V8, P5; Bird A, 2007, NOUS, V41, P64, DOI 10.1111/j.1468-0068.2007.00638.x; Bird A, 2008, STUD HIST PHILOS SCI, V39, P279, DOI 10.1016/j.shpsa.2008.03.019; Carnap R., 1962, LOGICAL FDN PROBABIL; CHISHOLM R., 1977, THEORY KNOWLEDGE; COLLINS A., 1987, CULTURAL MODELS LANG, P243; de Finetti B., 2008, PHILOS LECT PROBABIL; DE FINETTI B., 1974, THEORY PROBABILITY C, V1; DE PRICE D.S., 1975, SCI BABYLON; Dunn JM, 2008, HBK PHILOS SCI, V8, P581, DOI 10.1016/B978-0-444-51726-5.50019-4; Eells E, 2000, J PHILOS, V97, P663, DOI 10.2307/2678462; Esposito S, 2008, PHYSICA A, V387, P757, DOI 10.1016/j.physa.2007.10.029; Floridi L, 2005, PHILOS PHENOMEN RES, V70, P351, DOI 10.1111/j.1933-1592.2005.tb00531.x; FLORIDI L., 1996, SCEPTICISM FDN EPIST; Floridi L, 2004, MIND MACH, V14, P197, DOI 10.1023/B:MIND.0000021684.50925.c9; Gettier Edmund L., 1963, ANALYSIS, V23, P121, DOI 10.2307/3326922; GILLIES D, 1991, BRIT J PHILOS SCI, V42, P513, DOI 10.1093/bjps/42.4.513; Huber F, 2008, PHILOS SCI, V75, P413; HUBER F., 2007, INTERNET ENCY PHILOS; JACOBY LL, 1989, J PERS SOC PSYCHOL, V56, P326, DOI 10.1037//0022-3514.56.3.326; KELLEY CM, 1993, J MEM LANG, V32, P1, DOI 10.1006/jmla.1993.1001; KEYNES J. M., 1921, TREATISE PROBABILITY; LAGEMANN RT, 1977, AM J PHYS, V45, P281, DOI 10.1119/1.10643; LANGMUIR I., 1985, SPECULATIONS SCI TEC, V8, P77; Milne P, 1996, PHILOS SCI, V63, P21, DOI 10.1086/289891; MPEMBA E. B., 1969, PHYS EDUC, V4, P172, DOI 10.1088/0031-9120/4/3/312; NYE M. J., 1980, HIST STUD PHYS BIOL, V11, P125; POPPER K. R., 1968, LOGIC METHODOLOGY PH, P333; Popper K. R., 1972, OBJECTIVE KNOWLEDGE; Popper KR, 1983, REALISM AIM SCI; ROEDIGER HL, 1995, J EXP PSYCHOL LEARN, V21, P803, DOI 10.1037/0278-7393.21.4.803; Rowbottom DP, 2008, STUD HIST PHILOS SCI, V39, P277, DOI 10.1016/j.shpsa.2008.03.01; ROWBOTTOM D. P., 2012, METASCIENCE, V21, P193; Rowbottom DP, 2013, PAC PHILOS QUART, V94, P188, DOI 10.1111/j.1468-0114.2012.01451.x; Rowbottom DP, 2011, PHILOS SCI, V78, P1200, DOI 10.1086/662267; Rowbottom DP, 2010, INT STUD PHILOS SCI, V24, P241, DOI 10.1080/02698595.2010.522407; Rowbottom DP, 2008, STUD HIST PHILOS SCI, V39, P124, DOI 10.1016/j.shpsa.2007.11.010; SALMON W. C., 1990, SCI THEORIES, P175; SARTWELL C, 1992, J PHILOS, V89, P167, DOI 10.2307/2026639; SCHACTER DL, 1984, J VERB LEARN VERB BE, V23, P593, DOI 10.1016/S0022-5371(84)90373-6; STEUP M., 2006, STANFORD ENCY PHILOS; WILLIAMSON J. O. D., 2010, DEFENCE OBJECTIVE BA; Williamson T., 2000, KNOWLEDGE ITS LIMITS 47 0 0 CENTRE NATIONAL BELGE RECHERCHES LOGIQUE BRUSSELS VAKGROEP WIJSBEGEERTE, VUB, PLEINLAAN 2, BRUSSELS, B-1050, BELGIUM 0024-5836 LOG ANAL Log. Anal. JUN 2014 226 137 149 10.2143/LEA.226.0.3032652 13 Logic; Philosophy Science & Technology - Other Topics; Philosophy AM7VS WOS:000340077400003 J Van Laere, O; Schockaert, S; Tanasescu, V; Dhoedt, B; Jones, CB Van Laere, Olivier; Schockaert, Steven; Tanasescu, Vlad; Dhoedt, Bart; Jones, Christopher B. Georeferencing Wikipedia Documents Using Data from Social Media Sources ACM TRANSACTIONS ON INFORMATION SYSTEMS English Article Experimentation; Measurement; Geographic information retrieval; language models; semistructured Data Social media sources such as Flickr and Twitter continuously generate large amounts of textual information (tags on Flickr and short messages on Twitter). This textual information is increasingly linked to geographical coordinates, which makes it possible to learn how people refer to places by identifying correlations between the occurrence of terms and the locations of the corresponding social media objects. Recent work has focused on how this potentially rich source of geographic information can be used to estimate geographic coordinates for previously unseen Flickr photos or Twitter messages. In this article, we extend this work by analysing to what extent probabilistic language models trained on Flickr and Twitter can be used to assign coordinates to Wikipedia articles. Our results show that exploiting these language models substantially outperforms both (i) classical gazetteer-based methods (in particular, using Yahoo! Placemaker and Geonames) and (ii) language modelling approaches trained on Wikipedia alone. This supports the hypothesis that social media are important sources of geographic information, Which are valuable beyond the scope of individual applications. [Van Laere, Olivier] Yahoo Labs, Barcelona, Spain; [Dhoedt, Bart] Univ Ghent, iMinds, Dept Informat Technol, B-9000 Ghent, Belgium; [Schockaert, Steven; Tanasescu, Vlad; Jones, Christopher B.] Cardiff Univ, Sch Comp Sci & Informat, Cardiff CF10 3AX, S Glam, Wales Van Laere, O (reprint author), Yahoo Labs, Barcelona, Spain. vanlaere@yahoo-inc.com; S.Schockaert@cs.cardiff.ac.uk; V.Tanasescu@cs.cardiff.ac.uk; Bart.Dhoedt@intec.ugent.be; C.B.Jones@cs.cardiff.ac.uk Amitay E., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009040; Chen YR, 2006, MATER SCI FORUM, V505-507, P277, DOI 10.1145/1142473.1142505; Cheng Z, 2010, P 19 ACM INT C INF K, P759, DOI 10.1145/1871437.1871535; Crandall D. J., 2009, P 18 INT C WORLD WID, P761, DOI DOI 10.1145/1526709.1526812; De Rouck C., 2011, P TERR COGN 2011 WOR, P3; Eisenstein J, 2010, P 2010 C EMP METH NA, P1277; Hauff Claudia, 2012, Proceedings of the 35th Annual International ACM SIGIR Conference on Research & Development in Information Retrieval (SIGIR 2012), DOI 10.1145/2348283.2348376; Hauff C., WORK NOT MEDIAEVAL W; Hays J. H., 2008, P IEEE C COMP VIS PA, P1; Jones CB, 2008, INT J GEOGR INF SCI, V22, P1045, DOI [10.1080/13658810701850547, 10.1080/00207720802085245]; Kinsella S., 2011, P 3 INT WORKSH SEARC, P61, DOI DOI 10.1145/2065023.2065039; Krippner F., WORK NOT MEDIAEVAL W; Leidner J. L., 2007, THESIS U EDINBURGH; Lieberman M. D., 2010, P 6 WORKSH GEOGR INF; Manguinhas H., 2008, P 3 IEEE INT C DIG I, P146; Popescu A., 2008, P 8 ACM IEEE CS JOIN, P85, DOI 10.1145/1378889.1378906; Purves R., 2011, SIGSPATIAL SPECIAL, V3, P2; Purves RS, 2007, INT J GEOGR INF SCI, V21, P717, DOI 10.1080/13658810601169840; Rae A., 2012, WORK NOT MEDIAEVAL W; Rattenbury T., 2007, P 30 ANN INT ACM SIG, P103, DOI 10.1145/1277741.1277762; Rattenbury T, 2009, ACM T WEB, V3, DOI 10.1145/1462148.1462149; Roller S., 2012, P 2012 JOINT C EMP M, P1500; Serdyukov P, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P484, DOI 10.1145/1571941.1572025; Smart PD, 2010, LECT NOTES COMPUT SC, V6292, P234, DOI 10.1007/978-3-642-15300-6_17; Tobin R., 2010, P 6 WORKSH GEOGR INF, V7, P1; Twaroch F. A., 2008, P 2 INT WORKSH GEOGR, P43, DOI 10.1145/1460007.1460017; Van Laere O, 2011, P 1 ACM INT C MULT R, V48, P1; Van Laere O, 2013, INFORM SCIENCES, V238, P52, DOI 10.1016/j.ins.2013.02.045; Van Laere O, 2012, J WEB SEMANTICS; Weinberger K. Q., 2008, P ACM MULT, P111, DOI 10.1145/1459359.1459375; Wing B. P., 2011, P 49 ANN M ASS COMP, P955; Zhai C., 2001, P 24 ANN INT ACM SIG, P334, DOI DOI 10.1145/383952.384019 32 0 0 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 1046-8188 1558-2868 ACM T INFORM SYST ACM Trans. Inf. Syst. JUN 2014 32 3 12 10.1145/2629685 32 Computer Science, Information Systems Computer Science AL9NI WOS:000339467600003 J Cesario, J; Jonas, KJ Cesario, Joseph; Jonas, Kai J. REPLICABILITY AND MODELS OF PRIMING: WHAT A RESOURCE COMPUTATION FRAMEWORK CAN TELL US ABOUT EXPECTATIONS OF REPLICABILITY SOCIAL COGNITION English Article SOCIAL-BEHAVIOR; SPREADING ACTIVATION; NUMERICAL ASSESSMENT; AUTOMATICITY; PERCEPTION; RACE; CONFIDENCE; PSYCHOLOGY; RETRIEVAL; ATTENTION In this article, we argue that whether or not a replication attempt is informative is dependent on the accuracy of one's underlying model to explain the effect, as it is the explanatory model that enumerates the contingencies necessary for producing the effect. If the model is incorrect, then a researcher may unknowingly change variables that the model says are irrelevant but which are really essential, rendering the replication results ambiguous. The expectation that effects of priming on social behavior should be widely invariant makes sense only under the assumptions of strict direct expression and spreading activation models, yet it has been shown that these models cannot adequately explain findings from the priming literature. We describe one model of priming that predicts variability across experimental contexts and populations: the resource computation model. We highlight variables that have been uncovered under the assumptions of this model that cannot be accounted for by direct expression models and which can explain replication failures. The model is also consistent with evolutionary understandings of the mind, in which information from multiple sources beyond just stimulus information is incorporated into behavioral decisions. To the degree that anything other than a strict, direct expression, spreading activation model is correct, the expectation that priming of social behaviors should be widely invariant is unreasonable. [Cesario, Joseph] Michigan State Univ, E Lansing, MI 48824 USA; [Jonas, Kai J.] Univ Amsterdam, NL-1012 WX Amsterdam, Netherlands Cesario, J (reprint author), Psychol Bldg,316 Phys Rd Room 255, E Lansing, MI 48824 USA. cesario@msu.edu; k.j.jonas@uva.nl Amodio DM, 2006, J PERS SOC PSYCHOL, V91, P652, DOI 10.1037/0022-3514.91.4.652; Asendorpf JB, 2013, EUR J PERSONALITY, V27, P108, DOI 10.1002/per.1919; Bargh JA, 1999, AM PSYCHOL, V54, P462, DOI 10.1037/0003-066X.54.7.462; Bargh JA, 1996, J PERS SOC PSYCHOL, V71, P230, DOI 10.1037//0022-3514.71.2.230; Benson-Amram S, 2011, ANIM BEHAV, V82, P743, DOI 10.1016/j.anbehav.2011.07.004; Bry C, 2008, J EXP SOC PSYCHOL, V44, P751, DOI 10.1016/j.jesp.2007.06.005; Buss D. M., 2005, HDB EVOLUTIONARY PSY, P5; Cesario J, 2014, PERSPECT PSYCHOL SCI, V9, P40, DOI 10.1177/1745691613513470; Cesario J, 2014, SOC PSYCHOL PERS SCI, V5, P12, DOI 10.1177/1948550613485605; Cesario J, 2013, SOC COGNITION, V31, P260; Cesario J., 2013, ROLE INGROUPS UNPUB; Cesario J, 2006, J PERS SOC PSYCHOL, V90, P893, DOI 10.1037/0022-3514.90.6.893; Cesario J, 2010, PSYCHOL SCI, V21, P1311, DOI 10.1177/0956797610378685; COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407; De Dreu CKW, 2011, P NATL ACAD SCI USA, V108, P1262, DOI 10.1073/pnas.1015316108; Dijksterhuis A, 2001, ADV EXP SOC PSYCHOL, V33, P1, DOI 10.1016/S0065-2601(01)80003-4; Dijksterhuis A, 2000, J EXP SOC PSYCHOL, V36, P531, DOI 10.1006/jesp.2000.1427; Doyen S, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0029081; Faber TW, 2013, SOC COGNITION, V31, P301; FAZIO RH, 1995, J PERS SOC PSYCHOL, V69, P1013, DOI 10.1037//0022-3514.69.6.1013; Fessler DMT, 2013, PSYCHOL SCI, V24, P797, DOI 10.1177/0956797612461508; Gawronski B, 2013, PERS SOC PSYCHOL REV, V17, P187, DOI 10.1177/1088868313480096; Higgins E. T., 1996, SOCIAL PSYCHOL HDB B, P133; Jonas K. J., 2013, SOCIAL PERSONALITY P, V7, P689, DOI [10.1111/spc3.12060, DOI 10.1111/SPC3.12060]; Jonas KJ, 2006, J PERS SOC PSYCHOL, V90, P709, DOI 10.1037/0022-3514.90.5.709; Kurzban R, 2001, P NATL ACAD SCI USA, V98, P15387, DOI 10.1073/pnas.251541498; Loersch C, 2011, PERSPECT PSYCHOL SCI, V6, P234, DOI 10.1177/1745691611406921; Loersch C, 2014, SOC COGNITION, V32, P137; Loersch C, 2008, J EXP SOC PSYCHOL, V44, P1555, DOI 10.1016/j.jesp.2008.07.009; MCCOMB K, 1994, ANIM BEHAV, V47, P379, DOI 10.1006/anbe.1994.1052; MEYER DE, 1971, J EXP PSYCHOL, V90, P227, DOI 10.1037/h0031564; Mussweiler T, 2000, J EXP SOC PSYCHOL, V36, P194, DOI 10.1006/jesp.1999.1415; Navarrete CD, 2010, J PERS SOC PSYCHOL, V98, P933, DOI 10.1037/a0017931; NEELY JH, 1977, J EXP PSYCHOL GEN, V106, P226, DOI 10.1037/0096-3445.106.3.226; Pashler H, 2012, PERSPECT PSYCHOL SCI, V7, P528, DOI 10.1177/1745691612465253; Pleskac T. J., COMPUTATIONAL UNPUB; Pleskac TJ, 2010, PSYCHOL REV, V117, P864, DOI 10.1037/a0019737; Posner MI, 1975, INFORMATION PROCESSI, P55; Shantz A, 2009, ORGAN BEHAV HUM DEC, V109, P9, DOI 10.1016/j.obhdp.2009.01.001; Simmons JP, 2011, PSYCHOL SCI, V22, P1359, DOI 10.1177/0956797611417632; SMITH JM, 1973, NATURE, V246, P15, DOI 10.1038/246015a0; SMITH JM, 1979, PROC R SOC SER B-BIO, V205, P475, DOI 10.1098/rspb.1979.0080; Wilson ML, 2002, P ROY SOC B-BIOL SCI, V269, P1107, DOI 10.1098/rspb.2001.1926 43 4 4 GUILFORD PUBLICATIONS INC NEW YORK 72 SPRING STREET, NEW YORK, NY 10012 USA 0278-016X SOC COGNITION Soc. Cogn. JUN 2014 32 S SI 124 136 13 Psychology, Social Psychology AL8LC WOS:000339389200008 J Kerne, A; Webb, AM; Smith, SM; Linder, R; Lupfer, N; Qu, Y; Moeller, J; Damaraju, S Kerne, Andruid; Webb, Andrew M.; Smith, Steven M.; Linder, Rhema; Lupfer, Nic; Qu, Yin; Moeller, Jon; Damaraju, Sashikanth Using Metrics of Curation to Evaluate Information-Based Ideation ACM TRANSACTIONS ON COMPUTER-HUMAN INTERACTION English Article Creativity support tools; digital curation; ideation; creative cognition; interactive information; sensemaking; information foraging; information visualization; exploratory search CREATIVE PROCESS; DIVERGENT THINKING; ENGINEERING DESIGN; VISUAL ANALYTICS; INCUBATION; RETRIEVAL; FIXATION; TEXT; EMERGENCE; DISCOVERY Evaluating creativity support environments is challenging. Some approaches address people's experiences of creativity. The present method measures creativity, across conditions, in the products that people make. This research introduces information-based ideation (IBI), a paradigm for investigating open-ended tasks and activities in which users develop new ideas. IBI tasks span imagining, planning, and reflecting on a weekend, vacation, outfit, makeover, paper, internship, thesis, design, campaign, crisis response, career, or invention. What products do people create through engagement in IBI? Curation of digital media incorporates conceptualization, finding and choosing information objects, annotation, and synthesis. Through engagement in IBI tasks, people create curation products. This article formulates a quantitative methodology for evaluating IBI support tools, building on prior creative cognition research in engineering design to derive a battery of ideation metrics of curation. Elemental ideation metrics evaluate creativity within curated found objects. Holistic ideation metrics evaluate how a curation puts elements together. IBI support environments are characterized by their underlying medium of curation. Curation media include lists, such as listicles, and grids, such as the boards of Pinterest. An in-depth case study investigates information composition, an art-based medium representing a curation as a freeform visual semantic connected whole. We raise two creative cognition challenges for IBI. One challenge is overcoming fixation-for instance, when a person gets stuck in a counterproductive mental set. The other challenge is to bridge information visualization's synthesis gap, by providing support for connecting findings. To address the challenges, we develop mixed-initiative information composition ((MIC)-C-2), integrating human curation of information composition with automated agents of information retrieval and visualization. We hypothesize that (MIC)-C-2 generates provocative stimuli that help users overcome fixation to become more creative on IBI tasks. We hypothesize that (MIC)-C-2's integration of curation and visualization bridges the synthesis gap to help users become more creative. To investigate these hypotheses, we apply ideation metrics of curation to interpret results from experiments with 44 and 49 participants. [Kerne, Andruid; Webb, Andrew M.; Linder, Rhema; Lupfer, Nic; Qu, Yin; Moeller, Jon; Damaraju, Sashikanth] Texas A&M Univ, Interface Ecol Lab, College Stn, TX 77843 USA; [Smith, Steven M.] Texas A&M Univ, Dept Psychol, College Stn, TX 77843 USA Kerne, A (reprint author), Texas A&M Univ, Interface Ecol Lab, Dept Comp Sci & Engn, College Stn, TX 77843 USA. andruid@ecologylab.net; andrew@ecologylab.net; stevesmith@tamu.edu; rhema@ecologylab.net; nic@ecologylab.net; yin@ecologylab.net; jon@ecologylab.net; sashikanth@ecologylab.net National Science Foundation (NSF) [IIS-074742, IIS-1247126]; University of Nottingham's Horizon Institute of Digital Economy. This material is based upon work supported by the National Science Foundation (NSF) under grants IIS-074742 and IIS-1247126. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the NSF. Further support was provided by a sabbatical grant to the first author by the University of Nottingham's Horizon Institute of Digital Economy. Amabile Teresa M., 1996, 396239 HARV BUS SCH, P396; Amar Robert, 2004, P IEEE S INF VIS, P143; Babb Doug, 2012, MANAGING ISSUES CFM; Baddeley A. D., 1986, WORKING MEMORY; BATES MJ, 1989, ONLINE REV, V13, P407, DOI 10.1108/eb024320; Beghetto R.A., 2007, PSYCHOL AESTHET CREA, V1, P73, DOI DOI 10.1037/1931-3896.1.2.73; BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722; Benford S, 2011, ACM T COMPUT-HUM INT, V18, DOI 10.1145/1993060.1993061; Bertini Enrico, 2008, P BELIV 2008, P3913, DOI DOI 10.1145/1358628.1358955; Blake C, 2006, J AM SOC INF SCI TEC, V57, P1888, DOI 10.1002/asi.20486; Boden M., 2003, CREATIVE MIND MYTHS; Bowden EM, 2005, TRENDS COGN SCI, V9, P322, DOI 10.1016/j.tics.2005.05.012; Buxton Bill, 2007, SKETCHING USER EXPER; Cage John, 1961, SILENCE LECT WRITING; Card SK, 1999, READINGS INFORM VISU; CARD SK, 1980, COGNITIVE PSYCHOL, V12, P32, DOI 10.1016/0010-0285(80)90003-1; Carroll EA, 2009, C & C 09: PROCEEDINGS OF THE 2009 ACM SIGCHI CONFERENCE ON CREATIVITY AND COGNITION, P127; Chang R, 2009, IEEE COMPUT GRAPH, V29, P14, DOI 10.1109/MCG.2009.22; Chocano Carina, 2012, NEW YORK TIMES MAGAZ; Christensen BT, 2007, MEM COGNITION, V35, P29, DOI 10.3758/BF03195939; Corbin J, 2008, BASICS QUALITATIVE R; Council on Competitiveness, 2005, INN AM NAT INN IN SU; Dow SP, 2010, ACM T COMPUT-HUM INT, V17, DOI 10.1145/1879831.1879836; Dow Steven, 2012, P 2012 ACM C COMP SU, P1013; Dumais Susan, 2003, P 26 ANN INT ACM SIG, P72; Dym CL, 2005, J ENG EDUC, V94, P103; Emanuel Parzen and Subhadeep Mukhopadhyay, 2012, ARXIV12044699; Emmanuel Parzen, 1960, MODERN PROBABILITY T; Eunyee Koh, 2007, P 15 INT C MULT MULT, P228, DOI 10.1145/1291233.1291282; Finke R. A., 1992, CREATIVE COGNITION T; Gaver W.W., 2003, P SIGCHI C HUM FACT, P233, DOI DOI 10.1145/642611.642653; GLENBERG AM, 1992, J MEM LANG, V31, P129, DOI 10.1016/0749-596X(92)90008-L; Guilford JP, 1950, AM PSYCHOL, V5, P444, DOI 10.1037/h0063487; GUILFORD JP, 1956, PSYCHOL BULL, V53, P267, DOI 10.1037/h0040755; Guilford Joy P., 1968, INTELLIGENCE CREATIV; Hailpern J, 2007, CC2007-CREATIVITY AND COGNITION 2007 SEEDING CREATIVITY: TOOLS, MEDIA, AND ENVIRONMENTS, P193; Hampton JA, 1997, CREATIVE THOUGHT INV, P83, DOI 10.1037/10227-004; Harvard College Writing Program, 2011, WRIT RES; Hays W. L., 1988, STATISTICS; Hearst Marti A., 2008, P WORKSH COMP INT IN; Hochberg Y., 1987, MULTIPLE COMP PROCED; Hornecker E., 2008, P ACM 2008 C COMP SU, P167, DOI DOI 10.1145/1460563.1460589; Horvitz Eric, 1999, P ACM SIGCHI C HUM F, P159, DOI 10.1145/302979.303030; Illich Ivan, 2001, TOOLS CONVIVIALITY; Jansson D., 1991, DESIGN STUDIES, V12, P3, DOI 10.1016/0142-694X(91)90003-F; Johnson Benny, 2013, 24 DELIGHTFUL INAUGU; Jones W, 2002, P ASIST ANNU, V39, P391, DOI 10.1002/meet.1450390143; Kandinsky Wassily, 1994, KANDINSKY COMPLETE W; Kaufman J. C., 2010, CAMBRIDGE HDB CREATI; Kerne A, 2007, CC2007-CREATIVITY AND COGNITION 2007 SEEDING CREATIVITY: TOOLS, MEDIA, AND ENVIRONMENTS, P117; Kerne A, 2008, INT J HUM-COMPUT INT, V24, P460, DOI 10.1080/10447310802142243; Kerne A, 2007, New Review of Hypermedia and Multimedia, V13, DOI 10.1080/13614560701711859; Kerne Andruid, 2010, P 19 ACM INT C INF K, P1129, DOI 10.1145/1871437.1871580; Kerne A, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1416950.1416955; Kerne Andruid, 2006, P ACM IEEE JOINT C D, P11, DOI 10.1145/1141753.1141756; Kohn N, 2009, J CREATIVE BEHAV, V43, P102; Kohn NW, 2011, APPL COGNITIVE PSYCH, V25, P359, DOI 10.1002/acp.1699; Krauss Rosalind, 1998, OPTICAL UNCONSCIOUS; Kumar R., 2011, P SIGCHI C HUM FACT, P2197; Linder Rhema, 2014, P SIGCHI C HUM FACT, P2411; Linsey Julie S., 2005, P ASME DES THEOR MET, P24; Lippard Lucy, 1972, DADAS ART; Maccariello Kate, 2013, WORLD IS MY OYSTER; Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979; Marks Joe, 1997, P SIGGRAPH, P389, DOI DOI 10.1145/258734.258887; Marshall C. C., 1992, Proceeding of the ACM Conference on Hypertext, DOI 10.1145/168466.168490; Marshall Catherine C., 1993, P 5 ACM C HYP HYPERT, P217, DOI http://dx.doi.org/10.1145/168750.168826; Marshall Catherine C., 2005, P SIGCHI C HUM FACT, P111, DOI 10.1145/1054972.1054989; MEDNICK SA, 1962, PSYCHOL REV, V69, P220, DOI 10.1037/h0048850; Mi Jeong Kim, 2005, P HUM BEH DES 05; Mistry Pranav, 2012, SIXTHSENSE WEARABLE; National Academy of Engineering, 2005, ENG RES AM FUT M CHA; National Academy of Engineering, 2010, RIS GATH STORM REV R; Nelson BA, 2009, DESIGN STUD, V30, P737, DOI 10.1016/j.destud.2009.07.002; Norman R. F., 1931, J COMP PSYCHOL, V12, P181; North C, 2000, INT J HUM-COMPUT ST, V53, P715, DOI 10.1006/ijhc.2000.0418; Nunnally J.C., 1994, PSYCHOMETRIC THEORY; OpenDNS, 2013, OPENDNS COMM DOM CAT; Osborn A. F., 1963, APPL IMAGINATION PRI; Paul SA, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P1771; Perer A, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P265; Pirolli P., 1996, P ACM SIGCHI C HUM F, P213, DOI 10.1145/238386.238489; Pirolli P, 1999, PSYCHOL REV, V106, P643, DOI 10.1037/0033-295X.106.4.643; Quenqua Douglas, 2013, NY TIMES; Rector AL, 1997, ARTIF INTELL MED, V9, P139, DOI 10.1016/S0933-3657(96)00369-7; Ritchie Daniel, 2011, P 24 ANN ACM S US IN, P165; Rosenbaum Steven, 2011, CURATION NATION WIN; RUNCO MA, 1985, EDUC PSYCHOL MEAS, V45, P483; RUNCO MA, 1988, J YOUTH ADOLESCENCE, V17, P211, DOI 10.1007/BF01538162; Russell DM, 1993, P SIGCHI C HUM FACT, P269, DOI 10.1145/169059.169209; SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0; Seifert Colleen M., 1995, NATURE INSIGHT; Shah J. J., 2003, Design Studies, V24, DOI 10.1016/S0142-694X(02)00034-0; Shah Jami J., 1998, P ASME DES THEOR, V50; Shah JJ, 2001, J CREATIVE BEHAV, V35, P168; Shneiderman Ben, 1996, P IEEE S VIS LANG, P336; Shneiderman Ben, 2005, CREATIVITY SUPPORT T; Simon H. A., 1960, NEW SCI MANAGEMENT D; Simonton DK, 1999, PSYCHOL INQ, V10, P309; Skog Tobias, 2003, P IEEE S INF VIS, P233; Smith M. Smith, 1996, NATURE INSIGHT, P229; SMITH SM, 1991, AM J PSYCHOL, V104, P61, DOI 10.2307/1422851; SMITH SM, 1993, MEM COGNITION, V21, P837, DOI 10.3758/BF03202751; SMITH SM, 1989, B PSYCHONOMIC SOC, V27, P311; SMITH SM, 1991, MEM COGNITION, V19, P168, DOI 10.3758/BF03197114; Smith SM, 1997, J EXP PSYCHOL LEARN, V23, P355, DOI 10.1037/0278-7393.23.2.355; Smith Steven M., 1995, CREATIVE COGNITION A; Snoek C. G. M., 2009, FDN TRENDS INFORM RE, V4, P215; Snow R., 2008, P C EMP METH NAT LAN, P254, DOI 10.3115/1613715.1613751; Song S., 2004, P ASME DES THEOR MET, V3a, P351; Springmeyer R. R., 1992, Proceedings. Visualization '92 (Cat. No.92CH3201-1), DOI 10.1109/VISUAL.1992.235203; STEM to STEAM, 2013, WHAT IS STEAM; Sternberg R. J., 1999, HDB CREATIVITY; Teevan J, 2009, CHI2009: PROCEEDINGS OF THE 27TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1-4, P2023; Thiry Elizabeth, 2013, P SIGCHI C HUM FACT, P1619; Thomas JJ, 2005, ILLUMINATING PATH RE; Thomas JJ, 2006, IEEE COMPUT GRAPH, V26, P10, DOI 10.1109/MCG.2006.5; Thudt Alice, 2012, P 2012 ACM ANN C HUM, P1461; Tripathi Priyamvada, 2012, P ACM 2012 C COMP SU, P1203; Tufte E.R., 1990, ENVISIONING INFORM; Utley G, 1997, FOREIGN AFF, V76, P2, DOI 10.2307/20047932; Velcro, 2013, VELCR IND HIST G DEM; Vitruvius, 1914, 10 BOOKS ARCHITECTUR; Vul E, 2007, MEM COGNITION, V35, P701, DOI 10.3758/BF03193308; Webb Andrew, 2008, P WORK C ADV VIS INT, P91, DOI 10.1145/1385569.1385586; Webb Andrew M., 2011, P 11 ANN INT ACM IEE, P203; Webb Andrew M., 2013, P 9 ACM C CREAT COGN, P53; Weiser Mark, 1997, CALCULATION NEXT 50, P75; White Ryen W., 2006, COMMUN ACM, V49, P36, DOI 10.1145/1121949.1121978; Wilkenfeld MJ, 2001, J MEM LANG, V45, P21, DOI 10.1006/jmla.2000.2772; Wilson Max L., 2013, P CHI 13 HUM FACT CO, P3159; Woodruff Allison, 2001, P SIGCHI C HUM FACT, P198, DOI 10.1145/365024.365098; Wright W., 2006, P SIGCHI C HUM FACT, P801, DOI 10.1145/1124772.1124890; Yapalater Lauren, 2013, 22 MOST FABULOUS BEY; Yapalater Lauren, 2013, 23 REASONS SASHA MAL; Yee Ka-Ping, 2003, P SIGCHI C HUM FACT, P401, DOI DOI 10.1145/642611.642681 136 0 0 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 1073-0516 1557-7325 ACM T COMPUT-HUM INT ACM Trans. Comput.-Hum. Interact. JUN 2014 21 3 14 10.1145/2591677 48 Computer Science, Cybernetics; Computer Science, Information Systems Computer Science AL3DM WOS:000339005600001 J Schwartz, AJ; Boduroglu, A; Gutchess, AH Schwartz, Aliza J.; Boduroglu, Aysecan; Gutchess, Angela H. Cross-Cultural Differences in Categorical Memory Errors COGNITIVE SCIENCE English Article Culture; Cognition; Memory errors; Long-term memory; Categories FALSE RECOGNITION; CATEGORIZATION; PSYCHOLOGY; AMERICAN; CHINESE; ADULTS; WORDS Cultural differences occur in the use of categories to aid accurate recall of information. This study investigated whether culture also contributed to false ( erroneous) memories, and extended cross-cultural memory research to Turkish culture, which is shaped by Eastern and Western influences. Americans and Turks viewed word pairs, half of which were categorically related and half unrelated. Participants then attempted to recall the second word from the pair in response to the first word cue. Responses were coded as correct, as blanks, or as different types of errors. Americans committed more categorical errors than did Turks, and Turks mistakenly recalled more non-categorically related list words than did Americans. These results support the idea that Americans use categories either to organize information in memory or to support retrieval strategies to a greater extent than Turks and suggest that culture shapes not only accurate recall but also erroneous distortions of memory. [Schwartz, Aliza J.; Gutchess, Angela H.] Brandeis Univ, Dept Psychol, Waltham, MA 02454 USA; [Boduroglu, Aysecan] Bogazici Univ, Dept Psychol, TR-80815 Bebek, Turkey Gutchess, AH (reprint author), Brandeis Univ, Dept Psychol, 415 South St,MS 062, Waltham, MA 02454 USA. gutchess@brandeis.edu BATTIG WF, 1969, J EXP PSYCHOL, V80, P1, DOI 10.1037/h0027577; Boduroglu A, 2009, J CROSS CULT PSYCHOL, V40, P349, DOI 10.1177/0022022108331005; Brainerd CJ, 2008, PSYCHON B REV, V15, P1035, DOI 10.3758/PBR.15.6.1035; CHIU LH, 1972, INT J PSYCHOL, V7, P235, DOI 10.1080/00207597208246604; Dewhurst SA, 1999, MEM COGNITION, V27, P665, DOI 10.3758/BF03211560; Dewhurst SA, 2004, EUR J COGN PSYCHOL, V16, P403, DOI 10.1080/09541440340000088; Gutchess AH, 2011, LECT NOTES ARTIF INT, V6780, P67; Gutchess AH, 2009, PROG BRAIN RES, V178, P137, DOI 10.1016/S0079-6123(09)17809-3; Gutchess AH, 2006, GERONTOLOGY, V52, P314, DOI 10.1159/000094613; Henrich J, 2010, BEHAV BRAIN SCI, V33, P61, DOI 10.1017/S0140525X0999152X; Imamoglu O., 2007, ASIAN J SOC PSYCHOL, V10, P145; Ji LJ, 2004, J PERS SOC PSYCHOL, V87, P57, DOI 10.1037/0022-3514.87.1.57; KAGITCIBASI C, 1994, INT J PSYCHOL, V29, P729, DOI 10.1080/00207599408246562; Kashima E., 2000, ASIAN J SOC PSYCHOL, V3, P19, DOI DOI 10.1111/1467-839X.00053; Kucera H., 1967, COMPUTATIONAL ANAL P; Kutner M. H., 2004, APPL LINEAR REGRESSI; Loftus EF, 2005, LEARN MEMORY, V12, P361, DOI 10.1101/lm.94705; Masuda T, 2001, J PERS SOC PSYCHOL, V81, P922, DOI 10.1037//0022-3514.81.5.922; Nisbett RE, 2001, PSYCHOL REV, V108, P291, DOI 10.1037//0033-295X.108.2.291; Norman KA, 1997, MEM COGNITION, V25, P838, DOI 10.3758/BF03211328; Peynircioglu Z. F., 1988, INSAN BILIMLERI DERG, V7, P133; ROEDIGER HL, 1995, J EXP PSYCHOL LEARN, V21, P803, DOI 10.1037/0278-7393.21.4.803; Schacter DL, 1999, AM PSYCHOL, V54, P182, DOI 10.1037//0003-066X.54.3.182; Smith SM, 2000, MEM COGNITION, V28, P386, DOI 10.3758/BF03198554; Unsworth N, 2005, BEHAV RES METHODS, V37, P498, DOI 10.3758/BF03192720; Unsworth SJ, 2005, J CROSS CULT PSYCHOL, V36, P662, DOI 10.1177/0022022105280509; Wang Q, 2005, MEMORY, V13, P594, DOI 10.1080/09658210444000223; Wang Q, 2004, J PERS, V72, P911, DOI 10.1111/j.0022-3506.2004.00285.x 28 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0364-0213 1551-6709 COGNITIVE SCI Cogn. Sci. JUN 2014 38 5 997 1007 10.1111/cogs.12109 11 Psychology, Experimental Psychology AL4GY WOS:000339092100007 J Duden, T; Thust, A; Kumpf, C; Tautz, FS Duden, Thomas; Thust, Andreas; Kumpf, Christian; Tautz, F. Stefan Focal-Series Reconstruction in Low-Energy Electron Microscopy MICROSCOPY AND MICROANALYSIS English Article low-energy electron microscopy (LEEM); phase contrast imaging; phase retrieval IMAGE-RECONSTRUCTION; FOCUS; CONTRAST; HRTEM In low-energy electron microscopy (LEEM) we commonly encounter images which, beside amplitude contrast, also show signatures of phase contrast. The images are usually interpreted by following the evolution of the contrast during the experiment, and assigning gray levels to morphological changes. Through reconstruction of the exit wave, two aspects of LEEM can be addressed: (1) the resolution can be improved by exploiting the full information limit of the microscope and (2) electron phase shifts which contribute to the image contrast can be extracted. In this article, linear exit wave reconstruction from a through-focal series of LEEM images is demonstrated. As a model system we utilize a heteromolecular monolayer consisting of the organic molecules 3,4,9,10-perylene tetracarboxylic dianhydride and Cu-II-Phthalocyanine, adsorbed on a Ag(111) surface. [Duden, Thomas; Kumpf, Christian; Tautz, F. Stefan] Forschungszentrum Julich, Peter Grunberg Inst PGI 3, D-52425 Julich, Germany; [Thust, Andreas] Forschungszentrum Julich, ER C, D-52425 Julich, Germany; [Thust, Andreas] Forschungszentrum Julich, Peter Grunberg Inst PGI 5, D-52425 Julich, Germany; [Duden, Thomas; Thust, Andreas; Kumpf, Christian; Tautz, F. Stefan] Julich Aachen Res Alliance JARA Fundamentals Futu, D-52425 Julich, Germany Duden, T (reprint author), Forschungszentrum Julich, Peter Grunberg Inst PGI 3, D-52425 Julich, Germany. info@thomas-duden.de Technology Transfer Department of the Forschungszentrum Julich The authors would like to thank Markus Lentzen for fruitful discussions and Caroline Henneke for the sample preparation. They acknowledge financial support by the Technology Transfer Department of the Forschungszentrum Julich. COENE W., 1996, ULTRAMICROSCOPY, V64, P167; Coene WMJ, 1996, ULTRAMICROSCOPY, V64, P109, DOI 10.1016/0304-3991(96)00010-1; Glockler K, 1998, SURF SCI, V405, P1, DOI 10.1016/S0039-6028(97)00888-1; Hausler G., 1972, Optics Communications, V6, DOI 10.1016/0030-4018(72)90243-X; Koshikawa T, 2005, J PHYS-CONDENS MAT, V17, pS1371, DOI 10.1088/0953-8984/17/16/008; Kroger I, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/8/083038; LICHTE H, 1991, ULTRAMICROSCOPY, V38, P13, DOI 10.1016/0304-3991(91)90105-F; Pang AB, 2009, J PHYS-CONDENS MAT, V21, DOI 10.1088/0953-8984/21/31/314006; SAXTON WO, 1994, ULTRAMICROSCOPY, V55, P171, DOI 10.1016/0304-3991(94)90168-6; Schiske P, 2002, J MICROSC-OXFORD, V207, P154, DOI 10.1046/j.1365-2818.2002.01042.x; Schiske P., 1968, P 4 EUR C EL MICR RO, P145; Schramm SM, 2012, ULTRAMICROSCOPY, V115, P88, DOI 10.1016/j.ultramic.2011.11.005; Stadtmuller B, 2014, NAT COMMUN, V5, DOI 10.1038/ncomms4685; Stadtmuller B., 2014, TAILORING M IN PRESS; Thust A, 1996, ULTRAMICROSCOPY, V64, P211, DOI 10.1016/0304-3991(96)00011-3; Thust A., 1998, P 14 INT C EL MICR I, P119; Thust A., 2007, 38 IFF SPRING SCH 20; Thust A, 1996, ULTRAMICROSCOPY, V64, P249, DOI 10.1016/0304-3991(96)00022-8; van Gastel R, 2009, ULTRAMICROSCOPY, V110, P33, DOI 10.1016/j.ultramic.2009.09.002; Yu RP, 2010, MICRON, V41, P232, DOI 10.1016/j.micron.2009.10.010 20 0 0 CAMBRIDGE UNIV PRESS NEW YORK 32 AVENUE OF THE AMERICAS, NEW YORK, NY 10013-2473 USA 1431-9276 1435-8115 MICROSC MICROANAL Microsc. microanal. JUN 2014 20 3 968 973 10.1017/S1431927614000403 6 Materials Science, Multidisciplinary; Microscopy Materials Science; Microscopy AL5FK WOS:000339158700040 J Fitzgerald, PJ; Seemann, JR; Maren, S Fitzgerald, Paul J.; Seemann, Jocelyn R.; Maren, Stephen Can fear extinction be enhanced? A review of pharmacological and behavioral findings BRAIN RESEARCH BULLETIN English Review Fluoxetine; Propranolol; Yohimbine; L-Dopa; Massed extinction; Context MEDIAL PREFRONTAL CORTEX; CYCLOSERINE FACILITATES EXTINCTION; CONDITIONED EMOTIONAL RESPONSE; VIRTUAL-REALITY EXPOSURE; LONG-TERM EXTINCTION; CONTEXTUAL FEAR; MULTIPLE CONTEXTS; MEMORY RETRIEVAL; POTENTIATED STARTLE; LEARNED FEAR There is considerable interest, from both a basic and clinical standpoint, in gaining a greater understanding of how pharmaceutical or behavioral manipulations alter fear extinction in animals. Not only does fear extinction in rodents model exposure therapy in humans, where the latter is a cornerstone of behavioral intervention for anxiety disorders such as post-traumatic stress disorder and specific phobias, but also understanding more about extinction provides basic information into learning and memory processes and their underlying circuitry. In this paper, we briefly review three principal approaches that have been used to modulate extinction processes in animals and humans: a purely pharmacological approach, the more widespread approach of combining pharmacology with behavior, and a purely behavioral approach. The pharmacological studies comprise modulation by: brain derived neurotrophic factor (BDNF), D-cycloserine, serotonergic and noradrenergic drugs, neuropeptides, endocannabinoids, glucocorticoids, histone deacetylase (HDAC) inhibitors, and others. These studies strongly suggest that extinction can be modulated by drugs, behavioral interventions, or their combination, although not always in a lasting manner. We suggest that pharmacotherapeutic manipulations provide considerable promise for promoting effective and lasting fear reduction in individuals with anxiety disorders. This article is part of a Special Issue entitled 'Memory enhancement'. (C) 2014 Elsevier Inc. All rights reserved. [Fitzgerald, Paul J.; Maren, Stephen] Texas A&M Univ, Dept Psychol, College Stn, TX 77843 USA; [Seemann, Jocelyn R.; Maren, Stephen] Texas A&M Univ, Inst Neurosci, College Stn, TX 77843 USA Maren, S (reprint author), Texas A&M Univ, Dept Psychol, TAMU 4235, College Stn, TX 77843 USA. maren@tamu.edu National Institutes of Health [R01MH065961] Supported by a grant from the National Institutes of Health (R01MH065961). Abraham AD, 2012, LEARN MEMORY, V19, P67, DOI 10.1101/lm.024752.111; Abumaria N, 2011, J NEUROSCI, V31, P14871, DOI 10.1523/JNEUROSCI.3782-11.2011; Akirav I, 2006, EUR J NEUROSCI, V23, P758, DOI 10.1111/j.1460-9568.2006.04603.x; Andero R, 2011, AM J PSYCHIAT, V168, P163, DOI 10.1176/appi.ajp.2010.10030326; Bahari-Javan S, 2012, J NEUROSCI, V32, P5062, DOI 10.1523/JNEUROSCI.0079-12.2012; Balooch SB, 2012, BEHAV RES THER, V50, P604, DOI 10.1016/j.brat.2012.06.003; BAUM M, 1990, BEHAV RES THER, V28, P63, DOI 10.1016/0005-7967(90)90055-N; Berlau DJ, 2006, NEUROBIOL LEARN MEM, V86, P123, DOI 10.1016/j.nlm.2005.12.008; Bitencourt RM, 2008, EUR NEUROPSYCHOPHARM, V18, P849, DOI 10.1016/j.euroneuro.2008.07.001; BLANCHAR.RJ, 1969, J COMP PHYSIOL PSYCH, V67, P370, DOI 10.1037/h0026779; Bos MGN, 2012, BIOL PSYCHOL, V89, P598, DOI 10.1016/j.biopsycho.2012.01.007; Bouton ME, 2001, PSYCHOL REV, V108, P4, DOI 10.1037//0033-295X.108.1.4; BOUTON ME, 1990, BEHAV NEUROSCI, V104, P44, DOI 10.1037/0735-7044.104.1.44; Bouton ME, 2008, NEUROBIOL LEARN MEM, V90, P504, DOI 10.1016/j.nlm.2008.07.003; BOUTON ME, 1979, J EXP PSYCHOL ANIM B, V5, P368, DOI 10.1037/0097-7403.5.4.368; Bouton ME, 2006, BEHAV RES THER, V44, P983, DOI 10.1016/j.brat.2005.07.007; Bouton ME, 2006, BIOL PSYCHIAT, V60, P352, DOI 10.1016/j.biopsych.2005.12.015; Bowers ME, 2012, PHYSIOL BEHAV, V107, P699, DOI 10.1016/j.physbeh.2012.03.004; Bredy TW, 2007, LEARN MEMORY, V14, P268, DOI 10.1101/lm.500907; Bredy TW, 2008, LEARN MEMORY, V15, P39, DOI 10.1101/lm.801108; Brinks V, 2009, EXP NEUROL, V216, P375, DOI [10.1016/j.expneurol.2008.12.011, 10.1016/j.expneurol.2008.42.011]; Burghardt NS, 2013, BIOL PSYCHIAT, V73, P1078, DOI 10.1016/j.biopsych.2012.10.012; Cain CK, 2004, LEARN MEMORY, V11, P179, DOI 10.1101/lm.71504; Cain CK, 2003, J EXP PSYCHOL ANIM B, V29, P323, DOI 10.1037/0097-7403.29.4.323; Cammarota M, 2004, AN ACAD BRAS CIENC, V76, P573, DOI 10.1590/S0001-37652004000300011; Chan Wan Yee Macy, 2010, Learn Mem, V17, P512, DOI 10.1101/lm.1912510; Chang CH, 2011, LEARN MEMORY, V18, P221, DOI 10.1101/lm.2070111; Chang YJ, 2009, HIPPOCAMPUS, V19, P1142, DOI 10.1002/hipo.20581; Chhatwal JP, 2005, NEUROPSYCHOPHARMACOL, V30, P516, DOI 10.1038/sj.npp.1300655; Choi DC, 2010, P NATL ACAD SCI USA, V107, P2675, DOI 10.1073/pnas.0909359107; Craske MG, 2008, BEHAV RES THER, V46, P5, DOI 10.1016/j.brat.2007.10.003; Das RK, 2013, PSYCHOPHARMACOLOGY, V226, P781, DOI 10.1007/s00213-012-2955-y; Davis M, 2003, ANN NY ACAD SCI, V985, P218; de Quervain DJF, 2011, P NATL ACAD SCI USA, V108, P6621, DOI 10.1073/pnas.1018214108; Deschaux O., 2012, PSYCHOPHARMACOLOGY, V225, P209; Deschaux O, 2011, PSYCHOPHARMACOLOGY, V215, P231, DOI 10.1007/s00213-010-2134-y; Do-Monte FHM, 2010, NEUROBIOL LEARN MEM, V94, P318, DOI 10.1016/j.nlm.2010.07.004; Edelmann E., 2013, NEUROPHARMACOLOGY; Estes WK, 1941, J EXP PSYCHOL, V29, P390, DOI 10.1037/h0062283; Fanselow M.S., 1980, J BIOL SCI, V15, P177; Glautier S., 2013, LEARNING BEHAV; Gonzalez-Lima F, 2004, LEARN MEMORY, V11, P633, DOI 10.1101/lm.82404; Graham BM, 2011, BRIT J PHARMACOL, V164, P1230, DOI 10.1111/j.1476-5381.2010.01175.x; Graham BM, 2010, NEUROPSYCHOPHARMACOL, V35, P1348, DOI 10.1038/npp.2010.3; Graham BM, 2011, J NEUROSCI, V31, P14151, DOI 10.1523/JNEUROSCI.3014-11.2011; Graham BM, 2009, NEUROPSYCHOPHARMACOL, V34, P1875, DOI 10.1038/npp.2009.14; Guastella AJ, 2007, BEHAV RES THER, V45, P663, DOI 10.1016/j.brat.2006.07.005; Gunduz-Cinar O., 2012, MOL PSYCHIATR, V18, P813, DOI DOI 10.1038/MP.2012.72; Gunther LM, 1998, BEHAV RES THER, V36, P75, DOI 10.1016/S0005-7967(97)10019-5; Gutman AR, 2008, J NEUROSCI, V28, P12682, DOI 10.1523/JNEUROSCI.2305-08.2008; Haaker J., 2013, P NATL ACAD SCI USA, pE2428; Heinrichs SC, 2013, NEUROSCI LETT, V552, P108, DOI 10.1016/j.neulet.2013.07.035; Holmes A, 2010, TRENDS PHARMACOL SCI, V31, P2, DOI 10.1016/j.tips.2009.10.003; Holmes NM, 2013, J EXP PSYCHOL ANIM B, V39, P208, DOI 10.1037/a0031986; Janak PH, 2011, LEARN MEMORY, V18, P1, DOI 10.1101/lm.1923211; Johnson JS, 2010, LEARN MEMORY, V17, P639, DOI 10.1101/lm.1932310; Jungling K, 2008, NEURON, V59, P298, DOI 10.1016/j.neuron.2008.07.002; KALISH HI, 1954, J EXP PSYCHOL, V47, P1, DOI 10.1037/h0053732; Kaplan GB, 2011, PHARMACOL BIOCHEM BE, V99, P217, DOI 10.1016/j.pbb.2011.01.009; Karpova NN, 2011, SCIENCE, V334, P1731, DOI 10.1126/science.1214592; Kindt M, 2013, BIOL PSYCHOL, V92, P43, DOI 10.1016/j.biopsycho.2011.09.016; Klumpers F, 2012, J PSYCHOPHARMACOL, V26, P471, DOI 10.1177/0269881111431624; Knapska E, 2009, LEARN MEMORY, V16, P486, DOI 10.1101/lm.1463909; Kuriyama K, 2011, PSYCHOPHARMACOLOGY, V218, P589, DOI 10.1007/s00213-011-2353-x; Laborda MA, 2013, BEHAV THER, V44, P249, DOI 10.1016/j.beth.2012.11.001; Lach G, 2013, NEUROBIOL LEARN MEM, V103, P26, DOI 10.1016/j.nlm.2013.04.005; Lafentre P, 2007, PHARMACOL RES, V56, P367, DOI 10.1016/j.phrs.2007.09.006; Lattal KM, 2007, BEHAV NEUROSCI, V121, P1125, DOI 10.1037/0735-7044.121.5.1125; Ledgerwood L, 2005, BIOL PSYCHIAT, V57, P841, DOI 10.1016/j.biopsych.2005.01.023; LEDOUX JE, 1984, J NEUROSCI, V4, P683; Li AJ, 2002, EUR J NEUROSCI, V16, P1313, DOI 10.1046/j.1460-9568.2002.02193.x; Li SH, 2008, J EXP PSYCHOL ANIM B, V34, P336, DOI 10.1037/0097-7403.34.3.336; Lin HC, 2009, CEREB CORTEX, V19, P165, DOI 10.1093/cercor/bhn075; Lovibond PF, 2000, BEHAV RES THER, V38, P967, DOI 10.1016/S0005-7967(99)00121-7; MacKillop J, 2008, EXP CLIN PSYCHOPHARM, V16, P322, DOI [10.1037/a0012686, 10.1037/a0012686`]; Malvaez M, 2013, P NATL ACAD SCI USA, V110, P2647, DOI [10.1073/pnas.1213364110, 10.1073/pnas.1213364110/-/DCSupplemental]; Mao SC, 2006, J NEUROSCI, V26, P8892, DOI 10.1523/JNEUROSCI.0365-06.2006; Maren S, 2006, P NATL ACAD SCI USA, V103, P18020, DOI 10.1073/pnas.0608398103; Maren S, 2011, NEURON, V70, P830, DOI 10.1016/j.neuron.2011.04.023; Maren S, 2004, NAT REV NEUROSCI, V5, P844, DOI 10.1038/nrn1535; Maren S, 2013, NAT REV NEUROSCI, V14, P417, DOI 10.1038/nrn3492; Maren S, 2000, BEHAV BRAIN RES, V110, P97, DOI 10.1016/S0166-4328(99)00188-6; Marsicano G, 2002, NATURE, V418, P530, DOI 10.1038/nature00839; Matsuda S, 2010, PROG NEURO-PSYCHOPH, V34, P895, DOI 10.1016/j.pnpbp.2010.04.013; Matsumoto Y, 2013, PSYCHOPHARMACOLOGY, V229, P51, DOI 10.1007/s00213-013-3078-9; McConnell B.L., 2012, LEARN BEHAV, V41, P119; Milad MR, 2009, NEUROSCIENCE, V164, P887, DOI 10.1016/j.neuroscience.2009.09.011; Milad MR, 2012, ANNU REV PSYCHOL, V63, P129, DOI 10.1146/annurev.psych.121208.131631; Monfils MH, 2009, SCIENCE, V324, P951, DOI 10.1126/science.1167975; Moody EW, 2006, Q J EXP PSYCHOL, V59, P809, DOI 10.1080/17470210500299045; Morris MJ, 2013, J NEUROSCI, V33, P6401, DOI 10.1523/JNEUROSCI.1001-12.2013; Morris RW, 2007, BEHAV NEUROSCI, V121, P501, DOI 10.1037/0735-7044.121.3.501; Mueller D, 2010, BEHAV BRAIN RES, V208, P1, DOI 10.1016/j.bbr.2009.11.025; Mueller D, 2009, PSYCHOPHARMACOLOGY, V204, P599, DOI 10.1007/s00213-009-1491-x; Mueller D, 2008, J NEUROSCI, V28, P369, DOI 10.1523/JNEUROSCI.3248-07.2008; Murchison CF, 2004, CELL, V117, P131, DOI 10.1016/S0092-8674(04)00259-4; Myers KM, 2002, NEURON, V36, P567, DOI 10.1016/S0896-6273(02)01064-4; Neumann DL, 2007, BEHAV RES THER, V45, P385, DOI 10.1016/j.brat.2006.02.001; Okamura N., 2010, NEUROPSYCHOPHARMACOL, V36, P744; Orinstein AJ, 2010, BEHAV THER, V41, P14, DOI 10.1016/j.beth.2008.11.001; Orr SP, 2006, BIOL PSYCHOL, V73, P262, DOI 10.1016/j.biopsycho.2006.05.001; Ouyang M, 2005, P NATL ACAD SCI USA, V102, P9347, DOI 10.1073/pnas.0502315102; Pamplona FA, 2006, PSYCHOPHARMACOLOGY, V188, P641, DOI 10.1007/s00213-006-0514-0; Pamplona FA, 2008, NEUROBIOL LEARN MEM, V90, P290, DOI 10.1016/j.nlm.2008.04.003; Parnas AS, 2005, NEUROBIOL LEARN MEM, V83, P224, DOI 10.1016/j.nlm.2005.01.001; Parsons TD, 2008, J BEHAV THER EXP PSY, V39, P250, DOI 10.1016/j.jbtep.2007.07.007; Pavlov IP, 1927, CONDITIONED REFLEXES; Peters J, 2010, SCIENCE, V328, P1288, DOI 10.1126/science.1186909; Phelps EA, 2005, NEURON, V48, P175, DOI 10.1016/j.neuron.2005.09.025; Pitman RK, 2011, BEHAV NEUROSCI, V125, P632, DOI 10.1037/a0024364; Ponnusamy R, 2005, LEARN MEMORY, V12, P399, DOI 10.1101/lm.96605; Powers MB, 2009, J ANXIETY DISORD, V23, P350, DOI 10.1016/j.janxdis.2009.01.001; Quirk GJ, 2006, BIOL PSYCHIAT, V60, P337, DOI 10.1016/j.biopsych.2006.03.010; Rabinak CA, 2013, NEUROPHARMACOLOGY, V64, P396, DOI 10.1016/j.neuropharm.2012.06.063; Ren JT, 2013, PROG NEURO-PSYCHOPH, V44, P257, DOI 10.1016/j.pnpbp.2013.02.017; Rescorla RA, 2000, J EXP PSYCHOL ANIM B, V26, P251, DOI 10.1037//0097-7403.26.3.251; Ressler KJ, 2004, ARCH GEN PSYCHIAT, V61, P1136, DOI 10.1001/archpsyc.61.11.1136; Riddle MC, 2013, NEUROPSYCHOPHARMACOL, V38, P930, DOI 10.1038/npp.2012.268; Rodrigues SM, 2009, ANNU REV NEUROSCI, V32, P289, DOI 10.1146/annurev.neuro.051508.135620; Rodriguez-Romaguera J, 2009, BIOL PSYCHIAT, V65, P887, DOI 10.1016/j.biopsych.2009.01.009; Roozendaal B, 2011, BEHAV NEUROSCI, V125, P797, DOI 10.1037/a0026187; Rothbaum BO, 1999, BEHAV MODIF, V23, P507, DOI 10.1177/0145445599234001; Ruehle S, 2013, J NEUROSCI, V33, P10264, DOI 10.1523/JNEUROSCI.4171-12.2013; Saito Y., 2012, SYNAPSE, V67, P161; Santini E, 2010, J NEUROSCI, V30, P12379, DOI 10.1523/JNEUROSCI.1295-10.2010; Schiller D., 2013, P NATL ACAD SCI US; Schiller D, 2010, NATURE, V463, P49, DOI 10.1038/nature08637; Shiban Y, 2013, BEHAV RES THER, V51, P68, DOI 10.1016/j.brat.2012.10.007; SHIPLEY RH, 1974, J COMP PHYSIOL PSYCH, V87, P699, DOI 10.1037/h0036997; Si W, 2010, J NEUROCHEM, V115, P475, DOI 10.1111/j.1471-4159.2010.06947.x; Sierra-Mercado D., 2010, NEUROPSYCHOPHARMACOL, V36, P529; Smits JAJ, 2013, BIOL PSYCHIAT, V73, P1054, DOI 10.1016/j.biopsych.2012.12.009; Soeter M, 2011, LEARN MEMORY, V18, P357, DOI 10.1101/lm.2148511; Soeter M., 2011, NEUROPSYCHOPHARMACOL, V37, P1204; Spennato G, 2008, PSYCHOPHARMACOLOGY, V196, P583, DOI 10.1007/s00213-007-0993-7; Toth I, 2012, NEUROPHARMACOLOGY, V62, P1619, DOI 10.1016/j.neuropharm.2011.10.021; Urcelay GP, 2009, LEARN MOTIV, V40, P343, DOI 10.1016/j.lmot.2009.04.003; Urcelay GP, 2009, LEARN BEHAV, V37, P60, DOI 10.3758/LB.37.1.60; Vansteenwegen D, 2007, BEHAV RES THER, V45, P1169, DOI 10.1016/j.brat.2006.08.023; Verma D, 2012, BRIT J PHARMACOL, V166, P1461, DOI 10.1111/j.1476-5381.2012.01872.x; Vervliet B, 2007, BEHAV RES THER, V45, P375, DOI 10.1016/j.brat.2006.01.009; Walker DL, 2002, J NEUROSCI, V22, P2343; Weber M, 2007, NEUROBIOL LEARN MEM, V87, P476, DOI 10.1016/j.nlm.2006.12.010; Wei W, 2012, J NEUROSCI, V32, P11930, DOI 10.1523/JNEUROSCI.0178-12.2012; Woods AM, 2006, BEHAV NEUROSCI, V120, P1159, DOI 10.1037/0735-7044.120.5.1159; Yang YL, 2006, NEUROPSYCHOPHARMACOL, V31, P912, DOI 10.1038/sj.npp.1300899; Yang YL, 2007, NEUROPSYCHOPHARMACOL, V32, P1042, DOI 10.1038/sj.npp.1301215; Yang YL, 2005, NEUROSCIENCE, V134, P247, DOI 10.1016/j.neuroscience.2005.04.003; Zechel S, 2010, NEUROSCIENTIST, V16, P357, DOI 10.1177/1073858410371513; Zelikowsky M, 2013, BIOL PSYCHIAT, V73, P345, DOI 10.1016/j.biopsych.2012.08.006; Zushida K, 2007, J NEUROSCI, V27, P158, DOI 10.1523/JNEUROSCI.3842-06.2007 151 1 1 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0361-9230 1873-2747 BRAIN RES BULL Brain Res. Bull. JUN 2014 105 SI 46 60 10.1016/j.brainresbull.2013.12.007 15 Neurosciences Neurosciences & Neurology AK7MH WOS:000338611900007 J Nielsen, F; Nock, R; Amari, S Nielsen, Frank; Nock, Richard; Amari, Shun-ichi On Clustering Histograms with k-Means by Using Mixed alpha-Divergences ENTROPY English Article bag-of-X; alpha-divergence; Jeffreys divergence; centroid; k-means clustering; k-means seeding KULLBACK-LEIBLER DISTANCE; INFORMATION; CENTROIDS; APPROXIMATION Clustering sets of histograms has become popular thanks to the success of the generic method of bag-of-X used in text categorization and in visual categorization applications. In this paper, we investigate the use of a parametric family of distortion measures, called the alpha-divergences, for clustering histograms. Since it usually makes sense to deal with symmetric divergences in information retrieval systems, we symmetrize the alpha-divergences using the concept of mixed divergences. First, we present a novel extension of k-means clustering to mixed divergences. Second, we extend the k-means++ seeding to mixed alpha-divergences and report a guaranteed probabilistic bound. Finally, we describe a soft clustering technique for mixed alpha-divergences. [Nielsen, Frank] Sony Comp Sci Labs Inc, Tokyo 1410022, Japan; [Nielsen, Frank] Ecole Polytech, F-91128 Palaiseau, France; [Nock, Richard] NICTA, Alexandria, NSW 1435, Australia; [Nock, Richard] Australian Natl Univ, Alexandria, NSW 1435, Australia; [Amari, Shun-ichi] RIKEN, Brain Sci Inst, Wako, Saitama 3510198, Japan Nielsen, F (reprint author), Sony Comp Sci Labs Inc, Tokyo 1410022, Japan. Frank.Nielsen@acm.org; amari@brain.riken.jp Australian Government; Australian Research Council through the ICT Centre of Excellence program NICTA is funded by the Australian Government as represented by the Department of Broadband, Communication and the Digital Economy and the Australian Research Council through the ICT Centre of Excellence program. ALI SM, 1966, J ROY STAT SOC B, V28, P131; Amari SI, 2007, NEURAL COMPUT, V19, P2780, DOI 10.1162/neco.2007.19.10.2780; Amari S.I, 2000, METHODS INFORM GEOME; Amari SI, 2009, IEEE T INFORM THEORY, V55, P4925, DOI 10.1109/TIT.2009.2030485; Amari S.I., 2013, MATH SCI SUURIKAGAKU, P65; Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027; Baker L. D., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290970; Banerjee A, 2005, J MACH LEARN RES, V6, P1705; BARRY DA, 1995, ACM T MATH SOFTWARE, V21, P161, DOI 10.1145/203082.203084; BENTAL A, 1989, J MATH ANAL APPL, V139, P537, DOI 10.1016/0022-247X(89)90128-5; Besenyei A, 2012, MATH INEQUAL APPL, V15, P973, DOI 10.7153/mia-15-83; Bhattacharya A., 2014, THEORY APPL MODELS C, V8402, P7; Bigi B, 2003, LECT NOTES COMPUT SC, V2633, P305; Chandrasekhar V, 2012, INT J COMPUT VISION, V96, P384, DOI 10.1007/s11263-011-0453-z; CHERNOFF H, 1952, ANN MATH STAT, V23, P493, DOI 10.1214/aoms/1177729330; Cichocki A, 2011, ENTROPY-SWITZ, V13, P134, DOI 10.3390/e13010134; Csiszar I., 1967, STUD SCI MATH HUNG, V2, P229; Csurka G., 2004, WORKSH STAT LEARN CO, V1, P1; Heinz E., 1951, MATH ANN, V123, P415, DOI 10.1007/BF02054965; Jegou H, 2010, INT J COMPUT VISION, V87, P316, DOI 10.1007/s11263-009-0285-2; Kwitt R, 2012, MED IMAGE ANAL, V16, P1415, DOI 10.1016/j.media.2012.04.010; LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/TIT.1982.1056489; Lloyd S.P., 1957, RR5497 BELL LAB; Matsuyama Y, 2003, IEEE T INFORM THEORY, V49, P692, DOI 10.1109/TIT.2002.808105; MORIMOTO T, 1963, J PHYS SOC JPN, V18, P328, DOI 10.1143/JPSJ.18.328; Nielsen F, 2013, IEEE SIGNAL PROC LET, V20, P269, DOI 10.1109/LSP.2013.2243726; Nielsen F, 2009, 2009 6TH INTERNATIONAL SYMPOSIUM ON VORONOI DIAGRAMS (ISVD 2009), P71, DOI 10.1109/ISVD.2009.15; Nielsen F, 2010, 10094004 ARXIV; Nielsen F, 2011, IEEE T INFORM THEORY, V57, P5455, DOI 10.1109/TIT.2011.2159046; Nielsen F, 2009, IEEE T INFORM THEORY, V55, P2882, DOI 10.1109/TIT.2009.2018176; Nielsen F., 2009, ORG09114863 ARXIV; Nielsen F, 2013, IEEE SIGNAL PROC LET, V20, P657, DOI 10.1109/LSP.2013.2260538; Nock R., 2013, P 13 ACM IEEE CS JOI, P313; Nock R, 2008, LECT NOTES ARTIF INT, V5212, P154, DOI 10.1007/978-3-540-87481-2_11; Olszewski D, 2014, PATTERN RECOGN, V47, P2031, DOI 10.1016/j.patcog.2013.11.019; Romberg S., 2013, P 3 ACM C INT C MULT, P113; Steinhaus H., 1957, B ACAD POL SCI, V4, P801; Teboulle M, 2007, J MACH LEARN RES, V8, P65; Veldhuis R, 2002, IEEE SIGNAL PROC LET, V9, P96, DOI 10.1109/97.995827; Wu JX, 2009, IEEE I CONF COMP VIS, P630; Yu ZD, 2012, PROC CVPR IEEE, P781; Zhu HY, 1997, OPERAT RES COMP SCI, V8, P394 42 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy JUN 2014 16 6 3273 3301 10.3390/e16063273 29 Physics, Multidisciplinary Physics AK6NV WOS:000338545300018 J Iliyasu, AM; Venegas-Andraca, SE; Yan, F; Sayed, A Iliyasu, Abdullah M.; Venegas-Andraca, Salvador E.; Yan, Fei; Sayed, Ahmed Hybrid Quantum-Classical Protocol for Storage and Retrieval of Discrete-Valued Information ENTROPY English Article quantum computing; hybrid protocol; quantum technology; quantum applications MECHANICAL HAMILTONIAN MODELS; TURING-MACHINES; COMPUTERS; ALGORITHMS; PHYSICS; IMAGE In this paper we present a hybrid (i.e., quantum-classical) adaptive protocol for the storage and retrieval of discrete-valued information. The purpose of this paper is to introduce a procedure that exhibits how to store and retrieve unanticipated information values by using a quantum property, that of using different vector space bases for preparation and measurement of quantum states. This simple idea leads to an interesting old wish in Artificial Intelligence: the development of computer systems that can incorporate new knowledge on a real-time basis just by hardware manipulation. [Iliyasu, Abdullah M.; Sayed, Ahmed] Salman Bin Abdulaziz Univ, Coll Engn, Al Kharj Riyadh 11942, Saudi Arabia; [Iliyasu, Abdullah M.; Yan, Fei] Tokyo Inst Technol, Dept Computat Intelligence & Syst Sci, Yokohama, Kanagawa 2268502, Japan; [Venegas-Andraca, Salvador E.] Escuela Nacl Posgrad Ciencias Ingn & Tecnol, Tecnol Monterrey, Atizapan De Zaragoza 52926, Estado De Mexic, Mexico; [Venegas-Andraca, Salvador E.] Tecnol Monterrey, Atizapan De Zaragoza 52926, Estado De Mexic, Mexico; [Yan, Fei] Changchun Univ Sci & Technol, Sch Comp Sci & Technol, Changchun 130022, Peoples R China Venegas-Andraca, SE (reprint author), Escuela Nacl Posgrad Ciencias Ingn & Tecnol, Tecnol Monterrey, Carretera Lago Guadalupe Km 3-5, Atizapan De Zaragoza 52926, Estado De Mexic, Mexico. a.iliyasu@sau.edu.sa; salvador.venegas-andraca@keble.oxon.org; yanfei@cust.edu.cn; a.salama@sau.edu.sa Salman Bin Abdulaziz University via the Deanship for Scientific Research International partnership Programme; SNI-CONACyT (SNI) [41594]; Tecnologico de Monterrey The study is sponsored by the Salman Bin Abdulaziz University via the Deanship for Scientific Research International partnership Programme. Additionally, SVA gratefully acknowledges the support of SNI-CONACyT (SNI number 41594) and Tecnologico de Monterrey. All authors thank the anonymous reviewers of our manuscript for their insightful criticisms which greatly improved our work. Abal G, 2008, PHYSICA A, V387, P5326, DOI 10.1016/j.physa.2008.04.036; BENIOFF P, 1982, J STAT PHYS, V29, P515, DOI 10.1007/BF01342185; BENIOFF P, 1982, PHYS REV LETT, V48, P1581, DOI 10.1103/PhysRevLett.48.1581; BENIOFF PA, 1982, INT J THEOR PHYS, V21, P177, DOI 10.1007/BF01857725; BENIOFF P, 1980, J STAT PHYS, V22, P563, DOI 10.1007/BF01011339; Bergou J. A., 2013, INTRO THEORY QUANTUM; Brown KL, 2010, ENTROPY-SWITZ, V12, P2268, DOI 10.3390/e12112268; Childs A, 2003, P 35 ACM S THEOR COM, P59; Childs AM, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.180501; Childs AM, 2010, REV MOD PHYS, V82, P1, DOI 10.1103/RevModPhys.82.1; Crowston K., 2010, P 73 ASIA T ANN M NA; DEUTSCH D, 1985, P ROY SOC LOND A MAT, V400, P97, DOI 10.1098/rspa.1985.0070; Deutsch D, 2000, B SYMB LOG, V6, P265, DOI 10.2307/421056; ERA-Pilot, 2007, QUANTUM INFORM PROCE; Feynman R.P., 1999, FEYNMAN LECT COMPUTA; FEYNMAN RP, 1982, INT J THEOR PHYS, V21, P467, DOI 10.1007/BF02650179; FEYNMAN RP, 1986, FOUND PHYS, V16, P507, DOI 10.1007/BF01886518; Grover L. K., 1996, Proceedings of the Twenty-Eighth Annual ACM Symposium on the Theory of Computing, DOI 10.1145/237814.237866; Gruska J., 1999, QUANTUM COMPUTING; Harris SA, 2010, PHILOS T R SOC A, V368, P3581, DOI 10.1098/rsta.2010.0087; Horn D, 2002, PHYS REV LETT, V88, DOI 10.1103/PhysRevLett.88.018702; Huo R, 2011, J BREATH RES, V5, DOI 10.1088/1752-7155/5/4/046006; Iliyasu AM, 2013, ENTROPY-SWITZ, V15, P2874, DOI 10.3390/e15082874; Iliyasu AM, 2012, INFORM SCIENCES, V186, P126, DOI 10.1016/j.ins.2011.09.028; Iliyasu AM, 2011, INT J QUANTUM INF, V9, P1459, DOI 10.1142/S0219749911008015; Jordan SP, 2005, PHYS REV LETT, V95, DOI 10.1103/PhysRevLett.95.050501; Kitaev A, 2002, CLASSICAL QUANTUM CO; Lanzagorta M, 2011, QUANTUM RADAR; Lanzagorta M., 2009, QUANTUM COMPUTER SCI; Lanzagorta M, 2010, MATH STRUCT COMP SCI, V20, P1117, DOI 10.1017/S0960129510000411; Le P.Q., 2011, J ADV COMPUT INTELL, V15, P698; Le P.Q., 2011, THEOR COMPUT SCI, V412, P1046; Le P.Q., 2010, QUANTUM INF PROCESS, V10, P63; Margolus N, 2003, INT J THEOR PHYS, V42, P309, DOI 10.1023/A:1024403618093; Mariantoni M, 2011, SCIENCE, V334, P61, DOI 10.1126/science.1208517; Mermin N.D., 2007, QUANTUM COMPUTER SCI; Mermin ND, 2003, AM J PHYS, V71, P23, DOI 10.1119/1.1522741; Nielsen MA, 2000, QUANTUM COMPUTATION; QIST, 2004, ADV RES DEV ACT 2004; Rieffel E, 2000, ACM COMPUT SURV, V32, P300, DOI 10.1145/367701.367709; Shor PW, 1997, SIAM J COMPUT, V26, P1484, DOI 10.1137/S0097539795293172; Sun B, 2011, P IEEE 7 INT S INT S, P160; Trugenberger CA, 2002, PHYS REV LETT, V89, DOI 10.1103/PhysRevLett.89.277903; Trugenberger CA, 2002, QUANTUM INF PROCESS, V1, P471, DOI 10.1023/A:1024022632303; Trugenberger CA, 2001, PHYS REV LETT, V87, DOI 10.1103/PhysRevLett.87.067901; Venegas-Andraca SE, 2003, P SOC PHOTO-OPT INS, V5105, P137, DOI 10.1117/12.485960; Venegas-Andraca SE, 2010, QUANTUM INF PROCESS, V9, P1, DOI 10.1007/s11128-009-0123-z; Venegas-Andraca S.E., 2003, P IJCAI 2003 TELLIGE, P1563; Venegas-Andraca S.E., 2008, QUANTUM WALKS COMPUT; WOOTTERS WK, 1989, ANN PHYS-NEW YORK, V191, P363, DOI 10.1016/0003-4916(89)90322-9 50 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 1099-4300 ENTROPY-SWITZ Entropy JUN 2014 16 6 3537 3551 10.3390/e16063537 15 Physics, Multidisciplinary Physics AK6NV WOS:000338545300029 J Rasmy, M; Koike, T; Li, X Rasmy, Mohamed; Koike, Toshio; Li, Xin Applicability of Multi-Frequency Passive Microwave Observations and Data Assimilation Methods for Improving Numerical Weather Forecasting in Niger, Africa REMOTE SENSING English Article passive microwave remote sensing; data assimilation; numerical forecast; soil moisture; clouds; precipitation TIBETAN PLATEAU; PREDICTION SYSTEM; SATELLITE DATA; MODEL; PARAMETERIZATION; PRECIPITATION; INDEX The development of satellite-based forecasting systems is one of the few affordable solutions for developing regions (e.g., West Africa) that cannot afford ground-based observation networks. Although low-frequency passive microwave data have been used extensively for land surface monitoring, the use of high-frequency passive microwave data that contain cloud information is very limited over land because of strong heterogeneous land surface emissions. The Coupled Atmosphere and Land Data Assimilation System (CALDAS) was developed by merging soil moisture information estimated from low-frequency data with corresponding high-frequency data to estimate cloud information and, thus, improve weather forecasting over Niger, West Africa. The results showed that the assimilated soil moisture and cloud distributions were reasonably comparable to satellite retrievals of soil moisture and cloud observations. However, assimilating soil moisture alone within a mesoscale model produced only marginal improvements in the forecast, whereas the assimilation of both soil moisture and cloud distributions improved the simulation of temperature and humidity profiles. Rainfall forecasts from CALDAS also correlated well with satellite retrievals. This indicates the potential use of CALDAS as a reliable forecasting tool for developing regions. Further developments of CALDAS and the inclusion of data from several other sensors will be researched in future studies. [Rasmy, Mohamed; Koike, Toshio] Univ Tokyo, Dept Civil Engn, Tokyo 1138656, Japan; [Li, Xin] Chinese Acad Sci, Cold & Arid Regions Environm & Engn Res Inst, Lanzhou 730000, Peoples R China Rasmy, M (reprint author), Univ Tokyo, Dept Civil Engn, Tokyo 1138656, Japan. rasmy@hydra.t.u-tokyo.ac.jp; tkoike@hydra.t.u-tokyo.ac.jp; lixin@lzb.ac.cn Li, Xin/F-7473-2011 Li, Xin/0000-0003-2999-9818 Draper CS, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011650; DUAN QY, 1992, WATER RESOUR RES, V28, P1015, DOI 10.1029/91WR02985; Evensen G., 2003, OCEAN DYNAM, V53, P343, DOI DOI 10.1007/S10236-003-0036-9; Fujii H., 2009, J REMOTE SENS SOC JP, V29, P282; Fujii H, 2001, J METEOROL SOC JPN, V79, P475, DOI 10.2151/jmsj.79.475; Huang C., 2008, REMOTE SENS ENVIRON, V112, P889; *INT PAN CLIM CHAN, 2007, 4 INT PAN CLIM CHANG; Jackson T., 1997, IEEE T GEOSCI REMOTE, V37, P2136; JACKSON TJ, 1991, REMOTE SENS ENVIRON, V36, P203, DOI 10.1016/0034-4257(91)90057-D; Jackson TJ, 2010, IEEE T GEOSCI REMOTE, V48, P4256, DOI 10.1109/TGRS.2010.2051035; Jeu R., 2008, SURV GEOPHYS, V29, P399, DOI DOI 10.1007/S10712-008-9044-0; KAIN JS, 1993, METEOR MON, V24, P165; Koster RD, 2003, GEOPHYS RES LETT, V30, DOI 10.1029/2002GL016571; Koster RD, 2004, J HYDROMETEOROL, V5, P1049, DOI 10.1175/JHM-387.1; LIN YL, 1983, J CLIM APPL METEOROL, V22, P1065, DOI 10.1175/1520-0450(1983)022<1065:BPOTSF>2.0.CO;2; Liu GS, 1998, J METEOROL SOC JPN, V76, P335; Sellers PJ, 1996, J CLIMATE, V9, P706, DOI 10.1175/1520-0442(1996)009<0706:ARLSPF>2.0.CO;2; Mirza CR, 2008, IEEE T GEOSCI REMOTE, V46, P119, DOI 10.1109/TGRS.2007.907740; PALOSCIA S, 1988, IEEE T GEOSCI REMOTE, V26, P617, DOI 10.1109/36.7687; Rasmy M, 2011, IEEE T GEOSCI REMOTE, V49, P2847, DOI 10.1109/TGRS.2011.2112667; Rasmy M, 2012, IEEE T GEOSCI REMOTE, V50, P4227, DOI 10.1109/TGRS.2012.2190517; Reichle RH, 2002, MON WEATHER REV, V130, P103, DOI 10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2; Scipal K, 2008, ADV WATER RESOUR, V31, P1101, DOI 10.1016/j.advwatres.2008.04.013; Sohne N, 2008, MON WEATHER REV, V136, P4421, DOI 10.1175/2008MWR2432.1; Wentz F.J., 2007, ALGORITHM THEORET S1; Xue M, 2001, METEOROL ATMOS PHYS, V76, P143, DOI 10.1007/s007030170027 26 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. JUN 2014 6 6 5306 5324 10.3390/rs6065306 19 Remote Sensing Remote Sensing AK9RB WOS:000338763300028 J Im, S; Choi, J Im, Seokjin; Choi, JinTak A Distributed Air Index Based on Maximum Boundary Rectangle over Grid-Cells for Wireless Non-Flat Spatial Data Broadcast SENSORS English Article non-flat wireless data broadcast; distributed indexing scheme; spatial data; window query DEMAND DATA BROADCAST; MOBILE USERS; ACCESS; ENVIRONMENTS; ALGORITHMS; RETRIEVAL; EFFICIENT; PROTOCOLS; SCHEME In the pervasive computing environment using smart devices equipped with various sensors, a wireless data broadcasting system for spatial data items is a natural way to efficiently provide a location dependent information service, regardless of the number of clients. A non-flat wireless broadcast system can support the clients in accessing quickly their preferred data items by disseminating the preferred data items more frequently than regular data on the wireless channel. To efficiently support the processing of spatial window queries in a non-flat wireless data broadcasting system, we propose a distributed air index based on a maximum boundary rectangle (MaxBR) over grid-cells (abbreviated DAIM), which uses MaxBRs for filtering out hot data items on the wireless channel. Unlike the existing index that repeats regular data items in close proximity to hot items at same frequency as hot data items in a broadcast cycle, DAIM makes it possible to repeat only hot data items in a cycle and reduces the length of the broadcast cycle. Consequently, DAIM helps the clients access the desired items quickly, improves the access time, and reduces energy consumption. In addition, a MaxBR helps the clients decide whether they have to access regular data items or not. Simulation studies show the proposed DAIM outperforms existing schemes with respect to the access time and energy consumption. [Im, Seokjin] Sungkyul Univ, Dept Comp Engn, Anyang 430742, Gyeonggi Do, South Korea; [Choi, JinTak] Incheon Natl Univ, Dept Comp Engn, Inchon 406772, South Korea Choi, J (reprint author), Incheon Natl Univ, Dept Comp Engn, 119 Acad Ro, Inchon 406772, South Korea. imseokjin@sungkyul.edu; choi@incheon.ac.kr Incheon National University This work was supported by the Incheon National University (International Cooperative) Research Grant in 2012. Acharya S., 1995, P ACM SIGMOD C, P199, DOI 10.1145/223784.223816; Acharya S., 1997, P ACM SIGMOD INT C M, P183, DOI 10.1145/253260.253293; Aksoy D, 1999, IEEE ACM T NETWORK, V7, P846, DOI 10.1109/90.811450; Bujan D, 2013, SENSORS-BASEL, V13, P8060, DOI 10.3390/s130708060; Datta A, 1997, PROC INT CONF DATA, P124, DOI 10.1109/ICDE.1997.581745; Datta A, 1999, ACM T DATABASE SYST, V24, P1, DOI 10.1145/310701.310710; Im S, 2007, IEICE T COMMUN, VE90B, P2629, DOI 10.1093/ietcom/e90-b.9.2629; Im S., 2006, P 5 ACM WORKSH MOBID, P59, DOI 10.1145/1140104.1140117; Im S, 2011, J COMMUN NETW-S KOR, V13, P400; Imielinski T, 1994, P 4 INT C EXT DAT TE, P245; Imielinski T, 1997, IEEE T KNOWL DATA EN, V9, P353, DOI 10.1109/69.599926; Imielinski T., 1994, P 1994 ACM SIGMOD IN, P25, DOI 10.1145/191839.191846; Lee BG, 2012, SENSORS-BASEL, V12, P17536, DOI 10.3390/s121217536; Lee D.L, 2002, IEEE PERVAS COMPUT, V1, P65; Lee W. C., 2005, P 25 INT C DISTR COM, P349; Shi Y, 2013, SENSORS-BASEL, V13, P119, DOI 10.3390/s130100119; Su CJ, 1999, WIREL NETW, V5, P137, DOI 10.1023/A:1019134607998; Susi M, 2013, SENSORS-BASEL, V13, P1539, DOI 10.3390/s130201539; Vaidya NH, 1999, WIREL NETW, V5, P171, DOI 10.1023/A:1019142809816; Villa D., 2013, SENSORS, V14, P779; Xu JL, 2006, IEEE T PARALL DISTR, V17, P3; Yao YX, 2006, IEEE T KNOWL DATA EN, V18, P1111; Yu JX, 2000, WIREL NETW, V6, P89, DOI 10.1023/A:1019117026171; Zheng BH, 2009, VLDB J, V18, P959, DOI 10.1007/s00778-009-0137-2; Zheng BH, 2004, WIREL NETW, V10, P723, DOI 10.1023/B:WINE.0000044031.03597.97 25 0 0 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 1424-8220 SENSORS-BASEL Sensors JUN 2014 14 6 10619 10643 10.3390/s140610619 25 Chemistry, Analytical; Electrochemistry; Instruments & Instrumentation Chemistry; Electrochemistry; Instruments & Instrumentation AK7VK WOS:000338635600062 J El Haj, M; Allain, P; Kessels, RPC El Haj, Mohamad; Allain, Philippe; Kessels, Roy P. C. The cognitive and neuroanatomical underpinnings of destination memory TRANSLATIONAL NEUROSCIENCE English Article Destination memory; Episodic memory; Medial temporal cortex; Precuneus; Prefrontal cortex MEDIAL TEMPORAL-LOBE; PREFRONTAL CORTEX ACTIVITY; ALZHEIMERS-DISEASE; EPISODIC MEMORY; AUTOBIOGRAPHICAL MEMORY; FUNCTIONAL NEUROANATOMY; RECOGNITION MEMORY; SOURCE AMNESIA; OLDER-ADULTS; RETRIEVAL The ability to remember the destination to whom a piece of information has been addressed (e.g., did I tell you about the weekend?) has been labelled destination memory. Although this topic has been relatively scarcely studied, recent studies support the notion that destination recall can be the subject of important distortions in healthy younger and older adults and in individuals with Alzheimer's disease. This research also links destination recall to several cognitive domains such as episodic memory, executive function, and self-referential processes (e.g., did I tell you about the weekend?). The present review aims to assemble these findings into a comprehensive framework and shed light onto potential neuroanatomical underpinnings of destination memory, thus providing a promising venue for future exploration and research. [El Haj, Mohamad] Univ Montpellier 3, Epsylon Lab, EA 4556, F-34032 Montpellier, France; [El Haj, Mohamad] Univ North France, Dept Psychol, Res Unit Cognit & Affect Sci, Lille, France; [Allain, Philippe] Univ Nantes & Angers, LUNAM Univ, Lab Psychol Pays Loire, EA 4638, Nantes, France; [Allain, Philippe] CHU Angers, Ctr Memoire Ressources & Rech, Angers, France; [Kessels, Roy P. C.] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6525 ED Nijmegen, Netherlands; [Kessels, Roy P. C.] Korsako Clin, Vincent van Gogh Inst Psychiat, Venray, Netherlands; [Kessels, Roy P. C.] Radboud Univ Nijmegen, Med Ctr, Dept Med Psychol, NL-6525 ED Nijmegen, Netherlands El Haj, M (reprint author), Univ Montpellier 3, Epsylon Lab, EA 4556, F-34032 Montpellier, France. mohamad.el-haj@univ-montp3.fr Addis DR, 2004, NEUROIMAGE, V23, P1460, DOI 10.1016/j.neuroimage.2004.08.007; Bird CM, 2008, NAT REV NEUROSCI, V9, P182, DOI 10.1038/nrn2335; Cavanna AE, 2006, BRAIN, V129, P564, DOI 10.1093/brain/awl004; Conway MA, 2005, J MEM LANG, V53, P594, DOI 10.1016/j.jml.2005.08.005; Davachi L, 2006, CURR OPIN NEUROBIOL, V16, P693, DOI 10.1016/j.conb.2006.10.012; Diana RA, 2007, TRENDS COGN SCI, V11, P379, DOI 10.1016/j.tics.2007.08.001; Dobbins IG, 2006, J COGNITIVE NEUROSCI, V18, P1439, DOI 10.1162/jocn.2006.18.9.1439; Drag LL, 2009, J INT NEUROPSYCH SOC, V15, P399, DOI 10.1017/S1355617709090572; Dudukovic NM, 2007, NEUROPSYCHOLOGIA, V45, P2608, DOI 10.1016/j.neuropsychologia.2007.02.025; Dudukovic NM, 2006, ACTA PSYCHOL, V122, P160, DOI 10.1016/j.actpsy.2005.11.002; Eichenbaum H, 2007, ANNU REV NEUROSCI, V30, P123, DOI 10.1146/annurev.neuro.30.051606.094328; El Haj Mohamad, 2013, Dement Geriatr Cogn Dis Extra, V3, P342, DOI 10.1159/000354187; El Haj M., EXP AGING R IN PRESS; El Haj M, 2013, BEHAV NEUROL, V26, P215, DOI 10.3233/BEN-2012-129014; El Haj M, 2012, BRAIN COGNITION, V80, P185, DOI 10.1016/j.bandc.2012.06.004; El Haj M, 2012, NEUROPSYCHOL REHABIL, V22, P449, DOI 10.1080/09602011.2012.658267; El Haj M, 2014, J CLIN EXP NEUROPSYC, V36, P127, DOI 10.1080/13803395.2013.869309; El Haj M, 2012, GERIATR PSYCHOL NEUR, V10, P197, DOI 10.1684/pnv.2012.0342; El Haj M, 2013, CORTEX, V49, P82, DOI 10.1016/j.cortex.2011.11.014; Freton M., 2013, BRAIN STRUCT FUNCT, P1; Gardiner JM, 1998, CONSCIOUS COGN, V7, P1, DOI 10.1006/ccog.1997.0321; Glisky EL, 2001, J EXP PSYCHOL LEARN, V27, P1131, DOI 10.1037//0278-7393.27.5.1131; Glisky EL, 2008, J EXP PSYCHOL LEARN, V34, P809, DOI 10.1037/0278-7393.34.4.809; Gopie N, 2009, PSYCHOL SCI, V20, P1492, DOI 10.1111/j.1467-9280.2009.02472.x; Gopie N, 2010, PSYCHOL AGING, V25, P922, DOI 10.1037/a0019703; GROBER E, 1987, DEV NEUROPSYCHOL, V3, P13; Henson R, 2005, Q J EXP PSYCHOL-B, V58, P340, DOI 10.1080/02724990444000113; Kensinger EA, 2003, J NEUROSCI, V23, P2407; Kerr KM, 2007, HIPPOCAMPUS, V17, P697, DOI 10.1002/hipo.20315; Kessels RPC, 2012, NEUROPSYCHOL REV, V22, P117, DOI 10.1007/s11065-012-9202-5; Kircher TTJ, 2000, COGNITIVE BRAIN RES, V10, P133, DOI 10.1016/S0926-6410(00)00036-7; Kircher TTJ, 2002, NEUROPSYCHOLOGIA, V40, P683, DOI 10.1016/S0028-3932(01)00138-5; Lepage M, 1998, HIPPOCAMPUS, V8, P313, DOI 10.1002/(SICI)1098-1063(1998)8:4<313::AID-HIPO1>3.0.CO;2-I; Lipton P. A., 2008, NEURAL PLAST, DOI DOI 10.1155/2008/258467; Mayes A, 2007, TRENDS COGN SCI, V11, P126, DOI 10.1016/j.tics.2006.12.003; Mitchell KJ, 2009, PSYCHOL BULL, V135, P638, DOI 10.1037/a0015849; Mitchell KJ, 2004, J COGNITIVE NEUROSCI, V16, P921, DOI 10.1162/0898929041502724; Miyake A, 2000, COGNITIVE PSYCHOL, V41, P49, DOI 10.1006/cogp.1999.0734; Ranganath C, 2010, CURR DIR PSYCHOL SCI, V19, P131, DOI 10.1177/0963721410368805; Raye CL, 2000, PSYCHOBIOLOGY, V28, P197; Ruby P, 2001, NAT NEUROSCI, V4, P546; SCHACTER DL, 1984, J VERB LEARN VERB BE, V23, P593, DOI 10.1016/S0022-5371(84)90373-6; SHIMAMURA AP, 1987, J EXP PSYCHOL LEARN, V13, P464, DOI 10.1037//0278-7393.13.3.464; Shohamy D, 2008, NEURON, V60, P378, DOI 10.1016/j.neuron.2008.09.023; Simons JS, 2005, J NEUROPHYSIOL, V94, P813, DOI 10.1152/jn.01200.2004; SQUIRE LR, 1993, ANNU REV PSYCHOL, V44, P453, DOI 10.1146/annurev.ps.44.020193.002321; Staresina BP, 2006, J NEUROSCI, V26, P9162, DOI 10.1523/JNEUROSCI.2877-06.2006; Summerfield JJ, 2009, NEUROIMAGE, V44, P1188, DOI 10.1016/j.neuroimage.2008.09.033; Wheeler MA, 1997, PSYCHOL BULL, V121, P331, DOI 10.1037/0033-2909.121.3.331; Witter MP, 2000, HIPPOCAMPUS, V10, P398 50 0 0 DE GRUYTER OPEN LTD WARSAW SOLIPSKA 14A-1, 02-482 WARSAW, POLAND 2081-3856 2081-6936 TRANSL NEUROSCI Transl. Neurosci. JUN 2014 5 2 147 151 10.2478/s13380-014-0219-5 5 Neurosciences Neurosciences & Neurology AK8AV WOS:000338650400006 J Shaheen, M; Ezzeldin, AM Shaheen, Mohamed; Ezzeldin, Ahmed Magdy Arabic Question Answering: Systems, Resources, Tools, and Future Trends ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING English Review QA; Factoid questions; QA4MRE; Question analysis; Passage retrieval; Answer extraction; Answer validation; Test-sets; Evaluation; Metrics; Language resources; NLP; Information retrieval; Stemming; Corpus; NER; Stemming; Lemmatization; Morphological analysis; Part-of-speech tagging; Diacritization; Overview; Review; Survey Arabic is the 6th most wide-spread natural language in the world with more than 350 million native speakers. Arabic question answering systems are gaining great importance due to the increasing amounts of Arabic content on the Internet and the increasing demand for information that regular information retrieval techniques cannot satisfy. In spite of the importance of Arabic question answering, there is no review that covers Arabic question answering systems, tools, resources, and test-sets so far, which was the motivation for this work. In this survey, different Arabic question answering systems are demonstrated and analyzed and the main question answering tasks like question analysis, passage retrieval, and answer extraction are explored. The main difficulties of modern standard Arabic and how these difficulties are tamed and classified are also explained. Arabic question answering evaluation metrics, test-sets, and language resources are reviewed, and future trends are also highlighted to guide new research in this area. This survey provides guidance for new research in Arabic question answering to get up-to-date knowledge about the state-of-the-art approaches in this area. It also demonstrates the tools created and used by researchers to build an Arabic question answering system. [Shaheen, Mohamed; Ezzeldin, Ahmed Magdy] Arab Acad Sci Technol & Maritime Transport, Coll Comp & Informat Technol, Alexandria, Egypt Ezzeldin, AM (reprint author), Arab Acad Sci Technol & Maritime Transport, Coll Comp & Informat Technol, Alexandria, Egypt. cshaheen@hotmail.com; a.magdy@a1works.com Abdelbaki H., 2011, P AR LANG TECHN INT; Abdelrahman S., 2010, IJCSI, V1; Abouenour Lahsen, 2011, Natural Language Processing and Information Systems. Proceedings of the 16th International Conference on Applications of Natural Language to Information Systems, NLDB 2011, DOI 10.1007/978-3-642-22327-3_50; Abouenour L., 2009, P 3 INT C AR LANG PR; Abouenour L., 2010, EVALUATED SEMANTIC Q; Abouenour L., 2008, LANG RES EV C LREC M; Abouenour L., 2012, CLEF 2012 WORKSH QUE; Abuleil S., 1998, WORKSH SEM LANG PROC, P1; Al-Safadi L, 2013, ARAB J SCI ENG, V38, P2985, DOI 10.1007/s13369-012-0473-1; Alshalabi R., 2005, Information Technology Journal, V4; ATTIA M, 2008, ADV NATURAL LANGUAGE, V5221, P65, DOI 10.1007/978-3-540-85287-2_7; Attia M, 2009, IEEE T AUDIO SPEECH, V17, P916, DOI 10.1109/TASL.2009.2019298; AWADALLAH R, 2006, ADV INF RETR, V3936, P515, DOI 10.1007/11735106_54; Bekhti S, 2011, INT J ACAD RES, V3, P45; Benajiba Y., 2008, P WORKSH HLT NLP AR, V8, P143; BENAJIBA Y, 2007, COMPUT LINGUIST INTE, V4394, P530, DOI 10.1007/978-3-540-70939-8_47; Benajiba Y., 2007, P WORKSH NAT LANG IN; Benajiba Y., 2007, P WORKSH AR NAT LANG, P3; BENAJIBA Y, 2007, COMPUT LINGUIST INTE, V4394, P143, DOI 10.1007/978-3-540-70939-8_13; Bhaskar P., 2012, CLEF 2012 WORKSH QUE; Bouzouba K., 2007, ISCAL 07; Brini W., 2009, IEEE INT C NAT LANG, P1; Brini W., 2009, POST P NOOJ 2009 TOZ, P8; Buckwalter T, 2002, BUCKWALTER ARABIC MO; Buscaldi D., 2006, CLEF 2006 WORKSH; Diab M., 2009, P 2 INT C AR LANG RE, P285; Elghamry K., 2008, JADT 2008 9 JOURN IN; Elkateb S., 2006, P AR NLP MT C LOND U; Ferrucci D, 2010, AI MAG, V31, P59; Gomez J.M., 2005, LECT NOTES COMPUTER, P816; Habash N., 2009, P 2 INT C AR LANG RE, P102; Hammo B., 2002, P ACL 02 WORKSH COMP, P1, DOI 10.3115/1118637.1118644; Hammo B, 2004, COMPUT HUMANITIES, V38, P397, DOI 10.1007/s10579-004-1917-3; Harmanani H.M., INT ARAB J INF TECHN, V3, P265; Hatcher E., 2004, LUCENE ACTION; Kadri Y., 2006, P CHALL AR NLP MT C; Kanaan Ghassan, 2009, American Journal of Applied Sciences, V6, DOI 10.3844/ajas.2009.797.805; Khoja S., 1999, STEMMING ARABIC TEXT; Kontos J., 2005, P 7 HERCMA HELL EUR; Larkey L.S., 2006, ARABIC INFORM RETRIE; LARKEY LS, 2007, ARABIC COMPUTATIONAL, V38, P221, DOI 10.1007/978-1-4020-6046-5_12; Laurent D., 2006, P WORKSH MULT QUEST, P1, DOI 10.3115/1708097.1708099; Maamouri M., 2004, NEMLAR C AR LANG RES, P102; Manning C.D., 2008, INTRO INFORM RETRIEV, V1; Mesfar S., 2008, MORPHO SYNTACTIC ANA; Minock M., 2005, P IJCAI WORKSH KNOWL, P4; Mohammed F.A., 1993, ACM SIGART B, V4, P21, DOI 10.1145/165482.165488; Moldovan D., 2007, P 16 TEXT RETRIEVAL; Molla D, 2003, IEEE INTELL SYST, V18, P12, DOI 10.1109/MIS.2003.1217623; O'Steen D., 2009, NAMED ENTITY RECOGNI; Pelzer B., 2011, 3 INT C AG ART INT I, P492; Penas A., 2012, CLEF 2012 WORKSH QUE; Penas A., 2011, P 49 ANN M ASS COMP, V1, P1415; Rashwan MAA, 2011, IEEE T AUDIO SPEECH, V19, P166, DOI 10.1109/TASL.2010.2045240; Rosso P., 2005, P S INF COMM TECHN I; Rosso P., 2006, P 4 C SCI RES OUTL T, P11; Sidrine S., 2010, P 6 INT SYST THEOR A; Silberztein M., 2005, P HLT EMNLP INT DEM, P10, DOI 10.3115/1225733.1225739; Smucker M.D., 2008, IR655 U MASS CTR INT; Taghva K., 2005, IEEE INT C INF TECHN, V1, P152; Trigui O., 2010, WORKSH LANG RES HUM, P40; Trigui O., 2012, CLEF 2012 WORKSH QUE; Voorhees E.M., 2001, P 10 TEXT RETR C TRE, P1; Voorhees E. M., 2001, Proceedings of the 2001 ACM CIKM. Tenth International Conference on Information and Knowledge Management; Zaghouani W., 2010, P 7 INT C LANG RES E, P563 65 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1319-8025 2191-4281 ARAB J SCI ENG Arab. J. Sci. Eng. JUN 2014 39 6 4541 4564 10.1007/s13369-014-1062-2 24 Multidisciplinary Sciences Science & Technology - Other Topics AK1TK WOS:000338199100018 J Cohen, MS; Rissman, J; Suthana, NA; Castel, AD; Knowlton, BJ Cohen, Michael S.; Rissman, Jesse; Suthana, Nanthia A.; Castel, Alan D.; Knowlton, Barbara J. Value-based modulation of memory encoding involves strategic engagement of fronto-temporal semantic processing regions COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE English Article Value; Memory; Selective encoding; Reward; Metacognitive control; fMRI VENTROLATERAL PREFRONTAL CORTEX; OLDER-ADULTS; CA1 REGION; EPISODIC MEMORY; CEREBRAL-CORTEX; RETRIEVAL; INFORMATION; FMRI; ANTICIPATION; ACTIVATION A number of prior fMRI studies have focused on the ways in which the midbrain dopaminergic reward system coactivates with hippocampus to potentiate memory for valuable items. However, another means by which people could selectively remember more valuable to-be-remembered items is to be selective in their use of effective but effortful encoding strategies. To broadly examine the neural mechanisms of value on subsequent memory, we used fMRI to assess how differences in brain activity at encoding as a function of value relate to subsequent free recall for words. Each word was preceded by an arbitrarily assigned point value, and participants went through multiple study-test cycles with feedback on their point total at the end of each list, allowing for sculpting of cognitive strategies. We examined the correlation between value-related modulation of brain activity and participants' selectivity index, which measures how close participants were to their optimal point total, given the number of items recalled. Greater selectivity scores were associated with greater differences in the activation of semantic processing regions, including left inferior frontal gyrus and left posterior lateral temporal cortex, during the encoding of high-value words relative to low-value words. Although we also observed value-related modulation within midbrain and ventral striatal reward regions, our fronto-temporal findings suggest that strategic engagement of deep semantic processing may be an important mechanism for selectively encoding valuable items. [Cohen, Michael S.; Rissman, Jesse; Suthana, Nanthia A.; Castel, Alan D.; Knowlton, Barbara J.] Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA 90095 USA Cohen, MS (reprint author), Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA 90095 USA. mcohen1@ucla.edu NSF [BCS-0848246]; Scientific Research Network for Decision Neuroscience and Aging (SRNDNA) (under NIH) [AG039350] Funding was provided by NSF Grant No. BCS-0848246 to B.J.K., and by a grant from the Scientific Research Network for Decision Neuroscience and Aging (SRNDNA) (subaward under NIH Grant No. AG039350) to B.J.K., M. S. C., J.R., and A. D. C. We thank Susan Bookheimer, Martin Monti, Gregory Samanez-Larkin, Michael Vendetti, and Aimee Drolet Rossi for helpful suggestions related to the design and analysis of this study. We thank Brian Knutson and Gregory Samanez-Larkin for providing scripts to run the MID task, and Vishnu Murty for providing an anatomical VTA atlas. We also thank Shruti Ullas for assistance with running participants. Portions of this work were presented at the 20th Annual Meeting of the Cognitive Neuroscience Society, San Francisco, CA, and at the Mechanisms of Motivation, Cognition, and Aging Interactions (MoMCAI) conference, Washington, DC. Adcock RA, 2006, NEURON, V50, P507, DOI 10.1016/j.neuron.2006.03.036; Ariel R, 2014, EXP BRAIN RES, V232, P337, DOI 10.1007/s00221-013-3744-5; Badre D, 2007, NEUROPSYCHOLOGIA, V45, P2883, DOI 10.1016/j.neuropsychologia.2007.06.015; Badre D, 2005, NEURON, V47, P907, DOI 10.1016/j.neuron.2005.07.023; Bookheimer S, 2002, ANNU REV NEUROSCI, V25, P151, DOI 10.1146/annurev.neuro.25.112701.142946; Brodmann K., 1909, VEGLEICHENDE LOKALIS; Castel A. D., 2008, PSYCHOL LEARN MOTIV, V48, P225, DOI DOI 10.1016/S0079-7421(07)48006-9; Castel A. D., 2012, MEMORY AGING CURRENT, P245; Castel AD, 2009, NEUROPSYCHOLOGY, V23, P297, DOI 10.1037/a0014888; Castel AD, 2002, MEM COGNITION, V30, P1078, DOI 10.3758/BF03194325; Castel AD, 2011, DEV PSYCHOL, V47, P1553, DOI 10.1037/a0025623; Castel AD, 2007, MEM COGNITION, V35, P689, DOI 10.3758/BF03193307; Castel AD, 2013, PSYCHOL AGING, V28, P232, DOI 10.1037/a0030678; Conway ARA, 2005, PSYCHON B REV, V12, P769, DOI 10.3758/BF03196772; Costafreda SG, 2006, HUM BRAIN MAPP, V27, P799, DOI 10.1002/hbm.20221; CRAIK FIM, 1975, J EXP PSYCHOL GEN, V104, P268, DOI 10.1037//0096-3445.104.3.268; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; FREY U, 1991, NEUROSCI LETT, V129, P111, DOI 10.1016/0304-3940(91)90732-9; FREY U, 1990, BRAIN RES, V522, P69, DOI 10.1016/0006-8993(90)91578-5; Hanten G, 2007, DEV NEUROPSYCHOL, V32, P585; HOLM S, 1979, SCAND J STAT, V6, P65; HUANG YY, 1995, P NATL ACAD SCI USA, V92, P2446, DOI 10.1073/pnas.92.7.2446; Jay TM, 2003, PROG NEUROBIOL, V69, P375, DOI 10.1016/S0301-0082(03)00085-6; Jenkinson M, 2002, NEUROIMAGE, V17, P825, DOI 10.1006/nimg.2002.1132; Kane MJ, 2004, J EXP PSYCHOL GEN, V133, P189, DOI 10.1037/0096-3445.133.2.189; KAPUR S, 1994, P NATL ACAD SCI USA, V91, P2008, DOI 10.1073/pnas.91.6.2008; Kim H, 2011, NEUROIMAGE, V54, P2446, DOI 10.1016/j.neuroimage.2010.09.045; Kirby KN, 1999, J EXP PSYCHOL GEN, V128, P78, DOI 10.1037/0096-3445.128.1.78; Kirchhoff BA, 2006, NEURON, V51, P263, DOI 10.1016/j.neuron.2006.06.006; Knutson B, 2001, J Neurosci, V21, pRC159; Lancaster JL, 2007, HUM BRAIN MAPP, V28, P1194, DOI 10.1002/hbm.20345; Limbrick-Oldfield EH, 2012, NEUROIMAGE, V59, P1230, DOI 10.1016/j.neuroimage.2011.08.016; Lisman JE, 2005, NEURON, V46, P703, DOI 10.1016/j.neuron.2005.05.002; Liu X, 2011, NEUROSCI BIOBEHAV R, V35, P1219, DOI 10.1016/j.neubiorev.2010.12.012; LOFTUS GR, 1970, J EXP PSYCHOL, V85, P141, DOI 10.1037/h0029537; McGillivray S, 2011, PSYCHOL AGING, V26, P137, DOI 10.1037/a0022681; Miotto EC, 2006, HUM BRAIN MAPP, V27, P288, DOI 10.1002/hbm.20184; Murayama K, 2011, COGNITION, V119, P120, DOI 10.1016/j.cognition.2011.01.001; Murty VP, 2014, CEREB CORTEX, V24, P2160, DOI 10.1093/cercor/bht063; Murty VP, 2012, J NEUROSCI, V32, P8969, DOI 10.1523/JNEUROSCI.0094-12.2012; O'Doherty J. P., 2013, PRINCIPLES FRONTAL L, P302; O'Carroll CM, 2006, LEARN MEMORY, V13, P760, DOI 10.1101/lm.321006; Rosen VM, 1997, J EXP PSYCHOL GEN, V126, P211, DOI 10.1037//0096-3445.126.3.211; Samanez-Larkin GR, 2007, NAT NEUROSCI, V10, P787, DOI 10.1038/nn1894; Savage CR, 2001, BRAIN, V124, P219, DOI 10.1093/brain/124.1.219; Shermohammed M., 2012, 19 ANN M COGN NEUR S; Smith SM, 2002, HUM BRAIN MAPP, V17, P143, DOI 10.1002/hbm.10062; Soderstrom NC, 2011, J EXP PSYCHOL LEARN, V37, P1236, DOI 10.1037/a0023548; Spaniol J., 2014, J GERONTOL B-PSYCHOL, DOI [10.1093/geronb/gbt044, DOI 10.1093/GERONB/GBT044]; Stark CEL, 2001, P NATL ACAD SCI USA, V98, P12760, DOI 10.1073/pnas.221462998; STEIGER JH, 1980, PSYCHOL BULL, V87, P245, DOI 10.1037//0033-2909.87.2.245; Talairach J, 1988, COPLANAR STEREOTAXIC; Thompson-Schill SL, 1997, P NATL ACAD SCI USA, V94, P14792, DOI 10.1073/pnas.94.26.14792; Toglia MP, 2009, BEHAV RES METHODS, V41, P531, DOI 10.3758/BRM.41.2.531; Unsworth N, 2013, MEM COGNITION, V41, P242, DOI 10.3758/s13421-012-0261-x; Van Essen DC, 2001, J AM MED INFORM ASSN, V8, P443; Van Essen DC, 2012, CEREB CORTEX, V22, P2241, DOI 10.1093/cercor/bhr291; Wagner AD, 2001, NEURON, V31, P329, DOI 10.1016/S0896-6273(01)00359-2; Wagner AD, 1998, SCIENCE, V281, P1188, DOI 10.1126/science.281.5380.1188; Watkins M. J., 1999, SELECTIVITY MEMORY E; Whitney C, 2011, CEREB CORTEX, V21, P1066, DOI 10.1093/cercor/bhq180; Wolosin SM, 2012, J COGNITIVE NEUROSCI, V24, P1532, DOI 10.1162/jocn_a_00237; Woolrich MW, 2001, NEUROIMAGE, V14, P1370, DOI 10.1006/nimg.2001.0931; Wu CC, 2014, NEUROIMAGE, V84, P279, DOI [10.1016/j.neuroimage.2013.08.055, 10.1016/J.neuroimage.2013.08.055]; Yarkoni T, 2011, NAT METHODS, V8, P665, DOI [10.1038/nmeth.1635, 10.1038/NMETH.1635] 65 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1530-7026 1531-135X COGN AFFECT BEHAV NE Cogn. Affect. Behav. Neurosci. JUN 2014 14 2 578 592 10.3758/s13415-014-0275-x 15 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AK6CY WOS:000338516800008 J McCoy, SK; Hutchinson, S; Hawthorne, L; Cosley, BJ; Ell, SW McCoy, Shannon K.; Hutchinson, Steven; Hawthorne, Lauren; Cosley, Brandon J.; Ell, Shawn W. Is pressure stressful? The impact of pressure on the stress response and category learning COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE English Article Pressure; Stress; Category learning; Cognition WORKING-MEMORY; CHOKING; CATEGORIZATION; PERFORMANCE; SYSTEMS; CONSOLIDATION; INTEGRATION; RETRIEVAL; EXPLICIT; AROUSAL We examined the basic question of whether pressure is stressful. We proposed that when examining the role of stress or pressure in cognitive performance, it is important to consider the type of pressure, the stress response, and the aspect of cognition assessed. In Experiment 1, outcome pressure was not experienced as stressful but did lead to impaired performance on a rule-based (RB) category-learning task, but not on a more procedural information-integration (II) task. In Experiment 2, the addition of monitoring pressure resulted in a modest stress response to combined pressure and impairment on both tasks. Across experiments, higher stress appraisals were associated with decreased performance on the RB, but not on the II, task. In turn, higher stress reactivity (i.e., heart rate) was associated with enhanced performance on the II, but not on the RB, task. This work represents an initial step toward integrating the stress cognition and pressure cognition literatures and suggests that integrating these fields may require consideration of the type of pressure, the stress response, and the cognitive system mediating performance. [McCoy, Shannon K.; Hutchinson, Steven; Hawthorne, Lauren; Ell, Shawn W.] Univ Maine, Dept Psychol, Orono, ME 04469 USA; [Cosley, Brandon J.] Univ South Carolina Beaufort, Dept Social Sci, Blufton, SC USA; [Ell, Shawn W.] Univ Maine, Grad Sch Biomed Sci, Orono, ME 04469 USA McCoy, SK (reprint author), Univ Maine, Dept Psychol, 5742 Little Hall,Room 301, Orono, ME 04469 USA. shannon.mccoy@umit.maine.edu; shawn.ell@umit.maine.edu Ashby F. G., 1992, MULTIDIMENSIONAL MOD, P449; Ashby F. G., 1993, FDN PERCEPTUAL THEOR, P369; ASHBY FG, 1988, J EXP PSYCHOL LEARN, V14, P33, DOI 10.1037/0278-7393.14.1.33; Ashby FG, 2001, TRENDS COGN SCI, V5, P204, DOI 10.1016/S1364-6613(00)01624-7; ASHBY FG, 1986, PSYCHOL REV, V93, P154, DOI 10.1037//0033-295X.93.2.154; Ashby F Gregory, 2005, Annu Rev Psychol, V56, P149, DOI 10.1146/annurev.psych.56.091103.070217; Ashby FG, 1998, PSYCHOL REV, V105, P442, DOI 10.1037/0033-295X.105.3.442; Ashby FG, 2001, J EXP PSYCHOL GEN, V130, P77, DOI 10.1037/0096-3445.130.1.77; Ashby FG, 1999, PSYCHON B REV, V6, P363, DOI 10.3758/BF03210826; BAUMEISTER RF, 1984, J PERS SOC PSYCHOL, V46, P610, DOI 10.1037//0022-3514.46.3.610; Beilock SL, 2005, PSYCHOL SCI, V16, P101, DOI 10.1111/j.0956-7976.2005.00789.x; Beilock SL, 2007, J EXP PSYCHOL LEARN, V33, P983, DOI 10.1037/0278-7393.33.6.983; Blascovich J, 1996, ADV EXP SOC PSYCHOL, V28, P1, DOI 10.1016/S0065-2601(08)60235-X; Brainard DH, 1997, SPATIAL VISION, V10, P433, DOI 10.1163/156856897X00357; DeCaro MS, 2011, J EXP PSYCHOL GEN, V140, P390, DOI 10.1037/a0023466; Dickerson SS, 2004, PSYCHOL BULL, V130, P355, DOI 10.1037/0033-2909.130.3.355; DIENSTBIER RA, 1989, PSYCHOL REV, V96, P84, DOI 10.1037//0033-295X.96.1.84; Ell SW, 2011, PSYCHON B REV, V18, P96, DOI 10.3758/s13423-010-0018-0; Gimmig D, 2006, PSYCHON B REV, V13, P1005, DOI 10.3758/BF03213916; Green D. M., 1966, SIGNAL DETECTION THE; Joels M, 2009, NAT REV NEUROSCI, V10, P459, DOI 10.1038/nrn2632; Kassam KS, 2009, PSYCHOL SCI, V20, P1394, DOI 10.1111/j.1467-9280.2009.02455.x; KIRSCHBAUM C, 1993, NEUROPSYCHOBIOLOGY, V28, P76, DOI 10.1159/000119004; Lazarus RS, 1984, STRESS APPRAISAL COP; Lewandowsky S, 2012, J EXP PSYCHOL LEARN, V38, P881, DOI 10.1037/a0027298; Lewis BP, 1997, PERS SOC PSYCHOL B, V23, P937, DOI 10.1177/0146167297239003; Linden W, 1997, J PSYCHOSOM RES, V42, P117, DOI 10.1016/S0022-3999(96)00240-1; Lupien SJ, 2007, BRAIN COGNITION, V65, P209, DOI 10.1016/j.bandc.2007.02.007; MADDOX WT, 1993, PERCEPT PSYCHOPHYS, V53, P49, DOI 10.3758/BF03211715; Maddox WT, 2004, J EXP PSYCHOL LEARN, V30, P227, DOI 10.1037/0278-7393.30.1.227; Maddox WT, 2004, BEHAV PROCESS, V66, P309, DOI 10.1016/j.beproc.2004.03.011; Markman AB, 2006, PSYCHOL SCI, V17, P944, DOI 10.1111/j.1467-9280.2006.01809.x; MASTERS RSW, 1992, BRIT J PSYCHOL, V83, P343; MCEWEN BS, 1995, CURR OPIN NEUROBIOL, V5, P205, DOI 10.1016/0959-4388(95)80028-X; Payne JD, 2007, LEARN MEMORY, V14, P861, DOI 10.1101/lm.743507; Pelli DG, 1997, SPATIAL VISION, V10, P437, DOI 10.1163/156856897X00366; Plessow F, 2012, COGN AFFECT BEHAV NE, V12, P557, DOI 10.3758/s13415-012-0098-6; Roozendaal B, 2002, NEUROBIOL LEARN MEM, V78, P578, DOI 10.1006/nlme.2002.4080; Schoofs D, 2008, PSYCHONEUROENDOCRINO, V33, P643, DOI 10.1016/j.psyneuen.2008.02.004; Schwabe L, 2012, J NEUROSCI, V32, P11042, DOI 10.1523/JNEUROSCI.1484-12.2012; SCHWARZ G, 1978, ANN STAT, V6, P461, DOI 10.1214/aos/1176344136; Smeets T, 2008, PSYCHONEUROENDOCRINO, V33, P1378, DOI 10.1016/j.psyneuen.2008.07.009; Staal M. A., 2004, 2004212824 NASA TM; Wickens T. D., 1982, MODELS BEHAV STOCHAS; Worthy DA, 2009, ATTEN PERCEPT PSYCHO, V71, P924, DOI 10.3758/APP.71.4.924 45 1 1 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1530-7026 1531-135X COGN AFFECT BEHAV NE Cogn. Affect. Behav. Neurosci. JUN 2014 14 2 769 781 10.3758/s13415-013-0215-1 13 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AK6CY WOS:000338516800022 J Shaalan, K Shaalan, Khaled A Survey of Arabic Named Entity Recognition and Classification COMPUTATIONAL LINGUISTICS English Article As more and more Arabic textual information becomes available through the Web in homes and businesses, via Internet and Intranet services, there is an urgent need for technologies and tools to process the relevant information. Named Entity Recognition (NER) is an Information Extraction task that has become an integral part of many other Natural Language Processing (NLP) tasks, such as Machine Translation and Information Retrieval. Arabic NER has begun to receive attention in recent years. The characteristics and peculiarities of Arabic, a member of the Semitic languages family, make dealing with NER a challenge. The performance of an Arabic NER component affects the overall performance of the NLP system in a positive manner. This article attempts to describe and detail the recent increase in interest and progress made in Arabic NER research. The importance of the NER task is demonstrated, the main characteristics of the Arabic language are highlighted, and the aspects of standardization in annotating named entities are illustrated. Moreover, the different Arabic linguistic resources are presented and the approaches used in Arabic NER field are explained. The features of common tools used in Arabic NER are described, and standard evaluation metrics are illustrated. In addition, a review of the state of the art of Arabic NER research is discussed. Finally, we present our conclusions. Throughout the presentation, illustrative examples are used for clarification. [Shaalan, Khaled] Univ Edinburgh, Sch Informat, Edinburgh EH8 9YL, Midlothian, Scotland; [Shaalan, Khaled] British Univ Dubai, Dubai, U Arab Emirates Shaalan, K (reprint author), British Univ Dubai, POB 345015, Dubai, U Arab Emirates. khaled.shaalan@buid.ac.ae Abdallah Sherief, 2012, Computational Linguistics and Intelligent Text Processing. 13th International Conference (CICLing 2012). Proceedings, Part I, DOI 10.1007/978-3-642-28604-9_26; AbdelRahman Samir, 2010, IJCSI INT J COMPUTER, V7, P27; Abdul-Hamid Ahmed, 2010, P 2010 NAM ENT WORKS, P110; Abdul-Mageed Muhammad, 2011, P 49 ANN M ASS COMP, V2, P587; Abouenour Lahsen, 2010, P 7 INT C LANG RES E, P27; Abuleil Saleem, 2004, P COUPL APPR COUPL M, P638; Algahtani Shabib, 2011, THESIS U MANCHESTER; Al-Jumaily H, 2012, LANG RESOUR EVAL, V46, P543, DOI 10.1007/s10579-011-9146-z; Alkharashi Ibrahim, 2009, P 2 INT C AR LANG RE, P205; Al-Onaizan Yaser, 2002, P 40 ANN M ASS COMP, P400; Al-Onaizan Yaser, 2002, P ACL 02 WORKSH COMP, P1, DOI 10.3115/1118637.1118642; Al-Shalabi Riyad, 2009, INT C IT CEL S CHARM, P281; Al-Sughaiyer IA, 2004, J AM SOC INF SCI TEC, V55, P189, DOI 10.1002/asi.10368; Attia Mohammed, 2010, P 7 INT C LANG RES E, p[3, 614, 621]; Babych Bogdan, 2003, P EAMT EACL 2003 WOR, P1, DOI 10.3115/1609822.1609823; Badawy Osama, 2011, P AR LANG TECHN INT, P1; Balasuriya Dominic, 2009, P WORKSH PEOPL WEB M, P10, DOI 10.3115/1699765.1699767; Ben Hamadou Abdelmajid, 2010, RECOGNITION ARABIC F, P1; BENAJIBA Y, 2007, P 8 INT C COMP LING, V4394, P143; Benajiba Y, 2009, IEEE T AUDIO SPEECH, V17, P926, DOI 10.1109/TASL.2009.2019927; Benajiba Yassine, 2008, P WORKSH HLT NLP AR, P143; Benajiba Yassine, 2008, P C EMP METH NAT LAN, P284, DOI 10.3115/1613715.1613755; Benajiba Yassine, 2007, P WORKSH NAT LANG IN, p[1, 814, 823]; Benajiba Yassine, 2008, P AR INT C INF TECHN, P16; Bies Ann, 2012, CHALLENGES ARABIC MA, V322, P15; Buckwalter Tim, 2002, LDC2002L49; Burkett D, 2010, P 14 C COMP NAT LANG, P46; Chen Hsin-Hsi, 2003, P ACL 2003 WORKSH MU, P1; Collins Michael, 2002, P C EMP METH NAT LAN, V10, P1, DOI 10.3115/1118693.1118694; Cunningham H, 2011, TEXT PROCESSING GATE; Cunningham H, 2002, COMPUT HUMANITIES, V36, P223, DOI 10.1023/A:1014348124664; Diab M., 2009, P 2 INT C AR LANG RE, P285; Diab Mona, 2009, INT ARAB J INF TECHN, V6, P463; Diab Mona, 2004, P 5 M N AM CHAPT ASS, P149, DOI 10.3115/1613984.1614022; El Kholy Ahmed, 2010, P WORKSH LANG RES HU, P45; Elgibali Alaa, 2005, INVESTIGATING ARABIC; Elkateb Sabri, 2006, P AR NLP MET C LOND, P15; Ellouze Mariem, 2009, P NOOJ 2009 TOZ, P1; Elsebai Ali, 2011, P INT C INN INF TECH, P87; Elsebai Ali, 2009, P 11 INT BUS INF MAN, P53; Ezzeldin Ahmed, 2012, P 13 INT AR C INF TE, P1; FARBER B, 2008, P WORKSH HLT NLP AR, P2509; Farghaly Ali, 2009, ACM T ASIAN LANGUAGE, V8, P1; Fellbaum C., 2005, ENCY LANGUAGE LINGUI, P665; Grefenstette Gregory, 2005, P ACL WORKSH COMP AP, P31, DOI 10.3115/1621787.1621794; Guo JF, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P267, DOI 10.1145/1571941.1571989; Habash N., 2009, P 2 INT C AR LANG RE, P102; Habash Nizar, 2010, INTRO ARABIC NATURAL; Habash Nizar, 2005, P 43 ANN M ASS COMP, P573, DOI DOI 10.3115/1219840.1219911; Halpern Jack, 2009, P 2 INT C AR LANG RE, P193; Hassan A, 2007, P 2007 C REC ADV NAT, P1; Hassan Hany, 2005, P ACL WORKSH COMP AP, P87, DOI 10.3115/1621787.1621803; Hewavitharana Sanjika, 2011, P 4 WORKSH BUILD US, P61; Higgins Chiara, 2010, P NAACL HLT 2010 WOR, P89; Huang Shudong, 2004, P IJCNLP 04 WORKSH N, P112; Kim SeonYeong, 2012, P VLSI TECHN SYST AP, P1; KORAYEM M, 2012, ADV MACHINE LEARNING, V322, P128; KOULALI R, 2012, P 10 INT C ICT KNOWL, P46; Kumaran A, 2010, P 2010 NAM ENT WORKS, P21; Lahsen Abouenour, 2012, ONL WORK NOT LABS WO; Ma Xiaoyi, 2010, P LREC 2010 WORKSH M, P211; Maamouri Mohamed, 2004, P NEMLAR C AR LANG R, P102; Maloney John, 1998, P WORKSH COMP APPR S, P8, DOI 10.3115/1621753.1621756; Marton Yuval, 2010, P NAACL HLT 2010 1 W, P13; Maynard D, 2002, LECT NOTES ARTIF INT, V2443, P264; Maynard Diana, 2000, CS0006 U SHEFF; MESFAR S, 2007, P 12 INT C APPL NAT, V4592, P305; Minaei-Bidgoli Behrouz, 2012, P LANG RES EV REL TE, P1; Mohit Behrang, 2012, P 13 C EUR CHAPT ASS, P162; Molla Diego, 2006, P AUSTR LANG TECHN W, P51; Monem Azza Abdel, 2008, Machine Translation, V22, DOI 10.1007/s10590-009-9054-9; Mostefa Djamel, 2009, P 2 INT C AR LANG RE, P213; Nadeau D, 2007, LINGUISTICAE INVESTI, V30, P3, DOI DOI 10.1075/1I.30.1.03NAD; Nezda Luke, 2006, P 5 INT C LANG RES E, P41; Odile Piton, 2010, HAL ARCH, P1; Oudah Mai, 2012, P 24 INT C COMP LING, P2; Oudah Mai, 2013, Natural Language Processing and Information Systems. 18th International Conference on Applications of Natural Language to Information Systems, NLDB 2013. Proceedings: LNCS 7934, DOI 10.1007/978-3-642-38824-8_20; Pappu Aasish, 2009, P 23 PAC AS C LANG I, P779; Pouliquen Bruno, 2005, MULTILINGUAL PERSON, P1; Refaat Khaled, 2009, P 2 INT C AR LANG RE, P209; Roth D, 2009, P 13 C COMP NAT LANG, P147, DOI 10.3115/1596374.1596399; Ryding Karin, 2005, REFERENCE GRAMMAR MO; Salloum Wael, 2012, P INT C COMP LING DE, P385; Samy Doaa, 2005, P INT C REC ADV NAT, P459; SARAVANAN K, 2012, P 8 INT C LANG RES E, P3118; SHAALAN K, 2008, ADV NATURAL LANGUAGE, V5221, P440, DOI 10.1007/978-3-540-85287-2_42; Shaalan K, 2009, J AM SOC INF SCI TEC, V60, P1652, DOI 10.1002/asi.21090; SHAALAN K, 2012, P 8 INT C LANG RES E, P719; Shaalan Khaled, 2007, P 2007 WORKSH COMP A, P17, DOI 10.3115/1654576.1654581; Shaalan Khaled, 2010, INT J INFORM COMMUNI, V3, P11; Shihadeh Carolin, 2012, 4 WORKSH COMP APPR A, P24; Smrz Otakar, 2007, THESIS CHARLES U PRA; Steinberger R, 2012, LANG RESOUR EVAL, V46, P155, DOI 10.1007/s10579-011-9165-9; Steinberger Ralf, 2008, MINING MASSIVE DATA, V19, P217; Steinberger Ralf, 2009, INF ACC MULT WORLD P, P1; Strassel Stephanie, 2003, P ACL 2003 WORKSH MU, V15, P49, DOI 10.3115/1119384.1119391; Sudo Kiyoshi, 2002, P 3 INT C LANG RES E, P1; Traboulsi Hayssam, 2009, Proceedings of the 2009 International Multiconference on Computer Science and Information Technology (IMCSIT), DOI 10.1109/IMCSIT.2009.5352809; Trigui Omar, 2012, ONL WORK NOT LABS WO; Vichot Frantz, 2001, P 39 ANN M ACL 10 C, P426, DOI 10.3115/1073012.1073067; Witten IH, 2011, MOR KAUF D, P1; Yang Y., 1999, INFORM RETRIEVAL, V1, P69, DOI 10.1023/A:1009982220290; Zaghouani W., 2010, P 7 INT C LANG RES E, P563; Zaghouani Wajdi, 2012, ACM T ASIAN LANGUAGE, V11; Zaraket Fadi, 2012, P 25 INT FLOR ART IN, P256; Zayed Omnia, 2012, INFORMATICS, P44; Zitouni Imed, 2010, P ACL 2010 C ACLSHOR, P281; Zitouni Imed, 2005, P ACL WORKSH COMP AP, P63, DOI 10.3115/1621787.1621800 108 0 0 MIT PRESS CAMBRIDGE 55 HAYWARD STREET, CAMBRIDGE, MA 02142 USA 0891-2017 1530-9312 COMPUT LINGUIST Comput. Linguist. JUN 2014 40 2 469 510 10.1162/COLI_a_00178 42 Computer Science, Artificial Intelligence; Computer Science, Interdisciplinary Applications; Linguistics; Language & Linguistics Computer Science; Linguistics AK1SJ WOS:000338196000008 J De Jaeger, X; Courtey, J; Brus, M; Artinian, J; Villain, H; Bacquie, E; Roullet, P De Jaeger, Xavier; Courtey, Julie; Brus, Maina; Artinian, Julien; Villain, Helene; Bacquie, Elodie; Roullet, Pascal Characterization of spatial memory reconsolidation LEARNING & MEMORY English Article ANTERIOR CINGULATE CORTEX; OBJECT RECOGNITION MEMORY; CONTEXTUAL FEAR MEMORY; PROTEIN-SYNTHESIS; REMOTE MEMORIES; CONSOLIDATION; RETRIEVAL; REACTIVATION; MECHANISMS; INFORMATION Reconsolidation is necessary for the restabilization of reactivated memory traces. However, experimental parameters have been suggested as boundary conditions for this process. Here we investigated the role of a spatial memory trace's age, strength, and update on the reconsolidation process in mice. We first found that protein synthesis is necessary for reconsolidation to occur in the hippocampal CA3 region after reactivation of partially acquired and old memories but not for strongly acquired and recent memories. We also demonstrated that the update of a previously stable memory required, again, a memory reconsolidation in the hippocampal CA3. Finally, we found that the reactivation of a strongly acquired memory requires an activation of the anterior cingulate cortex as soon as 24 h after acquisition. This study demonstrates the importance of the knowledge of the task on the dynamic nature of memory reconsolidation processing. [De Jaeger, Xavier; Courtey, Julie; Brus, Maina; Artinian, Julien; Villain, Helene; Bacquie, Elodie; Roullet, Pascal] Univ Toulouse 3, Univ Toulouse, F-31062 Toulouse 9, France; [De Jaeger, Xavier; Courtey, Julie; Brus, Maina; Artinian, Julien; Villain, Helene; Bacquie, Elodie; Roullet, Pascal] CNRS, UMR 5169, Ctr Rech Cognit Anim, F-31062 Toulouse 9, France Roullet, P (reprint author), Univ Toulouse 3, Univ Toulouse, F-31062 Toulouse 9, France. pascal.roullet@univ-tlse3.fr CNRS ANR program [ANR-06-Neuro-027-03]; University Paul Sabatier Toulouse 3 This study was supported by CNRS ANR program (ANR-06-Neuro-027-03) and by the University Paul Sabatier Toulouse 3. We thank A. Mele for the helpful discussions and A. La Fontaine and S. Hebert-Seropian for proofreading this manuscript. Akirav I, 2006, CEREB CORTEX, V16, P1759, DOI 10.1093/cercor/bhj114; Alberini CM, 2005, TRENDS NEUROSCI, V28, P51, DOI 10.1016/j.tins.2004.11.001; Artinian J, 2007, HIPPOCAMPUS, V17, P181, DOI 10.1002/hipo.20256; Artinian J, 2008, EUR J NEUROSCI, V27, P3009, DOI 10.1111/j.1460-9568.2008.06262.x; Bontempi B, 1999, NATURE, V400, P671, DOI 10.1038/23270; Debiec J, 2002, NEURON, V36, P527, DOI 10.1016/S0896-6273(02)01001-2; Dudai Y, 2006, CURR OPIN NEUROBIOL, V16, P174, DOI 10.1016/j.conb.2006.03.010; Einarsson EO, 2012, LEARN MEMORY, V19, P449, DOI 10.1101/lm.027227.112; Eisenberg M, 2004, EUR J NEUROSCI, V20, P3397, DOI 10.1111/j.1460-9568.2004.03818.x; FLOOD JF, 1973, PHYSIOL BEHAV, V10, P555, DOI 10.1016/0031-9384(73)90221-7; Florian C, 2004, BEHAV BRAIN RES, V154, P365, DOI 10.1016/j.bbr.2004.03.003; Frankland PW, 2004, SCIENCE, V304, P881, DOI 10.1126/science.1094804; Frankland PW, 2005, NAT REV NEUROSCI, V6, P119, DOI 10.1038/nrn1607; Gisquet-Verrier P, 2012, LEARN MEMORY, V19, P401, DOI 10.1101/lm.026054.112; Goshen I, 2011, CELL, V147, P678, DOI 10.1016/j.cell.2011.09.033; Hernandez PJ, 2002, NAT NEUROSCI, V5, P1327, DOI 10.1038/nn973; Inda MC, 2005, J NEUROSCI, V25, P2070, DOI 10.1523/JNEUROSCI.4163-04.2005; Lee JLC, 2009, TRENDS NEUROSCI, V32, P413, DOI 10.1016/j.tins.2009.05.002; Lee JLC, 2008, NAT NEUROSCI, V11, P1264, DOI 10.1038/nn.2205; Lee JLC, 2005, NEURON, V47, P795, DOI 10.1016/j.neuron.2005.08.007; Lehmann H, 2011, LEARN MEMORY, V18, P132, DOI 10.1101/lm.2000811; McKenzie S, 2011, NEURON, V71, P224, DOI 10.1016/j.neuron.2011.06.037; Milekic MH, 2002, NEURON, V36, P521, DOI 10.1016/S0896-6273(02)00976-5; Morris RGM, 2006, NEURON, V50, P479, DOI 10.1016/j.neuron.2006.04.012; Nader K, 2003, TRENDS NEUROSCI, V26, P65, DOI 10.1016/S0166-2236(02)00042-5; Nader K, 2010, ANN NY ACAD SCI, V1191, P27, DOI 10.1111/j.1749-6632.2010.05443.x; Nader K, 2000, NATURE, V406, P722, DOI 10.1038/35021052; Pedreira ME, 2004, LEARN MEMORY, V11, P579, DOI 10.1016/lm.76904; Robinson MJF, 2010, BEHAV BRAIN RES, V213, P201, DOI 10.1016/j.bbr.2010.04.056; Rodriguez-Ortiz CJ, 2005, LEARN MEMORY, V12, P533, DOI 10.1101/lm.94505; Rossato JI, 2006, LEARN MEMORY, V13, P431, DOI 10.1101/lm.315206; Rossato JI, 2007, LEARN MEMORY, V14, P36, DOI 10.1101/lm.422607; Sara SJ, 2000, LEARN MEMORY, V7, P73, DOI 10.1101/lm.7.2.73; Suzuki A, 2004, J NEUROSCI, V24, P4787, DOI 10.1523/JNEUROSCI.5491-03.2004; Tronel S, 2005, PLOS BIOL, V3, P1630, DOI 10.1371/journal.pbio.0030293; Tronson NC, 2007, NAT REV NEUROSCI, V8, P262, DOI 10.1038/nrn2090; Tse D, 2007, SCIENCE, V316, P76, DOI 10.1126/science.1135935; Wang SH, 2009, NAT NEUROSCI, V12, P905, DOI 10.1038/nn.2350; Winters BD, 2009, LEARN MEMORY, V16, P545, DOI 10.1101/lm.1509909 39 0 0 COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT COLD SPRING HARBOR 1 BUNGTOWN RD, COLD SPRING HARBOR, NY 11724 USA 1072-0502 1549-5485 LEARN MEMORY Learn. Mem. JUN 2014 21 6 316 324 10.1101/lm.033415.113 9 Neurosciences; Psychology, Experimental Neurosciences & Neurology; Psychology AK5ZM WOS:000338506200003 J Hernandez-Gonzalez, Y; Garcia-Moreno, C; Rodriguez-Garcia, MA; Valencia-Garcia, R; Garcia-Sanchez, F Hernandez-Gonzalez, Yolanda; Garcia-Moreno, Carlos; Angel Rodriguez-Garcia, Miguel; Valencia-Garcia, Rafael; Garcia-Sanchez, Francisco A semantic-based platform for R&D project funding management COMPUTERS IN INDUSTRY English Article INFORMATION-RETRIEVAL; ONTOLOGY; SELECTION; FRAMEWORK; METHODOLOGY; DOCUMENTS; SYSTEMS; MODEL Innovation is one of the keys to success in the business world, particularly within the current economic climate. R&D projects constitute the building blocks of the innovation process, hence the importance of searching for funding for these projects. As ontologies and semantic technologies mature, they provide a consistent and reliable means to represent and aggregate knowledge from different sources. The present work explores the use of ontologies to model R&D grant funding calls and the application of semantic technologies to the development of an enhanced funding management system. Our experiments confirm the success of the proposed approach, and reveal that it may bring considerable benefits to R&D funding. (C) 2013 Elsevier B.V. All rights reserved. [Hernandez-Gonzalez, Yolanda; Garcia-Moreno, Carlos] Indra Software Labs, Madrid 28037, Spain; [Angel Rodriguez-Garcia, Miguel; Valencia-Garcia, Rafael; Garcia-Sanchez, Francisco] Univ Murcia, Dept Informat & Sistemas, E-30100 Murcia, Spain Valencia-Garcia, R (reprint author), Univ Murcia, Dept Informat & Sistemas, E-30100 Murcia, Spain. valencia@um.es Centro de Desarrollo Tecnologico Industrial (CDTI) through project IMAN [IDI-20110605]; Spanish Ministry of Economy and Competitiveness; European Commission (FEDER/ERDF) through project SeCloud [TIN2010-18650] This work has been supported by the Centro de Desarrollo Tecnologico Industrial (CDTI) through project IMAN (IDI-20110605) and by the Spanish Ministry of Economy and Competitiveness and the European Commission (FEDER/ERDF) through project SeCloud (TIN2010-18650). Alvarez JM, 2012, INT J SOFTW ENG KNOW, V22, P365, DOI 10.1142/S0218194012400086; Alves J., 2005, 9 EUR C CREAT INN EC; Bertaud-Gounot V, 2012, INFORM HEALTH SOC CA, V37, P51, DOI 10.3109/17538157.2011.590258; Blanco-Fernandez Y, 2008, KNOWL-BASED SYST, V21, P305, DOI 10.1016/j.knosys.2007.07.004; Bullinger HJ, 2005, LECT NOTES COMPUT SC, V3379, P280; Carrer-Neto W, 2012, EXPERT SYST APPL, V39, P10990, DOI 10.1016/j.eswa.2012.03.025; Castells P, 2007, IEEE T KNOWL DATA EN, V19, P261, DOI 10.1109/TKDE.2007.22; Chen JQ, 2009, EUR J OPER RES, V193, P23, DOI 10.1016/j.ejor.2007.10.040; Colomo-Palacios R, 2010, INT J INFORM MANAGE, V30, P465, DOI 10.1016/j.ijinfomgt.2010.05.012; Fensel D, 2001, IEEE INTELL SYST APP, V16, P8, DOI 10.1109/MIS.2001.1183337; Garcia-Moreno C, 2011, ADV INTEL SOFT COMPU, V91, P295; Garcia-Sanchez F, 2009, EXPERT SYST APPL, V36, P3167, DOI 10.1016/j.eswa.2008.01.037; Gharib TF, 2012, J UNIVERS COMPUT SCI, V18, P2234; Heidenberger K, 1996, EUR J OPER RES, V95, P284, DOI 10.1016/0377-2217(95)00259-6; Jung JJ, 2011, EXPERT SYST APPL, V38, P5774, DOI 10.1016/j.eswa.2010.10.055; Levenshtein VI, 1966, SOV PHYS DOKL, V10, P707; Lin F., 2010, COMP INF TECHN CIT I, P1292; Liu O, 2010, EXPERT SYST APPL, V37, P4626, DOI 10.1016/j.eswa.2009.12.046; Lupiani-Ruiz E, 2011, EXPERT SYST APPL, V38, P15565, DOI 10.1016/j.eswa.2011.06.003; Ma J, 2012, IEEE T SYST MAN CY A, V42, P784, DOI 10.1109/TSMCA.2011.2172205; Maedche A, 2003, IEEE INTELL SYST, V18, P26, DOI 10.1109/MIS.2003.1193654; Meade LA, 2002, IEEE T ENG MANAGE, V49, P59, DOI 10.1109/17.985748; Mendenhall W, 1995, STAT ENG SCI; Nobelius D., 2004, International Journal of Project Management, V22, DOI 10.1016/j.ijproman.2003.10.002; Prud'hommeaux E., SPARQL QUERY LANGUAG; Quelin B., 2000, EUROPEAN MANAGEMENT, V18, P476, DOI 10.1016/S0263-2373(00)00037-2; Ruiz-Martinez JM, 2011, EXPERT SYST APPL, V38, P12365, DOI 10.1016/j.eswa.2011.04.016; Salton G., 1986, INTRO MODERN INFORM; Santhanam R, 1996, EUR J OPER RES, V89, P380, DOI 10.1016/0377-2217(94)00257-6; Sirbiladze G., 2013, EUR J OPER RES; Studer R, 1998, DATA KNOWL ENG, V25, P161, DOI 10.1016/S0169-023X(97)00056-6; Valencia-Garcia R, 2008, EXPERT SYST, V25, P314, DOI 10.1111/j.1468-0394.2008.00464.x; Valencia-Garcia R, 2011, IEEE T SYST MAN CY A, V41, P121, DOI 10.1109/TSMCA.2010.2048029; Vidoni R, 2011, EXPERT SYST APPL, V38, P7430, DOI 10.1016/j.eswa.2010.12.080 34 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0166-3615 1872-6194 COMPUT IND Comput. Ind. JUN 2014 65 5 SI 850 861 10.1016/j.compind.2013.11.007 12 Computer Science, Interdisciplinary Applications Computer Science AJ7MY WOS:000337882000006 J Valle-Lisboa, JC; Pomi, A; Cabana, A; Elvevag, B; Mizraji, E Valle-Lisboa, Juan C.; Pomi, Andres; Cabana, Alvaro; Elvevag, Brita; Mizraji, Eduardo A modular approach to language production: Models and facts CORTEX English Article Language; Neural models; Model neuroimaging; Schizophrenia; Discourse trajectories LATENT SEMANTIC ANALYSIS; HALLUCINATED VOICES; NEURAL-NETWORK; SCHIZOPHRENIA; MEMORIES; REPRESENTATION; SPEECH; INFORMATION; RETRIEVAL; KNOWLEDGE Numerous cortical disorders affect language. We explore the connection between the observed language behavior and the underlying substrates by adopting a neurocomputational approach. To represent the observed trajectories of the discourse in patients with disorganized speech and in healthy participants, we design a graphical representation for the discourse as a trajectory that allows us to visualize and measure the degree of order in the discourse as a function of the disorder of the trajectories. Our work assumes that many of the properties of language production and comprehension can be understood in terms of the dynamics of modular networks of neural associative memories. Based upon this assumption, we connect three theoretical and empirical domains: (1) neural models of language processing and production, (2) statistical methods used in the construction of functional brain images, and (3) corpus linguistic tools, such as Latent Semantic Analysis (henceforth LSA), that are used to discover the topic organization of language. We show how the neurocomputational models intertwine with LSA and the mathematical basis of functional neuroimaging. Within this framework we describe the properties of a context-dependent neural model, based on matrix associative memories, that performs goal-oriented linguistic behavior. We link these matrix associative memory models with the mathematics that underlie functional neuroimaging techniques and present the "functional brain images" emerging from the model. This provides us with a completely "transparent box" with which to analyze the implication of some statistical images. Finally, we use these models to explore the possibility that functional synaptic disconnection can lead to an increase in connectivity between the representations of concepts that could explain some of the alterations in discourse displayed by patients with schizophrenia. (C) 2013 Elsevier Ltd. All rights reserved. [Valle-Lisboa, Juan C.; Pomi, Andres; Cabana, Alvaro; Mizraji, Eduardo] Univ Republica, Fac Ciencias, Grp Cognit Syst Modeling, Biophys Sect, Montevideo 11400, Uruguay; [Elvevag, Brita] Univ Tromso, Dept Clin Med, Psychiat Res Grp, Tromso, Norway; [Elvevag, Brita] Univ Hosp North Norway, Norwegian Ctr Integrated Care & Telemed NST, Tromso, Norway Mizraji, E (reprint author), Univ Republica, Fac Ciencias, Igua 4225, Montevideo 11400, Uruguay. mizraj@fcien.edu.uy PEDECIBA; CSIC-UdelaR; Northern Norwegian Regional Health Authority (Heise Nord RHF) AP, EM and JCVL acknowledge the partial financial support by PEDECIBA and CSIC-UdelaR. AC was supported by PEDECIBA. BE was supported by the Northern Norwegian Regional Health Authority (Helse Nord RHF). ANDERSON J A, 1972, Mathematical Biosciences, V14, P197, DOI 10.1016/0025-5564(72)90075-2; Anderson J. A., 1995, INTRO NEURAL NETWORK; ANDREASEN NC, 1986, SCHIZOPHRENIA BULL, V12, P348; ARBIB M. E., 1995, HDB BRAIN THEORY NEU; Baddeley A, 2007, OXFORD PSYCHOL SERIE; Bassett DS, 2010, PLOS COMPUT BIOL, V6, DOI 10.1371/journal.pcbi.1000748; Berry MW, 2005, SOFTW ENVIRON TOOLS, V17, P1, DOI 10.1137/1.9780898718164; Bienenstock E, 1998, ADV COMPLEX SYST, V1, P361, DOI 10.1142/S0219525998000223; Cabana A, 2011, SCHIZOPHR RES, V131, P157, DOI 10.1016/j.schres.2011.04.026; Cabana A, 2011, REV PSIQUIATRIA CLIN; Cooper LN, 1973, P NOB S COLL PROP PH; Cooper LN, 2000, INT J MOD PHYS A, V15, P4069, DOI 10.1142/S0217751X00002728; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; DeLisi LE, 2001, SCHIZOPHRENIA BULL, V27, P481; Doyle AC, 2005, COMPLETE S HOLMES AC; Dumais S, 2003, COGNITIVE SCI, V27, P491, DOI 10.1016/S0364-0213(03)00013-2; DUMAIS ST, 1991, BEHAV RES METH INSTR, V23, P229, DOI 10.3758/BF03203370; ELMAN JL, 1990, COGNITIVE SCI, V14, P179, DOI 10.1207/s15516709cog1402_1; Elman JL, 1997, RETHINKING INNATENES; Elvevag B, 2010, J NEUROLINGUIST, V23, P270, DOI 10.1016/j.jneuroling.2009.05.002; Elvevag B, 2007, SCHIZOPHR RES, V93, P304, DOI 10.1016/j.schres.2007.03.001; Foltz PW, 1998, DISCOURSE PROCESS, V25, P285; Friston Karl J, 2011, Brain Connect, V1, P13, DOI 10.1089/brain.2011.0008; Friston Karl J., 1994, Human Brain Mapping, V2, P56, DOI 10.1002/hbm.460020107; Gathercole SE, 1990, J MEM LANG, V22, P103; Griffiths TL, 2007, PSYCHOL REV, V114, P211, DOI 10.1037/0033-295X.114.2.211; Griffiths TL, P 24 ANN M COGN SCI; Gruning A, 2007, NEURAL COMPUT, V19, P3108, DOI 10.1162/neco.2007.19.11.3108; Hoffman P, 2011, J COGNITIVE NEUROSCI, V23, P2432, DOI 10.1162/jocn.2011.21614; Hoffman RE, 1997, AM J PSYCHIAT, V154, P1683; HOFFMAN RE, 1995, J COGNITIVE NEUROSCI, V7, P479, DOI 10.1162/jocn.1995.7.4.479; Hofmann T, 1999, P UNC ART INT UAI 99; Hofmann T, P 16 INT JOINT C ART, P688; HUMPHREYS MS, 1989, PSYCHOL REV, V96, P208, DOI 10.1037/0033-295X.96.2.208; Izhikevich E.M., 2006, DYNAMICAL SYSTEMS NE; Jones MN, 2006, J MEM LANG, V55, P534, DOI 10.1016/j.jml.2006.07.003; Kintsch W, 2001, COGNITIVE SCI, V25, P173, DOI 10.1207/s15516709cog2502_1; Koch C, 1992, SINGLE NEURON COMPUT; KOHONEN T, 1977, NEUROSCIENCE, V2, P1065, DOI 10.1016/0306-4522(77)90129-4; KOHONEN T, 1972, IEEE T COMPUT, VC 21, P353; Kohonen T., 1977, ASS MEMORY SYSTEM TH; Landauer T. K., 2007, HDB LATENT SEMANTIC; Landauer TK, 1997, PSYCHOL REV, V104, P211, DOI 10.1037/0033-295X.104.2.211; Landauer TK, 1998, DISCOURSE PROCESS, V25, P259; Lee L, 2006, NEUROIMAGE, V30, P1243, DOI 10.1016/j.neuroimage.2005.11.007; Levi-Montalcini R, 1989, PRAISE IMPERFECTION; McClelland JL, 1986, PSYCHOL BIOL MODELS; McCulloch Warren S., 1943, BULL MATH BIOPHYS, V5, P115, DOI 10.1007/BF02459570; McKenna PJ, 2005, SCHIZOPHRENIC SPEECH: MAKING SENSE OF BATHROOTS AND PONDS THAT FALL IN DOORWAYS, P1; MIZRAJI E, 1989, B MATH BIOL, V51, P195, DOI 10.1007/BF02458441; Mizraji E, 2011, B MATH BIOL, V73, P373, DOI 10.1007/s11538-010-9561-0; Mizraji E, 2009, COGN NEURODYNAMICS, V3, P401, DOI 10.1007/s11571-009-9084-2; Mizraji E, 2008, INT J GEN SYST, V37, P715, DOI 10.1080/03081070802037738; MIZRAJI E, 1994, BIOSYSTEMS, V32, P145, DOI 10.1016/0303-2647(94)90038-8; Nijinsky V, 1999, DAIRY V NIJINSKY UNE; OJA E, 1982, J MATH BIOL, V15, P267, DOI 10.1007/BF00275687; Ostwald P, 1991, V NIJINSKY LEAP MADN; Papadimitriou CH, 2000, J COMPUT SYST SCI, V61, P217, DOI 10.1006/jcss.2000.1711; Pena JL, 2001, SCIENCE, V292, P249, DOI 10.1126/science.1059201; Poirazi P, 2003, NEURON, V37, P989, DOI 10.1016/S0896-6273(03)00149-1; Pomi A, 2004, PHYS REV E, V70, DOI 10.1103/PhysRevE.70.066136; Pomi Brea Andres, 1999, Biosystems, V50, P173, DOI 10.1016/S0303-2647(99)00005-2; Pomi Andrés, 2006, BMC Med Inform Decis Mak, V6, P39, DOI 10.1186/1472-6947-6-39; Pulvermuller F, 2010, BIOLINGUISTICS, V4, P255; Pulvermuller F, 2012, J NEUROLINGUIST, V25, P423, DOI 10.1016/j.jneuroling.2011.03.004; Rogers TT, 2004, SEMANTIC COGNITION P; Roll M, 2011, CORTEX, V48, P1068; Rubinov M, 2010, NEUROIMAGE, V52, P1059, DOI 10.1016/j.neuroimage.2009.10.003; RUMELHART DE, 1986, NATURE, V323, P533, DOI 10.1038/323533a0; Rumelhart DE, 1986, FOUNDATIONS; SALTON G, 1965, COMMUN ACM, V8, P391, DOI 10.1145/364955.364990; Samaria F, 1994, SAR FLOR 2 IEEE WORK; SMOLENSKY P, 1990, ARTIF INTELL, V46, P159, DOI 10.1016/0004-3702(90)90007-M; Snowdon DA, 1996, JAMA-J AM MED ASSOC, V275, P528, DOI 10.1001/jama.275.7.528; Sporns O, 2010, NETWORKS BRAIN; Tagliazucchi E, 2011, NEUROSCI LETT, V488, P158, DOI 10.1016/j.neulet.2010.11.020; Valle-Lisboa JC, 2007, THESIS PEDECIBA U RE; Valle-Lisboa JC, 2007, INFORM SCIENCES, V177, P4122, DOI 10.1016/j.ins.2007.04.007; Valle-Lisboa JC, 2005, NEURAL NETWORKS, V18, P863, DOI 10.1016/j.neunet.2005.03.009; Xiang HD, 2010, CEREB CORTEX, V20, P549, DOI 10.1093/cercor/bhp119 80 1 1 ELSEVIER MASSON MILANO VIA PALEOCAPA 7, 20121 MILANO, ITALY 0010-9452 1973-8102 CORTEX Cortex JUN 2014 55 SI 61 76 10.1016/j.cortex.2013.02.005 16 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AJ5ZY WOS:000337770700006 J Golder, S; Loke, YK; Zorzela, L Golder, Su; Loke, Yoon K.; Zorzela, Liliane Comparison of search strategies in systematic reviews of adverse effects to other systematic reviews HEALTH INFORMATION AND LIBRARIES JOURNAL English Review bibliographic databases; database searching; information retrieval; review, literature; MEDLINE; meta analysis; review, systematic; search strategies; searching PAPER-BASED JOURNALS; COMPLEMENTARY MEDICINE; COCHRANE REVIEWS; METAANALYSES; QUALITY; SAFETY; INTERVENTIONS; METHODOLOGY; INFORMATION; MEDLINE Background: Research indicates that the methods used to identify data for systematic reviews of adverse effects may need to differ from other systematic reviews. Objectives: To compare search methods in systematic reviews of adverse effects with other reviews. Methods: The search methodologies in 849 systematic reviews of adverse effects were compared with other reviews. Results: Poor reporting of search strategies is apparent in both systematic reviews of adverse effects and other types of systematic reviews. Systematic reviews of adverse effects are less likely to restrict their searches to MEDLINE or include only randomised controlled trials (RCTs). The use of other databases is largely dependent on the topic area and the year the review was conducted, with more databases searched in more recent reviews. Adverse effects search terms are used by 72% of reviews and despite recommendations only two reviews report using floating subheadings. Conclusions: The poor reporting of search strategies in systematic reviews is universal, as is the dominance of searching MEDLINE. However, reviews of adverse effects are more likely to include a range of study designs (not just RCTs) and search beyond MEDLINE. [Golder, Su] Univ York, Ctr Reviews & Disseminat CRD, York YO10 5DD, N Yorkshire, England; [Loke, Yoon K.] Univ E Anglia, Sch Med Hlth Policy & Practice, Norwich NR4 7TJ, Norfolk, England; [Zorzela, Liliane] Univ Alberta, Dept Pediat, Edmonton, AB T6G 2M7, Canada Golder, S (reprint author), Univ York, Ctr Reviews & Disseminat CRD, York YO10 5DD, N Yorkshire, England. su.golder@york.ac.uk Alves C, 2012, PHARMACOEPIDEM DR S, V21, P21, DOI 10.1002/pds.2260; Bader J, 2004, J AM DENT ASSOC, V135, P464; Badgett R., 1999, LOCATING REPORTS SER; Booth A, 2006, J MED LIBR ASSOC, V94, P421; Brazier H, 1999, 2 S SYST REV BAS 5 7; Brazier H., 1998, 6 EUR C MED HLTH LIB; Breslow RA, 1998, AM J PUBLIC HEALTH, V88, P475, DOI 10.2105/AJPH.88.3.475; Centre for Reviews and Dissemination (CRD), 2008, DARE; Choi C., 2001, ANESTH ANALG, V92, P700; Cornelius V, 2008, DRUG SAFETY, V31, P891; Cornelius VR, 2009, PHARMACOEPIDEM DR S, V18, P1223, DOI 10.1002/pds.1844; Centre for Reviews and Dissemination, 2009, SYST REV CRDS GUID U; Delaney A, 2005, CRIT CARE, V9, pR575, DOI 10.1186/cc3803; Derry S, 2001, BMC Med Res Methodol, V1, P7, DOI 10.1186/1471-2288-1-7; Dixon E, 2005, ANN SURG, V241, P450, DOI 10.1097/01.sla.0000154258.30305.df; Egger M, 1998, BRIT MED J, V316, P140; Egger M, 1997, BRIT MED J, V315, P629; Ernst E, 2001, ARCH INTERN MED, V161, P125, DOI 10.1001/archinte.161.1.125-a; Ernst E, 2001, BRIT MED J, V323, P546, DOI 10.1136/bmj.323.7312.546; Fehrmann P., 2011, RES SYNH METHODS, V2, P15; Flores-Mir C, 2006, AM J ORTHOD DENTOFAC, V130, P214, DOI 10.1016/j.ajodo.2006.02.028; Glenny AM, 2003, EUR J ORAL SCI, V111, P85, DOI 10.1034/j.1600-0722.2003.00013.x; Golder S, 2012, INT J TECHNOL ASSESS, V28, P133, DOI 10.1017/S0266462312000128; Golder S, 2008, J CLIN EPIDEMIOL, V61, P440, DOI 10.1016/j.jclinepi.2007.06.005; Golder S, 2013, J CLIN EPIDEMIOL, V66, P253, DOI 10.1016/j.jclinepi.2012.09.013; Golder Su, 2006, BMC Med Res Methodol, V6, P3, DOI 10.1186/1471-2288-6-3; Golder S, 2010, HEALTH INFO LIBR J, V27, P176, DOI [10.1111/j.1471-1842.2010.00901.x, 10.1111/J.1471-1842.2010.00901.x]; Golder S, 2012, HEALTH INFO LIBR J, V29, P141, DOI [10.1111/j.1471-1842.2012.00980.x, 10.1111/j.1471-1842.2012.00980.X]; Golder S, 2009, J MED LIBR ASSOC, V97, P84, DOI 10.3163/1536-5050.97.2.004; Golder S, 2006, HEALTH INFO LIBR J, V23, P3, DOI 10.1111/j.1471-1842.2006.00634.x; Higgins JPT, 2011, COCHRANE HDB SYSTEMA; Hopewell S, 2008, J CLIN EPIDEMIOL, V61, P597, DOI 10.1016/j.jclinepi.2007.10.005; Jadad AR, 2000, BRIT MED J, V320, P537, DOI 10.1136/bmj.320.7234.537; Jadad AR, 1996, J CLIN EPIDEMIOL, V49, P235, DOI 10.1016/0895-4356(95)00062-3; Jadad AR, 1998, JAMA-J AM MED ASSOC, V280, P278, DOI 10.1001/jama.280.3.278; Kelly KD, 2001, ANN EMERG MED, V38, P518, DOI 10.1067/mem.2001.115881; Loke Yoon K, 2011, Ther Adv Drug Saf, V2, P59, DOI 10.1177/2042098611401129; Lundh A, 2009, CANCER TREAT REV, V35, P645, DOI 10.1016/j.ctrv.2009.08.010; Maggio LA, 2011, ACAD MED, V86, P1049, DOI 10.1097/ACM.0b013e31822221e7; Major Michael P, 2007, Evid Based Dent, V8, P66, DOI 10.1038/sj.ebd.6400504; Moher D, 2007, PLOS MED, V4, P447, DOI 10.1371/journal.pmed.0040078; Moher D., 2002, BMC PEDIATR, V2, P1021; Moseley A. M., 2009, J CLIN EPIDEMIOL, V62, P1; Parekh-Bhurke S, 2011, J CLIN EPIDEMIOL, V64, P349, DOI 10.1016/j.jclinepi.2010.04.022; Petticrew M, 1999, INT J TECHNOL ASSESS, V15, P671; Pilkington K, 2012, COMPLEMENT THER MED, V20, P73, DOI 10.1016/j.ctim.2011.10.002; Rigby K, 1996, INT J TECHNOL ASSESS, V12, P450; Roundtree AK, 2009, J CLIN EPIDEMIOL, V62, P128, DOI 10.1016/j.jclinepi.2008.08.003; SACKS HS, 1987, NEW ENGL J MED, V316, P450, DOI 10.1056/NEJM198702193160806; Sampson M, 2006, J CLIN EPIDEMIOL, V59, P1057, DOI 10.1016/j.jclinepi.2006.01.007; Sampson M., 2005, COCHR C 22 26 OCT ME; Sampson M, 2008, J CLIN EPIDEMIOL, V61, P748, DOI 10.1016/j.jclinepi.2007.10.009; Sampson M., 2011, RES SYNTHESIS METHOD, V2, P119; Shang H., METHODOLOGY; Shea B, 2006, J RHEUMATOL, V33, P9; Shea B, 2002, EVAL HEALTH PROF, V25, P116, DOI 10.1177/0163278702025001008; Smith AF, 1997, CAN J ANAESTH, V44, P405; Vandermeer B., 2006, 14 COCHR C 23 26 OCT; Wen J, 2008, J CLIN EPIDEMIOL, V61, P770, DOI 10.1016/j.jclinepi.2007.10.008; Wieland S, 2005, J CLIN EPIDEMIOL, V58, P560, DOI 10.1016/j.jclinepi.2004.11.018; Linde K, 2003, J ROY SOC MED, V96, P17, DOI 10.1258/jrsm.96.1.17; Yoshii A, 2009, J MED LIBR ASSOC, V97, P21, DOI 10.3163/1536-5050.97.1.004 62 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1471-1834 1471-1842 HEALTH INFO LIBR J Heatlth Info. Libr. J. JUN 2014 31 2 92 105 10.1111/hir.12041 14 Information Science & Library Science Information Science & Library Science AJ9EJ WOS:000338009700002 J Lee, G; Bulitko, V; Ludvig, EA Lee, Greg; Bulitko, Vadim; Ludvig, Elliot A. Automated Story Selection for Color Commentary in Sports IEEE TRANSACTIONS ON COMPUTATIONAL INTELLIGENCE AND AI IN GAMES English Article Artificial intelligence; automated narrative; information retrieval GENERATION; ROBOCUP Automated sports commentary is a form of automated narrative. Sports commentary exists to keep the viewer informed and entertained. One way to entertain the viewer is by telling brief stories relevant to the game in progress. We present a system called the sports commentary recommendation system (SCoReS) that can automatically suggest stories for commentators to tell during games. Through several user studies, we compared commentary using SCoReS to three other types of commentary and show that SCoReS adds significantly to the broadcast across several enjoyment metrics. We also collected interview data from professional sports commentators who positively evaluated a demonstration of the system. We conclude that SCoReS can be a useful broadcast tool, effective at selecting stories that add to the enjoyment and watchability of sports. SCoReS is a step toward automating sports commentary and, thus, automating narrative. [Lee, Greg; Bulitko, Vadim] Univ Alberta, Dept Comp Sci, Edmonton, AB T6G 2E8, Canada; [Ludvig, Elliot A.] Princeton Univ, Princeton Neurosci Inst, Princeton, NJ 08540 USA Lee, G (reprint author), Dalhousie Univ, NICHE Res Grp, Halifax, NS B3H 3J5, Canada. gmlee@ualberta.ca; bulitko@ualberta.ca; eludvig@princeton.edu Natural Sciences and Engineering Research Council of Canada (NSERC); International Council for Open Research and Open Education (iCORE) This work was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) and by the International Council for Open Research and Open Education (iCORE). *2K SPORTS, MLB 2K7; ALTMAN R, 1986, STUDIES ENTERTAINMEN, P39; Andre E, 2000, AI MAG, V21, P57; BRYANT J, 1977, J COMMUN, V27, P140, DOI 10.1111/j.1460-2466.1977.tb02140.x; BRYANT J, 1982, J COMMUN, V32, P109, DOI 10.1111/j.1460-2466.1982.tb00482.x; Chapelle O., 2009, P 18 ACM C INF KNOWL, P621, DOI 10.1145/1645953.1646033; Dix A., 1993, HUMAN COMPUTER INTER; DUNCAN MC, 1988, SOCIOLOGY SPORT J, V5, P1; Freund Y., 1995, P 2 EUR C COMP LEARN, P23; FREYTAG G, 1908, FREYTAGS TECHNIQUE D; HAMMOND KW, 1990, COMPUT BIOL MED, V20, P267, DOI 10.1016/0010-4825(90)90052-Q; *IGN, 2012, MLB12 SHOW REV; KENNEDY E, 2009, SPORTS MEDIA SOC; Kitano H, 1997, AI MAG, V18, P73; KORY M, 2012, BASEBALL PROSPE 0507; LEE G, 2012, P ART INT INT DIG EN, P32; LEE G, 2012, THESIS U ALBERTA EDM; LIU T, 2008, P INT C MACH LEARN, P1192; *MAJ LEAG BAS, 2011, LIV XML GAM SUMM; MICHENER J, 1976, SPORTS AM; NEYER R, 2008, R NEYERS BIG BOOK BA; *PLAYST, 2009, MLB 09 SHOW; Rhodes M, 2010, LECT NOTES COMPUT SC, V6432, P111, DOI 10.1007/978-3-642-16638-9_14; Riedl M. O., 2008, INT T SYSTEMS SCI AP, V4, P23; Riedl M. O., 2004, THESIS N CAROLINA ST; Roberts D. L., 2008, INT T SYST SCI APPL, V4, P61; RYAN ML, 1993, NARRATIVE, V1, P138; SMITH C, 1995, STORYTELLERS M ALLEN; WILLIAMS T, 1996, T WILLIAMS HIT LIST; Xu J, 2007, P 30 ANN INT ACM SIG, P391, DOI 10.1145/1277741.1277809; Young RM, 2007, INTERACT STUD, V8, P177, DOI 10.1075/is.8.2.02you 31 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1943-068X 1943-0698 IEEE T COMP INTEL AI IEEE Trans. Comput. Intell. AI Games JUN 2014 6 2 SI 144 155 10.1109/TCIAIG.2013.2275199 12 Computer Science, Artificial Intelligence; Computer Science, Software Engineering Computer Science AJ7TX WOS:000337901800005 J Guha, T; Ward, RK Guha, Tanaya; Ward, Rabab K. Image Similarity Using Sparse Representation and Compression Distance IEEE TRANSACTIONS ON MULTIMEDIA English Article Compression; image similarity; Kolmogorov complexity; overcomplete dictionary; sparse representation INFORMATION; RETRIEVAL A new line of research uses compression methods to measure the similarity between signals. Two signals are considered similar if one can be compressed significantly when the information of the other is known. The existing compression-based similarity methods, although successful in the discrete one dimensional domain, do not work well in the context of images. This paper proposes a sparse representation-based approach to encode the information content of an image using information from the other image, and uses the compactness (sparsity) of the representation as a measure of its compressibility (how much can the image be compressed) with respect to the other image. The sparser the representation of an image, the better it can be compressed and the more it is similar to the other image. The efficacy of the proposed measure is demonstrated through the high accuracies achieved in image clustering, retrieval and classification. [Guha, Tanaya; Ward, Rabab K.] Univ British Columbia, Image & Signal Proc Lab, Dept Elect & Comp Engn, Vancouver, BC V6T1R9, Canada Guha, T (reprint author), Univ British Columbia, Image & Signal Proc Lab, Dept Elect & Comp Engn, Vancouver, BC V6T1R9, Canada. tanaya@ece.ubc.ca; rababw@ece.ubc.ca Aharon M, 2006, IEEE T SIGNAL PROCES, V54, P4311, DOI 10.1109/TSP.2006.881199; Campana B. J. L., 2010, STAT ANAL DATA MININ, V3; Cerra D., 2008, P DCC, P509; CHAITIN GJ, 1966, J ACM, V13, P547, DOI 10.1145/321356.321363; Chen SSB, 1998, SIAM J SCI COMPUT, V20, P33, DOI 10.1137/S1064827596304010; Chen WY, 2011, IEEE T PATTERN ANAL, V33, P568, DOI 10.1109/TPAMI.2010.88; Chen X, 2004, IEEE T INFORM THEORY, V50, P1545, DOI 10.1109/TIT.2004.830793; Cilibrasi R, 2004, COMPUT MUSIC J, V28, P49, DOI 10.1162/0148926042728449; Cilibrasi R, 2005, IEEE T INFORM THEORY, V51, P1523, DOI 10.1109/TIT.2005.844059; Georghiades AS, 2001, IEEE T PATTERN ANAL, V23, P643, DOI 10.1109/34.927464; Girod B., 1993, DIGITAL IMAGES HUMAN; Kang LW, 2011, IEEE T MULTIMEDIA, V13, P1019, DOI 10.1109/TMM.2011.2159197; Keogh E., 2004, P 10 ACM SIGKDD INT, P206, DOI DOI 10.1145/1014052.1014077; Kolmogorov A.N., 1965, Problems of Information Transmission, V1; Lewicki MS, 2000, NEURAL COMPUT, V12, P337, DOI 10.1162/089976600300015826; Li M, 2004, IEEE T INFORM THEORY, V50, P3250, DOI 10.1109/TIT.2004.838101; Li M, 2006, LECT NOTES ARTIF INT, V3918, P704; Li M., 1997, INTRO KOLMOGOROV COM; Macedonas A, 2008, J VIS COMMUN IMAGE R, V19, P464, DOI 10.1016/j.jvcir.2008.06.006; Ng A.Y., 2001, P NIPS, P849; Olshausen BA, 1996, NATURE, V381, P607, DOI 10.1038/381607a0; Papadimitriou C., 1998, COMBINATORIAL OPTIMI; Pati Y., 1993, P AS SIGN SYST COMP; Pinho A., 2011, P 18 IEEE INT C IM P, P1993; Rubinstein R., 2008, TECHNICAL REPORT; Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054; Sheikh HR, 2006, IEEE T IMAGE PROCESS, V15, P430, DOI 10.1109/TIP.2005.859378; Silva D., 2013, P INT SOC MUS INF RE; SOLOMONOFF RJ, 1964, INFORM CONTROL, V7, P1, DOI 10.1016/S0019-9958(64)90223-2; Tataw O. M., 2013, P INT C DOC AN REC I, P180; Theodorakopoulos I., 2011, P IEEE ICCV, P1647; TVERSKY A, 1977, PSYCHOL REV, V84, P327, DOI 10.1037/0033-295X.84.4.327; Watanabe T, 2002, IEEE T PATTERN ANAL, V24, P579, DOI 10.1109/34.1000234; Wohlberg B, 2003, IEEE T SIGNAL PROCES, V51, P3053, DOI 10.1109/TSP.2003.819006; Zeppelzauer M., 2013, P ACM INT WORKSH MUL, P3 35 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia JUN 2014 16 4 980 987 10.1109/TMM.2014.2306175 8 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AJ8LN WOS:000337955800008 J Strong, G; Gong, ML Strong, Grant; Gong, Minglun Self-Sorting Map: An Efficient Algorithm for Presenting Multimedia Data in Structured Layouts IEEE TRANSACTIONS ON MULTIMEDIA English Article Algorithms; artificial neural networks; computational and artificial intelligence; computers and information processing; data visualization; neural networks; parallel algorithm; systems; man and cybernetics; user interfaces NONLINEAR DIMENSIONALITY REDUCTION; MULTIDIMENSIONAL PROJECTION; DATA VISUALIZATION; OVERLAP REMOVAL; DATA SETS; ORGANIZATION; COLLECTION; RETRIEVAL This paper presents the Self-Sorting Map (SSM), a novel algorithm for organizing and presenting multimedia data. Given a set of data items and a dissimilarity measure between each pair of them, the SSM places each item into a unique cell of a structured layout, where the most related items are placed together and the unrelated ones are spread apart. The algorithm integrates ideas from dimension reduction, sorting, and data clustering algorithms. Instead of solving the continuous optimization problem that other dimension reduction approaches do, the SSM transforms it into a discrete labeling problem. As a result, it can organize a set of data into a structured layout without overlap, providing a simple and intuitive presentation. The algorithm is designed for sorting all data items in parallel, making it possible to arrange millions of items in seconds. Experiments on different types of data demonstrate the SSM's versatility in a variety of applications, ranging from positioning city names by proximities to presenting images according to visual similarities, to visualizing semantic relatedness between Wikipedia articles. [Strong, Grant; Gong, Minglun] Mem Univ Newfoundland, Dept Comp Sci, St John, NF A1B 3X5, Canada Strong, G (reprint author), Mem Univ Newfoundland, Dept Comp Sci, St John, NF A1B 3X5, Canada. NSERC; Memorial University of Newfoundland The authors would like to thank the anonymous reviewers for their constructive and valuable comments. Thanks also go to Roberto De Phino, M. C. F. de Oliveira, and A. de Andrade Lopes, for providing us with their HexBoard implementation. This research is supported by the NSERC and Memorial University of Newfoundland. Algorithmics Group, 2009, MDSJ JAV LIB MULT SC; BORG I., 2005, MODERN MULTIDIMENSIO; Chen TT, 2008, IEEE INT CON INF VIS, P415; Ciura M., 2001, P 13 INT S FUND COMP, P106; de Pinho RD, 2010, MULTIMED TOOLS APPL, V50, P533, DOI 10.1007/s11042-010-0483-5; Di Battista G., 1999, GRAPH DRAWING ALGORI; Dwyer T, 2006, LECT NOTES COMPUT SC, V3843, P153; Gomez-Nieto E., 2013, P SIBGR C GRAPH IM V; Heesch D, 2008, MULTIMED TOOLS APPL, V40, P261, DOI 10.1007/s11042-008-0207-2; HOQUE E, 2011, P ATL WEB INT C, V86, P73; Hoque E, 2013, INFORM PROCESS MANAG, V49, P1122, DOI 10.1016/j.ipm.2012.12.001; Hotton S., 2004, HISEE VER 1 0 0; Jensen R. E., 2012, SELF SORTING MAP COL; Joia P, 2011, IEEE T VIS COMPUT GR, V17, P2563, DOI 10.1109/TVCG.2011.220; Kanungo T, 2002, IEEE T PATTERN ANAL, V24, P881, DOI 10.1109/TPAMI.2002.1017616; Kaski S, 2011, IEEE SIGNAL PROC MAG, V28, P100, DOI 10.1109/MSP.2010.940003; Kohonen T., 1995, SELF ORG MAPS; Kohonen T, 2000, IEEE T NEURAL NETWOR, V11, P574, DOI 10.1109/72.846729; Milne D., 2008, P AAAI WORKSH WIK AR; Morrison A., 2003, Information Visualization, V2, DOI 10.1057/palgrave.ivs.9500040; Paulovich FV, 2010, IEEE T VIS COMPUT GR, V16, P1281, DOI 10.1109/TVCG.2010.207; Paulovich FV, 2012, COMPUT SCI ENG, V14, P74; Paulovich FV, 2011, COMPUT GRAPH FORUM, V30, P1091, DOI 10.1111/j.1467-8659.2011.01958.x; Pinho R., 2009, P SAC 09 NEW YORK NY, P1757, DOI 10.1145/1529282.1529679; Pinho R, 2009, INFORMATION VISUALIZATION, IV 2009, PROCEEDINGS, P32, DOI 10.1109/IV.2009.12; Post F.-H, 2002, DATA VISUALIZATION S; Rodden K., 2001, P SIGCHI C HUM FACT, P190, DOI 10.1145/365024.365097; SAMMON JW, 1969, IEEE T COMPUT, VC 18, P401, DOI 10.1109/T-C.1969.222678; Strobelt H, 2012, COMPUT GRAPH FORUM, V31, P1135, DOI 10.1111/j.1467-8659.2012.03106.x; Strong G., 2009, P INT C IM VID RETR, P1, DOI 10.1145/1646396.1646401; Strong G, 2010, LECT NOTES COMPUT SC, V6454, P481, DOI 10.1007/978-3-642-17274-8_47; Strong G, 2010, LECT NOTES COMPUT SC, V6335, P424, DOI 10.1007/978-3-642-15470-6_44; Strong G., 2011, P GRAPH INT, P199; Strong G, 2008, LECT NOTES COMPUT SC, V5359, P390, DOI 10.1007/978-3-540-89646-3_38; Tenenbaum JB, 2000, SCIENCE, V290, P2319, DOI 10.1126/science.290.5500.2319; Tikhonova A., 2008, P EUR PAR GRAPH VIS, P25; van der Maaten LJP, 2009, 2009005 TICCTR TILB, V10, P1; Venna J, 2010, J MACH LEARN RES, V11, P451; Zhang J, 2009, IEEE T VIS COMPUT GR, V15, P1153, DOI 10.1109/TVCG.2009.202 39 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia JUN 2014 16 4 1045 1058 10.1109/TMM.2014.2306183 14 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AJ8LN WOS:000337955800013 J Soleymani, M; Larson, M; Pun, T; Hanjalic, A Soleymani, Mohammad; Larson, Martha; Pun, Thierry; Hanjalic, Alan Corpus Development for Affective Video Indexing IEEE TRANSACTIONS ON MULTIMEDIA English Article Benchmarks; content analysis; emotional characterization; multimedia; videos INFORMATION-RETRIEVAL; EMOTION; MUSIC; EXPRESSIONS; SOUNDS; WORDS; MODEL; MOOD Affective video indexing is the area of research that develops techniques to automatically generate descriptions of video content that encode the emotional reactions which the video content evokes in viewers. This paper provides a set of corpus development guidelines based on state-of-the-art practice intended to support researchers in this field. Affective descriptions can be used for video search and browsing systems offering users affective perspectives. The paper is motivated by the observation that affective video indexing has yet to fully profit from the standard corpora (data sets) that have benefited conventional forms of video indexing. Affective video indexing faces unique challenges, since viewer-reported affective reactions are difficult to assess. Moreover affect assessment efforts must be carefully designed in order to both cover the types of affective responses that video content evokes in viewers and also capture the stable and consistent aspects of these responses. We first present background information on affect and multimedia and related work on affective multimedia indexing, including existing corpora. Three dimensions emerge as critical for affective video corpora, and form the basis for our proposed guidelines: the context of viewer response, personal variation among viewers, and the effectiveness and efficiency of corpus creation. Finally, we present examples of three recent corpora and discuss how these corpora make progressive steps towards fulfilling the guidelines. [Soleymani, Mohammad] Univ London Imperial Coll Sci Technol & Med, Intelligent Behav Understanding Grp IBUG, London SW7 2AZ, England; [Larson, Martha; Hanjalic, Alan] Delft Univ Technol, Multimedia Informat Retrieval Lab, NL-2628 CD Delft, Netherlands; [Pun, Thierry] Univ Geneva, Comp Vis & Multimedia Lab, CH-1227 Carouge, GE, Switzerland Soleymani, M (reprint author), Univ London Imperial Coll Sci Technol & Med, Intelligent Behav Understanding Grp IBUG, London SW7 2AZ, England. m.soleymani@imperial.ac.uk; m.a.larson@tudelft.nl; thierry.pun@unige.ch; a.hanjalic@tudelft.nl European Research Area under the FP7 Marie Curie Intra-European Fellowship: Emotional continuous tagging using spontaneous behavior (EmoTag); European Community [287704] The work of M. Soleymani was supported by the European Research Area under the FP7 Marie Curie Intra-European Fellowship: Emotional continuous tagging using spontaneous behavior (EmoTag). The work of M. Larson and A. Hanjalic was supported in part by the European Community's Seventh Framework Program under grant agreement no. 287704 (CUbRIK). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Sen-Ching Cheung. Arapakis I., 2008, P 31 ANN INT ACM SIG, P395, DOI 10.1145/1390334.1390403; Benini S, 2011, IEEE T MULTIMEDIA, V13, P1356, DOI 10.1109/TMM.2011.2163058; Biel JI, 2013, IEEE T MULTIMEDIA, V15, P41, DOI 10.1109/TMM.2012.2225032; BRADLEY MM, 1994, J BEHAV THER EXP PSY, V25, P49, DOI 10.1016/0005-7916(94)90063-9; Chan CH, 2005, P 13 ANN ACM INT C M, P427, DOI 10.1145/1101149.1101243; Cowie R., 2000, ISCA TUT RES WORKSH; Demarty C.-H., 2012, LNCS, V7585, P416; Desmet P., 2003, MEASURING EMOTION DE, P111; Douglas-Cowie E., 2000, P ISCA WORKSH SPEECH, P39; Ekman P., 2005, BASIC EMOTIONS, P45; EKMAN P, 1987, J PERS SOC PSYCHOL, V53, P712, DOI 10.1037/0022-3514.53.4.712; Fontaine JRJ, 2007, PSYCHOL SCI, V18, P1050, DOI 10.1111/j.1467-9280.2007.02024.x; Hanjalic A, 2005, IEEE T MULTIMEDIA, V7, P1114, DOI 10.1109/TMM.2005.858397; Hanjalic A, 2005, IEEE T MULTIMEDIA, V7, P143, DOI 10.1109/TMM.2004.840618; Hanjalic A, 2006, IEEE SIGNAL PROC MAG, V23, P90, DOI 10.1109/MSP.2006.1621452; Hunter PG, 2011, EMOTION, V11, P1068, DOI 10.1037/a0023749; Hunter PG, 2008, COGNITION EMOTION, V22, P327, DOI 10.1080/02699930701438145; Irie G, 2010, IEEE T MULTIMEDIA, V12, P523, DOI 10.1109/TMM.2010.2051871; Janin A., 2010, P ACM INT C MULT 201, P1591, DOI DOI 10.1145/1873951.1874295; Jones G. J. F., 2012, AFFECT BASED INDEXIN, P321; Kang HB, 2003, P 11 ACM INT C MULT, P259; Kazemzadeh A, 2013, IEEE COMPUT INTELL M, V8, P34, DOI 10.1109/MCI.2013.2247824; Kittur A, 2008, CHI 2008: 26TH ANNUAL CHI CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS VOLS 1 AND 2, CONFERENCE PROCEEDINGS, P453; Laird J. D., 2007, FEELINGS PERCEPTION; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Lopatovska I, 2011, INFORM PROCESS MANAG, V47, P575, DOI 10.1016/j.ipm.2010.09.001; Marsella S., 2010, COMPUTATIONAL MODELS, P21; Morency L.-P., 2011, P 13 INT C MULT INT, P169; Morris R., 2011, P ACM CHI 11 WORKSH; Nathanson AI, 2003, LEA COMMUN SER, P107; Ortony A., 1988, COGNITIVE STRUCTURE; Otsuka I., 2009, SIGNALS COMMUNICATIO, P1; Paolacci G, 2010, JUDGM DECIS MAK, V5, P411; Picard R. W., 1995, 321 MED LAB PERC COM; Plutchik R., 1980, GEN PSYCHOEVOLUTIONA, P3; Quirin M, 2009, J PERS SOC PSYCHOL, V97, P500, DOI 10.1037/a0016063; Reips UD, 2001, BEHAV RES METH INS C, V33, P201, DOI 10.3758/BF03195366; Rottenberg J., 2007, AFFECTIVE SCI, P9; Rui Y., 2000, Proceedings ACM Multimedia 2000, DOI 10.1145/354384.354443; RUSSELL JA, 1980, J PERS SOC PSYCHOL, V39, P1161, DOI 10.1037/h0077714; RUSSELL JA, 1977, J RES PERS, V11, P273, DOI 10.1016/0092-6566(77)90037-X; RUSSELL JA, 1991, PSYCHOL BULL, V110, P426, DOI 10.1037/0033-2909.110.3.426; RUSSELL JA, 1989, J PERS SOC PSYCHOL, V57, P493, DOI 10.1037/0022-3514.57.3.493; Sander D, 2005, NEURAL NETWORKS, V18, P317, DOI 10.1016/j.neunet.2005.03.001; Schaefer A, 2010, COGNITION EMOTION, V24, P1153, DOI 10.1080/02699930903274322; SCHERER KR, 1993, COGNITION EMOTION, V7, P325, DOI 10.1080/02699939308409192; Scherer KR, 2005, SOC SCI INFORM, V44, P695, DOI 10.1177/0539018405058216; Smeaton A.F., 2006, P 8 ACM INT WORKSH M, P321, DOI 10.1145/1178677.1178722; Smeaton AF, 2009, SIGNALS COMMUN TECHN, P151, DOI 10.1007/978-0-387-76569-3_6; Snoek C. G. M., 2009, FDN TRENDS INFORM RE, V4, P215; Soleymani M, 2012, IEEE T AFFECT COMPUT, V3, P42, DOI 10.1109/T-AFFC.2011.25; Soleymani M., 2009, INT J SEMANTIC COMPU, V3, P235; Soleymani M., 2009, P AFF COMP INT INT; Soleymani M., P WORKSH CROWDS SEAR; Soleymani M., 2009, P INT C AFF COMP INT, P1; Soleymani M., 2013, CROWDMM 13, P1; Teixeira R. M., 2011, MULTIMED TOOLS APPL, P1; Thornley CV, 2011, J AM SOC INF SCI TEC, V62, P613, DOI 10.1002/asi.21494; Villon O., 2007, THESIS U NICE SOPHIA; Wang HL, 2006, IEEE T CIRC SYST VID, V16, P689, DOI 10.1109/TCSVT.2006.873781; Watson D., 1994, PANAS X MANUAL POSIT; Winoto P, 2010, EXPERT SYST APPL, V37, P6086, DOI 10.1016/j.eswa.2010.02.117; Wirth W., 2005, COMMUNICATION RES TR, V24, P3; Wundt W., 1905, GRUNDZUGE PHYSL PSYC; Xu M, 2008, P 16 ACM INT C MULT, P677, DOI DOI 10.1145/1459359.1459457; Yang YH, 2009, INT CONF ACOUST SPEE, P1657, DOI 10.1109/ICASSP.2009.4959919; Zeng ZH, 2009, IEEE T PATTERN ANAL, V31, P39, DOI 10.1109/TPAMI.2008.52; ZILLMANN D, 1996, LEA COMMUN SER, P199; Zillmann D., 1991, EMPATHY AFFECT BEARI, P135 69 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia JUN 2014 16 4 1075 1089 10.1109/TMM.2014.2305573 15 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AJ8LN WOS:000337955800015 J Xie, HT; Zhang, YD; Tan, JL; Guo, L; Li, JT Xie, Hongtao; Zhang, Yongdong; Tan, Jianlong; Guo, Li; Li, Jintao Contextual Query Expansion for Image Retrieval IEEE TRANSACTIONS ON MULTIMEDIA English Article Contextual query expansion; common visual patterns; image retrieval SEARCH; VIDEOS; SETS In this paper, we study the problem of image retrieval by introducing contextual query expansion to address the shortcomings of bag-of-words based frameworks: semantic gap of visual word quantization, and the efficiency and storage loss due to query expansion. Our method is built on common visual patterns (CVPs), which are the distinctive visual structures between two images and have rich contextual information. With CVPs, two contextual query expansions on visual word-level and image-level are explored, respectively. For visual word-level expansion, we find contextual synonymous visual words (CSVWs) and expand a word in the query image with its CSVWs to boost retrieval accuracy. CSVWs are the words that appear in the same CVPs and have same contextual meaning, i.e. similar spatial layout and geometric transformations. For image-level expansion, the database images that have the same CVPs are organized by linked list and the images that have the same CVPs as the query image, but not included in the results are automatically expanded. The main computation of these two expansions is carried out offline, and they can be integrated into the inverted file and efficiently applied to all images in the dataset. Experiments conducted on three reference datasets and a dataset of one million images demonstrate the effectiveness and efficiency of our method. [Xie, Hongtao; Tan, Jianlong; Guo, Li] Chinese Acad Sci, Natl Engn Lab Informat Secur Technol, Inst Informat Engn, Beijing 100093, Peoples R China; [Xie, Hongtao; Zhang, Yongdong; Li, Jintao] Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China Xie, HT (reprint author), Chinese Acad Sci, Natl Engn Lab Informat Secur Technol, Inst Informat Engn, Beijing 100093, Peoples R China. xiehongtao@iie.ac.cn; zhyd@ict.ac.cn; tanjianlong@iie.ac.cn; guoli@iie.ac.cn; jtli@ict.ac.cn Chinese Academy of Sciences [XDA06030602]; National High Technology Research and Development Program [2011AA010705]; National Nature Science Foundation of China [61303171, 61100087]; Beijing New Star Project on Science Technology [2007B071]; Natural Science Foundation of Beijing [4112055] This work was supported in part by the "Strategic Priority Research Program" of the Chinese Academy of Sciences (XDA06030602), National High Technology Research and Development Program (2011AA010705), National Nature Science Foundation of China (61303171, 61100087); Beijing New Star Project on Science & Technology (2007B071); Natural Science Foundation of Beijing (4112055). The associate editor coordinating the review of this manuscript and approving it for publication was Prof. Nicu Sebe. Arandjelovic R., 2012, P IEEE C COMP VIS PA; Berg B. T., 2005, P IEEE C COMP VIS PA; Chen D.M., 2011, P IEEE C COMP VIS PA; Chum O., 2007, P INT C COMP VIS; Chum O., 2011, P IEEE C COMP VIS PA; Duan JZ, 2013, IEEE SIGNAL PROC LET, V20, P831, DOI 10.1109/LSP.2013.2268206; Fernando B., 2012, P EUR C COMP VIS; Gavves E., 2010, P ACM C MULT; Holub A., 2008, P COMP VIS PATT REC; Hsiao JH, 2007, IEEE T IMAGE PROCESS, V16, P2069, DOI 10.1109/TIP.2007.900099; Jegou H, 2010, INT J COMPUT VISION, V87, P316, DOI 10.1007/s11263-009-0285-2; Jegou H., 2008, P EUR C COMP VIS; Joly A., 2009, P ACM C MULT; Kuo Y. H., 2009, P INT C MULT; Kuo Y.-H., 2011, P IEEE C COMP VIS PA; Liu H., 2010, P INT C MACH LEARN I; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Mikulik A., 2010, P EUR C COMP VIS; Nister D., 2006, P IEEE C COMP VIS PA; Olivares X., 2008, P ACM C MULT; Pavan M, 2007, IEEE T PATTERN ANAL, V29, P167, DOI 10.1109/TPAMI.2007.250608; Philbin J., 2010, P EUR C COMP VIS; Philbin J., 2008, P IEEE C COMP VIS PA; Philbin J., 2007, P IEEE C COMP VIS PA; Philbin J., 2010, INT J COMPUT VIS; Qin D., 2011, P IEEE C COMP VIS PA; Sivic J, 2009, IEEE T PATTERN ANAL, V31, P591, DOI 10.1109/TPAMI.2008.111; Tang JH, 2010, IEEE T MULTIMEDIA, V12, P131, DOI 10.1109/TMM.2009.2037373; Tang W., 2011, P ACM C MULT; Tian Q, 2011, MULTIMED TOOLS APPL, V51, P441, DOI 10.1007/s11042-010-0636-6; Tuytelaars T., 2008, FDN TRENDS COMPUTER, V3, P177, DOI [DOI 10.1561/0600000017, DOI 10.1561/0600000017>]; Weibull JW, 1997, EVOLUTIONARY GAME TH; Wu W., 2008, P ACM C MULT; Xie H., 2011, P ACM C MULT; Xie HT, 2011, IEEE T MULTIMEDIA, V13, P1319, DOI 10.1109/TMM.2011.2167224; Yuan J., 2007, P INT C COMP VIS; Zha Z., 2009, P ACM C MULT; Zhang D.-Q., P ACM C MULT; Zhang YD, 2014, INFORM SCIENCES, V281, P586, DOI 10.1016/j.ins.2013.12.043; Zhang Y., 2011, P IEEE C COMP VIS PA; Zhao WL, 2010, IEEE T MULTIMEDIA, V12, P448, DOI 10.1109/TMM.2010.2050651; Zhou W., 2012, P ACM C MULT; Zhou W., 2010, P ACM C MULT; Zhou W., 2013, TOMCCAP, V9 44 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1520-9210 1941-0077 IEEE T MULTIMEDIA IEEE Trans. Multimedia JUN 2014 16 4 1104 1114 10.1109/TMM.2014.2305909 11 Computer Science, Information Systems; Computer Science, Software Engineering; Telecommunications Computer Science; Telecommunications AJ8LN WOS:000337955800017 J Liu, Q; Tan, CC; Wu, J; Wang, GJ Liu, Qin; Tan, Chiu C.; Wu, Jie; Wang, Guojun Towards Differential Query Services in Cost-Efficient Clouds IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS English Article Cloud computing; cost efficiency; differential query services; privacy Cloud computing as an emerging technology trend is expected to reshape the advances in information technology. In a cost-efficient cloud environment, a user can tolerate a certain degree of delay while retrieving information from the cloud to reduce costs. In this paper, we address two fundamental issues in such an environment: privacy and efficiency. We first review a private keyword-based file retrieval scheme that was originally proposed by Ostrovsky. Their scheme allows a user to retrieve files of interest from an untrusted server without leaking any information. The main drawback is that it will cause a heavy querying overhead incurred on the cloud and thus goes against the original intention of cost efficiency. In this paper, we present three efficient information retrieval for ranked query (EIRQ) schemes to reduce querying overhead incurred on the cloud. In EIRQ, queries are classified into multiple ranks, where a higher ranked query can retrieve a higher percentage of matched files. A user can retrieve files on demand by choosing queries of different ranks. This feature is useful when there are a large number of matched files, but the user only needs a small subset of them. Under different parameter settings, extensive evaluations have been conducted on both analytical models and on a real cloud environment, in order to examine the effectiveness of our schemes. [Liu, Qin; Wang, Guojun] Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China; [Liu, Qin] Hunan Univ, Coll Informat Sci & Engn, Changsha 410082, Hunan, Peoples R China; [Tan, Chiu C.; Wu, Jie] Temple Univ, Dept Comp & Informat Sci, Philadelphia, PA 19122 USA Liu, Q (reprint author), Cent S Univ, Sch Informat Sci & Engn, Changsha 410083, Hunan, Peoples R China. gracelq628@hnu.edu.cn; cctan@temple.edu; jiewu@temple.edu; csgjwang@mail.csu.edu.cn NSF [ECCS 1231461, ECCS 1128209, CNS 1138963, CNS 1065444, CCF 1028167]; NSFC [61272151, 61073037]; ISTCP [2013DFB10070]; China Hunan Provincial Science & Technology Program [2012GK4106]; "Mobile Health'' Ministry of Education-China Mobile Joint Laboratory (MOE-DST) [[2012]311]; Fundamental Research Funds for the Central Universities This research was supported in part by NSF grants ECCS 1231461, ECCS 1128209, CNS 1138963, CNS 1065444, and CCF 1028167; NSFC grants 61272151 and 61073037, ISTCP grant 2013DFB10070, the China Hunan Provincial Science & Technology Program under Grant Number 2012GK4106, and the "Mobile Health'' Ministry of Education-China Mobile Joint Laboratory (MOE-DST No. [2012]311); and Fundamental Research Funds for the Central Universities. Berl A, 2010, COMPUT J, V53, P1045, DOI 10.1093/comjnl/bxp080; Bethencourt J, 2009, ACM T INFORM SYST SE, V12, DOI 10.1145/1455526.1455529; Bethencourt J., 2006, P IEEE SP, P1; Curtmola R., 2006, P 13 ACM C COMP COMM, P79, DOI 10.1145/1180405.1180417; Danezis G, 2007, LECT NOTES COMPUT SC, V4886, P148; Danezis G., 2006, 024 IACR EPR ARCH; Finiasz M., 2012, Proceedings of the 2012 IEEE International Symposium on Information Theory - ISIT, DOI 10.1109/ISIT.2012.6283979; Gelenbe E, 2012, 2012 IEEE SECOND SYMPOSIUM ON NETWORK CLOUD COMPUTING AND APPLICATIONS (NCCA 2012), P25, DOI 10.1109/NCCA.2012.16; Guo D., 2006, P 25 ANN JOINT C IEE, P1, DOI DOI 10.1109/INFOCOM.2006.325; Hore B., 2012, P SEC DAT MAN, P93; Liu Q, 2012, J PARALLEL DISTR COM, V72, P1019, DOI 10.1016/j.jpdc.2012.04.012; Liu Q, 2012, 2012 PROCEEDINGS IEEE INFOCOM, P2581; Mell P., 2011, NIST SPECIAL PUBLICA; Mitzenmacher M, 2002, IEEE ACM T NETWORK, V10, P604, DOI 10.1109/TNET.2002.803864; Ostrovsky R., 2005, P CRYPTO, P233; Ostrovsky R, 2007, J CRYPTOL, V20, P397, DOI 10.1007/s00145-007-0565-3; Paillier P, 1999, LECT NOTES COMPUT SC, V1592, P223; Wang GJ, 2011, COMPUT SECUR, V30, P320, DOI 10.1016/j.cose.2011.05.006; Yi Xing, 2011, P 10 ANN ACM WORKSH, P153; Yu S., 2010, P 10 INT C ALG ARCH, P1 20 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1045-9219 1558-2183 IEEE T PARALL DISTR IEEE Trans. Parallel Distrib. Syst. JUN 2014 25 6 1648 1658 10.1109/TPDS.2013.132 11 Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AJ8OU WOS:000337966400024 J Chakraborty, V; Chiu, V; Vasarhelyi, M Chakraborty, Vasundhara; Chiu, Victoria; Vasarhelyi, Miklos Automatic classification of accounting literature INTERNATIONAL JOURNAL OF ACCOUNTING INFORMATION SYSTEMS English Article Accounting literature; Automatic classification; Taxonomy; Attributes; Semantic parsing; Data mining INFORMATION; CONSTRUCTION; SYSTEM; STATE; ART This paper explores the possibility of using semantic parsing, information retrieval and data mining techniques to automatically classify accounting research. Literature taxonomization plays a critical role in understanding a discipline's knowledge attributes and structure. The traditional research classification is a manual process which is considerably time consuming and may introduce inconsistent classifications by different experts. Aiming at aiding this classification issue, this study conducted three studies to seek the most effective and accurate method to classify accounting publications' attributes. We found results in the third study most rewarding in which the classification accuracy reached 87.27% with decision trees and rule-based algorithms applied. Findings in the first and second studies also provided valuable implications on automatic literature classifications, e.g. abstracts are better measures to use than keywords and balancing under-represented subclasses does not contribute to more accurate classifications. All three studies' results also suggest that expanding article sample size is a key to strengthen automatic classification accuracy. Overall, the potential path of this line of research seems to be very promising and would have several collateral benefits and applications. (C) 2014 Elsevier Inc. All rights reserved. [Chakraborty, Vasundhara] Ramapo Coll, Mahwah, NJ 07430 USA; [Chiu, Victoria] SUNY Coll New Paltz, New Paltz, NY 12561 USA; [Vasarhelyi, Miklos] Rutgers State Univ, Piscataway, NJ 08855 USA Chiu, V (reprint author), SUNY Coll New Paltz, New Paltz, NY 12561 USA. vchiu626@gmail.com Ashton RH, HUMAN INFORM PROCESS; Badua F, 2011, ACCOUNT HIST J, V38, P31; Badua FA, 2005, THESIS RUTGERS U; BALL R, 1971, J ACCOUNTING RES, V9, P1, DOI 10.2307/2490199; BARKI H, 1993, MIS QUART, V17, P209, DOI 10.2307/249802; Birnberg J. G., 1989, BEHAV RES ACCOUNTING, V1, P23; BROWN LD, 1987, ACCOUNT ORG SOC, V12, P193, DOI 10.1016/0361-3682(87)90006-7; Brown LD, 1994, ACCOUNTING RES DIREC; Brown LD, 1989, CONTEMP ACCOUNT RES, V5, P793; Chatfield M., 1975, ACCOUNT REV, V1, P1; CHEN HC, 1995, J AM SOC INFORM SCI, V46, P175, DOI 10.1002/(SICI)1097-4571(199504)46:3<175::AID-ASI3>3.0.CO;2-U; Chen H, 1994, 9402 MAI WPS U AR CT; CHEN HC, 1992, IEEE T SYST MAN CYB, V22, P885, DOI 10.1109/21.179830; CRAWFORD RG, 1979, CAN J INFORM SCI, V4, P124; Crouch CJ, 1992, P 15 ANN INT ACM SIG; CROUCH CJ, 1990, INFORM PROCESS MANAG, V26, P629, DOI 10.1016/0306-4573(90)90106-C; DYCKMAN TR, 1984, J ACCOUNTING RES, V22, P225, DOI 10.2307/2490710; FELIX WL, 1982, ACCOUNT REV, V57, P245; Fisher IE, 2010, J EMERG TECHNOL ACC, V7, P1, DOI 10.2308/jeta.2010.7.1.1; Fleming RJ, 2000, ACCOUNTING HIST J, V27, P43; Gangolly J., 2000, New Review of Applied Expert Systems and Emerging Technologies, V6; Garnsey MR, 2006, J EMERG TECHNOL ACCO, V3, P21, DOI 10.2308/jeta.2006.3.1.21; Garnsey MR, 2008, J EMERG TECHNOL ACCO, V5, P17; GONEDES NJ, 1974, J ACCOUNTING RES, P48; Hakansson N., 1973, ACCOUNTING RES 1960, P137; Hall M, 2009, SIGKDD EXPLOR, P11; Heck JL, 2007, ACCOUNTING HIST J, V34, P109; Hofstedt T R, 1976, ACCOUNT ORG SOC, V1, P43, DOI 10.1016/0361-3682(76)90006-4; Hofstedt TR, 1975, J CONT BUSINESS AUT, P27; Hulth A, 2003, P EMNLP 03 P 2003 C; IJIRI Y, 1968, J ACCOUNTING RES, V6, P1, DOI 10.2307/2490119; Just A, WORKING PAPER; Krippendorff K., 2004, CONTENT ANAL; Libby R, 1977, ACCOUNT ORG SOC, V2, P245, DOI 10.1016/0361-3682(77)90015-0; LIBBY R, 1982, ACCOUNT ORG SOC, V7, P231, DOI 10.1016/0361-3682(82)90004-6; Liu B, 1998, KDD98; MARCH ST, 1995, DECIS SUPPORT SYST, V15, P251, DOI 10.1016/0167-9236(94)00041-2; Meyer M, 2001, BEHAV RES ACCOUNTING, V13, P253, DOI 10.2308/bria.2001.13.1.253; Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085; Nobata C, 1999, P NAT LANG IM PAC RI; Previts GJ, 1993, ACCOUNT HIS J, V20, P119; Salton G, 1996, INFORM PROCESS MANAG, V32, P127, DOI 10.1016/S0306-4573(96)85001-1; Tan P-N, 2005, INTRO DATA MINING; Thompson CA, P INT C MACH LEARN I; TRUESWELL JC, 1994, J MEM LANG, V33, P285, DOI 10.1006/jmla.1994.1014; TUCKER AB, 1984, COMMUN ACM, V27, P322, DOI 10.1145/358027.358035; Vasarhelyi MA, 1988, ACCOUNTING HIST J, V15, P45 47 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1467-0895 1873-4723 INT J ACCOUNT INF SY Int. J. Account. Inf. Syst. JUN 2014 15 2 122 148 10.1016/j.accinf.2014.01.001 27 Business; Business, Finance; Management Business & Economics AJ8CC WOS:000337929300004 J Kurtz, C; Beaulieu, CF; Napel, S; Rubin, DL Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L. A hierarchical knowledge-based approach for retrieving similar medical images described with semantic annotations JOURNAL OF BIOMEDICAL INFORMATICS English Article Image retrieval; Semantic image annotation; Semantic-based distances; Ontologies; Computed tomographic (CT) images; Liver lesions BIOMEDICAL DOMAIN; LIVER-LESIONS; CT IMAGES; CLASSIFICATION; RELATEDNESS; ONTOLOGY; DISTANCE; REPRESENTATION; RADIOLOGY; WORDS Computer-assisted image retrieval applications could assist radiologist interpretations by identifying similar images in large archives as a means to providing decision support. However, the semantic gap between low-level image features and their high level semantics may impair the system performances. Indeed, it can be challenging to comprehensively characterize the images using low-level imaging features to fully capture the visual appearance of diseases on images, and recently the use of semantic terms has been advocated to provide semantic descriptions of the visual contents of images. However, most of the existing image retrieval strategies do not consider the intrinsic properties of these terms during the comparison of the images beyond treating them as simple binary (presence/absence) features. We propose a new framework that includes semantic features in images and that enables retrieval of similar images in large databases based on their semantic relations. It is based on two main steps: (1) annotation of the images with semantic terms extracted from an ontology, and (2) evaluation of the similarity of image pairs by computing the similarity between the terms using the Hierarchical Semantic-Based Distance (HSBD) coupled to an ontological measure. The combination of these two steps provides a means of capturing the semantic correlations among the terms used to characterize the images that can be considered as a potential solution to deal with the semantic gap problem. We validate this approach in the context of the retrieval and the classification of 2D regions of interest(ROIs) extracted from computed tomographic (CT) images of the liver. Under this framework, retrieval accuracy of more than 0.96 was obtained on a 30-images dataset using the Normalized Discounted Cumulative Gain (NDCG) index that is a standard technique used to measure the effectiveness of information retrieval algorithms when a separate reference standard is available. Classification results of more than 95% were obtained on a 77-images dataset. For comparison purpose, the use of the Earth Mover's Distance (EMD), which is an alternative distance metric that considers all the existing relations among the terms, led to results retrieval accuracy of 0.95 and classification results of 93% with a higher computational cost. The results provided by the presented framework are competitive with the state-of-the-art and emphasize the usefulness of the proposed methodology for radiology image retrieval and classification. (C) 2014 Elsevier Inc. All rights reserved. [Kurtz, Camille; Beaulieu, Christopher F.; Napel, Sandy; Rubin, Daniel L.] Stanford Univ, Sch Med, Dept Radiol, Stanford, CA 94305 USA; [Kurtz, Camille] Univ Paris 05, LIPADE EA 2517, F-75006 Paris, France Kurtz, C (reprint author), Univ Paris 05, LIPADE EA 2517, 45 Rue St Peres, F-75006 Paris, France. camille.kurtz@parisdescartes.fr; dlrubin@stanford.edu National Cancer Institute, National Institutes of Health [U01CA142555-01, R01-CA160251]; GE Medical Systems The authors would like to thank Jarrett Rosenberg for his useful help on statistical evaluations. This project was funded in part by a grant from National Cancer Institute, National Institutes of Health, U01CA142555-01, R01-CA160251 and by a grant from GE Medical Systems. Aigrain P, 1996, MULTIMED TOOLS APPL, V3, P179, DOI 10.1007/BF00393937; Akgul CB, 2011, J DIGIT IMAGING, V24, P208, DOI 10.1007/s10278-010-9290-9; Al-Mubaid Hisham, 2006, Conf Proc IEEE Eng Med Biol Soc, V1, P2713; Al-Mubaid H, 2009, IEEE T SYST MAN CY C, V39, P389, DOI 10.1109/TSMCC.2009.2020689; Andre B, 2012, IEEE T MED IMAGING, V31, P1276, DOI 10.1109/TMI.2012.2188301; Barlow WE, 2004, J NATL CANCER I, V96, P1840, DOI 10.1093/jnci/djh333; Batet Montserrat, 2011, J Biomed Inform, V44, P118, DOI 10.1016/j.jbi.2010.09.002; Budanitsky A, 2006, COMPUT LINGUIST, V32, P13, DOI 10.1162/coli.2006.32.1.13; Cha SH, 2002, PATTERN RECOGN, V35, P1355, DOI 10.1016/S0031-3203(01)00118-2; Chen Y, 2013, J AM MED INFORM ASSN, V20, P1076, DOI 10.1136/amiajnl-2012-001380; Deng J, 2009, P IEEE C COMP VIS PA, P143; Deselaers T, 2011, PROC CVPR IEEE, P1777, DOI 10.1109/CVPR.2011.5995474; Forestier G, 2012, J BIOMED INFORM, V45, P255, DOI 10.1016/j.jbi.2011.11.002; Ganesan P, 2003, ACM T INFORM SYST, V21, P64, DOI 10.1145/635484.635487; Gimenez F, 2011, P IEEE INT C HEALTHC, P346; Gondra I, 2008, COMPUT VIS IMAGE UND, V111, P219, DOI 10.1016/j.cviu.2007.11.001; Guarino N, 1995, INT J HUM-COMPUT ST, V43, P625, DOI 10.1006/ijhc.1995.1066; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Korenblum D, 2011, J DIGIT IMAGING, V24, P739, DOI 10.1007/s10278-010-9328-z; Kurtz C, 2013, DATA KNOWL ENG, V87, P206, DOI 10.1016/j.datak.2013.06.002; Kurtz C, 2012, IEEE IMAGE PROC, P1157, DOI 10.1109/ICIP.2012.6467070; Kwitt R, 2012, MED IMAGE ANAL, V16, P1415, DOI 10.1016/j.media.2012.04.010; Langlotz CP, 2006, RADIOGRAPHICS, V26, P1595, DOI 10.1148/rg.266065168; Lee W.-N., 2008, AMIA ANN S P, V2008, P384; Li LJ, 2009, PROC CVPR IEEE, P2036; Ma H, 2010, IEEE T MULTIMEDIA, V12, P462, DOI 10.1109/TMM.2010.2051360; Mechouche A, 2009, IEEE T MED IMAGING, V28, P1165, DOI 10.1109/TMI.2009.2026746; Mojsilovic A, 2001, IEEE IMAGE PROC, P18; Napel SA, 2010, RADIOLOGY, V256, P243, DOI 10.1148/radiol.10091694; Niblack C. W., 1993, P SOC PHOTO-OPT INS, V1908, P173, DOI 10.1117/12.143648; Pedersen T, 2007, J BIOMED INFORM, V40, P288, DOI 10.1016/j.jbi.2006.06.004; Pivovarov R, 2012, J BIOMED INFORM, V45, P471, DOI 10.1016/j.jbi.2012.01.002; RADA R, 1989, IEEE T SYST MAN CYB, V19, P17, DOI 10.1109/21.24528; Robinson PJA, 1997, BRIT J RADIOL, V70, P1085; Rubin DL, 2009, IEEE INTELL SYST, V24, P57, DOI 10.1109/MIS.2009.3; Rubin DL, 2008, P ANN AM MED INF ASS, P626; Rubin DL, 2011, PSYCHIAT PSYCHOL, V18, P311; Rubin GD, 2000, EUR J RADIOL, V36, P74, DOI 10.1016/S0720-048X(00)00270-9; Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054; SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0; Sanchez D, 2011, J BIOMED INFORM, V44, P749, DOI 10.1016/j.jbi.2011.03.013; Sidney S, 1957, J NERV MENT DIS, V125, P497, DOI 10.1097/00005053-195707000-00032; SNEATH PHA, 1962, NATURE, V193, P855, DOI 10.1038/193855a0; Sujatha KS, 2012, PROCEDIA ENGINEER, V38, P2196, DOI 10.1016/j.proeng.2012.06.264; Tagarelli A, 2013, INFORM SCIENCES, V220, P244, DOI 10.1016/j.ins.2012.07.038; Torres JS, 2012, J BIOMED INFORM, V45, P1066, DOI 10.1016/j.jbi.2012.07.004; Tousch AM, 2012, PATTERN RECOGN, V45, P333, DOI 10.1016/j.patcog.2011.05.017; Turney PD, 2010, J ARTIF INTELL RES, V37, P141; Voorhees EM, 1999, LECT NOTES ARTIF INT, V1714, P32; Wang SJ, 2012, MED IMAGE ANAL, V16, P933, DOI 10.1016/j.media.2012.02.005; WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967; Wu Z, 1994, P 32 ANN M ASS COMP, P133, DOI 10.3115/981732.981751; Xu JJ, 2012, J DIGIT IMAGING, V25, P121, DOI 10.1007/s10278-011-9388-8; Yang W, 2012, J DIGIT IMAGING, V25, P708, DOI 10.1007/s10278-012-9495-1; Zhang DS, 2012, PATTERN RECOGN, V45, P346, DOI 10.1016/j.patcog.2011.05.013 55 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1532-0464 1532-0480 J BIOMED INFORM J. Biomed. Inform. JUN 2014 49 227 244 10.1016/j.jbi.2014.02.018 18 Computer Science, Interdisciplinary Applications; Medical Informatics Computer Science; Medical Informatics AJ6AN WOS:000337772200023 J Zhu, DQ; Wu, S; Carterette, B; Liu, HF Zhu, Dongqing; Wu, Stephen; Carterette, Ben; Liu, Hongfang Using large clinical corpora for query expansion in text-based cohort identification JOURNAL OF BIOMEDICAL INFORMATICS English Article Cohort identification; Information retrieval; Query expansion; Clinical text; Electronic medical records SYSTEM; INFORMATICS In light of the heightened problems of polysemy, synonymy, and hyponymy in clinical text, we hypothesize that patient cohort identification can be improved by using a large, in-domain clinical corpus for query expansion. We evaluate the utility of four auxiliary collections for the Text REtrieval Conference task of IR-based cohort retrieval, considering the effects of collection size, the inherent difficulty of a query, and the interaction between the collections. Each collection was applied to aid in cohort retrieval from the Pittsburgh NLP Repository by using a mixture of relevance models. Measured by mean average precision, performance using any auxiliary resource (MAP = 0.386 and above) is shown to improve over the baseline query likelihood model (MAP = 0.373). Considering subsets of the Mayo Clinic collection, we found that after including 2.5 billion term instances, retrieval is not improved by adding more instances. However, adding the Mayo Clinic collection did improve performance significantly over any existing setup, with a system using all four auxiliary collections obtaining the best results (MAP = 0.4223). Because optimal results in the mixture of relevance models would require selective sampling of the collections, the common sense approach of "use all available data" is inappropriate. However, we found that it was still beneficial to add the Mayo corpus to any mixture of relevance models. On the task of IR-based cohort identification, query expansion with the Mayo Clinic corpus resulted in consistent and significant improvements. As such, any IR query expansion with access to a large clinical corpus could benefit from the additional resource. Additionally, we have shown that more data is not necessarily better, implying that there is value in collection curation. (C) 2014 Elsevier Inc. All rights reserved. [Zhu, Dongqing; Carterette, Ben] Univ Delaware, Dept Comp & Informat Sci, Newark, DE 19716 USA; [Wu, Stephen; Liu, Hongfang] Mayo Clin, Div Biomed Stat & Informat, Rochester, MN 55905 USA Wu, S (reprint author), Mayo Clin, Div Biomed Stat & Informat, 200 First St SW, Rochester, MN 55905 USA. zhudq@cis.udel.edu; wu.stephen@mayo.edu; carteret@cis.udel.edu; hongfang@mayo.edu SHARPn (Strategic Health IT Advanced Research Projects) Area 4: Secondary Use of EHR Data Cooperative Agreement from the HHS Office of the National Coordinator, Washington, DC [DHHS 90TR000201] This work was supported in part by the SHARPn (Strategic Health IT Advanced Research Projects) Area 4: Secondary Use of EHR Data Cooperative Agreement from the HHS Office of the National Coordinator, Washington, DC. DHHS 90TR000201. Bedrick S, 2013, P 21 TEXT RETRIEVAL; Bodenreider O, 2004, NUCLEIC ACIDS RES, V32, pD267, DOI 10.1093/nar/gkh061; Buckley C., 2004, Proceedings of Sheffield SIGIR 2004. The Twenty-Seventh Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1008992.1009000; Chapman WW, 2001, J AM MED INFORM ASSN, P105; Cornet R, 2008, BMC MED INFORM DECIS, V8, DOI 10.1186/1472-6947-8-S1-S2; Croft B, 2009, SEARCH ENGINES INFOR; Daoud M, 2011, P 20 TEXT RETRIEVAL; D'Avolio LW, 2010, AM J MED, V123, pE32, DOI 10.1016/j.amjmed.2010.10.006; Demner-Fushman D, 2011, P 20 TEXT RETRIEVAL; Deshmukh VG, 2009, BMC MED RES METHODOL, V9, DOI 10.1186/1471-2288-9-70; Diaz F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148200; Goodwin T, 2011, P 20 TEXT RETRIEVAL; Hanauer DA, 2006, AMIA ANN S P, V331, P941; Harkema H, 2009, J BIOMED INFORM, V42, P839, DOI 10.1016/j.jbi.2009.05.002; Hersh W, 2012, P 21 TEXT RETRIEVAL; Hersh W, 2009, HEALTH INFORM SER, P3; Hersh WR, 2007, TREC; Kandula Sasikiran, 2011, J Biomed Inform, V44 Suppl 1, pS63, DOI 10.1016/j.jbi.2011.10.013; King B, 2011, P 20 TEXT RETRIEVAL; McCarty CA, 2011, BMC MED GENOMICS, V4, DOI 10.1186/1755-8794-4-13; Murphy SN, 2000, J AM MED INFORM ASSN, P1174; PORTER MF, 1980, PROGRAM-AUTOM LIBR, V14, P130, DOI 10.1108/eb046814; Qi Y, 2013, P 21 TEXT RETRIEVAL; Schuemie M, 2011, P 20 TEXT RETRIEVAL; Seyfried L, 2009, INT J MED INFORM, V78, pE13, DOI 10.1016/j.ijmedinf.2009.05.002; Uzuner O, 2011, J AM MED INFORM ASSN, V18, P552, DOI 10.1136/amiajnl-2011-000203; Voorhees EM, 2011, P 20 TEXT RETRIEVAL; Wu H, 2011, P 20 TEXT RETRIEVAL; Wu S, 2011, P AMIA 2011; Wu S, 2011, P 20 TEXT RETRIEVAL; Wu S, 2012, P AMIA JOINT SUMM CL; Wu ST, 2013, P ACM SIGIR WORKSH H; Yang L, 2011, AMIA ANN S P, P915; Yilmaz E, 2006, P 15 ACM INT C INF K, P102, DOI 10.1145/1183614.1183633; Zhu D, 2013, P 21 TEXT RETRIEVAL; Zhu D, 2011, P 20 TEXT RETRIEVAL 36 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1532-0464 1532-0480 J BIOMED INFORM J. Biomed. Inform. JUN 2014 49 275 281 10.1016/j.jbi.2014.03.010 7 Computer Science, Interdisciplinary Applications; Medical Informatics Computer Science; Medical Informatics AJ6AN WOS:000337772200027 J Sturm, BL Sturm, Bob L. The State of the Art Ten Years After a State of the Art: Future Research in Music Information Retrieval JOURNAL OF NEW MUSIC RESEARCH English Article information retrieval; machine learning; databases; music analysis EXPERIMENTAL COMPUTER-SCIENCE; INTER-GENRE SIMILARITY; SOCIAL TAGS; CLASSIFICATION; RECOGNITION; FEATURES; AUDIO; DISCRIMINATIONS; DATABASES; SYSTEMS A decade has passed since the first review of research on a 'flagship application' of music information retrieval (MIR): the problem of music genre recognition (MGR). During this time, about 500 works addressing MGR have been published, and at least 10 campaigns have been run to evaluate MGR systems. This makes MGR one of the most researched areas of MIR. So, where does MGR now lie? We show that in spite of this massive amount of work, MGR does not lie far from where it began, and the paramount reason for this is that most evaluation in MGR lacks validity. We perform a case study of all published research using the most-used benchmark dataset in MGR during the past decade: GTZAN. We show that none of the evaluations in these many works is valid to produce conclusions with respect to recognizing genre, i.e. that a system is using criteria relevant for recognizing genre. In fact, the problems of validity in evaluation also affect research in music emotion recognition and autotagging. We conclude by discussing the implications of our work for MGR and MIR in the next ten years. [Sturm, Bob L.] Aalborg Univ, DK-2450 Copenhagen, Denmark Sturm, BL (reprint author), Aalborg Univ, Audio Anal Lab, AD MT, AC Meyers Vaenge 15, DK-2450 Copenhagen, Denmark. bst@create.aau.dk Det Frie Forskningsrad [11-105218] This work was supported by Independent Postdoc Grant 11-105218 from Det Frie Forskningsrad. Ammer C., 2004, DICT MUSIC; Anglade A., 2009, P 10 INT C MUS INF R, P669; Aucouturier J.-J., 2009, LANGUAGE EVOLUTION B; Aucouturier JJ, 2008, J NEW MUSIC RES, V37, P87, DOI 10.1080/09298210802479318; Aucouturier JJ, 2003, J NEW MUSIC RES, V32, P83, DOI 10.1076/jnmr.32.1.83.16801; Aucouturier JJ, 2013, J INTELL INF SYST, V41, P483, DOI 10.1007/s10844-013-0251-x; Bagci U, 2007, IEEE SIGNAL PROC LET, V14, P521, DOI 10.1109/LSP.2006.891320; Bailey R. A., 2008, DESIGN COMP EXPT; Barbedo JGA, 2007, EURASIP J ADV SIG PR, DOI 10.1155/2007/64960; Barreira L, 2011, LECT NOTES ARTIF INT, V7026, P268, DOI 10.1007/978-3-642-24769-9_20; Barrington L., 2008, P ISMIR, P614; BASILI VR, 1986, IEEE T SOFTWARE ENG, V12, P733; Bergstra J, 2006, MACH LEARN, V65, P473, DOI 10.1007/s10994-006-9019-7; Bertin-Mahieux T., 2010, MACHINE AUDITION PRI; Bertin-Mahieux T, 2008, J NEW MUSIC RES, V37, P115, DOI 10.1080/09298210802479250; Bertin-Mahieux T., 2011, P INT SOC MUS INF RE, P591; Bowker G C, 1999, SORTING THINGS OUT C; Chang K., 2010, P 11 INT C MUS INF R, P387; Chase AR, 2001, ANIM LEARN BEHAV, V29, P336, DOI 10.3758/BF03192900; Craft A., 2007, P INT C MUS INF RETR, P73; Craft A., 2007, TECHNICAL REPORT; Cunningham S.J., 2012, P ISMIR, P259; Dougherty Edward R, 2013, EURASIP J Bioinform Syst Biol, V2013, P10, DOI 10.1186/1687-4153-2013-10; Dannenberg RB, 2010, STRUCTUE OF STYLE: ALGORITHMIC APPROACHES TO UNDERSTANDING MANNER AND MEANING, P45, DOI 10.1007/978-3-642-12337-5_3; De Mulder T., 2004, IEEE INT C AC SPEECH, V4, P233; DENNING PJ, 1980, COMMUN ACM, V23, P543, DOI 10.1145/359015.359016; DENNING PJ, 1981, COMMUN ACM, V24, P725, DOI 10.1145/358790.358791; Dixon S., 2004, P 5 INT C MUS INF RE, P509; Downie J Stephen, 2008, Acoustical Science and Technology, V29, DOI 10.1250/ast.29.247; Downie J. S., 2003, P 4 INT C MUS INF RE, P25; Downie JS, 2010, STUD COMPUT INTELL, V274, P93; Downie JS, 2004, COMPUT MUSIC J, V28, P12, DOI 10.1162/014892604323112211; Ellis D. P. W., 2005, P INT C MUS INF RETR, P594; Fabbri F., 1999, INT ASS STUD POP MUS; Fabbri F., 1980, 1 INT C POP MUS STUD; Feitelson D. G., 2006, TECHNICAL REPORT; FENTON N, 1994, IEEE SOFTWARE, V11, P86, DOI 10.1109/52.300094; Flexer A, 2006, J NEW MUSIC RES, V35, P113, DOI 10.1080/09298210600834946; Flexer A., 2007, P 8 INT C MUS INF RE, P341; Flexer A, 2010, COMPUT MUSIC J, V34, P20, DOI 10.1162/COMJ_a_00004; Frow J., 2005, GENRE; Fu ZY, 2011, IEEE T MULTIMEDIA, V13, P303, DOI 10.1109/TMM.2010.2098858; Fujinaga I., 2006, P INT C MUS INF RETR, P101; Gjerdingen R, 2008, J NEW MUSIC RES, V37, P93, DOI 10.1080/09298210802479268; Gouyon F., 2013, EVALUATION MUS UNPUB; Guaus E., 2009, THESIS U POMPEU FABR; HAND DJ, 1994, J ROY STAT SOC A STA, V157, P317, DOI 10.2307/2983526; Hartmann M. A., 2011, THESIS U JYVASKYLA J; Humphrey EJ, 2013, J INTELL INF SYST, V41, P461, DOI 10.1007/s10844-013-0248-5; Juszczak P., 2007, PR TOOLS4 1 MATLAB T; Kemp C., 2004, THESIS U JYVASKYLA J; KIMBALL AW, 1957, J AM STAT ASSOC, V52, P133, DOI 10.2307/2280840; Law E., 2011, MUSIC DATA MINING, P281; Levy M, 2009, IEEE T MULTIMEDIA, V11, P383, DOI 10.1109/TMM.2009.2012913; Li T, 2006, IEEE T MULTIMEDIA, V8, P564, DOI 10.1109/TMM.2006.870730; Li TLH, 2011, LECT NOTES COMPUT SC, V6523, P317; Lidy T., 2006, THESIS VIENNA U TECH; Mallat S., 2011, P INT SOC MUS INF RE, P657; Mallat S., 2013, ARXIV13046763V2CSSD; Markov K, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P1929; Marques C., 2011, P ISMIR, P699; Marques G., 2011, P INT SOC MUS INF RE, P795; Matityaho B., 1995, 18 CONV EL EL ENG IS, P1; Matsui T., 2012, P IEEE INT WORKSH MA, P1; Mayer R., 2008, P ISMIR, P337; MCCRACKEN DD, 1979, COMMUN ACM, V22, P503, DOI 10.1145/359146.362786; McLuhan M., 1964, UNDERSTANDING MEDIA; Moerchen F., 2006, P KNOWL DISC DAT MIN, P882, DOI 10.1145/1150402.1150523; Morato J., 2011, P ISMIR, P597; Noorzad P, 2012, EUR SIGNAL PR CONF, P674; Pachet F., 2000, P CONT BAS MULT INF, P1238; Pampalk E., 2006, THESIS VIENNA U TECH; Pampalk E., 2005, P 6 INT C MUS INF RE, P628; Panagakis Y., 2009, P EUSIPCO; Panagakis Y., 2009, P 10 INT SOC MUS INF, P249; Panagakis Y, 2010, INT CONF ACOUST SPEE, P249, DOI 10.1109/ICASSP.2010.5495984; Park SM, 2011, 2011 11TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION AND SYSTEMS (ICCAS), P1644; Pfungst O., 1911, CLEVER HANS HORSE VO; PORTER D, 1984, J EXP PSYCHOL ANIM B, V10, P138; Rauber A., 2005, P 6 INT C MUS INF RE, P34; Salzberg SL, 1997, DATA MIN KNOWL DISC, V1, P317, DOI 10.1023/A:1009752403260; Scaringella N, 2006, IEEE SIGNAL PROC MAG, V23, P133, DOI 10.1109/MSP.2006.1598089; Schnitzer D., 2009, P SOUND MUS COMP, P59; Serra X., 2013, ROADMAP MUSIC INFORM; Seyerlehner K., 2010, THESIS J KEPLER U LI; Seyerlehner K., 2010, P DAFX, P1; Shapiro Peter, 2005, TURN BEAT SECRET HIS; Slaney M., 1998, TECHNICAL REPORT; Sleep R., 2005, P 6 INT C MUS INF RE, P252; Sordo M., 2008, P 9 INT C MUS INF RE, P255; Sturm B. L., 2013, ARXIV13061461V2CSSD; Sturm B. L., 2012, P INT ACM WORKSH MIR, P7; Sturm B. L., 2013, SIMPLE METHOD UNPUB; Sturm B. L., 2012, P AD MULT RETR; Sturm B. L., 2013, P CMMR; Sturm B. L., 2013, P ICME, P1; Sturm B. L., 2013, P CMMR, P379; Sturm B. L., 2012, P ACM WORKSH MUS INF, P69; Sturm BL, 2013, J INTELL INF SYST, V41, P371, DOI 10.1007/s10844-013-0250-y; Sturm BL, 2013, IEEE SIGNAL PROC LET, V20, P1050, DOI 10.1109/LSP.2013.2280031; Theodoridis S, 2009, PATTERN RECOGNITION, 4RTH EDITION, P1; Thom B., 1997, P INT COMP MUS C, P344; Truzzi M., 1978, ZETETIC SCHOLAR, V1, P11; Tzanetakis G, 2002, IEEE T SPEECH AUDI P, V10, P293, DOI 10.1109/TSA.2002.800560; Urbano J., 2012, P ISMIR PORT PORT, P181; Urbano J., 2011, P INT SOC MUS INF RE, P609; Urbano J, 2013, J INTELL INF SYST, V41, P345, DOI 10.1007/s10844-013-0249-4; van den Berg E, 2008, SIAM J SCI COMPUT, V31, P890, DOI 10.1137/080714488; Vandewalle P, 2009, IEEE SIGNAL PROC MAG, V26, P37, DOI 10.1109/MSP.2009.932122; Wang A., 2003, P INT SOC MUS INF RE; Wiggins GA, 2009, 2009 11TH IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM 2009), P477, DOI 10.1109/ISM.2009.36; Wright J, 2009, IEEE T PATTERN ANAL, V31, P210, DOI 10.1109/TPAMI.2008.79; Xia G., 2012, P AUT AG MULT SYST, V1, P205 113 0 0 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND 0929-8215 1744-5027 J NEW MUSIC RES J. New Music Res. JUN 2014 43 2 147 172 10.1080/09298215.2014.894533 26 Computer Science, Interdisciplinary Applications; Music Computer Science; Music AJ8GL WOS:000337940800002 J Sterpenich, V; Schmidt, C; Albouy, G; Matarazzo, L; Vanhaudenhuyse, A; Boveroux, P; Degueldre, C; Leclercq, Y; Balteau, E; Collette, F; Luxen, A; Phillips, C; Maquet, P Sterpenich, Virginie; Schmidt, Christina; Albouy, Genevieve; Matarazzo, Luca; Vanhaudenhuyse, Audrey; Boveroux, Pierre; Degueldre, Christian; Leclercq, Yves; Balteau, Evelyne; Collette, Fabienne; Luxen, Andre; Phillips, Christophe; Maquet, Pierre Memory Reactivation during Rapid Eye Movement Sleep Promotes Its Generalization and Integration in Cortical Stores SLEEP English Article brain plasticity; conditioning; fMRI; memory consolidation; REM sleep EXPERIENCE-DEPENDENT CHANGES; PONTINE-WAVE GENERATOR; LONG-TERM POTENTIATION; EVENT-RELATED FMRI; REM-SLEEP; PARADOXICAL SLEEP; EMOTIONAL MEMORY; DECLARATIVE MEMORY; SYNAPTIC HOMEOSTASIS; AUDITORY-STIMULATION Study Objectives: Memory reactivation appears to be a fundamental process in memory consolidation. In this study we tested the influence of memory reactivation during rapid eye movement (REM) sleep on memory performance and brain responses at retrieval in healthy human participants. Participants: Fifty-six healthy subjects (28 women and 28 men, age [mean +/- standard deviation]: 21.6 +/- 2.2 y) participated in this functional magnetic resonance imaging (fMRI) study. Methods and Results: Auditory cues were associated with pictures of faces during their encoding. These memory cues delivered during REM sleep enhanced subsequent accurate recollections but also false recognitions. These results suggest that reactivated memories interacted with semantically related representations, and induced new creative associations, which subsequently reduced the distinction between new and previously encoded exemplars. Cues had no effect if presented during stage 2 sleep, or if they were not associated with faces during encoding. Functional magnetic resonance imaging revealed that following exposure to conditioned cues during REM sleep, responses to faces during retrieval were enhanced both in a visual area and in a cortical region of multisensory (auditory-visual) convergence. Conclusions: These results show that reactivating memories during REM sleep enhances cortical responses during retrieval, suggesting the integration of recent memories within cortical circuits, favoring the generalization and schematization of the information. [Sterpenich, Virginie; Schmidt, Christina; Albouy, Genevieve; Matarazzo, Luca; Vanhaudenhuyse, Audrey; Boveroux, Pierre; Degueldre, Christian; Leclercq, Yves; Balteau, Evelyne; Collette, Fabienne; Luxen, Andre; Phillips, Christophe; Maquet, Pierre] Univ Liege, Cyclotron Res Ctr, B-4000 Liege, Belgium; [Collette, Fabienne] Univ Liege, Dept Cognit Sci, B-4000 Liege, Belgium Maquet, P (reprint author), Univ Liege, Cyclotron Res Ctr, B30, B-4000 Liege, Belgium. pmaquet@ulg.ac.be Belgian Fonds National de la Recherche Scientifique (FNRS); Fondation Medicale Reine Elisabeth (FMRE); Research Fund of the University of Liege and "Interuniversity Attraction Poles Programme - Belgian State - Belgian Science Policy" This was not an industry supported study. This research was supported by the Belgian Fonds National de la Recherche Scientifique (FNRS), the Fondation Medicale Reine Elisabeth (FMRE), the Research Fund of the University of Liege and "Interuniversity Attraction Poles Programme - Belgian State - Belgian Science Policy." The authors have indicated no financial conflicts of interest. AstonJones G, 1996, PROG BRAIN RES, V107, P379; Baran B, 2012, J NEUROSCI, V32, P1035, DOI 10.1523/JNEUROSCI.2532-11.2012; Bonnet M., 1992, SLEEP, V15, P173; BRADLEY MM, 1994, J BEHAV THER EXP PSY, V25, P49, DOI 10.1016/0005-7916(94)90063-9; BUYSSE DJ, 1989, PSYCHIAT RES, V28, P193, DOI 10.1016/0165-1781(89)90047-4; Buzsaki G, 2003, SLEEP BRAIN PLASTICI; Cai DJ, 2009, P NATL ACAD SCI USA, V106, P10130, DOI 10.1073/pnas.0900271106; CALLAWAY CW, 1987, CELL MOL NEUROBIOL, V7, P105, DOI 10.1007/BF00711551; Damasio AR, 2000, NAT NEUROSCI, V3, P1049, DOI 10.1038/79871; Darsaud A, 2011, J COGNITIVE NEUROSCI, V23, P26, DOI 10.1162/jocn.2010.21448; Datta S, 2004, J NEUROSCI, V24, P1416, DOI 10.1523/JNEUROSCI.4111-03.2004; Datta S, 2008, EUR J NEUROSCI, V27, P1876, DOI 10.1111/j.1460-9568.2008.06166.x; Datta S, 2000, J NEUROSCI, V20, P8607; Devlin JT, 2006, NEUROIMAGE, V30, P1112, DOI 10.1016/j.neuroimage.2005.11.025; Diekelmann S, 2011, NAT NEUROSCI, V14, P381, DOI 10.1038/nn.2744; EKSTRAND BR, 1971, J EXP PSYCHOL, V88, P142, DOI 10.1037/h0030642; EMPSON JAC, 1970, NATURE, V227, P287, DOI 10.1038/227287a0; Fogel SM, 2007, BEHAV BRAIN RES, V180, P48, DOI 10.1016/j.bbr.2007.02.037; FOWLER MJ, 1973, SCIENCE, V179, P302, DOI 10.1126/science.179.4070.302; Gais S, 2002, J NEUROSCI, V22, P6830; Gais S, 2007, P NATL ACAD SCI USA, V104, P18778, DOI 10.1073/pnas.0705454104; GUERRIEN A, 1989, PHYSIOL BEHAV, V45, P947, DOI 10.1016/0031-9384(89)90219-9; HARS B, 1985, BEHAV BRAIN RES, V18, P241, DOI 10.1016/0166-4328(85)90032-4; Hasselmo ME, 1999, TRENDS COGN SCI, V3, P351, DOI 10.1016/S1364-6613(99)01365-0; Heinzel A, 2005, COGNITIVE BRAIN RES, V25, P348, DOI 10.1016/j.cogbrainres.2005.06.009; Hennevin E, 1998, BEHAV NEUROSCI, V112, P839, DOI 10.1037//0735-7044.112.4.839; HORNE J A, 1976, International Journal of Chronobiology, V4, P97; Hu P, 2006, PSYCHOL SCI, V17, P891, DOI 10.1111/j.1467-9280.2006.01799.x; Huber R, 2004, NATURE, V430, P78, DOI 10.1038/nature02663; Ishai A, 2002, NEUROIMAGE, V17, P1729, DOI 10.1006/nimg.2002.1330; Ishai A, 2008, NEUROIMAGE, V40, P415, DOI 10.1016/j.neuroimage.2007.10.040; JOHNS MW, 1991, SLEEP, V14, P540; Kanwisher N, 1998, COGNITION, V68, pB1, DOI 10.1016/S0010-0277(98)00035-3; KARNI A, 1994, SCIENCE, V265, P679, DOI 10.1126/science.8036518; Kiebel SJ, 2005, HUM BRAIN MAPP, V26, P170, DOI 10.1002/hbm.20153; Lang PJ, 1999, INT AFFECTIVE PICUTR; Laureys S, 2001, NEUROSCIENCE, V105, P521, DOI 10.1016/S0306-4522(01)00269-X; Leclercq Y, 2011, COMPUT INTEL NEUROSC, DOI 10.1155/2011/598206; Macaluso E, 2004, NEUROIMAGE, V21, P725, DOI 10.1016/j.neuroimage.2003.09.049; MAHO C, 1992, BRAIN RES, V581, P115, DOI 10.1016/0006-8993(92)90350-I; Maquet P, 2000, NAT NEUROSCI, V3, P831, DOI 10.1038/77744; MARR D, 1971, PHILOS T ROY SOC B, V262, P23, DOI 10.1098/rstb.1971.0078; MARR D, 1970, PROC R SOC SER B-BIO, V176, P161, DOI 10.1098/rspb.1970.0040; MCCLELLAND JL, 1995, PSYCHOL REV, V102, P419, DOI 10.1037/0033-295X.102.3.419; McKeon S, PLOS ONE, V7; Mednick S, 2003, NAT NEUROSCI, V6, P697, DOI 10.1038/nn1078; Nishida M, 2009, CEREB CORTEX, V19, P1158, DOI 10.1093/cercor/bhn155; OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4; Orban P, 2006, P NATL ACAD SCI USA, V103, P7124, DOI 10.1073/pnas.0510198103; Pastor MA, 2008, J NEUROPHYSIOL, V100, P1699, DOI 10.1152/jn.01156.2007; Payne JD, 2008, PSYCHOL SCI, V19, P781, DOI 10.1111/j.1467-9280.2008.02157.x; Payne JD, 2011, NAT NEUROSCI, V14, P272, DOI 10.1038/nn0311-272; Payne JD, 2009, NEUROBIOL LEARN MEM, V92, P327, DOI 10.1016/j.nlm.2009.03.007; Peigneux P, 2001, NEUROIMAGE, V14, P701, DOI 10.1006/nimg.2001.0874; Peigneux P, 2003, NEUROIMAGE, V20, P125, DOI 10.1016/S1053-8119(03)00278-7; Perrin F, 2002, NEUROREPORT, V13, P1345, DOI 10.1097/00001756-200207190-00026; Plihal W, 1997, J COGNITIVE NEUROSCI, V9, P534, DOI 10.1162/jocn.1997.9.4.534; Quirk GJ, 2008, NEUROPSYCHOPHARMACOL, V33, P56, DOI 10.1038/sj.npp.1301555; Rasch B, 2007, SCIENCE, V315, P1426, DOI 10.1126/science.1138581; Rauchs G, 2004, SLEEP, V27, P395; Ravassard P, 2009, SLEEP, V32, P227; Rechtschaffen A, 1968, NIH PUBLICATION, V204; Ribeiro S, 2002, J NEUROSCI, V22, P10914; Ribeiro Sidarta, 2007, Front Neurosci, V1, P43, DOI 10.3389/neuro.01.1.1.003.2007; Rose M, 2002, NEURON, V36, P1221, DOI 10.1016/S0896-6273(02)01105-4; Rudoy JD, 2009, SCIENCE, V326, P1079, DOI 10.1126/science.1179013; Siegel JM, 2001, SCIENCE, V294, P1058, DOI 10.1126/science.1063049; Simpson JR, 2000, J COGNITIVE NEUROSCI, V12, P157, DOI 10.1162/089892900564019; Smith APR, 2004, NEUROIMAGE, V22, P868, DOI 10.1016/j.neuroimage.2004.01.049; SMITH C, 1990, PSYCHIAT J U OTTAWA, V15, P85; SNODGRASS JG, 1988, J EXP PSYCHOL GEN, V117, P34, DOI 10.1037//0096-3445.117.1.34; Spielberger C. D., 1983, MANUAL STATE TRAIT A; Steer RA, 1997, PSYCHOL REP, V80, P443; STERIADE M, 1989, J NEUROSCI, V9, P2215; Sterpenich V, 2009, J NEUROSCI, V29, P5143, DOI 10.1523/JNEUROSCI.0561-09.2009; Sterpenich V, 2007, PLOS BIOL, V5, P2709, DOI 10.1371/journal.pbio.0050282; Sterpenich V, 2006, J NEUROSCI, V26, P7416, DOI 10.1523/JNEUROSCI.1001-06.2006; Stickgold R, 1999, J COGNITIVE NEUROSCI, V11, P182, DOI 10.1162/089892999563319; Takashima A, 2006, P NATL ACAD SCI USA, V103, P756, DOI 10.1073/pnas.0507774103; TILLEY AJ, 1978, BIOL PSYCHOL, V6, P293, DOI 10.1016/0301-0511(78)90031-5; Tononi G, 2003, BRAIN RES BULL, V62, P143, DOI 10.1016/j.brainresbull.2003.09.004; Tononi G, 2006, SLEEP MED REV, V10, P49, DOI 10.1016/j.smrv.2005.05.002; Vertes RP, 2000, BEHAV BRAIN SCI, V23, P867, DOI 10.1017/S0140525X00004003; Vertes RP, 2000, BEHAV BRAIN SCI, V23, P904; Vuilleumier P, 2001, NEURON, V30, P829, DOI 10.1016/S0896-6273(01)00328-2; Wagner U, 2001, LEARN MEMORY, V8, P112, DOI 10.1101/lm.36801; Walker MP, 2010, NAT REV NEUROSCI, V11, DOI 10.1038/nrn2762-c1; Walker MP, 2009, ANN NY ACAD SCI, V1156, P168, DOI 10.1111/j.1749-6632.2009.04416.x; YAROUSH R, 1971, J EXP PSYCHOL, V88, P361, DOI 10.1037/h0030914; Yoshiura T, 1999, NEUROREPORT, V10, P1683, DOI 10.1097/00001756-199906030-00011 90 1 1 AMER ACAD SLEEP MEDICINE WESTCHESTER ONE WESTBROOK CORPORATE CTR, STE 920, WESTCHESTER, IL 60154 USA 0161-8105 1550-9109 SLEEP Sleep JUN 1 2014 37 6 1061 U166 10.5665/sleep.3762 17 Clinical Neurology; Neurosciences Neurosciences & Neurology AJ7RK WOS:000337894400008 J Dong, SY; Shiradkar, R; Nanda, P; Zheng, GA Dong, Siyuan; Shiradkar, Radhika; Nanda, Pariksheet; Zheng, Guoan Spectral multiplexing and coherent-state decomposition in Fourier ptychographic imaging BIOMEDICAL OPTICS EXPRESS English Article PHASE RETRIEVAL; ELECTRON-MICROSCOPY; RESOLUTION; DIFFRACTION; DIVERSITY; TELESCOPES; FIELD Information multiplexing is important for biomedical imaging and chemical sensing. In this paper, we report a microscopy imaging technique, termed state-multiplexed Fourier ptychography (FP), for information multiplexing and coherent-state decomposition. Similar to a typical Fourier ptychographic setting, we use an array of light sources to illuminate the sample from different incident angles and acquire corresponding low-resolution images using a monochromatic camera. In the reported technique, however, multiple light sources are lit up simultaneously for information multiplexing, and the acquired images thus represent incoherent summations of the sample transmission profiles corresponding to different coherent states. We show that, by using the state-multiplexed FP recovery routine, we can decompose the incoherent mixture of the FP acquisitions to recover a high-resolution sample image. We also show that, color-multiplexed imaging can be performed by simultaneously turning on R/G/B LEDs for data acquisition. The reported technique may provide a solution for handling the partially coherent effect of light sources used in Fourier ptychographic imaging platforms. It can also be used to replace spectral filter, gratings or other optical components for spectral multiplexing and demultiplexing. With the availability of cost-effective broadband LEDs, the reported technique may open up exciting opportunities for computational multispectral imaging. (C) 2014 Optical Society of America [Dong, Siyuan; Shiradkar, Radhika; Nanda, Pariksheet; Zheng, Guoan] Univ Connecticut, Storrs, CT 06269 USA Dong, SY (reprint author), Univ Connecticut, Storrs, CT 06269 USA. guoan.zheng@uconn.edu Abbey B, 2011, NAT PHOTONICS, V5, P420, DOI [10.1038/nphoton.2011.125, 10.1038/NPHOTON.2011.125]; Akbari H, 2012, J BIOMED OPT, V17, DOI 10.1117/1.JBO.17.7.076005; Allen LJ, 2001, OPT COMMUN, V199, P65, DOI 10.1016/S0030-4018(01)01556-5; Baraniuk RG, 2007, IEEE SIGNAL PROC MAG, V24, P118, DOI 10.1109/MSP.2007.4286571; Batey DJ, 2014, ULTRAMICROSCOPY, V138, P13, DOI 10.1016/j.ultramic.2013.12.003; Bian ZC, 2013, OPT EXPRESS, V21, P32400, DOI 10.1364/OE.21.032400; Brady D., 2006, DEF SEC S; Dean BH, 2003, J OPT SOC AM A, V20, P1490, DOI 10.1364/JOSAA.20.001490; Dicker DT, 2006, CANCER BIOL THER, V5, P1033; Dierolf M, 2010, NEW J PHYS, V12, DOI 10.1088/1367-2630/12/3/035017; Dong SY, 2014, OPT EXPRESS, V22, P5455, DOI 10.1364/OE.22.005455; Edo TB, 2013, PHYS REV A, V87, DOI 10.1103/PhysRevA.87.053850; Elser V, 2003, J OPT SOC AM A, V20, P40, DOI 10.1364/JOSAA.20.000040; Faulkner HML, 2004, PHYS REV LETT, V93, DOI 10.1103/PhysRevLett.93.023903; FIENUP JR, 1982, APPL OPTICS, V21, P2758, DOI 10.1364/AO.21.002758; FIENUP JR, 1978, OPT LETT, V3, P27, DOI 10.1364/OL.3.000027; GERCHBER.RW, 1972, OPTIK, V35, P237; Gonsalves R. A., 1982, OPT ENG, V21; GONSALVES RA, 1987, J OPT SOC AM A, V4, P166, DOI 10.1364/JOSAA.4.000166; Guizar-Sicairos M, 2008, OPT EXPRESS, V16, P7264, DOI 10.1364/OE.16.007264; Gurcan Metin N, 2009, IEEE Rev Biomed Eng, V2, P147, DOI 10.1109/RBME.2009.2034865; HOPPE W, 1969, ACTA CRYSTALL A-CRYS, VA 25, P502, DOI 10.1107/S0567739469001057; Hoshino K, 2014, BIOMED OPT EXPRESS, V5, P1610, DOI [10.1364/BOE.5.001610, 10.1364/OE.22.001610]; Hue F, 2011, ULTRAMICROSCOPY, V111, P1117, DOI 10.1016/j.ultramic.2011.02.005; Humphry MJ, 2012, NAT COMMUN, V3, DOI 10.1038/ncomms1733; Lu CH, 2013, APPL OPTICS, V52, pD92, DOI 10.1364/AO.52.000D92; Maiden AM, 2010, OPT LETT, V35, P2585, DOI 10.1364/OL.35.002585; Marchesini S, 2013, INVERSE PROBL, V29, DOI 10.1088/0266-5611/29/11/115009; MEINEL AB, 1970, APPL OPTICS, V9, P2501, DOI 10.1364/AO.9.002501; Ou XZ, 2013, OPT LETT, V38, P4845, DOI 10.1364/OL.38.004845; PARMAR M, 2008, IEEE IMAGE PROC, P473; RODENBURG JM, 1992, PHILOS T ROY SOC A, V339, P521, DOI 10.1098/rsta.1992.0050; RYLE M, 1960, MON NOT R ASTRON SOC, V120, P220; Shenfield A, 2011, J APPL PHYS, V109, DOI 10.1063/1.3600235; TAYLOR LS, 1981, IEEE T ANTENN PROPAG, V29, P386, DOI 10.1109/TAP.1981.1142559; Thibault P, 2013, NATURE, V494, P68, DOI 10.1038/nature11806; Thibault P, 2008, SCIENCE, V321, P379, DOI 10.1126/science.1158573; Thibault P, 2009, ULTRAMICROSCOPY, V109, P338, DOI 10.1016/j.ultramic.2008.12.011; Waller L, 2010, OPT EXPRESS, V18, P22817, DOI 10.1364/OE.18.022817; Whitehead LW, 2009, PHYS REV LETT, V103, DOI 10.1103/PhysRevLett.103.243902; Willett R., 2007, ELECT IMAGING 2007; WOLF E, 1982, J OPT SOC AM, V72, P343, DOI 10.1364/JOSA.72.000343; Woolfe F., 1999, IEEE T MED IMAGING, V99, P9999; Zheng G., 2014, IEEE PHOTONICS J, V6, P1; Zheng GA, 2013, NAT PHOTONICS, V7, P739, DOI 10.1038/NPHOTON.2013.187 45 1 1 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 2156-7085 BIOMED OPT EXPRESS Biomed. Opt. Express JUN 1 2014 5 6 1757 1767 10.1364/BOE.5.001757 11 AJ2QM WOS:000337503900006 J Wu, MY; Pearce, PL Wu, Mao-Ying; Pearce, Philip L. Appraising netnography: towards insights about new markets in the digital tourist era CURRENT ISSUES IN TOURISM English Article netnography; digital tourist era; emerging phenomenon; Chinese RV tourists; Australia ONLINE COMMUNITIES; TRAVEL; MOTIVATIONS Netnography, a naturalistic and predominantly unobtrusive technique developed by Kozinets for exploring online contributions, was the centrepiece of this appraisal. The authors argue that netnography could play a valuable role in enhancing our understanding of (a) rapidly changing tourist markets, (b) the growth of new markets and (c) the perspectives of culturally distinctive groups. The analysis of the blogs of Chinese recreational vehicle tourists who had visited Australia was chosen as a case study. In studying an emerging market segment from a rapidly changing and culturally different community, the case represented a key test of the value of the approach in generating insights. Practical steps to employ the method - entree, data collection, data analysis, data interpretation and member checks - were illustrated. Issues arising from the case study for the application of netnography in tourism research were highlighted. They included the value of the detail inherent in the postings, the attendant ability to consider the material using conceptual schemes, the practicality of getting additional information, the need to fully address ethical concerns and the value of supplementary perspectives. Suggestions for ways to adapt the technique for better information retrieval and interpretation were also provided. [Wu, Mao-Ying] Zhejiang Univ, Sch Management, Hangzhou 310058, Zhejiang, Peoples R China; [Pearce, Philip L.] James Cook Univ, Sch Business, Townsville, Qld 4811, Australia Wu, MY (reprint author), Zhejiang Univ, Sch Management, Hangzhou 310058, Zhejiang, Peoples R China. maoying.wu@gmail.com Arlt W. G., 2013, Tourism Planning and Development, V10, P126, DOI 10.1080/21568316.2013.800350; Ayeh JK, 2013, TOURISM MANAGE, V35, P132, DOI 10.1016/j.tourman.2012.06.010; Banyai M, 2012, J TRAVEL RES, V51, P267, DOI 10.1177/0047287511410323; Beaven Z., 2007, Managing Leisure, V12, P120, DOI 10.1080/13606710701339322; Berge B. L., 2007, QUALITATIVE RES METH; Bosangit C., 2012, Journal of Vacation Marketing, V18, P207, DOI 10.1177/1356766712449367; China Tourism Academy, 2013, CHIN TOUR PERF REV F, P5; Corigliano M. A., 2011, Journal of China Tourism Research, V7, P396, DOI 10.1080/19388160.2011.627015; COTRI & PATA, 2010, AR YOU READ CHIN INT; Dann G. M. S, 1977, ANN TOURISM RES, V4, P184, DOI DOI 10.1016/0160-7383(77)90037-8; Diener E., 1978, ETHICS SOCIAL BEHAV; Haggerty K. D., 2004, QUALITATIVE SOCIOLOG, V27, P391, DOI [10.1023/B:QUAS.0000049239.15922.a3, DOI 10.1023/B:QUAS.0000049239.15922.A3]; Hamilton K, 2009, ADV CONSUM RES, V36, P502; Hardy A, 2011, ROUTL ADV TOUR, V17, P194; Hardy A, 2012, J TOUR CULT CHANGE, V10, P219, DOI 10.1080/14766825.2012.667415; Hsu CHC, 2008, TOURISM MANAGEMENT: ANALYSIS, BEHAVIOUR AND STRATEGY, P14, DOI 10.1079/9781845933234.0014; Huang SS, 2010, J SUSTAIN TOUR, V18, P845, DOI 10.1080/09669582.2010.484492; Jacobsen J. K. S., 2012, TOURISM MANAGEMENT P, V1, P39, DOI [10.1016/j.tmp.2011.12.005, DOI 10.1016/J.TMP.2011.12.005]; Janta H, 2012, TOURISM MANAGE, V33, P431, DOI 10.1016/j.tourman.2011.05.004; Jennings G, 2010, ASPEC TOUR, V44, P81; Kozinets R. V., 2010, NETNOGRAPHY DOING ET; Kozinets R. V., 1999, EUROPEAN MANAGEMENT, V17, P252, DOI DOI 10.1016/S0263-2373(99)00004-3; Kozinets RV, 2002, J MARKETING RES, V39, P61, DOI 10.1509/jmkr.39.1.61.18935; Kozinets RV, 1997, ADV CONSUM RES, V24, P470; Kozinets RV, 2010, J MARKETING, V74, P71; Kristensen A. E., 2013, Tourism Planning and Development, V10, P169; Langer R., 2005, QUALITATIVE MARKET R, V8, P189, DOI [10.1108/13522750510592454, DOI 10.1108/13522750510592454]; Lu WL, 2012, TOURISM MANAGE, V33, P702, DOI 10.1016/j.tourman.2011.08.003; McClymont H, 2011, ROUTL ADV TOUR, V17, P210; Mkono M., 2012, Tourism Analysis, V17, P553, DOI 10.3727/108354212X13473157390966; Mkono M., 2011, Tourist Studies, V11, P253, DOI 10.1177/1468797611431502; Mkono M., 2013, TOURISM MANAGEMENT P, V5, P68, DOI 10.1016/j.tmp.2012.10.007; Nelson MR, 2005, J BUS RES, V58, P89, DOI 10.1016/S0148-2963(02)00477-0; Onyx J., 2005, Journal of Tourism Studies, V16, P61; OTTI, 2012, 2011 MARK PROF; Patterson I., 2011, Tourism Analysis, V16, P283, DOI 10.3727/108354211X13110944387086; Pearce P. L., 2005, Journal of Travel Research, V43, P226, DOI 10.1177/0047287504272020; Pearce P. L., 2011, TOURIST BEHAV CONT W; Pearce P. L., 2013, TOURISM MANAGEMENT P, V7, P34, DOI 10.1016/j.tmp.2013.04.001; Pearce P. L., 2013, Tourism Recreation Research, V38, P145; Podoshen JS, 2011, TOURISM MANAGE, V32, P1332, DOI 10.1016/j.tourman.2011.01.007; Podoshen JS, 2013, TOURISM MANAGE, V35, P263, DOI 10.1016/j.tourman.2012.08.002; Shao J., 2012, TOURISM TRIBUNE, V27, P4; Sigala M., 2012, SOCIAL MEDIA TRAVEL; Tourism New Zealand, 2012, TOURISM NZ CHINA; Tourism Research Australia, 2012, CHIN TRAV SEGM CHIN; Tourism Research Australia, 2012, 2020 CHIN BUILD FDN; Tsaur SH, 2010, ANN TOURISM RES, V37, P1035, DOI 10.1016/j.annals.2010.04.001; Volo S., 2010, J VACATION MARKETING, V16, P297, DOI DOI 10.1177/1356766710380884; Wenger A., 2008, J VACATION MARKETING, V14, P169, DOI DOI 10.1177/1356766707087525; Xiang YiXian, 2013, Tourism Planning and Development, V10, P134, DOI 10.1080/21568316.2013.783740; Xu YY, 2012, TOURISM MANAGE, V33, P427, DOI 10.1016/j.tourman.2011.05.003 52 0 0 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND 1368-3500 1747-7603 CURR ISSUES TOUR Curr. Issues Tour. JUN 2014 17 5 463 474 10.1080/13683500.2013.833179 12 AJ4YS WOS:000337686900007 J Svensson, CM; Krusekopf, S; Lucke, J; Figge, MT Svensson, Carl-Magnus; Krusekopf, Solveigh; Luecke, Joerg; Figge, Marc Thilo Automated detection of circulating tumor cells with naive Bayesian classifiers CYTOMETRY PART A English Article biomedical image processing; cancer detection; support vector machines; Gaussian mixture model METASTATIC BREAST-CANCER; FLOW-CYTOMETRY DATA; IMAGE RETRIEVAL; PERIPHERAL-BLOOD; PROGRESSION-FREE; EM ALGORITHM; SURVIVAL; MODELS; IDENTIFICATION; ORGANIZATION Personalized medicine is a modern healthcare approach where information on each person's unique clinical constitution is exploited to realize early disease intervention based on more informed medical decisions. The application of diagnostic tools in combination with measurement evaluation that can be performed in a reliable and automated fashion plays a key role in this context. As the progression of various cancer diseases and the effectiveness of their treatments are related to a varying number of tumor cells that circulate in blood, the determination of their extremely low numbers by liquid biopsy is a decisive prognostic marker. To detect and enumerate circulating tumor cells (CTCs) in a reliable and automated fashion, we apply methods from machine learning using a naive Bayesian classifier (NBC) based on a probabilistic generative mixture model. Cells are collected with a functionalized medical wire and are stained for fluorescence microscopy so that their color signature can be used for classification through the construction of Red-Green-Blue (RGB) color histograms. Exploiting the information on the fluorescence signature of CTCs by the NBC does not only allow going beyond previous approaches but also provides a method of unsupervised learning that is required for unlabeled training data. A quantitative comparison with a state-of-the-art support vector machine, which requires labeled data, demonstrates the competitiveness of the NBC method. (c) 2014 International Society for Advancement of Cytometry [Svensson, Carl-Magnus; Figge, Marc Thilo] Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, D-07745 Jena, Germany; [Svensson, Carl-Magnus; Luecke, Joerg] Goethe Univ Frankfurt, Frankfurt Inst Adv Studies, D-60054 Frankfurt, Germany; [Krusekopf, Solveigh] GILUPI GmbH, Potsdam, Germany; [Luecke, Joerg] Carl von Ossietzky Univ Oldenburg, Cluster Excellence Hearing4all, D-26111 Oldenburg, Germany; [Luecke, Joerg] Carl von Ossietzky Univ Oldenburg, Dept Med Phys & Acoust, Sch Med & Hlth Sci, D-26111 Oldenburg, Germany; [Luecke, Joerg] Tech Univ Berlin, Fac Elect Engn & Comp Sci, Berlin, Germany; [Figge, Marc Thilo] Univ Jena, Jena, Germany Figge, MT (reprint author), Hans Knoell Inst, Leibniz Inst Nat Prod Res & Infect Biol, D-07745 Jena, Germany. thilo.figge@hki-jena.de Frankfurt Institute for Advanced Studies (FIAS); German Research Foundation (DFG) [LU 1196/4-2] Grant sponsor: Frankfurt Institute for Advanced Studies (FIAS)Grant sponsor: German Research Foundation (DFG); Grant number: LU 1196/4-2. Aghaeepour N, 2013, NAT METHODS, V10, P228, DOI [10.1038/nmeth.2365, 10.1038/NMETH.2365]; Aghaeepour N, 2011, CYTOM PART A, V79A, P6, DOI 10.1002/cyto.a.21007; Alili MS, 2012, IEEE T IMAGE PROCESS, V21, P1452; Allan AL, 2005, CYTOM PART A, V65A, P4, DOI 10.1002/cyto.a.20132; Amato RJ, 2013, UROLOGY, V81, P1303, DOI 10.1016/j.urology.2012.10.041; Balazar M, 1999, J MOL MED, V77, P699; Banys M, 2013, CLIN CHIM ACTA, V423, P39, DOI 10.1016/j.cca.2013.03.029; Beecks C., 2009, P ACM INT C MULT, P697, DOI 10.1145/1631272.1631391; Bhagat AAS, 2011, LAB CHIP, V11, P1870, DOI 10.1039/c0lc00633e; Buck TE, 2012, BIOESSAYS, V34, P791, DOI 10.1002/bies.201200032; Buhmann M. D, 2003, RADIAL BASIS FUNCTIO; Chapelle O, 1999, IEEE T NEURAL NETWOR, V10, P1055, DOI 10.1109/72.788646; Chausovsky G, 1999, CANCER, V86, P2395; Coelho LP, 2013, J OPEN RES SOFTW, V1, pe3, DOI DOI 10.5334/J0RS.AC; Cohen SJ, 2008, J CLIN ONCOL, V26, P3213, DOI 10.1200/JCO.2007.15.8923; Cristofanilli M, 2004, NEW ENGL J MED, V351, P781, DOI 10.1056/NEJMoa040766; Cristofanilli M, 2005, J CLIN ONCOL, V23, P1420, DOI 10.1200/JCO.2005.08.140; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; den Toonder J, 2011, LAB CHIP, V11, P375, DOI 10.1039/c0lc90100h; Do MN, 2002, IEEE T IMAGE PROCESS, V11, P146, DOI 10.1109/83.982822; Dougherty ER, 2003, HANDS ON MORPHOLOGIC; FLICKNER M, 1995, COMPUTER, V28, P23, DOI 10.1109/2.410146; Gascoyne PRC, 2009, ELECTROPHORESIS, V30, P1388, DOI 10.1002/elps.200800373; Gleghorn JP, 2010, LAB CHIP, V10, P27, DOI 10.1039/b917959c; Goutte C, 2005, LECT NOTES COMPUT SC, V3408, P345; Hastie T, 2009, ELEMENTS STAT LEARNI; Hayes DF, 2006, CLIN CANCER RES, V12, P4218, DOI 10.1158/1078-0432.CCR-05-2821; He W, 2007, P NATL ACAD SCI USA, V104, P11760, DOI 10.1073/pnas.0703875104; Hu WM, 2011, IEEE T SYST MAN CY C, V41, P797, DOI 10.1109/TSMCC.2011.2109710; Jaakkola T, 1998, ADV NEURAL INFORM PR, V11, P487; Kang JH, 2012, LAB CHIP, V12, P2175, DOI 10.1039/c2lc40072c; Krebs Matthew G, 2010, Ther Adv Med Oncol, V2, P351, DOI 10.1177/1758834010378414; Li JY, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0050514; Lin HK, 2010, CLIN CANCER RES, V16, P5011, DOI 10.1158/1078-0432.CCR-10-1105; Lo K, 2008, CYTOM PART A, V73A, P321, DOI 10.1002/cyto.a.20531; Lotufo R, 2000, COMPUT IMAGING VIS, V18, P341; Maji S, 2013, IEEE T PATTERN ANAL, V35, P66, DOI 10.1109/TPAMI.2012.62; Meinicke P, 2011, BIOINFORMATICS, V10, P1093; Mikolajczyk SD, 2011, J ONCOL, V2011, P10; Morgan NB, 2004, MAT SCI ENG A-STRUCT, V378, P16, DOI 10.1016/j.msea.2003.10.326; Nagrath S, 2007, NATURE, V450, P1235, DOI 10.1038/nature06385; NAKA KI, 1966, J PHYSIOL-LONDON, V185, P536; Neal RM, 1998, NATO ADV SCI I D-BEH, V89, P355; Niblack CW, 1993, SPIE P RETRIEVAL MET, V1908, P173; OTSU N, 1979, IEEE T SYST MAN CYB, V9, P62; Pantel K, 2008, NAT REV CANCER, V8, P329, DOI 10.1038/nrc2375; Pedregosa F, 2011, J MACH LEARN RES, V12, P2825; Peng T, 2011, CYTOM PART A, V79A, P383, DOI 10.1002/cyto.a.21066; Quellec G, 2010, MED IMAGE ANAL, V14, P227, DOI 10.1016/j.media.2009.11.004; Riethdorf S, 2007, CLIN CANCER RES, V13, P920, DOI 10.1158/1078-0432.CCR-06-1695; Roerdink JBTM, 2001, FUNDAMENTA INFORM, V41, P187; Rolle A., 2005, WORLD J SURG ONCOL, V3, P18, DOI 10.1186/1477-7819-3-18; Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054; Saucedo-Zeni N, 2012, INT J ONCOL, V41, P1241, DOI 10.3892/ijo.2012.1557; Scholtens TM, 2012, CYTOM PART A, V81A, P138, DOI 10.1002/cyto.a.22002; Sheng WA, 2012, ANAL CHEM, V84, P4199, DOI 10.1021/ac3005633; Stott SL, 2010, SCI TRANSL MED, V2, DOI 10.1126/scitranslmed.3000403; Tibbe AGJ, 2007, CYTOM PART A, V71A, P154, DOI 10.1002/cyto.a.20369; Tipping ME, 1999, NEURAL COMPUT, V11, P443, DOI 10.1162/089976699300016728; Tjensvoll K, 2014, INT J CANCER, V134, P1, DOI 10.1002/ijc.28134; Vasconcelos N, 2000, PROC CVPR IEEE, P216, DOI 10.1109/CVPR.2000.855822; Weight RM, 2010, 2010 ANN INT C IEEE; Wendt PTH, 2004, HUM PATHOL, V35, P122; Xie N, 2010, IEEE C COMP VIS PATT; Yu L, 2013, LAB CHIP, V13, P3163, DOI 10.1039/c3lc00052d; Yu M, 2011, J CELL BIOL, V192, P373, DOI 10.1083/jcb.201010021; Zheng XJ, 2011, LAB CHIP, V11, P3269, DOI 10.1039/c1lc20331b 67 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1552-4922 1552-4930 CYTOM PART A Cytom. Part A JUN 2014 85A 6 501 511 10.1002/cyto.a.22471 11 AJ2WS WOS:000337525900007 J Rajput, SK; Nishchal, NK Rajput, Sudheesh K.; Nishchal, Naveen K. An optical encryption and authentication scheme using asymmetric keys JOURNAL OF THE OPTICAL SOCIETY OF AMERICA A-OPTICS IMAGE SCIENCE AND VISION English Article RANDOM-PHASE ENCRYPTION; TRUNCATED FOURIER-TRANSFORMS; IMAGE ENCRYPTION; FRESNEL DOMAIN; FRACTIONAL FOURIER; PLAINTEXT ATTACK; POLARIZED-LIGHT; CRYPTOSYSTEM; ALGORITHM; COMPRESSION We propose a novel optical information encryption and authentication scheme that uses asymmetric keys generated by the phase-truncation approach and the phase-retrieval algorithm. Multiple images bonded with random phase masks are Fourier transformed, and obtained spectra are amplitude-and phase-truncated. The phase-truncated spectra are encoded into a single random intensity image using the phase-retrieval algorithm. Unlike most of the authentication schemes, in this study, only one encrypted reference image is required for verification of multiple secured images. The conventional double random phase encoding and correlation techniques are employed for authentication verification. Computer simulation results and theoretical explanation prove the effectiveness of the proposed scheme. (C) 2014 Optical Society of America [Rajput, Sudheesh K.; Nishchal, Naveen K.] Indian Inst Technol Patna, Dept Phys, Patna 800013, Bihar, India Nishchal, NK (reprint author), Indian Inst Technol Patna, Dept Phys, Patna 800013, Bihar, India. nkn@iitp.ac.in Defence Research & Development Organisation, Government of India [ERIP/ER/1200428/M/01/1473] The authors acknowledge funding from the Defence Research & Development Organisation, Government of India, under Grant No. ERIP/ER/1200428/M/01/1473. Abookasis D, 2001, OPT ENG, V40, P1584, DOI 10.1117/1.1388208; Alfalou A, 2010, OPT LETT, V35, P2185, DOI 10.1364/OL.35.002185; Alfalou A, 2013, OPT COMMUN, V307, P67, DOI 10.1016/j.optcom.2013.05.053; Alfalou A, 2010, OPT LETT, V35, P1914, DOI 10.1364/OL.35.001914; Alfalou A, 2009, ADV OPT PHOTONICS, V1, P589, DOI 10.1364/AOP.1.000589; Barrera JF, 2013, OPT EXPRESS, V21, P5373, DOI 10.1364/OE.21.005373; Carnicer A, 2005, OPT LETT, V30, P1644, DOI 10.1364/OL.30.001644; Chen LF, 2005, OPT COMMUN, V254, P361, DOI 10.1016/j.optcom.2005.05.052; Chen W, 2013, J OPT SOC AM A, V30, P806, DOI 10.1364/JOSAA.30.000806; Cho M, 2011, OPT LETT, V36, P861, DOI 10.1364/OL.36.000861; Cho M, 2013, OPT LETT, V38, P3198, DOI 10.1364/OL.38.003198; Frauel Y, 2007, OPT EXPRESS, V15, P10253, DOI 10.1364/OE.15.010253; GERCHBER.RW, 1972, OPTIK, V35, P237; JAVIDI B, 1989, APPL OPTICS, V28, P2358, DOI 10.1364/AO.28.002358; JAVIDI B, 1994, OPT ENG, V33, P1752, DOI 10.1117/12.170736; Kishk S, 2003, OPT LETT, V28, P167, DOI 10.1364/OL.28.000167; Markman A, 2014, J OPT SOC AM A, V31, P394, DOI 10.1364/JOSAA.31.000394; Nishchal NK, 2011, OPT ENG, V50, DOI 10.1117/1.3619825; Nishchal NK, 2011, OPT COMMUN, V284, P735, DOI 10.1016/j.optcom.2010.09.065; Perez-Cabre E, 2011, OPT LETT, V36, P22, DOI 10.1364/OL.36.000022; Perez-Cabre E, 2012, J OPTICS-UK, V14, DOI 10.1088/2040-8978/14/9/094001; Qin W, 2010, OPT LETT, V35, P118, DOI 10.1364/OL.35.000118; Rajput SK, 2012, OPT LASER ENG, V50, P1474, DOI 10.1016/j.optlaseng.2012.03.018; Rajput SK, 2013, APPL OPTICS, V52, P4343, DOI 10.1364/AO.52.004343; Rajput SK, 2013, OPT COMMUN, V309, P231, DOI 10.1016/j.optcom.2013.06.036; Rajput SK, 2012, APPL OPTICS, V51, P1446, DOI 10.1364/AO.51.001446; Rajput SK, 2013, APPL OPTICS, V52, P871, DOI 10.1364/AO.52.000871; Rajput SK, 2012, APPL OPTICS, V51, P5377, DOI 10.1364/AO.51.005377; Rajput SK, 2014, APPL OPTICS, V53, P418, DOI 10.1364/AO.53.000418; REFREGIER P, 1995, OPT LETT, V20, P767; Rodrigo JA, 2007, OPT COMMUN, V278, P279, DOI 10.1016/j.optcom.2007.06.023; Wan X., 2014, APPL OPTICS, V53, P208; Wang XG, 2012, OPT COMMUN, V285, P1078, DOI 10.1016/j.optcom.2011.12.017; Wang XG, 2013, OPT LETT, V38, P3684, DOI 10.1364/OL.38.003684; Chen YY, 2013, APPL OPTICS, V52, P5247, DOI 10.1364/AO.52.005247; Zalevsky Z, 1996, OPT LETT, V21, P842, DOI 10.1364/OL.21.000842 36 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1084-7529 1520-8532 J OPT SOC AM A J. Opt. Soc. Am. A-Opt. Image Sci. Vis. JUN 2014 31 6 1233 1238 10.1364/JOSAA.31.001233 6 AJ2PI WOS:000337499800011 J Dehkordi, MH; Alimoradi, R Dehkordi, Massoud Hadian; Alimoradi, Reza Certificateless identification protocols from super singular elliptic curve SECURITY AND COMMUNICATION NETWORKS English Article identification; super singular elliptic curve; challenge-response; identity-based cryptosystem; certificateless IDENTITY-BASED IDENTIFICATION; AGGREGATE SIGNATURE; SCHEME; EFFICIENT; SECURITY; PAIRINGS; CRYPTOGRAPHY To transmit information through very limited secure channels, we can use public key cryptosystems. A new kind of public key system, called identity-based public key system, sets the users' public identity, for example, their email addresses, as their public key. This characteristic of identity-based public key systems decreases expense and increases speed in executing many important protocols in data security such as cryptography, identification, key agreement, and digital signatures. But, the major shortcoming of these systems is Key Escrow (legal key retrieval) and also the key generation center's ability to impersonate users. In this paper, two certificateless identity-based identification schemes devoid of the aforementioned shortcomings are represented. These are Challenge-response Identification protocols. Also, the second scheme introduced in this paper has the batch verification quality. The security analysis of the introduced schemes will come at the end. Copyright (c) 2013 John Wiley & Sons, Ltd. [Dehkordi, Massoud Hadian; Alimoradi, Reza] Iran Univ Sci & Technol, Dept Math Sci, Tehran, Iran; [Alimoradi, Reza] Univ Qom, Fac Sci, Qom, Iran Alimoradi, R (reprint author), Iran Univ Sci & Technol, Dept Math Sci, Tehran, Iran. alimoradi.r@gmail.com Al-Riyami S., 2004, THESIS ROYAL HOLLOWA; Al-Riyami SS, 2003, LECT NOTES COMPUT SC, V2894, P452; Bellare M, 2004, LECT NOTES COMPUT SC, V3027, P268; Bentahar K, 2005, GENERIC CONSTRUCTION; BETH T, 1988, LECT NOTES COMPUT SC, V330, P77; Boneh D., 2001, LNCS, V2139, p[213, 27], DOI DOI 10.1007/3-540-44647-8_13; Castro R, EFFICIENT CERTIFICAT; Cheng Z, 2005, EFFICIENT CERTIFICAT; Dehkordi MH, 2009, LOBACHEVSKII J MATH, V30, P203; Dehkordi Massoud Hadian, 2009, Discrete Mathematics, Algorithms and Applications, V1; Du HZ, 2009, COMPUT STAND INTER, V31, P390, DOI 10.1016/j.csi.2008.05.013; Feige U., 1988, Journal of Cryptology, V1, DOI 10.1007/BF02351717; FIAT A, 1987, LECT NOTES COMPUT SC, V263, P186; Galbraith SD, 2002, LECT NOTES COMPUT SC, V2369, P324; Galindo D, 2006, LECT NOTES COMPUT SC, V4043, P81; Gennaro R, 2004, LECT NOTES COMPUT SC, V3329, P276; GIRAULT M, 1991, LECT NOTES COMPUT SC, V473, P481; Goldreich O, 1999, MODERN CRYPTOGRAPHY; GOLDWASSER S, 1989, SIAM J COMPUT, V18, P186, DOI 10.1137/0218012; Gong Z, 2007, IEEE SNPD 2007, V3, P188; Hu BC, 2007, DESIGN CODE CRYPTOGR, V42, P109, DOI 10.1007/s10623-006-9022-9; Joux A, 2002, LECT NOTES COMPUT SC, V2369, P20; KIM M, 2002, 7 AUSTR C INF SEC PR, V2384, P362; Kim M, 2002, SCIS 2002 2002 S CRY; Kurosawa K, 2004, LNCS, V2947; Paterson KG, 2002, ELECTRON LETT, V38, P1025, DOI 10.1049/el:20026682; Schnorr C. P., 1991, Journal of Cryptology, V4, DOI 10.1007/BF00196725; Shao J, 2004, INT S FUT SOFTW TECH; Shoup V, 1999, J CRYPTOL, V12, P247, DOI 10.1007/s001459900056; Smart NP, 2003, LECT NOTES COMPUT SC, V2612, P111; Swanson CM, 2009, THESIS U WATERLOO; Washington L, 2003, CRC PRESS SERIES DIS; Xiong H, 2013, INFORM SCIENCES, V219, P225, DOI 10.1016/j.ins.2012.07.004; Yao G, 2004, PROG COM SC, V23, P397; Zhang L, 2009, COMPUT COMMUN, V32, P1079, DOI 10.1016/j.comcom.2008.12.042; Zhang L, 2010, COMPUT NETW, V54, P2482, DOI 10.1016/j.comnet.2010.04.008; Zhang Z, 2005, SECURITY CERTIFICATE 37 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1939-0114 1939-0122 SECUR COMMUN NETW Secur. Commun. Netw. JUN 2014 7 6 979 986 10.1002/sec.815 8 AJ2TM WOS:000337516100005 J Keller, J; von der Gracht, HA Keller, Jonas; von der Gracht, Heiko A. The influence of information and communication technology (ICT) on future foresight processes - Results from a Delphi survey TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE English Article Foresight; Foresight support systems, ICT; Delphi method; Data PREDICTION MARKETS; DECISION-SUPPORT; KNOWLEDGE MANAGEMENT; SCENARIO MANAGEMENT; REAL-TIME; INNOVATION; CONSTRUCTION; UNCERTAINTY; PERSPECTIVE; INTEGRATION Information and communication technology (ICT) tools are increasingly being used to implement foresight exercises. Until now, it has not been analyzed how this development affects the quality and structure of foresight processes. In this paper, a Delphi study is conducted to analyze the future path of ICT in foresight and to identify channels by which KT drives progress in foresight and where there are limitations to this development. Using a real-time variant of the method, we posed 20 projections about ICT in 2020 to 177 foresight experts. In analyzing both quantitative and qualitative results of the study, we reveal that ICT will likely promote a shift in the focus of foresight exercises from scanning and data retrieval to more qualitative steps, such as interpretation, decision-making and implementation. In a growing foresight market, ICT should contribute to more efficient and accurate foresight processes with better accessibility to information, easy-to-use collaboration tools, data and knowledge linkages, quantitative modeling tools and process optimization. However, the qualitative nature of the discipline, value-driven challenges, as well as technological and competitive barriers should assure that foresight will remain a creative and human-centered activity with ICT tools (C) 2013 Elsevier Inc. All rights reserved. [Keller, Jonas; von der Gracht, Heiko A.] EBS Univ Wirtschaft & Recht, EBS Business Sch, D-65187 Wiesbaden, Germany Keller, J (reprint author), EBS Univ Wirtschaft & Recht, EBS Business Sch, Konrad Adenauer Ring 15, D-65187 Wiesbaden, Germany. jonas.keller@ebs.edu Ahmed DM, 2010, DECIS SUPPORT SYST, V49, P507, DOI 10.1016/j.dss.2010.06.004; Alcamo J., 1994, IMAGE 2 0 INTEGRATED; Andersen A. D., 2012, INNOVATION SYSTEM FO; Balkin JM, 2008, MINN LAW REV, V93, P1; Banuls VA, 2007, TECHNOL FORECAST SOC, V74, P750, DOI 10.1016/j.techfore.2006.05.015; Banuls VA, 2011, TECHNOL FORECAST SOC, V78, P1579, DOI 10.1016/j.techfore.2011.03.014; Banuls Victor A., 2011, International Journal of Foresight and Innovation Policy, V7, DOI 10.1504/IJFIP.2011.043023; Baum SD, 2011, TECHNOL FORECAST SOC, V78, P185, DOI 10.1016/j.techfore.2010.09.006; Bertino E, 2005, IEEE T DEPEND SECURE, V2, P2, DOI 10.1109/TDSC.2005.9; Brabham D. C., 2008, 1 MONDAY, V13, P1; Bullock S., 2004, TECHNICAL REPORTS HP; Campanella G, 2011, DECIS SUPPORT SYST, V52, P52, DOI 10.1016/j.dss.2011.05.003; Chaboud A., 2009, INT FINANCE DISCUSSI, V980; Chaffey Dave, 2009, INTERNET MARKETING S; Chan SWK, 2011, DECIS SUPPORT SYST, V52, P189, DOI 10.1016/j.dss.2011.07.003; Chermack T.J., 2011, SCENARIO PLANNING OR; Coates JF, 2010, TECHNOL FORECAST SOC, V77, P1428, DOI 10.1016/j.techfore.2010.07.009; Colecchia A, 2002, REV ECON DYNAM, V5, P408, DOI 10.1006/redy.2002.0170; Comes T, 2011, DECIS SUPPORT SYST, V52, P108, DOI 10.1016/j.dss.2011.05.008; Corbin A., 1990, QUAL SOCIOL, V13, P3; Courtney JF, 2001, DECIS SUPPORT SYST, V31, P17, DOI 10.1016/S0167-9236(00)00117-2; Czaplicka-Kolarz K, 2009, TECHNOL FORECAST SOC, V76, P327, DOI 10.1016/j.techfore.2008.05.007; Daheim C, 2008, TECHNOL ANAL STRATEG, V20, P321, DOI 10.1080/09537320802000047; Dalal S, 2011, TECHNOL FORECAST SOC, V78, P1426, DOI 10.1016/j.techfore.2011.03.021; Davenport TH, 2005, MIT SLOAN MANAGE REV, V46, P83; De Vet E, 2005, HEALTH EDUC RES, V20, P195, DOI 10.1093/her/cyg111; Ecken P, 2011, TECHNOL FORECAST SOC, V78, P1654, DOI 10.1016/j.techfore.2011.05.006; Erilcsson E. A., 2008, TECHNOL FORECAST SOC, V75, P462; Esmaeilzadeh H, 2011, ISCA 2011: PROCEEDINGS OF THE 38TH ANNUAL INTERNATIONAL SYMPOSIUM ON COMPUTER ARCHITECTURE, P365; European Foresight Platform, 2012, WHAT IS FOR; Farmer JD, 2009, NATURE, V460, P685, DOI 10.1038/460685a; Servan-Schreiber E., 2004, Electronic Markets, V14, DOI 10.1080/1019678042000245254; Gausemeier J, 1998, TECHNOL FORECAST SOC, V59, P111, DOI 10.1016/S0040-1625(97)00166-2; Georghiou L, 2013, TECHNOL FORECAST SOC, V80, P467, DOI 10.1016/j.techfore.2012.10.009; Gnatzy T, 2011, TECHNOL FORECAST SOC, V78, P1681, DOI 10.1016/j.techfore.2011.04.006; Godet M, 2011, STRATEGIC FORESIGHT; Goldmanis M, 2010, ECON J, V120, P651, DOI 10.1111/j.1468-0297.2009.02310.x; GOODMAN CM, 1987, J ADV NURS, V12, P729, DOI 10.1111/j.1365-2648.1987.tb01376.x; Gordon T, 2006, TECHNOL FORECAST SOC, V73, P321, DOI 10.1016/j.techfore.2005.09.005; Gordon TJ, 2005, TECHNOL FORECAST SOC, V72, P1064, DOI 10.1016/j.techfore.2004.11.008; Graefe A, 2011, INT J FORECASTING, V27, P183, DOI 10.1016/j.ijforecast.2010.05.004; Gunasekaran A, 2004, EUR J OPER RES, V159, P269, DOI 10.1016/j.ejor.2003.08.016; Hanson R, 2003, INFORM SYST FRONT, V5, P107, DOI 10.1023/A:1022058209073; Hasson F, 2011, TECHNOL FORECAST SOC, V78, P1695, DOI 10.1016/j.techfore.2011.04.005; Hayek FA, 1945, AM ECON REV, V35, P519; Heger T, 2012, TECHNOL FORECAST SOC, V79, P819, DOI 10.1016/j.techfore.2011.11.003; Heinonen S, 2012, FUTURES, V44, P248, DOI 10.1016/j.futures.2011.10.007; Ho TH, 2007, CALIF MANAGE REV, V50, P144; Kaufman LM, 2009, IEEE SECUR PRIV, V7, P61, DOI 10.1109/MSP.2009.87; Kerr NL, 2011, INT J FORECASTING, V27, P14, DOI 10.1016/j.ijforecast.2010.02.001; Klein GA, 2003, INTUITION WORK WHY D; Kostoff RN, 2006, TECHNOL FORECAST SOC, V73, P923, DOI 10.1016/j.techfore.2005.09.004; Landeta J, 2006, TECHNOL FORECAST SOC, V73, P467, DOI 10.1016/j.techfore.2005.09.002; Lee C, 2012, TECHNOL FORECAST SOC, V79, P16, DOI 10.1016/j.techfore.2011.06.009; Leiserowitz A., 2010, CLIMATEGATE PUBLIC O; Lidwell W., 2010, UNIVERSAL PRINCIPLES; Linstone HA, 2011, TECHNOL FORECAST SOC, V78, P1712, DOI 10.1016/j.techfore.2010.09.011; Macal CM, 2010, J SIMUL, V4, P151, DOI 10.1057/jos.2010.3; Martin B. M., 1989, RES FORESIGHT PRIORI; Mitchell V., 1996, J APPL MANAGEMENT ST, V5, P199; Olson DL, 2012, DECIS SUPPORT SYST, V52, P464, DOI 10.1016/j.dss.2011.10.007; Pang Alex Soojung-Kim, 2010, Foresight, V12, DOI 10.1108/14636681011020191; Parente R, 2011, TECHNOL FORECAST SOC, V78, P1705, DOI 10.1016/j.techfore.2011.07.005; Peter Bishop A. H., 2007, FORESIGHT, V9, P5; Plott Charles, 2008, HDB EXPT EC RESULTS, P742; Porter AL, 2004, TECHNOL FORECAST SOC, V71, P287, DOI 10.1016/j.techfore.2003.11.004; Porter AL, 2005, TECH MINING EXPLOITI; Reger G, 2001, TECHNOL ANAL STRATEG, V13, P533, DOI 10.1080/09537320127286; RIGGS WE, 1983, TECHNOL FORECAST SOC, V23, P89, DOI 10.1016/0040-1625(83)90073-2; Rinne M, 2004, TECHNOL FORECAST SOC, V71, P67, DOI 10.1016/j.techfore.2003.10.002; RITTEL HWJ, 1973, POLICY SCI, V4, P155, DOI 10.1007/BF01405730; Robinson J, 2011, TECHNOL FORECAST SOC, V78, P756, DOI 10.1016/j.techfore.2010.12.006; Rohrbeck R, 2011, CONTRIB MANAG SCI, P1, DOI 10.1007/978-3-7908-2626-5; Rohrbeck R., 2008, STRATEGIC FORESIGHT, P10; Roozbehani M, 2010, 2010 IEEE 1ST INTERNATIONAL CONFERENCE ON SMART GRID COMMUNICATIONS (SMARTGRIDCOMM), P543, DOI 10.1109/SMARTGRID.2010.5621994; Rowe G, 2005, TECHNOL FORECAST SOC, V72, P377, DOI 10.1016/j.techfore.2004.03.004; Rowe G, 2001, INT SER OPER RES MAN, V30, P125; Sackman H., 1974, DELPHI ASSESSMENT EX; SALANCIK JR, 1971, TECHNOL FORECAST SOC, V3, P65, DOI 10.1016/S0040-1625(71)80004-5; Salo A., 2004, INT J FORESIGHT INNO, V1, P249, DOI 10.1504/IJFIP.2004.004985; Sardar Z, 2010, FUTURES, V42, P177, DOI 10.1016/j.futures.2009.11.001; Scapolo F, 2006, TECHNOL FORECAST SOC, V73, P679, DOI 10.1016/j.techfore.2006.03.001; Scapolo F., 2008, FUTURE ORIENTED TECH, P149, DOI 10.1007/978-3-540-68811-2_11; Shim JP, 2002, DECIS SUPPORT SYST, V33, P111, DOI 10.1016/S0167-9236(01)00139-7; SNIEZEK JA, 1989, ORGAN BEHAV HUM DEC, V43, P1, DOI 10.1016/0749-5978(89)90055-1; Steinert M, 2009, TECHNOL FORECAST SOC, V76, P291, DOI 10.1016/j.techfore.2008.10.006; Surowiecki J., 2004, WISDOM CROWDS WHY MA; Tetlock P., 2010, FORESIGHT, V12, P5; Tseng FM, 2009, TECHNOL FORECAST SOC, V76, P897, DOI 10.1016/j.techfore.2009.02.003; Vaccaro A, 2010, TECHNOL FORECAST SOC, V77, P1076, DOI 10.1016/j.techfore.2010.02.006; Vance A., 2010, NY TIMES; von der Gracht HA, 2010, INT J PROD ECON, V127, P46, DOI 10.1016/j.ijpe.2010.04.013; WEBLER T, 1991, TECHNOL FORECAST SOC, V39, P253, DOI 10.1016/0040-1625(91)90040-M; WELTY G, 1972, ACAD MANAGE J, V15, P121, DOI 10.2307/254805; Wolfers J, 2004, J ECON PERSPECT, V18, P107, DOI 10.1257/0895330041371321 95 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0040-1625 1873-5509 TECHNOL FORECAST SOC Technol. Forecast. Soc. Chang. JUN 2014 85 81 92 10.1016/j.techfore.2013.07.010 12 AJ3GE WOS:000337553700008 J O'Connor, AM; Anderson, KM; Goodell, CK; Sargeant, JM O'Connor, A. M.; Anderson, K. M.; Goodell, C. K.; Sargeant, J. M. Conducting Systematic Reviews of Intervention Questions I: Writing the Review Protocol, Formulating the Question and Searching the Literature ZOONOSES AND PUBLIC HEALTH English Review Bibliographic databases; CAB Abstracts; information retrieval; literature searching; MEDLINE VETERINARY-MEDICINE; CONTROLLED-TRIALS; FILTERS; BIAS This article is the fourth of six articles addressing systematic reviews in animal agriculture and veterinary medicine. Previous articles in the series have introduced systematic reviews, discussed study designs and hierarchies of evidence, and provided details on conducting randomized controlled trials, a common design for use in systematic reviews. This article describes development of a review protocol and the first two steps in a systematic review: formulating a review question, and searching the literature for relevant research. The emphasis is on systematic reviews of questions related to interventions. The review protocol is developed prior to conducting the review and specifies the plan for the conduct of the review, identifies the roles and responsibilities of the review team and provides structured definitions related to the review question. For intervention questions, the review question should be defined by the PICO components: population, intervention, comparison and outcome(s). The literature search is designed to identify all potentially relevant original research that may address the question. Search terms related to some or all of the PICO components are entered into literature databases, and searches for unpublished literature also are conducted. All steps of the literature search are documented to provide transparent reporting of the process. [O'Connor, A. M.; Goodell, C. K.] Iowa State Univ, Coll Vet Med, Dept Vet Diagnost & Prod Anim Med, Ames, IA 50010 USA; [Anderson, K. M.] Univ Missouri, Zalk Vet Med Lib, Columbia, MO USA; [Sargeant, J. M.] Univ Guelph, Ctr Publ Hlth & Zoonoses, Guelph, ON N1G 2W1, Canada; [Sargeant, J. M.] Univ Guelph, Ontario Vet Coll, Dept Populat Med, Guelph, ON N1G 2W1, Canada; [Goodell, C. K.] IDEXX Livestock Poultry & Dairy, Westbrook, ME USA O'Connor, AM (reprint author), Iowa State Univ, Coll Vet Med, Bldg 4 Vet Med Res Inst, Ames, IA 50010 USA. oconnor@iastate.edu Laboratory for Foodborne Zoonoses, Public Health Agency of Canada; Canadian Institutes of Health Research (CIHR) Institute of Population and Public Health/Public Health Agency of Canada Applied Public Health Chair The authors thank Annette Wilkins for assistance with this manuscript. Partial funding was obtained from the Laboratory for Foodborne Zoonoses, Public Health Agency of Canada and the Canadian Institutes of Health Research (CIHR) Institute of Population and Public Health/Public Health Agency of Canada Applied Public Health Chair. Boeker M, 2013, BMC MED RES METHODOL, V13, DOI 10.1186/1471-2288-13-131; Counsell C, 1997, ANN INTERN MED, V127, P380; Deeks JJ, 2011, COCHRANE HDB SYSTEMA; Dudden Rosalind F., 2011, Medical Reference Services Quarterly, V30, P301, DOI 10.1080/02763869.2011.590425; Dwan K, 2008, PLOS ONE, V3, DOI 10.1371/journal.pone.0003081; European Food Safety Authority (EFSA), 2010, EFSA J, V8, P1, DOI DOI 10.2903/J.EFSA.2010.1627; Greenhalgh T, 2005, BRIT MED J, V331, P1064, DOI 10.1136/bmj.38636.593461.68; Grindlay DJC, 2012, J VET MED EDUC, V39, P404, DOI 10.3138/jvme.1111.109R; Higgins JPT, 2011, COCHRANE HDB SYSTEMA; Juni P, 2002, INT J EPIDEMIOL, V31, P115, DOI 10.1093/ije/31.1.115; Liberati A, 2009, PLOS MED, V6, DOI 10.1371/journal.pmed.1000100; McGowan J, 2005, J MED LIBR ASSOC, V93, P74; McKibbon KA, 2009, HEALTH INFO LIBR J, V26, P187, DOI 10.1111/j.1471-1842.2008.00827.x; McManus RJ, 1998, BRIT MED J, V317, P1562; Moher D, 2009, J CLIN EPIDEMIOL, V62, P1006, DOI 10.1016/j.jclinepi.2009.06.005; Murphy SA, 2003, J MED LIBR ASSOC, V91, P484; Murphy SA, 2002, J MED LIBR ASSOC, V90, P406; RevMan, 2012, REV MAN REVMAN COMP; Sampson M, 2009, J CLIN EPIDEMIOL, V62, P944, DOI 10.1016/j.jclinepi.2008.10.012; Sargeant JM, 2007, ZOONOSES PUBLIC HLTH, V54, P260, DOI 10.1111/j.1863-2378.2007.01059.x; Shojania KG, 2007, ANN INTERN MED, V147, P224 21 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1863-1959 1863-2378 ZOONOSES PUBLIC HLTH Zoonoses Public Health JUN 2014 61 1 SI 28 38 10.1111/zph.12125 11 AJ3RR WOS:000337585400006 J Zhai, XH; Peng, YX; Xiao, JG Zhai, Xiaohua; Peng, Yuxin; Xiao, Jianguo Learning Cross-Media Joint Representation With Sparse and Semisupervised Regularization IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY English Article Cross-media retrieval; joint representation learning (JRL); semisupervised regularization; sparse regularization SIMILARITY MEASURE; RETRIEVAL; MODEL Cross-media retrieval has become a key problem in both research and application, in which users can search results across all of the media types (text, image, audio, video, and 3-D) by submitting a query of any media type. How to measure the content similarity among different media is the key challenge. Existing cross-media retrieval methods usually focus on modeling the pairwise correlation or semantic information separately. In fact, these two kinds of information are complementary to each other and optimizing them simultaneously can further improve the accuracy. In this paper, we propose a novel feature learning algorithm for cross-media data, called joint representation learning (JRL), which is able to explore jointly the correlation and semantic information in a unified optimization framework. JRL integrates the sparse and semisupervised regularization for different media types into one unified optimization problem, while existing feature learning methods generally focus on a single media type. On one hand, JRL learns sparse projection matrix for different media simultaneously, so different media can align with each other, which is robust to the noise. On the other hand, both the labeled data and unlabeled data of different media types are explored. Unlabeled examples of different media types increase the diversity of training data and boost the performance of joint representation learning. Furthermore, JRL can not only reduce the dimension of the original features, but also incorporate the cross-media correlation into the final representation, which further improves the performance of both cross-media retrieval and single-media retrieval. Experiments on two datasets with up to five media types show the effectiveness of our proposed approach, as compared with the state-of-the-art methods. [Zhai, Xiaohua; Peng, Yuxin; Xiao, Jianguo] Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China Zhai, XH (reprint author), Peking Univ, Inst Comp Sci & Technol, Beijing 100871, Peoples R China. pengyuxin@pku.edu.cn National HiTech Research and Development Program (863 Program) of China [2014AA015102, 2012AA012503]; National Natural Science Foundation of China [61371128]; Ph.D. Programs Foundation of the Ministry of Education of China [20120001110097] This work was supported in part by the National HiTech Research and Development Program (863 Program) of China under Grants 2014AA015102 and 2012AA012503, in part by National Natural Science Foundation of China under Grant 61371128, and in part by the Ph.D. Programs Foundation of the Ministry of Education of China under Grant 20120001110097. This paper was recommended by Associate Editor S. Battiato. Battiato S, 2010, P 2 ACM WORKSH MULT, P65, DOI 10.1145/1877972.1877991; Battiato S., 2007, P SPIE 19 ANN S EL I; Battiato S, 2009, MULTIMED TOOLS APPL, V42, P5, DOI 10.1007/s11042-008-0250-z; Blaschko M., 2008, P IEEE C COMP VIS PA, P1; Bredin H, 2007, INT CONF ACOUST SPEE, P233; Chen DY, 2003, COMPUT GRAPH FORUM, V22, P223, DOI 10.1111/1467-8659.00669; Chung F.R.K., 1997, SPECTRAL GRAPH THEOR; Clinchant S., 2011, P ACM INT C MULT RET, p44P1; Escalante H., 2008, P 1 ACM INT C MULT I, P172, DOI 10.1145/1460096.1460125; Farahat A. K., 2011, Proceedings of the 2011 IEEE 11th International Conference on Data Mining (ICDM 2011), DOI 10.1109/ICDM.2011.22; Fu Z., 2011, P ACM MULT, P143; Grangier D, 2008, IEEE T PATTERN ANAL, V30, P1371, DOI 10.1109/TPAMI.2007.70791; Greenspan H, 2004, IEEE T PATTERN ANAL, V26, P384, DOI 10.1109/TPAMI.2004.1262334; Hotelling H, 1936, BIOMETRIKA, V28, P321, DOI 10.2307/2333955; Hu YQ, 2009, IEEE T MULTIMEDIA, V11, P1434, DOI 10.1109/TMM.2009.2032676; Jeon J., 2003, P 26 ANN INT ACM SIG, P119, DOI DOI 10.1145/860435.860459; Jia YQ, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P2407; Kidron E, 2005, PROC CVPR IEEE, P88; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Li D., 2003, P ACM INT C MULT, P604; Liu J., 2010, INT J MULTIMED INTEL, V1, P33; Liu Y., 2010, P ACM INT C IM VID R, P89, DOI 10.1145/1816041.1816057; Ma Z., 2011, P 19 ACM INT C MULT, P283; Nie F., 2010, P NIPS, P1813; Peng YX, 2006, IEEE T CIRC SYST VID, V16, P612, DOI 10.1109/TCSVT.2006.873157; Rasiwasia N., 2010, P ACM MULT, P251, DOI 10.1145/1873951.1873987; Typke R., 2005, P INT C MUS INF RETR, P153; Yang Y, 2008, IEEE T MULTIMEDIA, V10, P437, DOI 10.1109/TMM.2008.917359; Yang YS, 2009, PROCEEDINGS OF 2009 CONFERENCE ON SYSTEMS SCIENCE, MANAGEMENT SCIENCE & SYSTEM DYNAMICS, VOL 8, P175, DOI 10.1145/1631272.1631298; Yang Y., 2011, P 22 INT JOINT C ART, P1589; Yu J, 2008, IEEE T CIRC SYST VID, V18, P544, DOI 10.1109/TCSVT.2008.918763; Zhai XH, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), P2337; Zhai XH, 2012, LECT NOTES COMPUT SC, V7131, P312; Zhuang YT, 2008, IEEE T MULTIMEDIA, V10, P221, DOI 10.1109/FMM.2007.911822; Znaidia A., 2012, P 21 INT C PATT REC, P1509 35 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1051-8215 1558-2205 IEEE T CIRC SYST VID IEEE Trans. Circuits Syst. Video Technol. JUN 2014 24 6 965 978 10.1109/TCSVT.2013.2276704 14 Engineering, Electrical & Electronic Engineering AI8BC WOS:000337127700006 J Reid, DA; Nixon, MS; Stevenage, SV Reid, Daniel A.; Nixon, Mark S.; Stevenage, Sarah V. Soft Biometrics; Human Identification Using Comparative Descriptions IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Soft biometrics; human descriptions; retrieval; comparisons; regression; gait biometrics CRIME Soft biometrics are a new form of biometric identification which use physical or behavioral traits that can be naturally described by humans. Unlike other biometric approaches, this allows identification based solely on verbal descriptions, bridging the semantic gap between biometrics and human description. To permit soft biometric identification the description must be accurate, yet conventional human descriptions comprising of absolute labels and estimations are often unreliable. A novel method of obtaining human descriptions will be introduced which utilizes comparative categorical labels to describe differences between subjects. This innovative approach has been shown to address many problems associated with absolute categorical labels-most critically, the descriptions contain more objective information and have increased discriminatory capabilities. Relative measurements of the subjects' traits can be inferred from comparative human descriptions using the Elo rating system. The resulting soft biometric signatures have been demonstrated to be robust and allow accurate recognition of subjects. Relative measurements can also be obtained from other forms of human representation. This is demonstrated using a support vector machine to determine relative measurements from gait biometric signatures-allowing retrieval of subjects from video footage by using human comparisons, bridging the semantic gap. [Reid, Daniel A.; Nixon, Mark S.] Univ Southampton, Sch Elect & Comp Sci, Southampton, Hants, England; [Stevenage, Sarah V.] Univ Southampton, Sch Psychol, Southampton, Hants, England Reid, DA (reprint author), Univ Southampton, Sch Elect & Comp Sci, Southampton, Hants, England. Ailisto H., 2004, P 3 NORD C HUM COMP, P327, DOI 10.1145/1028014.1028065; BRADLEY RA, 1952, BIOMETRIKA, V39, P324, DOI 10.1093/biomet/39.3-4.324; Chapman G. B., 2002, HEURISTICS BIASES PS, P120; Dantcheva A., 2010, P IEEE INT C BIOM TH, P1; Denman Simon, 2009, Proceedings of the 2009 Digital Image Computing: Techniques and Applications (DICTA 2009), DOI 10.1109/DICTA.2009.38; Elo A., 1978, RATING CHESSPLAYERS; FLIN RH, 1986, HUM LEARN, V5, P29; Goffredo M, 2010, IEEE T SYST MAN CY B, V40, P997, DOI 10.1109/TSMCB.2009.2031091; Hu M., 2012, IET Biometrics, V1, DOI 10.1049/iet-bmt.2011.0004; JAIN AK, 2004, P ECCV INT WORKSH BI, V3087, P259; Joachims T, 2002, P 8 ACM SIGKDD INT C, P133, DOI DOI 10.1145/775047.775067; KUEHN LL, 1974, PERCEPT MOTOR SKILL, V39, P1159; KUMAR N, 2009, P IEEE INT C COMP VI, P365; Liu Z., 2004, P IEEE COMP SOC C CO, V4, P211; Loftus E. F., 1996, EYEWITNESS TESTIMONY; Luce RD, 1959, INDIVIDUAL CHOICE BE; MacLeod M. D., 1994, ADULT EYEWITNESS TES; Meissner C.A., 2007, HDB EYEWITNESS PSYCH, V2, P3; National Policing Improvement Agency, 2009, PNC US MAN, V2; Parik D., 2011, P IEEE INT C COMP VI; Park U., 2010, IEEE T INFORM FORENS, V5; Reid D.A., 2010, P 2 ACM WORKSH MULT, P25, DOI 10.1145/1877972.1877982; Reid DA, 2013, HANDB STAT, V31, P327, DOI 10.1016/B978-0-444-53859-8.00013-8; Samangooei S., 2010, MULTIMEDIA TOOLS APP, V49; Shutler JD, 2002, P 4 INT C REC ADV SO, P66; Thurstone LL, 1927, PSYCHOL REV, V34, P273, DOI 10.1037/h0070288; Wang G, 2010, PROC CVPR IEEE, P3525, DOI 10.1109/CVPR.2010.5539955; Wang L, 2003, IEEE T PATTERN ANAL, V25, P1505; YOO JH, 2005, P 7 INT C ADV CONC I, V3708, P138; YUILLE JC, 1986, J APPL PSYCHOL, V71, P291, DOI 10.1037//0021-9010.71.2.291 30 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. JUN 2014 36 6 1216 1228 10.1109/TPAMI.2013.219 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AI8AJ WOS:000337124200013 J Shen, XH; Lin, Z; Brandt, J; Wu, Y Shen, Xiaohui; Lin, Zhe; Brandt, Jonathan; Wu, Ying Spatially-Constrained Similarity Measure for Large-Scale Object Retrieval IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE English Article Object retrieval; bag-of-words; spatially-constrained similarity measure; k-NN re-ranking; product image search One fundamental problem in object retrieval with the bag-of-words model is its lack of spatial information. Although various approaches are proposed to incorporate spatial constraints into the model, most of them are either too strict or too loose so that they are only effective in limited cases. In this paper, a new spatially-constrained similarity measure (SCSM) is proposed to handle object rotation, scaling, view point change and appearance deformation. The similarity measure can be efficiently calculated by a voting-based method using inverted files. During the retrieval process, object localization in the database images can also be simultaneously achieved using SCSM without post-processing. Furthermore, based on the retrieval and localization results of SCSM, we introduce a novel and robust re-ranking method with the k-nearest neighbors of the query for automatically refining the initial search results. Extensive performance evaluations on six public data sets show that SCSM significantly outperforms other spatial models including RANSAC-based spatial verification, while k-NN re-ranking outperforms most state-of-the-art approaches using query expansion. We also adapted SCSM for mobile product image search with an iterative algorithm to simultaneously extract the product instance from the mobile query image, identify the instance, and retrieve visually similar product images. Experiments on two product image search data sets show that our approach can robustly localize and extract the product in the query image, and hence drastically improve the retrieval accuracy over baseline methods. [Shen, Xiaohui; Lin, Zhe; Brandt, Jonathan] Adobe Res, San Jose, CA 95110 USA; [Wu, Ying] Northwestern Univ, Dept Elect Engn & Comp Sci, Evanston, IL 60208 USA Shen, XH (reprint author), Adobe Res, 345 Pk Ave, San Jose, CA 95110 USA. xshen@adobe.com; zlin@adobe.com; jbrandt@adobe.com; yingwu@eecs.northwestern.edu Wu, Ying/B-7283-2009 Adobe Systems, Incorporated; US National Science Foundation [IIS-0347877, IIS-0916607]; US Army Research Laboratory; US Army Research Office [ARO W911NF-08-1-0504]; DARPA [FA 8650-11-1-7149] This work was partially supported by Adobe Systems, Incorporated, and in part by US National Science Foundation grant IIS-0347877, IIS-0916607, US Army Research Laboratory and the US Army Research Office under grant ARO W911NF-08-1-0504, and DARPA Award FA 8650-11-1-7149. Cao Y., 2010, P IEEE C COMP VIS PA; Chandrasekhar V., 2011, P ACM MULT SYST C; Chum O., 2010, P IEEE C COMP VIS PA; Chum O., 2011, P IEEE C COMP VIS PA; Chum O., 2009, P IEEE C COMP VIS PA; Chum O., 2007, P IEEE INT C COMP VI; Girod B., 2011, IEEE SIGNAL PROCESSI, V28; Griffin G., 2007, 7694 CALTECH; He J, 2012, P IEEE C COMP VIS PA; He J, 2011, P 19 ACM INT C MULT; Jegou H., 2009, P IEEE C COMP VIS PA; Jegou H., 2007, P IEEE C COMP VIS PA; Jegou H., 2008, P 10 EUR C COMP VIS; Jegou H., 2010, P IEEE C COMP VIS PA; Jing Y., 2008, P 17 INT C WORLD WID; Lampert C.H., 2009, P IEEE INT C COMP VI; Leibe B., 2004, P ECCV WORKSH STAT L; Lin X., 2008, P SPIE; Lin Z., 2010, P 11 EUR C COMP VIS; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Mikulik A., 2010, P 11 EUR C COMP VIS; Muja M., 2009, P VISAPP INT C COMP; Nister D., 2006, P IEEE C COMP VIS PA; Pedronette D.C.G., 2011, P ACM 1 INT C MULT R; Perd'och M., 2009, P IEEE C COMP VIS PA; Philbin J., 2010, P 11 EUR C COMP VIS; Philbin J., 2008, P IEEE C COMP VIS PA; Philbin J., 2007, P IEEE C COMP VIS PA; Qin D., 2011, P IEEE C COMP VIS PA; Rother C., 2004, P ACM SIGGRAPH; Shen X., 2012, P IEEE C COMP VIS PA; Shen X., 2012, P 12 EUR C COMP VIS; Sivic J., 2003, P IEEE INT C COMP VI; Tolias G., 2011, P IEEE INT C COMP VI; Wang X., 2011, P IEEE INT C COMP VI; Wu Z, 2009, P IEEE C COMP VIS PA; Zhang Y., 2011, P IEEE C COMP VIS PA 37 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 0162-8828 1939-3539 IEEE T PATTERN ANAL IEEE Trans. Pattern Anal. Mach. Intell. JUN 2014 36 6 1229 1241 10.1109/TPAMI.2013.237 13 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AI8AJ WOS:000337124200014 J Ding, ZM; Chen, ZK; Yang, Q Ding, Zhiming; Chen, Zhikui; Yang, Qi IoT-SVKSearch: a real-time multimodal search engine mechanism for the internet of things INTERNATIONAL JOURNAL OF COMMUNICATION SYSTEMS English Article search engines; index methods; sensor networks; spatial-temporal databases; internet of things; information retrieval for big data MANAGEMENT Recent advances on the Internet of Things (IoT) have posed great challenges to the search engine community. IoT systems manage huge numbers of heterogeneous sensors and/or monitoring devices, which continuously monitor the states of real-world objects, and most data are generated automatically through sampling. The sampling data are dynamically changing so that the IoT search engine should support real-time retrieval. Additionally, the IoT search involves not only keyword matches but also spatial-temporal searches and value-based approximate searches, as IoT sampling data are generally from spatial-temporal scenario. To meet these challenges, we propose a Hybrid Real-time Search Engine Framework for the Internet of Things based on Spatial-Temporal, Value-based, and Keyword-based Conditions' (IoT-SVK Search Engine' or simply IoT-SVKSearch' for short) in this paper. The experiments show that the IoT-SVK search engine has satisfactory performances in supporting real-time, multi-modal retrieval of massive sensor sampling data in the IoT. Copyright (c) 2013 John Wiley & Sons, Ltd. [Ding, Zhiming] Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China; [Chen, Zhikui] Dalian Univ Technol, Sch Software Technol, Dalian 116620, Peoples R China; [Yang, Qi] Natl Ctr ITS Engn & Technol, Beijing 100088, Peoples R China Ding, ZM (reprint author), Chinese Acad Sci, Inst Software, Beijing 100190, Peoples R China. zhiming@nfs.iscas.ac.cn National Natural Science Foundation of China (NSFC) [91124001]; National High-Tech. R&D Program of China (863 program) [2013AA01A603]; Strategic Priority Research Program of the Chinese Academy of Sciences [XDA06020600] The work was supported by the National Natural Science Foundation of China (NSFC) under grant number 91124001, the National High-Tech. R&D Program of China (863 program) under grant number 2013AA01A603, and the Strategic Priority Research Program of the Chinese Academy of Sciences under grant number XDA06020600. The authors would like to thank Xu Gao of ISCAS for his valuable work in the experiments. Almeida VT, 2005, GEOINFORMATICA, V9, P33; Atzori L, 2010, COMPUT NETW, V54, P2787, DOI 10.1016/j.comnet.2010.05.010; Balazinska M, 2007, IEEE PERVAS COMPUT, V6, P30, DOI 10.1109/MPRV.2007.27; Chatterjea S, 2007, INT J COMMUN SYST, V20, P889, DOI 10.1002/dac.850; [丁治明 Ding Zhiming], 2012, [计算机学报, Chinese Journal of Computers], V35, P1448; Ding Z, 2011, P 22 INT C DAT EXP S, P464; Ding ZM, 2013, J SUPERCOMPUT, V66, P1260, DOI 10.1007/s11227-012-0762-1; Ding ZM, 2012, 2012 IEEE INTERNATIONAL CONFERENCE ON GREEN COMPUTING AND COMMUNICATIONS, CONFERENCE ON INTERNET OF THINGS, AND CONFERENCE ON CYBER, PHYSICAL AND SOCIAL COMPUTING (GREENCOM 2012), P17, DOI 10.1109/GreenCom.2012.13; Fielding Roy, 2002, ACM T INTERNET TECHN, V2, P115, DOI [10.1145/514183.514185, DOI 10.1145/514183.514185]; Gao D, 2012, INT J COMMUNICATION, DOI 10.1002/dac.2492; Hsu IC, 2013, INT J COMMUN SYST, V26, P610, DOI 10.1002/dac.1365; Huang H, 2010, IEEE T PARALL DISTR, V21, P1188; Kansal A, 2007, IEEE MULTIMEDIA, V14, P8, DOI DOI 10.1109/MMUL.2007.82; Mayer S, 2011, P 2 INT WORKSH WEB T; Modi A, 2011, P 2011 INT C WORKSH, P707; Nadkarni PM, 2011, J AM MED INFORM ASSN, V18, P544, DOI 10.1136/amiajnl-2011-000464; Ning HS, 2012, INT J COMMUN SYST, V25, P1230, DOI 10.1002/dac.2373; Ostermaier B, 2010, P INT THINGS 2010, P1; Pfoser D., 2000, P 26 INT C VER LARG, P395; Romer K, 2010, P IEEE, V98, P1887, DOI 10.1109/JPROC.2010.2062470; Sundmaeker H., 2010, VISION CHALLENGES RE; Tamine-Lechani L, 2010, KNOWL INF SYST, V24, P1, DOI 10.1007/s10115-009-0231-1; Tan CC, 2010, ACM T EMBED COMPUT S, V9, DOI 10.1145/1721695.1721709; Wu Fei, 2010, Journal of Computer Aided Design & Computer Graphics, V22; Xiong Z, 2010, P 16 IEEE INT C PAR, P251; Yan L, 2008, WIREL NETW MOB COMMU, P1, DOI 10.1201/9781420052824; Yap KK, 2005, P 3 INT C EMB NETW S, P166, DOI 10.1145/1098918.1098937; Ye T, 2010, ANN BLUE BOOK CHINAS; Zeng D., 2011, J COMMUNICATIONS, V6, P424, DOI DOI 10.4304/JCM.6.6.424-438; Zhang C, 2011, J SYST SOFTWARE, V84, P2348, DOI 10.1016/j.jss.2011.07.027 30 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1074-5351 1099-1131 INT J COMMUN SYST Int. J. Commun. Syst. JUN 2014 27 6 SI 871 897 10.1002/dac.2647 27 Engineering, Electrical & Electronic; Telecommunications Engineering; Telecommunications AJ3ZH WOS:000337607000005 J Lu, WT; Li, L; Li, JX; Li, T; Zhang, HG; Guo, J Lu, Wenting; Li, Lei; Li, Jingxuan; Li, Tao; Zhang, Honggang; Guo, Jun A multimedia information fusion framework for web image categorization MULTIMEDIA TOOLS AND APPLICATIONS English Article Web image categorization; Multimedia information fusion; Text; Image; Dynamic weighting; Region-based semantic concept integration SEGMENTATION With the rapid development of technologies for fast Internet access and the popularization of digital cameras, an enormous number of digital images are posted and shared online everyday. Web images are usually organized by topic and are often assigned appropriate topic-related textual descriptions. Given a large set of images along with the corresponding texts, a challenging problem is how to utilize the available information to efficiently and effectively perform image retrieval tasks, such as image classification and image clustering. Previous approaches on image categorization focus on either adopting text or image features, or simply combining these two types of information together. In this paper, we improve our previously reported two multi-view classification approaches-(Dynamic Weighting and Region-based Semantic Concept Integration) for categorizing the images under the "supervision" of topic-related textual descriptions-by proposing a novel multimedia information fusion framework, in which these two proposed methods are seamlessly integrated by analyzing the special characteristics of different images. Notice that, the proposed framework is a generic multimedia information fusion framework which is not limited to our previously reported two approaches, and it can also be used to integrate other existing multi-view classification methods or models. Also, our proposed framework is capable of handling the large scale image categorization. Specifically, the proposed framework can automatically choose an appropriate classification model for each testing image according to its special characteristics and consequently achieve better classification performance with relatively less computation time for large scale datasets; Moreover, it is able to categorize images without any textual description in real world applications. Empirical experiments on two different types of web image datasets demonstrate the efficacy and efficiency of our proposed classification framework. [Lu, Wenting; Zhang, Honggang; Guo, Jun] Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligence Syst Lab, Beijing 100876, Peoples R China; [Lu, Wenting; Li, Lei; Li, Jingxuan; Li, Tao] Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA Li, T (reprint author), Florida Int Univ, Sch Comp & Informat Sci, Miami, FL 33199 USA. wlu@cs.fiu.edu; lli003@cs.fiu.edu; jli003@cs.fiu.edu; taoli@cs.fiu.edu; zhhg@bupt.edu.cn; guojun@bupt.edu.cn Army Research Office [W911NF-10-1-0366]; National Natural Science Foundation of China [61175011]; China Scholarship Council This work is partially supported by the Army Research Office under grant number W911NF-10-1-0366, the National Natural Science Foundation of China under Grant No. 61175011, and the China Scholarship Council. Allan M, 2009, BRIT MACH VIS C; Bishop C. M., 2006, PATTERN RECOGNITION; Blei D.M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460; Carter RJ, 2001, NUCLEIC ACIDS RES, V29, P3928; Chang C.C., 2001, ACM T INTEL SYST TEC, V2; Chatzichristofis S, 2008, P 6 INT C COMP VIS S, P312; Deng YN, 2001, IEEE T PATTERN ANAL, V23, P800, DOI 10.1109/34.946985; Giacinto G, 2002, P IEEE INNS ENNS INT, V3, P155; Gill PE, 1981, PRACTICAL OPTIMIZATI; Gionis A, 1999, PROCEEDINGS OF THE TWENTY-FIFTH INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P518; Hare J, 2010, P 2 ACM INT C MULT I; Hsu CW, 2002, IEEE T NEURAL NETWOR, V13, P415, DOI 10.1109/72.991427; Indyk P, 1999, PROCEEDINGS OF THE TENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P454; JORDAN MI, 1994, NEURAL COMPUT, V6, P181, DOI 10.1162/neco.1994.6.2.181; Kalva P, 2007, P 9 INT C DOC AN REC, P561; Lanckriet GRG, 2004, J MACH LEARN RES, V5, P27; Lee WJ, 2007, LECT NOTES COMPUT SC, V4472, P22; Li H, 2008, P 16 ACM INT C MULT, P813, DOI 10.1145/1459359.1459494; Li L, 2011, P FLAIRS, P45; Li LJ, 2009, PROC CVPR IEEE, P2036; Li T, 2005, KNOWL INF SYST, V7, P289, DOI 10.1007/s10115-004-0155-8; Liu XH, 2009, MPI STUD INTELL PROP, V6, P115, DOI 10.1145/1631272.1631291; Liu Y, 2008, PATTERN RECOGN, V41, P2554, DOI 10.1016/j.patcog.2007.12.003; McCallum A., 2002, MALLET MACHINE LEARN; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; Salton G., 1986, INTRO MODERN INFORM; Scholkopf B, 2002, LEARNING KERNELS SUP; Shao B, 2009, IEEE T AUDIO SPEECH, V17, P1602, DOI 10.1109/TASL.2009.2020893; Wang Y, 2007, P 6 ACM INT C IM VID, P425; Wu LZ, 1999, IEEE T MULTIMEDIA, V1, P334; WU Y, 2004, P 12 ANN ACM INT C M, P572, DOI 10.1145/1027527.1027665; Yin ZJ, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P957; Zhu Q, 2006, P 14 ACM C MULT SANT, P211, DOI 10.1145/1180639.1180698 33 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUN 2014 70 3 1453 1486 10.1007/s11042-012-1165-2 34 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI8IP WOS:000337156500005 J Qian, XM; Wang, H; Hou, XS Qian, Xueming; Wang, Huan; Hou, Xingsong Video text detection and localization in intra-frames of H.264/AVC compressed video MULTIMEDIA TOOLS AND APPLICATIONS English Article Text detection; DCT coefficient; H.264/AVC; Integer DCT; AC coefficient; Intra prediction CAPTION DETECTION; DIGITAL VIDEO; EXTRACTION; IMAGES; RECOGNITION; TRACKING; SYSTEM Video texts are closely related to the video content. The video text information can facilitate content based video analysis, indexing and retrieval. Video sequences are usually compressed before storage and transmission. A basic step of text-based applications is text detection and localization. In this paper, an overlaid text detection and localization method is proposed for H.264/AVC compressed videos by using the integer discrete cosine transform (DCT) coefficients of intra-frames. The main contributions of this paper are in the following two aspects: 1) coarse text blocks detection using block sizes and quantization parameters adaptive thresholds; 2) text line localization according to the characteristics of text in intra frames of H.264/AVC compressed domain. Comparisons are made with the pixel domain based text detection method for the H.264/AVC compressed video. Text detection results on five H.264/AVC video sequences under various qualities show the effectiveness of the proposed method. [Qian, Xueming; Hou, Xingsong] Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China; [Wang, Huan] Xi An Jiao Tong Univ, Xian 710049, Peoples R China Qian, XM (reprint author), Xi An Jiao Tong Univ, Sch Elect & Informat Engn, Xian 710049, Peoples R China. qianxm@mail.xjtu.edu.cn National Natural Science Foundation of China (NSFC) [60903121, 61173109]; Foundations of Microsoft Research Asia This work is supported in part by the National Natural Science Foundation of China (NSFC) Project No. 60903121, No. 61173109, and Foundations of Microsoft Research Asia. [Anonymous], 2003, JVTG050 ISO IEC MPEG; Chen DT, 2001, PROC CVPR IEEE, P621; Crandall D., 2001, Proceedings of Sixth International Conference on Document Analysis and Recognition, DOI 10.1109/ICDAR.2001.953910; Cui YT, 1997, PROC CVPR IEEE, P502, DOI 10.1109/CVPR.1997.609372; Ekin A, 2006, P INT C AC SPEECH SI, V2, pII753; Gargi U, 1998, INT C PATT RECOG, P916; Gordon S, 2003, JVT1022; Jain AK, 1998, INT C PATT RECOG, P1497; Jiang H, 2008, PROC ISM; Jung K, 2004, PATTERN RECOGN, V37, P977, DOI 10.1016/j.patcog.2003.10.012; Lee CW, 2003, PATTERN RECOGN LETT, V24, P2607, DOI 10.1016/S0167-8655(03)00105-3; Li HP, 2000, IEEE T IMAGE PROCESS, V9, P147, DOI 10.1109/83.817607; Lim YK, 2000, INT C PATT RECOG, P409; Liu Z, 2008, P ICPR; Lu S, 2008, INT CONF ACOUST SPEE, P1341; Lyu MR, 2005, IEEE T CIRC SYST VID, V15, P243, DOI 10.1109/TCSVT.2004.841653; Malvar HS, 2003, IEEE T CIRC SYST VID, V13, P598, DOI 10.1109/TCSVT.2003.814964; Mariano VY, 2000, INT C PATT RECOG, P539; Ngo CW, 2005, MULTIMEDIA SYST, V10, P261, DOI 10.1007/s00530-004-0157-0; Qi W, 2000, 2000 INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, VOL III, PROCEEDINGS, P520; Qian X, 2007, SIGNAL IMAGE VIDEO P, V4, P179; Qian X, 2006, P INT C AC SPEECH SI, V2, pII385; Qian XM, 2007, SIGNAL PROCESS-IMAGE, V22, P752, DOI 10.1016/j.image.2007.06.005; Qian XM, 2006, IEEE T CIRC SYST VID, V16, P1245, DOI 10.1109/TCSVT.2006.881858; Rainer L, 2002, IEEE T CIRCUITS SYST, V12, P256; Sato T, 1998, ICCV WORKSH IM VID R; Shen B, 1996, P SOC PHOTO-OPT INS, V2670, P404, DOI 10.1117/12.234779; Shivakumara P, 2009, INT C DOC AN REC, P1285; Snoek CGM, 2005, IEEE T MULTIMEDIA, V7, P638, DOI 10.1109/TMM.2005.850966; Sun L, 2009, P ICME; Tang X, 2002, IEEE T NEURAL NETWOR, V13, P961, DOI 10.1109/TNN.2002.1021896; Wang F, 2005, P 11 INT MULT MOD C, P115; Wang P, 2003, P 4 IEEE INT C INF C, V2, P787; Wang RR, 2004, INT C PATT RECOG, P449; Wiegand T, 2003, IEEE T CIRC SYST VID, V13, P560, DOI 10.1109/TCSVT.2003.815165; Wu V, 1999, IEEE T PATTERN ANAL, V21, P1224, DOI 10.1109/34.809116; Wu W, 2005, P INT C MULT EXP; Zhang HJ, 1997, PATTERN RECOGN, V30, P643, DOI 10.1016/S0031-3203(96)00109-4; Zhang J, 2008, P ICPR; Zhong Y, 2000, IEEE T PATTERN ANAL, V22, P385 40 1 1 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUN 2014 70 3 1487 1502 10.1007/s11042-012-1168-z 16 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI8IP WOS:000337156500006 J Huang, XD; Ma, HD; Ling, CX; Gao, GY Huang, Xiaodong; Ma, Huadong; Ling, Charles X.; Gao, Guangyu Detecting both superimposed and scene text with multiple languages and multiple alignments in video MULTIMEDIA TOOLS AND APPLICATIONS English Article Superimposed text; Scene text; Text detection; Motion field NATURAL SCENES; IMAGES; EXTRACTION; ALGORITHM; FEATURES; LOCALIZATION; FRAMES Video text often contains highly useful semantic information that can contribute significantly to video retrieval and understanding. Video text can be classified into scene text and superimposed text. Most of the previous methods detect superimposed or scene text separately due to different text alignments. Moreover, because different language characters have different edge and texture features, it is very difficult to detect the multilingual text. In this paper, we first perform a detailed analysis of motion patterns of video text, and show that the superimposed and scene text exhibit different motion patterns on consecutive frames, which is insensitive to multiple language characters and multiple text alignments. Based on our analysis, we define Motion Perception Field (MPF) to represent the text motion patterns. Finally, we propose a text detection algorithms using MPF for both superimposed and scene text with multiple languages and multiple alignments. Experimental results on diverse videos demonstrate that our algorithms are robust, and outperform previous methods for detecting both superimposed and scene texts with multiple languages and multiple alignments. [Huang, Xiaodong; Ma, Huadong; Ling, Charles X.; Gao, Guangyu] Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China; [Huang, Xiaodong] Capital Normal Univ, Beijing 100048, Peoples R China Ma, HD (reprint author), Beijing Univ Posts & Telecommun, Beijing Key Lab Intelligent Telecommun Software &, Beijing 100876, Peoples R China. mhd@bupt.edu.cn National Natural Science Foundation [60925010]; National Natural Science Foundation of China [60833009]; Beijing Committee of Education; Funds for Creative Research Groups of China [61121001]; Program for Changjiang Scholars and Innovative Research Team in University [IRT1049] The authors would like to thank the reviewers for their thorough comments and suggestions that helped to improve this paper. This work is supported by the National Natural Science Foundation for Distinguished Young Scholars under Grant No. 60925010; the National Natural Science Foundation of China under Grant No. 60833009; the Cosponsored Project of Beijing Committee of Education, the Funds for Creative Research Groups of China under Grant No. 61121001, and the Program for Changjiang Scholars and Innovative Research Team in University under Grant No. IRT1049. BARRON JL, 1994, INT J COMPUT VISION, V12, P43, DOI 10.1007/BF01420984; BORECZKY JS, 1998, ACOUST SPEECH SIG PR, P3741; Chen XL, 2004, IEEE T IMAGE PROCESS, V13, P87, DOI 10.1109/TIP.2003.819223; DIZENZO S, 1986, COMPUT VISION GRAPH, V33, P116, DOI 10.1016/0734-189X(86)90223-9; Gao J, 2001, PROC CVPR IEEE, P84; Goto H, 2008, INT J DOC ANAL RECOG, V11, P1, DOI 10.1007/s10032-008-0061-9; Harris C., 1988, 4 ALV VIS C, P147; Horn BKP, 1986, ROBOT VISION; Hua X, 2002, IEEE INT C IM PROC, V2, P397; Hua X- S, 2001, P ACM MULT 2001 WORK, P24; Huang XD, 2008, LECT NOTES COMPUT SC, V5353, P525; Kim KC, 2004, INT C PATT RECOG, P679, DOI 10.1109/ICPR.2004.1334350; Kim KI, 2003, IEEE T PATTERN ANAL, V25, P1631; Li HP, 2000, IEEE T IMAGE PROCESS, V9, P147, DOI 10.1109/83.817607; Li HP, 2000, INT C PATT RECOG, P223; Lyu MR, 2005, IEEE T CIRC SYST VID, V15, P243, DOI 10.1109/TCSVT.2004.841653; Mariano VY, 2000, INT C PATT RECOG, P539; Miao GY, 2008, 2008 IEEE INTERNATIONAL CONFERENCE ON MULTIMEDIA AND EXPO, VOLS 1-4, P569; Sato T, 1998, VIDEO OCR INDEXING D; Shivakumara P, 2009, P ICDAR, P156; Sin BK, 2002, INT C PATT RECOG, P489; Singh A, 1992, OPTIC FLOW COMPUTATI; Soffer A, 1997, PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON DOCUMENT ANALYSIS AND RECOGNITION, VOLS 1 AND 2, P233, DOI 10.1109/ICDAR.1997.619847; Wang RR, 2004, INT C PATT RECOG, P449; Wang YK, 2006, INT C PATT RECOG, P754; Winger LL, 2000, INT J PATTERN RECOGN, V14, P113, DOI 10.1142/S0218001400000106; Ye QX, 2004, LECT NOTES COMPUT SC, V3332, P858; Yi J, 2007, ACM C MULT, P847 28 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUN 2014 70 3 1703 1727 10.1007/s11042-012-1201-2 25 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI8IP WOS:000337156500016 J Chatzichristofis, SA; Iakovidou, C; Boutalis, YS; Angelopoulou, E Chatzichristofis, Savvas A.; Iakovidou, Chryssanthi; Boutalis, Yiannis S.; Angelopoulou, Elli Mean Normalized Retrieval Order (MNRO): a new content-based image retrieval performance measure MULTIMEDIA TOOLS AND APPLICATIONS English Article Image retrieval performance measures; Mean Average Precision; Average Normalized Modified Retrieval Rank INFORMATION-RETRIEVAL; DESCRIPTORS; FEATURES; COLOR; PRECISION; GRAPHS; SYSTEM The results of a content based image retrieval system can be evaluated by several performance measures, each one employing different evaluation criteria. Many of the methods used in the field of information retrieval have been adopted for use in image retrieval systems. This paper reviews the most widely used performance measures for retrieval evaluation with particular emphasis on the assumptions made during their design. More specifically, it focuses on the design principles of the commonly used Mean Average Precision (MAP) and Average Normalized Modified Retrieval Rank (ANMRR), pinpointing their limitations. It also proposes a new performance measure for image retrieval systems, the Mean Normalized Retrieval Order (MNRO), whose effectiveness is demonstrated through a wide range of experiments. Initial experiments were conducted on artificially produced query trials and evaluations. Experiments on a large database demonstrate the ability of MNRO to take into account the generality of the queries during the retrieval procedure. Furthermore, the results of a case study show that the proposed performance measure is closer to human evaluations, in comparison to MAP and ANMRR. Lastly, in order to encourage researchers and practitioners to use the proposed performance measure, we present the experimental results produced by a large number of state of the art descriptors applied on three well-known benchmarking databases. [Chatzichristofis, Savvas A.; Iakovidou, Chryssanthi; Boutalis, Yiannis S.] Democritus Univ Thrace, Dept Elect & Comp Engn, GR-67100 Xanthi, Greece; [Boutalis, Yiannis S.] Univ Erlangen Nurnberg, Dept Elect Elect & Commun Engn, D-91058 Erlangen, Germany; [Angelopoulou, Elli] Univ Erlangen Nurnberg, Pattern Recognit Lab, Dept Comp Sci, D-91054 Erlangen, Germany Chatzichristofis, SA (reprint author), Democritus Univ Thrace, Dept Elect & Comp Engn, GR-67100 Xanthi, Greece. schatzic@ee.duth.gr; ciakovid@ee.duth.gr; ybout@ee.duth.gr; elli@immd5.informatik.uni-erlangen.de European Union (European Social Fund-ESF); Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)-Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund This research has been co-financed by the European Union (European Social Fund-ESF) and Greek national funds through the Operational Program "Education and Lifelong Learning" of the National Strategic Reference Framework (NSRF)-Research Funding Program: Heracleitus II. Investing in knowledge society through the European Social Fund. Arampatzis A, 2011, LECT NOTES COMPUT SC, V6611, P326, DOI 10.1007/978-3-642-20161-5_33; Arevalillo-Herraez M, 2008, SIGNAL PROCESS-IMAGE, V23, P490, DOI 10.1016/j.image.2008.04.016; Aslam J. A., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Borghesani D, 2009, LECT NOTES COMPUT SC, V5716, P902, DOI 10.1007/978-3-642-04146-4_96; BOSTEELS K, 2007, FUZZY AUDIO SIMILARI, P361; Chatzichristofis SA, 2009, SISAP 2009: 2009 SECOND INTERNATIONAL WORKSHOP ON SIMILARITY SEARCH AND APPLICATIONS, PROCEEDINGS, P151, DOI 10.1109/SISAP.2009.16; Chatzichristofis SA, 2010, MULTIMED TOOLS APPL, V46, P493, DOI 10.1007/s11042-009-0349-x; Chatzichristofis SA, 2010, INT J PATTERN RECOGN, V24, P207, DOI 10.1142/S0218001410007890; Chatzichristofis Savvas A, 2010, ICAART 2010. Proceedings of the 2nd International Conference on Agents and Artificial Intelligence. Artificial Intelligence; CHATZICHRISTOFI.SA, 2010, RADIOENGINEERING, V4, P725; CHATZICHRISTOFI.SA, 2008, ICVS, P312; CHATZICHRISTOFI.SA, 2010, 6 IASTED INT C ADV C, P27; Choi Y, 2003, J AM SOC INF SCI TEC, V54, P498, DOI 10.1002/asi.10237; Croft W.B., 2009, SEARCH ENGINES INFOR; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; Davidson R., 2000, ECONOMET REV, V19, P55, DOI DOI 10.1080/07474930008800459; Deselaers T, 2008, INFORM RETRIEVAL, V11, P77, DOI 10.1007/s10791-007-9039-3; d'Onofrio A, 2011, MATH BIOSCI, V230, P45, DOI [10.1016/j.mbs.2011.01.001, 10.1016/j.mbs.2010.01.001]; Eidenberger H, 2007, MULTIMED TOOLS APPL, V35, P241, DOI 10.1007/s1142-007-0106-y; Fawcett T, 2006, PATTERN RECOGN LETT, V27, P861, DOI 10.1016/j.patrec.2005.10.010; GOMPERTZ B., 1825, PHILOS T ROY SOC LON, V115, P513, DOI DOI 10.1098/RSTL.1825.0026; HUANG J, 2001, Patent No. 6246790; Huijsmans DP, 2001, PROC CVPR IEEE, P26; Huijsmans DP, 2005, IEEE T PATTERN ANAL, V27, P245, DOI 10.1109/TPAMI.2005.30; HUISKES MJ, 2010, MULTIMEDIA INFORM RE, P527, DOI DOI 10.1145/1743384.1743475; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; JOSE JM, 1998, SIGIR P 21 ANN INT C, P232; Kraaij W., 1996, SIGIR Forum; Li J, 2003, IEEE T PATTERN ANAL, V25, P1075; LUPU M, 2009, 18 TEXT RETRIEVAL C; MACDONALD C, 2009, 18 TEXT RETRIEVAL C; Magdy W, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P611; Manjunath BS, 2001, IEEE T CIRC SYST VID, V11, P703, DOI 10.1109/76.927424; Manjunath BS, 2002, INTRO MPEG 7 MULTIME; Martinet J, 2011, INFORM PROCESS MANAG, V47, P391, DOI 10.1016/j.ipm.2010.10.003; Martinet J, 2008, MULTIMED TOOLS APPL, V39, P263, DOI 10.1007/s11042-008-0200-9; McDonald S., 2001, SIGIR Forum; MENG X, 2006, ITNG, P578; Mokhtarian F., 1997, IMAGE DATABASES MULT, P51; Moore D. S., 2005, INTRO PRACTICE STAT; *MPEG, 2000, M6029 ISOWG11; Muller H, 2002, LECT NOTES COMPUT SC, V2383, P38; Muller H, 2001, PATTERN RECOGN LETT, V22, P593, DOI 10.1016/S0167-8655(00)00118-5; MULLER H, 2010, IMAGECLEF EXPT EVALU; MULLER H, 2005, ACM MULTIMEDIA, P1014; Nister D., 2006, CVPR, P2161; Ohm J.-R., 2001, Computer Analysis of Images and Patterns. 9th International Conference, CAIP 2001. Proceedings (Lecture Notes in Computer Science Vol.2124); POPESCU A, 2010, CLEF NOTEBOOK PAPERS; RAGHAVAN VV, 1989, ACM T INFORM SYST, V7, P205, DOI 10.1145/65943.65945; ROBERTSON S, 2008, SIGIR C, P689; Robertson SE, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P603; Sakai T, 2008, INFORM RETRIEVAL, V11, P447, DOI 10.1007/s10791-008-9059-7; Salton G., 1971, SMART RETRIEVAL SYST; Sanderson M, 2010, INFORM RETRIEVAL SER, V32, P81, DOI 10.1007/978-3-642-15181-1_5; Schaefer G, 2004, P SOC PHOTO-OPT INS, V5307, P472; Smeaton AF, 2010, COMPUT VIS IMAGE UND, V114, P411, DOI 10.1016/j.cviu.2009.03.011; Smith JR, 1998, IEEE WORKSHOP ON CONTENT-BASED ACCESS OF IMAGE AND VIDEO LIBRARIES - PROCEEDINGS, P112; TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999; TANEVA B, 2010, WSDM, P431; THOMEE B, 2010, ACM MULTIMEDIA, P1473; Wang JZ, 2001, IEEE T PATTERN ANAL, V23, P947, DOI 10.1109/34.955109; Wong KM, 2005, IEEE INT SYMP CIRC S, P1541; Wu Z, 2011, IEEE T PATTERN ANAL, V33, P1991, DOI 10.1109/TPAMI.2011.111; Yilmaz E, 2008, KNOWL INF SYST, V16, P173, DOI 10.1007/s10115-007-0101-7; YUE Y, 2007, SIGIR 07, P271; Zagoris K, 2009, SISAP 2009: 2009 SECOND INTERNATIONAL WORKSHOP ON SIMILARITY SEARCH AND APPLICATIONS, PROCEEDINGS, P154, DOI 10.1109/SISAP.2009.15 66 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUN 2014 70 3 1767 1798 10.1007/s11042-012-1192-z 32 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI8IP WOS:000337156500019 J Zhang, HB; Li, SA; Chen, SY; Su, SZ; Duh, DJ; Li, SZ Zhang, Hong-Bo; Li, Shang-An; Chen, Shu-Yuan; Su, Song Zhi; Duh, Der-Jyh; Li, Shao Zi Adaptive photograph retrieval method MULTIMEDIA TOOLS AND APPLICATIONS English Article Photograph retrieval; Graphics retrieval; Image retrieval; Histogram of oriented gradient; Pixel-based retrieval; Graphics/image classification IMAGE RETRIEVAL Access to electronic books, electronic journals, and web portals, which may contain graphics (drawings or diagrams) and images, is now ubiquitous. However, users may have photographs that contain graphics or images and want to access an electronic database to retrieve this information. Hence, an effective photograph retrieval method is needed. Although many content-based retrieval methods have been developed for images and graphics, few are designed to retrieve graphics and images simultaneously. Moreover, existing graphics retrieval methods use contour-based rather than pixel-based approaches. Contour-based methods, which are concerned with lines or curves, are inappropriate for images. To retrieve graphics and images simultaneously, this work applies an adaptive retrieval method. The proposed method uses histograms of oriented gradient (HOG) as pixel-based features. However, the characteristics of graphics and images differ, and this affects feature extraction and retrieval accuracy. Thus, an adaptive method is proposed that selects different HOG-based features for retrieving graphics and images. Experimental results demonstrate the proposed method has high retrieval accuracy even under noisy conditions. [Zhang, Hong-Bo; Su, Song Zhi; Li, Shao Zi] Xiamen Univ, Sch Informat Sci & Technol, Xiamen, Fujian, Peoples R China; [Zhang, Hong-Bo; Su, Song Zhi; Li, Shao Zi] Xiamen Univ, Fujian Key Lab Brain Like Intelligent Syst, Xiamen, Fujian, Peoples R China; [Li, Shang-An; Chen, Shu-Yuan] Yuan Ze Univ, Dept Comp Sci & Engn, Taoyuan, Taiwan; [Duh, Der-Jyh] Chien Hsin Univ Sci & Technol, Dept Comp Sci & Informat Engn, Taoyuan, Taiwan Chen, SY (reprint author), Yuan Ze Univ, Dept Comp Sci & Engn, Taoyuan, Taiwan. cschen@saturn.yzu.edu.tw National Science Council of Taiwan [NSC-100-2221-E-155-086]; National Nature Science Foundation of China [61202143] This work was partially supported by National Science Council of Taiwan, under Grant No. NSC-100-2221-E-155-086 and National Nature Science Foundation of China, under Grant No. 61202143. Abbasi S, 1999, MULTIMEDIA SYST, V7, P467, DOI 10.1007/s005300050147; Alajlan N, 2008, IEEE T PATTERN ANAL, V30, P1003, DOI 10.1109/TPAMI.2008.37; Belongie S, 2002, IEEE T PATTERN ANAL, V24, P705; CANNY J, 1986, IEEE T PATTERN ANAL, V8, P679; Chalechale A, 2005, IEEE T SYST MAN CY A, V35, P28, DOI 10.1109/TSMCA.2004.838464; Chi YL, 2007, PATTERN RECOGN, V40, P244, DOI 10.1016/j.patcog.2006.06.009; Chio J-W, 2012, J PATTERN RECOGN RES, V7, P56; Dalal N., 2005, P IEEE C COMP VIS PA, V1, P886, DOI DOI 10.1109/CVPR.2005.177; Datta R, 2008, ACM COMPUT SURV, V40, DOI 10.1145/1348246.1348248; Duda R.O., 2001, PATTERN CLASSIFICATI; Huang Y-W, 2007, ASIAN J HLTH INFORM, V2, P79; Kahn CE, 2012, J DIGIT IMAGING, V25, P37, DOI 10.1007/s10278-011-9399-5; Lew MS, 2006, ACM T MULTIM COMPUT, V2, P1, DOI 10.1145/1126004.1126005; Ling HB, 2007, IEEE T PATTERN ANAL, V29, P286, DOI 10.1109/TPAMI.2007.41; Liu RJ, 2010, PATTERN RECOGN, V43, P1907, DOI 10.1016/j.patcog.2009.11.022; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Muller H, OVERVIEW CLEF 2010 M; Qi H, 2010, PATTERN RECOGN, V43, P2017, DOI 10.1016/j.patcog.2010.01.007; Sidiropoulos P, 2011, PATTERN RECOGN, V44, P739, DOI 10.1016/j.patcog.2010.09.014; Su SZ, 2010, ELECTRON LETT, V46, P996, DOI 10.1049/el.2010.1104; Torralba A, 2008, IEEE T PATTERN ANAL, V30, P1958, DOI 10.1109/TPAMI.2008.128; Vrochidis Stefanos, 2010, World Patent Information, V32, DOI 10.1016/j.wpi.2009.05.010 22 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. JUN 2014 70 3 2189 2209 10.1007/s11042-012-1233-7 21 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI8IP WOS:000337156500036 J Oltra-Cucarella, J; Perez-Elvira, R; Duque, P Oltra-Cucarella, J.; Perez-Elvira, R.; Duque, P. Benefits of deep encoding in Alzheimer Disease. Analysis of performance on a memory task using the Item Specific Deficit approach NEUROLOGIA Spanish Article Memory; Alzheimer disease; Encoding; Consolidation; Retrieval; Processing SUBCORTICAL VASCULAR DEMENTIA; MILD COGNITIVE IMPAIRMENT; NEURONORMA PROJECT NORMS; PARKINSONS-DISEASE; CUED-RECALL; DIAGNOSIS; SENSITIVITY; PROFILES; CRITERIA Introduction: the aim of this study is to test the encoding deficit hypothesis in Alzheimer disease (AD) using a recent method for correcting memory tests. To this end, a Spanish-language adaptation of the Free and Cued Selective Reminding Test was interpreted using the Item Specific Deficit Approach (ISDA), which provides three indices: Encoding Deficit Index, Consolidation Deficit Index, and Retrieval Deficit Index. Methods: We compared the performances of 15 patients with AD and 20 healthy control subjects and analysed results using either the task instructions or the ISDA approach. Results: patients with AD displayed deficient encoding of more than half the information, but items that were encoded properly could be retrieved later with the help of the same semantic clues provided individually during encoding. Virtually all the information retained over the long-term was retrieved by using semantic clues. Encoding was shown to be the most impaired process, followed by retrieval and consolidation. Discriminant function analyses showed that ISDA indices are more sensitive and specific for detecting memory impairments in AD than are raw scores. Conclusions: These results indicate that patients with AD present impaired information encoding, but they benefit from semantic hints that help them recover previously learned information. This should be taken into account for intervention techniques focusing on memory impairments in AD. (C) 2013 Sociedad Espanola de Neurologia. Published by Elsevier Espana, S.L. All rights reserved. [Oltra-Cucarella, J.] Hosp Clin Univ, Serv Neurol, Unidad Neuropsicol, Valencia, Spain; [Perez-Elvira, R.] NEPSA Rehabil Neurol, Salamanca, Spain; [Perez-Elvira, R.] Hosp Ctr Cuidados Laguna, Madrid, Spain; [Duque, P.] Hosp Virgen Macarena, Programa Neuropsicol Clin, Seville, Spain; [Duque, P.] Fdn Inst Valenciano Neurorrehabil, Valencia, Spain Oltra-Cucarella, J (reprint author), Hosp Clin Univ, Serv Neurol, Unidad Neuropsicol, Valencia, Spain. javixent@gmail.com Adlam ALR, 2006, CORTEX, V42, P675, DOI 10.1016/S0010-9452(08)70404-0; deWinstanley PA, 1996, MEMORY, V4, P31, DOI 10.1080/741940667; Brown LB, 2000, ARCH CLIN NEUROPSYCH, V15, P529, DOI 10.1016/S0887-6177(99)00042-6; BUSCHKE H, 1984, J CLIN NEUROPSYCHOL, V6, P433, DOI 10.1080/01688638408401233; Buschke H, 1997, NEUROLOGY, V48, P989; Castellanos Pinedo F, 2007, PRIMERA PARTE DEMENC; Cattie JE, 2012, CLIN NEUROPSYCHOL, V26, P288, DOI 10.1080/13854046.2011.653404; COHEN J, 1992, PSYCHOL BULL, V112, P155, DOI 10.1037//0033-2909.112.1.155; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; Dubois B, 2007, LANCET NEUROL, V6, P734, DOI 10.1016/S1474-4422(07)70178-3; Duque P, 2012, REV NEUROLOGIA, V54, P263; Emre M, 2007, MOVEMENT DISORD, V22, P1689, DOI 10.1002/mds.21507; Evans J, 2004, CLIN NEUROPSYCHOLOGY, P143; FOLSTEIN MF, 1975, J PSYCHIAT RES, V12, P189, DOI 10.1016/0022-3956(75)90026-6; Graham NL, 2004, J NEUROL NEUROSUR PS, V75, P61; Herlitz A, 1992, MEMORY FUNCTIONING D; Lipinska B, 1997, BRAIN COGNITION, V34, P274, DOI 10.1006/brcg.1997.0916; Lonie JA, 2009, J NEUROPSYCHOL, V3, P79, DOI 10.1348/174866408X289935; MCKHANN G, 1984, NEUROLOGY, V34, P939; Oltra-Cucarella J., 2013, NEUROPSYCHOLOGY NEW, P79; Palomo R, 2013, NEUROLOGIA, V28, P226, DOI 10.1016/j.nrl.2012.03.008; Pena-Casanova J, 2009, ARCH CLIN NEUROPSYCH, V24, P371, DOI 10.1093/arclin/acp041; Pena-Casanova J, 2009, ARCH CLIN NEUROPSYCH, V24, P395, DOI 10.1093/arclin/acp042; Reed BR, 2007, BRAIN, V130, P731, DOI 10.1093/brain/awl385; Remy F, 2005, NEUROIMAGE, V25, P253, DOI 10.1016/j.neuroimage.2004.10.045; Ropper AH, 2005, ADAMS VICTORS PRINCI; Saka E, 2009, PARKINSONISM RELAT D, V15, P688, DOI 10.1016/j.parkreldis.2009.04.008; Traykov L, 2005, J NEUROL SCI, V229, P75, DOI 10.1016/j.jns.2004.11.006; Troster AI, 2008, NEUROPSYCHOL REV, V18, P103, DOI 10.1007/s11065-008-9055-0; Whatmough CE, 2010, HANDBOOK OF MEDICAL NEUROPSYCHOLOGY: APPLICATIONS OF COGNITIVE NEUROSCIENCE, P277, DOI 10.1007/978-1-4419-1364-7_15; Wright MJ, 2009, J CLIN EXP NEUROPSYC, V31, P790, DOI 10.1080/13803390802508918; Wright MJ, 2010, J CLIN EXP NEUROPSYC, V32, P728, DOI 10.1080/13803390903512652 32 0 0 ELSEVIER DOYMA SL BARCELONA TRAVESERA DE GARCIA, 17-21, BARCELONA, 08021, SPAIN 0213-4853 1578-1968 NEUROLOGIA Neurologia JUN 2014 29 5 286 293 10.1016/j.nrl.2013.06.006 8 Clinical Neurology Neurosciences & Neurology AI9QB WOS:000337266700006 J Flores, A; Cobos, PL; Lopez, FJ; Godoy, A Flores, Amanda; Cobos, Pedro L.; Lopez, Francisco J.; Godoy, Antonio Detecting Fast, Online Reasoning Processes in Clinical Decision Making PSYCHOLOGICAL ASSESSMENT English Article diagnostic criteria; clinical reasoning; inconsistency paradigm; causal reasoning GLOBAL COHERENCE; COMPREHENSION; DIAGNOSIS; CRITERIA; INFERENCES; DISORDERS; KNOWLEDGE; SCRIPTS; EVENTS; MEMORY In an experiment that used the inconsistency paradigm, experienced clinical psychologists and psychology students performed a reading task using clinical reports and a diagnostic judgment task. The clinical reports provided information about the symptoms of hypothetical clients who had been previously diagnosed with a specific mental disorder. Reading times of inconsistent target sentences were slower than those of control sentences, demonstrating an inconsistency effect. The results also showed that experienced clinicians gave different weights to different symptoms according to their relevance when fluently reading the clinical reports provided, despite the fact that all the symptoms were of equal diagnostic value according to the Diagnostic and Statistical Manual of Mental Disorders (4th ed., text rev.; American Psychiatric Association, 2000). The diagnostic judgment task yielded a similar pattern of results. In contrast to previous findings, the results of the reading task may be taken as direct evidence of the intervention of reasoning processes that occur very early, rapidly, and online. We suggest that these processes are based on the representation of mental disorders and that these representations are particularly suited to fast retrieval from memory and to making inferences. They may also be related to the clinicians' causal reasoning. The implications of these results for clinician training are also discussed. [Flores, Amanda; Cobos, Pedro L.; Lopez, Francisco J.] Univ Malaga, Dept Psicol Basica, Malaga 29072, Spain; [Flores, Amanda; Cobos, Pedro L.; Lopez, Francisco J.] Inst Invest Biomed Malaga IBIMA, Malaga, Spain; [Godoy, Antonio] Univ Malaga, Dept Personalidad Evaluac & Tratamiento Psicol, Malaga 29072, Spain Flores, A (reprint author), Univ Malaga, Dept Psicol Basica, Fac Psicol, Campus Teatinos S-N, Malaga 29072, Spain. amandafm@uma.es ALBRECHT JE, 1993, J EXP PSYCHOL LEARN, V19, P1061, DOI 10.1037/0278-7393.19.5.1061; American Psychiatric Association, 2000, DIAGN STAT MAN MENT; BLACK JB, 1980, POETICS, V9, P223, DOI 10.1016/0304-422X(80)90021-2; Charlin B, 2007, MED EDUC, V41, P1178, DOI 10.1111/j.1365-2923.2007.02924.x; Charlin BD, 2000, ACAD MED, V75, P182, DOI 10.1097/00001888-200002000-00020; DAVIS RT, 1993, J ABNORM PSYCHOL, V102, P319, DOI 10.1037//0021-843X.102.2.319; Garb HN, 1996, PROF PSYCHOL-RES PR, V27, P272; GRAESSER AC, 1994, PSYCHOL REV, V101, P371, DOI 10.1037//0033-295X.101.3.371; Kahneman D., 2011, THINKING FAST SLOW; KENDELL RE, 1973, BRIT J PSYCHIAT, V122, P437, DOI 10.1192/bjp.122.4.437; Kendeou P, 2013, J EXP PSYCHOL LEARN, V39, P854, DOI 10.1037/a0029468; Kim NS, 2002, J EXP PSYCHOL GEN, V131, P451, DOI 10.1037//0096-3445.131.4.451; Long DL, 2001, J EXP PSYCHOL LEARN, V27, P1424, DOI 10.1037/0278-7393.27.6.1424; Long DL, 1996, DISCOURSE PROCESS, V22, P145; Maj M, 2011, WORLD PSYCHIATRY, V10, P81; MCKOON G, 1992, PSYCHOL REV, V99, P440, DOI 10.1037/0033-295X.99.3.440; Peracchi KA, 2004, MEM COGNITION, V32, P1044, DOI 10.3758/BF03196880; Posner M. I., 1989, FDN COGNITIVE SCI, P501; RUBINSON E, 1988, J NERV MENT DIS, V176, P480, DOI 10.1097/00005053-198808000-00005; SANDIFER MG, 1970, AM J PSYCHIAT, V126, P968; Schank R., 1975, CONCEPTUAL INFORM PR; SCHMIDT HG, 1990, ACAD MED, V65, P611, DOI 10.1097/00001888-199010000-00001; TRABASSO T, 1985, J MEM LANG, V24, P595, DOI 10.1016/0749-596X(85)90048-8; TRABASSO T, 1985, J MEM LANG, V24, P612, DOI 10.1016/0749-596X(85)90049-X; van Dijk T. A., 1983, STRATEGIES DISCOURSE; Westen D, 2000, J PERS DISORD, V14, P109; Westen D, 2012, WORLD PSYCHIATRY, V11, P16; Zwaan RA, 1998, PSYCHOL BULL, V123, P162, DOI 10.1037/0033-2909.123.2.162 28 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 1040-3590 1939-134X PSYCHOL ASSESSMENT Psychol. Assess. JUN 2014 26 2 660 665 10.1037/a0035151 6 Psychology, Clinical Psychology AJ5EU WOS:000337706000027 J Zhang, Y; Tian, T; Tian, JW; Gong, JB; Ming, DL Zhang, Yun; Tian, Tian; Tian, Jinwen; Gong, Junbin; Ming, Delie A novel biologically inspired local feature descriptor BIOLOGICAL CYBERNETICS English Article Local descriptor; Biologically inspired model; Pooling operation; Image matching; Object recognition OBJECT CLASS RECOGNITION; RECEPTIVE-FIELDS; STRIATE CORTEX; PATTERN-RECOGNITION; IMAGE RETRIEVAL; VISUAL-CORTEX; INVARIANT; MECHANISMS; VISION; MODEL Local feature descriptor is a fundamental representation for image patch which has been extensively used in many computer vision applications. In this paper, different from state-of-the-art features, a novel biologically inspired local descriptor (BILD) is proposed based on the visual information processing mechanism of ventral pathway in human brain. The local features used for constructing BILD are extracted by a two-layer network, which corresponds to the simple-to-complex cell hierarchy in the primary visual cortex (V1). It works in a similar way as the simple cell and complex cell do to get responses by applying the lateral inhibition from different orientations and operating an improved cortical pooling. To enhance the distinctiveness of BILD, we combine the local features from different orientations. Extensive evaluations have been performed for image matching and object recognition. Experimental results reveal that our proposed BILD outperforms many widely used descriptors such as SIFT and SURF, which demonstrate its efficiency for representing local regions. [Zhang, Yun; Tian, Tian; Tian, Jinwen; Ming, Delie] Huazhong Univ Sci & Technol, Sch Automat, Natl Key Lab Sci & Technol Multispectral Informat, Wuhan 430074, Peoples R China; [Gong, Junbin] China Ship Design & Res Ctr, Wuhan 430064, Peoples R China Ming, DL (reprint author), Huazhong Univ Sci & Technol, Sch Automat, Natl Key Lab Sci & Technol Multispectral Informat, Wuhan 430074, Peoples R China. zhangyun.2010@foxmail.com; mingdelie@hust.edu.cn National Natural Science Foundation of China (NSFC) [61004111, 61273279, 61273241] This work was partially supported by the National Natural Science Foundation of China (NSFC) under the Grant No. 61004111, No. 61273279 and No. 61273241. ADELSON EH, 1985, J OPT SOC AM A, V2, P284, DOI 10.1364/JOSAA.2.000284; Agrawal M, 2008, LECT NOTES COMPUT SC, V5305, P102, DOI 10.1007/978-3-540-88693-8_8; Alahi A, 2012, PROC CVPR IEEE, P510, DOI 10.1109/CVPR.2012.6247715; Azzopardi G, 2012, BIOL CYBERN, V106, P177, DOI 10.1007/s00422-012-0486-6; Azzopardi G, 2013, IEEE T PATTERN ANAL, V35, P490, DOI 10.1109/TPAMI.2012.106; Bay H, 2006, LECT NOTES COMPUT SC, V3951, P404; Beghdadi A, 2013, SIGNAL PROCESS-IMAGE, V28, P811, DOI 10.1016/j.image.2013.06.003; Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558; Berkes P, 2005, J VISION, V5, P579, DOI 10.1167/5.6.9; Calonder M, 2010, LECT NOTES COMPUT SC, V6314, P778, DOI 10.1007/978-3-642-15561-1_56; CARANDINI M, 1994, SCIENCE, V264, P1333, DOI 10.1126/science.8191289; Carneiro G, 2003, PROC CVPR IEEE, P736; Cui CH, 2013, IEEE T IMAGE PROCESS, V22, P2876, DOI 10.1109/TIP.2013.2246521; Dalal N, 2005, PROC CVPR IEEE, P886; DAUGMAN JG, 1985, J OPT SOC AM A, V2, P1160, DOI 10.1364/JOSAA.2.001160; Dorko G., 2003, P INT C COMP VIS, V1, P634; Fergus R, 2003, PROC CVPR IEEE, P264; Ferrari V, 2004, LECT NOTES COMPUT SC, V3021, P40; FREEMAN WT, 1991, IEEE T PATTERN ANAL, V13, P891, DOI 10.1109/34.93808; Fukushima K, 2003, NEUROCOMPUTING, V51, P161, DOI 10.1016/S0925-2312(02)00614-8; FUKUSHIMA K, 1980, BIOL CYBERN, V36, P193, DOI 10.1007/BF00344251; Griffin G., 2007, 7694 CALTECH; Grossberg S, 2007, PROG BRAIN RES, V165, P79, DOI 10.1016/S0079-6123(06)65006-1; Hebb DO, 1949, ORG BEHAV; Huang KQ, 2011, IEEE T SYST MAN CY B, V41, P307, DOI 10.1109/TSMCB.2009.2037923; Huang YZ, 2011, IEEE T SYST MAN CY B, V41, P1668, DOI 10.1109/TSMCB.2011.2158418; HUBEL DH, 1962, J PHYSIOL-LONDON, V160, P106; Jiang AW, 2010, P IEEE INT C ICPR, P758; JONES JP, 1987, J NEUROPHYSIOL, V58, P1233; Ke Y, 2004, PROC CVPR IEEE, P506; KOBATAKE E, 1994, J NEUROPHYSIOL, V71, P856; Kouh M, 2008, NEURAL COMPUT, V20, P1427, DOI 10.1162/neco.2008.02-07-466; Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Leutenegger S, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P2548, DOI 10.1109/ICCV.2011.6126542; Li J, 2008, NEUROCOMPUTING, V71, P1771, DOI 10.1016/j.neucom.2007.11.032; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Ma BP, 2012, P BRIT MACH VIS C, V57, P1; Ma BP, 2013, NEUROCOMPUTING, V115, P1, DOI 10.1016/j.neucom.2012.11.005; Marino J, 2005, NAT NEUROSCI, V8, P194, DOI 10.1038/nn1391; Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188; Mikolajczyk K., 2001, P ICCV, V1, P525, DOI DOI 10.1109/ICCV.2001.937561; Mikolajczyk K, 2005, IEEE I CONF COMP VIS, P1792; Moreels P, 2005, IEEE I CONF COMP VIS, P800; Mutch J, 2008, INT J COMPUT VISION, V80, P45, DOI 10.1007/s11263-007-0118-0; PERRETT DI, 1993, IMAGE VISION COMPUT, V11, P317, DOI 10.1016/0262-8856(93)90011-5; PETKOV N, 1995, FUTURE GENER COMP SY, V11, P451, DOI 10.1016/0167-739X(95)00015-K; Riesenhuber M, 1999, NAT NEUROSCI, V2, P1019; Salgian AS, 2008, INT C PATT RECOG, P3217; SCHAFFALITZKY F, 2002, P 7 EUR C COMP VIS, V2350, P414; Schmid C, 1997, IEEE T PATTERN ANAL, V19, P530, DOI 10.1109/34.589215; SCLAR G, 1982, EXP BRAIN RES, V46, P457; Serre T, 2007, IEEE T PATTERN ANAL, V29, P411, DOI 10.1109/TPAMI.2007.56; Serre T, 2007, P NATL ACAD SCI USA, V104, P6424, DOI 10.1073/pnas.0700622104; SILLITO AM, 1975, J PHYSIOL-LONDON, V250, P305; Stringer SM, 2006, BIOL CYBERN, V94, P128, DOI 10.1007/s00422-005-0030-z; Tanaka K, 1996, ANNU REV NEUROSCI, V19, P109, DOI 10.1146/annurev.ne.19.030196.000545; Tao DC, 2007, IEEE T PATTERN ANAL, V29, P1700, DOI 10.1109/TPAMI.2007.1096; Tao DC, 2006, IEEE T PATTERN ANAL, V28, P1088; Terzic K, 2013, P ICVS LECT NOTES CO, V7963, P113; Trzcinski T., 2012, P 12 EUR C COMP VIS, P228; Tuytelaars T, 2004, INT J COMPUT VISION, V59, P61, DOI 10.1023/B:VISI.0000020671.28016.e8; Van Gool L, 1996, P ECCV LECT NOTES CO, V1064, P642; van der Zant T, 2008, IEEE T PATTERN ANAL, V30, P1945, DOI 10.1109/TPAMI.2008.144; Yang JC, 2009, PROC CVPR IEEE, P1794; Yang KF, 2013, PROC CVPR IEEE, P2810, DOI 10.1109/CVPR.2013.362; Zhang J, 2012, P 12 EUR C COMP VIS, V7576, P312; Zhang SP, 2013, NEUROCOMPUTING, V100, P31, DOI 10.1016/j.neucom.2011.11.031 67 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0340-1200 1432-0770 BIOL CYBERN Biol. Cybern. JUN 2014 108 3 275 290 10.1007/s00422-013-0583-1 16 Computer Science, Cybernetics; Neurosciences Computer Science; Neurosciences & Neurology AI3XP WOS:000336799300003 J Wei, CP; Zhao, N; Tang, XJ Wei, Cuiping; Zhao, Na; Tang, Xijin Operators and Comparisons of Hesitant Fuzzy Linguistic Term Sets IEEE TRANSACTIONS ON FUZZY SYSTEMS English Article Hesitant fuzzy linguistic term sets (HFLTSs); multicriteria decision making (MCDM); possibility degree formula GROUP-DECISION-MAKING; OWA OPERATORS; AGGREGATION OPERATORS; INFORMATION-RETRIEVAL; PREFERENCE RELATIONS; INTERVAL NUMBERS; MODEL; SIMILARITY; SELECTION The theory of hesitant fuzzy linguistic term sets (HFLTSs) is very useful in objectively dealing with situations in which people are hesitant in providing linguistic assessments. The purpose of this paper is to develop comparison methods and study the aggregation theory for HFLTSs. We first define operations on HFLTSs and give possibility degree formulas for comparing HFLTSs. We then define two aggregation operators for HFLTSs: a hesitant fuzzy LWA operator and a hesitant fuzzy LOWA operator. In actual application, we use these operators and the comparison methods to deal with multicriteria decision-making problems with different situations in which importance weights of criteria or experts are known or unknown. [Wei, Cuiping; Zhao, Na] Qufu Normal Univ, Coll Management, Rizhao 276826, Peoples R China; [Tang, Xijin] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China Wei, CP (reprint author), Qufu Normal Univ, Coll Management, Rizhao 276826, Peoples R China. happywcp@126.com; zhaonawfxy@163.com; xjtang@iss.ac.cn National Natural Science Foundation of China [71171187, 11071142]; National Basic Research Program of China [2010CB731405]; Ministry of Education Foundation of Humanities and Social Sciences [10YJC630269] This work was supported in part by the National Natural Science Foundation of China (71171187,11071142), in part by the National Basic Research Program of China (2010CB731405), and in part by the Ministry of Education Foundation of Humanities and Social Sciences (10YJC630269). Bonissone P. P., 1986, P UNC ART INT N HOLL, P217; BORDOGNA G, 1993, J AM SOC INFORM SCI, V44, P70, DOI 10.1002/(SICI)1097-4571(199303)44:2<70::AID-ASI2>3.0.CO;2-I; Bordogna G, 1997, IEEE T SYST MAN CY A, V27, P126, DOI 10.1109/3468.553232; Chang SL, 2007, EUR J OPER RES, V177, P1013, DOI 10.1016/j.ejor.2006.01.032; Chen CT, 2006, INT J PROD ECON, V102, P289, DOI 10.1016/j.ijpe.2005.03.009; Degani R., 1988, International Journal of Approximate Reasoning, V2, DOI 10.1016/0888-613X(88)90105-3; DELGADO M, 1993, INT J INTELL SYST, V8, P351, DOI 10.1002/int.4550080303; Fan ZP, 2010, IEEE T SYST MAN CY B, V40, P1413, DOI 10.1109/TSMCB.2009.2039477; Herrera F, 2005, EUR J OPER RES, V166, P115, DOI 10.1016/j.ejor.2003.11.031; Herrera F, 2009, FUZZY OPTIM DECIS MA, V8, P337, DOI 10.1007/s10700-009-9065-2; Herrera F, 1996, FUZZY SET SYST, V79, P175, DOI 10.1016/0165-0114(95)00162-X; Herrera F, 2001, IEEE T SYST MAN CY B, V31, P227, DOI 10.1109/3477.915345; Herrera F, 2000, IEEE T FUZZY SYST, V8, P746, DOI 10.1109/91.890332; Herrera F, 1997, IEEE T SYST MAN CY A, V27, P646, DOI 10.1109/3468.618263; HERRERA F, 1995, INFORM SCIENCES, V85, P223, DOI 10.1016/0020-0255(95)00025-K; Herrera-Viedma E, 2005, INT J INTELL SYST, V20, P921, DOI 10.1002/int.20099; Kacprzyk J., 2005, INF SCI, V173, P1; Martinez L, 2005, INT J INTELL SYST, V20, P1161, DOI 10.1002/int.20107; Petry FE, 2012, IEEE T FUZZY SYST, V20, P248, DOI 10.1109/TFUZZ.2011.2172795; Rodriguez RM, 2012, IEEE T FUZZY SYST, V20, P109, DOI 10.1109/TFUZZ.2011.2170076; Sengupta A, 2000, EUR J OPER RES, V127, P28, DOI 10.1016/S0377-2217(99)00319-7; Torra V, 2009, 2009 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS, VOLS 1-3, P1378, DOI 10.1109/FUZZY.2009.5276884; Torra V., 1997, INT J INTELL SYST, V12, P53; Torra V, 2010, INT J INTELL SYST, V25, P529, DOI 10.1002/int.20418; Wang YM, 2005, COMPUT OPER RES, V32, P2027, DOI 10.1016/j.cor.2004.01.005; Wei C. P., 2011, INT J KNOWL SYST SCI, V2, P43; Wei C. P., 2010, SYST ENG THEOR PRACT, V29, P104; Wei CP, 2011, INT J INF TECH DECIS, V10, P1111, DOI 10.1142/S0219622011004737; Wilbik A, 2013, IEEE T FUZZY SYST, V21, P183, DOI 10.1109/TFUZZ.2012.2214225; Xu Z. S., 2007, P FUZZ SETS EXT REPR, P163; Xu ZS, 2005, OMEGA-INT J MANAGE S, V33, P249, DOI 10.1016/j.omega.2004.04.008; Xu ZS, 2004, INFORM SCIENCES, V166, P19, DOI 10.1016/j.ins.2003.10.006; Xu ZS, 2005, INT J INTELL SYST, V20, P843, DOI 10.1002/int.20097; YAGER RR, 1995, INT J APPROX REASON, V12, P237, DOI 10.1016/0888-613X(94)00035-2; YAGER RR, 1988, IEEE T SYST MAN CYB, V18, P183, DOI 10.1109/21.87068; YAGER RR, 1993, FUZZY SET SYST, V59, P125, DOI 10.1016/0165-0114(93)90194-M; Yager RR, 1998, INT J APPROX REASON, V18, P35, DOI 10.1016/S0888-613X(97)10003-2; Yager RR, 1996, INT J INTELL SYST, V11, P49, DOI 10.1002/(SICI)1098-111X(199601)11:1<49::AID-INT3>3.3.CO;2-L; ZADEH LA, 1975, INFORM SCIENCES, V8, P199, DOI 10.1016/0020-0255(75)90036-5 39 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1063-6706 1941-0034 IEEE T FUZZY SYST IEEE Trans. Fuzzy Syst. JUN 2014 22 3 575 585 10.1109/TFUZZ.2013.2269144 11 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AI8BK WOS:000337128900009 J Zhang, HW; Xiao, X; Hasegawa, O Zhang, Hongwei; Xiao, Xiong; Hasegawa, Osamu A Load-Balancing Self-Organizing Incremental Neural Network IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS English Article Document clustering; incremental learning; load-balancing; self-organizing neural network CLASSIFICATION; PARAMETER Clustering is widely used in machine learning, feature extraction, pattern recognition, image analysis, information retrieval, and bioinformatics. Online unsupervised incremental learning is an important branch of data clustering. However, accurately separating high-density overlapped areas in a network has a direct impact on the performance of the clustering algorithm. In this paper, we propose a load-balancing self-organizing incremental neural network (LB-SOINN) to achieve good clustering results and demonstrate that it is more stable than an enhanced SOINN (E-SOINN). LB-SOINN has all the advantages of E-SOINN, such as robustness to noise and online unsupervised incremental learning. It overcomes the shortcomings of the topology structure generated by E-SOINN, such as dependence on the sequence of the input data, and avoids the turbulence that occurs when separating a composite class into subclasses. Furthermore, we also introduce a distance combination framework to obtain good performance for high-dimensional space-clustering tasks. Experiments involving both artificial and real world data sets indicate that LB-SOINN has superior performance in comparison with E-SOINN and other methods. [Zhang, Hongwei; Xiao, Xiong; Hasegawa, Osamu] Tokyo Inst Technol, Imaging Sci & Engn Lab, Yokohama, Kanagawa 2268503, Japan Zhang, HW (reprint author), Tokyo Inst Technol, Imaging Sci & Engn Lab, Yokohama, Kanagawa 2268503, Japan. zhang.h.ac@m.titech.ac.jp; xiaoxiongsysu@yahoo.co.jp; hasegawa.o.aa@m.titech.ac.jp Japan Science and Technology Agency's CREST project This work was supported by the Japan Science and Technology Agency's CREST project. Bergstra J, 2012, J MACH LEARN RES, V13, P281; Botev Z. I., 2007, KERNEL DENSITY ESTIM; Datar M., 2004, P 20 ANN S COMP GEOM, P253, DOI 10.1145/997817.997857; Francois D, 2007, IEEE T KNOWL DATA EN, V19, P873, DOI 10.1109/TKDE.2007.1037; FRITZKE B, 1994, NEURAL NETWORKS, V7, P1441, DOI 10.1016/0893-6080(94)90091-4; Gionis A., 1999, P INT C VER LARG DAT, V99, P518; Gu XG, 2013, SIGNAL PROCESS, V93, P2244, DOI 10.1016/j.sigpro.2012.07.014; Hammouda KM, 2004, IEEE T KNOWL DATA EN, V16, P1279, DOI 10.1109/TKDE.2004.58; Kawewong A, 2011, 2011 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN), P749, DOI 10.1109/IJCNN.2011.6033296; Likas A, 2003, PATTERN RECOGN, V36, P451; Pinheiro RHW, 2012, EXPERT SYST APPL, V39, P12851, DOI 10.1016/j.eswa.2012.05.008; Shen F., 2010, P IJCNN, P1; Shen F, 2007, NEURAL NETWORKS, V20, P893; Shen Furao, 2008, Neural Netw, V21, P1537, DOI 10.1016/j.neunet.2008.07.001; Shen FR, 2010, NEURAL NETWORKS, V23, P135, DOI 10.1016/j.neunet.2009.06.002; Shen FR, 2006, NEURAL NETWORKS, V19, P90, DOI 10.1016/j.neunet.2005.04.006; Wan CH, 2012, EXPERT SYST APPL, V39, P11880, DOI 10.1016/j.eswa.2012.02.068; Xiao X., 2013, P 20 INT C NEUR INF 18 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2162-237X 2162-2388 IEEE T NEUR NET LEAR IEEE Trans. Neural Netw. Learn. Syst. JUN 2014 25 6 1096 1105 10.1109/TNNLS.2013.2287884 10 Computer Science, Artificial Intelligence; Computer Science, Hardware & Architecture; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI5OA WOS:000336917000007 J Blanchette, I; Gavigan, S; Johnston, K Blanchette, Isabelle; Gavigan, Sarah; Johnston, Kathryn Does Emotion Help or Hinder Reasoning? The Moderating Role of Relevance JOURNAL OF EXPERIMENTAL PSYCHOLOGY-GENERAL English Article emotion; deduction; conditional reasoning; relevance; arousal SELECTION TASK; SEMANTIC MEMORY; WORKING-MEMORY; DEPRESSED MOOD; STROOP TASK; RETRIEVAL; PERFORMANCE; MANIPULATION; INFORMATION; STRATEGIES Some prior research has shown that emotion impairs logicality in deductive reasoning tasks, while other research suggests improved performance with emotional contents. We suggest that relevance, whether the affective state is associated with the semantic contents of the reasoning task, may be crucial in explaining these apparently inconsistent findings. This hypothesis is based on a framework distinguishing between integral emotions, where affective responses are evoked by the semantic contents of the target task, and incidental emotions, where affective responses are not related to the task. In 4 experiments we examined the effect of emotion on conditional reasoning when affective responses were relevant and irrelevant. We used images presented simultaneously with the reasoning stimuli (Experiments 1, 2, and 3) or videos presented prior to the reasoning stimuli (Experiment 4) that were either emotional or neutral and semantically related or not to the conditional statements. Results showed that emotion decreased the proportion of normatively correct responses only in the irrelevant condition. In the relevant condition, emotion did not produce reliable deleterious effects. We used reaction time and skin conductance measures to investigate the physiological and cognitive correlates of these effects. Results are discussed in terms of the distinction between incidental and integral emotions. [Blanchette, Isabelle] Univ Quebec Trois Rivieres, Trois Rivieres, PQ G9A 5H7, Canada; [Gavigan, Sarah; Johnston, Kathryn] Univ Manchester, Sch Psychol Sci, Manchester, Lancs, England Blanchette, I (reprint author), Univ Quebec Trois Rivieres, CP 500, Trois Rivieres, PQ G9A 5H7, Canada. isabelle.blanchette@uqtr.ca Algom D, 2004, J EXP PSYCHOL GEN, V133, P323, DOI 10.1037/0096-3445.133.3.323; Barrouillet P., 1999, THINK REASONING, V5, P289, DOI DOI 10.1080/135467899393940; Blanchette I, 2004, PSYCHOL SCI, V15, P745, DOI 10.1111/j.0956-7976.2004.00751.x; Blanchette I, 2013, THINK REASONING, V19, P399, DOI 10.1080/13546783.2013.791642; Blanchette I, 2011, EXP PSYCHOL, V58, P235, DOI 10.1027/1618-3169/a000090; Blanchette I., PSYCHOL REC IN PRESS; Blanchette I, 2012, J COGN PSYCHOL, V24, P157, DOI 10.1080/20445911.2011.603693; Blanchette I, 2006, MEM COGNITION, V34, P1112, DOI 10.3758/BF03193257; Blanchette I, 2010, COGNITION EMOTION, V24, P561, DOI 10.1080/02699930903132496; Blanchette I, 2007, J EXP PSYCHOL-APPL, V13, P47, DOI 10.1037/1076-898X.13.1.47; Buchanan TW, 2007, PSYCHOL BULL, V133, P761, DOI 10.1037/0033-2909.133.5.761; CHANNON S, 1994, PERS INDIV DIFFER, V17, P707, DOI 10.1016/0191-8869(94)90148-1; Cuthbert B. N., 2008, A8 U FLOR; Damasio A. R., 1998, MIND BRAIN ENV LINAC, P57; De Neys W, 2002, MEM COGNITION, V30, P908; Descartes R., 1946, DISCOURS METHODE; Eschman A., 2002, E PRIME USERS GUIDE; Evans J. S. B. T., 1995, PERSPECTIVES THINKIN, P147; Evans J. S. B. T., 1996, RATIONALITY REASONIN; Evans JSBT, 1996, BRIT J PSYCHOL, V87, P223; Evans JST, 2006, PSYCHON B REV, V13, P378, DOI 10.3758/BF03193858; Forgues HL, 2010, THINK REASONING, V16, P221, DOI 10.1080/13546783.2010.503606; Gangemi A., 2013, EMOTION REASONING, P44; GILHOOLY KJ, 1993, MEM COGNITION, V21, P115, DOI 10.3758/BF03211170; Girotto V, 2001, COGNITION, V81, pB69, DOI 10.1016/S0010-0277(01)00124-X; Hebb D.O., 1949, ORG BEHAV NEUROPSYCH; Hilton DJ, 2005, J EXP PSYCHOL GEN, V134, P388, DOI 10.1037/0096-3445.134.3.388; Isen A. M., 2008, HDB EMOTIONS, P548; Johnson-Laird P. N., 1991, DEDUCTION; Johnson-Laird P. N., 2006, WE REASON; Johnson-Laird PN, 2000, HDB EMOTIONS, P458; Johnson-Laird PN, 2006, PSYCHOL REV, V113, P822, DOI 10.1037/0033-295X.113.4.822; Lefford A, 1946, J GEN PSYCHOL, V34, P127; Lerner JS, 2003, PSYCHOL SCI, V14, P144, DOI 10.1111/1467-9280.01433; Markovits H, 1998, CHILD DEV, V69, P742, DOI 10.1111/j.1467-8624.1998.00742.x; Markovits H., 2010, COGNITION CONDITIONA, P177, DOI [10.1093/acprof:oso/9780199233298.003.0010, DOI 10.1093/ACPROF:OSO/9780199233298.003.0010]; Markovits H, 2002, MEM COGNITION, V30, P696, DOI 10.3758/BF03196426; MELTON RJ, 1995, PERS SOC PSYCHOL B, V21, P788, DOI 10.1177/0146167295218001; Mercier H, 2011, BEHAV BRAIN SCI, V34, P57, DOI 10.1017/S0140525X10000968; Oaksford M, 1996, J EXP PSYCHOL LEARN, V22, P476, DOI 10.1037/0278-7393.22.2.476; RADENHAUSEN RA, 1988, PERCEPT MOTOR SKILL, V66, P855; RICHARDS A, 1992, BRIT J PSYCHOL, V83, P479; Richards A, 2004, EMOTION, V4, P275, DOI 10.1037/1527-3542.4.3.275; Rubin DC, 2009, MEMORY, V17, P802, DOI 10.1080/09658210903130764; Schaeken W, 2007, Mental Models Theory of Reasoning: Refinements and Extensions, p129A; Skinner B.F., 1948, WALDEN 2; Sperber D., 1995, RELEVANCE COMMUNICAT; SPERBER D, 1995, COGNITION, V57, P31, DOI 10.1016/0010-0277(95)00666-M; Stanovich K. E., 2004, ROBOTS REBELLION FIN, DOI [10.7208/chicago/9780226771199.001.0001, DOI 10.7208/CHICAGO/9780226771199.001.0001]; TOMS M, 1993, Q J EXP PSYCHOL-A, V46, P679; Yiend J, 2010, COGNITION EMOTION, V24, P3, DOI 10.1080/02699930903205698 51 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0096-3445 1939-2222 J EXP PSYCHOL GEN J. Exp. Psychol.-Gen. JUN 2014 143 3 1049 1064 10.1037/a0034996 16 Psychology, Experimental Psychology AI3TK WOS:000336786300012 J Jayanthi, J; Rathi, S Jayanthi, J.; Rathi, S. A Personalized Search Framework for Industrial Safety and Health Information Retrieval JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH English Article User profiles; SHMS; Page Ranking; Jaccard Co-efficient; Hamming Distance The emergence of the WWW brought about new searching and querying difficulties. It is evident that the Internet and its most popular service WWW have changed our everyday lives. Normally the search engines use the keyword based querying methods to retrieve the web documents as a result. But the fact is most of the results retrieved are not relevant to the users, because of the contextual ambiguities. A programmer may search for the query "SAFETY", referring to the state of being safe in various context. While it is searched by a home maker, it refers to the home safety. If it is searched by an expert who is training the people in safety and health management, his need may be of different nature. In order to produce the result based on the context, Personalization Methods are introduced. In the proposed system, personalization is done in two phases, (i) Building User profiles (ii) Reranking the SERPs (Search engine Result Pages). The browsing behavior of the user is represented in the form of user profile which consists of static initial information and dynamic search history of the user. Ranking algorithm takes the both static and dynamic factors weight as input for personalized reranking operation. The degree of personalization is also measured using the Jaccard Co-efficient and Hamming Distance. A Safety and Health Management System (SHMS) is a systematic approach that manages safety and health activities. It is covering occupational safety and health programs, policies, and objectives into organizational policies and Procedures. The dataset from the SHMS domain is used for the experiment and the profiles of different types are created. It shows the percentage of improvement in the relevancy of search results under various conditions in terms of precision and recall. [Jayanthi, J.] Sona Coll Technol Salem, Dept Comp Sci & Engn, Salem 636005, Tamil Nadu, India; [Rathi, S.] Govt Coll Technol Coimbatore, Dept Comp Sci & Engn, Coimbatore 13, Tamil Nadu, India Jayanthi, J (reprint author), Sona Coll Technol Salem, Dept Comp Sci & Engn, Salem 636005, Tamil Nadu, India. jayamithu2002@gmail.com Dadiyala C, 2013, INT J ADV RES CO JUN, V6; Dongsoo H, 2008, FRAMEWORK PERSONALIZ, P90; Dou Zhicheng, 2007, P 16 INT C WORLD WID, P581, DOI DOI 10.1145/1242572.1242651; Fernandez-Luque L, 2011, J MED INT RES, V13; Goel S, 2012, 6 INT AAAI C WEBL SO; Hannak Aniko, 2013, P 22 INT WORLD WID W, P527; Ibrahim F, 2012, EGYPT INFORM J, V13, P191; Kappel G, 2006, UBIQUITOUS WEB APPL; Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28; Matthijs N., 2011, P 4 ACM INT C WEB SE, P25, DOI 10.1145/1935826.1935840; Radlinski F., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148320; Shen X., 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747; Sieg A, 2007, IEEE INTELLIGENT INF, V8, P7; Teevan J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148326; Zhou X, 2006, IEEE COMPUTER SOC, P558; Zhu Y, 2010, P 19 INT C WORLD WID; Zhu Yangbo, 2008, P 31 INT ACM SIGIR C, P715, DOI 10.1145/1390334.1390466 17 0 0 NATL INST SCIENCE COMMUNICATION-NISCAIR NEW DELHI DR K S KRISHNAN MARG, PUSA CAMPUS, NEW DELHI 110 012, INDIA 0022-4456 0975-1084 J SCI IND RES INDIA J. Sci. Ind. Res. JUN 2014 73 6 407 414 8 Engineering, Multidisciplinary Engineering AI7YT WOS:000337119600008 J Wang, CJ; Chen, HH Wang, Chieh-Jen; Chen, Hsin-Hsi Intent mining in search query logs for automatic search script generation KNOWLEDGE AND INFORMATION SYSTEMS English Article Intent mining; Query log analysis; Search script generation; Web search enhancement WEB; ENGINES Capturing users' information needs is essential in decreasing the barriers in information access. This paper mines sequences of actions called search scripts from search query logs which keep large-scale users' search experiences. Search scripts can be applied to guide users to satisfy their information needs, improve the search effectiveness of retrieval systems, recommend advertisements at suitable places, and so on. Information quality, query ambiguity, topic diversity, and document relevancy are four major challenging issues in search script mining. In this paper, we determine the relevance of URLs for a query, adopt the Open Directory Project (ODP) categories to disambiguate queries and URLs, explore various features and clustering algorithms for intent clustering, identify critical actions from each intent cluster to form a search script, generate a nature language description for each action, and summarize a topic for each search script. Experiments show that the complete link hierarchical clustering algorithm with the features of query terms, relevant URLs, and disambiguated ODP categories performs the best. Applying the intent clusters created by the best model to intent boundary identification achieves an F score of 0.6666. The intent clusters then are applied to generate search scripts. [Wang, Chieh-Jen; Chen, Hsin-Hsi] Natl Taiwan Univ, Dept Comp Sci & Informat Engn, Taipei 10617, Taiwan Chen, HH (reprint author), Natl Taiwan Univ, Dept Comp Sci & Informat Engn, 1,Sec 4,Roosevelt Rd, Taipei 10617, Taiwan. cjwang@nlg.csie.ntu.edu.tw; hhchen@ntu.edu.tw National Science Council [99-2221-E-002-167-MY3]; Microsoft Research Asia Research of this paper was partially supported by National Science Council, under the contract 99-2221-E-002-167-MY3. We are also grateful to Microsoft Research Asia for the support of MSN Search Query Log excerpt. Altman A, 2005, P 6 ACM C EL COMM, P1, DOI 10.1145/1064009.1064010; Ashkan A, 2012, KNOWL INF SYST, V34, P425; BaezaYates R, 2004, LECT NOTES COMPUT SC, V3268, P588; Beeferman D., 2000, Proceedings. KDD-2000. Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, DOI 10.1145/347090.347176; Beitzel SM, 2007, J AM SOC INF SCI TEC, V58, P166, DOI 10.1002/asi.20464; Bille P, 2005, THEOR COMPUT SCI, V337, P217, DOI 10.1016/j.tcs.2004.12.030; Broder A., 2002, SIGIR Forum, V36; Cao H., 2008, P 14 ACM SIGKDD INT, P875, DOI 10.1145/1401890.1401995; Chapelle O., 2009, P 18 ACM C INF KNOWL, P621, DOI 10.1145/1645953.1646033; CRASWELL N, 2008, P INT C WEB SEARCH W, P87, DOI 10.1145/1341531.1341545; Craswell N, 2009, P 2009 WORKSH WEB SE, P95; El-Arini K, 2011, P 17 ACM SIGKDD INT, P439; Gu S, 2011, P 2011 IEEE 11 INT C, P221; Guo F., 2009, P 18 INT C WORLD WID, P11, DOI 10.1145/1526709.1526712; Guo F, 2009, P 2 ACM INT C WEB SE, P124, DOI 10.1145/1498759.1498818; Jansen BJ, 2007, J AM SOC INF SCI TEC, V58, P862, DOI 10.1002/asi.20564; Joachims Thorsten, 2007, ACM Transactions on Information Systems, V25, DOI 10.1145/1229179.1229181; Landis RJ, 1977, BIOMETRICS, V33, P159, DOI DOI 10.2307/2529310; Li Xiao, 2008, P SIGIR SING JUL, P339; Manshadi M, 2009, P JOINT C 47 ANN M A, V2, P861, DOI 10.3115/1690219.1690267; Montgomery AL, 2001, COMPUTER, V34, P94, DOI 10.1109/2.933515; Muhlestein D, 2011, KNOWL INF SYST, V26, P31, DOI 10.1007/s10115-009-0265-4; Nguyen V, 2007, QUER LOG AN SOC TECH; Perugini S, 2008, INFORM PROCESS MANAG, V44, P910, DOI 10.1016/j.ipm.2007.06.005; Saleh B, 2011, KNOWL INF SYST, V28, P311, DOI 10.1007/s10115-010-0361-5; Senkul P, 2012, KNOWL INF SYST, V30, P527, DOI 10.1007/s10115-011-0386-4; Shen X, 2005, 14 INT C WORLD WID W, P1102; Shie BE, 2013, KNOWL INF SYST, V37, P363, DOI 10.1007/s10115-012-0483-z; Silverstein C, 1998, ANAL VERY LARGE ALTA; Spink A, 2002, COMPUTER, V35, P107, DOI 10.1109/2.989940; Wan M, 2012, KNOWL INF SYST, V33, P89, DOI 10.1007/s10115-011-0453-x; Wang C., 2011, P 17 ACM SIGKDD INT, P448, DOI DOI 10.1145/2020408.2020480; Wang CJ, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P749; WARD JH, 1963, J AM STAT ASSOC, V58, P236, DOI 10.2307/2282967; Wen Ji-Rong, 2001, P 10 INT C WORLD WID, P162, DOI 10.1145/371920.371974; Zhang W, 2007, QUER LOG AN SOC TECH; Zhang Z, 2006, P 15 INT C WORLD WID, P1039, DOI 10.1145/1135777.1136004 37 0 0 SPRINGER LONDON LTD LONDON 236 GRAYS INN RD, 6TH FLOOR, LONDON WC1X 8HL, ENGLAND 0219-1377 0219-3116 KNOWL INF SYST Knowl. Inf. Syst. JUN 2014 39 3 513 542 10.1007/s10115-013-0620-3 30 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AI7BF WOS:000337034300002 J Bhattacharya, P; Gavrilova, M Bhattacharya, Priyadarshi; Gavrilova, Marina L. Rotation invariance for dense features inside regions of interest VISUAL COMPUTER English Article Computer vision; Landmark recognition; Rotation invariance; Regions of interest; Dense features IMAGE SEARCH; SCALE; CONSISTENCY; RETRIEVAL Interest points have traditionally been favoured over dense features for image retrieval tasks, where the goal is to retrieve images similar to a query image from an image corpus. While interest points are invariant to scale and rotation, their coverage of the image is not adequate for sub-image retrieval problems, where the query image occupies a small part of the corpus image. On the other hand, dense features provide excellent coverage but lack invariance, as they are computed at a fixed scale and orientation. Recently, we proposed a novel technique of combining dense features with interest points (Bhattacharya and Gavrilova, Vis Comput 29(6-8):491-499, 2013) to leverage the benefits of both worlds. This allows dense features to be scale invariant but not rotation invariant. In this paper, we build on this framework by incorporating rotation invariance for dense features and introducing several improvements in the voting and match score computation stages. Our method can produce high-quality recognition results that outperform bag of words even with geometric verification and several state-of-art methods that have considered spatial information. We achieve significant improvements in terms of both search speed and accuracy over (Bhattacharya and Gavrilova, Vis Comput 29(6-8):491-499, 2013). Experiments on Oxford Buildings, Holidays and UKbench datasets reveal that our method is not only robust to viewpoint and scale changes that occur in real-world photographs but also to geometric transformations. [Bhattacharya, Priyadarshi; Gavrilova, Marina L.] Univ Calgary, Dept Comp Sci, Calgary, AB T2N 1N4, Canada Bhattacharya, P (reprint author), Univ Calgary, Dept Comp Sci, 2500 Univ Dr NW, Calgary, AB T2N 1N4, Canada. bhattacp@ucalgary.ca; mgavrilo@ucalgary.ca Arandjelovic R, 2012, PROC CVPR IEEE, P2911, DOI 10.1109/CVPR.2012.6248018; Bhattacharya P, 2013, VISUAL COMPUT, V29, P491, DOI 10.1007/s00371-013-0813-5; Bosch A., 2007, ACM INT C IM VID RET, P401; Cao Y, 2010, PROC CVPR IEEE, P3352, DOI 10.1109/CVPR.2010.5540021; Chatfield K., 2011, BMVC, V76, P1; Chum O, 2011, PROC CVPR IEEE, P889, DOI 10.1109/CVPR.2011.5995601; Jegou H, 2010, INT J COMPUT VISION, V87, P316, DOI 10.1007/s11263-009-0285-2; Jegou H, 2009, PROC CVPR IEEE, P1169; Jegou H, 2008, LECT NOTES COMPUT SC, V5302, P304, DOI 10.1007/978-3-540-88682-2_24; Lin Z, 2010, LECT NOTES COMPUT SC, V6316, P294; Lowe, 2004, INT J COMPUT VISION, V60, P91; Matas J., 2002, BIOMETHANIZATION ORG, P1; Mikolajczyk K, 2004, INT J COMPUT VISION, V60, P63, DOI 10.1023/B:VISI.0000027790.02288.f2; Mikulik A, 2013, INT J COMPUT VISION, V103, P163, DOI 10.1007/s11263-012-0600-1; Nister D., 2006, CVPR, V2, P2161, DOI DOI 10.1109/CVPR.2006.264; Philbin J., 2008, CVPR, P1; Philbin J., 2007, CVPR, P1; Vedaldi A., VLFEAT OPEN PORTABLE; Wang XY, 2011, 2011 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), P209; Wu Z, 2009, PROC CVPR IEEE, P25; Yuan J., 2007, CVPR, P1; Zhang YM, 2011, PROC CVPR IEEE, P809; Zhao W., 2010, LIP VIREO LOCAL INTE 23 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0178-2789 1432-2315 VISUAL COMPUT Visual Comput. JUN 2014 30 6-8 569 578 10.1007/s00371-014-0964-z 10 Computer Science, Software Engineering Computer Science AI7GX WOS:000337054700002 J Zhou, LY; Lu, ZW; Leung, H; Shang, LF Zhou, Liuyang; Lu, Zhiwu; Leung, Howard; Shang, Lifeng Spatial temporal pyramid matching using temporal sparse representation for human motion retrieval VISUAL COMPUTER English Article Motion retrieval; Temporal sparse representation; Spatial temporal pyramid matching; Sparse coding; Motion capture DICTIONARIES An efficient retrieval mechanism is essential to search for a particular motion from a large corpus. This has proven to be a challenging task as human motion is high dimensional in both spatial and temporal domains. Besides, semantically similar motions are not necessary numerically similar because of the speed variations. In this paper, we propose a temporal sparse representation (TSR) for human motion retrieval. Compared with existing methods that adopt sparse representation, our TSR encodes the temporal information within motions and thus generates a more compact and discriminative representation. In addition, we propose a spatial temporal pyramid matching kernel based on TSR, which can be used for logical comparison between motions. Moreover, it improves the effectiveness of motion retrieval in terms of accuracy and speed. Through our experimental evaluations, we demonstrate that the proposed human motion retrieval system has better performance and allows the user to retrieve desired motions from the motion capture database. [Zhou, Liuyang; Leung, Howard] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China; [Lu, Zhiwu] Renmin Univ China, Sch Informat, Key Lab Data Engn & Knowledge Engn MOE, Beijing 100872, Peoples R China; [Shang, Lifeng] Huawei, Noahs Ark Lab, Shatin, Hong Kong, Peoples R China Leung, H (reprint author), City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China. zhiwu.lu@gmail.com; howard@cityu.edu.hk; lifengshang@gmail.com City University of Hong Kong [7004045]; National Natural Science Foundation of China [61202231]; Beijing Natural Science Foundation of China [4132037]; Ph.D. Programs Foundation of Ministry of Education of China [20120001120130] The work described in this paper was supported by a grant from City University of Hong Kong (Project No. 7004045), National Natural Science Foundation of China under Grant 61202231, Beijing Natural Science Foundation of China under Grant 4132037, and Ph.D. Programs Foundation of Ministry of Education of China under Grant 20120001120130. Aharon M, 2006, IEEE T SIGNAL PROCES, V54, P4311, DOI 10.1109/TSP.2006.881199; Chen SSB, 1998, SIAM J SCI COMPUT, V20, P33, DOI 10.1137/S1064827596304010; Corporation M, 2013, KIN WIND SDK BET PRO; Davis G., 1997, J CONSTR APPROX, V13, P57; Deng Z-G, 2009, P 2009 S INT 3D GRAP, P191, DOI 10.1145/1507149.1507181; Elad M, 2006, IEEE T IMAGE PROCESS, V15, P3736, DOI 10.1109/TIP.2006.881969; HUANG T, 2012, P 14 ACM INT C MULT, P209; Jin Y., 2011, ACM T MULTIM COMPUT, V7; Kapadia M., 2013, P ACM SIGGRAPH S INT, P19; Komura T, 2005, COMPUT ANIMAT VIRT W, V16, P213, DOI 10.1002/cav.101; Kovar L, 2004, ACM T GRAPHIC, V23, P559, DOI 10.1145/1015706.1015760; Lai R.Y.Q, 2012, ABS12011409 CORR; Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Lu ZW, 2011, IEEE T SYST MAN CY B, V41, P976, DOI 10.1109/TSMCB.2010.2102749; MALLAT SG, 1993, IEEE T SIGNAL PROCES, V41, P3397, DOI 10.1109/78.258082; Mou L., 2013, ACM T MULTIM COMPUT, V10; Muller M., 2006, P 2006 ACM SIGGRAPH, P137; Muller M., 2007, CG20072 U BONN; Pati Y. C., 1993, P 27 AS C SIGN SYST, P40; Pradhan GN, 2009, IEEE T INF TECHNOL B, V13, P802, DOI 10.1109/TITB.2009.2021262; Qi T, 2013, COMPUT ANIMAT VIRT W, V24, P399, DOI 10.1002/cav.1505; Shum H., 2012, P ACM S VIRT REAL SO, P17; Sun C., 2011, COMPUT GRAPH FORUM, V30; Tang JKT, 2012, PATTERN RECOGN LETT, V33, P420, DOI 10.1016/j.patrec.2011.06.005; Ward R.K., 2012, IEEE T PATTERN ANAL, V34, P1576; Wright J, 2009, IEEE T PATTERN ANAL, V31, P210, DOI 10.1109/TPAMI.2008.79; Wu SY, 2009, VISUAL COMPUT, V25, P499, DOI 10.1007/s00371-009-0345-1; Yang J., 2009, IEEE C COMP VIS PATT; Zhu M-Y, 2012, P ACM SIGGRAPH EUR S, P183 29 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0178-2789 1432-2315 VISUAL COMPUT Visual Comput. JUN 2014 30 6-8 845 854 10.1007/s00371-014-0957-y 10 Computer Science, Software Engineering Computer Science AI7GX WOS:000337054700026 J Weigold, A; Russell, EJ; Natera, SN Weigold, Arne; Russell, Elizabeth J.; Natera, Sara N. Correction of False Memory for Associated Word Lists by Collaborating Groups AMERICAN JOURNAL OF PSYCHOLOGY English Article RECALL; OLDER; INFORMATION; INHIBITION; PRESSURE Collaborative inhibition is often observed for both correct and false memories. However, research examining the mechanisms by which collaborative inhibition occurs, such as retrieval disruption, reality monitoring, or group filtering, is lacking. In addition, the creation of the nominal groups (i.e., groups artificially developed by combining individuals' recall) necessary for examining collaborative inhibition do not use statistical best practices. Using the Deese-Roediger-McDermott paradigm, we examined percentages of correct and false memories in individuals, collaborative interactive groups, and correctly created nominal groups, as well as the processes that the collaborative interactive groups used to determine which memories to report. Results showed evidence of the collaborative inhibition effect. In addition, analyses of the collaborative interactive groups' discussions found that these groups wrote down almost all presented words but less than half of nonpresented critical words, after discussing them, with nonpresented critical words being stated to the group with lower confidence and rejected by other group members more often. Overall, our findings indicated support for the group filtering hypothesis. [Weigold, Arne] Notre Dame Coll, South Euclid, OH 44121 USA; [Russell, Elizabeth J.] Univ Akron, Akron, OH 44325 USA; [Natera, Sara N.] Cleveland State Univ, Cleveland, OH 44115 USA Weigold, A (reprint author), Notre Dame Coll, Dept Psychol, 4545 Coll Rd, South Euclid, OH 44121 USA. aweigold@ndc.edu Basden BH, 2002, AM J PSYCHOL, V115, P211, DOI 10.2307/1423436; Basden BH, 1997, J EXP PSYCHOL LEARN, V23, P1176, DOI 10.1037/0278-7393.23.5.1176; Blumen HM, 2011, MEM COGNITION, V39, P147, DOI 10.3758/s13421-010-0023-6; DEESE J, 1959, J EXP PSYCHOL, V58, P17, DOI 10.1037/h0046671; Ekeocha JO, 2008, MEMORY, V16, P245, DOI 10.1080/09658210701807480; Finlay F, 2000, J EXP PSYCHOL LEARN, V26, P1556, DOI 10.1037//0278-7393.26.6.1556; Harris CB, 2011, DISCOURSE PROCESS, V48, P267, DOI 10.1080/0163853X.2010.541854; Hirst W, 2012, ANNU REV PSYCHOL, V63, P55, DOI 10.1146/annurev-psych-120710-100340; JOHNSON MK, 1981, PSYCHOL REV, V88, P67, DOI 10.1037//0033-295X.88.1.67; Kelley MR, 2010, BEHAV RES METHODS, V42, P36, DOI 10.3758/BRM.42.1.36; Maki RH, 2008, MEM COGNITION, V36, P598, DOI 10.3738/MC.36.3.598; Meade ML, 2009, MEMORY, V17, P39, DOI 10.1080/09658210802524240; Meade ML, 2011, MEMORY, V19, P417, DOI 10.1080/09658211.2011.583928; Pritchard ME, 2002, APPL COGNITIVE PSYCH, V16, P589, DOI 10.1002/acp.816; Rajaram S, 2010, PERSPECT PSYCHOL SCI, V5, P649, DOI 10.1177/1745691610388763; Reysen MB, 2007, MEM COGNITION, V35, P59, DOI 10.3758/BF03195942; Reysen MB, 2011, BRIT J PSYCHOL, V102, P646, DOI 10.1111/j.2044-8295.2011.02035.x; Roediger HL, 2001, PSYCHON B REV, V8, P365, DOI 10.3758/BF03196174; ROEDIGER HL, 1995, J EXP PSYCHOL LEARN, V21, P803, DOI 10.1037/0278-7393.21.4.803; Ross M, 2004, APPL COGNITIVE PSYCH, V18, P683, DOI 10.1002/acp.1023; Ross M, 2008, PSYCHOL AGING, V23, P85, DOI 10.1037/0882-7974.23.1.85; Takahashi M, 2007, BRIT J PSYCHOL, V98, P1, DOI 10.1348/000712606X101628; Thorley C, 2007, EUR J COGN PSYCHOL, V19, P867, DOI 10.1080/09541440600872068; Weldon MS, 1997, J EXP PSYCHOL LEARN, V23, P1160, DOI 10.1037/0278-7393.23.5.1160; Wright DB, 2007, BEHAV RES METHODS, V39, P460, DOI 10.3758/BF03193015 25 0 0 UNIV ILLINOIS PRESS CHAMPAIGN 1325 S OAK ST, CHAMPAIGN, IL 61820-6903 USA 0002-9556 1939-8298 AM J PSYCHOL Am. J. Psychol. SUM 2014 127 2 183 190 8 Psychology, Multidisciplinary Psychology AH7XV WOS:000336349600004 J Arora, V; Behera, L Arora, Vipul; Behera, Laxmidhar Musical Source Clustering and Identification in Polyphonic Audio IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING English Article Acoustic scene analysis; music information retrieval; polyphonic instrument identification FUNDAMENTAL-FREQUENCY ESTIMATION; INSTRUMENT CLASSIFICATION; SPEAKER DIARIZATION; RECOGNITION; RECORDINGS; SEPARATION; SIGNALS For music transcription or musical source separation, apart from knowing the multi-F0 contours, it is also important to know which F0 has been played by which instrument. This paper focuses on this aspect, i.e. given the polyphonic audio along with its multiple F0 contours, the proposed system clusters them so as to decide 'which instrument played when.' For the task of identifying the instrument or singers in the polyphonic audio, there are many supervised methods available. But many times individual source audio is not available for training. To address this problem, this paper proposes novel schemes using semi-supervised as well as unsupervised approach to source clustering. The proposed theoretical framework is based on auditory perception theory and is implemented using various tools like probabilistic latent component analysis and graph clustering, while taking into account various perceptual cues for characterizing a source. Experiments have been carried out over a wide variety of datasets - ranging from vocal to instrumental as well as from synthetic to real world music. The proposed scheme significantly outperforms a state of the art unsupervised scheme, which does not make use of the given F0 contours. The proposed semi-supervised approach also performs better than another semi-supervised scheme, which makes use of the given F0 information, in terms of computations as well as accuracy. [Arora, Vipul; Behera, Laxmidhar] Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India Arora, V (reprint author), Indian Inst Technol, Dept Elect Engn, Kanpur 208016, Uttar Pradesh, India. vipular@iitk.ac.in; lbehera@iitk.ac.in Arora V, 2013, IEEE T AUDIO SPEECH, V21, P520, DOI 10.1109/TASL.2012.2227731; Arora V., 2013, P EUR SIGN PROC C EU; Arora V., 2013, P INT S MUS INF RETR; Barbedo JGA, 2011, IEEE T AUDIO SPEECH, V19, P111, DOI 10.1109/TASL.2010.2045186; Becker J. M., 2012, LECT NOTES COMPUTER, P99; Benetos E., 2006, P IEEE INT C AC SPEE, P221; Bishop C. M., 2006, PATTERN RECOGNITION; Bregman A. S., 1994, AUDITORY SCENE ANAL; de Cheveigne A, 2002, J ACOUST SOC AM, V111, P1917, DOI 10.1121/1.1458024; Deng JD, 2008, IEEE T SYST MAN CY B, V38, P429, DOI 10.1109/TSMCB.2007.913394; Duan ZY, 2010, IEEE T AUDIO SPEECH, V18, P2121, DOI 10.1109/TASL.2010.2042119; Durrieu JL, 2011, IEEE J-STSP, V5, P1180, DOI 10.1109/JSTSP.2011.2158801; Eronen A., 2001, THESIS TAMPERE U TEC; Grindlay G, 2011, IEEE J-STSP, V5, P1159, DOI 10.1109/JSTSP.2011.2162395; Heittola T., 2009, P INT S MUS INF RETR; Hennequin R, 2011, INT CONF ACOUST SPEE, P45; Hsu CL, 2010, IEEE T AUDIO SPEECH, V18, P310, DOI 10.1109/TASL.2009.2026503; Jaiswal R, 2011, INT CONF ACOUST SPEE, P245; Kinnunen T, 2010, SPEECH COMMUN, V52, P12, DOI 10.1016/j.specom.2009.08.009; Kitahara T., 2007, EURASIP J APPL SIG P, V2007, P155; Martins L. G., 2007, P INT S MUS INF RETR; Meila M., 2001, NEURAL INF PROCESS S; Quatieri TF, 2002, DISCRETE TIME SPEECH; Reynolds DA, 2002, INT CONF ACOUST SPEE, P4072; RIGAUD F, 2013, P IEEE INT C AC SPEE, P11; Robles-Kelly A, 2004, PATTERN RECOGN, V37, P1387, DOI 10.1016/j.patcog.2003.10.017; Smaragdis P., 2006, ADV MODELS ACOUST PR, V148; Spiertz M., 2009, P INT C DIG AUD EFF; Tranter SE, 2006, IEEE T AUDIO SPEECH, V14, P1557, DOI 10.1109/TASL.2006.878256; Tsai WH, 2006, IEEE T AUDIO SPEECH, V14, P330, DOI 10.1109/TSA.2005.854091; Vijayasenan D, 2009, IEEE T AUDIO SPEECH, V17, P1382, DOI 10.1109/TASL.2009.2015698; von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z; Weintraub M., 1985, THESIS STANFORD U ST; Wu J, 2011, IEEE J-STSP, V5, P1124, DOI 10.1109/JSTSP.2011.2158064; Yeh C, 2010, IEEE T AUDIO SPEECH, V18, P1116, DOI 10.1109/TASL.2009.2030006 35 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 2329-9290 IEEE-ACM T AUDIO SPE IEEE-ACM Trans. Audio Speech Lang. JUN 2014 22 6 1003 1012 10.1109/TASLP.2014.2313404 10 Acoustics; Engineering, Electrical & Electronic Acoustics; Engineering AI3XL WOS:000336798900001 J Ben Mimoun, MS; Garnier, M; Ladwein, R; Benavent, C Ben Mimoun, Mohamed Slim; Garnier, Marion; Ladwein, Richard; Benavent, Christophe Determinants of e-consumer productivity in product retrieval on a commercial website: An experimental approach INFORMATION & MANAGEMENT English Article E-consumer productivity; Product retrieval; Site design; Online experiment; Cognitive absorption; Site complexity COMPUTER-MEDIATED ENVIRONMENTS; TECHNOLOGY ACCEPTANCE MODEL; WEB SEARCH BEHAVIOR; WORLD-WIDE-WEB; INFORMATION-RETRIEVAL; RECOMMENDATION AGENTS; MARKETING EXPERIMENTS; COGNITIVE ABSORPTION; PURCHASE INTENTIONS; FLOW EXPERIENCES This article investigates what determines e-consumer productivity, in the specific case of product retrieval, on a commercial website. With a 2 x 2 x 2 factorial design on 292 participants, an online experiment reveals that productivity in product retrieval (measured in terms of effectiveness, efficiency, and time) relates to website design (e.g., abstraction level of labels, animation), user characteristics (e.g., Internet experience, product category familiarity, cognitive absorption), and situational characteristics (e.g., task nature). The results also confirm interactive effects among the type of strategy used, the nature of the task, and the website design. These findings have notable implications for both research and practice. (C) 2014 Elsevier B.V. All rights reserved. [Ben Mimoun, Mohamed Slim; Garnier, Marion] Univ Lille Nord France, SKEMA Business Sch, F-59777 Lille, France; [Ladwein, Richard] IAE Lille, CNRS, LEM, UMR 8179, F-59043 Lille, France; [Benavent, Christophe] Univ Paris Ouest, CEROS, F-92000 Nanterre, France Ben Mimoun, MS (reprint author), Univ Lille Nord France, SKEMA Business Sch, Ave Willy Brandt, F-59777 Lille, France. m.slim_benmimoun@skema.edu; marion.garnier@skema.edu; richard.ladwein@univ-lille1.fr; christophe.benavent@u-paris10.fr Agarwal R, 2000, MIS QUART, V24, P665, DOI 10.2307/3250951; Anitsal I., 2005, THESIS U TENNESSEE K; Anitsal I., 2007, J MARKETING THEORY P, V15, DOI 10.2753/MTP1069-6679150405; Barry J., 1994, J CONSUM RES, V20, P644; Bensadoun-Medioni S., 1999, 272 DAUPH U DMSP RES; Berlyne D. E., 1960, CONFLICT AROUSAL CUR; BRUCKS M, 1985, J CONSUM RES, V12, P1, DOI 10.1086/209031; Bruner GC, 2000, J ADVERTISING RES, V40, P35; Chang H. H., 2009, INT J HUM-COMPUT ST, V68, P69; Chang HH, 2008, COMPUT HUM BEHAV, V24, P2336, DOI 10.1016/j.chb.2008.01.001; Chang HH, 2008, TECHNOVATION, V28, P564, DOI 10.1016/j.technovation.2008.03.006; Chebat JC, 2005, J BUS RES, V58, P1590, DOI 10.1016/j.jbusres.2004.02.006; Chen CM, 1996, HUM-COMPUT INTERACT, V11, P125, DOI 10.1207/s15327051hci1102_2; Cho JS, 2004, INFORM MANAGE-AMSTER, V41, P827, DOI 10.1016/j.im.2003.08.013; Cox R, 1948, J MARKETING, V12, P433, DOI 10.2307/1246623; CSIKSZENTMIHALYI M., 1990, FLOW PSYCHOL OPTIMAL; Davis K., 2003, MULTIPLE ANAL UNPUB; Dehn DM, 2000, INT J HUM-COMPUT ST, V52, P1, DOI 10.1006/ijhc.1999.0325; Eroglu S. A., 2000, P RET 2000 LAUNCH NE; Eroglu SA, 2001, J BUS RES, V54, P177, DOI 10.1016/S0148-2963(99)00087-9; Eroglu SA, 2003, PSYCHOL MARKET, V20, P139, DOI 10.1002/mar.10064; Farris J. S., 2003, THESIS KANSAS STATE; Flach J. M., 1995, ERGONOMICS DESIGN, V3, P19; Garson D., 2012, MULTIVARIATE GLM MAN; Gehrt K., 2004, INT J RETAIL DISTRIB, V32, P5, DOI 10.1108/09590550410515515; Geissler G., 2001, J ASSOC INF SYST, V2, P1; Gronroos C, 2004, J BUS RES, V57, P414, DOI 10.1016/S0148-2963(02)00275-8; Gupta R, 2005, ADV CONSUM RES, V32, P42; Hair J. F., 1998, MULTIVARIATE DATA AN; Harker P. T., 2002, J SERV RES-US, V4, P253, DOI 10.1177/1094670502004004003; Hausman AV, 2009, J BUS RES, V62, P5, DOI 10.1016/j.jbusres.2008.01.018; Hoffman DL, 2009, J INTERACT MARK, V23, P23, DOI 10.1016/j.intmar.2008.10.003; Hoffman DL, 1996, J MARKETING, V60, P50, DOI 10.2307/1251841; Novak TP, 2003, J CONSUM PSYCHOL, V13, P3, DOI 10.1207/S15327663JCP13-1&2_01; HOLBROOK MB, 1982, J CONSUM RES, V9, P132, DOI 10.1086/208906; Holscher C, 2000, COMPUT NETW, V33, P337, DOI 10.1016/S1389-1286(00)00031-1; Hostler RE, 2005, DECIS SUPPORT SYST, V41, P313, DOI 10.1016/j.dss.2004.07.002; Hoyle RH, 1995, STRUCTURAL EQUATION, P1; HSIEHYEE I, 1993, J AM SOC INFORM SCI, V44, P161, DOI 10.1002/(SICI)1097-4571(199304)44:3<161::AID-ASI5>3.0.CO;2-8; Hsieh-Yee I, 2001, LIBR INFORM SCI RES, V23, P167, DOI 10.1016/S0740-8188(01)00069-X; INGENE CA, 1982, J MARKETING, V46, P75, DOI 10.2307/1251364; INGENE CA, 1984, J RETAILING, V60, P15; Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960; Johnston R., 2004, INT J PRODUCTIVITY P, V53, P201, DOI 10.1108/17410400410523756; Joreskog K. G., 1998, LISREL 8 STRUCTURAL; Kalczynsk P. J., 2006, INT J ELECTRON COMM, V10, P123; Kamis A, 2010, INFORM SYST FRONT, V12, P157, DOI 10.1007/s10796-008-9135-y; Kapyla J., 2009, P INT FOR KNOWL ASS; Khalifa M, 2007, EUR J INFORM SYST, V16, P780, DOI 10.1057/palgrave.ejis.3000711; Khan J, 2011, J BUS RES, V64, P687, DOI 10.1016/j.jbusres.2010.08.009; Khan K, 1998, J AM SOC INFORM SCI, V49, P1248, DOI 10.1002/(SICI)1097-4571(1998)49:14<1248::AID-ASI3>3.3.CO;2-7; Kim KS, 2002, J AM SOC INF SCI TEC, V53, P109, DOI 10.1002/asi.10014; Kleiser S. B., 1994, AMA SUMM ED P AM MAR, P20; Koufaris M, 2002, INFORM SYST RES, V13, P205, DOI 10.1287/isre.13.2.205.83; Kukar-Kinney M, 2010, J ACAD MARKET SCI, V38, P240, DOI 10.1007/s11747-009-0141-5; Kumar N., 2005, E SERVICE J, V3, P87; Lankton NK, 2012, DECIS SUPPORT SYST, V53, P55, DOI 10.1016/j.dss.2011.12.004; Laroche M, 2005, J RETAILING, V81, P251, DOI 10.1016/j.jretai.2004.11.002; Lee G, 2011, INFORM MANAGE-AMSTER, V48, P288, DOI 10.1016/j.im.2011.09.003; Limayem M, 2000, IEEE T SYST MAN CY A, V30, P421, DOI 10.1109/3468.852436; Lowe RK, 2003, LEARN INSTR, V13, P157, DOI 10.1016/S0959-4752(02)00018-X; Madeja N., 2003, P 36 HAW INT C SYST; MARCHIONINI G, 1989, J AM SOC INFORM SCI, V40, P54, DOI 10.1002/(SICI)1097-4571(198901)40:1<54::AID-ASI6>3.0.CO;2-R; Martin CR, 2001, INT J SERV IND MANAG, V12, P137; Mazursky D, 2005, J BUS RES, V58, P1299, DOI 10.1016/j.jbusres.2005.01.003; Moles A., 1977, THEORIE ACTES; Moon JW, 2001, INFORM MANAGE, V38, P217, DOI 10.1016/S0378-7206(00)00061-6; Morgan G. A., 2005, SPSS INTERMEDIATE ST; Nachmias R., 2001, WORKING PAPER; Nadkarni S, 2007, MIS QUART, V31, P501; Ng CF, 2003, J ENVIRON PSYCHOL, V23, P439, DOI 10.1016/S0272-4944(02)00102-0; Nielsen J., 1997, SEARCH YOU MAY FIND; Nielsen J., 1993, USABILITY ENG; Nysveen H., 2005, International Journal of Internet Marketing and Advertising, V2, DOI 10.1504/IJIMA.2005.008103; O'Cass A., 2003, J RETAILING CONSUMER, V10, P81, DOI DOI 10.1016/S0969-6989(02)00004-8; Ojasalo K., 1999, THESIS SWEDISH SCH E; Ondrusek AL, 2004, LIBR INFORM SCI RES, V26, P221, DOI 10.1016/j.lisr.2004.01.002; Pace S, 2004, INT J HUM-COMPUT ST, V60, P327, DOI [10.1016/j.ijhcs.2003.08.005, 10.1016/j.ijhcs.2003.08.003]; Palmer JW, 2002, INFORM SYST RES, V13, P151, DOI 10.1287/isre.13.2.151.88; Pan Y., 2004, WEB SYSTEM DESIGN ON; Parasuraman A, 2002, MANAG SERV QUAL, V12, P6, DOI 10.1108/096045202104; PERDUE BC, 1986, J MARKETING RES, V23, P317, DOI 10.2307/3151807; Punj G, 2009, J BUS RES, V62, P644, DOI 10.1016/j.jbusres.2007.04.013; Rads S., 2007, EABR BUS ETLC TEACH; Ranganathan C., 2005, WEB SYSTEM DESIGN ON; Richard MO, 2005, J BUS RES, V58, P1632, DOI 10.1016/j.jbusres.2004.07.009; Rigou M., 2004, WEB SYSTEM DESIGN ON; Robbins SS, 2003, INFORM MANAGE-AMSTER, V40, P205, DOI 10.1016/S0378-7206(02)00002-2; Rosenfeld L., 1998, INFORM ARCHITECTURE; Russel J. A., 1974, APPROACH ENV PSYCHOL; Saade R, 2005, INFORM MANAGE-AMSTER, V42, P317, DOI 10.1016/j.im.2003.12.013; SAWYER AG, 1995, J CONSUM RES, V21, P581, DOI 10.1086/209420; Schaik P., 2006, CHB, V22, P870; Schlosser AE, 2003, J CONSUM RES, V30, P184, DOI 10.1086/376807; Shang RA, 2005, INFORM MANAGE-AMSTER, V42, P401, DOI 10.1016/j.im.2004.01.009; Sheth JN, 2002, J BUS RES, V55, P349, DOI 10.1016/S0148-2963(00)00164-8; Shneiderman B, 1997, INT J HUM-COMPUT ST, V47, P5, DOI 10.1006/ijhc.1997.0127; Sicilia M, 2010, ELECTRON COMMER R A, V9, P183, DOI 10.1016/j.elerap.2009.03.004; Siekpe J. S., 2005, J ELECTRON COMMER RE, V6, P31; Sink D. S., 1985, PRODUCTIVITY MANAGEM; Smith C. D., 1992, J MARKETING RES, V19, P296; Speier C, 2003, MIS QUART, V27, P397; Stahl DO, 1996, INT J IND ORGAN, V14, P243, DOI 10.1016/0167-7187(94)00474-9; Swaminathan V, 2003, J CONSUM PSYCHOL, V13, P93, DOI 10.1207/S15327663JCP13-1&2_08; Tabatabai D, 2005, LIBR INFORM SCI RES, V27, P222, DOI 10.1016/j.lisr.2005.01.005; Tan GW, 2006, ELECTRON COMMER R A, V5, P261, DOI 10.1016/j.elerap.2006.04.007; Tangen S., 2005, INT J PRODUCTIVITY P, V54, P34, DOI DOI 10.1108/17410400510571437; Timm NH, 2002, APPL MULTIVARIATE AN; Topi H, 2005, INT J HUM-COMPUT ST, V62, P349, DOI 10.1016/j.ijhcs.2004.10.003; Tractinsky N., 2007, ACAD MARKETING SCI R, V11, P1; Tung L., 2003, ELECTRON COMMER R A, V2, P61, DOI 10.1016/S1567-4223(03)00006-1; Turetken O., 2001, 22 INT C INF SYST; Verner R. W., 2001, THESIS BENEDICTINE U; Vessey I., 1994, Information and Management, V27, DOI 10.1016/0378-7206(94)90010-8; VESSEY I, 1991, DECISION SCI, V22, P219, DOI 10.1111/j.1540-5915.1991.tb00344.x; Villey-Migraine M., 2004, REV FRANCOPHONE MANA, V10, P3; Vredenburg K, 2002, USER CTR DESIGN INTE; Vuorinen I, 1998, INT J SERV IND MANAG, V9, P377, DOI 10.1108/09564239810228876; WOOD RE, 1986, ORGAN BEHAV HUM DEC, V37, P60, DOI 10.1016/0749-5978(86)90044-0; Xia L, 2002, J CONSUM PSYCHOL, V12, P265, DOI 10.1207/S15327663JCP1203_08; Xiao B, 2007, MIS QUART, V31, P137; Xue M, 2007, M&SOM-MANUF SERV OP, V9, P535, DOI 10.1287/msom.1060.0135; Zhang P., 1999, DOING BUSINESS INTER, P35; Zhang X., 2005, WEB SYSTEM DESIGN ON 124 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0378-7206 1872-7530 INFORM MANAGE-AMSTER Inf. Manage. JUN 2014 51 4 375 390 10.1016/j.im.2014.02.003 16 Computer Science, Information Systems; Information Science & Library Science; Management Computer Science; Information Science & Library Science; Business & Economics AI2QQ WOS:000336703500001 J Bashir, S; Khattak, AS Bashir, Shariq; Khattak, Akmal Saeed Producing efficient retrievability ranks of documents using normalized retrievability scoring function JOURNAL OF INTELLIGENT INFORMATION SYSTEMS English Article Information systems evaluation; Documents accessibility; Documents findability; Known-items search; Patent retrieval; Recall-oriented retrieval SEARCH; BIAS; WEB In this paper, we perform a number of experiments with large scale queries to analyze the retrieval bias of standard retrieval models. These experiments analyze how far different retrieval models differ in terms of retrieval bias that they imposed on the collection. Along with the retrieval bias analysis, we also exploit a limitation of standard retrievability scoring function and propose a normalized retrievability scoring function. Results of retrieval bias experiments show us that when a collection contains highly skewed distribution, then the standard retrievability calculation function does not take into account the differences in vocabulary richness across documents of collection. In such case, documents having large vocabulary produce many more queries and such documents thus have theoretically large probability of retrievability via a much large number of queries. We thus propose a normalized retrievability scoring function that tries to mitigate this effect by normalizing the retrievability scores of documents relative to their total number of queries. This provides an unbiased representation of the retrieval bias that could occurred due to vocabulary differences between the documents of collection without automatically inflicting a penalty on the retrieval models that favor or disfavor long documents. Finally, in order to examine, which retrievability scoring function has better effectiveness than other for correctly producing the retrievability ranks of documents, we perform a comparison between the both functions on the basis of known-items search method. Experiments on known-items search show that normalized retrievability scoring function has better effectiveness than the standard retrievability scoring function. [Bashir, Shariq] New York Univ Abu Dhabi, Ctr Sci & Engn, Abu Dhabi, U Arab Emirates; [Khattak, Akmal Saeed] Univ Leipzig, Dept Comp Sci, Nat Language Proc Res Grp, D-04109 Leipzig, Germany Bashir, S (reprint author), New York Univ Abu Dhabi, Ctr Sci & Engn, Abu Dhabi, U Arab Emirates. shariq.bashir@nyu.edu; akhattak@informatik.uni-leipzig.de Arampatzis A., 2007, P 16 TEXT RETR C TRE; Azzopardi L., 2007, SIGIR 07, P455; Azzopardi L, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P889; Azzopardi L., 2008, CIKM 08, P561; Bache R., 2010, T LARGE SCALE DATA 2, P103; Bashir S, 2009, LECT NOTES COMPUT SC, V5690, P753; Bashir S., 2010, T LARGE SCALE DATA 2, V2, P122; Bashir S, 2010, LECT NOTES COMPUT SC, V5993, P457, DOI 10.1007/978-3-642-12275-0_40; Bashir S., 2009, P CIKM 2009 HONG KON, P1863, DOI 10.1145/1645953.1646250; Callan J, 2001, ACM T INFORM SYST, V19, P97, DOI 10.1145/382979.383040; Chowdhury G.G., 2004, INTRO MODERN INFORM; GASTWIRT.JL, 1972, REV ECON STAT, V54, P306, DOI 10.2307/1937992; Harter P.S., 1997, ANNU REV INFORM SCI, V32, P3; Lauw W.H., 2006, P 12 ACM SIGKDD INT, P625; Lawrence S, 1999, NATURE, V400, P107, DOI 10.1038/21987; Lupu Mihai, 2009, SIGIR Forum, V43; Magdy W, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P611; Manning D., 2008, INTRO INFORM RETRIEV; Mowshowitz A, 2002, COMMUN ACM, V45, P56; Ounis I., 2006, P TEXT RETR C TREC 0; Owens C., 2009, THESIS U GLASGOW; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Robertson S. E., 1994, SIGIR '94. Proceedings of the Seventeenth Annual International ACM-SIGIR Conference on Research and Development in Information Retrieval; Sanderson M., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Singhal A., 2001, IEEE DATA ENG B, V24, P34; Singhal A., 1997, 6 TEXT RETR C TREC6, P227; Vaughan L, 2004, INFORM PROCESS MANAG, V40, P693, DOI 10.1016/S0306-4573(03)00063-3; Voorhees M.E., 2001, P TEXT RETR C TREC 0, P42; Voorhees M.E., 2005, TREC EXPT EVALUATION; Voorhees M.E., 2002, CLEF 01, P355; Zhai C., 2002, THESIS CARNEGIE MELL 31 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0925-9902 1573-7675 J INTELL INF SYST J. Intell. Inf. Syst. JUN 2014 42 3 457 484 10.1007/s10844-013-0274-3 28 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AH6YF WOS:000336277700006 J Shen, HY; Li, Z; Li, T Shen, Haiying; Li, Ze; Li, Ting Token list based information search in a multi-dimensional massive database JOURNAL OF INTELLIGENT INFORMATION SYSTEMS English Article Similarity data search; Proximity search; Locality sensitive hash; Database HIGH-DIMENSIONAL SPACES; SIMILARITY SEARCH; RETRIEVAL; TREE Finding proximity information is crucial for massive database search. Locality Sensitive Hashing (LSH) is a method for finding nearest neighbors of a query point in a high-dimensional space. It classifies high-dimensional data according to data similarity. However, the "curse of dimensionality" makes LSH insufficiently effective in finding similar data and insufficiently efficient in terms of memory resources and search delays. The contribution of this work is threefold. First, we study a Token List based information Search scheme (TLS) as an alternative to LSH. TLS builds a token list table containing all the unique tokens from the database, and clusters data records having the same token together in one group. Querying is conducted in a small number of groups of relevant data records instead of searching the entire database. Second, in order to decrease the searching time of the token list, we further propose the Optimized Token list based Search schemes (OTS) based on index-tree and hash table structures. An index-tree structure orders the tokens in the token list and constructs an index table based on the tokens. Searching the token list starts from the entry of the token list supplied by the index table. A hash table structure assigns a hash ID to each token. A query token can be directly located in the token list according to its hash ID. Third, since a single-token based method leads to high overhead in the results refinement given a required similarity, we further investigate how a Multi-Token List Search scheme (MTLS) improves the performance of database proximity search. We conducted experiments on the LSH-based searching scheme, TLS, OTS, and MTLS using a massive customer data integration database. The comparison experimental results show that TLS is more efficient than an LSH-based searching scheme, and OTS improves the search efficiency of TLS. Further, MTLS per forms better than TLS when the number of tokens is appropriately chosen, and a two-token adjacent token list achieves the shortest query delay in our testing dataset. [Shen, Haiying] Clemson Univ, Dept Elect & Comp Engn, Clemson, SC 29634 USA; [Li, Ze] MicroStrategy, Tysons Corner, Fairfax, VA 22182 USA; [Li, Ting] Wal Mart Stores Inc, Bentonville, AR 72716 USA Shen, HY (reprint author), Clemson Univ, Dept Elect & Comp Engn, Clemson, SC 29634 USA. shenh@clemson.edu; zel@clemson.edu; dragonflyting@hotmail.com U.S. NSF [IIS-1354123, CNS-1254006, CNS-1249603, OCI-1064230, CNS-1049947, CNS-0917056, CNS-1025652]; Microsoft [8300751]; United States Department of Defense [238866] This research was supported in part by U.S. NSF grants IIS-1354123, CNS-1254006, CNS-1249603, OCI-1064230, CNS-1049947, CNS-0917056 and, CNS-1025652, Microsoft Research Faculty Fellowship 8300751, Microsoft Research Faculty Fellowship 8300751, and the United States Department of Defense 238866. Early versions of this work were presented in the Proceedings of DMIN'08 (Li et al. 2008) and ICCIT'08 (Shen et al. 2008). We would like to thank Mr. Yuhua Lin for his valuable comments in addressing the review feedback. Aberer K., 2003, P 12 INT WORLD WID W; Alimohammadi D, 2003, ONLINE INFORM REV, V27, P238, DOI 10.1108/14684520310489023; Andoni A., 2005, E21SH 0 1 USER MANUA; Andoni A., 2005, LSH ALGORITHM IMPLEM; Arya S., 1994, P 5 ACM SIAM S DISCR; Bayer R., 1970, P 1970 ACM SIGFIDENT, P107, DOI 10.1145/1734663.1734671; BECKMANN N, 1990, SIGMOD REC, V19, P322, DOI 10.1145/93597.98741; Bennett K.P., 1999, P KDD; Bentley J. L., 1977, ACM T MATH SOFTWARE, V3, P209; Berchtold S, 1996, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES, P28; Berrani S.A., 2003, P CIKM; Berry MW, 1999, SIAM REV, V41, P335, DOI 10.1137/S0036144598347035; Blachman N., 2007, GOOGLE GUIDE MAKING; Bohm C, 2001, ACM COMPUT SURV, V33, P322, DOI 10.1145/502807.502809; Brin S., 1995, P 21 INT C VLDB; Chaudhuri S., 2007, P SIGIR; Chen H., 2008, P WWW, P989, DOI 10.1145/1367497.1367631; Chen HH, 2010, IEEE T COMPUT, V59, P969, DOI 10.1109/TC.2010.81; COMER D, 1979, COMPUT SURV, V11, P121; COVER TM, 1967, IEEE T INFORM THEORY, V13, P21, DOI 10.1109/TIT.1967.1053964; Datar M., 2003, P DIMACS WORKSH STRE; Datar M., 2004, P 20 ANN S COMP GEOM; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; Fagin R., 1998, P ACM S PRINC DAT SY; Filho R.F.S., 2001, P ICDE; Fu AW, 2000, VLDB J, V9, P154, DOI 10.1007/PL00010672; Gionis A., 1999, P INT C VER LARG DAT, V99, P518; Grossman D.A., 2004, IFLOW INFORM RETRIEV; Guttman A., 1984, P ACM SIGMOD INT C M, V14, P47, DOI 10.1145/971697.602266; Halevy A.Y., 2003, P 12 INT WORLD WID W; Hu J.J., 2005, 6 INT C WAIM; Indyk P., 1998, P 30 ANN ACM S THEOR; Kleinberg J.M., 1997, P ACM S THEOR COMP S; Kruskal JB, 1978, MULTIDIMENSIONAL SCA; Kulkarni S, 2006, LECT NOTES COMPUT SC, V4080, P738; Lam HT, 2009, LECT NOTES COMPUT SC, V5802, P511; Li C, 2002, IEEE T KNOWL DATA EN, V14, P792; Li T., 2008, P 4 INT C DAT MIN DM; Loccoz N.M., 2005, TR20050505 U GENEVE; Long X., 2005, P 14 INT C WORLD WID, P257, DOI 10.1145/1060745.1060785; Luu T., 2008, P VLDB END, V1, P1424; Nejdl W., 2003, P IPTPS; Nejdl W., 2003, P 12 INT WORLD WID W; Niblack C.W., 1993, P SPIE STOR RETR IM; Panigrahy R., 2006, D91 SECURESCM; Qi XG, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1459352.1459357; Salton G., 1983, INTRO MODERN INFORM; Sellis T., 1997, P 23 INT C VER LARG; Shen H., 2008, P INT C CONV HYBR IN; Skobeltsyn G, 2009, FUTURE GENER COMP SY, V25, P89, DOI 10.1016/j.future.2008.03.006; Weth C., 2012, IEEE INTERNET COMPUT, V16, P34; White D.A., 1996, VCL96101 U CAL; Yianlios P.N., 1993, P 4 ANN ACM SIAM S D; Zolotarev VM, 1986, ONE DIMENSIONAL STAB 54 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0925-9902 1573-7675 J INTELL INF SYST J. Intell. Inf. Syst. JUN 2014 42 3 567 594 10.1007/s10844-013-0289-9 28 Computer Science, Artificial Intelligence; Computer Science, Information Systems Computer Science AH6YF WOS:000336277700010 J Adamou, C; Avraamides, MN; Kelly, JW Adamou, Christina; Avraamides, Marios N.; Kelly, Jonathan W. Integration of visuospatial information encoded from different viewpoints PSYCHONOMIC BULLETIN & REVIEW English Article Spatial cognition; Spatial memory; Integration; Perspective taking MEMORY; KNOWLEDGE Two experiments investigated whether separate sets of objects viewed in the same environment but from different views were encoded as a single integrated representation or maintained as distinct representations. Participants viewed two circular layouts of objects that were placed around them in a round (Experiment 1) or a square (Experiment 2) room and were later tested on perspective-taking trials requiring retrieval of either one layout (within-layout trials) or both layouts (between-layout trials). Results from Experiment 1 indicated that participants did not integrate the two layouts into a single representation. Imagined perspective taking was more efficient on within- than on between-layout trials. Furthermore, performance for within-layout trials was best from the perspective that each layout was studied. Results from Experiment 2 indicated that the stable environmental reference frame provided by the square room caused many, but not all, participants to integrate all locations within a common representation. Participants who integrated performed equally well for within-layout and between-layout judgments and also represented both layouts using a common reference frame. Overall, these findings highlight the flexibility of organizing information in spatial memory. [Adamou, Christina; Avraamides, Marios N.] Univ Cyprus, Dept Psychol, Nicosia, Cyprus; [Avraamides, Marios N.] Ctr Appl Neurosci, Nicosia, Cyprus; [Kelly, Jonathan W.] Iowa State Univ, Dept Psychol, Ames, IA USA Avraamides, MN (reprint author), Univ Cyprus, Dept Psychol, POB 20537, Nicosia, Cyprus. mariosav@ucy.ac.cy Kelly, Jonathan/A-4793-2013 Avraamides M. N., FRONTIERS H IN PRESS; Giudice NA, 2009, SPAT COGN COMPUT, V9, P287, DOI 10.1080/13875860903305664; GOLLEDGE RG, 1993, J ENVIRON PSYCHOL, V13, P293, DOI 10.1016/S0272-4944(05)80252-X; Greenauer N, 2013, PSYCHOL RES-PSYCH FO, V77, P540, DOI 10.1007/s00426-012-0452-x; Kelly JW, 2011, COGNITION, V118, P444, DOI 10.1016/j.cognition.2010.12.006; Kelly JW, 2010, COGNITION, V116, P409, DOI 10.1016/j.cognition.2010.06.002; Klatzky R., 1998, LECT NOTES ARTIF INT, V1404, P1; Maguire EA, 1996, NEUROPSYCHOLOGIA, V34, P993, DOI 10.1016/0028-3932(96)00022-X; McNamara TP, 2003, LECT NOTES ARTIF INT, V2685, P174; Meilinger T, 2011, MEM COGNITION, V39, P1042, DOI 10.3758/s13421-011-0088-x; MOAR I, 1982, Q J EXP PSYCHOL-A, V34, P381; MONTELLO DR, 1993, ENVIRON BEHAV, V25, P457, DOI 10.1177/0013916593253002; Mou WM, 2002, J EXP PSYCHOL LEARN, V28, P162, DOI 10.1037//0278-7393.28.1.162; Shelton AL, 2001, COGNITIVE PSYCHOL, V43, P274, DOI 10.1006/cogp.2001.0758 14 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1069-9384 1531-5320 PSYCHON B REV Psychon. Bull. Rev. JUN 2014 21 3 659 665 10.3758/s13423-013-0538-5 7 Psychology, Mathematical; Psychology, Experimental Psychology AH8XZ WOS:000336422800008 J Thomson, DR; Smilek, D; Besner, D Thomson, David R.; Smilek, Daniel; Besner, Derek On the asymmetric effects of mind-wandering on levels of processing at encoding and retrieval PSYCHONOMIC BULLETIN & REVIEW English Article Recognition memory; Attention and memory EPISODIC MEMORY; META-AWARENESS; ATTENTION; FRAMEWORK; TASK The behavioral consequences of off-task thought (mind-wandering) on primary-task performance are now well documented across an increasing range of tasks. In the present study, we investigated the consequences of mind-wandering on the encoding of information into memory in the context of a levels-of-processing framework (Craik & Lockhart, 1972). Mind-wandering was assessed via subjective self-reports in response to thought probes that were presented under both semantic (size judgment) and perceptual (case judgment) encoding instructions. Mind-wandering rates during semantic encoding negatively predicted subsequent recognition memory performance, whereas no such relation was observed during perceptual encoding. We discuss the asymmetric effects of mind-wandering on levels of processing in the context of attentional-resource accounts of mind-wandering. [Thomson, David R.; Smilek, Daniel; Besner, Derek] Univ Waterloo, Dept Psychol, Waterloo, ON N2L 3G1, Canada Thomson, DR (reprint author), Univ Waterloo, Dept Psychol, 200 Univ Ave West, Waterloo, ON N2L 3G1, Canada. d5thomso@uwaterloo.ca BADDELEY AD, 1978, PSYCHOL REV, V85, P139, DOI 10.1037//0033-295X.85.3.139; Craik FIM, 2002, MEMORY, V10, P305, DOI 10.1080/09658210244000135; CRAIK FIM, 1975, J EXP PSYCHOL GEN, V104, P268, DOI 10.1037//0096-3445.104.3.268; CRAIK FIM, 1972, J VERB LEARN VERB BE, V11, P671, DOI 10.1016/S0022-5371(72)80001-X; He JB, 2011, HUM FACTORS, V53, P13, DOI 10.1177/0018720810391530; KAPUR S, 1994, P NATL ACAD SCI USA, V91, P2008, DOI 10.1073/pnas.91.6.2008; Kucera H., 1967, COMPUTATIONAL ANAL P; LOCKHART RS, 1990, CAN J PSYCHOL, V44, P87, DOI 10.1037/h0084237; McVay JC, 2009, J EXP PSYCHOL LEARN, V35, P196, DOI 10.1037/a0014104; Peirce JW, 2007, J NEUROSCI METH, V162, P8, DOI 10.1016/j.jneumeth.2006.11.017; Rajah M. N., 2013, MEMORY, DOI [10.1080/0958211.2012.761714, DOI 10.1080/0958211.2012.761714]; Riby LM, 2008, PSYCHOL REP, V102, P805, DOI 10.2466/PR0.102.3.805-818; Risko EF, 2012, APPL COGNITIVE PSYCH, V26, P234, DOI 10.1002/acp.1814; Schooler JW, 2011, TRENDS COGN SCI, V15, P319, DOI 10.1016/j.tics.2011.05.006; Seli P, 2013, FRONT PSYCHOL, V4, DOI 10.3389/fpsyg.2013.00430; Seli P, 2013, J EXP PSYCHOL HUMAN, V39, P1, DOI 10.1037/a0030954; Smallwood J, 2010, PSYCHOL BULL, V136, P202, DOI 10.1037/a0018673; Smallwood J, 2007, PSYCHON B REV, V14, P527, DOI 10.3758/BF03194102; Smallwood J, 2008, MEM COGNITION, V36, P1144, DOI 10.3758/MC.36.6.1144; Smallwood J, 2006, CONSCIOUS COGN, V15, P218, DOI 10.1016/j.concog.2005.03.003; Smallwood J, 2006, PSYCHOL BULL, V132, P946, DOI 10.1037/0033-2909.132.6.946; Thomson DR, 2013, FRONT PSYCHOL, V4, DOI 10.3389/fpsyg.2013.00360 22 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1069-9384 1531-5320 PSYCHON B REV Psychon. Bull. Rev. JUN 2014 21 3 728 733 10.3758/s13423-013-0526-9 6 Psychology, Mathematical; Psychology, Experimental Psychology AH8XZ WOS:000336422800019 J Kyung, EJ; Menon, G; Trope, Y Kyung, Ellie J.; Menon, Geeta; Trope, Yaacov Construal level and temporal judgments of the past: the moderating role of knowledge PSYCHONOMIC BULLETIN & REVIEW English Article Memory; Mindsets; Time perception; Construal level theory; Ease of retrieval PSYCHOLOGICAL DISTANCE; MEMORY; EVENTS; RETRIEVAL; FLUENCY; FUTURE; EASE The vast majority of work in construal level theory has found a robust relationship between construal level and temporal judgments for future events: Distance is associated with the abstract, and nearness is associated with the concrete. Our work looks at the past and proposes a critical moderator that reverses this relationship: knowledge. Through experiments involving real news events, we demonstrate that people with less knowledge about events felt nearer to them when recalling them in a concrete mindset versus an abstract one. However, this relationship reverses for those with greater knowledge: They feel closer to past events when recalling them in an abstract mindset versus a concrete one. We provide evidence that this reversal stems from feelings of metacognitive ease that inform temporal judgments when knowledge (which drives what information is held available and accessible in memory) and construal mindset (which drives what information is sought from memory) coincide. Our findings suggest that in memory, there are instances where the abstract seems near and the concrete seems distant. [Kyung, Ellie J.] Dartmouth Coll, Tuck Sch Business, Hanover, NH 03755 USA; [Menon, Geeta] NYU, Leonard N Stern Sch Business, New York, NY 10012 USA; [Trope, Yaacov] NYU, Dept Psychol, New York, NY 10003 USA Kyung, EJ (reprint author), Dartmouth Coll, Tuck Sch Business, 100 Tuck Hall, Hanover, NH 03755 USA. ellie.kyung@tuck.dartmouth.edu; gmenon@stern.nyu.edu; yaacov.trope@nyu.edu Aiken L., 1991, MULTIPLE REGRESSION; ALBA JW, 1987, J CONSUM RES, V13, P411, DOI 10.1086/209080; Alter AL, 2008, PSYCHOL SCI, V19, P161, DOI 10.1111/j.1467-9280.2008.02062.x; BROWN NR, 1985, COGNITIVE PSYCHOL, V17, P139, DOI 10.1016/0010-0285(85)90006-4; Caruso EM, 2013, PSYCHOL SCI, V24, P530, DOI 10.1177/0956797612458804; Chase W. G., 1981, COGNITIVE SKILLS THE, P141; Cohen J., 1988, STAT POWER BEHAV SCI; COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407; ERICSSON KA, 1995, PSYCHOL REV, V102, P211, DOI 10.1037//0033-295X.102.2.211; Freitas AL, 2004, J EXP SOC PSYCHOL, V40, P739, DOI 10.1016/j.jesp.2004.04.003; FRIEDMAN WJ, 1993, PSYCHOL BULL, V113, P44, DOI 10.1037//0033-2909.113.1.44; Fujita K, 2006, J PERS SOC PSYCHOL, V90, P351, DOI 10.1037/0022-3514.90.3.351; Herzog SM, 2007, J EXP SOC PSYCHOL, V43, P483, DOI 10.1016/j.jesp.2006.05.008; Koriat A, 2007, CAMB HANDB PSYCHOL, P289; Kyung EJ, 2010, J EXP SOC PSYCHOL, V46, P217, DOI 10.1016/j.jesp.2009.09.003; Menon G, 2003, J CONSUM RES, V30, P230, DOI 10.1086/376804; PETERSON C, 1980, SOC PSYCHOL QUART, V43, P372, DOI 10.2307/3033957; Preacher KJ, 2008, BEHAV RES METHODS, V40, P879, DOI 10.3758/BRM.40.3.879; Reyna VF, 2006, J EXP PSYCHOL-APPL, V12, P179, DOI 10.1037/1076-898X.12.3.179; Schwarz N, 1998, Pers Soc Psychol Rev, V2, P87, DOI 10.1207/s15327957pspr0202_2; Semin GR, 1999, J PERS SOC PSYCHOL, V76, P877, DOI 10.1037/0022-3514.76.6.877; Thompson C. P., 1996, AUTOBIOGRAPHICAL MEM; Trope Y, 2010, PSYCHOL REV, V117, P440, DOI 10.1037/a0018963; Tsai CI, 2011, PSYCHOL SCI, V22, P348, DOI 10.1177/0956797611398494; Zauberman G, 2010, PSYCHOL SCI, V21, P133, DOI 10.1177/0956797609356420 25 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1069-9384 1531-5320 PSYCHON B REV Psychon. Bull. Rev. JUN 2014 21 3 734 739 10.3758/s13423-013-0534-9 6 Psychology, Mathematical; Psychology, Experimental Psychology AH8XZ WOS:000336422800020 J Fleisch, MC; Hepp, P; Kaleta, T; Esch, JSA; Rein, D; Fehm, T; Beyer, I Fleisch, M. C.; Hepp, P.; Kaleta, T.; Esch, J. Schulte Am; Rein, D.; Fehm, T.; Beyer, I. Feasibility and first long-term results after laparoscopic rectal segment resection and vaginal specimen retrieval for deep infiltrating endometriosis ARCHIVES OF GYNECOLOGY AND OBSTETRICS English Article Deep infiltrating endometriosis; ENZIAN; Rectum; Laparoscopy QUALITY-OF-LIFE; COLORECTAL RESECTION; RECTOVAGINAL ENDOMETRIOSIS; RECTOSIGMOID RESECTION; SURGICAL-MANAGEMENT; RADICAL RESECTION; FOLLOW-UP; INVOLVEMENT; BOWEL; WOMEN Radical resection of deep infiltrating endometriosis (DIE), including bladder and bowel resection, provides relief from pain in symptomatic patients. The laparoscopic approach to treatment is well established for bowel resection but normally requires additional abdominal incisions for specimen retrieval. Here we describe our technique of laparoscopically assisted rectal resection and transvaginal specimen retrieval (LARRT) and provide follow-up information on pain scores and complications. Retrospective observational monocentric study on all DIE patients with rectal infiltration treated between 2008 and 2010 with LATRR at our department. Follow-up was obtained for at least 3 years, including baseline 1-year and 3-year pain scores. We identified four patients undergoing LARRT available for follow-up. DIE was confirmed by histology in all cases. There were no intraoperative complications. Two patients had transient postoperative urinary retention, one patient developed recto-vaginal fistula and required transient colostomy. One patient suffered from persistent vaginal dryness. All patients, however, reported persistent pain relief, including at the end of follow-up period. LARRT is a feasible variation of laparoscopic bowel resection for DIE with rectal infiltration. In our study it has promising results with respect to pain control. Larger studies will, however, be required to determine the safety of this procedure. [Fleisch, M. C.; Hepp, P.; Kaleta, T.; Fehm, T.; Beyer, I.] Univ Dusseldorf, Med Ctr, Dept Obstet & Gynecol, D-40225 Dusseldorf, Germany; [Esch, J. Schulte Am] Univ Dusseldorf, Med Ctr, Dept Colorectal Surg, D-40225 Dusseldorf, Germany; [Rein, D.] Elisabeth Hosp, Dept Obstet & Gynecol, Cologne, Germany Fleisch, MC (reprint author), Univ Dusseldorf, Med Ctr, Dept Obstet & Gynecol, Moorenstr 5, D-40225 Dusseldorf, Germany. Fleisch@uni-duesseldorf.de Abrao MS, 2005, INT J GYNECOL OBSTET, V91, P27, DOI 10.1016/j.ijgo.2005.014; BAILEY HR, 1994, DIS COLON RECTUM, V37, P747; Ballester M, 2012, HUM REPROD, V27, P1043, DOI 10.1093/humrep/des012; Boni L, 2007, SURG ONCOL, V16, pS157, DOI 10.1016/j.suronc.2007.10.003; Camara O, 2009, HUM REPROD, V24, P1407, DOI 10.1093/humrep/dep016; Chapron C, 2001, ACTA OBSTET GYN SCAN, V80, P349, DOI 10.1034/j.1600-0412.2001.080004349.x; Chapron C, 2004, ANN NY ACAD SCI, V1034, P326, DOI 10.1196/annals.1335.035; Darai E, 2007, SURG ENDOSC, V21, P1572, DOI 10.1007/s00464-006-9160-1; Darai E, 2010, ANN SURG, V251, P1018, DOI 10.1097/SLA.0b013e3181d9691d; Darai E, 2005, AM J OBSTET GYNECOL, V192, P394, DOI 10.1016/j.ajog.2004.08.033; Darai E, 2010, EUR J OBSTET GYN R B, V149, P210, DOI 10.1016/j.ejogrb.2009.12.032; Dubernard G, 2006, HUM REPROD, V21, P1243, DOI 10.1093/humrep/dei491; Duepree HJ, 2002, J AM COLL SURGEONS, V195, P754, DOI 10.1016/S1072-7515(02)01341-8; Ebert AD, 2009, J MINIM INVAS GYN, V16, P231, DOI 10.1016/j.jmig.2008.12.011; Fleisch MC, 2005, EUR J OBSTET GYN R B, V123, P224, DOI 10.1016/j.ejogrb.2005.04.007; Ford J, 2004, BJOG-INT J OBSTET GY, V111, P353, DOI 10.1111/.1471-0528.2004.00093.x; Ghezzi F, 2008, FERTIL STERIL, V90, P1964, DOI 10.1016/j.fertnstert.2007.09.002; Kavallaris A, 2011, ARCH GYNECOL OBSTET, V283, P1059, DOI 10.1007/s00404-010-1499-9; Keckstein J, 2005, MINIM INVASIV THER, V14, P160, DOI 10.1080/14017430510035916; Kondo W, 2013, GYNECOL OBSTET S, VS3, P001; NEZHAT C, 1994, SURG ENDOSC-ULTRAS, V8, P682, DOI 10.1007/BF00678566; Nezhat C, 1991, Surg Laparosc Endosc, V1, P106; Nezhat F, 2001, Surg Laparosc Endosc Percutan Tech, V11, P67, DOI 10.1097/00019509-200102000-00020; NEZHAT F, 1992, FERTIL STERIL, V57, P1129; Possover M, 2000, OBSTET GYNECOL, V96, P304, DOI 10.1016/S0029-7844(00)00839-5; Ruffo G, 2010, SURG ENDOSC, V24, P63, DOI 10.1007/s00464-009-0517-0; Seracchioli R, 2007, BJOG-INT J OBSTET GY, V114, P889, DOI 10.1111/j.1471-0528.2007.01363.x; Tuttlies F, 2005, Zentralbl Gynakol, V127, P275, DOI 10.1055/s-2005-836904; Urbach DR, 1998, DIS COLON RECTUM, V41, P1158, DOI 10.1007/BF02239439; Vercellini P, 2006, AM J OBSTET GYNECOL, V195, P1303, DOI 10.1016/j.ajog.2006.03.068; Wood SG, 2013, ANN SURG 31 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 0932-0067 1432-0711 ARCH GYNECOL OBSTET Arch. Gynecol. Obstet. JUN 2014 289 6 1241 1247 10.1007/s00404-014-3146-3 7 Obstetrics & Gynecology Obstetrics & Gynecology AH8NL WOS:000336394600018 J Elbialy, S; Mahmoud, A; Pradhan, B; Buchroithner, M Elbialy, S.; Mahmoud, A.; Pradhan, B.; Buchroithner, M. Application of spaceborne synthetic aperture radar data for extraction of soil moisture and its use in hydrological modelling at Gottleuba Catchment, Saxony, Germany JOURNAL OF FLOOD RISK MANAGEMENT English Article Flood; Germany; GIS; HEC-HMS; modelling; remote sensing; TerraSAR-X; soil moisture NEURAL-NETWORK MODEL; SURFACE-ROUGHNESS; FLOOD INUNDATION; OPTICAL-DATA; MALAYSIA; RETRIEVAL; MICROWAVE; VULNERABILITY; MANAGEMENT; IMAGERY Hydrological modelling is a powerful tool for hydrologists and engineers involved in the planning and management of water resources. With the recent advent of computational power and the growing availability of spatial data, remote sensing and geographical information systems technologies can augment to a great extent the conventional methods used in rainfall run-off studies. That means it is possible to accurately describe the characteristics of watershed in particularly when determining the run-off response to rainfall inputs. The main objective of this study is to apply the potential application of spaceborne synthetic aperture radar (SAR) data (i.e. TerraSAR-X and Advanced Land Observing Satellite/Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) for soil moisture retrieval) and to improve the spatial input parameters required for hydrological modelling. For the spatial database creation, a high-resolution 2-m aerial laser scanning digital terrain model, soil map, and land use map were used. Rainfall records were transformed into a run-off through hydrological parameterisation of the watershed using Hydrologic Engineering Center's Hydrologic Modelling System (HEC-HMS) software for rainfall run-off simulation. The Soil Conservation Services Curve Number and soil moisture accounting loss methods were selected to calculate the infiltration losses. In this research, soil moisture was derived from two different types of spaceborne SAR data: TerraSAR-X and ALOS PALSAR (L-band). The developed integrated hydrological model was applied to a flood prone area: Gottleuba Catchment in Pirna (Saxony, Germany). For model validation, 10 years historical precipitation data were used. The validated model was further optimised using the extracted soil moisture from SAR data. The simulation results showed a reasonable match between the simulated and the observed hydrographs. Hence, this paper confirms that TerraSAR-X and ALOS PALSAR (L-band) have a high potential for soil moisture mapping as a useful source of information and technique in hydrological modelling. [Elbialy, S.] Gulf Univ, Dept Civil Engn, Sanad, Bahrain; [Elbialy, S.; Mahmoud, A.; Buchroithner, M.] Tech Univ Dresden, Fac Forestry Geo & Hydro Sci, Inst Cartog, Dresden, Germany; [Mahmoud, A.] Fayoum Univ, Soil & Water Deptartment, Fac Agr, Al Fayyum, Egypt; [Pradhan, B.] Univ Putra Malaysia, Fac Engn, Dept Civil Engn, Serdang 43400, Malaysia Pradhan, B (reprint author), Univ Putra Malaysia, Fac Engn, Dept Civil Engn, Serdang 43400, Malaysia. biswajeet24@gmail.com German Aerospace Centre (DLR) [HYD0326] We would like to express our gratitude to (GeoSN) 'Staats-betrieb Geobasisinformation und ermessung Sachsen' for supporting and providing data used in this study. Thanks to Mr Hagen Linke from 'Landestal-sperrenverwaltung des Freistaates Sachsen' for providing Storage and Hydrological data. Dr Johannes Franke from Institute of Hydrology and Meteorology, TU-Dresden is gratefully acknowledged for collection and provision of Meteorological data. Firth two authors would like to thank the Cultural Affairs & Mission Sector, Ministry of Higher Education of Egypt for awarding PhD scholarships. The TerraSAR-X data used in this study was provided by German Aerospace Centre (DLR) under the project number HYD0326. Also, we would like to express our gratitude to Mr Thomas Hahmann of DLR, who helps in procuring the data. Thanks to two anonymous reviewers and editorial comment by David Balmforth for their helpful critical reviews on the earlier version of the manuscript. Al Fugura A, 2011, DISASTER ADV, V4, P20; Alvarez-Mozos J, 2005, BIOSYST ENG, V92, P119, DOI 10.1016/j.biosystemseng.2005.06.008; Arwa D. O., 2001, THESIS INT I AEROSPA; Baghdadi N, 2007, SENSORS-BASEL, V7, P2458, DOI 10.3390/s7102458; Baghdadi N, 2006, INT J REMOTE SENS, V27, P1907, DOI 10.1080/01431160500239032; Billa L., 2005, GEOINFORM DISASTER M, V18, P1357; Billa L., 2006, DISASTER PREVENTION, V15, P233, DOI 10.1108/09653560610659775; Billa L, 2011, J FLOOD RISK MANAG, V4, P318, DOI 10.1111/j.1753-318X.2011.01115.x; Biro K, 2013, LAND DEGRAD DEV, V24, P90, DOI 10.1002/ldr.1116; BRUCKLER L, 1988, REMOTE SENS ENVIRON, V26, P101, DOI 10.1016/0034-4257(88)90091-0; Buchroithner M. F., 1997, REMOTE SENSING REV, V16, P1; Cunderlik JM, 2002, J HYDROL, V261, P115, DOI 10.1016/S0022-1694(02)00019-7; Dixon B, 2005, J HYDROL, V309, P17, DOI 10.1016/j.jhydrol.2004.11.010; Engman E. T., 1991, REMOTE SENSING HYDRO; ENGMAN ET, 1995, REMOTE SENS ENVIRON, V51, P189, DOI 10.1016/0034-4257(94)00074-W; Federer C. A., 1995, BROOK90 SIMULATION M; Fritz T, 2007, TXGSDD3307; Gumbo B., 2005, 2 WARFSA WAT NET S I, P30; HEC-GeoHMS, 2010, EXT SUPP HEC HMS US; Horritt MS, 2002, J HYDROL, V268, P87, DOI 10.1016/S0022-1694(02)00121-X; Jackson SH, 2003, AGR WATER MANAGE, V58, P209, DOI 10.1016/S0378-3774(02)00078-1; JACOVIDES CP, 1995, AGR WATER MANAGE, V27, P365, DOI 10.1016/0378-3774(95)01152-9; Jagadeesha C. J., 1999, ASIAS 1 GIS GPS NOV; Kia MB, 2012, ENVIRON EARTH SCI, V67, P251, DOI 10.1007/s12665-011-1504-z; Le Hegarat-Mascle S, 2002, IEEE T GEOSCI REMOTE, V40, P2647, DOI 10.1109/TGRS.2002.806994; Lillesand TM, 2008, REMOTE SENSING IMAGE; Liu GX, 2011, J HYDROL ENG, V16, P266, DOI 10.1061/(ASCE)HE.1943-5584.0000308; Mahmoud A, 2011, ADV SPACE RES, V48, P799, DOI 10.1016/j.asr.2011.04.005; Maidment DR, 2002, ARC HYDRO GIS WATER; Mattia F, 2006, IEEE T GEOSCI REMOTE, V44, P900, DOI 10.1109/TGRS.2005.863483; Mohan S., 2000, P S REST LAK WETL NO; Moran MS, 1997, REMOTE SENS ENVIRON, V61, P96, DOI 10.1016/S0034-4257(96)00243-X; Moran MS, 2000, AGR FOREST METEOROL, V105, P69, DOI 10.1016/S0168-1923(00)00189-1; Muller U., 2008, DAMS RESERVOIRS, V18, P85, DOI 10.1680/dare.2008.18.2.85; NOHRSC, 2005, UN HYDR TECHN MAN; Oh Y, 2004, IEEE T GEOSCI REMOTE, V42, P596, DOI 10.1109/TGRS.2003.821065; Ponce V. M., 1989, ENG HYDROLOGY; Pradhan B, 2011, J FLOOD RISK MANAG, V4, P189, DOI 10.1111/j.1753-318X.2011.01103.x; Pradhan B., 2009, J SPATIAL HYDROLOGY, V9, P1; Pradhan B, 2010, ADV SPACE RES, V45, P1244, DOI 10.1016/j.asr.2010.01.006; Pradhan B., 2009, DISASTER ADV, V2, P7; Pradhan B, 2011, ENVIRON ECOL STAT, V18, P471, DOI 10.1007/s10651-010-0147-7; Pradhan B, 2010, ENVIRON ENG GEOSCI, V16, P107; Pradhan B, 2010, J INDIAN SOC REMOT, V38, P301, DOI 10.1007/s12524-010-0020-z; Rahman MM, 2008, REMOTE SENS ENVIRON, V112, P391, DOI 10.1016/j.rse.2006.10.026; Sanyal J, 2005, HYDROL PROCESS, V19, P3699, DOI 10.1002/hyp.5852; Sanyal J, 2004, NAT HAZARDS, V33, P283, DOI 10.1023/B:NHAZ.0000037035.65105.95; Satalino G, 2002, IEEE T GEOSCI REMOTE, V40, P2438, DOI 10.1109/TGRS.2002.803790; Seiler R, 2009, J ENVIRON MANAGE, V90, P2121, DOI 10.1016/j.jenvman.2007.07.035; Sinnakaudan SK, 2003, ENVIRON MODELL SOFTW, V18, P119, DOI 10.1016/S1364-8152(02)00068-3; Smith K., 1998, FLOODS PHYS PROCESSE, P3; Socher M., 2007, FLOOD RISK MANAGEMEN; Ulaby F. T., 1986, MICROWAVE REMOTE SEN, V3, P1098; USACE, HYDR MOD SYST HEC HM; Wang Z., 2004, 20 ISPRS C IST; Youssef AM, 2011, ENVIRON EARTH SCI, V62, P611, DOI 10.1007/s12665-010-0551-1; Zribi M, 2008, IEEE T GEOSCI REMOTE, V46, P438, DOI 10.1109/TGRS.2007.904582 57 3 3 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1753-318X J FLOOD RISK MANAG J. Flood Risk Manag. JUN 2014 7 2 159 175 10.1111/jfr3.12037 17 Environmental Sciences; Water Resources Environmental Sciences & Ecology; Water Resources AG4PD WOS:000335401600007 J Noda, K; Arie, H; Suga, Y; Ogata, T Noda, Kuniaki; Arie, Hiroaki; Suga, Yuki; Ogata, Tetsuya Multimodal integration learning of robot behavior using deep neural networks ROBOTICS AND AUTONOMOUS SYSTEMS English Article Object manipulation; Multimodal integration; Cross-modal memory retrieval; Deep learning MEMORY-IMPAIRED INDIVIDUALS; NEUROLOGICAL DAMAGE; RECOGNITION For humans to accurately understand the world around them, multimodal integration is essential because it enhances perceptual precision and reduces ambiguity. Computational models replicating such human ability may contribute to the practical use of robots in daily human living environments; however, primarily because of scalability problems that conventional machine learning algorithms suffer from, sensory-motor information processing in robotic applications has typically been achieved via modal-dependent processes. In this paper, we propose a novel computational framework enabling the integration of sensory-motor time-series data and the self-organization of multimodal fused representations based on a deep learning approach. To evaluate our proposed model, we conducted two behavior-learning experiments utilizing a humanoid robot; the experiments consisted of object manipulation and bell-ringing tasks. From our experimental results, we show that large amounts of sensory-motor information, including raw RGB images, sound spectrums, and joint angles, are directly fused to generate higher-level multimodal representations. Further, we demonstrated that our proposed framework realizes the following three functions: (1) cross-modal memory retrieval utilizing the information complementation capability of the deep autoencoder; (2) noise-robust behavior recognition utilizing the generalization capability of multimodal features; and (3) multimodal causality acquisition and sensory-motor prediction based on the acquired causality. (C) 2014 The Authors. Published by Elsevier B.V. [Noda, Kuniaki; Arie, Hiroaki; Suga, Yuki; Ogata, Tetsuya] Waseda Univ, Grad Sch Fundamental Sci & Engn, Dept Intermedia Art & Sci, Shinjuku Ku, Tokyo 1698555, Japan Noda, K (reprint author), Waseda Univ, Grad Sch Fundamental Sci & Engn, Dept Intermedia Art & Sci, Shinjuku Ku, 3-4-1 Okubo, Tokyo 1698555, Japan. kuniaki.noda@akane.waseda.jp; arie@aoni.waseda.jp; ysuga@ysuga.net; ogata@waseda.jp JST PRESTO "Information Environment and Humans"; MEXT [24119003] This work has been supported by JST PRESTO "Information Environment and Humans" and MEXT Grant-in-Aid for Scientific Research on Innovative Areas "Constructive Developmental Science" (24119003). Aldebaran Robotics, 2012, NAO HUM; Bekkerman R., 2011, SCALING MACHINE LEAR; Bengio Yoshua, 2009, Foundations and Trends in Machine Learning, V2, DOI 10.1561/2200000006; Brooks R. A., 1998, Proceedings Fifteenth National Conference on Artificial Intelligence (AAAI-98). Tenth Conference on Innovative Applications of Artificial Intelligence; Chuck Rosenberg, 2013, IMPROVING PHOTO SEAR; Coen M.H., 2001, INT JOINT C ARTIF IN, V17, P1417; Deneve S, 2004, J PHYSIOLOGY-PARIS, V98, P249, DOI 10.1016/j.jphysparis.2004.03.011; Dewey J., 1896, PSYCHOL REV, V3, P357; Ernst MO, 2004, TRENDS COGN SCI, V8, P162, DOI 10.1016/j.tics.2004.02.002; Franc V., 2008, STAT PATTERN RECOGNI; Gallagher I. I., 2000, TRENDS COGN SCI, V4, P14, DOI DOI 10.1016/S1364-6613(99)01417-5); Grilli MD, 2011, J INT NEUROPSYCH SOC, V17, P929, DOI 10.1017/S1355617711000737; Grilli MD, 2010, NEUROPSYCHOLOGY, V24, P698, DOI 10.1037/a0020318; Hinton G, 2012, IEEE SIGNAL PROC MAG, V29, P82, DOI 10.1109/MSP.2012.2205597; Hinton GE, 2006, SCIENCE, V313, P504, DOI 10.1126/science.1127647; Jauffret A., 2012, P 12 INT C SIM AD BE, V7426, P136; Kaneko K, 2004, IEEE INT CONF ROBOT, P1083, DOI 10.1109/ROBOT.2004.1307969; Kawabe T, 2013, P ROY SOC B-BIOL SCI, V280, DOI 10.1098/rspb.2013.0991; Krizhevsky A., 2012, ADV NEURAL INFORM PR, V25, P1106; Krizhevsky A., 2011, P 19 EUR S ART NEUR; Kuriyama T., 2010, P 10 INT C EP ROB OR, P57; LANG KJ, 1990, NEURAL NETWORKS, V3, P23, DOI 10.1016/0893-6080(90)90044-L; Le Q. V., 2012, P 29 INT C MACH LEAR, P81; Lecun Y, 1998, P IEEE, V86, P2278, DOI 10.1109/5.726791; Martens J., 2011, P INT C MACH LEARN, P1033; Martens J., 2010, P 27 INT C MACH LEAR, P735; Murphy R.R., 2000, INTRO AI ROBOTICS; Ngiam J., 2011, P 28 INT C MACH LEAR, P689; Ogino M, 2006, ROBOT AUTON SYST, V54, P414, DOI 10.1016/j.robot.2006.01.005; PEARLMUTTER BA, 1994, NEURAL COMPUT, V6, P147, DOI 10.1162/neco.1994.6.1.147; Pitti A., 2012, P IEEE INT C DEV LEA, P1; Pouget A, 2002, NAT REV NEUROSCI, V3, P741, DOI 10.1038/nrn914; Robert Hof, 2013, MEET GUY WHO HELPED; Sakagami Y., 2002, IEEE RSJ INT C INT R, V3, p2478 , DOI [DOI 10.1109/IRDS.2002.1041641, DOI 10.1109/IRDS.2002.104]; Sauser E., 2006, P 2006 IEEE RSJ INT, P5619; Schraudolph NN, 2002, NEURAL COMPUT, V14, P1723, DOI 10.1162/08997660260028683; Srivastava N., 2012, P ADV NEUR INF PROC, V25, P2231; Stein B. E., 1993, MERGING SENSES; Sutskever I., 2011, P 28 INT C MACH LEAR, P1017; Willow Grarage, PERSONAL ROBOT 2 PR2 40 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0921-8890 1872-793X ROBOT AUTON SYST Robot. Auton. Syst. JUN 2014 62 6 721 736 10.1016/j.robot.2014.03.003 16 Automation & Control Systems; Computer Science, Artificial Intelligence; Robotics Automation & Control Systems; Computer Science; Robotics AH9NF WOS:000336468400002 J Teodoro, G; Valle, E; Mariano, N; Torres, R; Meira, W; Saltz, JH Teodoro, George; Valle, Eduardo; Mariano, Nathan; Torres, Ricardo; Meira, Wagner, Jr.; Saltz, Joel H. Approximate similarity search for online multimedia services on distributed CPU-GPU platforms VLDB JOURNAL English Article Descriptor indexing; Multimedia databases; Information retrieval; Hypercurves; Filter-stream; GPGPU SPACE-FILLING CURVE; IMAGE RETRIEVAL; DESCRIPTORS; PERFORMANCE Similarity search in high-dimensional spaces is a pivotal operation for several database applications, including online content-based multimedia services. With the increasing popularity of multimedia applications, these services are facing new challenges regarding (1) the very large and growing volumes of data to be indexed/searched and (2) the necessity of reducing the response times as observed by end-users. In addition, the nature of the interactions between users and online services creates fluctuating query request rates throughout execution, which requires a similarity search engine to adapt to better use the computation platform and minimize response times. In this work, we address these challenges with Hypercurves, a flexible framework for answering approximate k-nearest neighbor (kNN) queries for very large multimedia databases. Hypercurves executes in hybrid CPU-GPU environments and is able to attain massive query-processing rates through the cooperative use of these devices. Hypercurves also changes its CPU-GPU task partitioning dynamically according to the observed load, aiming for optimal response times. In our empirical evaluation, dynamic task partitioning reduced query response times by approximately 50 % compared to the best static task partition. Due to a probabilistic proof of equivalence to the sequential kNN algorithm, the CPU-GPU execution of Hypercurves in distributed (multi-node) environments can be aggressively optimized, attaining superlinear scalability while still guaranteeing, with high probability, results at least as good as those from the sequential algorithm. [Teodoro, George; Saltz, Joel H.] Emory Univ, Ctr Comprehens Informat, Atlanta, GA 30322 USA; [Valle, Eduardo] Univ Estadual Campinas, Recod Lab DCA FEEC, Campinas, SP, Brazil; [Mariano, Nathan; Meira, Wagner, Jr.] Univ Fed Minas Gerais, Dept Comp Sci, Belo Horizonte, MG, Brazil; [Torres, Ricardo] Univ Estadual Campinas, Recod Lab DSI IC, Campinas, SP, Brazil Teodoro, G (reprint author), Emory Univ, Ctr Comprehens Informat, Atlanta, GA 30322 USA. glmteodoro@gmail.com; dovalle@dca.fee.unicamp.br; nathanr@dcc.ufmg.br; rtorres@ic.unicamp.br; meira@dcc.ufmg.br; jhsaltz@emory.edu CNPq [306580/2012-8, 484254/2012-0]; FAPESP; CAPES; FAPEMIG; InWeb; National Science Foundation [OCI-0910735] We would like to express our gratitude to the reviewers for their valuable comments, which helped us to improve our work both in terms of content and presentation. E. Valle thanks CNPq and FAPESP for the financial support of this work. R. Torres thanks CAPES, CNPq (grants 306580/2012-8, 484254/2012-0), and FAPESP for the financial support. W. Meira Jr. thanks CNPq, FAPEMIG and InWeb for financial support of this work. E. Valle and G. Teodoro thank the CENAPAD/UNICAMP for making available the computational resources required by the expensive experiments of this work. This research also used resources of the Keeneland Computing Facility at the Georgia Institute of Technology, which is supported by the National Science Foundation under Contract OCI-0910735. Adan I., 2001, LECT NOTES; Akune F., 2010, 20 INT C PATT REC IC; Arefin AS, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0044000; Augonnet C, 2009, LECT NOTES COMPUT SC, V5704, P863, DOI 10.1007/978-3-642-03869-3_80; B He, 2008, PARALLEL ARCHITECTUR; Beecks C., 2009, P ACM INT C MULT, P697, DOI 10.1145/1631272.1631391; Beecks C., 2010, P ACM INT C IM VID R, P438, DOI 10.1145/1816041.1816105; Beecks C., 2012, LECT NOTES COMPUTER, V7131; Bell N., 2011, GPU GEMS; Beynon M., 2000, IEEE S MASS STOR SYS, P119; Bhatti NT, 1998, ACM T COMPUT SYST, V16, P321, DOI 10.1145/292523.292524; Bohm C, 2001, ACM COMPUT SURV, V33, P322, DOI 10.1145/502807.502809; Bosilca G., 2011, IEEE INT C CLUST COM; Boureau YL, 2010, PROC CVPR IEEE, P2559, DOI 10.1109/CVPR.2010.5539963; BUTZ AR, 1971, IEEE T COMPUT, VC 20, P424, DOI 10.1109/T-C.1971.223258; Castelli V., 2002, MULTIDIMENSIONAL IND, P373; Chandrasekhar V., 2011, ISMIR, P801; Chavez E, 2001, ACM COMPUT SURV, V33, P273, DOI 10.1145/502807.502808; Datar M., 2004, P 20 ANN S COMP GEOM; Deisher Michael, 2011, Computer Science - Research and Development, V26, DOI 10.1007/s00450-011-0169-x; du Mouza C, 2009, VLDB J, V18, P933, DOI 10.1007/s00778-009-0135-4; Fagin R., 2001, P 20 ACM S PRINC DAT, P102, DOI 10.1145/375551.375567; Fagin R., 2003, P 2003 ACM SIGMOD IN; Faloutsos C., 1989, Proceedings of the Eighth ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, DOI 10.1145/73721.73746; Faloutsos C., 2002, MULTIMEDIA INDEXING, P435; FALOUTSOS C, 1988, IEEE T SOFTWARE ENG, V14, P1381, DOI 10.1109/32.6184; Garcia V., 2008, CVPR WORKSH COMP VIS; Harris M., 2007, GPU GEMS, V3, P851; Hua G, 2012, INT J COMPUT VISION, V96, P277, DOI 10.1007/s11263-011-0506-3; Huo X., 2011, 18 INT C HIGH PERF C; Indyk P., 1998, STOC, P604; Kato K., 2010, P 2010 10 IEEE ACM I; Krulis M, 2012, DISTRIB PARALLEL DAT, V30, P179, DOI 10.1007/s10619-012-7092-4; Kuang QS, 2009, PROCEEDINGS OF INTERNATIONAL SYMPOSIUM ON COMPUTER SCIENCE AND COMPUTATIONAL TECHNOLOGY (ISCSCT 2009), P151; Liao SW, 2001, PROC INT CONF DATA, P615, DOI 10.1109/ICDE.2001.914876; Linderman MD, 2008, ACM SIGPLAN NOTICES, V43, P287, DOI 10.1145/1353536.1346318; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Luk C.K., 2009, 42 INT S MICR MICRO; Mainar-Ruiz G, 2006, INT C PATT RECOG, P502; Megiddo N., 1997, 10093 RJ IBM ALM RES; Menasce D., 2002, CAPACITY PLANNING WE; Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188; Morton G. M., 1966, TECHNICAL REPORT; Muja M, 2009, VISAPP 2009: PROCEEDINGS OF THE FOURTH INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS, VOL 1, P331; OMALLEY SW, 1992, ACM T COMPUT SYST, V10, P110, DOI 10.1145/128899.128901; Pan J, 2010, IEEE INT C INT ROBOT, P2243; Pan J., 2011, 19 ACM SIGSPATIAL IN; Pang HH, 2010, VLDB J, V19, P437, DOI 10.1007/s00778-009-0174-x; Penatti OAB, 2012, J VIS COMMUN IMAGE R, V23, P359, DOI 10.1016/j.jvcir.2011.11.002; Ravi V., 2010, P 2010 INT C SUP, P137, DOI 10.1145/1810085.1810106; Sagan H., 1994, SPACE FILLING CURVES; Samet H., 2005, MORGAN KAUFMANN SERI; Satish N., 2009, IEEE INT PAR DISTR P; Shakhnarovich G., 2006, NEAREST NEIGHBOR MET; Shepherd J., 1999, SPIE C STOR RETR IM, VVII, P350; Sismanis N., 2012, PARALLEL SEARCH K NE; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Stone Z., 2008, IEEE COMP VIS PATT R; Sun L., 2010, TER GRID; Sunderam V. S., 1990, Concurrency: Practice and Experience, V2, DOI 10.1002/cpe.4330020404; Teodoro G., 2009, IEEE CLUST; Teodoro G., 2013, IPDPS 13; Teodoro G., 2008, 37 INT C PAR PROC IC; Teodoro G., 2010, P 19 ACM INT S HIGH; Teodoro G, 2012, INT PARALL DISTRIB P, P1093, DOI 10.1109/IPDPS.2012.101; Teodoro G., 2011, P 20 ACM INT C INF K; Tuytelaars T., 2008, FDN TRENDS COMPUTER, V3, P177, DOI [DOI 10.1561/0600000017, DOI 10.1561/0600000017>]; Valle E, 2010, IEEE T CONSUM ELECTR, V56, P1167, DOI 10.1109/TCE.2010.5606242; Valle E., 2008, P 8 ACM S DOC ENG DO; Valle E., 2008, P 17 ACM C INF KNOWL; Vetter JS, 2011, COMPUT SCI ENG, V13, P90, DOI 10.1109/MCSE.2011.83; Welsh M., 2001, SIGOPS OPER SYST REV, V35; Winder S.A.J., 2007, CVPR; Yiu ML, 2009, VLDB J, V18, P695, DOI 10.1007/s00778-008-0117-y; Yu H., 2000, P 14 INT C SUP ICS 0; Zezula P., 2010, SIMILARITY SEARCH ME 77 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1066-8888 0949-877X VLDB J VLDB J. JUN 2014 23 3 427 448 10.1007/s00778-013-0329-7 22 Computer Science, Hardware & Architecture; Computer Science, Information Systems Computer Science AH8JE WOS:000336383300004 J Guo, KH; Ma, JH; Duan, GH Guo, Kehua; Ma, Jianhua; Duan, Guihua DHSR: A Novel Semantic Retrieval Approach for Ubiquitous Multimedia WIRELESS PERSONAL COMMUNICATIONS English Article Data hiding; Ubiquitous multimedia; Semantic retrieval; Mobility intelligence management IMAGE ANNOTATION; ONTOLOGY; SYSTEM; SHAPES Semantic features are critical intelligence information for mobile ubiquitous multimedia, how to manage and retrieve the semantic information has been an important issue. In this paper, a novel semantic retrieval approach named Data Hiding based Semantic Retrieval (DHSR) for ubiquitous multimedia is proposed. This approach consists of the following features: (1) Every multimedia document has to be semantically annotated by several users before saved into multimedia database. (2) Semantic information described by object ontology will be hidden in the multimedia document data. (3) Semantic information will not be lost even if the multimedia document is copied, cut or leave the database. Our work provides a search engine with convenient user interfaces. The experimental results show that DHSR can search the multimedia documents reflecting users' query intent more effectively compared with some traditional approaches. [Guo, Kehua; Duan, Guihua] Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China; [Ma, Jianhua] Hosei Univ, Fac Comp & Informat Sci, Tokyo, Japan Guo, KH (reprint author), Cent S Univ, Sch Informat Sci & Engn, Changsha, Hunan, Peoples R China. guokehua@csu.edu.cn; jianhua@hosei.ac.jp; duangh@csu.edu.cn Natural Science Foundation of China [61202341, 61103203]; China Postdoctoral Fund [2012M521552]; Postdoctoral Fund of Hunan Province [2012RS4054]; Central South University Thanks for the invitation of Professor Laurence T. Yang in PiCom-2012. This work is supported by Natural Science Foundation of China (61202341, 61103203), China Postdoctoral Fund (2012M521552), Postdoctoral Fund of Hunan Province (2012RS4054) and Central South University. Adankon MM, 2009, PATTERN RECOGN, V42, P3264, DOI 10.1016/j.patcog.2008.10.023; Albertoni R, 2005, LECT NOTES COMPUT SC, V3762, P896; Attene M., 2009, LECT NOTES COMPUTER, V4816, P126; Attene M, 2007, 2007 INTERNATIONAL CONFERENCE ON CYBERWORLDS, PROCEEDINGS, P427, DOI 10.1109/CW.2007.8; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Carneiro G, 2007, IEEE T PATTERN ANAL, V29, P394, DOI 10.1109/TPAMI.2007.61; Chen Y., 2003, ISSPA 2003. Seventh International Symposium on Signal Processing and its Applications. Proceedings (Cat. No.03EX714); Chua T.-S., 2009, P ACM INT C IM VID R, P1, DOI DOI 10.1145/1646396.1646452; Djordjevic D, 2007, IEEE T CIRC SYST VID, V17, P313, DOI 10.1109/TCSVT.2007.890634; Gao Y., 2006, 14 ACM INT C MULT SA, V23, P901; Gijsenij A., 2010, IEEE T PATTERN ANAL, V33, P687; Gutierrez M, 2007, VISUAL COMPUT, V23, P207, DOI 10.1007/s00371-006-0093-4; Hofmann T, 2001, MACH LEARN, V42, P177, DOI 10.1023/A:1007617005950; Hoi S. C. H., 2008, IEEE C COMP VIS PATT, P1; Lei Wu, 2010, IEEE Transactions on Image Processing, V19, DOI 10.1109/TIP.2010.2045169; LI H, 2009, 2009 IEEE INT C DAT, P164; Li J., 2006, 14 ACM INT C MULT SA, P911; Li J, 2003, IEEE T PATTERN ANAL, V25, P1075; Li Z. J., 2008, IASME T J COMPUTING, V8, P1; Liu Y, 2007, PATTERN RECOGN, V40, P262, DOI 10.1016/j.patcog.2006.04.045; Monay F, 2007, IEEE T PATTERN ANAL, V29, P1802, DOI 10.1109/TPAMI.2007.1097; Rasiwasia N, 2007, IEEE T MULTIMEDIA, V9, P923, DOI 10.1109/TMM.2007.900138; Sabine B., 2008, INT C PATT REC TAMP, P1; Shi JB, 2000, IEEE T PATTERN ANAL, V22, P888; Tang XO, 2012, IEEE T PATTERN ANAL, V34, P1342, DOI 10.1109/TPAMI.2011.242; Uschold M, 1996, KNOWL ENG REV, V11, P93; Wang XY, 2008, LECT NOTES COMPUT SC, V4993, P335; Wong RCF, 2008, IEEE T PATTERN ANAL, V30, P1933, DOI 10.1109/TPAMI.2008.125; Yang C., 2006, IEEE C COMP VIS PATT, V2, P2057; Yang D, 2008, COMPUT AIDED DESIGN, V40, P863, DOI 10.1016/j.cad.2008.05.004; Yang S, 2007, IEEE T CIRC SYST VID, V17, P324, DOI 10.1109/TCSVT.2007.890829 31 0 0 SPRINGER NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0929-6212 1572-834X WIRELESS PERS COMMUN Wirel. Pers. Commun. JUN 2014 76 4 SI 779 793 10.1007/s11277-013-1327-1 15 Telecommunications Telecommunications AH7CN WOS:000336290200009 J Goyal, V; Hernandez, GA; Cohen, MG Goyal, Vishal; Hernandez, Gabriel A.; Cohen, Mauricio G. Successful Transradial Retrieval of an Embolized Guidewire During Transradial Vascular Access CATHETERIZATION AND CARDIOVASCULAR INTERVENTIONS English Article foreign body retrieval; catheterization; brachial; radial; ulnar; complications; diagnostic catheterization; complications; vascular access COMPLICATIONS; EXPERIENCE; FRAGMENT Transradial catheterization is associated with lower complication rates; however limited information is available regarding techniques to overcome unusual complications. We present a case of a 58-year-old male with suspected non-ST-elevated myocardial infarction who underwent transradial coronary angiography complicated by guidewire embolization into the radial artery and subsequent access loss. Successful retrieval of the embolized guidewire was achieved by re-accessing the same radial artery and the use of a 2 mm gooseneck microsnare. This technique was safe and prevented the need for surgical intervention or femoral access for retrieval, which are commonly described in the literature and can result in additional complications. (c) 2014 Wiley Periodicals, Inc. [Goyal, Vishal; Hernandez, Gabriel A.; Cohen, Mauricio G.] Univ Miami, Miller Sch Med, Univ Miami Hosp, Elaine & Sydney Sussman Catheterizat Labs, Miami, FL 33136 USA Cohen, MG (reprint author), Univ Miami Hosp, 1400 NW 12th Ave,Suite 1179, Miami, FL 33136 USA. mgcohen@med.miami.edu Bessoud B, 2003, AM J ROENTGENOL, V180, P527; Brilakis ES, 2005, CATHETER CARDIO INTE, V66, P333, DOI 10.1002/ccd.20449; Iturbe JM, 2012, J INVASIVE CARDIOL, V24, P215; Kanei Y, 2011, CATHETER CARDIO INTE, V78, P840, DOI 10.1002/ccd.22978; Kim JH, 2012, J INVASIVE CARDIOL, V24, P74; Kim JY, 2005, YONSEI MED J, V46, P166 6 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1522-1946 1522-726X CATHETER CARDIO INTE Catheter. Cardiovasc. Interv. JUN 1 2014 83 7 1089 1092 10.1002/ccd.25461 4 Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology AH2LV WOS:000335953900022 J Jensen, JL; McDaniel, MA; Woodard, SM; Kummer, TA Jensen, Jamie L.; McDaniel, Mark A.; Woodard, Steven M.; Kummer, Tyler A. Teaching to the TestaEuro broken vertical bar or Testing to Teach: Exams Requiring Higher Order Thinking Skills Encourage Greater Conceptual Understanding EDUCATIONAL PSYCHOLOGY REVIEW English Article Assessment; Bloom's taxonomy; Biology; Testing effect; Test expectancy LONG-TERM RETENTION; BLOOMS TAXONOMY; TEST-EXPECTANCY; COLLEGE BIOLOGY; ENHANCE; MEMORY; CLASSROOM; METACOMPREHENSION; RETRIEVAL; COURSES In order to test the effect of exam-question level on fostering student conceptual understanding, low-level and high-level quizzes and exams were administered in two sections of an introductory biology course. Each section was taught in a high-level inquiry based style but was assigned either low-level questions (memory oriented) on the quizzes and exams, or high-level questions (application, evaluation, and analysis) on the quizzes and exams for the entirety of the semester. A final exam consisting of 20 low-level and 21 high-level questions was given to both sections. We considered several theoretical perspectives based on testing effect, test expectancy, and transfer-appropriate processing literature as well as the theoretical underpinnings of Bloom's taxonomy. Reasoning from these theoretical perspectives, we predicted that high-level exams would encourage not only deeper processing of the information by students in preparation for the exam but also better memory for the core information (learned in the service of preparing for high-level questions). Results confirmed this prediction, with students in the high-level exam condition demonstrating higher performance on both the low-level final-exam items and the high-level final exam items. This pattern suggests that students who are tested throughout the semester with high-level questions acquire deep conceptual understanding of the material and better memory for the course information, and lends support to the proposed hierarchical nature of Bloom's taxonomy. [Jensen, Jamie L.; Woodard, Steven M.; Kummer, Tyler A.] Brigham Young Univ, Dept Biol, Provo, UT 84602 USA; [McDaniel, Mark A.] Washington Univ, Dept Psychol, St Louis, MO 63130 USA Jensen, JL (reprint author), Brigham Young Univ, Dept Biol, 401 WIDB, Provo, UT 84602 USA. Jamie.Jensen@byu.edu American Association for the Advancement of Science (AAAS), 2010, VIS CHANG CALL ACT; Anderson G. L., 2001, TAXONOMY LEARNING TE; Becker H. S., 1968, MAKING GRADE ACAD SI; Biggs J. B., 1987, STUDENT APPROACHES S; Bjork RA, 2013, ANNU REV PSYCHOL, V64, P417, DOI 10.1146/annurev-psych-113011-143823; Bloom BS, 1984, TAXONOMY ED OBJECTIV; Bybee R., 1993, INSTRUCTIONAL MODEL; CAMPBELL N.A., 2005, BIOLOGY; Carpenter SK, 2012, CURR DIR PSYCHOL SCI, V21, P279, DOI 10.1177/0963721412452728; Carpenter SK, 2007, PSYCHON B REV, V14, P474, DOI 10.3758/BF03194092; Carpenter SK, 2009, APPL COGNITIVE PSYCH, V23, P760, DOI 10.1002/acp.1507; Carpenter SK, 2006, MEM COGNITION, V34, P268, DOI 10.3758/BF03193405; Carpenter SK, 2008, MEM COGNITION, V36, P438, DOI 10.3758/MC.36.2.438; CARRIER M, 1992, MEM COGNITION, V20, P633, DOI 10.3758/BF03202713; Chan JCK, 2007, J EXP PSYCHOL LEARN, V33, P431, DOI 10.1037/0278-7393.33.2.431; Crowe A, 2008, CBE-LIFE SCI EDUC, V7, P368, DOI 10.1187/cbe.08-05-0024; Dickie L. O., 2003, CANADIAN J HIGHER ED, V33, P87; Finley JR, 2012, J EXP PSYCHOL LEARN, V38, P632, DOI 10.1037/a0026215; Fisher R. P., 1977, J EXPT PSYCHOL HUMAN, V3, P710; Harlen W., 2002, RES EVIDENCE ED LIB, V1; HILL PW, 1981, AM EDUC RES J, V18, P93, DOI 10.3102/00028312018001093; Johnson CI, 2009, J EDUC PSYCHOL, V101, P621, DOI 10.1037/a0015183; Joughin G, 2010, ASSESS EVAL HIGH EDU, V35, P335, DOI 10.1080/02602930903221493; Kang SHK, 2007, EUR J COGN PSYCHOL, V19, P528, DOI 10.1080/09541440601056620; Krathwohl DR, 2002, THEOR PRACT, V41, P212, DOI 10.1207/s15430421tip4104_2; Lawson A. E., 1978, J RES SCI TEACH, V15, P11, DOI 10.1002/tea.3660150103; Lawson AE, 2000, J RES SCI TEACH, V37, P81, DOI 10.1002/(SICI)1098-2736(200001)37:1<81::AID-TEA6>3.0.CO;2-I; Lawson AE, 2000, J RES SCI TEACH, V37, P996, DOI 10.1002/1098-2736(200011)37:9<996::AID-TEA8>3.0.CO;2-J; MADAUS GF, 1973, AM EDUC RES J, V10, P253, DOI 10.3102/00028312010004253; Mayer R. E., 2003, LEARN INSTR, V10, P253; McDaniel M. A., 2012, J APPL RES MEMORY CO, V1, P18, DOI DOI 10.1016/J.JARMAC.2011.10.001; McDaniel MA, 1996, J EDUC PSYCHOL, V88, P508; MCDANIEL MA, 1978, MEM COGNITION, V6, P156, DOI 10.3758/BF03197441; McDaniel MA, 2013, APPL COGNITIVE PSYCH, V27, P360, DOI 10.1002/acp.2914; McDaniel MA, 2007, EUR J COGN PSYCHOL, V19, P494, DOI 10.1080/09541440701326154; MCDANIEL MA, 1994, CONTEMP EDUC PSYCHOL, V19, P230, DOI 10.1006/ceps.1994.1019; McDermott K. B., J EXPT PSY IN PRESS; Miller C. M. L., 1974, MARK STUDY EXAMINATI; Momsen J, 2013, CBE-LIFE SCI EDUC, V12, P239, DOI 10.1187/cbe.12-08-0130; Momsen JL, 2010, CBE-LIFE SCI EDUC, V9, P435, DOI 10.1187/cbe.10-01-0001; National Research Council, 1996, NAT SCI ED STAND; Roediger HL, 2011, J EXP PSYCHOL-APPL, V17, P382, DOI 10.1037/a0026252; Roediger HL, 2006, PSYCHOL SCI, V17, P249, DOI 10.1111/j.1467-9280.2006.01693.x; Rohrer D, 2010, J EXP PSYCHOL LEARN, V36, P233, DOI 10.1037/a0017678; Seddon G. M., 1978, REV ED RES; Snyder G., 1951, HIDDEN CURRICULUM; Sternberg RJ, 2008, PERSPECT PSYCHOL SCI, V3, P486, DOI 10.1111/j.1745-6924.2008.00095.x; Struyven K., 2005, ASSESS EVAL HIGH EDU, V30, P325, DOI 10.1080/02602930500099102; Thiede KW, 2011, BRIT J EDUC PSYCHOL, V81, P264, DOI 10.1348/135910710X510494; Thomas AK, 2007, MEM COGNITION, V35, P668, DOI 10.3758/BF03193305; Van Etten S, 2008, J EDUC PSYCHOL, V100, P812, DOI 10.1037/0022-0663.100.4.812; Wiggins G., 1998, UNDERSTANDING DESIGN, P214; Witas H. W., 2007, INFECT GENET EVOL, V8, P146; ZOLLER U, 1993, J CHEM EDUC, V70, P195 54 0 0 SPRINGER/PLENUM PUBLISHERS NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 1040-726X 1573-336X EDUC PSYCHOL REV Educ. Psychol. Rev. JUN 2014 26 2 SI 307 329 10.1007/s10648-013-9248-9 23 Psychology, Educational Psychology AG9KI WOS:000335737700007 J Xiao, QM; Daito, Y; Matsumoto, K; Suzuki, M; Kita, K Xiao, Qingmei; Daito, Yusaku; Matsumoto, Kazuyuki; Suzuki, Motoyuki; Kita, Kenji Fast Search Method for Audio Fingerprinting Systems Based on Query Multiplexing ELECTRONICS AND COMMUNICATIONS IN JAPAN English Article query multiplexing; music information retrieval; hamming space; audio fingerprint In music information retrieval, a huge search space has to be explored because a query audio clip can start at any position of any music in the database, and also a query is often corrupted by highly significant noise and distortion. Audio fingerprints have attracted much attention recently for providing compact representation of the perceptually relevant parts of audio signals. In this paper, we propose an extremely fast method of exploring a huge Hamming space for audio fingerprinting systems. The effectiveness of our method has been evaluated by experiments using databases of 8740 real songs and 800 artificially corrupted and 268 real queries. [Xiao, Qingmei] Univ Tokushima, Grad Sch Adv Technol & Sci, Tokushima, Japan; [Daito, Yusaku; Matsumoto, Kazuyuki; Suzuki, Motoyuki; Kita, Kenji] Univ Tokushima, Tokushima, Japan Xiao, QM (reprint author), Univ Tokushima, Grad Sch Adv Technol & Sci, Tokushima, Japan. Allamanche E, 2001, 110 AES CONV; Broder A. Z., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276781; Charikar MS., 2002, P 34 ANN ACM S THEOR; Datar M., 2004, P 20 ANN S COMP GEOM; Fragoulis D, 2001, IEEE T SIGNAL PROCES, V49, P898, DOI 10.1109/78.912932; Gionis A, 1999, 25 INT C VER LARG DA; Haitsma J., 2002, P 3 INT C MUS INF RE, P107; Indyk P., 1998, P 30 ANN ACM S THEOR; Kita K, 2011, IPSJ SIG NOTES; Kulis B, 2009, P 12 IEEE INT C COMP; Kulis B., 2009, P NEURAL INFORM PROC, P1042; Logan B., 2000, P INT S MUS INF RETR; MANBER U, 1993, SIAM J COMPUT, V22, P935, DOI 10.1137/0222058; Manku G.S., 2007, P 16 INT C WORLD WID, P141, DOI 10.1145/1242572.1242592; Miller ML, 2005, J VLSI SIG PROC SYST, V41, P285, DOI 10.1007/s11265-005-4152-2; Ravichandran D, 2005, P ACL; Wang ALC., 2003, P 4 INT C MUS INF RE, P7 17 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1942-9533 1942-9541 ELECTR COMMUN JPN Electr. Commun. Jpn. JUN 2014 97 6 43 50 10.1002/ecj.11561 8 Engineering, Electrical & Electronic Engineering AG5SQ WOS:000335479300006 J Ding, W; Liang, P; Tang, A; van Vliet, H Ding, Wei; Liang, Peng; Tang, Antony; van Vliet, Hans Knowledge-based approaches in software documentation: A systematic literature review INFORMATION AND SOFTWARE TECHNOLOGY English Review Knowledge-based approach; Software documentation; Systematic literature review; Knowledge activity; Software architecture design ARCHITECTURAL KNOWLEDGE; MANAGEMENT; MAINTENANCE Context: Software documents are core artifacts produced and consumed in documentation activity in the software lifecycle. Meanwhile, knowledge-based approaches have been extensively used in software development for decades, however, the software engineering community lacks a comprehensive understanding on how knowledge-based approaches are used in software documentation, especially documentation of software architecture design. Objective: The objective of this work is to explore how knowledge-based approaches are employed in software documentation, their influences to the quality of software documentation, and the costs and benefits of using these approaches. Method: We use a systematic literature review method to identify the primary studies on knowledge-based approaches in software documentation, following a pre-defined review protocol. Results: Sixty studies are finally selected, in which twelve quality attributes of software documents, four cost categories, and nine benefit categories of using knowledge-based approaches in software documentation are identified. Architecture understanding is the top benefit of using knowledge-based approaches in software documentation. The cost of retrieving information from documents is the major concern when using knowledge-based approaches in software documentation. Conclusions: The findings of this review suggest several future research directions that are critical and promising but underexplored in current research and practice: (1) there is a need to use knowledge-based approaches to improve the quality attributes of software documents that receive less attention, especially credibility, conciseness, and unambiguity; (2) using knowledge-based approaches with the knowledge content in software documents which gets less attention in current applications of knowledge-based approaches in software documentation, to further improve the practice of software documentation activity; (3) putting more focus on the application of software documents using the knowledge-based approaches (knowledge reuse, retrieval, reasoning, and sharing) in order to make the most use of software documents; and (4) evaluating the costs and benefits of using knowledge-based approaches in software documentation qualitatively and quantitatively. (C) 2014 Elsevier B.V. All rights reserved. [Ding, Wei; Liang, Peng] Wuhan Univ, Sch Comp, State Key Lab Software Engn, Wuhan, Peoples R China; [Tang, Antony] Swinburne Univ Technol, Fac Informat & Commun Technol, Hawthorn, Vic 3122, Australia; [Liang, Peng; van Vliet, Hans] Vrije Univ Amsterdam, Dept Comp Sci, Amsterdam, Netherlands; [Ding, Wei] China Earthquake Adm, Inst Seismol, Key Lab Earthquake Geodesy, Chengdu, Peoples R China Liang, P (reprint author), Wuhan Univ, Sch Comp, State Key Lab Software Engn, Wuhan, Peoples R China. liangp@whu.edu.cn Tang, Antony/C-2459-2009 Tang, Antony/0000-0002-3574-3977 Natural Science Foundation of China (NSFC) [61170025]; KeS-RAD: Knowledge-enabled Software Requirements to Architecture Documentation; Dutch "Regeling Kenniswerkers" [KWR09164] This work has been partially sponsored by the Natural Science Foundation of China (NSFC) under the Grant No. 61170025, KeS-RAD: Knowledge-enabled Software Requirements to Architecture Documentation and the Dutch "Regeling Kenniswerkers" Project KWR09164, "Stephenson: Architecture knowledge sharing practices in software product lines for print systems". Alagar VS, 2011, TEXTS COMPUT SCI, P23, DOI 10.1007/978-0-85729-277-3_2; Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961; Ali MS, 2010, INFORM SOFTWARE TECH, V52, P871, DOI 10.1016/j.infsof.2010.05.003; Alves V, 2010, INFORM SOFTWARE TECH, V52, P806, DOI 10.1016/j.infsof.2010.03.014; [Anonymous], 2000, 14712000 IEEE; Barais O., 2008, SOFTWARE EVOLUTION, P233, DOI 10.1007/978-3-540-76440-3_10; Barker T.T., 2003, WRITING SOFTWARE DOC; Barmi Z.A., 2011, P 2011 IEEE 4 INT C, P476; Biehl M., 2010, ISRNKTHMMKR1006SE; BLAIR DC, 1985, COMMUN ACM, V28, P289, DOI 10.1145/3166.3197; Briand L. C., 2003, Proceedings Seventh European Conference on Software Maintenance and Reengineering; Briand L.C., 2002, P 14 SEKE IT, P3; Chen L, 2010, P 14 INT C EV ASS SO, P135; Cleland-Huang J., 2012, SOFTWARE SYSTEMS TRA; Clements P., 2010, DOCUMENTING SOFTWARE; Collobert R., 2008, P 25 INT C MACH LEAR, P160, DOI 10.1145/1390156.1390177; Davis A., 1993, Proceedings First International Software Metrics Symposium (Cat. No.93TH0518-1), DOI 10.1109/METRIC.1993.263792; Davis Alan M., 1993, SOFTWARE REQUIREMENT; de Graaf K.A., 2012, P JOINT 10 WORK IEEE, P121; de Boer RC, 2007, LECT NOTES COMPUT SC, V4880, P197; DEVANBU P, 1991, COMMUN ACM, V34, P34, DOI 10.1145/103167.103172; Dingsoyr T, 2002, INT J SOFTW ENG KNOW, V12, P391, DOI 10.1142/S0218194002000962; Dyba T, 2008, INFORM SOFTWARE TECH, V50, P833, DOI 10.1016/j.infsof.2008.01.006; Dzidek WJ, 2008, IEEE T SOFTWARE ENG, V34, P407, DOI 10.1109/TSE.2008.15; Easterbrook S., 2008, GUIDE ADV EMPIRICAL, P285, DOI 10.1007/978-1-84800-044-5_11; Forward A., 2002, P 2002 ACM S DOC ENG, P26; Gomez-Perez A, 2001, INT J INTELL SYST, V16, P391, DOI 10.1002/1098-111X(200103)16:3<391::AID-INT1014>3.0.CO;2-2; Gotel O. C. Z., 1994, Proceedings of the First International Conference on Requirements Engineering (Cat. No.94TH0613-0), DOI 10.1109/ICRE.1994.292398; Greenspan S.J., 1986, ACM SIGSOFT SOFTWARE, V11, P34; Haiduc S, 2012, PROC INT CONF SOFTW, P1273, DOI 10.1109/ICSE.2012.6227101; IEEE, 1998, 8301998 IEEE; IEEE, 1984, 8301984 IEEE; IEEE, 2004, GUID SOFTW ENG BOD K; IEEE, 1998, 10161998 IEEE; ISO, 1991, 900031991 ISO; Jansen A, 2009, J SYST SOFTWARE, V82, P1232, DOI 10.1016/j.jss.2009.04.052; Kitchenham B.A., 2007, EBSE200701 KEEL U U; KRAUT RE, 1995, COMMUN ACM, V38, P69, DOI 10.1145/203330.203345; Kruchten P, 2006, LECT NOTES COMPUT SC, V4214, P43; Kruchten P, 2009, SOFTWARE ARCHITECTURE KNOWLEDGE MANAGEMENT: THEORY AND PRACTICE, P39, DOI 10.1007/978-3-642-02374-3_3; Lethbridge TC, 2003, IEEE SOFTWARE, V20, P35, DOI 10.1109/MS.2003.1241364; Li ZY, 2013, INFORM SOFTWARE TECH, V55, P777, DOI 10.1016/j.infsof.2012.11.005; Liang P., 2010, P 3 INT WORKSH MAN R, P16; Liang P., 2009, RUGSEARCH09L02; Luckey M., 2010, P 6 ICSE WORKSH SOFT, P1, DOI 10.1145/1809100.1809101; Maalej W, 2013, IEEE T SOFTWARE ENG, V39, P1264, DOI 10.1109/TSE.2013.12; Maiden N, 2012, IEEE SOFTWARE, V29, P16, DOI 10.1109/MS.2012.152; Mirakhorli M., 2011, P 6 INT WORKSH SHARI, P45; Mirakhorli M, 2011, 2011 33RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), P1126, DOI 10.1145/1985793.1986014; Mylopoulos J, 1992, CONCEPTUAL MODELING, P49; Nakagawa E.Y., 2010, P 2010 ICSE WORKSH S, P29, DOI 10.1145/1833335.1833340; Nicolas J, 2009, INFORM SOFTWARE TECH, V51, P1291, DOI 10.1016/j.infsof.2009.04.001; Nonaka I., 1995, KNOWLEDGE CREATING C; Parnas DL, 2009, KNOWL-BASED SYST, V22, P132, DOI 10.1016/j.knosys.2008.11.001; Parnas D.L., 2011, FUTURE SOFTWARE ENG, P125, DOI 10.1007/978-3-642-15187-3_8; Paulk Mark, 1995, CAPABILITY MATURITY; Petersen K., 2008, P 12 INT C EV ASS SO, P68; Reeve L, 2005, P 2005 ACM S APPL CO, P1634, DOI DOI 10.1145/1066677.1067049; Rubin E, 2011, REQUIR ENG, V16, P117, DOI 10.1007/s00766-010-0113-9; Rus I, 2002, IEEE SOFTWARE, V19, P26, DOI 10.1109/MS.2002.1003450; Shahin M, 2009, 2009 JOINT WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE AND EUROPEAN CONFERENCE ON SOFTWARE ARCHITECTURE, P293, DOI 10.1109/WICSA.2009.5290823; Sommerville I., 2002, SOFTWARE ENG SUPPORT, V2, P171; Stark GE, 1999, J SOFTW MAINT-RES PR, V11, P293, DOI 10.1002/(SICI)1096-908X(199909/10)11:5<293::AID-SMR198>3.0.CO;2-R; Su MT, 2009, IEEE INT CONF AUTOM, P657, DOI 10.1109/ASE.2009.26; Tang A, 2007, J SYST SOFTWARE, V80, P918, DOI 10.1016/j.jss.2006.08.040; Tang A., 2005, P 5 WORK IEEE IFIP C, P89; Tang A, 2011, 2011 9TH WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE (WICSA), P252, DOI 10.1109/WICSA.2011.40; Tang A, 2010, J SYST SOFTWARE, V83, P352, DOI 10.1016/j.jss.2009.08.032; Tang A, 2009, 2009 JOINT WORKING IEEE/IFIP CONFERENCE ON SOFTWARE ARCHITECTURE AND EUROPEAN CONFERENCE ON SOFTWARE ARCHITECTURE, P253, DOI 10.1109/WICSA.2009.5290813; van der Kamp JW, 2006, Proceedings of the 3rd International Congress Flour - Bread '05 and 5th Croatian Congress of Cereal Technologists, P1; van Vliet Hans, 2010, Proceedings of the Tenth International Conference on Quality Software (QSIC 2010), DOI 10.1109/QSIC.2010.78; van Vliet H., 2008, P 19 AUSTR SOFTW ENG, P24; Wohlin C, 2012, EXPT SOFTWARE ENG 73 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0950-5849 1873-6025 INFORM SOFTWARE TECH Inf. Softw. Technol. JUN 2014 56 6 545 567 10.1016/j.infsof.2014.01.008 23 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AG5VH WOS:000335486300002 J Kinley, K; Tjondronegoro, D; Partridge, H; Edwards, S Kinley, Khamsum; Tjondronegoro, Dian; Partridge, Helen; Edwards, Sylvia Modeling Users' Web Search Behavior and Their Cognitive Styles JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article INFORMATION-RETRIEVAL INTERACTION; HUMAN INDIVIDUAL-DIFFERENCES; LEARNING STYLES; STRATEGIES; RELIABILITY; PERSPECTIVE; INDEX; NEEDS Previous studies have shown that users' cognitive styles play an important role during web searching. However, only a limited number of studies have showed the relationship between cognitive styles and web search behavior. Most importantly, it is not clear which components of web search behavior are influenced by cognitive styles. This article examines the relationships between users' cognitive styles and their web searching and develops a model that portrays the relationship. The study uses qualitative and quantitative analyses based on data gathered from 50 participants. A questionnaire was utilized to collect participants' demographic information, and Riding's (1991) Cognitive Styles Analysis (CSA) test to assess their cognitive styles. Results show that users' cognitive styles influenced their information-searching strategies, query reformulation behavior, web navigational styles, and information-processing approaches. The user model developed in this study depicts the fundamental relationships between users' web search behavior and their cognitive styles. Modeling web search behavior with a greater understanding of users' cognitive styles can help information science researchers and information systems designers to bridge the semantic gap between the user and the systems. Implications of the research for theory and practice, and future work, are discussed. [Kinley, Khamsum; Tjondronegoro, Dian; Partridge, Helen; Edwards, Sylvia] Queensland Univ Technol, Sch Informat Syst, Fac Sci & Engn, Brisbane, Qld 4001, Australia Kinley, K (reprint author), Queensland Univ Technol, Sch Informat Syst, Fac Sci & Engn, GPO Box 2434, Brisbane, Qld 4001, Australia. kkinley@acm.org; dian@qut.edu.au; h.partridge@qut.edu.au; s.edwards@qut.edu.au Allinson CW, 1996, J MANAGE STUD, V33, P119, DOI 10.1111/j.1467-6486.1996.tb00801.x; Belkin N., 1996, ISI, V96, P25; Borlund P., 2010, P 3 S INF INT CONT 2, P155, DOI 10.1145/1840784.1840808; Cheema I., 1991, EDUC PSYCHOL, V11, P193, DOI DOI 10.1080/0144341910110301; Chen SC, 2009, INTERACTIVE LEARNING, V25, P179; Chen SY, 2002, J AM SOC INF SCI TEC, V53, P3, DOI 10.1002/asi.10023; ELLIS D, 1993, LIBR QUART, V63, P469; Ericsson K., 1993, PROTOCOL ANAL VERBAL; Felder RM, 2005, INT J ENG EDUC, V21, P103; Fischer G, 2001, USER MODEL USER-ADAP, V11, P65, DOI 10.1023/A:1011145532042; Ford N, 2004, J DOC, V60, P183, DOI 10.1108/00220410410522052; Ford N, 2001, BRIT J EDUC TECHNOL, V32, P5, DOI 10.1111/1467-8535.00173; Ford N, 2009, J DOC, V65, P632, DOI 10.1108/00220410910970285; Ford N, 2005, J AM SOC INF SCI TEC, V56, P757, DOI 10.1002/asi.20173; Ford N, 2001, J AM SOC INF SCI TEC, V52, P1049, DOI 10.1002/asi.1165; Ford N, 2005, J AM SOC INF SCI TEC, V56, P741, DOI 10.1002/asi.20168; Frias-Martinez E, 2005, EXPERT SYST APPL, V29, P320, DOI 10.1016/j.eswa.2005.04.005; Frias-Martinez E, 2008, INT J INFORM MANAGE, V28, P355, DOI 10.1016/j.ijinfomgt.2007.10.003; Gwizdka J., 2008, P AM SOC INFORM SCI, V45, P1; Holscher C, 2000, COMPUT NETW, V33, P337, DOI 10.1016/S1389-1286(00)00031-1; Hudson L., 1968, CONTRARY IMAGINATION; Ingwersen P, 1996, J DOC, V52, P3, DOI 10.1108/eb026960; Jansen BJ, 2006, LIBR INFORM SCI RES, V28, P407, DOI 10.1016/j.lisr.2006.06.005; Julien H, 1996, LIBR INFORM SCI RES, V18, P53, DOI 10.1016/S0740-8188(96)90030-4; Kim J., 2006, THESIS RUTGERS U NEW; Kim KS, 2002, J AM SOC INF SCI TEC, V53, P109, DOI 10.1002/asi.10014; Kinley K., 2013, THESIS QUEENSLAND U; Kinley K., 2012, P 24 AUSTR COMP HUM, P299; Kinley K., 2012, P 17 AUSTR DOC COMP, P39; Knight SA, 2008, INFORM SCI KNOWL MAN, V14, P209; Lazonder AW, 2000, J AM SOC INFORM SCI, V51, P576, DOI 10.1002/(SICI)1097-4571(2000)51:6<576::AID-ASI9>3.0.CO;2-7; Moss N., 1999, P EUR C ED RES, P22; Ortiz-Cordova A, 2012, J AM SOC INF SCI TEC, V63, P1426, DOI 10.1002/asi.22640; Parkinson A, 2004, PERS INDIV DIFFER, V37, P1273, DOI 10.1016/j.paid.2003.12.012; PASK G, 1976, BRIT J EDUC PSYCHOL, V46, P128; Richardson A., 1977, J MENTAL IMAGERY, V1, P109; Riding R., 1991, COGNITIVE STYLES ANA; Riding R., 1998, COGNITIVE STYLES LEA; Riding R. J., 1997, EDUC PSYCHOL, V17, P29, DOI 10.1080/0144341970170102; RIDING RJ, 1981, BRIT J PSYCHOL, V72, P59; Saracevic T, 1997, P ASIS ANNU MEET, V34, P313; Schamber L, 2000, J AM SOC INFORM SCI, V51, P734, DOI 10.1002/(SICI)1097-4571(2000)51:8<734::AID-ASI60>3.0.CO;2-3; Spink A, 2007, ONLINE INFORM REV, V31, P845, DOI 10.1108/14684520710841801; Spink A, 2006, INFORM PROCESS MANAG, V42, P1366, DOI 10.1016/j.ipm.2006.01.007; Strauss A., 1990, BASICS QUALITATIVE R; Sutcliffe A, 1998, INTERACT COMPUT, V10, P321, DOI 10.1016/S0953-5438(98)00013-7; Tait H., 1998, IMPROVING STUDENT LE, P262; Tashakkori A., 1998, MIXED METHODOLOGY CO; TechSmith, 2009, CAMT STUD; Thurstone L., 1944, FACTORIAL STUDY PERC; Weber I., 2011, P 4 ACM INT C WEB SE, P15, DOI 10.1145/1935826.1935839; WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702; Witkin H. A., 1971, MANUAL EMBEDDED FIGU; WITKIN HA, 1977, REV EDUC RES, V47, P1; Witkin HA, 1981, PSYCHOL ISSUES, V51, P1; Wood F, 1996, J INFORM SCI, V22, P79, DOI 10.1177/016555159602200201 56 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUN 2014 65 6 1107 1123 10.1002/asi.23053 17 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FO WOS:000335583900002 J Dang, EKF; Luk, RWP; Allan, J Dang, Edward K. F.; Luk, Robert W. P.; Allan, James Beyond Bag-of-Words: Bigram-Enhanced Context-Dependent Term Weights JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article INFORMATION-RETRIEVAL; TEXT CLASSIFICATION; RELEVANCE FEEDBACK; MODEL; FREQUENCY; PROXIMITY; SYSTEMS While term independence is a widely held assumption in most of the established information retrieval approaches, it is clearly not true and various works in the past have investigated a relaxation of the assumption. One approach is to use n-grams in document representation instead of unigrams. However, the majority of early works on n-grams obtained only modest performance improvement. On the other hand, the use of information based on supporting terms or "contexts" of queries has been found to be promising. In particular, recent studies showed that using new context-dependent term weights improved the performance of relevance feedback (RF) retrieval compared with using traditional bag-of-words BM25 term weights. Calculation of the new term weights requires an estimation of the local probability of relevance of each query term occurrence. In previous studies, the estimation of this probability was based on unigrams that occur in the neighborhood of a query term. We explore an integration of the n-gram and context approaches by computing context-dependent term weights based on a mixture of unigrams and bigrams. Extensive experiments are performed using the title queries of the Text Retrieval Conference (TREC)-6, TREC-7, TREC-8, and TREC-2005 collections, for RF with relevance judgment of either the top 10 or top 20 documents of an initial retrieval. We identify some crucial elements needed in the use of bigrams in our methods, such as proper inverse document frequency (IDF) weighting of the bigrams and noise reduction by pruning bigrams with large document frequency values. We show that enhancing context-dependent term weights with bigrams is effective in further improving retrieval performance. [Dang, Edward K. F.; Luk, Robert W. P.] Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China; [Allan, James] Univ Massachusetts, Ctr Intelligent Informat Retrieval, Sch Comp Sci, Amherst, MA 01003 USA Dang, EKF (reprint author), Hong Kong Polytech Univ, Dept Comp, Hong Kong, Hong Kong, Peoples R China. cskfdang@comp.polyu.edu.hk; csrluk@comp.polyu.edu.hk; allan@cs.umass.edu Bekkerman R., 2003, IR408 CIIR U MASS; Buckley C., 1994, 3 TEXT RETR C TREC 3; Buckley C., 1994, P 17 ANN INT ACM SIG, P292; Callan J.P., 1994, P 17 ANN INT ACM SIG, P302; Cummins R, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P251, DOI 10.1145/1571941.1571986; Dang EKF, 2010, J AM SOC INF SCI TEC, V61, P2514, DOI 10.1002/asi.21425; Gao J., 2004, P 27 ANN INT ACM SIG, P170, DOI DOI 10.1145/1008992.1009024; Harman D, 1992, P 15 ANN INT ACM SIG, P1, DOI 10.1145/133160.133167; Kaszkiel M, 1997, PROCEEDINGS OF THE 20TH ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P178, DOI 10.1145/258525.258561; Kleinbaum D., 2002, LOGISTIC REGRESSION; Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI 10.1145/383952.383972; Lease M, 2008, LECT NOTES COMPUT SC, V5152, P687, DOI 10.1007/978-3-540-85760-0_87; Lease M., 2008, 17 TEXT RETR C TREC; Liu RL, 2010, J AM SOC INF SCI TEC, V61, P300, DOI 10.1002/asi.21260; Metzler D., 2005, P 28 ANN INT ACM SIG, P472, DOI DOI 10.1145/1076034.1076115; Peng FC, 2003, LECT NOTES COMPUT SC, V2633, P335; Pickens J., 2006, P CIKM 2006 NOV 2006, P559, DOI 10.1145/1183614.1183694; Ponte J. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291008; Rasolofo Y, 2003, LECT NOTES COMPUT SC, V2633, P207; Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582]; Robertson S. E., 1994, P 17 ANN INT ACM SIG, P232; ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302; Robertson SE, 2000, INFORM PROCESS MANAG, V36, P95, DOI 10.1016/S0306-4573(99)00046-1; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Ruthven I, 2003, KNOWL ENG REV, V18, P95, DOI 10.1017/S0269888903000638; SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0; SALTON G, 1990, J AM SOC INFORM SCI, V41, P288, DOI 10.1002/(SICI)1097-4571(199006)41:4<288::AID-ASI8>3.0.CO;2-H; Smucker M. D., 2007, P 16 ACM C INF KNOWL, P623, DOI 10.1145/1321440.1321528; Song F, 1999, PROCEEDINGS OF THE EIGHTH INTERNATIONAL CONFERENCE ON INFORMATION KNOWLEDGE MANAGEMENT, CIKM'99, P316, DOI 10.1145/319950.320022; Song RH, 2008, LECT NOTES COMPUT SC, V4956, P346; Sparck-Jones K, 2000, INFORM PROCESS MANAG, V36, P809, DOI 10.1016/S0306-4573(00)00016-9; SPARCKJONES K, 1972, J DOC, V28, P11; Srikanth M., 2002, Proceedings of SIGIR 2002. Twenty-Fifth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Tan CM, 2002, INFORM PROCESS MANAG, V38, P529, DOI 10.1016/S0306-4573(01)00045-0; Tao T., 2007, P 30 ANN INT ACM SIG, P295, DOI 10.1145/1277741.1277794; Tesar R, 2006, P 2006 ACM S DOC ENG, P138, DOI DOI 10.1145/1166160.1166197; Voorhees E. M., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291017; Wu HC, 2008, ACM T INFORM SYST, V26, DOI 10.1145/1361684.1361686; Wu HC, 2007, INFORM PROCESS MANAG, V43, P1308, DOI 10.1016/j.ipm.2006.10.009; Xu JX, 2000, ACM T INFORM SYST, V18, P79, DOI 10.1145/333135.333138 40 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUN 2014 65 6 1134 1148 10.1002/asi.23024 15 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FO WOS:000335583900004 J Liu, XZ; Guo, C; Zhang, L Liu, Xiaozhong; Guo, Chun; Zhang, Lin Scholar Metadata and Knowledge Generation With Human And Artificial Intelligence JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article Metadata Generation; Human Intelligence; Artificial Intelligence; Natural Language Processing NLP; Scholar Publication; User Evaluation; Cross-Folder Validation ARTICLES; CREATION Scholar metadata have traditionally centered on descriptive representations, which have been used as a foundation for scholarly publication repositories and academic information retrieval systems. In this article, we propose innovative and economic methods of generating knowledge-based structural metadata (structural keywords) using a combination of natural language processing-based machine-learning techniques and human intelligence. By allowing low-barrier participation through a social media system, scholars (both as authors and users) can participate in the metadata editing and enhancing process and benefit from more accurate and effective information retrieval. Our experimental web system ScholarWiki uses machine learning techniques, which automatically produce increasingly refined metadata by learning from the structural metadata contributed by scholars. The cumulated structural metadata add intelligence and automatically enhance and update recursively the quality of metadata, wiki pages, and the machine-learning model. [Liu, Xiaozhong; Guo, Chun] Indiana Univ, Dept Informat & Lib Sci, Sch Informat & Comp, Bloomington, IN 47405 USA; [Zhang, Lin] Dalian Maritime Univ, Transportat Management Coll, Dalian 116026, Peoples R China Liu, XZ (reprint author), Indiana Univ, Dept Informat & Lib Sci, Sch Informat & Comp, 1320 East 10th St,Li 011, Bloomington, IN 47405 USA. liu237@indiana.edu; chunguo@indiana.edu; joslin@dlmu.edu.cn HAYNES RB, 1987, ANN INTERN MED, V106, P598; ALLEN B, 1994, SOC STUD SCI, V24, P279, DOI 10.1177/030631279402400204; Bucheler T, 2011, PROCEDIA COMPUT SCI, V7, P327, DOI 10.1016/j.procs.2011.09.014; Demner-Fushman D, 2007, COMPUT LINGUIST, V33, P63, DOI 10.1162/coli.2007.33.1.63; Diekema AR, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P223, DOI 10.1145/1065385.1065436; Evans JA, 2011, SCIENCE, V331, P721, DOI 10.1126/science.1201765; Greenberg J., 2001, P INT C DUBL COR MET, V2, P38; Guo C., 2012, P AM SOC INFORM SCI, V49, P1, DOI [10.1002/meet.14504901152, DOI 10.1002/MEET.14504901152]; Guo C., 2013, ICONFERENCE 2013 P, P777, DOI [10.9776/13382, DOI 10.9776/13382]; Guo Y., 2010, P BIONLP, P99; Han J, 2006, DATA MINING CONCEPTS; Handschuh S, 2002, LECT NOTES ARTIF INT, V2473, P358; Hersh W., 2004, P 14 TEXT RETRIEVAL; Hirohata K., 2008, P 3 INT JOINT C NAT, P381; Hook K., 1997, FLEX HYP WORKSH 8 AS; International Federation of Library Associations, 1998, FUNCT REQ BIBL REC; Kajikawa Y, 2008, TECHNOL FORECAST SOC, V75, P1349, DOI 10.1016/j.techfore.2008.04.007; Lee PC, 2010, SCIENTOMETRICS, V85, P689, DOI 10.1007/s11192-010-0267-8; Liakata M., 2010, 7 INT C LANG RES EV; Lin J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148191; Lin J., 2006, P HLT NAACL 2006 WOR, P65; Liu X., J AM SOC IN IN PRESS; Liu XZ, 2013, J AM SOC INF SCI TEC, V64, P771, DOI 10.1002/asi.22744; Liu XZ, 2013, J AM SOC INF SCI TEC, V64, P1707, DOI 10.1002/asi.22851; Markines B., 2009, P 18 INT C WORLD WID, P641, DOI 10.1145/1526709.1526796; McKnight L., 2003, P 2003 ANN S AM MED, P440; Merity S., 2009, P 2009 WORKSH TEXT C, P19, DOI 10.3115/1699750.1699754; Milstead J, 1999, ONLINE, V23, P24; Mizuta Y, 2006, INT J MED INFORM, V75, P468, DOI 10.1016/j.ijmedinf.2005.06.013; Muller H. L., 2008, P 8 ACM IEEE CS JOIN, P157, DOI 10.1145/1378889.1378917; Qin J., 2008, METADATA; Quinlan JR, 1993, C4 5 PROGRAMS MACHIN; Rodriguez MA, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1462198.1462199; Shotton D, 2009, PLOS COMPUT BIOL, V5, DOI 10.1371/journal.pcbi.1000361; Siorpaes K, 2010, WORLD WIDE WEB, V13, P33, DOI 10.1007/s11280-009-0078-0; Tbahriti I, 2006, INT J MED INFORM, V75, P488, DOI 10.1016/j.ijmedinf.2005.06.007; Teufel S, 2009, P 2009 C EMP METH NA, V3, P1493, DOI 10.3115/1699648.1699696; Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936; Wade V., 2011, P 22 ACM C HYP HYP H, P37 39 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUN 2014 65 6 1187 1201 10.1002/asi.23013 15 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FO WOS:000335583900008 J Badia, A Badia, Antonio Data, Information, Knowledge: An Information Science Analysis JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article I analyze the text of an article that appeared in this journal in 2007 that published the results of a questionnaire in which a number of experts were asked to define the concepts of data, information, and knowledge. I apply standard information retrieval techniques to build a list of the most frequent terms in each set of definitions. I then apply information extraction techniques to analyze how the top terms are used in the definitions. As a result, I draw data-driven conclusions about the aggregate opinion of the experts. I contrast this with the original analysis of the data to provide readers with an alternative viewpoint on what the data tell us. Univ Louisville, Comp Engn & Comp Sci Dept, Duthie Ctr Engn, Louisville, KY 40292 USA Badia, A (reprint author), Univ Louisville, Comp Engn & Comp Sci Dept, Duthie Ctr Engn, Louisville, KY 40292 USA. abadia@louisville.edu Han J, 2012, MOR KAUF D, P1; Ribeiro-Neto B., 1999, MODERN INFORM RETRIE; Sarawagi S., 2008, INFORM EXTRACTION; Thelwall M, 2010, J AM SOC INF SCI TEC, V61, P2544, DOI 10.1002/asi.21416; Zins M., 2007, J AM SOC INFORM SCI, V58, P479 5 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH JUN 2014 65 6 1279 1287 10.1002/asi.23043 9 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FO WOS:000335583900014 J dos Reis, JC; Bonacin, R; Baranauskas, MCC dos Reis, Julio Cesar; Bonacin, Rodrigo; Baranauskas, M. Cecilia C. Addressing universal access in social networks: an inclusive search mechanism UNIVERSAL ACCESS IN THE INFORMATION SOCIETY English Article Inclusive social network; Inclusive search mechanism; Information retrieval; Ontology; Semantic search and semiotics Social network services (SNSs) have brought new possibilities and challenges to the design of software environments that respect people's cultural differences. These systems may represent an opportunity for social and digital inclusion. However, search mechanisms in these systems impose serious barriers for people in the process of acquiring digital literacy. One of the barriers is the difficulty of using the adequate terms/keywords to perform content searches. This paper presents an approach to allow ordinary, non-technology proficient people to access the content of a network through the use of search parameters that make sense to them. The proposal is grounded on Semantic Web technologies (Web ontology) combined with Organizational Semiotics concepts and methods to identify the users' profile and language. A case study was conducted with the search mechanism integrated into a SNS, and a preliminary evaluation reveals the advantages and drawbacks of the approach. [dos Reis, Julio Cesar] Publ Res Ctr Henri Tudor, Resource Ctr Hlth Care Technol SANTEC, L-4362 Esch Sur Alzette, Luxembourg; [dos Reis, Julio Cesar] Univ Paris 11, Lab Comp Sci LRI, PCRI, F-91405 Orsay, France; [Bonacin, Rodrigo] Fac Campo Limpo Paulista FACCAMP, BR-13231230 Campo Limpo Paulista, SP, Brazil; [Bonacin, Rodrigo] Ctr Informat Technol Renato Archer, Campinas, SP, Brazil; [Baranauskas, M. Cecilia C.] Univ Campinas UNICAMP, Inst Comp, Dept Informat Syst, Campinas, SP, Brazil dos Reis, JC (reprint author), Publ Res Ctr Henri Tudor, Resource Ctr Hlth Care Technol SANTEC, 6 Av Hauts Fourneaux, L-4362 Esch Sur Alzette, Luxembourg. julio.dosreis@tudor.lu; rodrigo.bonacin@cti.gov.br; cecilia@ic.unicamp.br Bonacin, Rodrigo/B-6650-2014 Microsoft Research-FAPESP Institute for IT Research [2007/54564-1]; CNPq/CTI [680.041/2006-0]; EcoWeb Project [560044/2010-0] This work was funded by Microsoft Research-FAPESP Institute for IT Research (#2007/54564-1), CNPq/CTI (#680.041/2006-0), and by EcoWeb Project (#560044/2010-0). The authors thank the commitment and collaboration provided by the people of 'Vila Uniao' and by the Telecenter 'Vila Monte Alegre.' The authors also thank colleagues from CTI Renato Archer, IC/UNICAMP, InterHAD, and NIED/UNICAMP. Baranauskas M.C.C., 2007, E CIDADANIA SYSTEMS; Baranauskas M.C.C., 2006, GRAND CHALLENGES IN; Berners-Lee T., 2001, SCIENTIFIC AMERICAN; Bonino D., 2004, WSEAS Transactions on Information Science and Applications, V1; Boyd D. M., 2008, J COMPUT-MEDIAT COMM, V13, P210, DOI DOI 10.1111/J.1083-6101.2007.00393.X; Brazilian Internet Steering Committee, 2010, SURVEY ON THE USE OF; Buitelaar P., 2005, ONTOLOGY LEARNING FR, V123; Choudhari A., 2008, SMARTSEEK SEMANTIC S; Egger M., 2009, LECTURE NOTES IN INF, V154; Fang WD, 2005, PROCEEDINGS OF 2005 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-9, P1913; Gibson J.J., 1977, PERCEIVING ACTING AN; Guha R, 2003, P 12 INT C WORLD WID, P700; Hamasaki M., 2008, PROCEEDINGS OF THE 1; Haynes J., 2009, PROC OF THE 3RD WORK; Heflin J, 2000, ARTIF INTELL, P35; Hendler J, 2010, ARTIF INTELL, V174, P156, DOI 10.1016/j.artint.2009.11.010; Hildebrand M., 2007, REPORT; Hoang H., 2006, PROCEEDINGS OF THE I, P06; Institute PauloMontenegro, 2009, INDICATOR OF FUNCTIO; Kassim J.M., 2009, INTERNATIONAL CONFER, V02, P380; Liu K., 2000, SEMIOTICS IN INFORMA; Liu KC, 2008, INFORM SOFTWARE TECH, V50, P1155, DOI 10.1016/j.infsof.2008.03.008; Lopes L., 2009, P 4 LANG TECHN C LTC, P427; Mangold Christoph, 2007, International Journal of Metadata, Semantics and Ontologies, V2, DOI 10.1504/IJMSO.2007.015073; Medelyan Olena, 2008, Journal of the American Society for Information Science and Technology, V59, DOI 10.1002/asi.20790; Mika P, 2005, LECT NOTES COMPUT SC, V3729, P522; Mori J, 2007, 20TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, P2820; Neris VPD, 2009, INFORMATION SYSTEMS IN THE CHANGING ERA: THEORY AND PRACTICE, P247; OSW, 1995, THE 1ST INTERNATIONA; Peirce C. S., 1931, COLLECTED PAPERS; Pereira R, 2008, ACM INTERNATIONAL CO, V378, P126; Pfeil U, 2010, UNIVERSAL ACCESS INF, V9, P1, DOI 10.1007/s10209-009-0154-3; Ramachandran D, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P1087; Reis J.C., 2010, P 30 C COMP BRAZ SOC, V1, P380; Reis J.C., 2010, P 12 INT C INF SEM O, P60; Reis J.C., 2011, TECHNICAL REPORT IC; Reis J.C., 2011, LECTURE NOTES IN BUS, V73, P555; Reis J.C., 2010, PROCEEDINGS OF THE I, P155; Salter A., 2002, 4TH INTERNATIONAL CO, P847; Santos T.M., 2008, P 10 INT C ENT INF S, V2, P305; Stamper R.K., 2000, INFORMATION ORGANISA; Stamper R.K., 1993, REQUIREMENTS ENGINEE; Vieira M.V., 2007, P 16 ACM C INF KNOWL, P563, DOI 10.1145/1321440.1321520; Wang W., 2008, INT J COMMUN SIWN, V3, P76; Wei W., 2007, TECH REP; Yu B., 2003, P 2 INT JOINT C AUT, P65 46 0 0 SPRINGER HEIDELBERG HEIDELBERG TIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY 1615-5289 1615-5297 UNIVERSAL ACCESS INF Univers. Access Inf. Soc. JUN 2014 13 2 125 145 10.1007/s10209-013-0290-7 21 Computer Science, Cybernetics; Ergonomics Computer Science; Engineering AG8OC WOS:000335677000001 J Biggers, LR; Bocovich, C; Capshaw, R; Eddy, BP; Etzkorn, LH; Kraft, NA Biggers, Lauren R.; Bocovich, Cecylia; Capshaw, Riley; Eddy, Brian P.; Etzkorn, Letha H.; Kraft, Nicholas A. Configuring latent Dirichlet allocation based feature location EMPIRICAL SOFTWARE ENGINEERING English Article Software evolution; Program comprehension; Feature location; Static analysis; Text retrieval SOURCE CODE; INFORMATION-RETRIEVAL; PROGRAM COMPREHENSION; TOPICS; COHESION Feature location is a program comprehension activity, the goal of which is to identify source code entities that implement a functionality. Recent feature location techniques apply text retrieval models such as latent Dirichlet allocation (LDA) to corpora built from text embedded in source code. These techniques are highly configurable, and the literature offers little insight into how different configurations affect their performance. In this paper we present a study of an LDA based feature location technique (FLT) in which we measure the performance effects of using different configurations to index corpora and to retrieve 618 features from 6 open source Java systems. In particular, we measure the effects of the query, the text extractor configuration, and the LDA parameter values on the accuracy of the LDA based FLT. Our key findings are that exclusion of comments and literals from the corpus lowers accuracy and that heuristics for selecting LDA parameter values in the natural language context are suboptimal in the source code context. Based on the results of our case study, we offer specific recommendations for configuring the LDA based FLT. [Biggers, Lauren R.; Eddy, Brian P.; Kraft, Nicholas A.] Univ Alabama, Dept Comp Sci, Tuscaloosa, AL 35487 USA; [Bocovich, Cecylia] Macalester Coll, Dept Math Stat & Comp Sci, St Paul, MN 55105 USA; [Capshaw, Riley] Hendrix Coll, Dept Math & Comp Sci, Conway, AR USA; [Etzkorn, Letha H.] Univ Alabama, Dept Comp Sci, Huntsville, AL 35899 USA Kraft, NA (reprint author), Univ Alabama, Dept Comp Sci, Tuscaloosa, AL 35487 USA. lbiggers@cs.ua.edu; cbocovic@macalester.edu; capshawrf@hendrix.edu; bpeddy@ua.edu; etzkorl@uah.edu; nkraft@cs.ua.edu National Science Foundation [0851824, 0915559, 1156563]; U.S. Department of Education [P200A100182] We thank the anonymous reviewers for their insightful comments and helpful suggestions. This material is based upon work supported by the National Science Foundation under Grant Nos. 0851824, 0915559, and 1156563 and by the U.S. Department of Education under Grant No. P200A100182. Abadi A, 2008, INT C PROGRAM COMPRE, P103, DOI 10.1109/ICPC.2008.30; Abebe SL, 2009, EUR CON SFTWR MTNCE, P189, DOI 10.1109/CSMR.2009.61; Abebe SL, 2009, WORK CONF REVERSE EN, P95, DOI 10.1109/WCRE.2009.26; Andrieu C, 2003, MACH LEARN, V50, P5, DOI 10.1023/A:1020281327116; Antoniol G, 2002, IEEE T SOFTWARE ENG, V28, P970, DOI 10.1109/TSE.2002.1041053; Asuncion A. U., 2009, P 25 ANN C UNC ART I, P27; Asuncion H, 2010, P 32 ACM IEEE INT C, P95, DOI 10.1145/1806799.1806817; Baldi PF, 2008, ACM SIGPLAN NOTICES, V43, P543, DOI 10.1145/1449955.1449807; Basili V., 1994, GOAL QUESTION METRIC; Beard M, 2011, P WCRE, P124; BIGGERSTAFF TJ, 1993, PROC INT CONF SOFTW, P482, DOI 10.1109/ICSE.1993.346017; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Canfora G, 2006, P INT WORKSH MIN SOF, P105, DOI 10.1145/1137983.1138009; Chang J, 2010, ANN APPL STAT, V4, P124, DOI 10.1214/09-AOAS309; Corley C.S., 2011, P 6 INT WKS TRAC EM, P31, DOI DOI 10.1145/1987856.1987863; DEERWESTER S, 1990, J AM SOC INFORM SCI, V41, P391, DOI 10.1002/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9; De Lucia A, 2007, ACM T SOFTW ENG METH, V16, DOI 10.1145/1276933.1276934; Dit B, 2013, J SOFTW-EVOL PROC, V25, P53, DOI 10.1002/smr.567; Dit B, 2011, CONF PROC INT SYMP C, P11, DOI 10.1109/ICPC.2011.47; Eaddy M, 2008, IEEE T SOFTWARE ENG, V34, P497, DOI 10.1109/TSE.2008.36; Eisenberg AD, 2005, PROC IEEE INT CONF S, P337; Fluri B, 2007, P 14 WORK C REV ENG, P70, DOI 10.1109/WCRE.2007.21; Fox C, 1992, INFORM RETRIEVAL DAT; Gay G, 2009, PROC IEEE INT CONF S, P351, DOI 10.1109/ICSM.2009.5306315; Gethers M., 2010, P 2010 IEEE INT C SO, P1, DOI DOI 10.1109/ICSM.2010.5609687; Griffiths TL, 2004, P NATL ACAD SCI USA, V101, P5228, DOI 10.1073/pnas.0307752101; Heinrich Gregor, 2009, PARAMETER ESTIMATION; Hill E, 2007, P 22 IEEE ACM INT C, P14, DOI 10.1145/1321631.1321637; Lawrie D., 2011, P 27 IEEE INT C SOFT, P113, DOI DOI 10.1109/ICSM.2011.6080778; Liu D., 2007, P 22 IEEE ACM INT C, P234, DOI DOI 10.1145/1321631.1321667; Liu YX, 2009, PROC IEEE INT CONF S, P233, DOI 10.1109/ICSM.2009.5306318; Lukins SK, 2008, WORK CONF REVERSE EN, P155, DOI 10.1109/WCRE.2008.33; Lukins SK, 2010, INFORM SOFTWARE TECH, V52, P972, DOI 10.1016/j.infsof.2010.04.002; Marcus A, 2005, PROC IEEE INT CONF S, P133; Marcus A, 2010, P FSE SDP WKSP FUT S, P229, DOI [10.1145/1882362.1882410, DOI 10.1145/1882362.1882410]; Marcus A, 2004, 11TH WORKING CONFERENCE ON REVERSE ENGINEERING, PROCEEDINGS, P214, DOI 10.1109/WCRE.2004.10; Maskeri G, 2008, P 1 C IND SOFTW ENG, DOI [10.1145/1342211.1342234, DOI 10.1145/1342211.1342234]; Minka T, 2009, ESTIMATING DIRICHLET; Oliveto Rocco, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.20; Poshyvanyk D, 2009, EMPIR SOFTW ENG, V14, P5, DOI 10.1007/s10664-008-9088-2; Poshyvanyk D, 2007, IEEE T SOFTWARE ENG, V33, P420, DOI 10.1109/TSE.2007.1016.; Rajlich V, 2006, COMMUN ACM, V49, P67, DOI 10.1145/1145287.1145289; Rajlich V, 2002, PROG COMPREHEN, P271, DOI 10.1109/WPC.2002.1021348; Rao S., 2011, P 8 WORK C MIN SOFTW, P43; Ratanotayanon Sukanya, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.33; Revelle M, 2009, INT C PROGRAM COMPRE, P218; Revelle Meghan, 2010, Proceedings of the 18th IEEE International Conference on Program Comprehension (ICPC 2010), DOI 10.1109/ICPC.2010.10; Salton G., 1989, AUTOMATIC TEXT PROCE; SALTON G, 1988, INFORM PROCESS MANAG, V24, P513, DOI 10.1016/0306-4573(88)90021-0; Savage T, 2010, P 26 INT C SOFTW MAI, P1, DOI [10.1109/ICSM.2010.5609654, DOI 10.1109/ICSM.2010.5609654]; Scanniello G, 2011, CONF PROC INT SYMP C, P1, DOI 10.1109/ICPC.2011.13; Shao P, 2012, INT J COMPUT APPL TE, V44, P61; Thomas SW, 2011, P 8 WORK C MIN SOFTW, P173, DOI DOI 10.1145/1985441.1985467; Tian K, 2009, 2009 6TH IEEE INTERNATIONAL WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES, P163, DOI 10.1109/MSR.2009.5069496; Vinz BL, 2006, INT C PROGRAM COMPRE, P69, DOI 10.1109/ICPC.2006.7; Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204; Zhao W, 2006, ACM T SOFTW ENG METH, V15, P195, DOI 10.1145/1131421.1131424 57 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1382-3256 1573-7616 EMPIR SOFTW ENG Empir. Softw. Eng. JUN 2014 19 3 465 500 10.1007/s10664-012-9224-x 36 Computer Science, Software Engineering Computer Science AF0ZA WOS:000334442700002 J Chen, XH; Huang, XL Chen, Xiuhong; Huang, Xianglei Usage of differential absorption method in the thermal IR: A case study of quick estimate of clear-sky column water vapor JOURNAL OF QUANTITATIVE SPECTROSCOPY & RADIATIVE TRANSFER English Article Total column water vapor; Retrieval; Differential absorption method; Thermal-IR; AIRS WINDOW RADIANCE MEASUREMENTS; SPLIT-WINDOW; PRECIPITABLE WATER; SURFACE TEMPERATURES; RETRIEVAL ALGORITHMS; AVHRR DATA; LAND; RESOLUTION; SATELLITE; AIRS/AMSU/HSB The concept of differential absorption has been widely used in UV and shortwave remote sensing. This study explores how to extend such concept to the thermal-IR for fast estimation of the total column water vapor (CWV) from clear-sky IR radiances. Using Atmospheric Infrared Sounder (AIRS) radiances as a case study, double difference of radiances at two pairs of pre-selected AIRS channels can be used to suppress the influence of continuum absorption and to highlight contrasts due to weak water vapor line absorptions. To take emission into account, another two AIRS channels are used as surrogates of surface temperature and lapse rate in the lower troposphere. As a result, a three-dimensional look-up table (LUT) can be constructed based on training data sets. Such LUT enables us a fast estimate of CWV directly from the spectral radiances without any a prior information or formal retrieval. The performance of the method is tested using synthetic AIRS radiances based on reanalysis as well as actual sounding profiles. It is also tested against AIRS L2 cloud-cleared radiances and CWV retrievals. The comparisons show that the mean bias of this method is within +/- 0.07 cm and the root-mean-square fractional error is about 33%. (C) 2014 Elsevier Ltd. All rights reserved. [Chen, Xiuhong; Huang, Xianglei] Univ Michigan, Dept Atmospher Ocean & Space Sci, Ann Arbor, MI 48109 USA Chen, XH (reprint author), Univ Michigan, Dept Atmospher Ocean & Space Sci, Ann Arbor, MI 48109 USA. xiuchen@umich.edu NASA [NNX11AH55G] We thank one anonymous reviewer for the suggestions that improve the clarity of this paper and visualization effect of Fig. 6. ECMWF ERA-Interim reanalysis are downloaded from http://data-portaLecmwf.int/data/d/. The AIRS L2 data are obtained from http://disc.sci.gsfc.nasa.gov/AIRS/data-holdings/by-data-product-V6. The authors wish to thank the Atmospheric Radiation Analysis group at Laboratoire de Meteorologie Dynamique for providing the TIGR data set. This work was supported by NASA Grants NNX11AH55G awarded to the University of Michigan. ALISHOUSE JC, 1990, IEEE T GEOSCI REMOTE, V28, P811, DOI 10.1109/36.58967; Aumann HH, 2003, IEEE T GEOSCI REMOTE, V41, P253, DOI 10.1109/TGRS.2002.808356; Baldridge AM, 2009, REMOTE SENS ENVIRON, V113, P711, DOI 10.1016/j.rse.2008.11.007; CHAHINE MT, 1992, NATURE, V359, P373, DOI 10.1038/359373a0; CHEDIN A, 1985, J CLIM APPL METEOROL, V24, P128, DOI 10.1175/1520-0450(1985)024<0128:TIIIMA>2.0.CO;2; Chen XH, 2013, J CLIMATE, V26, P478, DOI 10.1175/JCLI-D-12-00212.1; CHESTERS D, 1983, J CLIM APPL METEOROL, V22, P725, DOI 10.1175/1520-0450(1983)022<0725:LLWVFF>2.0.CO;2; Cihlar J, 2001, IEEE T GEOSCI REMOTE, V39, P173, DOI 10.1109/36.898679; Clough SA, 2005, J QUANT SPECTROSC RA, V91, P233, DOI 10.1016/j.jqsrt.2004.05.058; Dee DP, 2011, Q J ROY METEOR SOC, V137, P553, DOI 10.1002/qj.828; Frouin R, 1990, AM MET SOC S 1 ISLSC, P135; HEINEMANN G, 1995, METEOROL ATMOS PHYS, V55, P87, DOI 10.1007/BF01029604; Huang XL, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD013932; Huang XL, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD009219; IWASAKI H, 1994, J METEOROL SOC JPN, V72, P223; JEDLOVEC GJ, 1990, J APPL METEOROL, V29, P863, DOI 10.1175/1520-0450(1990)029<0863:PWEFHR>2.0.CO;2; KLEESPIES TJ, 1990, J APPL METEOROL, V29, P851, DOI 10.1175/1520-0450(1990)029<0851:ROPWFO>2.0.CO;2; KOMHYR WD, 1989, J GEOPHYS RES-ATMOS, V94, P9847, DOI 10.1029/JD094iD07p09847; Li ZL, 2003, INT J REMOTE SENS, V24, P5095, DOI 10.1080/0143116031000096014; Liu X, 2006, APPL OPTICS, V45, P201, DOI 10.1364/AO.45.000201; Loeb NG, 2005, J ATMOS OCEAN TECH, V22, P338, DOI 10.1175/JTECH1712.1; McMillin LM, 1971, THESIS IOWA STATE U; Nassar R, 2003, P SOC PHOTO-OPT INS, V5151, P173, DOI 10.1117/12.504639; Neely RR, 2011, J ATMOS OCEAN TECH, V28, P1141, DOI 10.1175/JTECH-D-10-05021.1; Noel S, 2002, ADV SPACE RES, V29, P1697, DOI 10.1016/S0273-1177(02)00099-6; Olsen ET, 2013, AIRS AMSU HSB VERSIO, P134; Ottle C, 1997, REMOTE SENS ENVIRON, V61, P410, DOI 10.1016/S0034-4257(97)00055-2; Pagano TS, 2002, SPIE P, VVII, P4814; Platt U, 1983, OPTICAL LASER REMOTE, P95; Pougatchev N, 2009, ATMOS CHEM PHYS, V9, P6453; PRABHAKARA C, 1982, J APPL METEOROL, V21, P59, DOI 10.1175/1520-0450(1982)021<0059:RSOPWO>2.0.CO;2; RAMANATHAN V, 1981, J ATMOS SCI, V38, P918, DOI 10.1175/1520-0469(1981)038<0918:TROOAI>2.0.CO;2; Schroedter-Homscheidt M, 2008, REMOTE SENS ENVIRON, V112, P249, DOI 10.1016/j.rse.2007.05.006; SOBRINO JA, 1994, IEEE T GEOSCI REMOTE, V32, P243, DOI 10.1109/36.295038; Sobrino JA, 1999, IEEE T GEOSCI REMOTE, V37, P1425, DOI 10.1109/36.763306; Soden BJ, 2005, SCIENCE, V310; Susskind J, 2013, PROC SPIE, V8866, DOI 10.1117/12.2023375; Susskind J, 2003, IEEE T GEOSCI REMOTE, V41, P390, DOI 10.1109/TGRS.2002.808236; Tobin DC, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006103; Ye HC, 2007, GEOPHYS RES LETT, V34, DOI 10.1029/2006GL028547 40 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0022-4073 1879-1352 J QUANT SPECTROSC RA J. Quant. Spectrosc. Radiat. Transf. JUN 2014 140 99 106 10.1016/j.jqsrt.2014.02.019 8 Spectroscopy Spectroscopy AG0JJ WOS:000335101500011 J Stokes, K; Farras, O Stokes, Klara; Farras, Oriol Linear spaces and transversal designs: k-anonymous combinatorial configurations for anonymous database search notes DESIGNS CODES AND CRYPTOGRAPHY English Article Anonymous database search; Combinatorial configuration; Partial linear space; Neighborhood; Linear space; Transversal design; k-Anonymity PRIVATE INFORMATION-RETRIEVAL Anonymous database search protocols allow users to query a database anonymously. This can be achieved by letting the users form a peer-to-peer community and post queries on behalf of each other. In this article we discuss an application of combinatorial configurations (also known as regular and uniform partial linear spaces) to a protocol for anonymous database search, as defining the key-distribution within the user community that implements the protocol. The degree of anonymity that can be provided by the protocol is determined by properties of the neighborhoods and the closed neighborhoods of the points in the combinatorial configuration that is used. Combinatorial configurations with unique neighborhoods or unique closed neighborhoods are described and we show how to attack the protocol if such configurations are used. We apply k-anonymity arguments and present the combinatorial configurations with k-anonymous neighborhoods and with k-anonymous closed neighborhoods. The transversal designs and the linear spaces are presented as optimal configurations among the configurations with k-anonymous neighborhoods and k-anonymous closed neighborhoods, respectively. [Stokes, Klara; Farras, Oriol] Univ Rovira & Virgili, Dept Comp Engn & Math, E-43007 Tarragona, Catalonia, Spain Stokes, K (reprint author), Univ Oberta Catalunya, Internet Interdisciplinary Inst IN3, Edifici Media TIC,Roc Boronat 117,7a Planta, Barcelona 08018, Catalonia, Spain. kstokes@uoc.edu; oriol.farras@urv.cat Spanish MEC [CSD2007-00004, TIN2009-11689, TIN2011-27076-C03-01]; Catalan Government [2009 SGR 1135] The authors want to thank Maria Bras-Amoros, Douglas R. Stinson, Colleen Swanson, Vicenc Torra and the anonymous referees for useful discussions and suggestions. Partial support by the Spanish MEC projects ARES (CONSOLIDER INGENIO 2010 CSD2007-00004), RIPUP (TIN2009-11689), CO-PRIVACY (TIN2011-27076-C03-01), and the Catalan Government Grant 2009 SGR 1135 is acknowledged. The authors are with the UNESCO Chair in Data Privacy, but their views do not necessarily reflect those of UNESCO nor commit that organization. Abel R.J.R., 2007, CRC HDB COMBINATORIA, P160; Ball S, 2013, J COMB DES, V21, P163, DOI 10.1002/jcd.21325; Bose R. C., 1960, CAN J MATH, V12, P189, DOI 10.4153/CJM-1960-016-5; Dalenius T., 1986, J OFF STAT, V2, P329; DALENIUS T, 1982, J STAT PLAN INFER, V6, P73, DOI 10.1016/0378-3758(82)90058-1; de Waal T., 2001, ELEMENTS STAT DISCLO; Domingo-Ferrer J, 2008, LECT NOTES COMPUT SC, V5262, P315; Domingo-Ferrer J, 2009, DATA KNOWL ENG, V68, P1237, DOI 10.1016/j.datak.2009.06.004; Domingo-Ferrer J, 2007, LECT NOTES COMPUT SC, V4721, P193; Gropp H., 2007, CRC HDB COMBINATORIA, P352; Grunbaum B., 2009, CONFIGURATIONS POINT; Hundepool A., 2012, STAT DISCLOSURE CONT; Lee J., 2005, IEEE WIR COMM NETW C, P6; Pfitzmann A, 2001, LECT NOTES COMPUTER, V2009, P1; Reiter M., 1998, ACM T INFORM SYST, V1, P66, DOI 10.1145/290163.290168; Samarati P., 1998, PROTECTING PRIVACY D; Spink A, 2001, J AM SOC INF SCI TEC, V52, P226, DOI 10.1002/1097-4571(2000)9999:9999<::AID-ASI1591>3.0.CO;2-R; Stokes K, 2010, COMPUT MATH APPL, V59, P1568, DOI 10.1016/j.camwa.2010.01.003; Stokes K., 2011, P WORKSH COMP SEC CR; Stokes K., 2011, P 4 INT WORKSH PRIV; Swanson C.M., 2012, ABS11122762 CORR; Sweeney L, 2002, INT J UNCERTAIN FUZZ, V10, P557, DOI 10.1142/S0218488502001648; Teevan J., 2005, P 29 ANN ACM C RES D, P703; Westin A. F., 1967, PRIVACY FREEDOM 24 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 0925-1022 1573-7586 DESIGN CODE CRYPTOGR Designs Codes Cryptogr. JUN 2014 71 3 503 524 10.1007/s10623-012-9745-8 22 Computer Science, Theory & Methods; Mathematics, Applied Computer Science; Mathematics AE7LJ WOS:000334179100008 J Simpson, MS; Demner-Fushman, D; Antani, SK; Thoma, GR Simpson, Matthew S.; Demner-Fushman, Dina; Antani, Sameer K.; Thoma, George R. Multimodal biomedical image indexing and retrieval using descriptive text and global feature mapping INFORMATION RETRIEVAL English Article Multimodal image retrieval; Image indexing; Clustering BAYES POINT MACHINES; ANNOTATION; SEARCH; QUANTIZATION; DATABASES; PICTURES; FUSION; SCALE; QUERY The images found within biomedical articles are sources of essential information useful for a variety of tasks. Due to the rapid growth of biomedical knowledge, image retrieval systems are increasingly becoming necessary tools for quickly accessing the most relevant images from the literature for a given information need. Unfortunately, article text can be a poor substitute for image content, limiting the effectiveness of existing text-based retrieval methods. Additionally, the use of visual similarity by content-based retrieval methods as the sole indicator of image relevance is problematic since the importance of an image can depend on its context rather than its appearance. For biomedical image retrieval, multimodal approaches are often desirable. We describe in this work a practical multimodal solution for indexing and retrieving the images contained in biomedical articles. Recognizing the importance of text in determining image relevance, our method combines a predominately text-based image representation with a limited amount of visual information, in the form of quantized content-based visual features, through a process called global feature mapping. The resulting multimodal image surrogates are easily indexed and searched using existing text-based retrieval systems. Our experimental results demonstrate that our multimodal strategy significantly improves upon the retrieval accuracy of existing approaches. In addition, unlike many retrieval methods that utilize content-based visual features, the response time of our approach is negligible, making it suitable for use with large collections. [Simpson, Matthew S.; Demner-Fushman, Dina; Antani, Sameer K.; Thoma, George R.] NIH, Lister Hill Natl Ctr Biomed Commun, US Natl Lib Med, Bethesda, MD 20892 USA Simpson, MS (reprint author), NIH, Lister Hill Natl Ctr Biomed Commun, US Natl Lib Med, Bldg 10, Bethesda, MD 20892 USA. simpsonmatt@mail.nih.gov; ddemner@mail.nih.gov; santani@mail.nih.gov; gthoma@mail.nih.gov U.S. National Library of Medicine, National Institutes of Health The authors would like to thank Dr. Md. Mahmudur Rahman and Srinivas Phadnis for extracting and preparing the content-based and text-based features of the images used in this work. This work is supported by the intramural research program of the U.S. National Library of Medicine, National Institutes of Health, and by an appointment to the NLM Research Participation Program administered by the Oak Ridge Institute for Science and Education. Alpkocak A., 2012, CLEF 2011 WORKSH; [Anonymous], 2010, ENTR PROGR UT HELP; Arthur D, 2007, PROCEEDINGS OF THE EIGHTEENTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P1027; Atrey PK, 2010, MULTIMEDIA SYST, V16, P345, DOI 10.1007/s00530-010-0182-0; Barnard K, 2003, J MACH LEARN RES, V3, P1107, DOI 10.1162/153244303322533214; Beatty M, 1997, INTERNATIONAL CONFERENCE ON IMAGE PROCESSING - PROCEEDINGS, VOL II, P835; Bezdek J. C., 1999, FUZZY MODELS ALGORIT; Blei D.M., 2003, P 26 ANN INT ACM SIG, P127, DOI DOI 10.1145/860435.860460; Brin S., 1995, P 21 INT C VER LARG, P574; Caicedo J. C., 2010, P MULT INF RETR, P359, DOI 10.1145/1743384.1743442; Callan J. P., 1992, DEXA 92. Database and Expert Systems Applications. Proceedings of the International Conference; Chang E, 2003, IEEE T CIRC SYST VID, V13, P26, DOI 10.1109/TCSVT.2002.808079; Chang SF, 2001, IEEE T CIRC SYST VID, V11, P688; Chatzichristofis SA, 2008, LECT NOTES COMPUT SC, V5008, P312; Chatzichristofis S.A., 2008, P 9 INT WORKSH IM AN, P191, DOI DOI 10.1109/WIAMIS.2008.24; Ciaccia P, 1997, PROCEEDINGS OF THE TWENTY-THIRD INTERNATIONAL CONFERENCE ON VERY LARGE DATABASES, P426; Clinchant S., 2010, CLEF 2010 WORKSH; Datta R., 2008, ACM COMPUT SURV, V40, P5; Datta R, 2007, IEEE MULTIMEDIA, V14, P24, DOI 10.1109/MMUL.2007.67; de Vries A. P., 1999, THESIS U TWENTE; Demner-Fushman D., 2008, P LANG RES CONT BAS, P18; Demner-Fushman D., 2009, INT J MED INFORM, V78, P59; Demner-Fushman D., 2012, J COMPUTING IN PRESS; Demner-Fushman D, 2007, COMPUT LINGUIST, V33, P63, DOI 10.1162/coli.2007.33.1.63; de Vries AP, 2004, IEEE IMAGE PROC, P2387; Duygulu P., 2006, LECT NOTES COMPUT SC, V2353, P349; Ferhatosmanoglu H, 2001, PROC INT CONF DATA, P503, DOI 10.1109/ICDE.2001.914864; Gkoufas Yiannis, 2011, Open Med Inform J, V5, P50, DOI 10.2174/1874431101105010050; Guttman A., 1984, P ACM SIGMOD INT C M, P47; Hamer OW, 2006, RADIOGRAPHICS, V26, P1637, DOI 10.1148/rg.266065004; Harris C., 1988, P 4 ALV VIS C, P147; Helbich TH, 1999, RADIOLOGY, V213, P537; Herbich R, 2001, J MACH LEARN RES, V1, P245, DOI 10.1162/153244301753683717; Hersh W, 2009, J DIGIT IMAGING, V22, P648, DOI 10.1007/s10278-008-9154-8; Ide NC, 2007, J AM MED INFORM ASSN, V14, P253, DOI 10.1197/jamia.M2233; Indyk P., 1998, Proceedings of the Thirtieth Annual ACM Symposium on Theory of Computing, DOI 10.1145/276698.276876; Indyk P., 2004, HDB DISCRETE COMPUTA, P877; Jegou H, 2011, IEEE T PATTERN ANAL, V33, P117, DOI 10.1109/TPAMI.2010.57; Kadir T, 2001, INT J COMPUT VISION, V45, P83, DOI 10.1023/A:1012460413855; Kalpathy-Cramer J., 2010, P INT C MULT INF RET, P165, DOI 10.1145/1743384.1743415; Kohonen T., 2001, SELF ORG MAPS INFORM, V30; Lacoste C, 2007, IEEE T CIRC SYST VID, V17, P889, DOI 10.1109/TCSVT.2007.897114; Lancaster F. W., 1973, INFORM RETRIEVAL ON; Langlotz CP, 2006, RADIOGRAPHICS, V26, P1595, DOI 10.1148/rg.266065168; Lavrenko V., 2003, P 17 ANN C NEUR INF, V16, P553; Li J, 2008, IEEE T PATTERN ANAL, V30, P985, DOI 10.1109/TPAMI.2007.70847; Lim JH, 2005, LECT NOTES COMPUT SC, V3689, P84; LINDBERG DAB, 1993, METHOD INFORM MED, V32, P281; LLOYD SP, 1982, IEEE T INFORM THEORY, V28, P129, DOI 10.1109/TIT.1982.1056489; Lowe D., 1999, P 7 IEEE INT C COMP, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Lux M., 2008, P 16 ACM INT C MULT, P1085, DOI DOI 10.1145/1459359.1459577; Maenpaa T., 2003, THESIS U OULU; MILLER GA, 1995, COMMUN ACM, V38, P39, DOI 10.1145/219717.219748; Muler H., 2003, NEW NAVIGATORS PROFE, V95, P480; Muller H., 2012, CLEF 2012 WORKSH; Muller H, 2010, INFORM RETRIEVAL SER, V32, P1, DOI 10.1007/978-3-642-15181-1; Muller H, 2004, INT J MED INFORM, V73, P1, DOI 10.1016/j.ijmedinf.2003.11.024; Muller H., 2010, CLEF 2010; Ng R, 1996, P SOC PHOTO-OPT INS, V2670, P50, DOI 10.1117/12.234809; Nowak E, 2006, LECT NOTES COMPUT SC, V3954, P490; Pham T. T., 2007, P 16 ACM C INF KNOWL, P439, DOI 10.1145/1321440.1321503; Rahman M. M., 2009, P 22 IEEE INT S COMP; Rahman MM, 2010, LECT NOTES COMPUT SC, V5853, P110; Rasiwasia N., 2010, P ACM MULT, P251, DOI 10.1145/1873951.1873987; Richardson W S, 1995, ACP J Club, V123, pA12; Rubner Y, 2000, INT J COMPUT VISION, V40, P99, DOI 10.1023/A:1026543900054; Sakai T., 2007, P 30 ANN INT ACM SIG, P71, DOI DOI 10.1145/1277741.1277756; SALTON G, 1975, COMMUN ACM, V18, P613, DOI 10.1145/361219.361220; Simpson M., 2009, CLEF 2009 WORKSH; Simpson M., 2011, CLEF 2011 WORKSH; Simpson M., 2010, CLEF 2010 WORKSH; Simpson M. S., 2012, CLEF 2012 WORKSH; Simpson M. S., 2012, P ANN S AM IN PRESS; Sivic J., 2003, P ICCV, V2, P1470, DOI DOI 10.1109/ICCV.2003.1238663]; Smucker M. D., 2007, P 16 ACM C INF KNOWL, P623, DOI 10.1145/1321440.1321528; Squire DM, 2000, PATTERN RECOGN LETT, V21, P1193, DOI 10.1016/S0167-8655(00)00081-7; Srinivasan G.N., 2008, P WORLD ACAD SCI ENG, V36, P1264; TAMURA H, 1978, IEEE T SYST MAN CYB, V8, P460, DOI 10.1109/TSMC.1978.4309999; UHLMANN JK, 1991, INFORM PROCESS LETT, V40, P175, DOI 10.1016/0020-0190(91)90074-R; Voorhees E. M., 2005, TREC EXPT EVALUATION; Wang CH, 2008, MULTIMEDIA SYST, V14, P205, DOI 10.1007/s00530-008-0128-y; Yang J., 2007, P INT WORKSH MULT IN, P197, DOI 10.1145/1290082.1290111; YIANILOS PN, 1993, PROCEEDINGS OF THE FOURTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P311; Zezula P., 2006, SIMILARITY SEARCH ME, V32; Zhou X, 2010, LECT NOTES COMPUT SC, V6388, P129 86 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1386-4564 1573-7659 INFORM RETRIEVAL Inf. Retr. JUN 2014 17 3 229 264 10.1007/s10791-013-9235-2 36 Computer Science, Information Systems Computer Science AF1SQ WOS:000334494200002 J Liu, XT; Chen, F; Fang, H; Wang, M Liu, Xitong; Chen, Fei; Fang, Hui; Wang, Min Exploiting entity relationship for query expansion in enterprise search INFORMATION RETRIEVAL English Article Entity centric; Enterprise search; Retrieval; Query expansion; Combining structured and unstructured data Enterprise search is important, and the search quality has a direct impact on the productivity of an enterprise. Enterprise data contain both structured and unstructured information. Since these two types of information are complementary and the structured information such as relational databases is designed based on ER (entity-relationship) models, there is a rich body of information about entities in enterprise data. As a result, many information needs of enterprise search center around entities. For example, a user may formulate a query describing a problem that she encounters with an entity, e.g., the web browser, and want to retrieve relevant documents to solve the problem. Intuitively, information related to the entities mentioned in the query, such as related entities and their relations, would be useful to reformulate the query and improve the retrieval performance. However, most existing studies on query expansion are term-centric. In this paper, we propose a novel entity-centric query expansion framework for enterprise search. Specifically, given a query containing entities, we first utilize both unstructured and structured information to find entities that are related to the ones in the query. We then discuss how to adapt existing feedback methods to use the related entities and their relations to improve search quality. Experimental results over two real-world enterprise collections show that the proposed entity-centric query expansion strategies are more effective and robust to improve the search performance than the state-of-the-art pseudo feedback methods for long natural language-like queries with entities. Moreover, results over a TREC ad hoc retrieval collections show that the proposed methods can also work well for short keyword queries in the general search domain. [Liu, Xitong; Fang, Hui] Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA; [Chen, Fei] HP Labs, Palo Alto, CA 94304 USA; [Wang, Min] Google Res, Mountain View, CA 94043 USA Liu, XT (reprint author), Univ Delaware, Dept Elect & Comp Engn, Newark, DE 19716 USA. xtliu@udel.edu; fei.chen4@hp.com; hfang@udel.edu; minwang@google.com HP Labs Innovation Research Program This material is based upon work supported by the HP Labs Innovation Research Program. We thank reviewers for their useful comments. Auer S, 2007, LECT NOTES COMPUT SC, V4825, P722; Bailey P., 2007, P TREC07; Bailey P., 2006, CIKM, P493; Balog K., 2006, SIGIR 06, P43; Balog K., 2008, CIKM, P489; Balog K., 2008, P TREC08; Balog K., 2007, SIGIR, P916; Balog K., 2010, P TREC; Bendersky M., 2012, SIGIR, P941; Bendersky M., 2010, P 3 ACM INT C WEB SE, P31, DOI DOI 10.1145/1718487.1718492; Bendersky M., 2011, SIGIR, P605; Brunnert J, 2007, LECT NOTES COMPUT SC, V4425, P674; Cao G., 2008, SIGIR 2008, P243; Carlson A., 2010, AAAI; Coffman J., 2013, KNOWLEDGE DATA ENG I, P1; Cohen W.W., 2003, IIWEB, P73; Craswell N., 2005, P TREC05; DEMARTINI G, 2009, FOCUSED RETRIEVAL EV, V5631, P243; Demartini G, 2010, LECT NOTES COMPUT SC, V6203, P254, DOI 10.1007/978-3-642-14556-8_26; Doan A., 2009, SIGMOD RECORD, V37; Fang H., 2006, SIGIR 06, P115; Feldman S., 2003, 29127 IDC; Freund L., 2006, SIGIR, P645; GARCIAMOLINA H, 2008, DATABASE SYSTEMS COM; Hawking D., 2004, P 15 AUSTR DAT C DAR, P15; Hearst MA, 2011, COMMUN ACM, V54, P60, DOI [10.1145/2018396.2018114, 10.1145/2018396.2018414]; J Xu, 1996, SIGIR 96, P4; Kolla M., 2007, SIGIR, P881; Lafferty J., 2001, SIGIR Forum; Lafferty J. D., 2001, ICML, P282; Lavrenko V., 2001, SIGIR Forum; Lin T., 2012, WWW, P589; Liu XiaoRan, 2011, China Vegetables, P47; Lv YH, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P579; Macdonald C., 2006, SIGIR, P675; Metzler D., 2007, SIGIR 07, P311; Metzler D., 2005, SIGIR 2005. Proceedings of the Twenty-Eighth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval; Mihalcea R., 2007, P 16 ACM C INF KNOWL, P233, DOI 10.1145/1321440.1321475; Miller DRH, 1999, SIGIR'99: PROCEEDINGS OF 22ND INTERNATIONAL CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P214; Ponte J. M., 1998, SIGIR 98, P275; Rizzolo N., 2010, LREC, V5; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Sah M., 2010, CIKM, P1665; Sarawagi Sunita, 2008, Foundations and Trends in Databases, V1, DOI 10.1561/1500000003; Serdyukov P., 2008, CIKM, P1133; Shen W., 2012, P 21 INT C WORLD WID, P449; Soboroff I., 2006, P TREC06; Suchanek F. M., 2007, WWW, P697, DOI DOI 10.1145/1242572.1242667; Tan B., 2007, SIGIR 07, P263; Tao T., 2006, SIGIR 2006, P162; Voorhees E. M., 2005, TREC EXPT EVALUATION; Wang L., 2012, SIGIR, P761; Weerkamp W., 2012, ACM T WEB, V6; Weerkamp W, 2009, LECT NOTES COMPUT SC, V5631, P292; Y Lv, 2009, SIGIR, P1895; Zelenko D, 2003, J MACH LEARN RES, V3, P1083, DOI 10.1162/153244303322533205; Zhai C., 2001, CIKM; Zhai C., 2001, SIGIR 2001, P334; Zhu J., 2009, WWW, P101 59 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1386-4564 1573-7659 INFORM RETRIEVAL Inf. Retr. JUN 2014 17 3 265 294 10.1007/s10791-013-9237-0 30 Computer Science, Information Systems Computer Science AF1SQ WOS:000334494200003 J Mojavezi, A; Ahmadian, MJ Mojavezi, Ahmad; Ahmadian, Mohammad Javad Working Memory Capacity and Self-Repair Behavior in First and Second Language Oral Production JOURNAL OF PSYCHOLINGUISTIC RESEARCH English Article First language; Second language; Self-repair behavior; Working memory capacity INDIVIDUAL-DIFFERENCES; LANGUAGE; L2; RETRIEVAL This study explores the relationship between working memory capacity and self-repair behavior in first (L1) and second language (L2) oral production. 40 Iranian intermediate EFL learners participated in this study. Their working memory capacity was measured via a version of listening span test. The participants performed two oral narrative tasks, one in their L2 (English) and one in their L1 (Farsi). Then, they were asked to listen to their own narrations and comment on the repairs they made in their speech. Self-repairs were analyzed and categorized taking into account the participants' stimulated recall comments. Results of the analyses pointed to positive correlations between the participants' working memory capacity and self-repairs in the L2 but not in the L1. Also, results revealed that whereas in the case of L1, the participants effectuated different-information and appropriacy repairs more than error-repairs, in the case of L2 more error-repairs were made. [Mojavezi, Ahmad] Islamic Azad Univ, Zahedan Branch, Dept English, Zahedan, Iran; [Ahmadian, Mohammad Javad] Univ Isfahan, Fac Foreign Languages, Dept English, Esfahan, Iran Mojavezi, A (reprint author), Islamic Azad Univ, Zahedan Branch, Dept English, Zahedan, Iran. a.mojavezi@gmail.com Acheson DJ, 2009, J MEM LANG, V60, P329, DOI 10.1016/j.jml.2008.12.002; Ahmadian M. J., 2012, INT J APPL LINGUISTI; Ahmadian M. J., WORKING MEMORY 2 LAN; Ahmadian M.J., CANADIAN MO IN PRESS; Ahmadian MJ, 2012, TESOL QUART, V46, P165, DOI 10.1002/tesq.8; Allan Dave, 1992, OXFORD PLACEMENT TES; Baddeley A, 2003, J COMMUN DISORD, V36, P189, DOI 10.1016/S0021-9924(03)00019-4; Baddeley AD, 1974, RECENT ADV LEARNING, V8; DANEMAN M, 1980, J VERB LEARN VERB BE, V19, P450, DOI 10.1016/S0022-5371(80)90312-6; DEBOT K, 1992, APPL LINGUIST, V13, P1, DOI 10.1093/applin/13.1.1; DELL GS, 1986, PSYCHOL REV, V93, P283, DOI 10.1037//0033-295X.93.3.283; Ganushchak LY, 2006, BRAIN RES, V1125, P104, DOI 10.1016/j.brainres.2006.09.096; Guara'-Tavares M. G., 2008, THESIS U FEDERAL SAN; Huitt W., 2003, INFORM PROCESSING AP; Jarrold C, 2006, NEUROSCIENCE, V139, P39, DOI 10.1016/j.neuroscience.2005.07.002; Juffs A, 2011, LANG TEACHING, V44, P137, DOI 10.1017/S0261444810000509; Kormos J, 1999, LANG LEARN, V49, P303, DOI 10.1111/0023-8333.00090; Kormos J., 2006, SPEECH PRODUCTION 2; Kormos J., 2000, STUDIES 2 LANGUAGE A, V22, P145; Kormos J., 1998, THESIS EOTVOS U BUDA; Levelt W. J. M., 1989, SPEAKING INTENTION A; Levelt W. J. M., 1999, NEUROCOGNITION LANGU, P83; LEVELT WJM, 1983, COGNITION, V14, P41, DOI 10.1016/0010-0277(83)90026-4; MACKAY DG, 1982, PSYCHOL REV, V89, P483, DOI 10.1037/0033-295X.89.5.483; Mackey A., 2002, INDIVIDUAL DIFFERENC, V2, P181; Oomen C. C. E., 2001, J PSYCHOLINGUISTIC R, V30, P184; OSAKA M, 1992, B PSYCHONOMIC SOC, V30, P287; Rosen VM, 1997, J EXP PSYCHOL GEN, V126, P211, DOI 10.1037//0096-3445.126.3.211; Van Hest E., 1996, SELF REPAIR L1 L2 PR 29 0 0 SPRINGER/PLENUM PUBLISHERS NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0090-6905 1573-6555 J PSYCHOLINGUIST RES J. Psycholinguist. Res. JUN 2014 43 3 289 297 10.1007/s10936-013-9253-7 9 Linguistics; Psychology, Experimental Linguistics; Psychology AE7LR WOS:000334179900006 J Ma, H; Song, JL; Wang, JD; Xiao, ZQ; Fu, Z Ma, Han; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang; Fu, Zhuo Improvement of spatially continuous forest LAI retrieval by integration of discrete airborne LiDAR and remote sensing multi-angle optical data AGRICULTURAL AND FOREST METEOROLOGY English Article Forest; LAI; Canopy height; LiDAR; MOMS; MISR LEAF-AREA INDEX; BIDIRECTIONAL REFLECTANCE; MODIS DATA; CANOPY; MODELS; VALIDATION; BIOMASS; ALBEDO; LEVEL; BRDF Forest leaf area index (LAI) is a critical variable in modeling climates and ecosystems, and is required on regional and global scales for models. However, forest LAI has proven to be difficult to obtain. In this study, we sought to improve forest LAI retrieval in a large study area in the Dayekou forest, Gansu province, by combining airborne discrete LiDAR, MODIS, and MISR data. In our retrieval scheme, canopy height is the key parameter, and the canopy height precision is of great importance when estimating LAI. To address this issue, we introduced LiDAR data and combined it with the MODIS and MISR products. First, the canopy height for the LiDAR data coverage was calculated using a local maximum filtering algorithm with a variable window size. Then, a multivariate linear regression model was developed to extrapolate the LiDAR-derived canopy height to the whole study area using the MODIS BRDF/Albedo product. In addition, the hi-directional reflectances from MODIS and MISR were used to invert the geometric-optical mutual-shadowing (GOMS) model structural parameters (nR(2), b/R, h/b, Delta h/b) of the forest. These structural parameters were then combined with the forest canopy height and field measurements to retrieve the LAI of the continuous forest area at a 500-m resolution. After comparison with the true LAI measured by LAI-2000 combined with TRAC, and by TRAC alone, the highest R-2 values of the estimated LAI were 0.73 and 0.69, respectively. The results indicate that the LiDAR canopy height derived from the optical multi-angle remote sensing data can be used to retrieve the large-scale forest LAI when combined with the canopy structure information derived from GOMS model. (C) 2014 Elsevier B.V. All rights reserved. [Ma, Han; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China; [Ma, Han; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang] Chinese Acad Sci, Inst Remote Sensing Applicat, Beijing 100875, Peoples R China; [Song, Jinling; Wang, Jindi; Xiao, Zhiqiang] Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China; [Ma, Han; Song, Jinling; Wang, Jindi; Xiao, Zhiqiang] Beijing Normal Univ, Sch Geog & Remote Sensing Sci, Beijing 100875, Peoples R China; [Fu, Zhuo] Minist Environm Protect, Satellite Environm Ctr, Beijing 100094, Peoples R China Song, JL (reprint author), Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China. songjl@bnu.edu.cn Special Funds for Major State Basic Research Project [2013CB733403, 2007CB714407]; National Natural Science Foundation of China [41171263]; National 863 Program [2013AA12A301, 2012AA12A303]; Independent Scientific Research Fund Project [104-105582GK]; Key Laboratory of Geo-Informatics of National Administration of Surveying, Mapping and Geoinformation of China [201120] This research was supported in part by the Special Funds for Major State Basic Research Project (2013CB733403 and 2007CB714407), the National Natural Science Foundation of China (No. 41171263), the National 863 Program (2013AA12A301, 2012AA12A303), the 2013 Independent Scientific Research Fund Project (104-105582GK) and the project funded by Key Laboratory of Geo-Informatics of National Administration of Surveying, Mapping and Geoinformation of China (201120). The authors would also like to thank anonymous reviewers who gave valuable suggestions that has helped to strengthen the paper. Boggs P.T., 1995, ACTA NUMERICA, V4, P1, DOI DOI 10.1017/S0962492900002518; Chen G, 2012, REMOTE SENS ENVIRON, V124, P384, DOI 10.1016/j.rse.2012.05.026; CHEN J, 1995, ACTA CRYSTALLOGR C, V51, P34, DOI 10.1107/S0108270194000521; Chen JM, 2006, AGR FOREST METEOROL, V140, P257, DOI 10.1016/j.agrformet.2006.08.005; Chen JM, 1996, AGR FOREST METEOROL, V80, P135, DOI 10.1016/0168-1923(95)02291-0; CHEN JM, 1992, PLANT CELL ENVIRON, V15, P421, DOI 10.1111/j.1365-3040.1992.tb00992.x; Demcsak M., 1997, 170TP006003 HUGH APP, V2; Diner DJ, 1998, IEEE T GEOSCI REMOTE, V36, P1072, DOI 10.1109/36.700992; Dubayah R., 2010, J GEOPHYS RES, V115; Fu Z, 2011, J APPL REMOTE SENS, V5, DOI 10.1117/1.3594171; Gower ST, 1999, REMOTE SENS ENVIRON, V70, P29, DOI 10.1016/S0034-4257(99)00056-5; Jensen JLR, 2011, REMOTE SENS ENVIRON, V115, P3625, DOI 10.1016/j.rse.2011.08.023; Kimes DS, 2006, REMOTE SENS ENVIRON, V100, P503, DOI 10.1016/j.rse.2005.11.004; Lefsky M. A., 2005, GEOPHYS RES LETT, V32; Lefsky MA, 2007, J APPL REMOTE SENS, V1, DOI 10.1117/1.2795724; Lefsky M.A., 2010, GEOPHYS RES LETT, V37, P1; Lefsky MA, 2002, BIOSCIENCE, V52, P19, DOI 10.1641/0006-3568(2002)052[0019:LRSFES]2.0.CO;2; LI XW, 1992, IEEE T GEOSCI REMOTE, V30, P276, DOI 10.1109/36.134078; Li XW, 1998, SCI CHINA SER D, V41, P580, DOI 10.1007/BF02878739; Liang S., 2004, QUANTITATIVE REMOTE; Lucht W, 2000, IEEE T GEOSCI REMOTE, V38, P977, DOI 10.1109/36.841980; Neilson RP, 1998, GLOB CHANGE BIOL, V4, P505, DOI 10.1046/j.1365-2486.1998.00202.x; Popescu SC, 2002, COMPUT ELECTRON AGR, V37, P71, DOI 10.1016/S0168-1699(02)00121-7; Popescu SC, 2011, REMOTE SENS ENVIRON, V115, P2786, DOI 10.1016/j.rse.2011.01.026; Riano D, 2004, AGR FOREST METEOROL, V124, P269, DOI 10.1016/j.agrformet.2004.02.005; ROUJEAN JL, 1992, J GEOPHYS RES-ATMOS, V97, P20455; S Hu, 2005, REMOTE SENSING INFOR, V4, P22; Schaaf CB, 2002, REMOTE SENS ENVIRON, V83, P135, DOI 10.1016/S0034-4257(02)00091-3; [SUN Guoqing 孙国清], 2006, [遥感学报, Journal of Remote Sensing], V10, P523; Swatantran A, 2011, REMOTE SENS ENVIRON, V115, P2917, DOI 10.1016/j.rse.2010.08.027; Tan B, 2006, REMOTE SENS ENVIRON, V105, P98, DOI 10.1016/j.rse.2006.06.008; Tarantola A., 2005, INVERSE PROBLEM THEO; Vermote E., 1999, ATMOSPHERIC CORRECTI; Wang ZS, 2011, REMOTE SENS ENVIRON, V115, P1595, DOI 10.1016/j.rse.2011.02.010; WANNER W, 1995, J GEOPHYS RES-ATMOS, V100, P21077, DOI 10.1029/95JD02371; X Li, 1997, J REMOTE SENSING, VS1, P113; Xing B., 2010, COMP DIFFERENT METHO, P1; Zhao KG, 2009, REMOTE SENS ENVIRON, V113, P1628, DOI 10.1016/j.rse.2009.03.006 38 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0168-1923 1873-2240 AGR FOREST METEOROL Agric. For. Meteorol. JUN 1 2014 189 60 70 10.1016/j.agrformet.2014.01.009 11 Agronomy; Forestry; Meteorology & Atmospheric Sciences Agriculture; Forestry; Meteorology & Atmospheric Sciences AE3BW WOS:000333852900008 J Wang, JJY; Bensmail, H Wang, Jim Jing-Yan; Bensmail, Halima Unified framework for representing and ranking PATTERN RECOGNITION English Article Database retrieval; Nearest neighbor classification; Data representation; Ranking score learning DIMENSIONALITY REDUCTION; IMAGE RETRIEVAL; SUBSPACE; CLASSIFICATION; INFORMATION; SELECTION In the database retrieval and nearest neighbor classification tasks, the two basic problems are to represent the query and database objects, and to learn the ranking scores of the database objects to the query. Many studies have been conducted for the representation learning and the ranking score learning problems, however, they are always learned independently from each other. In this paper, we argue that there are some inner relationships between the representation and ranking of database objects, and try to investigate their relationships by learning them in a unified way. To this end, we proposed the Unified framework for Representation and Ranking (UR2) of objects for the database retrieval and nearest neighbor classification tasks. The learning of representation parameter and the ranking scores are modeled within one single unified objective function. The objective function is optimized alternately with regard to representation parameter and the ranking scores. Based on the optimization results, iterative algorithms are developed to learn the representation parameter and the ranking scores on a unified way. Moreover, with two different formulas of representation (feature selection and subspace learning), we give two versions of UR2. The proposed algorithms are tested on two challenging tasks - MRI image based brain tumor retrieval and nearest neighbor classification based protein identification. The experiments show the advantage of the proposed unified framework over the state-of-the-art independent representation and ranking methods. (C) 2014 Published by Elsevier Ltd. [Wang, Jim Jing-Yan] SUNY Buffalo, Buffalo, NY 14203 USA; [Wang, Jim Jing-Yan] Soochow Univ, Prov Key Lab Comp Informat Proc Technol, Suzhou 215006, Peoples R China; [Bensmail, Halima] Qatar Comp Res Inst, Doha 5825, Qatar Wang, JJY (reprint author), SUNY Buffalo, Buffalo, NY 14203 USA. jimjywang@gmail.com; hbensmail@qf.org.qa Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China; Qatar Annual Research Forum Award [ARF2011] This work was supported by grants from Provincial Key Laboratory for Computer Information Processing Technology, Soochow University, China and 2011 Qatar Annual Research Forum Award (Grant no. ARF2011). Belkin M, 2006, J MACH LEARN RES, V7, P2399; De la Torre F, 2003, INT J COMPUT VISION, V54, P117, DOI 10.1023/A:1023709501986; DENOEUX T, 1995, IEEE T SYST MAN CYB, V25, P804, DOI 10.1109/21.376493; He X., 2005, ADV NEURAL INFORM PR, V18; He X, 2008, IEEE T KNOWL DATA EN, V20, P189, DOI 10.1109/TKDE.2007.190692; Jiang XD, 2011, IEEE SIGNAL PROC MAG, V28, P16, DOI 10.1109/MSP.2010.939041; Kohavi R, 1997, ARTIF INTELL, V97, P273, DOI 10.1016/S0004-3702(97)00043-X; Lee H, 2007, P NIPS, V20, P801; Marsolo K, 2008, KNOWL INF SYST, V14, P59, DOI 10.1007/s10115-007-0088-0; Pei H, 2012, J AM CHEM SOC, V134, P13843, DOI [10.1021/ja305814u, 10.1021/Ja305814u]; Peng HC, 2005, IEEE T PATTERN ANAL, V27, P1226; Roweis ST, 2000, SCIENCE, V290, P2323, DOI 10.1126/science.290.5500.2323; Smeulders AWM, 2000, IEEE T PATTERN ANAL, V22, P1349, DOI 10.1109/34.895972; Sun YJ, 2010, IEEE T PATTERN ANAL, V32, P1610, DOI 10.1109/TPAMI.2009.190; Wang J, 2012, INT C MULTIMED INFO, P963, DOI 10.1109/MINES.2012.127; Yang W, 2012, MED PHYS, V39, P6929, DOI 10.1118/1.4754305; Yang Y, 2012, IEEE T PATTERN ANAL, V34, P723, DOI 10.1109/TPAMI.2011.170; Zhang LJ, 2011, IEEE T PATTERN ANAL, V33, P2026, DOI 10.1109/TPAMI.2011.20; Zhou DY, 2004, ADV NEUR IN, V16, P169 19 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0031-3203 1873-5142 PATTERN RECOGN Pattern Recognit. JUN 2014 47 6 2293 2300 10.1016/j.patcog.2013.12.003 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic Computer Science; Engineering AE5CE WOS:000334004600017 J Jacob, IJ; Srinivasagan, KG; Jayapriya, K Jacob, I. Jeena; Srinivasagan, K. G.; Jayapriya, K. Local Oppugnant Color Texture Pattern for image retrieval system PATTERN RECOGNITION LETTERS English Article Content based image retrieval; Local Oppugnant Colored Texture Pattern; Colored Pattern Appearance Model BINARY PATTERNS; COOCCURRENCE MATRIX; CLASSIFICATION; REPRESENTATION; DATABASES; FEATURES; RECOGNITION; WAVELET The current scenario of image retrieval pays attention to local texture patterns. The recently proposed Local Tetra Pattern (LTrP) represents the image by the directional information and gives promising results. This paper proposes Local Oppugnant Color Texture Pattern (LOCTP), an enhancement of LTrP, which is able to discriminate the information derived from spatial inter-chromatic texture patterns of different spectral channels within a region. It determines the relationship in terms of the intensity and directional information between the referenced pixels and their oppugnant neighbors. The LOCTP strives to use the harmonized link between color and texture, which helps the system to incorporate the human perception. The experimental analysis of the proposed method is done with state-of-art techniques by using standard image databases Brodatz texture database (DB1) and Corel database (DB2). Also, the evaluation has been done in various color models like YCbCr, HSV, Lab, and RGB. In addition, a feature-level fusion framework is used to combine the Colored Pattern Appearance Model (CPAM) and the LOCTP for getting better result in natural images. The experimental results show considerable improvement in terms of average precision, average recall and average retrieval rate when compared with the previous works. (C) 2014 Elsevier B.V. All rights reserved. [Jacob, I. Jeena] SCAD Coll Engn & Technol, Dept Comp Sci & Engn, Cheranmahadevi, Tamil Nadu, India; [Srinivasagan, K. G.] Natl Engn Coll, Dept Comp Sci & Engn, Kovilpatti, Tamil Nadu, India; [Jayapriya, K.] Vin Solut, Tirunelveli, Tamil Nadu, India Jacob, IJ (reprint author), SCAD Coll Engn & Technol, Dept Comp Sci & Engn, Cheranmahadevi, Tamil Nadu, India. jeni.neha@gmail.com Abbadeni N, 2011, IEEE T IMAGE PROCESS, V20, P236, DOI 10.1109/TIP.2010.2060345; Ahmadian A, 2003, P ANN INT IEEE EMBS, V25, P930; Aralick R.M., 1973, IEEE T SYST MAN CYB, V3, P610; Belongie S, 2002, IEEE T PATTERN ANAL, V24, P509, DOI 10.1109/34.993558; Blakemore, 1969, J PSYCHOL, V204, P237; Chatfield K., 2009, NORDIA WORKSH ICCV; Chen Y., 1994, OPT ENG, V8, P2713; Distasi R, 2003, IEEE T IMAGE PROCESS, V12, P373, DOI 10.1109/TIP.2003.811041; Drimbarean A, 2001, PATTERN RECOGN LETT, V22, P1161, DOI 10.1016/S0167-8655(01)00058-7; Gevers T., 1997, P VIS INF SYST SAN D, P93; Guo ZH, 2010, IEEE T IMAGE PROCESS, V19, P1657, DOI 10.1109/TIP.2010.2044957; Guo ZH, 2010, PATTERN RECOGN, V43, P706, DOI 10.1016/j.patcog.2009.08.017; Han J, 2002, IEEE T IMAGE PROCESS, V11, P944, DOI 10.1109/TIP.2002.801585; Haralick R., 1979, P IEEE, V5, P786; HARVEY LO, 1981, J EXP PSYCHOL HUMAN, V7, P741; Heikkila M, 2009, PATTERN RECOGN, V42, P425, DOI 10.1016/j.patcog.2008.08.014; Heikkila M, 2006, IEEE T PATTERN ANAL, V28, P657, DOI 10.1109/TPAMI.2006.68; Howe NR, 2000, PROC CVPR IEEE, P239, DOI 10.1109/CVPR.2000.854798; Huang J, 1997, PROC CVPR IEEE, P762; Huang J., 1997, Proceedings ACM Multimedia 97, DOI 10.1145/266180.266383; Huang X., 2004, P INT C IM GRAPH, P184; Jain A, 1998, IEEE T IMAGE PROCESS, V7, P124, DOI 10.1109/83.650858; Johansson Bjorn, 2002, QBIC QUERY IMAGE CON; JULESZ B, 1975, SCI AM, V232, P34; Julesz Bela, 1983, P SPIE; Kaiser PK, 1996, HUMAN COLOR VISION; Kekre Dr H.B., 2010, SPRING INT C CONT CO; Kokare M, 2007, PATTERN RECOGN LETT, V28, P1240, DOI 10.1016/j.patrec.2007.02.006; Kouzani AZ, 2008, MACH VISION APPL, V19, P223, DOI 10.1007/s00138-007-0095-x; Lategahn H, 2010, IEEE T IMAGE PROCESS, V19, P1548, DOI 10.1109/TIP.2010.2042100; LERSKI RA, 1993, MAGN RESON IMAGING, V11, P873, DOI 10.1016/0730-725X(93)90205-R; Levine M., 1985, VISION MAN MACHINE; Li M, 2008, PATTERN RECOGN LETT, V29, P664, DOI 10.1016/j.patrec.2007.12.001; Liao S, 2009, IEEE T IMAGE PROCESS, V18, P1107, DOI 10.1109/TIP.2009.2015682; MA JQ, 2009, INT C WEB INF SYST M, P61, DOI DOI 10.1109/WISM.2009.20; Malekesmaeili M., 2011, IEEE T INF FOREN SEC, V6, P213; Mandelbrot B., 1997, FRACTALS FORMS CHANC; Ma WY, 1999, MULTIMEDIA SYST, V7, P184, DOI 10.1007/s005300050121; Manjunath BS, 2001, IEEE T CIRC SYST VID, V11, P703, DOI 10.1109/76.927424; Manjunath BS, 1996, IEEE T PATTERN ANAL, V18, P837, DOI 10.1109/34.531803; Marr D., 1982, COMPUTATIONAL INVEST; Murala S, 2012, IEEE T IMAGE PROCESS, V21, P2874, DOI 10.1109/TIP.2012.2188809; Ojala T, 1996, PATTERN RECOGN, V29, P51, DOI 10.1016/0031-3203(95)00067-4; Ojala T, 2002, IEEE T PATTERN ANAL, V24, P971, DOI 10.1109/TPAMI.2002.1017623; Pan YF, 2011, IEEE T IMAGE PROCESS, V20, P800, DOI 10.1109/TIP.2010.2070803; Paschos G, 2001, IEEE T IMAGE PROCESS, V10, P932, DOI 10.1109/83.923289; Pass G, 1997, P 4 ACM INT C MULT, P65; PENTLAND A, 1994, P SOC PHOTO-OPT INS, V2185, P34, DOI 10.1117/12.171786; Pi M, 2008, IET IMAGE PROCESS, V2, P218, DOI 10.1049/iet-ipr:20070055; Poirson B., 1993, J OPT SOC AM, V10, P2458; Qiu G., 2001, VISUAL COMMUNICATION; Rao J.R., 2001, ARTIF INTELL, V78, P461; Safia A., 2013, NEW BRODATZ BASED IM; Scott GJ, 2011, IEEE T GEOSCI REMOTE, V49, P1603, DOI 10.1109/TGRS.2010.2088404; Serra J., 1982, IMAGE ANAL MATH MORP; Shechtman E., 2005, CVPR; Smith J.R., 1995, CUCTR4089514; Stricker M., 1995, Proceedings of the SPIE - The International Society for Optical Engineering, V2420, DOI 10.1117/12.205308; Strzelecki M., 1995, THESIS; Su Ching-Hung, 2011, ELECT CONT ENG, P5746; Subrahmanyam M, 2013, COMPUT ELECTR ENG, V39, P762, DOI 10.1016/j.compeleceng.2012.11.023; Swain M., 1991, P 3 INT C COMP VIS, P11; Tan XY, 2010, IEEE T IMAGE PROCESS, V19, P1635, DOI 10.1109/TIP.2010.2042645; Unser M., 1993, IEEE T PATTERN ANAL, V15, P1186; UNSER M, 1986, SIGNAL PROCESS, V11, P61, DOI 10.1016/0165-1684(86)90095-2; Vadivel A, 2007, PATTERN RECOGN LETT, V28, P974, DOI 10.1016/j.patrec.2007.01.004; van de Sande KEA, 2010, IEEE T PATTERN ANAL, V32, P1582, DOI 10.1109/TPAMI.2009.154; Vizireanu D. N, 2007, J ELECTRON IMAGING, V16, P1; Vizireanu DN, 2008, J ELECTRON IMAGING, V19, P1; Vizireanu D.N., 2010, J ELECTRON IMAGING, V19, P1; Wang James Z., 2001, IEEE T PATTERN ANAL, V23; WESZKA JS, 1976, PATTERN RECOGN, V8, P195, DOI 10.1016/0031-3203(76)90039-X; Zhang BC, 2010, IEEE T IMAGE PROCESS, V19, P533, DOI 10.1109/TIP.2009.2035882; Zhang LN, 2012, IEEE T IMAGE PROCESS, V21, P2294, DOI 10.1109/TIP.2011.2177846; Zhou N, 2011, IEEE T PATTERN ANAL, V33, P1281, DOI 10.1109/TPAMI.2010.204 75 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0167-8655 1872-7344 PATTERN RECOGN LETT Pattern Recognit. Lett. JUN 1 2014 42 72 78 10.1016/j.patrec.2014.01.017 7 Computer Science, Artificial Intelligence Computer Science AD7NQ WOS:000333451300009 J Schuessler, O; Rodriguez, DGL; Doicu, A; Spurr, R Schuessler, Olena; Rodriguez, Diego Guillermo Loyola; Doicu, Adrian; Spurr, Robert Information Content in the Oxygen A-Band for the Retrieval of Macrophysical Cloud Parameters IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING English Article Information content of hyperspectral measurements; oxygen A-band; retrieval of macrophysical cloud parameters OPTICAL DEPTH; GOME; ALGORITHM; RADIATION; PRESSURE; CODE Current and future satellite sensors provide measurements in and around the oxygen A-band on a global basis. These data are commonly used for the determination of cloud and aerosol properties. In this paper, we assess the information content in the oxygen A-band for the retrieval of macrophysical cloud parameters using precise radiative transfer simulations covering a wide range of geophysical conditions in conjunction with advance inversion techniques. The information content of the signal with respect to the retrieved parameters is analyzed in a stochastic framework using two common criteria: the degrees of freedom for a signal and the Shannon information content. It is found that oxygen A-band measurements with moderate spectral resolution (0.2 nm) provide two pieces of independent information that allow the accurate retrieval of cloud-top height together with either cloud optical thickness or cloud fraction. Additionally, our results confirm previous studies indicating that the retrieval of cloud geometrical thickness (CGT) from single-angle measurements is not reliable in this spectral region. Finally, a sensitivity study shows that the retrieval of macrophysical cloud parameters is slightly sensitive to the uncertainty in the CGT and very sensitive to the uncertainty in the surface albedo. [Schuessler, Olena; Rodriguez, Diego Guillermo Loyola; Doicu, Adrian] Deutsch Zentrum Luft & Raumfahrt DLR, IMF, D-82234 Wessling, Germany; [Spurr, Robert] RT Solut Inc, Cambridge, MA 02138 USA Schuessler, O (reprint author), Deutsch Zentrum Luft & Raumfahrt DLR, IMF, D-82234 Wessling, Germany. Ahmad Z, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003395; Anton M., 2012, J GEOPHYS RES, V116; Bodhaine BA, 1999, J ATMOS OCEAN TECH, V16, P1854, DOI 10.1175/1520-0426(1999)016<1854:ORODC>2.0.CO;2; Boersma K., 2004, J GEOPHYS RES, V109; Boesche E, 2008, APPL OPTICS, V47, P3467, DOI 10.1364/AO.47.003467; Bovensmann H, 1999, J ATMOS SCI, V56, P127, DOI 10.1175/1520-0469(1999)056<0127:SMOAMM>2.0.CO;2; Burrows JP, 1999, J ATMOS SCI, V56, P151, DOI 10.1175/1520-0469(1999)056<0151:TGOMEG>2.0.CO;2; Callies J., 2000, ESA B, V102, P28; Doicu A, 2010, SPRINGER-PRAX BOOKS, P1, DOI 10.1007/978-3-642-05439-6; Ferlay N, 2010, J APPL METEOROL CLIM, V49, P2492, DOI 10.1175/2010JAMC2550.1; Gelman A, 2003, BAYESIAN DATA ANAL; Hadamard J., 2003, LECT CAUCHYS PROBLEM; Hahn CJ, 2001, J CLIMATE, V14, P11, DOI 10.1175/1520-0442(2001)014<0011:ICPAWS>2.0.CO;2; Hess M, 1998, B AM METEOROL SOC, V79, P831, DOI 10.1175/1520-0477(1998)079<0831:OPOAAC>2.0.CO;2; Ingmann P, 2012, REMOTE SENS ENVIRON, V120, P58, DOI 10.1016/j.rse.2012.01.023; Joiner J, 2006, IEEE T GEOSCI REMOTE, V44, P1272, DOI 10.1109/TGRS.2005.861385; L'Ecuyer TS, 2006, J APPL METEOROL CLIM, V45, P20, DOI 10.1175/JAM2326.1; Liu X, 2004, J QUANT SPECTROSC RA, V85, P337, DOI 10.1016/S0022-4073(03)00231-0; Loyola D., 2011, J GEOPHYS RES, V116; Loyola D., 2007, IEEE T GEOSCI REMOTE, V45, P2747, DOI DOI 10.1109/TGRS.2007.901043; Loyola DG, 2010, INT J REMOTE SENS, V31, P4295, DOI 10.1080/01431160903246741; Rodgers C. D., 2000, INVERSE METHODS ATMO; Rothman LS, 2009, J QUANT SPECTROSC RA, V110, P533, DOI 10.1016/j.jqsrt.2009.02.013; Schreier F, 2011, J QUANT SPECTROSC RA, V112, P1010, DOI 10.1016/j.jqsrt.2010.12.010; Schreier F, 2001, STUD GEO OP, P381; Shannon C, 1949, MATH THEORY COMMUNIC; Sneep M., 2008, J GEOPHYS RES, V113; Spurr R, 2008, S-P B ENVIRON SCI, P229, DOI 10.1007/978-3-540-48546-9_7; Spurr RJD, 2006, J QUANT SPECTROSC RA, V102, P316, DOI 10.1016/j.jqsrt.2006.05.005; Tikhonov A.N., 1977, SOLUTION ILL POSED P; Van Roozendael M, 2012, J GEOPHYS RES, V117; Van Roozendael M, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006375; Vasilkov A., 2008, J GEOPHYS RES, V113; Wang JH, 2000, J CLIMATE, V13, P3041, DOI 10.1175/1520-0442(2000)013<3041:CVSAIV>2.0.CO;2; WISCOMBE WJ, 1977, J ATMOS SCI, V34, P1408, DOI 10.1175/1520-0469(1977)034<1408:TDMRYA>2.0.CO;2; Wu D. L., 2009, GEOPHYS RES LETT, V36; Zelinka MD, 2012, J CLIMATE, V25, P3736, DOI 10.1175/JCLI-D-11-00249.1 37 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0196-2892 1558-0644 IEEE T GEOSCI REMOTE IEEE Trans. Geosci. Remote Sensing JUN 2014 52 6 3246 3255 10.1109/TGRS.2013.2271986 10 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AC4QB WOS:000332504700018 J Uslu, E; Albayrak, S Uslu, Erkan; Albayrak, Songul Curvelet-Based Synthetic Aperture Radar Image Classification IEEE GEOSCIENCE AND REMOTE SENSING LETTERS English Article Curvelet transform (CT); generalized Gaussian distribution (GGD); histogram of curvelets (HoC); land use classification; synthetic aperture radar (SAR) Curvelet transform (CT) is a multiscale directional transform that enables the use of texture and spatial locality information. In synthetic aperture radar (SAR) imaging, CT is mostly used in speckle noise reduction. This letter utilizes CT for feature extraction in land use classification. Two types of curvelet-based feature extraction methods are implemented for SAR. The first one is defined and used in content-based image retrieval and is based on generalized Gaussian distribution parameter estimation for each curvelet subband. The second implementation is a genuine method that utilizes the use of curvelet subband histograms, namely, histogram of curvelets (HoC). Using the proposed curvelet-based feature extraction method (HoC) on SAR data, better classification accuracies up to 99.56% are achieved compared to original data and H/A/alpha decomposition features. Compared to speckle-noise-reduced data classification results, it can be said that curvelet-based feature extraction is also robust against speckle noise. [Uslu, Erkan; Albayrak, Songul] Yildiz Tekn Univ, Dept Comp Engn, TR-34220 Istanbul, Turkey Uslu, E (reprint author), Yildiz Tekn Univ, Dept Comp Engn, TR-34220 Istanbul, Turkey. erkan@ce.yildiz.edu.tr; songul@ce.yildiz.edu.tr Albayrak, Songul/G-5329-2011 Yildiz Technical University through Bilimsel Arastirma Projeleri Koordinatorlugu [2011-04-01-DOP01] This work was supported in part by Yildiz Technical University through Bilimsel Arastirma Projeleri Koordinatorlugu under Grant 2011-04-01-DOP01. Anfinsen SN, 2011, IEEE T GEOSCI REMOTE, V49, P2281, DOI 10.1109/TGRS.2010.2103945; Ersahin K, 2010, IEEE T GEOSCI REMOTE, V48, P164, DOI 10.1109/TGRS.2009.2024303; Gomez F, 2011, PATTERN RECOGN LETT, V32, P2178, DOI 10.1016/j.patrec.2011.09.029; Grassin S, 1996, PROCEEDINGS OF THE IEEE-SP INTERNATIONAL SYMPOSIUM ON TIME-FREQUENCY AND TIME-SCALE ANALYSIS, P293, DOI 10.1109/TFSA.1996.547471; Ma JW, 2010, IEEE SIGNAL PROC MAG, V27, P118, DOI 10.1109/MSP.2009.935453; Spigai M, 2011, IEEE T GEOSCI REMOTE, V49, P2699, DOI 10.1109/TGRS.2011.2107914; Tu ST, 2012, IEEE T GEOSCI REMOTE, V50, P170, DOI 10.1109/TGRS.2011.2168532; Yi-Bo L., 2010, 2010 INT C COMP APPL, V1; Yongjian Yu, 2002, IEEE Transactions on Image Processing, V11, DOI 10.1109/TIP.2002.804276; Zhang YD, 2009, SENSORS-BASEL, V9, P7516, DOI 10.3390/s90907516 10 1 1 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1545-598X 1558-0571 IEEE GEOSCI REMOTE S IEEE Geosci. Remote Sens. Lett. JUN 2014 11 6 1071 1075 10.1109/LGRS.2013.2286089 5 Geochemistry & Geophysics; Engineering, Electrical & Electronic; Remote Sensing; Imaging Science & Photographic Technology Geochemistry & Geophysics; Engineering; Remote Sensing; Imaging Science & Photographic Technology AA5NJ WOS:000331146200009 J Deng, ZH; Luo, KH; Yu, HL Deng, Zhi-Hong; Luo, Kun-Hu; Yu, Hong-Liang A study of supervised term weighting scheme for sentiment analysis EXPERT SYSTEMS WITH APPLICATIONS English Article Sentiment analysis; Term weighting; Supervised learning; Experimentation; Performance TEXT CATEGORIZATION; SEARCH TERMS; CLASSIFICATION Term weighting is a strategy that assigns weights to terms to improve the performance of sentiment analysis and other text mining tasks. In this paper, we propose a supervised term weighting scheme based on two basic factors: Importance of a term in a document (ITD) and importance of a term for expressing sentiment (175), to improve the performance of analysis. For ITD, we explore three definitions based on term frequency. Then, seven statistical functions are employed to learn the ITS of each term from training documents with category labels. Compared with the previous unsupervised term weighting schemes originated from information retrieval, our scheme can make full use of the available labeling information to assign appropriate weights to terms. We have experimentally evaluated the proposed method against the state-of-the-art method. The experimental results show that our method outperforms the method and produce the best accuracy on two of three data sets. (C) 2013 Elsevier Ltd. All rights reserved. [Deng, Zhi-Hong; Luo, Kun-Hu; Yu, Hong-Liang] Peking Univ, Sch Elect Engn & Comp Sci, Dept Machine Intelligence, Key Lab Machine Percept,Minist Educ, Beijing 100871, Peoples R China Deng, ZH (reprint author), Peking Univ, Sch Elect Engn & Comp Sci, Dept Machine Intelligence, Key Lab Machine Percept,Minist Educ, Beijing 100871, Peoples R China. zhdeng@cis.pku.edu.cn National Natural Science Foundation of China; [61170091] This work is partially supported by Project 61170091, supported by National Natural Science Foundation of China. We would also like to thank the anonymous reviewers for their helpful comments. Baeza-Yates R., 2011, MODERN INFORM RETRIE; Church K.W., 1989, P ACL, P76; Das S., 2001, P AS PAC FIN ASS ANN; Debole F., 2003, P SAC 03 18 ACM S AP, P784; Deng ZH, 2004, LECT NOTES COMPUT SC, V3007, P588; Geng L, 2006, ACM COMPUTING SURVEY, V38; Jones K. S., 1972, Journal of Documentation, V28, DOI 10.1108/eb026526; Lan M, 2009, IEEE T PATTERN ANAL, V31, P721, DOI 10.1109/TPAMI.2008.110; Lee L., 2004, ACL 04 P 42 ANN M AS, P271; Li S., 2009, P 47 ANN M ACL, P692; Maas A.L., 2011, P 49 ANN M ASS COMP, P142; Manning C, 2008, INTRO INFORM RETRIEV; Martineau J., 2009, P 3 AAAI C WEBL SOC, P258; Mladenic D., 1998, P C AUT LEARN DISC C; Moffat Alistair, 2009, P 18 ACM C INF KNOWL, P601, DOI [10.1145/1645953.1646031, DOI 10.1145/1645953.1646031]; Ng V., 2006, P COLING ACL MAIN C; Nigam K, 2000, MACH LEARN, V39, P103, DOI 10.1023/A:1007692713085; Paltoglou G., 2010, P 48 ANN M ASS COMP, P1386; Pang B., 2008, OPINION MINING SENTI; Pang B., 2002, P 2002 C EMP METH NA; Quinlan J. R., 1986, Machine Learning, V1, DOI 10.1023/A:1022643204877; Ren FJ, 2013, INFORM SCIENCES, V236, P109, DOI 10.1016/j.ins.2013.02.029; Robertson S., 2004, P 13 ACM INT C INF K, P42, DOI DOI 10.1145/1031171.1031181; Robertson S. E., 1996, 5 TEXT RETR C TREC 5; ROBERTSON SE, 1976, J AM SOC INFORM SCI, V27, P129, DOI 10.1002/asi.4630270302; Robertson SE, 1994, TREC 3, P109; Salton G, 1988, J INFORM PROCESSING, V24, P513; Sebastiani F, 2002, ACM COMPUT SURV, V34, P1, DOI 10.1145/505282.505283; Soucy P, 2005, 19TH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-05), P1130; Tong R. M., 2001, P WORKSH OP TEXT CLA; Turney P. D., 2002, P 40 ANN M ASS COMP, P417, DOI DOI 10.3115/1073083.1073153; VANRIJSBERGEN CJ, 1981, INFORM PROCESS MANAG, V17, P77, DOI 10.1016/0306-4573(81)90029-7; Yang Y, 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 33 1 1 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0957-4174 1873-6793 EXPERT SYST APPL Expert Syst. Appl. JUN 1 2014 41 7 3506 3513 10.1016/j.eswa.2013.10.056 8 Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic; Operations Research & Management Science Computer Science; Engineering; Operations Research & Management Science AA3UM WOS:000331019800035 J Ceselli, A; Colombo, F; Cordone, R Ceselli, Alberto; Colombo, Fabio; Cordone, Roberto Balanced compact clustering for efficient range queries in metric spaces DISCRETE APPLIED MATHEMATICS English Article Similarity search; Clustering; Information retrieval; Integer programming; Tabu search Given a set of points in a metric space, an additional query point and a positive threshold, a range query determines the subset of points whose distance from the query point does not exceed the given threshold. This paper tackles the problem of clustering the set of points so as to minimize the number of distance evaluations required by a range query. This problem models the efficient extraction of information from a database when the user is not interested in an exact match retrieval, but in the search for similar items. Since this need has become widespread in the management of text, image, audio and video databases, several data structures have been proposed to support such queries. Their optimization, however, is still left to extremely simple heuristic rules, if not to random choices. We propose the Balanced Compact Clustering Problem (BCCP) as a combinatorial model of this problem. We discuss its approximation properties and the complexity of special cases. Then, we present two Integer Programming formulations, prove their equivalence and introduce valid inequalities and variable fixing procedures. We discuss the application of a general-purpose solver on the more efficient formulation. Finally, we describe a Tabu Search algorithm and discuss its application to randomly generated and to real-world benchmark instances up to one hundred thousands points. (C) 2013 Elsevier B.V. All rights reserved. [Ceselli, Alberto] Univ Milan, Dipartimento Informat, I-26013 Crema, Italy; [Colombo, Fabio; Cordone, Roberto] Univ Milan, Dipartimento Informat, I-20135 Milan, Italy Cordone, R (reprint author), Univ Milan, Dipartimento Informat, Via Comelico 39, I-20135 Milan, Italy. alberto.ceselli@unimi.it; fabio.colombo2@unimi.it; roberto.cordone@unimi.it Brin S., 1995, P 21 INT C VER LARG, P574; Bustos B, 2003, PATTERN RECOGN LETT, V24, P2357, DOI 10.1016/S0167-8655(03)00065-5; Chavez E., 2000, Proceedings Seventh International Symposium on String Processing and Information Retrieval. SPIRE 2000, DOI 10.1109/SPIRE.2000.878182; Ciaccia P, 1997, VLDB 97, P426; Cormen T. H., 2001, INTRO ALGORITHMS; Garey M., 1979, COMPUTERS INTRACTABI; Glover F., 1998, TABU SEARCH; Gupta M., ABS09012900 CORR; Hetland ML, 2009, STUD COMPUT INTELL, V242, P199, DOI 10.1007/978-3-642-03625-5_9; Levenshtein VI, 1966, SOV PHYS DOKL, V10, P707; Nemhauser GL, 1988, INTEGER COMBINATORIA; WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968; YIANILOS PN, 1993, PROCEEDINGS OF THE FOURTH ANNUAL ACM-SIAM SYMPOSIUM ON DISCRETE ALGORITHMS, P311 13 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0166-218X 1872-6771 DISCRETE APPL MATH Discret Appl. Math. MAY 31 2014 169 43 67 10.1016/j.dam.2013.12.019 25 Mathematics, Applied Mathematics AE6FU WOS:000334086800005 J Inoue, N; Watanabe, S Inoue, Naomi; Watanabe, Shigeru Effects of reversible deactivation of mossy fibers in the dentate-CA3 system on geometric center detection task in mice: Functional separation of spatial learning and its generalization to new environment PHYSIOLOGY & BEHAVIOR English Article Spatial learning; Hippocampus; Geometric center; Pattern completion HIPPOCAMPAL CA3-REGION; MEMORY CONSOLIDATION; DORSAL HIPPOCAMPUS; CA3; RETRIEVAL; SYNAPSES; GYRUS; RAT; LOCALIZATION; INFORMATION Using diethyldithiocarbamate (DEDTC), a zinc chelator, we deactivated the mossy fibers that project from the dentate gyrus (DG) to the CA3 during acquisition and testing of a center detection task in mice. The mice were trained to find a food pellet at the center of four objects in a circular area. DEDTC injection just before the training sessions impaired this learning, whereas DEDTC injection before the probe test did not impair recall of the memory. DEDTC injection before a pattern completion test in which only one of the four objects was presented did not cause deficits in this test. DEDTC injection did, however, cause severe deficits in an array shift test in which all four objects were moved to new positions. These results demonstrated that 1) the DG-CA3 system plays a crucial role in the learning of geometric center detection task but not in its recall or pattern completion, and 2) the DG-CA3 system is involved in generalization to a new environment but is not crucial for pattern completion. (C) 2014 Elsevier Inc All rights reserved. [Inoue, Naomi; Watanabe, Shigeru] Keio Univ, Dept Psychol, Minato Ku, Tokyo 1088345, Japan Watanabe, S (reprint author), Keio Univ, Dept Psychol, Minato Ku, 2-2-24 Mita, Tokyo 1088345, Japan. swat@flet.keio.ac.jp Amaral David G., 1995, P443; Cajal S., 1965, HISTOLOGY NERVOUS SY; Florian C, 2004, BEHAV BRAIN RES, V154, P365, DOI 10.1016/j.bbr.2004.03.003; Gilbert PE, 2009, PROG NEURO-PSYCHOPH, V33, P774, DOI 10.1016/j.pnpbp.2009.03.037; Gold AE, 2005, HIPPOCAMPUS, V15, P808, DOI 10.1002/hipo.20103; HAUG FMS, 1967, HISTOCHEMISTRY, V8, P355, DOI 10.1007/BF00401978; Hunsaker MR, 2008, HIPPOCAMPUS, V18, P955, DOI 10.1002/hipo.20455; Inoue N, 2012, BEHAV PROCESS, V91, P141, DOI 10.1016/j.beproc.2012.06.009; Jerman T, 2006, LEARN MEMORY, V13, P458, DOI 10.1101/lm.246906; Lassalle JM, 2000, NEUROBIOL LEARN MEM, V73, P243, DOI 10.1006/nlme.1999.3931; Lee I, 2004, HIPPOCAMPUS, V14, P66, DOI 10.1002/hipo.10167; Little R. J. A., 1987, STAT ANAL MISSING DA; Lu YM, 2000, SYNAPSE, V38, P187, DOI 10.1002/1098-2396(200011)38:2<187::AID-SYN10>3.0.CO;2-R; Manns JR, 2005, BEHAV NEUROSCI, V119, P1140, DOI 10.1037/0735-7044.119.4.1140; MARR D, 1971, PHILOS T ROY SOC B, V262, P23, DOI 10.1098/rstb.1971.0078; McHugh TJ, 2007, SCIENCE, V317, P94, DOI 10.1126/science.1140263; ALKON DL, 1991, BRAIN RES REV, V16, P193, DOI 10.1016/0165-0173(91)90005-S; MCNAUGHTON BL, 1987, TRENDS NEUROSCI, V10, P408, DOI 10.1016/0166-2236(87)90011-7; O'Keefe J., 1978, HIPPOCAMPUS COGNITIV; Okada K, 2009, BEHAV BRAIN RES, V200, P181, DOI 10.1016/j.bbr.2009.01.011; PEREZCLAUSELL J, 1985, BRAIN RES, V337, P91, DOI 10.1016/0006-8993(85)91612-9; Rolls E. T., 1998, NEURAL NETWORKS BRAI; Rolls ET, 1996, HIPPOCAMPUS, V6, P601, DOI 10.1002/(SICI)1098-1063(1996)6:6<601::AID-HIPO5>3.0.CO;2-J; Rolls ET, 2006, PROG NEUROBIOL, V79, P1, DOI 10.1016/j.pneurobio.2006.04.005; Rolls ET, 2007, LEARN MEMORY, V14, P714, DOI 10.1101/lm.631207; Stupien G, 2003, NEUROBIOL LEARN MEM, V80, P32, DOI 10.1016/S0174-7427(03)00022-4; Tommasi L, 2004, LEARN MEMORY, V11, P153, DOI 10.1101/lm.60904; TREVES A, 1992, HIPPOCAMPUS, V2, P189, DOI 10.1002/hipo.450020209; WITTER MP, 1993, HIPPOCAMPUS, V3, P33 29 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0031-9384 PHYSIOL BEHAV Physiol. Behav. MAY 28 2014 131 75 80 10.1016/j.physbeh.2014.04.015 6 Psychology, Biological; Behavioral Sciences Psychology; Behavioral Sciences AJ7HA WOS:000337866600011 J Bosch, SE; Jehee, JFM; Fernandez, G; Doeller, CF Bosch, Sander E.; Jehee, Janneke F. M.; Fernandez, Guillen; Doeller, Christian F. Reinstatement of Associative Memories in Early Visual Cortex Is Signaled by the Hippocampus JOURNAL OF NEUROSCIENCE English Article cortical reinstatement; hippocampus; multivariate analysis; visual cortex MEDIAL TEMPORAL-LOBE; WORKING-MEMORY; EPISODIC MEMORY; MENTAL-IMAGERY; RETRIEVAL; REPRESENTATIONS; ACTIVATION; ATTENTION; AREAS; RECOLLECTION The cortical reinstatement hypothesis of memory retrieval posits that content-specific cortical activity at encoding is reinstated at retrieval. Evidence for cortical reinstatement was found in higher-order sensory regions, reflecting reactivation of complex object-based information. However, it remains unclear whether the same detailed sensory, feature-based information perceived during encoding is subsequently reinstated in early sensory cortex and what the role of the hippocampus is in this process. In this study, we used a combination of visual psychophysics, functional neuroimaging, multivoxel pattern analysis, and a well controlled cued recall paradigm to address this issue. We found that the visual information human participants were retrieving could be predicted by the activation patterns in early visual cortex. Importantly, this reinstatement resembled the neural pattern elicited when participants viewed the visual stimuli passively, indicating shared representations between stimulus-driven activity and memory. Furthermore, hippocampal activity covaried with the strength of stimulus-specific cortical reinstatement on a trial-by-trial level during cued recall. These findings provide evidence for reinstatement of unique associative memories in early visual cortex and suggest that the hippocampus modulates the mnemonic strength of this reinstatement. [Bosch, Sander E.; Jehee, Janneke F. M.; Fernandez, Guillen; Doeller, Christian F.] Radboud Univ Nijmegen, Donders Inst Brain Cognit & Behav, NL-6525 EN Nijmegen, Netherlands; [Fernandez, Guillen] Radboud Univ Nijmegen, Med Ctr, Dept Cognit Neurosci, NL-6525 EN Nijmegen, Netherlands Bosch, SE (reprint author), Donders Inst Brain Cognit & Behav, Kapittelweg 29, NL-6525 EN Nijmegen, Netherlands. s.bosch@donders.ru.nl; c.doeller@donders.ru.nl Fernandez, Guillen/B-3771-2009; Fernandez Reumann, G.S.E./H-8029-2014 Fernandez, Guillen/0000-0002-5522-0604; European Research Council [RECONTEXT 261177, NEUROSCHEMA 268800]; Netherlands Organisation for Scientific Research VIDI [452-12-009] This work was supported by the European Research Council Starting Grant RECONTEXT 261177 and Netherlands Organisation for Scientific Research VIDI Grant 452-12-009. G.F. is supported by European Research Council Advanced Grant NEUROSCHEMA 268800. We thank Ruben van Bergen for help during retinotopy acquisition and Alexander Backus, Lorena Deuker, Alejandro Vicente Grabovetsky, and Marcel van Gerven for useful discussions. Albers AM, 2013, CURR BIOL, V23, P1427, DOI 10.1016/j.cub.2013.05.065; Brainard DH, 1997, SPATIAL VISION, V10, P433, DOI 10.1163/156856897X00357; Buchsbaum BR, 2012, J COGNITIVE NEUROSCI, V24, P1867, DOI 10.1162/jocn_a_00253; Chadwick MJ, 2010, CURR BIOL, V20, P544, DOI 10.1016/j.cub.2010.01.053; Chun MM, 2007, CURR OPIN NEUROBIOL, V17, P177, DOI 10.1016/j.conb.2007.03.005; Cichy RM, 2012, CEREB CORTEX, V22, P372, DOI 10.1093/cercor/bhr106; Davachi L, 2003, P NATL ACAD SCI USA, V100, P2157, DOI 10.1073/pnas.0337195100; Deuker L, 2013, J NEUROSCI, V33, P19373, DOI 10.1523/JNEUROSCI.0414-13.2013; DeYoe EA, 1996, P NATL ACAD SCI USA, V93, P2382, DOI 10.1073/pnas.93.6.2382; Diana RA, 2013, J EXP PSYCHOL GEN, V142, P1287, DOI 10.1037/a0034029; Doeller CF, 2010, NATURE, V463, P657, DOI 10.1038/nature08704; Duzel E, 2003, J NEUROSCI, V23, P9439; EICHENBAUM H, 1992, BEHAV NEURAL BIOL, V57, P2, DOI 10.1016/0163-1047(92)90724-I; Eldridge LL, 2000, NAT NEUROSCI, V3, P1149, DOI 10.1038/80671; Engel SA, 1997, CEREB CORTEX, V7, P181, DOI 10.1093/cercor/7.2.181; Friston KJ, 1998, MAGNET RESON MED, V39, P41, DOI 10.1002/mrm.1910390109; Gordon AM, 2013, CEREB CORTEX, DOI [10.1093/cercor/bht194, DOI 10.1093/CERCOR/BHT194.CR0SSREF]; Harrison SA, 2009, NATURE, V458, P632, DOI 10.1038/nature07832; Haynes JD, 2005, NAT NEUROSCI, V8, P686, DOI 10.1038/nn1445; James W, 1890, PRINCIPLES PSYCHOL; Jehee JFM, 2012, J NEUROSCI, V32, P16747, DOI 10.1523/JNEUROSCI.6112-11.2012; Jehee JFM, 2011, J NEUROSCI, V31, P8210, DOI 10.1523/JNEUROSCI.6153-09.2011; Ji DY, 2007, NAT NEUROSCI, V10, P100, DOI 10.1038/nn1825; Johnson JD, 2009, NEURON, V63, P697, DOI 10.1016/j.neuron.2009.08.011; Kamitani Y, 2005, NAT NEUROSCI, V8, P679, DOI 10.1038/nn1444; Khader P, 2005, NEUROIMAGE, V27, P805, DOI 10.1016/j.neuroimage.2005.05.006; Kok P, 2012, NEURON, V75, P265, DOI 10.1016/j.neuron.2012.04.034; KOSSLYN SM, 1995, NATURE, V378, P496, DOI 10.1038/378496a0; Kuhl BA, 2011, P NATL ACAD SCI USA, V108, P5903, DOI 10.1073/pnas.1016939108; Lamme VAF, 2000, TRENDS NEUROSCI, V23, P571, DOI 10.1016/S0166-2236(00)01657-X; Lee SH, 2012, NEUROIMAGE, V59, P4064, DOI 10.1016/j.neuroimage.2011.10.055; Lewis-Peacock JA, 2008, J NEUROSCI, V28, P8765, DOI 10.1523/JNEUROSCI.1953-08.2008; Liang JC, 2013, CEREB CORTEX, V23, P80, DOI 10.1093/cercor/bhr379; Liu T, 2007, NEURON, V55, P313, DOI 10.1016/j.neuron.2007.06.030; MARR D, 1971, PHILOS T ROY SOC B, V262, P23, DOI 10.1098/rstb.1971.0078; MUMFORD D, 1991, BIOL CYBERN, V65, P135, DOI 10.1007/BF00202389; Nyberg L, 2000, P NATL ACAD SCI USA, V97, P11120, DOI 10.1073/pnas.97.20.11120; Polyn SM, 2005, SCIENCE, V310, P1963, DOI 10.1126/science.1117645; Ranganath C, 2004, J NEUROSCI, V24, P3917, DOI 10.1523/JNEUROSCI.5053-03.2004; Ranganath C, 2005, HIPPOCAMPUS, V15, P997, DOI 10.1002/hipo.20141; Reddy L, 2010, NEUROIMAGE, V50, P818, DOI 10.1016/j.neuroimage.2009.11.084; Rugg MD, 2013, CURR OPIN NEUROBIOL, V23, P255, DOI 10.1016/j.conb.2012.11.005; Serences JT, 2007, NEURON, V55, P301, DOI 10.1016/j.neuron.2007.06.015; Serences JT, 2009, PSYCHOL SCI, V20, P207, DOI 10.1111/j.1467-9280.2009.02276.x; SERENO MI, 1995, SCIENCE, V268, P889, DOI 10.1126/science.7754376; Slotnick SD, 2006, NEUROPSYCHOLOGIA, V44, P2874, DOI 10.1016/j.neuropsychologia.2006.06.021; Smith SM, 2009, NEUROIMAGE, V44, P83, DOI 10.1016/j.neuroimage.2008.03.061; Squire LR, 2004, ANNU REV NEUROSCI, V27, P279, DOI 10.1146/annurev.neuro.27.070203.144130; Staresina BP, 2012, J NEUROSCI, V32, P18150, DOI 10.1523/JNEUROSCI.4156-12.2012; Staresina BP, 2013, J NEUROSCI, V33, P14184, DOI 10.1523/JNEUROSCI.1987-13.2013; Stokes M, 2009, J NEUROSCI, V29, P1565, DOI 10.1523/JNEUROSCI.4657-08.2009; Summerfield JJ, 2006, NEURON, V49, P905, DOI 10.1016/j.neuron.2006.01.021; Tambini A, 2013, P NATL ACAD SCI USA, V110, P19591, DOI 10.1073/pnas.1308499110; Thakral PP, 2013, NEUROPSYCHOLOGIA, V51, P482, DOI 10.1016/j.neuropsychologia.2012.11.020; Tong F, 2013, TRENDS COGN SCI, V17, P489, DOI 10.1016/j.tics.2013.08.005; TULVING E, 1973, PSYCHOL REV, V80, P352, DOI 10.1037/h0020071; van Dongen EV, 2012, P NATL ACAD SCI USA, V109, P10575, DOI 10.1073/pnas.1201072109; van Strien NM, 2009, NAT REV NEUROSCI, V10, P272, DOI 10.1038/nrn2614; Vicente-Grabovetsky A, 2014, CEREB CORTEX, V24, P281, DOI 10.1093/cercor/bhs313; Wandell BA, 2007, NEURON, V56, P366, DOI 10.1016/j.neuron.2007.10.012; WATSON AB, 1983, PERCEPT PSYCHOPHYS, V33, P113, DOI 10.3758/BF03202828; Wheeler ME, 2000, P NATL ACAD SCI USA, V97, P11125, DOI 10.1073/pnas.97.20.11125; Woodruff CC, 2005, NEUROPSYCHOLOGIA, V43, P1022, DOI 10.1016/j.neuropsychologia.2004.10.013; Xing Y, 2013, J NEUROSCI, V33, P10301, DOI 10.1523/JNEUROSCI.3754-12.2013 64 0 0 SOC NEUROSCIENCE WASHINGTON 11 DUPONT CIRCLE, NW, STE 500, WASHINGTON, DC 20036 USA 0270-6474 J NEUROSCI J. Neurosci. MAY 28 2014 34 22 7493 7500 10.1523/JNEUROSCI.0805-14.2014 8 Neurosciences Neurosciences & Neurology AI8CE WOS:000337131800010 J Watson, CE; Cardillo, ER; Bromberger, B; Chatterjee, A Watson, Christine E.; Cardillo, Eileen R.; Bromberger, Bianca; Chatterjee, Anjan The specificity of action knowledge in sensory and motor systems FRONTIERS IN PSYCHOLOGY English Article actions; functional magnetic resonance imaging (fMRI); motor system; semantic memory; visual motion INFERIOR FRONTAL GYRUS; PERCEPTUAL SYMBOL SYSTEMS; LATERAL TEMPORAL CORTEX; SPATIAL RELATIONS; ACTION WORDS; FMR-ADAPTATION; HUMAN BRAIN; LANGUAGE; REPETITION; RETRIEVAL Neuroimaging studies have found that sensorimotor systems are engaged when participants observe actions or comprehend action language. However, most of these studies have asked the binary question of whether action concepts are embodied or not, rather than whether sensory and motor areas of the brain contain graded amounts of information during putative action simulations. To address this question, we used repetition suppression (RS) functional magnetic resonance imaging to determine if functionally-localized motor movement and visual motion regions-of-interest (ROI) and two anatomical ROIs (inferior frontal gyrus, IFG; left posterior middle temporal gyrus, pMTG) were sensitive to changes in the exemplar (e.g., two different people "kicking") or representational format (e.g., photograph or schematic drawing of someone "kicking") within pairs of action images. We also investigated whether concrete versus more symbolic depictions of actions (i.e., photographs or schematic drawings) yielded different patterns of activation throughout the brain. We found that during a conceptual task, sensory and motor systems represent actions at different levels of specificity. While the visual motion ROI did not exhibit RS to different exemplars of the same action or to the same action depicted by different formats, the motor movement ROI did. These effects are consistent with "person-specific" action simulations: if the motor system is recruited for action understanding, it does so by activating one's own motor program for an action. We also observed significant repetition enhancement within the IFG ROI to different exemplars or formats of the same action, a result that may indicate additional cognitive processing on these trials. Finally, we found that the recruitment of posterior brain regions by action concepts depends on the format of the input: left lateral occipital cortex and right supramarginal gyrus responded more strongly to symbolic depictions of actions than concrete ones. [Watson, Christine E.] Einstein Healthcare Network, Moss Rehabil Res Inst, Elkins Pk, PA USA; [Watson, Christine E.; Cardillo, Eileen R.; Bromberger, Bianca; Chatterjee, Anjan] Univ Penn, Dept Neurol, Philadelphia, PA 19104 USA; [Watson, Christine E.; Cardillo, Eileen R.; Bromberger, Bianca; Chatterjee, Anjan] Univ Penn, Ctr Cognit Neurosci, Philadelphia, PA 19104 USA Watson, CE (reprint author), Moss Rehabil Res Inst, 50 Township Line Rd, Elkins Pk, PA 19027 USA. watsonch@einstein.edu Amorapanth P, 2012, BRAIN LANG, V120, P226, DOI 10.1016/j.bandl.2011.09.007; Assmus A, 2007, J COGNITIVE NEUROSCI, V19, P1004, DOI 10.1162/jocn.2007.19.6.1004; Aziz-Zadeh L, 2008, J PHYSIOLOGY-PARIS, V102, P35, DOI 10.1016/j.jphysparis.2008.03.012; Barsalou LW, 2008, ANNU REV PSYCHOL, V59, P617, DOI 10.1146/annurev.psych.59.103006.093639; Barsalou LW, 2003, PHILOS T ROY SOC B, V358, P1177, DOI 10.1098/rstb.2003.1319; Barsalou LW, 1999, BEHAV BRAIN SCI, V22, P577; Bavelier D, 2001, J NEUROSCI, V21, P8931; Beilock SL, 2008, P NATL ACAD SCI USA, V105, P13269, DOI 10.1073/pnas.0803424105; Boulenger V, 2009, CEREB CORTEX, V19, P1905, DOI 10.1093/cercor/bhn217; Calvo-Merino B, 2005, CEREB CORTEX, V15, P1243, DOI 10.1093/cercor/bhi007; Calvo-Merino B, 2006, CURR BIOL, V16, P1905, DOI 10.1016/j.cub.2006.07.065; Cardin V, 2012, J NEUROPHYSIOL, V108, P794, DOI 10.1152/jn.00002.2012; Caspers S, 2010, NEUROIMAGE, V50, P1148, DOI 10.1016/j.neuroimage.2009.12.112; Chatterjee Anjan, 2008, Seminars in Speech and Language, V29, P226, DOI 10.1055/s-0028-1082886; Chatterjee A, 2001, TRENDS COGN SCI, V5, P55, DOI 10.1016/S1364-6613(00)01598-9; Chatterjee A., 2010, LANGUAGE COGNITION, V2, P79, DOI DOI 10.1515/LANGCOG.2010.004; Damasio H, 2001, NEUROIMAGE, V13, P1053, DOI 10.1006/nimg.2001.0775; Deacon T. W., 1997, SYMBOLIC SPECIES CO; Decety J, 2006, BRAIN RES, V1079, P4, DOI 10.1016/j.brainres.2005.12.115; DIPELLEGRINO G, 1992, EXP BRAIN RES, V91, P176; Dumoulin SO, 2000, CEREB CORTEX, V10, P454, DOI 10.1093/cercor/10.5.454; Gallese V, 2005, COGN NEUROPSYCHOL, V22, P455, DOI 10.1080/02643290442000310; Gallese V, 2011, TRENDS COGN SCI, V15, P512, DOI 10.1016/j.tics.2011.09.003; Gennari S. P., 2012, LANG LINGUIST COMPAS, V6, P67, DOI 10.1002/lnc3.317; Grill-Spector K, 2006, TRENDS COGN SCI, V10, P14, DOI 10.1016/j.tics.2005.11.006; Grill-Spector K, 2001, ACTA PSYCHOL, V107, P293, DOI 10.1016/S0001-6918(01)00019-1; Grossman ED, 2010, FRONT HUM NEUROSCI, V4, DOI 10.3389/neuro.09.015.2010; Hauk O, 2004, NEURON, V41, P301, DOI 10.1016/S0896-6273(03)00838-9; Henson RNA, 2003, PROG NEUROBIOL, V70, P53, DOI 10.1016/S0301-0082(03)00086-8; Huk AC, 2002, J NEUROSCI, V22, P7195; Kable JW, 2005, J COGNITIVE NEUROSCI, V17, P1855, DOI 10.1162/089892905775008625; Kable JW, 2002, J COGNITIVE NEUROSCI, V14, P795, DOI 10.1162/08989290260138681; Kable JW, 2006, J COGNITIVE NEUROSCI, V18, P1498, DOI 10.1162/jocn.2006.18.9.1498; Kalenine S, 2010, BRAIN, V133, P3269, DOI 10.1093/brain/awq210; Kilner JM, 2009, J NEUROSCI, V29, P10153, DOI 10.1523/JNEUROSCI.2668-09.2009; Kranjec A, 2013, CORTEX, V49, P1983, DOI 10.1016/j.cortex.2013.03.005; Kuperberg GR, 2008, HUM BRAIN MAPP, V29, P544, DOI 10.1002/hbm.20419; Lingnau A, 2009, J VISION, V9, DOI 10.1167/9.13.3; LURIA AR, 1959, BRAIN, V82, P437, DOI 10.1093/brain/82.3.437; Maccotta L, 2004, J COGNITIVE NEUROSCI, V16, P1625, DOI 10.1162/0898929042568451; Moss HE, 2005, CEREB CORTEX, V15, P1723, DOI 10.1093/cercor/bhi049; Nichols T, 2003, STAT METHODS MED RES, V12, P419, DOI 10.1191/0962280203sm341ra; Nichols TE, 2002, HUM BRAIN MAPP, V15, P1, DOI 10.1002/hbm.1058; Peirce C.S, 1955, PHILOS WRITINGS PEIR; Pezzulo G, 2010, BRAIN COGNITION, V73, P68, DOI 10.1016/j.bandc.2010.03.002; Plaut DC, 2002, COGN NEUROPSYCHOL, V19, P603, DOI 10.1080/02643290244000112; Press C, 2012, NEUROIMAGE, V60, P1671, DOI 10.1016/j.neuroimage.2012.01.118; Pulvermuller F, 1999, BEHAV BRAIN SCI, V22, P253, DOI 10.1017/S0140525X9900182X; Raposo A, 2006, NEUROPSYCHOLOGIA, V44, P2284, DOI 10.1016/j.neuropsychologia.2006.05.017; Raposo A, 2009, NEUROPSYCHOLOGIA, V47, P388, DOI 10.1016/j.neuropsychologia.2008.09.017; RATCLIFF R, 1978, PSYCHOL REV, V85, P59, DOI 10.1037//0033-295X.85.2.59; Revill KP, 2008, P NATL ACAD SCI USA, V105, P13111, DOI 10.1073/pnas.0807054105; Saxe R, 2006, NEUROIMAGE, V30, P1088, DOI 10.1016/j.neuroimage.2005.12.062; Saygin AP, 2010, J COGNITIVE NEUROSCI, V22, P2480, DOI 10.1162/jocn.2009.21388; Segaert K, 2013, NEUROPSYCHOLOGIA, V51, P59, DOI 10.1016/j.neuropsychologia.2012.11.006; Thompson-Schill SL, 1997, P NATL ACAD SCI USA, V94, P14792, DOI 10.1073/pnas.94.26.14792; Thompson-Schill SL, 2003, NEUROPSYCHOLOGIA, V41, P280, DOI 10.1016/S0028-3932(02)00161-6; van Dam WO, 2012, HUM BRAIN MAPP, V33, P2322, DOI 10.1002/hbm.21365; Vigliocco G, 2004, COGNITIVE PSYCHOL, V48, P422, DOI 10.1016/j.cogpsych.2003.09.001; Wall MB, 2008, EUR J NEUROSCI, V27, P2747, DOI 10.1111/j.1460-9568.2008.06249.x; Watson CE, 2013, J COGNITIVE NEUROSCI, V25, P1191, DOI 10.1162/jocn_a_00401; Watson CE, 2011, NEUROLOGY, V76, P1428, DOI 10.1212/WNL.0b013e3182166e2c; Weigelt S., 2012, CEREB CORTEX, V23, P2169, DOI [10.1093/cercor/bhs192, DOI 10.1093/CERC0R/BHS192]; Wiggett AJ, 2011, J COGNITIVE NEUROSCI, V23, P1765, DOI 10.1162/jocn.2010.21552; Willems RM, 2012, FRONT PSYCHOL, V3, DOI 10.3389/fpsyg.2012.00582; WORSLEY KJ, 1992, J CEREBR BLOOD F MET, V12, P900 66 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. MAY 26 2014 5 494 10.3389/fpsyg.2014.00494 11 Psychology, Multidisciplinary Psychology AI5US WOS:000336935300001 J Stampoulis, D; Haddad, ZS; Anagnostou, EN Stampoulis, Dimitrios; Haddad, Ziad S.; Anagnostou, Emmanouil N. Assessing the drivers of biodiversity in Madagascar by quantifying its hydrologic properties at the watershed scale REMOTE SENSING OF ENVIRONMENT English Article Remote sensing; Hydrology; Biodiversity SEA-ICE; CONSERVATION PRIORITIES; SCATTEROMETER DATA; PASSIVE-MICROWAVE; SOIL-MOISTURE; SEAWINDS DATA; EVOLUTION; RETRIEVAL; DIVERSITY; PATTERNS Motivated by a theory developed by Wilme et al. (2006) according to which, watersheds of Madagascar with headwaters at high altitude respond differently to drought from those with headwaters confined to relatively low elevations, with possibly profound effects on the biodiversity patterns of the island, we analyzed multi-year basin-specific observations of soil moisture and vegetation water content (derived from NRL's WindSat radiometer) and their response to precipitation departures (derived from TRMM 3B42 V7) from its local mean. These datasets were analyzed to investigate the hydrologic properties at the basin scale, including the speed with which vegetation and soil moisture respond to precipitation anomalies. We also looked at the basin-specific normalized radar surface-backscattering cross-sections from NASA's QuikSCAT Scatterometer, to obtain information on the vegetation regimes of the various Malagasy basins. Finally, we correlated the basin response to the precipitation forcing, and compared the amplitude and time lag of the correlations across watersheds with high elevation headwaters versus those with low elevation headwaters in the aim of evaluating the drought-response hypothesis of Wilme et al. (2006). Our results indicate that the vegetation water content time series exhibit several features that are consistent with those of the majority of the bioclimatic zones of the island. Specifically, although the speed of the response of the vegetation water content varies significantly among the different basins, it is inter-annually consistent for each watershed, while the soil-moisture time series are less consistent than the vegetation water content time series. This study is a first step in the quantification of the hydrologic properties derived from microwave remote sensing, and which could potentially shed new light on the different intra-annual responses of watersheds to precipitation anomalies. Furthermore, this analysis offers important insights into the hydro-geomorphologic drivers associated with biodiversity patterns in Madagascar, contributing to a better understanding of the mechanisms that determine biotic diversification across the various bioclimatic regions of the island. (C) 2014 Elsevier Inc. All rights reserved. [Stampoulis, Dimitrios; Anagnostou, Emmanouil N.] Univ Connecticut, Dept Civil & Environm Engn, Storrs, CT 06269 USA; [Stampoulis, Dimitrios; Haddad, Ziad S.] CALTECH, Jet Prop Lab, Pasadena, CA USA Stampoulis, D (reprint author), Univ Connecticut, CEE, Storrs, CT 06269 USA. das09011@engr.uconn.edu NASA Precipitation Measurement Mission award [NNX07AE31G] This work was supported by a NASA Precipitation Measurement Mission award (NNX07AE31G). We also acknowledge and appreciate Dr. Joe F. Turk from the Jet Propulsion Laboratory (JPL) as well as Dr. Li Li from the Naval Research Laboratory (NRL) for providing the WindSat VWC and SM data. Z.S. Haddad's contribution was carried out at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Anderson HS, 2005, IEEE T GEOSCI REMOTE, V43, P647, DOI 10.1109/TGRS.2004.842017; Andreone F, 2005, CONSERV BIOL, V19, P1790, DOI 10.1111/j.1523-1739.2005.00249.x; Ashcraft I. S., 2000, P INT GEOSC REM SENS; Ashcraft I. S., 2005, IEEE T GEOSCI REMOTE, V43, P237; Brodzik M.J., 2002, DISCRETE GLOBAL GRID; Craul M, 2007, BMC EVOL BIOL, V83, P7, DOI DOI 10.1186/1471-2148-7-83; Dewar RE, 2007, P NATL ACAD SCI USA, V104, P13723, DOI 10.1073/pnas.0704346104; Drinkwater MR, 2001, J GEOPHYS RES-ATMOS, V106, P33935, DOI 10.1029/2001JD900107; Dumetz N, 1999, BIODIVERS CONSERV, V8, P273, DOI 10.1023/A:1008880718889; Dyal J., 1997, 97004 MERS BRIGH YOU; Early DS, 2001, IEEE T GEOSCI REMOTE, V39, P291, DOI 10.1109/36.905237; Forster RR, 2001, ANN GLACIOL, V33, P85, DOI 10.3189/172756401781818428; Ganzhorn JU, 2001, ORYX, V35, P346, DOI 10.1046/j.1365-3008.2001.00201.x; Gautier L., 2003, NATURAL HIST MADAGAS, P229; Goodman S.M., 2003, NATURAL HIST MADAGAS; Haarpaintner J, 2004, IEEE T GEOSCI REMOTE, V42, P1433, DOI 10.1109/TGRS.2004.828195; Huffman GJ, 1997, B AM METEOROL SOC, V78, P5, DOI 10.1175/1520-0477(1997)078<0005:TGPCPG>2.0.CO;2; Huffman GJ, 2007, J HYDROMETEOROL, V8, P38, DOI 10.1175/JHM560.1; Ingram JC, 2005, PHILOS T ROY SOC A, V363, P55, DOI 10.1098/rsta.2004.1476; JURY MR, 1991, METEOROL ATMOS PHYS, V47, P37, DOI 10.1007/BF01025825; Jury MR, 2003, NATURAL HIST MADAGAS, P75; Koechlin J., 1972, MONOGRAPHIAE BIOL, V21, P145; Kremen C, 2008, SCIENCE, V320, P222, DOI 10.1126/science.1155193; Krueger M., 2007, Annals of the Transvaal Museum, V44, P123; Li L, 2010, IEEE T GEOSCI REMOTE, V48, P2224, DOI 10.1109/TGRS.2009.2037749; Long DG, 2000, IEEE T GEOSCI REMOTE, V38, P1857, DOI 10.1109/36.851769; Long DG, 2000, INT GEOSCI REMOTE SE, P1220, DOI 10.1109/IGARSS.2000.858073; Michaelides S, 2009, ATMOS RES, V94, P512, DOI 10.1016/j.atmosres.2009.08.017; Myers N, 2000, NATURE, V403, P853, DOI 10.1038/35002501; Njoku EG, 1999, IEEE T GEOSCI REMOTE, V37, P79, DOI 10.1109/36.739125; Njoku EG, 2003, IEEE T GEOSCI REMOTE, V41, P215, DOI 10.1109/TGRS.2002.808243; Olivieri G, 2007, MOL PHYLOGENET EVOL, V43, P309, DOI 10.1016/j.ympev.2006.10.026; Pearson RG, 2009, EVOLUTION, V63, P959, DOI 10.1111/j.1558-5646.2008.00596.x; Phillipson P. B., 2006, AFRICAN PLANTS BIODI; Remund QP, 1999, J GEOPHYS RES-OCEANS, V104, P11515, DOI 10.1029/98JC02373; Remund QP, 2000, INT GEOSCI REMOTE SE, P491, DOI 10.1109/IGARSS.2000.861606; Remund QP, 2003, IEEE T GEOSCI REMOTE, V41, P1821, DOI 10.1109/TGRS.2003.813495; Spencer MW, 2000, IEEE T GEOSCI REMOTE, V38, P89, DOI 10.1109/36.823904; Stephen H, 2005, IEEE T GEOSCI REMOTE, V43, P238, DOI 10.1109/TGRS.2004.840646; STOREY M, 1995, SCIENCE, V267, P852, DOI 10.1126/science.267.5199.852; Ulaby FT, 1996, J HYDROL, V184, P57, DOI 10.1016/0022-1694(95)02968-0; Vences M, 2009, TRENDS ECOL EVOL, V24, P456, DOI 10.1016/j.tree.2009.03.011; Wells N. A., 2003, NATURAL HIST MADAGAS, P16; Wilme L, 2006, SCIENCE, V312, P1063, DOI 10.1126/science.1122806; Wollenberg KC, 2008, EVOLUTION, V62, P1890, DOI 10.1111/j.1558-5646.2008.00420.x; Wright C. P., 2007, DEV PRIMATOLOGY PROG, P385; Yoder AD, 2006, ANNU REV ECOL EVOL S, V37, P405, DOI 10.1146/annurev.ecolsys.37.091305.110239; Yoder AD, 2005, P NATL ACAD SCI USA, V102, P6587, DOI 10.1073/pnas.0502092102; Zhao YH, 2002, IEEE T GEOSCI REMOTE, V40, P1241, DOI 10.1109/TGRS.2002.800442 49 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. MAY 25 2014 148 1 15 10.1016/j.rse.2014.03.005 15 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AI3PE WOS:000336773600001 J Crowell, S; White, L; Wicker, L Crowell, Sean; White, Luther; Wicker, Louis Estimation of near surface wind speeds in strongly rotating flows APPLIED MATHEMATICS AND COMPUTATION English Article Vortex dynamics; Fluid mechanics; Vortex flows; Hyperbolic equations; Geophysical fluid dynamics; Axisymmetric dynamics TORNADO-LIKE VORTICES; VORTEX DYNAMICS; SWIRL RATIO; SIMULATION Modeling studies consistently demonstrate that the most violent winds in tornadic vortices occur in the lowest tens of meters above the surface. These velocities are unobservable by radar platforms due to line of sight considerations. In this work, a methodology is developed which utilizes parametric tangential velocity models derived from Doppler radar measurements, together with a tangential momentum and mass continuity constraint, to estimate the radial and vertical velocities in a steady axisymmetric frame. The main result is that information from observations aloft can be extrapolated into the surface layer of the vortex. The impact of the amount of information available to the retrieval is demonstrated through some numerical tests with pseudo-data. (C) 2014 Published by Elsevier Inc. [Crowell, Sean; White, Luther] Univ Oklahoma, Norman, OK 73019 USA; [Crowell, Sean; Wicker, Louis] NOAA, Natl Severe Storms Lab, Norman, OK 73072 USA Crowell, S (reprint author), Univ Oklahoma, Norman, OK 73019 USA. scrowell@ou.edu; lwhite@ou.edu; Louis.Wicker@noaa.gov CHURCH CR, 1977, B AM METEOROL SOC, V58, P900, DOI 10.1175/1520-0477(1977)058<0900:TVSAPU>2.0.CO;2; CHURCH CR, 1979, J ATMOS SCI, V36, P1755, DOI 10.1175/1520-0469(1979)036<1755:COTLVA>2.0.CO;2; Crowell S., 2011, THESIS U OKLAHOMA; Davies-Jones R, 2008, J ATMOS SCI, V65, P2469, DOI 10.1175/2007JAS2516.1; Dhall S., 2006, DYNAMIC DATA ASSIMIL; Dieudonne J., 1960, PURE APPL MATH, V10; Dowell DC, 2005, MON WEATHER REV, V133, P1501, DOI 10.1175/MWR2934.1; Evans L. C., 1998, GRADUATE STUDIES MAT, V19; FIEDLER BH, 1986, J ATMOS SCI, V43, P2328, DOI 10.1175/1520-0469(1986)043<2328:ATOTMW>2.0.CO;2; Lewellen DC, 2000, J ATMOS SCI, V57, P527, DOI 10.1175/1520-0469(2000)057<0527:TIOALS>2.0.CO;2; Lewellen W., 1993, GEOPHYS MONOGR AM GE, V79, P19; Lewellen W., 1980, NUREGCR1585; Lewellen WS, 1997, J ATMOS SCI, V54, P581, DOI 10.1175/1520-0469(1997)054<0581:LESOAT>2.0.CO;2; ROTUNNO R, 1979, J ATMOS SCI, V36, P140, DOI 10.1175/1520-0469(1979)036<0140:ASITLV>2.0.CO;2; SNOW JT, 1982, REV GEOPHYS, V20, P953, DOI 10.1029/RG020i004p00953; Tarantola A., 2005, INVERSE PROBLEM THEO; Wood VT, 2011, J ATMOS SCI, V68, P990, DOI 10.1175/2011JAS3588.1; Wurman J, 2005, MON WEATHER REV, V133, P97, DOI 10.1175/MWR-2856.1 18 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0096-3003 1873-5649 APPL MATH COMPUT Appl. Math. Comput. MAY 25 2014 235 201 211 10.1016/j.amc.2014.01.010 11 Mathematics, Applied Mathematics AH1RL WOS:000335898500022 J Qu, F; Zhang, D; Chen, LT; Wang, FF; Pan, JX; Zhu, YM; Ma, CM; Huang, YT; Ye, XQ; Sun, SJ; Zheng, WJ; Zhang, RJ; Xu, J; Xing, LF; Huang, HF Qu, Fan; Zhang, Dan; Chen, Lu-Ting; Wang, Fang-Fang; Pan, Jie-Xue; Zhu, Yi-Min; Ma, Chun-Mei; Huang, Yi-Ting; Ye, Xiao-Qun; Sun, Sai-Jun; Zheng, Wen-Jun; Zhang, Run-Ju; Xu, Jian; Xing, Lan-Feng; Huang, He-Feng Auricular Acupressure Reduces Anxiety Levels and Improves Outcomes of in Vitro Fertilization: A Prospective, Randomized and Controlled Study SCIENTIFIC REPORTS English Article ASSISTED REPRODUCTIVE TECHNOLOGY; AMSTERDAM PREOPERATIVE ANXIETY; EPIDERMAL-GROWTH-FACTOR; INFORMATION SCALE APAIS; HUMAN OVARIAN TISSUE; QUALITY-OF-LIFE; FOLLICULAR-FLUID; NEUROPEPTIDE-Y; EUROPEAN REGISTERS; FACTOR RECEPTOR The study was to explore whether auricular acupressure (AA) can relieve anxiety during the period from trans-vaginal oocyte retrieval to the embryo transfer in IVF treatment and whether AA can improve the outcomes of IVF. 305 infertile patients with tubal blockage who were referred for IVF were included. The women were randomized into a control group with 102 cases, a Sham-AA group with 102 cases and an AA group with 101 cases. The anxiety levels were rated with Spielberger's State Trait Anxiety Inventory and the Amsterdam Preoperative Anxiety and Information Scale. Data of clinical pregnancy rate (CPR), implantation rate (IR) and live birth rate (LBR) were obtained. The levels of neuropeptide Y (NPY) and transforming growth factor alpha (TGF-alpha) in the follicular fluids were detected with ELISA. After treatment, in AA group, the levels of state anxiety, preoperative anxiety and need-for-information were significantly lower, whereas CPR, IR, LBR and NPY levels in the follicular fluids were markedly higher than Sham-AA group and control group. We concluded that AA could help to reduce anxiety levels associated with IVF and improves the outcomes of IVF partly through increasing the levels of NPY in the follicular fluids. [Qu, Fan; Zhang, Dan; Chen, Lu-Ting; Wang, Fang-Fang; Pan, Jie-Xue; Zhu, Yi-Min; Ma, Chun-Mei; Huang, Yi-Ting; Ye, Xiao-Qun; Sun, Sai-Jun; Zheng, Wen-Jun; Zhang, Run-Ju; Xu, Jian; Xing, Lan-Feng; Huang, He-Feng] Zhejiang Univ, Sch Med, Womens Hosp, Hangzhou 310006, Zhejiang, Peoples R China; [Qu, Fan; Zhang, Dan; Wang, Fang-Fang; Zhu, Yi-Min; Zhang, Run-Ju; Xu, Jian; Xing, Lan-Feng; Huang, He-Feng] Minist Educ China, Key Lab Reprod Genet, Beijing 310006, Peoples R China; [Qu, Fan; Zhang, Dan; Wang, Fang-Fang; Zhu, Yi-Min; Zhang, Run-Ju; Xu, Jian; Xing, Lan-Feng; Huang, He-Feng] Key Lab Womens Reprod Hlth Zhejiang Prov, Hangzhou 310006, Zhejiang, Peoples R China Huang, HF (reprint author), Zhejiang Univ, Sch Med, Womens Hosp, Hangzhou 310006, Zhejiang, Peoples R China. xinglf@zju.edu.cn; huanghefg@hotmail.com Key Projects in the National Science Technology Pillar Program, China [2012BAI32B04] This work was supported by Key Projects in the National Science Technology Pillar Program in the Twelve Five year Plan Period, China (No. 2012BAI32B04). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank all the staff of Department of Chinese Medicine and Department of Reproductive Endocrinology, Women's Hospital, School of Medicine, Zhejiang University for their kind help with the present research and Professor Robert Norman (University of Adelaide, Australia) for his suggestions for the paper. Agarwal A, 2005, ANAESTHESIA, V60, P978, DOI 10.1111/j.1365-2044.2005.04332.x; Andersen AN, 2008, HUM REPROD, V23, P756, DOI 10.1093/humrep/den014; Berth Hendrik, 2007, Psychosoc Med, V4, pDoc01; Billig H, 1996, HUM REPROD UPDATE, V2, P103, DOI 10.1093/humupd/2.2.103; Bodhise Paul Brown, 2004, Hematology, V9, P235, DOI 10.1080/10245330410001701495; Boivin J, 1996, HUM REPROD, V11, P903; BOIVIN J, 1995, FERTIL STERIL, V64, P802; Boker A, 2002, CAN J ANAESTH, V49, P792; Campagne DM, 2006, HUM REPROD, V21, P1651, DOI 10.1093/humrep/del078; Chambers GM, 2009, FERTIL STERIL, V91, P2281, DOI 10.1016/j.fertnstert.2009.04.029; Chen TH, 2004, HUM REPROD, V19, P2313, DOI 10.1093/humrep/deh414; Cheung YL, 2003, PSYCHO-ONCOL, V12, P254, DOI 10.1002/pon.638; de Klerk C, 2008, HUM REPROD, V23, P112, DOI 10.1093/humrep/dem357; de Klerk C, 2006, HUM REPROD, V21, P721, DOI 10.1093/humrep/dei395; Eugster A, 1999, SOC SCI MED, V48, P575, DOI 10.1016/S0277-9536(98)00386-4; Fassoulaki A, 2003, ANESTH ANALG, V96, P885, DOI 10.1213/01.ANE.0000048713.41657.D3; Garip H, 2004, BRIT J ORAL MAX SURG, V42, P551, DOI 10.1016/j.bjoms.2004.08.001; Horsey K., 2006, ANN C EUR SOC HUM RE; Hsieh CH, 2011, AM J CHINESE MED, V39, P433, DOI 10.1142/S0192415X11008932; Hsieh CH, 2010, AM J CHINESE MED, V38, P675, DOI 10.1142/S0192415X10008147; Hui KKS, 2000, HUM BRAIN MAPP, V9, P13, DOI 10.1002/(SICI)1097-0193(2000)9:1<13::AID-HBM2>3.0.CO;2-F; JORGENSEN JC, 1989, AM J PHYSIOL, V257, pE220; JORGENSEN JC, 1990, ENDOCRINOLOGY, V127, P1682; Klonoff-Cohen H, 2004, FERTIL STERIL, V81, P982, DOI 10.1016/j.fertnstert.2003.08.050; Klonoff-Cohen H, 2005, Hum Reprod Update, V11, P179; Kober A, 2003, ANESTHESIOLOGY, V98, P1328, DOI 10.1097/00000542-200306000-00005; Kung YY, 2011, MENOPAUSE, V18, P638, DOI 10.1097/gme.0b013e31820159c1; Li XH, 2011, GYNECOL ENDOCRINOL, V27, P139, DOI 10.3109/09513590.2010.501871; Lintsen AME, 2007, HUM REPROD, V22, P2455, DOI 10.1093/humrep/dem183; Lintsen AME, 2009, HUM REPROD, V24, P1092, DOI 10.1093/humrep/den491; Liu CF, 2008, J ALTERN COMPLEM MED, V14, P303, DOI 10.1089/acm.2006.6064; Maa SH, 2003, J ALTERN COMPLEM MED, V9, P659, DOI 10.1089/107555303322524517; Markiewicz W, 2003, FOLIA HISTOCHEM CYTO, V41, P183; MCNEILL DL, 1987, NEUROSCI LETT, V80, P27, DOI 10.1016/0304-3940(87)90489-7; Moerman N, 1996, ANESTH ANALG, V82, P445, DOI 10.1097/00000539-199603000-00002; Mora B, 2007, J UROLOGY, V178, P160, DOI 10.1016/j.juro.2007.03.019; NEWTON CR, 1990, FERTIL STERIL, V54, P879; Nishimori M, 2002, QUAL LIFE RES, V11, P361, DOI 10.1023/A:1015561129899; Nygren KG, 2001, HUM REPROD, V16, P2459; Qu F, 2010, HUM REPROD, V25, P1441, DOI 10.1093/humrep/deq078; Qu JP, 2000, FERTIL STERIL, V74, P113, DOI 10.1016/S0015-0282(00)00549-5; Reeka N, 1998, HUM REPROD, V13, P2199, DOI 10.1093/humrep/13.8.2199; SHEK DTL, 1993, J CLIN PSYCHOL, V49, P349, DOI 10.1002/1097-4679(199305)49:3<349::AID-JCLP2270490308>3.0.CO;2-J; Smeenk JMJ, 2005, HUM REPROD, V20, P991, DOI 10.1093/humrep/deh739; Spielberger CD, 1970, MANUAL STATE TRAIT A; Stener-Victorin E, 2003, HUM REPROD, V18, P1454, DOI 10.1093/humrep/deg277; TAMURA M, 1995, HUM REPROD, V10, P1891; Templeton A, 1996, LANCET, V348, P1402, DOI 10.1016/S0140-6736(96)05291-9; TENENBAUM G, 1985, J CLIN PSYCHOL, V41, P239, DOI 10.1002/1097-4679(198503)41:2<239::AID-JCLP2270410218>3.0.CO;2-5; TSAFRIRI A, 1989, ENDOCRINOLOGY, V125, P1857; Verhaak CM, 2007, HUM REPROD UPDATE, V13, P27, DOI 10.1093/humupd/dml040; Volgsten H, 2008, HUM REPROD, V23, P2056, DOI 10.1093/humrep/den154; Wang MC, 2009, J ALTERN COMPLEM MED, V15, P235, DOI 10.1089/acm.2008.0164; Wang SM, 2005, ANESTH ANALG, V101, P666, DOI 10.1213/01.ANE.0000175212.17642.45; Wang SM, 2001, ANESTH ANALG, V92, P548; Wang SM, 2001, ANESTH ANALG, V93, P1178, DOI 10.1097/00000539-200111000-00024; Wang YZ, 2010, INT J NURS STUD, V47, P1089, DOI 10.1016/j.ijnurstu.2010.02.009; Zhang CS, 2010, CLIN OTOLARYNGOL, V35, P6, DOI 10.1111/j.1749-4486.2009.02067.x; Zhou J, 2008, J ALTERN COMPLEM MED, V14, P423, DOI 10.1089/acm.2007.0725; Zhou J, 2011, EVID-BASED COMPL ALT, P1, DOI 10.1093/ecam/nep001 60 0 0 NATURE PUBLISHING GROUP LONDON MACMILLAN BUILDING, 4 CRINAN ST, LONDON N1 9XW, ENGLAND 2045-2322 SCI REP-UK Sci Rep MAY 22 2014 4 5028 10.1038/srep05028 7 Multidisciplinary Sciences Science & Technology - Other Topics AH5YO WOS:000336207300004 J Raybuck, JD; Lattal, KM Raybuck, J. D.; Lattal, K. M. Differential effects of dorsal hippocampal inactivation on expression of recent and remote drug and fear memory NEUROSCIENCE LETTERS English Article Cocaine; Conditioned place preference; Fear conditioning; Systems consolidation; Muscimol CONDITIONED PLACE PREFERENCE; COCAINE-ASSOCIATED MEMORY; MEDIAL PREFRONTAL CORTEX; CONTEXTUAL FEAR; RETRIEVAL; CONSOLIDATION; ACQUISITION; EXTINCTION; RECEPTORS; SYSTEMS Drugs of abuse generate strong drug-context associations, which can evoke powerful drug cravings that are linked to reinstatement in animal models and to relapse in humans. Work in learning and memory has demonstrated that contextual memories become more distributed over time, shifting from dependence on the hippocampus for retrieval to dependence on cortical structures. Implications for such changes in the structure of memory retrieval to addiction are unknown. Thus, to determine if the passage of time alters the substrates of conditioned place preference (CPP) memory retrieval, we investigated the effects of inactivation of the dorsal hippocampus (DH) with the GABA-A receptor agonist muscimol on expression of recent or remote CPP. We compared these effects with the same manipulation on expression of contextual fear conditioning. DH inactivation produced similar deficits in expression of both recent and remote CPP, but blocked expression of recent but not remote contextual fear memory. We describe the implications of these findings for mechanisms underlying long-term storage of contextual information. (C) 2014 Elsevier Ireland Ltd. All rights reserved. [Raybuck, J. D.; Lattal, K. M.] Oregon Hlth & Sci Univ, Dept Behav Neurosci, Portland, OR 97239 USA Raybuck, JD (reprint author), Oregon Hlth & Sci Univ, Dept Behav Neurosci, 3181 SW Sam Jackson Pk Rd L470, Portland, OR 97239 USA. jdraybuck@gmail.com; lattalm@ohsu.edu NIDA; NIMH [R01DA025922, RO1MH077111, T32DA007262, F32DA031537] Grant sponsor: NIDA, NIMH. Grant number: R01DA025922, RO1MH077111 KML, T32DA007262, F32DA031537 JDR. Anagnostaras SG, 1999, J NEUROSCI, V19, P1106; Baier T, 2007, COMPUTATION STAT, V22, P91, DOI 10.1007/s00180-007-0023-6; Bardo MT, 2000, PSYCHOPHARMACOLOGY, V153, P31, DOI 10.1007/s002130000569; Beeman CL, 2013, LEARN MEMORY, V20, P336, DOI 10.1101/lm.031161.113; Bernardi RE, 2009, LEARN MEMORY, V16, P777, DOI 10.1101/lm.1648509; Cunningham CL, 2006, NAT PROTOC, V1, P1662, DOI 10.1038/nprot.2006.279; Dash PK, 2004, BRAIN RES REV, V45, P30, DOI 10.1016/j.brainresrev.2004.02.001; Dutra L, 2008, AM J PSYCHIAT, V165, P179, DOI 10.1176/appi.ajp.2007.06111851; Frankland PW, 2005, NAT REV NEUROSCI, V6, P119, DOI 10.1038/nrn1607; Goshen I, 2011, CELL, V147, P678, DOI 10.1016/j.cell.2011.09.033; Inda MC, 2011, J NEUROSCI, V31, P1635, DOI 10.1523/JNEUROSCI.4736-10.2011; Kalivas PW, 2011, MOL PSYCHIATR, V16, P974, DOI 10.1038/mp.2011.46; Koob GF, 2010, NEUROPSYCHOPHARMACOL, V35, P217, DOI 10.1038/npp.2009.110; Malvaez M, 2013, P NATL ACAD SCI USA, V110, P2647, DOI [10.1073/pnas.1213364110, 10.1073/pnas.1213364110/-/DCSupplemental]; Malvaez M, 2010, BIOL PSYCHIAT, V67, P36, DOI 10.1016/j.biopsych.2009.07.032; Meyers RA, 2003, NEUROREPORT, V14, P2127, DOI 10.1097/01.wnr.0000095709.83808.81; Meyers RA, 2006, BEHAV NEUROSCI, V120, P401, DOI 10.1037/0735-7044.120.2.401; Milekic MH, 2002, NEURON, V36, P521, DOI 10.1016/S0896-6273(02)00976-5; Milton A., 2012, CURR OPIN NEUROBIOL, V23, P706; Moscovitch M, 2005, J ANAT, V207, P35, DOI 10.1111/j.1469-7580.2005.00421.x; O'Brien CP, 1998, J PSYCHOPHARMACOL, V12, P15, DOI 10.1177/026988119801200103; Otis JM, 2011, NEUROPSYCHOPHARMACOL, V36, P1912, DOI 10.1038/npp.2011.77; Otis JM, 2013, J NEUROSCI, V33, P1271, DOI 10.1523/JNEUROSCI.3463-12.2013; Packard MG, 1996, NEUROBIOL LEARN MEM, V65, P65, DOI 10.1006/nlme.1996.0007; Penberthy Jennifer K, 2010, Curr Drug Abuse Rev, V3, P49; Quinn JJ, 2008, LEARN MEMORY, V15, P368, DOI 10.1101/lm.813608; Raybuck JD, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0015982; Raybuck JD, 2013, PHARMACOL BIOCHEM BE, V106, P109, DOI 10.1016/j.pbb.2013.02.009; Reichel CM, 2012, NEUROPSYCHOPHARMACOL, V37, P298, DOI 10.1038/npp.2011.164; Rezayof A., 2007, EUR NEUROPSYCHOPHARM, V17; Sutherland RJ, 2011, CURR OPIN NEUROBIOL, V21, P446, DOI 10.1016/j.conb.2011.04.007; Vanderschuren LJMJ, 2010, CURR TOP BEHAV NEURO, V3, P179, DOI 10.1007/7854_2009_21; Wang SH, 2009, NAT NEUROSCI, V12, P253, DOI 10.1038/nn.2263; Wiltgen BJ, 2010, CURR BIOL, V20, P1336, DOI 10.1016/j.cub.2010.06.068; Wiltgen BJ, 2013, NEUROBIOL LEARN MEM, V106, P365, DOI 10.1016/j.nlm.2013.06.001; Zarrindast MR, 2007, EUR J PHARMACOL, V568, P192, DOI 10.1016/j.ejphar.2007.04.015; Zarrindast MR, 2006, PHYSIOL BEHAV, V87, P31, DOI 10.1016/j.physbeh.2005.08.041; Zelikowsky M, 2012, J NEUROSCI, V32, P3393, DOI 10.1523/JNEUROSCI.4339-11.2012 38 0 0 ELSEVIER IRELAND LTD CLARE ELSEVIER HOUSE, BROOKVALE PLAZA, EAST PARK SHANNON, CO, CLARE, 00000, IRELAND 0304-3940 1872-7972 NEUROSCI LETT Neurosci. Lett. MAY 21 2014 569 1 5 10.1016/j.neulet.2014.02.063 5 Neurosciences Neurosciences & Neurology AH8AI WOS:000336356100001 J Ziemke, JR; Olsen, MA; Witte, JC; Douglass, AR; Strahan, SE; Wargan, K; Liu, X; Schoeberl, MR; Yang, K; Kaplan, TB; Pawson, S; Duncan, BN; Newman, PA; Bhartia, PK; Heney, MK Ziemke, J. R.; Olsen, M. A.; Witte, J. C.; Douglass, A. R.; Strahan, S. E.; Wargan, K.; Liu, X.; Schoeberl, M. R.; Yang, K.; Kaplan, T. B.; Pawson, S.; Duncan, B. N.; Newman, P. A.; Bhartia, P. K.; Heney, M. K. Assessment and applications of NASA ozone data products derived from Aura OMI/MLS satellite measurements in context of the GMI chemical transport model JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES English Article TROPICAL TROPOSPHERIC OZONE; VARIATIONAL STATISTICAL-ANALYSIS; 1997-1998 EL-NINO; COLUMN OZONE; INTERANNUAL VARIABILITY; MONITORING INSTRUMENT; RECURSIVE FILTERS; NUMERICAL ASPECTS; MLS MEASUREMENTS; ARCTIC OZONE Measurements from the Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS), both on board the Aura spacecraft, have been used to produce daily global maps of column and profile ozone since August 2004. Here we compare and evaluate three strategies to obtain daily maps of tropospheric and stratospheric ozone from OMI and MLS measurements: trajectory mapping, direct profile retrieval, and data assimilation. Evaluation is based on an assessment that includes validation using ozonesondes and comparisons with the Global Modeling Initiative (GMI) chemical transport model. We investigate applications of the three ozone data products from near-decadal and interannual time scales to day-to-day case studies. Interannual changes in zonal mean tropospheric ozone from all of the products in any latitude range are of the order 1-2 Dobson units while changes (increases) over the 8 year Aura record investigated vary by 2-4 Dobson units. It is demonstrated that all of the ozone products can measure and monitor exceptional tropospheric ozone events including major forest fire and pollution transport events. Stratospheric ozone during the Aura record has several anomalous interannual events including split stratospheric warmings in the Northern Hemisphere extratropics that are well captured using the data assimilation ozone profile product. Data assimilation with continuous daily global coverage and vertical ozone profile information is the best of the three strategies at generating a global tropospheric and stratospheric ozone product for science applications. [Ziemke, J. R.; Olsen, M. A.] Morgan State Univ, Goddard Earth Sci Technol & Res, Baltimore, MD 21239 USA; [Ziemke, J. R.; Olsen, M. A.; Douglass, A. R.; Strahan, S. E.; Wargan, K.; Pawson, S.; Duncan, B. N.; Newman, P. A.; Bhartia, P. K.] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Witte, J. C.; Wargan, K.; Heney, M. K.] Sci Syst & Applicat Inc, Lanham, MD USA; [Strahan, S. E.] Univ Space Res Assoc, Columbia, MD USA; [Liu, X.] Harvard Smithsonian Ctr Astrophys, Cambridge, MA 02138 USA; [Schoeberl, M. R.] Sci & Technol Corp, Lanham, MD USA; [Yang, K.] Univ Maryland, Dept Atmospher & Ocean Sci, College Pk, MD 20742 USA; [Kaplan, T. B.] INNOVIM, Greenbelt, MD USA Ziemke, JR (reprint author), Morgan State Univ, Goddard Earth Sci Technol & Res, Baltimore, MD 21239 USA. jerald.r.ziemke@gsfc.nasa.gov Douglass, Anne/D-4655-2012 NASA [NNH07ZDA001N-AST] The authors thank the Aura MLS and OMI instrument and algorithm teams for the extensive satellite measurements used in this study. We also thank the Editor and three reviewers for valuable comments that were very beneficial in improving the paper. OMI is a Dutch-Finnish contribution to the Aura mission. Funding for this research was provided in part by NASA NNH07ZDA001N-AST. Chandra S, 2002, J GEOPHYS RES-ATMOS, V107, DOI 10.1029/2001JD000447; Chandra S, 1998, GEOPHYS RES LETT, V25, P3867, DOI 10.1029/98GL02695; Chandra S, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002912; Doherty RM, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006849; Duncan B. N., 2008, ATMOS CHEM PHYS, V8, P22; Edwards DP, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002927; FISHMAN J, 1990, J GEOPHYS RES-ATMOS, V95, P3599, DOI 10.1029/JD095iD04p03599; Fishman J, 2003, ATMOS CHEM PHYS, V3, P893; Fishman J, 2005, J GEOPHYS RES-ATMOS, V110, DOI 10.1029/2005JD005868; Hogg R. V., 1978, INTRO MATH STAT; Hudson R. D., 1998, J GEOPHYS RES, V103, P129, DOI DOI 10.1029/98JD00729; Isaksen ISA, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL053876; Kar J, 2010, ATMOS CHEM PHYS, V10, P5213, DOI 10.5194/acp-10-5213-2010; Kim JH, 2001, J ATMOS SCI, V58, P2699, DOI 10.1175/1520-0469(2001)058<2699:DOTTOD>2.0.CO;2; Lee S, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013320; Lelieveld J, 2000, J GEOPHYS RES-ATMOS, V105, P3531, DOI 10.1029/1999JD901011; Liu X, 2010, ATMOS CHEM PHYS, V10, P2539; Liu X, 2010, ATMOS CHEM PHYS, V10, P2521; Livesey N. J., 2011, EOS MLS VERSION 3 3; MADDEN RA, 1994, MON WEATHER REV, V122, P814, DOI 10.1175/1520-0493(1994)122<0814:OOTDTO>2.0.CO;2; MADDEN RA, 1971, J ATMOS SCI, V28, P702, DOI 10.1175/1520-0469(1971)028<0702:DOADOI>2.0.CO;2; Manney GL, 2011, NATURE, V478, P469, DOI 10.1038/nature10556; Martin RV, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002622; McPeters R, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008802; McPeters RD, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2005JD006823; Molod A., 2012, NASA TECHNICAL REPOR, V28; Moxim WJ, 2000, J GEOPHYS RES-ATMOS, V105, P17393, DOI 10.1029/2000JD900175; Newchurch MJ, 2001, J GEOPHYS RES-ATMOS, V106, P20403, DOI 10.1029/2000JD000162; Oman LD, 2013, J GEOPHYS RES-ATMOS, V118, P965, DOI 10.1029/2012JD018546; Purser RJ, 2003, MON WEATHER REV, V131, P1536, DOI 10.1175//2543.1; Purser RJ, 2003, MON WEATHER REV, V131, P1524, DOI 10.1175//1520-0493(2003)131<1524:NAOTAO>2.0.CO;2; Rienecker MM, 2011, J CLIMATE, V24, P3624, DOI 10.1175/JCLI-D-11-00015.1; Ripesi P, 2012, Q J ROY METEOR SOC, V138, P1961, DOI 10.1002/qj.1935; Sauvage B, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2006JD008008; Schoeberl MR, 2007, J GEOPHYS RES-ATMOS, V112, DOI 10.1029/2007JD008773; Stajner I, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008863; Strahan SE, 2013, J GEOPHYS RES-ATMOS, V118, P1563, DOI 10.1002/jgrd.50181; Strahan SE, 2007, ATMOS CHEM PHYS, V7, P2435; Sudo K, 2001, GEOPHYS RES LETT, V28, P4091, DOI 10.1029/2001GL013335; Tan WW, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD004057; Thompson AM, 2003, J GEOPHYS RES-ATMOS, V108, DOI [10.1029/2001JD000967, 10.1029/2002JD002241]; Thompson AM, 2001, SCIENCE, V291, P2128, DOI 10.1126/science.291.5511.2128; Trenberth KE, 1997, B AM METEOROL SOC, V78, P2771, DOI 10.1175/1520-0477(1997)078<2771:TDOENO>2.0.CO;2; van der Werf GR, 2006, ATMOS CHEM PHYS, V6, P3423; Vasilkov A, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008689; Wang YH, 1998, J GEOPHYS RES-ATMOS, V103, P10713, DOI 10.1029/98JD00158; Witte JC, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039236; Witte JC, 2011, ATMOS CHEM PHYS, V11, P9287, DOI 10.5194/acp-11-9287-2011; Wozniak A. E., 2005, Journal of Geophysical Research-Part D-Atmospheres, V110, DOI 10.1029/2005JD005842; Wu WS, 2002, MON WEATHER REV, V130, P2905, DOI 10.1175/1520-0493(2002)130<2905:TDVAWS>2.0.CO;2; Yan H, 2012, ATMOS MEAS TECH, V5, P2635, DOI 10.5194/amt-5-2635-2012; Ziemke JR, 2010, ATMOS CHEM PHYS, V10, P3711; Ziemke JR, 1999, J GEOPHYS RES-ATMOS, V104, P21425, DOI 10.1029/1999JD900277; Ziemke JR, 2009, GEOPHYS RES LETT, V36, DOI 10.1029/2009GL039303; Ziemke JR, 2011, ATMOS CHEM PHYS, V11, P9237, DOI 10.5194/acp-11-9237-2011; Ziemke JR, 1998, J GEOPHYS RES-ATMOS, V103, P22115, DOI 10.1029/98JD01567; Ziemke JR, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2006JD007089 57 0 0 AMER GEOPHYSICAL UNION WASHINGTON 2000 FLORIDA AVE NW, WASHINGTON, DC 20009 USA 2169-897X 2169-8996 J GEOPHYS RES-ATMOS J. Geophys. Res.-Atmos. MAY 19 2014 119 9 5671 5699 10.1002/2013JD020914 29 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AK3QS WOS:000338340400042 J Evers-King, H; Bernard, S; Lain, LR; Probyn, TA Evers-King, Hayley; Bernard, Stewart; Lain, Lisl Robertson; Probyn, Trevor A. Sensitivity in reflectance attributed to phytoplankton cell size: forward and inverse modelling approaches OPTICS EXPRESS English Article INHERENT OPTICAL-PROPERTIES; OCEAN COLOR; NATURAL PHYTOPLANKTON; ABSORPTION-COEFFICIENTS; LIGHT-SCATTERING; CHLOROPHYLL-A; WATERS; VALIDATION; ALGORITHM; SEAWIFS Synoptic scale knowledge of the size structure of phytoplankton communities can offer insight in to primary ecosystem diversity and biogeochemical variability from operational to the decadal scales. Accordingly, obtaining estimates of size and other phytoplankton functional type descriptors within known confidence limits from remotely sensed data has become a major objective to extend the use of ocean colour data beyond chlorophyll a retrievals. Here, a new forward and inverse modelling structure is proposed to determine information about the cell size of phytoplankton communities using Standard size distributions of two layered spheres to derive a full suite of algal inherent optical properties for a coupled radiative transfer model. This new capability allows explicit quantification of the remote sensing reflectance signal attributable to changes in phytoplankton cell size. Inversion of this model reveals regions within the parameter space where ambiguity may limit potential of inversion algorithms. Validation of the algorithm within the Benguela upwelling system using independent data shows promise for ecosystem applications and further investigation of the interaction between phytoplankton functional types and optical signals. The results here suggest that the utility of assemblage related signals in spectral reflectance is highly sensitive to algal biomass, the presence of other absorbing and scattering constituents and the resultant constituent-specific inherent optical property budget. As such, optimal methods for determining phytoplankton size from (in situ or satellite) ocean colour data will likely rely on appropriately spectrally dense and optimised sensors, well characterised measurement errors including those from atmospheric correction, and an ability to appropriately limit ambiguity within the context of regional inherent optical properties. (C) 2014 Optical Society of America [Evers-King, Hayley; Bernard, Stewart; Lain, Lisl Robertson] Univ Cape Town, Dept Oceanog, ZA-7701 Cape Town, South Africa; [Bernard, Stewart] CSIR, ZA-7700 Cape Town, South Africa; [Probyn, Trevor A.] Dept Agr Forestry & Fisheries, ZA-8012 Cape Town, South Africa Evers-King, H (reprint author), Univ Cape Town, Dept Oceanog, ZA-7701 Cape Town, South Africa. hayleyeversking@gmail.com Marine Research (Ma-Re) institute BASICS programme; National Research Foundation of South Africa; Applied Centre for Climate and Earth System Science (ACCESS) The authors would like to thank the Marine Research (Ma-Re) institute BASICS programme, the National Research Foundation of South Africa and the Applied Centre for Climate and Earth System Science (ACCESS) for funding support as part of Hayley Evers-King's PhD research. Data was collected and provided through the BENCAL cruise, the Department for Agriculture, Forestry and Fisheries (DAFF) and the extensive support of Dr Grant Pitcher and Andre Du Randt. Additional thanks to the CSIR/DST SWEOS strategic research project and Ben Love-day for help with figures. The authors would also like to thank an anonymous reviewer for comments which significantly improved the structure of the manuscript and summary figures. AGUSTI S, 1987, LIMNOL OCEANOGR, V32, P983; AHN YH, 1992, DEEP-SEA RES, V39, P1835, DOI 10.1016/0198-0149(92)90002-B; Albert A, 2003, OPT EXPRESS, V11, P2873, DOI 10.1364/OE.11.002873; Alvain S, 2005, DEEP-SEA RES PT I, V52, P1989, DOI 10.1016/j.dsr.2005.06.015; Aurin DA, 2012, REMOTE SENS ENVIRON, V125, P181, DOI 10.1016/j.rse.2012.07.001; Barlow R. G., 2003, 2003206892 NASATM; Bernard S., 2006, LARGE MAR ECOSYST, V14, P281; Bernard S, 2007, OPT EXPRESS, V15, P1995, DOI 10.1364/OE.15.001995; Bernard S, 1998, S AFR J MARINE SCI, V19, P15; Bernard S., 2009, BIOGEOSCI DISCUSS, V6, P1; Brewin RJW, 2011, REMOTE SENS ENVIRON, V115, P325, DOI 10.1016/j.rse.2010.09.004; BRICAUD A, 1986, APPL OPTICS, V25, P571; BRICAUD A, 1995, J GEOPHYS RES-OCEANS, V100, P13321, DOI 10.1029/95JC00463; Ciotti AM, 2002, LIMNOL OCEANOGR, V47, P404; Defoin-Platel M, 2007, J GEOPHYS RES-OCEANS, V112, DOI 10.1029/2006JC003847; Dierssen HM, 2006, LIMNOL OCEANOGR, V51, P2646; HANSEN JE, 1974, SPACE SCI REV, V16, P527, DOI 10.1007/BF00168069; KITCHEN JC, 1992, LIMNOL OCEANOGR, V37, P1680; Konstadinov T., 2010, BIOGEOSCIENCES, V7, P3239; Maranon E, 2008, J PLANKTON RES, V30, P157, DOI 10.1093/plankt/fbm087; Matthews M., 2013, BIOGEOSCIENCES, V7, P3239; Matthews MW, 2012, REMOTE SENS ENVIRON, V124, P637, DOI 10.1016/j.rse.2012.05.032; Mobley C. D., 2008, THYDROLIGHT 5 0 TECH; Mobley CD, 2011, OPT EXPRESS, V19, P18927, DOI 10.1364/OE.19.018927; Morel A, 2001, J GEOPHYS RES-OCEANS, V106, P7163, DOI 10.1029/2000JC000319; MOREL A, 1977, LIMNOL OCEANOGR, V22, P709; Morel A, 2002, APPL OPTICS, V41, P6289, DOI 10.1364/AO.41.006289; NELDER JA, 1965, COMPUT J, V7, P308; O'Reilly JE, 1998, J GEOPHYS RES-OCEANS, V103, P24937, DOI 10.1029/98JC02160; Parsons T. R., 1984, MANUAL CHEM BIOL MET; Quirantes A, 2004, J QUANT SPECTROSC RA, V89, P311, DOI 10.1016/j.jqsrt.2004.05.031; Reda I, 2004, SOL ENERGY, V76, P577, DOI 10.1016/j.solener.2003.12.003; Rehm E, 2013, APPL OPTICS, V52, P795, DOI 10.1364/AO.52.000795; Robertson Lain L., OPT EXPRESS IN PRESS; ROESLER CS, 1995, J GEOPHYS RES-OCEANS, V100, P13279, DOI 10.1029/95JC00455; Roesler CS, 1998, LIMNOL OCEANOGR, V43, P1649; Sauer MJ, 2012, OPT EXPRESS, V20, P20920, DOI 10.1364/OE.20.020920; Uitz J, 2006, J GEOPHYS RES-OCEANS, V111, DOI 10.1029/2005JC003207; Werdell PJ, 2005, REMOTE SENS ENVIRON, V98, P122, DOI 10.1016/j.rse.2005.07.001; Whitmire AL, 2010, OPT EXPRESS, V18, P15073, DOI 10.1364/OE.18.015073; YENTSCH CS, 1962, LIMNOL OCEANOGR, V7, P207; ZANEVELD JRV, 1995, J GEOPHYS RES-OCEANS, V100, P13135, DOI 10.1029/95JC00453; Zhou W, 2012, OPT EXPRESS, V20, P11189, DOI 10.1364/OE.20.011189; Zibordi G, 2004, IEEE T GEOSCI REMOTE, V42, P401, DOI 10.1109/TGRS.2003.821064 44 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 1094-4087 OPT EXPRESS Opt. Express MAY 19 2014 22 10 11536 11551 10.1364/OE.22.011536 16 Optics Optics AI6CP WOS:000336957700016 J Leng, AGK; Singh, AK; Kumar, PR; Mohan, A Leng, Alex Goh Kwang; Singh, Ashutosh Kumar; Kumar, P. Ravi; Mohan, Anand TPRANK: CONTEND WITH WEB SPAM USING TRUST PROPAGATION CYBERNETICS AND SYSTEMS English Article spam mass; web spam filtering algorithms; TrustRank; adversarial information retrieval; trust propagation The quantity and quality of seed sets are key factors for the success of propagation-based anti-web spam techniques. This kind of approach is simple yet effective, but the manual evaluation of seed sets is time-consuming. Therefore, a manual evaluation process is vital. In this article, we propose Trust Propagation Rank (TPRank), which automatically propagates trust to demote web spam based on a small number of reputable and spam seeds. Moreover, the proposed algorithm is extended to trust propagation (TP) spam mass in detection of web spam. Experiments were performed on two public available data sets-WEBSPAM-UK2006 and WEBSPAM-UK2007-and the results showed that both TPRank and TP spam mass outperform the state-of-the-art TrustRank in demotion up to 10.623% and spam mass algorithm in detection up to 43.216%. [Leng, Alex Goh Kwang; Singh, Ashutosh Kumar; Kumar, P. Ravi] Curtin Univ, Dept Elect & Comp Engn, Miri, Malaysia; [Mohan, Anand] Natl Inst Technol, Kurukshetra, Haryana, India Singh, AK (reprint author), Curtin Univ, Dept Elect & Comp Engn, Sarawak Campus, Miri, Malaysia. ashutosh.s@curtin.edu.my Abernethy J., 2008, P 4 INT WORKSH ADV I, P41, DOI DOI 10.1145/1451983.1451994.URL; Becchetti L., 2008, ACM T WEB, V2, P1, DOI 10.1145/1326561.1326563; Becchetti L., 2006, P WORKSH WEB MIN WEB; Benczur A. A., 2010, ECML PKDD 2010 DISCO; Brinkmeier M., 2006, ACM Transactions on Internet Technology, V6, DOI 10.1145/1151087.1151090; Castillo C., 2006, SIGIR Forum, V40, DOI 10.1145/1189702.1189703; Fetterly D., 2004, P 7 INT WORKSH WEB D, P1, DOI 10.1145/1017074.1017077; Gyngyi Z., 2005, P 1 INT WORKSH ADV I; Gyongyi Z., 2006, P 32 INT C VER LARG, P439, DOI Seoul, Korea; Gyongyi Z., 2004, P 30 INT C VER LARG, V30, P576; Krishnan V., 2006, P 2 INT WORKSH ADV I; Leng AGK, 2012, CYBERNET SYST, V43, P459, DOI 10.1080/01969722.2012.707491; Li S., 2011, 2011 INT C INT COMP; Liang C., 2007, J COMPUTATIONAL INFO, V3, P1705; Nie L., 2007, P 30 ANN INT ACM SIG, P869, DOI 10.1145/1277741.1277950; Noi L.D., 2010, P 20 INT C ART NEU 2, P372; Qi C., 2008, P INT C COMP SCI SOF; Qureshi M. A., 2011, IMPROVING QUALITY WE; Scarselli F, 2009, IEEE T NEURAL NETWOR, V20, P81, DOI 10.1109/TNN.2008.2005141; Scarselli F., 2009, T NEURAL NETWORKS, V20, P61; Sobek M., 2002, PR0 GOOGLES PAGERANK; Wang D. Y., 2011, P 18 ACM C COMP COMM, P477; [王学春 WANG Xuechun], 2008, [干旱地区农业研究, Agricultural Research in the Arid Areas], V26, P1, DOI 10.1145/1344411.1344416; Wu B., 2005, P 14 INT WORLD WID W, P820, DOI 10.1145/1062745.1062762.; Wu B., 2006, WORLD WID WEB WWW200; Wu B, 2005, P 1 INT WORKSH ADV I; Wu B., 2006, P 15 INT C WORLD WID, P63, DOI 10.1145/1135777.1135792; Xiaofei N., 2010, NAT COMP ICNC 2010 6; Yahoo!, 2007, WEB SPAM COLL; Yang H., 2007, P 30 ANN INT ACM SIG, P431, DOI 10.1145/1277741.1277815; Zhang X., 2011, P 25 C ART INT AAAI, P1292; Zhang Y., 2009, P 18 ACM C INF KNOWL, P1839, DOI 10.1145/1645953.1646244 32 0 0 TAYLOR & FRANCIS INC PHILADELPHIA 520 CHESTNUT STREET, STE 850, PHILADELPHIA, PA 19106 USA 0196-9722 1087-6553 CYBERNET SYST Cybern. Syst. MAY 19 2014 45 4 SI 307 323 10.1080/01969722.2014.887938 17 Computer Science, Cybernetics Computer Science AG0PA WOS:000335116200001 J Lindskog, M; Winman, A Lindskog, Marcus; Winman, Anders Are All Data Created Equal? - Exploring Some Boundary Conditions for a Lazy Intuitive Statistician PLOS ONE English Article PROBABILISTIC INFERENCE; CONTINGENCY JUDGMENTS; BAYESIAN-INFERENCE; KNOWLEDGE; PRIMACY; BIASES; MODEL; FREQUENCY; EXEMPLAR; CATEGORIZATION The study investigated potential effects of the presentation order of numeric information on retrospective subjective judgments of descriptive statistics of this information. The studies were theoretically motivated by the assumption in the naive sampling model of independence between temporal encoding order of data in long-term memory and retrieval probability (i.e. as implied by a "random sampling'' from memory metaphor). In Experiment 1, participants experienced Arabic numbers that varied in distribution shape/variability between the first and the second half of the information sequence. Results showed no effects of order on judgments of mean, variability or distribution shape. To strengthen the interpretation of these results, Experiment 2 used a repeated judgment procedure, with an initial judgment occurring prior to the change in distribution shape of the information half-way through data presentation. The results of Experiment 2 were in line with those from Experiment 1, and in addition showed that the act of making explicit judgments did not impair accuracy of later judgments, as would be suggested by an anchoring and insufficient adjustment strategy. Overall, the results indicated that participants were very responsive to the properties of the data while at the same time being more or less immune to order effects. The results were interpreted as being in line with the naive sampling models in which values are stored as exemplars and sampled randomly from long-term memory. [Lindskog, Marcus; Winman, Anders] Uppsala Univ, Dept Psychol, Uppsala, Sweden Lindskog, M (reprint author), Uppsala Univ, Dept Psychol, Uppsala, Sweden. marcus.lindskog@psyk.uu.se Swedish Research Council This research was sponsored by the Swedish Research Council. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Aha DW, 1997, ARTIF INTELL REV, V11, P7, DOI 10.1023/A:1006538427943; ALLAN LG, 1993, PSYCHOL BULL, V114, P435, DOI 10.1037/0033-2909.114.3.435; ASHBY FG, 1993, J MATH PSYCHOL, V37, P372, DOI 10.1006/jmps.1993.1023; Black A. H., 1972, CLASSICAL CONDITION, P64, DOI DOI 10.1016/J.COGPSYCH.2004.11.001; BREHMER B, 1974, ORGAN BEHAV HUM PERF, V11, P1, DOI 10.1016/0030-5073(74)90002-6; BROWN NR, 1995, J EXP PSYCHOL LEARN, V21, P1539, DOI 10.1037//0278-7393.21.6.1539; Brown NR, 1996, PSYCHON B REV, V3, P385, DOI 10.3758/BF03210766; Cowan N, 2001, BEHAV BRAIN SCI, V24, P87, DOI 10.1017/S0140525X01003922; DeLosh EL, 1997, J EXP PSYCHOL LEARN, V23, P968, DOI 10.1037/0278-7393.23.4.968; Dennis MJ, 2001, MEM COGNITION, V29, P152, DOI 10.3758/BF03195749; Dougherty MR, 2008, PSYCHOL REV, V115, P199, DOI 10.1037/0033-295X.115.1.199; Fiedler K, 2006, INFORMATION SAMPLING AND ADAPTIVE COGNITION, P3; Fiedler K, 2000, PSYCHOL REV, V107, P659, DOI 10.1037//0033-295X.107.4.659; FLANNAGAN MJ, 1986, J EXP PSYCHOL LEARN, V12, P241, DOI 10.1037/0278-7393.12.2.241; FOX S, 1993, PERCEPT MOTOR SKILL, V76, P259; Galesic M, 2012, PSYCHOL SCI, V23, P1515, DOI 10.1177/0956797612445313; Galesic M, 2011, MED DECIS MAKING, V31, P444, DOI 10.1177/0272989X10373805; Gilovich T., 2002, HEURISTICS BIASES PS; Glautier S, 2008, MEM COGNITION, V36, P1087, DOI 10.3758/MC.36.6.1087; Griffiths TL, 2011, J EXP PSYCHOL GEN, V140, P725, DOI 10.1037/a0024899; Griffiths TL, 2006, PSYCHOL SCI, V17, P767, DOI 10.1111/j.1467-9280.2006.01780.x; HENDRICK C, 1970, PSYCHON SCI, V19, P121; Hertwig R, 2004, PSYCHOL SCI, V15, P534, DOI 10.1111/j.0956-7976.2004.00715.x; HINTZMAN DL, 1988, PSYCHOL REV, V95, P528, DOI 10.1037/0033-295X.95.4.528; HOGARTH RM, 1992, COGNITIVE PSYCHOL, V24, P1, DOI 10.1016/0010-0285(92)90002-J; Juslin P, 2008, COGNITION, V106, P259, DOI 10.1016/j.cognition.2007.02.003; Juslin P, 2007, PSYCHOL REV, V114, P678, DOI 10.1037/0033-295X.114.3.678; Juslin P, 2002, COGNITIVE SCI, V26, P563, DOI 10.1016/S0364-0213(02)00083-6; KAHNEMAN D, 1979, ECONOMETRICA, V47, P263, DOI 10.2307/1914185; Kalish ML, 2007, PSYCHON B REV, V14, P288, DOI 10.3758/BF03194066; Kalish ML, 2004, PSYCHOL REV, V111, P1072, DOI 10.1037/0033-295x.111.4.1072; Kareev Y, 2002, J EXP PSYCHOL GEN, V131, P287, DOI 10.1037//0096-3445.131.2.287; LICHTENSTEIN S, 1978, J EXP PSYCHOL-HUM L, V4, P551, DOI 10.1037//0278-7393.4.6.551; Lindskog M, 2013, J COGN PSYCHOL, V25, P994, DOI 10.1080/20445911.2013.841170; Lindskog M, 2012, J EXPT PSYCHOL LEARN, V39, P782, DOI 10.1037/a0029670; Lopez FJ, 1998, J EXP PSYCHOL LEARN, V24, P672, DOI 10.1037/0278-7393.24.3.672; MALMI RA, 1983, J VERB LEARN VERB BE, V22, P547, DOI 10.1016/S0022-5371(83)90337-7; NISBETT RE, 1985, J PERS SOC PSYCHOL, V48, P297, DOI 10.1037//0022-3514.48.2.297; Nosofsky RM, 2000, PSYCHON B REV, V7, P375; Oaksford M, 2009, BEHAV BRAIN SCI, V32, P69, DOI 10.1017/S0140525X09000284; PETERSON CR, 1967, PSYCHOL BULL, V68, P29, DOI 10.1037/h0024722; POLLARD P, 1984, Current Psychological Research and Reviews, V3, P5, DOI 10.1007/BF02686528; Shi L, 2010, PSYCHON B REV, V17, P443, DOI 10.3758/PBR.17.4.443; Tenenbaum JB, 2011, SCIENCE, V331, P1279, DOI 10.1126/science.1192788; TVERSKY A, 1974, SCIENCE, V185, P1124, DOI 10.1126/science.185.4157.1124; von Helversen B, 2008, J EXP PSYCHOL GEN, V137, P73, DOI 10.1037/0096-3445.137.1.73; YATES JF, 1986, ACTA PSYCHOL, V62, P293, DOI 10.1016/0001-6918(86)90092-2 47 0 0 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One MAY 16 2014 9 5 e97686 10.1371/journal.pone.0097686 10 Multidisciplinary Sciences Science & Technology - Other Topics AM1NZ WOS:000339614800063 J Liu, T; Zhang, WN; Cao, LJ; Zhang, Y Liu, Ting; Zhang, Wei-Nan; Cao, Liujuan; Zhang, Yu Question Popularity Analysis and Prediction in Community Question Answering Services PLOS ONE English Article With the blooming of online social media applications, Community Question Answering (CQA) services have become one of the most important online resources for information and knowledge seekers. A large number of high quality question and answer pairs have been accumulated, which allow users to not only share their knowledge with others, but also interact with each other. Accordingly, volumes of efforts have been taken to explore the questions and answers retrieval in CQA services so as to help users to finding the similar questions or the right answers. However, to our knowledge, less attention has been paid so far to question popularity in CQA. Question popularity can reflect the attention and interest of users. Hence, predicting question popularity can better capture the users' interest so as to improve the users' experience. Meanwhile, it can also promote the development of the community. In this paper, we investigate the problem of predicting question popularity in CQA. We first explore the factors that have impact on question popularity by employing statistical analysis. We then propose a supervised machine learning approach to model these factors for question popularity prediction. The experimental results show that our proposed approach can effectively distinguish the popular questions from unpopular ones in the Yahoo! Answers question and answer repository. [Liu, Ting; Zhang, Wei-Nan; Zhang, Yu] Harbin Inst Technol, Res Ctr Social Comp & Informat Retrieval, Harbin 150006, Heilongjiang, Peoples R China; [Cao, Liujuan] Xiamen Univ, Sch Informat Sci & Technol, Dept Comp Sci, Xiamen, Fujian, Peoples R China Cao, LJ (reprint author), Xiamen Univ, Sch Informat Sci & Technol, Dept Comp Sci, Xiamen, Fujian, Peoples R China. caoliujuan@gmail.com; zhangyu@ir.hit.edu.cn Natural Science Foundation of China [61133012, 61073129, 61073126]; Fundamental Research Funds for the Central Universities [2013121026, 2011121052]; Xiamen University 985 Project This work was supported by the Natural Science Foundation of China (Grant No. 61133012, 61073129, 61073126) and the Fundamental Research Funds for the Central Universities (No. 2013121026 and No. 2011121052) and Xiamen University 985 Project. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Bendersky M., 2008, P 31 ANN INT ACM SIG, P491, DOI 10.1145/1390334.1390419; BIAN J, 2009, P 18 INT C WORLD WID, P51, DOI 10.1145/1526709.1526717; BIAN J, 2008, P 17 INT C WORLD WID, P467, DOI 10.1145/1367497.1367561; CHEN BC, 2012, P 35 INT ACM SIGIR C, P781, DOI DOI 10.1145/2348283.2348388; FREUND Y, 1996, MACH LEARN P 13 INT; GAO Y, 2010, IEEE T IMAGE PROCESS, P123; Gao Y, 2013, IEEE T IMAGE PROCESS, V22, P363, DOI 10.1109/TIP.2012.2202676; GAO Y, 2011, ACM C MULT, P1517; GUPTA M, 2012, 75 ANN M AM SOC INF; HONG L, 2011, P 20 INT C COMP WORL, P57, DOI DOI 10.1145/1963192.1963222; JEON J, 2006, P 29 ANN INT ACM SIG, P228, DOI 10.1145/1148170.1148212; Ji RR, 2011, ACM T MULTIM COMPUT, V7, DOI 10.1145/2037676.2037688; LERMAN K, 2007, SOCIAL INFORMATION P, V11, P16, DOI DOI 10.1109/MIC.2007.136; Lerman K., 2010, P 19 INT C WORLD WID, P621, DOI DOI 10.1145/1772690.1772754; LI B, 2012, P 21 INT C COMP WORL, P775, DOI DOI 10.1145/2187980.2188200; QUAN X, 2012, 11 INT C COGN INF CO, P109; Richardson M., 2007, P 16 INT C WORLD WID, P521, DOI 10.1145/1242572.1242643; RODGERS JL, 1988, 13 WAYS LOOK CORRELA, V42, P59; SURYANTO MA, 2009, P 2 ACM INT C WEB SE, P142, DOI 10.1145/1498759.1498820; SZABO G, 2010, PREDICTING THE POPUL, V53, P80, DOI DOI 10.1145/1787234.1787254; WANG XJ, 2009, P 32 ANN INT ACM, P179, DOI DOI 10.1145/1571941.1571974.AVAILABLE:HTTP://D0I.ACM.0RG/10.1145/1571941.1571974; WITTEN IH, 2002, SIGMOD REC, V31, P76; YANG J, 2010, ICWSM; Yu B, 2011, LECT NOTES COMPUT SC, V6589, P317; ZHANG WN, 2012, COLING 2012, P3105; ZHANG WN, 2013, PLOS ONE; Zhang Y, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0064601; Zoie, 2008, P INT C WEB SEARCH W, DOI DOI 10.1145/1341531.1341557 28 0 0 PUBLIC LIBRARY SCIENCE SAN FRANCISCO 1160 BATTERY STREET, STE 100, SAN FRANCISCO, CA 94111 USA 1932-6203 PLOS ONE PLoS One MAY 16 2014 9 5 10.1371/journal.pone.0085236 12 Multidisciplinary Sciences Science & Technology - Other Topics AM1NZ WOS:000339614800001 J Barrera, JF; Mira-Agudelo, A; Torroba, R Fredy Barrera, John; Mira-Agudelo, Alejandro; Torroba, Roberto Experimental QR code optical encryption: noise-free data recovering OPTICS LETTERS English Article IMAGE ENCRYPTION; RETRIEVAL; SECURE We report, to our knowledge for the first time, the experimental implementation of a quick response (QR) code as a "container" in an optical encryption system. A joint transform correlator architecture in an interferometric configuration is chosen as the experimental scheme. As the implementation is not possible in a single step, a multiplexing procedure to encrypt the QR code of the original information is applied. Once the QR code is correctly decrypted, the speckle noise present in the recovered QR code is eliminated by a simple digital procedure. Finally, the original information is retrieved completely free of any kind of degradation after reading the QR code. Additionally, we propose and implement a new protocol in which the reception of the encrypted QR code and its decryption, the digital block processing, and the reading of the decrypted QR code are performed employing only one device (smartphone, tablet, or computer). The overall method probes to produce an outcome far more attractive to make the adoption of the technique a plausible option. Experimental results are presented to demonstrate the practicality of the proposed security system. (C) 2014 Optical Society of America [Fredy Barrera, John; Mira-Agudelo, Alejandro] Univ Antioquia UdeA, Fac Ciencias Exactas & Nat, Inst Fis, Grp Opt & Foton, Medellin, Colombia; [Torroba, Roberto] Univ Nacl La Plata, Fac Ingn, Ctr Invest Opt CONICET La Plata CIC, RA-1897 La Plata, Buenos Aires, Argentina; [Torroba, Roberto] Univ Nacl La Plata, Fac Ingn, UID OPTIMO, RA-1897 La Plata, Buenos Aires, Argentina Barrera, JF (reprint author), Univ Antioquia UdeA, Fac Ciencias Exactas & Nat, Inst Fis, Grp Opt & Foton, Calle 70 52-21, Medellin, Colombia. john.barrera@udea.edu.co CODI (Universidad de Antioquia-Colombia); COLCIENCIAS (Colombia); TWAS-UNESCO Associateship Scheme at Centres of Excellence in the South; International Centre for Theoretical Physics ICTP Associateship Scheme, CONICET (Argentina) [0863/09, 0549/12]; Facultad de Ingenieria, Universidad Nacional de La Plata (Argentina) [11/I168] This research was performed under grants from CODI (Universidad de Antioquia-Colombia), COLCIENCIAS (Colombia), TWAS-UNESCO Associateship Scheme at Centres of Excellence in the South, the International Centre for Theoretical Physics ICTP Associateship Scheme, CONICET Nos. 0863/09 and 0549/12 (Argentina), and Facultad de Ingenieria, Universidad Nacional de La Plata No. 11/I168 (Argentina). Alfalou A, 2009, ADV OPT PHOTONICS, V1, P589, DOI 10.1364/AOP.1.000589; Barrera JF, 2011, OPT COMMUN, V284, P4350, DOI 10.1016/j.optcom.2011.05.035; Barrera JF, 2013, OPT EXPRESS, V21, P5373, DOI 10.1364/OE.21.005373; Barrera JF, 2013, J OPTICS-UK, V15, DOI 10.1088/2040-8978/15/5/055404; Graydon O., 2013, NATURE PHOTON, V7, P343; Javidi B, 2000, APPL OPTICS, V39, P4117, DOI 10.1364/AO.39.004117; Liao K. C., 2010, J NETWORKS, V5, P937, DOI DOI 10.4304/JNW.5.8.937-941; Matoba O, 2002, OPT LETT, V27, P321, DOI 10.1364/OL.27.000321; Mosso F, 2011, OPT EXPRESS, V19, P5706, DOI 10.1364/OE.19.005706; Nishchal NK, 2011, OPT COMMUN, V284, P735, DOI 10.1016/j.optcom.2010.09.065; Nomura T, 2000, OPT ENG, V39, P2031, DOI 10.1117/1.1304844; Ohbuchi E., 2004, P 2004 INT C CYB CW, P260, DOI DOI 10.1109/CW.2004.23; Qin W, 2010, OPT LETT, V35, P118, DOI 10.1364/OL.35.000118; Qin W, 2011, OPT ENG, V50, DOI 10.1117/1.3607421; REFREGIER P, 1995, OPT LETT, V20, P767; Situ GH, 2005, OPT LETT, V30, P1306, DOI 10.1364/OL.30.001306; Unnikrishnan G, 2000, OPT LETT, V25, P887, DOI 10.1364/OL.25.000887; Vilardy JM, 2013, J OPTICS-UK, V15, DOI 10.1088/2040-8978/15/2/025401 18 0 0 OPTICAL SOC AMER WASHINGTON 2010 MASSACHUSETTS AVE NW, WASHINGTON, DC 20036 USA 0146-9592 1539-4794 OPT LETT Opt. Lett. MAY 15 2014 39 10 3074 3077 10.1364/OL.39.003074 4 Optics Optics AI6KI WOS:000336982500065 J Komes, J; Schweinberger, SR; Wiese, H Komes, Jessica; Schweinberger, Stefan R.; Wiese, Holger Fluency affects source memory for familiar names in younger and older adults: Evidence from event-related brain potentials NEUROIMAGE English Article Source memory; Fluency; Familiar names; Priming; ERPs; N400; Cognitive aging RECOGNITION MEMORY; LIFE-SPAN; EXPLICIT MEMORY; AGE-DIFFERENCES; RETRIEVAL; RECOLLECTION; REPETITION; ERP; WORD; FACE A current debate in memory research is whether and how the access to source information depends not only on recollection, but on fluency-based processes as well. In three experiments, we used event-related brain potentials (ERPs) to examine influences of fluency on source memory for famous names. At test, names were presented visually throughout, whereas visual or auditory presentation was used at learning. In Experiment 1, source decisions following old/new judgments were more accurate for repeated relative to non-repeated visually and auditorily learned names. ERPs were more positive between 300 and 600 ms for visually learned as compared to both auditorily learned and new names, resembling an N400 priming effect. In Experiment 2, we omitted the old/new decision to more directly test fast-acting fluency effects on source memory. We observed more accurate source judgments for repeated versus non-repeated visually learned names, but no such effect for repeated versus non-repeated auditorily learned names. Again, an N400 effect (300-600 ms) differentiated between visually and auditorily learned names. Importantly, this effect occurred for correct source decisions only. We interpret it as indexing fluency arising from within-modality priming of visually learned names at test. This idea was further supported in Experiment 3, which revealed an analogous pattern of results in older adults, consistent with the assumption of spared fluency processes in older age. In sum, our findings suggest that fluency affects person-related source memory via within-modality repetition priming in both younger and older adults. (C) 2014 Elsevier Inc. All rights reserved. [Komes, Jessica] Univ Jena, DFG Res Unit Person Percept, D-07743 Jena, Germany; [Komes, Jessica] Univ Jena, Dept Gen Psychol & Cognit Neurosci, D-07743 Jena, Germany Komes, J (reprint author), Univ Jena, DFG Res Unit Person Percept, Steiger 3-1, D-07743 Jena, Germany. jessica.komes@uni-jena.de Deutsche Forschungsgemeinschaft (DFG) [Wi 3219/4-2] This work was supported by a grant of the Deutsche Forschungsgemeinschaft (DFG) to H.W. (Wi 3219/4-2). We gratefully acknowledge help during data collection by Kathrin Rauscher, Kristin Oehler, and Franziska Krahmer. We thank the anonymous reviewers for helpful comments on an earlier version of the manuscript. Addante RJ, 2012, NEUROIMAGE, V62, P439, DOI 10.1016/j.neuroimage.2012.04.031; Anderson N.D., 2000, OXFORD HDB MEMORY; Angel L, 2010, BRAIN RES, V1335, P41, DOI 10.1016/j.brainres.2010.03.040; Bach M, 1996, OPTOMETRY VISION SCI, V73, P49, DOI 10.1097/00006324-199601000-00008; Ballesteros S, 2009, EUR J COGN PSYCHOL, V21, P366, DOI 10.1080/09541440802311956; Balota D.A., 2000, OXFORD HDB MEMORY; BALTES PB, 1987, DEV PSYCHOL, V23, P611, DOI 10.1037/0012-1649.23.5.611; Bastin C, 2013, PSYCHOL AGING, V28, P275, DOI 10.1037/a0031566; BRUCE V, 1985, BRIT J PSYCHOL, V76, P373; BURTON AM, 1990, BRIT J PSYCHOL, V81, P361; Cansino S, 2012, BIOL PSYCHOL, V90, P33, DOI 10.1016/j.biopsycho.2012.02.004; Cansino S, 2013, DEV PSYCHOL, V49, P973, DOI 10.1037/a0028894; Cohen G, 1993, Memory, V1, P249, DOI 10.1080/09658219308258237; COHEN G, 1986, BRIT J DEV PSYCHOL, V4, P187; Cruse D, 2009, NEUROPSYCHOLOGIA, V47, P2779, DOI 10.1016/j.neuropsychologia.2009.06.003; Curran T, 2000, MEM COGNITION, V28, P923, DOI 10.3758/BF03209340; Dewhurst SA, 1999, MEM COGNITION, V27, P665, DOI 10.3758/BF03211560; Diana RA, 2008, J EXP PSYCHOL LEARN, V34, P730, DOI 10.1037/0278-7393.34.4.730; Diana RA, 2011, BRAIN RES, V1367, P278, DOI 10.1016/j.brainres.2010.10.030; Duarte A, 2006, J COGNITIVE NEUROSCI, V18, P33, DOI 10.1162/089892906775249988; Dulas MR, 2012, CEREB CORTEX, V22, P37, DOI 10.1093/cercor/bhr056; Dywan J, 2002, BRAIN COGNITION, V49, P322, DOI 10.1006/brcg.2001.1503; Friedman D, 2013, FRONT BEHAV NEUROSCI, V7, DOI 10.3389/fnbeh.2013.00111; Friedman D, 2010, BRAIN RES, V1310, P124, DOI 10.1016/j.brainres.2009.11.016; Groh-Bordin C, 2006, NEUROIMAGE, V32, P1879, DOI 10.1016/j.neuroimage.2006.04.215; Hayes SM, 2012, COGN NEUROSCI-UK, V3, P216, DOI 10.1080/17588928.2012.689974; Henson RNA, 1999, J NEUROSCI, V19, P3962; HORN JL, 1968, PSYCHOL REV, V75, P242, DOI 10.1037/h0025662; Hornberger M, 2004, J COGNITIVE NEUROSCI, V16, P1196, DOI 10.1162/0898929041920450; Jacoby LL, 2006, CURR DIR PSYCHOL SCI, V15, P49, DOI 10.1111/j.0963-7214.2006.00405.x; Jacoby LL, 1999, J EXP PSYCHOL LEARN, V25, P3, DOI 10.1037//0278-7393.25.1.3; JOHNSON MK, 1993, PSYCHOL BULL, V114, P3, DOI 10.1037//0033-2909.114.1.3; Joyce CA, 1999, PSYCHOPHYSIOLOGY, V36, P655, DOI 10.1017/S0048577299981015; Kurilla BP, 2011, Q J EXP PSYCHOL, V64, P1609, DOI 10.1080/17470218.2011.561866; Kutas M, 2011, ANNU REV PSYCHOL, V62, P621, DOI 10.1146/annurev.psych.093008.131123; Lehrl S., 1977, MEHRFACHWAHL WORTSCH; LEIRER VO, 1990, EXP AGING RES, V16, P155, DOI 10.1080/07340669008251544; Li SC, 2004, PSYCHOL SCI, V15, P155, DOI 10.1111/j.0956-7976.2004.01503003.x; Lucas HD, 2012, NEUROPSYCHOLOGIA, V50, P3041, DOI 10.1016/j.neuropsychologia.2012.07.036; MANDLER G, 1980, PSYCHOL REV, V87, P252, DOI 10.1037//0033-295X.87.3.252; Mark RE, 1998, BRAIN, V121, P861, DOI 10.1093/brain/121.5.861; Mitchell KJ, 2009, PSYCHOL BULL, V135, P638, DOI 10.1037/a0015849; Mollison MV, 2012, NEUROPSYCHOLOGIA, V50, P2546, DOI 10.1016/j.neuropsychologia.2012.06.027; Mulligan NW, 2009, J EXP PSYCHOL LEARN, V35, P564, DOI 10.1037/a0014524; Nessler D, 2007, NEUROREPORT, V18, P1837; Old SR, 2012, PSYCHOL AGING, V27, P462, DOI 10.1037/a0025194; OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4; Oppenheimer DM, 2008, TRENDS COGN SCI, V12, P237, DOI 10.1016/j.tics.2008.02.014; Osorio A, 2009, BRAIN RES, V1289, P56, DOI 10.1016/j.brainres.2009.07.013; Osorio A, 2010, CORTEX, V46, P522, DOI 10.1016/j.cortex.2009.09.003; Paller KA, 2007, TRENDS COGN SCI, V11, P243, DOI 10.1016/j.tics.2007.04.002; PALMERI TJ, 1993, J EXP PSYCHOL LEARN, V19, P309, DOI 10.1037/0278-7393.19.2.309; Peters J, 2009, NEUROSCI LETT, V454, P182, DOI 10.1016/j.neulet.2009.03.019; Pickering EC, 2003, J EXP PSYCHOL LEARN, V29, P1298, DOI 10.1037/0278-7393.29.6.1298; Reales JM, 1999, J EXP PSYCHOL LEARN, V25, P644, DOI 10.1037/0278-7393.25.3.644; ROEDIGER HL, 1987, MEM COGNITION, V15, P379, DOI 10.3758/BF03197728; Rugg MD, 1995, ELECTROPHYSIOLOGY MI; Rugg MD, 2007, TRENDS COGN SCI, V11, P251, DOI 10.1016/j.tics.2007.04.004; Schaie KW, 1998, PSYCHOL AGING, V13, P8, DOI 10.1037/0882-7974.13.1.8; Schweinberger SR, 2002, NEUROPSYCHOLOGIA, V40, P2057, DOI 10.1016/S0028-3932(02)00050-7; Senkfor AJ, 1998, J EXP PSYCHOL LEARN, V24, P1005, DOI 10.1037/0278-7393.24.4.1005; Spencer WD, 1995, PSYCHOL AGING, V10, P527, DOI 10.1037/0882-7974.10.4.527; Swick D, 2006, BRAIN RES, V1107, P161, DOI 10.1016/j.brainres.2006.06.013; Swick D, 1997, J EXP PSYCHOL LEARN, V23, P123, DOI 10.1037/0278-7393.23.1.123; Trott CT, 1997, NEUROREPORT, V8, P3373, DOI 10.1097/00001756-199710200-00036; Valentine T, 1991, EUROPEAN J COGNITIVE, V3, P147, DOI 10.1080/09541449108406224; Verhaeghen P, 2012, AGING NEUROPSYCHOL C, V19, P1, DOI 10.1080/13825585.2011.645009; Vilberg KL, 2006, BRAIN RES, V1122, P161, DOI 10.1016/j.brainres.2006.09.023; Voss JL, 2012, COGN NEUROSCI-UK, V3, P193, DOI 10.1080/17588928.2012.674935; Wang TH, 2012, J COGNITIVE NEUROSCI, V24, P1055, DOI 10.1162/jocn_a_00129; Wechsler D, 1981, WECHSLER ADULT INTEL; Wiese H, 2012, NEUROPSYCHOLOGIA, V50, P3496, DOI 10.1016/j.neuropsychologia.2012.09.022; Wiese H, 2008, J EXP PSYCHOL LEARN, V34, P1246, DOI 10.1037/a0012937; Wilding EL, 1996, BRAIN, V119, P889, DOI 10.1093/brain/119.3.889; WILDING EL, 1995, NEUROPSYCHOLOGIA, V33, P743, DOI 10.1016/0028-3932(95)00017-W; Wittgenstein L, 1922, TRACTATUS LOGICU PHI; Wolk DA, 2009, BRAIN RES, V1250, P218, DOI 10.1016/j.brainres.2008.11.014; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864; Yovel G, 2004, NEUROIMAGE, V21, P789, DOI 10.1016/j.neuroimage.2003.09.034 79 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage MAY 15 2014 92 90 105 10.1016/j.neuroimage.2014.02.009 16 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging AG9BG WOS:000335713000011 J Hart, NA; Strohaber, J; Kaya, G; Kaya, N; Kolomenskii, AA; Schuessler, HA Hart, N. A.; Strohaber, J.; Kaya, G.; Kaya, N.; Kolomenskii, A. A.; Schuessler, H. A. Intensity-resolved above-threshold ionization of xenon with short laser pulses PHYSICAL REVIEW A English Article MULTIPHOTON IONIZATION; FIELD; ATOMS We present intensity-resolved above-threshold ionization (ATI) spectra of xenon using an intensity scanning and deconvolution technique. Experimental data were obtained with laser pulses of 58 fs and a central wavelength of 800 nm from a chirped-pulse amplifier. Applying a deconvolution algorithm, we obtained spectra that have higher contrast and are in excellent agreement with characteristic two and ten Up cutoff energies contrary to that found for raw data. The retrieved electron-ionization probability is consistent with the presence of a second electron from double ionization. This recovered ionization probability is confirmed with a calculation based on the Perelomov, Popov, and Terent'ev tunneling ionization model [Sov. Phys. JETP 23, 924 (1966)]. Thus, the measurements of the photoelectron yields and the developed deconvolution technique allowed retrieval of more accurate spectroscopic information from the ATI spectra and ionization probability features that usually are concealed by volume averaging. [Hart, N. A.; Strohaber, J.; Kaya, G.; Kaya, N.; Kolomenskii, A. A.; Schuessler, H. A.] Texas A&M Univ, Dept Phys, College Stn, TX 77843 USA; [Strohaber, J.] Florida A&M Univ, Dept Phys, Tallahassee, FL 32307 USA Hart, NA (reprint author), Texas A&M Univ, Dept Phys, College Stn, TX 77843 USA. nhart@physics.tamu.edu Robert A. Welch Foundation [A1546]; Qatar Foundation [NPRP 5-994-1-172, NPRP 6-465-1-091] This work was funded by the Robert A. Welch Foundation Grant No. A1546 and the Qatar Foundation under Grants No. NPRP 5-994-1-172 and No. NPRP 6-465-1-091. Boyd RW, 2008, NONLINEAR OPTICS, 3RD EDITION, P1; Bryan WA, 2006, PHYS REV A, V73, DOI 10.1103/PhysRevA.73.013407; Chartrand R, 2010, INT CONF ACOUST SPEE, P766, DOI 10.1109/ICASSP.2010.5494993; Cormier E, 1997, J PHYS B-AT MOL OPT, V30, P77, DOI 10.1088/0953-4075/30/1/010; Goodworth TRJ, 2005, J PHYS B-AT MOL OPT, V38, P3083, DOI 10.1088/0953-4075/38/17/001; Hansch P, 1996, OPT LETT, V21, P1286, DOI 10.1364/OL.21.001286; Kopold R, 2002, J PHYS B-AT MOL OPT, V35, P217, DOI 10.1088/0953-4075/35/2/302; LAMBROPOULOS P, 1993, AIP CONF PROC, V275, P499; Larochelle S, 1998, J PHYS B-AT MOL OPT, V31, P1201, DOI 10.1088/0953-4075/31/6/008; Le T, 2007, J MATH IMAGING VIS, V27, P257, DOI 10.1007/s10851-007-0652-y; MCILRATH TJ, 1989, PHYS REV A, V40, P2770, DOI 10.1103/PhysRevA.40.2770; Paulus GG, 2001, PHYS REV A, V64, DOI 10.1103/PhysRevA.64.021401; Paulus GG, 2000, PHYS REV LETT, V84, P3791, DOI 10.1103/PhysRevLett.84.3791; PAULUS GG, 1994, J PHYS B-AT MOL OPT, V27, pL703, DOI 10.1088/0953-4075/27/21/003; PERELOMO.AM, 1966, SOV PHYS JETP-USSR, V23, P924; SCHAFER KJ, 1990, PHYS REV A, V42, P5794, DOI 10.1103/PhysRevA.42.5794; Schultze M, 2011, NEW J PHYS, V13, DOI 10.1088/1367-2630/13/3/033001; SPEISER S, 1976, CHEM PHYS LETT, V44, P399, DOI 10.1016/0009-2614(76)80692-6; Strohaber J, 2010, PHYS REV A, V82, DOI 10.1103/PhysRevA.82.013403; Strohaber J, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.023002; Walker MA, 1998, PHYS REV A, V57, pR701, DOI 10.1103/PhysRevA.57.R701 21 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 1050-2947 1094-1622 PHYS REV A Phys. Rev. A MAY 15 2014 89 5 053414 10.1103/PhysRevA.89.053414 7 Optics; Physics, Atomic, Molecular & Chemical Optics; Physics AH1MO WOS:000335885100009 J Kent, C; Guest, D; Adelman, JS; Lamberts, K Kent, Christopher; Guest, Duncan; Adelman, James S.; Lamberts, Koen Stochastic accumulation of feature information in perception and memory FRONTIERS IN PSYCHOLOGY English Review information accumulation; perception; memory; cognition; categorization; identification; visual search; reading ACCURACY TRADE-OFF; INTERACTIVE ACTIVATION MODEL; INTEGRAL-DIMENSION STIMULI; RANDOM-WALK MODEL; RECALL-TO-REJECT; TIME-COURSE; VISUAL-SEARCH; SPEED-ACCURACY; RECOGNITION MEMORY; DECISION-MAKING It is now well established that the time course of perceptual processing influences the first second or so of performance in a wide variety of cognitive tasks. Over the last 20 years, there has been a shift from modeling the speed at which a display is processed, to modeling the speed at which different features of the display are perceived and formalizing how this perceptual information is used in decision making. The first of these models (Lamberts, 1995) was implemented to fit the time course of performance in a speeded perceptual categorization task and assumed a simple stochastic accumulation of feature information. Subsequently, similar approaches have been used to model performance in a range of cognitive tasks including identification, absolute identification, perceptual matching, recognition, visual search, and word processing, again assuming a simple stochastic accumulation of feature information from both the stimulus and representations held in memory. These models are typically fit to data from signal-to-respond experiments where by the effects of stimulus exposure duration on performance are examined, but response times (RTs) and RT distributions have also been modeled. In this article, we review this approach and explore the insights it has provided about the interplay between perceptual processing, memory retrieval, and decision making in a variety of tasks. In so doing, we highlight how such approaches can continue to usefully contribute to our understanding of cognition. [Kent, Christopher] Univ Bristol, Sch Expt Psychol, Bristol Tactile Act & Percept Lab, Bristol BS8 1TU, Avon, England; [Guest, Duncan] Nottingham Trent Univ, Sch Social Sci, Div Psychol, Nottingham, England; [Adelman, James S.] Univ Warwick, Dept Psychol, Coventry CV4 7AL, W Midlands, England; [Lamberts, Koen] Univ York, Vice Chancellors Dept, York YO10 5DD, N Yorkshire, England Kent, C (reprint author), Univ Bristol, Sch Expt Psychol, Bristol Tactile Act & Percept Lab, 12a Priory Rd, Bristol BS8 1TU, Avon, England. c.kent@bristol.ac.uk Adelman, James/E-6977-2011 Adelman JS, 2010, PSYCHOL SCI, V21, P1799, DOI 10.1177/0956797610387442; Adelman JS, 2011, PSYCHOL REV, V118, P570, DOI 10.1037/a0024811; Giordano AM, 2009, J VISION, V9, DOI 10.1167/9.3.30; Ashby E. G., 2011, FORMAL APPROACHES CA, P65; Ashby FG, 2003, NEUROPSYCHOLOGY, V17, P115, DOI 10.1037/0894-4105.17.1.115; ASHBY FG, 1986, PSYCHOL REV, V93, P154, DOI 10.1037//0033-295X.93.2.154; Ashby FG, 1998, PSYCHOL REV, V105, P442, DOI 10.1037/0033-295X.105.3.442; ASHBY FG, 1994, J MATH PSYCHOL, V38, P423, DOI 10.1006/jmps.1994.1032; Barsalou LW, 2008, ANNU REV PSYCHOL, V59, P617, DOI 10.1146/annurev.psych.59.103006.093639; Bausenhart KM, 2010, VISION RES, V50, P1025, DOI 10.1016/j.visres.2010.03.011; Biederman I, 1999, PSYCHOL RES-PSYCH FO, V62, P131, DOI 10.1007/s004260050047; Bogacz Rafal, 2010, Trends Neurosci, V33, P10, DOI 10.1016/j.tins.2009.09.002; Bower G., 1967, PSYCHOL LEARN MOTIV, V1, P230, DOI 10.1016/S0079-7421(08)60515-0; BOWER GH, 1994, PSYCHOL REV, V101, P290, DOI 10.1037//0033-295X.101.2.290; Brockdorff N, 2000, J EXP PSYCHOL LEARN, V26, P77, DOI 10.1037/0278-7393.26.1.77; Brown S, 2005, PSYCHOL REV, V112, P117, DOI 10.1037/0033-295X.112.1.117; Brown SD, 2008, PSYCHOL REV, V115, P396, DOI 10.1037/0033-295X.115.2.396; BUNDESEN C, 1990, PSYCHOL REV, V97, P523, DOI 10.1037/0033-295X.97.4.523; BUSEY TA, 1994, PSYCHOL REV, V101, P446, DOI 10.1037/0033-295X.101.3.446; Carrasco M, 2011, VISION RES, V51, P1484, DOI 10.1016/j.visres.2011.04.012; Carrasco M, 2001, P NATL ACAD SCI USA, V98, P5363, DOI 10.1073/pnas.081074098; Carrasco M, 2006, VISION RES, V46, P2028, DOI 10.1016/j.visres.2005.12.015; Carrasco Marisa, 2002, J Vis, V2, P467, DOI 10.1167/2.6.4; Cohen AL, 2003, J MATH PSYCHOL, V47, P150, DOI 10.1016/S0022-2496(02)00031-7; Cohen H., 2005, HDB CATEGORIZATION C, P573, DOI [10.1016/B978-008044612-7/50081-0, DOI 10.1016/B978-008044612-7/50081-0]; Cowan N, 2001, BEHAV BRAIN SCI, V24, P154; Dale R, 2007, MEM COGNITION, V35, P15, DOI 10.3758/BF03195938; Davis CJ, 2010, PSYCHOL REV, V117, P713, DOI 10.1037/a0019738; Diller DE, 2001, J EXP PSYCHOL LEARN, V27, P414, DOI 10.1037/0278-7393.27.2.414; Dosher BA, 2010, J EXP PSYCHOL HUMAN, V36, P1128, DOI 10.1037/a0020366; DOSHER BA, 1976, COGNITIVE PSYCHOL, V8, P291, DOI 10.1016/0010-0285(76)90009-8; Dosher BA, 2004, J EXP PSYCHOL HUMAN, V30, P3, DOI 10.1037/0096-1523.30.1.3; DOSHER BA, 1979, ACTA PSYCHOL, V43, P347, DOI 10.1016/0001-6918(79)90029-5; DOSHER BA, 1981, COGNITIVE PSYCHOL, V13, P551, DOI 10.1016/0010-0285(81)90020-7; Dunn JC, 2012, J EXP PSYCHOL LEARN, V38, P840, DOI 10.1037/a0027867; Eckstein MP, 1998, PSYCHOL SCI, V9, P111, DOI 10.1111/1467-9280.00020; ERIKSEN CW, 1979, PERCEPT PSYCHOPHYS, V25, P249, DOI 10.3758/BF03198804; Estes W. K., 1994, CLASSIFICATION COGNI, DOI [10.1093/acprof:oso/9780195073355.001.0001, DOI 10.1093/ACPROF:OSO/9780195073355.001.0001]; ESTES WK, 1950, PSYCHOL REV, V57, P94, DOI 10.1037/0033-295X.101.2.282; ESTES WK, 1953, PSYCHOL REV, V60, P276, DOI 10.1037/h0055775; Fific M, 2010, J EXP PSYCHOL LEARN, V36, P1290, DOI 10.1037/a0020123; Filoteo JV, 2005, NEUROPSYCHOLOGY, V19, P212, DOI 10.1037/0894-4105.19.2.212; Freeman JB, 2011, FRONT PSYCHOL, V2, DOI 10.3389/fpsyg.2011.00059; Freeman JB, 2014, PSYCHON B REV, V21, P85, DOI 10.3758/s13423-013-0470-8; Freeman JB, 2011, PSYCHOL REV, V118, P247, DOI 10.1037/a0022327; Friedman J, 2013, J MATH PSYCHOL, V57, P140, DOI 10.1016/j.jmp.2013.06.005; Garavan H, 1998, MEM COGNITION, V26, P263, DOI 10.3758/BF03201138; Gold JI, 2007, ANNU REV NEUROSCI, V30, P535, DOI 10.1146/annurev.neuro.29.051605.113038; Grainger J, 2003, MENTAL LEXICON: SOME WORDS TO TALK ABOUT WORDS, P1; Grainger J, 2008, LANG COGNITIVE PROC, V23, P1, DOI 10.1080/01690960701578013; Gronlund SD, 1997, J EXP PSYCHOL LEARN, V23, P1261, DOI 10.1037/0278-7393.23.5.1261; GRONLUND SD, 1989, J EXP PSYCHOL LEARN, V15, P846, DOI 10.1037/0278-7393.15.5.846; Guest D., 2008, 30 ANN C COGN SCI SO; Guest D, 2010, ATTEN PERCEPT PSYCHO, V72, P1079, DOI 10.3758/APP.72.4.1079; Guest D, 2010, J EXP PSYCHOL HUMAN, V36, P1609, DOI 10.1037/a0020562; Guest D, 2011, J EXP PSYCHOL HUMAN, V37, P1667, DOI 10.1037/a0025640; Gureckis TM, 2011, J COGNITIVE NEUROSCI, V23, P1697, DOI 10.1162/jocn.2010.21538; Healy A. F., 1992, LEARNING PROCESSES C, V1; Healy A. F., 1992, LEARNING PROCESSES C, V2; Heekeren HR, 2008, NAT REV NEUROSCI, V9, P467, DOI 10.1038/nrn2374; Heit E., 2008, MEMORY MIND FESTSCHR, P327; Heit E, 2003, PSYCHON B REV, V10, P718, DOI 10.3758/BF03196537; HINTZMAN DL, 1994, J MEM LANG, V33, P1, DOI 10.1006/jmla.1994.1001; HOCKLEY WE, 1987, PSYCHOL REV, V94, P341, DOI 10.1037/0033-295X.94.3.341; HUMMEL JE, 1992, PSYCHOL REV, V99, P480; Inglis M, 2013, COGNITION, V129, P63, DOI 10.1016/j.cognition.2013.06.003; Kanai R, 2011, NAT REV NEUROSCI, V12, P231, DOI 10.1038/nrn3000; Kent C, 2006, J EXP PSYCHOL HUMAN, V32, P920, DOI 10.1037/0096-1523.32.4.920; Kent C., 2014, LOND M EXP PSYCH SOC; Kent C., 2006, THESIS U WARWICK COV; Kent C, 2005, J EXP PSYCHOL LEARN, V31, P289, DOI 10.1037/0278-7393.312.289; Kent C, 2008, TRENDS COGN SCI, V12, P92, DOI 10.1016/j.tics.2007.12.004; Kent C, 2012, J VISION, V12, DOI 10.1167/12.1.13; Kent C, 2006, J MEM LANG, V55, P553, DOI 10.1016/j.jml.2006.08.010; Kruschke JK, 1999, J EXP PSYCHOL LEARN, V25, P1083, DOI 10.1037//0278-7393.25.5.1083; Kwantes PJ, 1999, CAN J EXP PSYCHOL, V53, P306, DOI 10.1037/h0087318; LaBerge D., 1959, STUDIES MATH LEARNIN, P53; LAMBERTS K, 1995, J EXP PSYCHOL GEN, V124, P161, DOI 10.1037/0096-3445.124.2.161; Lamberts K, 2008, J EXP PSYCHOL LEARN, V34, P688, DOI 10.1037/0278-7393.34.3.688; Lamberts K, 1999, J EXP PSYCHOL HUMAN, V25, P904, DOI 10.1037//0096-1523.25.4.904; Lamberts K, 2002, Q J EXP PSYCHOL-A, V55, P141, DOI 10.1080/02724980143000208; Lamberts K, 2002, J EXP PSYCHOL HUMAN, V28, P1176, DOI 10.1037//0096-1523.28.5.1176; Lamberts K, 2000, PSYCHOL REV, V107, P227, DOI 10.1037//0033-295X.107.2.227; Lamberts K, 1998, J EXP PSYCHOL LEARN, V24, P695, DOI 10.1037//0278-7393.24.3.695; Lamberts K, 2005, J EXP PSYCHOL HUMAN, V31, P14, DOI 10.1037/0096-1523.31.1.14; Lamberts K, 1999, PSYCHOL RES-PSYCH FO, V62, P107, DOI 10.1007/s004260050045; Lamberts K, 1997, MEM COGNITION, V25, P296, DOI 10.3758/BF03211285; Lamberts K, 2003, J EXP PSYCHOL GEN, V132, P351, DOI 10.1037/0096-3445.132.3.351; Lindell AK, 2003, Q J EXP PSYCHOL-A, V56, P287, DOI 10.1080/02724980244000341; Little DR, 2011, J EXP PSYCHOL LEARN, V37, P1, DOI 10.1037/a0021330; Little DR, 2013, J EXP PSYCHOL LEARN, V39, P801, DOI 10.1037/a0029667; Liu CC, 2009, J EXP PSYCHOL HUMAN, V35, P1329, DOI 10.1037/a0014255; Logan GD, 2004, ANNU REV PSYCHOL, V55, P207, DOI 10.1146/annurev.psych.55.090902.141415; Logan GD, 2002, PSYCHOL REV, V109, P376, DOI 10.1037//0033-295X.109.2.376; Luce R. D., 1963, HDB MATH PSYCHOL, V1, P103; Luce R. D., 1986, RESPONSE TIMES THEIR; LUCK SJ, 1994, J EXP PSYCHOL HUMAN, V20, P887, DOI 10.1037/0096-1523.20.4.887; Ma WJ, 2011, NAT NEUROSCI, V14, P783, DOI 10.1038/nn.2814; Maddox WT, 1998, PERCEPT PSYCHOPHYS, V60, P620, DOI 10.3758/BF03206050; MADDOX WT, 1993, PERCEPT PSYCHOPHYS, V53, P49, DOI 10.3758/BF03211715; Maddox WT, 2000, PERCEPT PSYCHOPHYS, V62, P984, DOI 10.3758/BF03212083; Maddox WT, 2001, PERCEPT PSYCHOPHYS, V63, P1183, DOI 10.3758/BF03194533; Maddox WT, 2003, J EXP PSYCHOL LEARN, V29, P467, DOI 10.1037/0278-7393.29.3.467; Malmberg KJ, 2008, COGNITIVE PSYCHOL, V57, P335, DOI 10.1016/j.cogpsych.2008.02.004; MARSLENWILSON W, 1984, ATTENTION PERFORM, V10, P125; MCCLELLAND JL, 1991, COGNITIVE PSYCHOL, V23, P1, DOI 10.1016/0010-0285(91)90002-6; MCCLELLAND JL, 1981, PSYCHOL REV, V88, P375, DOI 10.1037/0033-295X.88.5.375; MCELREE B, 1989, J EXP PSYCHOL GEN, V118, P346, DOI 10.1037//0096-3445.118.4.346; McElree B, 1999, J EXP PSYCHOL HUMAN, V25, P1517, DOI 10.1037/0096-1523.25.6.1517; MCGILL WJ, 1967, J MATH PSYCHOL, V4, P351, DOI 10.1016/0022-2496(67)90030-2; MEYER DE, 1988, PSYCHOL REV, V95, P183, DOI 10.1037/0033-295X.95.2.183; MILLER GA, 1956, PSYCHOL REV, V63, P81, DOI 10.1037/0033-295X.101.2.343; Newell BR, 2011, PSYCHOL LEARN MOTIV, V54, P167, DOI 10.1016/B978-0-12-385527-5.00006-1; Norman D. A., 1970, MODELS HUM MEMORY, P19; Norris D, 2012, PSYCHOL REV, V119, P517, DOI 10.1037/a0028450; NOSOFSKY RM, 1983, J EXP PSYCHOL HUMAN, V9, P299, DOI 10.1037/0096-1523.9.2.299; NOSOFSKY RM, 1988, J EXP PSYCHOL LEARN, V14, P700, DOI 10.1037/0278-7393.14.4.700; Nosofsky RM, 1997, PSYCHOL REV, V104, P266, DOI 10.1037//0033-295X.104.2.266; NOSOFSKY RM, 1991, J EXP PSYCHOL HUMAN, V17, P3, DOI 10.1037/0096-1523.17.1.3; NOSOFSKY RM, 1986, J EXP PSYCHOL GEN, V115, P39, DOI 10.1037/0096-3445.115.1.39; NOSOFSKY RM, 1992, ANNU REV PSYCHOL, V43, P25; Nosofsky RM, 2011, PSYCHOL REV, V118, P280, DOI 10.1037/a0022494; Gothe K, 2008, PSYCHOL RES-PSYCH FO, V72, P289, DOI 10.1007/s00426-007-0111-9; Oberauer K, 2002, J EXP PSYCHOL LEARN, V28, P411, DOI 10.1037//0278-7393.28.3.411; PAAP KR, 1982, PSYCHOL REV, V89, P573, DOI 10.1037/0033-295X.89.5.573; Pachella R. G., 1974, HUMAN INFORMATION PR, P41; Palmer J, 2000, VISION RES, V40, P1227, DOI 10.1016/j.visres.2008.02.022; Pezzulo G., 2013, FRONT PSYCHOL, V3, P612, DOI [10.3389/fpsyg.2012.00612, DOI 10.3389/FPSYG.2012.00612]; POSNER MI, 1980, Q J EXP PSYCHOL, V32, P3, DOI 10.1080/00335558008248231; Purcell BA, 2010, PSYCHOL REV, V117, P1113, DOI 10.1037/a0020311; Ratcliff R., 2009, PSYCH REV, V116, P283; RATCLIFF R, 1978, PSYCHOL REV, V85, P59, DOI 10.1037//0033-295X.85.2.59; Ratcliff R, 2004, PSYCHOL REV, V111, P333, DOI 10.1037/0033-295X.111.2.333; REED AV, 1973, SCIENCE, V181, P574, DOI 10.1126/science.181.4099.574; REED AV, 1976, MEM COGNITION, V4, P16, DOI 10.3758/BF03213250; Rehder B, 2005, J EXP PSYCHOL LEARN, V31, P811, DOI 10.1037/0278-7393.31.5.811; Rotello CM, 1999, J MEM LANG, V40, P432, DOI 10.1006/jmla.1998.2623; Rotello CM, 2000, MEM COGNITION, V28, P907, DOI 10.3758/BF03209339; RUMELHAR.DE, 1970, J MATH PSYCHOL, V7, P191, DOI 10.1016/0022-2496(70)90044-1; RUMELHART DE, 1982, PSYCHOL REV, V89, P60, DOI 10.1037//0033-295X.89.1.60; Salthouse TA, 1996, PSYCHOL REV, V103, P403, DOI 10.1037/0033-295X.103.3.403; Schall JD, 2001, NAT REV NEUROSCI, V2, P33, DOI 10.1038/35049054; Schneider DW, 2012, COGNITIVE PSYCHOL, V64, P127, DOI 10.1016/j.cogpsych.2011.11.001; SHEPARD RN, 1957, PSYCHOMETRIKA, V22, P325, DOI 10.1007/BF02288967; Smith PL, 2013, PSYCHOL REV, V120, P589, DOI 10.1037/a0033140; Song JH, 2009, TRENDS COGN SCI, V13, P360, DOI 10.1016/j.tics.2009.04.009; Spivey MJ, 2005, P NATL ACAD SCI USA, V102, P10393, DOI 10.1073/pnas.0503903102; Stewart N, 2005, PSYCHOL REV, V112, P881, DOI 10.1037/0033-295X.112.4.881; Takeda Y, 2007, Q J EXP PSYCHOL, V60, P186, DOI 10.1080/17470210601063142; Townsend J. T., 1983, STOCHASTIC MODELING; TREISMAN AM, 1980, COGNITIVE PSYCHOL, V12, P97, DOI 10.1016/0010-0285(80)90005-5; Treue S, 2001, TRENDS NEUROSCI, V24, P295, DOI 10.1016/S0166-2236(00)01814-2; TVERSKY A, 1972, J MATH PSYCHOL, V9, P341, DOI 10.1016/0022-2496(72)90011-9; Usher M, 2001, PSYCHOL REV, V108, P550, DOI 10.1037//0033-295X.108.3.550; Whitney C, 2001, PSYCHON B REV, V8, P221, DOI 10.3758/BF03196158; Wickelgren W. A., 1975, SHORT TERM MEMORY, P233; WICKELGREN WA, 1977, ACTA PSYCHOL, V41, P67, DOI 10.1016/0001-6918(77)90012-9; WOLFE JM, 1994, PSYCHON B REV, V1, P202, DOI 10.3758/BF03200774; WOLFORD G, 1975, PSYCHOL REV, V82, P184, DOI 10.1037//0033-295X.82.3.184 159 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. MAY 12 2014 5 412 10.3389/fpsyg.2014.00412 15 Psychology, Multidisciplinary Psychology AH3MP WOS:000336028200001 J Moran, MS; Schnitt, SJ; Giuliano, AE; Harris, JR; Khan, SA; Horton, J; Klimberg, S; Chavez-MacGregor, M; Freedman, G; Houssami, N; Johnson, PL; Morrow, M Moran, Meena S.; Schnitt, Stuart J.; Giuliano, Armando E.; Harris, Jay R.; Khan, Seema A.; Horton, Janet; Klimberg, Suzanne; Chavez-MacGregor, Mariana; Freedman, Gary; Houssami, Nehmat; Johnson, Peggy L.; Morrow, Monica Society of Surgical Oncology-American Society for Radiation Oncology Consensus Guideline on Margins for Breast-Conserving Surgery With Whole-Breast Irradiation in Stages I and II Invasive Breast Cancer JOURNAL OF CLINICAL ONCOLOGY English Article LOBULAR CARCINOMA; LOCAL RECURRENCE; RANDOMIZED-TRIAL; ADJUVANT BREAST; LOCOREGIONAL RECURRENCE; DOSE-ESCALATION; FOLLOW-UP; RADIOTHERAPY HYPOFRACTIONATION; CONSERVATIVE TREATMENT; RADICAL-MASTECTOMY Purpose Controversy exists regarding the optimal margin width in breast-conserving surgery for invasive breast cancer. Methods A multidisciplinary consensus panel used a meta-analysis of margin width and ipsilateral breast tumor recurrence (IBTR) from a systematic review of 33 studies including 28,162 patients as the primary evidence base for consensus. Results Positive margins (ink on invasive carcinoma or ductal carcinoma in situ) are associated with a two-fold increase in the risk of IBTR compared with negative margins. This increased risk is not mitigated by favorable biology, endocrine therapy, or a radiation boost. More widely clear margins do not significantly decrease the rate of IBTR compared with no ink on tumor. There is no evidence that more widely clear margins reduce IBTR for young patients or for those with unfavorable biology, lobular cancers, or cancers with an extensive intraductal component. Conclusion The use of no ink on tumor as the standard for an adequate margin in invasive cancer in the era of multidisciplinary therapy is associated with low rates of IBTR and has the potential to decrease re-excision rates, improve cosmetic outcomes, and decrease health care costs.J Clin Oncol 32. 2014 American Society of Clinical Oncology (R), American Society for Radiation Oncology (R), and Society of Surgical Oncology (R). All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without written permission by the American Society of Clinical Oncology, American Society for Radiation Oncology, and Society of Surgical Oncology. [Moran, Meena S.] Yale Univ, Sch Med, New Haven, CT USA; [Schnitt, Stuart J.; Harris, Jay R.] Harvard Univ, Sch Med, Boston, MA USA; [Giuliano, Armando E.] Cedars Sinai Med Ctr, Los Angeles, CA 90048 USA; [Khan, Seema A.] Northwestern Univ, Feinberg Sch Med, Chicago, IL 60611 USA; [Horton, Janet] Duke Univ, Med Ctr, Durham, NC USA; [Klimberg, Suzanne] Univ Arkansas Med Sci, Fayetteville, AR USA; [Chavez-MacGregor, Mariana] Univ Texas MD Anderson Canc Ctr, Houston, TX 77030 USA; [Freedman, Gary] Univ Penn, Sch Med, Philadelphia, PA 19104 USA; [Houssami, Nehmat] Univ Sydney, Sch Publ Hlth, Sch Med, Sydney, NSW 2006, Australia; [Johnson, Peggy L.] Susan G Komen Advocate Sci, Wichita, KS USA; [Morrow, Monica] Mem Sloan Kettering Canc Ctr, New York, NY 10065 USA Morrow, M (reprint author), Mem Sloan Kettering Canc Ctr, Breast Serv, Dept Surg, 300 East 66th St, New York, NY 10065 USA. morrowm@mskcc.org Abdulkarim BS, 2011, J CLIN ONCOL, V29, P2852, DOI 10.1200/JCO.2010.33.4714; Adkins FC, 2011, ANN SURG ONCOL, V18, P3164, DOI 10.1245/s10434-011-1920-z; Anders CK, 2008, J CLIN ONCOL, V26, P3324, DOI 10.1200/JCO.2007.14.2471; Anderson SJ, 2009, J CLIN ONCOL, V27, P2466, DOI 10.1200/JCO.2008.19.8424; Arvold ND, 2011, J CLIN ONCOL, V29, P3885, DOI 10.1200/JCO.2011.36.1105; Azu M, 2010, ANN SURG ONCOL, V17, P558, DOI 10.1245/s10434-009-0765-1; Bartelink H, 2007, J CLIN ONCOL, V25, P3259, DOI 10.1200/JCO.2007.11.4991; Bellon JR, 2005, J CLIN ONCOL, V23, P1934, DOI 10.1200/JCO.2005.04.032; Bentzen SM, 2008, LANCET ONCOL, V9, P331, DOI 10.1016/S1470-2045(08)60348-7; Bentzen SM, 2008, LANCET, V371, P1098; Biglia N, 2013, EJSO-EUR J SURG ONC, V39, P455, DOI 10.1016/j.ejso.2013.02.007; Cancello G, 2010, ANN ONCOL, V21, P1974, DOI 10.1093/annonc/mdq072; CARTER D, 1986, HUM PATHOL, V17, P330, DOI 10.1016/S0046-8177(86)80455-5; Chung MA, 1997, ANN SURG ONCOL, V4, P545, DOI 10.1007/BF02305534; Ciocca RM, 2008, ANN SURG ONCOL, V15, P2263, DOI 10.1245/s10434-008-9960-8; Darby S, 2011, LANCET, V378, P1707, DOI DOI 10.1016/S0140-6736(11)61629-2; de Bock GH, 2006, EUR J CANCER, V42, P351, DOI 10.1016/j.ejca.2005.10.006; Demirci S, 2012, INT J RADIAT ONCOL, V83, P814, DOI 10.1016/j.ijrobp.2011.09.001; Donovan E, 2007, RADIOTHER ONCOL, V82, P254, DOI 10.1016/j.radonc.2006.12.008; Downs-Kelly E, 2011, ARCH PATHOL LAB MED, V135, P737, DOI 10.1043/2010-0204-OA.1; Fisher B, 2002, NEW ENGL J MED, V347, P1233, DOI 10.1056/NEJMoa022152; Fisher B, 2004, J NATL CANCER I, V96, P1823, DOI 10.1093/jnci/djh338; Fisher ER, 2001, CANCER, V91, P1679, DOI 10.1002/1097-0142(20010415)91:8+<1679::AID-CNCR1183>3.0.CO;2-8; Formenti SC, 2007, J CLIN ONCOL, V25, P2236, DOI 10.1200/JCO.2006.09.1041; Formenti SC, 2012, JAMA-J AM MED ASSOC, V308, P861, DOI 10.1001/2012.jama.10759; Freedman GM, 2009, CANCER, V115, P946, DOI 10.1002/cncr.24094; Galimberti V, 2011, ANN SURG, V253, P580, DOI 10.1097/SLA.0b013e31820d9a81; Gangi A, JAMA SURG IN PRESS; Graham RA, 2002, AM J SURG, V184, P89, DOI 10.1016/S0002-9610(02)00902-9; Guidi AJ, 1997, CANCER, V79, P1568, DOI 10.1002/(SICI)1097-0142(19970415)79:8<1568::AID-CNCR19>3.0.CO;2-W; Haffty BG, 2006, J CLIN ONCOL, V24, P5652, DOI 10.1200/JCO.2006.06.5664; Haviland J, 2012, CANC RES S, V72; Hind D, 2007, Health Technol Assess, V11, piii; Hind D, 2007, HEALTH TECHNOL ASSES, V11, p[ix, 1]; Ho AY, 2012, CANCER-AM CANCER SOC, V118, P4944, DOI 10.1002/cncr.27480; HOLLAND R, 1990, J CLIN ONCOL, V8, P113; HOLLAND R, 1985, CANCER, V56, P979, DOI 10.1002/1097-0142(19850901)56:5<979::AID-CNCR2820560502>3.0.CO;2-N; Houssami N, ANN SURG ON IN PRESS; Houssami N, 2010, EUR J CANCER, V46, P3219, DOI 10.1016/j.ejca.2010.07.043; Institute of Medicine, 2011, CLIN PRACTICE GUIDEL; Jones HA, 2009, J CLIN ONCOL, V27, P4939, DOI 10.1200/JCO.2008.21.5764; King TA, 2011, J CLIN ONCOL, V29, P2158, DOI 10.1200/JCO.2010.29.4041; Kroman N, 2004, CANCER, V100, P688, DOI 10.1002/cncr.20022; Livi L, 2013, RADIOTHER ONCOL, V108, P273, DOI 10.1016/j.radonc.2013.02.009; Mamounas EP, 2012, J CLIN ONCOL, V30, P3960, DOI 10.1200/JCO.2011.40.8369; Mannino M, 2009, RADIOTHER ONCOL, V90, P14, DOI 10.1016/j.radonc.2008.05.002; MANSFIELD CM, 1995, CANCER, V75, P2328, DOI 10.1002/1097-0142(19950501)75:9<2328::AID-CNCR2820750923>3.0.CO;2-L; Mazouni C, 2013, AM J SURG, V205, P662, DOI 10.1016/j.amjsurg.2012.06.006; McCahill LE, 2012, JAMA-J AM MED ASSOC, V307, P467, DOI 10.1001/jama.2012.43; Moher D, 2009, J CLIN EPIDEMIOL, V62, P1006, DOI 10.1016/j.jclinepi.2009.06.005; Moran M, 1998, INT J RADIAT ONCOL, V40, P353, DOI 10.1016/S0360-3016(97)00573-7; Morrow M, 2009, JAMA-J AM MED ASSOC, V302, P1551, DOI 10.1001/jama.2009.1450; Murphy C, 2011, INT J RADIAT ONCOL, V81, P69, DOI 10.1016/j.ijrobp.2010.04.067; Neuschatz AC, 2003, CANCER, V97, P30, DOI 10.1002/cncr.10981; Partridge AH, 2013, J CLIN ONCOL, V31, P2692, DOI 10.1200/JCO.2012.44.1956; Pignol JP, 2008, J CLIN ONCOL, V26, P2085, DOI 10.1200/JCO.2007.15.2488; Pilewskie ML, 2013, ANN SURG ONCOL; Polgar C, 2002, STRAHLENTHER ONKOL, V178, P615, DOI 10.1007/s00066-002-1053-1; Romestaing P, 1997, J CLIN ONCOL, V15, P963; Romond EH, 2005, NEW ENGL J MED, V353, P1673, DOI 10.1056/NEJMoa052122; Sardanelli F, 2011, INVEST RADIOL, V46, P94, DOI 10.1097/RLI.0b013e3181f3fcdf; Sasson AR, 2001, CANCER, V91, P1862, DOI 10.1002/1097-0142(20010515)91:10<1862::AID-CNCR1207>3.0.CO;2-#; SCHNITT SJ, 1994, CANCER, V74, P1746, DOI 10.1002/1097-0142(19940915)74:6<1746::AID-CNCR2820740617>3.0.CO;2-Y; SCHNITT SJ, 1987, CANCER, V59, P675, DOI 10.1002/1097-0142(19870215)59:4<675::AID-CNCR2820590402>3.0.CO;2-U; SCHNITT SJ, 1984, CANCER, V53, P1049, DOI 10.1002/1097-0142(19840301)53:5<1049::AID-CNCR2820530506>3.0.CO;2-O; Smith I, 2007, LANCET, V369, P29, DOI 10.1016/S0140-6736(07)60028-2; Taghian A, 2005, ANN SURG, V241, P629, DOI 10.1097/01.sla.0000157272.04803.1b; Voduc KD, 2010, J CLIN ONCOL, V28, P1684, DOI 10.1200/JCO.2009.24.9284; Voogd AC, 2001, J CLIN ONCOL, V19, P1688; Wazer DE, 1998, INT J RADIAT ONCOL, V40, P851, DOI 10.1016/S0360-3016(97)00861-4; Whelan TJ, 2010, NEW ENGL J MED, V362, P513, DOI 10.1056/NEJMoa0906260; Winchester DJ, 1998, J AM COLL SURGEONS, V186, P416, DOI 10.1016/S1072-7515(98)00051-9 72 1 1 AMER SOC CLINICAL ONCOLOGY ALEXANDRIA 2318 MILL ROAD, STE 800, ALEXANDRIA, VA 22314 USA 0732-183X 1527-7755 J CLIN ONCOL J. Clin. Oncol. MAY 10 2014 32 14 1507 + 10.1200/JCO.2013.53.3935 10 Oncology Oncology AG5KL WOS:000335457800018 J Mommert, M; Hora, JL; Farnocchia, D; Chesley, SR; Vokrouhlicky, D; Trilling, DE; Mueller, M; Harris, AW; Smith, HA; Fazio, GG Mommert, M.; Hora, J. L.; Farnocchia, D.; Chesley, S. R.; Vokrouhlicky, D.; Trilling, D. E.; Mueller, M.; Harris, A. W.; Smith, H. A.; Fazio, G. G. CONSTRAINING THE PHYSICAL PROPERTIES OF NEAR-EARTH OBJECT 2009 BD ASTROPHYSICAL JOURNAL English Article infrared: planetary systems; minor planets, asteroids: individual (2009 BD) THERMAL-INFRARED OBSERVATIONS; SPITZER-SPACE-TELESCOPE; RADIATION PRESSURE; ASTEROIDAL FRAGMENTS; YARKOVSKY; POPULATION; TARGET; RADAR; MISSION; HAYABUSA We report on Spitzer Space Telescope Infrared Array Camera observations of near-Earth object 2009 BD that were carried out in support of the NASA Asteroid Robotic Retrieval Mission concept. We did not detect 2009 BD in 25 hr of integration at 4.5 mu m. Based on an upper-limit flux density determination from our data, we present a probabilistic derivation of the physical properties of this object. The analysis is based on the combination of a thermophysical model with an orbital model accounting for the non-gravitational forces acting upon the body. We find two physically possible solutions. The first solution shows 2009 BD as a 2.9 +/- 0.3 m diameter rocky body (rho = 2.9 +/- 0.5 g cm(-3)) with an extremely high albedo of 0.85(-0.10)(+0.20) that is covered with regolith-like material, causing it to exhibit a low thermal inertia (Gamma = 30(-10)(+20) SI units). The second solution suggests 2009 BD to be a 4 +/- 1 m diameter asteroid with pv = 0.45(-0.15)(+0.35) that consists of a collection of individual bare rock slabs (Gamma = 2000 +/- 1000 SI units, rho = 1.7(-0.4)(+0.7) g cm(-3)). We are unable to rule out either solution based on physical reasoning. 2009 BD is the smallest asteroid for which physical properties have been constrained, in this case using an indirect method and based on a detection limit, providing unique information on the physical properties of objects in the size range smaller than 10 m. [Mommert, M.; Trilling, D. E.] No Arizona Univ, Dept Phys & Astron, Flagstaff, AZ 86011 USA; [Hora, J. L.; Smith, H. A.; Fazio, G. G.] Harvard Smithsonian Ctr Astrophys, Cambridge, MA 02138 USA; [Farnocchia, D.; Chesley, S. R.] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA; [Vokrouhlicky, D.] Charles Univ Prague, Inst Astron, CZ-18000 Prague 8, Czech Republic; [Mueller, M.] Univ Groningen, SRON Netherlands Inst Space Res, NL-9700 AV Groningen, Netherlands; [Harris, A. W.] DLR Inst Planetary Res, D-12489 Berlin, Germany Mommert, M (reprint author), No Arizona Univ, Dept Phys & Astron, POB 6010, Flagstaff, AZ 86011 USA. michael.mommert@nau.edu NASA Postdoctoral Program at the Jet Propulsion Laboratory; California Institute of Technology; Grant Agency of the Czech Republic [P209-13- 01308S]; Propulsion Laboratory RSA [1367413] The authors of this work thank Tom Soifer, Director of the Spitzer Space Telescope, for the time allocation to observe 2009 BD. We also would like to thank Paul Chodas for his support and many informative conversations. We thank an anonymous referee for useful suggestions that improved this manuscript. D. Farnocchia was supported for this research by an appointment to the NASA Postdoctoral Program at the Jet Propulsion Laboratory, California Institute of Technology, administered by Oak Ridge Associated Universities through a contract with NASA. The work of S. Chesley was conducted at the Jet Propulsion Laboratory, California Institute of Technology under a contract with the National Aeronautics and Space Administration. The work of D. Vokrouhlicky was partially supported by the Grant Agency of the Czech Republic (grant P209-13- 01308S). J. L. Hora and H. A. Smith acknowledge partial support from Jet Propulsion Laboratory RSA No. 1367413. This work is based on observations made with the Spitzer Space Telescope, which is operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA. Abe M, 2006, SCIENCE, V312, P1334, DOI 10.1126/science.1125718; Ali-Lagoa V, 2014, ASTRON ASTROPHYS, V561, DOI 10.1051/0004-6361/201322215; [Anonymous], 2013, JPL SMALL BODY DATAB; [Anonymous], 2012, ARM SPITZ OBS MAN; Baer J., 2012, ASTEROID MASSES V3 0; Benner LAM, 2002, ICARUS, V158, P379, DOI 10.1006/icar.2002.6869; Binzel RP, 2004, ICARUS, V170, P259, DOI 10.1016/j.icarcus.2004.04.004; Bottke WF, 2006, ANNU REV EARTH PL SC, V34, P157, DOI 10.1146/annurev.earth.34.031405.125154; Britt D.T., 2002, ASTEROIDS, VIII, P485; Brooks H. E., 2006, BAAS, V38, P934; Bus SJ, 2002, ICARUS, V158, P146, DOI 10.1006/icar.2002.6856; Buzzi L., 2009, MPEC; Carpino M, 2003, ICARUS, V166, P248, DOI 10.1016/S0019-1035(03)00051-4; Chapman C. R., 1978, NASA C PUBL, V2053, P145; Chesley S. R., 2014, ICAR UNPUB; Chesley SR, 2003, SCIENCE, V302, P1739, DOI 10.1126/science.1091452; Consolmagno GJ, 2008, CHEM ERDE-GEOCHEM, V68, P1, DOI 10.1016/j.chemer.2008.01.003; de Pater I., 2001, PLANETARY SCI; Delbo M, 2007, ICARUS, V190, P236, DOI 10.1016/j.icarus.2007.03.007; Emery JP, 2014, ICARUS, V234, P17, DOI 10.1016/j.icarus.2014.02.005; Farnocchia D, 2013, ICARUS, V224, P1, DOI 10.1016/j.icarus.2013.02.004; Farnocchia D, 2014, ICARUS, V229, P321, DOI 10.1016/j.icarus.2013.09.022; Farnocchia D., 2014, CEMDA, P9; Fazio GG, 2004, ASTROPHYS J SUPPL S, V154, P10, DOI 10.1086/422843; Fujiwara A, 2006, SCIENCE, V312, P1330, DOI 10.1126/science.1125841; Harris AW, 2011, ASTRON J, V141, DOI 10.1088/0004-6256/141/3/75; Harris AW, 1998, ICARUS, V131, P291, DOI 10.1006/icar.1997.5865; Harris AW, 2007, ICARUS, V188, P414, DOI [10.1016/j.icarus.2006.12.003, 10.1016/j.carvus.2006.12.003]; JAKOSKY BM, 1986, ICARUS, V66, P117, DOI 10.1016/0019-1035(86)90011-4; Kiselev N. N., 2002, P C AST COM MET ACM, P887; Macke RJ, 2011, METEORIT PLANET SCI, V46, P1842, DOI 10.1111/j.1945-5100.2011.01298.x; Mainzer A, 2014, ASTROPHYS J, V784, DOI 10.1088/0004-637X/784/2/110; Mainzer A, 2011, ASTROPHYS J, V743, DOI 10.1088/0004-637X/743/2/156; Marchis F, 2008, ICARUS, V195, P295, DOI 10.1016/j.icarus.2007.12.010; Margot J. L., 2002, LUN PLAN I C ABSTR, V33, P1849; Margot JL, 2002, SCIENCE, V296, P1445, DOI 10.1126/science.1072094; MARSDEN BG, 1973, ASTRON J, V78, P211, DOI 10.1086/111402; Micheli M., 2012, ACM 2012 P ASTR COM, P1667; Micheli M, 2013, ICARUS, V226, P251, DOI 10.1016/j.icarus.2013.05.032; Micheli M, 2012, NEW ASTRON, V17, P446, DOI 10.1016/j.newast.2011.11.008; Mueller M, 2011, ASTRON J, V141, DOI 10.1088/0004-6256/141/4/109; Mueller M., 2007, THESIS FREIE U BERLI; Mueller M, 2010, ICARUS, V205, P505, DOI 10.1016/j.icarus.2009.07.043; Muller TG, 2004, ASTRON ASTROPHYS, V424, P1075, DOI 10.1051/0004-6361:20041061; Muller TG, 2011, ASTRON ASTROPHYS, V525, DOI 10.1051/0004-6361/201015599; Muller T. G., 2012, ASTRON ASTROPHYS, V548, P36; Muller TG, 2013, ASTRON ASTROPHYS, V558, DOI 10.1051/0004-6361/201321664; Ostro SJ, 2006, SCIENCE, V314, P1276, DOI 10.1126/science.1133622; Pravec P., 2002, ASTEROIDS, VIII, P113; Putzig NE, 2005, ICARUS, V173, P325, DOI 10.1016/j.icarus.2004.08.017; Richardson D.C., 2002, ASTEROIDS, VIII, P501; Roberts LC, 2007, ICARUS, V192, P469, DOI 10.1016/j.icarus.2007.08.007; Rozitis B, 2013, ASTRON ASTROPHYS, V555, DOI 10.1051/0004-6361/201321659; Sanchez JA, 2013, ICARUS, V225, P131, DOI 10.1016/j.icarus.2013.02.036; Sanchez P., 2014, M PS IN PRESS; Scheeres D. J., 2010, ICAR, V2010, P968; Shepard MK, 2006, ICARUS, V184, P198, DOI 10.1016/j.icarus.2006.04.019; Somers J. M., 2010, BAAS, V42, P1055; SPENCER JR, 1989, ICARUS, V78, P337, DOI 10.1016/0019-1035(89)90182-6; Tholen D. J., 2013, DPS MEETING, V45; Thomas CA, 2011, ASTRON J, V142, DOI 10.1088/0004-6256/142/3/85; Trilling D. E., 2013, AJ SUBMITTED; Trilling DE, 2010, ASTRON J, V140, P770, DOI 10.1088/0004-6256/140/3/770; Vokrouhlicky D, 2000, ASTRON ASTROPHYS, V362, P746; Vokrouhlicky D, 2008, ASTRON J, V135, P2336, DOI 10.1088/0004-6256/135/6/2336; Vokrouhlicky D, 2000, ICARUS, V148, P118, DOI 10.1006/icar.2000.6469; Vokrouhlicky D, 1998, ASTRON ASTROPHYS, V335, P1093; Vokrouhlicky D, 1999, ASTRON J, V118, P3049, DOI 10.1086/301138; Werner MW, 2004, ASTROPHYS J SUPPL S, V154, P1, DOI 10.1086/422992; Wolters SD, 2011, MON NOT R ASTRON SOC, V418, P1246, DOI 10.1111/j.1365-2966.2011.19575.x; Wright EL, 2010, ASTRON J, V140, P1868, DOI 10.1088/0004-6256/140/6/1868 71 1 1 IOP PUBLISHING LTD BRISTOL TEMPLE CIRCUS, TEMPLE WAY, BRISTOL BS1 6BE, ENGLAND 0004-637X 1538-4357 ASTROPHYS J Astrophys. J. MAY 10 2014 786 2 148 10.1088/0004-637X/786/2/148 9 Astronomy & Astrophysics Astronomy & Astrophysics AH1MI WOS:000335884500071 J Tanabe-Ishibashi, A; Ikeda, T; Osaka, N Tanabe-Ishibashi, Azumi; Ikeda, Takashi; Osaka, Naoyuki Raise two effects with one scene: scene contexts have two separate effects in visual working memory of target faces FRONTIERS IN PSYCHOLOGY English Article visual working memory; context effects; face memory; scene recognition; semantics ENCODING-RETRIEVAL MATCH; RECOGNITION MEMORY; ENVIRONMENTAL CONTEXT; NATURAL SCENES; OBJECT MEMORY; SPECIFICITY; CATEGORIZATION; INFORMATION; ATTENTION; CAPACITY Many people have experienced the inability to recognize a familiar face in a changed context, a phenomenon known as the "butcher-on-the-bus" effect. Whether this context effect is a facilitation of memory by old contexts or a disturbance of memory by novel contexts is of great debate. Here, we investigated how two types of contextual information associated with target faces influence the recognition performance of the faces using meaningful (scene) or meaningless (scrambled scene) backgrounds. The results showed two different effects of contexts: (1) disturbance on face recognition by changes of scene backgrounds and (2) weak facilitation of face recognition by the re-presentation of the same backgrounds, be it scene or scrambled. The results indicate that the facilitation and disturbance of context effects are actually caused by two different subcomponents of the background information: semantic information available from scene backgrounds and visual array information commonly included in a scene and its scrambled picture. This view suggests visual working memory system can control such context information, so that it switches the way to deal with the contexts information; inhibiting it as a distracter or activating it as a cue for recognizing the current target. [Tanabe-Ishibashi, Azumi; Osaka, Naoyuki] Kyoto Univ, Grad Sch Letters, Dept Psychol, Kyoto 6068501, Japan; [Ikeda, Takashi] Osaka Univ, Grad Sch Human Sci, Dept Mind & Brain Sci, Mino, Japan; [Ikeda, Takashi] Osaka Univ, Grad Sch Engn, Dept Adapt Machine Syst, Suita, Osaka, Japan Tanabe-Ishibashi, A (reprint author), Kyoto Univ, Grad Sch Letters, Dept Psychol, Sakyo Ku, Yoshida Honmachi, Kyoto 6068501, Japan. azumi1027@gmail.com Baddeley A, 2000, TRENDS COGN SCI, V4, P417, DOI 10.1016/S1364-6613(00)01538-2; Baddeley A. D., 1986, WORKING MEMORY; Cowan N, 2001, BEHAV BRAIN SCI, V24, P87, DOI 10.1017/S0140525X01003922; Dewhurst SA, 2010, MEM COGNITION, V38, P1101, DOI 10.3758/MC.38.8.1101; Dewhurst SA, 2007, Q J EXP PSYCHOL, V60, P543, DOI 10.1080/17470210601137086; Fitzgerald RJ, 2011, MEMORY, V19, P879, DOI 10.1080/09658211.2011.613843; Gardiner JM, 2006, MEM COGNITION, V34, P227, DOI 10.3758/BF03193401; GRAF P, 1990, J EXP PSYCHOL LEARN, V16, P978, DOI 10.1037/0278-7393.16.6.978; Gruppuso V, 2007, PSYCHON B REV, V14, P1085, DOI 10.3758/BF03193095; Hayes SM, 2010, J COGNITIVE NEUROSCI, V22, P2541, DOI 10.1162/jocn.2009.21379; Hayes SM, 2007, HIPPOCAMPUS, V17, P873, DOI 10.1002/hipo.20319; Hockley WE, 2008, J EXP PSYCHOL LEARN, V34, P1412, DOI 10.1037/a0013016; Hollingworth A, 2007, J EXP PSYCHOL HUMAN, V33, P31, DOI 10.1037/0096-1523.33.1.31; Hollingworth A, 2006, J EXP PSYCHOL LEARN, V32, P58, DOI 10.1037/0278-7393.32.1.58; Inoue K, 2012, VIS COGN, V20, P94, DOI 10.1080/13506285.2011.640648; Isarida T., 2005, JAPANESE J COGNITIVE, V3, P45, DOI [10.5265/jcogpsy.3.45, DOI 10.5265/JCOGPSY.3.45]; Li FF, 2002, P NATL ACAD SCI USA, V99, P9596, DOI 10.1073/pnas.092277599; Liu K, 2005, J VISION, V5, P650, DOI 10.1167/5.7.5; MANDLER G, 1980, PSYCHOL REV, V87, P252, DOI 10.1037//0033-295X.87.3.252; Meissner CA, 2001, PSYCHOL PUBLIC POL L, V7, P3, DOI 10.1037//1076-8971.7.1.3; Melcher D, 2011, FRONT PSYCHOL, V2, DOI 10.3389/fpsyg.2011.00262; Miyake A, 2000, COGNITIVE PSYCHOL, V41, P49, DOI 10.1006/cogp.1999.0734; Mulligan NW, 2006, PSYCHOL SCI, V17, P7, DOI 10.1111/j.1467-9280.2005.01657.x; Nairne JS, 2002, MEMORY, V10, P389, DOI 10.1080/09658210244000216; Nakashima R, 2011, PSYCHON B REV, V18, P309, DOI 10.3758/s13423-010-0045-x; Oliva A, 2006, PROG BRAIN RES, V155, P23, DOI 10.1016/S0079-6123(06)55002-2; Oliva A, 1997, COGNITIVE PSYCHOL, V34, P72, DOI 10.1006/cogp.1997.0667; Paivio A., 1971, IMAGERY AND VERBAL P; PAIVIO A, 1973, COGNITIVE PSYCHOL, V5, P176, DOI 10.1016/0010-0285(73)90032-7; Reder LM, 2002, MEM COGNITION, V30, P312, DOI 10.3758/BF03195292; Reingold EM, 2002, MEMORY, V10, P365, DOI 10.1080/09658210244000199; Rutherford A, 2004, Q J EXP PSYCHOL-A, V57, P107, DOI 10.1080/02724980343000152; RYAN TA, 1959, PSYCHOL BULL, V56, P26, DOI 10.1037/h0042478; SNODGRASS JG, 1988, J EXP PSYCHOL GEN, V117, P34, DOI 10.1037//0096-3445.117.1.34; Sporer SL, 2001, PSYCHOL PUBLIC POL L, V7, P36, DOI 10.1038//1076-8971.7.1.36; Sun HM, 2010, MEM COGNITION, V38, P1049, DOI 10.3758/MC.38.8.1049; Sun HM, 2009, VIS COGN, V17, P1259, DOI 10.1080/13506280802469510; Tanabe A, 2009, BEHAV RES METHODS, V41, P309, DOI 10.3758/BRM.41.2.309; Thorpe SJ, 2009, NEURON, V62, P168, DOI 10.1016/j.neuron.2009.04.012; Torralba A, 2009, VISUAL NEUROSCI, V26, P123, DOI 10.1017/S0952523808080930; TULVING E, 1973, PSYCHOL REV, V80, P352, DOI 10.1037/h0020071; Velisavljevic L, 2008, J VISION, V8, DOI 10.1167/8.16.8; Velisavljevic L, 2008, J VISION, V8, DOI 10.1167/8.4.28; Watkins O. C., 1975, J EXPT PSYCHOLOGY HU, V1, P442, DOI DOI 10.1037/0278-7393.1.4.442 44 0 0 FRONTIERS RESEARCH FOUNDATION LAUSANNE PO BOX 110, LAUSANNE, 1015, SWITZERLAND 1664-1078 FRONT PSYCHOL Front. Psychol. MAY 8 2014 5 400 10.3389/fpsyg.2014.00400 8 Psychology, Multidisciplinary Psychology AH2VX WOS:000335981200001 J Pennington, RS; Van den Broek, W; Koch, CT Pennington, Robert S.; Van den Broek, Wouter; Koch, Christoph T. Third-dimension information retrieval from a single convergent-beam transmission electron diffraction pattern using an artificial neural network PHYSICAL REVIEW B English Article CBED PATTERNS; BLOCH WAVES; MICROSCOPY; SCATTERING; SIMULATION; SEMICONDUCTORS; TOMOGRAPHY; HOLOGRAPHY; CONTRAST; IMAGES We have reconstructed third-dimension specimen information from convergent-beam electron diffraction (CBED) patterns simulated using the stacked-Bloch-wave method. By reformulating the stacked-Bloch-wave formalism as an artificial neural network and optimizing with resilient back propagation, we demonstrate specimen orientation reconstructions with depth resolutions down to 5 nm. To show our algorithm's ability to analyze realistic data, we also discuss and demonstrate our algorithm reconstructing from noisy data and using a limited number of CBED disks. Applicability of this reconstruction algorithm to other specimen parameters is discussed. [Pennington, Robert S.; Van den Broek, Wouter; Koch, Christoph T.] Univ Ulm, Inst Expt Phys, D-89081 Ulm, Germany Pennington, RS (reprint author), Univ Ulm, Inst Expt Phys, Albert Einstein Allee 11, D-89081 Ulm, Germany. robert.pennington@uni-ulm.de German Research Foundation [KO2911/7-1]; Carl Zeiss Foundation The authors acknowledge funding from the German Research Foundation (Grant No. KO2911/7-1) and the Carl Zeiss Foundation. Alpers A, 2013, ULTRAMICROSCOPY, V128, P42, DOI 10.1016/j.ultramic.2013.01.002; Batenburg K. J., 2007, IEEE C IM PROC, V4, pIV; Batson PE, 2002, NATURE, V418, P617, DOI 10.1038/nature00972; Beche A, 2013, ULTRAMICROSCOPY, V131, P10, DOI 10.1016/j.ultramic.2013.03.014; Bethe H, 1928, ANN PHYS-BERLIN, V87, P55; BIRD DM, 1990, ACTA CRYSTALLOGR A, V46, P202, DOI 10.1107/S0108767389011906; Cosgriff EC, 2008, ULTRAMICROSCOPY, V108, P1558, DOI 10.1016/j.ultramic.2008.05.009; EAGLESHAM DJ, 1989, PHILOS MAG A, V60, P161; Hirsch P., 1977, ELECT MICROSCOPY THI; Houdellier F, 2006, ULTRAMICROSCOPY, V106, P951, DOI 10.1016/j.ultramic.2006.04.011; Houdellier F, 2008, ULTRAMICROSCOPY, V108, P426, DOI 10.1016/j.ultramic.2007.06.002; Howie A, 2004, ULTRAMICROSCOPY, V98, P73, DOI 10.1016/j.ultramic.2003.08.002; HYTCH MJ, 1994, ULTRAMICROSCOPY, V53, P191, DOI 10.1016/0304-3991(94)90034-5; ISHIZUKA K, 1977, ACTA CRYSTALLOGR A, V33, P740, DOI 10.1107/S0567739477001879; ISHIZUKA K, 1982, ACTA CRYSTALLOGR A, V38, P773, DOI 10.1107/S0567739482001594; Jacob D, 2003, ULTRAMICROSCOPY, V96, P1, DOI 10.1016/S0304-3991(02)00342-X; KAMBE K, 1982, ULTRAMICROSCOPY, V10, P223, DOI 10.1016/0304-3991(82)90042-0; Knoll M, 1932, Z PHYS, V78, P318, DOI 10.1007/BF01342199; Koch CT, 2003, J PHYS A-MATH GEN, V36, P803, DOI 10.1088/0305-4470/36/3/314; Koch CT, 2011, ULTRAMICROSCOPY, V111, P828, DOI 10.1016/j.ultramic.2010.12.014; Krause FF, 2013, ULTRAMICROSCOPY, V134, P94, DOI 10.1016/j.ultramic.2013.05.015; LeBeau JM, 2008, PHYS REV LETT, V100, DOI 10.1103/PhysRevLett.100.206101; Lide D.R., 2008, CRC HDB CHEM PHYS; Lubk A, 2014, ULTRAMICROSCOPY, V136, P42, DOI 10.1016/j.ultramic.2013.07.007; MacGillavry CH, 1940, PHYSICA, V7, P329; Midgley PA, 2001, CHEM COMMUN, P907, DOI 10.1039/b101819c; Nellist PD, 2006, APPL PHYS LETT, V89, DOI 10.1063/1.2356699; Ohtsuka M, 2009, ACTA CRYSTALLOGR A, V65, P135, DOI 10.1107/S0108767308043316; Oliphant TE, 2007, COMPUT SCI ENG, V9, P10, DOI 10.1109/MCSE.2007.58; Peng LM, 1996, ACTA CRYSTALLOGR A, V52, P257, DOI 10.1107/S0108767395014371; Pennington RS, 2014, ULTRAMICROSCOPY, V141, P32, DOI 10.1016/j.ultramic.2014.03.003; REZ P, 1979, PHYS STATUS SOLIDI A, V55, pK79, DOI 10.1002/pssa.2210550158; Riedmiller M., 1993, IEEE INT C NEUR NETW, V1, P586; Rojas R., 1993, NEURAL NETWORKS SYST; Saunders M, 1996, J ELECTRON MICROSC, V45, P11; Schowalter M, 2009, ACTA CRYSTALLOGR A, V65, P5, DOI 10.1107/S0108767308031437; Spence J.C.H., 1992, ELECT MICRODIFFRACTI; Thust A, 2009, PHYS REV LETT, V102, DOI 10.1103/PhysRevLett.102.220801; Van den Broek W, 2013, PHYS REV B, V87, DOI 10.1103/PhysRevB.87.184108; Van den Broek W, 2012, PHYS REV LETT, V109, DOI 10.1103/PhysRevLett.109.245502; Vincent R, 1999, ULTRAMICROSCOPY, V76, P125, DOI 10.1016/S0304-3991(98)00076-X; Wang P, 2011, ULTRAMICROSCOPY, V111, P877, DOI 10.1016/j.ultramic.2010.10.012; Williams D. B., 2009, TRANSMISSION ELECT M; Yamazaki T, 2006, ACTA CRYSTALLOGR A, V62, P233, DOI 10.1107/S0108767306011974; ZUO JM, 1991, ULTRAMICROSCOPY, V35, P185, DOI 10.1016/0304-3991(91)90071-D; Zuo JM, 1997, J PHYS-CONDENS MAT, V9, P7541, DOI 10.1088/0953-8984/9/36/004 46 0 0 AMER PHYSICAL SOC COLLEGE PK ONE PHYSICS ELLIPSE, COLLEGE PK, MD 20740-3844 USA 1098-0121 1550-235X PHYS REV B Phys. Rev. B MAY 8 2014 89 20 205409 10.1103/PhysRevB.89.205409 8 Physics, Condensed Matter Physics AG6NP WOS:000335535600008 J Penolazzi, B; Stramaccia, DF; Braga, M; Mondini, S; Galfano, G Penolazzi, Barbara; Stramaccia, Davide Francesco; Braga, Miriam; Mondini, Sara; Galfano, Giovanni Human Memory Retrieval and Inhibitory Control in the Brain: Beyond Correlational Evidence JOURNAL OF NEUROSCIENCE English Article cognitive control; DLPFC; inhibition; memory; retrieval; tDCS DIRECT-CURRENT STIMULATION; LONG-TERM-MEMORY; UNWANTED MEMORIES; PREFRONTAL CORTEX; EXECUTIVE CONTROL; INTERFERENCE; MECHANISMS; DYNAMICS; MARKERS Retrieving information from long-term memory can result in the episodic forgetting of related material. One influential account states that this retrieval-induced forgetting (RIF) phenomenon reflects inhibitory mechanisms called into play to decrease retrieval competition. Recent neuroimaging studies suggested that the prefrontal cortex, which is critically engaged in inhibitory processing, is also involved in retrieval competition situations. Here, we used transcranial direct current stimulation (tDCS) to address whether inhibitory processes could be causally linked to RIF. tDCS was administered over the right dorsolateral prefrontal cortex during the retrieval-practice phase in a standard retrieval-practice paradigm. Sixty human participants were randomly assigned to anodal, cathodal, or sham-control groups. The groups showed comparable benefits for practiced items. In contrast, unlike both the sham and anodal groups, the cathodal group exhibited no RIF. This pattern is interpreted as evidence for a causal role of inhibitory mechanisms in episodic retrieval and forgetting. [Penolazzi, Barbara; Mondini, Sara] Univ Padua, Dept Gen Psychol, I-35131 Padua, Italy; [Stramaccia, Davide Francesco; Braga, Miriam; Galfano, Giovanni] Univ Padua, Dept Dev & Social Psychol, I-35131 Padua, Italy; [Mondini, Sara] Univ Padua, Human Inspired Technol Res Ctr HIT, I-35131 Padua, Italy; [Galfano, Giovanni] Univ Padua, Ctr Cognit Neurosci, I-35131 Padua, Italy; [Mondini, Sara] Figlie S Camillo, Neuropsychol Clin, I-26100 Cremona, Italy Galfano, G (reprint author), Univ Padua, Dept Dev & Social Psychol, Via Venezia 8, I-35131 Padua, Italy. giovanni.galfano@unipd.it Anderson MC, 2003, J MEM LANG, V49, P415, DOI 10.1016/j.jml.2003.08.006; ANDERSON MC, 1994, J EXP PSYCHOL LEARN, V20, P1063, DOI 10.1037/0278-7393.20.5.1063; Beeli G, 2008, BEHAV BRAIN FUNCT, V4, DOI 10.1186/1744-9081-4-33; Benoit RG, 2012, NEURON, V76, P450, DOI 10.1016/j.neuron.2012.07.025; Boccardi M, 1997, GIORNALE ITALIANO PS, V24, P425; Dayan E, 2013, NAT NEUROSCI, V16, P838, DOI 10.1038/nn.3422; De Neys W, 2008, PSYCHOL SCI, V19, P483, DOI 10.1111/j.1467-9280.2008.02113.x; Fertonani A, 2010, BEHAV BRAIN RES, V208, P311, DOI 10.1016/j.bbr.2009.10.030; Gagnepain P, 2014, P NATL ACAD SCI USA, V111, pE1310, DOI 10.1073/pnas.1311468111; Galfano G, 2011, PSYCHOPHYSIOLOGY, V48, P1681, DOI 10.1111/j.1469-8986.2011.01267.x; Hanslmayr S, 2012, J NEUROSCI, V32, P14742, DOI 10.1523/JNEUROSCI.1777-12.2012; Jacobson L, 2012, EXP BRAIN RES, V216, P1, DOI 10.1007/s00221-011-2891-9; Johnson SK, 2004, PSYCHOL SCI, V15, P448, DOI 10.1111/j.0956-7976.2004.00700.x; Juan CH, 2012, BRAIN STIMUL, V5, P63, DOI 10.1016/j.brs.2012.03.012; Knoch D, 2006, J NEUROSCI, V26, P6469, DOI 10.1523/JNEUROSCI.0804-06.2006; Koessler S, 2009, PSYCHOL SCI, V20, P1356, DOI 10.1111/j.1467-9280.2009.02450.x; Kuhl BA, 2007, NAT NEUROSCI, V10, P908, DOI 10.1038/nn1918; Levy BJ, 2002, TRENDS COGN SCI, V6, P299, DOI 10.1016/S1364-6613(02)01923-X; Manenti R, 2012, BRAIN STIMUL, V5, P103, DOI 10.1016/j.brs.2012.03.004; Masson MEJ, 2011, BEHAV RES METHODS, V43, P679, DOI 10.3758/s13428-010-0049-5; MENSINK GJ, 1988, PSYCHOL REV, V95, P434, DOI 10.1037/0033-295X.95.4.434; Penolazzi B, 2013, BEHAV BRAIN RES, V248, P129, DOI 10.1016/j.bbr.2013.04.007; Penolazzi B, 2010, PLOS ONE, V5, DOI 10.1371/journal.pone.0010623; Roman P, 2009, PSYCHOL SCI, V20, P1053, DOI 10.1111/j.1467-9280.2009.02415.x; Sandrini M, 2013, CURR BIOL, V23, P2181, DOI 10.1016/j.cub.2013.08.045; Storm BC, 2012, MEM COGNITION, V40, P827, DOI 10.3758/s13421-012-0211-7; Venkatakrishnan A, 2012, J NEUROPHYSIOL, V107, P1, DOI 10.1152/jn.00557.2011; Weller PD, 2013, J EXP PSYCHOL LEARN, V39, P1232, DOI 10.1037/a0030335; Wimber M, 2009, J COGNITIVE NEUROSCI, V21, P538, DOI 10.1162/jocn.2009.21043; Wimber M, 2008, J NEUROSCI, V28, P13419, DOI 10.1523/JNEUROSCI.1916-08.2008; Wimber M, 2011, TRANSL PSYCHIAT, V1, DOI 10.1038/tp.2011.15 31 1 1 SOC NEUROSCIENCE WASHINGTON 11 DUPONT CIRCLE, NW, STE 500, WASHINGTON, DC 20036 USA 0270-6474 J NEUROSCI J. Neurosci. MAY 7 2014 34 19 6606 6610 10.1523/JNEUROSCI.0349-14.2014 5 Neurosciences Neurosciences & Neurology AH8IF WOS:000336380500018 J Dvorak, D; Fenton, AA Dvorak, Dino; Fenton, Andre A. On Track with Two Gammas NEURON English Editorial Material HIPPOCAMPUS; OSCILLATIONS; REPLAY; RAT CA1 place cells discharge in prospective and retrospective modes, possibly reflecting memory retrieval and encoding, respectively. In this issue of Neuron, Bieri et al. (2014) report that slow and fast gamma oscillations associate with prospective and retrospective discharge, indicating that gamma oscillations organize information-processing modes. [Dvorak, Dino] SUNY Downstate, Brooklyn, NY 11203 USA; [Dvorak, Dino] NYU, Joint Grad Program Biomed Engn, Polytech Sch Engn, Brooklyn, NY 11203 USA; [Fenton, Andre A.] Suny Downstate Med Ctr, Robert F Furchgott Ctr Neural & Behav Sci, Dept Physiol & Pharmacol, Brooklyn, NY 11203 USA; [Fenton, Andre A.] NYU, Ctr Neural Sci, New York, NY 10003 USA Fenton, AA (reprint author), Suny Downstate Med Ctr, Robert F Furchgott Ctr Neural & Behav Sci, Dept Physiol & Pharmacol, 450 Clarkson Ave, Brooklyn, NY 11203 USA. afenton@nyu.edu Battaglia FP, 2004, J NEUROSCI, V24, P4541, DOI 10.1523/JNEUROSCI.4896-03.2004; Bieri KW, 2014, NEURON, V82, P670, DOI 10.1016/j.neuron.2014.03.013; BRAGIN A, 1995, J NEUROSCI, V15, P47; Colgin LL, 2009, NATURE, V462, P353, DOI 10.1038/nature08573; Dragoi G, 2013, ELIFE, V2, DOI 10.7554/eLife.01326; Fenton AA, 2010, J NEUROSCI, V30, P4613, DOI 10.1523/JNEUROSCI.5576-09.2010; Gupta AS, 2010, NEURON, V65, P695, DOI 10.1016/j.neuron.2010.01.034; Karlsson MP, 2009, NAT NEUROSCI, V12, P913, DOI 10.1038/nn.2344; Kelemen E, 2013, PLOS BIOL, V11, DOI 10.1371/journal.pbio.1001607; Lasztoczi B, 2014, NEURON, V81, P1126, DOI 10.1016/j.neuron.2014.01.021; O'Neill J, 2010, TRENDS NEUROSCI, V33, P220, DOI 10.1016/j.tins.2010.01.006; OKEEFE J, 1993, HIPPOCAMPUS, V3, P317, DOI 10.1002/hipo.450030307; OKEEFE J, 1971, BRAIN RES, V34, P171, DOI 10.1016/0006-8993(71)90358-1 13 0 0 CELL PRESS CAMBRIDGE 600 TECHNOLOGY SQUARE, 5TH FLOOR, CAMBRIDGE, MA 02139 USA 0896-6273 1097-4199 NEURON Neuron MAY 7 2014 82 3 506 508 10.1016/j.neuron.2014.04.027 3 Neurosciences Neurosciences & Neurology AG6BK WOS:000335503200003 J Bandara, R; Walker, JP; Rudiger, C Bandara, Ranmalee; Walker, Jeffrey P.; Ruediger, Christoph Towards soil property retrieval from space: Proof of concept using in situ observations JOURNAL OF HYDROLOGY English Article Surface soil moisture; Soil hydraulic parameter retrieval; JULES; Land surface modeling PARTICLE SWARM OPTIMIZATION; UNSATURATED HYDRAULIC CONDUCTIVITY; MOISTURE PLUME; DESIGN; FILTER Soil moisture is a key variable that controls the exchange of water and energy fluxes between the land surface and the atmosphere. However, the temporal evolution of soil moisture is neither easy to measure nor monitor at large scales because of its high spatial variability. This is mainly a result of the local variation in soil properties and vegetation cover. Thus, land surface models are normally used to predict the evolution of soil moisture and yet, despite their importance, these models are based on low-resolution soil property information or typical values. Therefore, the availability of more accurate and detailed soil parameter data than are currently available is vital, if regional or global soil moisture predictions are to be made with the accuracy required for environmental applications. The proposed solution is to estimate the soil hydraulic properties via model calibration to remotely sensed soil moisture observation, with in situ observations used as a proxy in this proof of concept study. Consequently, the feasibility is assessed, and the level of accuracy that can be expected determined, for soil hydraulic property estimation of duplex soil profiles in a semi-arid environment using near-surface soil moisture observations under naturally occurring conditions. The retrieved soil hydraulic parameters were then assessed by their reliability to predict the root zone soil moisture using the Joint UK Land Environment Simulator model. When using parameters that were retrieved using soil moisture observations, the root zone soil moisture was predicted to within an accuracy of 0.04 m(3)/m(3), which is an improvement of similar to 0.025 m(3)/m(3) on predictions that used published values or pedo-transfer functions. (C) 2014 Published by Elsevier B.V. [Bandara, Ranmalee; Walker, Jeffrey P.; Ruediger, Christoph] Monash Univ, Dept Civil Engn, Clayton, Vic 3800, Australia Bandara, R (reprint author), Monash Univ, Dept Civil Engn, Clayton, Vic 3800, Australia. ranmalee11@gmail.com MoistureMap Project through Australian Research Council Discovery Project [DP0879212]; Australian Research Council [DP0343778, DP0557543]; CRC for Catchment Hydrology; Monash University Institute of Graduate Research; Department of Civil Engineering, Monash University This work was supported by the MoistureMap Project, funded through the Australian Research Council Discovery Project Grant (DP0879212). Initial setup and maintenance of the Murrumbidgee monitoring network used in this study was funded by the Australian Research Council (DP0343778, DP0557543) and by the CRC for Catchment Hydrology. The authors also acknowledge Roger Young, Olaf Klimczak, Sandy Peischl, Robbie Ryan, Frank Winston and Mei Sun Yee for assistance in field work, Derek Chan, Ye Nan and Anthony Brosinsky for assistance with laboratory experiments, Ye Nan for help with MatLAB codes and Valentijn Pauwels for help with PSO codes. The Postgraduate Publication Award (PPA) by the Monash University Institute of Graduate Research is also acknowledged gratefully, as well as the departmental scholarship from the Department of Civil Engineering, Monash University. Abido MA, 2002, IEEE T ENERGY CONVER, V17, P406, DOI 10.1109/TEC.2002.801992; Abramowitz G., 2007, J HYDROMETEOROL, V8; Albergel C, 2009, HYDROL EARTH SYST SC, V13, P115; [Anonymous], D529810 ASTM; [Anonymous], 2006, GCOS107 WORLD MET OR; Bandara H.R.S., 2011, INT C MOD SIM; Bandara R, 2013, J HYDROL, V497, P198, DOI 10.1016/j.jhydrol.2013.06.004; Bandara R., 2013, LAND SURFACE MODEL I; Best M. J., 2011, GEOSCI MODEL DEV DIS, V4, DOI DOI 10.5194/GMDD-4-595-2011; Burke EJ, 1998, IEEE T GEOSCI REMOTE, V36, P454, DOI 10.1109/36.662729; Burke EJ, 1997, PROG PHYS GEOG, V21, P549, DOI 10.1177/030913339702100404; Burke EJ, 1997, WATER RESOUR RES, V33, P1689, DOI 10.1029/97WR01000; CAMILLO PJ, 1986, IEEE T GEOSCI REMOTE, V24, P930, DOI 10.1109/TGRS.1986.289708; CLAPP RB, 1978, WATER RESOUR RES, V14, P601, DOI 10.1029/WR014i004p00601; Clark D., 2011, GEOSCI MODEL DEV DIS, V4, P641, DOI [10.5194/gmdd-4-641-2011, DOI 10.5194/GMDD-4-641-2011]; Clark D., 2009, JOINT UK LAND ENV SI, P119; Cook F.J., 2002, SOIL PHYS MEASUREMEN, P119; COSBY BJ, 1984, WATER RESOUR RES, V20, P682, DOI 10.1029/WR020i006p00682; Cox PM, 1999, CLIM DYNAM, V15, P183, DOI 10.1007/s003820050276; DANE JH, 1983, SOIL SCI SOC AM J, V47, P619; De Lannoy G.J.M., 2012, 5 INT WORKSH CATCHM; Eberhart R.C., 2000, COMP INERTIA WEIGHT; Engelbrecht A. P., 2005, FUNDAMENTALS COMPUTA; Engelbrecht A.P, 2005, CEC 2005 TUTORIAL PA; Grayson R.B., 2006, CONTROLS PATTERNS SO, P41; Hu X, 2003, IEEE SWARM INT S 200; Imaoka K, 2010, P IEEE, V98, P717, DOI 10.1109/JPROC.2009.2036869; Ines A.V.M., 2008, WATER RESOUR RES, V44; Kannan S, 2004, ELECTR POW SYST RES, V70, P203, DOI 10.1016/j.epsr.2003.12.009; Kennedy J, 1995, 1995 IEEE INTERNATIONAL CONFERENCE ON NEURAL NETWORKS PROCEEDINGS, VOLS 1-6, P1942, DOI 10.1109/ICNN.1995.488968; Kerr Y.H., 2010, P IEEE, V98; Li C, 2011, VADOSE ZONE J, V10, P1205, DOI 10.2136/vzj2010.0159; McBratney AB, 2002, GEODERMA, V109, P41, DOI 10.1016/S0016-7061(02)00139-8; McKenzie D.H., 2002, SOIL PHYS MEASUREMEN, P131; Montzka C, 2011, J HYDROL, V399, P410, DOI 10.1016/j.jhydrol.2011.01.020; Nash J. E., 1970, J HYDROL, V10, P282, DOI DOI 10.1016/0022-1694(70)90255-6; Perez RE, 2007, COMPUT STRUCT, V85, P1579, DOI 10.1016/j.compstruc.2006.10.013; Pollacco J.A.P., 2011, VADOSE ZONE J; Qin J., 2009, J GEOPHYS RES, V114; Rawls W. J., 1982, T AM SOC AGR ENG, V25, P1328; Ritter A, 2003, AGR WATER MANAGE, V59, P77, DOI 10.1016/S0378-3774(02)00160-9; Santanello JA, 2007, REMOTE SENS ENVIRON, V110, P79, DOI 10.1016/j.rse.2007.02.007; Scheerlinck K, 2009, WATER RESOUR RES, V45, DOI 10.1029/2009WR008051; Seneviratne SI, 2010, EARTH-SCI REV, V99, P125, DOI 10.1016/j.earscirev.2010.02.004; Shi Y., 1998, P IEEE INT C EV COMP; Siriwardena L., 2003, 031 COOP RES CTR CAT; Smith AB, 2012, WATER RESOUR RES, V48, DOI 10.1029/2012WR011976; Trelea IC, 2003, INFORM PROCESS LETT, V85, P317, DOI 10.1016/S0020-0190(02)00447-7; VANGENUCHTEN MT, 1980, SOIL SCI SOC AM J, V44, P892; VEREECKEN H, 1990, SOIL SCI, V149, P1, DOI 10.1097/00010694-199001000-00001; VEREECKEN H, 1989, LAND QUALITIES IN SPACE AND TIME, P121; Ye M, 2005, WATER RESOUR RES, V41, DOI 10.1029/2004WR003735; Yeh TCJ, 2005, WATER RESOUR RES, V41, DOI 10.1029/2004WR003736 53 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 0022-1694 1879-2707 J HYDROL J. Hydrol. MAY 6 2014 512 27 38 10.1016/j.jhydrol.2014.02.031 12 Engineering, Civil; Geosciences, Multidisciplinary; Water Resources Engineering; Geology; Water Resources AG8ZH WOS:000335706900003 J Van Wittenberghe, S; Verrelst, J; Rivera, JP; Alonso, L; Moreno, J; Samson, R Van Wittenberghe, Shari; Verrelst, Jochem; Rivera, Juan Pablo; Alonso, Luis; Moreno, Jose; Samson, Roeland Gaussian processes retrieval of leaf parameters from a multi-species reflectance, absorbance and fluorescence dataset JOURNAL OF PHOTOCHEMISTRY AND PHOTOBIOLOGY B-BIOLOGY English Article Machine learning algorithm; Spectral features; Hyperspectral; Parameter retrieval; Chlorophyll; Leaf structure; Specific leaf area; Leaf water content INDUCED CHLOROPHYLL FLUORESCENCE; VERTICAL CANOPY GRADIENT; RED EDGE POSITION; BIOCHEMICAL-PROPERTIES; HYPERSPECTRAL INDEXES; SPECTRAL REFLECTANCE; OPTICAL-PROPERTIES; NITROGEN-CONTENT; RATIO F690/F730; LEAVES Biochemical and structural leaf properties such as chlorophyll content (Chl), nitrogen content (N), leaf water content (LWC), and specific leaf area (SLA) have the benefit to be estimated through nondestructive spectral measurements. Current practices, however, mainly focus on a limited amount of wavelength bands while more information could be extracted from other wavelengths in the full range (400-2500 nm) spectrum. In this research, leaf characteristics were estimated from a field-based multi-species dataset, covering a wide range in leaf structures and Chl concentrations. The dataset contains leaves with extremely high Chl concentrations (>100 mu g cm(-2)), which are seldom estimated. Parameter retrieval was conducted with the machine learning regression algorithm Gaussian Processes (GP), which is able to perform adaptive, nonlinear data fitting for complex datasets. Moreover, insight in relevant bands is provided during the development of a regression model. Consequently, the physical meaning of the model can be explored. Best estimates of SLA, LWC and Chl yielded a best obtained normalized root mean square error of 6.0%, 7.7%, 9.1%, respectively. Several distinct wavebands were chosen across the whole spectrum. A band in the red edge (710 nm) appeared to be most important for the estimation of Chl. Interestingly, spectral features related to biochemicals with a structural or carbon storage function (e.g. 1090, 1550, 1670, 1730 nm) were found important not only for estimation of SLA, but also for LWC, Chl or N estimation. Similar, Chl estimation was also helped by some wavebands related to water content (950, 1430 nm) due to correlation between the parameters. It is shown that leaf parameter retrieval by GP regression is successful, and able to cope with large structural differences between leaves. (C) 2014 Elsevier B.V. All rights reserved. [Van Wittenberghe, Shari; Samson, Roeland] Univ Antwerp, Fac Sci, Dept Biosci, B-2020 Antwerp, Belgium; [Verrelst, Jochem; Rivera, Juan Pablo; Alonso, Luis; Moreno, Jose] Univ Valencia, Image Proc Lab, E-46980 Valencia, Spain Van Wittenberghe, S (reprint author), Univ Antwerp, Fac Sci, Dept Biosci, Groenenborgerlaan 171, B-2020 Antwerp, Belgium. Shari.VanWittenberghe@ua.ac.be Belgian Science Policy Office (BELSPO) [SR/00/131]; Spanish Ministry for Science and Innovation [AYA 2010-21432-C02-01] The research presented in this paper is funded by the Belgian Science Policy Office (BELSPO) in the frame of the STEREO II program-project BIOHYPE (SR/00/131) and partially supported by the Spanish Ministry for Science and Innovation under the project AYA 2010-21432-C02-01. The authors wish to thank Adrian Del Amo for field assistance and laboratory analyses. Agati G, 2013, EUR J AGRON, V45, P39, DOI 10.1016/j.eja.2012.10.011; Alonso L, 2007, INT GEOSCI REMOTE SE, P3756; Apostol S, 2003, CAN J REMOTE SENS, V29, P57; Baret F., 2008, ADV LAND REMOTE SENS, P172, DOI DOI 10.1007/978-1-4020-6450-0_7; le Maire G, 2008, REMOTE SENS ENVIRON, V112, P3846, DOI 10.1016/j.rse.2008.06.005; Buschmann C, 2000, PHOTOSYNTHETICA, V38, P483, DOI 10.1023/A:1012440903014; Buschmann C, 2007, PHOTOSYNTH RES, V92, P261, DOI 10.1007/s11120-007-9187-8; Campbell PKE, 2007, J ENVIRON QUAL, V36, P832, DOI 10.2134/jeq2005.0396; Camps-Valls G, 2009, INT GEOSCI REMOTE SE, P2449; CHAPIN FS, 1983, ECOLOGY, V64, P376, DOI 10.2307/1937083; Cho MA, 2006, REMOTE SENS ENVIRON, V101, P181, DOI 10.1016/j.rse.2005.12.011; Clevers JGPW, 2013, INT J APPL EARTH OBS, V23, P344, DOI 10.1016/j.jag.2012.10.008; Curran PJ, 2001, REMOTE SENS ENVIRON, V76, P349, DOI 10.1016/S0034-4257(01)00182-1; DAMBROSIO N, 1992, RADIAT ENVIRON BIOPH, V31, P51, DOI 10.1007/BF01211512; Delegido J, 2014, ECOL INDIC, V40, P34, DOI 10.1016/j.ecolind.2014.01.002; ELVIDGE CD, 1990, INT J REMOTE SENS, V11, P1775; FILELLA I, 1994, INT J REMOTE SENS, V15, P1459; Fourty T, 1996, REMOTE SENS ENVIRON, V56, P104, DOI 10.1016/0034-4257(95)00234-0; Fourty T, 1998, INT J REMOTE SENS, V19, P1283, DOI 10.1080/014311698215441; GITELSON A, 1994, J PHOTOCH PHOTOBIO B, V22, P247, DOI 10.1016/1011-1344(93)06963-4; Gitelson AA, 1996, J PLANT PHYSIOL, V148, P494; Gitelson AA, 2003, J PLANT PHYSIOL, V160, P271, DOI 10.1078/0176-1617-00887; Gitelson AA, 1999, REMOTE SENS ENVIRON, V69, P296, DOI 10.1016/S0034-4257(99)00023-1; Grossman YL, 1996, REMOTE SENS ENVIRON, V56, P182, DOI 10.1016/0034-4257(95)00235-9; HAK R, 1990, RADIAT ENVIRON BIOPH, V29, P329, DOI 10.1007/BF01210413; Hansen PM, 2003, REMOTE SENS ENVIRON, V86, P542, DOI 10.1016/S0034-4257(03)00131-7; Hastie T, 2009, ELEMENTS STAT LEARNI; HORLER DNH, 1983, INT J REMOTE SENS, V4, P273; Jacquemoud S, 1996, REMOTE SENS ENVIRON, V56, P194, DOI 10.1016/0034-4257(95)00238-3; Johnson LF, 2001, REMOTE SENS ENVIRON, V78, P314, DOI 10.1016/S0034-4257(01)00226-7; Knudby A, 2010, REMOTE SENS ENVIRON, V114, P1230, DOI 10.1016/j.rse.2010.01.007; Langsdorf G, 2000, PHOTOSYNTHETICA, V38, P539, DOI 10.1023/A:1012409423487; LICHTENTHALER HK, 1990, PHOTOSYNTH RES, V25, P295, DOI 10.1007/BF00033170; Mariotto I, 2013, REMOTE SENS ENVIRON, V139, P291, DOI 10.1016/j.rse.2013.08.002; Ollinger SV, 2011, NEW PHYTOL, V189, P375, DOI 10.1111/j.1469-8137.2010.03536.x; Ourcival JM, 1999, NEW PHYTOL, V143, P351, DOI 10.1046/j.1469-8137.1999.00456.x; PENUELAS J, 1993, INT J REMOTE SENS, V14, P1887; Rasmussen CE, 2005, ADAPT COMPUT MACH LE, P1; Rivera J.P., 2014, IEEE J SEL TOP APPL; Rouse J.W., 1973, NASA, V1, P309; Schaepman ME, 2009, REMOTE SENS ENVIRON, V113, pS123, DOI 10.1016/j.rse.2009.03.001; Seelig HD, 2008, INT J REMOTE SENS, V29, P3701, DOI 10.1080/01431160701772500; Sims DA, 2002, REMOTE SENS ENVIRON, V81, P337, DOI 10.1016/S0034-4257(02)00010-X; STJACQUES C, 1991, TREE PHYSIOL, V8, P391; Thenkabail P.S., 2011, HYPERSPECTRAL REMOTE, P3; Thenkabail PS, 2004, REMOTE SENS ENVIRON, V91, P354, DOI 10.1016/j.rse.2004.03.013; Van Wittenberghe S, 2012, TREES-STRUCT FUNCT, V26, P1427, DOI 10.1007/s00468-012-0714-7; Van Wittenberghe S, 2014, SCI TOTAL ENVIRON, V466, P185, DOI 10.1016/j.scitotenv.2013.07.024; Van Wittenberghe S, 2013, ENVIRON POLLUT, V173, P29, DOI 10.1016/j.envpol.2012.10.003; Verrelst J, 2012, IEEE T GEOSCI REMOTE, V50, P1832, DOI 10.1109/TGRS.2011.2168962; Verrelst J, 2012, REMOTE SENS-BASEL, V4, P2866, DOI 10.3390/rs4092866; Verrelst J, 2012, REMOTE SENS ENVIRON, V118, P127, DOI 10.1016/j.rse.2011.11.002; Wang Q, 2012, ECOL INDIC, V14, P56, DOI 10.1016/j.ecolind.2011.08.021; YODER BJ, 1995, REMOTE SENS ENVIRON, V53, P199, DOI 10.1016/0034-4257(95)00135-N 54 0 0 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 1011-1344 J PHOTOCH PHOTOBIO B J. Photochem. Photobiol. B-Biol. MAY 5 2014 134 37 48 10.1016/j.jphotobiol.2014.03.010 12 Biochemistry & Molecular Biology; Biophysics Biochemistry & Molecular Biology; Biophysics AI4YZ WOS:000336873900006 J Jay, S; Guillaume, M Jay, Sylvain; Guillaume, Mireille A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data REMOTE SENSING OF ENVIRONMENT English Article Bathymetry; Depth retrieval; Hyperspectral; Maximum likelihood estimation; Multi-resolution mapping; Remote-sensing; Water quality retrieval SEMIANALYTICAL MODEL; COASTAL ENVIRONMENTS; SHALLOW WATERS; TURBID LAKES; BATHYMETRY; BENTHOS; IMAGES; MERIS This article presents a novel statistical method for mapping water column properties from hyperspectral remote-sensing data. Usual inversion methods are based on a pixel-by-pixel approach. Therefore, they do not consider the spatial correlation between neighboring pixels, even though such pixels are often affected by the same water column if the spatial resolution is high enough. The proposed method uses such redundant information performing local maximum likelihood (ML) estimation of depth and water quality in large zones. It provides multi-resolution maps, in which resolution depends on local depth. In shallow water, the signal-to-noise ratio is high so an accurate mapping can be performed. Therefore, we propose to model local depth variations with a linear model while water quality is still supposed to be locally homogeneous. In deep water, the signal-to-noise ratio is lower so estimating only the local mean depth with standard ML estimation is more robust and reliable. The entire image is divided into appropriate meshes. In every mesh, water column properties are estimated using both linear and constant depth models. Final maps are obtained combining these estimates. The deeper the water, the higher the influence of standard ML estimation maps. Using local information provided by neighboring pixels makes this method robust to noise. Moreover, the hyperspectral image and estimated bathymetry have the same resolution in shallow water since depth is modeled for every pixel. The efficiency of our method was assessed with simulated and real hyperspectral images. Results proved that depth modeling improves depth and water quality estimations, especially in shallow water. In deep water, assuming that the bottom is locally flat is reasonable since depth variations are small relatively to depth mean value. With the considered water quality, the estimated bathymetry was accurate for depths up to 14 m. Estimated concentration maps were consistent for the whole range Of depths. The spatial resolution of estimated bathymetry was 50 cm in shallow water (for depths up to 10 m), and ranged from 2.5 m to 5 m in deep water (for depths between 10 m and 14 m). The influence of bio-optical modeling is also demonstrated. We show that reliable models are absolutely necessary to obtain good estimation results. (C) 2014 Elsevier Inc. All rights reserved. [Jay, Sylvain; Guillaume, Mireille] Aix Marseille Univ, CNRS, Ecole Cent Marseille, Inst Fresnel,UMR 7249, F-13013 Marseille, France Jay, S (reprint author), Irstea UMR ITAP, 361 Rue JF Breton, F-34196 Montpellier, France. sylvain.jay@fresnel.fr Office for Advanced Research and Innovation (DGA/MRIS) This work was supported by the Office for Advanced Research and Innovation (DGA/MRIS). We are also grateful to Actimar, which carried out the field measurement campaign within the exploratory research and innovation project named "HypLitt" and funded by the French Defense Agency (DGA). Actimar is a company specialized in operational oceanography and high resolution remote sensing, based in Brest, France (www.actimar.fr). Bergmann M., 2004, THESIS I NATL POLYTE; Brando VE, 2009, REMOTE SENS ENVIRON, V113, P755, DOI 10.1016/j.rse.2008.12.003; BRICAUD A, 1995, J GEOPHYS RES-OCEANS, V100, P13321, DOI 10.1029/95JC00463; Clark R.N., 2007, US GEOLOGICAL SURVEY, V231; Dekker AG, 2011, LIMNOL OCEANOGR-METH, V9, P396, DOI 10.4319/lom.2011.9.396; Gerardino-Neira C., 2008, P IEEE INT GEOSC REM; Giardino C, 2012, COMPUT GEOSCI-UK, V45, P313, DOI 10.1016/j.cageo.2011.11.022; Hedley J, 2009, REMOTE SENS ENVIRON, V113, P2527, DOI 10.1016/j.rse.2009.07.008; Hedley JD, 2005, INT J REMOTE SENS, V26, P2107, DOI 10.1080/01431160500034086; Jay S, 2012, IEEE J-STARS, V5, P1213, DOI 10.1109/JSTARS.2012.2185488; Jay S., 2011, P 3 WORKSH HYP IM SI, P1; Jay S., 2012, THESIS ECOLE CENTRAL; Jay S, 2010, INT GEOSCI REMOTE SE, P2820, DOI 10.1109/IGARSS.2010.5650257; Kiefer J., 1953, P AM MATH SOC; Klonowski W., 2007, J APPL REMOTE SENS, V1; Kutser T, 2001, SCI TOTAL ENVIRON, V268, P47, DOI 10.1016/S0048-9697(00)00682-3; Le CF, 2009, REMOTE SENS ENVIRON, V113, P1175, DOI 10.1016/j.rse.2009.02.005; Lee Z., 1999, APPL OPTICS, P3831; Lee Z., 1994, THESIS U S FLORIDA; Lee ZP, 1998, APPL OPTICS, V37, P6329, DOI 10.1364/AO.37.006329; Louchard EM, 2003, LIMNOL OCEANOGR, V48, P511; Lyzenga D., 1978, APPL OPTICS, P379; Manolakis D., 2003, Lincoln Laboratory Journal, V14; MARITORENA S, 1994, LIMNOL OCEANOGR, V39, P1689; Matthews MW, 2010, REMOTE SENS ENVIRON, V114, P2070, DOI 10.1016/j.rse.2010.04.013; Minghelli-Roman A, 2007, IEEE GEOSCI REMOTE S, V4, P274, DOI 10.1109/LGRS.2007.890548; Mobley CD, 2005, APPL OPTICS, V44, P3576, DOI 10.1364/AO.44.003576; O'Reilly JE, 1998, J GEOPHYS RES-OCEANS, V103, P24937, DOI 10.1029/98JC02160; Philpot W., 1989, APPL OPTICS, P1569; Richardson L., 2006, REMOTE SENSING AQUAT; Richter R., 2011, 5650211 DLR IB; Segelstein D. J., 1981, THESIS U MISSOURI KA; Smet S., 2010, TECHNICAL REPORT; Volpe V, 2011, REMOTE SENS ENVIRON, V115, P44, DOI 10.1016/j.rse.2010.07.013; Xiu P, 2009, CONT SHELF RES, V29, P2270, DOI 10.1016/j.csr.2009.09.003 35 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. MAY 5 2014 147 121 132 10.1016/j.rse.2014.01.026 12 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AH5QR WOS:000336186400011 J Rautiainen, K; Lemmetyinen, J; Schwank, M; Kontu, A; Menard, CB; Matzler, C; Drusch, M; Wiesmann, A; Ikonen, J; Pulliainen, J Rautiainen, Kimmo; Lemmetyinen, Juha; Schwank, Mike; Kontu, Anna; Menard, Cecile B.; Maetzler, Christian; Drusch, Matthias; Wiesmann, Andreas; Ikonen, Jaakko; Pulliainen, Jouni Detection of soil freezing from L-band passive microwave observations REMOTE SENSING OF ENVIRONMENT English Article Microwave radiometry; L-band; Soil freeze/thaw; SMOS; SMAP FROZEN; MOISTURE; VEGETATION; RADIOMETER; EMISSION; SEASONS; BOREAL; CYCLES We present a novel algorithm for detecting seasonal soil freezing processes using L-band microwave radiometry. L-band is the optimal choice of frequency for the monitoring of soil freezing, due to the inherent high contrast of the microwave signature between the frozen and thawed states of the soil medium. Dual-polarized observations of L-band brightness temperature at a range of observation angles were collected from a tower-based instrument, and evaluated against ancillary information on soil and snow properties over four winter seasons. During the first three winter periods the measurement site was located over mineral soil on a forest clearing, for the fourth winter the instrument was moved to a wetland site. Both sites are located in Sodankyla, Northern Finland. The test sites represent two environments typical for the northern boreal forest zone. The data were applied to derive an empirical relation between the onset and progress of soil freezing and the observed passive L-band signature. A retrieval algorithm was developed using the observations at the forest opening site. The algorithm exploits the perceived change in brightness temperature and the change in the relative difference between the signatures at horizontal and vertical polarization. With the collected experimental dataset, these features were linked optimally to the progress of soil freezing by choice of observation angle, polarization and temporal averaging. The wetland site observations provided the first opportunity for demonstrating the developed algorithm over a different soil type, giving a first estimate of the algorithm performance over larger heterogeneous targets. The future objective is to adapt the algorithm to L-band satellite observations. The present study is highly relevant for the development of freeze-thaw algorithms from current and future L-band satellite missions such as SMOS and SMAP. (C) 2014 Elsevier Inc. All rights reserved. [Rautiainen, Kimmo; Lemmetyinen, Juha; Kontu, Anna; Menard, Cecile B.; Ikonen, Jaakko; Pulliainen, Jouni] Arctic Res, Finnish Meteorol Inst, FI-00101 Helsinki, Finland; [Schwank, Mike; Maetzler, Christian; Wiesmann, Andreas] Gamma Remote Sensing AG, CH-3073 Gumlingen, Switzerland; [Drusch, Matthias] Estec, European Space Agcy, NL-2200 AG Noordwijk, Netherlands Rautiainen, K (reprint author), Arctic Res, Finnish Meteorol Inst, POB 503, FI-00101 Helsinki, Finland. Menard, Cecile/F-7860-2014 STSE Program [4000105184/12/I-BG] This research has been performed within the frame of the STSE Program through the SMOS + Innovation "Application of SMOS data for the characterization of the freeze/thaw cycle and the retrieval of permafrost information" (contract 4000105184/12/I-BG) activity. The authors would like to thank ESA for the loan of the ELBARA-II radiometer (ESTEC ELBARA-II Loan Agreement 21013/07/NL/FF). Furthermore, the authors would like to thank The Finnish Environment Institute (SYKE) for providing frost tube observation network data. Entekhabi D, 2010, P IEEE, V98, P704, DOI 10.1109/JPROC.2010.2043918; Fierz C., 2009, IACS CONTRIBUTION, V1, P83; Frolking S, 1996, GLOB CHANGE BIOL, V2, P343, DOI 10.1111/j.1365-2486.1996.tb00086.x; Frolking S, 1999, J GEOPHYS RES-ATMOS, V104, P27895, DOI 10.1029/1998JD200093; HALLIKAINEN MT, 1985, IEEE T GEOSCI REMOTE, V23, P25, DOI 10.1109/TGRS.1985.289497; HAO ZG, 2011, GEOSC REM SENS S IGA, P3097; Iwata Y, 2012, COLD REG SCI TECHNOL, V71, P111, DOI 10.1016/j.coldregions.2011.10.010; JACKSON TJ, 1989, IEEE T GEOSCI REMOTE, V27, P225, DOI 10.1109/36.20301; Kerr YH, 2012, IEEE T GEOSCI REMOTE, V50, P1384, DOI 10.1109/TGRS.2012.2184548; Kerr YH, 2010, P IEEE, V98, P666, DOI 10.1109/JPROC.2010.2043032; Kim Y, 2012, REMOTE SENS ENVIRON, V121, P472, DOI 10.1016/j.rse.2012.02.014; Matzler Christian, 2006, Thermal Microwave Radiation: Applications for Remote Sensing, DOI 10.1049/PBEW052E_ch5; MECKLENBURG S, 2010, P 38 COSPAR SCI ASS, P5; Mironov VL, 2013, IEEE J-STARS, V6, P1781, DOI 10.1109/JSTARS.2013.2262108; NAEIMI V, 2011, IEEE T GEOSCI REMOTE, V50, P2566; Rautiainen K, 2012, IEEE T GEOSCI REMOTE, V50, P1483, DOI 10.1109/TGRS.2011.2167755; RIGNOT E, 1994, REMOTE SENS ENVIRON, V49, P131, DOI 10.1016/0034-4257(94)90049-3; Schwank M, 2012, IEEE T GEOSCI REMOTE, V50, P1587, DOI 10.1109/TGRS.2012.2184126; SCHWANK M, 2014, IEEE T GEOSCIE UNPUB; Schwank M, 2010, SENSORS-BASEL, V10, P584, DOI 10.3390/s100100584; Schwank M, 2004, IEEE T GEOSCI REMOTE, V42, P1252, DOI 10.1109/TGRS.2004.825592; SKOGLAND T, 1988, SOIL BIOL BIOCHEM, V20, P851, DOI 10.1016/0038-0717(88)90092-2; SMITH S, 2009, GLOBAL TERRESTRIAL O; ULABY FT, 1982, MICROWAVE REMOTE SEN, V2, pCH11; Watanabe K, 2002, COLD REG SCI TECHNOL, V34, P103, DOI 10.1016/S0165-232X(01)00063-5; WILLIS WO, 1961, SOIL SCI SOC AM J, V41, P115; Zhang T, 2001, GEOPHYS RES LETT, V28, P763, DOI 10.1029/2000GL011952; Zhang T., 2003, P 8 INT C PERM, P1289; Zhao SJ, 2012, INT J REMOTE SENS, V33, P860, DOI 10.1080/01431161.2011.577836 29 0 0 ELSEVIER SCIENCE INC NEW YORK 360 PARK AVE SOUTH, NEW YORK, NY 10010-1710 USA 0034-4257 1879-0704 REMOTE SENS ENVIRON Remote Sens. Environ. MAY 5 2014 147 206 218 10.1016/j.rse.2014.03.007 13 Environmental Sciences; Remote Sensing; Imaging Science & Photographic Technology Environmental Sciences & Ecology; Remote Sensing; Imaging Science & Photographic Technology AH5QR WOS:000336186400017 J Horner, AJ; Burgess, N Horner, Aidan J.; Burgess, Neil Pattern Completion in Multielement Event Engrams CURRENT BIOLOGY English Article MEDIAL TEMPORAL-LOBE; HUMAN BRAIN; MEMORY CONSOLIDATION; NEURAL REPRESENTATIONS; HIPPOCAMPAL; RECALL; BOUNDARIES; CONTEXT; MODELS; INFORMATION Personally experienced events include multiple elements, such as locations, people, and objects. These events are thought to be stored in episodic memory as coherent representations [1] that allow the retrieval of all elements from a partial cue ("pattern completion" [2-6]). However, direct evidence for coherent multielement representations is lacking. Their presence would predict that retrieval of one element from an event should be dependent on retrieval of the other elements from that event. If we remember where we were, we should be more likely to remember who we met and what object they gave us. Here we provide evidence for this type of dependency in remembering three-element events. Dependency was seen when all three elements were encoded simultaneously, or when the three overlapping pairwise associations comprising an event were learned on separate trials. However, dependency was only seen in the separated encoding condition when all possible within-event associations were encoded. These results suggest that episodic memories are stored as coherent representations in which associations between all within-event elements allow retrieval via pattern completion. They also show that related experiences encountered at different times can be flexibly integrated into these coherent representations. [Horner, Aidan J.; Burgess, Neil] UCL, Inst Cognit Neurosci, London WC1N 3AR, England; [Horner, Aidan J.; Burgess, Neil] UCL, Inst Neurol, London WC1N 3AR, England Horner, AJ (reprint author), UCL, Inst Cognit Neurosci, 17 Queen Sq, London WC1N 3AR, England. a.horner@ucl.ac.uk; n.burgess@ucl.ac.uk Burgess, Neil/B-2420-2009 Medical Research Council UK; Wellcome Trust We thank Dharshan Kumaran and Peter Dayan for helpful comments on an earlier version of this manuscript. This work was funded by the Medical Research Council UK and the Wellcome Trust. ALVAREZ P, 1994, P NATL ACAD SCI USA, V91, P7041, DOI 10.1073/pnas.91.15.7041; Bartlett F. C., 1932, REMEMBERING STUDY EX; Bentin S, 1998, ACTA PSYCHOL, V98, P311, DOI 10.1016/S0001-6918(97)00048-6; Ben-Yakov A, 2011, J NEUROSCI, V31, P9032, DOI 10.1523/JNEUROSCI.0702-11.2011; Bishop Y.M.M., 1975, DISCRETE MULTIVARIAT; DuBrow S, 2013, J EXP PSYCHOL GEN, V142, P1277, DOI 10.1037/a0034024; Dumay N, 2007, PSYCHOL SCI, V18, P35, DOI 10.1111/j.1467-9280.2007.01845.x; Durrant SJ, 2013, CEREB CORTEX, V23, P2467, DOI 10.1093/cercor/bhs244; EICH E, 1984, MEM COGNITION, V12, P105, DOI 10.3758/BF03198423; Eichenbaum H, 2004, NEURON, V44, P109, DOI 10.1016/j.neuron.2004.08.028; Ekstrom AD, 2003, NATURE, V425, P184, DOI 10.1038/nature01964; ESTES WK, 1955, PSYCHOL REV, V62, P145, DOI 10.1037/h0048509; Ezzyat Y, 2011, PSYCHOL SCI, V22, P243, DOI 10.1177/0956797610393742; GARDNERMEDWIN AR, 1976, PROC R SOC SER B-BIO, V194, P375, DOI 10.1098/rspb.1976.0084; Homer A.J., 2013, J EXP PSYCHOL GEN, V142, P1370; HOPFIELD JJ, 1982, P NATL ACAD SCI-BIOL, V79, P2554, DOI 10.1073/pnas.79.8.2554; Howard MW, 2002, J MATH PSYCHOL, V46, P269, DOI 10.1006/jmps.2001.1388; JACOBY LL, 1993, J EXP PSYCHOL GEN, V122, P139, DOI 10.1037//0096-3445.122.2.139; JONES GV, 1976, J EXP PSYCHOL GEN, V105, P277, DOI 10.1037//0096-3445.105.3.277; Kahana MJ, 2005, J EXP PSYCHOL LEARN, V31, P933, DOI 10.1037/0278-7393.31.5.933; Kahana M.J., 2000, OXFORD HDB MEMORY, P59; Kinsbourne M., 1975, SHORT TERM MEMORY, P257; Kumaran D, 2012, PSYCHOL REV, V119, P573, DOI 10.1037/a0028681; MARR D, 1971, PHILOS T ROY SOC B, V262, P23, DOI 10.1098/rstb.1971.0078; MCCLELLAND JL, 1995, PSYCHOL REV, V102, P419, DOI 10.1037/0033-295X.102.3.419; Meiser T, 2008, J EXP PSYCHOL LEARN, V34, P32, DOI 10.1037/0278-7393.34.1.32; Meiser T, 2002, J EXP PSYCHOL LEARN, V28, P116, DOI 10.1037//0278-7393.28.1.116; Nadel L, 1997, CURR OPIN NEUROBIOL, V7, P217, DOI 10.1016/S0959-4388(97)80010-4; Nakazawa K, 2002, SCIENCE, V297, P211, DOI 10.1126/science.1071795; OKEEFE J, 1971, BRAIN RES, V34, P171, DOI 10.1016/0006-8993(71)90358-1; Orban G, 2008, P NATL ACAD SCI USA, V105, P2745, DOI 10.1073/pnas.0708424105; Quiroga RQ, 2005, NATURE, V435, P1102, DOI 10.1038/nature03687; ROSS BH, 1981, MEM COGNITION, V9, P1, DOI 10.3758/BF03196946; Schapiro AC, 2013, NAT NEUROSCI, V16, P486, DOI 10.1038/nn.3331; SCOVILLE WB, 1957, J NEUROL NEUROSUR PS, V20, P11, DOI 10.1136/jnnp.20.1.11; Shohamy D, 2008, NEURON, V60, P378, DOI 10.1016/j.neuron.2008.09.023; Speer NK, 2007, PSYCHOL SCI, V18, P449, DOI 10.1111/j.1467-9280.2007.01920.x; SQUIRE LR, 1991, SCIENCE, V253, P1380, DOI 10.1126/science.1896849; Starns JJ, 2005, J EXP PSYCHOL LEARN, V31, P1213, DOI 10.1037/0278-7393.31.6.1213; Stickgold R, 2013, NAT NEUROSCI, V16, P139, DOI 10.1038/nn.3303; Tse D, 2007, SCIENCE, V316, P76, DOI 10.1126/science.1135935; Tulving E, 1983, ELEMENTS EPISODIC ME; TULVING E, 1975, PSYCHOL REV, V82, P261, DOI 10.1037/h0076782; van Kesteren MTR, 2010, P NATL ACAD SCI USA, V107, P7550, DOI 10.1073/pnas.0914892107; Wagner U, 2004, NATURE, V427, P352, DOI 10.1038/nature02223; Wills TJ, 2005, SCIENCE, V308, P873, DOI 10.1126/science.1108905; Zacks JM, 2001, NAT NEUROSCI, V4, P651, DOI 10.1038/88486; Zeithamova D, 2012, NEURON, V75, P168, DOI 10.1016/j.neuron.2012.05.010 48 0 0 CELL PRESS CAMBRIDGE 600 TECHNOLOGY SQUARE, 5TH FLOOR, CAMBRIDGE, MA 02139 USA 0960-9822 1879-0445 CURR BIOL Curr. Biol. MAY 5 2014 24 9 988 992 10.1016/j.cub.2014.03.012 5 Biochemistry & Molecular Biology; Cell Biology Biochemistry & Molecular Biology; Cell Biology AG6QA WOS:000335542300022 J Fang, YL; Kwok, RCW; Schroeder, A Fang, Yulin; Kwok, Ron Chi-Wai; Schroeder, Andreas Knowledge processes in virtual teams: consolidating the evidence BEHAVIOUR & INFORMATION TECHNOLOGY English Review knowledge management; virtual community; distributed cognition FACE-TO-FACE; COMPUTER-MEDIATED COMMUNICATION; INFORMATION-TECHNOLOGY; TRANSACTIVE MEMORY; MANAGEMENT-SYSTEMS; DISTRIBUTED TEAMS; TACIT KNOWLEDGE; PERFORMANCE; WORK; COLLABORATION This article takes stock of the current state of research on knowledge processes in virtual teams (VTs) and consolidates the extent research findings. Virtual teams, on the one hand, constitute important organisational entities that facilitate the integration of diverse and distributed knowledge resources. On the other hand, collaborating in a virtual environment creates particular challenges for the knowledge processes. The article seeks to consolidate the diverse evidence on knowledge processes in VTs with a specific focus on identifying the factors that influence the effectiveness of these knowledge processes. The article draws on the four basic knowledge processes outlined by Alavi and Leidner (2001) (i.e. creation, transferring, storage/retrieval and application) to frame the investigation and discuss the extent research. The consolidation of the existing research findings allows us to recognise the gaps in the understanding of knowledge processes in VTs and identify the important avenues for future research. [Fang, Yulin; Kwok, Ron Chi-Wai] City Univ Hong Kong, Dept Informat Syst, Hong Kong, Hong Kong, Peoples R China; [Schroeder, Andreas] Univ Buckingham, Sch Business, Buckingham MK18 1EG, England Schroeder, A (reprint author), Univ Buckingham, Sch Business, Hunter St, Buckingham MK18 1EG, England. andreas.schroeder@buckingham.ac.uk Research Grants Council of the Hong Kong Special Administrative Region, China [CityU 142810] The work described in this article was supported by a grant from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project CityU 142810). *ACC, 2005, MISS CRIT WORKF PERF; Ahuja G, 2000, ADMIN SCI QUART, V45, P425, DOI 10.2307/2667105; Alavi M, 2001, MIS QUART, V25, P107, DOI 10.2307/3250961; Alavi M, 2002, INFORM SYST RES, V13, P404, DOI 10.1287/isre.13.4.404.72; Alavi M, 2002, J AM SOC INF SCI TEC, V53, P1029, DOI 10.1002/asi.10107; ALAVI M, 1994, MIS QUART, V18, P159, DOI 10.2307/249763; ARCHER NP, 1990, BEHAV INFORM TECHNOL, V9, P307; Argyris C., 1978, ORG LEARNING THEORY; Assudani RH, 2009, J KNOWL MANAG, V13, P521, DOI 10.1108/13673270910997169; Baba ML, 2004, J ORGAN BEHAV, V25, P547, DOI 10.1002/job.259; Behrend FD, 2009, J KNOWL MANAG, V13, P99, DOI 10.1108/13673270910971860; Bosch-Sijtsema PM, 2009, J KNOWL MANAG, V13, P533, DOI 10.1108/13673270910997178; BOUTELLIER EA, 1998, R&D MANAGE, V28, P13; Capece G, 2009, KNOWL MAN RES PRACT, V7, P329, DOI 10.1057/kmrp.2009.25; CHIDAMBARAM L, 1993, MIS QUART, V17, P465, DOI 10.2307/249588; Choi SY, 2010, MIS QUART, V34, P855; CIANNI M, 1977, ACAD MANAGEMENT EXEC, V11, P105; CLARK H, 1982, MUTUAL KNOWLEDGE, P332; Cohen SG, 1997, J MANAGE, V23, P239, DOI 10.1016/S0149-2063(97)90034-9; Cornelius C, 2003, COMMUN RES, V30, P147, DOI 10.1177/0093650202250874; Cramton CD, 2001, ORGAN SCI, V12, P346, DOI 10.1287/orsc.12.3.346.10098; Crossan MM, 1999, ACAD MANAGE REV, V24, P522, DOI 10.2307/259140; DAVIDOW WH, 1992, CALIFORNIA BUSINESS, V27, P34; Dean JW, 1996, ACAD MANAGE J, V39, P368, DOI 10.2307/256784; Edmondson A, 1999, ADMIN SCI QUART, V44, P350, DOI 10.2307/2666999; Edmondson AC, 2002, ORGAN SCI, V13, P128, DOI 10.1287/orsc.13.2.128.530; Gammelgaard J, 2010, BEHAV INFORM TECHNOL, V29, P349, DOI 10.1080/01449290903548406; Gibson CB, 2006, ADMIN SCI QUART, V51, P451; Gonzalez-Pereira B, 2010, J INFORMETR, V4, P379, DOI 10.1016/j.joi.2010.03.002; GOVINDARAJAN AKG, 2000, STRATEGIC MANAGEMENT, V21, P473, DOI DOI 10.1002/(SICI)1097-0266(200004)21:4473::AID-SMJ843.0.CO;2-I; Grant RM, 1996, STRATEGIC MANAGE J, V17, P109; GREENO JG, 1974, KNOWLEDGE COGNITION, P17; Griffith TL, 2001, RES ORGAN BEHAV, V23, P379, DOI 10.1016/S0191-3085(01)23009-3; Griffith TL, 2003, MIS QUART, V27, P265; Griffith TL, 1999, ACAD MANAGE REV, V24, P472, DOI 10.2307/259137; Guo ZN, 2008, DECIS SUPPORT SYST, V44, P673, DOI 10.1016/j.dss.2007.09.006; GURVITCH G, 1971, SOCIAL FRAMEWORKS KN; Haas MR, 2006, ORGAN SCI, V17, P367, DOI 10.1287/orsc.1060.0187; Hackman J. R., 1992, HDB IND ORG PSYCHOL, V3, P199; HOLZNER B., 1979, KNOWLEDGE APPL KNOWL; Hong Jacky F L, 2008, Journal of General Management, V34; Huber GP, 2001, EUR J INFORM SYST, V10, P72, DOI 10.1057/palgrave.ejis.3000399; *INT CORP, 2004, EWORKFORCE EN GLOB V; Kang M, 2010, J AM SOC INF SCI TEC, V61, P483, DOI 10.1002/asi.21267; King WR, 2006, INFORM MANAGE-AMSTER, V43, P740, DOI 10.1016/j.im.2006.05.003; Kolb D. A., 1975, THEORIES GROUP PROCE, P33; Hertel G., 2005, Human Resources Management Review, V15, DOI 10.1016/j.hrmr.2005.01.002; Krauss R. M., 1990, INTELLECTUAL TEAMWOR, P111; Kruempel K, 2000, IEEE T PROF COMMUN, V43, P185, DOI 10.1109/47.843645; Kwok RCW, 2002, J MANAGE INFORM SYST, V19, P185; Langfred CW, 1998, SMALL GR RES, V29, P124, DOI 10.1177/1046496498291005; Lee H, 2003, J MANAGE INFORM SYST, V20, P179; Leonard D, 1998, CALIF MANAGE REV, V40, P112; Luckmann T., 1967, SOCIAL CONSTRUCTION; MAIER R, 2007, KNOWLEDGE MANAGEMENT; Majchrzak A, 2000, MIS QUART, V24, P569, DOI 10.2307/3250948; Majchrzak A, 2005, INFORM SYST RES, V16, P9, DOI 10.1287/isre.1050.0044; Majchrzak A., 2000, Information Resources Management Journal, V13, DOI 10.4018/irmj.2000010104; Malhotra A., 2004, J KNOWLEDGE MANAGEME, V8, P75, DOI [10.1108/13673270410548496, DOI 10.1108/13673270410548496]; Malhotra A, 2001, MIS QUART, V25, P229, DOI 10.2307/3250930; Markus ML, 2000, SLOAN MANAGE REV, V42, P13; Martins LL, 2004, J MANAGE, V30, P805, DOI 10.1016/j.jm.2004.05.002; MILES RE, 1992, CALIF MANAGE REV, V34, P53; Ocker RJ, 1999, GROUP DECIS NEGOT, V8, P427, DOI 10.1023/A:1008621827601; Olivera F, 2004, SMALL GR RES, V35, P440, DOI 10.1177/1046496404263765; Palanisamy R, 2007, J COMPUT INFORM SYST, V48, P100; Paul DL, 2006, J MANAGE INFORM SYST, V22, P143, DOI 10.2753/MIS0742-1222220406; Peachey T., 2005, International Journal of Knowledge Management, V1, DOI 10.4018/jkm.2005070104; Penrose E., 1959, THEORY GROWTH FIRM; Pinsonneault A., 2005, International Journal of e-Collaboration, V1, DOI 10.4018/jec.2005070101; Powell A., 2004, ACM SIGMIS DATABASE, V35, P6, DOI DOI 10.1145/968464.968467; Qureshi S, 2001, GROUP DECIS NEGOT, V10, P27, DOI 10.1023/A:1008756811139; Ratcheva V, 2009, INT J PROJ MANAG, V27, P206, DOI 10.1016/j.ijproman.2008.02.008; Robert LP, 2008, INFORM SYST RES, V19, P314, DOI 10.1287/isre.1080.0177; Robey D, 2000, TECH COMMUN, V47, P51; Rubenstein-Montano B, 2001, DECIS SUPPORT SYST, V31, P5, DOI 10.1016/S0167-9236(00)00116-0; SAUNDERS CS, 2000, FRAMING DOMAIN IT MA; Senge P. M., 1990, 5 DISCIPLINE ART PRA; SHARDA R, 1988, MANAGE SCI, V34, P139, DOI 10.1287/mnsc.34.2.139; Siebdrat F, 2009, MIT SLOAN MANAGE REV, V50, P63; Sole D, 2002, BRIT J MANAGE, V13, P17; Staples DS, 2008, INFORM SYST J, V18, P617, DOI 10.1111/j.1365-2575.2007.00244.x; SZULANSKI, 1996, STRATEGIC MANAGEMENT, V17, P27; Townsend A., 1998, ACAD MANAGEMENT EXEC, V12, P17; Vaccaro A, 2009, RES POLICY, V38, P1278, DOI 10.1016/j.respol.2009.06.012; Vogel DR, 2001, IEEE T PROF COMMUN, V44, P114, DOI 10.1109/47.925514; Wagner C, 2010, TECH COMMUN-STC, V57, P68; WALSH JP, 1995, ORGAN SCI, V6, P280, DOI 10.1287/orsc.6.3.280; Workman M, 2007, J AM SOC INF SCI TEC, V58, P794, DOI 10.1002/asi.20545; Wu CH, 2010, BEHAV INFORM TECHNOL, V29, P513, DOI 10.1080/01449290903490666; Yoo Y., 2001, INT J ORG ANAL, V9, P187, DOI DOI 10.1108/EB028933; Yuan YC, 2011, J AM SOC INF SCI TEC, V62, P535, DOI 10.1002/asi.21472 92 1 1 TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0144-929X 1362-3001 BEHAV INFORM TECHNOL Behav. Inf. Technol. MAY 4 2014 33 5 486 501 10.1080/0144929X.2012.719033 16 Computer Science, Cybernetics; Ergonomics Computer Science; Engineering AF7PO WOS:000334907200006 J Hubber, PJ; Gilmore, C; Cragg, L Hubber, Paula J.; Gilmore, Camilla; Cragg, Lucy The roles of the central executive and visuospatial storage in mental arithmetic: A comparison across strategies QUARTERLY JOURNAL OF EXPERIMENTAL PSYCHOLOGY English Article Visuospatial sketchpad.; Mathematical cognition; Central executive; Addition; Visuospatial working memory; Mental arithmetic VISUAL WORKING-MEMORY; SHORT-TERM-MEMORY; INDIVIDUAL-DIFFERENCES; NUMERICAL ABILITIES; PERFORMANCE; CHILDREN; DIFFICULTIES; VERIFICATION; MATHEMATICS; COMPONENTS Previous research has demonstrated that working memory plays an important role in arithmetic. Different arithmetical strategies rely on working memory to different extents-for example, verbal working memory has been found to be more important for procedural strategies, such as counting and decomposition, than for retrieval strategies. Surprisingly, given the close connection between spatial and mathematical skills, the role of visuospatial working memory has received less attention and is poorly understood. This study used a dual-task methodology to investigate the impact of a dynamic spatial n-back task (Experiment 1) and tasks loading the visuospatial sketchpad and central executive (Experiment 2) on adults' use of counting, decomposition, and direct retrieval strategies for addition. While Experiment 1 suggested that visuospatial working memory plays an important role in arithmetic, especially when counting, the results of Experiment 2 suggested this was primarily due to the domain-general executive demands of the n-back task. Taken together, these results suggest that maintaining visuospatial information in mind is required when adults solve addition arithmetic problems by any strategy but the role of domain-general executive resources is much greater than that of the visuospatial sketchpad. [Hubber, Paula J.; Cragg, Lucy] Univ Nottingham, Sch Psychol, Nottingham NG7 2RD, England; [Gilmore, Camilla] Univ Loughborough, Math Educ Ctr, Loughborough, Leics, England Hubber, PJ (reprint author), Univ Nottingham, Sch Psychol, East Dr,Univ Pk Campus, Nottingham NG7 2RD, England. lpxph@nottingham.ac.uk ESRC (Economic and Social Research Council) [RES-062-23-3280]; British Academy This project was funded by ESRC (Economic and Social Research Council) project RES-062-23-3280. C. G. is funded by a British Academy Postdoctoral Fellowship. ASHCRAFT MH, 1992, COGNITION, V44, P75, DOI 10.1016/0010-0277(92)90051-I; Baddeley A, 2003, NAT REV NEUROSCI, V4, P829, DOI 10.1038/nrn1201; Baddeley Alan, 1996, Quarterly Journal of Experimental Psychology Section A Human Experimental Psychology, V49, P5, DOI 10.1080/027249896392784; Baddeley A, 2000, TRENDS COGN SCI, V4, P417, DOI 10.1016/S1364-6613(00)01538-2; Baddeley A. D., 1974, RECENT ADV LEARNING, V8, P47; Bull R, 2008, DEV NEUROPSYCHOL, V33, P205, DOI 10.1080/87565640801982312; Bull R, 1999, DEV NEUROPSYCHOL, V15, P421; Campbell J. I., 1995, MATH COGNITION, V1, P121; Campbell JID, 2002, MEM COGNITION, V30, P988, DOI 10.3758/BF03195782; Campbell JID, 1996, MEM COGNITION, V24, P156, DOI 10.3758/BF03200878; DEHAENE S, 1992, COGNITION, V44, P1, DOI 10.1016/0010-0277(92)90049-N; DEHAENE S, 1993, J EXP PSYCHOL GEN, V122, P371, DOI 10.1037/0096-3445.122.3.371; de Hevia MD, 2008, NEUROSCI BIOBEHAV R, V32, P1361, DOI 10.1016/j.neubiorev.2008.05.015; De Rammelaere S, 1999, PSYCHOL RES-PSYCH FO, V62, P72, DOI 10.1007/s004260050041; DeStefano D, 2004, EUR J COGN PSYCHOL, V16, P353, DOI 10.1080/09541440244000328; Dumontheil I, 2012, CEREB CORTEX, V22, P1078, DOI 10.1093/cercor/bhr175; Furst AJ, 2000, MEM COGNITION, V28, P774, DOI 10.3758/BF03198412; Gilmore C, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0067374; Hanich LB, 2001, J EDUC PSYCHOL, V93, P615, DOI 10.1037//0022-0663.93.3.615; HEATHCOTE D, 1994, CAH PSYCHOL COGN, V13, P207; Hecht SA, 2002, MEM COGNITION, V30, P447, DOI 10.3758/BF03194945; Hegarty M, 1999, J EDUC PSYCHOL, V91, P684, DOI 10.1037//0022-0663.91.4.684; Imbo I, 2007, J EXP CHILD PSYCHOL, V96, P284, DOI 10.1016/j.jecp.2006.09.001; Imbo I, 2007, Q J EXP PSYCHOL, V60, P1246, DOI 10.1080/17470210600943419; Imbo I, 2007, MEM COGNITION, V35, P454, DOI 10.3758/BF03193285; Imbo I, 2010, MEM COGNITION, V38, P176, DOI 10.3758/MC.38.2.176; Jordan NC, 2003, CHILD DEV, V74, P834, DOI 10.1111/1467-8624.00571; Lee KM, 2002, COGNITION, V83, pB63, DOI 10.1016/S0010-0277(02)00010-0; LeFevre J.-A., 2005, HDB MATH COGNITION, P361; LOGIE RH, 1994, MEM COGNITION, V22, P395, DOI 10.3758/BF03200866; Mix KS, 2012, ADV CHILD DEV BEHAV, V42, P197; Noel MP, 2001, MEM COGNITION, V29, P34, DOI 10.3758/BF03195738; Peirce JW, 2007, J NEUROSCI METH, V162, P8, DOI 10.1016/j.jneumeth.2006.11.017; Pickering SJ, 2001, Q J EXP PSYCHOL-A, V54, P397, DOI 10.1080/02724980042000174; Raghubar KP, 2010, LEARN INDIVID DIFFER, V20, P110, DOI 10.1016/j.lindif.2009.10.005; Reuhkala M, 2001, ED PSYCHOL, V21, P387, DOI DOI 10.1080/01443410120090786; Robert N. D., 2013, RES MATH ED, V15, P165, DOI [10.1080/14794802.2013.797748, DOI 10.1080/14794802.2013.797748]; SERON X, 1992, COGNITION, V44, P159, DOI 10.1016/0010-0277(92)90053-K; Seyler DJ, 2003, J EXP PSYCHOL LEARN, V29, P1339, DOI 10.1037/0278-7393.29.6.1339; Siegler R. S., 1984, ORIGINS COGNITIVE SK, P229; Simmons FR, 2012, J EXP CHILD PSYCHOL, V111, P139, DOI 10.1016/j.jecp.2011.08.011; Towse JN, 1998, BEHAV RES METH INS C, V30, P583, DOI 10.3758/BF03209475; Trbovich PL, 2003, MEM COGNITION, V31, P738, DOI 10.3758/BF03196112; van Dijck JP, 2011, COGNITION, V119, P114, DOI 10.1016/j.cognition.2010.12.013 44 0 0 ROUTLEDGE JOURNALS, TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXFORDSHIRE, ENGLAND 1747-0218 1747-0226 Q J EXP PSYCHOL Q. J. Exp. Psychol. MAY 4 2014 67 5 936 954 10.1080/17470218.2013.838590 19 Psychology, Biological; Physiology; Psychology; Psychology, Experimental Psychology; Physiology AG0FC WOS:000335089700008 J Prakash, S; Sathiyamoorthy, V; Mahesh, C; Gairola, RM Prakash, Satya; Sathiyamoorthy, V.; Mahesh, C.; Gairola, R. M. An evaluation of high-resolution multisatellite rainfall products over the Indian monsoon region INTERNATIONAL JOURNAL OF REMOTE SENSING English Article SATELLITE PRECIPITATION PRODUCTS; GAUGE DATA; PASSIVE MICROWAVE; VALIDATION; TMPA; VARIABILITY; PROJECT; SCALES To date, more than half a dozen merged rainfall data sets are available to the research community. These data sets use different approaches for rainfall retrieval and combine different satellites or/and ground-based rainfall observations. However, these data sets appear to differ among themselves and deviate from in situ observations at regional and seasonal scales. Hence, it is becoming difficult to choose a suitable data set from these products for regional rainfall analyses. In the present study, four independently developed multisatellite high-resolution precipitation products (HRPPs), namely Climate Prediction Center Morphing (CMORPH) version 1.0, Naval Research Laboratory (NRL)-blended, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA)-3B42 version 7 are compared with quality-controlled gridded rain gauge data over India developed by the India Meteorological Department (IMD). A preliminary analysis is carried out for a 6 year period from 2004 to 2009 at daily scale for the summer monsoon season of June to September. Comparison of all-India seasonal (June to September) mean rainfall with rain gauge data shows a considerable underestimation by all HRPPs, although the underestimation is comparatively less for TMPA. Moreover, all the HRPPs are able to capture the important characteristic features of the summer monsoon rainfall such as intra-seasonal (active/break spells) and inter-annual (excess/deficient) variabilities reasonably well. Regional differences between observed rainfall and the HRPPs are also analysed. Results suggest that TMPA is comparatively closer to the ground-truth, possibly due to the incorporation of rain gauge observations. Furthermore, all the HRPPs show high probability of detection, low false alarm ratio, and high threat score in detection of rainfall events over most parts of India. It is also observed that all these HRPPs have certain issues in rainfall detection over the rain-shadow region of southeast peninsular India, semi-arid northwest parts of India, and hilly northern parts. Hence, results of the 6 year analysis over a region with a dense network of surface observations of rainfall suggest that the TMPA merged rainfall product is better than the other HRPPs due to (1) lower underestimation of rainfall, (2) higher correlation and lower root-mean-square error (RMSE), and (3) better performance over the west coast. Therefore, TMPA can be used with confidence as compared to other HRPPs for monsoon studies, particularly over the Indian land region with a considerable rain gauge network. This study also clarifies the fact that the merged satellite rainfall products with sufficient ground-truths can be the ideal product for monsoon and hydrological studies. [Prakash, Satya; Sathiyamoorthy, V.; Mahesh, C.; Gairola, R. M.] Indian Space Res Org, Atmospher & Ocean Sci Grp, Ctr Space Applicat, Ahmadabad 380015, Gujarat, India Prakash, S (reprint author), Earth Syst Sci Org, Natl Ctr Medium Range Weather Forecasting, Minist Earth Sci, Noida 201309, India. spsharma_01@yahoo.co.in Adler RF, 2003, J HYDROMETEOROL, V4, P1147, DOI 10.1175/1525-7541(2003)004<1147:TVGPCP>2.0.CO;2; AghaKouchak A, 2012, GEOPHYS RES LETT, V39, DOI 10.1029/2012GL051592; Ebert EE, 2007, B AM METEOROL SOC, V88, P47, DOI 10.1175/BAMS-88-1-47; Gadgil S, 2003, ANNU REV EARTH PL SC, V31, P429, DOI 10.1146/annurev.earth.31.100901.141251; Gao YC, 2013, HYDROL EARTH SYST SC, V17, P837, DOI 10.5194/hess-17-837-2013; Gebregiorgis AS, 2013, IEEE T GEOSCI REMOTE, V51, P704, DOI 10.1109/TGRS.2012.2196282; Goswami BN, 2001, J CLIMATE, V14, P1180, DOI 10.1175/1520-0442(2001)014<1180:IOAIVO>2.0.CO;2; Houze RA, 2012, REV GEOPHYS, V50, DOI 10.1029/2011RG000365; Hsu KL, 1997, J APPL METEOROL, V36, P1176, DOI 10.1175/1520-0450(1997)036<1176:PEFRSI>2.0.CO;2; Huffman GJ, 2010, SATELLITE RAINFALL APPLICATIONS FOR SURFACE HYDROLOGY, P3, DOI 10.1007/978-90-481-2915-7_1; Huffman GJ, 2007, J HYDROMETEOROL, V8, P38, DOI 10.1175/JHM560.1; Joyce RJ, 2004, J HYDROMETEOROL, V5, P487, DOI 10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2; Karaseva MO, 2012, THEOR APPL CLIMATOL, V108, P147, DOI 10.1007/s00704-011-0509-6; Kidd C, 2003, J HYDROMETEOROL, V4, P1088, DOI 10.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2; Kidd C, 2012, J HYDROMETEOROL, V13, P67, DOI 10.1175/JHM-D-11-042.1; Krishnamurthy V, 2007, J CLIMATE, V20, P3, DOI 10.1175/JCLI3981.1; Kubota T, 2007, IEEE T GEOSCI REMOTE, V45, P2259, DOI 10.1109/TGRS.2007.895337; Mishra A, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012157; Mitra AK, 2009, J METEOROL SOC JPN, V87A, P265, DOI 10.2151/jmsj.87A.265; Nair S, 2009, J METEOROL SOC JPN, V87, P927, DOI 10.2151/jmsj.87.927; Prakash S, 2010, METEOROL ATMOS PHYS, V110, P45, DOI 10.1007/s00703-010-0106-8; Prakash S, 2013, REMOTE SENS LETT, V4, P677, DOI 10.1080/2150704X.2013.783248; Prakash S, 2012, NAT HAZARDS, V61, P689, DOI 10.1007/s11069-011-0055-7; Rahman SH, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD011694; Rajeevan M, 2009, CURR SCI INDIA, V96, P558; Sapiano MRP, 2009, J HYDROMETEOROL, V10, P149, DOI 10.1175/2008JHM1052.1; Shin DB, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2010JD015483; Sorooshian S, 2011, B AM METEOROL SOC, V92, P1353, DOI 10.1175/2011BAMS3158.1; Tian Y, 2007, J HYDROMETEOROL, V8, P1165, DOI 10.1175/2007JHM859.1; Tian YD, 2010, GEOPHYS RES LETT, V37, DOI 10.1029/2010GL046008; Turk FJ, 2005, IEEE T GEOSCI REMOTE, V43, P1059, DOI 10.1109/TGRS.2004.841627; Uma R, 2013, J EARTH SYST SCI, V122, P573; Gairola R. M., 2004, Indian Journal of Radio & Space Physics, V33; Wilks D. S., 2006, STAT METHODS ATMOSPH, V648 34 0 0 TAYLOR & FRANCIS LTD ABINGDON 4 PARK SQUARE, MILTON PARK, ABINGDON OX14 4RN, OXON, ENGLAND 0143-1161 1366-5901 INT J REMOTE SENS Int. J. Remote Sens. MAY 3 2014 35 9 3018 3035 10.1080/01431161.2014.894661 18 Remote Sensing; Imaging Science & Photographic Technology Remote Sensing; Imaging Science & Photographic Technology AF7OU WOS:000334905200005 J Hung, CJ; Shieh, CJ; Yeh, SP Hung, Chia-Jen; Shieh, Chich-Jen; Yeh, Shang-Pao Effects of Interaction in Teacher Professional Community on Teachers' Innovational Instruction ANTHROPOLOGIST English Article Teacher Specialty; Community Interaction; Innovative Environment; Teaching Strategies; Information Technology Modern changes of low birthrate have induced the dilemma and competition in the educational environment. To keep up with the knowledge and technology explosion, teachers, with the responsibilities of teaching the fundamental relationship between oneself and the society, telling the knowledge and skills for careers, and helping answer questions in the learning process, cannot fall behind the trend of knowledge and technology. The continuous professional development to enhance the role function would not be reduced with time that the establishment of teacher communities would assist in the professional development and actually benefit the members in professional communities, the served objects, and the public. Total 200 copies of questionnaires are distributed to the instructors in ten public and private universities in southern Taiwan. Total 124 valid copies are retrieved, with the retrieval rate 62%. The research analyses show 1. significantly positive correlations between Interaction in Teacher Professional Community and Innovative Approach in Innovational Instruction, 2. remarkably positive correlations between Interaction in Teacher Professional Community and Innovative Teaching Materials in Innovational Instruction, 3. partially positive correlations between Interaction in Teacher Professional Community and Innovative Environment in Innovational Instruction, and, 4. notably partial effects of-Teachers' Background on the correlations between Interaction in Teacher Professional Community and Innovational Instruction. [Hung, Chia-Jen] Vanung Univ, Dept Food & Beverage Management, Taoyuan 32061, Taiwan; [Shieh, Chich-Jen] Chang Jung Christian Univ, Dept Int Business, Tainan 71101, Taiwan; [Yeh, Shang-Pao] I Shou Univ, Dept Tourism, Kaohsiung 84001, Taiwan Yeh, SP (reprint author), I Shou Univ, Dept Tourism, Kaohsiung 84001, Taiwan. shangpao@ms12.hinet.net Bessant J, 2001, MANAGEMENT INNOVATIO; Chang Pao-chen, 2010, LEARNING DEV, V558, P12; Chen Chun-chin, 2011, ED MONTHLY, V464, P1; Chu Hui-ping, 2010, J ED RESOURCES RES, V55, P109; Dogan Yadigar, 2013, ANTHROPOLOGIST, V6, P117; Hatch M. J., 1997, EUR J MARKETING, V31, P356, DOI 10.1108/eb060636; Hsing-ming Lee, 2009, EDUC REV, V28, P63; Jeou-shyan Hong, 2010, J NATL TAIWAN NORMAL, V52, P49; Kuo Ming-te, 2011, J NATL TAIWAN SPORT, V12, P61; Lin Tien-yo, 2010, J EDUC RES, V191, P125; Little D., 2002, LANG TEACHING, V35, P182; Lun-szu Lo, 2010, J SECONDARY ED, V56, P112; Mirah JD, 2001, ADV SPECIAL ED, V14, P69; Moate J, 2011, TEACH TEACH, V17, P255, DOI 10.1080/13540602.2011.539804; Paez D, 1998, SOCIAL IDENTITY INT, P1; Pei-chen Sun, 2011, SUN YAT SEN MANAGEME, V19, P423; Slimplicio JSC, 2000, EDUCATION, V120, P675; Ting Wen-chi, 2011, J EDUC RES, V203, P57; Tokumasu K, 2007, JPN J EDUC PSYCHOL, V55, P34; Wang Chen-hung, 2010, J EDUC RES, V134, P106; Wang Wei-kuo, 2010, J CURRICULUM STUD, V2, P41; Wei-wen Lin, 2009, J EDUC RES, V157, P31; Wenger E., 2002, CULTIVATING COMMUNIT; Wu CC, 1999, COMPUT EDUC, V32, P239, DOI 10.1016/S0360-1315(99)00006-8; Wu Ching-shan, 2012, TAIWAN ED REV MONTHL, V614, P2; Yang Chih-hsien, 2009, J ED SCI, V9, P165; Yang Yu-fu, 2011, J DESIGN, V5, P75; Yi-yin Lo, 2011, NEWSLETTER TEACHING, V15, P76 28 0 0 KAMLA-RAJ ENTERPRISES DELHI 2273, GALI BARI PAHARWALI, CHAWRI BAZAR, DELHI, 00000, INDIA 0972-0073 ANTHROPOLOGIST Anthropologist MAY 2014 17 3 735 741 7 Anthropology; Multidisciplinary Sciences; Social Sciences, Interdisciplinary Anthropology; Science & Technology - Other Topics; Social Sciences - Other Topics AM7AC WOS:000340016100005 J Chou, YH; Pai, CH Chou, Yu-Hsien; Pai, Chih-Hung A Study the Effect of Brand Innovation on Consumer Decision in Catering Industry ANTHROPOLOGIST English Article Traditions; Beliefs; Demographic Variables; Diet; Marketing Abilities BEHAVIOR Catering industry in Taiwan is gradually concerned internationally, presenting the boom of Taiwanese diet. The increasing competitors also have local catering be popular and continuously develop. This study aims to discuss the effects of Brand Innovation on Consumer Decision. By distributing and collecting questionnaires on-site, 300 copies of questionnaires areweredistributed to the consumers of Howard Taipei, and 194 valid copies are retrieved, with the retrieval rate 65%. Each retrieved copy iswas regarded as a valid sample. The research results show partially positive effects of Brand Innovation on Information Input, Information Processing, Decision-Making Process, and Situational Factors in Consumer Decision and notable effects of demographic variables on the correlations between Brand Innovation and Consumer Decision. The research results are expected to provide some suggestions and reference for catering businesses which intend to practice Brand Innovation management. [Chou, Yu-Hsien; Pai, Chih-Hung] Taoyuan Innovat Inst Technol, Dept Hospitality Management, Taoyuan 32091, Taiwan Pai, CH (reprint author), Taoyuan Innovat Inst Technol, Dept Hospitality Management, 414,Sec 3,Jhongshan E Rd, Taoyuan 32091, Taiwan. book.tw@msa.hinet.net; bair@tiit.edu.tw Aaker D. A., 1991, MANAGING BRAND EQUIT; Blackwell RD, 2006, CONSUMER BEHAV; Chien Ming-hui, 2010, CONSUMER BEHAV; Chou Pei-ying, 2010, CHIAO TA MANAGEMENT, V25, P97; de Mooij Marieke, 2010, CONSUMER BEHAV CULTU; Grant J, 2006, BRAND INNOVATION MAN; Hinz O, 2008, INFORM SYST RES, V19, P351, DOI 10.1287/isre.1080.0190; Horng JS, 2010, TOURISM MANAGE, V31, P74, DOI 10.1016/j.tourman.2009.01.009; Kapferer JN, 2005, 2 BUSINESS CULTURES; Kotler P, 1986, PRINCIPLES MARKETING; Kotler P, 1996, MARKETING MANAGEMENT; Mowen JC, 2001, CONSUMER BEHAV FRAME; Richards G, 2002, ANN TOURISM RES, V29, P1048, DOI 10.1016/S0160-7383(02)00026-9; Schiffman LG, 2004, CONSUMER BEHAV; Schmitt B., 1999, J MARKETING MANAGEME, V15, P53, DOI 10.1362/026725799784870496; Tourism Bureau ROC, 2009, 2008 ANN SURV REP VI; Vigneron F., 2004, J BRAND MANAGEMENT, V11, P484, DOI 10.1057/palgrave.bm.2540194; Wang JH, 2014, ANTHROPOLOGIST, V17, P93; Wilkie W. L., 1994, CONSUMER BEHAV 19 0 0 KAMLA-RAJ ENTERPRISES DELHI 2273, GALI BARI PAHARWALI, CHAWRI BAZAR, DELHI, 00000, INDIA 0972-0073 ANTHROPOLOGIST Anthropologist MAY 2014 17 3 743 750 8 Anthropology; Multidisciplinary Sciences; Social Sciences, Interdisciplinary Anthropology; Science & Technology - Other Topics; Social Sciences - Other Topics AM7AC WOS:000340016100006 J Chen, BL; Chen, YW; Chen, KY; Wang, HM; Yu, KT Chen, Berlin; Chen, Yi-Wen; Chen, Kuan-Yu; Wang, Hsin-Min; Yu, Kuen-Tyng Enhancing Query Formulation for Spoken Document Retrieval JOURNAL OF INFORMATION SCIENCE AND ENGINEERING English Article spoken document retrieval; language modeling; query modeling; pseudo-relevance feedback; speech recognition PSEUDO-RELEVANCE FEEDBACK; MODELING TECHNIQUES; SPEECH RECOGNITION The popularity and ubiquity of multimedia associated with spoken documents has spurred a lot of research interest in spoken document retrieval (SDR) in the recent past. Beyond much effort devoted to developing robust indexing and modeling techniques for representing spoken documents, a recent line of thought targets at the improvement of query modeling for better reflecting the user's information need. Pseudo-relevance feedback is by far the most commonly-used paradigm for query reformulation, which assumes that a small amount of top-ranked feedback documents obtained from the initial round of retrieval are relevant and can be utilized for this purpose. Nevertheless, simply taking all of the top-ranked feedback documents obtained from the initial retrieval for query modeling does not always perform well, especially when the top-ranked documents contain much redundant or non-relevant information. In the view of this, we explore in this paper an interesting problem of how to effectively glean useful cues from the top-ranked documents so as to achieve more accurate query modeling. Towards this end, various sources of information cues are considered and integrated into the process of feedback document selection so as to achieve better retrieval effectiveness. Furthermore, we also investigate representing the query and documents with different granularities of index features to work in conjunction with the query and document models. A series of experiments conducted on the TDT (Topic Detection and Tracking) task seem to demonstrate the effectiveness of our query modeling framework for SDR. [Chen, Berlin; Chen, Yi-Wen; Yu, Kuen-Tyng] Natl Taiwan Normal Univ, Dept Comp Sci & Engn, Taipei 106, Taiwan; [Chen, Kuan-Yu; Wang, Hsin-Min] Acad Sinica, Inst Informat Sci, Nankang 115, Taiwan Chen, BL (reprint author), Natl Taiwan Normal Univ, Dept Comp Sci & Engn, Taipei 106, Taiwan. "Aim for the Top University Project" of National Taiwan Normal University (NTNU); Ministry of Education, Taiwan; Ministry of Science and Technology, Taiwan [NSC 101-2221-E-003-024-MY3, NSC 102-2221-E-003-014-, NSC 101-2511-S-003-057-MY3, NSC 101-2511-S-003-047-MY3, NSC 103-2911-1-003-301] This research is supported in part by the "Aim for the Top University Project" of National Taiwan Normal University (NTNU), sponsored by the Ministry of Education, Taiwan, and by the Ministry of Science and Technology, Taiwan, under Grants NSC 101-2221-E-003-024-MY3, NSC 102-2221-E-003-014-, NSC 101-2511-S-003-057-MY3, NSC 101-2511-S-003-047-MY3 and NSC 103-2911-1-003-301. Baeza-Yates R., 2011, MODERN INFORM RETRIE; Blei DM, 2003, J MACH LEARN RES, V3, P993, DOI 10.1162/jmlr.2003.3.4-5.993; Carbonell J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.291025; Chelba C, 2008, IEEE SIGNAL PROC MAG, V25, P39, DOI 10.1109/MSP.200S.917992; Chen B, 2013, INFORM PROCESS MANAG, V49, P807, DOI 10.1016/j.ipm.2013.01.005; Chen B., 2012, IEEE T AUDIO SPEECH, V20, P199; Chen B., 2009, ACM T ASIAN LANG INF, V8, P1; Chen B, 2013, INFORM PROCESS MANAG, V49, P1, DOI 10.1016/j.ipm.2011.12.002; Chen B, 2012, IEEE T AUDIO SPEECH, V20, P2602, DOI 10.1109/TASL.2012.2208628; Chen KY, 2012, IEICE T INF SYST, VE95D, P1195, DOI 10.1587/transinf.E95.D.1195; Chen Y.-W., 2012, P C TECHN APPL ART I, P140; Chen YW, 2013, INT CONF ACOUST SPEE, P8535; Chia T. K., 2010, ACM T INFORM SYST, V28, p[2, 1, 30]; DEMPSTER AP, 1977, J ROY STAT SOC B MET, V39, P1; Furui S, 2012, IEEE SIGNAL PROC MAG, V29, P16, DOI 10.1109/MSP.2012.2209906; Garofolo J., 2000, P 8 TEXT RETR C, P107; Hoffmann T., 2001, MACH LEARN, V42, P177; Jelinek F., 1999, STAT METHODS SPEECH; KULLBACK S, 1951, ANN MATH STAT, V22, P79, DOI 10.1214/aoms/1177729694; Lavrenko V., 2001, P 24 ANN INT ACM SIG, P120, DOI 10.1145/383952.383972; LDC, 2000, PROJ TOP DET TRACK; Lee KS, 2013, INFORM PROCESS MANAG, V49, P792, DOI 10.1016/j.ipm.2013.01.001; Lee LS, 2005, IEEE SIGNAL PROC MAG, V22, P42; Lu Y, 2011, INFORM RETRIEVAL, V14, P178, DOI 10.1007/s10791-010-9141-9; Lv Y., 2011, P INT C MACH LEARN I, P165; Meng HM, 2004, COMPUT SPEECH LANG, V18, P163, DOI 10.1016/j.csl.2003.09.003; Ostendorf M., 2008, IEEE SIGNAL PROCESS, V25, P150; Parlak S, 2012, IEEE T AUDIO SPEECH, V20, P731, DOI 10.1109/TASL.2011.2164531; Rocchio J. J., 1971, SMART RETRIEVAL SYST, P313; Shen X., 2005, P 28 ANN INT ACM SIG, P55; Turunen V. T., 2007, P ACM SIGIR C RES DE, P631, DOI 10.1145/1277741.1277849; Wei X., 2006, P 29 ANN INT ACM SIG, P178, DOI DOI 10.1145/1148170.1148204; Xu Z., 2007, P EUR C IR RES, P245; Zhai C., 2008, FDN TRENDS INF RETR, V2, P137, DOI DOI 10.1561/1500000008; Zhai C., 2001, P 10 INT C INF KNOWL, P403, DOI 10.1145/502585.502654 35 0 0 INST INFORMATION SCIENCE TAIPEI ACADEMIA SINICA, TAIPEI 115, TAIWAN 1016-2364 J INF SCI ENG J. Inf. Sci. Eng. MAY 2014 30 3 553 569 17 Computer Science, Information Systems Computer Science AM7UX WOS:000340075300002 J Liu, Z; Han, HJ; Yan, H Liu, Zheng; Han, Huijian; Yan, Hua Tagging Social Images by Parallel Tag Graph Partitioning JOURNAL OF INFORMATION SCIENCE AND ENGINEERING English Article social image; Flickr; tag; parallel graph partitioning; image retrieval ANNOTATION; ALIGNMENT In recent years, we have witnessed a great success of social community websites. Large-scale social images with rich metadata are increasingly available on the Web. In this paper, we focus on efficiently tagging social images by partitioning the large-scale tag graph in parallel. Vertices of the tag graph are constructed by the candidate tags which are extended from initial tags. Initial tags are extracted from the rich metadata of social images, including user supplied tags, notes data and group information. Edge weight of the tag graph is calculated by combining two parameters, which are related to image visual features and tag co-occurrence. Both global and local features are considered in parameter 1. For each candidate tag, a neighbor images voting algorithm is performed to calculated parameter 2. As the tag graph may be large-scale, we utilize a parallel graph partitioning algorithm to accelerate the graph partitioning process. After the tag graph is partitioned, we rank all the sub-graphs according to the edge weight within one sub-graph. Afterwards, final tags are selected from the top ranked sub-graphs. Experimental results on Flickr image collection well demonstrate the effectiveness and efficiency of the proposed algorithm. Furthermore, we apply our social image tagging algorithm in tag-based image retrieval to illustrate that our algorithm can really enhance the performance of social image tagging related applications. [Liu, Zheng; Yan, Hua] Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Peoples R China; [Liu, Zheng; Han, Huijian; Yan, Hua] Shandong Prov Key Lab Digital Media Technol, Jinan 250014, Peoples R China Liu, Z (reprint author), Shandong Univ Finance & Econ, Sch Comp Sci & Technol, Jinan 250014, Peoples R China. National Natural Science Foundation of China [61303090, 61272431, 61020106001, 61101162, 60970048, 60903109]; NSFC Joint Fund with Guangdong [01201258]; Scientific Research Foundation for the Excellent Middle-Aged and Youth Scientists of Shandong Province [BS2012DX028, BS2011DX024, BS2013DX013]; Shandong Provincial Natural Science Foundation [ZR2012FM002, ZR2011FL020, ZR2011FL029, ZR2013FL010]; Humanities and Social Sciences Project of Education Ministry [13YJC860023]; Doctoral Foundation of Shandong University of Finance and Economics [B13034]; Shandong Province Higher Educational Science and Technology Program [J10LG69]; Science and Technology Project of Ji'nan [201303012]; Program for Scientific Research Innovation Team in Colleges and Universities of Shandong Province This work is supported by the National Natural Science Foundation of China (Grant No. 61303090, No. 61272431, No. 61020106001, No. 61101162, No. 60970048, and No. 60903109), NSFC Joint Fund with Guangdong (01201258), the Scientific Research Foundation for the Excellent Middle-Aged and Youth Scientists of Shandong Province (Grant No. BS2012DX028, BS2011DX024, and No. BS2013DX013), Shandong Provincial Natural Science Foundation (Grant No. ZR2012FM002, ZR2011FL020, ZR2011FL029, and ZR2013FL010), the Humanities and Social Sciences Project of Education Ministry (Grant No. 13YJC860023), the Doctoral Foundation of Shandong University of Finance and Economics (Grant No. B13034), Project of Shandong Province Higher Educational Science and Technology Program (Grant No. J10LG69), the Science and Technology Project of Ji'nan (Grant No. 201303012) and Program for Scientific Research Innovation Team in Colleges and Universities of Shandong Province. We also appreciate the anonymous reviewers for thoroughly reading the paper and providing thoughtful comments. Ames M, 2007, CONFERENCE ON HUMAN FACTORS IN COMPUTING SYSTEMS, VOLS 1 AND 2, P971; Banos R, 2004, J HEURISTICS, V10, P315, DOI 10.1023/B:HEUR.0000026898.11874.e7; Chang T, 1993, IEEE T IMAGE PROCESS, V2, P429, DOI 10.1109/83.242353; Chen M., 2008, P ACM MM, P737; Chua T., 2009, P ACM CIVR, P368; Cilibrasi RL, 2007, IEEE T KNOWL DATA EN, V19, P370, DOI 10.1109/TKDE.2007.48; Duygulu P, 2002, LECT NOTES COMPUT SC, V2353, P97; Guan NY, 2011, IEEE T IMAGE PROCESS, V20, P2030, DOI 10.1109/TIP.2011.2105496; Guan NY, 2011, IEEE T NEURAL NETWOR, V22, P1218, DOI 10.1109/TNN.2011.2157359; Guan NY, 2012, IEEE T SIGNAL PROCES, V60, P2882, DOI 10.1109/TSP.2012.2190406; Guan NY, 2012, IEEE T NEUR NET LEAR, V23, P1087, DOI 10.1109/TNNLS.2012.2197827; Huiskes M., 2008, P 1 ACM INT C MULT I, P39, DOI DOI 10.1145/1460096.1460104; Jin Y., 2008, P IEEE INT C COMP VI, P1; Jin YH, 2010, J SIGNAL PROCESS SYS, V58, P387, DOI 10.1007/s11265-009-0391-y; Karypis G, 1998, J PARALLEL DISTR COM, V48, P96, DOI 10.1006/jpdc.1997.1404; Karypis G., 1997, P 1997 ACM IEEE C SU, P1, DOI 10.1145/509593.509621; Karypis G., 1998, P 1998 ACM IEEE C SU, P1; Kennedy L. S., 2006, P ACM WORKSH MULT IN, P249, DOI 10.1145/1178677.1178712; Li XR, 2009, IEEE T MULTIMEDIA, V11, P1310, DOI 10.1109/TMM.2009.2030598; LINDE Y, 1980, IEEE T COMMUN, V28, P84, DOI 10.1109/TCOM.1980.1094577; Liu D., 2010, P ACM MULT, P491, DOI 10.1145/1873951.1874031; Liu D., 2009, P 18 INT C WORLD WID, P351, DOI 10.1145/1526709.1526757; Liu J, 2009, PATTERN RECOGN, V42, P218, DOI 10.1016/j.patcog.2008.04.012; Liu Z, 2012, INFORMATION-TOKYO, V15, P249; [刘峥 Liu Zheng], 2011, [计算机研究与发展, Journal of Computer Research and Development], V48, P1246; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Negoescu R. A., 2008, P 2008 INT C CONT BA, P417, DOI 10.1145/1386352.1386406; Rui X., 2007, P ACM INT C MULT, P585, DOI 10.1145/1291233.1291378; Schloegel K, 2000, LECT NOTES COMPUT SC, V1900, P296; Schmitz P., 2006, P COLL WEB TAGG WORK, P210; Si S, 2012, MULTIMED TOOLS APPL, V56, P91, DOI 10.1007/s11042-010-0567-2; Sigurbjornsson B., 2008, P 17 INT C WORLD WID, P327, DOI 10.1145/1367497.1367542; Tang JH, 2012, IEEE T IMAGE PROCESS, V21, P2354, DOI 10.1109/TIP.2011.2180916; Tian X., 2012, ACM T MULTIMEDIA COM; Tian XM, 2010, IEEE T IMAGE PROCESS, V19, P805, DOI 10.1109/TIP.2009.2035866; Wang C., 2007, P COMP VIS PATT REC, P1, DOI DOI 10.1109/FUZZY.2007.4295670; Wu L., 2008, P ACM MULT, P31, DOI DOI 10.1145/1459359.1459364; Wu L., 2009, P 18 INT C WORLD WID, P361, DOI 10.1145/1526709.1526758; Wu P., 2011, P 4 ACM INT C WEB SE, P197, DOI 10.1145/1935826.1935865; Xia TA, 2010, IEEE T SYST MAN CY B, V40, P1438, DOI 10.1109/TSMCB.2009.2039566; Xie B, 2011, IEEE T SYST MAN CY B, V41, P1088, DOI 10.1109/TSMCB.2011.2106208; Xu H., 2009, P ACM CIVR, P352; Zhang L., 2011, P 1 ACM INT C MULT R; Zhou N, 2011, LECT NOTES COMPUT SC, V6524, P46; Zhu G., 2010, P INT C MULT, P461, DOI 10.1145/1873951.1874028; Zhuang J., 2011, P 4 ACM INT C WEB SE, P625, DOI 10.1145/1935826.1935913 46 0 0 INST INFORMATION SCIENCE TAIPEI ACADEMIA SINICA, TAIPEI 115, TAIWAN 1016-2364 J INF SCI ENG J. Inf. Sci. Eng. MAY 2014 30 3 911 932 22 Computer Science, Information Systems Computer Science AM7UX WOS:000340075300022 J Hou, AY; Kakar, RK; Neeck, S; Azarbarzin, AA; Kummerow, CD; Kojima, M; Oki, R; Nakamura, K; Iguchi, T Hou, Arthur Y.; Kakar, Ramesh K.; Neeck, Steven; Azarbarzin, Ardeshir A.; Kummerow, Christian D.; Kojima, Masahiro; Oki, Riko; Nakamura, Kenji; Iguchi, Toshio THE GLOBAL PRECIPITATION MEASUREMENT MISSION BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY English Article DATA ASSIMILATION SYSTEM; PASSIVE MICROWAVE OBSERVATIONS; RAIN-PROFILING ALGORITHM; DUAL-WAVELENGTH RADAR; TROPICAL RAINFALL; RETRIEVAL ALGORITHM; BAYESIAN-ESTIMATION; TRMM SATELLITE; RADIOMETER; IMPACT Precipitation affects many aspects of our everyday life. It is the primary source of freshwater and has significant socioeconomic impacts resulting from natural hazards such as hurricanes, floods, droughts, and landslides. Fundamentally, precipitation is a critical component of the global water and energy cycle that governs the weather, climate, and ecological systems. Accurate and timely knowledge of when, where, and how much it rains or snows is essential for understanding how the Earth system functions and for improving the prediction of weather, climate, freshwater resources, and natural hazard events. The Global Precipitation Measurement (GPM) mission is an international satellite mission specifically designed to set a new standard for the measurement of precipitation from space and to provide a new generation of global rainfall and snowfall observations in all parts of the world every 3 h. The National Aeronautics and Space Administration (NASA) and the Japan Aerospace and Exploration Agency (JAXA) successfully launched the Core Observatory satellite on 28 February 2014 carrying advanced radar and radiometer systems to serve as a precipitation physics observatory. This will serve as a transfer standard for improving the accuracy and consistency of precipitation measurements from a constellation of research and operational satellites provided by a consortium of international partners. GPM will provide key measurements for understanding the global water and energy cycle in a changing climate as well as timely information useful for a range of regional and global societal applications such as numerical weather prediction, natural hazard monitoring, freshwater resource management, and crop forecasting. [Hou, Arthur Y.; Azarbarzin, Ardeshir A.] NASA, Goddard Space Flight Ctr, Greenbelt, MD 20771 USA; [Kakar, Ramesh K.; Neeck, Steven] Natl Aeronaut & Space Adm Headquarters, Washington, DC USA; [Kummerow, Christian D.] Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA; [Kojima, Masahiro] Japan Aerosp Explorat Agcy, Tsukuba Space Ctr, Tsukuba, Ibaraki 3058505, Japan; [Oki, Riko] Japan Aerosp Explorat Agcy, Earth Observat Res Ctr, Tsukuba, Ibaraki, Japan; [Nakamura, Kenji] Dokkyo Univ, Dept Econ Sustainabil, Saitama, Japan; [Iguchi, Toshio] Natl Inst Informat & Commun Technol, Tokyo, Japan Kummerow, CD (reprint author), Colorado State Univ, Dept Atmospher Sci, Ft Collins, CO 80523 USA. kummerow@atmos.colostate.edu NASA Earth Science Division Flight Programs The authors thank Christopher Kidd, Eric Wood, and Gail Skofronick-Jackson for valuable comments on the manuscript. It is also a pleasure to acknowledge contributions to this article by members of the NASA PMM Science Team, the JAXA PMM Science Team, the NASA GPM Advisory Panel on Ground Validation, the GMI Calibration Task Force, the GMI High-Frequency Channels Advisory Group, the U.S.-Japan CEOS Precipitation Constellation Study Team, and the GPM Flight Project at NASA Goddard Space Flight Center in particular, Robert Adler, Emmanouil Anagnostou, Ana Barros, Peter Bauer, Rafael Bras, Scott Braun, Candace Carlisle, V. Chandrasekar, John Durning, Ralph Ferraro, Kinji Furukawa, Efi Foufoula-Georgiou, Ziad Haddad, Steve Horowitz, Robert Houze, David Hudak, George Huffman, Paul Joe, Linwood Jones, Dalia Kirschbaum, Jarkko Koskinen, Sergey Krimchansky, William Lau, Dennis Lettenmaier, Vincenzo Levizzani, Xin Lin, Guosheng Liu, Robert Meneghini, Joe Munchak, William Olson, Christa Peters-Lidard, Walter Petersen, Fritz Policelli, Didier Renaut, Remy Roca, Christopher Ruf, Steven Rutledge, Mathew Schwaller, Marshall Shepherd, James Shiue, Eric Smith, Soroosh Sorooshian, Erich Stocker, Wei-Kuo Tao, Joe Turk, Fuzhong Weng, Thomas Wilheit, and Edward Zipser. This work is supported by NASA Earth Science Division Flight Programs. Adler RH, 2007, J HYDROMETEOROL, V8, P38; Andreae MO, 2004, SCIENCE, V303, P1337, DOI 10.1126/science.1092779; Aonashi K, 2004, J METEOROL SOC JPN, V82, P671, DOI 10.2151/jmsj.2004.671; Asrar G, 2001, B AM METEOROL SOC, V82, P1309, DOI 10.1175/1520-0477(2001)082<1309:NRSFES>2.3.CO;2; Avissar R, 2005, J HYDROMETEOROL, V6, P134, DOI 10.1175/JHM406.1; Back LE, 2006, GEOPHYS RES LETT, V33, DOI 10.1029/2006GL026672; Bauer P, 2006, Q J ROY METEOR SOC, V132, P2307, DOI 10.1256/qj.06.07; Bell TL, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2007JD008623; Bhatt C. B., 2005, MON WEA REV, V133, P149; Bindlish R, 2000, GLOBAL PLANET CHANGE, V25, P111, DOI 10.1016/S0921-8181(00)00024-2; Bunin S. L., 2004, 20 INT C INT INF PRO, V16; Cherny I, 2002, INT GEOSCI REMOTE SE, P2660; Cotton WR, 2007, HUMAN IMPACT ON WEATHER AND CLIMATE, 2ND EDITION, P1, DOI 10.2277/ 0521600561; Dabberdt WF, 2005, B AM METEOROL SOC, V86, P961, DOI 10.1175/BAMS-86-7-961; Del Genio AD, 2005, J CLIMATE, V18, P2376, DOI 10.1175/JCLI3413.1; Desbois M, 2003, P SOC PHOTO-OPT INS, V4899, P172, DOI 10.1117/12.466703; Edward P., 2000, ESA B, V102, P6; EVANS KF, 1995, J APPL METEOROL, V34, P260, DOI 10.1175/1520-0450-34.1.260; Fekete BM, 2004, J CLIMATE, V17, P294, DOI 10.1175/1520-0442(2004)017<0294:UIPATI>2.0.CO;2; Futrel J. H., 2005, PNWD3597 NSFDOE, DOI [10.2172/1046481., DOI 10.2172/1046481]; GPM, PREC MEAS MISS; GPM ATBD, GLOB PREC MEAS ALG B; Grecu M, 2006, J APPL METEOROL CLIM, V45, P416, DOI 10.1175/JAM2360.1; Grecu M, 2004, J APPL METEOROL, V43, P562, DOI 10.1175/1520-0450(2004)043<0562:ROPPFM>2.0.CO;2; Gu GJ, 2007, J CLIMATE, V20, P4033, DOI 10.1175/JCLI4227.1; Haddad ZS, 1997, J METEOROL SOC JPN, V75, P799; Hossain F, 2006, WATER RESOUR RES, V42, DOI 10.1029/2006WR005202; Hou AY, 2004, MON WEATHER REV, V132, P2094, DOI 10.1175/1520-0493(2004)132<2094:VCAOTA>2.0.CO;2; Hou AY, 2007, J ATMOS SCI, V64, P3865, DOI 10.1175/2006JAS2028.1; Hudak D., 2006, P 4 EUR C RAD HYDR M, P609; Huffman GJ, 2001, J HYDROMETEOROL, V2, P36, DOI 10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2; Iguchi T, 2000, J APPL METEOROL, V39, P2038, DOI 10.1175/1520-0450(2001)040<2038:RPAFTT>2.0.CO;2; Joyce RJ, 2011, J HYDROMETEOROL, V12, P1547, DOI 10.1175/JHM-D-11-022.1; Kelley OA, 2004, GEOPHYS RES LETT, V31, DOI 10.1029/2004GL021616; Kidd C, 2003, J HYDROMETEOROL, V4, P1088, DOI 10.1175/1525-7541(2003)004<1088:SREUCP>2.0.CO;2; Kidd C, 2011, HYDROL EARTH SYST SC, V7, P8157, DOI [10.5194/hessd-8-8157-2010., DOI 10.5194/HESSD-8-8157-2010]; Kirschbaum D, 2012, J HYDROMETEOROL, V13, P1536, DOI 10.1175/JHM-D-12-02.1; Kubota T, 2007, IEEE T GEOSCI REMOTE, V45, P2259, DOI 10.1109/TGRS.2007.895337; Kucera PA, 2013, B AM METEOROL SOC, V94, P365, DOI 10.1175/BAMS-D-11-00171.1; Kulie MS, 2009, J APPL METEOROL CLIM, V48, P2564, DOI 10.1175/2009JAMC2193.1; Kuligowski RJ, 2002, J HYDROMETEOROL, V3, P112, DOI 10.1175/1525-7541(2002)003<0112:ASCRTG>2.0.CO;2; Kumar SV, 2006, ENVIRON MODELL SOFTW, V21, P1402, DOI 10.1016/j.envsoft.2005.07.004; KUMMEROW C, 1994, J APPL METEOROL, V33, P3, DOI 10.1175/1520-0450(1994)033<0003:APMTFE>2.0.CO;2; Kummerow C, 1996, IEEE T GEOSCI REMOTE, V34, P1213, DOI 10.1109/36.536538; Kummerow C, 1998, J ATMOS OCEAN TECH, V15, P809, DOI 10.1175/1520-0426(1998)015<0809:TTRMMT>2.0.CO;2; Kummerow CD, 2011, J ATMOS OCEAN TECH, V28, P113, DOI 10.1175/2010JTECHA1468.1; Kunkee DB, 2008, IEEE T GEOSCI REMOTE, V46, P863, DOI 10.1109/TGRS.2008.917980; Lau WKM, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD016510; Liao L, 2005, J ATMOS OCEAN TECH, V22, P1494, DOI 10.1175/JTECH1808.1; Mardiana R, 2004, IEEE T GEOSCI REMOTE, V42, P2214, DOI 10.1109/TGRS.2004.834647; Marecal V, 2003, Q J ROY METEOR SOC, V129, P3137, DOI 10.1256/qj.02.120; Marzano FS, 1999, IEEE T GEOSCI REMOTE, V37, P596, DOI 10.1109/36.739124; Masunaga H, 2006, J ATMOS SCI, V63, P2777, DOI 10.1175/JAS3783.1; Masunaga H, 2005, J ATMOS OCEAN TECH, V22, P909, DOI 10.1175/JTECH1751.1; Meneghini R, 1997, IEEE T GEOSCI REMOTE, V35, P487, DOI 10.1109/36.581956; Mitchell K. E., 2004, J GEOPHYS RES, V109, DOI DOI 10.1029/2003JD003823; Montaigne F., 2002, NATL GEOGRAPHIC MAGA, V202, P2; Morita J, 2006, DYNAM ATMOS OCEANS, V42, P107, DOI 10.1016/j.dynatmoce.2006.02.002; Mugnai A, 2007, ADV GLOB CHANGE RES, V28, P655, DOI 10.1007/978-1-4020-5835-6_49; Negri AJ, 2004, J CLIMATE, V17, P1306, DOI 10.1175/1520-0442(2004)017<1306:TIOADO>2.0.CO;2; Nesbitt SW, 2000, J CLIMATE, V13, P4087, DOI 10.1175/1520-0442(2000)013<4087:ACOPFI>2.0.CO;2; Nijssen B, 2004, J GEOPHYS RES-ATMOS, V109, DOI 10.1029/2003JD003497; NRC, 2010, WEATH MATT SCI SERV; NRC, 2011, GLOB CHANG EXTR HYDR; NRC, 2005, ASS BEN EXT TROP RAI; NRC, 2007, EARTH SCI APPL SPAC; NRC, 2007, NOAAS ROL SPAC BAS G; NSMC, STORM 3 FY 3 PROGR; NSTC, 2004, SCI TECHN SUPP FRESH; Rodell M, 2004, B AM METEOROL SOC, V85, P381, DOI 10.1175/BAMS-85-3-381; Rose CR, 2006, J ATMOS OCEAN TECH, V23, P1372, DOI 10.1175/JTECH1921.1; Rosenfeld D, 2001, P NATL ACAD SCI USA, V98, P5975, DOI 10.1073/pnas.101122798; Shepherd JM, 2002, J APPL METEOROL, V41, P689, DOI 10.1175/1520-0450(2002)041<0689:RMBMUA>2.0.CO;2; Shige S, 2007, J APPL METEOROL CLIM, V46, P1098, DOI 10.1175/JAM2510.1; Shimoda H, 2005, INT GEOSCI REMOTE SE, P4201; Simpson J, 1998, METEOROL ATMOS PHYS, V67, P15, DOI 10.1007/BF01277500; Sorooshian S, 2000, B AM METEOROL SOC, V81, P2035, DOI 10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2; Tao WK, 2010, J CLIMATE, V23, P1874, DOI 10.1175/2009JCLI3278.1; Trenberth KE, 2007, J HYDROMETEOROL, V8, P758, DOI 10.1175/JHM600.1; Turk FJ, 2005, IEEE T GEOSCI REMOTE, V43, P1059, DOI 10.1109/TGRS.2004.841627; Ushio T, 2009, J METEOROL SOC JPN, V87A, P137, DOI 10.2151/jmsj.87A.137; Vasiloff SV, 2007, B AM METEOROL SOC, V88, P1899, DOI 10.1175/BAMS-88-12-1899; Wall CL, 2012, J HYDROMETEOROL, V13, P310, DOI 10.1175/JHM-D-11-031.1; Wang JF, 2000, J HYDROMETEOROL, V1, P267, DOI 10.1175/1525-7541(2000)001<0267:TIOODO>2.0.CO;2; Wilheit TT, 2013, IEEE T GEOSCI REMOTE, V51, P1453, DOI 10.1109/TGRS.2012.2207122; Wu H, 2012, J HYDROMETEOROL, V13, P1268, DOI 10.1175/JHM-D-11-087.1; Zhang SQ, 2013, MON WEATHER REV, V141, P754, DOI 10.1175/MWR-D-12-00055.1; Zupanski D., 2010, J HYDROMETEOROL, V12, P118; Zupanski M, 2002, MON WEATHER REV, V130, P1967, DOI 10.1175/1520-0493(2002)130<1967:FDVDAF>2.0.CO;2 89 5 5 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693 USA 0003-0007 1520-0477 B AM METEOROL SOC Bull. Amer. Meteorol. Soc. MAY 2014 95 5 701 + 10.1175/BAMS-D-13-00164.1 24 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AL4GS WOS:000339091500008 J Yang, C; Zhang, M; Zheng, W; Liu, R; Zhuang, G Yang, C.; Zhang, M.; Zheng, W.; Liu, R.; Zhuang, G. The upgrade of the J-TEXT experimental data access and management system FUSION ENGINEERING AND DESIGN English Article MDSplus; Tokamak; Web; Signal management; Browser/sever The experimental data of J-TEXT tokamak are stored in the MDSplus database. The old J-TEXT data access system is based on the tools provided by MDSplus. Since the number of signals is huge, the data retrieval for an experiment is difficult. To solve this problem, the J-TEXT experimental data access and management system (DAMS) based on MDSplus has been developed. The DAMS left the old MDSplus system unchanged providing new tools, which can help users to handle all signals as well as to retrieve signals they need thanks to the user information requirements. The DAMS also offers users a way to create their jScope configuration files which can be downloaded to the local computer. In addition, the DAMS provides a JWeb-Scope tool to visualize the signal in a browser. JWeb-Scope adopts segment strategy to read massive data efficiently. Users can plot one or more signals on their own choice and zoom-in, zoom-out smoothly. The whole system is based on B/S model, so that the users only need of the browsers to access the DAMS. The DAMS has been tested and it has a better user experience. It will be integrated into the J-TEXT remote participation system later. (C) 2014 Elsevier B.V. All rights reserved. [Yang, C.; Zhang, M.; Zheng, W.; Liu, R.; Zhuang, G.] Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China; [Yang, C.; Zhang, M.; Zheng, W.; Liu, R.; Zhuang, G.] Huazhong Univ Sci & Technol, Sch Elect & Elect Engn, Wuhan 430074, Peoples R China Zheng, W (reprint author), Huazhong Univ Sci & Technol, State Key Lab Adv Electromagnet Engn & Technol, Wuhan 430074, Peoples R China. yangchao_353@hust.edu.cn; zhengwei@hust.edu.cn National ITER Project of China [2010GB108004] This work is supported by the National ITER Project of China (No. 2010GB108004). Davis W, 2002, 19TH IEEE/NPSS SYMPOSIUM ON FUSION ENGINEERING, PROCEEDINGS, P176; Ningning D., 2009, 19 IEEE NPSS REAL TI, P141; Stillerman JA, 1997, REV SCI INSTRUM, V68, P939, DOI 10.1063/1.1147719; Zhuang G, 2011, NUCL FUSION, V51, DOI 10.1088/0029-5515/51/9/094020 4 0 0 ELSEVIER SCIENCE SA LAUSANNE PO BOX 564, 1001 LAUSANNE, SWITZERLAND 0920-3796 1873-7196 FUSION ENG DES Fusion Eng. Des. MAY 2014 89 5 726 730 10.1016/j.fusengdes.2014.02.027 5 Nuclear Science & Technology Nuclear Science & Technology AL4VT WOS:000339133600046 J Valenzuela, REG; Schwartz, WR; Pedrini, H Gonzalez Valenzuela, Ricardo Eugenio; Schwartz, William Robson; Pedrini, Helio Linear dimensionality reduction applied to scale invariant feature transformation and speeded up robust feature descriptors JOURNAL OF ELECTRONIC IMAGING English Article linear dimensionality reduction; feature vector; image descriptors PARTIAL LEAST-SQUARES; REPRESENTATION; SURF; PCA Robust local descriptors usually consist of high-dimensional feature vectors to describe distinctive characteristics of images. The high dimensionality of a feature vector incurs considerable costs in terms of computational time and storage. It also results in the curse of dimensionality that affects the performance of several tasks that use feature vectors, such as matching, retrieval, and classification of images. To address these problems, it is possible to employ some dimensionality reduction techniques, leading frequently to information lost and, consequently, accuracy reduction. This work aims at applying linear dimensionality reduction to the scale invariant feature transformation and speeded up robust feature descriptors. The objective is to demonstrate that even risking the decrease of the accuracy of the feature vectors, it results in a satisfactory trade-off between computational time and storage requirements. We perform linear dimensionality reduction through random projections, principal component analysis, linear discriminant analysis, and partial least squares in order to create lower dimensional feature vectors. These new reduced descriptors lead us to less computational time and memory storage requirements, even improving accuracy in some cases. We evaluate reduced feature vectors in a matching application, as well as their distinctiveness in image retrieval. Finally, we assess the computational time and storage requirements by comparing the original and the reduced feature vectors. (C) 2014 SPIE and IS&T [Gonzalez Valenzuela, Ricardo Eugenio; Pedrini, Helio] Univ Estadual Campinas, Inst Comp, BR-13083852 Campinas, SP, Brazil; [Schwartz, William Robson] Univ Fed Minas Gerais, Dept Comp Sci, BR-31270010 Belo Horizonte, MG, Brazil Pedrini, H (reprint author), Univ Estadual Campinas, Inst Comp, Av Albert Einstein 1251, BR-13083852 Campinas, SP, Brazil. helio@ic.unicamp.br FAPESP; CNPq; FAPEMIG; CAPES The authors are thankful to FAPESP, CNPq, FAPEMIG, and CAPES for their financial support. Agarwal S, 2002, LECT NOTES COMPUT SC, V2353, P113; Balakrishnama S., 1998, P INT S INF PROC, P1; Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014; Bay H, 2006, LECT NOTES COMPUT SC, V3951, P404; Bingham E, 2001, P 7 ACM SIGKDD INT C, P245, DOI 10.1145/502512.502546; Brown M., 2002, P BRIT MACH VIS C, P656; Chandrasekhar V., 2010, P 2 INT WORKSH MOB M, P1; Dasgupta S., 1999, TR99006, P1; Gonzalez RC, 2001, DIGITAL IMAGE PROCES; Hoffmann H, 2007, PATTERN RECOGN, V40, P863, DOI 10.1016/j.patcog.2006.07.009; INRIA, 2013, GRAFF DAT; Johnson W. B., 1984, CONT MATH, V26, P189; Jolliffe IT, 2002, PRINCIPAL COMPONENT; Juan L., 2009, INT J IMAGE PROCESSI, V3, P143; Ke Y, 2004, PROC CVPR IEEE, P506; Leibe B., 2013, TU DARMSTADT DATABAS; Li J, 2008, NEUROCOMPUTING, V71, P1771, DOI 10.1016/j.neucom.2007.11.032; Lowe D., 1999, P 7 IEEE INT C COMP, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Maitra S., 2008, PRINCIPAL COMPONENT, p79~90; Mallat S., 1999, WAVELET TOUR SIGNAL; Mikolajczyk K, 2005, IEEE T PATTERN ANAL, V27, P1615, DOI 10.1109/TPAMI.2005.188; MIR Flickr, 2013, MIRFLICKR RETR EV; Noonan R., 1977, SCAND J EDUC RES, V21, P33, DOI 10.1080/0031383770210103; Park CH, 2008, PATTERN RECOGN, V41, P1083, DOI 10.1016/j.patcog.2007.07.022; Pearson K, 1901, PHILOS MAG, V2, P559; Randen T, 1999, IEEE T PATTERN ANAL, V21, P291, DOI 10.1109/34.761261; Rosipal R, 2006, LECT NOTES COMPUT SC, V3940, P34; Sulic V., 2010, P 15 COMP VIS WINT W, P29; Tuytelaars T., 2008, FDN TRENDS COMPUTER, V3, P177, DOI [DOI 10.1561/0600000017, DOI 10.1561/0600000017>]; Valenzuela R., 2012, P 11 IEEE C CYB INT, P58; Valenzuela R., 2013, P 4 ECCOMAS THEM C C, P31; Watcharapinchai N., 2009, P INT S INT SIGN PRO, P1; WOLD S, 1984, SIAM J SCI STAT COMP, V5, P735, DOI 10.1137/0905052; Ye J., 2009, DISCRIMINANT ANAL DI; Zhao G., 2010, P INT C MULT FIR IT, P1175, DOI 10.1145/1873951.1874180 36 0 0 IS&T & SPIE BELLINGHAM 1000 20TH ST, BELLINGHAM, WA 98225 USA 1017-9909 1560-229X J ELECTRON IMAGING J. Electron. Imaging MAY-JUN 2014 23 3 033017 10.1117/1.JEI.23.3.033017 13 Engineering, Electrical & Electronic; Optics; Imaging Science & Photographic Technology Engineering; Optics; Imaging Science & Photographic Technology AL2VU WOS:000338984500018 J Andrews-Hanna, JR; Saxe, R; Yarkoni, T Andrews-Hanna, Jessica R.; Saxe, Rebecca; Yarkoni, Tal Contributions of episodic retrieval and mentalizing to autobiographical thought: Evidence from functional neuroimaging, resting-state connectivity, and fMRI meta-analyses NEUROIMAGE English Article Default network; Default mode; Autobiographical; Episodic memory; Mentalizing; Theory of mind; Social; Self TEMPORO-PARIETAL JUNCTION; BRAINS DEFAULT NETWORK; POSTERIOR CINGULATE CORTEX; MEDIAL PREFRONTAL CORTEX; SELF-DEFINING MEMORIES; HUMAN CEREBRAL-CORTEX; EVENT-RELATED FMRI; SOCIAL COGNITION; MODE NETWORK; STRUCTURAL CONNECTIVITY A growing number of studies suggest the brain's "default network" becomes engaged when individuals recall their personal past or simulate their future. Recent reports of heterogeneity within the network raise the possibility that these autobiographical processes comprised of multiple component processes, each supported by distinct functional-anatomic subsystems. We previously hypothesized that a medial temporal subsystem contributes to autobiographical memory and future thought by enabling individuals to retrieve prior information and bind this information into a mental scene. Conversely, a dorsal medial subsystem was proposed to support social-reflective aspects of autobiographical thought, allowing individuals to reflect on the mental states of one's self and others (i.e. "mentalizing"). To test these hypotheses, we first examined activity in the default network subsystems as participants performed two commonly employed tasks of episodic retrieval and mentalizing. In a subset of participants, relationships among task-evoked regions were examined at rest, in the absence of an overt task. Finally, large-scale fMRI meta-analyses were conducted to identify brain regions that most strongly predicted the presence of episodic retrieval and mentalizing, and these results were compared to meta-analyses of autobiographical tasks. Across studies, laboratory-based episodic retrieval tasks were preferentially linked to the medial temporal subsystem, while mentalizing tasks were preferentially linked to the dorsal medial subsystem. In turn, autobiographical tasks engaged aspects of both subsystems. These results suggest the default network is a heterogeneous brain system whose subsystems support distinct component processes of autobiographical thought. (C) 2014 Elsevier Inc. All rights reserved. [Andrews-Hanna, Jessica R.; Yarkoni, Tal] Univ Colorado, Inst Cognit Sci, Boulder, CO 80309 USA; [Saxe, Rebecca] MIT, Dept Brain & Cognit Sci, Cambridge, MA 02139 USA Andrews-Hanna, JR (reprint author), Univ Colorado, Inst Cognit Sci, UCB 594, Boulder, CO 80309 USA. jandrewshanna@gmail.com; saxe@mit.edu; talyarkoni@gmail.com National Institutes of Health [F32MH093985, R01MH096906, F32NR012081]; Howard Hughes Medical Institute We wish to thank Randy Buckner for his valuable contribution and support, Renee Poulin, Marissa Hollingshead, and Jamie Parker for their assistance with data collection and analysis, and Tor Wager, Itamar Kahn, Fenna Krienen, Tanveer Talukdar, and three anonymous reviewers for their helpful discussion. This work was supported by the National Institutes of Health: F32MH093985 (J.A.), R01MH096906 (T.Y.), and F32NR012081 (T.Y.), and the Howard Hughes Medical Institute (Randy L Buckner). Addis DR, 2007, NEUROPSYCHOLOGIA, V45, P1363, DOI 10.1016/j.neuropsychologia.2006.10.016; Andrews-Hanna JR, 2012, NEUROSCIENTIST, V18, P251, DOI 10.1177/1073858411403316; Andrews-Hanna JR, 2013, FRONT PSYCHOL, V4, DOI 10.3389/fpsyg.2013.00900; Andrews-Hanna J.R., 2014, ANN N Y ACA IN PRESS; Andrews-Hanna JR, 2010, J NEUROPHYSIOL, V104, P322, DOI 10.1152/jn.00830.2009; Andrews-Hanna JR, 2010, NEURON, V65, P550, DOI 10.1016/j.neuron.2010.02.005; Baetens K., 2014, SOC COGN AF IN PRESS; Bar M, 2009, PHILOS T R SOC B, V364, P1235, DOI 10.1098/rstb.2008.0310; Bar M, 2007, TRENDS COGN SCI, V11, P280, DOI 10.1016/j.tics.2007.05.005; Beer JS, 2006, BRAIN RES, V1079, P98, DOI 10.1016/j.brainres.2006.01.002; Benoit RG, 2010, NEUROIMAGE, V50, P1340, DOI 10.1016/j.neuroimage.2009.12.091; Binder JR, 2009, CEREB CORTEX, V19, P2767, DOI 10.1093/cercor/bhp055; Binder JR, 2011, TRENDS COGN SCI, V15, P527, DOI 10.1016/j.tics.2011.10.001; Brainard DH, 1997, SPATIAL VISION, V10, P433, DOI 10.1163/156856897X00357; Bruneau EG, 2012, NEUROPSYCHOLOGIA, V50, P219, DOI 10.1016/j.neuropsychologia.2011.11.008; Buckner RL, 2008, ANN NY ACAD SCI, V1124, P1, DOI 10.1196/annals.1440.011; Buckner RL, 2009, J NEUROSCI, V29, P1860, DOI 10.1523/JNEUROSCI.5062-08.2009; Buckner RL, 2007, TRENDS COGN SCI, V11, P49, DOI 10.1016/j.tics.2006.11.004; Burianova H, 2010, NEUROIMAGE, V49, P865, DOI 10.1016/j.neuroimage.2009.08.066; Cabeza R, 2007, TRENDS COGN SCI, V11, P219, DOI 10.1016/j.tics.2007.02.005; Cabeza R, 2008, NAT REV NEUROSCI, V9, P613, DOI 10.1038/nrn2459; Cabeza R, 2004, J COGNITIVE NEUROSCI, V16, P1583, DOI 10.1162/0898929042568578; CAVADA C, 1989, J COMP NEUROL, V287, P422, DOI 10.1002/cne.902870403; Chang LJ, 2013, CEREB CORTEX, V23, P739, DOI 10.1093/cercor/bhs065; Cloutier J, 2011, NEUROIMAGE, V57, P583, DOI 10.1016/j.neuroimage.2011.04.051; Contreras JM, 2012, SOC COGN AFFECT NEUR, V7, P764, DOI 10.1093/scan/nsr053; Conway MA, 2009, NEUROPSYCHOLOGIA, V47, P2305, DOI 10.1016/j.neuropsychologia.2009.02.003; Dale AM, 1999, HUM BRAIN MAPP, V8, P109, DOI 10.1002/(SICI)1097-0193(1999)8:2/3<109::AID-HBM7>3.0.CO;2-W; D'Argembeau A., 2009, J COGNITIVE NEUROSCI, P1701; D'Argembeau A., 2014, SOC COGN AF IN PRESS; Dastjerdi M, 2011, P NATL ACAD SCI USA, V108, P3023, DOI 10.1073/pnas.1017098108; Denny BT, 2012, J COGNITIVE NEUROSCI, V24, P1742, DOI 10.1162/jocn_a_00233; Dohnel K, 2012, NEUROIMAGE, V60, P1652, DOI 10.1016/j.neuroimage.2012.01.073; Dunbar RIM, 1997, HUM NATURE-INT BIOS, V8, P231, DOI 10.1007/BF02912493; Frith U, 2003, PHILOS T R SOC B, V358, P459, DOI 10.1098/rstb.2002.1218; GIAMBRA LM, 1979, INT J AGING HUM DEV, V10, P1, DOI 10.2190/01BD-RFNE-W34G-9ECA; Gilbert SJ, 2007, SOC COGN AFFECT NEUR, V2, P217, DOI 10.1093/scan/nsm014; Gilboa A, 2004, NEUROPSYCHOLOGIA, V42, P1336, DOI 10.1016/j.neuropsychologia.2004.02.014; Gilboa A, 2004, CEREB CORTEX, V14, P1214, DOI 10.1093/cercor/bhh082; Greicius MD, 2009, CEREB CORTEX, V19, P72, DOI 10.1093/cercor/bhn059; Greicius MD, 2003, P NATL ACAD SCI USA, V100, P253, DOI 10.1073/pnas.0135058100; Hagmann P, 2008, PLOS BIOL, V6, P1479, DOI 10.1371/journal.pbio.0060159; Hassabis D, 2007, TRENDS COGN SCI, V11, P299, DOI 10.1016/j.tics.2007.05.001; Hassabis D, 2009, PHILOS T R SOC B, V364, P1263, DOI 10.1098/rstb.2008.0296; Hassabis D, 2007, J NEUROSCI, V27, P14365, DOI 10.1523/JNEUROSCI.4549-07.2007; Hassabis D., 2014, CEREB CORTE IN PRESS; Hutchinson J.B., 2012, CEREB CORTEX, V24, P49; Immordino-Yang MH, 2012, PERSPECT PSYCHOL SCI, V7, P352, DOI 10.1177/1745691612447308; Kahn I, 2008, J NEUROPHYSIOL, V100, P129, DOI 10.1152/jn.00077.2008; Kim H, 2010, NEUROIMAGE, V50, P1648, DOI 10.1016/j.neuroimage.2010.01.051; Kim H, 2012, NEUROIMAGE, V61, P966, DOI 10.1016/j.neuroimage.2012.03.025; Kobayashi Y, 2003, J COMP NEUROL, V466, P48, DOI 10.1002/cne.10883; Krienen FM, 2010, J NEUROSCI, V30, P13906, DOI 10.1523/JNEUROSCI.2180-10.2010; Lardi C, 2010, MEMORY, V18, P293, DOI 10.1080/09658211003601522; Leech R, 2014, BRAIN, V137, P12, DOI 10.1093/brain/awt162; Leech R, 2011, J NEUROSCI, V31, P3217, DOI 10.1523/JNEUROSCI.5626-10.2011; Levine B, 2004, BRAIN COGNITION, V55, P54, DOI 10.1016/s0278-2626(03)00280-x; Libby LA, 2012, J NEUROSCI, V32, P6550, DOI 10.1523/JNEUROSCI.3711-11.2012; Lieberman MD, 2007, ANNU REV PSYCHOL, V58, P259, DOI 10.1146/annurev.psych.58.110405.085654; Macrae CN, 2004, CEREB CORTEX, V14, P647, DOI 10.1093/cercor/bhh025; Mar RA, 2011, ANNU REV PSYCHOL, V62, P103, DOI 10.1146/annurev-psych-120709-145406; Mar RA, 2012, CONSCIOUS COGN, V21, P401, DOI 10.1016/j.concog.2011.08.001; Margulies DS, 2009, P NATL ACAD SCI USA, V106, P20069, DOI 10.1073/pnas.0905314106; Mars R.B., 2012, FRONT HUM NEUROSCI, V6, P1; McDermott KB, 2009, NEUROPSYCHOLOGIA, V47, P2290, DOI 10.1016/j.neuropsychologia.2008.12.025; Mitchell JP, 2006, BRAIN RES, V1079, P66, DOI 10.1016/j.brainres.2005.12.113; Mitchell JP, 2006, NEURON, V50, P655, DOI 10.1016/j.neuron.2006.03.040; Mitchell JP, 2009, PHILOS T R SOC B, V364, P1309, DOI 10.1098/rstb.2008.0318; Moran JM, 2006, J COGNITIVE NEUROSCI, V18, P1586, DOI 10.1162/jocn.2006.18.9.1586; Moran JM, 2009, SOC NEUROSCI-UK, V4, P197, DOI 10.1080/17470910802250519; Moran JM, 2011, J COGNITIVE NEUROSCI, V23, P2222, DOI 10.1162/jocn.2010.21580; Nelson SM, 2010, NEURON, V67, P156, DOI 10.1016/j.neuron.2010.05.025; Nieto-Castanon A., 2013, NEUROIMAGE, V63, P1646; Ochsner KN, 2004, J COGNITIVE NEUROSCI, V16, P1746, DOI 10.1162/0898929042947829; Olson IR, 2013, SOC COGN AFFECT NEUR, V8, P123, DOI 10.1093/scan/nss119; Patterson K, 2007, NAT REV NEUROSCI, V8, P976, DOI 10.1038/nrn2277; Pearson JM, 2011, TRENDS COGN SCI, V15, P143, DOI 10.1016/j.tics.2011.02.002; Poldrack RA, 2006, TRENDS COGN SCI, V10, P59, DOI 10.1016/j.tics.2005.12.004; Prebble SC, 2013, PSYCHOL BULL, V139, P815, DOI 10.1037/a0030146; Rabin JS, 2010, J COGNITIVE NEUROSCI, V22, P1095, DOI 10.1162/jocn.2009.21344; Rabin JS, 2012, NEUROIMAGE, V62, P520, DOI 10.1016/j.neuroimage.2012.05.002; Raichle ME, 2001, P NATL ACAD SCI USA, V98, P676, DOI 10.1073/pnas.98.2.676; RAJARAM S, 1993, MEM COGNITION, V21, P89, DOI 10.3758/BF03211168; Ranganath C, 2012, NAT REV NEUROSCI, V13, P713, DOI 10.1038/nrn3338; Ross L.A., 2011, NEUROIMAGE, V9, P3452; Roy M, 2012, TRENDS COGN SCI, V16, P147, DOI 10.1016/j.tics.2012.01.005; Rubin DC, 2006, PERSPECT PSYCHOL SCI, V1, P277, DOI 10.1111/j.1745-6916.2006.00017.x; Ruby FJM, 2013, PLOS ONE, V8, DOI 10.1371/journal.pone.0077554; Rugg MD, 2013, CURR OPIN NEUROBIOL, V23, P255, DOI 10.1016/j.conb.2012.11.005; Rugg MD, 2003, TRENDS COGN SCI, V7, P313, DOI 10.1016/S1364-6613(03)00131-1; Saxe R, 2003, NEUROIMAGE, V19, P1835, DOI 10.1016/S1053-8119(03)00230-1; Saxe R, 2006, PSYCHOL SCI, V17, P692, DOI 10.1111/j.1467-9280.2006.01768.x; Saxe R, 2006, CURR OPIN NEUROBIOL, V16, P235, DOI 10.1016/j.conb.2006.03.001; Saxe R, 2005, NEUROPSYCHOLOGIA, V43, P1391, DOI 10.1016/j.neuropsychologia.2005.02.013; Saxe R, 2006, NEUROIMAGE, V30, P1088, DOI 10.1016/j.neuroimage.2005.12.062; Schacter DL, 2012, NEURON, V76, P677, DOI 10.1016/j.neuron.2012.11.001; Schacter DL, 2007, NAT REV NEUROSCI, V8, P657, DOI 10.1038/nrn2213; Schacter DL, 2008, ANN NY ACAD SCI, V1124, P39, DOI 10.1196/annals.1440.001; Schilbach L, 2012, PLOS ONE, V7, DOI 10.1371/journal.pone.0030920; Schilbach L, 2008, CONSCIOUS COGN, V17, P457, DOI 10.1016/j.concog.2008.03.013; Seghier ML, 2012, FRONT PSYCHOL, V3, DOI 10.3389/fpsyg.2012.00281; Singer J, 2007, MEMORY, V15, P886, DOI 10.1080/09658210701754351; Singer J. L., 1966, DAYDREAMING INTRO EX; Skipper LM, 2011, NEUROPSYCHOLOGIA, V49, P3419, DOI 10.1016/j.neuropsychologia.2011.07.033; Smallwood Jonathan, 2012, Brain Res, V1428, P60, DOI 10.1016/j.brainres.2011.03.072; Spaniol J, 2009, NEUROPSYCHOLOGIA, V47, P1765, DOI 10.1016/j.neuropsychologia.2009.02.028; Spreng RN, 2012, BRAIN RES, V1428, P43, DOI 10.1016/j.brainres.2010.12.024; Spreng RN, 2010, J COGNITIVE NEUROSCI, V22, P1112, DOI 10.1162/jocn.2009.21282; Spreng RN, 2009, J COGNITIVE NEUROSCI, V21, P489, DOI 10.1162/jocn.2008.21029; Stawarczyk D, 2013, FRONT PSYCHOL, V4, DOI 10.3389/fpsyg.2013.00425; Svoboda E, 2006, NEUROPSYCHOLOGIA, V44, P2189, DOI 10.1016/j.neuropsychologia.2006.05.023; Szpunar K.K, 2012, PSYCHOL SCI, V5, P142; Szpunar K.K., 2014, SOC COGN AF IN PRESS; Szpunar KK, 2007, P NATL ACAD SCI USA, V104, P642, DOI 10.1073/pnas.0610082104; Thorne A, 2004, J PERS, V72, P513, DOI 10.1111/j.0022-3506.2004.00271.x; TULVING E, 1985, AM PSYCHOL, V40, P385, DOI 10.1037/0003-066X.40.4.385; Uddin LQ, 2010, CEREB CORTEX, V20, P2636, DOI 10.1093/cercor/bhq011; van den Heuvel M, 2008, J NEUROSCI, V28, P10844, DOI 10.1523/JNEUROSCI.2964-08.2008; van der Meer L, 2010, NEUROSCI BIOBEHAV R, V34, P935, DOI 10.1016/j.neubiorev.2009.12.004; Van Dijk KRA, 2010, J NEUROPHYSIOL, V103, P297, DOI 10.1152/jn.00783.2009; Van Essen DC, 2005, NEUROIMAGE, V28, P635, DOI 10.1016/j.neuroimage.2005.06.058; Vann SD, 2009, NAT REV NEUROSCI, V10, P792, DOI 10.1038/nrn2733; Van Overwalle F, 2009, HUM BRAIN MAPP, V30, P829, DOI 10.1002/hbm.20547; Vilberg KL, 2008, NEUROPSYCHOLOGIA, V46, P1787, DOI 10.1016/j.neuropsychologia.2008.01.004; Vincent JL, 2010, J NEUROPHYSIOL, V103, P793, DOI 10.1152/jn.00546.2009; Vincent JL, 2006, J NEUROPHYSIOL, V96, P3517, DOI 10.1152/jn.00048.2006; Vincent JL, 2008, J NEUROPHYSIOL, V100, P3328, DOI 10.1152/jn.90355.2008; Vogt BA, 2006, NEUROIMAGE, V29, P452, DOI 10.1016/j.neuroimage.2005.07.048; Wagner AD, 2005, TRENDS COGN SCI, V9, P445, DOI 10.1016/j.tics.2005.07.001; Ward B. D., 2000, SIMULTANEOUS INFEREN; Wheeler ME, 2004, NEUROIMAGE, V21, P1337, DOI 10.1016/j.neuroimage.2003.11.001; Yang X., 2013, FRONT PSYCHOL, V3, P1; Yarkoni T, 2008, NEUROIMAGE, V41, P1408, DOI 10.1016/j.neuroimage.2008.03.062; Yarkoni T, 2011, NAT METHODS, V8, P665, DOI [10.1038/nmeth.1635, 10.1038/NMETH.1635]; Yeo BTT, 2011, J NEUROPHYSIOL, V106, P1125, DOI 10.1152/jn.00338.2011; Yonelinas AP, 2002, J MEM LANG, V46, P441, DOI 10.1006/jmla.2002.2864; Young L, 2010, NEUROPSYCHOLOGIA, V48, P2658, DOI 10.1016/j.neuropsychologia.2010.05.012; ZAITCHIK D, 1990, COGNITION, V35, P41, DOI 10.1016/0010-0277(90)90036-J 138 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 1053-8119 1095-9572 NEUROIMAGE Neuroimage MAY 1 2014 91 324 335 10.1016/j.neuroimage.2014.01.032 12 Neurosciences; Neuroimaging; Radiology, Nuclear Medicine & Medical Imaging Neurosciences & Neurology; Radiology, Nuclear Medicine & Medical Imaging AL1VJ WOS:000338914100033 J Torres-Salinas, D; Jimenez-Contreras, E; Robinson-Garcia, N Torres-Salinas, Daniel; Jimenez-Contreras, Evaristo; Robinson-Garcia, Nicolas Trends in science mapping: Co-use of scientific literatures as evidence of researchers' interests PROFESIONAL DE LA INFORMACION Spanish Article Science maps; Information visualization; Co-use; Consumption of scientific literature; Metodologies; Information retrieval; Research interests; University of Navarre JOURNAL USAGE; IMPACT; WEBOMETRICS; COCITATION; METRICS; WEB The possibility of constructing science maps based on co-use of scientific literature by academic users is explored. We define co-use as the co-occurrence of scientific information requests by pairs of users recorded in platforms of scientific journals. We used search data from the University of Navarre to the ScienceDirect platform in 2012 in order to test the validity and to analyze the possibilities of this methodological approach. We conclude by emphasizing the viability of this methodology when exploring the research interests of an academic institution along with the relations among different disciplines. [Torres-Salinas, Daniel; Jimenez-Contreras, Evaristo; Robinson-Garcia, Nicolas] Univ Granada, Res Grp EC3, E-18071 Granada, Spain Torres-Salinas, D (reprint author), Univ Granada, Res Grp EC3, Campus Cartuja, E-18071 Granada, Spain. torressalinas@gmail.com; evaristo@ugr.es; elrobin@ugr.es Aguillo IF, 2006, J AM SOC INF SCI TEC, V57, P1296, DOI 10.1002/asi.20433; Almind TC, 1997, J DOC, V53, P404, DOI 10.1108/EUM0000000007205; Bollen J, 2005, INFORM PROCESS MANAG, V41, P1419, DOI 10.1016/j.ipm.2005.03.024; Borrego Angel, 2005, PROF INFORM, V14, P30; Brody T, 2006, J AM SOC INF SCI TEC, V57, P1060, DOI 10.1002/asi.20373; Haustein S, 2011, J INFORMETR, V5, P446, DOI 10.1016/j.joi.2011.04.002; Kurz Michael J., 2010, ANNU REV INFORM SCI, V44, P1; Leydesdorff L, 2008, J AM SOC INF SCI TEC, V59, P77, DOI 10.1002/asi.20732; LUUKKONEN T, 1992, SCI TECHNOL HUM VAL, V17, P101, DOI 10.1177/016224399201700106; Nicholas D, 2005, J DOC, V61, P248, DOI 10.1108/00220410510585214; Noyons Christiaan M., 2004, HDB QUANTITATIVE SCI, P237; Priem Jason, 2010, ALTMETRICS ORG; Rodriguez-Bravo B, 2012, PROF INFORM, V21, P585, DOI 10.3145/epi.2012.nov.05; Rowlands I, 2007, ASLIB PROC, V59, P222, DOI 10.1108/00012530710752025; SMALL H, 1973, J AM SOC INFORM SCI, V24, P265, DOI 10.1002/asi.4630240406; Tenopir Carol, LEARNED PUBLISHING; Thelwall M, 2008, J INF SCI, V34, P605, DOI 10.1177/0165551507087238; Torres-Salinas D, 2011, PROF INFORM, V20, P111, DOI 10.3145/epi.2011.ene.14; Torres-Salinas Daniel, 2013, ANUARIO THINKEPI, V7, P114; Torres-Salinas Daniel, 2013, COMUNICAR, V41, P53; Van-Noorden Richard, 2014, NATURE 21 0 0 EPI BARCELONA APARTADO 32 280, BARCELONA, 08080, SPAIN 1386-6710 PROF INFORM Prof. Inf. MAY-JUN 2014 23 3 253 258 10.3145/epi.2014.may.05 6 Information Science & Library Science Information Science & Library Science AL3OB WOS:000339037000005 J Morato, J; Sanchez-Cuadrado, S; Ruiz-Robles, A; Moreiro-Gonzalez, JA Morato, Jorge; Sanchez-Cuadrado, Sonia; Ruiz-Robles, Alejandro; Moreiro-Gonzalez, Jose-Antonio Information visualization and retrieval in the semantic web PROFESIONAL DE LA INFORMACION Spanish Article Linked open data; LOD; Semantic web; Queries; Search engines; Visualization; Graphical display; Evaluation; Conceptual browsing; DBpedia The authors have questioned how the semantic web search engines work and if the graphical display of the search results improves the retrieval. The types of queries that can be performed, as well as the main features of a semantic search engine, have been analyzed. A query was selected and executed in semantic search engines with online access to DBpedia. The basic and desirable functionality in a semantic web search engine, and reviewed studies about the evaluation of semantic search engines have been studied. Finally, there have been analyzed the graphic solutions for displaying the results of some search engines. The main findings underlines the idea that graphical solutions to display query results do not meet the expectations proposed in the bibliography. [Morato, Jorge] Univ Carlos III Madrid, Dept Informat, Leganes 28911, Madrid, Spain; [Sanchez-Cuadrado, Sonia] JOT Internet Media, Madrid 28020, Spain; [Ruiz-Robles, Alejandro] Univ Piura, Dep Ingn Ind & Sistemas, Piura, Peru; [Moreiro-Gonzalez, Jose-Antonio] Univ Carlos III Madrid, Dept Bibliotecon & Documentac, E-28903 Getafe, Madrid, Spain Morato, J (reprint author), Univ Carlos III Madrid, Dept Informat, Avda Univ 30, Leganes 28911, Madrid, Spain. jmorato@inf.uc3m.es; ssanchec@gmail.com; alejandro.ruiz@udep.pe; jamore@bib.uc3m.es Andago Martin O., 2010, INT J ADV SCI ARTS, V1, P55; Baeza-Yates Ricardo, 2004, PROF INFORM, V13, P168; Bauer Florian, 2011, LINKED OPEN DATA ESS; BIZER C, 2009, INT J SEMANTIC WEB I, V5, P1; Broader Andrei, 2002, SIGIR FORUM, V36, P3; DIMARTINO B, 2010, ICWE 2010 WORKSH, V6385, P211; ESMAILI KS, 2006, AICCSA, P171; Fazzinga Bettina, 2010, SEMANT WEB, V1, P89; Fuentes-Lorenzo D, 2009, INFORM SYST FRONT, V11, P471, DOI 10.1007/s10796-009-9159-y; Hirsch Christian, 2009, WORKSH VIS INT SOC S; Lopez Vanessa, 2011, SEMANTIC WEB INTEROP, V2, P125; Mangold Christoph, 2007, International Journal of Metadata, Semantics and Ontologies, V2, DOI 10.1504/IJMSO.2007.015073; Mejla-Sanchez-Bermejo Antonio, 2013, SIMILITUD SEMANTICA; Mendez E, 2012, PROF INFORM, V21, P236; Milicic Vuk, 2011, BEW CITNAMES; Morato JL, 2013, LIBR HI TECH, V31, P638, DOI 10.1108/LHT-03-2013-0026; Strasunskas D, 2010, PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INFORMATION MANAGEMENT AND EVALUATION, P380; Turner Duygu, 2009, 4 INT C INT MON PROT, P51; Uren V, 2007, KNOWL ENG REV, V22, P361, DOI 10.1017/S0269888907001233; Wei Wang, 2008, INT J COMMUN SIWN, P76 20 0 0 EPI BARCELONA APARTADO 32 280, BARCELONA, 08080, SPAIN 1386-6710 PROF INFORM Prof. Inf. MAY-JUN 2014 23 3 319 329 10.3145/epi.2014.may.12 11 Information Science & Library Science Information Science & Library Science AL3OB WOS:000339037000012 J Medrano-Corrales, I; Ruiz-Macias, D; Escalona-Cuaresma, MJ Medrano-Corrales, Isabel; Ruiz-Macias, Diego; Escalona-Cuaresma, Maria-Jose Searching records visually PROFESIONAL DE LA INFORMACION Spanish Article Records; Procurement dossiers; Archives; Visualization; Search and records retrieval; Timelines; Network diagrams; Clusters; Maps We present the results of the application of visualization techniques to search large sets of records, under the framework of the "Project of innovation in record management applied to transport infrastructure services and works procurement dossiers". We started from a metadata schema as the origin of the information used to create visualizations. Four visualization. techniques timeline, cluster, cloud and map- and their metadata are exhaustively described. [Medrano-Corrales, Isabel] Agencia Obra Publ Junta Andalucia, Seville 41071, Spain; [Ruiz-Macias, Diego] Tecnocom Espana Solut, Seville 41020, Spain; [Escalona-Cuaresma, Maria-Jose] Univ Seville, ETS Ingn Informat, E-41012 Seville, Spain Medrano-Corrales, I (reprint author), Agencia Obra Publ Junta Andalucia, Av Diego Martinez Barrio 10, Seville 41071, Spain. isabel.medrano@aopandalucia.es; diego.ruiz@tecnocom.es; mjescalona@us.es Bengochea L., 2005, Revista Espanola de Documentacion Cientifica, V28; Bosque-Sendra Joaquin, 2002, GEOFOCUS REV INT CIE, P61; Chen Chaomei, 1999, INFORM VISUALIZATION; Costa Joan, 1998, INFORM VISUAL CONOCI; Dietrich Daniel, 2012, DATA VISUALIZATION; Dursteler Juan-Carlos, 2002, VISUALIZACION INFORM; Fernandez-Molina Juan-Carlos, 1999, ORG CONOCIMIENTO SIS, P295; Marcos Mari-Carmen, 2004, INTERACCION INTERFAC; Red.es, 2013, REC HERR PROC VIS DA 9 0 0 EPI BARCELONA APARTADO 32 280, BARCELONA, 08080, SPAIN 1386-6710 PROF INFORM Prof. Inf. MAY-JUN 2014 23 3 330 338 10.3145/epi.2014.may.13 9 Information Science & Library Science Information Science & Library Science AL3OB WOS:000339037000013 J Wu, QH; Li, ZY; Zhou, JE; Jiang, H; Hu, ZY; Liu, YJ; Xie, GG Wu, Qinghua; Li, Zhenyu; Zhou, Jianer; Jiang, Heng; Hu, Zhiyang; Liu, Yunjie; Xie, Gaogang SOFIA: Toward Service-Oriented Information Centric Networking IEEE NETWORK English Article This article presents SOFIA, a service-oriented information-centric networking architecture. SOFIA is designed by exploring the design space between host abstraction and content abstraction. It can facilitate ICN to easily support various applications beyond content retrieval. SOFIA decouples flexible service processing and efficient data transmission into two stack layers: the service and network layers. Service requesting is driven by service name and processed by intermediate routers according to processing rules at the service layer. Applications build on the service layer and manipulate information over service sessions. A service session can further map to multiple service connections for flexibility and high throughput. We also detail three important communication types enabled by SOFIA. Finally, we implement the SOFIA stack based on a Click modular router and develop a few applications. [Wu, Qinghua; Li, Zhenyu; Zhou, Jianer; Jiang, Heng; Hu, Zhiyang; Liu, Yunjie; Xie, Gaogang] Chinese Acad Sci, ICT, Beijing, Peoples R China; [Wu, Qinghua] Univ CAS, Beijing, Peoples R China Wu, QH (reprint author), Chinese Acad Sci, ICT, Beijing, Peoples R China. wuqinghua@ict.ac.cn; zyli@ict.ac.cn; zhoujianer@ict.ac.cn; jiangheng@ict.ac.cn; huzhiyang@ict.ac.cn; liuyj@chinaunicom.cn; xie@ict.ac.cn National Basic Research Program of China [2012CB315801]; National Natural Science Foundation of China (NSFC) [61133015, 61272473]; National High-Tech R&D Program of China [2013AA013501]; [CNGI-12-03-007] This work was supported by the National Basic Research Program of China with Grant 2012CB315801, the National Natural Science Foundation of China (NSFC) with Grants 61133015 and 61272473, the National High-Tech R&D Program of China with Grant 2013AA013501 and CNGI-12-03-007 project. Dannewitz C., 2009, P 3 GI ITG KUVS WKSP; Dogar F. R., 2012, P 8 INT C EM NETW EX, P13; Ford A., 2011, RFC6182; Fotiou N., 2012, BROADBAND COMMUNICAT, P1; Ghodsi A, 2011, P ACM SIGCOMM WORKSH, P1; Han D., 2012, P 9 USENIX NSDI; Jacobson V., 2009, P 5 INT C EM NETW EX, P1, DOI DOI 10.1145/1658939.1658941; Kim D., 2012, ACM S, P13; Ko B., 2012, ACM SIGCOMM ICN WKSP, P79; Kohler E, 2000, ACM T COMPUT SYST, V18, P263, DOI 10.1145/354871.354874; Koponen T, 2007, ACM SIGCOMM COMP COM, V37, P181, DOI 10.1145/1282427.1282402; Lee J, 2012, IEEE COMMUN MAG, V50, P28; Mathieu B, 2012, IEEE COMMUN MAG, V50, P44, DOI 10.1109/MCOM.2012.6231278; Nordstrom E., 2012, P 9 USENIX NSDI; Zhang L., 2010, NAMED DATA NETWORKIN 15 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 0890-8044 1558-156X IEEE NETWORK IEEE Netw. MAY-JUN 2014 28 3 12 18 7 Computer Science, Hardware & Architecture; Computer Science, Information Systems; Engineering, Electrical & Electronic; Telecommunications Computer Science; Engineering; Telecommunications AK8YF WOS:000338714000003 J More, L; Jensen, G More, Lorenzo; Jensen, Greg Acquisition of conditioned responding in a multiple schedule depends on the reinforcement's temporal contingency with each stimulus LEARNING & MEMORY English Article INTERTRIAL DURATIONS; MEMORY; REPRESENTATION; INFORMATION; HIPPOCAMPUS; INHIBITION; MECHANISMS; RETRIEVAL; TRIAL; DELAY Forty mice acquired conditioned responses to stimuli presented in a multiple schedule with variable inter-trial intervals (ITIs). In some trials, reinforcement was preceded by a variable conditioned stimulus (CS), while other trials were reinforced following distinctive fixed-duration CS. A third stimulus was presented but never paired with reinforcement. Subjects in five groups experienced ITIs of different durations. Acquisition of responding to each stimulus depended only on the cycle-to-trial ratio (C/T), and thus on the temporal contingency of each stimulus. Acquisition was unaffected by whether CSs were of fixed or variable duration. [More, Lorenzo] Ist Sci San Raffaele, Mol Genet Mental Retardat Unit, Dept Biotechnol, Dulbecco Telethon Inst, I-20132 Milan, Italy; [Jensen, Greg] Columbia Univ, Dept Psychol, New York, NY 10027 USA More, L (reprint author), Ist Sci San Raffaele, Mol Genet Mental Retardat Unit, Dept Biotechnol, Dulbecco Telethon Inst, I-20132 Milan, Italy. lorenzo.more@gmail.com; greg.guichard.jensen@gmail.com Abel T, 2001, CURR OPIN NEUROBIOL, V11, P180, DOI 10.1016/S0959-4388(00)00194-X; Alberini CM, 2013, CURR BIOL, V23, pR746, DOI 10.1016/j.cub.2013.06.046; Balsam P. D., 2010, COMP COGN BEHAV REV, V5, P1, DOI DOI 10.3819/CCBR.2010.50001; Balsam PD, 2006, J EXP PSYCHOL ANIM B, V32, P284, DOI 10.1037/0097-7403.32.3.284; Balsam PD, 2009, TRENDS NEUROSCI, V32, P73, DOI 10.1016/j.tins.2008.10.004; Black A. H., 1972, CLASSICAL CONDITION, P64, DOI DOI 10.1016/J.COGPSYCH.2004.11.001; Bock J, 2013, CEREB CORTEX, DOI [10.1093/cercor/bht148, DOI 10.1093/CERCOR/BHT148]; Brandon SE, 2003, BEHAV PROCESS, V62, P5, DOI 10.1016/S0376-6357(03)00016-0; Correa SAL, 2012, J NEUROSCI, V32, P13039, DOI 10.1523/JNEUROSCI.0930-12.2012; da Silva BM, 2013, LEARN MEMORY, V21, P28; Doyle M, 2011, EMBO J, V30, P3540, DOI 10.1038/emboj.2011.278; Flesher MM, 2011, NEUROBIOL LEARN MEM, V96, P181, DOI 10.1016/j.nlm.2011.04.008; Gallistel CR, 2004, P NATL ACAD SCI USA, V101, P13124, DOI 10.1097/pnas.0404965101; Gallistel CR, 2000, PSYCHOL REV, V107, P289, DOI 10.1037//0033-295X.107.2.289; Gallistel CR, 2013, ANNU REV PSYCHOL, V64, P169, DOI 10.1146/annurev-psych-113011-143807; GIBBON J, 1977, J EXP PSYCHOL ANIM B, V3, P264, DOI 10.1037/0097-7403.3.3.264; Gibbon J, 1997, CURR OPIN NEUROBIOL, V7, P170, DOI 10.1016/S0959-4388(97)80005-0; Gottlieb DA, 2008, J EXP PSYCHOL ANIM B, V34, P185, DOI 10.1037/0097-7403.34.2.185; Jennings DJ, 2013, J EXP PSYCHOL ANIM B, V39, P233, DOI 10.1037/a0032151; Jensen G, 2013, J EXP ANAL BEHAV, V100, P408, DOI 10.1002/jeab.49; Jensen G., 2013, PEERJ PREPRINTS, V1; Kirkpatrick K, 2002, BEHAV PROCESS, V57, P89, DOI 10.1016/S0376-6357(02)00007-4; Kirkpatrick K, 2000, J EXP PSYCHOL ANIM B, V26, P206, DOI 10.1037/0097-7403.26.2.206; Lattal KM, 1999, J EXP PSYCHOL ANIM B, V25, P433, DOI 10.1037/0097-7403.25.4.433; London M, 2005, ANNU REV NEUROSCI, V28, P503, DOI 10.1146/annurev.neuro.28.061604.135703; Machado A, 1997, PSYCHOL REV, V104, P241, DOI 10.1037/0033-295X.104.2.241; McLaren IPL, 2000, ANIM LEARN BEHAV, V28, P211, DOI 10.3758/BF03200258; Papper M, 2011, LEARN MEMORY, V18, P58, DOI 10.1101/lm.2024811; Pavlov IP, 1927, CONDITIONED REFLEXES; Reichelt AC, 2013, LEARN MEMORY, V20, P51, DOI 10.1101/lm.027482.112; Sanderson DJ, 2010, NEUROPSYCHOLOGIA, V48, P2303, DOI 10.1016/j.neuropsychologia.2010.03.018; Sanderson DJ, 2008, PROG BRAIN RES, V169, P159, DOI 10.1016/S0079-6123(07)00009-X; Stout SC, 2007, PSYCHOL REV, V114, P759, DOI 10.1037/0033-295X.114.3.759; Vogel EH, 2004, BRAIN RES BULL, V63, P173, DOI 10.1016/j.brainresbull.2004.01.005; Vogel EH, 2003, BEHAV PROCESS, V62, P27, DOI 10.1016/S0376-6357(03)00050-0; Wagner A.R., 1981, P5; Ward RD, 2012, J EXP PSYCHOL ANIM B, V38, P217, DOI [10.1037/a0027621, 10.1037/a0027621.supp]; Ward RD, 2013, BEHAV PROCESS, V95, P3, DOI 10.1016/j.beproc.2013.01.005; Whitlock JR, 2006, SCIENCE, V313, P1093, DOI 10.1126/science.1128134; WILCOXON F, 1945, BIOMETRICS BULL, V1, P80, DOI 10.2307/3001968; WINDHOLZ G, 1986, PAVLOVIAN J BIOL SCI, V21, P141 41 0 0 COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT COLD SPRING HARBOR 1 BUNGTOWN RD, COLD SPRING HARBOR, NY 11724 USA 1072-0502 1549-5485 LEARN MEMORY Learn. Mem. MAY 2014 21 5 258 262 10.1101/lm.034231.113 5 Neurosciences; Psychology, Experimental Neurosciences & Neurology; Psychology AK5ZE WOS:000338505300002 J Matsumoto, Y; Sandoz, JC; Devaud, JM; Lormant, F; Mizunami, M; Giurfa, M Matsumoto, Yukihisa; Sandoz, Jean-Christophe; Devaud, Jean-Marc; Lormant, Flore; Mizunami, Makoto; Giurfa, Martin Cyclic nucleotide-gated channels, calmodulin, adenylyl cyclase, and calcium/calmodulin-dependent protein kinase II are required for late, but not early, long-term memory formation in the honeybee LEARNING & MEMORY English Article LATE-PHASE LTP; PROBOSCIS EXTENSION RESPONSE; PRIMARY OLFACTORY CENTER; CRITICAL TIME-WINDOW; APIS-MELLIFERA; MUSHROOM BODIES; INSECT BRAIN; FUNCTIONAL-CHARACTERIZATION; DROSOPHILA-MELANOGASTER; CREB PHOSPHORYLATION Memory is a dynamic process that allows encoding, storage, and retrieval of information acquired through individual experience. In the honeybee Apis mellifera, olfactory conditioning of the proboscis extension response (PER) has shown that besides short-term memory (STM) and mid-term memory (MTM), two phases of long-term memory (LTM) are formed upon multiple-trial conditioning: an early phase (e-LTM) which depends on translation from already available mRNA, and a late phase (l-LTM) which requires de novo transcription and translation. Here we combined olfactory PER conditioning and neuropharmacological inhibition and studied the involvement of the NO-cGMP pathway, and of specific molecules, such as cyclic nucleotide-gated channels (CNG), calmodulin (CaM), adenylyl cyclase (AC), and Ca2+/calmodulin-dependent protein kinase (CaMKII), in the formation of olfactory LTM in bees. We show that in addition to NO-cGMP and cAMP-PKA, CNG channels, CaM, AC, and CaMKII also participate in the formation of a l-LTM (72-h post-conditioning) that is specific for the learned odor. Importantly, the same molecules are dispensable for olfactory learning and for the formation of both MTM (in the minute and hour range) and e-LTM (24-h post-conditioning), thus suggesting that the signaling pathways leading to l-LTM or e-LTM involve different molecular actors. [Matsumoto, Yukihisa; Sandoz, Jean-Christophe; Devaud, Jean-Marc; Lormant, Flore; Giurfa, Martin] Univ Toulouse, UPS, Res Ctr Anim Cognit, F-31062 Toulouse 9, France; [Matsumoto, Yukihisa; Sandoz, Jean-Christophe; Devaud, Jean-Marc; Lormant, Flore; Giurfa, Martin] CNRS, Res Ctr Anim Cognit, F-31062 Toulouse 9, France; [Matsumoto, Yukihisa; Mizunami, Makoto] Hokkaido Univ, Grad Sch Life Sci, Sapporo, Hokkaido 0600810, Japan Matsumoto, Y (reprint author), Tokyo Med & Dent Univ, Coll Liberal Arts & Sci, 2-8-30 Kounodai, Ichikawa, Chiba 2720827, Japan. yukihisa.las@tmd.ac.jp; martin.giurfa@univ-tlse3.fr Fyssen Foundation; CNRS; University Paul Sabatier We thank three anonymous reviewers and G. Isabel (Toulouse) for useful comments and corrections. M.G. thanks U. Muller (Saarbrucken) and D. Eisenhardt (Berlin) for fruitful information exchange and discussions. Y.M. thanks the Fyssen Foundation for postdoctoral support. J.-M.D, J.C.S, and M.G. thank the CNRS and the University Paul Sabatier for financial support. M.G. acknowledges the specific support of a scientific grant of the University Paul Sabatier (Project Apigene) and the valuable support of the Institut Universitaire de France. Abel T, 1997, CELL, V88, P615, DOI 10.1016/S0092-8674(00)81904-2; Akalal DBG, 2010, J NEUROSCI, V30, P16699, DOI 10.1523/JNEUROSCI.1882-10.2010; ANHOLT RRH, 1994, TRENDS NEUROSCI, V17, P37, DOI 10.1016/0166-2236(94)90033-7; Arenas A, 2012, EUR J NEUROSCI, V35, P682, DOI 10.1111/j.1460-9568.2012.07999.x; Balfanz S, 2012, INSECT BIOCHEM MOLEC, V42, P435, DOI 10.1016/j.ibmb.2012.02.005; BARTSCH D, 1995, CELL, V83, P979, DOI 10.1016/0092-8674(95)90213-9; Bhattacharya A, 2004, J BIOL CHEM, V279, P37291, DOI 10.1074/jbc.M403819200; BITTERMAN ME, 1983, J COMP PSYCHOL, V97, P107, DOI 10.1037/0735-7036.97.2.107; Broillet MC, 1996, J NEUROBIOL, V30, P49, DOI 10.1002/(SICI)1097-4695(199605)30:1<49::AID-NEU5>3.0.CO;2-G; Busto GU, 2010, PHYSIOLOGY, V25, P338, DOI 10.1152/physiol.00026.2010; Chen CC, 2012, SCIENCE, V335, P678, DOI 10.1126/science.1212735; Crittenden JR, 1998, LEARN MEMORY, V5, P38; Davis RL, 2005, ANNU REV NEUROSCI, V28, P275, DOI 10.1146/annurev.neuro.28.061604.135651; Eisenhardt D, 2003, INSECT MOL BIOL, V12, P373, DOI 10.1046/j.1365-2583.2003.00421.x; Eisenhardt D, 2006, ANIM BIOL, V56, P259, DOI 10.1163/157075606777304249; Eisenhardt D, 2001, INSECT MOL BIOL, V10, P173, DOI 10.1046/j.1365-2583.2001.00252.x; Eisenhardt D, 2006, INSECT MOL BIOL, V15, P551, DOI 10.1111/j.1365-2583.2006.00668.x; ENSLEN H, 1994, J BIOL CHEM, V269, P15520; ERBER J, 1980, PHYSIOL ENTOMOL, V5, P343, DOI 10.1111/j.1365-3032.1980.tb00244.x; Fiala A, 1999, J NEUROSCI, V19, P10125; Friedrich A, 2004, J NEUROSCI, V24, P4460, DOI 10.1523/JNEUROSCI.0669-04.2004; Fuss N, 2010, INSECT BIOCHEM MOLEC, V40, P573, DOI 10.1016/j.ibmb.2010.05.004; Gauthier M, 2010, ADV EXP MED BIOL, V683, P97; Gervasi N, 2010, NEURON, V65, P516, DOI 10.1016/j.neuron.2010.01.014; Giurfa M, 2012, LEARN MEMORY, V19, P54, DOI 10.1101/lm.024711.111; Giurfa M, 2007, J COMP PHYSIOL A, V193, P801, DOI 10.1007/s00359-007-0235-9; GRAY DC, 1984, Q J EXP PHYSIOL CMS, V69, P171; Grunbaum L, 1998, J NEUROSCI, V18, P4384; Grunewald B, 1999, J COMP PHYSIOL A, V185, P565, DOI 10.1007/s003590050417; Guerrieri F, 2005, PLOS BIOL, V3, P718, DOI 10.1371/journal.pbio.0030060; HAMMER M, 1993, NATURE, V366, P59, DOI 10.1038/366059a0; HAN PL, 1992, NEURON, V9, P619, DOI 10.1016/0896-6273(92)90026-A; Heinrich R, 2001, P NATL ACAD SCI USA, V98, P9919, DOI 10.1073/pnas.151131998; Hourcade B, 2009, LEARN MEMORY, V16, P607, DOI 10.1101/lm.1445609; Hourcade B, 2010, J NEUROSCI, V30, P6461, DOI 10.1523/JNEUROSCI.0841-10.2010; JH Zar, 1999, BIOSTATISTICAL ANAL; Kamikouchi A, 2000, J COMP NEUROL, V417, P501, DOI 10.1002/(SICI)1096-9861(20000221)417:4<501::AID-CNE8>3.0.CO;2-4; Kandel ER, 2001, SCIENCE, V294, P1030, DOI 10.1126/science.1067020; Kaupp UB, 2002, PHYSIOL REV, V82, P769, DOI 10.1152/physrev.00008.2002; Kelliher KR, 2003, P NATL ACAD SCI USA, V100, P4299, DOI 10.1073/pnas.0736071100; Kemenes I, 2002, J NEUROSCI, V22, P1414; Leboulle G, 2004, FEBS LETT, V576, P216, DOI 10.1016/j.febslet.2004.08.079; LEVIN LR, 1992, CELL, V68, P479, DOI 10.1016/0092-8674(92)90185-F; Lewin MR, 1999, NAT NEUROSCI, V2, P18; Limback-Stokin K, 2004, J NEUROSCI, V24, P10858, DOI 10.1523/JNEUROSCI.1022-04.2004; LIVINGSTONE MS, 1984, CELL, V37, P205, DOI 10.1016/0092-8674(84)90316-7; Lu YF, 1999, J NEUROSCI, V19, P10250; Lu YF, 2002, J NEUROPHYSIOL, V88, P1270, DOI 10.1152/jn.01036.2001; LUNNEY GH, 1970, J EDUC MEAS, V7, P263, DOI 10.1111/j.1745-3984.1970.tb00727.x; Makhinson M, 1999, J NEUROSCI, V19, P2500; MALENKA RC, 1989, NATURE, V340, P554, DOI 10.1038/340554a0; Malik BR, 2013, FRONT NEURAL CIRCUIT, V7, DOI 10.3389/fncir.2013.00052; Mao ZM, 2004, P NATL ACAD SCI USA, V101, P198, DOI 10.1073/pnas.0306128101; Margrie TW, 1998, NAT NEUROSCI, V1, P378; Margulies C, 2005, CURR BIOL, V15, pR700, DOI 10.1016/j.cub.2005.08.024; Matsumoto Y, 2012, J NEUROSCI METH, V211, P159, DOI 10.1016/j.jneumeth.2012.08.018; Matsumoto Y, 2006, LEARN MEMORY, V13, P35, DOI 10.1101/lm.130506; McGuire Sean E, 2004, Sci STKE, V2004, ppl6, DOI 10.1126/stke.2202004pl6; Mehren JE, 2004, J NEUROSCI, V24, P10584, DOI 10.1523/JNEUROSCI.3560-04.2004; Menzel R, 2001, LEARN MEMORY, V8, P53, DOI 10.1101/lm.38801; Menzel R., 1974, P195; Menzel R, 1999, J COMP PHYSIOL A, V185, P323, DOI 10.1007/s003590050392; Micheau J, 1999, CELL MOL LIFE SCI, V55, P534, DOI 10.1007/s000180050312; Miyazu M, 2000, INSECT MOL BIOL, V9, P283, DOI 10.1046/j.1365-2583.2000.00186.x; Muller U, 2000, NEURON, V27, P159, DOI 10.1016/S0896-6273(00)00017-9; Muller U, 1997, J NEUROBIOL, V33, P33, DOI 10.1002/(SICI)1097-4695(199707)33:1<33::AID-NEU4>3.0.CO;2-E; Muller U, 1996, NEURON, V16, P541, DOI 10.1016/S0896-6273(00)80073-2; NAKAZAWA H, 1995, NEUROSCIENCE, V69, P585, DOI 10.1016/0306-4522(95)00293-R; Pasch E, 2011, J COMP NEUROL, V519, P3700, DOI 10.1002/cne.22683; Poser S, 2001, INT J DEV NEUROSCI, V19, P387, DOI 10.1016/S0736-5748(00)00094-0; REDPATH NT, 1993, BIOCHEM J, V293, P31; Schwarzel M, 2006, CELL MOL LIFE SCI, V63, P989, DOI 10.1007/s00018-006-6024-8; Shan Q, 2008, J NEUROSCI, V28, P12864, DOI 10.1523/JNEUROSCI.2413-08.2008; TAKEDA K, 1961, J INSECT PHYSIOL, V6, P168, DOI 10.1016/0022-1910(61)90060-9; Wachten S, 2006, J NEUROCHEM, V96, P1580, DOI 10.1111/j.1471-4159.2006.03666.x; Wan HM, 2010, J NEUROSCI, V30, P56, DOI 10.1523/JNEUROSCI.2577-09.2010; Wang HB, 2004, NAT NEUROSCI, V7, P635, DOI 10.1038/nn1248; Watanabe T, 2007, DEV NEUROBIOL, V67, P456, DOI 10.1002/dneu.20359; Wei JY, 1998, J MOL NEUROSCI, V10, P53, DOI 10.1007/BF02737085; WITTSTOCK S, 1993, J NEUROSCI, V13, P1379; Wong ST, 1999, NEURON, V23, P787, DOI 10.1016/S0896-6273(01)80036-2; Wustenberg D, 1998, EUR J NEUROSCI, V10, P2742, DOI 10.1046/j.1460-9568.1998.t01-1-00319.x; Yin JCP, 1996, CURR OPIN NEUROBIOL, V6, P264, DOI 10.1016/S0959-4388(96)80082-1; Zannat MT, 2006, NEUROSCI LETT, V398, P274, DOI 10.1016/j.neulet.2006.01.007; Zars T, 2000, LEARN MEMORY, V7, P18, DOI 10.1101/lm.7.1.18 85 0 0 COLD SPRING HARBOR LAB PRESS, PUBLICATIONS DEPT COLD SPRING HARBOR 1 BUNGTOWN RD, COLD SPRING HARBOR, NY 11724 USA 1072-0502 1549-5485 LEARN MEMORY Learn. Mem. MAY 2014 21 5 272 286 10.1101/lm.032037.113 15 Neurosciences; Psychology, Experimental Neurosciences & Neurology; Psychology AK5ZE WOS:000338505300004 J Arroyo, AA; Camps, A; Aguasca, A; Forte, GF; Monerris, A; Rudiger, C; Walker, JP; Park, H; Pascual, D; Onrubia, R Alonso Arroyo, Alberto; Camps, Adriano; Aguasca, Albert; Forte, Giuseppe F.; Monerris, Alessandra; Ruediger, Christoph; Walker, Jeffrey P.; Park, Hyuk; Pascual, Daniel; Onrubia, Raul Dual-Polarization GNSS-R Interference Pattern Technique for Soil Moisture Mapping IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING English Article Brewster angle; global navigation satellite systems (GNSS); GNSS reflectometry (GNSS-R); interference pattern technique (IPT); soil moisture (SM) GPS BISTATIC RADAR; REFLECTOMETRY; OCEAN; PERMITTIVITY; SURFACE; SIGNALS; SMEX02 The interference pattern technique (IPT) consists of the coherent addition of the direct and reflected global navigation satellite systems (GNSS) signals in the receiving antenna. The detected power oscillates (fading), and the amplitude of these oscillations is very sensitive to the soil reflection coefficient at the specular reflection point. Therefore, variations of the reflection coefficient can be mapped, and thus dielectric constant variations, from which soil moisture can be retrieved. This work extends the use of the IPT technique from vertical polarization (V-Pol) to horizontal polarization (H-Pol). Moreover, the IPT equations are reformulated to facilitate the combination of dual-polarization retrievals. Simulations of the interference patterns at V-and H-Pol are presented for different soil moisture conditions. An upgrade of the SMIGOL GNSS-R instrument for dual-polarization observations is presented. This instrument was deployed in a flat, dry grassland in Yanco, Australia, in order to validate the proposed concepts. Finally, a comparison between the data retrieved from the SMIGOL instrument and the ground-truth soil moisture data is presented showing a good agreement between them and rainfall information. [Alonso Arroyo, Alberto; Camps, Adriano; Aguasca, Albert; Forte, Giuseppe F.; Park, Hyuk; Pascual, Daniel; Onrubia, Raul] Univ Politecn Catalunya BarcelonaTech UPC, Dept Signal Theory & Commun, Barcelona 08034, Spain; [Monerris, Alessandra; Ruediger, Christoph; Walker, Jeffrey P.] Monash Univ, Dept Civil Engn, Melbourne, Vic 3168, Australia Arroyo, AA (reprint author), Univ Politecn Catalunya BarcelonaTech UPC, Dept Signal Theory & Commun, Barcelona 08034, Spain. alberto.alonso.arroyo@tsc.upc.edu; camps@tsc.upc.edu; aguasca@tsc.upc.edu; giuseppe.forte@tsc.upc.edu; sandra.monerris-belda@monash.edu; chris.rudiger@monash.edu; jeff.walker@monash.edu; park.hyuk@tsc.upc.edu; daniel.pascual@tsc.upc.edu; onrubia@tsc.upc.edu Spanish Ministry of Science and Innovation [AYA2011-29183-C02-01/ESP]; Monash University Faculty of Engineering; ACROSS (Advanced Remote Sensing Ground-Truth Demo and Test Facilities) - German Helmholtz-Association; TERENO (Terrestrial Environmental Observatories) - German Helmholtz-Association; CosmOz network; Commonwealth Scientific and Industrial Research Organisation; OzNet hydrological monitoring network This work was supported in part by the Spanish Ministry of Science and Innovation, "AROSA-Advanced Radio Ocultations and Scatterometry Applications using GNSS and other opportunity signals," code AYA2011-29183-C02-01/ESP, in part by a Monash University Faculty of Engineering Seed Grant 2013, in part by ACROSS (Advanced Remote Sensing Ground-Truth Demo and Test Facilities), in part by TERENO (Terrestrial Environmental Observatories) funded by the German Helmholtz-Association, in part by the CosmOz network and the funding provided by the Commonwealth Scientific and Industrial Research Organisation, and in part by the OzNet hydrological monitoring network. AUBER JC, 1994, PROCEEDINGS OF ION GPS-94: 7TH INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION, PTS 1 AND 2, P1155; BECKMANN P, 1993, SCATTERING ELECTROMA; *BUR MET, BUR MET MEAS RAINF A; Cardellach E., 2011, RADIO SCI, V46; Chew CC, 2014, IEEE T GEOSCI REMOTE, V52, P537, DOI 10.1109/TGRS.2013.2242332; CHOUDHURY BJ, 1979, J GEOPHYS RES-OC ATM, V84, P5699, DOI 10.1029/JC084iC09p05699; EGIDO A, 2008, SOIL MOISTURE MONITO; Egido A, 2012, REMOTE SENS-BASEL, V4, P2356, DOI 10.3390/rs4082356; Jin SG, 2010, ADV SPACE RES, V46, P111, DOI 10.1016/j.asr.2010.01.014; Jin SG, 2011, ADV SPACE RES, V47, P1645, DOI 10.1016/j.asr.2011.01.036; Katzberg SJ, 2006, REMOTE SENS ENVIRON, V100, P17, DOI 10.1016/j.rse.2005.09.015; Kavak A, 1998, ELECTRON LETT, V34, P254, DOI 10.1049/el:19980180; Larson KM, 2010, IEEE J-STARS, V3, P91, DOI 10.1109/JSTARS.2009.2033612; LERONDEL G, 1999, APPL PHYS LETT, V74; MARTINEIRA M, 1993, ESA J-EUR SPACE AGEN, V17, P331; Masters D, 2004, REMOTE SENS ENVIRON, V92, P507, DOI 10.1016/j.rse.2004.05.016; Moccia A, 2011, IEEE T GEOSCI REMOTE, V49, P3487, DOI 10.1109/TGRS.2011.2115250; Njoku EG, 1996, J HYDROL, V184, P101, DOI 10.1016/0022-1694(95)02970-2; Pozar D. M., 2012, MICROWAVE ENG; Rodriguez-Alvarez N, 2011, IEEE T GEOSCI REMOTE, V49, P71, DOI 10.1109/TGRS.2010.2049023; Rodriguez-Alvarez N, 2009, IEEE T GEOSCI REMOTE, V47, P3616, DOI 10.1109/TGRS.2009.2030672; RUFFINI G, 2004, GNSS R INTERFEROMETR; Seneviratne SI, 2010, EARTH-SCI REV, V99, P125, DOI 10.1016/j.earscirev.2010.02.004; SMITH AB, 2012, WATER RESOUR RES, V48; *STEV WAT MON SYST, SDI 12 SM PROB; Valencia E, 2013, IEEE J-STARS, V6, P217, DOI 10.1109/JSTARS.2012.2210392; WANG JR, 1980, IEEE T GEOSCI REMOTE, V18, P288, DOI 10.1109/TGRS.1980.350304; Wang L, 2009, FRONTIERS EARTH SCI, V3, P237, DOI DOI 10.1007/S11707-009-0023-7; Zavorotny V, 2003, INT GEOSCI REMOTE SE, P781; Zavorotny VU, 2000, IEEE T GEOSCI REMOTE, V38, P951, DOI 10.1109/36.841977; Zavorotny VU, 2010, IEEE J-STARS, V3, P100, DOI 10.1109/JSTARS.2009.2033608 31 0 0 IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC PISCATAWAY 445 HOES LANE, PISCATAWAY, NJ 08855-4141 USA 1939-1404 2151-1535 IEEE J-STARS IEEE J. Sel. Top. Appl. Earth Observ. Remote Sens. MAY 2014 7 5 SI 1533 1544 10.1109/JSTARS.2014.2320792 12 Engineering, Electrical & Electronic; Geography, Physical; Remote Sensing; Imaging Science & Photographic Technology Engineering; Physical Geography; Remote Sensing; Imaging Science & Photographic Technology AJ7TQ WOS:000337900700014 J Wang, ZQ; Xu, G; Li, H; Zhang, M Wang, Ziqi; Xu, Gu; Li, Hang; Zhang, Ming A Probabilistic Approach to String Transformation IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article String transformation; log linear model; spelling error correction; query reformulation SEARCH Many problems in natural language processing, data mining, information retrieval, and bioinformatics can be formalized as string transformation, which is a task as follows. Given an input string, the system generates the k most likely output strings corresponding to the input string. This paper proposes a novel and probabilistic approach to string transformation, which is both accurate and efficient. The approach includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method is applied to correction of spelling errors in queries as well as reformulation of queries in web search. Experimental results on large scale data show that the proposed approach is very accurate and efficient improving upon existing methods in terms of accuracy and efficiency in different settings. [Wang, Ziqi; Zhang, Ming] Peking Univ, Sch EECS, Beijing 100871, Peoples R China; [Xu, Gu] Microsoft Bing, Bellevue, WA 98004 USA; [Li, Hang] Huawei Noahs Ark Lab, Shatin, Hong Kong, Peoples R China Wang, ZQ (reprint author), Peking Univ, Sch EECS, Beijing 100871, Peoples R China. wangziqi@pku.edu.cn; guxu@microsoft.com; hangli.hl@huawei.com; mzhang@net.pku.edu.cn National Natural Science Foundation of China [61272343]; Doctoral Program of Higher Education of China [20120001110112] This research was supported in part by the the National Natural Science Foundation of China under Grant 61272343, and in part by the Doctoral Program of Higher Education of China under Grant 20120001110112. Ahmad Farooq, 2005, P C HUM LANG TECHN E, P955, DOI 10.3115/1220575.1220695; AHO AV, 1975, COMMUN ACM, V18, P333, DOI 10.1145/360825.360855; Arasu A., 2009, PVLDB, V2, P514; Behm A, 2009, PROC INT CONF DATA, P604; Brill Eric, 2000, P 38 ANN M ASS COMP, P286, DOI 10.3115/1075218.1075255; BYRD RH, 1995, SIAM J SCI COMPUT, V16, P1190, DOI 10.1137/0916069; Chen Q., P 2007 JOINT C EMP M, P181; Dreyer M., 2008, P C EMP METH NAT LAN, P1080, DOI 10.3115/1613715.1613856; Duan Huizhong, 2011, P 20 INT C WORLD WID, P117; Golding AR, 1999, MACH LEARN, V34, P107, DOI 10.1023/A:1007545901558; Guo Jiafeng, 2008, P 31 ANN INT ACM SIG, P379, DOI 10.1145/1390334.1390400; Hadjieleftheriou M., 2009, P VLDB ENDOWMENT, V2, P1660; Islam A., P 2009 C EMP METH NA, P1241; Islam A, 2011, LECT NOTES ARTIF INT, V6657, P192, DOI 10.1007/978-3-642-21043-3_23; Ji S., 2009, P 18 INT C WORLD WID, P371, DOI 10.1145/1526709.1526760; Jones R., 2006, P 15 INT C WORLD WID, P387, DOI DOI 10.1145/1135777.1135835; Li C., 2007, P INT C VER LARG DAT, P303; Li C, 2008, PROC INT CONF DATA, P257, DOI 10.1109/ICDE.2008.4497434; Li M, 2006, COLING/ACL 2006, VOLS 1 AND 2, PROCEEDINGS OF THE CONFERENCE, P1025; McCallum A., 2005, P 21 C UNC ART INT U, P388; Okazaki N., 2008, P C EMP METH NAT LAN, P447, DOI 10.3115/1613715.1613772; Oncina J., 2005, WORKSH GRAMM INF APP; Ristad ES, 1998, IEEE T PATTERN ANAL, V20, P522, DOI 10.1109/34.682181; Tejada S., 2002, P 8 ACM SIGKDD INT C, P350; Toutanova Kristina, 2002, P 40 ANN M ASS COMP, P144; Vernica R., 2009, P 1 INT WORKSH KEYW, P9, DOI 10.1145/1557670.1557677; Wang XL, 2008, Proceedings of the 27th Chinese Control Conference, Vol 7, P479, DOI 10.1145/1458082.1458147; Whitelaw C., P 2009 C EMP METH NA, P890; Xu Jingfang, 2011, P 4 ACM INT C WEB SE, P615, DOI 10.1145/1935826.1935912; Yang X., P 2008 ACM SIGMOD IN, P353; Yang Z., 2010, P AAAI C ART INT AAA, P1467 31 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. MAY 2014 26 5 1063 1075 10.1109/TKDE.2013.11 13 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Computer Science; Engineering AJ8OP WOS:000337965900003 J Gu, Y; Gao, CP; Cong, G; Yu, G Gu, Yu; Gao, Chunpeng; Cong, Gao; Yu, Ge Effective and Efficient Clustering Methods for Correlated Probabilistic Graphs IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Clustering; correlated; probabilistic graphs; algorithm NETWORKS Recently, probabilistic graphs have attracted significant interests of the data mining community. It is observed that correlations may exist among adjacent edges in various probabilistic graphs. As one of the basic mining techniques, graph clustering is widely used in exploratory data analysis, such as data compression, information retrieval, image segmentation, etc. Graph clustering aims to divide data into clusters according to their similarities, and a number of algorithms have been proposed for clustering graphs, such as the pKwikCluster algorithm, spectral clustering, k-path clustering, etc. However, little research has been performed to develop efficient clustering algorithms for probabilistic graphs. Particularly, it becomes more challenging to efficiently cluster probabilistic graphs when correlations are considered. In this paper, we define the problem of clustering correlated probabilistic graphs. To solve the challenging problem, we propose two algorithms, namely the PEEDR and the CPGS clustering algorithm. For each of the proposed algorithms, we develop several pruning techniques to further improve their efficiency. We evaluate the effectiveness and efficiency of our algorithms and pruning methods through comprehensive experiments. [Gu, Yu; Gao, Chunpeng; Yu, Ge] Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110816, Peoples R China; [Cong, Gao] Nanyang Technol Univ, Singapore 639798, Singapore Gu, Y (reprint author), Northeastern Univ, Coll Informat Sci & Engn, Shenyang 110816, Peoples R China. guyu@ise.neu.edu.cn; gchphao@gmail.com; gaocong@ntu.edu.sg; yuge@ise.neu.edu.cn Cong , Gao/A-3726-2011 National Basic Research Program of China [2012CB316201]; National Natural Science Foundation of China [61003058, 61272179]; Fundamental Research Funds for the Central Universities [N130404010] This work was supported in part by the National Basic Research Program of China under Grant 2012CB316201, in part by the National Natural Science Foundation of China (61003058, 61272179), and in part by the Fundamental Research Funds for the Central Universities (N130404010). Ackermann MR, 2014, ALGORITHMICA, V69, P184, DOI 10.1007/s00453-012-9717-4; Aggarwal CC, 2010, ADV DATABASE SYST, V40, P1, DOI 10.1007/978-1-4419-6045-0; Bach FR, 2006, J MACH LEARN RES, V7, P1963; Corso G. M. D., 1997, SIAM J MATRIX ANAL A, V18, P913; Dhillon I., 2004, P 10 ACM SIGKDD INT, P551, DOI DOI 10.1145/1014052.1014118; Flake G. W., 2003, INTERNET MATH, V1, P385, DOI DOI 10.1080/15427951.2004.10129093; Gibson D., 2005, P 31 INT C VER LARG, P721; Girvan M, 2002, P NATL ACAD SCI USA, V99, P7821, DOI 10.1073/pnas.122653799; Hua M., 2010, P 13 INT C EXT DAT T, P347, DOI 10.1145/1739041.1739084; Jain AK, 1999, ACM COMPUT SURV, V31, P264, DOI 10.1145/331499.331504; Jin R., 2011, P VLDB ENDOWMENT, V4, P551; Kannan R, 2004, J ACM, V51, P497, DOI 10.1145/990308.990313; Kollios G, 2013, IEEE T KNOWL DATA EN, V25, P325, DOI 10.1109/TKDE.2011.243; Lian X., 2010, P VLDB ENDOWMENT, V4, P12; MOHAR B, 1992, DISCRETE MATH, V109, P171, DOI 10.1016/0012-365X(92)90288-Q; Pei J., 2005, P 11 ACM SIGKDD INT, P228, DOI DOI 10.1145/1081870.1081898; Potamias M., 2010, P VLDB END PVLDB, V3, P997; Sen P., 2007, P 23 INT C DAT ENG, P596; Shamir R, 2004, DISCRETE APPL MATH, V144, P173, DOI 10.1016/j.dam.2004.01.007; von Luxburg U, 2007, STAT COMPUT, V17, P395, DOI 10.1007/s11222-007-9033-z; Wang WC, 2000, J CONSTR ENG M ASCE, V126, P458, DOI 10.1061/(ASCE)0733-9364(2000)126:6(458); Yan DH, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P907; Yuan Y., 2012, P VLDB ENDOWMENT, V5, P800 23 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. MAY 2014 26 5 1117 1130 10.1109/TKDE.2013.123 14 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Computer Science; Engineering AJ8OP WOS:000337965900007 J Paulet, R; Kaosar, MG; Yi, X; Bertino, E Paulet, Russell; Kaosar, Md. Golam; Yi, Xun; Bertino, Elisa Privacy-Preserving and Content-Protecting Location Based Queries IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Location based query; private query; private information retrieval; oblivious transfer INFORMATION-RETRIEVAL; LOGARITHMS; NETWORKS; MODEL In this paper we present a solution to one of the location-based query problems. This problem is defined as follows: (i) a user wants to query a database of location data, known as Points Of Interest (POIs), and does not want to reveal his/her location to the server due to privacy concerns; (ii) the owner of the location data, that is, the location server, does not want to simply distribute its data to all users. The location server desires to have some control over its data, since the data is its asset. We propose a major enhancement upon previous solutions by introducing a two stage approach, where the first step is based on Oblivious Transfer and the second step is based on Private Information Retrieval, to achieve a secure solution for both parties. The solution we present is efficient and practical in many scenarios. We implement our solution on a desktop machine and a mobile device to assess the efficiency of our protocol. We also introduce a security model and analyse the security in the context of our protocol. Finally, we highlight a security weakness of our previous work and present a solution to overcome it. [Paulet, Russell; Kaosar, Md. Golam; Yi, Xun] Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia; [Bertino, Elisa] Purdue Univ, Dept Comp Sci & Cyber Ctr, W Lafayette, IN 47907 USA Paulet, R (reprint author), Victoria Univ, Sch Sci & Engn, Melbourne, Vic 8001, Australia. russell.paulet@live.vu.edu.au; glmksr@live.com; xun.yi@vu.edu.au; bertino@cs.purdue.edu ARC [DP0988411]; NSF [1016722] This work was supported in part by ARC Discovery Project (DP0988411) "Private Data Warehouse Query" and in part by NSF award (1016722) "TC: Small: Collaborative: Protocols for Privacy-Preserving Scalable Record Matching and Ontology Alignment". Bellare M., 1990, P ADV CRYPTOLOGY, P547; Beresford AR, 2003, IEEE PERVAS COMPUT, V2, P46, DOI 10.1109/MPRV.2003.1186725; Bettini C, 2005, LECT NOTES COMPUT SC, V3674, P185; Chen X., 2012, P CODASPY12, P49; Chor B, 1998, J ACM, V45, P965, DOI 10.1145/293347.293350; Damiani M., 2010, T DATA PRIVACY, V3, P123; Duckham M., 2005, LECT NOTES COMPUTER, V3468, P243; ELGAMAL T, 1985, IEEE T INFORM THEORY, V31, P469, DOI 10.1109/TIT.1985.1057074; Gedik B, 2005, INT CON DISTR COMP S, P620, DOI 10.1109/ICDCS.2005.48; Gentry C, 2005, LECT NOTES COMPUT SC, V3580, P803; Ghinita G, 2009, LECT NOTES COMPUT SC, V5644, P98, DOI 10.1007/978-3-642-02982-0_9; Ghinita G., 2010, GEOINFORMATICA, V15, P1; Ghinita G., 2010, P GIS 10, P3, DOI 10.1145/1869790.1869795; Ghinita G., 2008, P ACM SIGMOD INT C M, P121, DOI DOI 10.1145/1376616.1376631; Gruteser M, 2003, PROCEEDINGS OF MOBISYS 2003, P31, DOI 10.1145/1066116.1189037; Hashem T, 2007, LECT NOTES COMPUT SC, V4717, P372; Hoh B., 2005, P 1 INT C SEC PRIV E, P194, DOI DOI 10.1109/SECURECOMM.2005.33; Kalnis P, 2007, IEEE T KNOWL DATA EN, V19, P1719, DOI 10.1109/TKDE.2007.190662; Kido H., 2005, Proceedings. International Conference on Pervasive Services 2005 (IEEE Cat. No. 05EX1040); Krumm J, 2009, PERS UBIQUIT COMPUT, V13, P391, DOI 10.1007/s00779-008-0212-5; Kushilevitz E., 1997, Proceedings. 38th Annual Symposium on Foundations of Computer Science (Cat. No.97CB36150), DOI 10.1109/SFCS.1997.646125; Marconi L, 2010, LECT NOTES COMPUT SC, V6476, P325, DOI 10.1007/978-3-642-17650-0_23; Mascetti S., 2007, P 1 INT WORKSH PRIV, P258; Mokbel M. F., 2006, P 32 INT C VER LARG, P763; Naor M., 1999, P CRYPTO SANT BARB C, V1666, P791; Paillier P, 1999, LECT NOTES COMPUT SC, V1592, P223; Palanisamy B, 2011, PROC INT CONF DATA, P494, DOI 10.1109/ICDE.2011.5767898; Paulet R, 2012, PROC INT CONF DATA, P44, DOI 10.1109/ICDE.2012.95; POHLIG SC, 1978, IEEE T INFORM THEORY, V24, P106, DOI 10.1109/TIT.1978.1055817; Shoup V., 2011, NUMBER THEORY LIB; Sweeney L, 2002, INT J UNCERTAIN FUZZ, V10, P557, DOI 10.1142/S0218488502001648; Xu T, 2009, CCS'09: PROCEEDINGS OF THE 16TH ACM CONFERENCE ON COMPUTER AND COMMUNICATIONS SECURITY, P348 32 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. MAY 2014 26 5 1200 1210 10.1109/TKDE.2013.87 11 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Computer Science; Engineering AJ8OP WOS:000337965900013 J de Campos, LM; Fernandez-Luna, JM; Huete, JF; Vicente-Lopez, E de Campos, Luis M.; Fernandez-Luna, Juan M.; Huete, Juan F.; Vicente-Lopez, Eduardo Using Personalization to Improve XML Retrieval IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING English Article Information retrieval; XML; personalization; query expansion; reranking; CAS queries STRUCTURED DOCUMENT-RETRIEVAL; INFORMATION-RETRIEVAL; RELEVANCE FEEDBACK; WEB SEARCH; SYSTEM; MODEL As the amount of information increases every day and the users normally formulate short and ambiguous queries, personalized search techniques are becoming almost a must. Using the information about the user stored in a user profile, these techniques retrieve results that are closer to the user preferences. On the other hand, the information is being stored more and more in an semi-structured way, and XML has emerged as a standard for representing and exchanging this type of data. XML search allows a higher retrieval effectiveness, due to its ability to retrieve and to show the user specific parts of the documents instead of the full document. In this paper we propose several personalization techniques in the context of XML retrieval. We try to combine the different approaches where personalization may be applied: query reformulation, re-ranking of results and retrieval model modification. The experimental results obtained from a user study using a parliamentary document collection support the validity of our approach. [de Campos, Luis M.; Fernandez-Luna, Juan M.; Huete, Juan F.; Vicente-Lopez, Eduardo] Univ Granada, Dept Ciencias Comp & Inteligencia Artificial, ETSI Informat & Telecomunicac, CITIC UGR, E-18071 Granada, Spain de Campos, LM (reprint author), Univ Granada, Dept Ciencias Comp & Inteligencia Artificial, ETSI Informat & Telecomunicac, CITIC UGR, E-18071 Granada, Spain. lci@decsai.ugr.es; jmfluna@decsai.ugr.es; jhg@decsai.ugr.es; evicente@decsai.ugr.es Spanish "Consejeria de Innovacion, Ciencia y Empresa de la Junta de Andalucia"; Ministerio de Ciencia e Innovacion; research programme "Consolider Ingenio" [P09-TIC-4526, TIN2011-28538-C02-02, MIPRCV:CSD2007-00018] This paper has been supported in part by the Spanish "Consejeria de Innovacion, Ciencia y Empresa de la Junta de Andalucia," the "Ministerio de Ciencia e Innovacion" and the research programme "Consolider Ingenio 2010" under the projects P09-TIC-4526, TIN2011-28538-C02-02, and MIPRCV:CSD2007-00018, respectively. Allan J., 2002, WORKSH HELD CTR INT, P1; Amer-Yahia S., 2005, P 31 INT C VLDB TRON, P1310; Amer-Yahia S., 2007, P INT C DAT ENG ICDE, P906; Belkin N. J., 2008, P 30 EUR C INF RETR; Carpineto C, 2012, ACM COMPUT SURV, V44, DOI 10.1145/2071389.2071390; Chang H., 2000, P 17 INT C MACH LEAR, P127; Chernishev G., P SYRCODIS 2008 C DA; Chirita P. A., 2007, P 30 ANN INT ACM SIG, P7, DOI 10.1145/1277741.1277746; Chirita Paul-Alexandru, 2006, P 15 ACM INT C INF K, P287, DOI 10.1145/1183614.1183658; Croft B. W., 2001, P 2 DELOS WORKSH PER; de Campos L. M., 2013, P 28 ACM SAC COIMBR, P872; de Campos L. M., 2010, P INT C KNOWL DISC I, P418; de Campos LM, 2009, LECT NOTES COMPUT SC, V5631, P39; de Campos LM, 2009, PROGRAM-ELECTRON LIB, V43, P156, DOI 10.1108/00330330910954370; de Campos LM, 2009, LECT NOTES ARTIF INT, V5822, P617, DOI 10.1007/978-3-642-04957-6_53; de Campos LM, 2004, INFORM PROCESS MANAG, V40, P829, DOI 10.1016/j.ipm.2004.04.014; de Campos LM, 2010, INFORM PROCESS MANAG, V46, P514, DOI 10.1016/j.ipm.2009.11.006; de Campos LM, 2005, LECT NOTES COMPUT SC, V3408, P215; Dou Zhicheng, 2007, P 16 INT C WORLD WID, P581, DOI DOI 10.1145/1242572.1242651; Furh N., 2006, LNCS, V3977; Furh N., 2008, LNCS, V4862; Haveliwala TH, 2003, IEEE T KNOWL DATA EN, V15, P784, DOI 10.1109/TKDE.2003.1208999; Hlaoua Lobna, 2010, International Journal on Digital Libraries, V11, DOI 10.1007/s00799-010-0061-5; Hsu W, 2004, PROC INT C TOOLS ART, P526; Jarvelin K, 2002, ACM T INFORM SYST, V20, P422, DOI 10.1145/582415.582418; Jeh G., 2003, P 12 INT C WORLD WID, P271, DOI DOI 10.1145/775152.775191; Kamps J, 2008, LECT NOTES COMPUT SC, V4862, P24, DOI 10.1007/978-3-540-85902-4_2; Kazai G, 2006, LECT NOTES COMPUT SC, V3977, P16; Lalmas M., 2009, XML RETRIEVAL; Liu F, 2004, IEEE T KNOWL DATA EN, V16, P28; Macdonald C., 2007, P ACM INT C INF KNOW, P341, DOI 10.1145/1321440.1321490; Mass Y, 2005, LECT NOTES COMPUT SC, V3493, P303; Matthijs N., 2011, P 4 ACM INT C WEB SE, P25, DOI 10.1145/1935826.1935840; Meister L, 2011, INFORM RETRIEVAL, V14, P413, DOI 10.1007/s10791-010-9150-8; Meister L., 2009, IEIS200901 ISR I TEC; Nie L., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148189; Pan HL, 2004, LECT NOTES COMPUT SC, V3268, P187; Pitkow J, 2002, COMMUN ACM, V45, P50; Sakai T., 2005, ACM T ASIAN LANG INF, V2, P111; Schenkel R, 2006, LECT NOTES COMPUT SC, V3896, P331; Schenkel R, 2006, LECT NOTES COMPUT SC, V3936, P326; Shen X., 2005, P 14 ACM INT C INF K, P824, DOI DOI 10.1145/1099554.1099747; Sieg A., 2007, P 16 ACM C INF KNOWL, P525, DOI 10.1145/1321440.1321515; Steichen B, 2012, INFORM PROCESS MANAG, V48, P698, DOI 10.1016/j.ipm.2011.12.004; Sugiyama K., 2004, P 13 INT C WORLD WID, P675, DOI 10.1145/988672.988764; Tamine-Lechani L, 2010, KNOWL INF SYST, V24, P1, DOI 10.1007/s10115-009-0231-1; Tao XH, 2011, IEEE T KNOWL DATA EN, V23, P496, DOI 10.1109/TKDE.2010.145; Teevan J, 2010, ACM T COMPUT-HUM INT, V17, DOI 10.1145/1721831.1721835; Teevan J., 2005, P 28 ANN INT ACM SIG, P449, DOI 10.1145/1076034.1076111; Trotman A, 2005, LECT NOTES COMPUT SC, V3493, P16; Zighelnic L., 2008, P 31 ANN INT ACM SIG, P825, DOI 10.1145/1390334.1390524 51 0 0 IEEE COMPUTER SOC LOS ALAMITOS 10662 LOS VAQUEROS CIRCLE, PO BOX 3014, LOS ALAMITOS, CA 90720-1314 USA 1041-4347 1558-2191 IEEE T KNOWL DATA EN IEEE Trans. Knowl. Data Eng. MAY 2014 26 5 1280 1292 10.1109/TKDE.2013.75 13 Computer Science, Artificial Intelligence; Computer Science, Information Systems; Engineering, Electrical & Electronic Computer Science; Engineering AJ8OP WOS:000337965900019 J Kornell, N Kornell, Nate Where Is the "Meta" in Animal Metacognition? JOURNAL OF COMPARATIVE PSYCHOLOGY English Article metacognition; humans; animals; certainty; confidence MONKEYS MACACA-MULATTA; REINFORCEMENT SIGNALS; UNCERTAINTY RESPONSES; MEMORY PREDICTIONS; CUE-FAMILIARITY; JUDGMENTS; METAMEMORY; INFORMATION; HUMANS; RETRIEVAL Apes, dolphins, and some monkeys seem to have metacognitive abilities: They can accurately evaluate the likelihood that their response in cognitive task was (or will be) correct. These certainty judgments are seen as significant because they imply that animals can evaluate internal cognitive states, which may entail meaningful self-reflection. But little research has investigated what is being reflected upon: Researchers have assumed that when animals make metacognitive judgments they evaluate internal memory strength. Yet decades of research have demonstrated that humans cannot directly evaluate internal memory strength. Instead, they make certainty judgments by drawing inferences from cues they can evaluate, such as familiarity and ease of processing. It seems likely that animals do the same, but this hypothesis has not been tested. I suggest two strategies for investigating the internal cues that underlie animal metacognitive judgments. It is possible that animals, like humans, are capable of making certainty judgments based on internal cues without awareness or meaningful self-reflection. Williams Coll, Dept Psychol, Williamstown, MA 01267 USA Kornell, N (reprint author), Williams Coll, Dept Psychol, Williamstown, MA 01267 USA. nkornell@gmail.com Alter AL, 2009, PERS SOC PSYCHOL REV, V13, P219, DOI 10.1177/1088868309341564; Baertlein L., 2011, REUTERS; BEGG I, 1989, J MEM LANG, V28, P610, DOI 10.1016/0749-596X(89)90016-8; Benjamin A. S., 1996, IMPLICIT MEMORY META, P309; Benjamin AS, 1998, J EXP PSYCHOL GEN, V127, P55, DOI 10.1037/0096-3445.127.1.55; Beran MJ, 2013, PSYCHOL SCI, V24, P660, DOI 10.1177/0956797612458936; Beran MJ, 2009, J EXP PSYCHOL ANIM B, V35, P371, DOI 10.1037/a0014626; Besken M, 2013, MEM COGNITION, V41, P897, DOI 10.3758/s13421-013-0307-8; Bjork R. A., 2008, HDB METAMEMORY MEMOR; Bjork RA, 2013, ANNU REV PSYCHOL, V64, P417, DOI 10.1146/annurev-psych-113011-143823; BOLLES R, 1954, J COMP PHYSIOL PSYCH, V47, P378, DOI 10.1037/h0060058; Call J, 2010, ANIM COGN, V13, P689, DOI 10.1007/s10071-010-0317-x; Call J, 2001, ANIM COGN, V3, P207, DOI [DOI 10.1007/S100710100078, 10.1007/s100710100078]; Couchman JJ, 2010, J COMP PSYCHOL, V124, P356, DOI 10.1037/a0020129; Crystal JD, 2011, CURR ZOOL, V57, P531; DUNLOSKY J, 1992, MEM COGNITION, V20, P374, DOI 10.3758/BF03210921; GALLUP GG, 1970, SCIENCE, V167, P86, DOI 10.1126/science.167.3914.86; Hampton RR, 2001, P NATL ACAD SCI USA, V98, P5359, DOI 10.1073/pnas.071600998; Hampton RR, 2004, ANIM COGN, V7, P239, DOI 10.1007/s10071-004-0215-1; Hampton RR, 2009, COMP COGNITION BEHAV, V4, P17, DOI DOI 10.3819/CCBR.2009.40002; HART JT, 1967, J VERB LEARN VERB BE, V6, P685, DOI 10.1016/S0022-5371(67)80072-0; Inman A, 1999, J EXP PSYCHOL ANIM B, V25, P389, DOI 10.1037/0097-7403.25.3.389; JACOBY LL, 1987, PERS SOC PSYCHOL B, V13, P314, DOI 10.1177/0146167287133003; JAMESON KA, 1990, ACTA PSYCHOL, V73, P55, DOI 10.1016/0001-6918(90)90058-N; KELLEY CM, 1993, J MEM LANG, V32, P1, DOI 10.1006/jmla.1993.1001; Kelley CM, 1996, J MEM LANG, V35, P157, DOI 10.1006/jmla.1996.0009; Koriat A, 1997, J EXP PSYCHOL GEN, V126, P349, DOI 10.1037/0096-3445.126.4.349; Kornell N., 2008, HDB MEMORY METAMEMOR, P333; Kornell N, 2007, PSYCHOL SCI, V18, P64, DOI 10.1111/j.1467-9280.2007.01850.x; Kornell N, 2011, PSYCHOL SCI, V22, P787, DOI 10.1177/0956797611407929; Kornell N, 2009, CURR DIR PSYCHOL SCI, V18, P11, DOI 10.1111/j.1467-8721.2009.01597.x; Le Pelley ME, 2012, J EXP PSYCHOL LEARN, V38, P686, DOI 10.1037/a0026478; Magnussen S, 2006, MEMORY, V14, P595, DOI 10.1080/09658210600646716; Matvey G, 2001, MEM COGNITION, V29, P222, DOI 10.3758/BF03194916; Metcalfe J, 2003, BEHAV BRAIN SCI, V26, P350; METCALFE J, 1993, J EXP PSYCHOL LEARN, V19, P851, DOI 10.1037//0278-7393.19.4.851; Metcalfe J, 2005, MISSING LINK COGNITI, DOI 10.1093/acprof:oso/9780195161564.003.0002; NELSON TO, 1994, PSYCHOL SCI, V5, P207, DOI 10.1111/j.1467-9280.1994.tb00502.x; Nestle M., 2011, ATLANTIC; REDER LM, 1992, J EXP PSYCHOL LEARN, V18, P435, DOI 10.1037/0278-7393.18.3.435; Reder L. M., 1996, IMPLICIT MEMORY META, P45; REDER LM, 1987, COGNITIVE PSYCHOL, V19, P90, DOI 10.1016/0010-0285(87)90005-3; Redford JS, 2010, J EXP PSYCHOL LEARN, V36, P248, DOI 10.1037/a0017809; Rhodes MG, 2008, J EXP PSYCHOL GEN, V137, P615, DOI 10.1037/a0013684; Rhodes MG, 2009, PSYCHON B REV, V16, P550, DOI 10.3758/PBR.16.3.550; Schwartz B. L., 2002, TIP TONGUE STATES PH; Schwartz BL, 1997, CURR DIR PSYCHOL SCI, V6, P132, DOI 10.1111/1467-8721.ep10772899; SCHWARTZ BL, 1992, J EXP PSYCHOL LEARN, V18, P1074, DOI 10.1037/0278-7393.18.5.1074; Shields WE, 1997, J EXP PSYCHOL GEN, V126, P147, DOI 10.1037/0096-3445.126.2.147; Smith J. D., 2009, COMP COGNITION BEHAV, V4, P40, DOI [10.3819/ccbr.2009.40004, DOI 10.3819/CCBR.2009.40004]; SMITH JD, 1995, J EXP PSYCHOL GEN, V124, P391, DOI 10.1037/0096-3445.124.4.391; Smith JD, 1998, J EXP PSYCHOL GEN, V127, P227, DOI 10.1037//0096-3445.127.3.227; Smith JD, 2009, TRENDS COGN SCI, V13, P389, DOI 10.1016/j.tics.2009.06.009; Smith JD, 2003, BEHAV BRAIN SCI, V26, P317; Smith JD, 2008, PSYCHON B REV, V15, P679, DOI 10.3758/PBR.15.4.679; Smith JD, 2006, J EXP PSYCHOL GEN, V135, P282, DOI 10.1037/0096-3445.135.2.282; Son L. K., 2005, MISSING LINK COGNITI, P296, DOI 10.1093/acprof:oso/9780195161564.003.0012; Sutton JE, 2008, J EXP PSYCHOL ANIM B, V34, P266, DOI 10.1037/0097-7403.34.2.266; Templer VL, 2012, ANIM COGN, V15, P409, DOI 10.1007/s10071-011-0468-4; Terrace H. S., 2005, MISSING LINK COGNITI, DOI [10.1093/acprof:oso/9780195161564.001.0001, DOI 10.1093/ACPROF:OSO/9780195161564.001.0001]; Terrace HS, 2009, CURR OPIN NEUROBIOL, V19, P67, DOI 10.1016/j.conb.2009.06.004; Undorf M, 2011, J EXP PSYCHOL LEARN, V37, P1264, DOI 10.1037/a0023719 62 5 5 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0735-7036 1939-2087 J COMP PSYCHOL J. Comp. Psychol. MAY 2014 128 2 SI 143 149 10.1037/a0033444 7 Behavioral Sciences; Psychology; Psychology, Multidisciplinary; Zoology Behavioral Sciences; Psychology; Zoology AJ7TB WOS:000337899000006 J Vlassova, L; Perez-Cabello, F; Nieto, H; Martin, P; Riano, D; de la Riva, J Vlassova, Lidia; Perez-Cabello, Fernando; Nieto, Hector; Martin, Pilar; Riano, David; de la Riva, Juan Assessment of Methods for Land Surface Temperature Retrieval from Landsat-5 TM Images Applicable to Multiscale Tree-Grass Ecosystem Modeling REMOTE SENSING English Article land surface temperature; Landsat; multitemporal THERMAL-INFRARED DATA; DIFFERENCE VEGETATION INDEX; SENSIBLE HEAT-FLUX; ATMOSPHERIC CORRECTION; EMISSIVITY RETRIEVAL; WINDOW ALGORITHM; SATELLITE DATA; EVAPORATION; MODIS; RADIOMETER Land Surface Temperature (LST) is one of the key inputs for Soil-Vegetation-Atmosphere transfer modeling in terrestrial ecosystems. In the frame of BIOSPEC (Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of global change) and FLUXPEC (Monitoring changes in water and carbon fluxes from remote and proximal sensing in Mediterranean. dehesa. ecosystem) projects LST retrieved from Landsat data is required to integrate ground-based observations of energy, water, and carbon fluxes with multi-scale remotely-sensed data and assess water and carbon balance in ecologically fragile heterogeneous ecosystem of Mediterranean wooded grassland (dehesa). Thus, three methods based on the Radiative Transfer Equation were used to extract LST from a series of 2009-2011 Landsat-5 TM images to assess the applicability for temperature input generation to a Landsat-MODIS LST integration. When compared to surface temperatures simulated using MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) with atmospheric profiles inputs (LSTref), values from Single-Channel (SC) algorithm are the closest (root-mean-square deviation (RMSD) = 0.50 degrees C); procedure based on the online Radiative Transfer Equation Atmospheric Correction Parameters Calculator (RTE-ACPC) shows RMSD = 0.85 degrees C; Mono-Window algorithm (MW) presents the highest RMSD (2.34 degrees C) with systematical LST underestimation (bias = 1.81 degrees C). Differences between Landsat-retrieved LST and MODIS LST are in the range of 2 to 4 degrees C and can be explained mainly by differences in observation geometry, emissivity, and time mismatch between Landsat and MODIS overpasses. There is a seasonal bias in Landsat-MODIS LST differences due to greater variations in surface emissivity and thermal contrasts between landcover components. [Vlassova, Lidia; Perez-Cabello, Fernando; de la Riva, Juan] Univ Zaragoza, Dept Geog & Spatial Management, IUCA, GEOFOREST Grp, E-50009 Zaragoza, Spain; [Vlassova, Lidia] Tech State Univ Quevedo, Dept Environm Sci, Los Rios, Ecuador; [Nieto, Hector] Univ Copenhagen, Dept Geosci & Nat Resource Management, DK-1350 Copenhagen K, Denmark; [Martin, Pilar; Riano, David] Spanish Council Sci Res, Ctr Human & Social Sci, Madrid 28037, Spain; [Riano, David] Univ Calif Davis, Dept Land Air & Water Resources, Ctr Spatial Technol & Remote Sensing CSTARS, Davis, CA 95616 USA Vlassova, L (reprint author), Univ Zaragoza, Dept Geog & Spatial Management, IUCA, GEOFOREST Grp, Pedro Cerbuna 12, E-50009 Zaragoza, Spain. vlassova@unizar.es; fcabello@unizar.es; hn@geo.ku.dk; mpilar.martin@cchs.csic.es; david.riano@cchs.csic.es; delariva@unizar.es BIOSPEC, Ministry of Science and Innovation, Spain [CGL2008-02301/CLI]; FLUXPEC, Ministry of Economy and Competitiveness, Spain [CGL2012-34383]; Aragon Government; Obra Social "La Caixa" (DGA-La Caixa), Spain [GA-LC-042/2011] This research has been financially supported by the BIOSPEC project "Linking spectral information at different spatial scales with biophysical parameters of Mediterranean vegetation in the context of Global Change" [28] (CGL2008-02301/CLI, Ministry of Science and Innovation, Spain), the FLUXPEC project "Monitoring changes in water and carbon fluxes from remote and proximal sensing in a Mediterranean dehesa ecosystem" [29] (CGL2012-34383, Ministry of Economy and Competitiveness, Spain), and by a collaboration agreement between the Aragon Government and the Obra Social "La Caixa" (DGA-La Caixa, GA-LC-042/2011), Spain. The authors also appreciate the financial support provided to this research by SENESCYT, Ecuador. We are grateful to Research Group AIRE of Physics Department, University of Extremadura, for establishing and maintaining the Caceres AERONET site database used in this research. Agam N, 2007, REMOTE SENS ENVIRON, V107, P545, DOI 10.1016/j.rse.2006.10.006; Baldocchi D, 2001, B AM METEOROL SOC, V82, P2415, DOI 10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2; Barsi J.A., 2003, P IEEE INT, V5, P3014; Barsi Julia A, 2005, Proceedings of the SPIE - The International Society for Optical Engineering, V5882, DOI 10.1117/12.619990; BECKER F, 1990, REMOTE SENS ENVIRON, V32, P17, DOI 10.1016/0034-4257(90)90095-4; Berk A, 1999, P SOC PHOTO-OPT INS, V3756, P348, DOI 10.1117/12.366388; Berk A, 2011, MODTRAN 5 2 1 USERS; CHOUDHURY BJ, 1994, REMOTE SENS ENVIRON, V50, P1, DOI 10.1016/0034-4257(94)90090-6; Cleugh HA, 2007, REMOTE SENS ENVIRON, V106, P285, DOI 10.1016/j.rse.2006.07.007; Coll C, 2012, REMOTE SENS ENVIRON, V117, P199, DOI 10.1016/j.rse.2011.09.018; Coll C, 2012, REMOTE SENS ENVIRON, V116, P211, DOI 10.1016/j.rse.2010.01.027; Copertino V.A., 2012, TETHYS, V9, P25; Cristobal J, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010616; French AN, 2003, REMOTE SENS ENVIRON, V87, P326, DOI 10.1016/j.rse.2003.08.001; Gamon JA, 2004, REMOTE SENS ENVIRON, V89, P139, DOI 10.1016/j.rse.2003.08.017; Gillespie A, 1998, IEEE T GEOSCI REMOTE, V36, P1113, DOI 10.1109/36.700995; Gutman G, 1998, INT J REMOTE SENS, V19, P1533, DOI 10.1080/014311698215333; Hesslerova P, 2013, ECOL ENG, V54, P145, DOI 10.1016/j.ecoleng.2013.01.036; Jia L, 2001, ADV GLOB CHANGE RES, V7, P23; Jimenez-Munoz JC, 2009, IEEE T GEOSCI REMOTE, V47, P339, DOI 10.1109/TGRS.2008.2007125; Jimenez-Munoz JC, 2010, REMOTE SENS ENVIRON, V114, P2195, DOI 10.1016/j.rse.2010.04.022; Jimenez-Munoz J.C., 2004, J GEOPHYS RES, V108, P46; Kalma JD, 2008, SURV GEOPHYS, V29, P421, DOI 10.1007/s10712-008-9037-z; Kistler R., 2001, NCEP NCAR 50 YEAR RE; Kneizys F.X., 1988, USERS GUIDE LOWTRAN; Kustas W, 2009, AGR FOREST METEOROL, V149, P2071, DOI 10.1016/j.agrformet.2009.05.016; Li FQ, 2004, REMOTE SENS ENVIRON, V92, P521, DOI 10.1016/j.rse.2004.02.018; Li H, 2010, INT GEOSCI REMOTE SE, P2448, DOI 10.1109/IGARSS.2010.5649801; Li ZL, 2013, INT J REMOTE SENS, V34, P3084, DOI 10.1080/01431161.2012.716540; Li ZL, 2013, REMOTE SENS ENVIRON, V131, P14, DOI 10.1016/j.rse.2012.12.008; Moran MS, 1996, AGR FOREST METEOROL, V80, P87, DOI 10.1016/0168-1923(95)02292-9; Noyes E., 2006, P 2 WORK M MERIS AAT; Nunez Corchero M., 2001, CLIMATOLOGIA EXTREMA; OLIOSO A, 1995, INT J REMOTE SENS, V16, P3211; Qian Y.-G., 2012, INT J REMOTE SENS, V34, P3140; Qin Z, 2001, INT J REMOTE SENS, V22, P3719, DOI 10.1080/01431160010006971; Quattrochi D.A., 2004, THERMAL REMOTE SENSI; Rasmussen MO, 2010, IEEE T GEOSCI REMOTE, V48, P3123, DOI 10.1109/TGRS.2010.2044509; Rubio E, 1997, REMOTE SENS ENVIRON, V59, P490, DOI 10.1016/S0034-4257(96)00123-X; Schmugge T, 1998, REMOTE SENS ENVIRON, V65, P121, DOI 10.1016/S0034-4257(98)00023-6; Snyder WC, 1998, INT J REMOTE SENS, V19, P2753, DOI 10.1080/014311698214497; SOBRINO JA, 1991, REMOTE SENS ENVIRON, V38, P19, DOI 10.1016/0034-4257(91)90069-I; Sobrino JA, 2001, REMOTE SENS ENVIRON, V75, P256, DOI 10.1016/S0034-4257(00)00171-1; Sobrino JA, 2004, REMOTE SENS ENVIRON, V90, P434, DOI 10.1016/j.rse.2004.02.003; Sobrino J.A., 2013, THERMAL INFRARED REM, P197; Sobrino JA, 2000, INT J REMOTE SENS, V21, P353, DOI 10.1080/014311600210876; Sobrino JA, 2008, IEEE T GEOSCI REMOTE, V46, P316, DOI 10.1109/TGRS.2007.904834; Soria G., 2008, P 2 MERIS AATSR US W; Srivastava PK, 2009, ADV SPACE RES, V43, P1563, DOI 10.1016/j.asr.2009.01.023; TANG H, 2014, QUANTITATIVE REMOTE, P1; Trigo IF, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD010035; Valor E, 1996, REMOTE SENS ENVIRON, V57, P167, DOI 10.1016/0034-4257(96)00039-9; VANDEGRIEND AA, 1993, INT J REMOTE SENS, V14, P1119; Walker JJ, 2012, REMOTE SENS ENVIRON, V117, P381, DOI 10.1016/j.rse.2011.10.014; Wan Z, 2008, INT J REMOTE SENS, V29, P5373, DOI 10.1080/01431160802036565; Wan ZM, 1996, IEEE T GEOSCI REMOTE, V34, P892; Zhan X, 1996, REMOTE SENS ENVIRON, V58, P242, DOI 10.1016/S0034-4257(96)00049-1; Zhao L., 2010, P AS PAC POW EN ENG, P1, DOI 10.1145/1838574.1838598 58 1 1 MDPI AG BASEL POSTFACH, CH-4005 BASEL, SWITZERLAND 2072-4292 REMOTE SENS-BASEL Remote Sens. MAY 2014 6 5 4345 4368 10.3390/rs6054345 24 Remote Sensing Remote Sensing AI8JW WOS:000337160700039 J Hao, N; Yuan, H; Hu, Y; Grabner, RH Hao, Ning; Yuan, Huan; Hu, Yi; Grabner, Roland H. Interaction effect of body position and arm posture on creative thinking LEARNING AND INDIVIDUAL DIFFERENCES English Article Creative thinking; Embodiment; Body position; Arm motor action AVOIDANCE MOTOR ACTIONS; VALENCED INFORMATION; DIVERGENT THINKING; DOMAIN KNOWLEDGE; IDEA GENERATION; COGNITION; ATTITUDES; INCUBATION; RETRIEVAL; TOLERANCE Previous studies revealed that in the seated body position, an approach motor action of arm flexion can improve creative thinking compared to an avoidance motor action of arm extension. However in the lying body position, the associations of arm flexion/extension to approach/avoidance motor action are converse. Therefore, there is an opposite prediction for the effect of arm posture on creative thinking. The study reported here asked the participants to work on Alternative Uses Task (AUT) problems while performing arm flexion and arm extension, in the body contexts of being seated on a chair or lying in bed. The results demonstrated that arm flexion and extension in the lying body position exerted effects on AUT performance in a converse pattern compared to that in the seated body position. This is the first study that revealed an interaction effect of body position and arm posture on creative thinking. (C) 2014 Elsevier Inc. All rights reserved. [Hao, Ning; Yuan, Huan; Hu, Yi] E China Normal Univ, Sch Psychol & Cognit Sci, Key Lab Brain Funct Genom MOE & STCSM, Shanghai 200062, Peoples R China; [Grabner, Roland H.] Univ Gottingen, Georg Elias Muller Inst Psychol, D-37073 Gottingen, Germany Hao, N (reprint author), E China Normal Univ, Sch Psychol & Cognit Sci, Key Lab Brain Funct Genom MOE & STCSM, Shanghai 200062, Peoples R China. nhao@psy.ecnu.edu.cn Abele B. A., 1992, POLISH PSYCHOL B, V23, P203; Baird B, 2012, PSYCHOL SCI, V23, P1117, DOI 10.1177/0956797612446024; Barsalou LW, 2008, ANNU REV PSYCHOL, V59, P617, DOI 10.1146/annurev.psych.59.103006.093639; Bohns VK, 2012, J EXP SOC PSYCHOL, V48, P341, DOI 10.1016/j.jesp.2011.05.022; CACIOPPO JT, 1993, J PERS SOC PSYCHOL, V65, P5, DOI 10.1037//0022-3514.65.1.5; Carney DR, 2010, PSYCHOL SCI, V21, P1363, DOI 10.1177/0956797610383437; Clore G, 1994, HDB SOCIAL COGNITION, V1, P323; Fernandez-Abascal EG, 2013, CREATIVITY RES J, V25, P213, DOI 10.1080/10400419.2013.783759; Forgeard MJC, 2011, PERS INDIV DIFFER, V51, P904, DOI 10.1016/j.paid.2011.07.015; Forster J, 1997, PERCEPT MOTOR SKILL, V85, P1419; Forster J, 1998, J PERS SOC PSYCHOL, V75, P1115, DOI 10.1037/0022-3514.75.5.1115; Forster J, 1998, PERCEPT MOTOR SKILL, V86, P1423; Forster J, 2010, PSYCHOL INQ, V21, P175, DOI 10.1080/1047840X.2010.487849; Forster J, 2006, J EXP SOC PSYCHOL, V42, P133, DOI 10.1016/j.jesp.2005.02.004; Friedman RS, 2002, J EXP SOC PSYCHOL, V38, P41, DOI 10.1006/jesp.2001.1488; Friedman RS, 2010, PSYCHOL BULL, V136, P875, DOI 10.1037/a0020495; Friedman RS, 2000, J PERS SOC PSYCHOL, V79, P477, DOI 10.1037//0022-3514.79.4.477; Friedman RS, 2008, HANDBOOK OF APPROACH AND AVOIDANCE MOTIVATION, P235; Gilhooly K. J., 2012, THINK REASONING, V19, P137, DOI 10.1080/13546783.2012.749812; Gilhooly KJ, 2012, MEM COGNITION, V40, P966, DOI 10.3758/s13421-012-0199-z; GUILFORD J. P., 1967, NATURE HUMAN INTELLI; Hao N, 2010, J CREATIVE BEHAV, V44, P237; Koch S, 2008, COGNITION, V109, P133, DOI 10.1016/j.cognition.2008.07.014; Kuschel S, 2010, SOC PSYCHOL PERS SCI, V1, P4, DOI 10.1177/1948550609345023; Markman AB, 2005, PSYCHOL SCI, V16, P6, DOI 10.1111/j.0956-7976.2005.00772.x; Neumann R, 2000, J PERS SOC PSYCHOL, V79, P39, DOI 10.1037/0022-3514.79.1.39; Newton DP, 2013, THINK SKILLS CREAT, V8, P34, DOI 10.1016/j.tsc.2012.05.006; Niedenthal PM, 2005, PERS SOC PSYCHOL REV, V9, P184, DOI 10.1207/s15327957pspr0903_1; Priester JR, 1996, PERS SOC PSYCHOL B, V22, P442, DOI 10.1177/0146167296225002; Runco M. A, 1991, DIVERGENT THINKING; Runco MA, 2012, CREATIVITY RES J, V24, P66, DOI 10.1080/10400419.2012.652929; Runco M.A., 1999, ENCY CREATIVITY, VI, P577; Schwarz N., 1991, EMOTION SOCIAL JUDGM, P55; Stafford LD, 2010, PERS INDIV DIFFER, V48, P827, DOI 10.1016/j.paid.2010.02.005; STRACK F, 1988, J PERS SOC PSYCHOL, V54, P768, DOI 10.1037//0022-3514.54.5.768; Thibodeau R, 2011, PSYCHOPHYSIOLOGY, V48, P1011, DOI 10.1111/j.1469-8986.2010.01159.x; Ward TB, 2008, LEARN INDIVID DIFFER, V18, P363, DOI 10.1016/j.lindif.2007.07.002; Ward TB, 2004, CREATIVITY RES J, V16, P1, DOI 10.1207/s15326934crj1601_1; Zenasni F, 2011, THINK SKILLS CREAT, V6, P49, DOI 10.1016/j.tsc.2010.10.005 39 0 0 ELSEVIER SCIENCE BV AMSTERDAM PO BOX 211, 1000 AE AMSTERDAM, NETHERLANDS 1041-6080 1873-3425 LEARN INDIVID DIFFER Learn. Individ. Differ. MAY 2014 32 261 265 10.1016/j.lindif.2014.03.025 5 Psychology, Educational Psychology AI4FF WOS:000336820400030 J Stegmaier, F; Kosch, H; Klamma, R; Lux, M; Damiani, E Stegmaier, Florian; Kosch, Harald; Klamma, Ralf; Lux, Mathias; Damiani, Ernesto Multimedia on the web - editorial MULTIMEDIA TOOLS AND APPLICATIONS English Editorial Material Streaming video has recently surpassed peer-to-peer networks in terms of network capacity hunger. Reports estimate a share of 40 % of peak network capacity dedicated to entertainment, mostly streaming video. A large share of this traffic originates from web-based services. YouTube alone takes up to 8 % of the prime time Internet traffic. While transmission currently works in a best effort system, multimedia information systems on the web are far from being perfect. Retrieval, annotation, validated and useful metadata, reliable and trusted services, and user interaction and context-based adaptation are still under discussion and issues to research. Currently, the Web itself faces dramatic changes, looking for example at the spread of social networks, like Facebook, Twitter or the YouTube community, which become part of everyday life of more and more people. Technologies like Linked Data, HTML5, WebM, or WebRTC are enablers that make these changes possible and let people experience the Web without knowing details about technology. Also the availability of the Web has changed a lot. Many people create multimedia data on the go, put videos and photos on microblogs, or share audiovisual data with their loved ones or colleagues. These activities also have a deep effect on multimedia data formats and usage, multimedia systems and multimedia service providers. "Multimedia everywhere" was a common concept before, but this has changed to "relevant media at your finger tips", so new methods for finding, annotating, sharing, remixing, summarizing, transmitting, storing and consuming multimedia data have to be found. [Stegmaier, Florian; Kosch, Harald] Univ Passau, Lehrstuhl Verteilte Informat Syst, D-94032 Passau, Germany; [Klamma, Ralf] Rhein Westfal TH Aachen, Lehrstuhl Informat, Aachen, Germany; [Lux, Mathias] Klagenfurt Univ, Inst Informat Technol, Klagenfurt, Austria; [Damiani, Ernesto] Univ Milan, Dept Comp Sci, Milan, Italy Kosch, H (reprint author), Univ Passau, Lehrstuhl Verteilte Informat Syst, D-94032 Passau, Germany. Florian.Stegmaier@uni-passau.de; Harald.Kosch@uni-passau.de; klamma@dbis.rwth-aachen.de; mlux@itec.uni-klu.ac.at; Ernesto.Damiani@unimi.it 0 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2014 70 2 821 826 10.1007/s11042-013-1729-9 6 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI6OW WOS:000336995900012 J Ionescu, BE; Seyerlehner, K; Mironica, I; Vertan, C; Lambert, P Ionescu, Bogdan Emanuel; Seyerlehner, Klaus; Mironica, Ionut; Vertan, Constantin; Lambert, Patrick An audio-visual approach to web video categorization MULTIMEDIA TOOLS AND APPLICATIONS English Article Audio block-based descriptors; Color perception; Action assessment; Video relevance feedback; Video genre classification RELEVANCE FEEDBACK; GENRE CLASSIFICATION; IMAGE RETRIEVAL; FEATURES; SVM In this paper, we discuss and audio-visual approach to automatic web video categorization. To this end, we propose content descriptors which exploit audio, temporal, and color content. The power of our descriptors was validated both in the context of a classification system and as part of an information retrieval approach. For this purpose, we used a real-world scenario, comprising 26 video categories from the blip.tv media platform (up to 421 h of video footage). Additionally, to bridge the descriptor semantic gap, we propose a new relevance feedback technique which is based on hierarchical clustering. Experiments demonstrated that with this technique retrieval performance can be increased significantly and becomes comparable to that of high level semantic textual descriptors. [Ionescu, Bogdan Emanuel; Mironica, Ionut; Vertan, Constantin] Univ Politehn Bucuresti, LAPI, Bucharest 061071, Romania; [Ionescu, Bogdan Emanuel; Lambert, Patrick] Univ Savoie, Polytech Annecy Chambery, LISTIC, F-74944 Savoie, France; [Seyerlehner, Klaus] Johannes Kepler Univ Linz, DCP, A-4040 Linz, Austria Ionescu, BE (reprint author), Univ Politehn Bucuresti, LAPI, Bucharest 061071, Romania. bionescu@alpha.imag.pub.ro; klaus.seyerlehner@jku.at; imironica@alpha.imag.pub.ro; constantin.vertan@upb.ro; patrick.lambert@univ-savoie.fr Romanian Sectoral Operational Programme Human Resources Development [POSDRU/89/1.5/S/62557, POSDRU/89/1.5/S/64109]; Austrian Science Fund (FWF) [L511-N15] This work was supported by the Romanian Sectoral Operational Programme Human Resources Development 2007-2013 through the Financial Agreement POSDRU/89/1.5/S/62557 and POSDRU/89/1.5/S/64109, and by the Austrian Science Fund (FWF): L511-N15. We also acknowledge the 2011 Genre Tagging Task of the MediaEval Multimedia Benchmark [16] for providing the test data set. Al Z, 2011, EURASIP JIVP, DOI [10.1155/2011/537372, DOI 10.1155/2011/537372]; Brezeale D, 2008, IEEE T SYST MAN CY C, V38, P416, DOI 10.1109/TSMCC.2008.919173; Cigarran J, 2011, P MEDIAEVAL 2011 WOR; Fan J, 2004, ACM IEEE C DIG LIB T, P192; Fernando W.A.C., 1999, IEEE INT C IM PROC K, P299; Forman G., 2003, Journal of Machine Learning Research, V3, DOI 10.1162/153244303322753670; Gibert X, 2003, ICME, V2, P345; Hong GY, 2005, KYBERNETES, V34, P784, DOI 10.1108/03684920510595490; Huang SH, 2006, MULTIMEDIA SYST, V12, P14, DOI 10.1007/s00530-006-0028-y; Ionescu B, 2012, 18 INT C MULTIMEDIA; Ionescu B, 2008, EURASIP J IMAGE VIDE, DOI 10.1155/2008/849625; Ivanovici M, 2010, IEEE T IMAGE PROCESS, V20, P227; Kelm P, 2009, 2009 10TH INTERNATIONAL WORKSHOP ON IMAGE ANALYSIS FOR MULTIMEDIA INTERACTIVE SERVICES, P25, DOI 10.1109/WIAMIS.2009.5031423; Krzanowski W. J., 1993, PRINCIPLES MULTIVARI; Larson M, 2011, P MEDIAEVAL 2011 WOR, V807; Lee MH, 2003, IEEE INT C MULT EXP, V2, P157; Liang S, 2008, PATTERN RECOGN LETT, V29, P1733, DOI 10.1016/j.patrec.2008.05.004; Lienhart R, 2001, INT J IMAGE GRAPHICS, V1, P469, DOI 10.1142/S021946780100027X; Liu Z, 1998, IEEE WORKSH MULT SIG, P27; MacArthur SD, 2002, COMPUT VIS IMAGE UND, V88, P55, DOI 10.1006/cviu.2002.0977; Manning C, 2008, INTRO INFORM RETRIEV; Montagnuolo M, 2009, MULTIMED TOOLS APPL, V41, P125, DOI 10.1007/s11042-008-0222-3; Nguyen NV, 2009, INT C MACH LEARN PAT; Perea-Ortega JM, 2011, P MEDIAEVAL 2011 WOR; Perez-Iglesias J, 2009, CORR; Rasheed Z, 2005, IEEE T CIRC SYST VID, V15, P52, DOI 10.1109/TCSVT.2004.839993; Rasheed Z, 2002, INT C PATT RECOG, P1086; ROACH MJ, 2001, ACOUST SPEECH SIG PR, P1557; Rouvier M, 2011, P MEDIAEVAL 2011 WOR; Rudinac S, 2011, P MEDIAEVAL 2011 WOR; Rui Y, 1998, IEEE T CIRC SYST VID, V8, P644; Schmiedeke S, 2011, P MEDIAEVAL 2011 WOR; Semela T, 2011, P MEDIAEVAL 2011 WOR; Seyerlehner K., 2010, 6 ANN MUS INF RETR E; Smeaton AF, 2009, SIGNALS COMMUN TECHN, P151, DOI 10.1007/978-0-387-76569-3_6; Snoek CGM, 2005, ACM INT C MULT NEW Y; Srinivasan U, 2005, MULTIMED TOOLS APPL, V27, P105, DOI 10.1007/s11042-005-2716-6; Su CW, 2005, IEEE T MULTIMEDIA, V7, P1106, DOI 10.1109/TMM.2005.858394; Tiwari R, 2011, P MEDIAEVAL 2011 WOR; Truong BT, 2000, INT C PATT RECOG, P230; Wang HL, 2003, J VIS COMMUN IMAGE R, V14, P150, DOI 10.1016/S1047-3203(03)00019-1; Wang ZS, 2010, PROC CVPR IEEE, P879, DOI 10.1109/CVPR.2010.5540125; Wei G, 2000, 2000 IEEE INT C MULT, V3, P1345, DOI 10.1109/ICME.2000.871015; Wu YM, 2004, MULTIMEDIA SYST, V10, P41, DOI 10.1007/s00530-004-0136-5; Xu L-Q, 2003, IEEE INT C MULT EXP, P485; Yuan X, 2006, IEEE IMAGE PROC, P2905, DOI 10.1109/ICIP.2006.313037 46 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2014 70 2 1007 1032 10.1007/s11042-012-1097-x 26 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI6OW WOS:000336995900019 J Cusano, C; Santini, S Cusano, Claudio; Santini, Simone With a little help from my friends Community-based assisted organization of personal photographs MULTIMEDIA TOOLS AND APPLICATIONS English Article Personal photography; Automatic image annotation; Content-based image analysis; Social image retrieval IMAGE CLASSIFICATION; FEATURES In this paper, we propose a content-based method the for semi-automatic organization of photo albums based on the analysis of how different users organize their own pictures. The goal is to help the user in dividing his pictures into groups characterized by a similar semantic content. The method is semi-automatic: the user starts to assign labels to the pictures and unlabeled pictures are tagged with proposed labels. The user can accept the recommendation or made a correction. To formulate the suggestions is exploited the knowledge encoded in how other users have partitioned their images. The method is conceptually articulated in two parts. First, we use a suitable feature representation of the images to model the different classes that the users have collected, second, we look for correspondences between the criteria used by the different users. Boosting is used to integrate the information provided by the analysis of multiple users. A quantitative evaluation of the proposed approach is obtained by simulating the amount of user interaction needed to annotate the albums of a set of members of the flickr (R) photo-sharing community. [Cusano, Claudio] Univ Milano Bicocca, Dept Informat Syst & Commun DISCo, I-20126 Milan, Italy; [Santini, Simone] Univ Autonoma Madrid, Escuela Politecn Super, E-28049 Madrid, Spain Cusano, C (reprint author), Univ Milano Bicocca, Dept Informat Syst & Commun DISCo, Viale Sarca 336, I-20126 Milan, Italy. claudio.cusano@disco.unimib.it; simone.santini@uam.es Ministerio de Educacion y Ciencia [MEC TIN2008-06566-C04-02] S. Santini was supported in part by the Ministerio de Educacion y Ciencia under the grant N. MEC TIN2008-06566-C04-02, Information Retrieval on different media based on multidimensional models: relevance, novelty, personalization and context. Boutell M, 2004, PROC CVPR IEEE, P623; Chen L, 2003, INT J IMAGE GRAPHICS; Das M, 2003, IEEE SYS MAN CYBERN, P3726; Furht B, 1998, HDB MULTIMEDIA COMPU; Grauman K, 2005, IEEE I CONF COMP VIS, P1458; Ivory MY, 2001, ACM COMPUT SURV, V33, P470, DOI 10.1145/503112.503114; Jaimes A, 2000, P INT C IM PROC, V3, P528; Li J., 2003, P 4 INT C INF COMM S, V3, P1536, DOI 10.1109/ICICS.2003.1292724; Li X, 2008, P 1 ACM INT C MULT I, P180, DOI 10.1145/1460096.1460126; Loui AC, 2003, IEEE T MULTIMEDIA, V5, P390, DOI 10.1109/TMM.2003.814723; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Mulhem P, 2003, P INT C IM VID RETR, P308; Nister D., 2006, CVPR, V2, P2161, DOI DOI 10.1109/CVPR.2006.264; Obrador P., 2010, P ACM MULT C, P561, DOI DOI 10.1145/1873951.1874; Platt J. C., 2000, Proceedings IEEE Workshop on Content-Based Access of Image and Video Libraries, DOI 10.1109/IVL.2000.853847; Shevade B, 2003, P ACM SIGMM WORKSH E, P91, DOI 10.1145/982484.982502; Sun Y, 2002, P 10 ACM INT C MULT, P81; Vailaya A, 1998, PATTERN RECOGN, V31, P1921, DOI 10.1016/S0031-3203(98)00079-X; Vailaya A, 2001, IEEE T IMAGE PROCESS, V10, P117, DOI 10.1109/83.892448; Vedaldi A, 2005, SIFT LIGHTWEIGHT C I; Wallraven C., 2003, P 9 IEEE INT C COMP, V1, P257; Wenyin L, 2000, P 8 ACM INT C MULT, P479; Zhang J, 2007, INT J COMPUT VISION, V73, P213, DOI 10.1007/s11263-006-9794-4; Zhang L, 2003, P 11 ACM INT C MULT, P355; Zhu J, 2005, D91 SECURESCM 25 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2014 70 2 1033 1048 10.1007/s11042-012-1096-y 16 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI6OW WOS:000336995900020 J Liebeskind, DS Liebeskind, David S. Collateral lessons from recent acute ischemic stroke trials NEUROLOGICAL RESEARCH English Article Stroke; Ischemia; Collateral; Neuroprotection; Reperfusion ENDOVASCULAR TREATMENT; REVASCULARIZATION; THERAPY; PERFUSION; FLOW; STANDARDS; EFFICACY; NEUROFLO; SAFETY Numerous acute ischemic stroke trials have recently published detailed results, providing an opportunity to consider the role of collaterals in stroke pathophysiology and their influential effect on patient outcomes. Safety and Efficacy of NeuroFlo Technology in Ischemic Stroke (SENTIS), the largest randomized controlled trial of device therapy to date, tested the potential augmentation of collateral perfusion. SYNTHESIS Expansion, Mechanical Retrieval and Recanalization of Stroke Clots Using Embolectomy (MR RESCUE), and Interventional Management of Stroke (IMS) III chronicled the saga of endovascular therapy trialed against medical treatment for acute ischemic stroke. These recent randomized studies, however, largely neglect current device technology available for endovascular therapy as advanced by the TREVO2 and SOLITAIRE(TM) FR With the Intention For Thrombectomy (SWIFT) studies. Such exhaustive efforts in recent trials have failed to introduce a new treatment for stroke that unequivocally improves patient outcomes. Collateral perfusion is widely recognized to vary across individuals in any population and exerts a dramatic effect on baseline variables including the time course of ischemic injury, stroke severity, imaging findings, and therapeutic opportunities. Similarly, collaterals have been recognized to influence recanalization, reperfusion, hemorrhagic transformation, and subsequent neurological outcomes after stroke. Collateral lessons may be gleaned from these trials, to expand consideration of overall study results and perhaps most importantly, alter ongoing and new trials in development. Detailed analyses of available information on collaterals from these trials demonstrate that collaterals may be more influential than the choice of treatment modality or intervention. Univ Calif Los Angeles, Stroke Ctr, Los Angeles, CA 90095 USA Liebeskind, DS (reprint author), Univ Calif Los Angeles, Stroke Ctr, 710 Westwood Plaza, Los Angeles, CA 90095 USA. davidliebeskind@yahoo.com National Institutes of Health National Institutes of Health. Azizyan A, 2011, AM J NEURORADIOL, V32, P1771, DOI 10.3174/ajnr.A2265; Bang OY, 2008, J NEUROL NEUROSUR PS, V79, P625, DOI 10.1136/jnnp.2007.132100; Bang OY, 2011, STROKE, V42, P2235, DOI 10.1161/STROKEAHA.110.604603; Bang OY, 2011, STROKE, V42, P693, DOI 10.1161/STROKEAHA.110.595256; Barber PA, 2000, LANCET, V355, P1670, DOI 10.1016/S0140-6736(00)02237-6; Broderick JP, 2013, NEW ENGL J MED, V368, P893, DOI 10.1056/NEJMoa1214300; Ciccone A, 2013, NEW ENGL J MED, V368, P904, DOI 10.1056/NEJMoa1213701; Higashida RT, 2003, STROKE, V34, pE109, DOI 10.1161/01.STR.0000082721.62796.09; Kidwell CS, 2013, NEW ENGL J MED, V368, P914, DOI 10.1056/NEJMoa1212793; Leker RR, 2012, CEREBROVASC DIS, V34, P263, DOI 10.1159/000342668; Liebeskind DS, 2003, STROKE, V34, P2279, DOI 10.1161/01.STR.0000086465.41263.06; Liebeskind David S, 2004, Expert Rev Neurother, V4, P255, DOI 10.1586/14737175.4.2.255; Liebeskind DS, 2012, INT J STROKE, V7, P309, DOI 10.1111/j.1747-4949.2012.00818.x; Liebeskind DS, 2013, STROKE, V44, pAWP5; Liebeskind DS, 2009, STROKE, V40, pE30, DOI 10.1161/STROKEAHA.108.541441; Liebeskind DS, 2013, INT J STROKE, V8, P258, DOI 10.1111/ijs.12090; Liebeskind DS, 2010, CURR OPIN NEUROL, V23, P36, DOI 10.1097/WCO.0b013e328334da32; Nogueira RG, 2012, LANCET, V380, P1231, DOI 10.1016/S0140-6736(12)61299-9; Sanossian N, 2009, AM J NEURORADIOL, V30, P564, DOI 10.3174/ajnr.A1388; Saver JL, 2012, LANCET, V380, P1241, DOI 10.1016/S0140-6736(12)61384-1; Schellinger PD, 2013, STROKE, V44, P1606, DOI 10.1161/STROKEAHA.111.000709; Shuaib A, 2011, STROKE, V42, P1680, DOI 10.1161/STROKEAHA.110.609933; Shuaib A, 2011, LANCET NEUROL, V10, P909, DOI 10.1016/S1474-4422(11)70195-8; Yoo AJ, 2013, STROKE, V44, P2509, DOI 10.1161/STROKEAHA.113.001990; Zaidat OO, 2013, STROKE, V44, P2650, DOI 10.1161/STROKEAHA.113.001972 25 0 0 MANEY PUBLISHING LEEDS STE 1C, JOSEPHS WELL, HANOVER WALK, LEEDS LS3 1AB, W YORKS, ENGLAND 0161-6412 1743-1328 NEUROL RES Neurol. Res. MAY 2014 36 5 SI 397 402 10.1179/1743132814Y.0000000348 6 Clinical Neurology; Neurosciences Neurosciences & Neurology AI7VR WOS:000337109400003 J Juang, LH; Wu, MN; Weng, ZZ Juang, Li-Hong; Wu, Ming-Ni; Weng, Zhi-Zhong Object identification using mobile devices MEASUREMENT English Article Object recognition; Texture; Color features; Vector distance; Mobile phone IMAGE RETRIEVAL; COOCCURRENCE MATRIX; COLOR; FEATURES; CLASSIFICATION; SIMILARITY; HISTOGRAMS; SHAPE To detect object from complex background, illumination variations and texture by machine is very difficult but important for adaptive information service. In this research, we present a preliminary design and experimental results of object recognition from a mobile device that utilizes the texture and the color features by image pre-processing with a simple vector distance matching classifier to train and extract the characteristics. The result shows that the proposed method can adopt the few characteristic values and the accuracy can reach up to 100% of object identification rate when making a querying in a mobile phone. The Euclidean distance is also used to represent the object similarity. The similarity can reach 87.5%, 62.5%, 75% and 87.5% respectively. (C) 2014 Elsevier Ltd. All rights reserved. [Juang, Li-Hong] Shantou Univ, Dept Civil Engn, Shantou, Guangdong, Peoples R China; [Juang, Li-Hong] Shantou Univ, Key Lab Digital Signal & Image Proc Guangdong Pro, Shantou, Guangdong, Peoples R China; [Wu, Ming-Ni; Weng, Zhi-Zhong] Natl Taichung Univ Technol, Dept Informat Management, Taichung, Taiwan Juang, LH (reprint author), Shantou Univ, Dept Civil Engn, Shantou, Guangdong, Peoples R China. puuan.juang@msa.hinet.net Shantou University, Guangdong, P.R. China under the STU Scientific Research Foundation for Talents plan The authors deeply acknowledge the financial support from Shantou University, Guangdong, P.R. China under the STU Scientific Research Foundation for Talents plan. Amet A.L., 2000, IMAGE VISION COMPUT, V18, P543; Brunelli R, 2001, PATTERN RECOGN, V34, P1625, DOI 10.1016/S0031-3203(00)00054-6; Bruns E, 2009, PERS UBIQUIT COMPUT, V13, P165, DOI 10.1007/s00779-008-0194-3; Cerra D, 2012, J VIS COMMUN IMAGE R, V23, P293, DOI 10.1016/j.jvcir.2011.10.009; Chan YK, 2004, J SYST SOFTWARE, V71, P65, DOI 10.1016/S0164-1212(02)00140-1; Gonzalez R. C., 2007, DIGITAL IMAGE PROCES; HARALICK RM, 1973, IEEE T SYST MAN CYB, VSMC3, P610, DOI 10.1109/TSMC.1973.4309314; Huang PW, 2003, PATTERN RECOGN, V36, P665, DOI 10.1016/S0031-3203(02)00083-3; Hurtut T, 2008, COMPUT VIS IMAGE UND, V112, P101, DOI 10.1016/j.cviu.2007.12.006; Jhanwar N, 2004, IMAGE VISION COMPUT, V22, P1211, DOI 10.1016/j.imavis.2004.03.026; JULESZ B, 1981, NATURE, V290, P91, DOI 10.1038/290091a0; JULESZ B, 1986, BIOL CYBERN, V54, P245, DOI 10.1007/BF00318420; Lai CC, 2011, IEEE T INSTRUM MEAS, V60, P3318, DOI 10.1109/TIM.2011.2135010; Liu GH, 2008, PATTERN RECOGN, V41, P3521, DOI 10.1016/j.patcog.2008.06.010; Liu GH, 2011, PATTERN RECOGN, V44, P2123, DOI 10.1016/j.patcog.2011.02.003; Liu GH, 2010, PATTERN RECOGN, V43, P2380, DOI 10.1016/j.patcog.2010.02.012; Lowe D., 1999, ICCV, V2, P1150, DOI DOI 10.1109/ICCV.1999.790410; Lowe DG, 2004, INT J COMPUT VISION, V60, P91, DOI 10.1023/B:VISI.0000029664.99615.94; Luo JB, 2006, IEEE T IMAGE PROCESS, V15, P1443, DOI 10.1109/TIP.2006.871081; Mehtre BM, 1997, INFORM PROCESS MANAG, V33, P319, DOI 10.1016/S0306-4573(96)00069-6; Min R, 2009, PATTERN RECOGN, V42, P147, DOI 10.1016/j.patcog.2008.07.001; Nezamabadi-Pour H, 2004, PATTERN RECOGN LETT, V25, P1547, DOI 10.1016/j.patrec.2004.05.019; Palm C, 2004, PATTERN RECOGN, V37, P965, DOI 10.1016/j.patcog.2003.09.010; Qi H, 2010, PATTERN RECOGN, V43, P2017, DOI 10.1016/j.patcog.2010.01.007; Shu X, 2011, IMAGE VISION COMPUT, V29, P286, DOI 10.1016/j.imavis.2010.11.001; SWAIN MJ, 1991, INT J COMPUT VISION, V7, P11, DOI 10.1007/BF00130487; Wei CH, 2009, PATTERN RECOGN, V42, P386, DOI 10.1016/j.patcog.2008.08.019; Xiao X., 2009, ACM T WEB, V3 28 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 0263-2241 1873-412X MEASUREMENT Measurement MAY 2014 51 100 111 10.1016/j.measurement.2014.01.029 12 Engineering, Multidisciplinary; Instruments & Instrumentation Engineering; Instruments & Instrumentation AH3IH WOS:000336016400011 J Setoguchi, S; Zhu, Y; Jalbert, JJ; Williams, LA; Chen, CY Setoguchi, Soko; Zhu, Ying; Jalbert, Jessica J.; Williams, Lauren A.; Chen, Chih-Ying Validity of Deterministic Record Linkage Using Multiple Indirect Personal Identifiers Linking a Large Registry to Claims Data CIRCULATION-CARDIOVASCULAR QUALITY AND OUTCOMES English Article databases, factual; information storage and retrieval; insurance claim reporting; medical record linkage; medical records systems, computerized; Medicare; registries CLINICAL EFFECTIVENESS; MEDICARE PATIENTS; STRATEGIES Background-Linking patient registries with administrative databases can enhance the utility of the databases for epidemiological and comparative effectiveness research. However, registries often lack direct personal identifiers, and the validity of record linkage using multiple indirect personal identifiers is not well understood. Methods and Results-Using a large contemporary national cardiovascular device registry and 100% Medicare inpatient data, we linked hospitalization-level records. The main outcomes were the validity measures of several deterministic linkage rules using multiple indirect personal identifiers compared with rules using both direct and indirect personal identifiers. Linkage rules using 2 or 3 indirect, patient-level identifiers (ie, date of birth, sex, admission date) and hospital ID produced linkages with sensitivity of 95% and specificity of 98% compared with a gold standard linkage rule using a combination of both direct and indirect identifiers. Conclusions-Ours is the first large-scale study to validate the performance of deterministic linkage rules without direct personal identifiers. When linking hospitalization-level records in the absence of direct personal identifiers, provider information is necessary for successful linkage. [Setoguchi, Soko; Jalbert, Jessica J.; Williams, Lauren A.; Chen, Chih-Ying] Brigham & Womens Hosp, Dept Med, Div Pharmacoepidemiol, Boston, MA 02115 USA; [Setoguchi, Soko] Duke Univ, Sch Med, Duke Clin Res Inst, Durham, NC USA; [Setoguchi, Soko] Duke Univ, Sch Med, Dept Med, Durham, NC 27706 USA; [Zhu, Ying] Univ Tokyo, Grad Sch Med, Dept Biostat, Tokyo, Japan Setoguchi, S (reprint author), Duke Clin Res Inst, POB 17969, Durham, NC 27715 USA. soko@post.harvard.edu Agency for Healthcare Research and Quality, US Department of Health and Human Services [HHSA29020050016I]; Centers for Medicare and Medicaid Services, US Department of Health and Human Services [HHSM500201000001I]; Agency for Healthcare Research and Quality [K02HS017731] This project was funded under contract #HHSA29020050016I from the Agency for Healthcare Research and Quality, US Department of Health and Human Services, as part of the Developing Evidence to Inform Decisions about Effectiveness (DEcIDE) program; and contract No. HHSM500201000001I from the Centers for Medicare and Medicaid Services, US Department of Health and Human Services. Dr Setoguchi was supported by midcareer development award K02HS017731 from the Agency for Healthcare Research and Quality. The funding agency had no role in the design and conduct of the study and in the collection, analysis, and interpretation of the data. The manuscript was based on a report done under contract to AHRQ; AHRQ had the draft report reviewed by independent peer reviewers before acceptance of the final report. [Anonymous], 2011, MED MANAGED CARE ENR; [Anonymous], 2008, IMPROVING METHODS CO; Blakely T, 2002, INT J EPIDEMIOL, V31, P1246, DOI 10.1093/ije/31.6.1246; Bohensky MA, 2011, ANAESTH INTENS CARE, V39, P202; Clark DE, 2004, INJURY PREV, V10, P186, DOI 10.1136/ip.2003.004580; Crystal S, 2007, MED CARE, V45, pS58; Douglas PS, 2009, J AM COLL CARDIOL, V53, P1629, DOI 10.1016/j.jacc.2009.03.005; Gliklich RE, 2010, REGISTRIES EVALUATIN; Greenberg JD, 2011, ANN RHEUM DIS, V70, P576, DOI 10.1136/ard.2010.129916; Hammill BG, 2009, AM HEART J, V157, P995, DOI 10.1016/j.ahj.2009.04.002; Hernandez AF, 2010, CIRC-HEART FAIL, V3, P7, DOI 10.1161/CIRCHEARTFAILURE.109.884395; Li Q, 2011, PHARMACOEPIDEM DR S, V20, P700, DOI 10.1002/pds.2146; Schneeweiss S, 2005, J CLIN EPIDEMIOL, V58, P323, DOI 10.1016/j.jclinepi.2004.10.012; Setoguchi S, 2011, CLIN PHARMACOL THER, V89, P674, DOI 10.1038/clpt.2011.17; Setoguchi S, 2007, CANCER CAUSE CONTROL, V18, P561, DOI 10.1007/s10552-007-0131-1; Weintraub WS, 2012, NEW ENGL J MED, V366, P1467, DOI 10.1056/NEJMoa1110717; Wilkinson NMR, 2007, J RHEUMATOL, V34, P224 17 0 0 LIPPINCOTT WILLIAMS & WILKINS PHILADELPHIA 530 WALNUT ST, PHILADELPHIA, PA 19106-3621 USA 1941-7705 1941-7713 CIRC-CARDIOVASC QUAL Circ.-Cardiovasc. Qual. Outcomes MAY 2014 7 3 475 480 10.1161/CIRCOUTCOMES.113.000294 6 Cardiac & Cardiovascular Systems Cardiovascular System & Cardiology AI3WQ WOS:000336796300019 J Reuerberi, C; Cherubini, P; Balolinelli, S; Luzzi, S Reuerberi, Carlo; Cherubini, Paolo; Balolinelli, Sara; Luzzi, Simona Semantic fluency: Cognitive basis and diagnostic performance in focal dementias and Alzheimer's disease CORTEX English Article Semantic fluency; Multivariate analysis; Control functions; Executive functions; Semantic memory; Semantic dementia; Primary progressive aphasia; Fronto-temporal dementia; Alzheimer's Disease INFERIOR FRONTAL GYRUS; PRIMARY-PROGRESSIVE-APHASIA; VERBAL FLUENCY; FRONTOTEMPORAL DEMENTIA; LOBE LESIONS; WORD RETRIEVAL; FREE-RECALL; MEMORY; CORTEX; IMPAIRMENT Semantic fluency is widely used both as a clinical test and as a basic tool for understanding how humans extract information from the semantic store. Recently, major efforts have been made to devise fine-grained scoring procedures to measure the multiple cognitive processes underlying fluency performance. Nevertheless, it is still unclear how many and which independent components are necessary to thoroughly describe performance on the fluency task. Furthermore,, whether a combination of multiple indices can improve the diagnostic performance of the test should be assessed. In this study, we extracted multiple indices of performance on the semantic fluency test from a large sample of healthy controls (n = 307) and patients (n = 145) suffering from three types of focal dementia or Alzheimer's Disease (AD). We found that five independent components underlie semantic fluency performance. We argue that these components functionally map onto the generation and application of a search strategy (component 2), to the monitoring of the overall sequence to avoid repetitions (component 3) and out-of-category items (component 4), and to the full integrity of the semantic store (component 5). The integrated and effective work of all these components would relate to a "general effectiveness" component (component 1). Importantly, while all the focal dementia groups were equally impaired on general effectiveness measures, they showed differential patterns of failure in the other components. This finding suggests that the cognitive deficit that impairs fluency differs among the three focal dementia groups: a semantic store deficit in the semantic variant of primary progressive aphasia (sv-PPA), a strategy deficit in the non-fluent variant of primary progressive aphasia (nfv-PPA), and an initiation deficit in the behavioural variant of fronto-temporal dementia (bv-FTD). Finally, we showed that the concurrent use of multiple fluency indices improves the diagnostic accuracy of semantic fluency both for focal dementias and for AD. More generally, our study suggests that a formal evaluation of fine-grained patterns of performance would improve the diagnostic accuracy of neuropsychological tests. (C) 2014 Elsevier Ltd. All rights reserved. [Reuerberi, Carlo; Cherubini, Paolo] Univ Milano Bicocca, Dept Psychol, I-20126 Milan, Italy; [Balolinelli, Sara; Luzzi, Simona] Polytech Univ Marche, Dept Clin & Expt Med, Ancona, Italy Reuerberi, C (reprint author), Univ Milano Bicocca, Dept Psychol, Piazza Ateneo Nuovo 1, I-20126 Milan, Italy. carlo.reverberi@unimib.it Abwender DA, 2001, ASSESSMENT, V8, P323, DOI 10.1177/107319110100800308; Badre D, 2007, NEUROPSYCHOLOGIA, V45, P2883, DOI 10.1016/j.neuropsychologia.2007.06.015; Baldo JV, 2006, J INT NEUROPSYCH SOC, V12, P896, DOI 10.1017/S1355617706061078; Baldo JV, 1998, NEUROPSYCHOLOGY, V12, P259, DOI 10.1037/0894-4105.12.2.259; BLAND JM, 1995, BRIT MED J, V310, P170; Bousfield W. A., 1944, J GEN PSYCHOL, V52, P83; Burgess PW, 1996, NEUROPSYCHOLOGIA, V34, P263, DOI 10.1016/0028-3932(95)00104-2; Capitani E, 1999, CORTEX, V35, P273, DOI 10.1016/S0010-9452(08)70800-1; Capitani E, 2009, NEUROPSYCHOLOGIA, V47, P423, DOI 10.1016/j.neuropsychologia.2008.09.016; CATTELL RB, 1977, MULTIVAR BEHAV RES, V12, P289, DOI 10.1207/s15327906mbr1203_2; Chang C.-C., 2011, ACM T INTEL SYST TEC, V2, P1, DOI DOI 10.1145/1961189.1961199; Costafreda SG, 2006, HUM BRAIN MAPP, V27, P799, DOI 10.1002/hbm.20221; Duncan J, 2010, TRENDS COGN SCI, V14, P172, DOI 10.1016/j.tics.2010.01.004; Fagundo AB, 2008, INT J GERIATR PSYCH, V23, P1007, DOI 10.1002/gps.2025; Gorno-Tempini ML, 2004, ANN NEUROL, V55, P335, DOI 10.1002/ana.10825; Gorno-Tempini ML, 2011, NEUROLOGY, V76, P1006, DOI 10.1212/WNL.0b013e31821103e6; Grossman M, 2010, NAT REV NEUROL, V6, P88, DOI 10.1038/nrneurol.2009.216; GRUENEWALD PJ, 1980, J EXP PSYCHOL-HUM L, V6, P225, DOI 10.1037//0278-7393.6.3.225; Henry JD, 2004, NEUROPSYCHOLOGY, V18, P284, DOI 10.1037/0894-4105.18.2.284; Hirshorn EA, 2006, NEUROPSYCHOLOGIA, V44, P2547, DOI 10.1016/j.neuropsychologia.2006.03.035; Ho AK, 2002, NEUROPSYCHOLOGIA, V40, P1277, DOI 10.1016/S0028-3932(01)00217-2; HODGES JR, 1995, MEMORY, V3, P463, DOI 10.1080/09658219508253161; HODGES JR, 1992, BRAIN, V115, P1783, DOI 10.1093/brain/115.6.1783; KATZ S, 1970, GERONTOLOGIST, V10, P20; Katzev M, 2013, J NEUROSCI, V33, P7837, DOI 10.1523/JNEUROSCI.3147-12.2013; Kloppel S, 2008, BRAIN, V131, P2969, DOI 10.1093/brain/awn239; Laisney M, 2009, J NEUROL, V256, P1083, DOI 10.1007/s00415-009-5073-y; Mayr U, 2002, NEUROPSYCHOLOGIA, V40, P562, DOI 10.1016/S0028-3932(01)00132-4; McKhann GM, 2011, ALZHEIMERS DEMENT, V7, P263, DOI 10.1016/j.jalz.2011.03.005; MOSCOVITCH M, 1992, J COGNITIVE NEUROSCI, V4, P257, DOI 10.1162/jocn.1992.4.3.257; Neary D, 2005, LANCET NEUROL, V4, P771, DOI 10.1016/S1474-4422(05)70223-4; Neary D, 1998, NEUROLOGY, V51, P1546; Patterson K, 2007, NAT REV NEUROSCI, V8, P976, DOI 10.1038/nrn2277; Pereira F, 2009, NEUROIMAGE, V45, pS199, DOI 10.1016/j.neuroimage.2008.11.007; Price SE, 2012, NEUROPSYCHOLOGY, V26, P490, DOI 10.1037/a0028567; Rascovsky K, 2011, BRAIN, V134, P2456, DOI 10.1093/brain/awr179; Reverberi C, 2012, J NEUROSCI, V32, P17420, DOI 10.1523/JNEUROSCI.2344-12.2012; Reverberi C., 2004, GIORNALE ITALIANO PS, V31, P497; Reverberi C, 2006, NEUROPSYCHOLOGIA, V44, P469, DOI 10.1016/j.neuropsychologia.2005.05.011; Reverberi C, 1991, CEREB CORTEX, V22, P1237; Robinson G, 2012, BRAIN, V135, P2202, DOI 10.1093/brain/aws142; Roca M, 2010, BRAIN, V133, P234, DOI 10.1093/brain/awp269; Rogers TT, 2004, PSYCHOL REV, V111, P205, DOI 10.1037/0033-295X.111.1.205; Rosen VM, 1997, J EXP PSYCHOL GEN, V126, P211, DOI 10.1037//0096-3445.126.3.211; Salmon E, 2003, NEUROIMAGE, V20, P435, DOI 10.1016/S1053-8119(03)00346-X; Snowden J. S, 1989, BEHAV NEUROL, V2, P167; Snowden JS, 2011, BRAIN, V134, P2478, DOI 10.1093/brain/awr189; Snowden J.S., 1996, FRONTOTEMPORAL LOBAR; Stuss D.T., 1994, NEUROPSYCHOLOGY, V8, P355, DOI 10.1037/0894-4105.8.3.355; Stuss DT, 1998, J INT NEUROPSYCH SOC, V4, P265; Thompson JC, 2005, J NEUROL NEUROSUR PS, V76, P920, DOI 10.1136/jnnp.2003.033779; Troster AI, 1998, NEUROPSYCHOLOGIA, V36, P295, DOI 10.1016/S0028-3932(97)00153-X; Troyer AK, 1998, NEUROPSYCHOLOGIA, V36, P499, DOI 10.1016/S0028-3932(97)00152-8; Troyer AK, 1997, NEUROPSYCHOLOGY, V11, P138, DOI 10.1037//0894-4105.11.1.138; Unsworth N, 2011, Q J EXP PSYCHOL, V64, P447, DOI 10.1080/17470218.2010.505292; WARRINGTON EK, 1975, Q J EXP PSYCHOL, V27, P635, DOI 10.1080/14640747508400525; WIXTED JT, 1994, PSYCHON B REV, V1, P89, DOI 10.3758/BF03200763 57 0 0 ELSEVIER MASSON MILANO VIA PALEOCAPA 7, 20121 MILANO, ITALY 0010-9452 1973-8102 CORTEX Cortex MAY 2014 54 150 164 10.1016/j.cortex.2014.02.006 15 Behavioral Sciences; Neurosciences Behavioral Sciences; Neurosciences & Neurology AH9LW WOS:000336464900013 J Tailby, C; Weintrob, DL; Saling, MM; Fitzgerald, C; Jackson, GD Tailby, Chris; Weintrob, David L.; Saling, Michael M.; Fitzgerald, Carly; Jackson, Graeme D. Reading difficulty is associated with failure to lateralize temporooccipital function EPILEPSIA English Article Epilepsy; Reading disorder; Dyslexia; Functional magnetic resonance imaging TEMPORAL-LOBE EPILEPSY; DEVELOPMENTAL DYSLEXIA; LANGUAGE LATERALIZATION; CHILDREN; BRAIN; FMRI; CORTEX; ORGANIZATION; FLUENCY; MRI ObjectiveStudies of focal epilepsy have revealed abnormalities of language organization; however, little attention has been paid to disorders of reading in this group. We hypothesized that language functional magnetic resonance imaging (fMRI) would reveal differences in language organization between focal epilepsy patients with and without reading difficulties. MethodsWe conducted language fMRI studies of 10 focal epilepsy patients with reading difficulties, 34 focal epilepsy patients without reading difficulties, and 42 healthy controls. ResultsWe defined regions of interests on the basis of activation patterns on an orthographic lexical retrieval task. Comparison of activations within these ROIs on a second Noun-Verb task revealed epilepsy-related effects (relative to healthy controls: reduced activation in left inferior frontal cortex), as well as greater activation in the right temporooccipital cortex specific to the reading difficulty group. SignificanceThese findings identify a focal epilepsy effect in the left frontal region (present in patients with and without reading difficulties), and a functional abnormality specific to the reading difficulty group localized to right temporooccipital cortexa region implicated in lexicosemantic processing. Our observations suggest a failure of left hemisphere specialization among focal epilepsy patients with reading difficulties. A PowerPoint slide summarizing this article is available for download in the Supporting Information section . [Tailby, Chris; Fitzgerald, Carly; Jackson, Graeme D.] Florey Inst Neurosci & Mental Hlth, Melbourne, Vic, Australia; [Tailby, Chris; Saling, Michael M.] Univ Melbourne, Sch Psychol Sci, Melbourne, Vic, Australia; [Weintrob, David L.; Saling, Michael M.] Austin Hlth, Dept Neuropsychol, Melbourne, Vic, Australia; [Jackson, Graeme D.] Univ Melbourne, Dept Med, Melbourne, Vic, Australia Jackson, GD (reprint author), Melbourne Brain Centre, Florey Inst Neurosci & Mental Hlth, 245 Burgundy St, Heidelberg, Vic 3084, Australia. gjackson@brain.org.au Jackson, Graeme/A-9064-2013 National Health and Medical Research Council of Australia [628952]; NHMRC [527800, 368650, 318900, 628725]; Operational Infrastructure Support Program of the State Government of Victoria, Australia We thank all the patients and healthy controls who participated in this project. This study was supported by the National Health and Medical Research Council of Australia program grant (628952) and an NHMRC practitioner fellowship to GDJ (527800), NHMRC Project grants 368650, 318900, and 628725, and the Operational Infrastructure Support Program of the State Government of Victoria, Australia. Abbott DF, 2010, NEUROIMAGE, V50, P1446, DOI 10.1016/j.neuroimage.2010.01.059; Anderson DP, 2006, EPILEPSIA, V47, P998, DOI 10.1111/j.1528-1167.2006.00572.x; Beghi M, 2006, EPILEPSIA, V47, P14, DOI 10.1111/j.1528-1167.2006.00681.x; Bell BD, 2001, NEUROPSYCHOLOGY, V15, P434, DOI 10.1037//0894-4105.15.4.434; Berl MM, 2005, NEUROLOGY, V65, P1604, DOI 10.1212/01.wnl.0000184502.06647.28; Breier JI, 2000, J CLIN EXP NEUROPSYC, V22, P804, DOI 10.1076/jcen.22.6.804.948; Briellmann RS, 2006, EPILEPSIA, V47, P916, DOI 10.1111/j.1528-1167.2006.00513.x; Chang BS, 2005, NEUROLOGY, V64, P799; Chang BS, 2007, NEUROLOGY, V69, P2146, DOI 10.1212/01.wnl.0000286365.41070.54; De Clercq-Quaegebeur M, 2010, J LEARN DISABIL-US, V43, P563, DOI 10.1177/0022219410375000; Deblaere K, 2004, NEURORADIOLOGY, V46, P413, DOI 10.1007/s00234-004-1196-0; Dow C, 2004, EPILEPSY BEHAV, V5, P919, DOI 10.1016/j.yebeh.2004.08.007; Dronkers NF, 1996, NATURE, V384, P159, DOI 10.1038/384159a0; Everts R, 2010, EPILEPSIA, V51, P627, DOI 10.1111/j.1528-1167.2009.02406.x; Fletcher JM, 2007, LEARNING DISABILITIE; Galaburda AM, 2006, NAT NEUROSCI, V9, P1213, DOI 10.1038/nn1772; Hamberger MJ, 2007, NEUROPSYCHOL REV, V17, P477, DOI 10.1007/s11065-007-9046-6; Hickok G, 2007, NAT REV NEUROSCI, V8, P393, DOI 10.1038/nrn2113; HYND GW, 1990, ARCH NEUROL-CHICAGO, V47, P919; Kriegeskorte N, 2009, NAT NEUROSCI, V12, P535, DOI 10.1038/nn.2303; Lillywhite LM, 2009, EPILEPSIA, V50, P2276, DOI 10.1111/j.1528-1167.2009.02065.x; Lyon GR, 2003, ANN DYSLEXIA, V53, P1, DOI 10.1007/s11881-003-0001-9; Martin RC, 2000, NEUROPSYCHOLOGY, V14, P501, DOI 10.1037//0894-4105.14.4.501; Milne RD, 2002, NEUROCASE, V8, P205, DOI 10.1093/neucas/8.3.205; MOHR JP, 1978, NEUROLOGY, V28, P311; Paulesu E, 1997, NEUROREPORT, V8, P2011, DOI 10.1097/00001756-199705260-00042; Pennington BF, 2012, J ABNORM PSYCHOL, V121, P212, DOI 10.1037/a0025823; Peterson RL, 2012, LANCET, V379, P1997, DOI 10.1016/S0140-6736(12)60198-6; Pugh KR, 2000, PSYCHOL SCI, V11, P51, DOI 10.1111/1467-9280.00214; Richlan F, 2009, HUM BRAIN MAPP, V30, P3299, DOI 10.1002/hbm.20752; Ruff RM, 1997, BRAIN LANG, V57, P394, DOI 10.1006/brln.1997.1755; RUMSEY JM, 1992, ARCH NEUROL-CHICAGO, V49, P527; SCHACHTER SC, 1993, ANN NY ACAD SCI, V682, P402; Shaywitz BA, 2002, BIOL PSYCHIAT, V52, P101, DOI 10.1016/S0006-3223(02)01365-3; Shaywitz SE, 1998, P NATL ACAD SCI USA, V95, P2636, DOI 10.1073/pnas.95.5.2636; SILLANPAA M, 1992, EPILEPSIA, V33, P444, DOI 10.1111/j.1528-1157.1992.tb01689.x; Springer JA, 1999, BRAIN, V122, P2033, DOI 10.1093/brain/122.11.2033; Stella F, 2003, ARQ NEURO-PSIQUIAT, V61, P335, DOI 10.1590/S0004-282X2003000300003; Sveller C, 2006, NEUROLOGY, V67, P1813, DOI 10.1212/01.wnl.0000244465.74707.42; Vinckier F, 2007, NEURON, V55, P143, DOI 10.1016/j.neuron.2007.05.031; Wood AG, 2001, NEUROIMAGE, V14, P162, DOI 10.1006/nimg.2001.0778; Wood AG, 2004, NEUROLOGY, V63, P1035 42 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0013-9580 1528-1167 EPILEPSIA Epilepsia MAY 2014 55 5 746 753 10.1111/epi.12607 8 Clinical Neurology Neurosciences & Neurology AI1PK WOS:000336623200021 J Farhangi, MM; Soryani, M; Fathy, M Farhangi, Mohammad Mehdi; Soryani, Mohsen; Fathy, Mahmood Informative visual words construction to improve bag of words image representation IET IMAGE PROCESSING English Article RECOGNITION; RETRIEVAL; FEATURES Bag of visual words model has recently attracted much attention from computer vision society because of its notable success in analysing images and exploring their content. This study improves this model by utilising the adjacency information between words. To explore this information, a binary tree structure is constructed from the visual words in order to model the is a relationships in the vocabulary. Informative nodes of this tree are extracted by using the. 2 criterion and are used to capture the adjacency information of visual words. This approach is a simple and computationally effective way for modelling the spatial relations of visual words, which improves the image classification performance. The authors evaluated our method for visual classification of three known datasets: 15 natural scenes, Caltech-101 and Graz-01. [Farhangi, Mohammad Mehdi; Soryani, Mohsen; Fathy, Mahmood] Iran Univ Sci & Technol, Sch Comp Engn, Tehran 1684613114, Iran Farhangi, MM (reprint author), Iran Univ Sci & Technol, Sch Comp Engn, Tehran 1684613114, Iran. mehdi.farhangi@gmail.com Alpaydin E, 2004, INTRO MACHINE LEARNI; Bay H, 2006, LECT NOTES COMPUT SC, V3951, P404; Bouachir W., 2010, P 5 INT I V COMM MOB, P1; Farhangi M.M., 2012, P 2 INT C ADV COMP I, P681; Fei-Fei L., 2004, P WORKSH GEN MOD BAS; Fei-Fei L, 2005, PROC CVPR IEEE, P524; Gemert J.C., 2010, IEEE T PATTERN ANAL, V32, P1271; Harris C., 1988, ALV VIS C, V15, P147; Herve N, 2009, IEEE INT CON MULTI, P430; Jegou H, 2009, PROC CVPR IEEE, P1169; Jiang YG, 2010, IEEE T MULTIMEDIA, V12, P42, DOI 10.1109/TMM.2009.2036235; Jiang Y.G., 2008, P 31 INT ACM SIGIR C, P769, DOI 10.1145/1390334.1390495; Jiang YG, 2009, COMPUT VIS IMAGE UND, V113, P405, DOI 10.1016/j.cviu.2008.10.002; Jurie F, 2005, IEEE I CONF COMP VIS, P604; Krishnamoorthy R, 2012, IET IMAGE PROCESS, V6, P635, DOI 10.1049/iet-ipr.2011.0303; Lazebnik S., 2006, P IEEE C COMP VIS PA, V2, P2169, DOI DOI 10.1109/CVPR.2006.68; Li T, 2011, IEEE T CIRC SYST VID, V21, P381, DOI 10.1109/TCSVT.2010.2041828; Lowe K.D., 2004, INT J COMPUT VISION, V60, P91; Mei L., 2011, IEEE T CIRCUITS SYST, V21, P381; Milkojcyk K., 2005, IEEE T PATTERN ANAL, V27, P1615; Nister D., 2006, CVPR, V2, P2161, DOI DOI 10.1109/CVPR.2006.264; NOWAK E, 2006, P EUR C COMP VIS ECC, V3954, P490; Opelt A, 2004, LECT NOTES COMPUT SC, V3022, P71; Russell SJ, 2003, ARTIFICIAL INTELLIGE; Sivic J., 2003, COMP VIS IEEE INT C, V2, P1470; Tirilly P., 2008, P 2008 INT C CONT BA, P249, DOI 10.1145/1386352.1386388; Vogel J, 2007, INT J COMPUT VISION, V72, P133, DOI 10.1007/s11263-006-8614-1; Wu L., 2007, P INT WORKSH MULT IN, P115, DOI 10.1145/1290082.1290101; Wu XB, 2007, PROCEEDINGS OF THE 2007 INTERNATIONAL CONFERENCE ON AGRICULTURE ENGINEERING, P162; Yang F, 2012, IET IMAGE PROCESS, V6, P115, DOI 10.1049/iet-ipr.2010.0127; Yang Y, 1997, P 14 INT C MACH LEAR, P412, DOI DOI 10.1016/J.ESWA.2008.05.026 31 0 0 INST ENGINEERING TECHNOLOGY-IET HERTFORD MICHAEL FARADAY HOUSE SIX HILLS WAY STEVENAGE, HERTFORD SG1 2AY, ENGLAND 1751-9659 1751-9667 IET IMAGE PROCESS IET Image Process. MAY 2014 8 5 310 318 10.1049/iet-ipr.2013.0449 9 Engineering, Electrical & Electronic Engineering AI5ND WOS:000336913700006 J Ruocco, M; Ramampiaro, H Ruocco, Massimiliano; Ramampiaro, Heri A scalable algorithm for extraction and clustering of event-related pictures MULTIMEDIA TOOLS AND APPLICATIONS English Article Event detection; Image clustering; Suffix tree SUFFIX TREES The event detection problem, which is closely related to clustering, has gained a lot of attentions within event detection for textual documents. However, although image clustering is a problem that has been treated extensively in both Content-Based Image Retrieval (CBIR) and Text-Based Image Retrieval (TBIR) systems, event detection within image management is a relatively new area. Having this in mind, we propose a novel approach for event extraction and clustering of images, taking into account textual annotations, time and geographical positions. Our goal is to develop a clustering method based on the fact that an image may belong to an event cluster. Here, we stress the necessity of having an event clustering and cluster extraction algorithm that are both scalable and allow online applications. To achieve this, we extend a well-known clustering algorithm called Suffix Tree Clustering (STC), originally developed to cluster text documents using document snippets. The idea is that we consider an image along with its annotation as a document. Further, we extend it to also include time and geographical position so that we can capture the contextual information from each image during the clustering process. This has appeared to be particularly useful on images gathered from online photo-sharing applications such as Flickr. Hence, our STC-based approach is aimed at dealing with the challenges induced by capturing contextual information from Flickr images and extracting related events. We evaluate our algorithm using different annotated datasets mainly gathered from Flickr. As part of this evaluation we investigate the effects of using different parameters, such as time and space granularities, and compare these effects. In addition, we evaluate the performance of our algorithm with respect to mining events from image collections. Our experimental results clearly demonstrate the effectiveness of our STC-based algorithm in extracting and clustering events. [Ruocco, Massimiliano; Ramampiaro, Heri] Norwegian Univ Sci & Technol NTNU, Dept Comp & Informat Sci, N-7491 Trondheim, Norway Ruocco, M (reprint author), Norwegian Univ Sci & Technol NTNU, Dept Comp & Informat Sci, N-7491 Trondheim, Norway. ruocco@idi.ntnu.no; heri@idi.ntnu.no Research Council of Norway under VERDIKT program [176858] We would like to thank Symeon Papadopoulos for being helpful and providing us the Barcelona dataset. This work is supported by the Research Council of Norway, grant number 176858 under the VERDIKT program. Allan J., 1998, Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/290941.290954; ALLAN J, 1998, SIGIR 98, P37; Allan James, 1998, P DARPA BROADC NEWS, P194; Baeza-Yates R., 2011, MODERN INFORM RETRIE; Bay H, 2008, COMPUT VIS IMAGE UND, V110, P346, DOI 10.1016/j.cviu.2007.09.014; BECKER H, 2010, WSDM 10, P291, DOI DOI 10.1145/1718487.1718524; BRANTS T, 2003, SIGIR 03, P330; BTTCHER S, 2010, INFORM RETRIEVAL IMP; Carpineto C, 2009, ACM COMPUT SURV, V41, DOI 10.1145/1541880.1541884; CHEN L, 2009, CIKM 2009 HONG KONG, P523; Das M, 2009, 2009 IEEE THIRD INTERNATIONAL CONFERENCE ON SEMANTIC COMPUTING (ICSC 2009), P116, DOI 10.1109/ICSC.2009.36; Ester M., 1996, P 2 INT C KNOWL DISC, V1996, P226; FIALHO A, 2010, EVENTS10; FUNG GPC, 2005, VLDB, P181; Gammeter S, 2009, IEEE I CONF COMP VIS, P614, DOI 10.1109/ICCV.2009.5459180; Gao B., 2005, P 13 ANN ACM INT C M, P112, DOI 10.1145/1101149.1101167; GRANVILLE B, 2007, INT C ENT INF SYST W; GULLA JA, 2008, EMERGING TECHNOLOGIE, V3237, P184; HAYS J, 2008, IEEE C COMP VIS PATT; HERNANDEZARANDA D, 2010, MEDIAEVAL 2010 WORKI; HU M, 2008, WIDM 08, P1; Jin Y, 2007, INFORM PROCESS MANAG, V43, P365, DOI 10.1016/j.ipm.2006.07.007; Kamps J, 2003, LECT NOTES COMPUT SC, V3237, P152; Kurtz S, 1999, SOFTWARE PRACT EXPER, V29, P1149, DOI 10.1002/(SICI)1097-024X(199911)29:13<1149::AID-SPE274>3.0.CO;2-O; LARSON M, 2010, P MEDIAEVAL 2010 WOR; Li L, 2007, P IEEE 11 INT C COMP, V2, P1, DOI DOI 10.1109/ICCV.2007.4408872; LIN Y, 2011, SIGIR, P405; Loui AC, 2003, IEEE T MULTIMEDIA, V5, P390, DOI 10.1109/TMM.2003.814723; NEMRAVA J, 2006, P 15 INT C KNOWL ENG, P33; Papadopoulos S, 2011, IEEE MULTIMEDIA, V18, P52, DOI 10.1109/MMUL.2010.68; PORTER MF, 1997, ALGORITHM SUFFIX STI; QUACK T, 2008, P INT C CONT BAS IM, P47, DOI DOI 10.1145/1386352.1386363; RATTENBURY T, 2007, SIGIR 2007, P103; RUOCCO M, 2010, 2010 IEEE INT S MULT, P41, DOI 10.1109/ISM.2010.16; SERDYUKOV P, 2009, P 32 INT ACM SIGIR C; Shapira B, 2005, ONLINE INFORM REV, V29, P374, DOI 10.1108/14684520510617820; SMITH DA, 2002, JCDL 02; Trieschnigg D., 2005, Journal of Digital Information Management, V3; TRONCY R, 2010, ACM INT C P SERIES; UKKONEN E, 1995, ALGORITHMICA, V14, P249, DOI 10.1007/BF01206331; WARTENA C, 2010, MEDIAEVAL 2010 WORKI; YANG Y, 1998, SIGIR, P28; YUAN J, 2008, MIR 08, P2; ZAMIR O, 1998, SIGIR, P46; ZHANG K, 2007, SIGIR 07, P215; Zhang W., 2006, P 3 INT S 3D DAT PRO, P33 46 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2014 70 1 55 88 10.1007/s11042-012-1087-z 34 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI4AK WOS:000336807300004 J Talukder, A; Panangadan, A Talukder, Ashit; Panangadan, Anand Extreme event detection and assimilation from multimedia sources MULTIMEDIA TOOLS AND APPLICATIONS English Article Event detection; Information retrieval; Matching; Multimedia; Representation; Search; Spatio-temporal trajectory; Tracking VIDEO; MODEL A new event-based multimedia processing framework for detection, retrieval, and cross-media content assimilation of geo-spatiotemporal phenomena is described. Multimedia information relevant to geo-spatiotemporal events are available from sources such as remote satellites, in-situ sensors as image streams, and other outlets such as news articles, weather bulletins as text documents and in various formats, each with widely varying properties. We pose an event-based framework to automatically detect geo-spatiotemporal phenomena from raw untagged remote-sensing satellite image streams, extract attributes for such events, match the spatiotemporal properties of the detected phenomenon with events in a database to automatically derive a media-independent event description, and subsequently use the media-independent event descriptions to assimilate relevant information of the same event across other media sources from the web using a mashup. A virtual globe interface enables simultaneous visualization of the assimilated spatiotemporal information annotated with Internet sources. This framework is demonstrated for the automatic detection of tropical cyclones from satellite imagery followed by the retrieval and assimilation of related information from government-run weather sites and commercial news portals. [Talukder, Ashit] NIST, Gaithersburg, MD 20899 USA; [Panangadan, Anand] CALTECH, Jet Prop Lab, Pasadena, CA 91109 USA Talukder, A (reprint author), NIST, 100 Bur Dr,Mail Stop 8940, Gaithersburg, MD 20899 USA. ashit.talukder@nist.gov; Anand.V.Panangadan@jpl.nasa.gov National Aeronautics and Space Administration (NASA) Applied Information Systems Research (AISR) Program The research described in this paper was carried out at the Jet Propulsion Laboratory, California Institute of Technology with funding from the National Aeronautics and Space Administration (NASA) Applied Information Systems Research (AISR) Program. The authors acknowledge the contributions of Eric Rigor, Andrew Bingham, and Shen-shyang Ho. Bobick AF, 1998, PROC CVPR IEEE, P196, DOI 10.1109/CVPR.1998.698609; Castano R, 2006, 12 ACM SIGKDD INT C, P845; DeMenthon D, 2003, P 11 ACM INT C MULT; Dvorak VF, 1984, TROPICAL CYCLONE INT, V11; FOWLER C, 2009, CONT FILM DIRECT, P1; Francois ARJ, 2005, IEEE MULTIMEDIA, V12, P76, DOI 10.1109/MMUL.2005.87; Franklin J, 2011, NATL HURRICANE CTR F; Ho S-s, 2009, WORKSH CROSS MED INF; Ho S-S, 2008, INT C KNOWL DISC DAT, P928; Hongeng S, 2000, C COMP VIS PATT REC, P1818; Hwang TH, 2003, INT GEOSCI REMOTE SE, P3641; Khalid S, 2005, 7 IEEE WORKSH APPL C; Kim S-S, 2004, MULT EXP 2004 ICME 0, V812, P811; Koprinska I, 2001, SIGNAL PROCESS-IMAGE, V16, P477, DOI 10.1016/S0923-5965(00)00011-4; Li ZY, 2005, IEEE INT SYMP CIRC S, P3845; Lippman A, 1980, SIGGRAPH COMPUT GRAP, V14, P32; Mazzoni D, 2007, REMOTE SENS ENVIRON, V107, P149, DOI 10.1016/j.rse.2006.06.021; Medioni G, 2001, IEEE T PATTERN ANAL, V23, P873, DOI 10.1109/34.946990; Moncrieff S, 2001, MULT EXP 2001 ICME 2, P989; Navarrete T, 2006, INT SEM WEB C ISWC; Nguyen NT, 2005, PROC CVPR IEEE, P955; Nobre EMN, 2002, P 6 EUR WORKSH MULT; Oliver N, 2002, FOURTH IEEE INTERNATIONAL CONFERENCE ON MULTIMODAL INTERFACES, PROCEEDINGS, P3; Panangadan A, 2009, 17 ACM SIGSPATIAL IN; Pasch RJ, 2003, B AM METEOROL SOC, V84, P1415, DOI 10.1175/BAMS-84-10-1415; PEUQUET DJ, 1995, INT J GEOGR INF SYST, V9, P7, DOI 10.1080/02693799508902022; Rath TM, 2003, PROC CVPR IEEE, P521; Rhome JR, 2009, TECHNICAL SUMMARY NA; Rui Y, 2000, P 8 ACM INT C MULT M; Sakaki T, 2010, P 19 INT C WORLD WID; Stefanidis A, 2003, 11 ACM INT S ADV GEO, P86; Wei H, 2006, INT GEOSCI REMOTE SE, P835, DOI 10.1109/IGARSS.2006.214; Westerman U, 2007, IEEE MULTIMEDIA, V4, P19; Wimmers A, 2004, 26 C HURR TROP MET; Wulder M, 2005, GEO WORLD NOV; Zeinalipour-Yazti D, 2006, 15 ACM C INF KNOWL M; Zhai Y, 2006, SEMANTIC LEARNING AP; Zotkin D, 2001, DET REC EV VID 2001, P20 38 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2014 70 1 237 261 10.1007/s11042-012-1088-y 25 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI4AK WOS:000336807300010 J Lourenco, A; da Silva, HP; Carreiras, C; Alves, AP; Fred, ALN Lourenco, Andre; da Silva, Hugo Placido; Carreiras, Carlos; Alves, Ana Priscila; Fred, Ana L. N. A web-based platform for biosignal visualization and annotation MULTIMEDIA TOOLS AND APPLICATIONS English Article Ground truth; Annotation; Biosignals; Data representation; Multimodal; Metadata EXCHANGE; FORMAT; EEG With the advent of wearable sensing and mobile technologies, biosignals have seen an increasingly growing number of application areas, leading to the collection of large volumes of data. One of the difficulties in dealing with these data sets, and in the development of automated machine learning systems which use them as input, is the lack of reliable ground truth information. In this paper we present a new web-based platform for visualization, retrieval and annotation of biosignals by non-technical users, aimed at improving the process of ground truth collection for biomedical applications. Moreover, a novel extendable and scalable data representation model and persistency framework is presented. The results of the experimental evaluation with possible users has further confirmed the potential of the presented framework. [Lourenco, Andre] ISEL, Lisbon, Portugal; [Lourenco, Andre; da Silva, Hugo Placido; Carreiras, Carlos; Alves, Ana Priscila; Fred, Ana L. N.] IT, Lisbon, Portugal; [da Silva, Hugo Placido; Fred, Ana L. N.] Inst Super Tecn, Lisbon, Portugal Lourenco, A (reprint author), ISEL, Lisbon, Portugal. arlourenco@lx.it.pt; hsilva@lx.it.pt; carlos.carreiras@lx.it.pt; anapriscila.alves@lx.it.pt; afred@lx.it.pt Fundacao para a Ciencia e Tecnologia (FCT) [PTDC/EIA-CCO/103230/2008, SFRH/BD/65248/2009, SFRH/PROTEC/49512/2009]; Instituto de Telecomunicacoes under the grant "Android Biometric System"; Area Departamental de Engenharia de Electronica e Telecomunicacoes e de Computadores - ISEL This work was partially funded by Fundacao para a Ciencia e Tecnologia (FCT) under the grants PTDC/EIA-CCO/103230/2008, SFRH/BD/65248/2009 and SFRH/PROTEC/49512/2009, by the Instituto de Telecomunicacoes under the grant "Android Biometric System" and by Area Departamental de Engenharia de Electronica e Telecomunicacoes e de Computadores - ISEL, whose support the authors gratefully acknowledge. Anderson JC, 2010, COUCHDB DEFINITIVE G; Bangor A, 2008, INT J HUM-COMPUT INT, V24, P574, DOI 10.1080/10447310802205776; Borsci S, 2009, COGN PROCESS, V10, P193, DOI 10.1007/s10339-009-0268-9; Brooks D, 2009, IEEE ENG MED BIO, P3881, DOI 10.1109/IEMBS.2009.5332642; Brooks DJ, 2011, IEEE ENG MED BIO, P5670, DOI 10.1109/IEMBS.2011.6091372; Chang F, 2008, ACM T COMPUT SYST, V26, DOI 10.1145/1365815.1365816; Chang HH, 2009, BIOMEDICAL SIGNAL PR; Chodorow K., 2010, MONGODB DEFINITIVE G; Clarys Jan Pieter, 1993, Journal of Sports Sciences, V11, P379, DOI 10.1080/02640419308730010; Crockford D., 2006, APPL JSON MEDIA TYPE; Dean J., 2004, P 6 C S OP SYST DES, V6, P10; Engelse W, 1979, IEEE COMPUT CARD, P37; EPI Mobile Solutions, EPI LIF DOCT YOUR PO; Fensli R, 2005, 18 INT S COMP BAS ME; Freivalds A., 2011, BIOMECHANICS UPPER L; Goldberger A, NY TIMES 1026, V23, P215; Hellmann G, 1996, ELECTROEN CLIN NEURO, V99, P426, DOI 10.1016/S0013-4694(96)96502-5; Jordan P. W., 1996, USABILITY EVALUATION; Kemp B, 2003, CLIN NEUROPHYSIOL, V114, P1755, DOI 10.1016/S1388-2457(03)00123-8; Lourenco A, 2012, P INT C BIOINSP SYST; Magrabi F, 1999, INT J MED INFORM, V54, P145, DOI 10.1016/S1386-5056(98)00177-4; McGuinness DL, 2003, ONTOLOGIES COME AGE; Moody GB, 2001, IEEE ENG MED BIOL, V20, P70, DOI 10.1109/51.932728; Preece J., 2002, INTERACTION DESIGN H; Russell BC, 2008, INT J COMPUT VISION, V77, P157, DOI 10.1007/s11263-007-0090-8; Schwartz M, 2005, BIOFEEDBACK PRACTITI; Shimada T, 2000, IEEE T BIO-MED ENG, V47, P369, DOI 10.1109/10.827301; Silva H, 2011, P PORT C PATT REC RE; Silva M, 2012, PROCEDIA COMPUTER SC; Sorokin Alexander, 2008, IEEE COMP SOC C COMP, P1, DOI DOI 10.1109/CVPRW.2008.4562953; Stanford V, 2004, IEEE PERVAS COMPUT, V3, P99, DOI 10.1109/MPRV.2004.1269140; Strauch C., 2011, NOSQL DATABASES; The HDF Group, 2010, HIER DAT FORM VERS 5 33 0 0 SPRINGER DORDRECHT VAN GODEWIJCKSTRAAT 30, 3311 GZ DORDRECHT, NETHERLANDS 1380-7501 1573-7721 MULTIMED TOOLS APPL Multimed. Tools Appl. MAY 2014 70 1 433 460 10.1007/s11042-013-1397-9 28 Computer Science, Information Systems; Computer Science, Software Engineering; Computer Science, Theory & Methods; Engineering, Electrical & Electronic Computer Science; Engineering AI4AK WOS:000336807300020 J Abel, S; Weiller, C; Huber, W; Willmes, K Abel, Stefanie; Weiller, Cornelius; Huber, Walter; Willmes, Klaus Neural underpinnings for model-oriented therapy of aphasic word production NEUROPSYCHOLOGIA English Article Aphasia; Treatment; Naming; fMRI; Semantics; Phonology INFERIOR FRONTAL GYRUS; POSTSTROKE APHASIA; UNIFIED SEGMENTATION; ANOMIA TREATMENT; SPOKEN LANGUAGE; NAMING THERAPY; RECOVERY; FMRI; STROKE; RECRUITMENT Model-oriented therapies of aphasic word production have been shown to be effective, with item-specific therapy effects being larger than generalisation effects for untrained items. However, it remains unclear whether semantic versus phonological therapy lead to differential effects, depending on type of lexical impairment. Functional imaging studies revealed that mainly left-hemisphere, perisylvian brain areas were involved in successful therapy-induced recovery of aphasic word production. However, the neural underpinnings for model-oriented therapy effects have not received much attention yet. We aimed at identifying brain areas indicating (1) general therapy effects using a naming task measured by functional magnetic resonance imaging (fMRI) in 14 patients before and after a 4-week naming therapy, which comprised increasing semantic and phonological cueing-hierarchies. We also intended to reveal differential effects (2) of training versus generalisation, (3) of therapy methods, and (4) of type of impairment as assessed by the connectionist Dell model. Training effects were stronger than generalisation effects, even though both were significant. Furthermore, significant impairment-specific therapy effects were observed for patients with phonological disorders (P-patients). (1) Left inferior frontal gyrus, pars opercularis (IFGoper), was a positive predictor of therapy gains while the right caudate was a negative predictor. Moreover, less activation decrease due to therapy in left-hemisphere temporo-parietal language areas was positively correlated with therapy gains. (2) Naming of trained compared to untrained words yielded less activation decrease in left superior temporal gyrus (STG) and precuneus, bilateral thalamus, and right caudate due to therapy. (3) Differential therapy effects could be detected in the right superior parietal lobule for the semantic method, and in regions involving bilateral anterior and mid cingulate, right precuneus, and left middle/superior frontal gyrus for the phonological method. (4) Impairment-specific changes of activation were found for P-patients in left IFGoper. Patients with semantic disorders (S-patients) relied on right frontal areas involving IFG, pars triangularis. After therapy, they revealed less activation decrease in areas involving left STG, caudate, paracentral lobule, and right rolandic operculum. Regarding naming performance, the present study corroborates previous findings on training and generalisation effects and reveals differential therapy effects for P-patients. Moreover, brain imaging results confirm a predominance of (1) general effects in the left brain hemisphere. (2) Brain regions related to visual strategy, monitoring/feedback, and articulatory patterns were characteristic for the familiar trained items. (3) Distinct regions associated with strategies, monitoring capacities, and linguistic information indicate the specific therapeutic influence on word retrieval. (4) While P-patients relied more on preserved phonological functions in the left hemisphere, S-patients revealed right-sided compensation of semantic processing as well as increased strategic efforts in both hemispheres. (C) 2014 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/). [Abel, Stefanie; Willmes, Klaus] Rhein Westfal TH Aachen, Sect Neuropsychol, Dept Neurol, D-52074 Aachen, Germany; [Abel, Stefanie; Huber, Walter] Rhein Westfal TH Aachen, Dept Neurol, Aachen, Germany; [Abel, Stefanie; Huber, Walter; Willmes, Klaus] Aachen Juelich Res Alliance, JARA BRAIN, Aachen, Germany; [Abel, Stefanie] SRH Univ Appl Sci Gera, Gera, Germany; [Weiller, Cornelius] Univ Med Ctr Freiburg, Dept Neurol, Freiburg, Germany; [Willmes, Klaus] Rhein Westfal TH Aachen, Interdisciplinary Ctr Clin Res, Aachen, Germany Abel, S (reprint author), Univ Hosp RWTH, Sect Neuropsychol, Dept Neurol, Pauwelsstr 30, D-52074 Aachen, Germany. sabel@ukaachen.de Bundesministerium fur Bildung und Forschung (Research collaboration, BMBF) [01GW 0661]; German Research Foundation (DFG) [HU 292/10-1, AB 282/2-1] This research was supported by the Bundesministerium fur Bildung und Forschung (Research collaboration, BMBF grant 01GW 0661 for "Mechanisms of Brain Reorganisation in the Language Network") and the German Research Foundation (DFG research grants HU 292/10-1 and AB 282/2-1 for "Model-oriented treatment of word production disorders in aphasia"). We thank Ruth Bitzer, Sibylle Hufner and Stephanie Elkeles for assistance in the delivery of speech therapy and the two reviewers for their many helpful comments. Abel S., 2013, EUR WORKSH COGN NEUR; Abel S, 2003, BRAIN LANG, V87, P143, DOI 10.1016/S0093-934X(03)00240-2; Abel S., 2014, THERAPY INDUCED BRAI; Abel Stefanie, 2011, Brain Connect, V1, P219, DOI 10.1089/brain.2011.0024; Abel S, 2009, APHASIOLOGY, V23, P1, DOI 10.1080/02687030903022203; Abel S, 2007, APHASIOLOGY, V21, P411, DOI 10.1080/02687030701192687; Abel S, 2009, NEUROSCI LETT, V463, P167, DOI 10.1016/j.neulet.2009.07.077; Ashburner J, 2005, NEUROIMAGE, V26, P839, DOI 10.1016/j.neuroimage.2005.02.018; Baayen R. H., 1995, CELEX LEX DAT; Cabeza R, 2000, J COGNITIVE NEUROSCI, V12, P1, DOI 10.1162/08989290051137585; Calvert GA, 2000, BRAIN LANG, V71, P391, DOI 10.1006/brln.1999.2272; Cicerone KD, 2000, ARCH PHYS MED REHAB, V81, P1596, DOI 10.1053/apmr.2000.19240; Comelissen K., 2003, J COGNITIVE NEUROSCI, V15, P444; Crinion J, 2007, NEUROIMAGE, V37, P866, DOI 10.1016/j.neuroimage.2007.04.065; Crinion JT, 2007, CURR OPIN NEUROL, V20, P667; Crosson B, 2007, NEUROPSYCHOL REV, V17, P157, DOI 10.1007/s11065-007-9024-z; Crosson B, 2005, J COGNITIVE NEUROSCI, V17, P392, DOI 10.1162/0898929053279487; Cusack R, 2005, HUM BRAIN MAPP, V24, P299, DOI 10.1002/hbm.20085; Doesborgh SJC, 2004, STROKE, V35, P141, DOI 10.1161/01.STR.0000105460.52928.A6; Foygel D, 2000, J MEM LANG, V43, P182, DOI 10.1006/jmla.2000.2716; Fridriksson J, 2009, HUM BRAIN MAPP, V30, P2487, DOI 10.1002/hbm.20683; Fridriksson J, 2006, NEUROIMAGE, V32, P1403, DOI 10.1016/j.neuroimage.2006.04.194; Fridriksson J, 2012, NEUROIMAGE, V60, P854, DOI 10.1016/j.neuroimage.2011.12.057; Fridriksson J, 2010, J NEUROSCI, V30, P11558, DOI 10.1523/JNEUROSCI.2227-10.2010; Fridriksson J, 2007, NEUROPSYCHOLOGIA, V45, P1812, DOI 10.1016/j.neuropsychologia.2006.12.017; Genzel S., 1995, NEUROLINGUISTIK, V9, P41; Gold BT, 2000, BRAIN LANG, V73, P456, DOI 10.1006/brln.2000.2317; Heiss WD, 1999, ANN NEUROL, V45, P430, DOI 10.1002/1531-8249(199904)45:4<430::AID-ANA3>3.0.CO;2-P; Heiss WD, 2006, BRAIN LANG, V98, P118, DOI 10.1016/j.bandl.2006.02.002; Hillis AE, 2006, J NEUROSCI, V26, P8069, DOI 10.1523/JNEUROSCI.2088-06.2006; Howard D, 2006, APHASIOLOGY, V20, P921, DOI 10.1080/02687030600782679; Huber W., 1983, AACHENER APHASIE TES; Katzev M, 2013, J NEUROSCI, V33, P7837, DOI 10.1523/JNEUROSCI.3147-12.2013; Laine M., 2006, ANOMIA THEORETICAL C; Leger A, 2002, NEUROIMAGE, V17, P174, DOI 10.1006/nimg.2002.1238; Marcotte K, 2012, NEUROPSYCHOLOGIA, V50, P1776, DOI 10.1016/j.neuropsychologia.2012.04.001; Meinzer M, 2011, APHASIOLOGY, V25, P271, DOI 10.1080/02687038.2010.530672; Meinzer M, 2008, NEUROIMAGE, V39, P2038, DOI 10.1016/j.neuroimage.2007.10.008; Meinzer M, 2007, NEUROPSYCHOLOGIA, V45, P1247, DOI 10.1016/j.neuropsychologia.2006.10.003; Menke R, 2009, BMC NEUROSCI, V10, DOI 10.1186/1471-2202-10-118; Musso M, 1999, BRAIN, V122, P1781, DOI 10.1093/brain/122.9.1781; Naeser MA, 2005, BRAIN LANG, V93, P95, DOI 10.1016/j.bandl.2004.08.004; OLDFIELD RC, 1971, NEUROPSYCHOLOGIA, V9, P97, DOI 10.1016/0028-3932(71)90067-4; Price C. J., 2000, J ANAT, V197, P377; Price CJ, 2012, NEUROIMAGE, V62, P816, DOI 10.1016/j.neuroimage.2012.04.062; Raboyeau G, 2008, NEUROLOGY, V70, P290, DOI 10.1212/01.wnl.0000287115.85956.87; Richter M, 2008, BRAIN, V131, P1391, DOI 10.1093/brain/awn043; Ridderinkhof K. R., 2004, BRAIN COGNITION, V56, P140; Riddoch M. J., 2005, BIRMINGHAM OBJECT RE; Rochon E, 2010, BRAIN LANG, V114, P164, DOI 10.1016/j.bandl.2010.05.005; Saur D, 2006, BRAIN, V129, P1371, DOI 10.1093/brain/awl090; Schwartz M. F., 2006, J MEM LANG, V54, P223; Schwartz MF, 2012, BRAIN, V135, P3799, DOI 10.1093/brain/aws300; Schwartz MF, 2009, BRAIN, V132, P3411, DOI 10.1093/brain/awp284; Seghier ML, 2008, NEUROIMAGE, V41, P1253, DOI 10.1016/j.neuroimage.2008.03.028; Slotnick SD, 2003, COGNITIVE BRAIN RES, V17, P75, DOI 10.1016/S0926-6410(03)00082-X; SNODGRASS JG, 1980, J EXP PSYCHOL-HUM L, V6, P174, DOI 10.1037/0278-7393.6.2.174; Vigneau M, 2011, NEUROIMAGE, V54, P577, DOI 10.1016/j.neuroimage.2010.07.036; Vigneau M, 2006, NEUROIMAGE, V30, P1414, DOI 10.1016/j.neuroimage.2005.11.002; Vitali P, 2007, NEUROREHAB NEURAL RE, V21, P152, DOI 10.1177/1545968306294735; WEILLER C, 1995, ANN NEUROL, V37, P723, DOI 10.1002/ana.410370605; Willmes K., 2010, HDB CLIN NEUROPSYCHO, P28; Winhuisen L, 2005, STROKE, V36, P1759, DOI 10.1161/01.STR.0000174487.81126.ef; Wisenburn B, 2009, APHASIOLOGY, V23, P1338, DOI 10.1080/02687030902732745; Wisenburn B., 2010, PSHA J, P4; Zahn R, 2006, J PHYSIOL-PARIS, V99, P370, DOI 10.1016/j.jphysparis.2006.03.013 66 0 0 PERGAMON-ELSEVIER SCIENCE LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, ENGLAND 0028-3932 1873-3514 NEUROPSYCHOLOGIA Neuropsychologia MAY 2014 57 154 165 10.1016/j.neuropsychologia.2014.03.010 12 Behavioral Sciences; Neurosciences; Psychology, Experimental Behavioral Sciences; Neurosciences & Neurology; Psychology AI3OP WOS:000336772100015 J Deng, T; Fan, WF Deng, Ting; Fan, Wenfei On the Complexity of Query Result Diversification ACM TRANSACTIONS ON DATABASE SYSTEMS English Article Design; Algorithms; Theory; Result diversification; relevance; diversity; recommender systems; database queries; combined complexity; data complexity; counting problems Query result diversification is a bi-criteria optimization problem for ranking query results. Given a database D, a query Q, and a positive integer k, it is to find a set of k tuples from Q(D) such that the tuples are as relevant as possible to the query, and at the same time, as diverse as possible to each other. Subsets of Q(D) are ranked by an objective function defined in terms of relevance and diversity. Query result diversification has found a variety of applications in databases, information retrieval, and operations research. This article investigates the complexity of result diversification for relational queries. (1) We identify three problems in connection with query result diversification, to determine whether there exists a set of k tuples that is ranked above a bound with respect to relevance and diversity, to assess the rank of a given k-element set, and to count how many k-element sets are ranked above a given bound based on an objective function. (2) We study these problems for a variety of query languages and for the three objective functions proposed in Gollapudi and Sharma [2009]. We establish the upper and lower bounds of these problems, all matching, for both combined complexity and data complexity. (3) We also investigate several special settings of these problems, identifying tractable cases. Moreover, (4) we reinvestigate these problems in the presence of compatibility constraints commonly found in practice, and provide their complexity in all these settings. [Deng, Ting] Beihang Univ, SKLSDE, RCBD, Beijing, Peoples R China; [Deng, Ting] Beihang Univ, Sch Comp Sci & Engn, Beijing, Peoples R China; [Fan, Wenfei] Univ Edinburgh, Sch Informat, Lab Fdn Comp Sci, Edinburgh, Midlothian, Scotland; [Fan, Wenfei] Beihang Univ, Beijing, Peoples R China Deng, T (reprint author), Beihang Univ, SKLSDE, RCBD, 37 Xuyuan Rd, Beijing, Peoples R China. dengt-ing@act.buaa.edu.cn 973 Program [2014CB340302, 2012CB316200]; NSFC [61133002]; Guangdong Innovative Research Team Program [2011D005]; Shenzhen Peacock Program [1105100030834361]; EPSRC [EP/J015377/1]; [863 2012AA011203] The authors are supported in part by 973 Program 2014CB340302. T. Deng is also supported in part by the 863 2012AA011203. W. Fan is also supported in part by NSFC 61133002, 973 Program 2012CB316200, Guangdong Innovative Research Team Program 2011D005, Shenzhen Peacock Program 1105100030834361, and EPSRC EP/J015377/1. Abiteboul S., 1995, FDN DATABASES; Adomavicius G, 2005, IEEE T KNOWL DATA EN, V17, P734, DOI 10.1109/TKDE.2005.99; Agrawal R., 2009, P 2 ACM INT C WEB SE, P5, DOI 10.1145/1498759.1498766; Amer-Yahia S., 2011, IEEE DATA ENG B, V34, P69; Amer-Yahia S., 2013, P INT C WORLD WID WE, P79; Berbeglia G., 2010, DISCRETE APPL MATH, V157, P2541; Borodin A., 2012, P 31 S PRINC DAT SYS, P155; Capannini G., 2011, P VLDB ENDOW, V4, P451; Chen Zhiyuan, 2007, P 2007 ACM SIGMOD IN, P641, DOI 10.1145/1247480.1247551; Demidova E, 2010, SIGIR 2010: PROCEEDINGS OF THE 33RD ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH DEVELOPMENT IN INFORMATION RETRIEVAL, P331; Deng T., 2013, P VLDB ENDOW, V6, P577; Deng T., 2012, P 31 ACM SIGMOD SIGA, P261; Drosou M, 2010, SIGMOD RECORD, V39, P41, DOI 10.1145/1860702.1860709; Drosou M., 2009, IEEE DATA ENG B, V32, P4; Durand A, 2005, THEOR COMPUT SCI, V340, P496, DOI 10.1016/j.tcs.2005.03.012; Fagin R, 2003, J COMPUT SYST SCI, V66, P614, DOI 10.1016/S0022-0000(03)00026-6; Feuerstein E., 2007, P LAT AM WWW C LA WE, P22; Fraternali P., 2012, P ACM SIGMOD INT C M, P421; Gollapudi S., 2009, P 18 INT C WORLD WID, P381, DOI 10.1145/1526709.1526761; Hemaspaandra L. A., 1995, SIGACT News, V26; ILYAS I. F., 2008, ACM COMPUT SURV, V40, P4; Jin W., 2011, P ACM SIGMOD INT C M, P601; Koutrika G, 2009, ACM SIGMOD/PODS 2009 CONFERENCE, P745; LADNER RE, 1989, SIAM J COMPUT, V18, P1087, DOI 10.1137/0218073; Lappas T, 2009, KDD-09: 15TH ACM SIGKDD CONFERENCE ON KNOWLEDGE DISCOVERY AND DATA MINING, P467; Li C., 2005, P 25 INT C VER LARG, P1342; LIU Z., 2009, P VLDB ENDOW, V2, P313; Minack E., 2009, P 1 INT WORKSH LIV W; Papadimitriou C. H., 1994, COMPUTATIONAL COMPLE; Parameswaran A. G., 2010, P CIKM, P919, DOI 10.1145/1871437.1871555; Parameswaran A. G., 2011, ACM T INFORM SYST, V29, P4; Prokopyev OA, 2009, EUR J OPER RES, V197, P59, DOI 10.1016/j.ejor.2008.06.005; Schnaitter K., 2008, PODS, P43, DOI 10.1145/1376916.1376924; Stefanidis K., 2010, P INT C EXT DAT TECH, P585, DOI 10.1145/1739041.1739111; Valiant L. G., 1979, Theoretical Computer Science, V8, DOI 10.1016/0304-3975(79)90044-6; Vardi M., 1982, P 14 ACM S THEOR COM, P137, DOI 10.1145/800070.802186; Vee E, 2008, PROC INT CONF DATA, P228, DOI 10.1109/ICDE.2008.4497431; Vieira MR, 2011, PROC INT CONF DATA, P1163, DOI 10.1109/ICDE.2011.5767846; Xie M, 2012, FRONT COMPUT SCI-CHI, V6, P264, DOI 10.1007/s11704-012-2014-1; Yu C., 2009, P 12 INT C EXT DAT T, P368, DOI 10.1145/1516360.1516404; Yu C, 2009, PROC INT CONF DATA, P1299; Zhang M, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P123; Ziegler CN, 2005, P 14 INT WORLD WID W, P22, DOI DOI 10.1145/1060745.1060754 43 0 0 ASSOC COMPUTING MACHINERY NEW YORK 2 PENN PLAZA, STE 701, NEW YORK, NY 10121-0701 USA 0362-5915 1557-4644 ACM T DATABASE SYST ACM Trans. Database Syst. MAY 2014 39 2 15 10.1145/2602136 46 Computer Science, Information Systems; Computer Science, Software Engineering Computer Science AH9EI WOS:000336443800006 J Hope, L; Gabbert, F; Fisher, RP; Jamieson, K Hope, Lorraine; Gabbert, Fiona; Fisher, Ronald P.; Jamieson, Kat Protecting and Enhancing Eyewitness Memory: The Impact of an Initial Recall Attempt on Performance in an Investigative Interview APPLIED COGNITIVE PSYCHOLOGY English Article SELF-ADMINISTERED INTERVIEW; MISLEADING POSTEVENT INFORMATION; COGNITIVE INTERVIEW; COOPERATIVE WITNESSES; SENTENCE RECOGNITION; RETENTION INTERVAL; HYPERMNESIA; RETRIEVAL; EVENTS; REMINISCENCE Evidence-gathering begins at the scene of an incident. Available witnesses may be asked to provide an account of what happened, either in response to an open request for information or, in some regions, by completing a Self-Administered Interview (SAI (c)). In both cases, an investigative interview may be conducted at some later date. This study sought to determine the impact of an initial retrieval attempt on a subsequent interview. After exposure to a mock crime, participants completed an SAI (c) or a free recall (FR), or did not engage in an initial retrieval (Control). One week later, participants were interviewed with a Cognitive Interview. SAI (c) participants reported more correct information and maintained higher accuracy than FR and Control participants. Consistency analyses revealed that the SAI (c) was effective because it preserved more of the originally recalled items (Time 1) than did an initial FR, and not because it yielded new recollections at Time 2. Copyright (c) 2014 John Wiley & Sons, Ltd. [Hope, Lorraine] Univ Portsmouth, Portsmouth, Hants, England; [Gabbert, Fiona] Univ London, London, England; [Fisher, Ronald P.] Florida Int Univ, Miami, FL 33199 USA; [Jamieson, Kat] Univ Abertay, Dundee, Scotland Hope, L (reprint author), Univ Portsmouth, Dept Psychol, King Henry Bldg,King Henry I St, Portsmouth, Hants, England. lorraine.hope@port.ac.uk ANDERSON JR, 1983, J VERB LEARN VERB BE, V22, P261, DOI 10.1016/S0022-5371(83)90201-3; ANDERSON MC, 1994, J EXP PSYCHOL LEARN, V20, P1063, DOI 10.1037/0278-7393.20.5.1063; Ayers MS, 1998, PSYCHON B REV, V5, P1, DOI 10.3758/BF03209454; Bailey F. L., 1985, SUCCESSFUL TECHNIQUE; BEGG I, 1974, MEM COGNITION, V2, P353, DOI 10.3758/BF03209009; Bjork R. A., 1988, PRACTICAL ASPECTS ME, V1, P396; Bornstein BH, 1998, APPL COGNITIVE PSYCH, V12, P119, DOI 10.1002/(SICI)1099-0720(199804)12:2<119::AID-ACP500>3.0.CO;2-4; Brock P, 1999, PSYCHOL CRIME LAW, V5, P29, DOI 10.1080/10683169908414992; Cohen J, 1988, STAT POWER ANAL BEHA; Ebbesen EB, 1998, J APPL PSYCHOL, V83, P745, DOI 10.1037/0021-9010.83.5.745; Ebbinghaus H., 1885, MEMORY CONTRIBUTION; Ellison Louise, 2001, LEGAL STUDIES, V21, p[353, 353], DOI DOI 10.1111/J.1748-121X.2001.TB00172.X; Erdelyi M. H., 1996, RECOVERY UNCONSCIOUS; ERDELYI MH, 1974, COGNITIVE PSYCHOL, V6, P159, DOI 10.1016/0010-0285(74)90008-5; Evans JR, 2011, APPL COGNITIVE PSYCH, V25, P501, DOI 10.1002/acp.1722; Fisher R., 2009, HDB PSYCHOL INVESTIG, P121, DOI 10.1002/9780470747599.ch8; Fisher R. P., 1995, PSYCHOL LAW CRIMINAL; Fisher R. P., 1992, MEMORY ENHANCING TEC; FISHER RP, 1987, J POLICE SCI ADMIN, V15, P291; FISHER RP, 1989, J APPL PSYCHOL, V74, P722, DOI 10.1037//0021-9010.74.5.722; Fisher RP, 1996, BEHAV BRAIN SCI, V19, P197; Fisher RP, 2011, CURR DIR PSYCHOL SCI, V20, P16, DOI 10.1177/0963721410396826; Fisher RP, 2010, LEGAL CRIMINOL PSYCH, V15, P25, DOI 10.1348/135532509X441891; Gabbert F, 2012, APPL COGNITIVE PSYCH, V26, P568, DOI 10.1002/acp.2828; Gabbert F, 2009, LAW HUMAN BEHAV, V33, P298, DOI 10.1007/s10979-008-9146-8; Gilbert JAE, 2006, APPL COGNITIVE PSYCH, V20, P723, DOI 10.1002/acp.1232; Glissan J. L., 1991, CROSS EXAMINATION PR; Goldsmith M, 2005, J MEM LANG, V52, P505, DOI 10.1016/j.jml.2005.01.010; Granhag P. A., 2001, NEW TREND CRIMINAL I, V2, P309; HASHTROUDI S, 1994, PSYCHOL AGING, V9, P160, DOI 10.1037//0882-7974.9.1.160; Hope L, 2011, LEGAL CRIMINOL PSYCH, V16, P211, DOI 10.1111/j.2044-8333.2011.02015.x; Koriat A, 2003, J EXP PSYCHOL LEARN, V29, P1095, DOI 10.1037/0278-7393.29.6.1095; La Rooy D, 2005, J EXP CHILD PSYCHOL, V90, P235, DOI 10.1016/j.jecp.2004.11.002; Levene H, 1960, CONTRIBUTIONS PROBAB, P278; Levy BJ, 2002, TRENDS COGN SCI, V6, P299, DOI 10.1016/S1364-6613(02)01923-X; LOFTUS EF, 1978, J EXP PSYCHOL-HUM L, V4, P19, DOI 10.1037//0278-7393.4.1.19; Macleod M, 2002, APPL COGNITIVE PSYCH, V16, P135, DOI 10.1002/acp.782; Marsh E. J., 2005, APPL COGNITIVE PSYCH, V19, P1; MCCAULEY MR, 1995, J APPL PSYCHOL, V80, P510, DOI 10.1037//0021-9010.80.4.510; MCCLOSKEY M, 1985, J EXP PSYCHOL GEN, V114, P1; MCDANIEL MA, 1991, CONTEMP EDUC PSYCHOL, V16, P192, DOI 10.1016/0361-476X(91)90037-L; Mello EW, 1996, APPL COGNITIVE PSYCH, V10, P403, DOI 10.1002/(SICI)1099-0720(199610)10:5<403::AID-ACP395>3.0.CO;2-X; Memon A, 2010, PSYCHOL PUBLIC POL L, V16, P340, DOI 10.1037/a0020518; MORRIS CD, 1977, J VERB LEARN VERB BE, V16, P519, DOI 10.1016/S0022-5371(77)80016-9; Oeberst A, 2012, LAW HUMAN BEHAV, V36, P266, DOI 10.1037/h0093966; Pansky A., 2012, J APPL RES MEMORY CO, V1, P2; Penrod S., 1982, PSYCHOL COURTROOM, P119; Potter R., 1999, PSYCHIAT PSYCHOL LAW, V6, P97; REYNA VF, 1994, DEV PSYCHOL, V30, P178, DOI 10.1037/0012-1649.30.2.178; ROEDIGER HL, 1982, J EXP PSYCHOL-HUM L, V8, P66, DOI 10.1037//0278-7393.8.1.66; ROEDIGER HL, 1982, J VERB LEARN VERB BE, V21, P635, DOI 10.1016/S0022-5371(82)90810-6; ROEDIGER HL, 1983, PSYCHOL RES-PSYCH FO, V45, P275, DOI 10.1007/BF00308707; Rubin D. C., 1996, PSYCHOL REV, V103, P743; SCRIVNER E, 1988, J APPL PSYCHOL, V73, P371, DOI 10.1037//0021-9010.73.3.371; SHAW JS, 1995, PSYCHON B REV, V2, P249, DOI 10.3758/BF03210965; Stuesser L., 1993, INTRO ADVOCACY; SUENGAS AG, 1988, J EXP PSYCHOL GEN, V117, P377, DOI 10.1037//0096-3445.117.4.377; Tuckey MR, 2003, J EXP PSYCHOL-APPL, V9, P101, DOI 10.1037/1076-898X.9.2.101; WILKINSON AC, 1984, J MATH PSYCHOL, V28, P43, DOI 10.1016/0022-2496(84)90019-1; Wixted J., 1997, MEM COGNITION, V23, P731; WIXTED JT, 1991, PSYCHOL SCI, V2, P409, DOI 10.1111/j.1467-9280.1991.tb00175.x; ZARAGOZA MS, 1994, J EXP PSYCHOL LEARN, V20, P934, DOI 10.1037/0278-7393.20.4.934 62 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0888-4080 1099-0720 APPL COGNITIVE PSYCH Appl. Cogn. Psychol. MAY 2014 28 3 304 313 10.1002/acp.2984 10 Psychology, Experimental Psychology AE3ZE WOS:000333916200004 J Balducci, C; Forloni, G Balducci, Claudia; Forloni, Gianluigi In Vivo Application of beta Amyloid Oligomers: A Simple Tool to Evaluate Mechanisms of Action and New Therapeutic Approaches CURRENT PHARMACEUTICAL DESIGN English Article Alzheimer's disease; mice; A beta oligomers; behaviour; memory; synaptic dysfunction; prion protein; gamma-secretase modulators; inflammation; toll-like receptor 4 GAMMA-SECRETASE MODULATOR; OBJECT RECOGNITION MEMORY; CELLULAR PRION PROTEIN; LONG-TERM POTENTIATION; CENTRAL-NERVOUS-SYSTEM; HIPPOCAMPAL SYNAPTIC PLASTICITY; ALZHEIMERS-DISEASE-MODEL; TRANSGENIC MOUSE MODEL; A-BETA; PRECURSOR PROTEIN Alzheimer's disease (AD) is a neurodegenerative disorder characterized by cerebral accumulation of extracellular amyloid beta (A beta) and neurofibrillary tangles made of hyperphosphorylated tau protein, two main lesions which appear sequentially during the disease progression. In the last decade numerous studies have proposed small soluble aggregates of A beta, known as oligomers, as the species responsible for synaptic dysfunction, memory loss and neurodegeneration typical of AD. In vitro and in vivo experiments have identified A beta oligomers as the elements that can alter synaptic function by a reversible mechanism, which gradually becomes permanent when exposure is continuous. Here we show that intracerebroventricular (ICV) injection in mice of a solution containing specifically A beta(1-42) oligomers substantially affects their memory when tested in the novel object recognition task. This acute mouse model enabled us to distinguish whether oligomers were affecting specific phases of the memory processing. A single injection of A beta(1-42) oligomers before memory consolidation abolished information processing, leading to memory impairment, whereas no such effects were observed when the injection was done once the information had been processed, indicating that the oligomers affect memory consolidation rather than retrieval. Beside A beta(1-42), A beta(1-40) oligomers also impaired memory, and both isoforms were antagonized by the anti-A beta 4G8 monoclonal antibody. This simple and reliable paradigm is useful to investigate the mechanisms through which A beta oligomers interfere with neuronal processes and to test the efficacy of new therapeutic approaches specifically against these species. We tested several molecules by direct co-incubation with A beta oligomers, ICV injections preceding A beta oligomers, and the systemic treatment with drugs that cross the blood brain barrier. We also examined the proposed involvement of cellular prion protein as a mediator of the oligomer-induced memory impairment. [Balducci, Claudia; Forloni, Gianluigi] Ist Ric Farmacol Mario Negri, IRCCS, Dept Neurosci, I-20156 Milan, Italy Forloni, G (reprint author), Ist Ric Farmacol Mario Negri, IRCCS, Via La Masa 19, I-2156 Milan, Italy. forloni@marionegri.it Regione Lombardia [14501A] We thank Dr Mario Salmona, Dr Marten Beeg, Dr Massimo Messa and Dr Laura Colombo for providing us with A beta oligomers and AFM characterization. We also thank Dr Bruno P. Imbimbo, at Chiesi Farmaceutici (Parma, Italy), for providing the compound CHF5074. The authors declare they have no conflicts of interest. This study has been partially funded by Regione Lombardia under Institutional Agreement n. 14501A Ait-Ghezala G, 2008, CYTOKINE, V44, P283, DOI 10.1016/j.cyto.2008.08.013; Akiyama H, 2000, NEUROBIOL AGING, V21, P383, DOI 10.1016/S0197-4580(00)00124-X; Aoki C, 2005, J COMP NEUROL, V483, P383, DOI 10.1002/cne.20449; Balducci C, 2010, P NATL ACAD SCI USA, V107, P2295, DOI 10.1073/pnas.0911829107; Balducci C, 2011, J ALZHEIMERS DIS, V24, P799, DOI 10.3233/JAD-2011-101839; Balducci C, 2010, J ALZHEIMERS DIS, V21, P1367, DOI 10.3233/JAD-2010-100675; Balducci C, 2011, NEUROMOL MED, V13, P117, DOI 10.1007/s12017-010-8141-7; Barres BA, 2008, NEURON, V60, P430, DOI 10.1016/j.neuron.2008.10.013; Beeg M, 2011, ANAL BIOCHEM, V411, P297, DOI 10.1016/j.ab.2010.12.032; Benilova I, 2012, NAT NEUROSCI, V15, P349, DOI 10.1038/nn.3028; BERTONIFREDDARI C, 1989, PATHOL RES PRACT, V185, P799; Bird CM, 2008, NAT REV NEUROSCI, V9, P182, DOI 10.1038/nrn2335; Bourne JN, 2008, ANNU REV NEUROSCI, V31, P47, DOI 10.1146/annurev.neuro.31.060407.125646; Brouillette J, 2012, J NEUROSCI, V32, P7852, DOI 10.1523/JNEUROSCI.5901-11.2012; Buchhave P, 2009, NEUROSCI LETT, V450, P56, DOI 10.1016/j.neulet.2008.10.091; Calella AM, 2010, EMBO MOL MED, V2, P306, DOI 10.1002/emmm.201000082; Chapman PF, 1999, NAT NEUROSCI, V2, P271; Cleary JP, 2005, NAT NEUROSCI, V8, P79, DOI 10.1038/nn1372; Coin I, 2007, NAT PROTOC, V2, P3247, DOI 10.1038/nprot.2007.454; Cooke SF, 2006, BRAIN, V129, P1659, DOI 10.1093/brain/awl082; COWBURN R, 1988, NEUROSCI LETT, V86, P109, DOI 10.1016/0304-3940(88)90192-9; CROSS AJ, 1987, NEUROSCI LETT, V79, P213, DOI 10.1016/0304-3940(87)90699-9; DaRocha-Souto B, 2011, J NEUROPATH EXP NEUR, V70, P360, DOI 10.1097/NEN.0b013e318217a118; DAVIES CA, 1987, J NEUROL SCI, V78, P151, DOI 10.1016/0022-510X(87)90057-8; De Felice FG, 2009, P NATL ACAD SCI USA, V106, P1971, DOI 10.1073/pnas.0809158106; DEKOSKY ST, 1990, ANN NEUROL, V27, P457, DOI 10.1002/ana.410270502; Dere E, 2007, NEUROSCI BIOBEHAV R, V31, P673, DOI 10.1016/j.neubiorev.2007.01.005; Desideri G, 2008, NEUROBIOL AGING, V29, P348, DOI 10.1016/j.neurobiolaging.2006.10.019; D'Hooge R, 2001, BRAIN RES REV, V36, P60, DOI 10.1016/S0165-0173(01)00067-4; Dineley KT, 2002, J BIOL CHEM, V277, P22768, DOI 10.1074/jbc.M200164200; Dodart JC, 2002, NAT NEUROSCI, V5, P452, DOI 10.1038/nn842; El-Agnaf OMA, 2003, LANCET NEUROL, V2, P461, DOI 10.1016/S1474-4422(03)00481-2; Encalada SE, 2008, J MOL NEUROSCI, V34, P9, DOI 10.1007/s12031-007-0011-x; ENNACEUR A, 1988, BEHAV BRAIN RES, V31, P47, DOI 10.1016/0166-4328(88)90157-X; Fancy DA, 2000, CHEM BIOL, V7, P697, DOI 10.1016/S1074-5521(00)00020-X; Forloni G, 2011, PRION, V5, P10, DOI 10.4161/prion.5.1.14367; Forloni G, 1997, J NEUROCHEM, V69, P2048; Frank S, 2009, NEUROSCI LETT, V453, P41, DOI 10.1016/j.neulet.2009.01.075; Freir DB, 2011, NEUROBIOL AGING, V32, P2211, DOI 10.1016/j.neurobiolaging.2010.01.001; Funato H, 1999, AM J PATHOL, V155, P23, DOI 10.1016/S0002-9440(10)65094-8; Ghosh AK, 2012, J NEUROCHEM, V120, P71, DOI 10.1111/j.1471-4159.2011.07476.x; Ghosh AK, 2008, CURR ALZHEIMER RES, V5, P121, DOI 10.2174/156720508783954730; Gimbel DA, 2010, J NEUROSCI, V30, P6367, DOI 10.1523/JNEUROSCI.0395-10.2010; Greene JDW, 1996, NEUROPSYCHOLOGIA, V34, P537, DOI 10.1016/0028-3932(95)00151-4; Hardy J, 2002, SCIENCE, V297, P353, DOI 10.1126/science.1072994; HARDY J, 1987, NEUROSCI LETT, V73, P77, DOI 10.1016/0304-3940(87)90034-6; HARDY JA, 1992, SCIENCE, V256, P184, DOI 10.1126/science.1566067; Hsia AY, 1999, P NATL ACAD SCI USA, V96, P3228, DOI 10.1073/pnas.96.6.3228; Huang SM, 2006, J BIOL CHEM, V281, P17941, DOI 10.1074/jbc.M601372200; HYMAN BT, 1987, ANN NEUROL, V22, P37, DOI 10.1002/ana.410220110; Imbimbo BP, 2010, J ALZHEIMERS DIS, V20, P159, DOI 10.3233/JAD-2010-1366; Imbimbo BP, 2012, ALZHEIMER D IN PRESS; Imbimbo BP, 2007, J PHARMACOL EXP THER, V323, P822, DOI 10.1124/jpet.107.129007; Imbimbo BP, 2009, BRIT J PHARMACOL, V156, P982, DOI 10.1111/j.1476-5381.2008.00097.x; Jacobsen JS, 2006, P NATL ACAD SCI USA, V103, P5161, DOI 10.1073/pnas.0600948103; Janowsky JS, 1996, NEUROPSYCHOLOGIA, V34, P527, DOI 10.1016/0028-3932(95)00138-7; JARRETT JT, 1993, BIOCHEMISTRY-US, V32, P4693, DOI 10.1021/bi00069a001; Kakimura J, 2002, FASEB J, V16, P601, DOI 10.1096/fj.01-0530fje; KATZMAN R, 1986, NEW ENGL J MED, V314, P964, DOI 10.1056/NEJM198604103141506; Kessels HW, 2011, NATURE, V466, pE4; Kessels HW, 2011, NATURE, V466, pE3; Kielian T, 2006, J NEUROSCI RES, V83, P711, DOI 10.1002/jnr.20767; Klein WL, 2001, TRENDS NEUROSCI, V24, P219, DOI 10.1016/S0166-2236(00)01749-5; Klyubin I, 2008, J NEUROSCI, V28, P4231, DOI 10.1523/JNEUROSCI.5161-07.2008; Klyubin I, 2005, NAT MED, V11, P556, DOI 10.1038/nm1234; KOH JY, 1990, BRAIN RES, V533, P315, DOI 10.1016/0006-8993(90)91355-K; Kokubo H, 2005, BRAIN RES, V1031, P222, DOI 10.1016/j.brainres.2004.10.041; Kopec KK, 1998, J NEUROCHEM, V71, P2123; Kotilinek LA, 2002, J NEUROSCI, V22, P6331; Kukar TL, 2008, NATURE, V453, P925, DOI 10.1038/nature07055; Kuo YM, 1996, J BIOL CHEM, V271, P4077; Lacor PN, 2004, J NEUROSCI, V24, P10191, DOI 10.1523/JNEUROSCI.3432-04.2004; Lacor PN, 2007, J NEUROSCI, V27, P796, DOI 10.1523/JNEUROSCI.3501-06.2007; Lambert MP, 1998, P NATL ACAD SCI USA, V95, P6448, DOI 10.1073/pnas.95.11.6448; Larson J, 1999, BRAIN RES, V840, P23, DOI 10.1016/S0006-8993(99)01698-4; Lauren J, 2009, NATURE, V457, P1128, DOI 10.1038/nature07761; Lesne S, 2006, NATURE, V440, P352, DOI 10.1038/nature04533; LEVINE H, 1995, NEUROBIOL AGING, V16, P755, DOI 10.1016/0197-4580(95)00052-G; Linden R, 2008, PHYSIOL REV, V88, P673, DOI 10.1152/physrev.00007.2007; Lopez-Perez E, 2001, J NEUROCHEM, V76, P1532, DOI 10.1046/j.1471-4159.2001.00180.x; Lowndes G, 2007, NEUROPSYCHOL REV, V17, P193, DOI 10.1007/s11065-007-9032-z; Lue LF, 1999, AM J PATHOL, V155, P853, DOI 10.1016/S0002-9440(10)65184-X; Maillard I, 2003, IMMUNITY, V19, P781, DOI 10.1016/S1074-7613(03)00325-X; Masliah E, 2001, NEUROLOGY, V56, P127; McLean CA, 1999, ANN NEUROL, V46, P860, DOI 10.1002/1531-8249(199912)46:6<860::AID-ANA8>3.0.CO;2-M; Moechars D, 1999, J BIOL CHEM, V274, P6483, DOI 10.1074/jbc.274.10.6483; Morrison JH, 1997, SCIENCE, V278, P412, DOI 10.1126/science.278.5337.412; Mouri A, 2007, FASEB J, V21, P2135, DOI 10.1096/fj.06-7685com; Mucke L, 2000, J NEUROSCI, V20, P4050; NEARY D, 1986, J NEUROL NEUROSUR PS, V49, P229, DOI 10.1136/jnnp.49.3.229; Nilsberth C, 2001, NAT NEUROSCI, V4, P887, DOI 10.1038/nn0901-887; Oddo S, 2003, NEURON, V39, P409, DOI 10.1016/S0896-6273(03)00434-3; OHare E, 1996, BEHAV PHARMACOL, V7, P742; PALMER AM, 1986, NEUROSCI LETT, V66, P199, DOI 10.1016/0304-3940(86)90190-4; Peretto I, 2005, J MED CHEM, V48, P5705, DOI 10.1021/jm0502541; Perrin RJ, 2009, NATURE, V461, P916, DOI 10.1038/nature08538; PODLISNY MB, 1995, J BIOL CHEM, V270, P9564; Podlisny MB, 1998, BIOCHEMISTRY-US, V37, P3602, DOI 10.1021/bi972029u; Poling A, 2008, BEHAV BRAIN RES, V193, P230, DOI 10.1016/j.bbr.2008.06.001; Portelius E, 2011, CURR PHARM DESIGN, V17, P2594; PROCTER AW, 1988, J NEUROCHEM, V50, P790, DOI 10.1111/j.1471-4159.1988.tb02983.x; Puzzo D, 2008, J NEUROSCI, V28, P14537, DOI 10.1523/JNEUROSCI.2692-08.2008; Rao A, 1998, J NEUROSCI, V18, P1217; Richardson RL, 2002, BRAIN RES, V954, P1, DOI 10.1016/S0006-8993(02)03006-8; Scheff SW, 2007, NEUROLOGY, V68, P1501, DOI 10.1212/01.wnl.0000260698.46517.8f; Scholtzova H, 2008, J NEUROSCI RES, V86, P2784, DOI 10.1002/jnr.21713; Selkoe DJ, 2002, SCIENCE, V298, P789, DOI 10.1126/science.1074069; Selkoe DJ, 2001, PHYSIOL REV, V81, P741; Shankar GM, 2007, J NEUROSCI, V27, P2866, DOI 10.1523/JNEUROSCI.4970-06.2007; Shankar GM, 2008, NAT MED, V14, P837, DOI 10.1038/nm1782; Sheng M, 1999, ANN NY ACAD SCI, V868, P483, DOI 10.1111/j.1749-6632.1999.tb11317.x; Snyder EM, 2005, NAT NEUROSCI, V8, P1051, DOI 10.1038/nn1503; Soto Claudio, 1995, Neuroscience Letters, V200, P105, DOI 10.1016/0304-3940(95)12089-M; Stanger BZ, 2005, P NATL ACAD SCI USA, V102, P12443, DOI 10.1073/pnas.0505690102; Stern EA, 2004, J NEUROSCI, V24, P4535, DOI 10.1523/JNEUROSCI.0462-04.2004; Stine WB, 2003, J BIOL CHEM, V278, P11612, DOI 10.1074/jbc.M210207200; Stravalaci M, 2012, J BIOL CHEM, V287, P27796, DOI 10.1074/jbc.M111.334979; Sze CI, 1997, J NEUROPATH EXP NEUR, V56, P933, DOI 10.1097/00005072-199708000-00011; Tahara K, 2006, BRAIN, V129, P3006, DOI 10.1093/brain/awl249; Tan J, 1999, SCIENCE, V286, P2352, DOI 10.1126/science.286.5448.2352; Tang SC, 2008, EXP NEUROL, V213, P114, DOI 10.1016/j.expneurol.2008.05.014; Teplow DB, 1998, AMYLOID, V5, P121, DOI 10.3109/13506129808995290; TERRY RD, 1991, ANN NEUROL, V30, P572, DOI 10.1002/ana.410300410; Tomiyama T, 2008, ANN NEUROL, V63, P377, DOI 10.1002/ana.21321; Townsend M, 2006, J PHYSIOL-LONDON, V572, P477, DOI 10.1113/jphysiol.2005.103754; Tsai J, 2004, NAT NEUROSCI, V7, P1181, DOI 10.1038/nn1335; Verdier Y, 2004, J PEPT SCI, V10, P229, DOI 10.1002/psc.573; Walsh DM, 2002, NATURE, V416, P535, DOI 10.1038/416535a; Walsh DM, 2000, BIOCHEMISTRY-US, V39, P10831, DOI 10.1021/bi001048s; Walsh DM, 2002, BIOCHEM SOC T, V30, P552; Walsh DM, 2004, NEURON, V44, P181, DOI 10.1016/j.neuron.2004.09.010; Walter S, 2007, CELL PHYSIOL BIOCHEM, V20, P947, DOI 10.1159/000110455; Wang J, 2002, BRAIN RES, V943, P181, DOI 10.1016/S0006-8993(02)02617-3; Wang J, 1999, EXP NEUROL, V158, P328, DOI 10.1006/exnr.1999.7085; Wang QW, 2004, J NEUROSCI, V24, P3370, DOI 10.1523/JNEUROSCI.1633-03.2004; Weggen S, 2001, NATURE, V414, P212, DOI 10.1038/35102591; Weldon DT, 1998, J NEUROSCI, V18, P2161; Westerman MA, 2002, J NEUROSCI, V22, P1858; Winters BD, 2008, NEUROSCI BIOBEHAV R, V32, P1055, DOI 10.1016/j.neubiorev.2008.04.004; Winters BD, 2005, J NEUROSCI, V25, P4243, DOI 10.1523/JNEUROSCI.0480-05.2005; Winters BD, 2005, J NEUROSCI, V25, P52, DOI 10.1523/JNEUROSCI.3827-04.2005; Wolfe MS, 2012, J NEUROCHEM, V120, P89, DOI 10.1111/j.1471-4159.2011.07501.x; Wu CC, 2004, P NATL ACAD SCI USA, V101, P7141, DOI 10.1073/pnas.0402147101; Yu WD, 2012, NEURAL PLAST, DOI 10.1155/2012/247150; Zhang L, 2006, BEHAV BRAIN RES, V173, P246, DOI 10.1016/j.bbr.2006.06.034 145 1 1 BENTHAM SCIENCE PUBL LTD SHARJAH EXECUTIVE STE Y-2, PO BOX 7917, SAIF ZONE, 1200 BR SHARJAH, U ARAB EMIRATES 1381-6128 1873-4286 CURR PHARM DESIGN Curr. Pharm. Design MAY 2014 20 15 2491 2505 15 Pharmacology & Pharmacy Pharmacology & Pharmacy AH5KX WOS:000336169300005 J Iwabuchi, H; Yamada, S; Katagiri, S; Yang, P; Okamoto, H Iwabuchi, Hironobu; Yamada, Soichiro; Katagiri, Shuichiro; Yang, Ping; Okamoto, Hajime Radiative and Microphysical Properties of Cirrus Cloud Inferred from Infrared Measurements Made by the Moderate Resolution Imaging Spectroradiometer (MODIS). Part I: Retrieval Method JOURNAL OF APPLIED METEOROLOGY AND CLIMATOLOGY English Article Cirrus clouds; Ice crystals; Ice particles; Infrared radiation; Cloud retrieval; Satellite observations LAND-SURFACE TEMPERATURE; ICE CLOUDS; SCATTERING PROPERTIES; EFFECTIVE EMISSIVITY; INFORMATION-CONTENT; OPTICAL-THICKNESS; MODIS; ALGORITHM; AVHRR; MODEL An optimal estimation-based algorithm is developed to infer the global-scale distribution of cirrus cloud radiative and microphysical properties from the measurements made by the Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) at three infrared (IR) window bands centered at 8.5, 11, and 12 m. Cloud-top and underlying surface temperatures, as a priori information, are obtained from the MODIS operational products. A fast-forward model based on semianalytical equations for the brightness temperature is used. The modeling errors in brightness temperature are mainly from the uncertainties in model parameters including surface emissivity, precipitable water, and cloud-base temperature. The total measurement-model errors are well correlated for the three bands, which are considered in the retrieval. The most important factors for the accurate retrieval of cloud optical thickness and the effective particle radius are cloud-top and surface temperatures, whereas model parameter uncertainties constitute a moderately significant error source. The three-band IR method is suitable for retrieving optical thickness and effective radius for cloud optical thicknesses within a range of 0.5-6, where the typical root-mean-square error is less than 20% in optical thickness and less than 40% in effective particle radius. A tropical-region case study demonstrates the advantages of the methodin particular, the ability to be applied to more pixels in optically thin cirrus in comparison with a solar-reflection-based methodand the ability of the optimal estimation framework to produce useful diagnostics of the retrieval quality. Collocated comparisons with spaceborne active remote sensing data exhibit reasonable consistency with respect to retrieved particle size. [Iwabuchi, Hironobu; Yamada, Soichiro; Katagiri, Shuichiro] Tohoku Univ, Grad Sch Sci, Ctr Atmospher & Ocean Studies, Sendai, Miyagi 9808578, Japan; [Yang, Ping] Texas A&M Univ, Dept Atmospher Sci, College Stn, TX USA; [Okamoto, Hajime] Kyushu Univ, Appl Mech Res Inst, Fukuoka 812, Japan Iwabuchi, H (reprint author), Tohoku Univ, Grad Sch Sci, Ctr Atmospher & Ocean Studies, Sendai, Miyagi 9808578, Japan. hiroiwa@m.tohoku.ac.jp Yang, Ping/B-4590-2011 Japan Society for the Promotion of Science (JSPS) [24340116]; Ministry of Education, Culture, Sports, Science, and Technology of Japan [25247078] Hironobu Iwabuchi is grateful to Dr. Hitoshi Irie of Chiba University, Chiba, Japan, for participating in valuable discussions. This work was partly supported by a grant-in-aid for scientific research (Kiban B 24340116) from the Japan Society for the Promotion of Science (JSPS). Author HO was supported by the Ministry of Education, Culture, Sports, Science, and Technology of Japan through grants-in-aid for scientific research (Kiban A 25247078). The MODIS data were obtained from NASA websites, including the Level 1 and Atmosphere Archive and Distribution System (LAADS). The radiative transfer model RSTAR was obtained from the OpenCLASTR project. Baran AJ, 2007, Q J ROY METEOR SOC, V133, P1899, DOI 10.1002/qj.164; Baum BA, 2005, J APPL METEOROL, V44, P1885, DOI 10.1175/JAM2308.1; Baum BA, 2011, J APPL METEOROL CLIM, V50, P1037, DOI 10.1175/2010JAMC2608.1; Brown O. B., 1999, MODIS INFRARED SEA S; Chiriaco M, 2004, MON WEATHER REV, V132, P1684, DOI 10.1175/1520-0493(2004)132<1684:IROCCP>2.0.CO;2; Cole BH, 2013, J APPL METEOROL CLIM, V52, P186, DOI 10.1175/JAMC-D-12-097.1; Cooper SJ, 2003, J GEOPHYS RES-ATMOS, V108, DOI 10.1029/2002JD002611; Cooper SJ, 2006, J APPL METEOROL CLIM, V45, P42, DOI 10.1175/JAM2327.1; Eichler H, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2009JD012215; FOOT JS, 1988, Q J ROY METEOR SOC, V114, P145, DOI 10.1002/qj.49711447908; Garnier A, 2012, J APPL METEOROL CLIM, V51, P1407, DOI 10.1175/JAMC-D-11-0220.1; Garrett KJ, 2009, J APPL METEOROL CLIM, V48, P818, DOI 10.1175/2008JAMC1915.1; Giraud V, 1997, J APPL METEOROL, V36, P664, DOI 10.1175/1520-0450-36.6.664; Hagihara Y, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012344; Heidinger AK, 2009, J APPL METEOROL CLIM, V48, P1100, DOI 10.1175/2008JAMC1882.1; HEIDINGER AK, 2010, J GEOPHYS RES, V115, DOI DOI 10.1029/2009JD012379; Holz RE, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD009837; Hu YX, 2009, J ATMOS OCEAN TECH, V26, P2293, DOI 10.1175/2009JTECHA1280.1; INOUE T, 1985, J METEOROL SOC JPN, V63, P88; Iwabuchi H, 2011, J QUANT SPECTROSC RA, V112, P2520, DOI 10.1016/j.jqsrt.2011.06.017; Iwabuchi H, 2012, J GEOPHYS RES-ATMOS, V117, DOI 10.1029/2011JD017020; JOSEPH JH, 1976, J ATMOS SCI, V33, P2452, DOI 10.1175/1520-0469(1976)033<2452:TDEAFR>2.0.CO;2; Katagiri S, 2004, J METEOROL SOC JPN, V82, P81, DOI 10.2151/jmsj.82.81; LIOU KN, 1986, MON WEATHER REV, V114, P1167, DOI 10.1175/1520-0493(1986)114<1167:IOCCOW>2.0.CO;2; Liou K., 2002, INT GEOPHYS SERIES, V84; MASUDA K, 2012, PAP METEOROL GEOPHYS, V63, P1, DOI DOI 10.2467/MRIPAPERS.63.1; Menzel WP, 2008, J APPL METEOROL CLIM, V47, P1175, DOI 10.1175/2007JAMC1705.1; NAKAJIMA T, 1986, J QUANT SPECTROSC RA, V35, P13, DOI 10.1016/0022-4073(86)90088-9; NAKAJIMA T, 1988, J QUANT SPECTROSC RA, V40, P51, DOI 10.1016/0022-4073(88)90031-3; Newman SM, 2005, Q J ROY METEOR SOC, V131, P2539, DOI 10.1256/qj.04.150; Okamoto H, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD009812; Okamoto H, 2007, SEIKAGAKU, V79, P1; Okamoto H, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD013383; Onogi K, 2007, J METEOROL SOC JPN, V85, P369, DOI 10.2151/jmsj.85.369; PAROL F, 1991, J APPL METEOROL, V30, P973, DOI 10.1175/1520-0450-30.7.973; Platnick S, 2003, IEEE T GEOSCI REMOTE, V41, P459, DOI 10.1109/TGRS.2002.808301; Poulsen CA, 2012, ATMOS MEAS TECH, V5, P1889, DOI 10.5194/amt-5-1889-2012; Rodgers C. D., 2000, INVERSE METHODS ATMO; Sassen K, 2008, J GEOPHYS RES-ATMOS, V113, DOI 10.1029/2008JD009972; Sassen K, 2001, J ATMOS SCI, V58, P2113, DOI 10.1175/1520-0469(2001)058<2113:AMCCCF>2.0.CO;2; Seemann S. W., 2006, MODIS ATMOSPHERIC PR; Stubenrauch CJ, 2006, J CLIMATE, V19, P5531, DOI 10.1175/JCLI3929.1; Tobin DC, 2006, J GEOPHYS RES-ATMOS, V111, DOI 10.1029/2005JD006095; Waliser DE, 2009, J GEOPHYS RES-ATMOS, V114, DOI 10.1029/2008JD010015; Walther A, 2012, J APPL METEOROL CLIM, V51, P1371, DOI 10.1175/JAMC-D-11-0108.1; Wan Z, 2004, INT J REMOTE SENS, V25, P261, DOI 10.1080/0143116031000116417; Wan ZM, 1997, IEEE T GEOSCI REMOTE, V35, P980; Wang CX, 2011, J APPL METEOROL CLIM, V50, P2283, DOI 10.1175/JAMC-D-11-067.1; Wang W, 2008, REMOTE SENS ENVIRON, V112, P623, DOI 10.1016/j.rse.2007.05.024; Watts PD, 2011, J GEOPHYS RES-ATMOS, V116, DOI 10.1029/2011JD015883; Xiong XX, 2009, IEEE T GEOSCI REMOTE, V47, P803, DOI 10.1109/TGRS.2008.2005109; Yang P, 2008, IEEE T GEOSCI REMOTE, V46, P1948, DOI 10.1109/TGRS.2008.916472; Yang P, 2013, J ATMOS SCI, V70, P330, DOI 10.1175/JAS-D-12-039.1; Yoshida R, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2009JD012334; Zhang Z, 2009, ATMOS CHEM PHYS, V9, P7115; Zhang ZB, 2010, J GEOPHYS RES-ATMOS, V115, DOI 10.1029/2010JD013835 56 0 0 AMER METEOROLOGICAL SOC BOSTON 45 BEACON ST, BOSTON, MA 02108-3693 USA 1558-8424 1558-8432 J APPL METEOROL CLIM J. Appl. Meteorol. Climatol. MAY 2014 53 5 1297 1316 10.1175/JAMC-D-13-0215.1 20 Meteorology & Atmospheric Sciences Meteorology & Atmospheric Sciences AG6ZF WOS:000335567000011 J Brezis, RS; Galili, T; Wong, T; Piggot, JI Brezis, Rachel S.; Galili, Tal; Wong, Tiffany; Piggot, Judith I. Impaired Social Processing in Autism and its Reflections in Memory: A Deeper View of Encoding and Retrieval Processes JOURNAL OF AUTISM AND DEVELOPMENTAL DISORDERS English Article Social memory; Autism; Encoding; Retrieval; Levels of processing ASPERGERS-SYNDROME; INDIVIDUALS; SELF; DISORDERS; COGNITION; CHILDREN; ABILITY; RATINGS; ADULTS; WORDS Previous studies of memory in autism spectrum conditions (ASC) have consistently shown that persons with ASC have reduced memories for social information, relative to a spared memory for non-social facts. The current study aims to reproduce these findings, while examining the possible causes leading to this difference. Participants' memory for trait-words was tested after they had viewed the words in three study contexts: visuo-motor, letter-detection, and social judgment. While participants with ASC showed a levels-of-processing effect, such that their memory for words viewed in the social judgment context was greater than their memory for words viewed in the letter-detection context, their memory for socially-processed words was reduced relative to comparison participants. This interaction effect could not be explained by a speed/accuracy trade-off, nor could it be explained solely by differences in encoding. These results suggest that social memory deficits in ASC arise from difficulties both in orienting towards and encoding social content, as well as retaining and retrieving it. Implications for theory and clinical practice are discussed. [Brezis, Rachel S.] Univ Chicago, Dept Comparat Human Dev, Chicago, IL 60637 USA; [Brezis, Rachel S.] Univ Calif Los Angeles, Ctr Autism Res & Treatment, Los Angeles, CA 90024 USA; [Galili, Tal] Tel Aviv Univ, Dept Stat & Operat Res, IL-69978 Tel Aviv, Israel; [Wong, Tiffany; Piggot, Judith I.] Univ Calif Los Angeles, Dept Psychiat & Biobehav Sci, Los Angeles, CA 90024 USA Brezis, RS (reprint author), Univ Calif Los Angeles, Ctr Culture & Hlth, Dept Psychiat & Biobehav Sci, 760 Westwood Plaza,Box 62,Room B7-435, Los Angeles, CA 90024 USA. rsbrezis@ucla.edu ANDERSON NH, 1968, J PERS SOC PSYCHOL, V9, P272, DOI 10.1037/h0025907; Baron-Cohen S., 1999, UNDERSTANDING OTHER; Ben Shalom D, 2003, CORTEX, V39, P1129; Blair RJR, 1996, J AUTISM DEV DISORD, V26, P571; Boucher J, 2012, AUTISM, V16, P603, DOI 10.1177/1362361311417738; Boucher J., 2008, MEMORY AUTISM THEORY; Bowler DM, 2004, J AUTISM DEV DISORD, V34, P533, DOI 10.1007/s10803-004-2548-7; Brezis R. S., MEMORY SELF IN PRESS; Constantino J. N., 2005, SOCIAL RESPONSIVENES; Crane L, 2008, J AUTISM DEV DISORD, V38, P498, DOI 10.1007/s10803-007-0420-2; Danker JF, 2010, PSYCHOL BULL, V136, P87, DOI 10.1037/a0017937; Henderson HA, 2009, J CHILD PSYCHOL PSYC, V50, P853, DOI 10.1111/j.1469-7610.2008.02059.x; HOBSON RP, 1986, J CHILD PSYCHOL PSYC, V27, P321, DOI 10.1111/j.1469-7610.1986.tb01836.x; Klin A, 2009, NATURE, V459, P257, DOI 10.1038/nature07868; Kuperman V, 2012, BEHAV RES METHODS, V44, P978, DOI 10.3758/s13428-012-0210-4; Lind SE, 2010, AUTISM, V14, P430, DOI 10.1177/1362361309358700; Lombardo MV, 2007, PLOS ONE, V2, DOI 10.1371/journal.pone.0000883; Lord C., 1999, AUTISM DIAGNOSTIC OB; Lord C., 2003, SOCIAL COMMUNICATION; Losh M, 2003, J AUTISM DEV DISORD, V33, P239, DOI 10.1023/A:1024446215446; Merriam-Webster, 2009, MERR WEBST WORDCENTR; Minshew NJ, 2007, ARCH NEUROL-CHICAGO, V64, P945, DOI 10.1001/archneur.64.7.945; MORRIS CD, 1977, J VERB LEARN VERB BE, V16, P519, DOI 10.1016/S0022-5371(77)80016-9; Mottron L, 2001, J CHILD PSYCHOL PSYC, V42, P253, DOI 10.1017/S0021963001006722; Piggot J, 2004, J AM ACAD CHILD PSY, V43, P473, DOI 10.1097/01.chi.0000111363.94169.37; Rutter M., 2003, AUTISM DIAGNOSTIC IN; Sinzig J, 2008, EUR CHILD ADOLES PSY, V17, P63, DOI 10.1007/s00787-007-0637-9; Stanislaw H, 1999, BEHAV RES METH INS C, V31, P137, DOI 10.3758/BF03207704; Takeda T, 2007, PSYCHIAT CLIN NEUROS, V61, P407, DOI 10.1111/j.1440-1819.2007.01678.x; Toichi M, 2002, NEUROPSYCHOLOGIA, V40, P964, DOI 10.1016/S0028-3932(01)00163-4; Wechsler D., 1999, WECHSLER ABBREVIATED; Whitehouse AJO, 2007, AUTISM, V11, P241, DOI 10.1177/1362361307076860 32 0 0 SPRINGER/PLENUM PUBLISHERS NEW YORK 233 SPRING ST, NEW YORK, NY 10013 USA 0162-3257 1573-3432 J AUTISM DEV DISORD J. Autism Dev. Disord. MAY 2014 44 5 1183 1192 10.1007/s10803-013-1980-y 10 Psychology, Developmental Psychology AH7RD WOS:000336330200017 J Huff, MJ; Bodner, GE Huff, Mark J.; Bodner, Glen E. All varieties of encoding variability are not created equal: Separating variable processing from variable tasks JOURNAL OF MEMORY AND LANGUAGE English Article Encoding variability; Item-specific and relational processing; Free recall; Recognition; False recognition SPREADING ACTIVATION THEORY; ITEM-SPECIFIC INFORMATION; AGE-RELATED DIFFERENCES; REDUCES FALSE MEMORIES; FREE-RECALL; SUBJECTIVE ORGANIZATION; DISTRIBUTED PRACTICE; RETRIEVAL PROCESSES; CUMULATIVE-RECALL; LEXICAL DECISION Whether encoding variability facilitates memory is shown to depend on whether item-specific and relational processing are both performed across study blocks, and whether study items are weakly vs. strongly related. Variable-processing groups studied a word list once using an item-specific task and once using a relational task. Variable-task groups' two different study tasks recruited the same type of processing each block. Repeated-task groups performed the same study task each block. Recall and recognition were greatest in the variable-processing group, but only with weakly related lists. A variable-processing benefit was also found when task-based processing and list-type processing were complementary (e.g., item-specific processing of a related list) rather than redundant (e.g., relational processing of a related list). That performing both item-specific and relational processing across trials, or within a trial, yields encoding-variability benefits may help reconcile decades of contradictory findings in this area. (C) 2014 Published by Elsevier Inc. [Huff, Mark J.; Bodner, Glen E.] Univ Calgary, Dept Psychol, Calgary, AB T2N 1N4, Canada Huff, MJ (reprint author), Washington Univ, Dept Psychol, St Louis, MO 63130 USA. mhuff@wustl.edu Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship; NIH [5T32AG000030-38]; NSERC Discovery Grant This study comprises the first author's doctoral dissertation and was supported by the Natural Sciences and Engineering Research Council of Canada (NSERC) Postgraduate Scholarship and by NIH grant 5T32AG000030-38 to M.J.H. and by an NSERC Discovery Grant to G.E.B. M.J.H. is now at Washington University in St. Louis. We extend special thanks to Sara Davis, Aram Lee, and Erin Shumlich for assistance in data collection. ANDERSON JR, 1983, J VERB LEARN VERB BE, V22, P261, DOI 10.1016/S0022-5371(83)90201-3; BALOTA DA, 1986, J EXP PSYCHOL LEARN, V12, P336, DOI 10.1037//0278-7393.12.3.336; Balota DA, 2007, FOUNDATIONS OF REMEMBERING: ESSAYS IN HONOR OF HENRY L. ROEDIGER, III, P83; BALOTA DA, 1989, PSYCHOL AGING, V4, P3, DOI 10.1037//0882-7974.4.1.3; BATTIG WF, 1969, J EXP PSYCHOL, V80, P1, DOI 10.1037/h0027577; BIRD CP, 1978, AM J PSYCHOL, V91, P713, DOI 10.2307/1421519; BOBROW SA, 1970, J VERB LEARN VERB BE, V9, P363, DOI 10.1016/S0022-5371(70)80074-3; BOWER GH, 1969, J VERB LEARN VERB BE, V8, P481, DOI 10.1016/S0022-5371(69)80092-7; Burns DJ, 1998, J EXP PSYCHOL LEARN, V24, P1041, DOI 10.1037/0278-7393.24.4.1041; Burns DJ, 2011, J EXP PSYCHOL LEARN, V37, P206, DOI 10.1037/a0021325; Burns DJ, 2005, MEMORY, V13, P189, DOI 10.1080/09608210344000670; Cepeda NJ, 2006, PSYCHOL BULL, V132, P354, DOI 10.1037/0033-2909.132.3.354; Chwilla DJ, 2002, MEM COGNITION, V30, P217, DOI 10.3758/BF03195282; COLLINS AM, 1975, PSYCHOL REV, V82, P407, DOI 10.1037//0033-295X.82.6.407; DAGOSTIN.PR, 1973, J VERB LEARN VERB BE, V12, P108, DOI 10.1016/S0022-5371(73)80066-0; DEMPSTER FN, 1987, J EDUC PSYCHOL, V79, P162, DOI 10.1037//0022-0663.79.2.162; EINSTEIN GO, 1980, J EXP PSYCHOL-HUM L, V6, P588, DOI 10.1037/0278-7393.6.5.588; ELMES DG, 1975, J VERB LEARN VERB BE, V14, P30, DOI 10.1016/S0022-5371(75)80004-1; Engelkamp J, 1998, MEMORY, V6, P307; ESTES WK, 1950, PSYCHOL REV, V57, P94, DOI 10.1037/0033-295X.101.2.282; GALBRAITH RC, 1975, MEM COGNITION, V3, P282, DOI 10.3758/BF03212912; GARTMAN LM, 1972, J VERB LEARN VERB BE, V11, P801, DOI 10.1016/S0022-5371(72)80016-1; GLENBERG AM, 1977, J EXP PSYCHOL-HUM L, V3, P282, DOI 10.1037/0278-7393.3.3.282; GLENBERG AM, 1979, MEM COGNITION, V7, P95, DOI 10.3758/BF03197590; GREENE RL, 1995, J MEM LANG, V34, P468, DOI 10.1006/jmla.1995.1021; Gunter RW, 2007, MEM COGNITION, V35, P1083, DOI 10.3758/BF03193480; Hege ACG, 2004, J EXP PSYCHOL LEARN, V30, P787, DOI 10.1037/0278-7393.30.4.787; Hintzman D. L., 1974, THEOR COGN PSYCH LOY, P386; Hintzman DL, 2004, MEM COGNITION, V32, P336, DOI 10.3758/BF03196863; HINTZMAN DL, 1978, J EXP PSYCHOL-HUM L, V4, P539, DOI 10.1037//0278-7393.4.5.539; Hodge MH, 1996, MEM COGNITION, V24, P110, DOI 10.3758/BF03197277; Huff M. J., 2013, J EXPT PSYCHOL LEARN; Huff MJ, 2011, MEMORY, V19, P317, DOI 10.1080/09658211.2011.568494; Huff MJ, 2012, J EXP PSYCHOL LEARN, V38, P1720, DOI 10.1037/a0028476; HUNT RR, 1993, J MEM LANG, V32, P421, DOI 10.1006/jmla.1993.1023; Hunt RR, 2011, J MEM LANG, V65, P378, DOI 10.1016/j.jml.2011.06.003; HUNT RR, 1984, J EXP PSYCHOL LEARN, V10, P454, DOI 10.1037//0278-7393.10.3.454; HUNT RR, 1981, J VERB LEARN VERB BE, V20, P497, DOI 10.1016/S0022-5371(81)90138-9; JACOBY LL, 1991, J MEM LANG, V30, P513, DOI 10.1016/0749-596X(91)90025-F; JOHNSTON WA, 1976, J EXP PSYCHOL-HUM L, V2, P153; JOHNSTON WA, 1972, J VERB LEARN VERB BE, V11, P784, DOI 10.1016/S0022-5371(72)80013-6; POSTMAN L, 1983, J VERB LEARN VERB BE, V22, P133, DOI 10.1016/S0022-5371(83)90101-9; MANDLER G, 1980, PSYCHOL REV, V87, P252, DOI 10.1037//0033-295X.87.3.252; MARTIN E, 1968, PSYCHOL REV, V75, P421, DOI 10.1037/h0026301; MASKARINEC AS, 1976, MEM COGNITION, V4, P741, DOI 10.3758/BF03213242; McCabe DP, 2004, PSYCHON B REV, V11, P1074, DOI 10.3758/BF03196739; MCDANIEL MA, 1988, J MEM LANG, V27, P521, DOI 10.1016/0749-596X(88)90023-X; MCDANIEL MA, 1985, J EXP PSYCHOL LEARN, V11, P371, DOI 10.1037/0278-7393.11.2.371; MCDANIEL MA, 1988, MEM COGNITION, V16, P8, DOI 10.3758/BF03197740; McDaniel MA, 1989, EDUC PSYCHOL REV, V1, P113, DOI 10.1007/BF01326639; MELTON AW, 1970, J VERB LEARN VERB BE, V9, P596, DOI 10.1016/S0022-5371(70)80107-4; Morris C. D., 1977, J VERB LEARN VERB BE, V16, P249; Mulligan NW, 2005, PSYCHOL RES-PSYCH FO, V69, P272, DOI 10.1007/s00426-004-0178-5; Roediger HL, 2006, PSYCHOL SCI, V17, P249, DOI 10.1111/j.1467-9280.2006.01693.x; ROEDIGER HL, 1995, J EXP PSYCHOL LEARN, V21, P803, DOI 10.1037/0278-7393.21.4.803; Roediger III H. L., 2011, 52 ANN M PSYCH SOC S; ROENKER DL, 1971, PSYCHOL BULL, V76, P45, DOI 10.1037/h0031355; Schacter DL, 1999, J MEM LANG, V40, P1, DOI 10.1006/jmla.1998.2611; Senkova Olesya, 2012, Adv Cogn Psychol, V8, P292, DOI 10.2478/v10053-008-0124-y; Toglia MP, 1999, MEMORY, V7, P233, DOI 10.1080/741944069; TULVING E, 1966, J VERB LEARN VERB BE, V5, P193, DOI 10.1016/S0022-5371(66)80016-6; TULVING E, 1962, PSYCHOL REV, V69, P344, DOI 10.1037/h0043150; TULVING E, 1968, PSYCHON SCI, V10, P53; TULVING E, 1973, PSYCHOL REV, V80, P352, DOI 10.1037/h0020071; Van Overschelde JP, 2004, J MEM LANG, V50, P289, DOI 10.1016/j.jml.2003.10.003; WILLIAMS RF, 1970, J EXP PSYCHOL, V86, P317, DOI 10.1037/h0029998; WINOGRAD E, 1974, J EXP PSYCHOL, V102, P1061, DOI 10.1037/h0036386; YOUNG DR, 1982, J EXP PSYCHOL LEARN, V8, P545 68 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0749-596X 1096-0821 J MEM LANG J. Mem. Lang. MAY 2014 73 43 58 10.1016/j.jml.2014.02.004 16 Linguistics; Psychology; Psychology, Experimental Linguistics; Psychology AH4PS WOS:000336111100004 J Soderstrom, NC; Bjork, RA Soderstrom, Nicholas C.; Bjork, Robert A. Testing facilitates the regulation of subsequent study time JOURNAL OF MEMORY AND LANGUAGE English Article Testing; Metacognition; Test-potentiated learning; Study-time allocation; Self-regulated learning LONG-TERM RETENTION; EDUCATIONAL PRACTICE; RETRIEVAL; MEMORY; JUDGMENTS; ALLOCATION; ILLUSIONS; COMPETENCE; STRATEGIES; REGION We examined how testing potentiates self-regulated learning and alleviates the foresight bias an illusion of competence that arises from information being present during study but absent at test and whether such benefits can transfer to non-tested material. After studying paired associates that varied in difficulty, participants either restudied or were tested on all the pairs (Experiment 1); were tested on only half of the pairs (Experiment 2); or were tested on half of the pairs and restudied the remaining pairs (Experiments 3 and 4). All items were then restudied at participants' own pace before a final cued-recall test. In Experiment 1, interim tests enhanced the effectiveness of subsequent study time and alleviated the foresight bias, whereas interim restudying had no such benefits. Experiments 2, 3, and 4 demonstrated that such test-potentiated self-regulated learning can transfer to non-tested items if restudied intermixed with items that were tested. The results demonstrate yet another practical benefit of testing and suggest that retrieval practice can foster metacognitive sophistication among learners, serving as an experiencebased debiasing procedure. (C) 2014 Elsevier Inc. All rights reserved. [Soderstrom, Nicholas C.; Bjork, Robert A.] Univ Calif Los Angeles, Dept Psychol, Los Angeles, CA 90095 USA Soderstrom, NC (reprint author), Univ Calif Los Angeles, Dept Psychol, 1285 Franz Hall, Los Angeles, CA 90095 USA. nsoderstrom@psych.ucla.edu James S. McDonnell Foundation [29192G] Grant 29192G from the James S. McDonnell Foundation supported this research. We thank Lakshan Fonseka and Gayan Seneviratna for their help with data collection, and members of CogFog for insightful comments regarding this research. Anderson MC, 2003, J MEM LANG, V49, P415, DOI 10.1016/j.jml.2003.08.006; ANDERSON MC, 1994, J EXP PSYCHOL LEARN, V20, P1063, DOI 10.1037/0278-7393.20.5.1063; Ariel R, 2009, J EXP PSYCHOL GEN, V138, P432, DOI 10.1037/a0015928; Arnold KM, 2013, J EXP PSYCHOL LEARN, V39, P940, DOI 10.1037/a0029199; Bahrick HP, 2005, J MEM LANG, V52, P566, DOI 10.1016/j.jml.2005.01.012; Benjamin AS, 1998, J EXP PSYCHOL GEN, V127, P55, DOI 10.1037/0096-3445.127.1.55; Benjamin AS, 2003, MEM COGNITION, V31, P297, DOI 10.3758/BF03194388; Bertsch S, 2007, MEM COGNITION, V35, P201, DOI 10.3758/BF03193441; Bjork EL, 2007, PSYCHON B REV, V14, P207, DOI 10.3758/BF03194053; Bjork R. A., 1975, INFORM PROCESSING CO, P123; Bjork RA, 1999, ATTENTION PERFORM, V17, P435; Bjork RA, 2013, ANNU REV PSYCHOL, V64, P417, DOI 10.1146/annurev-psych-113011-143823; Carpenter SK, 2012, CURR DIR PSYCHOL SCI, V21, P279, DOI 10.1177/0963721412452728; Castel AA, 2008, MEM COGNITION, V36, P429, DOI 10.3758/MC.36.2.429; Castel AD, 2007, PSYCHON B REV, V14, P107, DOI 10.3758/BF03194036; Chan JCK, 2010, MEMORY, V18, P49, DOI 10.1080/09658210903405737; Chan JCK, 2009, J MEM LANG, V61, P153, DOI 10.1016/j.jml.2009.04.004; Chan JCK, 2006, J EXP PSYCHOL GEN, V135, P553, DOI 10.1037/0096-3445.135.4.553; DeWinstanley PA, 2004, MEM COGNITION, V32, P945, DOI 10.3758/BF03196872; Dunlosky J, 1998, ACTA PSYCHOL, V98, P37, DOI 10.1016/S0001-6918(97)00051-6; Dunlosky J, 2011, PSYCHOL LEARN MOTIV, V54, P103, DOI 10.1016/B978-0-12-385527-5.00004-8; Dunlosky J, 1998, EDUC PSYCHO, P249; Finn B, 2007, J EXP PSYCHOL LEARN, V33, P238, DOI 10.1037/0278-7393.33.1.238; Grimaldi PJ, 2012, MEM COGNITION, V40, P505, DOI 10.3758/s13421-011-0174-0; Hartwig MK, 2012, PSYCHON B REV, V19, P126, DOI 10.3758/s13423-011-0181-y; Hays MJ, 2013, J EXP PSYCHOL LEARN, V39, P290, DOI 10.1037/a0028468; Hertzog C, 2003, J EXP PSYCHOL LEARN, V29, P22, DOI 10.1037/0278-7393.29.1.22; Hertzog C, 2004, PSYCHOL LEARN MOTIV, V45, P215, DOI 10.1016/S0079-7421(03)45006-8; IZAWA C, 1971, J MATH PSYCHOL, V8, P200, DOI 10.1016/0022-2496(71)90012-5; IZAWA C, 1966, PSYCHOL REP, V18, P879; IZAWA C, 1970, J EXP PSYCHOL, V83, P340, DOI 10.1037/h0028541; IZAWA C, 1968, PSYCHOL REP, V23, P947; Karpicke JD, 2007, J MEM LANG, V57, P151, DOI 10.1016/j.jml.2006.09.004; Karpicke JD, 2009, J EXP PSYCHOL GEN, V138, P469, DOI 10.1037/a0017341; KING JF, 1980, AM J PSYCHOL, V93, P329, DOI 10.2307/1422236; Koriat A, 2006, MEM COGNITION, V34, P959, DOI 10.3758/BF03193244; Koriat A, 2006, J EXP PSYCHOL LEARN, V32, P1133, DOI 10.1037/0278-7393.32.5.1133; Koriat A, 2005, J EXP PSYCHOL LEARN, V31, P187, DOI 10.1037/0278-7393.31.2.187; Koriat A, 2004, J EXP PSYCHOL GEN, V133, P643, DOI 10.1037/0096-3445.133.4.643; Koriat A, 2002, J EXP PSYCHOL GEN, V131, P147, DOI 10.1037//0096-3445.131.2.147; Koriat A, 1997, J EXP PSYCHOL GEN, V126, P349, DOI 10.1037/0096-3445.126.4.349; Kornell N., 2008, HDB MEMORY METAMEMOR, P333; Kornell N, 2009, J EXP PSYCHOL LEARN, V35, P989, DOI 10.1037/a0015729; Kornell N, 2006, J EXP PSYCHOL LEARN, V32, P609, DOI 10.1037/0278-7393.32.3.609; Kornell N, 2009, MEMORY, V17, P493, DOI 10.1080/09658210902832915; Little JL, 2012, PSYCHOL SCI, V23, P1337, DOI 10.1177/0956797612443370; McDaniel MA, 2008, PSYCHON B REV, V15, P237, DOI 10.3758/PBR.15.2.237; Metcalfe J, 2005, J MEM LANG, V52, P463, DOI 10.1016/j.jml.2004.12.001; Metcalfe J, 2002, J EXP PSYCHOL GEN, V131, P349, DOI 10.1037//0096-3445.131.3.349; Nelson D. L., 1998, U S FLORIDA WORD ASS; Nelson T. O., 1990, PSYCHOL LEARN MOTIV, V26, P125, DOI [10.1016/S0079-7421(08) 60053-5, DOI 10.1016/S0079-7421(08)60053-5]; NELSON TO, 1988, J EXP PSYCHOL LEARN, V14, P676, DOI 10.1037//0278-7393.14.4.676; NELSON TO, 1991, PSYCHOL SCI, V2, P267, DOI 10.1111/j.1467-9280.1991.tb00147.x; NELSON TO, 1994, PSYCHOL SCI, V5, P207, DOI 10.1111/j.1467-9280.1994.tb00502.x; Pyc MA, 2010, SCIENCE, V330, P335, DOI 10.1126/science.1191465; Pyc MA, 2012, J EXP PSYCHOL LEARN, V38, P737, DOI 10.1037/a0026166; Rhodes MG, 2011, PSYCHOL BULL, V137, P131, DOI 10.1037/a0021705; Roediger HL, 2006, PERSPECT PSYCHOL SCI, V1, P181, DOI 10.1111/j.1745-6916.2006.00012.x; Roediger HL, 2011, PSYCHOL LEARN MOTIV, V55, P1, DOI 10.1016/B978-0-12-387691-1.00001-6; SLAMECKA NJ, 1978, J EXP PSYCHOL-HUM L, V4, P592, DOI 10.1037//0278-7393.4.6.592; Soderstrom NC, 2011, J EXP PSYCHOL LEARN, V37, P1236, DOI 10.1037/a0023548; Son LK, 2000, J EXP PSYCHOL LEARN, V26, P204, DOI 10.1037//0278-7393.26.1.204; Szpunar KK, 2013, P NATL ACAD SCI USA, V110, P6313, DOI [10.1073/pnas.1221764110, 10.1073/pnas.122764110]; Szpunar KK, 2008, J EXP PSYCHOL LEARN, V34, P1392, DOI 10.1037/a0013082; Wissman KT, 2011, PSYCHON B REV, V18, P1140, DOI 10.3758/s13423-011-0140-7; ZECHMEISTER EB, 1980, B PSYCHONOMIC SOC, V15, P41 66 0 0 ACADEMIC PRESS INC ELSEVIER SCIENCE SAN DIEGO 525 B ST, STE 1900, SAN DIEGO, CA 92101-4495 USA 0749-596X 1096-0821 J MEM LANG J. Mem. Lang. MAY 2014 73 99 115 10.1016/j.jml.2014.03.003 17 Linguistics; Psychology; Psychology, Experimental Linguistics; Psychology AH4PS WOS:000336111100007 J Chiaravalloti, ND; Ibarretxe-Bilbao, N; DeLuca, J; Rusu, O; Pena, J; Garcia-Gorostiaga, I; Ojeda, N Chiaravalloti, Nancy D.; Ibarretxe-Bilbao, Naroa; DeLuca, John; Rusu, Olga; Pena, Javier; Garcia-Gorostiaga, Ines; Ojeda, Natalia The Source of the Memory Impairment in Parkinson's Disease: Acquisition Versus Retrieval MOVEMENT DISORDERS English Article learning; memory; Parkinson's disease; cognition; Open Trial Selective Reminding Test TRAUMATIC BRAIN-INJURY; ADULT AGE-DIFFERENCES; MULTIPLE-SCLEROSIS; COGNITIVE IMPAIRMENT; PROCESSING-SPEED; WORKING-MEMORY; RECOGNITION MEMORY; SUBCORTICAL DEMENTIA; DEFICIT HYPOTHESIS; TEST-PERFORMANCE Memory deficits are common in persons with Parkinson's disease (PD) even without the presence of a frank dementia. These memory deficits have traditionally been attributed to inability of patients to retrieve information from long-term memory, referred to as the retrieval failure hypothesis. However, some studies additionally document problems in recognition memory, noted to be inconsistent with the retrieval failure hypothesis. Given the neuroanatomical abnormalities observed in the hippocampus of PD patients and the role of the hippocampus in learning new information, the current study was designed to specifically examine learning abilities in a nondemented PD sample through the application of a learning paradigm, the Open Trial Selective Reminding Test. We examined 27 patients with PD without dementia and 27 age-, gender-, and education-matched healthy controls (HCs) with a neuropsychological test battery designed to assess new learning and memory. Results indicated a significant difference between the groups in terms of their ability to learn a list of 10 semantically related words. However, once the groups were equated on learning abilities, no significant difference was noted between the PD and HC participants in recall or recognition of the newly learned material. The memory deficit observed in nondemented PD patients is thus largely the result of a deficit in learning new information. This finding should be used to guide treatment for memory deficits in persons with PD, and future research should seek to identify novel means of improving new learning in this population. (c) 2014 International Parkinson and Movement Disorder Society [Chiaravalloti, Nancy D.; DeLuca, John] Kessler Fdn, W Orange, NJ USA; [Chiaravalloti, Nancy D.; DeLuca, John] Rutgers New Jersey Med Sch, Dept Phys Med & Rehabil, Newark, NJ USA; [Ibarretxe-Bilbao, Naroa; Rusu, Olga; Pena, Javier; Ojeda, Natalia] Univ Deusto, Fac Psychol & Educ, Dept Methods & Expt Psychol, Neuropsychol Psychiat & Neurol Disorders Res Team, Bilbao, Spain; [DeLuca, John] Rutgers New Jersey Med Sch, Dept Neurol & Neurosci, Newark, NJ USA; [Garcia-Gorostiaga, Ines] Galdakao Hosp, Dept Neurol, Galdakao, Spain Ibarretxe-Bilbao, N (reprint author), Univ Deusto, Fac Psychol & Educ, Univ 24, E-48007 Bilbao, Spain. naroa.ibarretxe@deusto.es Health Department of Basque Government [2011111117]; Spanish Ministry of Economy and Competitiveness [PSI2012-32441] This study was supported by the Health Department of Basque Government (2011111117; to N.I.B) and the Spanish Ministry of Economy and Competitiveness (PSI2012-32441; to N.I.B.). Aarsland D, 2009, NEUROLOGY, V72, P1121, DOI 10.1212/01.wnl.0000338632.00552.cb; American Psychiatric Association, 2000, DIAGN STAT MAN MENT; Beatty WW, 2003, ARCH CLIN NEUROPSYCH, V18, P509, DOI 10.1016/S0887-6177(02)00148-8; BEGG I, 1988, MEM COGNITION, V16, P232, DOI 10.3758/BF03197756; BELLEZZA FS, 1989, J EXP PSYCHOL LEARN, V15, P990, DOI 10.1037//0278-7393.15.5.990; Bouchard TP, 2008, NEUROBIOL AGING, V29, P1027, DOI 10.1016/j.neurobiolaging.2007.02.002; Brandt J., 1991, CLIN NEUROPSYCHOL, V5, P125, DOI DOI 10.1080/13854049108403297; Bronnick K, 2011, NEUROPSYCHOLOGY, V25, P114, DOI 10.1037/a0020857; Bruck A, 2004, J NEUROL NEUROSUR PS, V75, P1467, DOI 10.1136/jnnp.2003.031237; BUSCHKE H, 1973, J VERB LEARN VERB BE, V12, P543, DOI 10.1016/S0022-5371(73)80034-9; Camicioli R, 2003, MOVEMENT DISORD, V18, P784, DOI 10.1002/mds.10444; Chiaravalloti N, 2009, CLIN NEUROPSYCHOL, V23, P231, DOI 10.1080/13854040802121158; Chiaravalloti ND, 2013, J CLIN EXP NEUROPSYC, V35, P180, DOI 10.1080/13803395.2012.760537; Chiaravalloti ND, 2013, NEUROLOGY, V81, P2066, DOI 10.1212/01.wnl.0000437295.97946.a8; Chiaravalloti ND, 2003, CLIN REHABIL, V17, P58, DOI 10.1191/0269215503cr586oa; Chiaravalloti ND, 2002, ARCH PHYS MED REHAB, V83, P1070, DOI 10.1053/apmr.2002.33729; COOPER JA, 1991, BRAIN, V114, P2095, DOI 10.1093/brain/114.5.2095; CUMMINGS JL, 1984, ARCH NEUROL-CHICAGO, V41, P874; Daniel S E, 1993, J Neural Transm Suppl, V39, P165; Delis DC, 1987, CALIFORNIA VERBAL LE; Delis DCKJ, 2000, CALIFORNIA VERBAL LE; DeLuca J, 2004, J CLIN EXP NEUROPSYC, V26, P550, DOI 10.1080/13803390490496641; DELUCA J, 1994, J CLIN EXP NEUROPSYC, V16, P183, DOI 10.1080/01688639408402629; DeLuca J, 2000, ARCH PHYS MED REHAB, V81, P1327, DOI 10.1053/apmr.2000.9390; DeLuca J, 1998, J CLIN EXP NEUROPSYC, V20, P376; Dubois B, 1998, COGNITIVE BEHAV ASPE; Dubois B, 2007, MOVEMENT DISORD, V22, P2314, DOI 10.1002/mds.21844; Eigh E, 2009, EUROPEAN J NEUROLOGY, V16, P1278; Emre M, 2007, MOVEMENT DISORD, V22, P1689, DOI 10.1002/mds.21507; FLOWERS KA, 1984, J NEUROL NEUROSUR PS, V47, P1174, DOI 10.1136/jnnp.47.11.1174; Foltynie T, 2004, BRAIN, V127, P550, DOI 10.1093/brain/awh067; Gaudino EA, 2001, NEUROPSY NEUROPSY BE, V14, P32; Genova HM, 2013, J CLIN EXP NEUROPSYC, V35, P631, DOI 10.1080/13803395.2013.806649; Gonzalez M, 1991, CREACION VALIDACION; Goverover Y, 2009, AM J OCCUP THER, V63, P543; Goverover Y, 2009, J CLIN EXP NEUROPSYC, V31, P513, DOI 10.1080/13803390802287042; Hely MA, 2008, MOVEMENT DISORD, V23, P837, DOI 10.1002/mds.21956; Higginson CI, 2005, J CLIN EXP NEUROPSYC, V27, P516, DOI 10.1080/13803390490515469; Ibarretxe-Bilbao N, 2011, J NEUROL SCI, V310, P70, DOI 10.1016/j.jns.2011.07.054; Ibarretxe-Bilbao N, 2009, MOVEMENT DISORD, V24, pS748, DOI 10.1002/mds.22670; Jokinen P, 2009, PARKINSONISM RELAT D, V15, P88, DOI 10.1016/j.parkreldis.2008.03.005; Junque C, 2005, MOVEMENT DISORD, V20, P540, DOI 10.1002/mds.20371; LARRABEE GJ, 1986, J CLIN EXP NEUROPSYC, V8, P275, DOI 10.1080/01688638608401318; Lengenfelder J, 2007, REHABIL PSYCHOL, V52, P290, DOI 10.1037/0090-5550.52.3.290; Lezak MD, 1995, NEUROPSYCHOLOGICAL A; Liozidou A, 2012, J GERIATR PSYCH NEUR, V25, P215, DOI 10.1177/0891988712466456; Mahurin RK, 1993, NEUROPSYCHOLOGY ALZH; Muslimovic D, 2005, NEUROLOGY, V65, P1239, DOI 10.1212/01.wnl.0000180516.69442.95; Ojeda N, 2012, SCHIZOPHR RES, V135, P72, DOI 10.1016/j.schres.2011.12.004; OWEN AM, 1992, BRAIN, V115, P1727, DOI 10.1093/brain/115.6.1727; Owen AM, 2004, NEUROSCIENTIST, V10, P525, DOI 10.1177/1073858404266776; Salthouse TA, 1996, PSYCHOL REV, V103, P403, DOI 10.1037/0033-295X.103.3.403; SALTHOUSE TA, 1991, DEV PSYCHOL, V27, P763, DOI 10.1037/0012-1649.27.5.763; Sumowski JF, 2010, NEUROPSYCHOLOGY, V24, P267, DOI 10.1037/a0017533; Tam CWC, 2005, NEUROLOGY, V64, P861; TAYLOR AE, 1986, BRAIN, V109, P845, DOI 10.1093/brain/109.5.845; TROSTER AI, 1995, BEHAV NEUROL, V8, P59, DOI 10.3233/BEN-1995-8201; van Hooren SAH, 2007, AGING NEUROPSYCHOL C, V14, P40, DOI 10.1080/138255890969483; Wechsler D., 1997, WECHSLER MEMORY SCAL; Whittington CJ, 2000, NEUROPSYCHOLOGY, V14, P233, DOI 10.1037//0894-4105.14.2.233; Williams JM, 1991, COGNITIVE BEHAV RATI; YESAVAGE JA, 1983, J PSYCHIAT RES, V17, P37, DOI 10.1016/0022-3956(82)90033-4 62 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0885-3185 1531-8257 MOVEMENT DISORD Mov. Disord. MAY 2014 29 6 765 771 10.1002/mds.25842 7 Clinical Neurology Neurosciences & Neurology AG4BI WOS:000335363300011 J Chuffart, F; Yvert, G Chuffart, Florent; Yvert, Gael MyLabStocks: a web-application to manage molecular biology materials YEAST English Article yeast strains; plasmids; oligonucleotides; software; laboratory management Laboratory stocks are the hardware of research. They must be stored and managed with mimimum loss of material and information. Plasmids, oligonucleotides and strains are regularly exchanged between collaborators within and between laboratories. Managing and sharing information about every item is crucial for retrieval of reagents, for planning experiments and for reproducing past experimental results. We have developed a web-based application to manage stocks commonly used in a molecular biology laboratory. Its functionalities include user-defined privileges, visualization of plasmid maps directly from their sequence and the capacity to search items from fields of annotation or directly from a query sequence using BLAST. It is designed to handle records of plasmids, oligonucleotides, yeast strains, antibodies, pipettes and notebooks. Based on PHP/MySQL, it can easily be extended to handle other types of stocks and it can be installed on any server architecture. MyLabStocks is freely available from: under an open source licence. (c) 2014 Laboratoire de Biologie Moleculaire de la Cellule CNRS. Yeast published by John Wiley & Sons, Ltd. [Chuffart, Florent; Yvert, Gael] Univ Lyon, Ecole Normale Super Lyon, Lab Biol Mol Cellule, CNRS, Lyon, France Yvert, G (reprint author), CNRS, Ecole Normale Super Lyon, Lab Biol Mol Cellule, 46 Allee Italie, F-69007 Lyon, France. Gael.Yvert@ens-lyon.fr European Research Council under the European Union [281359] We are grateful to David Wishart for making PlasMapper available, developers of BLAST, phpMyAdmin, phpMyEdit and Ubuntu for their software. This work was supported by the European Research Council under the European Union's Seventh Framework Programme FP7/2007-2013 (Grant SiGHT No. 281359]. ALTSCHUL SF, 1990, J MOL BIOL, V215, P403, DOI 10.1006/jmbi.1990.9999; Dong XL, 2004, NUCLEIC ACIDS RES, V32, pW660, DOI 10.1093/nar/gkh410; Nayler O, 1999, BIOTECHNIQUES, V26, P1186; Olhovsky M, 2011, NAT METHODS, V8, P612, DOI 10.1038/nmeth.1658 4 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 0749-503X 1097-0061 YEAST Yeast MAY 2014 31 5 179 184 10.1002/yea.3008 6 Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Microbiology; Mycology Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; Microbiology; Mycology AG1XZ WOS:000335210800003 J Kuhn, JH; Bao, YM; Bavari, S; Becker, S; Bradfute, S; Brauburger, K; Brister, JR; Bukreyev, AA; Cai, YY; Chandran, K; Davey, RA; Dolnik, O; Dye, JM; Enterlein, S; Gonzalez, JP; Formenty, P; Freiberg, AN; Hensley, LE; Hoenen, T; Honko, AN; Ignatyev, GM; Jahrling, PB; Johnson, KM; Klenk, HD; Kobinger, G; Lackemeyer, MG; Leroy, EM; Lever, MS; Muhlberger, E; Netesov, SV; Olinger, GG; Palacios, G; Patterson, JL; Paweska, JT; Pitt, L; Radoshitzky, SR; Ryabchikova, EI; Saphire, EO; Shestopalov, AM; Smither, SJ; Sullivan, NJ; Swanepoel, R; Takada, A; Towner, JS; van der Groen, G; Volchkov, VE; Volchkova, VA; Wahl-Jensen, V; Warren, TK; Warfield, KL; Weidmann, M; Nichol, ST Kuhn, Jens H.; Bao, Yiming; Bavari, Sina; Becker, Stephan; Bradfute, Steven; Brauburger, Kristina; Brister, J. Rodney; Bukreyev, Alexander A.; Cai, Yingyun; Chandran, Kartik; Davey, Robert A.; Dolnik, Olga; Dye, John M.; Enterlein, Sven; Gonzalez, Jean-Paul; Formenty, Pierre; Freiberg, Alexander N.; Hensley, Lisa E.; Hoenen, Thomas; Honko, Anna N.; Ignatyev, Georgy M.; Jahrling, Peter B.; Johnson, Karl M.; Klenk, Hans-Dieter; Kobinger, Gary; Lackemeyer, Matthew G.; Leroy, Eric M.; Lever, Mark S.; Muehlberger, Elke; Netesov, Sergey V.; Olinger, Gene G.; Palacios, Gustavo; Patterson, Jean L.; Paweska, Janusz T.; Pitt, Louise; Radoshitzky, Sheli R.; Ryabchikova, Elena I.; Saphire, Erica Ollmann; Shestopalov, Aleksandr M.; Smither, Sophie J.; Sullivan, Nancy J.; Swanepoel, Robert; Takada, Ayato; Towner, Jonathan S.; van der Groen, Guido; Volchkov, Viktor E.; Volchkova, Valentina A.; Wahl-Jensen, Victoria; Warren, Travis K.; Warfield, Kelly L.; Weidmann, Manfred; Nichol, Stuart T. Virus nomenclature below the species level: a standardized nomenclature for filovirus strains and variants rescued from cDNA ARCHIVES OF VIROLOGY English Article RECOMBINANT MARBURG VIRUS; EBOLA-VIRUS; FAMILY FILOVIRIDAE; CELL-CULTURE; GUINEA-PIGS; IRF-3 ACTIVATION; IN-VITRO; REPLICATION; TRANSCRIPTION; DOMAINS Specific alterations (mutations, deletions, insertions) of virus genomes are crucial for the functional characterization of their regulatory elements and their expression products, as well as a prerequisite for the creation of attenuated viruses that could serve as vaccine candidates. Virus genome tailoring can be performed either by using traditionally cloned genomes as starting materials, followed by site-directed mutagenesis, or by de novo synthesis of modified virus genomes or parts thereof. A systematic nomenclature for such recombinant viruses is necessary to set them apart from wild-type and laboratory-adapted viruses, and to improve communication and collaborations among researchers who may want to use recombinant viruses or create novel viruses based on them. A large group of filovirus experts has recently proposed nomenclatures for natural and laboratory animal-adapted filoviruses that aim to simplify the retrieval of sequence data from electronic databases. Here, this work is extended to include nomenclature for filoviruses obtained in the laboratory via reverse genetics systems. The previously developed template for natural filovirus genetic variant naming, < virus name > (< strain >/)< isolation host-suffix >/< country of sampling >/< year of sampling >/< genetic variant designation >-< isolate designation >, is retained, but we propose to adapt the type of information added to each field for cDNA clone-derived filoviruses. For instance, the full-length designation of an Ebola virus Kikwit variant rescued from a plasmid developed at the US Centers for Disease Control and Prevention could be akin to "Ebola virus H.sapiens-rec/COD/1995/Kikwit-abc1" (with the suffix "rec" identifying the recombinant nature of the virus and "abc1" being a placeholder for any meaningful isolate designator). Such a full-length designation should be used in databases and the methods section of publications. Shortened designations (such as "EBOV H.sap/COD/95/Kik-abc1") and abbreviations (such as "EBOV/Kik-abc1") could be used in the remainder of the text, depending on how critical it is to convey information contained in the full-length name. "EBOV" would suffice if only one EBOV strain/variant/isolate is addressed. [Kuhn, Jens H.; Cai, Yingyun; Hensley, Lisa E.; Jahrling, Peter B.; Lackemeyer, Matthew G.; Wahl-Jensen, Victoria] NIAID, Integrated Res Facil Ft Detrick IRF Frederick, DCR, NIH, Frederick, MD 21702 USA; [Bao, Yiming; Brister, J. Rodney] NIH, Informat Engn Branch, Natl Ctr Biotechnol Informat, Natl Lib Med, Bethesda, MD 20892 USA; [Bavari, Sina; Dye, John M.; Honko, Anna N.; Olinger, Gene G.; Palacios, Gustavo; Pitt, Louise; Radoshitzky, Sheli R.; Warren, Travis K.] United States Army Med Res Inst Infect Dis, Frederick, MD USA; [Becker, Stephan; Dolnik, Olga; Klenk, Hans-Dieter] Univ Marburg, Inst Virol, D-35032 Marburg, Germany; [Bradfute, Steven] Univ New Mexico, Albuquerque, NM 87131 USA; [Brauburger, Kristina; Muehlberger, Elke] Boston Univ, Sch Med, Dept Microbiol, Boston, MA 02118 USA; [Brauburger, Kristina; Muehlberger, Elke] Boston Univ, Sch Med, Natl Emerging Infect Dis Lab, Boston, MA 02118 USA; [Bukreyev, Alexander A.; Freiberg, Alexander N.] Univ Texas Med Branch, Dept Pathol, Galveston, TX 77555 USA; [Bukreyev, Alexander A.; Freiberg, Alexander N.] Univ Texas Med Branch, Galveston Natl Lab, Galveston, TX 77555 USA; [Chandran, Kartik] Albert Einstein Coll Med, Dept Microbiol & Immunol, Bronx, NY 10467 USA; [Davey, Robert A.; Patterson, Jean L.] Texas Biomed Res Inst, Dept Virol & Immunol, San Antonio, TX USA; [Enterlein, Sven; Warfield, Kelly L.] Integrated BioTherapeut Inc, Gaithersburg, MD USA; [Gonzalez, Jean-Paul] Inst Rech Dev, Dept Hlth, Marseille, France; [Gonzalez, Jean-Paul] Metabiota Inc, San Francisco, CA USA; [Formenty, Pierre] World Hlth Org, Geneva, Switzerland; [Hoenen, Thomas] NIAID, Virol Lab, Div Intramural Res, NIH, Hamilton, MT USA; [Ignatyev, Georgy M.] Minist Hlth Russian Federat, Fed State Unitary Co Microgen Sci Ind Co Immunobi, Moscow, Russia; [Kobinger, Gary] Publ Hlth Agcy Canada, Natl Microbiol Lab, Special Pathogens Program, Winnipeg, MB, Canada; [Leroy, Eric M.] Ctr Int Rech Med Franceville, Franceville, Gabon; [Lever, Mark S.; Smither, Sophie J.] Dstl, Dept Biomed Sci, Salisbury, Wilts, England; [Netesov, Sergey V.; Shestopalov, Aleksandr M.] Novosibirsk State Univ, Novosibirsk 630090, Novosibirsk Reg, Russia; [Paweska, Janusz T.] Natl Inst Communicable Dis, Ctr Emerging & Zoonot Dis, Natl Hlth Lab Serv, Sandringham Johannesburg, Gauteng, South Africa; [Ryabchikova, Elena I.] Russian Acad Sci, Siberian Branch, Inst Chem Biol & Fundamental Med, Novosibirsk, Novosibirsk Reg, Russia; [Saphire, Erica Ollmann] Scripps Res Inst, Dept Immunol & Microbial Sci, La Jolla, CA 92037 USA; [Saphire, Erica Ollmann] Scripps Res Inst, Skaggs Inst Chem Biol, La Jolla, CA 92037 USA; [Shestopalov, Aleksandr M.] State Res Ctr Virol & Biotechnol Vector, Koltsov, Novosibirsk Reg, Russia; [Sullivan, Nancy J.] NIAID, Vaccine Res Ctr, NIH, Bethesda, MD 20892 USA; [Swanepoel, Robert] Univ Pretoria, Zoonoses Res Unit, ZA-0002 Pretoria, South Africa; [Takada, Ayato] Hokkaido Univ, Res Ctr Zoonosis Control, Div Global Epidemiol, Sapporo, Hokkaido, Japan; [Towner, Jonathan S.; Nichol, Stuart T.] Ctr Dis Control & Prevent CDC, Natl Ctr Emerging & Zoonot Infect Dis NCEZID, Div High Consequence Pathogens Pathol DHCPP, Viral Special Pathogens Branch VSPB, Atlanta, GA 30333 USA; [van der Groen, Guido] Prins Leopold Inst Trop Geneeskunde, Antwerp, Belgium; [Volchkov, Viktor E.; Volchkova, Valentina A.] Univ Lyon, Lab Filovirus, INSERM, U758,UCB Lyon 1,Ecole Normale Super Lyon, Lyon, France; [Weidmann, Manfred] Univ Med Gottingen, Abt Virol, Gottingen, Germany Kuhn, JH (reprint author), NIAID, Integrated Res Facil Ft Detrick IRF Frederick, DCR, NIH, B-8200 Res Plaza, Frederick, MD 21702 USA. kuhnjens@mail.nih.gov; stn1@cdc.gov Joint Science and Technology Office for Chem Bio Defense [TMTI0048_09_RD_T]; NIAID [HHSN272200700016I]; Intramural Research Program of the NIH, National Library of Medicine; Intramural Research Program of the NIH, NIAID The content of this publication does not necessarily reflect the views or policies of the US Department of the Army, the US Department of Defense or the US Department of Health and Human Services or of the institutions and companies affiliated with the authors. This work was funded in part by the Joint Science and Technology Office for Chem Bio Defense (proposal #TMTI0048_09_RD_T to SB). YC, JHK, and VWJ performed this work as employees of Tunnell Consulting, Inc., and MGL as an employee of Lovelace Respiratory Research Institute, both subcontractors to Battelle Memorial Institute under its prime contract with NIAID, under Contract No. HHSN272200700016I. This research was also supported in part by the Intramural Research Program of the NIH, National Library of Medicine (YB and JRB), and the Intramural Research Program of the NIH, NIAID (TH). Adams MJ, 2012, ARCH VIROL, V157, P1411, DOI 10.1007/s00705-012-1299-6; Calisher CH, 2009, ZOOTAXA, P63; Ebihara H, 2007, J INFECT DIS, V196, pS313, DOI 10.1086/520590; Ebihara H, 2006, PLOS PATHOG, V2, P705, DOI 10.1371/journal.ppat.0020073; Enterlein S, 2009, J VIROL, V83, P4508, DOI 10.1128/JVI.02429-08; Enterlein S, 2006, J VIROL, V80, P1038, DOI 10.1128/JVI.80.2.1038-1043.2006; Feldmann H, 2005, VIRUS TAXONOMY, P645; Groseth A, 2012, PLOS PATHOG, V8, DOI 10.1371/journal.ppat.1002847; Hartman AL, 2006, J VIROL, V80, P6430, DOI 10.1128/JVI.00044-06; Hartman AL, 2008, J VIROL, V82, P2699, DOI 10.1128/JVI.02344-07; Hoenen T, 2011, ANTIVIR RES, V91, P195, DOI 10.1016/j.antiviral.2011.06.003; Hoenen T, 2013, ANTIVIR RES, V99, P207, DOI [10.1016/j.antiviral.2013.05.017, 10.1016/j.antivira1.2013.05.017]; Hoenen T, 2012, J VIROL, V86, P11779, DOI 10.1128/JVI.01525-12; International Committee on Standardized Genetic Nomenclature for Mice, 2011, GUID NOM MOUS RAT ST; Krahling V, 2010, PLOS NEGLECT TROP D, V4, DOI 10.1371/journal.pntd.0000802; Kuhn JH, 2013, ARCH VIROL, V158, P301, DOI 10.1007/s00705-012-1454-0; Kuhn JH, 2010, ARCH VIROL, V155, P2083, DOI 10.1007/s00705-010-0814-x; Kuhn JH, 2011, VIRUS TAXONOMY, P665; Kuhn JH, 2013, ARCH VIROL, V158, P1425, DOI 10.1007/s00705-012-1594-2; Lubaki NM, 2013, J VIROL, V87, P7471, DOI 10.1128/JVI.03316-12; Martinez MJ, 2011, J INFECT DIS, V204, pS934, DOI 10.1093/infdis/jir320; Martinez MJ, 2008, J VIROL, V82, P12569, DOI 10.1128/JVI.01395-08; Mateo M, 2011, J INFECT DIS, V204, pS1011, DOI 10.1093/infdis/jir338; Mateo M, 2011, J INFECT DIS, V204, pS892, DOI 10.1093/infdis/jir311; Mittler E, 2013, CELL MICROBIOL, V15, P270, DOI 10.1111/cmi.12076; Mpanju OM, 2006, VIRUS RES, V121, P205, DOI 10.1016/j.virusres.2006.06.002; Neumann G, 2002, J VIROL, V76, P406, DOI 10.1128/JVI.76.1.406-410.2002; Neumann G, 2005, J VIROL, V79, P10300, DOI 10.1128/JVI.79.16.10300-10307.2005; Prins KC, 2010, J VIROL, V84, P3004, DOI 10.1128/JVI.02459-09; Schmidt KM, 2011, J INFECT DIS, V204, pS861, DOI 10.1093/infdis/jir308; Shabman RS, 2013, PLOS PATHOG, V9, DOI 10.1371/journal.ppat.1003147; Theriault S, 2004, VIRUS RES, V106, P43, DOI 10.1016/j.virusres.2004.06.002; Towner JS, 2005, VIROLOGY, V332, P20, DOI 10.1016/j.virol.2004.10.048; Volchkov VE, 2001, SCIENCE, V291, P1965, DOI 10.1126/science.1057269; Volchkova VA, 2011, J INFECT DIS, V204, pS941, DOI 10.1093/infdis/jir321 35 1 1 SPRINGER WIEN WIEN SACHSENPLATZ 4-6, PO BOX 89, A-1201 WIEN, AUSTRIA 0304-8608 1432-8798 ARCH VIROL Arch. Virol. MAY 2014 159 5 1229 1237 10.1007/s00705-013-1877-2 9 Virology Virology AG9ZB WOS:000335777600048 J De Visscher, A; Noel, MP De Visscher, Alice; Noel, Marie-Pascale Arithmetic facts storage deficit: the hypersensitivity-to-interference in memory hypothesis DEVELOPMENTAL SCIENCE English Article WORKING-MEMORY; DEVELOPMENTAL DYSCALCULIA; MATHEMATICAL SKILLS; DISABLED-CHILDREN; PROBLEM SIZE; INFORMATION; DISABILITY; SIMILARITY; RETRIEVAL; DIFFICULTIES Dyscalculia, or mathematics learning disorders, is currently known to be heterogeneous (Wilson & Dehaene, ). While various profiles of dyscalculia coexist, a general and persistent hallmark of this math learning disability is the difficulty in memorizing arithmetic facts (Geary, Hoard & Hamson, ; Jordan & Montani, ; Slade & Russel, ). Arithmetic facts are simple arithmetic problems that are solved by direct retrieval from memory. Recently, De Visscher and Noel () showed hypersensitivity-to-interference in memory in an adult suffering from a specific deficit of arithmetic facts storage. According to the authors, arithmetic facts share many features. The overlapping of these features between arithmetic facts may provoke interference. Consequently, learners who are hypersensitive-to-interference could have considerable difficulties in storing arithmetic facts. The present study aims at testing this new hypothesis on fourth-grade children who are learning multiplication tables. Among 101 children that were assessed, 23 low arithmetic facts learners were selected because of their low score in arithmetic facts fluency (controlling for processing speed). Twenty-three control children were selected, matched for classroom, gender, and age. In addition to a subtest of global reasoning, these participants were given a multiplication production task and a memorization task of low- and high-interference associations. The results show that children with low arithmetic fluencies experience hypersensitivity-to-interference in memory compared with children with typical arithmetic fluencies. [De Visscher, Alice; Noel, Marie-Pascale] Catholic Univ Louvain, Ctr Neurosci Cognit & Syst, Inst Rech Sci Psychol, B-1348 Louvain, Belgium De Visscher, A (reprint author), Catholic Univ Louvain, Pl Cardinal Mercier 10, B-1348 Louvain, Belgium. alice.devisscher@uclouvain.be Barrouillet P, 2005, J EXP CHILD PSYCHOL, V91, P183, DOI 10.1016/j.jecp.2005.03.002; Campbell J. I., 1995, MATH COGNITION, V1, P121; CAMPBELL JID, 1985, CAN J PSYCHOL, V39, P338, DOI 10.1037/h0080065; Censabella S, 2004, CAH PSYCHOL COGN, V22, P635; Conlin JA, 2005, Q J EXP PSYCHOL-A, V58, P1434, DOI 10.1080/02724980443000683; CONRAD R, 1964, BRIT J PSYCHOL, V55, P429; De Brauwer J, 2006, J EXP CHILD PSYCHOL, V94, P43, DOI 10.1016/j.jecp.2005.11.004; De Smedt B, 2011, NEUROIMAGE, V57, P771, DOI 10.1016/j.neuroimage.2010.12.037; De Visscher A, 2013, CORTEX, V49, P50, DOI 10.1016/j.cortex.2012.01.003; Farrell S, 2002, PSYCHON B REV, V9, P59, DOI 10.3758/BF03196257; Friedman NP, 2004, J EXP PSYCHOL GEN, V133, P101, DOI 10.1037/0096-3445.133.1.101; GARNETT K, 1983, LEARN DISABILITY Q, V6, P223, DOI 10.2307/1510801; GEARY DC, 1991, DEV PSYCHOL, V27, P787, DOI 10.1037/0012-1649.27.5.787; Geary DC, 1999, J EXP CHILD PSYCHOL, V74, P213, DOI 10.1006/jecp.1999.2515; Geary DC, 2012, J LEARN DISABIL-US, V45, P291, DOI 10.1177/0022219410392046; Geary DC, 2004, J LEARN DISABIL-US, V37, P4, DOI 10.1177/00222194040370010201; Grabner RH, 2009, NEUROPSYCHOLOGIA, V47, P604, DOI 10.1016/j.neuropsychologia.2008.10.013; HALL JF, 1971, AM J PSYCHOL, V84, P521, DOI 10.2307/1421169; Holmes VM, 2007, MEM COGNITION, V35, P2041, DOI 10.3758/BF03192936; Jonides J, 2006, NEUROSCIENCE, V139, P181, DOI 10.1016/j.neuroscience.2005.06.042; Jordan NC, 2003, J EXP CHILD PSYCHOL, V85, P103, DOI 10.1016/S0022-0965(03)00032-8; Jordan NC, 1997, J LEARN DISABIL-US, V30, P624; LeFevre JA, 2013, J EXP CHILD PSYCHOL, V114, P243, DOI 10.1016/j.jecp.2012.10.005; McCloskey M., 1989, PSYCHOL LEARN MOTIV, V24, P109; McLean JF, 1999, J EXP CHILD PSYCHOL, V74, P240, DOI 10.1006/jecp.1999.2516; MONSELL S, 1978, COGNITIVE PSYCHOL, V10, P465, DOI 10.1016/0010-0285(78)90008-7; Noel M-P., 2008, OPEN PSYCHOL J, V1, P26, DOI 10.2174/1874350100801010026; Oberauer K, 2001, EUR J COGN PSYCHOL, V13, P187; Oberauer K, 2006, J MEM LANG, V55, P601, DOI 10.1016/j.jml.2006.08.009; Oberauer K, 2008, J MEM LANG, V58, P730, DOI 10.1016/j.jml.2007.09.006; Passolunghi MC, 1999, MEM COGNITION, V27, P779; Passolunghi MC, 2004, J EXP CHILD PSYCHOL, V88, P348, DOI 10.1016/j.jecp.2004.04.002; Piazza M, 2010, COGNITION, V116, P33, DOI 10.1016/j.cognition.2010.03.012; Shalev RS, 2000, EUR CHILD ADOLES PSY, V9, P58; Simmons FR, 2012, J EXP CHILD PSYCHOL, V111, P139, DOI 10.1016/j.jecp.2011.08.011; SLADE PD, 1971, PSYCHOL MED, V1, P292; Swanson HL, 2006, REV EDUC RES, V76, P249, DOI 10.3102/00346543076002249; Wechsler D., 2005, ECHELLE INTELLIGENCE; Wilson A. J., 2007, HUMAN BEHAV LEARNING, P212 39 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 1467-7687 DEVELOPMENTAL SCI Dev. Sci. MAY 2014 17 3 434 442 10.1111/desc.12135 9 Psychology, Developmental; Psychology, Experimental Psychology AF4OO WOS:000334693300010 J Pfeifer, M; Lefebvre, V; Gardner, TA; Arroyo-Rodriguez, V; Baeten, L; Banks-Leite, C; Barlow, J; Betts, MG; Brunet, J; Cerezo, A; Cisneros, LM; Collard, S; D'Cruze, N; Motta, CD; Duguay, S; Eggermont, H; Eigenbrod, F; Hadley, AS; Hanson, TR; Hawes, JE; Scalley, TH; Klingbeil, BT; Kolb, A; Kormann, U; Kumar, S; Lachat, T; Fraser, PL; Lantschner, V; Laurance, WF; Leal, IR; Lens, L; Marsh, CJ; Medina-Rangel, GF; Melles, S; Mezger, D; Oldekop, JA; Overal, WL; Owen, C; Peres, CA; Phalan, B; Pidgeon, AM; Pilia, O; Possingham, HP; Possingham, ML; Raheem, DC; Ribeiro, DB; Neto, JDR; Robinson, WD; Robinson, R; Rytwinski, T; Scherber, C; Slade, EM; Somarriba, E; Stouffer, PC; Struebig, MJ; Tylianakis, JM; Tscharntke, T; Tyre, AJ; Urbina-Cardona, JN; Vasconcelos, HL; Wearn, O; Wells, K; Willig, MR; Wood, E; Young, RP; Bradley, AV; Ewers, RM Pfeifer, Marion; Lefebvre, Veronique; Gardner, Toby A.; Arroyo-Rodriguez, Victor; Baeten, Lander; Banks-Leite, Cristina; Barlow, Jos; Betts, Matthew G.; Brunet, Joerg; Cerezo, Alexis; Cisneros, Laura M.; Collard, Stuart; D'Cruze, Neil; da Silva Motta, Catarina; Duguay, Stephanie; Eggermont, Hilde; Eigenbrod, Felix; Hadley, Adam S.; Hanson, Thor R.; Hawes, Joseph E.; Scalley, Tamara Heartsill; Klingbeil, Brian T.; Kolb, Annette; Kormann, Urs; Kumar, Sunil; Lachat, Thibault; Lakeman Fraser, Poppy; Lantschner, Victoria; Laurance, William F.; Leal, Inara R.; Lens, Luc; Marsh, Charles J.; Medina-Rangel, Guido F.; Melles, Stephanie; Mezger, Dirk; Oldekop, Johan A.; Overal, William L.; Owen, Charlotte; Peres, Carlos A.; Phalan, Ben; Pidgeon, Anna M.; Pilia, Oriana; Possingham, Hugh P.; Possingham, Max L.; Raheem, Dinarzarde C.; Ribeiro, Danilo B.; Ribeiro Neto, Jose D.; Robinson, W. Douglas; Robinson, Richard; Rytwinski, Trina; Scherber, Christoph; Slade, Eleanor M.; Somarriba, Eduardo; Stouffer, Philip C.; Struebig, Matthew J.; Tylianakis, Jason M.; Tscharntke, Teja; Tyre, Andrew J.; Urbina-Cardona, J. Nicolas; Vasconcelos, Heraldo L.; Wearn, Oliver; Wells, Konstans; Willig, Michael R.; Wood, Eric; Young, Richard P.; Bradley, Andrew V.; Ewers, Robert M. BIOFRAG - a new database for analyzing BIOdiversity responses to forest FRAGmentation ECOLOGY AND EVOLUTION English Article Bioinformatics; data sharing; database; edge effects; forest fragmentation; global change; landscape metrics; matrix contrast; species turnover HABITAT FRAGMENTATION; LANDSCAPE STRUCTURE; BREEDING BIRDS; EDGE; MATRIX; CONSERVATION; METAANALYSIS; COVER; SIZE; AREA Habitat fragmentation studies have produced complex results that are challenging to synthesize. Inconsistencies among studies may result from variation in the choice of landscape metrics and response variables, which is often compounded by a lack of key statistical or methodological information. Collating primary datasets on biodiversity responses to fragmentation in a consistent and flexible database permits simple data retrieval for subsequent analyses. We present a relational database that links such field data to taxonomic nomenclature, spatial and temporal plot attributes, and environmental characteristics. Field assessments include measurements of the response(s) (e.g., presence, abundance, ground cover) of one or more species linked to plots in fragments within a partially forested landscape. The database currently holds 9830 unique species recorded in plots of 58 unique landscapes in six of eight realms: mammals 315, birds 1286, herptiles 460, insects 4521, spiders 204, other arthropods 85, gastropods 70, annelids 8, platyhelminthes 4, Onychophora 2, vascular plants 2112, nonvascular plants and lichens 320, and fungi 449. Three landscapes were sampled as long-term time series (>10years). Seven hundred and eleven species are found in two or more landscapes. Consolidating the substantial amount of primary data available on biodiversity responses to fragmentation in the context of land-use change and natural disturbances is an essential part of understanding the effects of increasing anthropogenic pressures on land. The consistent format of this database facilitates testing of generalizations concerning biologic responses to fragmentation across diverse systems and taxa. It also allows the re-examination of existing datasets with alternative landscape metrics and robust statistical methods, for example, helping to address pseudo-replication problems. The database can thus help researchers in producing broad syntheses of the effects of land use. The database is dynamic and inclusive, and contributions from individual and large-scale data-collection efforts are welcome. [Pfeifer, Marion; Lefebvre, Veronique; Banks-Leite, Cristina; Owen, Charlotte; Pilia, Oriana; Possingham, Hugh P.; Tylianakis, Jason M.; Wearn, Oliver; Bradley, Andrew V.; Ewers, Robert M.] Univ London Imperial Coll Sci Technol & Med, Dept Life Sci, Ascot SL5 7PY, Berks, England; [Gardner, Toby A.] Stockholm Environm Inst, Stockholm, Sweden; [Arroyo-Rodriguez, Victor] Univ Nacl Autonoma Mexico, Ctr Invest Ecosistemas, Morelia, Michoacan, Mexico; [Baeten, Lander] Univ Ghent, Dept Forest & Water Management, B-9000 Ghent, Belgium; [Barlow, Jos] Univ Lancaster, Lancaster Environm Ctr, Lancaster, England; [Betts, Matthew G.; Hadley, Adam S.] Oregon State Univ, Dept Forest Ecosyst & Soc, Corvallis, OR 97331 USA; [Brunet, Joerg] Swedish Univ Agr Sci, Southern Swedish Forest Res Ctr, Alnarp, Sweden; [Cerezo, Alexis] Univ Buenos Aires, Dept Metodos Cuantitat & Sistemas Informac, Buenos Aires, DF, Argentina; [Cisneros, Laura M.; Klingbeil, Brian T.; Willig, Michael R.] Univ Connecticut, Dept Ecol & Evolutionary Biol, Storrs, CT USA; [Cisneros, Laura M.; Klingbeil, Brian T.; Willig, Michael R.] Univ Connecticut, Ctr Environm Sci & Engn, Storrs, CT USA; [Collard, Stuart] Nat Conservat Soc South Australia, Adelaide, SA, Australia; [D'Cruze, Neil] World Soc Protect Anim, London, England; [da Silva Motta, Catarina] INPA, Dept Entomol, Manaus, Amazonas, Brazil; [Duguay, Stephanie] Carleton Univ, Geomat & Landscape Ecol Res Lab, Ottawa, ON K1S 5B6, Canada; [Eggermont, Hilde; Lens, Luc] Univ Ghent, Terr Ecol Unit, B-9000 Ghent, Belgium; [Eigenbrod, Felix; Phalan, Ben] Univ Southampton, Ctr Biol Sci, Southampton, Hants, England; [Hawes, Joseph E.; Peres, Carlos A.] Univ E Anglia, Sch Environm Sci, Norwich NR4 7TJ, Norfolk, England; [Scalley, Tamara Heartsill] USDA Forestry Serv, Int Inst Trop Forestry, Rio Piedras, PR USA; [Kolb, Annette] Univ Bremen, Inst Ecol, FB2, D-28359 Bremen, Germany; [Kormann, Urs; Scherber, Christoph; Tscharntke, Teja] Univ Gottingen, Dept Crop Sci, D-37073 Gottingen, Germany; [Kumar, Sunil] Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO 80523 USA; [Lachat, Thibault] Swiss Fed Inst Forest Snow & Landscape Res WSL, Birmensdorf, Switzerland; [Lakeman Fraser, Poppy] Univ London Imperial Coll Sci Technol & Med, OPAL, London, England; [Lantschner, Victoria] Bariloche CONICET, INTA EEA, San Carlos De Bariloche, Rio Negro, Argentina; [Laurance, William F.] James Cook Univ, Ctr Trop Environm & Sustainabil Sci, Cairns, Qld, Australia; [Laurance, William F.] James Cook Univ, Sch Marine & Trop Biol, Cairns, Qld, Australia; [Leal, Inara R.; Ribeiro Neto, Jose D.] Univ Fed Pernambuco, Dept Bot, Recife, PE, Brazil; [Marsh, Charles J.] Univ Leeds, Fac Biol Sci, Leeds, W Yorkshire, England; [Medina-Rangel, Guido F.] Univ Nacl Colombia, ICN, Bogota, Colombia; [Melles, Stephanie] Univ Toronto, Dept Ecol & Evolutionary Biol, Toronto, ON, Canada; [Mezger, Dirk] Field Museum Nat Hist, Dept Zool, Chicago, IL 60605 USA; [Oldekop, Johan A.] Univ Sheffield, Sheffield Inst Int Dev, Sheffield, S Yorkshire, England; [Overal, William L.] Museu Paraense Emilio Goeldi, Dept Entomol, Belem, Para, Brazil; [Pidgeon, Anna M.; Wood, Eric] Univ Wisconsin Madison, Dept Forest & Wildlife Ecol, Madison, WI USA; [Possingham, Hugh P.] Univ Queensland, Brisbane, Qld, Australia; [Raheem, Dinarzarde C.] Royal Belgian Inst Nat Sci, Brussels, Belgium; [Raheem, Dinarzarde C.] Nat Hist Museum, Dept Life Sci, London SW7 5BD, England; [Ribeiro, Danilo B.] Univ Fed Mato Grosso do Sul, Ctr Ciencias Biol & Saude, Campo Grande, Brazil; [Robinson, W. Douglas] Oregon State Univ, Dept Fisheries & Wildlife, Corvallis, OR USA; [Robinson, Richard] Manjimup Res Ctr, Dept Pk & Wildlife, Manjimup, WA, Australia; [Rytwinski, Trina] Carleton Univ, Dept Biol, Ottawa, ON K1S 5B6, Canada; [Slade, Eleanor M.] Univ Oxford, Dept Zool, Oxford OX1 3PS, England; [Somarriba, Eduardo] CATIE, Turrialba, Costa Rica; [Stouffer, Philip C.] Louisiana State Univ, Ctr Agr, Sch Renewable Nat Resources, Baton Rouge, LA 70803 USA; [Stouffer, Philip C.] INPA, BDFFP, Manaus, Amazonas, Brazil; [Struebig, Matthew J.] Univ Kent, Sch Anthropol & Conservat, Durrell Inst Conservat & Ecol, Canterbury, Kent, England; [Tylianakis, Jason M.] Univ Canterbury, Sch Biol Sci, Canterbury, New Zealand; [Tyre, Andrew J.] Univ Nebraska, Sch Nat Resources, Lincoln, NE USA; [Urbina-Cardona, J. Nicolas] Pontificia Univ Javeriana, Sch Rural & Environm Studies, Ecol & Terr Dept, Bogota, Colombia; [Vasconcelos, Heraldo L.] Univ Fed Uberlandia, Inst Biol, Uberlandia, MG, Brazil; [Wearn, Oliver] Zool Soc London, Inst Zool, London NW1 4RY, England; [Wells, Konstans] Univ Adelaide, Sch Earth & Environm Sci, Inst Environm, Adelaide, SA, Australia; [Young, Richard P.] Durrell Wildlife Conservat Trust, Trinity, Jersey, England Pfeifer, M (reprint author), Univ London Imperial Coll Sci Technol & Med, Dept Life Sci, Silwood Pk Campus, Ascot SL5 7PY, Berks, England. m.pfeifer@imperial.ac.uk Barlow, Jos/E-7861-2014; Urbina-Cardona, J. Nicolas/B-5447-2008; Marsh, Charles/D-2891-2012; Phalan, Ben/A-5783-2009; Wells, Konstans/A-7232-2010; Leal, Inara/E-8362-2010 Urbina-Cardona, J. Nicolas/0000-0002-4174-8467; Phalan, Ben/0000-0001-7876-7226; Wells, Konstans/0000-0003-0377-2463; European Research Council (ERC) [281986] This paper is a contribution to Imperial College's Grand Challenges in Ecosystems and the Environment initiative. The research is funded by the European Research Council under the 7th Framework Program (FP7 ERC no. 281986). Arroyo-Rodriguez V, 2010, AM J PRIMATOL, V72, P1, DOI 10.1002/ajp.20753; Banks-Leite C, 2012, BIOTROPICA, V44, P378, DOI 10.1111/j.1744-7429.2011.00801.x; Barlow J, 2007, P NATL ACAD SCI USA, V104, P18555, DOI 10.1073/pnas.0703333104; Bender DJ, 2003, LANDSCAPE ECOL, V18, P17, DOI 10.1023/A:1022937226820; Bender DJ, 2005, ECOLOGY, V86, P1023, DOI 10.1890/03-0769; Betts MG, 2007, CONSERV BIOL, V21, P1046, DOI 10.1111/j.1523-1739.2007.00723.x; Betts MG, 2006, ECOL APPL, V16, P1076, DOI 10.1890/1051-0761(2006)016[1076:IEOFOF]2.0.CO;2; Bierregaard RO, 2001, LESSONS AMAZONIA ECO; Chalfoun AD, 2002, CONSERV BIOL, V16, P306, DOI 10.1046/j.1523-1739.2002.00308.x; Codd E. F., 1971, IBM RES REPORT; Cushman SA, 2006, BIOL CONSERV, V128, P231, DOI 10.1016/j.biocon.2005.09.031; Debinski DM, 2006, J BIOGEOGR, V33, P1791, DOI 10.1111/j.1365-2699.2006.01596.x; Didham R. K., 2010, ENCY LIFE SCI, DOI [10.1002/9780470015902.a0021904., DOI 10.1002/9780470015902.A0021904]; Didham RK, 1996, TRENDS ECOL EVOL, V11, P255, DOI 10.1016/0169-5347(96)20047-3; Didham RK, 2012, OIKOS, V121, P161, DOI 10.1111/j.1600-0706.2011.20273.x; Driscoll DA, 2005, CONSERV BIOL, V19, P182, DOI 10.1111/j.1523-1739.2005.00586.x; Eigenbrod F, 2011, BIOL CONSERV, V144, P298, DOI 10.1016/j.biocon.2010.09.007; Ewers Robert M., 2002, Weta, V24, P25; Ewers RM, 2007, ECOLOGY, V88, P96, DOI 10.1890/0012-9658(2007)88[96:SIBEAA]2.0.CO;2; Ewers RM, 2006, J APPL ECOL, V43, P527, DOI 10.1111/j.1365-2664.2006.01151.x; Ewers RM, 2008, P NATL ACAD SCI USA, V105, P5426, DOI 10.1073/pnas.0800460105; Ewers RM, 2011, PHILOS T R SOC B, V366, P3292, DOI 10.1098/rstb.2011.0049; Ewers RM, 2006, BIOL REV, V81, P117, DOI 10.1017/S1464793105006949; Ewers RM, 2010, TRENDS ECOL EVOL, V25, P699, DOI 10.1016/j.tree.2010.09.008; Ewers RM, 2009, BIOL CONSERV, V142, P2872, DOI 10.1016/j.biocon.2009.06.022; Ewers RM, 2007, CONSERV BIOL, V21, P926, DOI 10.1111/j.1523-1739.2007.00720.x; Fagan WE, 1999, AM NAT, V153, P165, DOI 10.1086/303162; Fahrig L, 2003, ANNU REV ECOL EVOL S, V34, P487, DOI 10.1146/annurev.ecolsys.34.011802.132419; Fletcher RJ, 2007, CAN J ZOOL, V85, P1017, DOI 10.1139/Z07-100; Fletcher RJ, 2005, J ANIM ECOL, V74, P342, DOI 10.1111/j.1365-2656.2005.00930.x; Fonseca CR, 2007, RESTOR ECOL, V15, P613; Forman RTT, 1995, LAND MOSAICS ECOLOGY; Friedl MA, 2010, REMOTE SENS ENVIRON, V114, P168, DOI 10.1016/j.rse.2009.08.016; Gardner T., 2012, MONITORING FOREST BI; Gardner TA, 2009, ECOL LETT, V12, P561, DOI 10.1111/j.1461-0248.2009.01294.x; Gardner TA, 2007, BIOL CONSERV, V138, P166, DOI 10.1016/j.biocon.2007.04.017; Giam XL, 2012, P ROY SOC B-BIOL SCI, V279, P67, DOI 10.1098/rspb.2011.0433; Hadley AS, 2012, BIOL REV, V87, P526, DOI 10.1111/j.1469-185X.2011.00205.x; Hansen MC, 2013, SCIENCE, V342, P850, DOI 10.1126/science.1244693; Harper KA, 2005, CONSERV BIOL, V19, P768, DOI 10.1111/j.1523-1739.2005.00045.x; Hassan R., 2005, ECOSYSTEMS HUMAN WEL; Huxel GR, 1998, AM NAT, V152, P460, DOI 10.1086/286182; IUCN & UNEP-WCMC, 2010, WORLD DAT PROT AR WD; Jetz W, 2007, PLOS BIOL, V5, P1211, DOI 10.1371/journal.pbio.0050157; Kennedy CM, 2010, ECOL MONOGR, V80, P651, DOI 10.1890/09-0904.1; Krauss J, 2010, ECOL LETT, V13, P597, DOI 10.1111/j.1461-0248.2010.01457.x; Kupfer JA, 2006, GLOBAL ECOL BIOGEOGR, V15, P8, DOI 10.1111/j.1466-822x.2006.00204.x; Lampila P, 2005, CONSERV BIOL, V19, P1537, DOI 10.1111/j.1523-1739.2005.00201.x; Larsen LG, 2012, ECOL APPL, V22, P2204, DOI 10.1890/11-1948.1; Larsen TH, 2005, BIOTROPICA, V37, P322, DOI 10.1111/j.1744-7429.2005.00042.x; Laurance WF, 2006, ECOLOGY, V87, P469, DOI 10.1890/05-0064; Laurance WF, 2011, BIOL CONSERV, V144, P56, DOI 10.1016/j.biocon.2010.09.021; Laurance WF, 1998, ECOLOGY, V79, P2032, DOI 10.1890/0012-9658(1998)079[2032:RFFATD]2.0.CO;2; Lefebvre V., 2013, NEW FRONTIERS TROPIC; Lindenmayer DB, 2007, TRENDS ECOL EVOL, V22, P127, DOI 10.1016/j.tree.2006.11.006; Magrach A, 2011, PLOS ONE, V6, DOI 10.1371/journal.pone.0021596; MALCOLM JR, 1994, ECOLOGY, V75, P2438, DOI 10.2307/1940897; Malhi Y, 2008, SCIENCE, V319, P169, DOI 10.1126/science.1146961; MARGULES CR, 1992, ENVIRON CONSERV, V19, P316; McGarigal K., 2002, ISSUES PERSPECTIVES, P112; MCGARIGAL K, 1995, ECOL MONOGR, V65, P235, DOI 10.2307/2937059; McGarigal K, 2002, FRAGSTATS SPATIAL PA; Melles S, 2012, AVIAN CONSERV ECOL, V7, DOI 10.5751/ACE-00530-070203; Mittermeier R.A., 2011, BIODIVERSITY HOTSPOT, P3; MURCIA C, 1995, TRENDS ECOL EVOL, V10, P58, DOI 10.1016/S0169-5347(00)88977-6; Newbold T., 2012, FRONT BIOGEOGRAP, V4, P155; Newbold Tim, 2013, Proceedings of the Royal Society Biological Sciences Series B, V280, P1; Nichols E, 2007, BIOL CONSERV, V137, P1, DOI 10.1016/j.biocon.2007.01.023; Niemela J, 2001, EUR J ENTOMOL, V98, P127; Olson DM, 2002, ANN MO BOT GARD, V89, P199, DOI 10.2307/3298564; Platts P. J., 2012, THESIS U YORK YORK; Prevedello JA, 2010, BIODIVERS CONSERV, V19, P1205, DOI 10.1007/s10531-009-9750-z; Prugh LR, 2008, P NATL ACAD SCI USA, V105, P20770, DOI 10.1073/pnas.0806080105; Prugh LR, 2009, ECOL APPL, V19, P1300, DOI 10.1890/08-1524.1; RAMAGE BS, 2012, CONSERV BIOL, V27, P364, DOI DOI 10.1111/C0BI.12004); Rand TA, 2006, ECOL LETT, V9, P603, DOI 10.1111/j.1461-0248.2006.00911.x; Reino L, 2009, BIOL CONSERV, V142, P824, DOI 10.1016/j.biocon.2008.12.011; Restrepo C, 1999, ECOLOGY, V80, P668, DOI 10.1890/0012-9658(1999)080[0668:AETGAF]2.0.CO;2; Ries L, 2004, ANNU REV ECOL EVOL S, V35, P491, DOI 10.1146/annurev.ecolsys.35.112202.130148; Ryall KL, 2006, ECOLOGY, V87, P1086, DOI 10.1890/0012-9658(2006)87[1086:ROPTLA]2.0.CO;2; Scalley TH, 2010, BIOTROPICA, V42, P455, DOI 10.1111/j.1744-7429.2009.00609.x; Sexton JO, 2013, INT J DIGIT EARTH, V6, P427, DOI 10.1080/17538947.2013.786146; Slade EM, 2013, ECOLOGY, V94, P1519, DOI 10.1890/12-1366.1; Truxa C, 2012, EUR J ENTOMOL, V109, P77; Trzcinski MK, 1999, ECOL APPL, V9, P586; Tscharntke T, 2012, BIOL REV, V87, P661, DOI 10.1111/j.1469-185X.2011.00216.x; Villard MA, 1999, CONSERV BIOL, V13, P774, DOI 10.1046/j.1523-1739.1999.98059.x; Vogt P, 2009, ECOL INDIC, V9, P64, DOI 10.1016/j.ecolind.2008.01.011; Wagner HH, 2005, ECOLOGY, V86, P1975, DOI 10.1890/04-0914; Watling JI, 2011, GLOBAL ECOL BIOGEOGR, V20, P209, DOI 10.1111/j.1466-8238.2010.00586.x; Wells K, 2013, METHODS ECOL EVOL, V4, P1, DOI 10.1111/j.2041-210x.2012.00249.x; Westphal MI, 2007, LANDSCAPE URBAN PLAN, V81, P56, DOI 10.1016/j.landurbplan.2006.10.015; Westphal MI, 2003, ECOL APPL, V13, P543, DOI 10.1890/1051-0761(2003)013[0543:TUOSDP]2.0.CO;2; Willis KJ, 2012, BIOL CONSERV, V147, P3, DOI 10.1016/j.biocon.2011.11.001; With KA, 2004, RISK ANAL, V24, P803, DOI 10.1111/j.0272-4332.2004.00480.x 95 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2045-7758 ECOL EVOL Ecol. Evol. MAY 2014 4 9 1524 1537 10.1002/ece3.1036 14 Ecology Environmental Sciences & Ecology AG2SQ WOS:000335267000003 J Zhang, Y Zhang, Yan Beyond Quality and Accessibility: Source Selection in Consumer Health Information Searching JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article NATIONAL TRENDS SURVEY; WORLD-WIDE-WEB; INTERNET USE; CARE INFORMATION; SEEKING; MODEL; CONTEXT; COMPLEXITY; RETRIEVAL; ENGINEERS A systematic understanding of factors and criteria that affect consumers' selection of sources for health information is necessary for the design of effective health information services and information systems. However, current studies have overly focused on source attributes as indicators for 2 criteria, source quality and accessibility, and overlooked the role of other factors and criteria that help determine source selection. To fill this gap, guided by decision-making theories and the cognitive perspective to information search, we interviewed 30 participants about their reasons for using a wide range of sources for health information. Additionally, we asked each of them to report a critical incident in which sources were selected to fulfill a specific information need. Based on the analysis of the transcripts, 5 categories of factors were identified as influential to source selection: source-related factors, user-related factors, user-source relationships, characteristics of the problematic situation, and social influences. In addition, about a dozen criteria that mediate the influence of the factors on source-selection decisions were identified, including accessibility, quality, usability, interactivity, relevance, usefulness, familiarity, affection, anonymity, and appropriateness. These results significantly expanded the current understanding of the nature of costs and benefits involved in source-selection decisions, and strongly indicated that a personalized approach is needed for information services and information systems to provide effective access to health information sources for consumers. Univ Texas Austin, Sch Informat, Austin, TX 78701 USA Zhang, Y (reprint author), Univ Texas Austin, Sch Informat, 1616 Guadalupe, Austin, TX 78701 USA. yanz@ischool.utexas.edu Agarwal NK, 2011, J AM SOC INF SCI TEC, V62, P1087, DOI 10.1002/asi.21513; ASHFORD SJ, 1986, ACAD MANAGE J, V29, P465, DOI 10.2307/256219; Atkinson NL, 2009, J MED INTERNET RES, V11, DOI 10.2196/jmir.1035; Baker L, 2003, JAMA-J AM MED ASSOC, V289, P2400, DOI 10.1001/jama.289.18.2400; Baron J., 2006, THINKING DECIDING; BELKIN NJ, 1983, J INFORM SCI, V5, P153; BELKIN NJ, 1982, J DOC, V38, P61, DOI 10.1108/eb026722; Borlund P, 2003, J AM SOC INF SCI TEC, V54, P913, DOI 10.1002/asi.10286; Brunswik E., 1952, CONCEPTUAL FRAMEWORK; BYSTROM K, 1995, INFORM PROCESS MANAG, V31, P191, DOI 10.1016/0306-4573(94)00041-Z; Cangelosi J D Jr, 1994, Health Mark Q, V12, P23, DOI 10.1300/J026v12n01_04; Case D. O., 2002, LOOKING INFORM SURVE; Case DO, 2005, J MED LIBR ASSOC, V93, P353; Case DO, 2004, J AM SOC INF SCI TEC, V55, P660, DOI 10.1002/asi.20000; Charmaz K., 2006, CONSTRUCTING GROUNDE; Cline RJW, 2001, HEALTH EDUC RES, V16, P671, DOI 10.1093/her/16.6.671; Courtright C, 2007, ANNU REV INFORM SCI, V41, P273, DOI 10.1002/aris.2007.1440410113; DAVIS FD, 1989, MIS QUART, V13, P319, DOI 10.2307/249008; Dervin B., 1983, INT COMM ASS ANN M D; Dervin B., 1982, COMMUNICATION YB, V5, P807; Dickerson S, 2004, J AM MED INFORM ASSN, V11, P499, DOI 10.1197/jamia.M1460; Dolan G., 2003, INT J CONSUM STUD, V27, P241, DOI 10.1046/j.1470-6431.2003.00308_33.x; Escoffery C, 2005, J AM COLL HEALTH, V53, P183, DOI 10.3200/JACH.53.4.183-188; Eysenbach G, 2002, JAMA-J AM MED ASSOC, V287, P2691, DOI 10.1001/jama.287.20.2691; Eysenbach G, 2000, BRIT MED J, V320, P1713, DOI 10.1136/bmj.320.7251.1713; Eysenbach G, 2004, BRIT MED J, V328, P1166, DOI 10.1136/bmj.328.7449.1166; Eysenbach G, 2002, BRIT MED J, V324, P573, DOI 10.1136/bmj.324.7337.573; Fidel R, 2004, INFORM PROCESS MANAG, V40, P563, DOI 10.1016/S0306-4573(03)00003-7; FLANAGAN JC, 1954, PSYCHOL BULL, V51, P327, DOI 10.1037/h0061470; Fox S., 2009, SOCIAL LIFE HLTH INF; Fox S., 2010, CHRONIC DIS INTERNET; Geana MV, 2011, J HEALTH COMMUN, V16, P583, DOI 10.1080/10810730.2011.551992; GERSTBER.PG, 1968, J APPL PSYCHOL, V52, P272, DOI 10.1037/h0026041; Glaser BG, 1967, DISCOVERY GROUNDED T; Gollop CJ, 1997, B MED LIBR ASSOC, V85, P141; Gray NJ, 2002, J MED SYST, V26, P545, DOI 10.1023/A:1020296710179; Griffiths KM, 2005, J MED INTERNET RES, V7, P53, DOI 10.2196/jmir.7.5.e55; Han JY, 2010, J COMPUT-MEDIAT COMM, V15, P367, DOI 10.1111/j.1083-6101.2010.01508.x; Hesse BW, 2005, ARCH INTERN MED, V165, P2618, DOI 10.1001/archinte.165.22.2618; Hogarth R., 1987, JUDGEMENT CHOICE; Ingwersen P., 2005, INFORM RETRIEVAL SER; Johnson J. D., 1997, CANC RELATED INFORM; Johnson J D, 1991, J Health Care Mark, V11, P37; Kealey E, 2010, J HEALTH COMMUN, V15, P236, DOI 10.1080/10810730.2010.522693; Kim P, 1999, BRIT MED J, V318, P647; Kuhlthau CC, 1999, J AM SOC INFORM SCI, V50, P399, DOI 10.1002/(SICI)1097-4571(1999)50:5<399::AID-ASI3>3.0.CO;2-L; Laurent MR, 2009, J AM MED INFORM ASSN, V16, P471, DOI 10.1197/jamia.M3059; Lemire M, 2008, INT J MED INFORM, V77, P723, DOI 10.1016/j.ijmedinf.2008.03.002; Lenz E R, 1984, ANS Adv Nurs Sci, V6, P59; MANFREDI C, 1993, CANCER, V71, P1326, DOI 10.1002/1097-0142(19930215)71:4<1326::AID-CNCR2820710426>3.0.CO;2-K; Marchionini G., 1997, INFORM SEEKING ELECT; McKenzie PJ, 2003, J DOC, V59, P19, DOI 10.1108/00220410310457993; Mead Nicola, 2003, J Health Serv Res Policy, V8, P33, DOI 10.1258/13558190360468209; Morahan-Martin JM, 2004, CYBERPSYCHOL BEHAV, V7, P497, DOI 10.1089/cpb.2004.7.497; Morrison EW, 2000, J MANAGE, V26, P119, DOI 10.1016/S0149-2063(99)00040-9; National Network of Libraries of Medicine (NNLM), 2012, HLTH LIT; Newman M. W., 2011, P ACM 2011 C COMP SU, P341; O'Keefe D. J., 2002, PERSUASION THEORY RE; OREILLY CA, 1982, ACAD MANAGE J, V25, P756, DOI 10.2307/256097; Oh S., 2012, 74 ANN C AM SOC INF; O'Malley AS, 1999, AM J PREV MED, V17, P198, DOI 10.1016/S0749-3797(99)00067-7; Pennbridge J, 1999, WESTERN J MED, V171, P302; Purcell GP, 2002, BRIT MED J, V324, P557, DOI 10.1136/bmj.324.7337.557; Rieh S.Y., 2008, DIGITAL MEDIA YOUTH, P49; Rieh SY, 2002, J AM SOC INF SCI TEC, V53, P145, DOI 10.1002/asi.10017.abs; Rutten LJF, 2005, PATIENT EDUC COUNS, V57, P250, DOI 10.1016/j.pec.2004.06.006; Saracevic T, 1997, P ASIS ANNU MEET, V34, P313; Saracevic T, 2007, J AM SOC INF SCI TEC, V58, P1915, DOI 10.1002/asi.20682; Savolainen R, 2006, LIBR INFORM SCI RES, V28, P110, DOI 10.1016/j.lisr.2005.11.001; Silberg WM, 1997, JAMA-J AM MED ASSOC, V277, P1244, DOI 10.1001/jama.277.15.1244; Sillence E., 2004, P SIGCHI C HUM FACT, P663, DOI 10.1145/985692.985776; Smith D., 2011, J COMMUNICATION HEAL, V4, P200; Sonnenwald D., 2001, 2001 ASS LIB INF SCI; Spink A, 2001, LIBR INFORM SCI RES, V23, P45, DOI 10.1016/S0740-8188(00)00067-0; Stanovich K. E., 1999, WHO IS RATIONAL STUD; Toms Elaine G, 2007, Health Informatics J, V13, P223, DOI 10.1177/1460458207079901; Wang PL, 1998, J AM SOC INFORM SCI, V49, P115, DOI 10.1002/(SICI)1097-4571(1998)49:2<115::AID-ASI3>3.0.CO;2-1; Warner D, 2004, J AM SOC INF SCI TEC, V55, P709, DOI 10.1002/asi.20016; Wildemuth B. M., 2009, APPL SOCIAL RES METH; Wildemuth BM, 2004, J AM SOC INF SCI TEC, V55, P246, DOI 10.1002/asi.10367; Wilson P, 1983, 2 HAND KNOWLEDGE INQ; WILSON TD, 1981, J DOC, V37, P3, DOI 10.1108/eb026702; Xu YJ, 2006, J AM SOC INF SCI TEC, V57, P1666, DOI 10.1002/asi.20339; Yeung TM, 2012, DIS COLON RECTUM, V55, P85, DOI 10.1097/DCR.0b013e3182351eec; Zhang Y., 2009, APPL SOCIAL RES METH, P308; Zhang Y., 2012, 74 ANN C AM SOC INF; Zhang YM, 2012, INFORM RES, V871, P17 87 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH MAY 2014 65 5 911 927 10.1002/asi.23023 17 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FN WOS:000335583800004 J Liu, XZ; Qin, J Liu, Xiaozhong; Qin, Jian An Interactive Metadata Model for Structural, Descriptive, and Referential Representation of Scholarly Output JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article ARTICLES The scientific metadata model proposed in this article encompasses both classical descriptive metadata such as those defined in the Dublin Core Metadata Element Set (DC) and the innovative structural and referential metadata properties that go beyond the classical model. Structural metadata capture the structural vocabulary in research publications; referential metadata include not only citations but also data about other types of scholarly output that is based on or related to the same publication. The article describes the structural, descriptive, and referential (SDR) elements of the metadata model and explains the underlying assumptions and justifications for each major component in the model. ScholarWiki, an experimental system developed as a proof of concept, was built over the wiki platform to allow user interaction with the metadata and the editing, deleting, and adding of metadata. By allowing and encouraging scholars (both as authors and as users) to participate in the knowledge and metadata editing and enhancing process, the larger community will benefit from more accurate and effective information retrieval. The ScholarWiki system utilizes machine-learning techniques that can automatically produce self-enhanced metadata by learning from the structural metadata that scholars contribute, which will add intelligence to enhance and update automatically the publication of metadata Wiki pages. [Liu, Xiaozhong] Indiana Univ, Sch Informat & Comp, Dept Informat & Lib Sci, Bloomington, IN 47405 USA; [Qin, Jian] Syracuse Univ, Sch Informat Studies, Syracuse, NY 13244 USA Liu, XZ (reprint author), Indiana Univ, Sch Informat & Comp, Dept Informat & Lib Sci, 1320 East 10th St, Bloomington, IN 47405 USA. liu237@indiana.edu; jqin@syr.edu ALLEN B, 1994, SOC STUD SCI, V24, P279, DOI 10.1177/030631279402400204; Bide M., 2000, INDECS METADATA FRAM; Dabrowski M., 2009, SEMANTIC DIGITAL LIB, P103, DOI 10.1007/978-3-540-85434-0_8; Demner-Fushman D, 2007, COMPUT LINGUIST, V33, P63, DOI 10.1162/coli.2007.33.1.63; Diekema AR, 2005, Proceedings of the 5th ACM/IEEE Joint Conference on Digital Libraries, Proceedings, P223, DOI 10.1145/1065385.1065436; Duval E., 2002, D LIB MAGAZINE, V8; Evans JA, 2011, SCIENCE, V331, P721, DOI 10.1126/science.1201765; Giasson F., 2009, BIBLIO ONTOLOGY SPEC; Greenberg J., 2001, P INT C DUBL COR MET, V2, P38; Guarino N, 1997, INT J HUM-COMPUT ST, V46, P293, DOI 10.1006/ijhc.1996.0091; Guo C., 2012, P 74 AM SOC INF SCI; Guo Y., 2010, P 2010 WORKSH BIOM N; Handschuh S., 2002, P 13 INT C KNOWL ENG; Hersh W., 2004, 14 TEXT RETRIEVAL C; Hirohata K., 2008, P 3 INT JOINT C NAT, P381; Hook K., 1997, FLEX HYP WORKSH HELD; Hyland Ken, 1998, HEDGING SCI RES ARTI; IFLA Section on Cataloguing, 1998, FUNCT REQ BIBL REC F; Lagoze C., 2001, JCDL, P54; Lagoze C., 2001, J DIGITAL INFORM, V2; Liakata M., 2010, P 7 INT C LANG RES E; Liddy E. D., 1994, P 2 TEXT RETR C TREC; Lin J., 2006, Proceedings of the Twenty-Ninth Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, DOI 10.1145/1148170.1148191; Lin J., 2006, P HLT NAACL 2006 WOR, P65; Liu X., 2013, J AM SOC IN IN PRESS; Liu XZ, 2013, J AM SOC INF SCI TEC, V64, P771, DOI 10.1002/asi.22744; Liu XZ, 2013, J AM SOC INF SCI TEC, V64, P1707, DOI 10.1002/asi.22851; Markines B., 2009, P 18 INT C WORLD WID, P641, DOI 10.1145/1526709.1526796; McCray AT, 2001, ST HEAL T, V84, P216; McKnight L., 2003, P 2003 ANN S AM MED, P440; Milstead J., 1999, METADATA CATALOGING; Mizuta Y, 2006, INT J MED INFORM, V75, P468, DOI 10.1016/j.ijmedinf.2005.06.013; Domingue J., 1999, Knowledge Acquisition, Modeling and Management. 11th European Workshop, EKAW'99. Proceedings; Tonkin Emma, 2008, Joint Conference on Digital Libraries (JCDL 2008), DOI 10.1145/1378889.1378917; Nilsson M., 2008, SINGAPORE FRAMEWORK; Powell A., 2007, DCMI ABSTRACT MODEL; Prado J. C., 2006, SERIE COMUNICACAO IN, V4, P143; Priss UE, 1998, LANG SPEECH & COMMUN, P179; Qin J., 2008, METADATA; Goasdoue F., 1999, Knowledge Acquisition, Modeling and Management. 11th European Workshop, EKAW'99. Proceedings; Rodriguez MA, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1462198.1462199; Shotton D., 2009, BIOONTOLOGIES 2009; Tbahriti I, 2006, INT J MED INFORM, V75, P488, DOI 10.1016/j.ijmedinf.2005.06.007; Teufel S, 2009, P 2009 C EMP METH NA, V3, P1493, DOI 10.3115/1699648.1699696; Teufel S, 2002, COMPUT LINGUIST, V28, P409, DOI 10.1162/089120102762671936; Wade V., 2011, HT 11 P 22 ACM C HYP, P37; Weinstein P. C., 1998, Digital 98 Libraries. Third ACM Conference on Digital Libraries 47 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH MAY 2014 65 5 964 983 10.1002/asi.23007 20 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FN WOS:000335583800007 J Zhao, DZ; Strotmann, A Zhao, Dangzhi; Strotmann, Andreas The Knowledge Base and Research Front of Information Science 2006-2010: An Author Cocitation and Bibliographic Coupling Analysis JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article LIBRARY This study continues a long history of author cocitation analysis (and more recently, author bibliographic coupling analysis) of the intellectual structure of information science (IS) into the time period 2006 to 2010 (IS 2006-2010). We find that web technologies continue to drive developments, especially at the research front, although perhaps more indirectly than before. A broadening of perspectives is visible in IS 2006-2010, where network science becomes influential and where full-text analysis methods complement traditional computer science influences. Research in the areas of the h-index and mapping of science appears to have been highlights of IS 2006-2011. This study tests and confirms a forecast made previously by comparing knowledge-base and research-front findings for IS 2001-2005, which expected both the information retrieval (IR) systems and webometrics specialties to shrink in 2006 to 2010. A corresponding comparison of the knowledge base and research front of IS 2006-2010 suggests a continuing decline of the IR systems specialty in the near future, but also a considerable (re) growth of the webometrics area after a period of decline from 2001 to 2005 and 2006 to 2010, with the latter due perhaps in part to its contribution to an emerging web science. [Zhao, Dangzhi] Univ Alberta, Sch Lib & Informat Studies, Edmonton, AB T6G 2J4, Canada; [Strotmann, Andreas] GESIS Leibniz Inst Social Sci, D-50667 Cologne, Germany Zhao, DZ (reprint author), Univ Alberta, Sch Lib & Informat Studies, Edmonton, AB T6G 2J4, Canada. dzhao@ualberta.ca; andreas.strotmann@gesis.org Bates M. J., 2010, ENCY LIB INFORM SCI; Chen CM, 2010, J AM SOC INF SCI TEC, V61, P1386, DOI 10.1002/asi.21309; Cornelius B, 2006, ENTREP THEORY PRACT, V30, P375, DOI 10.1111/j.1540-6520.2006.00125.x; Finlay C. S., 2012, LIB Q, V82, P29; Hair J. F., 1998, MULTIVARIATE DATA AN; Hirsch JE, 2005, P NATL ACAD SCI USA, V102, P16569, DOI 10.1073/pnas.0507655102; Klavans R, 2011, J AM SOC INF SCI TEC, V62, P1, DOI 10.1002/asi.21444; Lariviere V, 2012, J AM SOC INF SCI TEC, V63, P997, DOI 10.1002/asi.22645; Lu K, 2012, J AM SOC INF SCI TEC, V63, P1973, DOI 10.1002/asi.22628; MCCAIN KW, 1990, J AM SOC INFORM SCI, V41, P433, DOI 10.1002/(SICI)1097-4571(199009)41:6<433::AID-ASI11>3.0.CO;2-Q; Milojevic S, 2011, J AM SOC INF SCI TEC, V62, P1933, DOI 10.1002/asi.21602; PERSSON O, 1994, J AM SOC INFORM SCI, V45, P31, DOI 10.1002/(SICI)1097-4571(199401)45:1<31::AID-ASI4>3.0.CO;2-G; Rousseau R, 2013, J INFORMETR, V7, P294, DOI 10.1016/j.joi.2012.11.012; Saracevic T., 2010, ENCY LIB INFORM SCI, P2570; Strotmann A, 2012, J AM SOC INF SCI TEC, V63, P1820, DOI 10.1002/asi.22695; Sugimoto CR, 2011, J AM SOC INF SCI TEC, V62, P185, DOI 10.1002/asi.21435; White H. D., 1990, SCHOLARLY COMMUNICAT, P84; White HD, 1998, J AM SOC INFORM SCI, V49, P327, DOI 10.1002/(SICI)1097-4571(19980401)49:4<327::AID-ASI4>3.0.CO;2-W; Yan EJ, 2012, J AM SOC INF SCI TEC, V63, P1313, DOI 10.1002/asi.22680; Zhao D., 2008, J AM SOC INFORM SCI, V59, P2070; Zhao DZ, 2008, J AM SOC INF SCI TEC, V59, P916, DOI 10.1002/asi.20799; Zhao DZ, 2011, J AM SOC INF SCI TEC, V62, P654, DOI 10.1002/asi.21495; Zhao DZ, 2008, J INFORMETR, V2, P229, DOI 10.1016/j.joi.2008.05.004; Zhao DZ, 2011, SCIENTOMETRICS, V87, P115, DOI 10.1007/s11192-010-0317-2 24 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH MAY 2014 65 5 995 1006 10.1002/asi.23027 12 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FN WOS:000335583800009 J Zhang, Y; Broussard, R; Ke, WM; Gong, XM Zhang, Yan; Broussard, Ramona; Ke, Weimao; Gong, Xuemei Evaluation of a Scatter/Gather Interface for Supporting Distinct Health Information Search Tasks JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY English Article PEOPLES MENTAL MODELS; WEB; RETRIEVAL; SEEKING; MEDLINEPLUS; DESIGN; USERS Web search engines are important gateways for users to access health information. This study explored whether a search interface based on the Bing API and enabled by Scatter/Gather, a well-known document-clustering technique, can improve health information searches. Forty participants without medical backgrounds were randomly assigned to two interfaces: a baseline interface that resembles typical web search engines and a Scatter/Gather interface. Both groups performed two lookup and two exploratory health-related tasks. It was found that the baseline group was more likely to rephrase queries and less likely to access general-purpose sites than the Scatter/Gather group when completing exploratory tasks. Otherwise, the two groups did not differ in behavior and task performance, with participants in the Scatter/Gather group largely overlooking the features (key words, clusters, and the recluster function) designed to facilitate the exploration of semantic relationships between information objects, a potentially useful means for users in the rather unfamiliar domain of health. The results suggest a strong effect of users' mental models of search on their use of search interfaces and a high cognitive cost associated with using the Scatter/Gather features. It follows that novel features of a search interface should not only be compatible with users' mental models but also provide sufficient affordance to inform users of how they can be used. Compared with the interface, tasks showed more significant impacts on search behavior. In future studies, more effort should be devoted to identify salient features of health-related information needs. [Zhang, Yan; Broussard, Ramona] Univ Texas Austin, Sch Informat, Austin, TX 78701 USA; [Ke, Weimao; Gong, Xuemei] Drexel Univ, Coll Informat Sci & Technol, Philadelphia, PA 19104 USA Zhang, Y (reprint author), Univ Texas Austin, Sch Informat, 1616 Guadalupe St, Austin, TX 78701 USA. yanz@ischool.utexas.edu; ramona@ischool.utexas.edu; wk@drexel.edu; xg45@drexel.edu Alumni Fellowship from the School of Information at the University of Texas at Austin We thank all of our participants for their valuable time and input. This work was partially supported by the Alumni Fellowship from the School of Information at the University of Texas at Austin. Arora NK, 2008, J GEN INTERN MED, V23, P223, DOI 10.1007/s11606-007-0406-y; Arthur D., 2007, P 18 ANN ACM SIAM S, V1, P1027, DOI DOI 10.1145/1283383.1283494; Baron J., 2000, THINKING DECIDING; Boden C., 2009, J CANADIAN HLTH LIB, V30, P75; Bundorf MK, 2006, HEALTH SERV RES, V41, P819, DOI 10.1111/j.1475-6773.2006.00510.x; Capra R, 2007, PROCEEDINGS OF THE 7TH ACM/IEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P442, DOI 10.1145/1255175.1255267; Carroll J. M., 1987, MENTAL MODELS HUMAN; Cartright M, 2011, P ACM SIGIR, P65; Cutting D. R., 1992, P 15 ANN INT ACM SIG, V1, P318; Dickson J., 1984, Cataloging & Classification Quarterly, V4, DOI 10.1300/J104v04n03_02; Dunne C, 2012, J AM SOC INF SCI TEC, V63, P2351, DOI 10.1002/asi.22652; Eysenbach G, 2002, BRIT MED J, V324, P573, DOI 10.1136/bmj.324.7337.573; Fogg B. J., 2001, P CHI 01 C HUM FACT, V3, P61, DOI DOI 10.1145/365024.365037]; Fox S., 2009, SOCIAL LIFE HLTH INF; Fox S., 2011, SOCIAL LIFE HLTH INF; Gong X., 2012, P AM SOC INFORM SCI; Gossen T., 2012, P S HUM COMP INT INF; Gwizdka J., 2009, INFORM RES, V14; Hall M., 2009, SIGKDD EXPLORATIONS, V11, P10, DOI DOI 10.1145/1656274.1656278; Hearst M., 1996, P 19 ANN INT ACM SIG, P76, DOI 10.1145/243199.243216; Hearst M. A., 2009, SEARCH USER INTERFAC; Jensen E. C., 2002, Proceedings of the Eleventh International Conference on Information and Knowledge Management. CIKM 2002; Johnson-Laird P., 1983, MENTAL MODELS; Ke W., 2008, P AM SOC INFORM SCI, V45, P1; Ke WM, 2009, PROCEEDINGS 32ND ANNUAL INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL, P19, DOI 10.1145/1571941.1571947; Keselman A, 2008, J AM MED INFORM ASSN, V15, P484, DOI 10.1197/jamia.M2449; Kules B, 2009, JCDL 09: PROCEEDINGS OF THE 2009 ACM/IEEE JOINT CONFERENCE ON DIGITAL LIBRARIES, P313; Kules B, 2008, INFORM PROCESS MANAG, V44, P463, DOI 10.1016/j.ipm.2007.07.014; Marchionini G., 2008, P WORKSH HUM COMP IN; Marchionini G, 1998, ANNU REV INFORM SCI, V33, P89; Marchionini G, 2006, COMMUN ACM, V49, P41, DOI 10.1145/1121949.1121979; Mu XM, 2011, J BIOMED INFORM, V44, P576, DOI 10.1016/j.jbi.2011.01.008; Nielsen J., 2005, MENTAL MODELS SEARCH; Norman DA, 1988, DESIGN EVERYDAY THIN; Pirolli P., 1996, P ACM SIGCHI C HUM F, P213, DOI 10.1145/238386.238489; Pratt W, 1997, J AM MED INFORM ASSN, P480; Rieh SY, 2006, INFORM PROCESS MANAG, V42, P751, DOI 10.1016/j.ipm.2005.05.005; Robertson S, 2004, J DOC, V60, P503, DOI [10.1108/00220410410560582, 10.1108/00220410560582]; Saracevic T, 1997, P ASIS ANNU MEET, V34, P313; Sillence E., 2004, P SIGCHI C HUM FACT, P663, DOI 10.1145/985692.985776; Spink Amanda, 2004, Health Info Libr J, V21, P44, DOI 10.1111/j.1471-1842.2004.00481.x; Tang R, 1999, J AM SOC INFORM SCI, V50, P254, DOI 10.1002/(SICI)1097-4571(1999)50:3<254::AID-ASI8>3.0.CO;2-Y; Toms E., 2008, HLTH INFORM J, V13, P223; UPTON GJG, 1992, J ROY STAT SOC A STA, V155, P395, DOI 10.2307/2982890; Vakkari P, 2003, ANNU REV INFORM SCI, V37, P413, DOI 10.1002/aris.1440370110; White RW, 2009, ACM T INFORM SYST, V27, DOI 10.1145/1629096.1629101; Wildemuth B., 2009, P 3 WORKSH HUM COMP; Wilson M, 2011, J PETROL, V52, P1, DOI 10.1093/petrology/egq092; Witten IH, 2011, MOR KAUF D, P1; Woodruff A, 2002, J AM SOC INF SCI TEC, V53, P172, DOI 10.1002/asi.10029; Zeng QT, 2006, J AM MED INFORM ASSN, V13, P80, DOI 10.1197/jamia.M1820; Zhang Y, 2013, LIBR INFORM SCI RES, V35, P159, DOI 10.1016/j.lisr.2012.11.004; Zhang Y., 2012, P ACM IHI 12, P641; Zhang Y, 2008, J AM SOC INF SCI TEC, V59, P2087, DOI 10.1002/asi.20915; Zhang Y, 2010, J AM SOC INF SCI TEC, V61, P2206, DOI 10.1002/asi.21406; Zhang Y., 2013, J AM SOC IN IN PRESS; Zhang Y, 2012, INFORM PROCESS MANAG, V48, P107, DOI 10.1016/j.ipm.2011.02.007; Zhang Y., 2011, P WORKSH HUM COMP IN; Zhang Y., 2012, P 75 ANN C AM SOC IN; Zhang Y., J AM SOC IN IN PRESS; Zickuhr K., 2010, GENERATIONS 2010; Zun Leslie S, 2011, West J Emerg Med, V12, P448, DOI 10.5811/westjem.2010.10.1607 62 0 0 WILEY-BLACKWELL HOBOKEN 111 RIVER ST, HOBOKEN 07030-5774, NJ USA 2330-1635 2330-1643 J ASSOC INF SCI TECH MAY 2014 65 5 1028 1041 10.1002/asi.23011 14 Computer Science, Information Systems; Information Science & Library Science Computer Science; Information Science & Library Science AG7FN WOS:000335583800012 J Shanks, MF; McGeown, WJ; Guerrini, C; Venneri, A Shanks, Michael F.; McGeown, William J.; Guerrini, Chiara; Venneri, Annalena Awareness and Confabulation NEUROPSYCHOLOGY English Article personal memory; autobiographical memory; mild cognitive impairment; Alzheimer's disease; deja vecu ALZHEIMERS-DISEASE; STRATEGIC RETRIEVAL; DEJA VECU; MEMORY; REALITY; DELUSION; DISTORTIONS; IMPAIRMENT; SYSTEM Objective: A single case study with control and normative data of a 74-year-old retired businessman with amnestic mild cognitive impairment, who had spontaneous confabulations concerning fantastic exploits and magical powers as well as deja vecu experiences. Methods and Results: His neuropsychological profile showed episodic memory impairment including deficits of recent episodic autobiographical memories and of recognition, but performance was within normal limits on tests assessing source memory for words, the ability to suppress irrelevant items on a continuous recognition memory task, and the detection of stimulus frequency. There were discrete impairments in an ad hoc test measuring his ability to detect and discriminate the source of a range of material including information derived from personal and public events, invented material, and episodes culled from his personal reading. Although his source memory for autobiographical information was normal, he attributed 20% of the invented material and personal readings and 15% of the public events either to his own experience or to that of someone he knew personally or to someone else. Conclusions: This evidence suggests that none of the current theoretical accounts of spontaneous confabulations is sufficiently explanatory. Instead, an argument is developed that both fantastic confabulation and deja vecu arose from a more fundamental disorder of awareness. [Shanks, Michael F.; Venneri, Annalena] Univ Sheffield, Dept Neurosci, Sheffield S5 7JT, S Yorkshire, England; [McGeown, William J.] Univ Strathclyde, Dept Psychol, Glasgow, Lanark, Scotland; [Guerrini, Chiara] Univ Hull, Dept Psychol, Kingston Upon Hull, Yorks, England; [Venneri, Annalena] IRCCS San Camillo Hosp Fdn, Venice, Italy Shanks, MF (reprint author), Univ Sheffield, Dept Neurosci, Unit Acad Psychiat, Longley Ctr, Norwood Grange Dr, Sheffield S5 7JT, S Yorkshire, England. m.f.shanks@sheffield.ac.uk MIUR The authors thank HF and his wife for their time and cooperation. MFS, WJMcG, and AV were employed by the University of Hull at the time when data collection for this study took place. This study was supported by a grant from MIUR to AV. BERLYNE N, 1972, BRIT J PSYCHIAT, V120, P31, DOI 10.1192/bjp.120.554.31; Burge T., 2010, ORIGINS OBJECTIVITY, DOI [10.1093/acprof:oso/9780199581405.001.0001, DOI 10.1093/ACPROF:OSO/9780199581405.001.0001]; Cooper JM, 2006, NEUROPSYCHOLOGIA, V44, P1697, DOI 10.1016/j.neuropsychologia.2006.03.029; CUMMINGS JL, 1994, NEUROLOGY, V44, P2308; Dalla Barba G, 1997, NEUROCASE, V3, P425, DOI 10.1093/neucas/3.6.425; Dalla Barba G, 1999, COGNITIVE NEUROPSYCH, V16, P385; DALLABARBA G, 2010, DELUSIONS CONFABULAT, V15, P95, DOI DOI 10.1080/13546800902758017; Fotopoulou A, 2007, NEUROPSYCHOLOGIA, V45, P2180, DOI 10.1016/j.neuropsychologia.2007.03.003; Fotopoulou A, 2004, NEUROPSYCHOLOGIA, V42, P727, DOI 10.1016/j.neuropsychologia.2003.11.008; Gilboa A, 2006, BRAIN, V129, P1399, DOI 10.1093/brain/awl093; Ivanoiu A, 2006, NEUROPSYCHOLOGIA, V44, P1936, DOI 10.1016/j.neuropsychologia.2006.01.030; Johnson MK, 1997, BRAIN COGNITION, V34, P189, DOI 10.1006/brcg.1997.0873; KOPELMAN MD, 1987, J NEUROL NEUROSUR PS, V50, P1482, DOI 10.1136/jnnp.50.11.1482; Kopelman M. D., 1990, AUTOBIOGRAPHICAL MEM; Kopelman MD, 2010, COGN NEUROPSYCHIATRY, V15, P14, DOI 10.1080/13546800902732830; Langdon R, 2010, COGN NEUROPSYCHIATRY, V15, P1, DOI 10.1080/13546800903519095; Lee E, 2007, J GERIATR PSYCH NEUR, V20, P34, DOI 10.1177/0891988706292760; Moscovitch M, 1997, NEUROPSYCHOLOGIA, V35, P1017, DOI 10.1016/S0028-3932(97)00028-6; Moulin CJA, 2005, NEUROPSYCHOLOGIA, V43, P1362, DOI 10.1016/j.neuropsychologia.2004.12.008; NELSON HE, 1976, CORTEX, V12, P313; O'Connor AR, 2010, COGN NEUROPSYCHIATRY, V15, P118, DOI 10.1080/13546800903113071; Petersen RC, 2001, ARCH NEUROL-CHICAGO, V58, P1985, DOI 10.1001/archneur.58.12.1985; Politis M, 2012, PSYCHOPATHOLOGY, V45, P337, DOI 10.1159/000337748; Schnider A, 2001, BRAIN RES REV, V36, P150, DOI 10.1016/S0165-0173(01)00090-X; Schnider A, 1999, NAT NEUROSCI, V2, P677, DOI 10.1038/10236; Schnider A, 2000, NEUROLOGY, V55, P74; Shanks Michael F, 2002, Cogn Neuropsychiatry, V7, P317, DOI 10.1080/13546800244000021; SNODGRASS JG, 1980, J EXP PSYCHOL-HUM L, V6, P174, DOI 10.1037/0278-7393.6.2.174; Sokal R., 1981, BIOMETRY PRINCIPLES; Turner M, 2010, COGN NEUROPSYCHIATRY, V15, P346, DOI 10.1080/13546800903441902; Turner MS, 2008, CORTEX, V44, P637, DOI 10.1016/j.cortex.2007.01.002; Venneri A., FUNCTIONAL NEUROANAT, P263; Venneri A, 2000, NEUROPSYCHOLOGIA, V38, P213, DOI 10.1016/S0028-3932(99)00061-5; Venneri A, 2004, NEUROPSYCHOLOGIA, V42, P230, DOI 10.1016/S0028-3932(03)00171-4; Venneri A., 2010, NEUROPSYCHOANALYSIS, V12, P185; VENNERI A, 1993, ADV BIOSCI, V87, P81 36 0 0 AMER PSYCHOLOGICAL ASSOC WASHINGTON 750 FIRST ST NE, WASHINGTON, DC 20002-4242 USA 0894-4105 1931-1559 NEUROPSYCHOLOGY Neuropsychology MAY 2014 28 3 406 414 10.1037/neu0000031 9 Psychology, Clinical; Neurosciences; Psychology Psychology; Neurosciences & Neurology AG3QH WOS:000335334000011 J Paesen, R; Sanen, K; Smisdom, N; Michiels, L; Ameloot, M Paesen, Rik; Sanen, Kathleen; Smisdom, Nick; Michiels, Luc; Ameloot, Marcel Polarization second harmonic generation by image correlation spectroscopy on collagen type I hydrogels ACTA BIOMATERIALIA English Article Image correlation spectroscopy; Polarization second harmonic generation; Collagen type I hydrogel; Tissue engineering FLUORESCENCE CORRELATION SPECTROSCOPY; MECHANICAL-PROPERTIES; EXTRACELLULAR-MATRIX; CELLS FEEL; MICROSCOPY; TISSUE Successful engineering of biomimetic tissue relies on an accurate quantification of the mechanical properties of the selected scaffold. To improve this quantification, typical bulk rheological measurements are often complemented with microscopic techniques, including label-free second harmonic generation (SHG) imaging. Image correlation spectroscopy (ICS) has been applied to obtain quantitative information from SHG images of fibrous scaffolds. However, the typical polarization SHG (P-SHG) effect, which partly defines the shape of the autocorrelation function (ACF), has never been taken into account. Here we propose a new and flexible model to reliably apply ICS to P-SHG images of fibrous structures. By starting from a limited number of straightforward assumptions and by taking into account the P-SHG effect, we were able to cope with the typically observed ACF particularities. Using simulated datasets, the resulting model was thoroughly evaluated and compared with models previously described in the literature. We showed that our new model has no restrictions concerning the fibre length for the density retrieval. For certain length ranges, the model can additionally be used to obtain the average fibre length and the P-SHG related non-zero susceptibility tensor element ratios. From experimental data on collagen type I hydrogels, values of SHG tensor element ratios and fibre thickness were determined which match values reported in the literature, thereby underpinning the validity and applicability of our new model. (C) 2014 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved. [Paesen, Rik; Sanen, Kathleen; Smisdom, Nick; Michiels, Luc; Ameloot, Marcel] Hasselt Univ, BIOMED, B-3590 Diepenbeek, Belgium Ameloot, M (reprint author), Hasselt Univ, BIOMED, Agoralaan, B-3590 Diepenbeek, Belgium. marcel.ameloot@uhasselt.be European Union; local governments; research institutes; SMEs; Federal Science Policy of Belgium [IAP-7/05]; FWO-onderzoeksgemeenschap "Scanning and Wide Field Microscopy of (Bio)-organic Systems; Province of Limburg (Belgium) within the tUL IMPULS FASE II program This research is part of the Interreg EMR IV-A project BioMIMedics (www.biomimedics.org) and is co-financed by the European Union, local governments, research institutes and SMEs. M.A. acknowledges the Federal Science Policy of Belgium (IAP-7/05), the support by the FWO-onderzoeksgemeenschap "Scanning and Wide Field Microscopy of (Bio)-organic Systems" and the Province of Limburg (Belgium) for the financial support within the tUL IMPULS FASE II program, allowing for the upgrading of the laser source used in this work. Abreu EL, 2010, J BIOMED MATER RES A, V93A, P150, DOI 10.1002/jbm.a.32508; Bayan C, 2009, J APPL PHYS, V105, DOI 10.1063/1.3116626; Carey SP, 2012, BIOMATERIALS, V33, P4157, DOI 10.1016/j.biomaterials.2012.02.029; Ghazaryan A, 2013, J BIOMED OPT, V18, DOI 10.1117/1.JBO.18.3.031105; Buxboim A, 2010, J CELL SCI, V123, P297, DOI 10.1242/jcs.041186; Discher DE, 2005, SCIENCE, V310, P1139, DOI 10.1126/science.1116995; Eichhorn SJ, 2005, J ROY SOC INTERFACE, V2, P309, DOI 10.1098/rsif.2005.0039; Friess W, 1998, EUR J PHARM BIOPHARM, V45, P113, DOI 10.1016/S0939-6411(98)00017-4; Hess ST, 2002, BIOPHYS J, V83, P2300; Hunt NC, 2010, BIOTECHNOL LETT, V32, P733, DOI 10.1007/s10529-010-0221-0; Kniazeva E, 2012, INTEGR BIOL-UK, V4, P431, DOI 10.1039/c2ib00120a; Kolin DL, 2007, CELL BIOCHEM BIOPHYS, V49, P141, DOI 10.1007/s12013-007-9000-5; KOPPEL DE, 1974, PHYS REV A, V10, P1938, DOI 10.1103/PhysRevA.10.1938; Lo CM, 2000, BIOPHYS J, V79, P144, DOI DOI 10.1016/S0006-3495(00)76279-5.PUBMED:10866943; Mir SM, 2012, BIOMED OPT EXPRESS, V3, P215, DOI 10.1364/BOE.3.000215; Psilodimitrakopoulos S, 2009, OPT EXPRESS, V17, P10168, DOI 10.1364/OE.17.010168; Raub CB, 2008, BIOPHYS J, V94, P2361, DOI 10.1529/biophysj.107.120006; Robertson C, 2012, J BIOMED OPT, V17, DOI 10.1117/1.JBO.17.8.080801; Stoller P, 2002, BIOPHYS J, V82, P3330; Thompson N., 2002, TOP FLUORESC SPECTRO, P337; Tiaho F, 2007, OPT EXPRESS, V15, P12286, DOI 10.1364/OE.15.012286; Tuer AE, 2011, J PHYS CHEM B, V115, P12759, DOI 10.1021/jp206308k; Ulrich TA, 2010, BIOMATERIALS, V31, P1875, DOI 10.1016/j.biomaterials.2009.10.047 23 0 0 ELSEVIER SCI LTD OXFORD THE BOULEVARD, LANGFORD LANE, KIDLINGTON, OXFORD OX5 1GB, OXON, ENGLAND 1742-7061 1878-7568 ACTA BIOMATER Acta Biomater. MAY 2014 10 5 2036 2042 10.1016/j.actbio.2014.01.011 7 Engineering, Biomedical; Materials Science, Biomaterials Engineering; Materials Science AG0HA WOS:000335095300025