Refereed Publications

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北大考研-经济学院研究生导师简介-秦雪征

爱考机构-北大考研-经济学院研究生导师简介-秦雪征秦雪征秦雪征/XuezhengQin经济学系副教授硕士生导师地址（Add）：北京大学经济学院320室，100871电话（Tel）：+86(10)62757237传真（Fax）：+86(10)62754237电邮（Email）：qin.econpku@接待学生日星期一下午14：00-17：00（请提前发邮件预约）教育背景/EDUCATION美国纽约州立大学布法罗分校经济学博士2009Ph.D.inEconomics,StateUniversityofNewYorkatBuffalo,2009美国纽约州立大学布法罗分校经济学硕士2005M.A.inEconomics,StateUniversityofNewYorkatBuffalo,2005北京大学经济学院经济学学士2002B.A.inEconomics,PekingUniversity,2002工作经历/EMPLOYMENT北京大学经济学院讲师（2009-2012）副教授（2012-至今）SchoolofEconomics,PekingUniversity,AssistantProfessor(2009-2012),AssociateProfessor(2012-pr esent)北京大学卫生经济与管理研究院研究员2009-至今HealthEconomicsandManagementInstitute,PekingUniversity,ResearchAssociate,2009-present美国纽约州立大学布法罗分校兼职讲师2007StateUniversityofNewYorkatBuffalo,AdjunctLecturer,2007美国纽约州人力资本与经济发展研究中心研究助理2006-2009 NewYorkStateCenterofExcellenceonHumanCapitalandEconomicDevelopment,ResearchAssistant, 2006-2009研究领域/RESEARCHINTERESTS卫生经济学，劳动经济学，应用计量经济学HealthEconomics,LaborEconomics,AppliedEconometrics讲授课程/TEACHING本科课程：应用经济计量，专业英语，经济学原理UndergraduateCourses:AppliedEconometrics,EnglishforEconomicsMajors,PrinciplesofEconomics 研究生课程：高级计量经济学，高级卫生经济学，当代西方经济理论前沿GraduateCourses:AdvancedEconometrics,AdvancedHealthEconomics,FrontiersofModernEconom icResearch经理人课程：管理经济学，中国经济转型，医疗政策与改革ExecutiveCourses:ManagerialEconomics,China’sEconomicTransition,HealthCarePolicyandReform学术论文发表/REFEREEDPUBLICATIONS秦雪征，尹志锋，周建波，孔欣欣：“国家科技计划与中小型企业创新：基于匹配模型的分析”，《管理世界》，2012年第4期。

外籍人才个人英文简历

外籍⼈才个⼈英⽂简历外籍⼈才个⼈英⽂简历stanford university, stanford, cam.s. degree in engineering economic systems and operations research in june 2000.ph.d. degree in management science and engineering june 2004.dissertation title: "multi-agent learning and coordination algorithms for distributed dynamic resource allocation." dissertation advisor: nicholas bambosmassachusetts institute of technology, cambridge, mab.s. degree in mathematics in june 1997.m.s. degree in systems science and control engineering from the department of electrical engineering and computer science in june 1998. masters thesis topic: context-sensitive planning for autonomous vehicles operating in complex, uncertain, and nonstationary environments.experiencesun microsystems laboratories, menlo park, caapril 2003 – present:conceiving, developing and implementing self-managing and self-optimizing capabilities in computer systems, covering domains such as: cache-aware thread scheduling and cpu power management, dynamic sharing of cpu/memory/bandwidth, dynamic data migration in distributed storage systems, dynamic job scheduling and job pricing in cloud computing, dynamic user migration in distributed virtual environments, etc.principal investigator for the adaptive optimization project since 2006.multiple patent applications filed, conference/journal papers published, multiple successful adaptive learning systems designed and implemented. the publicly available case studies are in the “technical reports” section of/people/vengerov/publications.html.intelligent inference systems corp., sunnyvale, ca research scientistapril 2002 – april 2003: started a new research initiative in applying the acfrl algorithm and the previously developed multi-agent coordination algorithms to power control in wireless networks. published several conference papers on this topic. results demonstrate an improvement by more than a factor of 2 in comparison with the algorithms used in is-95 andcdma2000 standards.april 2002 – april 2003: wrote a phase i sttr proposal to the office of naval research and received funding for the topic of “perception-based co-evolutionary reinforcement learning for uav sensor allocation.” developed theoretical algorithms and designed a practical implementation strategy, which demonstrated excellent results in a high-fidelity robotic simulator. published a conference paper.october 1998 – april 2002: wrote a proposal to the nasa program in thinking systems and received multi-year funding for the topic of cooperation and coordination in multi-agent systems. developed, ｅｖａｌuated, and published new reinforcement learning algorithms for dynamic resource allocation among distributed agents operating jointly in complex, uncertain, and nonstationary environments.fall 2000: developed a new algorithm for single-agent learning in noisy dynamic environments with delayed rewards: actor-critic fuzzy reinforcement learning (acfrl). published a conference and a journal paper with a convergence proof for acfrl. us patent (number 6,917,925) was granted for the acfrl algorithm on july 12, 2005.chaincast inc., san jose, caaug 2000 – oct 2000: conducted a survey of techniques for dynamic updating of multicasting trees and suggested a novel approach based on using multi-agent learning.nasa ames research center, moffet field, ca summer 1998: designed a framework for multiple agents operating in a complex,uncertain, and nonstationary environment. agents learn to improve their policies using fuzzy reinforcement learning.sri international, artificial intelligence center, menlo park, casummer 1998: developed a methodology for representing a replanning problem in the space of plans as a reinforcement learning problem.bear, stearns & co., inc. - proprietory trading department, new york, nysummer 1996, 1997: conducted a comprehensive study of time series forecasting models with neural networks. recommended a hybrid model combining best features of the existing models and implemented it in c++.summer 1995: developed a stock forecasting system based on conventional econometric techniques and implemented it in sas language. gained exposure to various proprietary trading models.alphatech, inc., burlington, mafeb 1997 - may 1997: developed an algorithm for optimal control of macroeconomic systems described by simultaneous-time equations and implemented it in matlab.arthur andersen, inc., boston, mafeb 1996 - may 1996: developed an internal system dynamics cashflow model of startup businesses. gained experience in management level client interactions and in project presentation skills.summer 1996: independently designed a game theoretic bid forecasting system in procurement auctions for a large construction company. the project involved extensive on-site client interactions during model development as well as a final presentation to the top level management.property & portfolio research, inc., boston, mafeb 1994 - may 1995: designed a mortgage portfolio analysis model and implemented it in visual basic for excel. developed a methodology for grouping real estate time series using cluster and factor analyses in spss. designed an optimal investment strategy for a class of mortgage backed securities based on the efficient frontier characteristics. gained broad exposure to real estate markets and models.donaldson, lufkin & jenrette, inc. — pershing division, jersey city, njsummer 1994: developed a stock forecasting system based on technical analysis and economic indicators. developed a djia trading strategy based on s&p 500 futures and demonstrated its profitability.mit laboratory for information and decision systems, cambridge, maaug 1993 - may 1994: developed a trading strategy for us treasury bonds based on multi-resolution wavelet analysis. demonstrated its profitability as compared to the conventional moving average models.programmingc++, java, matlab; various packages for statistics, neural networks and system dynamics.publicationspublished 13 papers in refereed conferences, 8 journal papers, 1 book chapter. the complete list, including technical reports, is available at /people/vengerov/publications.html.patentsfour patents granted, 10 patent applications are currently under review at the us patent bureau.personalunited states citizen. fluent in russian and english. black belt and instructor in tae kwon do.last ｕｐｄａｔｅd 5/26/2009david vengerov【外籍⼈才个⼈英⽂简历】相关⽂章：1.2.3.4.5.6. 7. 8. 9.。

翻译学国际顶级权威期刊.附官方网址

翻译学国际顶级权威期刊Babel (International Journal of Traduction –Quarterly journal devoted to information, development and research in the field of translation and interpreting Babel is a scholarly journal designed primarily for translators and interpreters, yet of interest also for the non-specialist concerned with current issues and events in the field of translation.Babel includes articles on translation theory and practice, as well as discussions of the legal, financial and social aspects of the translator’s profession; it reports on new methods of translating, such as machine-aided translation, the use of computerized dictionaries or word banks; it also focuses on schools, special courses, degrees, and prizes for translators. An established publication, Babel will appeal to all those who make translation their business.Contributions are written in French and English and occasionally in German, Italian and Russian.Babel is published for the Federation of Translators (FIT)./cgi-bin/t_seriesview.cgi?series=BabelMeta deals with all aspects of translation and interpretation: translation studies (theories of translation), teaching translation, interpretation research, stylistics, comparative terminological studies, computer-assisted translation (machine translation), documentation, etc. While aimed particularly at translators, interpreters and terminologists, the publication addresses everyone interested in language phenomena./revue/meta/Perspectives: Studies in Translatology encourages studies of all types of interlingual transmission, such as translation, interpreting, subtitling etc.The emphasis lies on analyses of authentic translation work, translation practices, procedures and strategies. Based on real-life examples, studies in the journal place their findings in an international perspective from a practical, theoretical or pedagogical angle in order to address important issues in the craft, the methods and the results of translation studies worldwide.The journal is published quarterly, each issue consisting of approximately 80 pages. The language of publication is English although the issues discussed involve all languages and language pairs./multi/journals/journals_pst.aspTarget promotes the scholarly study of translational phenomena from a thoroughly interdisciplinary and international point of view. Rather than reducing research on translation to the practical questions asked by translators, their committers or their audience, the aim is to examine the role of translation in communication in general, with emphasis on cultural situations and theoretical, methodological and didactic matters. Attention is given to the relationship between translation and thesocietal organisation of communication. Target provides a forum for innovative approaches to translation. It publishes original studies on theoretical, methodological and descriptive-explanatory nature into translation problems and corpora, reflecting various socio-cultural approaches. The review section discusses the most important publications in the field in order to reflect the evolution of the discipline./cgi-bin/t_seriesview.cgi?series=TargetThe Translator is a refereed international journal that publishes articles on a variety of issues related to translation and interpreting as acts of intercultural communication. It aims to provide a meeting point for existing as well as future approaches and to stimulate interaction between various groups who share a common concern for translation as a profession and translation studies as a discipline. The Translator puts equal emphasis on rigour and readability and is not restricted in scope to any particular school of thought or academic group.https:///tsa/journal/1/Translation Review, started in 1978, is published three times a year. The Review is unique in the English-speaking world. While many literary journals publish translations of the works of international authors in English translation, Translation Review focuses on the theoretical and critical aspects of transplanting a literary text from one culture into another. The pages of Translation Review present in-depth interviews with translators; articles that deal with the evaluation of existing translations; profiles on small, commercial and university publishers of foreign literature in translation; comparative studies of multiple translations into English of the same work; investigations of methodologies to develop translation workshops and courses in literary translations; and information concerning ongoing research in translation studies in the United States and abroad.Through Translation Review, translators have a forum to talk about the reconstruction of the translation process to give readers a sense of the tremendous difficulties involved in transplanting a text from a foreign culture into English. Many considerations that illuminate the many ways languages interpret the world. Thus, the practice of translation can also be considered an important methodological tool to initiate and promote interdisciplinary thinking. Translation Review serves as a major critical and scholarly journal to facilitate cross-cultural communication through the refined art and craft of literary translations.Translators and scholars who are interested in contributing to Translation Review should contact the editor, Rainer Schulte. We are looking for articles and essays that deal with the reconstruction of the translation process. We are particularly interested in thoughtful reviews of significant new translations into English. Furthermore, we would like to expand studies that deal with the anthropological and cultural aspects of translation. Articles focusing on the practical implications of translation for the teaching of literature and the humanities are of particular interest to the editors. /research/cts/tr/翻译季刊Translation Quarterly, published four times a year, is the official publication of the Hong Kong Translation Society. The first of its kind in Hong Kong, the journal aims at providing a forum for the exchange of views and ideas concerning translation both as a professional activity and a field for scholarly investigation. Its first issue appeared in 1995, and since that time it has published articles, book reviews, articles and brief notices by over seventy translation scholars, teachers and researchers. Besides its bilingual format, the journal is also characterized by a concern with the special nature of translation as it is practiced and studied in Hong Kong..hk/cgi-bin/pub.plRenditions, published by the Research Centre for Translation, is the leading international journal of Chinese literature in English translation. First published in 1973, its issues cover over 2000 years of Chinese literature, from classical works of poetry, prose and fiction to their contemporary counterparts, as well as articles on art, Chinese studies and translation studies./renditions/。

北大考研-光华管理学院研究生导师简介-ZHOU,Lian

爱考机构-北大考研-光华管理学院研究生导师简介-ZHOU,LianZHOU,LianDepartmentChairAppliedEconomicsProfessorTEL：Email：zhoula@CVinChineseCVinEnglishBiographyPublicationsCurrentResearchTeachingLi-AnZhoureceivedhisBAandMAinEconomicsatPekingUniversity,andhisPh.D.inEconomicsatStan fordUniversity.HeservesasProfessorofEconomicsandChairofAppliedEconomicsDepartmentatGuan ghuaSchoolofManagement,PekingUniversity.HeisassociatedirectorandresearchfellowofMirrlessIns tituteofEconomicResearch(IEPR).HeistheauthorofthebookIncentivesandGovernance:China’sLocalGovernments(CengageLearning,2010),andco-authorofover30academicpapersinpoliticalecon omy,healtheconomics,andeconomicdevelopmentwhichhavebeenpublishedintheleadinginternational anddomesticjournalsofeconomicsandmanagement,suchasReviewofEconomicsandStatistics,Journal ofPublicEconomics,JournalofHealthEconomics,andStrategicManagementJournal.ResearchAreaspoliticaleconomyindustrialorganizationdevelopmentandtransitionchineseeconomyEducation2002Ph.D.EconomicsStanfordUniversity1991MAEconomicsPekingUniversity1988BAInternationalEconomicsPekingUniversityProfessionalExperiencesJuly2006-January2011AssociateChair,DepartmentofAppliedEconomics,GuanghuaSchoolofManagement,PekingUniversit yJanuary2011-presentChair,DepartmentofAppliedEconomics,GuanghuaSchoolofManagement,PekingUniversityAugust2006-presentAssociateDirector,InstituteofEconomicPolicyResearch(IEPR),PekingUniversityPUBLICATIONSINREFEREEDJOURNALS(ENGLISH) [13]"FamilyTiesandOrganizationalDesign:EvidencefromChinesePrivateFirms"(withHongbinCai,H ongbinLi,andAlbertPark),forthcoming,ReviewofEconomicsandStatistics[12]“ReturneesVersusLocals:WhoPerformsBetterinChina’sTechnologyEntrepreneurship?”(withHaiyangLi,YanZhang,YuLi,andWeiyingZhang),StrategicEntrepreneurshipJournal6:257-272,2 012[11]."Intra-IndustryKnowledgeSpilloversfromForeignDirectInvestmentinR&D:EvidencefromChin a's'SiliconValley'"(withYasuyukiTodoandWeiyingZhang),ReviewofDevelopmentEconomics15(3), 569-585,2011.[10]."FDISpilloversinanEmergingMarket:TheRoleofForeignFirms'CountryOriginDiversityandDo mesticFirms’AbsorptiveCapacity"(withAntheaZhang,HaiyangLiandYuLi),StrategicManagementJournal31:969–989,2010.[9]."IncomeandConsumptionInequalityinUrbanChina:1992-2003"(withHongbinCaiandYuyuChen), EconomicDevelopmentandCulturalChange58(3):385-413,2010,leadarticle. [8]."Incentives,Equality,andContractRenegotiations:TheoryandEvidenceintheChineseBankingIndu stry"(withHongbinCaiandHongbinLi),JournalofIndustrialEconomics58(1):156-189,2010. [7]."PoliticalConnections,FinancingandFirmPerformance:EvidencefromChinesePrivateEntreprene urs"(withHongbinLi,LingshengMeng,andQianWang),JournalofDevelopmentEconomics87:283-29 9,2008. ListedasoneoftheMostCitedJournalofDevelopmentEconomicsArticlessince2007withtherankof13th outofTop25,2012[6]."TheLong-TermHealthandEconomicConsequencesofthe1959-61FamineinChina"(withYuyuCh en),JournalofHealthEconomics26:659-681,2007,leadarticle. ListedasoneoftheMostCitedJournalofHealthEconomicsArticlessince2007withtherankof11thoutofT op25,2012[5]“IncentiveContractsandBankPerformance:EvidencefromtheBankingIndustryinRuralChina"(with HongbinLiandScottRozelle),EconomicsofTransition15(1):109-124,2007. [4]"RelativePerformanceEvaluationandtheTurnoverofProvincialLeadersinChina"(withYeChenand HongbinLi),EconomicsLetters88:421-425,2005.[3]“PoliticalTurnoverandEconomicPerformance:TheIncentiveRoleofPersonnelControlinChina”(withHongbinLi),JournalofPublicEconomics89:1743-1762,2005.Reprintedin"GoverningRapidGro wthinChina:EquityandInstitutions",editedbyRaviKanburandXiaoboZhang,2009,LondonandNewYo rk:Routledge. ListedasoneoftheMostCitedJournalofPublicEconomicsArticlessince2005withtherankof3rdoutofTo p10,2010.[2]"ParentalChildcareandChildren’sEducationalAttainment:EvidencefromChina"(withHongbinLi,XianguoYao,andJunshengZhang),A ppliedEconomics,37(18):2067-2076,2005.[1]“HowPrudentAreCommunityRepresentativeConsumers?”(withYuyuChen)TopicsinMacroeconomics:Vol.3:No.1,Article4.2003.WORKINGPAPERS [1]."TheEffectofMicroinsuranceonEconomicActivities:EvidencefromaRandomizedNaturalFieldEx periment"(withHongbinCai,YuyuChenandHanmingFang),R&RatReviewofEconomicsandStatistics [2]“HowtoPromoteTrust:TheoryandEvidencefromChina"(withHongbinCai,GingerZ.Jin,andChongL iu),submitted[3]“ChildHealthandtheIncomeGradient:EvidencefromChina”(withYiChenandXiaoyanLei),submitted[4]“DoMultinationals'R&DActivitiesStimulateIndigenousEntrepreneurship?EvidencefromChina's ‘SiliconValley’"NBERWorkingPaper(withHongbinCaiandYasuyukiTodo) [5]."InstitutionalEmbeddednessandStrategicChoice:SurvivalofTechnologyVenturesinChina'sTransitionEconomy:1995-2002"(withHaiyangLiandWeiyingZhang)[6]“TheInsuranceRoleofRoscasinthePresenceofCreditMarkets:TheoryandEvidence"(withHanmingF angandRongzhuKe)[7]"TournamentwithTargets:TheoryandEvidencefromChina"(withXingLi,ChongLiu,andXiWeng)REFEREESERVICESJournalofPublicEconomics,EconomicJournal,JournalofHealthEconomics,WorldPolitics,Journalof ComparativeEconomics,EconomicDevelopmentandCulturalChange,EconomicsofTransition,Journ alofPolitics,ChineseEconomicReview,HealthEconomics2004-presentUndergraduateProgram–IntermediateMicroeconomics2002-2005GraduateProgram–AdvancedMicroeconomics2003-2006GraduateProgram–TopicsinAdvancedMicroeconomics2003-presentGraduateProgram–AppliedEconometrics2004-2007GraduateProgram–TopicsinIncentivesandOrganizations。

陈盈秀 (Ying-Hsiu Chen)

陳盈秀 (Ying-Hsiu Chen)財金系助理教授(Assistant Professor, Department of Finance)簡歷 (Curriculum Vitae)【學歷】Education淡江大學產業經濟學研究所博士【經歷】Professional Experience 元培科技大學財務金融學系助理教授 (2008/08~迄今)台灣經濟研究院研究二所副研究員 (2007/09~2008/07) 淡江大學經濟學系兼任講師 (2004/09~2006/06)【專長領域】ResearchInterests產業經濟學、計量經濟學、效率與生產力分析【任教科目】TeachingCourses經濟學、總體經濟學、貨幣銀行學、銀行管理、線性代數【聯絡資訊】Phone, Email & Location 元培科技大學財務金融學系30015新竹市元培街306 號電話: (03) 538-1183轉6847傳真: (03) 610-2367郵件:yhchen@.twDepartment of Information ManagementYuanpei UniversityNo. 306, Yuanpei Street, Hsin Chu, 30015, TaiwanTel: +886-3-538-1183 Ext.8647Fax: +886-3-610-2367E-mail: yhchen@.tw【論文著作】Publications （一）學術期刊論文（Refereed Papers）1. 黃台心、陳盈秀 (2005)，「應用三階段估計法探討台灣地區銀行業經濟效率」，貨幣市場，第9卷第4期，1-29頁。

2. 黃台心、陳盈秀與陳珮欣 (2007)，「台灣地區本國銀行業長期效率的動態分析」，經濟論文[TSSCI, Econ. Lit.]，第35卷第1期， 83-114頁。

3. Tai-Hsin Huang, Ying-Hsiu Chen (2009)，, “A study on long-run inefficiencylevels of a panel dynamic cost frontier under the framework of forward-looking rational expectations,” Journal of Banking and Finance [SSCI], Vol. 33, 3, pp.842-849.4. 黃台心、陳盈秀與王美惠 (2009)，「我國與東亞諸國總體生產效率與生產力之研究」，經濟論文叢刊 [TSSCI, Econ. Lit.]，第37卷第4期，379-414頁。

20种新闻学、传播学研究名刊

20种新闻学、传播学研究名刊根据世界新闻实验室(JournalismLab)的测评和统计，下面20种新闻学、传播学研究名刊值得订阅和学习。

1. 《哥伦比亚新闻评论》（美国－双月刊）Columbia journalism review主页是当前文章，但同时提供近3年的全部期刊内容，并可以检索；可供选择的第三个网址可以看到1994年至今的文章/ (中文版)/p/articles/mi _qa36132. 《美国新闻评论》（美国－双月刊）American journalism review该刊物涉及出版物、电视、电台和网络媒体的所有方面，以新闻分析与评论为主。

/3. 《在线新闻评论》（Online Journalism Review）关于新媒体研究的一本杂志/4. 《编辑与出版商》（美国－月刊）Editor &Publisher:America's Oldest Journal Covering the Newspaper Industry Editor & Publisher is the authoritative weekly magazine covering the newspaper industry in North America. The magazine dates back to 1884, when The Journalist, a weekly, was founded. E&P was launched in 1901 and merged with The Journalist in 1907. E&P later acquired Newspaperdom, a trade journal for the newspaper industry that started in 1892.5. 《新闻学与传播学专论》（美国）Journalism and communication monographs (also known as: Journalism & communication monographs)—Columbia, SC : Association for Education inJournalism and Mass Communication,c1999-—ISSN: 1522-63796.《新闻与大众传播教育者》（美国－季刊）Journalism and mass communication educator (also known as: Journalism & mass communication educator, Educator, Journalism educator) —Columbia, SC : Association for Education in Journalism and Mass Communication in cooperation with the Association of Schools of Journalism and Mass Communication,c1995-—ISSN: 1077-6958美国教育协会和新闻与大众传播学院协会合作出版发行的季刊。

北京市属高校国外访问学者英文简历模板

附件五 (Name)Ph.DCurriculum Vitae, April 2009Finance Department Date of Birth: February 22,1959 University of Minnesota Citizenship: U.S.A.321-19 Avenue South SEX:Minneapolis Major:Professional Title:Office Telephone: E-mail:Mobile: School Website:Fax:ACADEMIC EXPERIENCEMinnesota Banking Industry Professor of Finance,Carlson School of Management,University of Minnesote,2002-presentAssociate Professor,Finance Department, Carlson School of Management, University of Minnesote,1998-2002Visiting Scholar,Financial Markets Group,London School of Economics,December 1995-1996……OTHER EXPERIENCEConsultant,Federal Reserve Bank of Cleveland,1996Research Analyst,CoreStates Financial Corp/Philadelphia National Bank, 1981-1983……….EDUCATIONThe Wharton School,University of Pennsylvania, Philadelphia,PAPh.D.,Finance,August 1990Ph.D.Thesis Title:Ph.D.Thesis Research was performed at:M.B.A.,Finance,Mar 1982Princeton University,Princeton,NJA.B., Mathematics,June 1980RESEARCH INTERESTSFinancial contracting, security design, and corporate finance.Structure and regulation of financial institutions.REFEREED JOURNAL ARTICLES“Risk Overhang and Market Behavior,” (with Anne ). Journal of Business74:4 (October 2001), 591-612.“Delegated Monitoring and Bank Structure in a Finite Economy.” Journal of Financial Intermediation, 4:2 (April 1995), 158-187.……WORKING PAPERS AND WORK IN PROGRESS“Risk Overhang and Loan Portfolio Decisions,” (with Robert and Anne ).May 2006.“When Do Institutional Investors Become Activists? Trading Incentivesand Shareholder Activism,” (with Chen Liu). September 2003.OTHER PUBLICATIONS“The Social Cost of Bank Capital,” (with Gary Gorton). Proceedings ofthe 32nd Annual Conference on Bank Structure and Competition, May1996, Federal Reserve Bank of Chicago.……COURSES TAUGHTMBA: Corporate Finance; Managing Financial Institutions.Non-Degree Executive Education: Financial Strategy.……TEACHING AWARDSTeaching commendations from Kellogg Dean's Office, every academic year 1991-1998.……SCHOOL SERVICEChair, Carlson Finance Department, 2008-Carlson Finance Department Recruiting Committee, 2006-2007……PROFESSIONAL ACTIVITIESEditorial Activities:Associate Editor, Journal of Financial Intermediation, January 2002-present Associate Editor, Journal of Finance, March 2000-present……Association Service:Society for Financial Studies Nominating Committee, 2000……Conferences, 2005-presentProgram Committee and session chair, Western Finance AssociationMeetings, June 2008.Program Committee and session chair, Western Finance AssociationMeetings, June 2005.……Seminar Presentations, 2005-PresentAmerican University, November 2008Stanford University, May 2006……填写说明:1、模版中的红色字体为范例,仅供参考；2、该表格将提交申请访学学校，直接关系到申请人的录取，请如实详细填写。

Uninformative variable elimination NIRS

An ASABE Meeting PresentationPaper Number: 1008570Application of Uninformative Variable EliminationAlgorithm to Determine Organophosphorus Pesticide Concentration with Near-infrared SpectroscopyJingjing Chen, PhD studentChina Agricultural University, College of Engineering, cjjsym@Yankun Peng, Professor, Corresponding authorChina Agricultural University, College of Engineering, ypeng@Yongyu Li, InstructorChina Agricultural University, College of Engineering, YYLI@Jianhu Wu, PhD studentChina Agricultural University, College of Engineering, wjhu180509@Jiajia Shan, Master studentChina Agricultural University, College of Engineering, jia.jia.1986@Written for presentation at the2010 ASABE Annual International MeetingSponsored by ASABEDavid L. Lawrence Convention CenterPittsburgh, PennsylvaniaJune 20 – June 23, 2010Abstract.The traditional methods of determination pesticide concentration are time-consuming, complicated, and require a lot of pretreatment processes. The objective of this research was to develop a new method for determination the pesticide concentration by using NIR spectroscopy. Organophosphorus pesticide (chlorpyrifos) solution was prepared by dissolving the commercial pesticide into distilled water at different concentrations, and samples were prepared by pipetting the solutions onto the filter papers and then were evaporated by vacuum drying oven. The spectra of filter paper samples were acquired in the range of 4000-10000 cm-1. Partial least squares regression (PLSR) was used to establish prediction models for predicting pesticide concentration. The uninformative variable elimination (UVE) was used for variable selection of NIR spectra data in order to develop an effective PLSR prediction model. The UVE algorithm reduced more than 70% of the variable number. Prediction results indicated that the UVE-PLSR models which multiplicative scatter correction (MSC) and standard normal variate (SNV) were used as pre-processing of spectral data were able to predict the concentration of chlorpyrifos with the correlation coefficient (R) is 0.94 for validation set, and the root mean square errors of prediction (RMSEP) is 0.36 for validation set. Keywords. NIR spectroscopy, Organphosphorus pesticide, Uninformative variable elimination.The authors are solely responsible for the content of this technical presentation. The technical presentation does not necessarily reflect the official position of the American Society of Agricultural and Biological Engineers (ASABE), and its printing and distribution does not constitute an endorsement of views which may be expressed. Technical presentations are not subject to the formal peer review process by ASABE editorial committees; therefore, they are not to be presented as refereed publications. Citation of this work should state that it is from an ASABE meeting paper. EXAMPLE: Author's Last Name, Initials. 2010. Title of Presentation. ASABE Paper No. 10----. St. Joseph, Mich.: ASABE. For information about securing permission to reprint or reproduce a technical presentation, please contact ASABE at rutter@ or 269-429-0300 (2950 Niles Road, St. Joseph, MI 49085-9659 USA).IntroductionPesticides are essential for agricultural and horticultural crops production. Pesticides are commonly classified as Insecticide, fungicide, herbicide, rodenticide, etc. These pesticides act against insects, rodents, weeds which are harmful in agricultural or horticultural planting. Normally, farmers use the pesticides following the instruction written in the package. In most cases, the pesticides are mixed with water and sprayed over the plants. Basically, after spraying fruits or vegetables with pesticide, a period of 10 to 14 days is required to allow the chemical to degrade. However, the full degradation of pesticide is not always achieved. In recent years, some farmers ignored to use the pesticide correctly and rationally. In order to chase a better insecticidal effect and the economic interests, the phenomenon of using pesticide excessively, or selling the fruits or vegetables just after spraying the pesticide in few days are not difficult to see. And the pesticides overdosing also have the potential to contaminate the soil, air, and river. It can be concluded that misusing pesticides are harmful to not only human beings but also the environment, and it is vital to control the pesticide concentration on agricultural products for maintaining public heath conditions and protecting the entire environment.At present, there are several ways to determine the concentration of pesticide residue, such as gas chromatography (GC), high-performance liquid chromatography (HPLC), thin-layer chromatography, supercritical fluid chromatography, chromatography-mass spectrometry, capillary electrophoresis, enzyme inhibition method, immunoassay method, and bio-sensor method and so on. In all of these analysis methods, the accuracy of instrumental analysis method is best, like GC and HPLC (Gambacorta et al., 2005). But the use of instrumental analysis method for controlling all produce is not possible due to the limitations in time and labor. Normally, at least 30 minutes are needed to measure the pesticide concentration of a single sample because of the complication in the testing process. And these instrument analysis methods just can be used in laboratory for accurate analysis and statutory inspection (Luypaert et al., 2003). Biological and chemical analysis methods were developed in recent years, but there are also some flaws, such as the pre-treatments are needed and the demanding of experimental conditions.Nowadays, there is an ongoing interest to develop safe, fast, reliable and low-cost analytical methods for the determination of pesticide residue which could avoid the use of organic solvents and reduce the contact of operator with the toxic substances. In order to achieve this goal, a new analysis method and technology should be developed. In recent years, spectroscopy based procedures is regarded as a potential method which could solve the above problems. Spectroscopy analysis methods have been widely used in chemical industry, agriculture, medicine and other areas (Peng et al., 2008, 2009; ElMasry et al., 2007). Among the optical analysis methods, near-infrared (NIR) spectroscopy is the most popular method because of its non-destructive nature, the low cost of using equipment and the fast response times (Armenta et al., 2007), and it also has been successfully applied to quality control in food (Pi et al., 2009; Leroy et al., 2003; Subbiah et al., 2008), petrochemical, pharmaceutical, clinical and biomedical and environmental sectors (Ripoll et al., 2008). There are some researches about determination of pesticide or fungicide by using near-infrared spectroscopy. Such as Saranwong and Kawano (2005) developed a system for rapid fungicide determination by using near-infrared spectroscopy; Khanmohammadi et al. (2008) used near-infrared and mid-infrared spectroscopy method to detect metribuzin concentration in soil samples, and obtained the minimum detection limit of 17mg/kg and 9mg/kg respectively; Armenta, et al. (2007) developed PLS-NIR procedure provides a non-destructive, solvent free, fast and accurate method which allows the determination of 120 samples per hour for determination of pesticides in commercialformulations. All of this researches show the feasibility of using NIR spectroscopy to detect trace pesticides.Main objectives of this research were to evaluate NIR spectroscopy as a tool for determining the pesticide concentration, and use statistical algorithm to develop a satisfied prediction model. Materials and MethodsSamplesPesticide solution: A commercial pesticide, containing 40% chlorpyrifos (Noposion, China) was used. Chlorpyrifos is an organophosphorus pesticide, normally used in the paddy, wheat, cotton, fruit trees, and vegetables. Distilled water was prepared in order to provide the solutions with different concentrations. A total of 24 concentration levels, from 1 mg/kg to 400 mg/kg of active ingredient were diluted based on the amount of chlorpyrifos. After preparation, the solutions were kept in conical flasks and preserved in a cool place in order to prevent chemical degradation and contamination.Figure 1. Platform for filter paper.Filter paper samples: It is well known that the control level of pesticide does not lie at the percent level but at the 10-6 level, even 10-9 level. It is hard to obtain a satisfactory result by the use of NIR spectroscopy to determine the concentration of pesticide solution. The reason being that water has several strong absorption peaks in near-infrared bands; as a result it is difficult to get the information of pesticide compared to water in the solution. In order to obtain the absorption of trace chemicals, a special method to concentrate the amount of chemicals on samples was developed. Filter paper was used as substrate, water was removed from wet substrate by drying, and then the NIR measurement was performed on the dried substrate. Normal filter papers (Shuangquan, China), in 9 cm diameter were selected. First of all, every piece of 9 cm filter-papers was sheared into four pieces each 30 mm diameter by using a special mold. Then put the filter paper onto the special platform, which was made of polystyrene foam and pins (Figure 1). Each platform was almost 20 cm long and 5 cm wide, and four pieces of filter paper could be placed on. After putting the filter papers onto the platform, 200µL of pesticide solution was gently pipetted onto each filter paper (the amount of 200µL is the volumeabsorbable by filter paper without any overflow). Several pieces of filter paper samples were prepared for each concentration level. A total of 99 filter paper samples were prepared.Drying filter paper samples: The platform with filter paper samples were carefully moved into the vacuum drying oven, at room temperature for 1 hour. After drying, samples were stored into vacuum packing bags immediately and marked with different concentrations.Spectrum AcquisitionAn Antaris FT-NIR spectrometer (Thermo Nicolet, Waltham, Massachusetts, USA), equipped with an InGaAs detector was used. The filter paper sample was placed in a specially modified sample cell. The spectra were acquired in the range of 4000 cm-1 to 10000 cm-1 at 8 cm-1 interval. For each sample, three points were chosen randomly for the NIR measurement, and 32 scans were co-added for each point. The sample was then removed, and the spectra were collected again in the same manner. Three spectra were obtained for each sample at the same state, and averaged spectra were calculated for further evaluation. To prevent the interference of water vapor in the air, the spectra of samples were acquired immediately after taking out from the vacuum packing bags.Pre-processing Method and Data AnalysisThe Matlab 7.0 software (MathWorks, USA) was used for all calculations. A total of 99 filter paper samples were divided into two groups, 75 samples were selected as calibration set; the left 24 samples in each concentration level were put into validation sample set. Partial least squares regression (PLSR) was used to develop a prediction model. Multiplicative scatter correction (MSC) and standard normal variate (SNV) were used in PLSR for pre-processing of spectral data. MSC efficiently eliminates the base line drift of the spectra which in turn reflects the more detailed characteristics of the spectra, and also removes additive and/or multiplicative signal effects (Brunet et al., 2009). The main advantage of SNV is to avoid attributes in greater numeric ranges dominate those in smaller numeric ranges. The PLSR model basing on all variables of the spectra is complex, thus a special algorithm uninformative variable elimination (UVE) was used as a method for variables selection of NIR spectra data of samples in order to develop the effective PLSR prediction model for determination the concentrations of pesticide samples.UVE is an algorithm based on the regression coefficient b of PLSR (Chen et al., 2005; Wu et al., 2009). In the PLSR-NIR prediction model, there is a relationship between X (spectral matrix) and Y (concentration matrix):Y = X b + e(1) where b is the regression coefficient vector, e is the error vector. The following five steps were taken to get a new spectral matrix with fewer wave bands:1. PLSR was used to develop a prediction model in the entire wave range from 4000 cm-1 to10000 cm-1. Cross validation was applied to the calibration set. Each time, one sample was taken out from the calibration set. A calibration model was established for the remaining samples and the model was then used to predict the sample left out. Thereafter, the sample was placed back into the calibration set and a second sample was taken out. The procedure was repeated until all samples have been left out once. The root mean square error of cross validation (RMSEcv) was calculated for each of all wavelength combinations.The best principal component (PC) number with the highest Rcv (correlation coefficient of cross validation) and lowest RMSEcv value was selected.2. A random matrix Ra have the same number of variables with independent variable matrixwas added into spectral matrix to be a new matrix XRa.3. Partial least squares regression (PLSR) was used again. Leave one out cross validationwas carried between the new matrix XRa and concentration matrix Y. After each step of leave one out cross validation, a regression coefficient b was obtained.4. Analyzing the stability of C value which is the ratio of the mean value of vector b and thestandard deviation of vector b :)()(C i i i b std b mean = (2)5. According to the absolute value of C i to discriminate the each spectra variable is effective ornot. All effective variables were selected and put into a new independent variable matrix, and then this new matrix and Y were used to establish a new PLSR prediction model. Results and DiscussionNIR SpectraA total of 99 filter samples’ NIR original spectra are shown in figure 2, and the spectra of samples after pre-processing with MSC are shown in figure 3. It is obviously seen that the base line drift of the spectra is reduced in the figure 3 compared to figure 2 by the application of MSC.Figure 2. NIR transmittance spectra of filter-paper samples with different chlorpyrifos content.Figure 3. NIR transmittance spectra of filter-paper samples after MSC.Results of PLSR in Full BandsFor the total sets, two spectrum pre-processing methods MSC and SNV were used. Figure 4 illustrates the results of the cross validation when MSC and SNV were used as the spectrum pre-processing method.Figure 4. Optimal PC number of prediction model for filter-paper samples.The total sample sets were separated into calibration set and validation set. Cross validation was first used in calibration sample set to find the optimal component number. From figure 4 we can see the best principle component number to be 10 with corresponding highest Rcv of 0.91and lowest RMSEcv of 0.41. Model accuracy was then evaluated on the validation set using the root mean square error of prediction (RMSEP), correlation coefficient (R) between predicted and actual data. The results obtained are shown in table 1 corresponding to R = 0.95 and RMSEP= 0.32 mg/kg.Table 1. Calibration and validation results for chlorpyrifos concentration by using PLSR method. Pre-processing method LV Rcv RMSEcv (mg/kg)R RMSEP (mg/kg) MSC + SNV 10 0.91 0.41 0.95 0.32LV: the optimal principal component (PC) number used in cross-validationRcv: correlation coefficient of cross validationRMSEcv: root mean square error of cross validationR: correlation coefficients in validation setRMSEP: root mean square error of predictionResults of UVE-PLSRFrom table 1 we can see that PLSR method do get a satisfied prediction result, but PLSR method used in full bands of the spectra to develop a calibration model are time-consuming of running the computer program. In the full bands of samples’ spectra, some variables are effective, some variables are not. A special algorithm uninformative variable elimination (UVE) here was used to find out the effective variables, the variables with useless information were eliminated from the spectra of samples.MSC and SNV were used as the pre-processing method. According the result in table1, the optimal principal component number was chosen as 10. Then UVE algorithm was used to select the effective variables. The results were shown in figure 5.Figure 5. Variables selected by UVE.In figure 5, the dotted line indicates the threshold of variables selection. In the range of [1, 1557], the variables corresponding the C value within the threshold range are not effective, and368 variables left after ineffective variables were eliminated. A new PLSR prediction model was developed by using these 368 variables. The results showed that the correlation coefficient (Rcv) in cross validation is 0.91, the root mean square error of cross validation (RMSEcv) is 0.42 mg/kg, and the correlation coefficient (R) in validation set is 0.94, the root mean square error of prediction (RMSEP) is 0.36 mg/kg. Compared with the results of the PLSR used in full bands, the UVE-PLSR could get similar results but using fewer wave bands. In the UVE algorithm, the random matrix which was added into the original matrix was different each time, so the results would be different in every prediction model. In order to prove the stability of UVE algorithm, another 4 times of UVE-PLSR was used. The results of a total of five times UVE-PLSR are shown in table 2.Table 2. Prediction results of UVE-PLSR methods.Model Number ofVariablesLV RcvRMSEcv(mg/kg)RRMSEP(mg/kg)VariablesThresholdsUVE-PLSR-1 368 7 0.91 0.42 0.94 0.36 ±29.16 UVE-PLSR-2 281 7 0.90 0.47 0.94 0.37 ±31.31 UVE-PLSR-3 395 7 0.90 0.43 0.94 0.36 ±27.61 UVE-PLSR-4 379 7 0.90 0.43 0.94 0.37 ±28.23 UVE-PLSR-5 330 7 0.90 0.43 0.94 0.36 ±30.27 LV: the optimal principal component (PC) number used in cross-validationRcv: correlation coefficient of cross validationRMSEcv: root mean square error of cross validationR: correlation coefficients in validation setRMSEP: root mean square error of predictionFigure 6 shows the variables selection results by the use of another 4 times UVE-PLSR based on different random matrix. As the results shown in table 2, the differences between each UVE-PLSR method are small. The number of variables is ranging from 281 to 395, and the prediction results are almost identical to each other. Considering the different random matrix, the Rcv (correlation coefficient of cross validation) range from 0.90 to 0.91, RMSEcv (cross validation) range from 0.42 to 0.47 mg/kg, R (validation set) is 0.94, RMSEP range from 0.36 to 0.37mg/kg which MSC and SNV were used as the pre-processing method. It could be concluded that the differences of random matrix have very weak affection in the process of developing a prediction model, and the numbers of variables used in UVE-PLSR could be declined by more than 70%. ConclusionIn this study, a special technique for preparing the filter paper samples for pesticide determination was developed. PLSR method was used in full bands to establish a prediction model of pesticide concentration, and the prediction results with R of 0.95 and RMSEP of 0.32 mg/kg were obtained which MSC and SNV were used as the pre-processing method. In order to make the calculation more efficient and fast, a special algorithm UVE was used to eliminate the variables which are not effective in the spectra of samples. And the stability of UVE algorithm was proved by running the UVE program for several times, and the prediction results with the highest R of 0.94, the lowest RMSEP of 0.36 mg/kg. These results indicated that the prediction capability of UVE-PLSR is similar as the PLSR used in full bands. In the future, more researches should be done to improve the accuracy of prediction models. So, it can beconcluded that NIR determination of pesticide is a low cost, an environment friendly and a potential method compared to the traditional methods, and the UVE-PLSR algorithm is an efficient method to select the effective variables of spectra and develop a prediction model of pesticide concentration with fewer wave bands.(a) (b)(c) (d)Figure 6. Variables selected by UVE with different random matrix.AcknowledgementsThe authors greatly acknowledge National High-tech R&D Program, China (Project NO. 2008AA10Z210), and Basic Scientific Research and Innovation Fund of Graduate Student, China Agricultural University (15059201) for supporting this research.ReferencesArmenta, S., S. Garrigues, and M. de la Guardia.2007. Partial least squares-near infrared determination of pesticides in commercial formulations. Journal of Vibrational Spectroscopy. 44: 273-278.Brunet, D., T. Woignier, M. Lesueur-Jannoyer, R. Achard, L. Rangon, and B.G. Barthes. 2009.Determination of soil content in chlordecone (organochlorine pesticide) using near infrared reflectance spectroscopy (NIRS). Environmental Pollution. 157: 3120-3125. Chen, B., and D. Chen. 2005. The application of uninformative variables elimination in near-infrared spectroscopy. Spectronic Instruments and Analysis. 04: 26-30.ElMasry, G., N. Wang, A. ElSayed, and M. Ngadi. 2007. Hyperspectral imaging for nondestructive determination of some quality attributes for strawberry. Journal of Food Engineering.81: 98-107.Gambacorta, G., M. Faccia, C. Lamacchia, A. Di Luccia, and E. La Notte. 2005. Pesticide residues in tomato grown in open field. Food control. 16: 629-632.Khanmohammadi M., S. Arment, S. Garrigues and M. de la Guardia. 2008. Mid-and near-infrared determination of metribuzin in agrochemicals. Journal of Vibrational Spectroscopy. 46: 82-88.Leroy, B., S. Lambotte, O. Dotreppr, H. Lecocq, L. Istasse, and A. Clinquart. 2003. Prediction of technological and organoleptic properties of beef longissimus thoracis from near-infrared reflection and transmission spectra. Meat Science. 66: 45-54.Luypaert, J., M.H. Zhang, and D.L. Massart. 2003. Feasibility study for the use of near infrared spectroscopy in the qualitative and quantitative analysis of green tea, Camellia sinensis (L.). Analytica Chimica Acta. 478: 303–312.Peng, Y., and R. Lu. 2008. Analysis of spatially resolved hyperspectral scattering images for assessing apple fruit firmness and soluble solids content. Postharvest Biology and Technology. 48: 52-62.Peng, Y., and J. H. Wu. 2008. Hyperspectral scattering profiles for prediction of beef tenderness. ASABE Paper No. 080004. Rhode Island convention center, Rhode, USA. Peng, Y., J. Zhang, and J.H. Wu. 2009. Hyperspectral scattering profiles for prediction of the microbial spoilage of beef. SPIE Paper No. 7315-25, Orlando, Florida, USA.Ripoll, G., P. Alberti, B. Panea, J.L. Olleta, and C. Sanudo. 2008. Near-infrared reflectance spectroscopy for predicting chemical, instrumental and sensory quality of beef. Meat Science. 80: 697-702.Saranwong, S., and S. Kawano. 2005. Rapid determination of fungicide contaminated on tomato surface. Journal of Near infrared spectrosc. 13：169-175.Subbiah, J., C.R. Calkins, A. Samal, and G.E. Meyer. 2008. Visible/near-infrared hyperspectral imaging for beef tenderness prediction. 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Research Summary

Research Summaryby Jun YangMy areas of interest include multimedia information retrieval, Web searching, digital library, computer vision, and multimedia database, which share a common theme – the management of multimedia data. This interest is originated from the insight that, without effective access and management tools, the pervasive and expanding multimedia information will become more frustrating and less valuable to end users. Starting from the 3rd year of my undergraduate study, I have worked in the broad area of multimedia for 4 years in 4 different research institutions, including Microsoft Visual Perception Lab of Zhejiang University, Siemens in Vienna, Austria, Microsoft Research Asia, and Dept of Computer Engineering and Information Technology at City University of Hong Kong. I have published over 15 refereed papers in international conferences and journals or as book chapters, and built several prototype systems. In this document, I summarize my research achievements by domains with reference to my representative publications.1. Multi-modal Information RetrievalThis work is motivated by the conservative “one system, one media” framework observed among existing multimedia information retrieval systems, i.e., each system can deal with only a single type of media based on a single type of knowledge (e.g., content-based image retrieval). To remedy this limitation, we advocate “one system for all” framework by proposing multi-modal information retrieval, where the keyword “multi-modal” is defined at three levels: (1) multiple types of media data (text, images, videos, etc) are retrieved in an integrated manner; (2) multiple sources of knowledge are explored; and (3) multiple retrieval approaches/techniques are employed. Two research projects along this direction are described below: • Octopus – an aggressive search mechanism for multi-modal information [1]: Octopus is a mechanism for aggressive retrieval of multi-modal data (i.e., a mixture of text, images, videos, etc) in an integrated manner. It is based on a multifaceted knowledge base constructed on a layered graph model (LGM), which describes the relevance relationships among media objects deduced from low-level features, contextual knowledge (e.g., hyperlinks), and user-system interactions. Link analysis technique, an extensively used technique in Web searching, is applied to explore the LGM to search for relevant media objects for user queries. Furthermore, an incremental relevance feedback technique is proposed to update the knowledge base by learning from user-system interactions, therefore enhancing the retrieval performance in a “hill-climbing” manner. Octopus advocates a highly flexible retrieval scenario, where users are free to submit any media (objects) as query example and receive any media (objects) as results.Our recent work has addressed the interface design [15] of Octopus.• CoSEEM – a cooperative search engine for multimedia in digital libraries[2,3]: As the predecessor of Octopus, CoSEEM is a retrieval framework for multimedia information in digital libraries.It focuses on the use of uniform semantic descriptions (keywords) to retrieve various types of media objects in an integrated manner. A learning-from-elements strategy is proposed to propagate and update descriptive keywords associated with media objects, and a cross-media search mechanism is devised to search for media objects by combining their low-level features and semantic descriptions.2. Semantics and Content based Image RetrievalDue to the “semantic gulf” between low-level features and high-level user queries, Content-based Image Retrieval (CBIR) is still of limited practicability in general settings, while its killer applications in specificdomains are yet to be found. In view of this, my research on CBIR emphasizes exploring the role of human-computer interaction to achieve semantics-based and personalized image retrieval. The specific techniques/devices that I have investigated for this general goal include lexical thesaurus [4,5], user profiling [6], graphic-theoretic model [7], and “peer indexing” [8, 9], as summarized below:• Thesaurus-aided image retrieval and browsing [4,5]: This approach explores the power of lexical thesaurus (specifically, WordNet) to support fuzzy match in keyword-based image retrieval. By examining the similarity between different keywords, our approach is able to match a keyword query with images annotated by different but relevant keywords (e.g., matching a query of “animal” with images annotated with “tiger”), which is not supported by simple keyword matching. Although WordNet has been extensively used in IR, our approach is unique in terms of a combination of semantic keywords and low-level image features for image retrieval. Moreover, we propose a dynamic semantic hierarchy, which can be automatically constructed from WordNet to support image navigation by semantic subjects.• Personalized image retrieval based on user profiling [6]: The objective of this work is towards personalized image retrieval based on a synergy of relevance feedback techniques and information filtering/recommendation techniques. Specifically, a “common profile” and a set of “user profiles” are constructed from user feedbacks to model the common knowledge and the personal views of individual users respectively. Our profile-based image retrieval approach enables “learning from others” by exploring the common profile, as well as “learning from history” by exploring user profiles. Therefore, the retrieval results generated by our approach strike the balance between matching the commonsense of the entire user community and catering for the personal interests of each individual user.• A graphic-theoretic model for image retrieval [7]: In attempt to remedy the limitation of traditional “non-memory” relevance feedback techniques, we have proposed a graphic-theoretic model for incremental relevance feedback in image retrieval. A two-layered graph model is introduced to memorize the semantic correlations (among images) progressively derived from user feedbacks, and link analysis technique is adopted to explore the graph model for image retrieval. This approach outperforms traditional approaches in both short-term (intra-session) and long-term (inter-session) performance.• Data and user-adaptive image retrieval based on “peer indexing” [8]: Peer indexing is based on an intuitive idea – indexing an image by its semantically related peer images. The peer index of an image, as a list of weighted peer images, can be acquired from user feedbacks by a suggested learning strategy.Due to the analogy between a keyword and a peer image as a “visual keyword”, mature techniques in the IR area (e.g., TF/IDF weighting scheme, cosine similarity metric) are applied to image retrieval based on peer indexing in cooperation with low-level image features. Our recent work along this direction has focused on data and user-adaptive image retrieval [9] by applying two-level peer indexing.3. Vector-based Media (Flash™) ManagementRecent years witness the phenomenal growth of Flash, a new format of vector-based animation set forth by Macromedia Inc., which has over 440 million of viewers worldwide. This remarkable popularity justifies the need of investigating the management issues of Flash, which are critical to the better utilization of the enormous Flash resource but unfortunately overlooked by the research community. We therefore propose FLAME [10,11], namely FL ash A ccess and M anagement E nvironment, which covers a variety of management issues of Flash animations.Currently, FLAME consists of three functional components, including (1) content-based retrieval component, which addresses the indexing, retrieval, and query specification of Flash animations by exploringtheir content characteristics on their embedded media ingredients, spatio-temporal features, and user interactions; (2) classification component, which automatically classifies Flash animations into pre-defined categories, such as MTV, commercial advertisement, cartoon, e-postcard, based on their content characteristics; and (3) segmentation component, which partitions long Flash animations into shot/scene structures defined similarly to their counterparts in video segmentation. Further issues to be explored under FLAME include Flash search engine, copyright protection, and sample-based Flash authoring.4. Multimedia DatabaseMy primary goal in this area is to apply database technology to address the efficiency and scalability problem that plagues data-intensive multimedia information systems. One specific problem is the “semantic gap” between semantics-intensive multimedia applications and conventional databases, which are inadequate to model the context-dependent semantics of multimedia data. We have managed to propose MediaView [12,13] as an extended object-oriented view mechanism to bridge this semantic gap. Specifically, this mechanism captures the dynamic semantics of multimedia using a modeling construct named media view, which formulates a customized context where heterogeneous media objects with related semantics are characterized by semantic properties and relationships.Another proposal is a self-adaptive semantic schema mechanism (SSM) for multimedia databases [14]. The SSM is implemented based on an object-oriented data model, in which classes are organized into a semantic hierarchy. As its unique feature, SSM supports adaptive evolution of a schema in the form of expansion with new classes and/or compaction by removing inefficient classes, when the conditions of predefined ECA-rules are satisfied. This self-adaptive evolution strategy allows a data schema to be automatically optimized for each particular multimedia application, (esp. multimedia retrieval systems), thereby achieving a dynamic, application-specific balance between modeling capability and efficiency.5. Video-based Human AnimationTo overcome the shortcomings of conventional human animation techniques, we have proposed a video-based human animation approach [16]. Given a video clip containing human motion, we first recognize and track the human joints with the aid of Kalman filter and morph-block matching in a sequence of video frames. From the recognized human joints, we construct the corresponding 3-D human motion skeleton sequence under the perspective projection, using camera calibration techniques and human anatomy knowledge. Finally, a motion library is established by annotating multiform motion attributes, which can be browsed and queried by animators. This approach has the advantages of rich source materials, low computational cost, efficient production, and realistic animation result.6. Video SegmentationSegmentation of video clips serves as the basis of video indexing and retrieval. We have developed a prototype system for parsing video clips, especially news videos, into a sequence of shots and scenes. The shot boundaries are detected by examining the difference between the color histograms of consecutive frames using “twin-comparison” algorithm, which is robust in detecting gradual transitions (zoom, fade in/out, dissolve, etc). Particularly, for news videos with a prior model of the temporal video structure, we group the segmented shots into higher-level units such as news stories, weather forecast, and commercials. Reference:1. Jun Yang, Qing Li, Yueting Zhuang, “Octopus: Aggressive Search of Multi-Modality Data Using Multifaceted KnowledgeBase”, Proc. of 11th Int'l Conf. on World Wide Web, pp.54-64, Hawaii, USA, May, 2002.2. Jun Yang, Yueting Zhuang, Qing Li, “Search for Multi-Modality Data in Digital Libraries”, Proc. of 2nd IEEE Pacific-RimConf. on Multimedia, pp. 482-489, Beijing, China, 2001.3. Jun Yang, Yueting Zhuang, Qing Li, “Multi-Modal Retrieval for Multimedia Digital Libraries: Issues, Architecture, andMechanisms”, Proc. of Int'l Workshop on Multimedia Information Systems, pp. 81-88, Capri, Italy, 2001.4. Jun Yang, Liu Wenyin, Hongjiang Zhang, Yueting Zhuang, “Thesaurus-aided Approach for Image Retrieval and Browsing”,Proc. of 2nd IEEE Int'l Conf. on Multimedia and Expo, pp. 313-316. Tokyo, Japan, 2001.5. Jun Yang, Liu Wenyin, Hongjiang Zhang, Yueting Zhuang, “An Approach to Semantics-based Image Retrieval andBrowsing”, Proc. of 7th Int’ l Conference on Distributed Multimedia Systems, Taiwan, 2001.6. Qing Li, Jun Yang, Yueting Zhuang, “Web-based Multimedia Retrieval: Balancing out between Common Knowledge andPersonalized Views”, Proc. of 2nd Int'l Conf. on Web Information System Engineering, pp. 92-101, Kyoto, Japan, 2001.7. Yueting Zhuang, Jun Yang, Qing Li, “A Graphic-Theoretic Model for Incremental Relevance Feedback in Image Retrieval”,Proc. of 2002 Int'l Conf. on Image Processing, New York, Sep., 2002.8. Jun Yang, Qing Li, Yueting Zhuang, "Image Retrieval and Relevance Feedback using Peer Indexing", Proc. of 2002 IEEEInt'l Conf. on Multimedia and Expo, Lausanne, Switzerland, Aug, 2002.9. Jun Yang, Qing Li, Yueting Zhuang, "Modeling Data and User Characteristics by Peer Indexing in Content-based ImageRetrieval", The 9th Int'l Conf. on Multimedia Modeling, Taiwan, 2003. (accepted)10. Jun Yang, Qing Li, Liu Wenyin, Yueting Zhuang, "Search for Flash Movies on the Web", Proc. of the 3rd Int'l Conf. on WebInformation Systems Engineering, workshop on Mining for Enhanced Web Search, Singapore, 2002.11. Jun Yang, Qing Li, Liu Wenyin, Yueting Zhuang, "FLAME: A Generic Framework for Content-based FlashRetrieval", Proc. of the 4th Int'l Workshop on Multimedia Information Retrieval, in conjunction with ACM Multimedia 2002, Juan-les-Pins, France, 2002.12. Qing Li, J un Yang, Yueting Zhuang, "MediaView: A Semantic View Mechanism for Multimedia Modeling", Proc. of the3rd IEEE Pacific-Rim Conf. on Multimedia, Taiwan, Dec. 2002. (accepted)13. Qing Li, Jun Yang, Yueting Zhuang, “Chapter 9: A Semantic Data Modeling Mechanism for Multimedia Databases”, inMultimedia Information Retrieval and Management, edited by Hong-jiang Zhang, etc.14. Jun Yang, Qing Li, and Yueting Zhuang, "A Self-adaptive Semantic Schema Mechanism for Multimedia Databases", SPIEPhotonics Asia: Electronic Imaging and Multimedia Technology III, pp.69-79, Proc. vol. 4926, Shanghai, China, Oct. 2000.15. Jun Yang, Qing Li, Yueting Zhuang, "A Multimodal Information Retrieval System: Mechanism and Interface", IEEE Trans.on Multimedia (submitted).16. Zhuang Yueting, Liu xiaoming, Pan Yunhe, Yang Jun, "Human Three Dimension Motion Skeleton Reconstruction ofMotion Image Sequence", Journal of Computer-aided Design & Computer Graphics, 12(4), 245-251, 2002. (in Chinese)。

Honors And Awards

TARUN BANSAL2600 Waterview Pkwy, Apt#3932, Richardson TX 75080tarun@; 248.504.7423Research InterestsComputer Networks, large scale distributed systems, wireless and mobile systemsEducation•M.S. (Master of Science), Computer Science, The University of Texas at Dallas (Graduation Expected: 2009) GPA 4.0/4.0• B.Tech., Computer Science and Engg., IIT Roorkee (Roorkee), India (2006) with CGPA 7.25/10.0 Professional ActivityPresent: Graduate Research Assistant, Department of Computer Science, Univ. of Texas at DallasPresent WorkCognitive Radios:Working on MAC layer based neighbor discovery protocols for Cognitive Radio Networks. Also working on designing efficient cross layer protocol for Cognitive RadiosStabilization detection in dynamic distributed systems:Designing efficient algorithms to detect system wide stabilization properties in a completely distributed system with continuous arrival and departure of processesRefereed PublicationsT.Bansal,P.Ghanshani,R.Joshi,“An Application Dependent Communication Protocol for Wireless Sensor Networks,” in 5th International Conference for Networking (ICN), April 2006 and IEEE Explore.P.Ghanshani,T.Bansal, “Oasis: A Hierarchical EMST Based P2P Network,” in7th International Workshop on Distributed Computing (IWDC) Dec 2005, LNCS 3741, pp. 201 – 212, Springer- Verlag.T. Bansal and P. Ghanshani, “An EMST Based Look-up Protocol for Peer to Peer Networks,” Journal of Networks, Academy Publisher, July 2006. (extended version of OASIS)P. Ghanshani, A. Yadav, T. Bansal, K. Garg and S. Jain, “A Centralized Dynamic Power-Aware Communication Protocol for Wireless Sensor Networks,” Proc. 4th Asian International Mobile Computing Conference (AMOC), Jan 2006. Professional ServicesReviewer and Technical Program Committee member for conferences:–The Seventh International Conference on Networking (ICN), 2008–International Conference on Systems and Networks Communications (ICSNC) 2006 – 2008–International Conference on Wireless and Mobile Communications (ICWMC) 2006 – 2008–Second International Conference on Sensor Technologies and Applications (SENSORCOMM) 2008–Second International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM) 2008Honors And Awards•Recipient of Jonsson School Graduate School Computer Science Department Graduate Scholarship(at UT Dallas) which includes a partial waiver of the tuition fee and an annual stipend.•Secured an All India Rank of 535 out of 153,000 candidates in IIT-JEE 2002.•Selected among top 60 students all over India for Physics Olympiad2002 conducted by Homi Bhabha Center for Science and Education (HBCSE).•Winner of National Talent Search Examination (NTSE) in 2000 conducted by NCERT.•Secured 13th position in Regional Mathematical Olympiad (RMO 2001 for North West Region) conducted by National Board of Higher Mathematics and was selected to participate in the Indian National Mathematics Olympiad (INMO) - 2001.Advanced Courses Taken@ University of Texas at Dallas (Fall 07):Distributed Computing, Combinatorics and Graph Algorithms, Design and Analysis of Computer Algorithms, Advanced Computer Networks,, Advanced Operating Systems, Performance of Computer NetworksB.Tech Dissertation•Title: “Energy Efficient Routing Protocol For Wireless Sensor Networks”•Proposed a new routing protocol for sensor networks, ADCP (An Application Dependent Communication Protocol for Wireless Sensor Networks) which was later selected for publication in International Conference of Networking(ICN), 2006•Finally a Process Control and Monitoring System was implemented on the Crossbow MICA mote kit. Programming SkillsProgramming languages: Assembly Language, C, C++, Java, nesCOperating Systems: Various distributions of Linux, TinyOS, UNIX and WindowsScripting Languages: LaTeX , Perl, UNIX Shell Scripting, Otcl, SQLReferencesAvailable on Request。

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RAMESH M.NALLAPATIDepartment of Computer Science 140Governors Drive University of Massachusetts Amherst,MA01003Email:nmramesh@URL:/∼nmramesh Work:(413)545-3059Home:(413)687-5145Research Interests Machine Learning and its application to Information Retrieval(IR)and other text related problems.Education•Doctoral Candidate,Department of Computer Science September2000-June2006(Expected) U NIVERSITY OF M ASSACHUSETTS,A MHERST Amherst,MA(Current GPA:4.00)Thesis Title:The Smoothed Dirichlet distribution:explaining KL-divergence ranking in IRAdvisor:Prof.James AllanCommittee:Prof.Bruce Croft(CS,UMass),Prof.Sridhar Mahadevan(CS,UMass),Prof.JohnStaudenmayer(Statistics,UMass),Dr.Thomas Minka(Microsoft Research,Cambridge,UK)•Master of Science(M.S.)in Computer Science September2000-February2004U NIVERSITY OF M ASSACHUSETTS,A MHERST Amherst,MA(GPA:4.00)•Master of Science(M.S.)in Mechanical Engineering September1998-May2001U NIVERSITY OF M ASSACHUSETTS,A MHERST Amherst,MA(GPA:3.87)•Bachelor of Technology(B.Tech.)in Mechanical Engineering July1994-May1998I NDIAN I NSTITUTE OF T ECHNOLOGY,B OMBAY Mumbai,India(GPA:7.5/10.0)Refereed Publications 1.Ramesh Nallapati,Ao Feng,Fuchun Peng and James Allan,Event Threading within News Topics,ACM Conference on Information and Knowledge Management,pp.446-453,2004.2.Ramesh Nallapati,Discriminative Models for Information Retrieval,ACM Special Interest Groupin Information Retrieval,pp.64-71,2004.3.Ramesh Nallapati and James Allan,An Adaptive Local Dependency language Model:Relaxingthe Na¨ive Bayes Assumption,Workshop on Mathematical and Formal Models in IR,ACM Special Interest Group in Information Retrieval,2003.4.Ramesh Nallapati,Bruce Croft and James Allan,Relevant Query Feedback in Statistical LanguageModeling,ACM Conference on Information and Knowledge Management,pp.560-563,2003. 5.Ramesh Nallapati,Semantic Language Models for Topic Detection and Tracking,Student Re-search Workshop,Human Language Technologies-North American Association of Computational Linguistics,2003.6.Ramesh Nallapati and James Allan,Capturing Term Dependencies using a Sentence Tree basedLanguage Model,ACM Conference on Information and Knowledge Management,pp.383-390, 2002.UnRefereed Publications 1.Ramesh Nallapati,Thomas Minka and Stephen Robertson,Smoothed Dirichlet Distribution:anew building block for topical models.Available as CIIR technical report IR-461.2.UMass at TDT2002,James Allan,Victor Lavrenko and Ramesh Nallapati,Topic Detection andTracking workshop,2002.3.Extraction of key-words from news stories,Ramesh Nallapati,James Allan and Sridhar Mahade-van,CIIR Technical report IR-345.Experience•Research Assistant Jan’01onwardsC ENTER FOR I NTELLIGENT I NFORMATION R ETRIEVAL UMass AmherstAdvisor:Prof.James AllanPerformed research in applying machine learning techniques for information retrieval.Built ad-vanced language models and discriminative models for information retrieval in the early part.Aspart of my thesis,proposed a novel distribution called Smoothed-Dirichlet to model text.•Research Intern June’04-September’04M ICROSOFT R ESEARCH Cambridge,UKMentors:Dr.Stephen Robertson,Dr.Thomas Minka and Dr.Hugo ZaragozaDeveloped the smoothed-Dirichlet distribution for text that forms part of my thesis.Have beencollaborating with Dr.Robertson and Dr.Minka on my thesis following my internship.•Research Intern June’03-August’03P ALO A LTO R ESEARCH C ENTER Palo Alto,CA,USAMentors:Dr.Francine Chen,Dr.Ayman Farahat,Dr.Annie ZaenonWorked on topic-wise segmentation of ed SVMs to classify paragraph boundariesinto segments or non-segments using several features such as PLSA topics,named-entity chains,word similarity,punctuation,etc.,on either side of the paragraph boundary.•Teaching Assistant Sept’00-Dec’00D EPARTMENT OF C OMPUTER S CIENCE UMass AmherstAssisted in conducting a class in Computer literacy.Responsibilities included grading,organizinglabs and answering students’questions.Honors•Won a scholarship to attend the NAACL summer workshop in language technologies,Johns Hop-kins University,2002.•Was ranked in the top0.7%in the All India Joint Entrance Examination1994for admission to IIT(Indian Institute of Technology),out of approximately100,000appeared.•Received a state government scholarship for academic merit in10th grade.Relevant Course Work •Information Retrieval:Information Retrieval,Statistical Natural Language Processing seminar, Language modeling seminar,Information Extraction,Advanced Information Retrieval seminar.•Machine Learning:Artiﬁcial Intelligence,Machine Learning,Advanced Machine Learning,Man-ifold learning methodsProfessional Activities •Reviewer for one or more papers in the following journals:ACM TOIS,IEEE TKDE.•Elected Graduate Representative in the Computer Science Department at UMass,Jan’05-Jan’06.Responsibilities included organizing faculty seminars and serving as a liaison between faculty and students.Talks andPresentationsList of talks excluding conference presentations of my papers.•Classiﬁcation models for Information Retrieval,Presented at Indian Institute of Information Tech-nology,Hyderabad,India June2005.•Classiﬁcation models for Information Retrieval,Presented at Indian Institute of Technology,Bom-bay,June2005.•Smoothed Dirichlet Distribution for Information Retrieval,Presented at Yahoo Research Labs,Birbank,CA,Novemeber,2005.ReferencesProf.James Allan Prof.Bruce CroftAssociate Professor ProfessorDepartment of Computer Science Department of Computer Science140Governors Dr.140Governors Dr.University of Massachusetts University of Massachusetts Amherst,MA01003Amherst,MA01003Tel:(413)545-3240Tel:(413)545-0463Fax:(413)545-1789Fax:(413)545-1789allan@ croft@/∼allan /∼croftDr.Stephen Robertson Dr.Thomas MinkaResearcher ResearcherMicrosoft Research Ltd Microsoft Research LtdRoger Needham House Roger Needham House7J J Thomson Avenue7J J Thomson AvenueCambridge CB30FB,U.K.Cambridge CB30FB,U.K.ser@ minka@/users/robertson//users/minka Prof.Sridhar Mahadevan Prof.Andrew McCallumAssociate Professor Associate ProfessorDepartment of Computer Science Department of Computer Science140Governors Dr.140Governors Dr.University of Massachusetts University of Massachusetts Amherst,MA01003Amherst,MA01003Tel:(413)545-3140Tel:(413)545-1323Fax:(413)545-1249Fax:(413)545-1789mahadeva@ mccallum@/∼mahadeva /∼mccallum。