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119th ASEE Annual Conference and Exposition

作者： Anderson-Rowland, Mary R. Rodriguez, Armando A. Hall Jr., Richard A. McBride, Phil Blake Pangasa, Rakesh Saber, John M. Vangilder, Clark Grierson, Anita Department of Computing Informatics and Systems Design Engineering Arizona State University United States ASU Faculty Arizona State University United States Math Science and Health Science METSTEP Program Cochise Community College United States Eastern Arizona College United States METSTEP Program Arizona Western College United States METSTEP Program Exxon Research and Development Labs Mohave Community College Baton Rouge United States METSTEP Program Central Arizona College United States Arizona State University United States

ISBN: (纸本)9780878232413

In response to our nation's cry for more engineers, this paper describes how a major university has leveraged four NSF program grants to produce a highly successful program focused on retention, graduation, and graduate degrees. One of the programs is for lower division students, one is for upper division transfer students, one is for non-transfer upper division students and graduate students that have come up through the system, and one is for upper division students who come from five selected non-metropolitan community colleges in Arizona. The last program ties everything all together with a Transfer Student Center, a place for the S-STEM Academic Scholarship Success Class to meet, a place to host potential transfer students, and a place for transfer students to study and network. An emphasis of all of these programs is the encouragement and support of female and underrepresented minority students and students with unmet financial need. These programs have attained a 90-95% retention rate. For upper division transfer students not in the program, the graduation rate is about 70% for males and 64% for females. The transfer GPA shock was shown to be negligent for new program transfer students, compared with a half grade point drop for new upper division transfer students not in the program. Most importantly, during the past three years over 50% of the graduated scholarship students have gone right on to graduate school full-time. Seventy percent of the transfer students who did not intend to go to graduate school when they matriculated to Arizona State University, but who have now chosen to go on to graduate school, have reported that this program was a major influence in this decision change. Several of the students are now in PhD programs preparing to become professors. Even though parts of this program have been in existence for nearly 10 years, this is a continual work in progress as we strive for more excellence. This paper highlights the successes of being abl

关键词： Students

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Evaluation of Process Variability on Current for Nanotechnologies Devices

Evaluation of Process Variability on Current for Nanotechnol...

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IEEE Latin American Symposium on Circuits and Systems

作者： Cristina Meinhardt Ricardo Reis Computer Science Center - C3 Federal University of Rio Grande - FURG Graduate Program in Computing Institute of Informatics Federal University of Rio Grande do Sul - UFRGS

ISBN: (纸本)9781467312073

The continuous shrinking of devices has introduced new challenges to integrated circuit design, mainly to deal with the parametric variations in process parameters. This paper presents an evaluation of the process variability on the current Ids of nanotechnologies devices, individually and simultaneously, taking into account the correlation among them. The results show that the deviation from mean value is quite significant ≈ 16% for high performance models. The variation of L has the dominant effect on the overall variation of the device in high performance models while the dominant effect on the overall variation of the device in low power models still being due to Vth variations. Lastly, the effect of process parameter variations deteriorates with technology scaling, with a considerable increase in the deviation from the 22nm to 16nm technology.

关键词： Cutting parameters Nanotechnologies currents electric current HIGH-PERFORMANCE parameter variation Deviation mean value powered models

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A Heuristic Rule Reduction Approach to Software Fault-proneness Prediction

A Heuristic Rule Reduction Approach to Software Fault-pronen...

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Asia-Pacific Conference on Software Engineering

作者： Akito Monden Jacky Keung Shuji Morisaki Yasutaka Kamei Ken-Ichi Matsumoto Graduate School of Information Science Nara Institute of Science and Technology Ikoma Nara Japan Department of Computing Hong Kong Polytechnic University Hong Kong China Faculty of Informatics Shizuoka University Hamamatsu Shizuoka Japan Graduate School and Faculty of Information Science and Electrical Engineering Kyushu University Fukuoka Japan

Background: Association rules are more comprehensive and understandable than fault-prone module predictors (such as logistic regression model, random forest and support vector machine). One of the challenges is that there are usually too many similar rules to be extracted by the rule mining. Aim: This paper proposes a rule reduction technique that can eliminate complex (long) and/or similar rules without sacrificing the prediction performance as much as possible. Method: The notion of the method is to removing long and similar rules unless their confidence level as a heuristic is high enough than shorter rules. For example, it starts with selecting rules with shortest length (length=1), and then it continues through the 2nd shortest rules selection (length=2) based on the current confidence level, this process is repeated on the selection for longer rules until no rules are worth included. Result: An empirical experiment has been conducted with the Mylyn and Eclipse PDE datasets. The result of the Mylyn dataset showed the proposed method was able to reduce the number of rules from 1347 down to 13, while the delta of the prediction performance was only. 015 (from. 757 down to. 742) in terms of the F1 prediction criteria. In the experiment with Eclipsed PDE dataset, the proposed method reduced the number of rules from 398 to 12, while the prediction performance even improved (from. 426 to. 441.) Conclusion: The novel technique introduced resolves the rule explosion problem in association rule mining for software proneness prediction, which is significant and provides better understanding of the causes of faulty modules.

关键词： Measurement Association rules Explosions Software Predictive models Educational institutions

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31st Annual Meeting and Associated programs of the Society for Immunotherapy of Cancer (SITC 2016): part two Abstracts

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JOURNAL FOR IMMUNOTHERAPY OF CANCER 2016年第SUPPL 1期4卷 107-221页

作者： [Anonymous] Department of Immunology University of Texas MD Anderson Cancer Center Houston TX USA Department of Cancer Medicine University of Texas MD Anderson Cancer Center Houston TX USA Immune Design Seattle WA USA Innate Pharma Marseille Provence-Alpes-Cote d’Azur France MedImmune Cambridge England UK Georgetown-Lombardi Comprehensive Cancer Center Washington DC USA Westmead and Blacktown Hospitals Melanoma Institute Australia and the University of Sydney Westmead New South Wales Australia University of California Los Angeles CA USA University of Washington Seattle WA USA Dana-Farber Cancer Institute/Brigham and Women’s Hospital Harvard University Boston MA USA University of Texas MD Anderson Cancer Center Houston TX USA Beth Israel Deaconess Medical Center Boston MA USA Gallipoli Medical Research Foundation Greenslopes Private Hospital and the University of Queensland Greenslopes Queensland Australia Olivia Newton-John Cancer Research Institute Heidelberg Victoria Australia Canterbury District Health Board Christchurch Hospital Christchurch New Zealand Waikato Hospital Regional Cancer Centre Hamilton New Zealand Royal Prince Alfred Hospital Melanoma Institute Australia the University of Sydney and Chris O’Brien Lifehouse Camperdown New South Wales Australia Tasman Oncology Research Southport Gold Coast Queensland Australia Merck & Co. Inc. Kenilworth NJ USA Quantitative Solutions A Certara Company Oss Netherlands Melanoma Institute Australia the University of Sydney Mater Hospital and Royal North Shore Hospital Wollstonecraft New South Wales Australia Columbia University Medical Center New York NY USA Institut d’Investigacions Biomediques August Pi i Sunyer Barcelona Catalonia Spain Icahn School of Medicine at Mount Sinai New York NY USA New York Presbyterian/Columbia University Medical Center New York NY USA Memorial Sloan Kettering Cancer Center New York NY USA Peregrine Pharmaceuticals Inc. Tustin CA USA Regeneron Tarrytown NY USA University of

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Code Compression in ARM Embedded Systems Using Multiple Dictionaries

Code Compression in ARM Embedded Systems Using Multiple Dict...

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IEEE International Conference on Computational science and Engineering, CSE

作者： Wanderson Roger Azevedo Dias Edward David Moreno Institute of Computing-IComp Post-graduate Program in Informatics-PPGI Federal University of Amazonas Manaus Amazonas Brazil Center for Exact Sciences and Technology-CCET Department of Computer Science-DComp Federal University of Sergipe Aracaju Sergipe Brazil

This paper presents a new code compression method where we use different dictionaries, all of them are based in traditional Huffman algorithm. Our method reduces code size by up to 34.5% (including all extra costs) and presents an improvement of almost 7% over the traditional Huffman method. We performed simulations using Simple Scalar and analyzes using the applications from benchmark MiBench and use the embedded processor ARM.

关键词： Dictionaries Embedded systems Analytical models Benchmark testing Memory management Decoding Energy consumption

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Proceedings of the 3rd Biennial Conference of the Society for Implementation Research Collaboration (SIRC) 2015: advancing efficient methodologies through community partnerships and team science : Seattle, WA, USA. 24-26 September 2015

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Implementation science : IS 2016年第1期11 Suppl 1卷 85页

作者： Cara Lewis Doyanne Darnell Suzanne Kerns Maria Monroe-DeVita Sara J. Landes Aaron R. Lyon Cameo Stanick Shannon Dorsey Jill Locke Brigid Marriott Ajeng Puspitasari Caitlin Dorsey Karin Hendricks Andria Pierson Phil Fizur Katherine A. Comtois Lawrence A. Palinkas Patricia Chamberlain Gregory A. Aarons Amy E. Green Mark. G. Ehrhart Elise M. Trott Cathleen E. Willging Maria E. Fernandez Nicholas H. Woolf Shuting Lily Liang Natalia I. Heredia Michelle Kegler Betsy Risendal Andrea Dwyer Vicki Young Dayna Campbell Michelle Carvalho Yvonne Kellar-Guenther Laura J. Damschroder Julie C. Lowery Sarah S. Ono Kathleen F. Carlson Erika K. Cottrell Maya E. O’Neil Travis L. Lovejoy Joanna J. Arch Jill L. Mitchell Cara C. Lewis Brigid R. Marriott Kelli Scott Jennifer Schurer Coldiron Eric J. Bruns Alyssa N. Hook Benjamin C. Graham Katelin Jordan Rochelle F. Hanson Angela Moreland Benjamin E. Saunders Heidi S. Resnick Shannon Wiltsey Stirman Cassidy A. Gutner Jennifer Gamarra Dawne Vogt Michael Suvak Jennifer Schuster Wachen Katherine Dondanville Jeffrey S. Yarvis Jim Mintz Alan L. Peterson Elisa V. Borah Brett T. Litz Alma Molino Stacey Young McCaughan Patricia A. Resick Nancy Pandhi Nora Jacobson Neftali Serrano Armando Hernandez Elizabeth Zeidler- Schreiter Natalie Wietfeldt Zaher Karp Michael D. Pullmann Barbara Lucenko Bridget Pavelle Jacqueline A. Uomoto Andrea Negrete Molly Cevasco Suzanne E. U. Kerns Robert P. Franks Christopher Bory Edward J. Miech Teresa M. Damush Jason Satterfield Derek Satre Maria Wamsley Patrick Yuan Patricia O’Sullivan Helen Best Susan Velasquez Miya Barnett Lauren Brookman-Frazee Jennifer Regan Nicole Stadnick Alison Hamilton Anna Lau Scott Roesch Byron J. Powell Thomas J. Waltz Matthew J. Chinman Laura Damschroder Jeffrey L. Smith Monica M. Matthieu Enola K. Proctor JoAnn E. Kirchner Monica J. Matthieu Craig S. Rosen Sarah C. Walker Asia S. Bishop Mariko Lockhart Allison L. Rodriguez Luisa Manfredi Andrea Nevedal Joel Rosenthal Daniel M. Blonigen Anne M. Mauricio Thomas D. Dishion Jenna Rudo-Stern Department of Psychological and Brain Sciences Indiana University Bloomington USA Department of Psychiatry & Behavioral Sciences University of Washington Seattle USA Psychology Department University of Montana Missoula USA Department of Psychology La Salle University Philadelphia USA Division of Public Behavioral Health and Justice Policy University of Washington Seattle USA National Center for PTSD VA Palo Alto Health Care System Menlo Park USA Department of Psychiatry University of Arkansas for Medical Sciences Little Rock USA Department of Psychology University of Washington Seattle USA Department of Speech and Hearing Sciences University of Washington Seattle USA Department of Psychological Sciences University of Missouri Columbia USA Department of Children Youth and Families School of Social Work University of Southern California Los Angeles USA Oregon Social Learning Center Eugene USA Department of Psychiatry University of California San Diego La Jolla USA Child and Adolescent Services Research Center (CASRC) San Diego USA Department of Psychology San Diego State University San Diego USA Pacific Institute for Research and Evaluation Behavioral Health Research Center of the Southwest Albuquerque USA School of Public Health University of Texas Health Science Center at Houston Houston USA Woolf Consulting Santa Barbara USA Rollins School of Public Health Emory University Atlanta USA University of Colorado Cancer Center at Denver Aurora USA South Carolina Primary Health Care Association Columbia USA Veterans Affairs (VA) Ann Arbor Center for Clinical Management Research Ann Arbor USA Personalizing Options through Veteran Engagement (PROVE) Quality Enhancement Research Initiative (QUERI) Program Ann Arbor USA Center to Improve Veteran Involvement in Care (CIVIC) Veterns Affairs (VA) Portland Health Care System (VAPORHCS) Portland USA Department of Family Medicine Oregon Health & Science University Portland USA Division of General Internal Medi

Introduction to the 3rd Biennial Conference of the Society for Implementation Research Collaboration: advancing efficient methodologies through team science and community partnerships Cara Lewis, Doyanne Darnell, Suzanne Kerns, Maria Monroe-DeVita, Sara J. Landes, Aaron R. Lyon, Cameo Stanick, Shannon Dorsey, Jill Locke, Brigid Marriott, Ajeng Puspitasari, Caitlin Dorsey, Karin Hendricks, Andria Pierson, Phil Fizur, Katherine A. Comtois A1: A behavioral economic perspective on adoption, implementation, and sustainment of evidence-based interventions Lawrence A. Palinkas A2: Towards making scale up of evidence-based practices in child welfare systems more efficient and affordable Patricia Chamberlain A3: Mixed method examination of strategic leadership for evidence-based practice implementation Gregory A. Aarons, Amy E. Green, Mark. G. Ehrhart, Elise M. Trott, Cathleen E. Willging A4: Implementing practice change in Federally Qualified Health Centers: Learning from leaders’ experiences Maria E. Fernandez, Nicholas H. Woolf, Shuting (Lily) Liang, Natalia I. Heredia, Michelle Kegler, Betsy Risendal, Andrea Dwyer, Vicki Young, Dayna Campbell, Michelle Carvalho, Yvonne Kellar-Guenther A3: Mixed method examination of strategic leadership for evidence-based practice implementation Gregory A. Aarons, Amy E. Green, Mark. G. Ehrhart, Elise M. Trott, Cathleen E. Willging A4: Implementing practice change in Federally Qualified Health Centers: Learning from leaders’ experiences Maria E. Fernandez, Nicholas H. Woolf, Shuting (Lily) Liang, Natalia I. Heredia, Michelle Kegler, Betsy Risendal, Andrea Dwyer, Vicki Young, Dayna Campbell, Michelle Carvalho, Yvonne Kellar-Guenther A5: Efficient synthesis: Using qualitative comparative analysis and the Consolidated Framework for Implementation Research across diverse studies Laura J. Damschroder, Julie C. Lowery A6: Establishing a veterans engagement group to empower patients and inform Veterans Affairs (VA) health services research Sara

关键词： Veteran Affair Motivational Interview Veteran Health Administration Dialectical Behavior Therapy Qualitative Comparative Analysis

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ESICM LIVES 2016: part three

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Intensive Care Medicine Experimental 2016年第1期4卷 405-607页

作者： Velasquez, T. Mackey, G. Lusk, J. Kyle, U. G. Fontenot, T. Marshall, P. Shekerdemian, L. S. Coss-Bu, J. A. Nishigaki, A. Yatabe, T. Tamura, T. Yamashita, K. Yokoyama, M. Ruiz-Rodriguez, J. C. Encina, B. Belmonte, R. Troncoso, I. Tormos, P. Riveiro, M. Baena, J. Sanchez, A. Bañeras, J. Cordón, J. Duran, N. Ruiz, A. Caballero, J. Nuvials, X. Riera, J. Serra, J. Rutten, A. M. F. van Ieperen, S. N. M. Der Kinderen, E. P. H. M. Van Logten, T. Kovacikova, L. Skrak, P. Zahorec, M. Akcan-Arikan, A. Silva, J. C. Goldsworthy, M. Wood, D. Harrison, D. Parslow, R. Davis, P. Pappachan, J. Goodwin, S. Ramnarayan, P. Chernyshuk, S. Yemets, H. Zhovnir, V. Pulitano’, S. M. De Rosa, S. Mancino, A. Villa, G. Tosi, F. Franchi, P. Conti, G. Patel, B. Khine, H. Shah, A. Sung, D. Singer, L. Haghbin, S. Inaloo, S. Serati, Z. Idei, M. Nomura, T. Yamamoto, N. Sakai, Y. Yoshida, T. Matsuda, Y. Yamaguchi, Y. Takaki, S. Yamaguchi, O. Goto, T. Longani, N. Medar, S. Abdel-Aal, I. R. El Adawy, A. S. Mohammed, H. M. E. H. Mohamed, A. N. Parry, S. M. Knight, L. D. Denehy, L. De Morton, N. Baldwin, C. E. Sani, D. Kayambu, G. da Silva, V. Z. M. Phongpagdi, P. Puthucheary, Z. A. Granger, C. L. Rydingsward, J. E. Horkan, C. M. Christopher, K. B. McWilliams, D. Jones, C. Reeves, E. Atkins, G. Snelson, C. Aitken, L. M. Rattray, J. Kenardy, J. Hull, A. M. Ullman, A. Le Brocque, R. Mitchell, M. Davis, C. Macfarlane, B. Azevedo, J. C. Rocha, L. L. De Freitas, F. F. M. Cavalheiro, A. M. Lucinio, N. M. Lobato, M. S. Ebeling, G. Kraegpoeth, A. Laerkner, E. De Brito-Ashurst, I. White, C. Gregory, S. Forni, L. G. Flowers, E. Curtis, A. Wood, C. A. Siu, K. Venkatesan, K. Muhammad, J. B. H. Ng, L. Seet, E. Baptista, N. Escoval, A. Tomas, E. Agrawal, R. Mathew, R. Varma, A. Dima, E. Charitidou, E. Perivolioti, E. Pratikaki, M. Vrettou, C. Giannopoulos, A. Zakynthinos, S. Routsi, C. Atchade, E. Houzé, S. Jean-Baptiste, S. Thabut, G. Genève, C. Tanaka, S. Lortat-Jacob, B. Augustin, P. Desmard, M. Montravers, P. de Molina, F. J. González Barbadillo, S. Alejandro, R. Texas Children’s Hospital Clinical Nutriiton Houston USA Texas Children’s Hospital Intensive Care Houston USA Baylor College of Medicine Pediatrics Houston USA Kochi Medical School Department of Anesthesiology and Intensive Care Medicine Nankoku Japan Vall d’ Hebron University Hospital Critical Care Department Barcelona Spain Vall d’ Hebron University Hospital Anesthesia & Reanimation Department Barcelona Spain Vall d’ Hebron University Hospital Neurocritical Care Department Barcelona Spain Vall d’ Hebron University Hospital Coronary Care Unit Barcelona Spain Sagrat Cor University Hospital Critical Care Department Barcelona Spain St Elisabeth Twee Steden Hospital ICU Tilburg Netherlands National Institute of Cardiovascular Diseases PCICU Bratislava Slovakia National Institute of Cardiovascular Diseases Bratislava Slovakia National Institute of Cardiovascular Diseases Pediatric Cardiac Intensive Care Unit Bratislava Slovakia Texas Children’s Hospital Renal Service Houston USA Bristol Royal Hospital for Children Paediatric Intensive Care Unit Bristol UK Intensive Care National Audit & Research Centre (ICNARC) London UK University of Leeds Leeds UK Southampton University Hospitals NHS Trust Southampton UK Great Ormond Street Hospital London UK UCCC ICU Kyiv Ukraine Catholic University Department of Anesthesia and Intensive Care Rome Italy International Renal Research Institute of Vicenza (IRRIV) Department of Nephrology Dialysis and Transplantation Vicenza Italy University of Florence Department of Health Science Section of Anaesthesiology and Intensive Care Florence Italy The Children’s Hospital at Montefiore Pediatric Critical Care Bronx USA The Children’s Hospital at Montefiore Bronx USA Shiraz University of Medical Sciences Pediatric intensive Care Shiraz Islamic Republic of Iran Shiraz University of Medical Sciences Shiraz Islamic Republic of Iran Yokohama City University Hospital Intensive Care Unit Yokohama Japan Yokohama City Univ

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Erratum: Search for new phenomena in final states with large jet multiplicities and missing transverse momentum at = 8 TeV proton-proton collisions using the ATLAS experiment

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Journal of High Energy Physics 2014年第1期2014卷 1-18页

作者： G. Aad T. Abajyan B. Abbott J. Abdallah S. Abdel Khalek O. Abdinov R. Aben B. Abi M. Abolins O. S. AbouZeid H. Abramowicz H. Abreu Y. Abulaiti B. S. Acharya L. Adamczyk D. L. Adams T. N. Addy J. Adelman S. Adomeit T. Adye S. Aefsky T. Agatonovic-Jovin J. A. Aguilar-Saavedra M. Agustoni S. P. Ahlen A. Ahmad M. Ahsan G. Aielli T. P. A. Åkesson G. Akimoto A. V. Akimov M. A. Alam J. Albert S. Albrand M. J. Alconada Verzini M. Aleksa I. N. Aleksandrov F. Alessandria C. Alexa G. Alexander G. Alexandre T. Alexopoulos M. Alhroob M. Aliev G. Alimonti L. Alio J. Alison B. M. M. Allbrooke L. J. Allison P. P. Allport S. E. Allwood-Spiers J. Almond A. Aloisio R. Alon A. Alonso F. Alonso A. Altheimer B. Alvarez Gonzalez M. G. Alviggi K. Amako Y. Amaral Coutinho C. Amelung V. V. Ammosov S. P. Amor Dos Santos A. Amorim S. Amoroso N. Amram C. Anastopoulos L. S. Ancu N. Andari T. Andeen C. F. Anders G. Anders K. J. Anderson A. Andreazza V. Andrei X. S. Anduaga S. Angelidakis P. Anger A. Angerami F. Anghinolfi A. V. Anisenkov N. Anjos A. Annovi A. Antonaki M. Antonelli A. Antonov J. Antos F. Anulli M. Aoki L. Aperio Bella R. Apolle G. Arabidze I. Aracena Y. Arai A. T. H. Arce S. Arfaoui J.-F. Arguin S. Argyropoulos E. Arik M. Arik A. J. Armbruster O. Arnaez V. Arnal O. Arslan A. Artamonov G. Artoni D. Arutinov S. Asai N. Asbah S. Ask B. Åsman L. Asquith K. Assamagan R. Astalos A. Astbury M. Atkinson N. B. Atlay B. Auerbach E. Auge K. Augsten M. Aurousseau G. Avolio D. Axen G. Azuelos Y. Azuma M. A. Baak C. Bacci A. M. Bach H. Bachacou K. Bachas M. Backes M. Backhaus J. Backus Mayes E. Badescu P. Bagiacchi P. Bagnaia Y. Bai D. C. Bailey T. Bain J. T. Baines O. K. Baker S. Baker P. Balek F. Balli E. Banas Sw. Banerjee D. Banfi A. Bangert V. Bansal H. S. Bansil L. Barak S. P. Baranov T. Barber E. L. Barberio D. Barberis M. Barbero D. Y. Bardin T. Barillari M. Barisonzi T. Barklow N. Barlow B. M. Barnett R. M. Barnett A. Baroncelli G. Barone A. J. Barr F. Barreiro J. Barreiro Guimarães da Costa R. Bartoldus A. E. Barton V. Bartsch A. B Fakultät für Mathematik und Physik Albert-Ludwigs-Universität Freiburg Germany Physikalisches Institut University of Bonn Bonn Germany Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman United States of America Institut de Física d’Altes Energies and Departament de Física de la Universitat Autònoma de Barcelona Barcelona Spain LAL Université Paris-Sud and CNRS/IN2P3 Orsay France Institute of Physics Azerbaijan Academy of Sciences Baku Azerbaijan Nikhef National Institute for Subatomic Physics and University of Amsterdam Amsterdam Netherlands Department of Physics Oklahoma State University Stillwater United States of America Department of Physics and Astronomy Michigan State University East Lansing United States of America Department of Physics University of Toronto Toronto Canada Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel DSM/IRFU (Institut de Recherches sur les Lois Fondamentales de l’Univers) CEA Saclay (Commissariat à l’Energie Atomique et aux Energies Alternatives) Gif-sur-Yvette France Department of Physics Stockholm University Stockholm Sweden INFN Gruppo Collegato di Udine Udine Italy Department of Physics King’s College London London United Kingdom AGH University of Science and Technology Faculty of Physics and Applied Computer Science Krakow Poland Physics Department Brookhaven National Laboratory Upton United States of America Department of Physics Hampton University Hampton United States of America Department of Physics Yale University New Haven United States of America Fakultät für Physik Ludwig-Maximilians-Universität München München Germany Particle Physics Department Rutherford Appleton Laboratory Didcot United Kingdom Department of Physics Brandeis University Waltham United States of America Vinca Institute of Nuclear Sciences University of Belgrade Belgrade Serbia Laboratorio de Instrumentacao e Fisica Experimental de Particulas - LIP Lisboa Portug

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Reports of the AAAI 2011 conference workshops

Reports of the AAAI 2011 conference workshops

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作者： Agmon, Noa Agrawal, Vikas Aha, David W. Aloimonos, Yiannis Buckley, Donagh Doshi, Prashant Geib, Christopher Grasso, Floriana Green, Nancy Johnston, Benjamin Kaliski, Burt Kiekintveld, Christopher Law, Edith Lieberman, Henry Mengshoel, Ole J. Metzler, Ted Modayil, Joseph Oard, Douglas W. Onder, Nilufer O'Sullivan, Barry Pastra, Katerina Precup, Doina Ramachandran, Sowmya Reed, Chris Sariel-Talay, Sanem Selker, Ted Shastri, Lokendra Singh, Satinder Smith, Stephen F. Srivastava, Siddharth Sukthankar, Gita Uthus, David C. Williams, Mary-Anne Department of Computer Science University of Texas at Austin United States Center for Knowledge Driven Information Systems Infosys Labs India Adaptive Systems Section Naval Research Laboratory Washington DC United States Department of Computer Science University of Maryland College Park United States EMC's Center of Excellence in Ireland Research Europe at EMC Ireland Department of Computer Science University of Georgia United States School of Informatics University of Edinburgh United Kingdom Department of Computer Science University of Liverpool United Kingdom Department of Computer Science University of North Carolina Greensboro United States Faculty of Engineering and Information Technology University of Technology Sydney Australia VeriSign Inc. United States Department of Computer Science University of Texas at El Paso United States Machine Learning Department Carnegie Mellon University United States Massachusetts Institute of Technology Media Laboratory United States Carnegie Mellon University's Silicon Valley Campus United States Darrell W. Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computing Science University of Alberta Canada College of Information Studies Institute for Advanced Computer Studies University of Maryland College Park United States Department of Computer Science Michigan Technological University United States Department of Computer Science University College Cork Ireland Cognitive Systems Research Insitute Athens Greece School of Computer Science McGill University Canada Stottler Henke Inc. United States School of Computing University of Dundee United Kingdom Department of Computer Engineering Istanbul Technical University Turkey Convergence Lab Infosys Technologies Ltd. United States Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor United States Robotics Institute Carnegie Mellon University United States University of Wisconsin

The AAAI-11 workshop program was held Sunday and Monday, August 7-18, 2011, at the Hyatt Regency San Francisco in San Francisco, California USA. The AAAI-11 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages;Analyzing Microtext;Applied Adversarial Reasoning and Risk Modeling;Artificial Intelligence and Smarter Living: The Conquest of Complexity;Artifiicial Intelligence for Data Center Management and Cloud computing;Automated Action Planning for Autonomous Mobile Robots;Computational Models of Natural Argument;Generalized Planning;Human Computation;Human-Robot Interaction in Elder Care;Interactive Decision Theory and Game Theory, 2010;Language-Action Tools for Cognitive Artificial Agents: Integrating Vision, Action, and Language;Lifelong Learning from Sensorimotor Experience;Plan, Activity, and Intent Recognition;and Scalable Integration of Analytics and Visualization. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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Fluctuation free matrix representation based random data partitioning through HDMR

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AIP Conference Proceedings 2012年第1期1504卷

作者： M. Alper Tunga Metin Demiralp Bahçeşehir University Faculty of Engineering Department of Software Engineering Beşiktaş 34349 İstanbul Turkey İstanbul Technical University Informatics Institute Computational Science and Engineering Program Group for Science and Methods of Computing Maslak 34469 İstanbul Turkey

High Dimensional Model Representation (HDMR) method does not have the random data partitioning capability because of its nature. This paper focuses on how we can bring the random data partitioning feature to the HDMR method by virtue of the fluctuation free approximation method for the multivariate interpolation problems. The main aim of this work is to construct the universal fluctuation free matrix and to determine the best eigenvalues and the corresponding eigenvectors needed in our new method for partitioning the given multivariate random data through the HDMR method.

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