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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： J. Ion Titapiccolo M. Ferrario C. Barbieri D. Marcelli F. Mari E. Gatti S. Cerutti P. Smyth M. G. Signorini Politecnico di Milano Department of Bioengineering P.zza Leonardo da Vinci 32 20133 Milano Italy Fresenius Medical Care E Kroenerstrasse 1 61352 Bad Homburg University of California Irvine CA 92697-3435 Department of Computer Science Center for Machine Learning and Intelligent Systems

ISBN: (纸本)9781424441198

The administration of hemodialysis (HD) treatment leads to the continuous collection of a vast quantity of medical data. Many variables related to the patient health status, to the treatment, and to dialyzer settings can be recorded and stored at each treatment session. In this study a dataset of 42 variables and 1526 patients extracted from the Fresenius Medical Care database EuCliD was used to develop and apply a random forest predictive model for the prediction of cardiovascular events in the first year of HD treatment. A ridge-lasso logistic regression algorithm was then applied to the subset of variables mostly involved in the prediction model to get insights in the mechanisms underlying the incidence of cardiovascular complications in this high risk population of patients.

关键词： hemodialysis (HD) forest predictive risk population

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Reports of the AAAI 2012 conference workshops

Reports of the AAAI 2012 conference workshops

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作者： Agrawal, Vikas Baier, Jorge Bekris, Kostas Chen, Yiling D'Avila Garcez, Artur S. Hitzler, Pascal Haslum, Patrik Jannach, Dietmar Law, Edith Lecue, Freddy Lamb, Luis C. Matuszek, Cynthia Palacios, Hector Srivastava, Biplav Shastri, Lokendra Sturtevant, Nathan Stern, Roni Tellex, Stefanie Vassos, Stavros Center for Knowledge Driven Intelligent Systems Enterprise Technology Research Labs Infosys Limited India Pontificia Universidad Católica de Chile Chile Rutgers University NJ United States Harvard University United States City University London United Kingdom Wright State University Dayton OH United States Australian National University Australia TU Dortmund Germany Machine Learning Department Carnegie Mellon University United States IBM Research - Smarter Cities Technology Centre Dublin Ireland Federal University of Rio Grande do Sul Porto Alegre Brazil Computer Science and Engineering Department University of Washington United States Universidad Carlos III de Madrid Spain IBM Research - India India University of Denver United States Ben Gurion University Negev Israel Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology United States Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

The AAAI-12 Workshop program was held Sunday and Monday, July 22-23, 2012, at the Sheraton Centre Toronto Hotel in Toronto, Ontario, Canada. The AAAI-12 workshop program included nine workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were Activity Context Representation: Techniques and Languages, Cognitive Robotics, Grounding Language for Physical systems, Human Computation, intelligent Techniques for Web Personalization and Recommendation systems, Multiagent Pathfinding, Neural-Symbolic learning and Reasoning, Problem Solving Using Classical Planners, and Semantic Cities. This article presents short summaries of those events. Copyright © 2012, Association for the Advancement of Artificial Intelligence.

关键词： Problem solving

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics): Preface

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2012年 7263 LNAI卷 V-VII页

作者： Langs, Georg Rish, Irina Grosse-Wentrup, Moritz Murphy, Brain Menze, Bjoern Sabuncu, Mert Medical University of Vienna Department of Radiology CIR Lab. Währinger Gürtel 18-20 1090 Wien Austria IBM T.J.Watson Research Center Computational Biology Center 1101 Kitchawan Road Yorktown Heights NY 10598 United States Max Planck Institute for Intelligent Systems Spemannstraße 38 72076 Tübingen Germany Carnegie Mellon University Machine Learning Department 5000 Forbes Avenue Pittsburgh PA 15213-3891 United States ETH Zürich Switzerland CSAIL MIT United States Harvard Medical School United States

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Software traceability with topic modeling 10

Software traceability with topic modeling

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32nd ACM/IEEE International Conference on Software Engineering, ICSE 2010

作者： Asuncion, Hazeline U. Asuncion, Arthur U. Taylor, Richard N. Institute for Software Research University of California Irvine CA United States Center for Machine Learning and Intelligent Systems University of California Irvine CA United States

ISBN: (纸本)9781605587196

Software traceability is a fundamentally important task in software engineering. The need for automated traceability increases as projects become more complex and as the number of artifacts increases. We propose an automated technique that combines traceability with a machine learning technique known as topic modeling. Our approach automatically records traceability links during the software development process and learns a probabilistic topic model over artifacts. The learned model allows for the semantic categorization of artifacts and the topical visualization of the software system. To test our approach, we have implemented several tools: an artifact search tool combining keyword-based search and topic modeling, a recording tool that performs prospective traceability, and a visualization tool that allows one to navigate the software architecture and view semantic topics associated with relevant artifacts and architectural components. We apply our approach to several data sets and discuss how topic modeling enhances software traceability, and vice versa. © 2010 ACM.

关键词： Software architecture

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Software traceability with topic modeling

Software traceability with topic modeling

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International Conference on Software Engineering (ICSE)

作者： Hazeline U. Asuncion Arthur U. Asuncion Richard N. Taylor Institute for Software Research University of California Irvine USA Center of Machine Learning and Intelligent Systems University of California Irvine USA

关键词： Semantics Software machine learning Visualization Large scale integration Probabilistic logic Resource management

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AAAI 2008 workshop reports

AAAI 2008 workshop reports

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作者： Anand, Sarabjot Singh Bunescu, Razvan Carvcdho, Vitor Chomicki, Jan Conitzer, Vincent Cox, Michael T. Dignum, Virginia Dodds, Zachary Dredze, Mark Furcy, David Gabrilovich, Evgeniy Göker, Mehmet H. Guesgen, Hans Hirsh, Haym Jannach, Dietmar Junker, Ulrich Ketter, Wolfgang Kobsa, Alfred Koenig, Sven Lau, Tessa Lewis, Lundy Matson, Eric Metzler, Ted Mihalcea, Rada Mobasher, Bamshad Pineau, Joelle Poupart, Pascal Raja, Anita Ruml, Wheeler Sadeh, Norman Shani, Guy Shapiro, Daniel Smith, Trey Taylor, Matthew E. Wagstaff, Kiri Walsh, William Zhou, Rong Department of Computer Science University of Warwick United Kingdom School of Electrical Engineering and Computer Science Ohio University United States Microsoft Live Labs United States computer science and engineering University at Buffalo United States Computer science and economics Duke University United States Intelligent Computing group of BBN Technologies Utrecht University Netherlands Department of Computer science Harvey Mudd College United States University of Pennsylvania United States University of Wisconsin United States Yahoo Research PricewaterhouseCoopers Center for Advanced Research United States Department of Computer science School of Engineering and Advanced Technology Massey University New Zealand Departent of Computer science Rutgers University United States Department of Computer Science Dortmund University of Technology. Germany ILOG Rotterdam School of Management Erasmus University Netherlands Donald Bren School of Information and Computer Sciences University of California Irvine United States Depatrment of Computer science University of Southern California United States IBM Almaden Research Center United States Department of Computer Information Technology Southern New Hampshire University United States Computer and Information Technology Program College of Technology Purdue University United States Hughes Program for Religion and Science Dialogue Oklahoma City University United States Department of Computer Science and Engineering University of North Texas United States School of Computing DePaul University United States Department of Computer Science McGill University Canada School of Computer Science University of Waterloo Canada Department of Software and Information Systems University of North Carolina Charlotte United States University of New Hampshire United States Microsoft Research Institute for the Study of Learning and Expertise Applied Reactivity Inc. United States NASA Ames Research Center Carnegie Mellon University West

AAAI was pleased to present the AAAI-08 Workshop Program, held Sunday and Monday, July 13-14, in Chicago, Illinois, USA. The program included the following 15 workshops: Advancements in POMDP Solvers;AI Education Workshop Colloquium;Coordination, Organizations, Institutions, and Norms in Agent systems, Enhanced Messaging;Human Implications of Human-Robot Interaction;intelligent Techniques for Web Personalization and Recommender systems;Metareasoning: Thinking about Thinking;Multidisciplinary Workshop on Advances in Preference Handling;Search in Artificial Intelligence and Robotics;Spatial and Temporal Reasoning;Trading Agent Design and Analysis;Transfer learning for Complex Tasks;What Went Wrong and Why: Lessons from AI Research and Applications;and Wikipedia and Artificial Intelligence: An Evolving Synergy. Copyright © 2009, Association for the Advancement of Artificial Intelligence. All rights reserved.

关键词： Human robot interaction

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A Study On Iterative learning Control With Adjustment Of learning Interval For Monotone Convergence In The Sense Of Sup-Norm

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Asian Journal of Control 2008年第1期4卷

作者： Kwang-Hyun Park Zeungnam Bien Division of EE Department of EECS Korea Advanced Institute of Science and Technology 373–1 Kusong-dong Yusong-gu Taejon 305–701 Korea. Zeungname Bien:received the B.S. degree in electronics engineering from Seoul National University Seoul Korea in 1969 and the M.S. and Ph.D. degrees in electrical engineering from the University of Iowa Iowa City Iowa U.S.A. in 1972 and 1975 respectively. During 1976–1977 academic years he taught as assistant professor at the Department of Electrical Engineering University of Iowa. Then Dr. Bien joined Korea Advanced Institute of Science and Technology summer 1977 and is now Professor of Control Engineering at the Department of Electrical Engineering and Computer Science KAIST. Dr. Bien was the president of the Korea Fuzzy Logic and Intelligent Systems Society during 1990–1995 and also the general chair of IFSA World Congress 1993 and for FUZZ-IEEE99 respectively. He is currently co-Editor-in-Chief for International Journal of Fuzzy Systems (IJFS) Associate Editor for IEEE Transactions on Fuzzy Systems and a regional editor for the International Journal of Intelligent Automation and Soft Computing. He has been serving as Vice President for IFSA since 1997 and is now Chief Chairman of Institute of Electronics Engineers of Korea and Director of Humanfriendly Welfare Robot System Research Center. His current research interests include intelligent control methods with emphasis on fuzzy logic systems service robotics and rehabilitation engineering and large-scale industrial control systems. Kwang-Hyun Park:received the B.S. M.S. and Ph.D. degrees in electrical engineering and computer science from KAIST Korea in 1994 19997 and 2001 respectively. He is now a researcher at Human-friendly Welfare Robot System Research Center. His research interests include learning control machine learning human-friendly interfaces and service robotics.

It has been found that some huge overshoot in the sense of sup-norm may be observed when typical iterative learning control (ILC) algorithms are applied to LTI systems, even though monotone convergence in the sense of λ-norm is guaranteed. In this paper, a new ILC algorithm with adjustment of learning interval is proposed to resolve such an undesirable phenomenon, and it is shown that the output error can be monotonically converged to zero in the sense of sup-norm when the proposed ILC algorithm is applied. A numerical example is given to show the effectiveness of the proposed algorithm.

关键词： Iterative learning control monotone convergence learning interval sup-norm

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Deep learning in Biomedical Data Science

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Biomedical Data Science 1000年第1期1卷 181-205页

作者： Pierre Baldi Department of Computer Science Institute for Genomics and Bioinformatics and Center for Machine Learning and Intelligent Systems University of California Irvine California 92697 USA email: pfbaldi@uci.edu

Since the 1980s, deep learning and biomedical data have been coevolving and feeding each other. The breadth, complexity, and rapidly expanding size of biomedical data have stimulated the development of novel deep learning methods, and application of these methods to biomedical data have led to scientific discoveries and practical solutions. This overview provides technical and historical pointers to the field, and surveys current applications of deep learning to biomedical data organized around five subareas, roughly of increasing spatial scale: chemoinformatics, proteomics, genomics and transcriptomics, biomedical imaging, and health care. The black box problem of deep learning methods is also briefly discussed.

关键词： neural networks machine learning omic data biomedical imaging electronic medical records

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Informed machine learning 1

Informed Machine Learning

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丛书名： Cognitive Technologies

1000年

作者： Daniel Schulz Christian Bauckhage

ISBN: (数字)9783031830976

ISBN: (纸本)9783031830969;9783031830990

This open access book presents the concept of Informed machine learning and demonstrates its practical use with a compelling collection of applications of this paradigm in industrial and business use cases. These range from health care over manufacturing and material science to more advanced combinations with deep learning, say, in the form of physical informed neural networks. The book is intended for those interested in modern informed machine learning for a wide range of practical applications where the aspect of small data sets is a challenge. machine learning with small amounts of data? After the recent success of Artificial Intelligence based on training with massive amounts of data, this idea may sound exotic. However, it addresses crucial needs of practitioners in industry. While many industrial applications stand to benefit from the use of AI, the amounts of data needed by current learning paradigms are often hard to come by in industrial settings. As an alternative, learning methods and models are called for which integrate other sources of knowledge in order to compensate for the lack of data. This is where the principle of “Informed machine learning” comes into play. Informed machine learning combines purely data driven learning and knowledge-based techniques to learn from both data and knowledge. This has several advantages. It reduces the need for data, it often results in smaller, less complex and more robust models, and even makes machine learning applicable in settings where data is scarce. The kind of knowledge to be incorporated into learning processes can take many different forms, for example, differential equations, analytical models, simulation results, logical rules, knowledge graphs, or human feedback which makes the approach overall very powerful and widely applicable.

关键词： machine learning Artificial Intelligence Natural Language Processing (NLP) Data Structures and Information Theory Knowledge based systems Multiagent systems

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machine learning and Interpretation in Neuroimaging 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Georg Langs Irina Rish Moritz Grosse-Wentrup Brian Murphy

ISBN: (数字)9783642347139

ISBN: (纸本)9783642347122

Brain imaging brings together the technology, methodology, research questions and approaches of a wide range of scientific fields including physics, statistics, computer science, neuroscience, biology, and engineering. Thus, methodological and technological advances that enable us to obtain measurements, examine relationships across observations, and link these data to neuroscientific hypotheses happen in a highly interdisciplinary environment. The dynamic field of machine learning with its modern approach to data mining provides many relevant approaches for neuroscience and enables the exploration of open questions. This state-of-the-art survey offers a collection of papers from the Workshop on machine learning and Interpretation in Neuroimaging, MLINI 2011, held at the 25th Annual Conference on Neural Information Processing, NIPS 2011, in the Sierra Nevada, Spain, in December 2011. Additionally, invited speakers agreed to contribute reviews on various aspects of the field, adding breadth and perspective to the volume. The 32 revised papers were carefully selected from 48 submissions. At the interface between machine learning and neuroimaging the papers aim at shedding some light on the state of the art in this interdisciplinary field. They are organized in topical sections on coding and decoding, neuroscience, dynamcis, connectivity, and probabilistic models and machine learning.

关键词： Computer Imaging, Vision, Pattern Recognition and Graphics Pattern Recognition Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Image Processing and Computer Vision Computer Applications

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