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Meeting Report from the Second "Minimum Information for Biological and Biomedical Investigations" (MIBBI) workshop

Standards in genomic sciences 2010年第3期3卷 259-66页

作者： Carsten Kettner Dawn Field Susanna-Assunta Sansone Chris Taylor Jan Aerts Nigel Binns Andrew Blake Cedrik M Britten Ario de Marco Jennifer Fostel Pascale Gaudet Alejandra González-Beltrán Nigel Hardy Jan Hellemans Henning Hermjakob Nick Juty Jim Leebens-Mack Eamonn Maguire Steffen Neumann Sandra Orchard Helen Parkinson William Piel Shoba Ranganathan Philippe Rocca-Serra Annapaola Santarsiero David Shotton Peter Sterk Andreas Untergasser Patricia L Whetzel Beilstein-Institut Frankfurt am Main Germany Centre for Ecology & Hydrology UK Oxford e-Research Centre University of Oxford UK The European Bioinformatics Institute (EBI) UK Faculty of Engineering - ESAT/SCD Leuven University Leuven-Heverlee Belgium Division of Pathway Medicine University of Edinburgh Medical School Edinburgh UK MRC Harwell UK Medical Department University Medical Center Johannes Gutenberg University-Mainz Mainz Germany Consortium for Genomic Technology Milano Italy University of Nova Gorica Nova Gorica Slovenia NIEHS NIH US National Toxicology Program USA Swiss Institute of Bioinformatics Geneva Switzerland Computational and Systems Medicine and Department of Computer Science University College London London UK Department of Computer Science Aberystwyth University Aberystwyth UK Center for Medical Genetics Ghent University Ghent Belgium Department of Plant Biology University of Georgia Athens USA Department of Stress- and Developmental Biology Institute for Plant Biochemistry Halle Germany Peabody Museum of Natural History Yale University New Haven USA Macquarie University Sydney Australia National University of Singapore Singapore Cancer Pharmacology The Mario Negri Institute for Pharmacological Research Milan Italy Image Bioinformatics Research Group Department of Zoology University of Oxford Oxford UK Wellcome Trust Sanger Institute UK Zentrum für Molekulare Biologie der Universität Heidelberg Heidelberg Germany The National Center for Biomedical Ontology / Stanford Center for Biomedical Informatics Research Stanford University Stanford USA

This report summarizes the proceedings of the second workshop of the 'Minimum Information for Biological and Biomedical Investigations' (MIBBI) consortium held on Dec 1-2, 2010 in Rüdesheim, Germany through the sponsorship of the Beilstein-institute. MIBBI is an umbrella organization uniting communities developing Minimum Information (MI) checklists to standardize the description of data sets, the workflows by which they were generated and the scientific context for the work. This workshop brought together representatives of more than twenty communities to present the status of their MI checklists and plans for future development. Shared challenges and solutions were identified and the role of MIBBI in MI checklist development was discussed. The meeting featured some thirty presentations, wide-ranging discussions and breakout groups. The top outcomes of the two-day workshop as defined by the participants were: 1) the chance to share best practices and to identify areas of synergy; 2) defining a series of tasks for updating the MIBBI Portal; 3) reemphasizing the need to maintain independent MI checklists for various communities while leveraging common terms and workflow elements contained in multiple checklists; and 4) revision of the concept of the MIBBI Foundry to focus on the creation of a core set of MIBBI modules intended for reuse by individual MI checklist projects while maintaining the integrity of each MI project. Further information about MIBBI and its range of activities can be found at http://***/.

关键词： Minimum Information European Bioinformatics institute Biomedical Ontology Stable Link Breakout Group

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HotMobile 2008: Postconference report

HotMobile 2008: Postconference report

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作者： Lindqvist, Janne Pawar, Pravin Stuntebeck, Erich Department of Computer Science and Engineering Helsinki University of Technology Finland Remote Monitoring and Treatment Group Department of Electrical Engineering Mathematics and Computer Science University of Twente Netherlands College of Computing's Ubiquitous Computing Research Group Georgia Institute of Technology

The 9th Workshop on Mobile computing Systems and Applications (HotMobile 08) took place in February in Napa Valley, California. Mark Corner from University of Massachusetts, opened the workshop and introduced the keynote speaker, B.J. Fogg, founder of the Stanford Persuasive technology Lab. The first paper session focused on interpreting data obtained from sensors. Erich Stuntebeck presented 'HealthSense: Classification of Health-Related Sensor Data through User-Assisted Machine Learning'. Deborah Estrin presented her group's research on 'Seeing Our Signals: Combining Location Traces and Web-Based Models for Personal Discovery'. Roy Want from Intel Research presented 'Dynamic Composable computing', addressing the problem of giving users access to a full personal-computing experience with the mobility that smart phones afford. Suman Banerjee from University of Wisconsin, presented his group's paper 'Supporting Continuous Mobility through Multi-Rate Wireless Paketization'.

关键词： Smartphones

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A scalable assistant librarian: Hierarchical subject classification of books

A scalable assistant librarian: Hierarchical subject classif...

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31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008

作者： Crain, Steven P. Jian, Huang Hongyuan, Zha Division of Computational Science and Engineering Georgia Institute of Technology Atlanta GA 30332 United States College of Information Sciences and Technology Pennsylvania State University University Park PA 16802 United States

ISBN: (纸本)9781605581644

In this paper, we discuss our work in progress towards a scalable hierarchical classification system for books using the Library of Congress subject hierarchy. We examine the characteristics of this domain which make the problem very challenging, and we look at several appropriate performance measurements. We show that both Hieron and Hierarchical Support Vector Machines perform moderately well.

关键词： Support vector machines

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Learning to rank with ties

Learning to rank with ties

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31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, ACM SIGIR 2008

作者： Zhou, Ke Xue, Gui-Rong Zha, Hongyuan Yu, Yong Dept. of Computer Science and Engineering Shanghai Jiao-Tong University No. 800 Dongchuan Road Shanghai 200240 China College of Computing Georgia Institute of Technology Atlanta GA 30032 United States

ISBN: (纸本)9781605581644

Designing effective ranking functions is a core problem for information retrieval and Web search since the ranking functions directly impact the relevance of the search results. The problem has been the focus of much of the research at the intersection of Web search and machine learning, and learning ranking functions from preference data in particular has recently attracted much interest. The objective of this paper is to empirically examine several objective functions that can be used for learning ranking functions from preference data. Specifically, we investigate the roles of ties in the learning process. By ties, we mean preference judgments that two documents have equal degree of relevance with respect to a query. This type of data has largely been ignored or not properly modeled in the past. In this paper, we analyze the properties of ties and develop novel learning frameworks which combine ties and preference data using statistical paired comparison models to improve the performance of learned ranking functions. The resulting optimization problems explicitly incorporating ties and preference data are solved using gradient boosting methods. Experimental studies are conducted using three publicly available data sets which demonstrate the effectiveness of the proposed new methods. Copyright 2008 ACM.

关键词： Information retrieval

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Modified binary particle swarm optimization

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Progress in Natural science:Materials International 2008年第9期18卷 1161-1166页

作者： Sangwook Lee Sangmoon Soak Sanghoun Oh Witold Pedrycz Moongu Jeon College of Computing Georgia Institute of Technology Atlanta GA 30332 USA Information Systems Examination Team Korean Intellectual Property Ofice (KIPO) Government Complex Daejeon Building 4 920 Dunsandong Seogu Republic of Korea Department of Information and Communications Gwangju Institute of Science and Technology 261 Cheomdan-gwagiro Buk-gu Gwanju Republic of Korea Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada T6G 2V4

This paper presents a modified binary particle swarm optimization (BPSO) which adopts concepts of the genotype-phenotype rep-resentation and the mutation operator of genetic *** main feature is that the BPSO can be treated as a continuous *** proposed BPSO algorithm is tested on various benchmark functions, and its performance is compared with that of the original *** results show that the modified BPSO outperforms the original BPSO algorithm.

关键词： Binary particle swarm optimization Genotype-phenotype Mutation

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Exploring Social Annotations for Information Retrieval 08

Exploring Social Annotations for Information Retrieval

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17th International Conference on World Wide Web 2008, WWW'08

作者： Zhou, Ding Bian, Jiang Zheng, Shuyi Zha, Hongyuan C. Lee Giles Facebook Inc. 156 University Avenue Palo Alto CA 94301 United States College of Computing Georgia Institute of Technology Atlanta GA 30332 United States Computer Science and Engineering Pennsylvania State University University Park PA 16802 United States Information Sciences and Technology Computer Science and Engineering Pennsylvania State University PA 16802 United States

ISBN: (纸本)9781605580852

Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) discovering topics in the contents and annotations of documents while categorizing the users by domains;and (2) enhancing document and query language models by incorporating user domain interests as well as topical background models. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization framework to incorporate the topical information to language models. Experiments are carried out on a real-world annotation data set sampled from ***. Our results demonstrate significant improvements over traditional approaches.

关键词： Information retrieval

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Energy-aware opcode design

Energy-aware opcode design

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Balaji V. Iyer Jason A. Poovey Thomas M. Conte School of Computer Science College of Computing Georgia Institute of Technology Atlanta GA USA Department of Electrical & Computer Engineering North Carolina State University Raleigh NC USA

Embedded processors are required to achieve high performance while running on batteries. Thus, they must exploit all the possible means available to reduce energy consumption while not sacrificing performance. In this work, one technique to reduce energy is explored to intelligently design the instruction-opcodes of a processor based on a target-workload. The optimization is done using a heuristic that not-only minimizes switching between adjacent instructions, but also simplifies the decoding to reduce latches to save dynamic energy. On average, an optimized opcode is able to be decoded using 40-60% less latches in the decoder. In addition, it is shown that a decoder optimized for algorithms that had similar program structure, similar data-types or similar behavior exhibited consistent patterns of energy reduction. The techniques presented in this paper yield an average 10% reduction in the total dynamic energy. It is also shown that this heuristic can be used to achieve similar results on different issue-width processors.

关键词： Decoding Process design Instruction sets Embedded computing High performance computing Batteries Energy consumption Computer science Educational institutions Design engineering

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Learning multiple graphs for document recommendations 08

Learning multiple graphs for document recommendations

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17th International Conference on World Wide Web 2008, WWW'08

作者： Zhou, Ding Zhu, Shenghuo Yu, Kai Song, Xiaodan Tseng, Belle L. Zha, Hongyuan Giles, C. Lee Face Book Inc. 156 University Avenue Palo Alto CA 94301 United States NEC Labs America 10080 N Wolfe Road Cupertino CA 95014 United States Google Inc. 1600 Amphitheatre Pkway Mountain View CA 94043 United States Yahoo Inc. 701 First Avenue Sunnyvale CA 94089 United States College of Computing Georgia Institute of Technology Atlanta GA 30332 United States Information Sciences and Technology Computer Science and Engineering Pennsylvania State University University Park PA 16802 United States

ISBN: (纸本)9781605580852

The Web offers rich relational data with different semantics. In this paper, we address the problem of document recommendation in a digital library, where the documents in question are networked by citations and are associated with other entities by various relations. Due to the sparsity of a single graph and noise in graph construction, we propose a new method for combining multiple graphs to measure document similarities, where different factorization strategies are used based on the nature of different graphs. In particular, the new method seeks a single low-dimensional embedding of documents that captures their relative similarities in a latent space. Based on the obtained embedding, a new recommendation framework is developed using semi-supervised learning on graphs. In addition, we address the scalability issue and propose an incremental algorithm. The new incremental method significantly improves the efficiency by calculating the embedding for new incoming documents only. The new batch and incremental methods are evaluated on two real world datasets prepared from Cite Seer. Experiments demonstrate significant quality improvement for our batch method and significant efficiency improvement with tolerable quality loss for our incremental method.

关键词： Collaborative filtering

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Simulating Solid‐Solid Phase Transition in Shape‐Memory Alloy Microstructure by Face‐Offsetting Method

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AIP Conference Proceedings 2008年第1期973卷

作者： Ravi S. Bellur Ramaswamy Eliot Fried Xiangmin Jiao Daniel A. Tortorelli Department of Mechanical Science and Engineering University of Illinois at Urbana‐Champaign 1206 W. Green Street Urbana IL 61801 USA Department of Mechanical and Aerospace Engineering Washington University Campus Box 1185 St. Louis MO 63130‐4899 Computational Science and Engineering Division College of Computing Georgia Institute of Technology 801 Atlantic Drive Atlanta GA 30332

Advances in the understanding of martensitic transformations (diffusionless, solid‐solid phase transformations) have been instrumental to the recent discovery of new low hysteresis alloys. However, some key fundamental issues must be better understood to design still better alloys. Restricting attention to antiplane shear, we use finite element analysis to model the shape‐memory alloy microstructure within the Abeyaratne‐Knowles continuum thermomechanical framework and use an interface kinetic relation of the kind proposed by Rosakis and Tsai. Geometric singularities and topological changes associated with microstructural evolution pose significant numerical challenges. We address such challenges with a recently developed front‐tracking scheme called the face‐offsetting method (FOM) to explicitly model phase interfaces. Initial results demonstrate the effectiveness of FOM in resolving needle‐like twinned microstructures.

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Topic segmentation with shared topic detection and alignment of multiple documents

Proceedings of the 30th Annual International ACM SIGIR Confe...

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Proceedings of the 30th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'07 2007年 199-206页

作者： Sun, Bingjun Mitra, Prasenjit Giles, C. Lee Yen, John Zha, Hongyuan Department of Computer Science and Engineering Pennsylvania State University University Park PA 16802 College of Information Sciences and Technology Pennsylvania State University University Park PA 16802 College of Computing Georgia Institute of Technology Atlanta GA 30332

ISBN: (纸本)1595935975

Topic detection and tracking and topic segmentation play an important role in capturing the local and sequential information of documents. Previous work in this area usually focuses on single documents, although similar multiple documents are available in many domains. In this paper, we introduce a novel unsupervised method for shared topic detection and topic segmentation of multiple similar documents based on mutual information (MI) and weighted mutual information (WMI) that is a combination of MI and term weights. The basic idea is that the optimal segmentation maximizes MI (or WMI). Our approach can detect shared topics among documents. It can find the optimal boundaries in a document, and align segments among documents at the same time. It also can handle single-document segmentation as a special case of the multi-document segmentation and alignment. Our methods can identify and strengthen cue terms that can be used for segmentation and partially remove stop words by using term weights based on entropy learned from multiple documents. Our experimental results show that our algorithm works well for the tasks of single-document segmentation, shared topic detection, and multi-document segmentation. Utilizing information from multiple documents can tremendously improve the performance of topic segmentation, and using WMI is even better than using MI for the multi-document segmentation. Copyright 2007 ACM.

关键词： Information retrieval systems

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