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22nd International Conference on program Comprehension, ICPC 2014

作者： Vasconcelos, Renan Schots, Marcelo Werner, Cláudia Systems Engineering and Computer Science Program COPPE/UFRJ PO Box 68511 Rio de Janeiro RJCEP21945-970 Brazil

ISBN: (纸本)9781450328791

Software development comprises the execution of a variety of tasks, such as bug discovery, finding reusable assets, dependency analysis etc. A better understanding of the task at hand and its surroundings can improve the development performance in general. Software visualizations can support such understanding by addressing different issues according to the necessity of stakeholders. However, knowing which visualizations better fit a given task in progress is not a trivial skill. In this sense, a feature model, intended for organizing the knowledge of a given domain and allowing the reuse of components, can support the identification, categorization and selection of information visualization elements. This work presents an ongoing domain analysis performed for building an information visualization feature model, whose goal is to support the process of choosing and building proper, suitable software visualizations. Copyright © 2014 ACM.

关键词： Visualization

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Correction: Liquid-capped encoded microcapsules for multiplex assays

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Lab on a chip 2017年第4期17卷 738页

作者： Younghoon Song Yunjin Jeong Taehong Kwon Daewon Lee Dong Yoon Oh Tae-Joon Park Junhoi Kim Jiyun Kim Sunghoon Kwon Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Department of Electrical and Computer Science Seoul National University Seoul 151-742 Republic of Korea. Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Interdisciplinary Program of Bioengineering Seoul National University Seoul 151-742 Republic of Korea. Nano Systems Institute Seoul National University Seoul 151-742 Republic of Korea. Department of Materials Science and Engineering Ulsan National Institute of Science and Technology Ulsan 44919 Republic of Korea. Institutes of Entrepreneurial BioConvergence Seoul National University Seoul 151-742 Republic of Korea. skwon@snu.ac.kr and Department of Electrical and Computer Science Seoul National University Seoul 151-742 Republic of Korea and Interdisciplinary Program of Bioengineering Seoul National University Seoul 151-742 Republic of Korea and Nano Systems Institute Seoul National University Seoul 151-742 Republic of Korea and Seoul National University Hospital Biomedical Research Institute Seoul National University Hospital Seoul 151-742 Republic of Korea and Quantamatrix Inc. Seoul 151-742 Republic of Korea.

Correction for 'Liquid-capped encoded microcapsules for multiplex assays' by Younghoon Song et al., Lab Chip, 2017, DOI: .

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Using HMM in strategic games 9

Using HMM in strategic games

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9th International Workshop on Developments in Computational Models, DCM 2013

作者： Benevides, Mario Lima, Isaque Nader, Rafael Rougemont, Pedro Systems and Computer Engineering Program and Computer Science Department Federal University of Rio de Janeiro Brazil

In this paper we describe an approach to resolve strategic games in which players can assume different types along the game. Our goal is to infer which type the opponent is adopting at each moment so that we can increase the player's odds. To achieve that we use Markov games combined with hidden Markov model. We discuss a hypothetical example of a tennis game whose solution can be applied to any game with similar characteristics. © M. Benevides, I. Lima, R. Nader & P. Rougemont This work is licensed under the Creative Commons Attribution License.

关键词： Tennis

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Creation and analysis of biochemical constraint-based models: The COBRA Toolbox v3.0

arXiv

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arXiv 2017年

作者： Heirendt, Laurent Arreckx, Sylvain Pfau, Thomas Mendoza, Sebastián N. Richelle, Anne Heinken, Almut Haraldsdóttir, Hulda S. Wachowiak, Jacek Keating, Sarah M. Vlasov, Vanja Magnusdóttir, Stefania Ng, Chiam Yu Preciat, German Žagare, Alise Chan, Siu H.J. Aurich, Maike K. Clancy, Catherine M. Modamio, Jennifer Sauls, John T. Noronha, Alberto Bordbar, Aarash Cousins, Benjamin El Assal, Diana C. Valcarcel, Luis V. Apaolaza, Iñigo Ghaderi, Susan Ahookhosh, Masoud Guebila, Marouen Ben Kostromins, Andrejs Sompairac, Nicolas Le, Hoai M. Ma, Ding Sun, Yuekai Wang, Lin Yurkovich, James T. Oliveira, Miguel A.P. Vuong, Phan T. El Assal, Lemmer P. Kuperstein, Inna Zinovyev, Andrei Scott Hinton, H. Bryant, William A. Aragón Artacho, Francisco J. Planes, Francisco J. Stalidzans, Egils Maass, Alejandro Vempala, Santosh Hucka, Michael Saunders, Michael A. Maranas, Costas D. Lewis, Nathan E. Sauter, Thomas Palsson, Bernhard Ø. Thiele, Ines Fleming, Ronan M.T. Luxembourg Centre for Systems Biomedicine University of Luxembourg 6 avenue du Swing BelvauxL-4367 Luxembourg Life Sciences Research Unit University of Luxembourg 6 avenue du Swing BelvauxL-4367 Luxembourg University of Chile Blanco Encalada 2085 Santiago Chile Department of Pediatrics University of California San Diego School of Medicine San DiegoCA92093 United States Hinxton CambridgeCB10 1SD United Kingdom Department of Chemical Engineering Pennsylvania State University University University ParkPA16802 United States Department of Physics University of California San Diego 9500 Gilman Dr. San DiegoCA92093 United States Bioinformatics and Systems Biology Program University of California San Diego San DiegoCA United States Sinopia Biosciences San DiegoCA United States School of Computer Science Algorithms and Randomness Center Georgia Institute of Technology AtlantaGA United States Biomedical Engineering and Sciences Department TECNUN University of Navarra Paseo de Manuel Lardizabal 13 San Sebastian20018 Spain Institute of Microbiology and Biotechnology University of Latvia Jelgavas iela 1 RigaLV-1004 Latvia Department of Management Science and Engineering Stanford University StanfordCA94305-4026 United States Bioengineering Department University of California San Diego San DiegoCA United States Utah State University Research Foundation 1695 North Research Park Way North LoganUT84341 United States Centre for Integrative Systems Biology and Bioinformatics Department of Life Sciences Imperial College London London United Kingdom Department of Mathematics University of Alicante Spain California Institute of Technology Computing and Mathematical Sciences MC 305-16 1200 E. California Blvd. PasadenaCA91125 United States Mathomics Center for Mathematical Modeling University of Chile Beauchef 851 Santiago Chile Novo Nordisk Foundation Center for Biosustainability University of California San Diego San DiegoCA92093 Uni

COnstraint-Based Reconstruction and Analysis (COBRA) provides a molecular mechanistic framework for integrative analysis of experimental data and quantitative prediction of physicochemically and biochemically feasible phenotypic states. The COBRA Toolbox is a comprehensive software suite of interoperable COBRA methods. It has found widespread applications in biology, biomedicine, and biotechnology because its functions can be flexibly combined to implement tailored COBRA protocols for any biochemical network. Version 3.0 includes new methods for quality controlled reconstruction, modelling, topological analysis, strain and experimental design, network visualisation as well as network integration of chemoinformatic, metabolomic, transcriptomic, proteomic, and thermochemical data. New multi-lingual code integration also enables an expansion in COBRA application scope via high-precision, high-performance, and nonlinear numerical optimisation solvers for multi-scale, multi-cellular and reaction kinetic modelling, respectively. This protocol can be adapted for the generation and analysis of a constraint-based model in a wide variety of molecular systems biology scenarios. This protocol is an update to the COBRA Toolbox 1.0 and 2.0. The COBRA Toolbox 3.0 provides an unparalleled depth of constraint-based reconstruction and analysis methods. Copyright © 2017, The Authors. All rights reserved.

关键词： Data integration

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Gene expression imputation across multiple brain regions provides insights into schizophrenia risk (vol 51, pg 659, 2019)

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NATURE GENETICS 2019年第6期51卷 1068-1068页

作者： Huckins, Laura M. Dobbyn, Amanda Ruderfer, Douglas M. Hoffman, Gabriel Wang, Weiqing Pardinas, Antonio F. Rajagopal, Veera M. Als, Thomas D. Nguyen, Hoang T. Girdhar, Kiran Boocock, James Roussos, Panos Fromer, Menachem Kramer, Robin Domenici, Enrico Gamazon, Eric R. Purcell, Shaun Demontis, Ditte Borglum, Anders D. Walters, James T. R. O'Donovan, Michael C. Sullivan, Patrick Owen, Michael J. Devlin, Bernie Sieberts, Solveig K. Cox, Nancy J. Im, Hae Kyung Sklar, Pamela Stahl, Eli A. Pamela Sklar Division of Psychiatric Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Genetics and Genomics Icahn School of Medicine at Mount Sinai New York NY USA Department of Psychiatry Icahn School of Medicine at Mount Sinai New York NY USA Icahn Institute for Genomics and Multiscale Biology Icahn School of Medicine at Mount Sinai New York NY USA Vanderbilt University Medical Center Nashville TN USA MRC Centre for Neuropsychiatric Genetics and Genomics Cardiff University Cardiff UK Department of Biomedicine Aarhus University Aarhus Denmark The Lundbeck Foundation Initiative for Integrative Psychiatric Research iPSYCH Denmark Center for Integrative Sequencing Aarhus University Aarhus Denmark Department of Human Genetics David Geffen School of Medicine University of California Los Angeles Los Angeles CA USA Human Brain Collection Core National Institute of Mental Health Bethesda MD USA Laboratory of Neurogenomic Biomarkers Centre for Integrative Biology (CIBIO) University of Trento Trento Italy Clare Hall University of Cambridge Cambridge UK University of North Carolina at Chapel Hill Chapel Hill NC USA Karolinska Institutet Stockholm Sweden Department of Psychiatry University of Pittsburgh Pittsburgh PA USA Systems Biology Sage Bionetworks Seattle WA USA Section of Genetic Medicine Department of Medicine University of Chicago Chicago IL USA Integrated Technology Research Laboratories Pharmaceutical Research Division Takeda Pharmaceutical Company Limited Fujisawa Japan Neuropsychiatry Section Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Neuropsychiatric Signaling Program Department of Psychiatry Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Psychiatry JJ Peters Virginia Medical Center Bronx NY USA Department of Neuroscience Icahn School of Medicine at Mount Sinai New York New York NY USA Friedman Brain Institute

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression.

关键词： Gene expression Genome-wide association studies Schizophrenia Transcriptomics

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Real-Time Music Tracking Based on a Weightless Neural Network

Real-Time Music Tracking Based on a Weightless Neural Networ...

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International Conference on Complex, Intelligent and Software Intensive systems, CISIS

作者： Diego F. P. De Souza Felipe M.G. Franca Priscila M.V. Lima Syst. Eng. & Comput. Sci. Program - COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro RJ BR System Engineering and Computer Science Program - COPPE Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Inst. Tercio Pacitti (NCE) Univ. Fed. do Rio de Janeiro Janeiro Brazil

Music tracking is a useful technique for many music related tasks. Some applications include evaluating musicians performance, automatic score page turning and syncing music lyrics. In this work, we describe a WiSARD-based system for real-time music tracking and evaluate the performance of the corresponding implementation. Given any audio example, the system is capable of recognizing which point of the original signal is currently playing and of dynamically tracking it. In other words, if the music restarts or jumps to another position, the system is capable of following it. This is accomplished thanks to the low complexity training and classifying of the models used, which continually keep track of all possible points of the music, and not only of the current neighboring region. Experiments are provided in order to analyze the performance of the system in three test scenarios: tracking continuous playing, tracking multiple jumps and self predicting errors. The final results demonstrate that the system is efficient even when applied to musics with some level of repetitions and short periods of silence.

关键词： Training Random access memory Music Robot sensing systems Hidden Markov models Neurons

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Author Correction: Reference component analysis of single-cell transcriptomes elucidates cellular heterogeneity in human colorectal tumors

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Nature genetics 2018年第12期50卷 1754页

作者： Huipeng Li Elise T Courtois Debarka Sengupta Yuliana Tan Kok Hao Chen Jolene Jie Lin Goh Say Li Kong Clarinda Chua Lim Kiat Hon Wah Siew Tan Mark Wong Igor Cima Min-Han Tan Lawrence J K Wee Axel M Hillmer Iain Beehuat Tan Paul Robson Shyam Prabhakar Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. Department of Computer Science and Engineering and Center for Computational Biology Indraprastha Institute of Information Technology Delhi India. Synthetic Biology Genome Institute of Singapore Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. Department of Pathology Singapore General Hospital Singapore Singapore. Department of Colorectal Surgery Singapore General Hospital Singapore Singapore. Institute of Bioengineering and Nanotechnology Singapore Singapore. Data Analytics Department Institute for Infocomm Research Singapore Singapore. Cancer Therapeutics and Stratified Oncology Genome Institute of Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Department of Medical Oncology National Cancer Centre Singapore Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. iain.tan.b.h@singhealth.com.sg. Developmental Cellomics Laboratory Genome Institute of Singapore Singapore Singapore. paul.robson@***. The Jackson Laboratory for Genomic Medicine Farmington Connecticut USA. paul.robson@***. Department of Genetics and Genome Sciences Institute for Systems Genomics University of Connecticut Farmington Connecticut USA. paul.robson@***. Department of Biological Sciences National University of Singapore Singapore Singapore. paul.robson@***. Computational and Systems Biology Genome Institute of Singapore Singapore Singapore. prabhakars@gis.a-star.edu.sg.

In the version of the article published, the author list is not accurate. Igor Cima and Min-Han Tan should have been authors, appearing after Mark Wong in the author list, while Paul Jongjoon Choi should not have been listed as an author. Igor Cima and Min-Han Tan both have the affiliation Institute of Bioengineering and Nanotechnology, Singapore, Singapore, and their contributions should have been noted in the Author Contributions section as "I.C. preprocessed Primary Cell Atlas data with inputs from M.-H.T." The following description of the contribution of Paul Jongjoon Choi should not have appeared: "P.J.C. supported the smFISH experiments." In the 'RCA: global panel' section of the Online Methods, the following sentence should have appeared as the second sentence, "An expression atlas of human primary cells (the Primary Cell Atlas) was preprocessed similarly to in ref. 55," with new reference 55 (Cima, I. et al. Tumor-derived circulating endothelial cell clusters in colorectal cancer. science Transl. Med. 8, 345ra89, 2016).

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DevCAFE 1.0: A participatory platform for assessing development initiatives in the field

DevCAFE 1.0: A participatory platform for assessing developm...

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IEEE Global Humanitarian Technology Conference (GHTC)

作者： Brandie Nonnecke Sanjay Krishnan Jay Patel Mo Zhou Laura Byaruhanga Dorothy Masinde Maria Elena Meneses Alejandro Martin del Campo Camille Crittenden Ken Goldberg CITRIS Connected Communities Initiative UC Berkeley Electrical Engineering and Computer Science UC Berkeley Industrial Engineering and Operations Research UC Berkeley Uganda Program Iowa State University Global Resource Systems Iowa State University Tecnologico de Monterrey CITRIS Connected Communities Initiative UC Berkeley

The design and assessment of development initiatives is increasingly participatory, where decision makers consider feedback from affected populations. While digital data collection facilitates faster and more reliable analysis, existing data collection tools are not optimized for unstructured qualitative (textual) data and peer-to-peer participant collaboration. In this paper, we propose a system called the Development Collaborative Assessment and Feedback Engine version 1.0 (DevCAFE), a customizable participatory assessment platform that collects and integrates quantitative assessment, qualitative feedback and peer-to-peer collaborative filtering. DevCAFE incorporates a library of statistical analyses for researchers to quickly identify quantitative and qualitative trends while collecting field data. DevCAFE can run on any mobile device with a Web-browser and can work with or without Internet connectivity. We present results from two pilot projects: (1) 137 participants evaluating family planning education trainings at three Nutrition Education Centers in rural Uganda, and (2) 4,518 participants evaluating policy priorities for elected leaders in the June 2015 Mexico mid-term elections. DevCAFE collected over 19,000 peer-to-peer ratings of 336 submitted ideas. Feedback gathered through DevCAFE enabled targeted reforms to the family planning efforts in Uganda and the need for increased government attention to public safety in Mexico. Case studies and interactive demos are available at: http://***/devcafe/.

关键词： Data collection Filtering Collaboration Principal component analysis Peer-to-peer computing Internet Visualization

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Monitoring collaboration in software processes using social networks

Monitoring collaboration in software processes using social ...

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20th International Conference on Collaboration and Technology, CRIWG 2014

作者： Costa, Gabriella C. B. Santana, Francisco Magdaleno, Andréa M. Werner, Cláudia M. L. UFRJ - Federal University of Rio de Janeiro COPPE - Systems Engineering and Computer Science Department P.O. Box 68511 21945-970 - Rio de Janeiro - RJ Brazil

ISBN: (纸本)9783319101651

Collaboration monitoring in software process is important to check if the collaboration is indeed happening as planned, but there are few approaches that define how to measure and monitor collaboration. By assessing collaboration during an ongoing process execution, project managers can take corrective actions that might improve the process execution and, consequently, reflect on quality gains of the final product. This research work proposes to evaluate the level of coordination achieved by a running software process through social network analysis metrics. © 2014 Springer International Publishing.

关键词： Social sciences computing

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Towards autonomous navigation of unsignalized intersections under uncertainty of human driver intent

Towards autonomous navigation of unsignalized intersections ...

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IEEE/RSJ International Conference on Intelligent Robots and systems

作者： V. Sezer T. Bandyopadhyay D. Rus E. Frazzoli D. Hsu Department of Mechanical Engineering Istanbul Technical University Istanbul Turkey Tirthankar Bandyopadhyay is with Autonomous Systems Program Commonwealth Scientific and Industrial Research Organization Australia Daniela Rus and Emilio Frazzoli are with the Department of Electri-cal&Engineering and Aeronautics&Astronautic respectively Massachusetts Institute of Technology USA Department of Computer Science National University of Singapore Singapore

In a mixed environment of autonomous driverless vehicles and human driven vehicles operating on the same road, identifying intentions of human drivers and interacting with them in a compliant and responsible manner becomes a challenging problem for the driverless vehicles. In this paper, the problem of vehicle interaction at an intersection merging scenario is formulated as an Intention-Aware motion planning problem using the tools from Mixed Observability Markov Decision Process (MOMDP). We utilize the tools from recent intention aware planning framework to demonstrate a merging behavior in the presence of human drivers by trying to infer and act according to the intentions of the human drivers. A driver behavior model for T-junction intersections is developed in order to calculate the probabilistic state transition functions of the MOMDP model. With proposed solution, it is demonstrated that using intention aware planning improves performance in comparison to present time to merge approach by lowering accident probability and intersection navigation duration. The proposed method is tested on a real autonomous vehicle (AV) in the presence of human driven vehicles to validate our approach.

关键词： Vehicles Navigation Mobile robots Roads Hidden Markov models Uncertainty

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