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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROscience 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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Predictive efficacy of a new association football league format in Polish Ekstraklasa 2

Predictive efficacy of a new association football league for...

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2nd Workshop on Machine Learning and Data Mining for Sports Analytics, MLSA 2015

作者： Lasek, Jan Gagolewski, Marek Interdisciplinary PhD Studies Program Institute of Computer Science Polish Academy of Sciences Jana Kazimierza 5 Warsaw01-248 Poland Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw01-447 Poland Faculty of Mathematics and Information Science Warsaw University of Technology Koszykowa 75 Warsaw00-662 Poland

In this study we investigate the recently introduced competition format for the top association football division in Poland (similar to one used in, e.g., Belgium and Kazakhstan). We compare it to the double round-robin tournament which is the most prevalent league for-mat among European leagues. In a simulation study we show that the new league format has better ability to determine the strongest competitor as the winner of a league as well as it yields higher correlation with theoretical latent teams' strength parameters in the model.

关键词： Routers

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Bayesian group factor analysis with structured sparsity

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2016年第1期17卷

作者： Kevin Murphy Bernhard Schölkopf Shiwen Zhao Chuan Gao Sayan Mukherjee Barbara E. Engelhardt Google MPI for Intelligent Systems Computational Biology and Bioinformatics Program Department of Statistical Science Duke University Durham NC Department of Statistical Science Duke University Durham NC Departments of Statistical Science Computer Science Mathematics Duke University Durham NC Department of Computer Science Center for Statistics and Machine Learning Princeton University Princeton NJ

Latent factor models are the canonical statistical tool for exploratory analyses of low-dimensional linear structure for a matrix of p features across n samples. We develop a structured Bayesian group factor analysis model that extends the factor model to multiple coupled observation matrices; in the case of two observations, this reduces to a Bayesian model of canonical correlation analysis. Here, we carefully define a structured Bayesian prior that encourages both element-wise and column-wise shrinkage and leads to desirable behavior on high-dimensional data. In particular, our model puts a structured prior on the joint factor loading matrix, regularizing at three levels, which enables element-wise sparsity and unsupervised recovery of latent factors corresponding to structured variance across arbitrary subsets of the observations. In addition, our structured prior allows for both dense and sparse latent factors so that covariation among either all features or only a subset of features can be recovered. We use fast parameter-expanded expectation-maximization for parameter estimation in this model. We validate our method on simulated data with substantial structure. We show results of our method applied to three high-dimensional data sets, comparing results against a number of state-of-the-art approaches. These results illustrate useful properties of our model, including i) recovering sparse signal in the presence of dense effects; ii) the ability to scale naturally to large numbers of observations; iii) flexible observation- and factor-specific regularization to recover factors with a wide variety of sparsity levels and percentage of variance explained; and iv) tractable inference that scales to modern genomic and text data sizes.

关键词： bayesian structured sparsity canonical correlation analysis mixture models parameter expansion sparse and low-rank matrix decomposition sparse priors

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Efforts to Overcome Cyber Crime Actions in Indonesia

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Journal of Physics: Conference Series 2018年第1期1114卷

作者： Muhammad Hatta Ramalinggam Rajamanickam Dahlan Abdullah H Hartono Marten Bunga Hari Sapto Adji Amiruddin Hardianto Djanggih Nasrun Hipan S Salle S Salmawati Abdul Azis Mirnawanti Wahab Kisman Karinda A A Saleh I Ketut Sudarsana Agung Purnomo Fatkul Anam Dian Rianita Department of Criminal Law Universitas Malikusssaleh Aceh Indonesia Faculty of Law National University of Malaysia Selangor Malaysia Department of Informatics Universitas Malikussaleh Aceh Indonesia Department of Computer Science STMIK IBBI Medan Indonesia Department of Law Universitas Gorontalo Indonesia Department of Law Universitas Tompotika Luwuk Banggai Indonesia Department of Law Universitas Muhammadiyah Luwuk Banggai Indonesia Department of Law Universitas Muslim Indonesia Indonesia Department of State Adminstration Sekolah Tinggi Ilmu Administrasi YAPPI Makassar Indonesia Department of Management Sekolah Tinggi Ilmu Ekonomi AMKOP Makassar Indonesia Department of Goverment Studies Universitas Muhammadiyah Luwuk Banggai Indonesia Statistics Financial & Social Sciences Research Group Indonesia Department of Religious Education Institut Hindu Dharma Negeri Denpasar Denpasar Indonesia Entrepreneurship Program Bina Nusantara Institute of Creative Technology Malang Indonesia Department of Mathematics Education Universitas Wijaya Kusuma Surabaya Indonesia Department of Administration University of Lancang Kuning Riau Indonesia

The presence of the internet can penetrate the boundaries between countries and accelerate the spread and exchange of information throughout the world. Although there are many positive impacts of internet use, there are also negative impacts that disturb the community. One can easily become a criminal (cyber crime) only by creating blogs, accounts, applications, programs, sites in various mass media and social media to commit fraud, data theft, illegal use of credit cards, the spread of pornographic content, online gambling, online prostitution, hate speech, radicalism, terrorism and so on. To overcome crime in the virtual space, the government has issued Law No. 19 of 2016 concerning Amendments to Law No. 11 of 2008 concerning Information and Electronic Transactions. But not all internet users know and understand the substance of the law. Therefore, the government, employers and institutions that are engaged in information and communication technology have the responsibility to socialize the law to the public to prevent crime in the virtual space.

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Retraction: Efforts to Overcome Cyber Crime Actions in Indonesia (J. Phys.: Conf. Ser. 1114 012081)

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Journal of Physics: Conference Series 2018年第1期1114卷

This paper was retracted by IOP Publishing on 12 December 2018. This paper was published due to a technical error and was not intended to be included in this journal. Retraction published: 8 February 2019

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Detection of CMB-Cluster Lensing using Polarization Data from SPTpol

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Physical Review Letters 2019年第18期123卷 181301-181301页

作者： S. Raghunathan S. Patil E. Baxter B. A. Benson L. E. Bleem T. M. Crawford G. P. Holder T. McClintock C. L. Reichardt T. N. Varga N. Whitehorn P. A. R. Ade S. Allam A. J. Anderson J. E. Austermann S. Avila J. S. Avva D. Bacon J. A. Beall A. N. Bender F. Bianchini S. Bocquet D. Brooks D. L. Burke J. E. Carlstrom J. Carretero F. J. Castander C. L. Chang H. C. Chiang R. Citron M. Costanzi A. T. Crites L. N. da Costa S. Desai H. T. Diehl J. P. Dietrich M. A. Dobbs P. Doel S. Everett A. E. Evrard C. Feng B. Flaugher P. Fosalba J. Frieman J. Gallicchio J. García-Bellido E. Gaztanaga E. M. George T. Giannantonio A. Gilbert R. A. Gruendl J. Gschwend N. Gupta G. Gutierrez T. de Haan N. W. Halverson N. Harrington J. W. Henning G. C. Hilton D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes N. Huang J. Hubmayr K. D. Irwin T. Jeltema M. Carrasco Kind L. Knox N. Kuropatkin O. Lahav A. T. Lee D. Li M. Lima A. Lowitz M. A. G. Maia J. L. Marshall J. J. McMahon P. Melchior F. Menanteau S. S. Meyer R. Miquel L. M. Mocanu J. J. Mohr J. Montgomery C. Corbett Moran A. Nadolski T. Natoli J. P. Nibarger G. Noble V. Novosad R. L. C. Ogando S. Padin A. A. Plazas C. Pryke D. Rapetti A. K. Romer A. Roodman A. Carnero Rosell E. Rozo J. E. Ruhl E. S. Rykoff B. R. Saliwanchik E. Sanchez J. T. Sayre V. Scarpine K. K. Schaffer M. Schubnell S. Serrano I. Sevilla-Noarbe C. Sievers G. Smecher M. Smith M. Soares-Santos A. A. Stark K. T. Story E. Suchyta M. E. C. Swanson G. Tarle C. Tucker K. Vanderlinde T. Veach J. De Vicente J. D. Vieira V. Vikram G. Wang W. L. K. Wu V. Yefremenko Y. Zhang Department of Physics and Astronomy University of California Los Angeles California 90095 USA School of Physics University of Melbourne Parkville VIC 3010 Australia Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Fermi National Accelerator Laboratory MS209 P.O. Box 500 Batavia Illinois 60510 USA Kavli Institute for Cosmological Physics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA Department of Astronomy and Astrophysics University of Chicago 5640 South Ellis Avenue Chicago Illinois 60637 USA High Energy Physics Division Argonne National Laboratory 9700 S. Cass Avenue Argonne Illinois 60439 USA Astronomy Department University of Illinois at Urbana-Champaign 1002 W. Green Street Urbana Illinois 61801 USA Department of Physics University of Illinois Urbana-Champaign 1110 W. Green Street Urbana Illinois 61801 USA Canadian Institute for Advanced Research CIFAR Program in Gravity and the Extreme Universe Toronto Ontario M5G 1Z8 Canada Department of Physics University of Arizona Tucson Arizona 85721 USA Max Planck Institute for Extraterrestrial Physics Giessenbachstrasse Garching 85748 Germany Universitäts-Sternwarte Fakultät für Physik LudwigMaximilians Universität München Scheinerstr. 1 München 81679 Germany Cardiff University Cardiff CF10 3XQ United Kingdom Fermi National Accelerator Laboratory P. O. Box 500 Batavia Illinois 60510 USA NIST Quantum Devices Group 325 Broadway Mailcode 817.03 Boulder Colorado 80305 USA Instituto de Fisica Teorica UAM/CSIC Universidad Autonoma de Madrid 28049 Madrid Spain Department of Physics University of California Berkeley California 94720 USA Institute of Cosmology & Gravitation University of Portsmouth Dennis Sciama Building Burnaby Road Portsmouth PO1 3FX United Kingdom Faculty of Physics Ludwig-Maximilians-Universität Scheinerstr. 1 Munich 81679 Germany Department of Physics & Astronomy University College London Gower

We report the first detection of gravitational lensing due to galaxy clusters using only the polarization of the cosmic microwave background (CMB). The lensing signal is obtained using a new estimator that extracts the lensing dipole signature from stacked images formed by rotating the cluster-centered Stokes QU map cutouts along the direction of the locally measured background CMB polarization gradient. Using data from the SPTpol 500 deg2 survey at the locations of roughly 18 000 clusters with richness λ≥10 from the Dark Energy Survey (DES) Year-3 full galaxy cluster catalog, we detect lensing at 4.8σ. The mean stacked mass of the selected sample is found to be (1.43±0.40)×1014M⊙ which is in good agreement with optical weak lensing based estimates using DES data and CMB-lensing based estimates using SPTpol temperature data. This measurement is a key first step for cluster cosmology with future low-noise CMB surveys, like CMB-S4, for which CMB polarization will be the primary channel for cluster lensing measurements.

关键词： Cosmic microwave background Gravitational lenses Galaxy clusters

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Response to Shen et al

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American journal of human genetics 2016年第5期99卷 1220-1221页

作者： Kevin J Galinsky Gaurav Bhatia Po-Ru Loh Stoyan Georgiev Sayan Mukherjee Nick J Patterson Alkes L Price Department of Biostatistics Harvard T.H. Chan School of Public Health Boston MA 02115 USA Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Electronic address: galinsky@fas.harvard.edu. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA 02142 USA Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA 02115 USA. Google Palo Alto CA 94043 USA. Departments of Statistical Science Computer Science and Mathematics Duke University Durham NC 27708 USA. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA 02142 USA. Program in Medical and Population Genetics Broad Institute of MIT and Harvard Cambridge MA 02142 USA Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA 02115 USA. Electronic address: aprice@hsph.harvard.edu.

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A Data Mining Analysis of Asthma Risk Factors

A Data Mining Analysis of Asthma Risk Factors

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International Conference on Applied Computing and Information Technology/International Conference on Computational science and Intelligence (ACIT-CSI)

作者： Alma Pochini Ben M. Williams Hasanboy M. Isomitdinov Gongzhu Hu Departments of Finance Law & Departments of Mathematics Data Mining Graduate Certificate Program Department of Economics Department of Computer Science Central Michigan University Michigan USA

Asthma is a chronic inflammatory disease of the airways. Although causes of asthma may directly related to environment factors such as dust mites, pollens, cold air, etc.,we would like to find out if demographic, social and behavior factors also play a role. In this paper, we present a study of the behavioral rick factors in relation to people getting asthma. This study applies data mining techniques to the 2013 Behavioral Risk Factor Surveillance System (BRFSS) dataset of over 490,000 observations with 336 variables. The results reveal some relationships between some of these variables and asthma that may help asthma prevention.

关键词： Logistics Diseases Data mining Neural networks Regression tree analysis

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PySHMEM: A High Productivity OpenSHMEM Interface for Python

PySHMEM: A High Productivity OpenSHMEM Interface for Python

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International Conference on Partitioned Global Address Space programming Models (PGAS)

作者： Aaron Welch Pavel Shamis Pengfei Hao Barbara Chapman Department of Computer Science University of Houston Houston TX High Performance Computing Research Program Oak Ridge National Laboratory Oak Ridge Tennessee Computer Science and Mathematics Division Oak Ridge National Laboratory Oak Ridge Tennessee

OpenSHMEM is a well known high performance communication library implementing the Partitioned Global Address Space (PGAS) programming model. It exposes a broad range of one-sided communication semantics which maps well to modern network technologies and can achieve a level of performance that is close to that of network hardware. In this paper we explore how OpenSHMEM semantics can be integrated within the context of Python, a high level programming language that is widely used by the scientific community. In this work we introduce the PySHMEM library that provides simple and convenient interfaces for accessing OpenSHMEM functionality. The goal of this paper is twofold: (1) investigate how high-level programming semantics can be used to expose a high productivity interface for accessing low-level communication semantics, and (2) evaluate performance overheads imposed by Python with respect to native (C) OpenSHMEM performance.

关键词： Semantics Arrays Libraries Memory management Productivity Resource management programming

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Absolute change pivot rule for the simplex algorithm

Absolute change pivot rule for the simplex algorithm

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International MultiConference of Engineers and computer Scientists, IMECS 2014

作者： Chankong, Kittiphong Intiyot, Boonyarit Sinapiromsaran, Krung Applied Mathematics and Computational Science Program Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok Thailand

ISBN: (纸本)9789881925336

The simplex algorithm is a widely used method for solving a linear programming problem (LP) which is first presented by George B. Dantzig. One of the important steps of the simplex algorithm is applying an appropriate pivot rule, the rule to select the entering variable. An effective pivot rule can lead to the optimal solution of LP with the small number of iterations. In a minimization problem, Dantzig's pivot rule selects an entering variable corresponding to the most negative reduced cost. The concept is to have the maximum improvement in the objective value per unit step of the entering variable. However, in some problems, Dantzig's rule may visit a large number of extreme points before reaching the optimal solution. In this paper, we propose a pivot rule that could reduce the number of such iterations over the Dantzig's pivot rule. The idea is to have the maximum improvement in the objective value function by trying to block a leaving variable that makes a little change in the objective function value as much as possible. Then we test and compare the efficacy of this rule with Dantzig' original rule.

关键词： Linear programming

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