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17th Genetic and Evolutionary Computation Conference, GECCO 2015

作者： Zheng, Jinhua Bai, Hui Shen, Ruimin Li, Miqing Institute of Information Engineering Xiangtan University Hunan411105 China Institute of Mathematics and Computational Science Xiangtan University Hunan411105 China Department of Information Systems and Computing Brunel University Uxbridge MiddlesexUB8 3PH United Kingdom

ISBN: (纸本)9781450334884

This study exhaustively compares the abilities to solve manyobjective problems of eight representative algorithms from four different classes (i.e., Pareto-, aggregation-, indicator-, and diversity-based EMO algorithms). The eight compared algorithms are tested on four types of well-defined continuous, discontinuous and combinatorial problems, through three performance metrics as well as a visual observation in the decision space. We can conclude from the experimental results that the performance of the eight algorithms differ not only on the dimensionality of the problems, but also on the shape and features of the Pareto front. From this it suggests an appropriate choice for researchers and practitioners when solving many-objective problems.

关键词： Problem solving

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A fully implicit method for lattice boltzmann equations

A fully implicit method for lattice boltzmann equations

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作者： Huang, Jizu Yang, Chao Cai, Xiao-Chuan Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China Institute of Software Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Computer Science Beijing100190 China Department of Computer Science University of Colorado Boulder BoulderCO80309 United States

Existing approaches for solving the lattice Boltzmann equations with finite difference methods are explicit and semi-implicit;both have certain stability constraints on the time step size. In this work, a fully implicit second-order finite difference scheme is developed. We focus on a parallel, highly scalable, Newton-Krylov-RAS algorithm for the solution of a large sparse nonlinear system of equations arising at each time step. Here, RAS is a restricted additive Schwarz preconditioner based on a first-order spatial discretization. We show numerically that by using the fully implicit method the time step size is no longer constrained by the CFL condition, and the Newton-Krylov- RAS algorithm is scalable on a supercomputer with more than ten thousand processors. Moreover, to calculate the steady state solution we investigate an adaptive time stepping strategy. The total compute time required by the implicit method with adaptive time stepping is much smaller than that of an explicit method for several test cases. © 2015 Society for Industrial and Applied Mathematics.

关键词： Finite difference method

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M-Statistic for Kernel Change-Point Detection 15

M-Statistic for Kernel Change-Point Detection

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Annual Conference on Neural Information Processing Systems

作者： Shuang Li Yao Xie Hanjun Dai Le Song H. Milton Stewart School of Industrial and Systems Engineering Georgian Institute of Technology Computational Science and Engineering College of Computing Georgia Institute of Technology

ISBN: (纸本)9781510825024

Detecting the emergence of an abrupt change-point is a classic problem in statistics and machine learning. Kernel-based nonparametric statistics have been proposed for this task which make fewer assumptions on the distributions than traditional parametric approach. However, none of the existing kernel statistics has provided a computationally efficient way to characterize the extremal behavior of the statistic. Such characterization is crucial for setting the detection threshold, to control the significance level in the offline case as well as the average run length in the online case. In this paper we propose two related computationally efficient M-statistics for kernel-based change-point detection when the amount of background data is large. A novel theoretical result of the paper is the characterization of the tail probability of these statistics using a new technique based on change-of-measure. Such characterization provides us accurate detection thresholds for both offline and online cases in computationally efficient manner, without the need to resort to the more expensive simulations such as bootstrapping. We show that our methods perform well in both synthetic and real world data.

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Analysis on a Superlinearly Convergent Augmented Lagrangian Method

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Acta Mathematica Sinica,English Series 2014年第1期30卷 1-10页

作者： Ya Xiang YUAN State Key Laboratory of Scientific/Engineering Computing Institute of Computational Mathematics and Scientific/Engineering ComputingAcademy of Mathematics and Systems ScienceChinese Academy of Sciences

The augmented Lagrangian method is a classical method for solving constrained ***,the augmented Lagrangian method attracts much attention due to its applications to sparse optimization in compressive sensing and low rank matrix optimization ***,most Lagrangian methods use first order information to update the Lagrange multipliers,which lead to only linear *** this paper,we study an update technique based on second order information and prove that superlinear convergence can be *** properties of the update formula are given and some implementation issues regarding the new update are also discussed.

关键词： Nonlinearly constrained optimization augmented Lagrange function Lagrange multiplier convergence

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Erratum to: Towards the abstract system theory of system science for cognitive and intelligent systems

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Complex & Intelligent Systems 2016年第1期1卷 23-23页

作者： Yingxu Wang Laboratory for Computational Intelligence and Software Science International Institute of Cognitive Informatics and Cognitive Computing (ICIC) Department of Electrical and Computer Engineering Schulich School of Engineering and Hotchkiss Brain Institute University of Calgary Calgary Canada

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Madness: A multiresolution, adaptive numerical environment for scientific simulation

Madness: A multiresolution, adaptive numerical environment f...

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作者： Harrison, Robert J. Beylkin, Gregory Bischoff, Florian A. Calvin, Justus A. Fann, George I. Fosso-Tande, Jacob Galindo, Diego Hammond, Jeff R. Hartman-Baker, Rebecca Hill, Judith C. Jia, Jun Kottmann, Jakob S. Ou, M-J. Yvonne Pei, Junchen Ratcliff, Laura E. Reuter, Matthew G. Richie-Halford, Adam C. Romero, Nichols A. Sekino, Hideo Shelton, William A. Sundahl, Bryan E. Thornton, W. Scott Valeev, Edward F. Vázquez-Mayagoitia, Álvaro Vence, Nicholas Yanai, Takeshi Yokoi, Yukina Stony Brook University Stony BrookNY11794 United States University of Colorado at Boulder BoulderCO80309 United States Institut für Chemie Humboldt-Universität Zu Berlin Unter den Linden 6 Berlin10099 Germany Department of Chemistry Virginia Tech. BlacksburgVA24061 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemistry and Biochemistry Florida State University TallahasseeFL32306 United States Parallel Computing Lab Intel Corporation PortlandOR97219 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States LinkedIn Mountain ViewCA94043 United States Department of Mathematical Sciences University of Delaware NewarkDE19716 United States State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Department of Physics University of Washington SeattleWA98195 United States Computer Science and Engineering Toyohashi University of Technology ToyohashiAichi441-8580 Japan Louisiana State University Baton RougeLA70803 United States Department of Physics LaSierra University RiversideCA92505 United States Theoretical and Computational Molecular Science Institute for Molecular Science OkazakiAichi444-8585 Japan

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision that are based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics. © 2016 Society for Industrial and Applied Mathematics.

关键词： Multiresolution analysis

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Dimensionless ratios: Characteristics of quantum liquids and their phase transitions

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Physical Review B 2016年第19期94卷 195129-195129页

作者： Yi-Cong Yu Yang-Yang Chen Hai-Qing Lin Rudolf A. Römer Xi-Wen Guan State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Chinese Academy of Sciences Wuhan 430071 China Beijing Computational Science Research Center Beijing 100094 China Department of Physics and Centre for Scientific Computing University of Warwick Coventry CV4 7AL United Kingdom Center for Cold Atom Physics Chinese Academy of Sciences Wuhan 430071 China Department of Theoretical Physics Research School of Physics and Engineering Australian National University Canberra ACT 0200 Australia

Dimensionless ratios of physical properties can characterize low-temperature phases in a wide variety of materials. As such, the Wilson ratio (WR), the Kadowaki-Woods ratio, and the Wiedemann-Franz law capture essential features of Fermi liquids in metals, heavy fermions, etc. Here we prove that the phases of many-body interacting multicomponent quantum liquids in one dimension (1D) can be described by WRs based on the compressibility, susceptibility, and specific heat associated with each component. These WRs arise due to additivity rules within subsystems reminiscent of the rules for multiresistor networks in series and parallel—a novel and useful characteristic of multicomponent Tomonaga-Luttinger liquids (TLL) independent of microscopic details of the systems. Using experimentally realized multispecies cold atomic gases as examples, we prove that the Wilson ratios uniquely identify phases of TLL, while providing universal scaling relations at the boundaries between phases. Their values within a phase are solely determined by the stiffnesses and sound velocities of subsystems and identify the internal degrees of freedom of said phase such as its spin degeneracy. This finding can be directly applied to a wide range of 1D many-body systems and reveals deep physical insights into recent experimental measurements of the universal thermodynamics in ultracold atoms and spins.

关键词： Fermi gases Specific heat Fermi liquid theory

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Historical typewritten document recognition using minimal user interaction 15

Historical typewritten document recognition using minimal us...

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3rd International Workshop on Historical Document Imaging and Processing, HIP 2015

作者： Retsinas, George Gatos, Basilis Antonacopoulos, Apostolos Louloudis, Georgios Stamatopoulos, Nikolaos Computational Intelligence Laboratory Institute of Informatics and Telecommunications National Center for Scientific Research Demokritos AthensGR-15310 Greece School of Electrical and Computer Engineering National Technical University of Athens AthensGR-15773 Greece Research Lab School of Computing Science and Engineering University of Salford Greater ManchesterM5 4WT United Kingdom

ISBN: (纸本)9781450336024

Recognition of low-quality historical typewritten documents can still be considered as a challenging and difficult task due to several issues i.e. the existence of faint and degraded characters, stains, tears, punch holes etc. In this paper, we exploit the unique characteristics of historical typewritten documents in order to propose an efficient recognition methodology that requires minimum user interaction. It is based on a pre-processing stage in order to enhance the quality and extract connected components, on a semi-supervised clustering for detecting the most representative character samples and on a segmentation-free recognition stage based on a template matching and cross-correlation technique. Experimental results prove that even with minimum user interaction, the proposed method can lead to promising accuracy results. © 2015 ACM.

关键词： Template matching

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Saliency Detection via Nonconvex Regularization Based Matrix Decomposition

Saliency Detection via Nonconvex Regularization Based Matrix...

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International Conference on computational Intelligence and Security

作者： Zhixiang He Xiaoli Sun Xiujun Zhang Chen Xu College of Mathematics and Computational Science Shenzhen University Shenzhen China College of Information and Engineering Shenzhen University Shenzhen China Institute of Intelligent Computing Science Shenzhen University Shenzhen China

In this paper, a nonconvex regularization based matrix decomposition model (NRMD) is proposed. In NRMD, the non-salient regions are regarded as a low rank part, and the salient regions are viewed as a sparse part. Dif... 详细信息

关键词： computational intelligence Security

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