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2015欧亚经济论坛-2015第五届国际纳米科技大会

作者： Benzhuo Lu Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences

Continuum models(implicit solvent models) are widely applied to simulating molecular solvation and ionic transport in wide ion channels.A web-based graphical user interface(GUI) is presented for simulations of Poisson-Boltzmann electrostatics and Poisson-Nernst-Planck electrodiffusion of ion transport in channel proteins or *** webserver is designed to help users avoid most of the technical difficulties encountered in setting up and simulating complex ***-based 3D finite element/boundary element simulation platforms are built for these simulations,as well as design of *** the case of nanopore,the volume tetrahedral mesh is automatically generated on a web interface through typing a series of parameters describing the nanopore,such as the upper and lower pore radius,membrane thickness and box *** pore can carry surface charge density described as a given *** solvation energies,and the ionic concentration and electric potential profiles,and ionic current in the channel/pore cases can be *** are given for different types of simulations.

关键词： Web Interface for Continuum Modeling of Molecular Solvation and Ion Transport

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A separability-entanglement classifier via machine learning

arXiv

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arXiv 2017年

作者： Lu, Sirui Huang, Shilin Li, Keren Li, Jun Chen, Jianxin Lu, Dawei Ji, Zhengfeng Shen, Yi Zhou, Duanlu Zeng, Bei Department of Physics Tsinghua University Beijing100084 China Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Department of Mathematics & Statistics University of Guelph GuelphONN1G 2W1 Canada Joint Center for Quantum Information and Computer Science University of Maryland College ParkMD United States Department of Physics Southern University of Science and Technology Shenzhen518055 China Centre for Quantum Computation & Intelligent Systems School of Software Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China Department of Statistics and Actuarial Science University of Waterloo WaterlooONN2L 3G1 Canada Institute of Physics Chinese Academy of Sciences Beijing100190 China

The problem of determining whether a given quantum state is entangled lies at the heart of quantum information processing, which is known to be an NP-hard problem in general. Despite the proposed many methods such as the positive partial transpose (PPT) criterion and the k-symmetric extendibility criterion to tackle this problem in practice, none of them enables a general, effective solution to the problem even for small dimensions. Explicitly, separable states form a high-dimensional convex set, which exhibits a vastly complicated structure. In this work, we build a new separability-entanglement classifier underpinned by machine learning techniques. Our method outperforms the existing methods in generic cases in terms of both speed and accuracy, opening up the avenues to explore quantum entanglement via the machine learning approach. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum entanglement

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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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One-Sided Interface for Matrix Operations Using MPI-3 RMA: A Case Study with Elemental

One-Sided Interface for Matrix Operations Using MPI-3 RMA: A...

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International Conference on Parallel Processing (ICPP)

作者： Sayan Ghosh Jeff R. Hammond Antonio J. Peña Pavan Balaji Assefaw H. Gebremedhin Barbara Chapman School of Electrical Engineering and Computer Science Washington State University Pullman WA USA Parallel Computing Lab Intel Corp. Portland OR USA Centro Nacional de Supercomputacion Barcelona ES Mathematics and Computer Science Division Argonne National Laboratory Lemont IL USA Institute for Advanced Computational Science Stony Brook University Stony Brook NY USA

A one-sided programming model separates communication from synchronization, and is the driving principle behind partitioned global address space (PGAS) libraries such as Global Arrays (GA) and SHMEM. PGAS models expose a rich set of functionality that a developer needs in order to implement mathematical algorithms that require frequent multidimensional array accesses. However, use of existing PGAS libraries in application codes often requires significant development effort in order to fully exploit these programming models. On the other hand, a vast majority of scientific codes use MPI either directly or indirectly via third-party scientific computation libraries, and need features to support application-specific communication requirements (e.g., asynchronous update of distributed sparse matrices, commonly arising in machine learning workloads). For such codes it is often impractical to completely shift programming models in favor of special one-sided communication middleware. Instead, an elegant and productive solution is to exploit the one-sided functionality already offered by MPI-3 RMA (Remote Memory Access). We designed a general one-sided interface using the MPI-3 passive RMA model for remote matrix operations in the linear algebra library Elemental, we call the interface we designed RMAInterface. Elemental is an open source library for distributed-memory dense and sparse linear algebra and optimization. We employ RMAInterface to construct a Global Arrays-like API and demonstrate its performance scalability and competitivity with that of the existing GA (with ARMCI-MPI) for a quantum chemistry application.

关键词： Libraries Arrays Two dimensional displays computational modeling Matrices Data models

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A comparative study use of OTL for many-objective optimization 15

A comparative study use of OTL for many-objective optimizati...

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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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25th Annual computational Neuroscience Meeting CNS-2016, Seogwipo City, South Korea, July 2-7, 2016 Abstracts

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BMC NEUROSCIENCE 2016年第1期17卷 1-112页

作者： [Anonymous] Computational Neurobiology Laboratory The Salk Institute for Biological Studies San Diego USA UNIC CNRS Gif sur Yvette France The European Institute for Theoretical Neuroscience (EITN) Paris France ATR Computational Neuroscience Laboratories Kyoto Japan Krembil Research Institute University Health Network Toronto Canada Department of Physiology University of Toronto Toronto Canada Department of Medicine (Neurology) University of Toronto Toronto Canada Department of Physics University of New Hampshire Durham USA Department of Neurophysiology Nencki Institute of Experimental Biology Warsaw Poland Department of Theory Wigner Research Centre for Physics of the Hungarian Academy of Sciences Budapest Hungary Department of Mathematical Sciences KAIST Daejoen Republic of Korea Department of Mathematics University of Houston Houston USA Department of Biochemistry & Cell Biology and Institute of Biosciences and Bioengineering Rice University Houston USA Department of Biology and Biochemistry University of Houston Houston USA Grupo de Neurocomputación Biológica Dpto. de Ingeniería Informática Escuela Politécnica Superior Universidad Autónoma de Madrid Madrid Spain Department of Biological Sciences University of Southern California Los Angeles USA Center for Neuroscience Korea Institute of Science and Technology Seoul South Korea Department of Neurology Albert Einstein College of Medicine Bronx USA Center for Neuroscience KIST Seoul South Korea Department of Neuroscience University of Science and Technology Daejon South Korea Systems Neuroscience Group QIMR Berghofer Medical Research Institute Herston Australia Department of Psychology Yonsei University Seoul South Korea Department of Psychiatry Kyung Hee University Hospital at Gangdong Seoul South Korea Department of Psychiatry Veterans Administration Boston Healthcare System and Harvard Medical School Brockton USA Department of Electrical and Electronic Engineering The University of Melbourne Parkvil

A1 Functional advantages of cell-type heterogeneity in neural circuits Tatyana O. Sharpee A2 Mesoscopic modeling of propagating waves in visual cortex Alain Destexhe A3 Dynamics and biomarkers of mental disorders Mitsuo Kawato F1 Precise recruitment of spiking output at theta frequencies requires dendritic h-channels in multi-compartment models of oriens-lacunosum/moleculare hippocampal interneurons Vladislav Sekulić, Frances K. Skinner F2 Kernel methods in reconstruction of current sources from extracellular potentials for single cells and the whole brains Daniel K. Wójcik, Chaitanya Chintaluri, Dorottya Cserpán, Zoltán Somogyvári F3 The synchronized periods depend on intracellular transcriptional repression mechanisms in circadian clocks. Jae Kyoung Kim, Zachary P. Kilpatrick, Matthew R. Bennett, Kresimir Josić O1 Assessing irregularity and coordination of spiking-bursting rhythms in central pattern generators Irene Elices, David Arroyo, Rafael Levi, Francisco B. Rodriguez, Pablo Varona O2 Regulation of top-down processing by cortically-projecting parvalbumin positive neurons in basal forebrain Eunjin Hwang, Bowon Kim, Hio-Been Han, Tae Kim, James T. McKenna, Ritchie E. Brown, Robert W. McCarley, Jee Hyun Choi O3 Modeling auditory stream segregation, build-up and bistability James Rankin, Pamela Osborn Popp, John Rinzel O4 Strong competition between tonotopic neural ensembles explains pitch-related dynamics of auditory cortex evoked fields Alejandro Tabas, André Rupp, Emili Balaguer-Ballester O5 A simple model of retinal response to multi-electrode stimulation Matias I. Maturana, David B. Grayden, Shaun L. Cloherty, Tatiana Kameneva, Michael R. Ibbotson, Hamish Meffin O6 Noise correlations in V4 area correlate with behavioral performance in visual discrimination task Veronika Koren, Timm Lochmann, Valentin Dragoi, Klaus Obermayer O7 Input-location dependent gain modulation in cerebellar nucleus neurons Maria Psarrou, Maria Schilstra, Neil Davey, Benjamin Torben-Ni

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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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Outcomes from the DOE workshop on turbulent flow simulation at the exascale 46

Outcomes from the DOE workshop on turbulent flow simulation ...

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46th AIAA Fluid Dynamics Conference, 2016

作者： Sprague, Michael A. Boldyrev, Stanislav Chang, Choong-Seock Fischer, Paul Grout, Ray Gustafson, William I. Hittinger, Jeffrey A. F. Merzari, Elia Moser, Robert National Renewable Energy Laboratory 15013 DenverWest Parkway GoldenCO80401 United States University of Wisconsin-Madison MadisonWI53706 United States Princeton University PrincetonNJ08543 United States University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Renewable Energy Laboratory GoldenCO80401 United States Pacific Northwest National Laboratory RichlandWA99352 United States Lawrence Livermore National Laboratory LivermoreCA94551 United States Argonne National Laboratory ArgonneIL60439 United States University of Texas at Austin AustinTX78712 United States Computational Science Center 15013 DenverWest Parkway United States Physics Department 1150 University Avenue United States Princeton Plasma Physics Laboratory P.O. Box 451 United States Department of Computer Science 201 North Goodwin Avenue United States Computational Science Center 5013 Denver West Parkway United States Atmospheric Sciences and Global Change Division P.O. Box 999 K9-30 United States Scientific Computing Group Center for Applied Scientific Computing PO Box 808 L-561 United States Mathematics and Computer Science 9700 S. Cass Avenue United States Institute for Computational Engineering and Sciences and Department of Mechanical Engineering 201 East 24th United States

ISBN: (数字)9781624104367

ISBN: (纸本)9781624104367

This paper summarizes the outcomes from the Turbulent Flow Simulation at the Exascale: Opportunities and Challenges Workshop, which was held 4-5 August 2015, and was sponsored by the U.S. Department of Energy (DOE) Office of Advanced Scientific computing Research (ASCR). The workshop objectives were to define and describe the challenges and opportunities that computing at the exascale will bring to turbulent-flow simulations in applied science and technology. The need for accurate simulation of turbulent flows is evident across the DOE applied-science and engineering portfolios, including combustion, plasma physics, nuclear-reactor physics, wind energy, and atmospheric science. The work-shop brought together experts in turbulent-flow simulation, computational mathematics, and high-performance computing. Building upon previous ASCR workshops on exascale computing, participants defined a research agenda and path forward that will enable scientists and engineers to continually leverage, engage, and direct advances in computational systems on the path to exascale computing. © 2016, American institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Turbulent flow

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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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STUDIES ON STRUCTURAL AND MECHANICAL PROPERTIES UNDER ISOSTATIC COMPRESSION WITH LARGE-SCALE DISCRETE ELEMENT SIMULATIONS

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Acta Mechanica Solida Sinica 2014年第2期27卷 129-136页

作者： Jianguo Liu Qicheng Sun Feng Jin Qingkai Liu State Key Laboratory of Hydroscience and Engineering Tsinghua University High Performance Computing Center Institute of Applied Physics and Computational Mathematics

Granular systems undergo a jamming transition at point J simply by increasing the packing fraction. A large-scale parallel discrete element code （THDEM： TsingHua Discrete Element Method） was used to obtain a satisfying statistical description of the structural and me- chanical properties near point J. The isostatic compressions of 100,000 polydispersed frictionless particles were simulated on high performance computers to clearly observe the sophisticated con- figurations of force chains. The first peak of the pair correlation function, coordination number, spatial distribution of the packing fraction, and stress were calculated to analyze their variations with increasing packing fraction. The critical packing fraction at point J is determined to be 0.62. The incremental stress and coordination number from point J scale well with the power law, and coincide with previous theoretical predications. The distribution of the packing frac- tion is a normal distribution around the average value. The standard deviation decreases with increasing packing fraction, indicating the system is more uniform with a denser packing.

关键词： granular matter jamming transition force chains parallel computing

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