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Whole-proteome based phylogenetic tree construction with inter-amino-acid distances and the conditional geometric distribution profiles

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Molecular phylogenetics and evolution 2015年 89卷 37-45页

作者： Xian-Hua Xie Zu-Guo Yu Guo-Sheng Han Wei-Feng Yang Vo Anh Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China School of Mathematics and Computer Science Gannan Normal University Jiangxi 341000 PR China. Electronic address: xxianhua@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: yuzuguo@***. Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China. Electronic address: korea10282003@***. Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University Hunan 411105 PR China. Electronic address: yangweifeng09@***. School of Mathematical Sciences Queensland University of Technology GPO Box 2434 Brisbane QLD 4001 Australia. Electronic address: v.anh@qut.edu.au.

There has been a growing interest in alignment-free methods for whole genome comparison and phylogenomic studies. In this study, we propose an alignment-free method for phylogenetic tree construction using whole-proteome sequences. Based on the inter-amino-acid distances, we first convert the whole-proteome sequences into inter-amino-acid distance vectors, which are called observed inter-amino-acid distance profiles. Then, we propose to use conditional geometric distribution profiles (the distributions of sequences where the amino acids are placed randomly and independently) as the reference distribution profiles. Last the relative deviation between the observed and reference distribution profiles is used to define a simple metric that reflects the phylogenetic relationships between whole-proteome sequences of different organisms. We name our method inter-amino-acid distances and conditional geometric distribution profiles (IAGDP). We evaluate our method on two data sets: the benchmark dataset including 29 genomes used in previous published papers, and another one including 67 mammal genomes. Our results demonstrate that the new method is useful and efficient.

关键词： Alignment-free whole-proteome comparison Conditional geometric distribution profile Inter-amino-acid distances Phylogenetic tree

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Research on scene organize algorithm based on BVH with dynamic rendering tree

Research on scene organize algorithm based on BVH with dynam...

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2nd International Conference on Mechatronics and Industrial Informatics, ICMII 2014

作者： Xu, Li Liu, Mao Zhen Computer College Beijing Information Science and Technology University Beijing 100101 China Virtual Reality and Simulation Laboratory Beijing Information Science and Technology University Beijing 100192 China

ISBN: (纸本)9783038351764

The real-time rendering of 3D scene need excellent algorithm to support scene management. After considering the algorithm complexity and scene scale factors, combining the advantages of Bounding Volume Hierarchy (BVH) and rendering tree in scene management, this paper puts forward a kind of scene organize algorithm, to solve the problem of render states redundant calculation when using BVH technology to solve the problem of deformable collision detection, by choosing the appropriate strategy of Hierarchy Bounding Volume tree construction and optimizing rendering tree of the scene. Experiments prove the proposed algorithm can effectively improve the rendering efficiency of 3D scene, enhance the visual effect. © (2014) Trans Tech Publications, Switzerland.

关键词： Trees (mathematics)

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Reputation-Based Conditional Investment Enhances the Evolution of Cooperation in Spatial Public Goods Game

Reputation-Based Conditional Investment Enhances the Evoluti...

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International Conference on Identification, Information and Knowledge in the Internet of Things (IIKI)

作者： Hong Ding Lin Cao Hongjun Qiu Zhangbing Zhou Lei Shu Yizhi Ren Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education China Hangzhou Dianzi University Hangzhou China School of Computer Science and Technologv Hangzhou Dianzi University Hangzhou China School of Information Engineering China University of Geosciences Beijing China Guangdong Petrochemical Equipment Fault Diagnosis Key Laboratory Guangdong University of Petrochemical Technology Maoming China

Most previous studies involving public goods games are investigated under a simplifying assumption that participators are compulsive in collective interactions and contribute unconditionally to the public pool. Nevertheless, how the conditional investment mechanism based on individual's reputation affects the evolution of cooperation in structured populations is still unclear. Here we introduce a reputation-based conditional investment rule for constituting participant groups into spatial threshold public goods game, where the public goods game can be conducted only if the participant number is not less than the threshold parameter. Interestingly, we find that large threshold parameter results in the optimal environment for cooperators' viability.

关键词： Games Investment Sociology Statistics Lattices Evolution (biology) Fault diagnosis

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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A comprehensive methodology for vision-based progress and activity estimation of excavation processes for productivity assessment 21

A comprehensive methodology for vision-based progress and ac...

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21st International Workshop of the European Group for Intelligent Computing in Engineering, EG-ICE 2014

作者： Bügler, Maximilian Ogunmakin, Gbolabo Teizer, Jochen Vela, Patricio A. Borrmann, André Department of Computational Modeling and Simulation Technische Universität München Germany School of Electrical and Computer Engineering Georgia Institute of Technology United States RAPIDS Construction Safety and Technology Laboratory United States

ISBN: (纸本)9780993080708

The high level of complexity in modern construction projects causes a-priori project schedules to be highly sensitive to delays in the involved processes. At underground construction sites the earthwork processes are very vital, as most of the following tasks depend on it. This paper presents a novel method for tracking the progress of earthwork processes by combing two technologies based on computer vision: photogrammetry and video analysis. While the former is applied to determine the volume of the excavated soil in regular intervals, the latter is used to generate statistics regarding the construction activities, such as loading times and idle times. Combining these two data sources allows exact measurement of the productivity of the machinery and determining site-specific performance factors. Most importantly, reasons for low productivity - such as an insufficient number of trucks - can be identified easily. The paper presents in detail the vision-based techniques applied and the methods used for combining both data sources. The suitability of the approach has been proved by an extensive case study - a real-world excavation project in Munich, Germany.

关键词： Productivity

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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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Scaling Hadoop clusters with virtualized volunteer computing environment

Scaling Hadoop clusters with virtualized volunteer computing...

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International Joint Conference on computer Science and Software Engineering (JCSSE)

作者： Ekasit Kijsipongse Suriya U-ruekolan Large-Scale Simulation Research Laboratory National Electronics and Computer Technology Center (NECTEC) Pathumthani Thailand

ISBN: (纸本)9781479958238

MapReduce framework has commonly been used to perform large-scale data processing, such as social network analysis, data mining as well as machine learning, on cluster computers. However, building a large dedicated cluster for MapReduce is not cost effective if the system is underutilized. To speedup the MapReduce computation with low cost, the computing resources donated from idle desktop/notebook computers in an organization become true potential. The MapReduce framework is then implemented into Volunteer Computing environment to allow such data processing tasks to be carried out on the unused computers. Virtualization technology is deployed to resolve the security and heterogeneity problem in Volunteer Computing so that the MapReduce jobs can always run under a unified runtime and isolated environment. This paper presents a Hadoop cluster that can be scaled into virtualized Volunteer Computing environment. The system consists of a small fixed set of dedicate nodes plus a variable number of volatile volunteer nodes which give additional computing power to the cluster. To this end, we consolidate Apache Hadoop, the most popular MapReduce implementation, with the virtualized BOINC platform. We evaluate the proposed system on our testbed with MapReduce benchmark that represents different workload patterns. The performance of the Hadoop cluster is measured when its computing capability is expanded with volunteer nodes. The results show that the system can be scaled preferably for CPU-intensive jobs, as opposed to data-intensive jobs which their scalability is more restricted.

关键词： MapReduce Volunteer Computing Virtualization Virtualization Distributed processing computers jobs Environments Clusters Ganglia Volunteers

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Density estimation with adaptive sparse grids for large data sets 14

Density estimation with adaptive sparse grids for large data...

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14th SIAM International Conference on Data Mining, SDM 2014

作者： Peherstorfer, Benjamin Pflüger, Dirk Bungartz, Hans-Joachim Aerospace Computational Design Laboratory AeroAstro Massachusetts Institute of Technology United States Simulation of Large Systems Group Institute for Parallel and Distributed Systems University of Stuttgart Germany Scientific Computing Group Department of Computer Science Technische Universität München Germany

ISBN: (纸本)9781510811515

Nonparametric density estimation is a fundamental problem of statistics and data mining. Even though kernel density estimation is the most widely used method, its performance highly depends on the choice of the kernel bandwidth, and it can become computationally expensive for large data sets. WTe present an adaptive sparse-grid-based density estimation method which discretizes the estimated density function on basis functions centered at grid points rather than on kernels centered at the data points. Thus, the costs of evaluating the estimated density function are independent from the number of data points. We give details on how to estimate density functions on sparse grids and develop a cross validation technique for the parameter selection. We show numerical results to confirm that our sparse-grid-based method is well-suited for large data sets, and, finally, employ our method for the classification of astronomical objects to demonstrate that it is competitive to current kernel-based density estimation approaches with respect to classification accuracy and runtime. Copyright © SIAM.

关键词： Data mining

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A Lane Marking Detection and Tracking Algorithm Based on Sub-Regions

A Lane Marking Detection and Tracking Algorithm Based on Sub...

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2014 International Conference on Informative and Cybernetics for Computational Social Systems(ICCSS2014)

作者： Wenhui Li Xiaohu Gong Ying Wang Peixun Liu College of Computer Science and Technology and State Key Laboratory of Automotive Simulation and Control Jilin University College of Software Jilin University

In this paper,we present a lane marking detection and tracking method for the Lane Departure Warning system(LDWs).The method can both detect straight and curve lanes on the highway and urban *** lane marking detection part is divided into three ***,bisect the ROI(region of interest).Then get lines by processing Hough transformation on each sub-region,after this phase,we can get a set of seed ***,judge the lane’s type(straight or curve)by its *** it’s a curve line,fitting the lane by using the rational cubic B-spline *** the lane tracking part,we use Extended Kalman Filter to make sure continuous lane detection *** the experiment part,we use a dataset which includes virtual videos and real videos to measure our method’s performance under the virtual and real *** results show that the average detection rate of virtual video is 80%,and the real video is 85%.And the average processing time of virtual video is about5.3ms/frame,and the real video is about 14.0ms/frame.

关键词： Kalman A Lane Marking Detection and Tracking Algorithm Based on Sub-Regions

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Design and implementation of multiple volume servers block level storage system based on load-balanced strategy

International Journal of Database Theory and Application

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International Journal of Database Theory and Application 2014年第4期7卷 59-78页

作者： Wan, Jian Chen, Xun You, Xindong School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex System Modeling and Simulation Ministry of Education China

Block level storage system has been widely used as it can provide users raw block device. On the basis of Orthrus system, this paper designs and implements a multiple volume servers block level storage system based on load-balanced strategy-Orthrus Plus. This system uses the dynamic load-balanced strategy based on genetic algorithm strategy to load balance the volume servers clusters and proposes an iSCSI pause and resume method to solve the breakpoint retransmission problem during load-balancing. At last, we design a hierarchical management mechanism based on volume life cycle at storage. Experiments show that the load balancing capability in Orthrus Plus is more accurate and efficient under the situation of larger amount of data, and the hierarchical storage strategy also increases the I/O throughout capacity. © 2013 SERSC.

关键词： Genetic algorithms

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