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2013 IEEE Conference Anthology, ANTHOLOGY 2013

作者： Lin, Chuanwen Gu, Naijie Cai, Songsong School of Computer Science and Technology University of Science and Technology of China Anhui Province Key Laboratory of Computing and Communication Software Hefei China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781479916603

Cache plays an important role in multilevel storage system. It can greatly reduce the memory access latency. So the cache hit rate has a significant impact on the performance of the application. Many processors provide cache locking mechanism, which can lock the certain lines in cache. It enables an application to affect the cache replacement decisions under software control. This paper presents a novel method, which uses cache locking mechanism to reduce the run-time of java virtual machine (JVM). JVM often uses just-in-time compiler (JIT) to improve the performance. JIT compiles the method that has been invoked certain times, and then JVM executes the compiled method when invoking this method the next time. This paper analyzes the calling situation of the compiled method in JVM, and then proposes a heuristic approach to lock the compiled method in cache for an appropriate period. It can reduce cache miss rate when JVM executes the compiled method. The algorithm has been implemented in HotSpot based on Loongson-3A. Also, it can be implemented in other run-time systems. Experiment results show that the cache locking heuristic algorithm averagely reduces the cache miss rate by 8.5%, and improves the performance by 4% on the benchmark SPECjvm2008. © 2013 IEEE.

关键词： Application programs

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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 qualitative modeling and simulation method based on cloud model

A qualitative modeling and simulation method based on cloud ...

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2013 5th International Conference on Modeling, Simulation and Applied Optimization, ICMSAO 2013

作者： Shao, Chenxi Sun, Kelu Zhenzhong, Shao Computer Science and Technology Dept. University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China Jianghuai Automobile Co. Ltd. Hefei 230022 China

ISBN: (纸本)9781467358149

Qualitative simulation is effective methods processing incomplete knowledge, how to represent qualitative knowledge and reduce spurious behaviors is its research emphasis. Contraposing the limitations of existing methods representing qualitative knowledge and filtering spurious behaviors, an approach is proposed to qualitatively model and simulate on the basis of normal cloud model. In this research, the cloud numeric characteristics and cloud measurement are introduced into this approach;consequently, it can more accurately assess the qualitative knowledge, and expand the variables expression and quantity space;meanwhile, it is constructed the means of modeling and simulation by adopting more amply portraying the system status, more intuitively transforming status and more accurately filtering spurious behaviors. Furthermore, in simulation algorithm, cloud measurement indices are employed to significantly select the successor status which can not be determined by existing constraint means. The results of simulation example demonstrate that our approach effectively improves the ability to filter spurious behaviors, and it is a feasible method. © 2013 IEEE.

关键词： Cloud computing

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Accurate classification of time series based on a new robust similarity measure

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Journal of computational Information Systems 2013年第9期9卷 3521-3528页

作者： He, Xiaoxu Shao, Chenxi Xiong, Yan School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei 230027 China

Among different time series data mining, time series classification is one of the most important aspects. How to design a suitable similarity measure of similarity is a burning issue for accurate time series classification. In this paper, we propose a new similarity measure based on feature exaction of the original time series. The new similarity measure is used in classification with the nearest neighbor rule. In order to provide a comprehensive comparison, we conducted three sets of experiments, testing effectiveness on different time series datasets from a wide variety of application domains. Experimental evaluations show that the proposed similarity measure can tolerate the most distortions than other three typical similarity;besides, the new classifier is not only superior to other conventional classifier, but also more excellent in 1nn classifier than other similarity measure. © 2013 by Binary Information Press.

关键词： Data mining

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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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Estimating properties of chaotic system based on S-NURBS resampling method

Estimating properties of chaotic system based on S-NURBS res...

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International Conference on Measurement, Information and Control (MIC)

作者： Chenxi Shao Qingqing Liu Tingting Wang Binghong Wang Anhui Province Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei China Computer Science and Technology Department University of Science and Technology of China Hefei China

ISBN: (纸本)9781479913916

Physical properties of the chaotic system play important roles on studying the innate character and deciding the practical predictability of the dynamics. For a short piece of undersampled chaotic signals, it is very hard to abstract the physical properties of the signal source from the sequence. In this paper, we model this type of data with global S-NURBS method to reconstruct a smooth trajectory and resample the trajectory to get enough series. In this way, the problem of estimating the physical properties from small undersampled data is turned to the work of calculating the properties of the resampled time series. The new interpolation method is named as S-NURBS resampling method, and the simulation experiment demonstrates that the new method has a good performance in studying physical systems from the observed time series.

关键词： Time series analysis Interpolation Trajectory Chaos Splines (mathematics) Surface topography Surface reconstruction

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A qualitative modeling and simulation method based on cloud model

A qualitative modeling and simulation method based on cloud ...

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International Conference on Modeling, Simulation and Applied Optimization (ICMSAO)

作者： Chenxi Shao Kelü Sun Zhenzhong Shao Computer Science and Technology Department University of Science and Technology of China Hefei China Anhui Province Key Laboratory of Software in Computing and Communication Hefei China Jianghuai Automobile Company Limited Hefei China

ISBN: (纸本)9781467358125

Qualitative simulation is effective methods processing incomplete knowledge, how to represent qualitative knowledge and reduce spurious behaviors is its research emphasis. Contraposing the limitations of existing methods representing qualitative knowledge and filtering spurious behaviors, an approach is proposed to qualitatively model and simulate on the basis of normal cloud model. In this research, the cloud numeric characteristics and cloud measurement are introduced into this approach; consequently, it can more accurately assess the qualitative knowledge, and expand the variables expression and quantity space; meanwhile, it is constructed the means of modeling and simulation by adopting more amply portraying the system status, more intuitively transforming status and more accurately filtering spurious behaviors. Furthermore, in simulation algorithm, cloud measurement indices are employed to significantly select the successor status which can not be determined by existing constraint means. The results of simulation example demonstrate that our approach effectively improves the ability to filter spurious behaviors, and it is a feasible method.

关键词： Uncertainty Coordinate measuring machines computational modeling Entropy Measurement uncertainty Numerical models Size measurement

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An artificial endocrine-emotion model based on fuzzy logic 1

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International Conference on Information Engineering and Applications, IEA 2011

作者： Liang, Jianwei Dong, Dayuan Wang, Xufa Department of Computer Science and Technology Anhui Key Laboratory of Software in Computing and Communication University of Science and Technology of China Hefei China

ISBN: (数字)9781447123866

ISBN: (纸本)9781447123859

For the past few years, the research of intelligence model based on biologicalinformation processing mechanisms has become an important area of artificial intelligence. The biological information processing system is mainly composed of nervous system, immune system and endocrine system. There is not only large loop among them, but also direct reciprocity, which plays an important role in the growing development and metabolism of organisms and maintains their vital movement together. As an important part of information processing system in living organisms, the endocrine system contains very complex and particular information processing mechanisms, including hormonal regulation mechanism, information delivery mechanism and emotion reacting mechanism. Extracting these mechanisms which are peculiar to endocrine system and establishing corresponding models and algorithms has important research value, which has been a hot issue in the research field of artificial intelligence. Shibata presented a hormone-like mechanism with an emphasis on human-robot interaction. Shen proposed a bio-inspired control method called Digital Hormone Model (DHM) to control the tasking and executing of robot swarms based on local communication,signal propagation, and stochastic reactions. Neal and Timmis put forward artificial endocrine system (AES) for the first time in 2003. They proposed an artificial homeostasis system later. Motivated by the high-level regulation mechanism of endocrine system, an agent self-organization algorithm of behavior was put forward. A hormone based tracking strategy for mobile target tracking in wireless sensor networks was presented. © 2012 Springer-Verlag London Limited.

关键词： Stochastic systems

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Stochastic prefix-based fault tolerant source routing algorithm for network on chip

Stochastic prefix-based fault tolerant source routing algori...

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2012 International Conference on Computer and Information Science, ICCIS 2012 - A Conference of World Engineering, Science and Technology Congress, ESTCON 2012

作者： Xu, Gengchun Ren, Kaixin Gu, Naijie Anhui Province Key Laboratory of Computing and Communication Software Hefei China USTC-SICT Joint Laboratory on Network and Communication Hefei China School of Computer Science and Technology University of Science and Technology of China Hefei China

ISBN: (纸本)9781467319386

In this paper, a novel fault-tolerant source routing algorithm for Network on Chip (NOC) called Stochastic Prefix-based Source Routing (SPSR) is proposed. Compared to non-source routing algorithms, it releases the intermediate node from complicated decision-making or routing table maintenance. Unlike the traditional source routing algorithms which use broadcast packets to record the YD of every intermediate node in Route Discovery, it predetermine prospective routes, which can greatly reduces the packets generated and forwarded in the Route Discovery phase, and therefore reduces the power consumption. The proposed routing algorithm is implemented on the open source simulator Nirgam [14], incorporating Orion [15] to estimate the power consumption. Experiment results show that the proposed algorithm can greatly reduce the power consumption in the route discovery and the latency of SPSR is comparable with that of the broadcasting mechanism. © 2012 IEEE.

关键词： Network-on-chip

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Fault diagnosis design based on qualitative simulation

Fault diagnosis design based on qualitative simulation

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2012 3rd International Conference on Intelligent Control and Information Processing, ICICIP 2012

作者： Shao, Chenxi Shao, Zhenzhong Zhang, Juntao Bai, Fanzhou College of Computer Science and Technology University of Science and Technology of China Hefei 230026 China Anhui Province Key Laboratory of Software in Computing and Communication Hefei Anhui 230027 China Jianghuai Automobile Co. Ltd. Hefei 230022 China

ISBN: (纸本)9781457721427

Based on the QSIM reasoning of Kuipers, comparative constraint is presented to reduce the diagnosis space. The transfer regulation of qualitative constrain is used in the simulation and reasoning of system diagnosis. This arithmetic takes observed faulty state as the beginning, to diagnosis the discrepancy of variable and location of the causation according to the transfer regulation of qualitative constraint, reasons from faulty diagnosed to examine the result and delete the redundancy of diagnosis result. In the example of Condensation refrigeration system, the constraint relation is built according to qualitative difference equation. Aim at the bad efficiency of refrigeration, the mixture of air or Freon superfluously is diagnosed as the result faulty source, is consistent with the factual system. © 2012 IEEE.

关键词： Fault detection

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