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

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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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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Geometric control and modeling of genome reprogramming

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Bioarchitecture 2016年第4期6卷 76-84页

作者： Caroline Uhler G V Shivashankar a Department of Electrical Engineering & Computer Science and Institute for Data Systems and Society MIT Cambridge MA USA. b Mechanobiology Institute National University of Singapore Singapore. c FIRC Institute of Molecular Oncology (IFOM) Milan Italy.

Cell geometry is tightly coupled to gene expression patterns within the tissue microenvironment. This perspective synthesizes evidence that the 3D organization of chromosomes is a critical intermediate for geometric control of genomic programs. Using a combination of experiments and modeling we outline approaches to decipher the mechano-genomic code that governs cellular homeostasis and reprogramming.

关键词： cell geometry chromosome organization gene expression nuclear morphology sphere packing models

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Bloom-Filter-Based Profile Matching for Proximity-Based Mobile Social Networking

Bloom-Filter-Based Profile Matching for Proximity-Based Mobi...

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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Kun Xie Xin Wang Wei Li Zhe Zheng Gaogang Xie Jigang Wen College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781509017331

The popularity of smart phones fosters the growth of Proximity-based Mobile Social Networking (PMSN). Although some profile matching approaches have been proposed to facilitate a user to find another user that shares his/her interest in the proximity, these approaches usually model the matching problem as a Private Set Intersection problem or a Private Set Intersection Cardinality problem and require high complexity of computation. Different from current studies, to facilitate more effective building of PMSNs, we propose a novel similarity metric to evaluate the common interests of mobile users by considering the time-dependent features of their interests. To calculate the metric in a low cost and privacy- protection way, we propose a novel time-dependent bloom filter to encode the time-dependent interest and a novel probabilistic algorithm to estimate the time- dependent similarity metric based on the bloom filter. Based on the proposed BF-based profile matching approach, we further propose InterestMatch, a novel distributed mobile communication system to facilitate more efficient social networking among strangers in the physical proximity. We have done extensive experiments on real-world phones, our experiment results demonstrate that our approach is promising for facilitating mobile social interactions in the physical proximity due to its low complexity and consequently low power consumption.

关键词： Mobile communication Measurement Social network services Mobile computing Probabilistic logic Privacy Arrays

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Global and Asymptotically Efficient Identification of Nonlinear Rational Systems via a Two-step Method

Global and Asymptotically Efficient Identification of Nonlin...

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第35届中国控制会议

作者： Biqiang Mu Er-Wei Bai Wei Xing Zheng Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of Sciences Department of Electrical and Computer Engineering University of Iowa School of Computing Engineering and MathematicsWestern Sydney University

ISBN: (纸本)9781509009107

Identification of nonlinear rational systems defined as the ratio of two nonlinear functions of past inputs and outputs is considered in this paper. Although this problem has a long history, there is still lack of a globally consistent identification algorithm for such identification problem. This paper develops a globally consistent algorithm by the following steps: model transformation, bias analysis, noise variance estimation, and compensation. First, the paper studies the prediction error type estimator(nonlinear least square estimators) and the corresponding solving algorithm(Gauss-Newton algorithms). It is shown that the Gauss-Newton algorithm is locally convergent but actually asymptotically efficient by calculating the Crame′r-Rao lower bound under Gaussian observation noises. This motivates that a global and asymptotically efficient estimator can be constructed by combining the proposed globally consistent estimator with the Gauss-Newton algorithm. So, a two-step method is proposed,which consists of first executing the globally consistent algorithm and then applying the Gauss-Newton algorithm with the consistent estimate serving as the initial value. A simulation example is provided to verify the good performance of the proposed two-step method.

关键词： Nonlinear rational systems nonlinear least squares Gauss-Newton algorithms local convergence corrected least squares global convergence asymptotical efficiency

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Accurate Recovery of Internet Traffic data: A Tensor Completion Approach

Accurate Recovery of Internet Traffic Data: A Tensor Complet...

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Annual IEEE International Conference on computer Communications

作者： Kun Xie Lele Wang Xin Wang Gaogang Xie Jigang Wen Guangxing Zhang College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York at Stony Brook USA Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781467399548

The inference of traffic volume of the whole network from partial traffic measurements becomes increasingly critical for various network engineering tasks, such as traffic prediction, network optimization, and anomaly detection. Previous studies indicate that the matrix completion is a possible solution for this problem. However, as a two-dimension matrix cannot sufficiently capture the spatial-temporal features of traffic data, these approaches fail to work when the data missing ratio is high. To fully exploit hidden spatial-temporal structures of the traffic data, this paper models the traffic data as a 3-way traffic tensor and formulates the traffic data recovery problem as a low-rank tensor completion problem. However, the high computation complexity incurred by the conventional tensor completion algorithms prevents its practical application for the traffic data recovery. To reduce the computation cost, we propose a novel Sequential Tensor Completion algorithm (STC) which can efficiently exploit the tensor decomposition result for the previous traffic data to deduce the tensor decomposition for the current data. To the best of our knowledge, we are the first to apply the tensor to model Internet traffic data to well exploit their hidden structures and propose a sequential tensor completion algorithm to significantly speed up the traffic data recovery process. We have done extensive simulations with the real traffic trace as the input. The simulation results demonstrate that our algorithm can achieve significantly better performance compared with the literature tensor and matrix completion algorithms even when the data missing ratio is high.

关键词： Tensile stress data models Computational modeling Correlation Internet Algorithm design and analysis Feature extraction

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Provable multiple replication data possession with full dynamics for secure cloud storage

Provable multiple replication data possession with full dyna...

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作者： Zhang, Yafang Ni, Jianbing Tao, Xiaoling Wang, Yong Yu, Yong Big Data Research Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada College of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Cloud Security and Cloud Service Engineering Research Center Guilin University of Electronic Technology Guilin541004 China

Cloud storage has been gaining tremendous popularity among individuals and corporations because of its low maintenance cost and on-demand services for the clients. To improve the availability and the reliability of critical data, storing multiple replicas on multiple servers is a commonly used strategy. Currently, several provable data possession (PDP) protocols for multiple replicas of dynamic data have been proposed to ensure the integrity of outsourced multi-copy data, but the efficiency of these protocols on verifying multiple replicas one by one is not satisfactory. In this paper, we propose a provable multiple replication data possession protocol with full dynamics, named MR-DPDP. In MR-DPDP, we utilize a novel authenticated data structure called Merkle hash tree with rank to support both full dynamic data updates and efficient integrity verification. In addition, our construction with RSA signature can support both variable-sized file blocks and public verification. Through security proof and performance evaluation, we demonstrate that MR-DPDP not only is sound but also incurs less communication overhead when updating data blocks as well as verifying a proof of the integrity of multiple replicas. Copyright © 2015 John Wiley & Sons, Ltd.

关键词： Cloud storage

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Parkes Pulsar Timing Array constraints on ultralight scalar-field dark matter

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Physical Review D 2018年第10期98卷 102002-102002页

作者： Nataliya K. Porayko Xingjiang Zhu Yuri Levin Lam Hui George Hobbs Aleksandra Grudskaya Konstantin Postnov Matthew Bailes N. D. Ramesh Bhat William Coles Shi Dai James Dempsey Michael J. Keith Matthew Kerr Michael Kramer Paul D. Lasky Richard N. Manchester Stefan Osłowski Aditya Parthasarathy Vikram Ravi Daniel J. Reardon Pablo A. Rosado Christopher J. Russell Ryan M. Shannon Renée Spiewak Willem van Straten Lawrence Toomey Jingbo Wang Linqing Wen Xiaopeng You Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 D-53121 Bonn Germany School of Physics and Astronomy Monash University Clayton VIC 3800 Australia School of Physics University of Western Australia Crawley WA 6009 Australia OzGrav: Australian Research Council Centre of Excellence for Gravitational Wave Discovery Hawthorn VIC 3122 Australia Center for Theoretical Physics Department of Physics Columbia University New York New York 10027 USA Center for Computational Astrophysics Flatiron Institute New York New York 10010 USA CSIRO Astronomy and Space Science P.O. Box 76 Epping NSW 1710 Australia Sternberg Astronomical Institute Lomonosov Moscow State University Universitetskii pr. 13 Moscow 119234 Russia Kazan Federal University Kremlevskaya 18 Kazan 420008 Russia Centre for Astrophysics and Supercomputing Swinburne University of Technology P.O. Box 218 Hawthorn VIC 3122 Australia International Centre for Radio Astronomy Research Curtin University Bentley WA 6102 Australia Department of Electrical and Computer Engineering University of California at San Diego La Jolla California 92093 USA CSIRO Information Management and Technology PO Box 225 Dickson ACT 2602 Australia Jodrell Bank Center for Astrophysics University of Manchester Manchester M13 9PL UK Space Science Division Naval Research Laboratory Washington DC 20375 USA Cahill Center for Astronomy and Astrophysics MC 249-17 California Institute of Technology Pasadena California 91125 USA CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 Alexandria NSW 1435 Australia Institute for Radio Astronomy & Space Research Auckland University of Technology Private Bag 92006 Auckland 1142 New Zealand Xinjiang Astronomical Observatory Chinese Academy of Sciences 150 Science 1-Street Urumqi Xinjiang 830011 China School of Physical Science and Technology Southwest University Chongqing 400715 China

It is widely accepted that dark matter contributes about a quarter of the critical mass-energy density in our Universe. The nature of dark matter is currently unknown, with the mass of possible constituents spanning nearly one hundred orders of magnitude. The ultralight scalar field dark matter, consisting of extremely light bosons with m∼10−22 eV and often called “fuzzy” dark matter, provides intriguing solutions to some challenges at sub-Galactic scales for the standard cold dark matter model. As shown by Khmelnitsky and Rubakov, such a scalar field in the Galaxy would produce an oscillating gravitational potential with nanohertz frequencies, resulting in periodic variations in the times of arrival of radio pulses from pulsars. The Parkes Pulsar Timing Array (PPTA) has been monitoring 20 millisecond pulsars at two- to three-week intervals for more than a decade. In addition to the detection of nanohertz gravitational waves, PPTA offers the opportunity for direct searches for fuzzy dark matter in an astrophysically feasible range of masses. We analyze the latest PPTA data set which includes timing observations for 26 pulsars made between 2004 and 2016. We perform a search in this data set for evidence of ultralight dark matter in the Galaxy using Bayesian and Frequentist methods. No statistically significant detection has been made. We, therefore, place upper limits on the local dark matter density. Our limits, improving on previous searches by a factor of 2 to 5, constrain the dark matter density of ultralight bosons with m≤10−23 eV to be below 6 GeV cm−3 with 95% confidence in the Earth neighborhood. Finally, we discuss the prospect of probing the astrophysically favored mass range m≳10−22 eV with next-generation pulsar timing facilities.

关键词： Dark matter Neutron stars & pulsars Electromagnetic radiation astronomy Gravitational wave detection

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Evidence of energy and charge sign dependence of the recovery time for the december 2006 forbush event measured by the pamela experiment

arXiv

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arXiv 2018年

作者： Munini, R. Boezio, M. Bruno, A. Christian, E.C. de Nolfo, G.A. Di Felice, V. Martucci, M. Merge, M. Richardson, I.G. Ryan, J.M. Stochaj, S. Adriani, O. Barbarino, G.C. Bazilevskaya, G.A. Bellotti, R. Bongi, M. Bonvicini, V. Bottai, S. Cafagna, F. Campana, D. Carlson, P. Casolino, M. Castellini, G. de Santis, C. Galper, A.M. Karelin, A.V. Koldashov, S.V. Koldobskiy, S. Krutkov, S.Y. Kvashnin, A.N. Leonov, A. Malakhov, V. Marcelli, L. Mayorov, A.G. Menn, W. Mikhailov, V.V. Mocchiutti, E. Monaco, A. Mori, N. Osteria, G. Panico, B. Papini, P. Pearce, M. Picozza, P. Ricci, M. Ricciarini, S.B. Simon, M. Sparvoli, R. Spillantini, P. Stozhkov, Y.I. Vacchi, A. Vannuccini, E. Vasilyev, G. Voronov, S.A. Yurkin, Y.T. Zampa, G. Zampa, N. Potgieter, M.S. INFN Sezione di Trieste TriesteI-34149 Italy INFN Sezione di Bari BariI-70126 Italy Heliophysics Division NASA Goddard Space Flight Ctr GreenbeltMD United States INFN Sezione di Rome "Tor Vergata" RomeI-00133 Italy Science Data Center FrascatiI-00044 Italy University of Rome "Tor Vergata" Department of Physics RomeI-00133 Italy INFN Laboratori Nazionali di Frascati Via Enrico Fermi 40 FrascatiI-00044 Italy GPHI and Department of Astronomy University of Maryland College Park United States Space Science Center University of New Hampshire DurhamNH United States Electrical and Computer Engineering New Mexico State University Las CrucesNM United States University of Florence Department of Physics Sesto FiorentinoFlorenceI-50019 Italy INFN Sezione di Florence Sesto FiorentinoFlorenceI-50019 Italy University of Naples "Federico II" Department of Physics NaplesI-80126 Italy INFN Sezione di Naples NaplesI-80126 Italy Lebedev Physical Institute MoscowRU-119991 Russia University of Bari Department of Physics BariI-70126 Italy KTH Department of Physics Oskar Klein Centre for Cosmoparticle Physics AlbaNova University Centre StockholmSE-10691 Sweden RIKEN Advanced Science Institute Wako-shiSaitama Japan IFAC Sesto FiorentinoFlorenceI-50019 Italy National Research Nuclear University MEPhI MoscowRU-115409 Russia Ioffe Physical Technical Institute St. PetersburgRU-194021 Russia Universität Siegen Department of Physics SiegenD-57068 Germany University of Udine Department of Mathematics and Informatics UdineI-33100 Italy INFN Sezione di Florence Sesto Fiorentino FlorenceItalI-50019 Italy North-West University Centre for Space Research Potchefstroom2520 South Africa

New results on the short-term galactic cosmic ray (GCR) intensity variation (Forbish decrease) in December 2006 measured by the PAMELA instrument are presented. Forbush decreases are sudden suppressions of the GCR intensities which are associated with the passage of interplanetary transients such as shocks and interplanetary coronal mass ejections (ICMEs). Most of the past measurements of this phenomenon were carried out with ground-based detectors such as neutron monitors or muon telescopes. These techniques allow only the indirect detection of the overall GCR intensity over an integrated energy range. For the first time, thanks to the unique features of the PAMELA magnetic spectrometer, the Forbush decrease commencing on 2006 December 14, following a CME at the Sun on 2006 December 13 was studied in a wide rigidity range (0.4 − 20 GV) and for different species of GCRs detected directly in space. The daily averaged GCR proton intensity was used to investigate the rigidity dependence of the amplitude and the recovery time of the Forbush decrease. Additionally, for the first time, the temporal variations in the helium and electron intensities during a Forbush decrease were studied. Interestingly, the temporal evolutions of the helium and proton intensities during the Forbush decrease were found in good agreement, while the low rigidity electrons ( Copyright © 2018, The Authors. All rights reserved.

关键词： Rigidity

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Unsupervised classification of data streams based on typicality and eccentricity data analytics

Unsupervised classification of data streams based on typical...

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2016 IEEE International Conference on Fuzzy Systems, FUZZ-IEEE 2016

作者： Costa, Bruno Sielly Jales Bezerra, Clauber Gomes Guedes, Luiz Affonso Angelov, Plamen Parvanov Federal Institute of Rio Grande do Norte - IFRN Campus Natal - Zona Norte Natal Brazil Federal Institute of Rio Grande do Norte - IFRN Campus EaD Natal Brazil Department of Computer Engineering and Automation Federal University of Rio Grande do Norte - UFRN Brazil Data Science Group School of Computing and Communications Lancaster University LancasterLA14WA United Kingdom Department of Excellence Carlos III University Madrid Spain

ISBN: (纸本)9781509006250

In this paper, we propose a novel approach to unsupervised and online data classification. The algorithm is based on the statistical analysis of selected features and development of a self-evolving fuzzy-rule-basis. It starts learning from an empty rule basis and, instead of offline training, it learns "on-The-fly". It is free of parameters and, thus, fuzzy rules, number, size or radius of the classes do not need to be pre-defined. It is very suitable for the classification of online data streams with realtime constraints. The past data do not need to be stored in memory, since that the algorithm is recursive, which makes it memory and computational power efficient. It is able to handle concept-drift and concept-evolution due to its evolving nature, which means that, not only rules/classes can be updated, but new classes can be created as new concepts emerge from the data. It can perform fuzzy classification/soft-labeling, which is preferred over traditional crisp classification in many areas of application. The algorithm was validated with an industrial pilot plant, where online calculated period and amplitude of control signal were used as input to a fault diagnosis application. The approach, however, is generic and can be applied to different problems and with much higher dimensional inputs. The results obtained from the real data are very significant. © 2016 IEEE.

关键词： Classification (of information)

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