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Variable selection in identification of a high dimensional nonlinear non-parametric system

Control Theory and Technology 2015年第1期13卷 1-16页

作者： Er-Wei BAI Wenxiao ZHAO Weixing ZHENG Department of Electrical and Computer Engineering University of Iowa School of Electronics Electrical Engineering and Computer Science Queen's UniversityBelfast U.K. Key Laboratory of Systems and Control Academy of Mathematics and Systems Science National Center for Mathematics and Interdisciplinary Sciences Chinese Academy of Sciences School of Computing Engineering and Mathematics University of Western Sydney

The problem of variable selection in system identification of a high dimensional nonlinear non-parametric system is described. The inherent difficulty, the curse of dimensionality, is introduced. Then its connections to various topics and research areas are briefly discussed, including order determination, pattern recognition, data mining, machine learning, statistical regression and manifold embedding. Finally, some results of variable selection in system identification in the recent literature are presented.

关键词： System identification variable selection nonlinear non-parametric system curse of dimensionality

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A decade of Gamma-Ray Bursts observed by Fermi-LAT:

arXiv

引用

arXiv 2019年

作者： Ajello, M. Arimoto, M. Axelsson, M. Baldini, L. Barbiellini, G. Bastieri, D. Bellazzini, R. Bhat, P.N. Bissaldi, E. Blandford, R.D. Bonino, R. Bonnell, J. Bottacini, E. Bregeon, J. Bruel, P. Buehler, R. Cameron, R.A. Caputo, R. Caraveo, P.A. Cavazzuti, E. Chen, S. Cheung, C.C. Chiaro, G. Ciprini, S. Costantin, D. Crnogorcevic, M. Cutini, S. Dainotti, M. D’Ammando, F. de la Torre Luque, P. de Palma, F. Desai, A. Desiante, R. Di Lalla, N. Di Venere, L. Fana Dirirsa, F. Fegan, S.J. Franckowiak, A. Fukazawa, Y. Funk, S. Fusco, P. Gargano, F. Gasparrini, D. Giglietto, N. Giordano, F. Giroletti, M. Green, D. Grenier, I.A. Grove, J.E. Guiriec, S. Hays, E. Hewitt, J.W. Horan, D. Jóhannesson, G. Kocevski, D. Kuss, M. Latronico, L. Li, J. Longo, F. Loparco, F. Lovellette, M.N. Lubrano, P. Maldera, S. Manfreda, A. Martí-Devesa, G. Mazziotta, M.N. Mereu, I. Meyer, M. Michelson, P.F. Mirabal, N. Mitthumsiri, W. Mizuno, T. Monzani, M.E. Moretti, E. Morselli, A. Moskalenko, I.V. Negro, M. Nuss, E. Ohno, M. Omodei, N. Orienti, M. Orlando, E. Palatiello, M. Paliya, V.S. Paneque, D. Persic, M. Pesce-Rollins, M. Petrosian, V. Piron, F. Poolakkil, S. Poon, H. Porter, T.A. Principe, G. Racusin, J.L. Rainò, S. Rando, R. Razzano, M. Razzaque, S. Reimer, A. Reimer, O. Reposeur, T. Ryde, F. Serini, D. Sgrò, C. Siskind, E.J. Sonbas, E. Spandre, G. Spinelli, P. Suson, D.J. Tajima, H. Takahashi, M. Tak, D. Thayer, J.B. Torres, D.F. Troja, E. Valverde, J. Veres, P. Vianello, G. von Kienlin, A. Wood, K. Yassine, M. Zhu, S. Zimmer, S. Department of Physics and Astronomy Clemson University Kinard Lab of Physics ClemsonSC29634-0978 United States Faculty of Mathematics and Physics Institute of Science and Engineering Kanazawa University Kakuma Kanazawa Ishikawa920-1192 Department of Physics and Oskar Klein Center for Cosmoparticle Physics Stockholm University Stockholm106 91 Sweden Department of Physics KTH Royal Institute of Technology AlbaNova StockholmSE-106 91 Sweden Università di Pisa Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Trieste TriesteI-34127 Italy Dipartimento di Fisica Università di Trieste TriesteI-34127 Italy Istituto Nazionale di Fisica Nucleare Sezione di Padova PadovaI-35131 Italy Dipartimento di Fisica e Astronomia "G. Galilei" Università di Padova PadovaI-35131 Italy Istituto Nazionale di Fisica Nucleare Sezione di Pisa PisaI-56127 Italy University of Alabama in Huntsville HuntsvilleAL35899 United States Dipartimento di Fisica "M. Merlin" dell’Università e del Politecnico di Bari BariI-70126 Italy Istituto Nazionale di Fisica Nucleare Sezione di Bari BariI-70126 Italy W. W. Hansen Experimental Physics Laboratory Kavli Institute for Particle Astrophysics and Cosmology Department of Physics SLAC National Accelerator Laboratory Stanford University StanfordCA94305 United States Istituto Nazionale di Fisica Nucleare Sezione di Torino TorinoI-10125 Italy Dipartimento di Fisica Università degli Studi di Torino TorinoI-10125 Italy NASA Goddard Space Flight Center GreenbeltMD20771 United States Department of Astronomy University of Maryland College ParkMD20742 United States Department of Physics and Astronomy University of Padova Vicolo Osservatorio 3 PadovaI-35122 Italy Laboratoire Univers et Particules de Montpellier Université Montpellier CNRS/IN2P3 MontpellierF-34095 France Laboratoire Leprince-Ringuet École polytechnique CNRS/IN2P3 PalaiseauF-91128 France

The Large Area Telescope (LAT) aboard the Fermi spacecraft routinely observes high-energy emission from gamma-ray bursts (GRBs). Here we present the second catalog of LAT-detected GRBs, covering the first 10 years of operations, from 2008 August 4 to 2018 August 4. A total of 186 GRBs are found;of these, 91 show emission in the range 30–100 MeV (17 of which are seen only in this band) and 169 are detected above 100 MeV. Most of these sources were discovered by other instruments (Fermi/GBM, Swift/BAT, AGILE, INTEGRAL) or reported by the Interplanetary Network (IPN);the LAT has independently triggered on 4 GRBs. This catalog presents the results for all 186 GRBs. We study onset, duration and temporal properties of each GRB, as well as spectral characteristics in the 100 MeV–100 GeV energy range. Particular attention is given to the photons with highest energy. Compared with the first LAT GRB catalog, our rate of detection is significantly improved. The results generally confirm the main findings of the first catalog: the LAT primarily detects the brightest GBM bursts, and the high-energy emission shows delayed onset as well as longer duration. However, in this work we find delays exceeding 1 ks, and several GRBs with durations over 10 ks. Furthermore, the larger number of LAT detections shows that these GRBs cover not only the high-fluence range of GBM-detected GRBs, but also samples lower fluences. In addition, the greater number of detected GRBs with redshift estimates allows us to study their properties in both the observer and rest frames. Comparison of the observational results with theoretical predictions reveals that no model is currently able to explain all results, highlighting the role of LAT observations in driving theoretical models. Copyright © 2019, The Authors. All rights reserved.

关键词： Stars

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Decentralized Context Sharing in Vehicular Delay Tolerant Networks with Compressive Sensing

Decentralized Context Sharing in Vehicular Delay Tolerant Ne...

引用

International Conference on Distributed computing Systems

作者： Kun Xie Wang Luo Xin Wang Dongliang Xie Jiannong Cao Jigang Wen Gaogang Xie College of Computer Science and Electronics Engineering Hunan University Changsha China Department of Electrical and Computer Engineering State University of New York Stony Brook USA State key Laboratory of Networking and Switching Technology Beijing Univ. of Posts and Telecomm. China Department of Computing The Hong Kong Polytechnic University Hong Kong Institute of Computing Technology Chinese Academy of Sciences Beijing China

Vehicles equipped with various types of sensors can act as mobile sensors to monitor the road conditions. To speed up the information collection process, the monitoring data can be shared among vehicles upon their encounters to facilitate drivers to find a good route. The vehicular network experiences intermittent connectivity as a result of the mobility, which makes the inter-vehicle contact duration a scarce resource for data transmissions and the support of monitoring applications over vehicular networks a challenge. We propose a novel compressive sensing (CS)-based scheme to enable efficient decentralized context sharing in vehicular delay tolerant networks, called CS-Sharing. To greatly reduce the data transmission overhead and speed up the monitoring processing, CS-sharing exploits two techniques: sending an aggregate message in each vehicle encounter, and quick collection of information taking advantage of data sharing and the sparsity of events in vehicle networks to significantly reduce the number of measurements needed for global information recovery. We propose a novel data structure, and an aggregation method that can take advantage of the random and opportunistic vehicle encounters to form the measurement matrix. We prove that the measurement matrix satisfies the Restricted Isometry Property (RIP) property required by the CS technique. Our results from extensive simulations demonstrate that CS-Sharing allows vehicles in a large network to quickly obtain the full context data with the successful recovery ratio larger than 90%.

关键词： Vehicles Context Sensors Roads Monitoring Compressed sensing Wireless sensor networks

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Enhancing quantum control by bootstrapping a quantum processor of 12 qubits

arXiv

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arXiv 2017年

作者： Lu, Dawei Li, Keren Li, Jun Katiyar, Hemant Park, Annie Jihyun Feng, Guanru Xin, Tao Li, Hang Long, Guilu Brodutch, Aharon Baugh, Jonathan Zeng, Bei Laflamme, Raymond Department of Physics Southern University of Science and Technology Shenzhen518055 China Institute for Quantum Computing Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada State Key Laboratory of Low-Dimensional Quantum Physics Department of Physics Tsinghua University Beijing100084 China Beijing Computational Science Research Center Beijing100193 China Max-Planck-Institutfür Quantenoptik GarchingD-85748 Germany Center for Quantum Information and Quantum Control Department of Physics Department of Electrical and Computer Engineering University of Toronto TorontoONM5S 3H6 Canada Department of Mathematics and Statistics University of Guelph GuelphONN1G 2W1 Canada Perimeter Institute for Theoretical Physics Waterloo ONN2L 2Y5 Canada

Accurate and efficient control of quantum systems is one of the central challenges for quantum information processing. Current state-of-the-art experiments rarely go beyond 10 qubits and in most cases demonstrate only limited control. Here we demonstrate control of a 12-qubit system, and show that the system can be employed as a quantum processor to optimize its own control sequence by using measurement-based feedback control (MQFC). The final product is a control sequence for a complex 12-qubit task: preparation of a 12-coherent state. The control sequence is about 10% more accurate than the one generated by the standard (classical) technique, showing that MQFC can correct for unknown imperfections. Apart from demonstrating a high level of control over a relatively large system, our results show that even at the 12-qubit level, a quantum processor can be a useful lab instrument. As an extension of our work, we propose a method for combining the MQFC technique with a twirling protocol, to optimize the control sequence that produces a desired Clifford gate. Copyright © 2017, The Authors. All rights reserved.

关键词： Quantum optics

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Corrigendum to “Road detection algorithm for Autonomous Navigation Systems based on dark channel prior and vanishing point in complex road scenes” [Robot. Auton. Syst. 85 (2016) 1–11]

引用

Robotics and Autonomous Systems 2017年 88卷 202-202页

作者： Yong Li Weili Ding Xuguang Zhang Zhaojie Ju Laboratory of Pattern Recognition and Intelligent Systems Key Laboratory of Industrial Computer Control Engineering of Hebei Province Department of Automation Institute of Electrical Engineering Yanshan University Qinghuangdao Hebei 066004 China College of Information Science and Engineering Northeastern University Shenyang Liaoning 110004 China School of Computing University of Portsmouth PO1 3HE UK

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Side channel analysis of multiple PMU data in electric power systems

Side channel analysis of multiple PMU data in electric power...

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2015 Clemson University Power Systems Conference, PSC 2015

作者： Zhong, Xingsi Arunagirinathan, Paranietharan Ahmadi, Afshin Brooks, Richard Venayagamoorthy, Ganesh Kumar Yu, Lu Fu, Yu Real-Time Power and Intelligent Systems Laboratory Clemson University ClemsonSC29634 United States Holcombe Department of Electrical and Computer Engineering Clemson University ClemsonSC29634 United States

ISBN: (纸本)9781479919512

The deployment of Phasor Measurement Units (PMUs) in an electric power grid will enhance real-time monitoring and analysis of grid operations. The PMU collects bus voltage phasors, branch current phasors, and bus frequency measurements and uses a communication network to transmit the measurements to the respective substation(s)/control center(s). PMU information is sensitive, since missing or incorrect PMU data could lead to grid failure and/or damage. It is important to use encrypted communicate channels to avoid cyber attacks. In this study, a side-channel attack using inter-packet delays to isolate the stream of packets of one PMU from an encrypted tunnel is shown. Also, encryption in power system VPNs and vulnerabilities due to side channel analysis is discussed. © 2015 IEEE.

关键词： Phasor measurement units

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

引用

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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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

引用

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

引用

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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Design of a configurable fixed-point FFT processor

Design of a configurable fixed-point FFT processor

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IET International Radar Conference 2015

作者： Yang, Chen Xie, Yi-Zhuang Chen, Liang Chen, He Deng, Yi Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China Department of Electrical and Computer Engineering Virginia Polytechnic Institute State University ArlingtonVA United States

ISBN: (纸本)9781849199940

In this paper, we focus on the widely-used radix-22 Decimation-In-Frequency (DIF) Fast Fourier Transform (FFT) algorithm. A variable-length, controllable-precision FFT processor is proposed. The processor can perform 16, 64, 256 and 1024 point FFT/IFFT and provide flexible wordlength scaling modes for different FFT stages. Thus, the processor is suitable for different precision and FFT length requirements in various applications. Aiming at high throughput performance, single-path delay feedback (SDF) pipeline architecture is adopted. The design is testified on Xilinx Virtex6 xc6vcx240t FPGA. Accordingly, we make a clear comparison between the proposed design and Xilinx FFT v7.1. Our design achieves better signal-to-quantizationnoise ratio (SQNR) property and shorter pipeline latency. Meanwhile, the occupied resource is approximately the same. Moreover, SQNR performance of different FFT length and wordlength scaling modes is analysed.

关键词： Field programmable gate arrays (FPGA)

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