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Neutron Imaging for Intermetallic Alloy using a Delay Line Current-Biased Kinetic-Inductance Detector

Journal of Physics: Conference Series 2021年第1期1975卷

作者： Hiroaki Shishido The Dang Vu Kazuya Aizawa Kenji M. Kojima Tomio Koyama Kenichi Oikawa Masahide Harada Takayuki Oku Kazuhiko Soyama Shigeyuki Miyajima Mutsuo Hidaka Soh Y. Suzuki Manobu M. Tanaka Shuichi Kawamata Takekazu Ishida Department of Physics and Electronics Graduate School of Engineering Osaka Prefecture University Sakai Osaka 599-8531 Japan NanoSquare Research Institute Osaka Prefecture University Sakai Osaka 599-8570 Japan Materials and Life Science Division J-PARC Center Japan Atomic Energy Agency Tokai Ibaraki 319-1195 Japan Centre for Molecular and Materials Science TRIUMF and Stewart Blusson Quantum Matter Institute University of British Columbia Vancouver BC V6T 2A3 and V6T 1Z4 Canada Division of Quantum and Radiation Engineering Osaka Prefecture University Sakai Osaka 599-8570 Japan Advanced ICT Research Institute National Institute of Information and Communications Technology 588-2 Iwaoka Nishi-ku Kobe Hyogo 651-2492 Japan National Institute of Advanced Industrial Science and Technology Tsukuba Ibaraki 305-8568 Japan Computing Research Center Applied Research Laboratory High Energy Accelerator Research Organization (KEK) Tsukuba Ibaraki 305-0801 Japan Institute of Particle and Nuclear Studies High Energy Accelerator Research Organization (KEK) Tsukuba Ibaraki 305-0801 Japan

The current-biased kinetic-inductance detector (CB-KID) is a solid-state superconducting neutron detector with high spatial and temporal resolutions, and multi-hit tolerance. We demonstrate high temperature operation of CB-KID at 7.9 K with the delay-line method. high temperature operation reduces imaging pixel size by suppressing signal propagation velocity. high spatial neutron transmission image for a mixed metal alloy consisting of heavy elements Sm and Sn is successfully constructed. We also examine the capability of element discrimination imaging based on the resonance dip analysis.

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Block-Based Projection Matrix Design for Compressed Sensing

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Chinese Journal of Electronics 2016年第3期25卷 551-555页

作者： LI Zhetao XIE Jingxiong ZHU Gengming PENG Xin XIE Yanrong CHOI Youngjune The College of Information Engineering Xiangtan University School of Computer National University of Defense Technology Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University School of Computer Science and Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University

The objective of optimizing a projection matrix is to decrease the mutual coherence between a projection matrix and a basis matrix. In this paper, a novel block-based method is proposed to design a projection matrix in compressed sensing. Here, the projection matrix is divided into two blocks. The relationship between the two blocks was obtained by reasoning and proving. Theoretical analysis demonstrates that the mutual coherence between the whole projection matrix and the whole basis matrix keeps as good as the mutual coherence between the block matrix and blocked basis matrix. Experimental results show that the proposed method obtains better performance compared to existing methods.

关键词： Compressed sensing Projection matrix Block optimization Mutual coherence

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Measurement of Ξcc++ production in pp collisions at s1/2=13 TeV

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Chinese Physics C 2020年第2期44卷 13-23页

作者： R.Aaij C.Abellán Beteta T.Ackernley B.Adeva M.Adinolfi H.Afsharnia C.A.Aidala S.Aiola Z.Ajaltouni S.Akar P.Albicocco J.Albrecht F.Alessio M.Alexander A.Alfonso Albero G.Alkhazov P.Alvarez Cartelle A.A.Alves Jr S.Amato Y.Amhis L.An L.Anderlini G.Andreassi M.Andreotti F.Archilli A.Artamonov M.Artuso K.Arzymatov E.Aslanides M.Atzeni B.Audurier S.Bachmann J.J.Back S.Baker V.Balagura W.Baldini A.Baranov R.J.Barlow S.Barsuk W.Barter M.Bartolini F.Baryshnikov J.M.Basels G.Bassi V.Batozskaya B.Batsukh A.Battig A.Bay M.Becker F.Bedeschi I.Bediaga A.Beiter L.J.Bel V.Belavin S.Belin V.Bellee K.Belous I.Belyaev G.Bencivenni E.Ben-Haim S.Benson S.Beranek A.Berezhnoy *** D.Berninghoff H.C.Bernstein C.Bertella E.Bertholet A.Bertolin C.Betancourt F.Betti M.O.Bettler Ia.Bezshyiko S.Bhasin J.Bhom M.S.Bieker S.Bifani P.Billoir A.Bizzeti M.Bjørn M.P.Blago T.Blake F.Blanc S.Blusk D.Bobulska V.Bocci O.Boente Garcia T.Boettcher A.Boldyrev A.Bondar N.Bondar S.Borghi M.Borisyak M.Borsato J.T.Borsuk T.J.V.Bowcock C.Bozzi M.J.Bradley S.Braun A.Brea Rodriguez M.Brodski J.Brodzicka A.Brossa Gonzalo D.Brundu E.Buchanan A.Buonaura C.Burr A.Bursche A.Butkevich J.S.Butter J.Buytaert W.Byczynski S.Cadeddu H.Cai R.Calabrese L.Calero Diaz S.Cali R.Calladine M.Calvi M.Calvo Gomez P.Camargo Magalhaes A.Camboni P.Campana D.H.Campora Perez A.F.Campoverde Quezada L.Capriotti A.Carbone G.Carboni R.Cardinale A.Cardini I.Carli P.Carniti K.Carvalho Akiba A.Casais Vidal G.Casse M.Cattaneo G.Cavallero S.Celani R.Cenci J.Cerasoli M.G.Chapman M.Charles Ph.Charpentier G.Chatzikonstantinidis M.Chefdeville V.Chekalina C.Chen S.Chen A.Chernov S.-G.Chitic V.Chobanova S.Cholak M.Chrzaszcz A.Chubykin P.Ciambrone M.F.Cicala X.Cid Vidal G.Ciezarek F.Cindolo P.E.L.Clarke M.Clemencic H.V.Cliff J.Closier J.L.Cobbledick V.Coco J.A.B.Coelho J.Cogan E.Cogneras L.Cojocariu P.Collins T.Colombo A.Comerma-Montells A.Contu N.Cooke G.Coombs S.Coquereau G.Corti C.M.Costa Sobral B.Couturier D.C.Craik J.Crkovska A.Crocombe M.Cruz Torres R.Currie C.L.Da Silva E.Dall'Occo J.Dalseno C.D' Centro Brasileiro de Pesquisas Físicas(CBPF) Rio de JaneiroBrazil Universidade Federal do Rio de Janeiro(UFRJ) Rio de JaneiroBrazil Center for High Energy Physics Tsinghua UniversityBeijingChina School of Physics State Key Laboratory of Nuclear Physics and Technology Peking UniversityBeijingChina University of Chinese Academy of Sciences BeijingChina Institute Of High Energy Physics(IHEP) BeijingChina Institute of Particle Physics Central China Normal UniversityWuhanHubeiChina Univ.Grenoble Alpes Univ.Savoie Mont BlancCNRSIN2P3-LAPPAnnecyFrance UniversitéClermont Auvergne CNRS/IN2P3LPCClermont-FerrandFrance Aix Marseille Univ CNRS/IN2P3CPPMMarseilleFrance LAL Univ.Paris-SudCNRS/IN2P3UniversitéParis-SaclayOrsayFrance LPNHE Sorbonne UniversitéParis Diderot Sorbonne Paris CitéCNRS/IN2P3ParisFrance I.Physikalisches Institut RWTH Aachen UniversityAachenGermany Fakultät Physik Technische Universität DortmundDortmundGermany Max-Planck-Institut für Kernphysik(MPIK) HeidelbergGermany Physikalisches Institut Ruprecht-Karls-Universität HeidelbergHeidelbergGermany School of Physics University College DublinDublinIreland INFN Sezione di Bari BariItaly INFN Sezione di Bologna BolognaItaly INFN Sezione di Ferrara FerraraItaly INFN Sezione di Firenze FirenzeItaly INFN Laboratori Nazionali di Frascati FrascatiItaly INFN Sezione di Genova GenovaItaly INFN Sezione di Milano-Bicocca MilanoItaly INFN Sezione di Milano MilanoItaly INFN Sezione di Cagliari MonserratoItaly INFN Sezione di Padova PadovaItaly INFN Sezione di Pisa PisaItaly INFN Sezione di Roma Tor Vergata RomaItaly INFN Sezione di Roma La Sapienza RomaItaly Nikhef National Institute for Subatomic Physics AmsterdamNetherlands Nikhef National Institute for Subatomic Physics and VU University Amsterdam AmsterdamNetherlands Henryk Niewodniczanski Institute of Nuclear Physics Polish Academy of Sciences KrakówPoland AGH-University of Science and Technology Faculty of Physics and Applied Computer ScienceKrakówPoland Nationa

The production of ■baryons in proton-proton collisions at a centre-of-mass energy of √s = 13 TeV is measured in the transverse-momentum range 4<pT<15GeV/c and the rapidity range2.0<y<*** data used in thi... 详细信息

The production of ■baryons in proton-proton collisions at a centre-of-mass energy of √s = 13 TeV is measured in the transverse-momentum range 4

关键词： doubly charmed baryons hadron production QCD

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Search for continuous gravitational waves from 20 accreting millisecond x-ray pulsars in O3 LIGO data

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Physical Review D 2022年第2期105卷 022002-022002页

作者： R. Abbott T. D. Abbott F. Acernese K. Ackley C. Adams N. Adhikari R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain T. Akutsu S. Albanesi A. Allocca P. A. Altin A. Amato C. Anand S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando T. Andrade N. Andres T. Andrić S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koji Arai Koya Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson K. G. Arun H. Asada Y. Asali G. Ashton Y. Aso M. Assiduo S. M. Aston P. Astone F. Aubin C. Austin S. Babak F. Badaracco M. K. M. Bader C. Badger S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer A. Balsamo G. Baltus S. Banagiri D. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo F. Barone B. Barr L. Barsotti M. Barsuglia D. Barta J. Bartlett M. A. Barton I. Bartos R. Bassiri A. Basti M. Bawaj J. C. Bayley A. C. Baylor M. Bazzan B. Bécsy V. M. Bedakihale M. Bejger I. Belahcene V. Benedetto D. Beniwal T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser D. Beveridge R. Bhandare U. Bhardwaj D. Bhattacharjee S. Bhaumik I. A. Bilenko G. Billingsley S. Bini R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini L. D. Bonavena F. Bondu E. Bonilla R. Bonnand P. Booker B. A. Boom R. Bork V. Boschi N. Bose S. Bose V. Bossilkov V. Boudart Y. Bouffanais A. Bozzi C. Bradaschia P. R. Brady A. Bramley A. Branch M. Branchesi J. E. Brau M. Breschi T. Briant J. H. Briggs A. Brillet M. Brinkmann P. Brockill A. F. Brooks J. Brooks D. D. Brown S. Brunett G. Bruno R. Bruntz J. Bryant T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic C. Buy R. L. Byer L. Cadonati G. Cagnoli C. Cahillane J. Calderón Bustillo J. D. Callaghan T. A. Callister E. Ca LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Dipartimento di Farmacia Università di Salerno I-84084 Fisciano Salerno Italy INFN Sezione di Napoli Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 Victoria Australia LIGO Livingston Observatory Livingston Louisiana 70754 USA University of Wisconsin-Milwaukee Milwaukee Wisconsin 53201 USA OzGrav Australian National University Canberra Australian Capital Territory 0200 Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) D-30167 Hannover Germany Leibniz Universität Hannover D-30167 Hannover Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Theoretisch-Physikalisches Institut Friedrich-Schiller-Universität Jena D-07743 Jena Germany University of Birmingham Birmingham B15 2TT United Kingdom Center for Interdisciplinary Exploration & Research in Astrophysics (CIERA) Northwestern University Evanston Illinois 60208 USA Instituto Nacional de Pesquisas Espaciais 12227-010 São José dos Campos São Paulo Brazil Gravity Exploration Institute Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa I-56127 Pisa Italy Gravitational Wave Science Project National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan (NAOJ) Mitaka City Tokyo 181-8588 Japan INFN Sezione di Torino I-10125 Torino Italy Università di Napoli “Federico II” Complesso Universitario di Monte S. Angelo I-80126 Napoli Italy Université de Lyon Université Claude Bernard Lyon 1 CNRS Institut Lumière Matière F-69622 Villeurbanne France Department of Physics The University of Tokyo Bunkyo-ku Tokyo 113-0033 Japan Research Center for the Early Un

Results are presented of searches for continuous gravitational waves from 20 accreting millisecond x-ray pulsars with accurately measured spin frequencies and orbital parameters, using data from the third observing run of the Advanced LIGO and Advanced Virgo detectors. The search algorithm uses a hidden Markov model, where the transition probabilities allow the frequency to wander according to an unbiased random walk, while the J-statistic maximum-likelihood matched filter tracks the binary orbital phase. Three narrow subbands are searched for each target, centered on harmonics of the measured spin frequency. The search yields 16 candidates, consistent with a false alarm probability of 30% per subband and target searched. These candidates, along with one candidate from an additional target-of-opportunity search done for SAX J1808.4−3658, which was in outburst during one month of the observing run, cannot be confidently associated with a known noise source. Additional follow-up does not provide convincing evidence that any are a true astrophysical signal. When all candidates are assumed nonastrophysical, upper limits are set on the maximum wave strain detectable at 95% confidence, h095%. The strictest constraint is h095%=4.7×10−26 from IGR J17062−6143. Constraints on the detectable wave strain from each target lead to constraints on neutron star ellipticity and r-mode amplitude, the strictest of which are ε95%=3.1×10−7 and α95%=1.8×10−5 respectively. This analysis is the most comprehensive and sensitive search of continuous gravitational waves from accreting millisecond x-ray pulsars to date.

关键词： Gravitational waves Binary stars Neutron stars & pulsars

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Robust Online Spectrum Prediction with Incomplete and Corrupted Historical Observations

Robust Online Spectrum Prediction with Incomplete and Corrup...

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作者： Ding, Guoru Wu, Fan Wu, Qihui Tang, Shaojie Song, Fei Vasilakos, Athanasios V. Tsiftsis, Theodoros A. National Mobile Communications Research Laboratory Southeast University Nanjing210018 China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Electronics and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210007 China Department of Information Systems University of Texas at Dallas RichardsonTX75080 United States College of Communications Engineering PLA University of Science and Technology Nanjing210007 China Department of Computer Science Electrical and Space Engineering Lulea University of Technology Lulea97187 Sweden School of Engineering Nazarbayev University Astana010000 Kazakhstan

A range of emerging applications, from adaptive spectrum sensing to proactive spectrum mobility, depend on the ability to foresee spectrum state evolution. Despite a number of studies appearing about spectrum prediction, fundamental issues still remain unresolved: 1) The existing studies do not explicitly account for anomalies, which may incur serious performance degradation;2) they focus on the design of batch spectrum prediction algorithms, which limit the scalability to analyze massive spectrum data in real time;3) they assume the historical data are complete, which may not hold in reality. To address these issues, we develop a Robust Online Spectrum Prediction (ROSP) framework, with incomplete and corrupted observations, in this paper. We first present data analytics of real-world spectrum measurements to reveal the correlation structures of spectrum evolution and to analyze the impact of anomalies on the rank distribution of spectrum matrices. Then, from a spectral-temporal 2-D perspective, we formulate the ROSP as a joint optimization problem of matrix completion and recovery by effectively integrating the time series forecasting techniques and develop an alternating direction optimization method to efficiently solve it. We apply ROSP to a wide range of real-world spectrum matrices of popular wireless services. Experiment results show that ROSP outperforms state-of-the-art spectrum prediction schemes. © 1967-2012 IEEE.

关键词： Forecasting

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Examining the Reproducibility of Using Dynamic Loop Scheduling Techniques in Scientific Applications

Examining the Reproducibility of Using Dynamic Loop Scheduli...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Franziska Hoffeins Florina M. Ciorba Ioana Banicescu Technische Universität Dresden Center for Information Services and High Performance Computing Dresden Germany Department of Mathematics and Computer Science University of Basel Basel Switzerland Department of Computer Science and Engineering Mississippi State Mississippi State University MS USA

Reproducibility of the execution of scientific applications on parallel and distributed systems is a growing concern, underlying the trustworthiness of the experiments and the conclusions derived from experiments. Dynamic loop scheduling (DLS) techniques are an effective approach towards performance improvement of scientific applications via load balancing. These techniques address algorithmic and systemic sources of load imbalance by dynamically assigning tasks to processing elements. The DLS techniques have demonstrated their effectiveness when applied in real applications. Complementing native experiments, simulation is a powerful tool for studying the behavior of parallel and distributed applications. In earlier work, the scalability [1], robustness [2], and resilience [3] of the DLS techniques were investigated using the MSG interface of the SimGrid simulation framework [4]. The present work complements the earlier work and concentrates on the verification via reproducibility of the implementation of the DLS techniques in SimGrid-MSG. This work describes the challenges of verifying the performance of using DLS techniques in earlier implementations of scientific applications. The verification is performed via reproducibility of simulations based on SimGrid-MSG. To simulate experiments selected from earlier literature, the reproduction process begins by extracting the information needed from the earlier literature and converting it into the input required by SimGrid-MSG. The reproducibility study is carried out by comparing the performance of SimGrid-MSG-based experiments with those reported in two selected publications in which the DLS techniques were originally proposed. While the reproduction was not successful for experiments from one of the selected publications, it was successful for experiments from the other. This successful reproduction implies the verification of the DLS implementation in SimGrid-MSG for the considered applications and systems, and thus

关键词： Dynamic scheduling Processor scheduling Computational modeling Heuristic algorithms Analytical models Load modeling Scalability

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Improving stock market prediction via heterogeneous information fusion

arXiv

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arXiv 2018年

作者： Zhang, Xi Zhang, Yunjia Wang, Senzhang Yao, Yuntao Fang, Binxing Yu, Philip S. Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Computer Science University of Illinois at ChicagoIL60607 United States Institute for Data Science Tsinghua University Beijing100084 China Institute of Electronic and Information Engineering of UESTC in Guangdong Dongguan Guangdong523808 China

Traditional stock market prediction approaches commonly utilize the historical price-related data of the stocks to forecast their future trends. As the Web information grows, recently some works try to explore financial news to improve the prediction. Effective indicators, e.g., the events related to the stocks and the people’s sentiments towards the market and stocks, have been proved to play important roles in the stocks’ volatility, and are extracted to feed into the prediction models for improving the prediction accuracy. However, a major limitation of previous methods is that the indicators are obtained from only a single source whose reliability might be low, or from several data sources but their interactions and correlations among the multi-sourced data are largely ignored. In this work, we extract the events from Web news and the users’ sentiments from social media, and investigate their joint impacts on the stock price movements via a coupled matrix and tensor factorization framework. Specifically, a tensor is firstly constructed to fuse heterogeneous data and capture the intrinsicrelations among the events and the investors’ sentiments. Due to the sparsity of the tensor, two auxiliary matrices, the stock quantitative feature matrix and the stock correlation matrix, are constructed and incorporated to assist the tensor decomposition. The intuition behind is that stocks that are highly correlated with each other tend to be affected by the same event. Thus, instead of conducting each stock prediction task separately and independently, we predict multiple correlated stocks simultaneously through their commonalities, which are enabled via sharing the collaboratively factorized low rank matrices between matrices and the tensor. Evaluations on the China A-share stock data and the HK stock data in the year 2015 demonstrate the effectiveness of the proposed model. Copyright © 2018, The Authors. All rights reserved.

关键词： Tensors

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Large-Capacity Three-Party Quantum Digital Secret Sharing Using Three Particular Matrices Coding

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Communications in Theoretical Physics 2016年第11期65卷 501-508页

作者：赖红罗明星 Josef Pieprzyk 陶丽刘志明 Mehmet A.Orgun School of Computer and Information Science Southwest University Information Security and National Computing Grid Laboratory School of Information Science and TechnologySouthwest Jiaotong University School of Electrical Engineering and Computer Science Queensland University of Technology Institute of Computer Science Polish Academy of Sciences Department of Computing Macquarie University Faculty of Information Technology Macao University of Science and Technology

In this paper, we develop a large-capacity quantum digital secret sharing(QDSS) scheme, combined the Fibonacci- and Lucas-valued orbital angular momentum(OAM) entanglement with the recursive Fibonacci and Lucas matrices. To be exact, Alice prepares pairs of photons in the Fibonacci- and Lucas-valued OAM entangled states, and then allocates them to two participants, say, Bob and Charlie, to establish the secret key. Moreover, the available Fibonacci and Lucas values from the matching entangled states are used as the seed for generating the Fibonacci and Lucas matrices. This is achieved because the entries of the Fibonacci and Lucas matrices are recursive. The secret key can only be obtained jointly by Bob and Charlie, who can further recover the secret. Its security is based on the facts that nonorthogonal states are indistinguishable, and Bob or Charlie detects a Fibonacci number, there is still a twofold uncertainty for Charlie'(Bob') detected value.

关键词： Fibonacci-and Lucas-valued orbital angular momentum(OAM) states the first kind of Chebyshev maps lower error rates longer destances

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A throughput aware with collision-free MAC for wireless LANs

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science China(information sciences) 2016年第2期59卷 240-242页

作者： Tingrui PEI Yafeng DENG Zhetao LI Gengming ZHU Gaofeng PAN Youngjune CHOI Hiroo SEKIYA College of Information Engineering Xiangtan University Key Laboratory of Intelligent Computing & Information Processing of Ministry of Education Xiangtan University School of Computer Science & Engineering Hunan University of Science and Technology School of Electronic and Information Engineering Southwest University Department of Information and Computer Engineering Ajou University Graduate School of Advanced Integration Science Chiba University

Dear editor,The Carrier Sense Multiple Access with Collision Avoidance(CSMA/CA)hybrid with the resource reservation approach from Time Division Multiple Address(TDMA)has been emerged as a promising method to solve collision problems in wireless LANs[1–6].In the hybrid method,a TDMA circle contains multiple slots for nodes to contend for the ***,it allows a

关键词： MAC CSCF free TDMA A throughput aware with collision-free MAC for wireless LANs

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