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Upper limits on the isotropic gravitational-wave background from Advanced LIGO and Advanced Virgo’s third observing run

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Physical Review D 2021年第2期104卷 022004-022004页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams 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 K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. Bankar J. C. Barayoga C. Barbieri B. C. Barish D. Barker P. Barneo S. Barnum 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 M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini 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 A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic R. L. Byer L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India 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 Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 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 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 Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università 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 California State University Fullerton Fullerton California 92831 USA NCSA University of Illinois at Urbana-Champaign Urbana Illinois 61801 USA Università di Napoli “Federico II” Complesso Universitario

We report results of a search for an isotropic gravitational-wave background (GWB) using data from Advanced LIGO’s and Advanced Virgo’s third observing run (O3) combined with upper limits from the earlier O1 and O2 runs. Unlike in previous observing runs in the advanced detector era, we include Virgo in the search for the GWB. The results of the search are consistent with uncorrelated noise, and therefore we place upper limits on the strength of the GWB. We find that the dimensionless energy density ΩGW≤5.8×10−9 at the 95% credible level for a flat (frequency-independent) GWB, using a prior which is uniform in the log of the strength of the GWB, with 99% of the sensitivity coming from the band 20–76.6 Hz; ΩGW(f)≤3.4×10−9 at 25 Hz for a power-law GWB with a spectral index of 2/3 (consistent with expectations for compact binary coalescences), in the band 20–90.6 Hz; and ΩGW(f)≤3.9×10−10 at 25 Hz for a spectral index of 3, in the band 20–291.6 Hz. These upper limits improve over our previous results by a factor of 6.0 for a flat GWB, 8.8 for a spectral index of 2/3, and 13.1 for a spectral index of 3. We also search for a GWB arising from scalar and vector modes, which are predicted by alternative theories of gravity; we do not find evidence of these, and place upper limits on the strength of GWBs with these polarizations. We demonstrate that there is no evidence of correlated noise of magnetic origin by performing a Bayesian analysis that allows for the presence of both a GWB and an effective magnetic background arising from geophysical Schumann resonances. We compare our upper limits to a fiducial model for the GWB from the merger of compact binaries, updating the model to use the most recent data-driven population inference from the systems detected during O3a. Finally, we combine our results with observations of individual mergers and show that, at design sensitivity, this joint approach may yield stronger constraints on the merger rate of binary black holes at z≳2

关键词： Gravitational wave detection

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All-sky search for continuous gravitational waves from isolated neutron stars in the early O3 LIGO data

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Physical Review D 2021年第8期104卷 082004-082004页

作者： R. Abbott T. D. Abbott S. Abraham F. Acernese K. Ackley A. Adams C. Adams R. X. Adhikari V. B. Adya C. Affeldt D. Agarwal M. Agathos K. Agatsuma N. Aggarwal O. D. Aguiar L. Aiello A. Ain P. Ajith T. Akutsu K. M. Aleman G. Allen A. Allocca P. A. Altin A. Amato S. Anand A. Ananyeva S. B. Anderson W. G. Anderson M. Ando S. V. Angelova S. Ansoldi J. M. Antelis S. Antier S. Appert Koya Arai Koji Arai Y. Arai S. Araki A. Araya M. C. Araya J. S. Areeda M. Arène N. Aritomi N. Arnaud S. M. Aronson H. Asada Y. Asali G. Ashton Y. Aso S. M. Aston P. Astone F. Aubin P. Aufmuth K. AultONeal C. Austin S. Babak F. Badaracco M. K. M. Bader S. Bae Y. Bae A. M. Baer S. Bagnasco Y. Bai L. Baiotti J. Baird R. Bajpai M. Ball G. Ballardin S. W. Ballmer M. Bals A. Balsamo G. Baltus S. Banagiri D. Bankar R. S. 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 M. G. Benjamin T. F. Bennett J. D. Bentley M. BenYaala F. Bergamin B. K. Berger S. Bernuzzi D. Bersanetti A. Bertolini J. Betzwieser R. Bhandare A. V. Bhandari D. Bhattacharjee S. Bhaumik J. Bidler I. A. Bilenko G. Billingsley R. Birney O. Birnholtz S. Biscans M. Bischi S. Biscoveanu A. Bisht B. Biswas M. Bitossi M.-A. Bizouard J. K. Blackburn J. Blackman C. D. Blair D. G. Blair R. M. Blair F. Bobba N. Bode M. Boer G. Bogaert M. Boldrini 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 A. Buikema T. Bulik H. J. Bulten A. Buonanno R. Buscicchio D. Buskulic R. L. Byer L. Cadonati M. Caesar G. Cagnoli C. Cahillane H. W. Cain, III J. Calderón Bustillo J. LIGO Laboratory California Institute of Technology Pasadena California 91125 USA Louisiana State University Baton Rouge Louisiana 70803 USA Inter-University Centre for Astronomy and Astrophysics Pune 411007 India 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 and Astronomy Monash University Clayton 3800 Victoria Australia Christopher Newport University Newport News Virginia 23606 USA LIGO Livingston Observatory Livingston Louisiana 70754 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 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 and 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 Gran Sasso Science Institute (GSSI) I-67100 L’Aquila Italy INFN Laboratori Nazionali del Gran Sasso I-67100 Assergi Italy INFN Sezione di Pisa I-56127 Pisa Italy Università di Pisa I-56127 Pisa Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India 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 California State University Fullerton Fullerton California 92831 USA NCSA University of Illi

We report on an all-sky search for continuous gravitational waves in the frequency band 20–2000 Hz and with a frequency time derivative in the range of [−1.0,+0.1]×10−8 Hz/s. Such a signal could be produced by a nearby, spinning and slightly nonaxisymmetric isolated neutron star in our Galaxy. This search uses the LIGO data from the first six months of Advanced LIGO’s and Advanced Virgo’s third observational run, O3. No periodic gravitational wave signals are observed, and 95% confidence-level (C.L.) frequentist upper limits are placed on their strengths. The lowest upper limits on worst-case (linearly polarized) strain amplitude h0 are ∼1.7×10−25 near 200 Hz. For a circularly polarized source (most favorable orientation), the lowest upper limits are ∼6.3×10−26. These strict frequentist upper limits refer to all sky locations and the entire range of frequency derivative values. For a population-averaged ensemble of sky locations and stellar orientations, the lowest 95% C.L. upper limits on the strain amplitude are ∼1.4×10−25. These upper limits improve upon our previously published all-sky results, with the greatest improvement (factor of ∼2) seen at higher frequencies, in part because quantum squeezing has dramatically improved the detector noise level relative to the second observational run, O2. These limits are the most constraining to date over most of the parameter space searched.

关键词： Astrophysical studies of gravity Gravitational wave detection Gravitational wave sources Gravitational waves

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Open Data from the Third Observing Run of LIGO, Virgo, KAGRA, and GEO

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Astrophysical Journal, Supplement Series 2023年第2期267卷 29-29页

作者： Abbott, R. Abe, H. Acernese, F. Ackley, K. Adhicary, S. Adhikari, N. Adhikari, R.X. Adkins, V.K. Adya, V.B. Affeldt, C. Agarwal, D. Agathos, M. Aguiar, O.D. Aiello, L. Ain, A. Ajith, P. Akutsu, T. Albanesi, S. Alfaidi, R.A. Al-Jodah, A. Alléné, C. Allocca, A. Almualla, M. Altin, P.A. Amato, A. Amez-Droz, L. Amorosi, A. Anand, S. Ananyeva, A. Andersen, R. Anderson, S.B. Anderson, W.G. Andia, M. Ando, M. Andrade, T. Andres, N. Andrés-Carcasona, M. Andrić, T. Ansoldi, S. Antelis, J.M. Antier, S. Aoumi, M. Apostolatos, T. Appavuravther, E.Z. Appert, S. Apple, S.K. Arai, K. Araya, A. Araya, M.C. Areeda, J.S. Arène, M. Aritomi, N. Arnaud, N. Arogeti, M. Aronson, S.M. Arun, K.G. Asada, H. Ashton, G. Aso, Y. Assiduo, M. Assis de Souza Melo, S. Aston, S.M. Astone, P. Aubin, F. AultONeal, K. Babak, S. Badalyan, A. Badaracco, F. Badger, C. Bae, S. Bagnasco, S. Bai, Y. Baier, J.G. Baiotti, L. Baird, J. Bajpai, R. Baka, T. Ball, M. Ballardin, G. Ballmer, S.W. Baltus, G. Banagiri, S. Banerjee, B. Bankar, D. Baral, P. Barayoga, J.C. Barber, J. Barish, B.C. Barker, D. Barneo, P. Barone, F. Barr, B. Barsotti, L. Barsuglia, M. Barta, D. Barthelmy, S.D. Barton, M.A. Bartos, I. Basak, S. Basalaev, A. Bassiri, R. Basti, A. Bawaj, M. Bayley, J.C. Baylor, A.C. Bazzan, M. Bécsy, B. Bedakihale, V.M. Beirnaert, F. Bejger, M. Bell, A.S. Benedetto, V. Beniwal, D. Benoit, W. Bentley, J.D. Ben Yaala, M. Bera, S. Berbel, M. Bergamin, F. Berger, B.K. Bernuzzi, S. Beroiz, M. Berry, C.P.L. Bersanetti, D. Bertolini, A. Betzwieser, J. Beveridge, D. Bevins, N. Bhandare, R. Bhandari, A.V. Bhardwaj, U. Bhatt, R. Bhattacharjee, D. Bhaumik, S. Bianchi, A. Bilenko, I.A. Bilicki, M. Billingsley, G. Bini, S. Birnholtz, O. Biscans, S. Bischi, M. Biscoveanu, S. Bisht, A. Biswas, B. Bitossi, M. Bizouard, M.-A. Blackburn, J.K. Blair, C.D. Blair, D.G. Blair, R.M. Bobba, F. Bode, N. Boër, M. Bogaert, G. Boileau, G. Boldrini, M. Bolingbroke, G.N. Bonavena, L.D. Bondarescu, R. Bondu, F. Bonilla, E. Bonilla, G.S. Bonnand, R. Booker, P. Bork, R. Boschi, V. Bose, N. LIGO Laboratory California Institute of Technology Pasadena 91125 CA United States Graduate School of Science Tokyo Institute of Technology 2-12-1 Ookayama Meguro-ku Tokyo 152-8551 Japan Dipartimento di Farmacia Università di Salerno Salerno Fisciano I-84084 Italy INFN Sezione di Napoli Napoli I-80126 Italy OzGrav School of Physics & Astronomy Monash University Clayton 3800 VIC Australia The Pennsylvania State University University Park 16802 PA United States University of Wisconsin-Milwaukee Milwaukee 53201 WI United States Louisiana State University Baton Rouge 70803 LA United States OzGrav Australian National University Canberra 0200 ACT Australia Max Planck Institute for Gravitational Physics (Albert Einstein Institute) Hannover D-30167 Germany Leibniz Universität Hannover Hannover D-30167 Germany Inter-University Centre for Astronomy and Astrophysics Pune 411007 India University of Cambridge Cambridge CB2 1TN United Kingdom Instituto Nacional de Pesquisas Espaciais São Paulo São José dos Campos 12227-010 Brazil Cardiff University Cardiff CF24 3AA United Kingdom INFN Sezione di Pisa Pisa I-56127 Italy International Centre for Theoretical Sciences Tata Institute of Fundamental Research Bengaluru 560089 India Gravitational Wave Science Project National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Advanced Technology Center National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka City Tokyo 181-8588 Japan Dipartimento di Fisica Università degli Studi di Torino Torino I-10125 Italy INFN Sezione di Torino Torino I-10125 Italy SUPA University of Glasgow Glasgow G12 8QQ United Kingdom OzGrav University of Western Australia Crawley 6009 WA Australia Univ. Savoie Mont Blanc CNRS Laboratoire d'Annecy de Physique des Particules - IN2P3 Annecy F-74000 France Università di Napoli “Federico II” Napoli I-80126 Italy University of Minnesota Minneapolis 55455 MN United States M

The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in 2019 April and lasting six months, O3b starting in 2019 November and lasting five months, and O3GK starting in 2020 April and lasting two weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://***. The main data set, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages. © 2023. The Author(s). Published by the American Astronomical Society.

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Fast sparse representation classification using transfer learning 1st

Fast sparse representation classification using transfer lea...

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1st International Conference on cloud computing and Security, ICCCS 2015

作者： Zhu, Qi Dai, Baisheng Fan, Zizhu Zhang, Zheng College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Key Laboratory of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Nanjing University of Science and Technology Nanjing China School of Computing Science and Technology Harbin Institute of Technology Harbin China School of Basic Science East China Jiaotong University Nanchang China Shenzhen Graduate School Harbin Institute of Technology Shenzhen China

ISBN: (纸本)9783319270500

Under certain conditions, the sparsest solution to the combination coefficients can be achieved by L1-norm minimization. Many algorithms of L1-norm minimization have been studied in recent years, but they suffer from the expensive computational problem, which constrains the applications of SRC in large-scale problems. This paper aims to improve the computation efficiency of SRC by speeding up the learning of the combination coefficients. We show that the coupled representations in the original space and PCA space have the similar sparse representation model (coefficients). By using this trick, we successfully avoid the curse of dimensionality of SRC in computation and develop the Fast SRC (FSRC). Experimental results on several face datasets illustrate that FSRC has comparable classification accuracy to SRC. Compared to PCA +SRC, FSRC achieves higher classification accuracy. © Springer International Publishing Switzerland 2015.

关键词： Classification (of information)

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On-Demand Single Photons with High Extraction Efficiency and Near-Unity Indistinguishability from a Resonantly Driven Quantum Dot in a Micropillar

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Physical Review Letters 2016年第2期116卷 020401-020401页

作者： Xing Ding Yu He Z.-C. Duan Niels Gregersen M.-C. Chen S. Unsleber S. Maier Christian Schneider Martin Kamp Sven Höfling Chao-Yang Lu Jian-Wei Pan Shanghai Branch National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Shanghai 201315 China CAS Center for Excellence and Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Hefei Anhui 230026 China CAS-Alibaba Quantum Computing Laboratory Shanghai 201315 China DTU Fotonik Department of Photonics Engineering Technical University of Denmark Building 343 DK-2800 Kongens Lyngby Denmark Technische Physik Physikalisches Instität and Wilhelm Conrad Röntgen-Center for Complex Material Systems Universitat Würzburg Am Hubland D-97074 Wüzburg Germany SUPA School of Physics and Astronomy University of St. Andrews St. Andrews KY16 9SS United Kingdom

Scalable photonic quantum technologies require on-demand single-photon sources with simultaneously high levels of purity, indistinguishability, and efficiency. These key features, however, have only been demonstrated separately in previous experiments. Here, by s-shell pulsed resonant excitation of a Purcell-enhanced quantum dot-micropillar system, we deterministically generate resonance fluorescence single photons which, at π pulse excitation, have an extraction efficiency of 66%, single-photon purity of 99.1%, and photon indistinguishability of 98.5%. Such a single-photon source for the first time combines the features of high efficiency and near-perfect levels of purity and indistinguishabilty, and thus opens the way to multiphoton experiments with semiconductor quantum dots.

关键词： Semiconductor quantum optics Quantum dots

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Author Correction: The challenge of mapping the human connectome based on diffusion tractography

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Nature communications 2019年第1期10卷 5059页

作者： Klaus H Maier-Hein Peter F Neher Jean-Christophe Houde Marc-Alexandre Côté Eleftherios Garyfallidis Jidan Zhong Maxime Chamberland Fang-Cheng Yeh Ying-Chia Lin Qing Ji Wilburn E Reddick John O Glass David Qixiang Chen Yuanjing Feng Chengfeng Gao Ye Wu Jieyan Ma Renjie He Qiang Li Carl-Fredrik Westin Samuel Deslauriers-Gauthier J Omar Ocegueda González Michael Paquette Samuel St-Jean Gabriel Girard François Rheault Jasmeen Sidhu Chantal M W Tax Fenghua Guo Hamed Y Mesri Szabolcs Dávid Martijn Froeling Anneriet M Heemskerk Alexander Leemans Arnaud Boré Basile Pinsard Christophe Bedetti Matthieu Desrosiers Simona Brambati Julien Doyon Alessia Sarica Roberta Vasta Antonio Cerasa Aldo Quattrone Jason Yeatman Ali R Khan Wes Hodges Simon Alexander David Romascano Muhamed Barakovic Anna Auría Oscar Esteban Alia Lemkaddem Jean-Philippe Thiran H Ertan Cetingul Benjamin L Odry Boris Mailhe Mariappan S Nadar Fabrizio Pizzagalli Gautam Prasad Julio E Villalon-Reina Justin Galvis Paul M Thompson Francisco De Santiago Requejo Pedro Luque Laguna Luis Miguel Lacerda Rachel Barrett Flavio Dell'Acqua Marco Catani Laurent Petit Emmanuel Caruyer Alessandro Daducci Tim B Dyrby Tim Holland-Letz Claus C Hilgetag Bram Stieltjes Maxime Descoteaux Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. k.maier-hein@dkfz.de. Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg 69120 Germany. Sherbrooke Connectivity Imaging Lab (SCIL) Université de Sherbrooke Sherbrooke QC J1K 0A5 QC Canada. Department of Intelligent Systems Engineering School of Informatics and Computing Indiana University Bloomington IN 47408 USA. Krembil Research Institute University Health Network Toronto Canada M5G 2C4. Department of Neurological Surgery University of Pittsburgh School of Medicine Pittsburgh PA 15213 USA. IMT-Institute for Advanced Studies Lucca 55100 Italy. Department of Diagnostic Imaging St. Jude Children's Research Hospital Memphis TN 38105 USA. University of Toronto Institute of Medical Science Toronto Canada M5S 1A8. Institute of Information Processing and Automation Zhejiang University of Technology Hangzhou 310023 Zhejiang China. United Imaging Healthcare Co Shanghai 201807 China. Shanghai Advanced Research Institute Shanghai 201210 China. Laboratory of Mathematics in Imaging Harvard Medical School Boston MA 02215 USA. Center for Research in Mathematics Guanajuato 36023 Mexico. PROVIDI Lab Image Sciences Institute University Medical Center Utrecht Utrecht 3508 The Netherlands. Cardiff University Brain Research Imaging Centre School of Psychology Cardiff University Maindy Road Cardiff CF24 4HQ UK. Department of Radiology University Medical Center Utrecht Utrecht 3508 The Netherlands. Centre de recherche institut universitaire de geriatrie de Montreal (CRIUGM) Université de Montréal Montreal QC Canada H3W 1W5. Sorbonne Universités UPMC Univ Paris 06 CNRS INSERM Laboratoire d'Imagerie Biomédicale (LIB) 75013 Paris France. Center for Advanced Research in Sleep Medicine Hôpital du Sacré-Coeur de Montréal Montreal Canada H4J 1C5. Neuroimaging Unit Institute of Bioimaging and Molecular Physiology (IBFM) Nat

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Instant Discovery of Moment Companion Vehicles from Big Streaming Traffic Data

Instant Discovery of Moment Companion Vehicles from Big Stre...

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International Conference on cloud computing and Big Data (CCBD)

作者： Meiling Zhu Chen Liu Jianwu Wang Xiongbin Wang Yanbo Han School of Computer Science and Technology Tianjin University Tianjin China Cloud Computing Research Center North China University of Technology Beijing China Department of Information Systems University of Maryland Baltimore County Baltimore MD U.S.A.

With more and more traffic monitoring cameras installed in large cities, big streaming data are being generated continuously and provides a lot of new application opportunities. Among the new applications, companion vehicle discovery is to identify vehicle groups that move together. To quickly identify companion vehicles from a special type of streaming traffic data, called Automatic Number Plate Recognition (ANPR) data, this paper proposes a framework and two algorithms following distributed data-parallel programming model. The main challenge is how to handle the scale of ANPR data and detect companion vehicles as soon as possible. The proposed framework is designed to instantly output companion vehicles when they pass through monitoring cameras. Our framework can be used in many time-sensitive scenarios like surveillance on suspect trackers for specific vehicles. Experiments with real ANPR data in a distributed environment verify that our approach can process streaming ANPR data directly and discover the companion vehicles in nearly real time. We also analyze the performance affecting factors from the experiments.

关键词： Vehicles Cameras Sparks Urban areas distributed databases Algorithm design and analysis Heuristic algorithms

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Hybrid phase-locked loop with fast locking time and low spur in a 0.18-μm CMOS process

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Chinese Physics B 2014年第7期23卷 748-753页

作者：朱思衡司黎明郭超史君宇朱卫仁 Beijing Key Laboratory of Millimeter Wave and Terahertz Technology Department of Electronic Engineering School of Information and ElectronicsBeijing Institute of Technology Advanced Computing and Simulation Laboratory (AχL) Department of Electrical and Computer Systems EngineeringMonash University

We propose a novel hybrid phase-locked loop （PLL） architecture for overcoming the trade-off between fast locking time and low spur. To reduce the settling time and meanwhile suppress the reference spurs, we employ a wide-band single-path PLL and a narrow-band dual-path PLL in a transient state and a steady state, respectively, by changing the loop bandwidth according to the gain of voltage controlled oscillator （VCO） and the resister of the loop filter. The hybrid PLL is implemented in a 0.18-μm complementary metal oxide semiconductor （CMOS） process with a total die area of 1.4×0.46 mm2. The measured results exhibit a reference spur level of lower than -73 dB with a reference frequency of 10 MHz and a settling time of 20 μs with 40 MHz frequency jump at 2 GHz. The total power consumption of the hybrid PLL is less than 27 mW with a supply voltage of 1.8 V.

关键词： phase-locked loop （PLL） fast locking time low spur complementary metal oxide semiconductor（CMOS）

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Computational Offloading or Data Binding? Bridging the cloud Infrastructure to the Proximity of the Mobile User

Computational Offloading or Data Binding? Bridging the Cloud...

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IEEE International Conference on Mobile cloud computing, Services, and Engineering

作者： Huber Flores Satish Narayana Srirama Rajkumar Buyya University of Tartu Institute of Computer Science Mobile Cloud Lab University of Melbourne Department of Computing and Information Systems Cloud Computing and Distributed Systems Laboratory (CLOUDS)

ISBN: (纸本)9781479944248

Mobile and cloud computing are converging as the prominent technologies that are leading the change to the post personal computing (PC) era. Computational offloading and data binding are the core techniques that foster to elastically augment the capabilities of low-power devices, such as smartphones. Mobile applications may be bonded to cloud resources by following a task delegation or code offloading criteria. In a delegation model, a handset can utilize the cloud in a service-oriented manner to delegate asynchronously a resource-intensive mobile task by direct invocation of the service. In contrast, in an offloading model, a mobile application is partitioned and analyzed so that the most computational expensive operations at code level can be identified and offloaded to a remote cloud-based surrogate. We compared in this paper, the mobile cloud computing models for offloading and delegation. We utilized our own frameworks for computational offloading and data binding in the analysis. While in principle, offloading and delegation are viable methods to augment the capabilities of the mobile devices with cloud power, they enrich the mobile applications from different perspectives at diverse computational scales.

关键词： Mobile cloud computing Access Models Code Offloading Task Delegation Middleware Mobile Crowdsourcing

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Peer Comparison of XSEDE and NCAR Publication Data

Peer Comparison of XSEDE and NCAR Publication Data

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IEEE International Conference on Cluster computing

作者： Gregor von Laszewski Fugang Wang Geoffrey C. Fox David L. Hart Thomas R. Furlani Robert L. DeLeon Steven M. Gallo School of Informatics and Computing Indiana University Bloomington Indiana U.S.A. Computational and Information Systems Laboratory NCAR Boulder CO U.S.A. Center for Computational Research SUNY Buffalo New York U.S.A.

We present a framework that compares the publication impact based on a comprehensive peer analysis of papers produced by scientists using XSEDE and NCAR resources. The analysis is introducing a percentile ranking based approach of citations of the XSEDE and NCAR papers compared to peer publications in the same journal that do not use these resources. This analysis is unique in that it evaluates the impact of the two facilities by comparing the reported publications from them to their peers from within the same journal issue. From this analysis, we can see that papers that utilize XSEDE and NCAR resources are cited statistically significantly more often. Hence we find that reported publications indicate that XSEDE and NCAR resources exert a strong positive impact on scientific research.

关键词： Resource management Data analysis Ranking (statistics) Atmospheric measurements Extraterrestrial measurements Conferences

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