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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

Machine discovery of partial differential equations from spatiotemporal data

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arXiv 2019年

作者： Yuan, Ye Li, Junlin Li, Liang Jiang, Frank Tang, Xiuchuan Zhang, Fumin Liu, Sheng Goncalves, Jorge Voss, Henning U. Li, Xiuting Kurths, Jürgen Ding, Han School of Artificial Intelligence and Automation State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan430074 China School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm11428 Sweden School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan430074 China School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta30332 United States School of Power and Mechanical Engineering Wuhan University Wuhan430074 China Department of Engineering University of Cambridge CambridgeCB2 1PZ United Kingdom Luxembourg Centre for Systems Biomedicine University of Luxembourg BelvauxL-4367 Luxembourg Citigroup Biomedical Imaging Center Weill Cornell Medical College New York10021 United States Research Domain IV - Transdisciplinary Concepts & Methods Potsdam Institute for Climate Impact Research Potsdam14473 Germany School of Mechanical Science and Engineering State Key Laboratory of Digital Manufacturing Equipments and Technology Huazhong University of Science and Technology Wuhan430074 China

The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data. The method, called Sparse Spatiotemporal System Discovery (S3d), decides which physical terms are necessary and which can be removed (because they are physically negligible in the sense that they do not affect the dynamics too much) from a pool of candidate functions. The method is built on the recent development of Sparse Bayesian Learning;which enforces the sparsity in the to-be-identified PDEs, and therefore can balance the model complexity and fitting error with theoretical guarantees. Without leveraging prior knowledge or assumptions in the discovery process, we use an automated approach to discover ten types of PDEs, including the famous Navier-Stokes and sine-Gordon equations, from simulation data alone. Moreover, we demonstrate our data-driven discovery process with the Complex Ginzburg-Landau Equation (CGLE) using data measured from a traveling-wave convection experiment. Our machine discovery approach presents solutions that has the potential to inspire, support and assist physicists for the establishment of physical laws from measured spatiotemporal data, especially in notorious fields that are often too complex to allow a straightforward establishment of physical law, such as biophysics, fluid dynamics, neuroscience or nonlinear optics. Copyright © 2019, The Authors. All rights reserved.

关键词： Nonlinear optics

Optimal design of SWIPT-aware fog computing networks

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arXiv 2019年

作者： Liu, Jingxian Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong

This paper studies a simultaneous wireless information and power transfer (SWIPT)-aware fog computing network, where a multiple antenna fog function integrated hybrid access point (F-HAP) transfers information and energy to multiple heterogeneous single-antenna sensors and also helps some of them fulfill computing tasks. By jointly optimizing energy and information beamforming designs at the F-HAP, the bandwidth allocation and the computation offloading distribution, an optimization problem is formulated to minimize the required energy under communication and computation requirements, as well as energy harvesting constraints. Two optimal designs, i.e., fixed offloading time (FOT) and optimized offloading time (OOT) designs, are proposed. As both designs get involved in solving non-convex problems, there are no known solutions to them. Therefore, for the FOT design, the semidefinite relaxation (SDR) is adopted to solve it. It is theoretically proved that the rank-one constraints are always satisfied, so the global optimal solution is guaranteed. For the OOT design, since its non-convexity is hard to deal with, a penalty dual decomposition (PDD)-based algorithm is proposed, which is able to achieve a suboptimal solution. The computational complexity for two designs are analyzed. Numerical results show that the partial offloading mode is superior to binary benchmark modes. It is also shown that if the system is with strong enough computing capability, the OOT design is suggested to achieve lower required energy;Otherwise, the FOT design is preferred to achieve a relatively low computation complexity. Copyright © 2019, The Authors. All rights reserved.

关键词： Fog computing

Optimal online transmission policy in wireless powered networks with urgency-aware age of information

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arXiv 2019年

作者： Lu, Yang Xiong, Ke Fan, Pingyi Zhong, Zhangdui Letaief, Khaled Ben School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Lab of Rail Traffic Control and Safety Beijing Jiaotong University Beijing100044 China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications Beijing Jiaotong University Beijing100044 China Hong Kong University of Science and Technology Hong Kong

This paper investigates the age of information (AoI) for a radio frequency (RF) energy harvesting (EH) enabled network, where a sensor first scavenges energy from a wireless power station and then transmits the collected status update to a sink node. To capture the thirst for the fresh update becoming more and more urgent as time elapsing, urgency-aware AoI (U-AoI) is defined, which increases exponentially with time between two received updates. Due to EH, some waiting time is required at the sensor before transmitting the status update. To find the optimal transmission policy, an optimization problem is formulated to minimize the long-term average U-AoI under constraint of energy causality. As the problem is non-convex and with no known solution, a two-layer algorithm is presented to solve it, where the outer loop is designed based on Dinklebach's method, and in the inner loop, a semi-closed-form expression of the optimal waiting time policy is derived based on Karush-Kuhn-Tucker (KKT) optimality conditions. Numerical results shows that our proposed optimal transmission policy outperforms the the zero time waiting policy and equal time waiting policy in terms of long-term average U-AoI, especially when the networks are non-congested. It is also observed that in order to achieve the lower U-AoI, the sensor should transmit the next update without waiting when the network is congested while should wait a moment before transmitting the next update when the network is non-congested. Additionally, it also shows that the system U-AoI first decreases and then keep unchanged with the increments of EH circuit's saturation level and the energy outage probability. Copyright © 2019, The Authors. All rights reserved.

关键词： Energy harvesting

Publisher Correction: A vaccine targeting the RBD of the S protein of SARS-CoV-2 induces protective immunity

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Nature 2021年第7844期590卷 E23页

作者： Jingyun Yang Wei Wang Zimin Chen Shuaiyao Lu Fanli Yang Zhenfei Bi Linlin Bao Fei Mo Xue Li Yong Huang Weiqi Hong Yun Yang Yuan Zhao Fei Ye Sheng Lin Wei Deng Hua Chen Hong Lei Ziqi Zhang Min Luo Hong Gao Yue Zheng Yanqiu Gong Xiaohua Jiang Yanfeng Xu Qi Lv Dan Li Manni Wang Fengdi Li Shunyi Wang Guanpeng Wang Pin Yu Yajin Qu Li Yang Hongxin Deng Aiping Tong Jiong Li Zhenling Wang Jinliang Yang Guobo Shen Zhiwei Zhao Yuhua Li Jingwen Luo Hongqi Liu Wenhai Yu Mengli Yang Jingwen Xu Junbin Wang Haiyan Li Haixuan Wang Dexuan Kuang Panpan Lin Zhengtao Hu Wei Guo Wei Cheng Yanlin He Xiangrong Song Chong Chen Zhihong Xue Shaohua Yao Lu Chen Xuelei Ma Siyuan Chen Maling Gou Weijin Huang Youchun Wang Changfa Fan Zhixin Tian Ming Shi Fu-Sheng Wang Lunzhi Dai Min Wu Gen Li Guangyu Wang Yong Peng Zhiyong Qian Canhua Huang Johnson Yiu-Nam Lau Zhenglin Yang Yuquan Wei Xiaobo Cen Xiaozhong Peng Chuan Qin Kang Zhang Guangwen Lu Xiawei Wei Laboratory of Aging Research and Cancer Drug Target State Key Laboratory of Biotherapy and Cancer Center National Clinical Research Center for Geriatrics West China Hospital Sichuan University Chengdu China. National Kunming High-level Biosafety Primate Research Center Institute of Medical Biology Chinese Academy of Medical Sciences and Peking Union Medical College Yunnan China. Key Laboratory of Human Disease Comparative Medicine Chinese Ministry of Health Beijing Key Laboratory for Animal Models of Emerging and Remerging Infectious Diseases Institute of Laboratory Animal Science Chinese Academy of Medical Sciences and Comparative Medicine Center Peking Union Medical College Beijing China. National Institutes for Food and Drug Control (NIFDC) Beijing China. National Chengdu Center for Safety Evaluation of Drugs State Key Laboratory of Biotherapy and Cancer Center West China Hospital Sichuan University Collaborative Innovation Center for Biotherapy Chengdu China. Shanghai Key Laboratory of Chemical Assessment and Sustainability School of Chemical Science & Engineering Tongji University Shanghai China. Treatment and Research Center for Infectious Diseases The Fifth Medical Center of PLA General Hospital National Clinical Research Center for Infectious Diseases Beijing China. Center for Biomedicine and Innovations Faculty of Medicine Macau University of Science and Technology Macau China. Department of Computer Science and Technology Tsinghua University Beijing China. Department of Applied Biology and Chemical Technology Hong Kong Polytechnic University Hong Kong China. Sichuan Provincial Key Laboratory for Human Disease Gene Study and Institute of Laboratory Medicine Sichuan Provincial People's Hospital University of Electronic Science and Technology of China Chengdu China. State Key Laboratory of Medical Molecular Biology Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical College Beijing China. Center for Biomedicine and Innovation

Correction to: Nonsymmorphic nodal-line metals in the two-dimensional rare earth monochalcogenides MX (M = Sc, Y;X = S, Se, Te)

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Journal of Materials science 2020年第32期55卷 15636-15636页

作者： Guo, Hao Zhao, Jianzhou Chen, Cong Li, Si Jiang, Wentao Fan, Haidong Tian, Xiaobao Yang, Shengyuan A. Department of Mechanics and Engineering Sichuan University Chengdu China Research Laboratory for Quantum Materials Singapore University of Technology and Design Singapore Singapore Co-Innovation Center for New Energetic Materials Southwest University of Science and Technology Mianyang China Key Laboratory of Micro-nano Measurement-Manipulation and Physics (Ministry of Education) School of Physics Beihang University Beijing China Key Laboratory of Low-Dimensional Quantum Structures and Quantum Control of Ministry of Education Department of Physics and Synergetic Innovation Center for Quantum Effects and Applications Hunan Normal University Changsha China

The corresponding author was incorrectly designated in the original article

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Optimal multicast subgrouping in mobility-aware 5G systems: Challenges, modeling, and opportunities 12

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Optimal multicast subgrouping in mobility-aware 5G systems: ...

Selected Papers of the 12th International Workshop on Applied Problems in Theory of Probabilities and Mathematical Statistics (Summer Session) in the Framework of the Conference on Information and Telecommunication Technologies and Mathematical Modeling of High-Tech Systems, APTP+MS 2018

作者： Beschastnyi, Vitalii A. Ostrikova, Darya Yu. Zeifman, Alexander I. Gudkova, Irina. A. 6 Miklukho-Maklaya St. Moscow117198 Russia Department of Applied Mathematics Vologda State University 6 Orlova St. Vologda160000 Russia Institute of Informatics Problems Federal Research Center Computer Science and Control of the Russian Academy of Sciences 44-2 Vavilov St. Moscow119333 Russia Faculty of Electrical Engineering and Communication Brno University of Technology 3058/10 Technická Brno61600 Czech Republic

The expected growth in the mobile video (including streaming video, video downloading, conferencing, etc.) now is the key driver for rapid development of 5G wireless network technologies. This paradigm forces wireless networks to manage their resources as effectively as possible. One of the most appropriate solutions for video traffic that may provide the sufficient spectral efficiency is multicasting. Multicast sessions allows a group of users to simultaneously access the same multimedia content with the same QoE parameters. In this paper we consider different methods of multicast subgrouping. An analytical framework in the from of a queuing network is proposed to study the network performance, and a comparative study of network performance metrics under different user movement scenarios is presented. Copyright © 2018 for the individual papers by the papers' authors.

关键词： Multicasting

Mobility-aware caching scheduling for fog computing in mmwave band

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arXiv 2018年

作者： Niu, Yong Liu, Yu Li, Yong Zhong, Zhangdui Ai, Bo Hui, Pan State Key Laboratory of Rail Traffic Control and Safety Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China Department of Electronic Engineering Tsinghua University Beijing100084 China Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong

As an extension of cloud computing, fog computing at the edge of networks provides low latency, location awareness, and real-time interactions. At the same time, millimeter wave (mmWave) communications are able to provide directional multigigabit transmission rates with large available bandwidth. Based on the user mobile trajectories in a region, several activity hotspots that users pass by frequently can be obtained. By caching popular content at the edge nodes near the hotspots, users can download the cached content directly at a short distance, and the user experience can be significantly improved. Considering multiple hotspots in a region, how to efficiently schedule the transmission for the caching at edge nodes becomes a key problem. In this paper, we focus on the problem of mobility aware transmission scheduling for caching at edge nodes near hotspots, and utilize multi-hop relaying and concurrent transmissions to achieve better performance. After formulating the optimal scheduling problem as a stochastic nonlinear mixed integer program, we propose a mobility aware caching scheduling scheme, called MHRC (Multi-Hop Relaying based Caching), where multi-hop D2D paths are established for edge nodes, and concurrent transmissions are exploited in the scheduling of caching at edge nodes. Extensive performance evaluation demonstrates MHRC achieves more than 1x higher expected cached data amount compared with state-of-the-art schemes. Copyright © 2018, The Authors. All rights reserved.

关键词： Fog computing

Generative Adversarial Networks:Introduction and Outlook

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IEEE/CAA Journal of Automatica Sinica 2017年第4期4卷 588-598页

作者： Kunfeng Wang Chao Gou Yanjie Duan Yilun Lin Xinhu Zheng Fei-Yue Wang IEEE State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Qingdao Academy of Intelligent Industries State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences University of Chinese Academy of Sciences Department of Computer Science and Engineering University of Minnesota Research Center for Computational Experiments and Parallel Systems Technology National University of Defense Technology

Recently, generative adversarial networks(GANs)have become a research focus of artificial intelligence. Inspired by two-player zero-sum game, GANs comprise a generator and a discriminator, both trained under the adversarial learning *** goal of GANs is to estimate the potential distribution of real data samples and generate new samples from that *** their initiation, GANs have been widely studied due to their enormous prospect for applications, including image and vision computing, speech and language processing, etc. In this review paper, we summarize the state of the art of GANs and look into the future. Firstly, we survey GANs' proposal background,theoretic and implementation models, and application ***, we discuss GANs' advantages and disadvantages, and their development trends. In particular, we investigate the relation between GANs and parallel intelligence,with the conclusion that GANs have a great potential in parallel systems research in terms of virtual-real interaction and integration. Clearly, GANs can provide substantial algorithmic support for parallel intelligence.

关键词： ACP approach adversarial learning generative adversarial networks(GANs) generative models parallel intelligence zero-sum game