检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Xue, Xiao Bian, Ligong Shu, Jing Yuan, Qiang Zhu, Xingjiang Bhat, N.D. Ramesh Dai, Shi Feng, Yi Goncharov, Boris Hobbs, George Howard, Eric Manchester, Richard N. Russell, Christopher J. Reardon, Daniel J. Shannon, R.M. Spiewak, Renée Thyagarajan, Nithyanandan Wang, Jingbo CAS Key Laboratory of Theoretical Physics Institute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China School of Physical Sciences University of Chinese Academy of Sciences Beijing100049 China II. Institute of Theoretical Physics Universität Hamburg Hamburg22761 Germany Department of Physics Chongqing University Chongqing401331 China Chongqing Key Laboratory for Strongly Coupled Physics Chongqing401331 China CAS Key Laboratory of Theoretical Physics Insitute of Theoretical Physics Chinese Academy of Sciences Beijing100190 China CAS Center for Excellence in Particle Physics Beijing100049 China School of Fundamental Physics and Mathematical Sciences Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Hangzhou310024 China Center for High Energy Physics Peking University Beijing100871 China International Center for Theoretical Physics Asia-Pacific Hangzhou Beijing China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Nanjing210023 China School of Astronomy and Space Science University of Science and Technology of China Hefei230026 China School of Physics and Astronomy Monash University ClaytonVIC3800 Australia OzGrav: The ARC Centre of Excellence for Gravitational Wave Discovery HawthornVIC3122 Australia Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China International Centre for Radio Astronomy Research Curtin University BentleyWA6102 Australia Western Sydney University Locked Bag 1797 Penrith South DCNSW1797 Australia University of Chinese Academy of Sciences Beijing100049 China CSIRO Astronomy and Space Science P.O. Box 76 EppingNSW1710 Australia Macquarie University Department of Physics and Astronomy SydneyNSW2109 Australia CSIRO Scientific Computing Australian Technology Park Locked Bag 9013 AlexandriaNSW1435 Australia Centre for Astrophysics and Supercomputing Swinburne University of

A cosmological first-order phase transition is expected to produce a stochastic gravitational wave background. If the phase transition temperature is on the MeV scale, the power spectrum of the induced stochastic gravitational waves peaks around nanohertz frequencies, and can thus be probed with high-precision pulsar timing observations. We search for such a stochastic gravitational wave background with the latest data set of the Parkes Pulsar Timing Array. We find no evidence for a Hellings-Downs spatial correlation as expected for a stochastic gravitational wave background. Therefore, we present constraints on first-order phase transition model parameters. Our analysis shows that pulsar timing is particularly sensitive to the low-temperature (T ∼ 1 − 100 MeV) phase transition with a duration (β/H∗)−1 ∼ 10−2 − 10−1 and therefore can be used to constrain the dark and QCD phase transitions. © 2021, CC BY.

关键词： Pulsars

来源：评论

学校读者我要写书评

暂无评论

Vlbi data processing on coronal radio-sounding experiments of mars express

arXiv

引用

arXiv 2021年

作者： Ma, Maoli Calvés, Guifré Molera Cimò, Giuseppe Zhang, Peijin Ming, Xiong Li, Peijia Kummamuru, Pradyumna Chu, Zhanghu Jiang, Tianyu Xia, Bo Tetsuro, Kondo Tong, Fengxian de Vicente, Pablo Quick, Jonathan Zhang, Hua Chen, Zhong Shanghai Astronomical Observatory 80 Nandan Road Shanghai China Shanghai Astronomical Observatory Key Laboratory of Radio Astronomy Chinese Academy of Sciences 10 Yuanhua Road JiangSu Nanjing210033 China University of Tasmania Private Bag 37 HobartTAS7001 Australia Joint Institute for VLBI ERIC Oude Hoogeveensedijk 4 PD Dwingeloo7991 Netherlands University of Science and Technology of China 96 Jinzhai Road Hefei China National Space Science Center NO.1 Nanertiao Zhongguancun Haidian district Beijing China Centro de Desarrollos Tecnológicos Observatorio de Yebes Guadalajara Spain Hartebeesthoek Radio Astronomy Observatory PO Box 443 Krugersdorp South Africa Xinjiang Astronomical Observatory CAS 150 Science 1-Street Urumqi China National Basic Science Data Center Building No.2 4 Zhongguancun South 4th Street Haidian District Beijing100190 China

The ESA's Mars Express solar corona experiments were performed at two solar conjunctions in the years 2015 and 2017 by a number of radio telescopes in the European VLBI network. This paper presents the methods to measure the frequency and phase fluctuations of the spacecraft radio signal, and the applications to study the characteristics of the plasma turbulence effects on the signal at a single station and at multiple stations via cross-correlation. The power spectra of the frequency fluctuations observed between 4.9 and 76.3 Rs have a power-law shape close to a Kolmogorov spectrum over the frequency interval νlo up, where the nominal value of νlo is set to 3 mHz and νup is in the range of 0.03 ∼ 0.15 Hz. The RMS of the frequency fluctuations is presented as a function of the heliocentric distance. Furthermore, we analyse the variations of the electron column density fluctuations at solar offsets 4.9 Rs and 9.9 Rs and the cross-correlation products between the VLBI stations. The power density of the differential fluctuations between different stations decreases at ν 700 km s−1 are derived from the cross-correlation of frequency fluctuations at ν Copyright © 2021, The Authors. All rights reserved.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Optimizing network resource allocation with graph pointer neural network in large-scale AI systems

引用

Digital Communications and networks 2024年

作者： Peiying Zhang Yilin Li Athanasios V. Vasilakos Konstantin Igorevich Kostromitin Jianyong Zhang Mohsen Guizani Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao 266580 China State Key Laboratory of Integrated Services Networks Xidian University Xi'an 710071 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan 250013 China Department of Networks and Communications College of Computer Science and Information Technology Imam Abdulrahman Bin Faisal University P.O. Box 1982 Dammam 31441 Saudi Arabia Department of Physics of Nanoscale Systems South Ural State University Chelyabinsk 454080 the Russian Federation Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Key Lab of All Optical Network and Advanced Telecommunication of EMC Institute of Lightwave Technology Beijing Jiaotong University Beijing 100044 China Machine Learning Department Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI) Abu Dhabi 999041 United Arab Emirates

For increasingly complex communication demands of large-scale AI communication systems, the Space-Air-Ground Integrated network (SAGIN) better caters to demands but also raises concerns about resource scarcity and diversity. This paper innovatively combines Graph Pointer Neural networks (GPNN) and Reinforcement Learning (RL) to enhance resource allocation efficiency. The method leverages the advantages of GPNN in handling graph data and RL in optimizing decisions in dynamic environments. It also targets the optimization goal of maximizing resource allocation while minimizing deployment latency. This paper begins by modeling SAGIN and elucidating the SAGIN logical architecture based on Software-defined networking (SDN). Subsequently, it introduces an SFC deployment algorithm aimed at joint optimization of resource allocation and latency. The algorithm leverages GPNN and RL to deploy virtual nodes and links, with the goal of optimizing resource allocation and deployment latency. Experiment findings conclusively demonstrate that the efficacy of proposed algorithm in effectively weighing limited heterogeneous resources and minimum mapping delay. Notably, when compared to three other SFC mapping algorithms MLRL, NFVdeep, and RL, the proposed algorithm consistently outperforms them, with an average improvement of 10.17% in long-term average reward/cost, 11.21% in link resource utilization ratio, 15.34% in node resource utilization ratio, and 16.38% in acceptance ratio.

关键词： Space-air-ground integrated network Topological structure Reinforcement learning Service function chain Graph pointer neural network

来源：评论

学校读者我要写书评

暂无评论

Insight-HXMT and GECAM-C observations of the brightest-of-all-time GRB 221009A

arXiv

引用

arXiv 2023年

作者： An, Zheng-Hua Antier, S. Bi, Xing-Zi Bu, Qing-Cui Cai, Ce Cao, Xue-Lei Camisasca, Anna-Elisa Chang, Zhi Chen, Gang Chen, Li Chen, Tian-Xiang Chen, Wen Chen, Yi-Bao Chen, Yong Chen, Yu-Peng Coughlin, Michael W. Cui, Wei-Wei Dai, Zi-Gao Hussenot-Desenonges, T. Du, Yan-Qi Du, Yuan-Yuan Du, Yun-Fei Fan, Cheng-Cheng Frontera, Filippo Gao, He Gao, Min Ge, Ming-Yu Gong, Ke Gu, Yu-Dong Guan, Ju Guo, Dong-Ya Guo, Zhi-Wei Guidorzi, Cristiano Han, Da-Wei He, Jian-Jian He, Jun-Wang Hou, Dong-Jie Huang, Yue Huo, Jia Ji, Zhen Jia, Shu-Mei Jiang, Wei-Chun Kann, David Alexander Klotz, A. Kong, Ling-Da Lan, Lin Li, An Li, Bing Li, Chao-Yang Li, Cheng-Kui Li, Gang Li, Mao-Shun Li, Ti-Pei Li, Wei Li, Xiao-Bo Li, Xin-Qiao Li, Xu-Fang Li, Yan-Guo Li, Zheng-Wei Liang, Jing Liang, Xiao-Hua Liao, Jin-Yuan Lin, Lin Liu, Cong-Zhan Liu, He-Xin Liu, Hong-Wei Liu, Jia-Cong Liu, Xiao-Jing Liu, Ya-Qing Liu, Yu-Rong Lu, Fang-Jun Lu, Hong Lu, Xue-Feng Luo, Qi Luo, Tao Ma, Bin-Yuan Ma, Fu-Li Ma, Rui-Can Ma, Xiang Maccary, Romain Mao, Ji-Rong Meng, Bin Nie, Jian-Yin Orlandini, Mauro Ou, Ge Peng, Jing-Qiang Peng, Wen-Xi Qiao, Rui Qu, Jin-Lu Ren, Xiao-Qin Shi, Jing-Yan Shi, Qi Song, Li-Ming Song, Xin-Ying Su, Ju Sun, Gong-Xing Sun, Liang Sun, Xi-Lei Tan, Wen-Jun Tan, Ying Tao, Lian Tuo, You-Li Turpin, Damien Wang, Jin-Zhou Wang, Chen Wang, Chen-Wei Wang, Hong-Jun Wang, Hui Wang, Jin Wang, Ling-Jun Wang, Peng-Ju Wang, Ping Wang, Wen-Shuai Wang, Xiang-Yu Wang, Xi-Lu Wang, Yu-Sa Wang, Yue Wen, Xiang-Yang Wu, Bo-Bing Wu, Bai-Yang Wu, Hong Xiao, Sheng-Hui Xiao, Shuo Xiao, Yun-Xiang Xie, Sheng-Lun Xiong, Shao-Lin Xiong, Sen-Lin Xu, Dong Xu, He Xu, Yan-Jun Xu, Yan-Bing Xu, Ying-Chen Xu, Yu-Peng Xue, Wang-Chen Yang, Sheng Yang, Yan-Ji Yang, Zi-Xu Ye, Wen-Tao Yi, Qi-Bin Yi, Shu-Xu Yin, Qian-Qing You, Yuan Yu, Yun-Wei Yu, Wei Yu, Wen-Hui Zeng, Ming Zhang, Bing Zhang, Bin-Bin Zhang, Da-Li Zhang, Fan Zhang, Hong-Mei Zhang, Juan Key Laboratory of Particle Astrophysics Institute of High Energy Physics Chinese Academy of Sciences 19B Yuquan Road Beijing100049 China Institut für Astronomie und Astrophysik Kepler Center for Astro and Particle Physics Eberhard Karls Universität Sand 1 Tübingen72076 Germany College of Physics Hebei Key Laboratory of Photophysics Research and Application Hebei Normal University Hebei Shijiazhuang050024 China Department of Physics and Earth Science University of Ferrara Via Saragat 1 Ferrara44122 Italy Department of Astronomy Beijing Normal University Beijing100875 China CAS Key Laboratory for Research in Galaxies and Cosmology Department of Astronomy University of Science and Technology of China Hefei230026 China Department of Astronomy Tsinghua University Beijing100084 China Southwest Jiaotong University Chengdu610092 China INAF Osservatorio di Astrofisica e Scienza dello Spazio di Bologna Via Piero Gobetti 101 Bologna40129 Italy University of Chinese Academy of Sciences Chinese Academy of Sciences Beijing100049 China College of physics Sciences Technology Hebei University No. 180 Wusi Dong Road Lian Chi District Hebei Baoding071002 China INFN - Sezione di Ferrara Via Saragat 1 Ferrara44122 Italy Physics and Space Science College China West Normal University China Yunnan Observatories Chinese Academy of Sciences Yunnan Province Kunming650011 China Key Laboratory of Space Astronomy and Technology National Astronomical Observatories Chinese Academy of Sciences Beijing100012 China School of Astronomy and Space Science Nanjing University Nanjing210093 China School of Physics and Optoelectronics Xiangtan University Yuhu District Hunan Xiangtan411105 China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guizhou Normal University Guiyang550001 China Institute of Astrophysics Central China Normal University Wuhan430079 China Nevada Center for Astrophysics Department of Physics and Astronomy University of

GRB 221009A is the brightest gamma-ray burst ever detected since the discovery of this kind of energetic explosions. However, an accurate measurement of the prompt emission properties of this burst is very challenging due to its exceptional brightness. With joint observations of Insight-HXMT and GECAM-C, we made an unprecedentedly accurate measurement of the emission during the first ∼1800 s of GRB 221009A, including its precursor, main emission (ME, which dominates the burst in flux), flaring emission and early afterglow, in the hard X-ray to soft gamma-ray band from ∼ 10 keV to ∼ 6 MeV. Based on the GECAM-C unsaturated data of the ME, we measure a record-breaking isotropic equivalent energy (Eiso) of ∼ 1.5 × 1055 erg, which is about eight times the total rest-mass energy of the Sun. The early afterglow data require a significant jet break between 650 s and 1100 s, most likely at ∼ 950 s from the afterglow starting time TAG, which corresponds to a jet opening angle of ∼ 0.7◦ (ηγn)1/8, where n is the ambient medium density in units of cm−3 and ηγ is the ratio between γ-ray energy and afterglow kinetic energy. The beaming-corrected total γ-ray energy Eγ is ∼ 1.15 × 1051 (ηγn)1/4 erg, which is typical for long GRBs. These results suggest that this GRB may have a special central engine, which could launch and collimate a very narrowly beamed jet with an ordinary energy budget, leading to exceptionally luminous gamma-ray radiation per unit solid angle. Alternatively, more GRBs might have such a narrow and bright beam, which are missed by an unfavorable viewing angle or have been detected without distance measurement. Copyright © 2023, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Scintillation Arc from FRB 20220912A

arXiv

引用

arXiv 2023年

作者： Wu, Zi-Wei Main, Robert A. Zhu, Wei-Wei Zhang, Bing Jiang, Peng Niu, Jia-Rui Han, Jin-Lin Li, Di Lee, Ke-Jia Li, Dong-Zi Yang, Yuan-Pei Wang, Fa-Yin Luo, Rui Wang, Pei Niu, Chen-Hui Xu, Heng Wang, Bo-Jun Wang, Wei-Yang Zhang, Yong-Kun Feng, Yi Zhou, De-Jiang Xu, Yong-Hua Deng, Can-Min Zhu, Yu-Hao National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 Bonn53121 Germany Institute for Frontier in Astronomy and Astrophysics Beijing Normal University Beijing102206 China Nevada Center for Astrophysics Department of Physics and Astronomy University of Nevada Las VegasNV89154 United States CAS Key Laboratory of FAST National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China School of Astronomy University of Chinese Academy of Sciences Beijing China NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-Natal Durban4000 South Africa Department of Astronomy Peking University Beijing100871 China Kavli Institute for Astronomy and Astrophysics Peking University Beijing100871 China Cahill Center for Astronomy and Astrophysics California Institute of Technology MC 249-17 PasadenaCA91125 United States South-Western Institute For Astronomy Research Yunnan University Yunnan650504 China School of Astronomy and Space Science Nanjing University Nanjing210093 China Ministry of Education China CSIRO Space and Astronomy PO Box 76 EppingNSW1710 Australia Department of Astronomy School of Physics and Materials Science Guangzhou University Guangzhou510006 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Yunnan Observatories Chinese Academy of Sciences Kunming650011 China Guangxi Key Laboratory for Relativistic Astrophysics Department of Physics Guangxi University Nanning530004 China

We present the interstellar scintillation analysis of fast radio burst (FRB) 20220912A during its extremely active episode in 2022 using data from the Five-hundred-meter Aperture Spherical Radio Telescope (FAST). We detect a scintillation arc in the FRB’s secondary spectrum, which describes the power in terms of the scattered FRB signals’ time delay and Doppler shift. The arc indicates that the scintillation is caused by a highly localized region. Our analysis favors a Milky Way origin of the ionized interstellar medium (IISM) for the localized scattering medium but cannot rule out a host galaxy origin. We present our method for detecting the scintillation arc, which can be applied generally to sources with irregularly spaced bursts or pulses. These methods could help shed light on the complex interstellar environment surrounding the FRBs and in our Galaxy. Copyright © 2023, The Authors. All rights reserved.

关键词： Scintillation

来源：评论

学校读者我要写书评

暂无评论

SUPER-RESOLVED IMAGE OF M87 OBSERVED WITH EAST ASIAN VLBI network

arXiv

引用

arXiv 2023年

作者： Tazaki, Fumie Cui, Yuzhu Hada, Kazuhiro Kino, Motoki Cho, Ilje Zhao, Guang-Yao Akiyama, Kazunori Mizuno, Yosuke Ro, Hyunwook Honma, Mareki Lu, Ru-Sen Shen, Zhi-Qiang Cui, Lang Yonekura, Yoshinori Tokyo Electron Technology Solutions Limited Iwate023-1101 Japan Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Research Center for Intelligent Computing Platforms Zhejiang Laboratory Hangzhou311100 China Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan Academic Support Center Kogakuin University of Technology and Engineering Tokyo192-0015 Japan Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Massachusetts Institute Technology Haystack Observatory WestfordMA01886 United States Black Hole Initiative Harvard University CambridgeMA02138 United States School of Physics & Astronomy Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China Institut für Theoretische Physik Goethe-Universität Frankfurt Max-von-Laue-Straße 1 Frankfurt am MainD-60438 Germany Department of Astronomy Yonsei University Seodaemun-gu Seoul03722 Korea Republic of Korea Astronomy and Space Science Institute Yuseong-gu Daejeon34055 Korea Republic of Department of Astronomical Science The Graduate University for Advanced Studies SOKENDAI Tokyo181-8588 Japan Institute of Astronomy The University of Tokyo Tokyo181-0015 Japan Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Radio Astronomy Chinese Academy of Sciences Nanjing210008 China Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Xinjiang Astronomical Observatory CAS 150 Science 1-Street Urumqi830011 China Center for Astronomy Ibaraki University Ibaraki310-8512 Japan

Obtaining high-resolution images at centimeter-or-longer wavelengths is vital for understanding the physics of jets. We reconstructed images from the M87 22 GHz data observed with the East Asian VLBI network (EAVN) by using the regularized maximum likelihood (RML) method, which is different from the conventional imaging method CLEAN. Consequently, a bright core and jet extending about 30 mas to the northwest were detected with a higher resolution than in the CLEAN image. The width of the jet was 0.5 mas at 0.3 mas from the core, consistent with the width measured in the 86 GHz image in the previous study. In addition, three ridges were able to be detected at around 8 mas from the core, even though the peak-to-peak separation was only 1.0 mas. This indicates that the RML image's spatial resolution is at least 30% higher than that of the CLEAN image. This study is an important step for future multi-frequency and high-cadence observations of the EAVN to discuss the more detailed structure of the jet and its time variability. © 2023, CC BY-SA.

关键词： Active Galactic Nuclei

来源：评论

学校读者我要写书评

暂无评论

FAST discovery of an extremely radio-faint millisecond pulsar from the Fermi-LAT unassociated source 3FGL J0318.1+0252

arXiv

引用

arXiv 2021年

作者： Wang, Pei Li, Di Clark, Colin J. Saz Parkinson, Pablo M. Hou, Xian Zhu, Weiwei Qian, Lei Yue, Youling Pan, Zhichen Liu, Zhijie Yu, Xuhong You, Shanping Xie, Xiaoyao Zhi, Qijun Zhang, Hui Yao, Jumei Yan, Jun Zhang, Chengmin Fan, Kwok Lung Ray, Paul S. Kerr, Matthew Smith, David A. Michelson, Peter F. Ferrara, Elizabeth C. Thompson, David J. Shen, Zhiqiang Wang, Na CAS Key Laboratory of FAST National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China University of Chinese Academy of Sciences Beijing100049 China Jodrell Bank Centre for Astrophysics Department of Physics and Astronomy University of Manchester ManchesterM13 9PL United Kingdom Department of Physics Laboratory for Space Research University of Hong Kong Hong Kong999077 Hong Kong Santa Cruz Institute for Particle Physics University of California Santa CruzCA95064 United States Yunnan Observatories Chinese Academy of Sciences 396 Yangfangwang Guandu District Kunming650216 China Key Laboratory for the Structure and Evolution of Celestial Objects Chinese Academy of Sciences 396 Yangfangwang Guandu District Kunming650216 China School of Computer Science and Technology Key Laboratory of Information and Computing Guizhou Province Guizhou Normal University Guiyang550001 China School of Physics and Electronic Science Guizhou Normal University Guiyang550001 China Phys. Dept. Stanford University StanfordCA94305 United States UMCP CRESST GSFC NASA GreenbeltMD20771 United States GSFC NASA GreenbeltMD20771 United States Centre d’Études Nucléaires de Bordeaux Gradignan IN2P3 CNRS Université Bordeaux Gradignan33175 France Space Science Division U.S. Naval Research Laboratory WashingtonDC20375-5352 United States Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Xinjiang Astronomical Observatory 150 Science-1 Street Xinjiang Urumqi830011 China Department of Physics University of Maryland College ParkMD20742 United States NAOC-UKZN Computational Astrophysics Centre University of KwaZulu-Natal Durban4000 South Africa

High sensitivity radio searches of unassociated γ-ray sources have proven to be an effective way of finding new pulsars. Using the Five-hundred-meter Aperture Spherical radio Telescope (FAST) during its commissioning phase, we have carried out a number of targeted deep searches of Fermi Large Area Telescope (LAT) γ-ray sources. On Feb. 27th, 2018 we discovered an isolated millisecond pulsar (MSP), PSR J0318+0253, coincident with the unassociated γ-ray source 3FGL J0318.1+0252. PSR J0318+0253 has a spin period of 5.19 milliseconds, a dispersion measure (DM) of 26 pc cm−3 corresponding to a DM distance of about 1.3 kpc, and a period-averaged flux density of ∼11 ± 2 µJy at L-band (1.05-1.45 GHz). Among all high energy MSPs, PSR J0318+0253 is the faintest ever detected in radio bands, by a factor of at least ∼4 in terms of L-band fluxes. With the aid of the radio ephemeris, an analysis of 9.6 years of Fermi-LAT data revealed that PSR J0318+0253 also displays strong γ-ray pulsations. Follow-up observations carried out by both Arecibo and FAST suggest a likely spectral turn-over around 350 MHz. This is the first result from the collaboration between FAST and the Fermi-LAT teams as well as the first confirmed new MSP discovery by FAST, raising hopes for the detection of many more MSPs. Such discoveries will make a significant contribution to our understanding of the neutron star zoo while potentially contributing to the future detection of gravitational waves, via pulsar timing array (PTA) experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Redefining pandemic preparedness:Multidisciplinary insights from the CERP modelling workshop in infectious diseases,workshop report

引用

Infectious Disease Modelling 2024年第2期9卷 501-518页

作者： Marta C.Nunes Edward Thommes Holger Fröhlich Antoine Flahault Julien Arino Marc Baguelin Matthew Biggerstaff Gaston Bizel-Bizellot Rebecca Borchering Giacomo Cacciapaglia Simon Cauchemez Alex Barbier-Chebbah Carsten Claussen Christine Choirat Monica Cojocaru Catherine Commaille-Chapus Chitin Hon Jude Kong Nicolas Lambert Katharina B.Lauer Thorsten Lehr Cédric Mahe Vincent Marechal Adel Mebarki Seyed Moghadas Rene Niehus Lulla Opatowski Francesco Parino Gery Pruvost Andreas Schuppert Rodolphe Thiébaut Andrea Thomas-Bachli Cecile Viboud Jianhong Wu Pascal Crépey Laurent Coudeville Center of Excellence in Respiratory Pathogens(CERP) Hospices Civils de Lyon(HCL)and Centre International de Recherche en Infectiologie(CIRI)Équipe SantéPubliqueÉpidémiologie etÉcologieÉvolutive des Maladies Infectieuses(PHE3ID)Inserm U1111CNRS UMR5308ENS de LyonUniversitéClaude Bernard Lyon 1LyonFrance South African Medical Research Council Vaccines&Infectious Diseases Analytics Research UnitFaculty of Health SciencesUniversity of the WitwatersrandJohannesburgSouth Africa New Products and Innovation(NPI) Sanofi Vaccines(Global)TorontoOntarioCanada Department of Mathematics and Statistics University of GuelphGuelphOntarioCanada Fraunhofer Institute for Algorithms and Scientific Computing(SCAI) Department of BioinformaticsSchloss BirlinghovenSankt AugustinGermany University of Bonn Bonn-Aachen International Center for IT(b-it)BonnGermany Institute of Global Health Faculty of MedicineUniversity of GenevaGenevaSwitzerland and Swiss School of Public HealthZürichSwitzerland Department of Mathematics University of ManitobaWinnipegManitobaCanada MRC Centre for Global Infectious Disease Analysis School of Public HealthImperial College LondonLondonUK Centre for Mathematical Modelling of Infectious Diseases Department of Infectious Disease EpidemiologyLondon School of Hygiene&Tropical MedicineLondonUK National Center for Immunization and Respiratory Diseases(NCIRD) US Centers for Disease Control and Prevention(CDC)AtlantaGAUSA Departement of Computational Biology Departement of Global HealthInstitut PasteurParisFrance Institut de Physique des Deux Infinis de Lyon(IP2I) UMR5822IN2P3/CNRSUniversitéClaude Bernard Lyon 1VilleurbanneFrance Mathematical Modelling of Infectious Diseases Unit Institut PasteurUniversitéParis CitéUMR2000 CNRSParisFrance Decision and Bayesian Computation Institut PasteurUniversitéParis CitéCNRS UMR 3571France Fraunhofer-Institute for Translational Medicine and Pharmacology HamburgGermany Institute of Global Health Faculty of MedicineUniversity of

In July 2023,the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 *** report summarizes the rich discussions that occurred during the *** workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data,social media,and wastewater *** advancements were noted in the development of predictive models,with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease *** role of open collaboration between various stakeholders in modelling was stressed,advocating for the continuation of such partnerships beyond the pandemic.A major gap identified was the absence of a common international framework for data sharing,which is crucial for global pandemic ***,the workshop underscored the need for robust,adaptable modelling frameworks and the integration of different data sources and collaboration across sectors,as key elements in enhancing future pandemic response and preparedness.

关键词： Modelling Covid-19 Infectious diseases Pandemic preparedness Workshop

来源：评论

学校读者我要写书评

暂无评论

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

来源：评论

学校读者我要写书评

暂无评论

Overview of AI and Communication for 6G network: Fundamentals, Challenges, and Future Research Opportunities

arXiv

引用

arXiv 2024年

作者： Cui, Qimei You, Xiaohu Ni, Wei Nan, Guoshun Zhang, Xuefei Zhang, Jianhua Lyu, Xinchen Ai, Ming Tao, Xiaofeng Feng, Zhiyong Zhang, Ping Wu, Qingqing Tao, Meixia Huang, Yongming Huang, Chongwen Liu, Guangyi Peng, Chenghui Pan, Zhiwen Sun, Tao Niyato, Dusit Chen, Tao Khan, Muhammad Khurram Jamalipour, Abbas Guizani, Mohsen Yuen, Chau National Engineering Research Center of Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing100876 China Department of Broadband Communication Peng Cheng Laboratory Shenzhen518055 China National Mobile Communications Research Laboratory Southeast University Nanjing210096 China Fudan University Shanghai200433 China School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing100876 China CICT Mobile Communication Technology Co. Ltd. Beijing100020 China Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China Shanghai Jiao Tong University Shanghai200240 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310058 China China Mobile Research Institute Beijing100053 China Huawei Technologies Shanghai201206 China College of Computing and Data Science Nanyang Technological University Singapore117583 Singapore VTT Technical Research Centre of Finland Ltd. EspooFI-02044 VTT Finland Center of Excellence in Information Assurance King Saud University Riyadh11362 Saudi Arabia School of Electrical and Information Engineering University of Sydney NSW2006 Australia Abu Dhabi United Arab Emirates School of Electrical and Electronics Engineering Nanyang Technological University Singapore639798 Singapore

With the growing demand for seamless connectivity and intelligent communication, the integration of artificial intelligence (AI) and sixth-generation (6G) communication networks has emerged as a transformative paradigm. By embedding AI capabilities across various network layers, this integration enables optimized resource allocation, improved efficiency, and enhanced system robust performance, particularly in intricate and dynamic environments. This paper presents a comprehensive overview of AI and communication for 6G networks, with a focus on emphasizing their foundational principles, inherent challenges, and future research opportunities. We first review the integration of AI and communications in the context of 6G, exploring the driving factors behind incorporating AI into wireless communications, as well as the vision for the convergence of AI and 6G. The discourse then transitions to a detailed exposition of the envisioned integration of AI within 6G networks, delineated across three progressive developmental stages. The first stage, AI for network, focuses on employing AI to augment network performance, optimize efficiency, and enhance user service experiences. The second stage, network for AI, highlights the role of the network in facilitating and buttressing AI operations and presents key enabling technologies, such as digital twins for AI and semantic communication. The final stage, AI as a Service, it is anticipated that future 6G networks will innately provide AI functions as services, supporting application scenarios like immersive communication and intelligent industrial robots. Specifically, we have defined the quality of AI service, which refers to a framework for measuring AI services within the network. We further summarize the standardization process of AI for wireless networks, highlighting key milestones and ongoing efforts. In addition, we conduct an in-depth analysis of the critical challenges faced by the integration of AI and communications

关键词： Resource allocation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：