检索结果-内蒙古大学图书馆

Design and Implementation of Variable Precision Algorithm for Transcendental Functions

Journal of Physics: Conference Series 2019年第1期1325卷

作者： Hao Jiangwei Xu Jinchen Guo Shaozhong Xia Yuanyuan Liu Dan State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou China

mathematical function is an essential part of numerical program, and it is also the key factor that affects the precision and performance of a program. In the design of mathematical functions, in order to satisfy most application scenarios, it is necessary to correctly round and cover the calculated interval as much as possible. But in certain applications, correct rounding and full coverage of definition domain may not be required. Therefore, mathematical functions can be customized according to application requirements to avoid precision waste as well as improve performance. However, manually implementing mathematical functions is a time-consuming and error-prone task. Tools like Metalibm is designed to automatically generate mathematical function code, which is difficult to take advantage of all the mathematical properties of a function and results in the generated code performing slowly than the corresponding mathematical library function. A method of generating variable precision code based on mathematical properties is proposed for the transcendental function in this work. Experiments showed the performance of the proposed method is comparable to Glibc mathematical function.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Geometrical reconstruction of fluorescence events observed by the LHAASO experiment

引用

Chinese Physics C 2021年第4期45卷 416-425页

作者： F.Aharonian Q.An Axikegu L.X.Bai Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi H.Cai J.T.Cai Z.Cao Z.Cao J.Chang J.F.Chang X.C.Chang B.M.Chen J.Chen L.Chen L.Chen L.Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen X.L.Chen Y.Chen N.Cheng Y.D.Cheng S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai Danzengluobu D.della Volpe B.DEtorre Piazzoli X.J.Dong J.H.Fan Y.Z.Fan Z.X.Fan J.Fang J.Fang C.F.Feng L.Feng S.H.Feng Y.L.Feng B.Gao C.D.Gao Q.Gao W.Gao M.M.Ge L.S.Geng G.H.Gong Q.B.Gou M.H.Gu J.G.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.C.He S.L.He X.B.He Y.He M.Heller Y.K.Hor C.Hou X.Hou H.B.Hu S.Hu S.C.Hu X.J.Hu D.H.Huang Q.L.Huang W.H.Huang X.T.Huang Z.C.Huang F.Ji X.L.Ji H.Y.Jia K.Jiang Z.J.Jiang C.Jin D.Kuleshov K.Levochkin B.B.Li C.Li C.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li K.Li W.L.Li X.Li X.Li X.R.Li Y.Li Y.Z.Li Z.Li Z.Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.L.Liu J.S.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.N.Liu Z.X.Liu W.J.Long R.Lu H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao A.Masood W.Mitthumsiri T.Montaruli Y.C.Nan B.Y..Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi D.Ruffolo V.Rulev A.Saiz L.Shao O.Shchegolev X.D.Sheng J.R.Shi H.C.Song Yu.V.Stenkin V.Stepanov Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Z.B.Tang W.W.Tian B.D.Wang C.Wang H.Wang H.G.Wang J.C.Wang J.S.Wang L.P.Wang L.Y.Wang R.N.Wang W.Wang W.Wang X.G.Wang X.J.Wang X.Y.Wang Y.D.Wang Y.J.Wang Y.P.Wang Z.Wang Z.Wang Z.H.Wang Z.X.Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu W.X.Wu X.F.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang G.Xiao H.B.Xiao G.G.Xin Y.L.Xin Y.Xing D.L.Xu R.X.Xu L.Xue D.H.Yan C.W.Yang F.F.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.D.Zeng T.X.Zeng W.Zeng Z.K.Zeng M.Zha X.X.Zhai B.B.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang J.W.Zhang L.Zhang L.Zhang L.X.Zhang P.F.Zhang P.F.Zhang R.Zhang S.R.Zhang S.S.Zhang X.Zhang X.P.Zhang Y.Zhang Y.Zhang Y.F.Zhang Y.L.Zhang B.Zhao J.Zhao L.Zhao L.Z.Zhao S.P.Zhao F.Zheng Y.Zheng B.Zhou H.Zhou J.N.Zhou P Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China TIANFU Cosmic Ray Research Center ChengduSichuanChina State Key Laboratory of Paricle Detection and Electronics China University of Science and Technology of China HefeiAnhui 230026China Department of Engineering Physics Tsinghua UniversityBeijing 100084China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China National Space Science Center Chinese Academy of SciencesBeijing 100190China School of Physics Peking UniversityBeijing 100871China Center for Astrophysics Guangzhou UniversityGuangzhouGuangdong 510006China School of Physics and Astronomy&School of Physics(Guangzhou) Sun Yat-sen UniversityZhuhaiGuangdong 519000China School of Physical Science and Technology Guangxi UniversityNanningGuangxi 530004China Hebei Normal University ShijiazhuangHebei 050024China School of Physics and Microelectronics Zhengzhou UniversityZhengzhouHenan 450001China School of Astronomy and Space Science Nanjing UniversityNanjingJiangsu 210023China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjingJiangsu 210023China Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdaoShandong 266237China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of SciencesShanghai 200030China Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong UniversityShanghai 200240China School of Physical Science and Technology Southwest Jiaotong UniversityChengduSichuan 610031China College of Physics Sichuan UniversityChengduSichuan 610065China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasaTibet 850000China School of physics and technology Wuhan UniversityWuhanHubei 430072Ch

The LHAASO-WFCTA experiment,which aims to observe cosmic rays in the sub-EeV range using the fluorescence technique,uses a new generation of high-performance *** ensure that the experiment has ex-cellent detection capability associated with the measurement of the energy spectrum,the primary composition of cosmic rays,and so on,an accurate geometrical reconstruction of air-shower events is *** paper de-scribes the development and testing of geometrical reconstruction for stereo viewed events using the WFCTA(Wide Field of view Cherenkov/Fluorescence Telescope Array)*** approaches,which take full advantage ofthe WFCTA *** *** is the stereo-angular method,which uses the pointing of triggered SiPMs in the shower trajectory,and the other is the stereo-timing method,which uses the triggering time of the fired *** results show that both methods have good geometrical resolution:the resolution of the stereo-timing method is slightly better than the stereo-angular method because the resolution of the latter is slightly limited by the shower track length.

关键词： cosmic ray fluorescence telescope stereo observation geometrical reconstruction

来源：评论

学校读者我要写书评

暂无评论

Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

引用

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

Master stability functions for complete, intralayer, and interlayer synchronization in multiplex networks of coupled Rössler oscillators

引用

Physical Review E 2019年第1期99卷 012304-012304页

作者： Longkun Tang Xiaoqun Wu Jinhu Lü Jun-an Lu Raissa M. D'Souza Fujian Province University Key Laboratory of Computation Science School of Mathematical Science Huaqiao University Quanzhou 362021 China School of Mathematics and Statistics Wuhan University Wuhan 430072 China Department of Computer Science University of California Davis California 95616 USA School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China

Synchronization phenomena are of broad interest across disciplines and increasingly of interest in a multiplex network setting. For the multiplex network of coupled Rössler oscillators, here we show how the master stability function, a celebrated framework for analyzing synchronization on a single network, can be extended to certain classes of multiplex networks with different intralayer and interlayer coupling functions. We derive three master stability equations that determine, respectively, the necessary regions of complete synchronization, intralayer synchronization, and interlayer synchronization. We calculate these three regions explicitly for the case of a two-layer network of Rössler oscillators and show that the overlap of the regions determines the type of synchronization achieved. In particular, if the interlayer or intralayer coupling function is such that the interlayer or intralayer synchronization region is empty, complete synchronization cannot be achieved regardless of the coupling strength. Furthermore, for any network structure, the occurrence of intralayer and interlayer synchronization depends mainly on the coupling functions of nodes within a layer and across layers, respectively. Our mathematical analysis requires that the intralayer and interlayer supra-Laplacians commute. But, we show this is only a sufficient, and not necessary, condition and that the results can be applied more generally.

关键词： Collective dynamics Synchronization Synchronization transition

来源：评论

学校读者我要写书评

暂无评论

Observation of the Crab Nebula with LHAASO-KM2A−a performance study

引用

Chinese Physics C 2021年第2期45卷 518-530页

作者： F.Aharonian Q.An Axikegu L.X.Bai Y.X.Bai Y.W.Bao D.Bastieri X.J.Bi Y.J.Bi H.Cai J.T.Cai Z.Cao J.Chang J.F.Chang X.C.Chang B.M.Chen J.Chen L.Chen M.J.Chen M.L.Chen Q.H.Chen S.H.Chen S.Z.Chen T.L.Chen X.L.Chen Y.Chen N.Cheng Y.D.Cheng S.W.Cui X.H.Cui Y.D.Cui B.Z.Dai H.L.Dai Z.G.Dai D.della Volpe B.D'Ettorre Piazzoli X.J.Dong J.H.Fan Y.Z.Fan Z.X.Fan J.Fang K.Fang C.F.Feng L.Feng S.H.Feng Y.L.Feng B.Gao C.D.Gao Q.Gao W.Gao M.M.Ge L.S.Geng G.H.Gong Q.B.Gou M.H.Gu J.G.Guo X.L.Guo Y.Q.Guo Y.Y.Guo Y.A.Han H.H.He H.N.He J.C.He S.L.He X.B.He Y.He M.Heller Y.K.Hor C.Hou X.Hou H.B.Hu S.Hu S.C.Hu X.J.Hu D.H.Huang Q.L.Huang W.H.Huang X.T.Huang Z.C.Huang F.Ji X.L.Ji H.Y.Jia K.Jiang Z.J.Jiang C.Jin D.Kuleshov K.Levochkin B.B.Li C.Li F.Li H.B.Li H.C.Li H.Y.Li J.Li K.Li W.L.Li X.Li X.R.Li Y.Li Y.Z.Li Z.Li E.W.Liang Y.F.Liang S.J.Lin B.Liu C.Liu D.Liu H.Liu H.D.Liu J.Liu J.L.Liu J.S.Liu J.Y.Liu M.Y.Liu R.Y.Liu S.M.Liu W.Liu Y.N.Liu Z.X.Liu W.J.Long R.Lu H.K.Lv B.Q.Ma L.L.Ma X.H.Ma J.R.Mao A.Masood W.Mitthumsiri T.Montaruli Y.C.Nan B.Y.Pang P.Pattarakijwanich Z.Y.Pei M.Y.Qi D.Ruffolo V.Rulev A.Sáiz L.Shao O.Shchegolev X.D.Sheng J.R.Shi H.C.Song Yu.V.Stenkin V.Stepanov Q.N.Sun X.N.Sun Z.B.Sun P.H.T.Tam Z.B.Tang W.W.Tian B.D.Wang C.Wang H.Wang H.G.Wang J.C.Wang J.S.Wang L.P.Wang L.Y.Wang R.N.Wang W.Wang X.G.Wang X.J.Wang X.Y.Wang Y.D.Wang Y.J.Wang Y.P.Wang Z.Wang Z.H.Wang Z.X.Wang D.M.Wei J.J.Wei Y.J.Wei T.Wen C.Y.Wu H.R.Wu S.Wu W.X.Wu X.F.Wu S.Q.Xi J.Xia J.J.Xia G.M.Xiang G.Xiao H.B.Xiao G.G.Xin Y.L.Xin Y.Xing D.L.Xu R.X.Xu L.Xue D.H.Yan C.W.Yang F.F.Yang J.Y.Yang L.L.Yang M.J.Yang R.Z.Yang S.B.Yang Y.H.Yao Z.G.Yao Y.M.Ye L.Q.Yin N.Yin X.H.You Z.Y.You Y.H.Yu Q.Yuan H.D.Zeng T.X.Zeng W.Zeng Z.K.Zeng M.Zha X.X.Zhai B.B.Zhang H.M.Zhang H.Y.Zhang J.L.Zhang J.W.Zhang L.Zhang L.X.Zhang P.F.Zhang P.P.Zhang R.Zhang S.R.Zhang S.S.Zhang X.Zhang X.P.Zhang Y.Zhang Y.F.Zhang Y.L.Zhang B.Zhao J.Zhao L.Zhao L.Z.Zhao S.P.Zhao F.Zheng Y.Zheng B.Zhou H.Zhou J.N.Zhou P.Zhou R.Zhou X.X.Zhou C.G.Zhu F.R.Zhu H.Zhu K.J.Zhu X.Zuo Key Laboratory of Particle Astrophyics&Experimental Physics Division&Computing Center Institute of High Energy PhysicsChinese Academy of Sciences100049 BeijingChina University of Chinese Academy of Sciences 100049 BeijingChina TIANFU Cosmic Ray Research Center ChengduSichuanChina State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China 230026 HefeiAnhuiChina Department of Engineering Physics Tsinghua University100084 BeijingChina National Astronomical Observatories Chinese Academy of Sciences100101 BeijingChina National Space Science Center Chinese Academy of Sciences100190 BeijingChina School of Physics Peking University100871 BeijingChina Center for Astrophysics Guangzhou University510006 GuangzhouGuangdongChina School of Physics and Astronomy&School of Physics(Guangzhou) Sun Yat-sen University519000 ZhuhaiGuangdongChina School of Physical Science and Technology Guangxi University530004 NanningGuangxiChina Hebei Normal University 050024 ShijiazhuangHebeiChina School of Physics and Microelectronics Zhengzhou University450001 ZhengzhouHenanChina School of Astronomy and Space Science Nanjing University210023 NanjingJiangsuChina Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of Sciences210023 NanjingJiangsuChina Institute of Frontier and Interdisciplinary Science Shandong University266237 QingdaoShandongChina Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical ObservatoryChinese Academy of Sciences200030 ShanghaiChina Tsung-Dao Lee Institute&School of Physics and Astronomy Shanghai Jiao Tong University200240 ShanghaiChina School of Physical Science and Technology&School of Information Science and Technology Southwest Jiaotong University610031 ChengduSichuanChina College of Physics Sichuan University610065 ChengduSichuanChina Key Laboratory of Cosmic Rays(Tibet University) Ministry of Education850000 LhasaTibetChina School of Physics and Tec

A sub-array of the Large High Altitude Air Shower Observatory(LHAASO),KM2A is mainly designed to observe a large fraction of the northern sky to hunt for γ-ray sources at energies above 10 *** though the detector construction is still underway,half of the KM2A array has been operating stably since the end of *** this paper,we present the KM2A data analysis pipeline and the first observation of the Crab Nebula,a standard candle in very high energy γ-ray *** detect γ-ray signals from the Crab Nebula in both energy ranges of 10-100 TeV and>100 TeV with high significance,by analyzing the KM2A data of 136 live days between December 2019 and May *** the observations,we test the detector performance,including angular resolution,pointing accuracy and cosmic-ray background rejection *** energy spectrum of the Crab Nebula in the energy range 10-250 TeV fits well with a single power-law function dN/dE=(1.13±0.05stat±0.08sys)×10^(-14).(E/20 TeV)-309±0.06stat±0.02syscm^(-2) s^(-1) TeV^(-1).It is consistent with previous measurements by other *** opens a new window of γ-ray astronomy above 0.1 PeV through which new ultrahigh-energy γ-ray phenomena,such as cosmic PeVatrons,might be discovered.

关键词： γ-ray Crab Nebula extensive air showers cosmic rays

来源：评论

学校读者我要写书评

暂无评论

A Gene-Inspired Malware Detection Approach

引用

Journal of Physics: Conference Series 2019年第6期1168卷

作者： Yihang Chen Zheng Shan Fudong Liu Guanghui Liang Binglin Zhao Xingwei Li Meng Qiao State Key of Laboratory of Mathematical Engineering and Advanced Computing 450001 Zhengzhou Henan China

Malware detection is an important topic in cyber security. The research presented in this paper mainly studies on disassembly codes of Windows executable files, learns from the research route of bioinformatics and proposes the concept of software gene. A distance-based method is also proposed to measure the difference of genes and the dimensionality reduction based on a modified clustering algorithm of biological phylogenetic model. Finally a gene-inspired malware detector is constructed using Random Forest model. The software gene extraction proposed in this paper is more flexible and generates less data than the widely-used n-gram method. The detector based on genes also performs better. The clustering-based dimensionality reduction retains more comprehensive features and maintains the interpretability in software analysis area. The detector based on gene-inspired malware detection approach can reach the precision 96.14%, which is better than traditional methods.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Parallel Probabilistic Approach to Factorize a Semiprime

引用

American Journal of Computational Mathematics 2018年第2期8卷 175-183页

作者： Jianhui Li Department of Computer Science Guangdong Neusoft Institute Foshan City Foshan China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

In accordance with the distributive traits of semiprimes’ divisors, the article proposes an approach that can find out the small divisor of a semiprime by parallel computing. The approach incorporates a deterministic search with a probabilistic search, requires less memory and can be implemented on ordinary multicore computers. Experiments show that certain semiprimes of 27 to 46 decimal-bits can be validly factorized with the approach on personal computer in expected time.

关键词： Parallel Probabilistic Integer Factorization Semiprime

来源：评论

学校读者我要写书评

暂无评论

ChanDet: Detection Model for Potential Channel of iOS Applications

引用

Journal of Physics: Conference Series 2019年第4期1187卷

作者： GuoMiao Zhou Ming Duan Qi Xi Hao Wu State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China

Despite providing iOS the security, comfortable, powerful mobile operating system, Apple has too many restrictions. Many users prefer to jailbreaking the iOS by using jailbreaking tool, which allows them to do more unavailable things on their devices. This behaviour may cause risks toward applications and many researches have focused on application security in various aspects. We find that a legal application (we call it potential channel hereafter) can be hijacked and it acts as a channel between the malware in device and the remote control terminal. In this paper, we introduce a channel model based on five conditions after analysing the entire operation procedure and comparing the similarities and differences of various applications. To approve our argument, we show iOS Messages which meets the five conditions, and demonstrate how to intercept messages, that means, a legal application can be hijacked and become a channel. To eliminate the risks, we propose a solution ChanDet and describe how to test whether an application is a potential channel or not. Finally, we give some protection strategies for applications, and we expect that ChanDet will play a significant role in application security of iOS.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spatio-temporal propagation of COVID-19 pandemics

arXiv

引用

arXiv 2020年

作者： Gross, Bnaya Zheng, Zhiguo Liu, Shiyan Chen, Xiaoqi Sela, Alon Li, Jianxin Li, Daqing Havlin, Shlomo Department of Physics Bar-Ilan University Ramat-Gan52900 Israel School of Reliability and Systems Engineering Beihang University Beijing100191 China Department of Industrial Engineering Ariel University Ariel Israel Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China State Key Laboratory of Software Development Environment Beihang University Beijing100083 China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing100191 China

The new coronavirus known as COVID-19 is rapidly spreading since December 2019. Without any vaccination or medicine, the means of controlling it are limited to quarantine and social distancing. Here we study the spatio-temporal propagation of the COVID-19 virus in China and compare it to other global locations. Our results suggest that the disease propagation is highly related to population migration from Hubei resembling a Lévy flight which is characteristic of human mobility and thus could be controlled by efficient quarantines. Since quarantine is usually applied on a city level, more insight can be obtained in analyzing the epidemic in cities. Our results suggest that the disease spread in a city in China is characterized by two-stages process. At early times, at order of few days, the infection rate in the city is close to constant probably due to the lack of means to detect infected individuals before infection signs are observed and at later times it decays approximately exponentially due to quarantines. These two stages can explain the significant differences between the propagation in China and in other world-wide locations. While most cities in China control the disease which resulted in the decaying stage, in other world-wide countries the situation is still becoming worse probably due to less social interactions control and overloaded health systems which reflects in the death and recovery rates. Copyright © 2020, The Authors. All rights reserved.

关键词： Population dynamics

来源：评论

学校读者我要写书评

暂无评论

DLocRL: A deep learning pipeline for fine-grained location recognition and linking in tweets

arXiv

引用

arXiv 2019年

作者： Xu, Canwen Pei, Jiaxin Li, Jing Li, Chenliang Luo, Xiangyang Ji, Donghong Wuhan University School of Computer Science Hubei China Inception Institute of Artificial Intelligence United Arab Emirates Wuhan University School of Cyber Science and Engineering Hubei China State Key Lab of Mathematical Engineering and Advanced Computing Henan China

In recent years, with the prevalence of social media and smart devices, people causally reveal their locations such as shops, hotels, and restaurants in their tweets. Recognizing and linking such fine-grained location mentions to well-defined location profiles are beneficial for retrieval and recommendation systems. In this paper, we propose DLocRL, a new deep learning pipeline for fine-grained location recognition and linking in tweets, and verify its effectiveness on a real-world Twitter dataset. Copyright © 2019, The Authors. All rights reserved.

关键词： Social networking (online)

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：