检索结果-内蒙古大学图书馆

arXiv 2021年

作者： Li, Cheng-Long Zhang, Kai-Yi Zhang, Xingjian Yang, Kui-Xing Han, Yu Cheng, Su-Yi Cui, Hongrui Liu, Wen-Zhao Li, Ming-Han Liu, Yang Bai, Bing Dong, Hai-Hao Zhang, Jun Ma, Xiongfeng Yu, Yu Fan, Jingyun Zhang, Qiang Pan, Jian-Wei Hefei National Laboratory for Physical Sciences at Microscale Department of Modern Physics University of Science and Technology of China Hefei230026 China Shanghai Branch CAS Center for Excellence Synergetic Innovation Center in Quantum Information and Quantum Physics University of Science and Technology of China Shanghai201315 China Shanghai Research Center for Quantum Sciences Shanghai201315 China Shanghai Jiao Tong University Shanghai200240 China Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Shenzhen Institute for Quantum Science and Engineering Department of Physics Southern University of Science and Technology Shenzhen518055 China State Key Laboratory of Mathematical Engineering and Advanced Computing Henan Zhengzhou450001 China Jinan Institute of Quantum Technology Jinan250101 China

Zero-knowledge proof (ZKP) is a fundamental cryptographic primitive that allows a prover to convince a verifier of the validity of a statement without leaking any further information[1]. As an efficient variant of ZKP, non-interactive zero-knowledge proof (NIZKP) adopting the Fiat-Shamir heuristic is essential to a wide spectrum of applications, such as federated learning[2], blockchain and social networks[3]. However, the heuristic is typically built upon the random oracle model making ideal assumptions about hash functions, which does not hold in reality and thus undermines the security of the protocol[4]. Here, we present a quantum resolution to the problem. Instead of resorting to a random oracle model, we implement a quantum randomness service. This service generates random numbers certified by the loophole-free Bell test[5, 6] and delivers them with post-quantum cryptography (PQC) authentication[7]. Employing this service, we conceive and implement a NIZKP of the three-colouring problem[8]. By bridging together three prominent research themes, quantum non-locality, PQC and ZKP, we anticipate this work to open a new paradigm of quantum information science. Copyright © 2021, The Authors. All rights reserved.

关键词： Hash functions

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Loop-nest Auto-vectorization Method Based on Benefit Analysis

Loop-nest Auto-vectorization Method Based on Benefit Analysi...

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作者： Yao Jinyang Zhao Rongcai Wang Qi Tao Xiaohan State Key Laboratory of Mathematical Engineering and Advanced Computing

The SIMD(Single Instruction Multiple Data) extension component has been widely used in modern processors to improve the execution efficiency of the *** mainstream compilers(such as GCC,ICC,OPEN64,etc.) have supported the SIMD automatic *** current compilers can only vectorize the innermost layer of the loop,but this way often does not get the optimal performance *** at this kind of problem and combining with the processor architecture,a loopnest vectorization method based on the benefit analysis model is proposed and implemented in the GCC *** method can analyze different loop-nest vectorization schemes effectively and choose the most profitable one as the final optimization *** article first reviewed the researches about loop-nest vectorization,then introduces this new code generation method for loop-nest ***,we use several common application examples for *** experimental results show that the results of the benefit model are similar to the actual test results,and accordingly increase the efficiency for loop-nest vectorization.

关键词： Vectorization Loop-nest Benefit Analysis Cost Model

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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

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A Scalable Attention Mechanism Based Neural Network for Text Classification

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Journal of Physics: Conference Series 2020年第2期1486卷

作者： Jianyun Zheng Jianmin Pang Xiaochuan Zhang Di Sun Xin Zhou Kai Zhang Dong Wang MingLiang Li Jun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou 450002 China Xi'an Institute of Surveying and Mapping Xi'an 710000 China

In general, deep learning based text classification methods are considered to be effective but tend to be relatively slow especially for model training. In this work, we present a powerful, so-called 'scalable attention mechanism', which performs better than conventional attention mechanism in terms of both effectiveness and the speed of model training. Based on the scalable attention mechanism, we propose a neural network for text classification. The experimental results on eight representative datasets show that our method can obtain similar accuracy to state-of-the-art methods with training in less than 4 minutes on an NVIDIA GTX 1080Ti GPU. To the best of our knowledge, our method is at least twice faster than all the published deep learning classifiers.

关键词：

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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

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Spatio-temporal propagation of COVID-19 pandemics

arXiv

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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

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On s-uniform property of compressing sequences derived from primitive sequences modulo odd prime powers

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Science China(Information Sciences) 2017年第5期60卷 84-95页

作者： Yupeng JIANG Qun-Xiong ZHENG Dongdai LIN State Key Laboratory of Information Security Institute of Information EngineeringChinese Academy of Sciences State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Information Science and Technology Institute

Let Z/（p;） be the integer residue ring modulo pewith p an odd prime and e 2. We consider the suniform property of compressing sequences derived from primitive sequences over Z/（p;）. We give necessary and sufficient conditions for two compressing sequences to be s-uniform with α provided that the compressing map is of the form φ(x;, x;,..., x;) = g(x;) + η(x;, x;,..., x;), where g(x;) is a permutation polynomial over Z/（p） and η is an（e-1）-variable polynomial over Z/（p）.

关键词： compressing map linear recurring sequence primitive sequence permutation polynomial s uniform

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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A Distributed Privacy-Preserving Learning Dynamics in General Social Networks

arXiv

引用

arXiv 2020年

作者： Tao, Youming Chen, Shuzhen Li, Feng Yu, Dongxiao Yu, Jiguo Sheng, Hao School of Computer Science and Technology Shandong University Qingdao266237 China Big Data Institute Qilu University of Technology Jinan250353 China State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

In this paper, we study a distributed privacy-preserving learning problem in social networks with general topology. The agents can communicate with each other over the network, which may result in privacy disclosure, since the trustworthiness of the agents cannot be guaranteed. Given a set of options which yield unknown stochastic rewards, each agent is required to learn the best one, aiming at maximizing the resulting expected average cumulative reward. To serve the above goal, we propose a four-staged distributed algorithm which efficiently exploits the collaboration among the agents while preserving the local privacy for each of them. In particular, our algorithm proceeds iteratively, and in every round, each agent i) randomly perturbs its adoption for the privacy-preserving purpose, ii) disseminates the perturbed adoption over the social network in a nearly uniform manner through random walking, iii) selects an option by referring to the perturbed suggestions received from its peers, and iv) decides whether or not to adopt the selected option as preference according to its latest reward feedback. Through solid theoretical analysis, we quantify the trade-off among the number of agents (or communication overhead), privacy preserving and learning utility. We also perform extensive simulations to verify the efficacy of our proposed social learning algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： Iterative methods

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Super resolution of arterial spin labeling MR imaging using unsupervised multi-scale generative adversarial network

arXiv

引用

arXiv 2020年

作者： Cui, Jianan Gong, Kuang Han, Paul Liu, Huafeng Li, Quanzheng State Key Laboratory of Modern Optical Instrumentation College of Optical Science and Engineering Zhejiang University HangzhouZhejiang310027 China Center for Advanced Medical Computing and Analysis Massachusetts General Hospital Harvard Medical School BostonMA02114 United States Gordon Center for Medical Imaging Massachusetts General Hospital/Harvard Medical School BostonMA02114 United States

Arterial spin labeling (ASL) magnetic resonance imaging (MRI) is a powerful imaging technology that can measure cerebral blood flow (CBF) quantitatively. However, since only a small portion of blood is labeled compared to the whole tissue volume, conventional ASL suffers from low signal-to-noise ratio (SNR), poor spatial resolution, and long acquisition time. In this paper, we proposed a super-resolution method based on a multi-scale generative adversarial network (GAN) through unsupervised training. The network only needs the low-resolution (LR) ASL image itself for training and the T1-weighted image as the anatomical prior. No training pairs or pre-training are needed. A low-pass filter guided item was added as an additional loss to suppress the noise interference from the LR ASL image. After the network was trained, the super-resolution (SR) image was generated by supplying the upsampled LR ASL image and corresponding T1-weighted image to the generator of the last layer. Performance of the proposed method was evaluated by comparing the peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) using normal-resolution (NR) ASL image (5.5 min acquisition) and high-resolution (HR) ASL image (44 min acquisition) as the ground truth. Compared to the nearest, linear, and spline interpolation methods, the proposed method recovers more detailed structure information, reduces the image noise visually, and achieves the highest PSNR and SSIM when using HR ASL image as the ground-truth. Copyright © 2020, The Authors. All rights reserved.

关键词： Generative adversarial networks

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