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arXiv 2024年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. Tian, W.W. Wang, C. Wang, C.B. Key Laboratory of Particle Astrophysics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics 69029 Heidelberg103980 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-Sen University Guangdong Guangzhou510275 China Sun Yat-Sen University Guangdong Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Université Paris Cité CNRS IN2P3 CEA/IRFU Observatoire de Paris Paris119 75205 France Department of Engineering Physics Tsinghua Univers

KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overflow. Some simplifications are used to significantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement. © 2024, CC BY-NC-ND.

关键词： Cosmic rays

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2020 roadmap on two-dimensional materials for energy storage and conversion

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Chinese Chemical Letters 2019年第12期30卷 2053-2064页

作者： Baolin Xu Shihan Qi Mengmeng Jin Xiaoyi Cai Linfei Lai Zhouting Sun Xiaogang Han Zifeng Lin Hui Shao Peng Peng Zhonghua Xiang Johan E.ten Elshof Rou Tan Chen Liu Zhaoxi Zhang Xiaochuan Duan Jianmin Ma School of Physics and Electronics Hunan UniversityChangsha 410082China Key Laboratory of Flexible Electronics&Institute of Advanced Materials(IAM) Jiangsu National Synergistic Innovation Center for Advanced Materials(SICAM)Nanjing Tech UniversityNanjing 210009China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore State Key Lab of Electrical Insulation and Power Equipment Xi'an Jiaotong UniversityXi'an 710049China College of Materials Science and Engineering Sichuan UniversityChengdu 610065China UniversitéPaul Sabatier Laboratoire CIRIMAT UMR CNRS 5085Toulouse 31062France Réseau sur le Stockage Electrochimique de l’Energie(RS2E) CNRS 3459AmiensFrance State Key Laboratory of Organic–Inorganic Composites Beijing University of Chemical TechnologyBeijing 100029China University of Twente AE Enschede 7500the Netherlands Pen-Tung Sah Institute of Micro-Nano Science and Technology Xiamen UniversityXiamen 361005China Key Laboratory of Materials Processing and Mold(Zhengzhou University) Ministry of EducationZhengzhou UniversityZhengzhou 450002China

Energy storage and conversion have attained significant intere st owing to its important applications that reduce CO2 emission through employing green *** promising technologies are included metalair batteries,metal-sulfur batteries,metal-ion batteries,electrochemical capacitors,***,metal elements are involved with lithium,sodium,and *** these devices,electrode materials are of importance to obtain high ***-dimensional(2 D) materials are a large kind of layered structured materials with promising future as energy storage materials,which include graphene,black phosporu s,MXenes,covalent organic frameworks(COFs),2 D oxides,2 D chalcogenides,and *** progress has been achieved to go ahead for 2 D materials in energy storage and *** researchers will join in this research *** the background,it has motivated us to contribute with a roadmap on ’two-dimensional materials for energy storage and conversion.

关键词： Graphene Black phosphorus MXenes Covalent organic frameworks Oxides Chalcogenides Metal-air batteries Metal-sulfur batteries Metal-ion batteries Electrochemical capacitors

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Jacobi Spectral Collocation Method Based on Lagrange Interpolation Polynomials for Solving Nonlinear Fractional Integro-Differential Equations

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Advances in Applied Mathematics and Mechanics 2018年第6期10卷 1440-1458页

作者： Xingfa Yang Yin Yang Yanping Chen Jie Liu State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body Hunan UniversityChangsha 410082HunanChina Hunan Key Laboratory for Computation and Simulation in Science and Engineering Key Laboratory of Intelligent Computing&Information Processing of Ministry of EducationSchool of Mathematics and Computational ScienceXiangtan UniversityXiangtan 411105HunanChina School of Mathematical Sciences South China Normal UniversityGuangzhou 510631GuangdongChina Center for Research on Leading Technology of Special Equipment School of Mechanical and Electric EngineeringGuangzhou UniversityGuangzhou 510006GuangdongChina

In this paper,we study a class of nonlinear fractional integro-differential equations,the fractional derivative is described in the Caputo *** the properties of the Caputo derivative,we convert the fractional integro-differential equations into equivalent integral-differential equations of Volterra type with singular kernel,then we propose and analyze a spectral Jacobi-collocation approximation for nonlinear integro-differential equations of Volterra *** provide a rigorous error analysis for the spectral methods,which shows that both the errors of approximate solutions and the errors of approximate fractional derivatives of the solutions decay exponentially in L^(∞)-norm and weighted L^(2)-norm.

关键词： Spectral method nonlinear fractional derivative Volterra integro-differential equations Caputo derivative

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Adversarial directed graph embedding

arXiv

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arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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Observing Geometry of Quantum states in a Three-Level System

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Physical Review Letters 2020年第15期125卷 150401-150401页

作者： Jie Xie Aonan Zhang Ningping Cao Huichao Xu Kaimin Zheng Yiu-Tung Poon Nung-Sing Sze Ping Xu Bei Zeng Lijian Zhang National Laboratory of Solid State Microstructures Key Laboratory of Intelligent Optical Sensing and Manipulation (Ministry of Education) College of Engineering and Applied Sciences and School of Physics Nanjing University Nanjing 210093 China Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing 210093 China Department of Mathematics & Statistics University of Guelph Guelph N1G 2W1 Ontario Canada Institute for Quantum Computing University of Waterloo Waterloo N2L 3G1 Ontario Canada Department of Mathematics Iowa State University Ames Iowa 50011 USA Department of Applied Mathematics The Hong Kong Polytechnic University 999077 Hong Kong China Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha 410073 China Department of Physics The Hong Kong University of Science and Technology Clear Water Bay Kowloon 999077 Hong Kong China

In quantum mechanics, geometry has been demonstrated as a useful tool for inferring nonclassical behaviors and exotic properties of quantum systems. One standard approach to illustrate the geometry of quantum systems is to project the quantum state space onto the Euclidean space via measurements of observables on the system. Despite the great success of this method in studying two-level quantum systems (qubits) with the celebrated Bloch sphere representation, it is still difficult to reveal the geometry of multidimensional quantum systems. Here we report the first experiment measuring the geometry of such projections beyond the qubit. Specifically, we observe the joint numerical ranges of a triple of observables in a three-level photonic system, providing a complete classification of these ranges. We further show that the geometry of different classes reveals ground-state degeneracies of a Hamiltonian as a linear combination of the observables, which is related to quantum phases in the thermodynamic limit. Our results offer a versatile geometric approach for exploring the properties of higher-dimensional quantum systems.

关键词： Geometric & topological phases Quantum foundations Quantum measurements Quantum phase transitions

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A practical and aggressive loop fission technique 18th

A practical and aggressive loop fission technique

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18th International Conference on Algorithms and Architectures for Parallel Processing - ICA3PP 2018 Collocated Workshops: ICA3PP 2018 Workshop on Intelligent Algorithms for Large-scale Complex Optimization Problems, IALCOP 2018, ICA3PP 2018 Workshop on Security and Privacy in Data Processing, SPDP 2018

作者： Zhao, Bo Li, Yingying Han, Lin Zhao, Jie Gao, Wei Zhao, Rongcai Yahyapour, Ramin Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen Göttingen37077 Germany State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China French Institute for Research in Computer Science and Automation Rocquencourt France

ISBN: (纸本)9783030052331

Loop fission is an effective loop optimization for exploiting fine-grained parallelism. Currently, loop fission is widely used in existing parallelizing compilers. To fully exploit the optimization, we proposed and implemented a practical and aggressive loop fission technique. First, we present an aggressive dependence graph pruning method to eliminate pseudo dependences caused by the conservativeness of compilers. Second, we introduce a topological sort based loop fission algorithm to distribute loops correctly. Finally, to enhance the performance of the generated programs which have potential of loop fission, we propose an advanced loop fission strategy. We evaluate these techniques and algorithms in the experimental section. © Springer Nature Switzerland AG 2018.

关键词： Program compilers

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An anti-side-channel virtual CPU scheduling algorithm based on leakage evaluation for virtual machine security 7

An anti-side-channel virtual CPU scheduling algorithm based ...

引用

2017 7th International Workshop on Computer Science and engineering, WCSE 2017

作者： Du, Yuanzhi Du, Xuehui Yang, Zhi State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9789811136719

Infrastructure as a service (IaaS) which is one of the cloud computing's service modes provides virtual machines to clients via shared physical machines. This Service provides convenience for many enterprises, but also introduces new security threats. Many studies have shown that the co-residency side-channel can be used to extract sensitive information by malicious users. Remarkably, Soo-Jin has present a migration-based system called Nomad to mitigating known and future side-channel which is more universal than the traditional method. However, large scale migration will lead to huge network overheads. To solve the above problems, the characteristics of co-residency side-channel on the single physical server is analyzed, based on which two virtual machine schedule algorithms according the leakage model in Nomad were proposed. The simulation results show that the algorithm can mitigate the threats effectively.

关键词： Virtual machine

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Design and Implementation of Variable Precision Algorithm for Transcendental Functions

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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：

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Geometrical reconstruction of fluorescence events observed by the LHAASO experiment

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

arXiv

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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

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