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Performance of LHAASO-WCDA and observation of the Crab Nebula as a standard candle

Chinese Physics C 2021年第8期45卷 166-181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.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 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.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 B.Q.Qiao 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.P.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 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 Particle Detection and Electronics China University of Science and Technology of China Hefei 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 UniversityGuangzhou 510006China School of Physics and Astronomy&School of Physics(Guangzhou) Sun Yat-sen UniversityZhuhai 519082China School of Physical Science and Technology Guangxi UniversityNanning 530004China Hebei Normal University Shijiazhuang 050024China School of Physics and Microelectronics Zhengzhou UniversityZhengzhou 450001China School of Astronomy and Space Science Nanjing UniversityNanjing 210023China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain ObservatoryChinese Academy of SciencesNanjing 210023China Institute of Frontier and Interdisciplinary Science Shandong UniversityQingdao 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&School of Information Science and Technology Southwest Jiaotong UniversityChengdu 610031China College of Physics Sichuan UniversityChengdu 610065China Key Laboratory of Cosmic Rays(Tibet University) Ministry of EducationLhasa 850000China School of Physics and Technology Wuhan UniversityWuhan 430072China School of Physics and Astronomy Yunnan University

The first Water Cherenkov detector of the LHAASO experiment(WCDA-1)has been operating since April *** data for the first year have been analyzed to test its performance by observing the Crab Nebula as a standard *** WCDA-1 achieves a sensitivity of 65 mCU per year,with a statistical threshold of 5 *** accomplish this,a 97.7%cosmic-ray background rejection rate around 1 TeV and 99.8%around 6 TeV with an ap proximate photon acceptance of 50%is achieved after applying an algorithm to separate gamma-induced *** angular resolution is measured using the Crab Nebula as a point source to be approximately 0.45°at 1 TeV and better than 0.2°above 6 TeV,with a pointing accuracy better than 0.05°.These values all match the design *** energy resolution is found to be 33%for gamma rays around 6 *** spectral energy distribution of the Crab Nebula in the range from 500 GeV to 15.8 TeV is measured and found to be in agreement with the results from other TeV gamma ray observatories.

关键词： LHAASO-WCDA Crab Nebula angular resolution spectral energy distribution

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A Format Protection Method of Greybox Fuzzing

A Format Protection Method of Greybox Fuzzing

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International Conference on Communication Technology (ICCT)

作者： Shibin Zhao Tianyang Zhou Xiaoli Duan Hongyang Lin Xinlei Ying Qibo Shi Jianshan Peng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China China United Network Communications Group Co. Ltd Shanxi Yuncheng Branch Yuncheng China Ant-financial Light-Year Security Lab hangzhou China

Greybox fuzzing, which can fuzz without knowledge of the format, is one of the most advanced fuzzing techniques. However, the lack of format knowledge makes the test cases' format messy as the fuzzing proceeds and reduces the effectiveness of fuzzing. In this paper, we introduce a format protection method that can protect the format of test cases during greybox fuzzing. Based on prior knowledge, the Naive Bayes classifier is applied to achieve a format check method, which is used to improve the favourite seed selection process and induce the position of variation. The experiment results indicate that this method not only has a higher code coverage ratio and more unique crashes in less time than other fuzzers but also maintains the invalid format ratio at a low level. 24 previously unreported CVEs were exposed.

关键词： Fuzzing Image edge detection Software Genetic algorithms Security Tools Feature extraction

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Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A

引用

Physical Review Letters 2023年第15期131卷 151001-151001页

作者： Zhen Cao F. Aharonian Q. An Axikegu Y. X. Bai Y. W. Bao D. Bastieri X. J. Bi Y. J. Bi J. T. Cai Q. Cao W. Y. Cao Zhe Cao J. Chang J. F. Chang 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. Chen N. Cheng Y. D. Cheng M. Y. Cui S. W. Cui X. H. Cui Y. D. Cui B. Z. Dai H. L. Dai Z. G. Dai Danzengluobu D. della Volpe X. Q. Dong K. K. Duan J. H. Fan Y. Z. Fan J. Fang K. Fang C. F. Feng L. Feng S. H. Feng X. T. Feng Y. L. Feng S. Gabici B. Gao C. D. Gao L. Q. Gao Q. Gao W. Gao W. K. Gao M. M. Ge L. S. Geng G. Giacinti G. H. Gong Q. B. Gou M. H. Gu F. L. Guo X. L. Guo Y. Q. Guo Y. Y. Guo Y. A. Han H. H. He H. N. He J. Y. He X. B. He Y. He M. Heller Y. K. Hor B. W. Hou C. Hou X. Hou H. B. Hu Q. Hu S. C. Hu D. H. Huang T. Q. Huang W. J. Huang X. T. Huang X. Y. Huang Y. Huang Z. C. Huang X. L. Ji H. Y. Jia K. Jia K. Jiang X. W. Jiang Z. J. Jiang M. Jin M. M. Kang T. Ke D. Kuleshov K. Kurinov 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 X. R. Li Xin Li Y. Z. Li Zhe Li Zhuo 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. Y. Liu M. Y. Liu R. Y. Liu S. M. Liu W. Liu Y. Liu Y. N. Liu R. Lu Q. Luo H. K. Lv B. Q. Ma L. L. Ma X. H. Ma J. R. Mao Z. Min W. Mitthumsiri H. J. Mu Y. C. Nan A. Neronov Z. W. Ou B. Y. Pang P. Pattarakijwanich Z. Y. Pei M. Y. Qi Y. Q. Qi B. Q. Qiao J. J. Qin D. Ruffolo A. Sáiz D. Semikoz C. Y. Shao L. Shao O. Shchegolev X. D. Sheng F. W. Shu H. C. Song Yu. V. Stenkin V. Stepanov Y. Su Q. N. Sun X. N. Sun Z. B. Sun P. H. T. Tam Q. W. Tang Z. B. Tang W. W. Tian C. Wang C. B. Wang G. W. Wang H. G. Wang H. H. Wang J. C. Wang K. Wang L. P. Wang L. Y. Wang P. H. Wang R. Wang W. Wang X. G. Wang X. Y. Wang Y. Wang Y. D. Wang Y. J. Wang Z. H. Wang Z. X. Wang Zhen Wang Zheng Wang D. M. Wei J. J. Wei Y. J. Wei T. Wen C. Y. Wu H. R. Wu S. Wu X. F. Wu Y. S. Wu S. Q. Xi J. Xia J. J. Xia G. M. Xiang D. X. Xiao G. Xiao G. G. Xin Y. L. Xin Y. Xing Z. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences 100049 Beijing China University of Chinese Academy of Sciences 100049 Beijing China Tianfu Cosmic Ray Research Center 610000 Chengdu Sichuan China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 69029 Heidelberg Germany State Key Laboratory of Particle Detection and Electronics 230026 Hefei China University of Science and Technology of China 230026 Hefei Anhui China School of Physical Science and Technology & School of Information Science and Technology Southwest Jiaotong University 610031 Chengdu Sichuan China School of Astronomy and Space Science Nanjing University 210023 Nanjing Jiangsu China Center for Astrophysics Guangzhou University 510006 Guangzhou Guangdong China Hebei Normal University 050024 Shijiazhuang Hebei China Key Laboratory of Dark Matter and Space Astronomy & Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences 210023 Nanjing Jiangsu China Tsung-Dao Lee Institute & School of Physics and Astronomy Shanghai Jiao Tong University 200240 Shanghai China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences 200030 Shanghai China Key Laboratory of Cosmic Rays (Tibet University) Ministry of Education 850000 Lhasa Tibet China National Astronomical Observatories Chinese Academy of Sciences 100101 Beijing China School of Physics and Astronomy (Zhuhai) & School of Physics (Guangzhou) & Sino-French Institute of Nuclear Engineering and Technology (Zhuhai) Sun Yat-sen University 519000 Zhuhai & 510275 Guangzhou Guangdong China School of Physics and Astronomy Yunnan University 650091 Kunming Yunnan China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet

The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10 TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of −2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of −2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60 TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.

关键词： Cosmic rays & astroparticles Gamma ray astronomy

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The YAG Lidar System Applied in LHAASO 37

The YAG Lidar System Applied in LHAASO

引用

37th International Cosmic Ray Conference, ICRC 2021

作者： Sun, Q.N. Geng, L.S. Li, X. Chen, L. Liu, H. Wang, Y. Zhu, F.R. Zhang, Y. Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, H. Cai, J.T. Cao, Zhe Chang, J. Chang, J.F. Chen, B.M. Chen, E.S. Chen, J. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. D’Ettorre Piazzoli, B. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.J. Duan, K.K. Fan, J.H. Fan, Y.Z. Fan, Z.X. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Ge, M.M. Geng, L.S. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, J.G. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.C. He, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hu, H.B. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, Q.L. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Z.C. Ji, F. Ji, X.L. Jia, H.Y. Jiang, K. Jiang, Z.J. Jin, C. Ke, T. Kuleshov, D. Levochkin, K. Li, B.B. Li, Cheng Li, Cong Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. Li, Xin Li, Xin Li, Y. 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.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Liu, Z.X. Long, W.J. Lu, R. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Montaruli, T. Nan, Y.C. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Rulev, V. Sáiz, A. Shao, L. Shchegolev, O. Sheng, X.D. Shi, J.Y. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Key Laboratory of Particle Astrophyics & 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 Dublin02 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 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 College of Physics Sichuan University Sichuan Chengdu610065 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China School of Physics and Technology Wuhan University Hubei Wuhan430072 China Key Laboratory of Dark Matter and Space Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Dipartimento di Fisica dell’Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo

The atmospheric quality plays an important role in the air shower observation by the Wide Field-of-view Cherenkov Telescope Array (WFCTA) of LHAASO. A YAG imaging lidar system was developed to continuously monitor the calorimetric information. The accuracy of atmospheric monitoring is dependent on the pulse energy, the YAG laser’s beam parameters and the angular repeatability of a High-precision 3D lifting Rotating Platform (HiRoP). Therefore, we designed an optical system for this lidar with a beam splitter to divide the laser beam into a reference beam and a calibrating beam with a certain ratio and coupled the beam paths with the movement of HiRoP. Thus, every pulse energy of the calibrating beam, which has the same energy fluctuation with respect to the reference beam recorded by a power meter, could be calculated by the ratio of the two beams. Great cares were also taken to characterize the beam size, polarization and divergence of the laser. Meanwhile, a high-precision home-made thermotank was designed to control the temperature and humidity to improve the performance and stability of our laser system, which resulting in a thermal fluctuation less than 2 ◦C inside the container in the winter at an altitude of 4410 m. As a result, the pulse energy fluctuation of the laser beam for calibration was improved from 5 % to less than 2 %. As a result, we have successfully attained distinguishable full-WFCTA-view scanning Laser images in different air conditions, which could be used for the atmospheric quality analysis in further. © Copyright owned by the author(s).

关键词： Yttrium aluminum garnet

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Constructing PLC Binary Program Model for Detection Purposes

Constructing PLC Binary Program Model for Detection Purposes

引用

1st International Conference on advanced Algorithms and Control engineering, ICAACE 2018

作者： Chang, Tianyou Wei, Qiang Geng, Yangyang Zhang, Hongwei State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450001 China People's Bank of China Center Branch of Zhoukou City Zhoukou Henan466000 China

Programmable Logic Controller (PLC) programs are vulnerable to tampering attacks with addition of malware, which can substantially cause severe physical destructions. In order to solve the problems, We propose a static analysis method of Binary PLC programs for the detection of PLC malwares. Firstly, disassemble the binary program into STL program. Secondly, constructed the control flow graph of STL program according to the characteristics of STL language. Thirdly, generate the program execution paths according to traverse the control flow graph and analysis loop paths. Finally the PLC program output and input mapping relations are obtained based on the program execution paths. The experiment shows that the proposed method has achieved the analysis of PLC programs and get each path predicate and the mapping relation between output and input. © Published under licence by IOP Publishing Ltd.

关键词： Malware

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Topology-Aware Software Assignment in Networked Systems

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Journal of Physics: Conference Series 2019年第4期1213卷

作者： Junchao Wang Hao Liu Zhenwu Liu Zheng Zhang Jianmin Pang State key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Cyberspace mimic defense has been proven to be a revolutionary defense technology that 'changes the rules of the game' to ensure the security of cyberspace. However, the software diversity inherent in mimic defense technology may increase the difficulty in managing software executable binaries, especially when updating or debugging a software. In this paper, we study the problem of software assignment in a networked system to minimise the number of binaries generated by mimic compilers. Mimic compilers can help to generate functional equivelant software executables which exhibits diverse characteristics such as binary size etc. Theoretically the software assignment problem is equal to the traditional graph coloring problem. To guarantee network severity, we apply a Welsh-Powell-based Software Assignment (WPSA) algorithm to determine the number of binaries needed and the assignment of these binaries to hosts in a network. We conduct experiments on real world network topologies. Experimental results show that our algorithm can effectively reduce the number of binaries needed in networked systems.

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A Framework for Multi-Variant Execution Environment

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

作者： Wang Junchao Liu Zhenwu Liu Hao Pang Jianmin State key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Software diversity has been proven to be an effective approach to enhance system security. To make the best of the advantage brought by software diversity, a multi-variant execution environment is needed. However, although some MVEEs have been proposed, most of them are either too simple or only focusing one aspect which limits their widely adoption in industry. In this paper we propose a framework for multi-variant execution environment to enhance the security of software systems. The framework addresses different aspects when implementing a MVEE and can help to make the best of software diversity to enhance the system's security.

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

arXiv

引用

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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Attacking Intel UEFI by Using Cache Poisoning

引用

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

作者： Dong Wang Wei Yu Dong State Key Laboratory of Mathematical Engineering And Advanced Computing Zhengzhou China

The Unified Extensible Firmware Interface (UEFI) is a software interface between an operating system and platform firmware designed to replace a traditional BIOS. In this paper, we evaluated the security mechanisms used to protected SPI Flash, and then analyzed the attack surface presented by those security mechanisms. Intel provides several registers in its chipset relevant to locking down the SPI Flash chip that contains the UEFI in order to prevent arbitrary writes. Since these registers implement their functions through the system management mode, the main attack surface is concentrated in the system management mode. In this paper, we propose an attack vector for the system management mode, which uses the method of cache poisoning to attack the system management mode and destroy the protection mechanism of SPI Flash. This method can overcome the limitations for the traditional attacks. Experimental results proved that this kind of attack can arbitrarily write to the UEFI.

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swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

arXiv

引用

arXiv 2019年

作者： Li, Mingzhen Liu, Changxi Liao, Jianjin Zheng, Xuegui Yang, Hailong Sun, Rujun Xu, Jun Gan, Lin Yang, Guangwen Luan, Zhongzhi Qian, Depei School of Computer Science and Engineering Beihang University Beijing China State Key Laboratory of Software Development Environment Beijing China National University of Singapore Singapore Singapore State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China Science and Technology on Special System Simulation Laboratory Beijing Simulation Center Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79× on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor. Copyright © 2019, The Authors. All rights reserved.

关键词： Program compilers

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