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37th International Cosmic Ray Conference, ICRC 2021

作者： Nan, Yuncheng Li, Zhe Chen, Songzhan Feng, Cunfeng Wu, Sha Li, Yizhuo Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. 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. Shandong University Institute of Frontier and Interdisciplinary Science 72 Binhai Road Jimo Shandong Qingdao China Key Laboratory of Particle Astrophysics Institute of High Energy Physics CAS 19-yi Yuquan Road Shijingshan District Beijing 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 2 Dublin 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 Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yu

The KM2A is a major ground-based array for the researches on cosmic rays around the "knee" region and the ultra high energy gamma-ray astronomy, which depends heavily on its performance. The half array of the KM2A has been operated from December 27, 2019 to November 30, 2020. The cosmic ray Moon shadow, which moves between ±25◦ of declination and is observed with significance up to -25 standard deviation each month, is used as an unique and powerful source to test the array’s performance. Through the observation of the characteristics of the Moon shadow, including the position displacement, shape, deficit, and their variation with the time and energy, we test the pointing error, angular resolution, long-term stability of the KM2A and the absolute energy scale of the primary cosmic-ray particles. In particular, the pointing errors for showers from different declinations are discussed in this work. © Copyright owned by the author(s).

关键词： Cosmic rays

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In Situ High Selectivity Contact-Electroreduction of CO2to Methanol Using an Imine-Mediated Metal-Free Vitrimer Catalyst

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Angewandte Chemie 2025年

作者： Nannan Wang Haisong Feng Jing Yang Jie Zheng Yong-Wei Zhang Nikos Hadjichristidis Zibiao Li Institute of Sustainability for Chemicals Energy and Environment (ISCE2) Agency for Science Technology and Research (A*STAR) 1 Pesek Road Jurong Island Singapore 627833 Republic of Singapore State Key Laboratory of Chemical Resource Engineering Beijing Advanced Innovation Center for Soft Matter Science and Engineering Beijing University of Chemical Technology Beijing 100029 P.R. China Institute of High Performance Computing (IHPC) Agency for Science Technology and Research (A*STAR) 1 Fusionopolis Way #16-16 Connexis Singapore 138632 Republic of Singapore Polymer Synthesis Laboratory KAUST Catalysis Center Physical Science and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal 23955 Saudi Arabia Institute of Materials Research and Engineering (IMRE) Agency for Science Technology and Research (A*STAR) 2 Fusionopolis Way Innovis #08-03 Singapore 138634 Republic of Singapore Department of Materials Science and Engineering National University of Singapore 9 Engineering Drive 1 Singapore 117576 Singapore

Metal catalysts for the CO 2 reduction reaction (CO 2 RR) face challenges such as high cost, limited durability, and environmental impact. Although various structurally diverse and functional metal-free catalysts have been developed, they often suffer from slow kinetics, low selectivity, and nonrecyclability, significantly limiting their practical applications. In this study, we introduce a recyclable nonmetallic polymer material (vitrimer) as a catalyst for a new platform in contact-electrocatalysis. This approach harnesses the contact charges generated between water droplets and vitrimer to drive CO 2 RR, achieving methanol selectivity exceeding 90%. The imine groups within the vitrimer play a dual role, facilitating CO 2 adsorption and enriching friction-generated electrons, thereby mediating efficient electron transfer between the imine groups and CO 2 to promote CO 2 RR. After 84 h of CO 2 RR, the system achieved a methanol production rate of 13 nmol·h −1 , demonstrating the excellent stability of the method. Moreover, the vitrimer retains its high-performance electrocatalytic activity even after recycling. Mechanistic studies reveal that, compared to traditional metal catalysts, the N─O bond in the imine, which adsorbs the key intermediate *OCH 3 , breaks more readily to produce methanol, resulting in enhanced product selectivity and yield. This efficient and environmentally friendly contact-electroreduction strategy for CO 2 offers a promising pathway toward a circular carbon economy by leveraging natural water droplet-based contact-electrochemistry.

关键词： CO2 reduction Contact-electrocatalysis High-selectivity Metal-free catalyst Vitrimer

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Analysis of cryptosystem recognition scheme based on Euclidean distance feature extraction in three machine learning classifiers

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Journal of Physics: Conference Series 2019年第1期1314卷

作者： SiJie Fan YaQun Zhao State Key Laboratory of Mathematical Engineering and Advanced Computing 450001 China State Key Laboratory of Cryptography and Science 450001 China

In reality, many cryptographic analysis techniques are based on a specific cryptographic system or a large number of encrypted ciphertext. The identification and detection of cryptographic system is of great significance for evaluating the security of the algorithm and guiding the design and improvement of the algorithm. In this paper, we transcode each character in ciphertext into a decimal number, construct these numbers into one-dimensional arrays, and obtain the Euclidean distance between these one-dimensional arrays. Then we use these distances as features and input them into three machine learning classifiers: random forest, logistic regression and support vector machine to recognize cryptosystem and compare their recognition accuracy. The subjects include 8 common block ciphers (DES, 3DES, AES-128, AES-256, IDEA, SMS4, Blowfish, Camellia-128). The experimental results show that using the feature extraction scheme not only shortens the experimental time, reduces the computational cost, but also improves the recognition accuracy of eight typical block cipher algorithms. The classification accuracy of the ECB mode in the random forest classifier is 75%, which is higher than the existing published literature experimental results. The classification accuracy rate of CBC mode is higher than 13.5%, which is higher than the accuracy of random classification.

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Current Status of Electromagnetic Particle Detectors for LHAASO-KM2A

Current Status of Electromagnetic Particle Detectors for LHA...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Liu, Jia Zhang, Xiaopeng Sheng, Xiangdong Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. 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. Tang, Z.B. Key Laboratory of Particle Astrophyics Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu 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 Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 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 via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D’epartement de Physique Nucl’eaire et Corpusculaire Facult’e de Sciences Universi

The Large High Altitude Air Shower Observatory (LHAASO) is a new generation hybrid cosmic ray observatory which is expected to reveal the mystery of the origin of cosmic rays. The one square kilometer array (KM2A) containing 5195 Electromagnetic particle Detectors (ED) and 1188 Muon Detectors (MD) is a sub-array of the LHAASO. The EDs are designed to measure the density and arriving time of the secondary particles of cosmic rays. KM2A reaches its 3/4 scale by December 2020. 3978 electromagnetic particle detectors (EDs) and 917 muon detectors (MDs) are now in stable operation. In this paper, we will introduce the construction process of ED, the performances and long-term stability of the detectors. © Copyright owned by the author(s).

关键词： Observatories

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Efficient and secure authentication scheme based on certificateless aggregate signature in space networks 7

Efficient and secure authentication scheme based on certific...

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

作者： Hu, Zhiyan Du, Xuehui Cao, Lifeng State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9789811136719

According to the requirements of the space networks for computing cost, communication cost and security, a new efficient certificateless aggregate signature algorithm is proposed. The signature length is only 2 group elements, the verification stage requires only 3 pairing operations, and the algorithm is proved secure based on the computational Diffie-Hellman problem under the random oracle model. Compared with the other similar algorithm, this algorithm achieves higher security with higher efficiency and lower cost of communication. Then according to the signature algorithm proposed in this paper, an authentication scheme with the function of mutual authentication, aggregate authentication and group key agreement is presented. The analysis results show that the scheme is secure and efficient, especially suitable for the large-scale space networks.

关键词： Authentication

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A compact construction for non-monotonic online/offline CP-ABE scheme 10th

A compact construction for non-monotonic online/offline CP-A...

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Zhang, Junqi Cheng, Qingfeng Wei, Fushan Zhang, Xinglong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783319723945

Nowadays the mobile devices are becoming the necessities in our life, while they are generally resource-constrained, CP-ABE schemes designed for mobile devices should have the property of low computational complexity, therefore Online/Offline mechanism has prospect future in cryptographic mechanism. In this paper, we attempt to construct an unbounded Online/Offline CP-ABE scheme based on a non-monotonic access structure. During the offline phase, most of the computations for encryption are done;during the online phase, we transform the non-monotonic access structure with positive attribute sets into a monotonic access structure which is based on the LSSS access structure with positive and negative attribute sets, then it only needs a small amount of addition and multiplication operations for the rest components of encryption. Compared with the original non-monotonic CP-ABE scheme, our scheme remains the same on the public keys and the master secret keys, with only a small increase in computational complexity. The computational complexity during online phase is very small. © Springer International Publishing AG 2017.

关键词： Computational complexity

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HTTP Tunnel Trojan Detection Model Based on Deep Learning

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

作者： Yubo He Yuefei Zhu Wei Lin State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China

Aiming at reducing the high False Negative rate of the existing Trojan horse detection method based on behavior, this paper utilized the sequence characteristics of tunnel Trojan communication extracted from the transport layer and the bi-directional recurrent neural network in deep learning to build a HTTP tunnel Trojan detection model. The experimental result showed that the deep learning-based detection model reduced the false positive rate of normal network traffic and improved the Trojan detection rate. We also found that the deep learning-based detection model reduced the feature selecting and data cleaning process of generating samples and improved the easy-using of the HTTP tunnel Trojan detection model.

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Detection of emission from Cygnus Cocoon above 100TeV with LHAASO

Detection of emission from Cygnus Cocoon above 100TeV with L...

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37th International Cosmic Ray Conference, ICRC 2021

作者： Li, Cong Chen, Songzhan Wu, Sha Wang, Lingyu Yang, Ruizhi Liu, Ruoyu 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, 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, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, K. Li, W.L. Li, X.R. 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. Tang, Z.B. Tian, W.W. Wang, B.D. Key Laboratory of Particle Astrophyics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China TIANFU Cosmic Ray Research Center Sichuan Chengdu China University of Science and Technology of China Anhui Hefei230026 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China University of Chinese Academy of Sciences Beijing100049 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics 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 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 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 Zhuhai519000 China Dipartimento di Fisica Università di Napoli `‘Federico II" Complesso Universitario di Monte Sant’Angelo via Cinthia Napoli80126 Italy School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China D’epartement de Physique Nucl’eaire et Corpusculaire Facult’e de Sciences Universit’e de Gen‘eve 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Fr

The origin of PeV cosmic rays in the Galaxy is a long-standing puzzle. Superbubble or massive star cluster (MSC) has been suggested as powerful cosmic-ray accelerators. Cygnus Cocoon, an extended gamma-ray source, is likely associated with the MSC Cygnus OB2. Previous observations have shown that its spectrum extends up to 100 TeV with a break around 10 TeV. The spatial coincidence between the gamma-ray emission and molecular clouds in the region implies the hadronic origin of the gamma-ray emission, supporting Cygnus cocoon as a cosmic-ray proton accelerator. However, lack of a precise measurement of the spectrum beyond 100 TeV as well as relevant morphology prevent people from concluding that it is the source of PeV cosmic rays. We here report the observation of LHAASO-KM2A on this region. The maximum significance above 25 TeV is about 21.8σ. A photo with energy up to 1.4 PeV is detected from this region, which indicates the spectrum can extend up to 1 PeV. Such a result may be considered as the evidence for cosmic ray accelerated beyond PeV in Cygnus Cocoon. © Copyright owned by the author(s).

关键词： Cosmic rays

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A formal android permission model based on the B method 10th

A formal android permission model based on the B method

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10th International Conference on Security, Privacy and Anonymity in Computation, Communication and Storage, SpaCCS 2017

作者： Ren, Lu Chang, Rui Yin, Qing Man, Yujia State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China

ISBN: (纸本)9783319723884

The rapid development of Android devices brings the increase of security requirements, especially for access control. Recently, many enhancements have been put forward towards the Android permission mechanism. However, few researches focus on the formalization and verification of security schemes. In this paper, we propose a formal Android permission model based on the B method, describing mechanism specifications and proving security properties. All model components are type checked by AtelierB, with 87% (154 out of 178) of generated proof obligations proved yet. The model is fully animated and checked by ProB. The results show that all specifications are well-defined without any deadlock and invariant violation. The proposed B model is for not only security analysis, but also system animation and extension. It presents a feasible approach to specify and verify the security scheme in the embedded system, which is able to translate into executable codes and implement practical module as well. © 2017, Springer International Publishing AG.

关键词： Specifications

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Efficient Block Pruning Based on Kernel and Feature Stablization

Efficient Block Pruning Based on Kernel and Feature Stabliza...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Sheng Xu Hanlin Chen Kexin Liu Jinhu Lii Baochang Zhang School of Automatic Science and Electrical Engineering Beihang University China State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China

With the development of computer vision research, the architecture of convolutional neural network becomes more and more complex to reach the state-of-the-art performance. Is the complexity of the model necessarily proportional to its accuracy? To answer this, the compression of the network has attracted much attention in the academy and industry. Existing network pruning methods mostly rely on the scoring mechanism of complexity or diversity of kernels to compress the network, and then build the network model after removing the kernels by tuning or training on the input data. These methods are cumbersome and depend on a well-trained pre-trained model. In this paper, we propose an end-to-end block pruning method based on kernel and feature stability by pruning blocks efficiently. To accomplish this, we firstly introduce a mask to scale the output of the blocks, and the L1 regularization term to monitor the mask update. Second, we introduce the Center Loss to guarantee that the feature does not deviate greatly during learning. To converge fast, we introduce fast iterative shrinkage-thresholding algorithm (FISTA) to optimize the mask, by which a more fast and reliable pruning process is achieved. We implement experiments on different datasets, including CIFAR-10 and ImageNet ILSVRC2012. All the experiments have achieved the state-of-the-art accuracy.

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