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38th International Cosmic Ray Conference, ICRC 2023

作者： Chen, Xuejian Zhou, Xunxiu Yang, Ci Huang, Daihui 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 della Volpe, D. 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. Heller, M. 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. School of Physical Science and Technology Southwest Jiaotong University Chengdu610031 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 Dublin 2 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 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 School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Sun Yat-sen University Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Départeme

A sub-array of the Large High Altitude Air Shower Observatory (LHAASO), KM2A contains 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). For each shower event that meets the trigger conditions (at least 20 fired EDs within a time window of 400 ns) in KM2A array, there are many signals from ED and MD detectors. The information on the arrival time and location of each signal are recorded, and the ED hits are used for shower reconstruction. Due to the acceleration and deflection by the atmospheric electric field (AEF) during a thunderstorm, the arrival time and position of the ground-level particles are modified, resulting in the changes in the inferred shower detection. To understand the shower event variation during thunderstorms, the particle arrival time is studied by analyzing the KM2A data. We can measure the distribution of changes of arrival time in the thunderstorm electric field. The variation amplitude is not only dependent on the AEF, but also highly correlated with the direction of the shower event. With the increase of the AEF strength and the zenith angle, the variation in amplitude of the arrival time becomes larger. Our results are useful in understanding the variation of shower rate detected by LHAASO-KM2A, and will also provide important information for shower reconstruction during thunderstorms. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Thunderstorms

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Poison over troubled forwarders: a cache poisoning attack targeting DNS forwarding device 20

Poison over troubled forwarders: a cache poisoning attack ta...

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Proceedings of the 29th USENIX Conference on Security Symposium

作者： Xiaofeng Zheng Chaoyi Lu Jian Peng Qiushi Yang Dongjie Zhou Baojun Liu keyu Man Shuang Hao Haixin Duan Zhiyun Qian Tsinghua University and Qi An Xin Technology Research Institute Tsinghua University Qi An Xin Technology Research Institute State Key Laboratory of Mathematical Engineering and Advanced Computing University of California Riverside University of Texas at Dallas

ISBN: (纸本)9781939133175

In today's DNS infrastructure, DNS forwarders are devices standing in between DNS clients and recursive resolvers. The devices often serve as ingress servers for DNS clients, and instead of resolving queries, they pass the DNS requests to other servers. Because of the advantages and several use cases, DNS forwarders are widely deployed and queried by Internet users. However, studies have shown that DNS forwarders can be more vulnerable devices in the DNS *** this paper, we present a cache poisoning attack targeting DNS forwarders. Through this attack, attackers can inject rogue records of arbitrary victim domain names using a controlled domain, and circumvent widely-deployed cache poisoning defences. By performing tests on popular home router models and DNS software, we find several vulnerable implementations, including those of large vendors (e.g., D-Link, Linksys, dnsmasq and MS DNS). Further, through a nationwide measurement, we estimate the population of Chinese mobile clients which are using vulnerable DNS forwarders. We have been reporting the issue to the affected vendors, and so far have received positive feedback from three of them. Our work further demonstrates that DNS forwarders can be a soft spot in the DNS infrastructure, and calls for attention as well as implementation guidelines from the community.

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Cross-domain object detection through coarse-to-fine feature adaptation

arXiv

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arXiv 2020年

作者： Zheng, Yangtao Huang, Di Liu, Songtao Wang, Yunhong Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University School of Computer Science and Engineering Beihang University Beijing100191 China

Recent years have witnessed great progress in deep learning based object detection. However, due to the domain shift problem, applying off-the-shelf detectors to an unseen domain leads to significant performance drop. To address such an issue, this paper proposes a novel coarseto-fine feature adaptation approach to cross-domain object detection. At the coarse-grained stage, different from the rough image-level or instance-level feature alignment used in the literature, foreground regions are extracted by adopting the attention mechanism, and aligned according to their marginal distributions via multi-layer adversarial learning in the common feature space. At the fine-grained stage, we conduct conditional distribution alignment of foregrounds by minimizing the distance of global prototypes with the same category but from different domains. Thanks to this coarse-to-fine feature adaptation, domain knowledge in foreground regions can be effectively transferred. Extensive experiments are carried out in various cross-domain detection scenarios. The results are state-of-the-art, which demonstrate the broad applicability and effectiveness of the proposed approach. Copyright © 2020, The Authors. All rights reserved.

关键词： Object recognition

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Simulation study on cosmic ray shower rate variations with LHAASO-KM2A during thunderstorms 38

Simulation study on cosmic ray shower rate variations with L...

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38th International Cosmic Ray Conference, ICRC 2023

作者： Yang, Ci Zhou, Xunxiu Chen, Xuejian Huang, Daihui 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 della Volpe, D. 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. Heller, M. 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. School of Physical Science and Technology Southwest Jiaotong University Chengdu610031 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 Dublin 2 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 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 School of Physics and Astronomy Sun Yat-sen University Zhuhai519000 China School of Physics Sun Yat-sen University Guangdong Guangzhou510275 China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-sen University Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Départeme

The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA, and WFCTA. As the major array of LHAASO, KM2A has been operating stably in shower mode. To study the near-earth atmospheric electric field (AEF) effect on the trigger event rate during thunderstorms, Monte Carlo simulations are performed with CORSIKA and G4KM2A. According to the simulations, the shower rate variations are found to be strongly dependent on the strength and polarity of the AEF. The shower rates increase with the field intensity. In positive AEF (defined as the direction pointing towards the ground), the increased amplitude is less than that in negative AEF. With the same field strength 1000 V/cm, the value exceeds 12% in a negative field, and merely is up to 6% in the positive one. The dependence of the trigger rate variation on the thickness of the AEF layer is also simulated. The shower event rate increases dramatically at small thickness, and then the trend of variation slows down with the AEF layer thickness. This indicates that the AEF with larger layer thickness has more deflection effects on the development of an extensive air shower. The shower rate variations are also found to be dependent on the primary zenith angle. Our simulation results could be useful in understanding the variation of trigger rate detected by LHAASO-KM2A during thunderstorms. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Cosmology

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A Security-Sensitive Function Mining Framework for Source Code 1

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5th International Conference on Artificial Intelligence and Security, ICAIS 2019

作者： Chen, Lin Yang, Chunfang Liu, Fenlin Gong, Daofu Ding, Shichang Zhengzhou Science and Technology Institute Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China University of Göttingen Goldschmidtstr. 7 Göttingen37077 Germany

ISBN: (数字)9783030242688

ISBN: (纸本)9783030242671

The security-sensitive functions can be effectively used to improve the efficiency of vulnerability mining techniques, but mining security-sensitive functions of the large-scale code base is difficult. An automatic mining framework for security-sensitive functions is proposed. Firstly, a class of high-resolution code features is used to extract suspected security-sensitive function sets, and then a class of code features is applied to measure the sensitivity of each suspected security-sensitive function. Ultimately, the final security-sensitive function set is ensured based on the measurement result. Established along the framework, a mining algorithm for a type security-sensitive function is proposed. Through the mining experiments on three well-known open source codes, the performance of this algorithm is better than the existing methods. © 2019, Springer Nature Switzerland AG.

关键词： Open source software

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Comments on Privacy-preserving Yoking proof with key exchange in the three-party setting

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International Journal of Network Security 2019年第2期21卷 355-358页

作者： Cheng, Qingfeng Zhang, Xinglong State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan Province450001 China

In 2017, Tian, Yang and Mu presented a new three-party key exchange protocol YPKE in radio frequency identification environment, which is based on the HMQV protocol. They claimed that the proposed YPKE protocol in the three-party setting meets user privacy and session key security. In this comment, we point out that the YPKE protocol still has some weaknesses. Our results show that the proposed YPKE protocol cannot provide perfect forward secrecy, and also cannot resist impersonation attack. At the same time, the YPKE protocol is lack of the security of ephemeral private key leakage and unknown key-share, which the original HMQV protocol can achieve. © 2019 Femto Technique Co., Ltd.

关键词： Radio frequency identification (RFID)

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Optical gyroscope with coherent-boosted two-mode squeezed beams

Optical gyroscope with coherent-boosted two-mode squeezed be...

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2020 Frontiers in Optics Conference, FiO 2020

作者： Xiao, Xiao-Qi Matekole, Elisha S. Zhao, Jiankang Zeng, Guihua Dowling, Jonathan P. Lee, Hwang Department of Communication Engineering Shanghai Dianji University Shanghai200240 China Hearne Institute for Theoretical Physics Department of Physics and Astronomy Louisiana State University Baton RougeLA70803 United States State Key Laboratory of Advanced Optical Communication Systems and Networks Institute of Sensing and Navigation Department of Electronic Engineering Shanghai Jiaotong University Shanghai200030 China National Institute of Information and Communications Technology Tokyo184-8795 Japan NYU-ECNU Institute of Physics NYU Shanghai Shanghai200062 China CAS-Alibaba Quantum Computing Laboratory USTC Shanghai201315 China

ISBN: (纸本)9781943580804

We propose a new gyroscope scheme with a two-mode squeezed coherent light as the source. With the intensity-difference measurement, the phase sensitivity is better than the shot-noise limit, and can saturate the Crámer-Rao Bound. The loss inside the gyroscope can be compensated by adjusting the squeezing parameters. © OSA 2020 © 2020 The Author(s)

关键词： Shot noise

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Spin-induced orbital frustration in a hexagonal optical lattice

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Physical Review Research 2021年第3期3卷 033274-033274页

作者： Yongqiang Li Jianmin Yuan Xiaoji Zhou Xiaopeng Li Department of Physics National University of Defense Technology Changsha 410073 People's Republic of China Department of Physics Graduate School of China Academy of Engineering Physics Beijing 100193 People's Republic of China State Key Laboratory of Advanced Optical Communication System and Network Department of Electronics Peking University Beijing 100871 People's Republic of China State Key Laboratory of Surface Physics Institute of Nanoelectronics and Quantum Computing and Department of Physics Fudan University Shanghai 200438 People's Republic of China Shanghai Qi Zhi Institute AI Tower Xuhui District Shanghai 200232 People's Republic of China

Complex lattices provide a versatile ground for fascinating quantum many-body physics. Here, we propose an exotic mechanics for generating orbital frustration in hexagonal lattices. We study two-component (pseudospin-12) Bose gases in p-orbital bands of two-dimensional hexagonal lattices, and find that the system exhibits previously untouched orbital frustration as a result of the interplay of spin and orbital degrees of freedom, in contrast to normal Ising-type orbital ordering of spinless p-orbital band bosons in two-dimensional hexagonal lattices. Based on the classification by symmetry analysis, we find the interplay of orbital frustration and strong interaction leads to exotic Mott and superfluid phases with spin-orbital intertwined orders, in spite of the complete absence of spin-orbital interaction in the Hamiltonian. Our study implies many-body correlations in a multiorbital setting could induce rich spin-orbital intertwined physics in complex lattice structures.

关键词： Bose-Einstein condensates Cold gases in optical lattices Exotic phases of matter Quantum phase transitions Dynamical mean field theory

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A dynamic network change detection method using network embedding 4th

A dynamic network change detection method using network embe...

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4th International Conference on Cloud computing and Security, ICCCS 2018

作者： Sun, Tong Liu, Yan China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030000059

Dynamic networks are ubiquitous. Detecting dynamic network changes is helpful to understand the network development trend and discover network anomalies in time. It is a research hotspot at present. The structure of the network in the real world is very complex, the current feature learning method is difficult to capture a variety of network connectivity patterns, and the definition of efficient network features requires a large number of neighborhood knowledge and computational costs. In order to overcome this limitation, this paper presents a method of dynamic network change detection using network embedding, which automates the whole process by using feature extraction as a embedding problem, and carries out dynamic network change detection by analyzing the distribution of nodes in space after network embedding processing. We use this method to simulate dynamic network and real dynamic network datasets to prove the validity of this method. © Springer Nature Switzerland AG 2018.

关键词： Network embeddings

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Truth discovery on categorical multi-valued attributes with source relations 4

Truth discovery on categorical multi-valued attributes with ...

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4th IEEE International Conference on Computer and Communications, ICCC 2018

作者： Zheng, Yan Luo, Junyong Yin, Meijuan Yang, Wen China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781538683392

In the era of big data, a mass of data fusion applications need to obtain accurate and complete attribute information about various objects. Most existing truth discovery methods are for single-valued attributes, which will be invalid for multi-valued attributes. They are usually based on the assumption that the data sources are independent of each other. It is not in line with the reality that related relationship occurs between them. Since multi-valued attributes are at large discrete, this paper proposes a truth discovery method incorporating the qulity and the correlations of data sources, namely Categorical Multi-Truths Finder algorithm (CMTF), which iteratively derives the accuracy and the recall rate of data source along with the truth set for each attribute. The experimental results show that CMTF outperforms the state-of-the-art. © 2018 IEEE.

关键词： Data fusion

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