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arXiv 2022年

作者： Aharonian, F. An, Q. Axikegu Bai, L.X. Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Zhe Cao, Zhen Chang, J. Chang, J.F. Chen, E.S. Chen, Liang Chen, Liang Chen, Long Chen, M.J. Chen, M.L. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, X.J. Chen, Y. Cheng, H.L. Cheng, N. Cheng, Y.D. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. 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, X.T. Feng, Y.L. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. 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, S.L. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S. Hu, S.C. Hu, X.J. Huang, D.H. Huang, W.H. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Li, B.B. Li, Cheng Li, Cong 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, 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.S. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Long, W.J. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Masood, A. Min, Z. Mitthumsiri, W. Nan, Y.C. 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. Shao, C.Y. 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. Wang, C. Wang, H. Wang, H.G. Wang, J.C. Wang, J.S. 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 Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 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 Hebei Normal University Hebei Shijiazhuang050024 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Facultéde Sciences Universitéde Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China Department of Engineering Physics Tsinghua University Beijing100084 China School

The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA and WFCTA. The flux variations of cosmic ray air showers were studied by analyzing the KM2A data during the thunderstorm on 10 June 2021. The number of shower events that meet the trigger conditions increases significantly in atmospheric electric fields, with maximum fractional increase of 20%. The variations of trigger rates (increases or decreases) are found to be strongly dependent on the primary zenith angle. The flux of secondary particles increases significantly, following a similar trend with that of the shower events. To better understand the observed behavior, Monte Carlo simulations are performed with CORSIKA and G4KM2A (a code based on GEANT4). We find that the experimental data (in saturated negative fields) are in good agreement with simulations, assuming the presence of a uniform electric field of -700 V/cm with a thickness of 1500 m in the atmosphere above the observation level. Due to the acceleration/deceleration by the atmospheric electric field, the number of secondary particles with energy above the detector threshold is modified, resulting in the changes in shower detection rate. © 2022, CC BY-NC-SA.

关键词： Cosmic rays

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A Forensic Method for Efficient File Extraction in HDFS Based on Three-Level Mapping

引用

Wuhan University Journal of Natural Sciences 2017年第2期22卷 114-126页

作者： GAO Yuanzhao LI Binglong The Forth College Information Engineering UniversityZhengzhou 450001 Henan China State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou 450001 Henan China

The large scale and distribution of cloud computing storage have become the major challenges in cloud forensics for file extraction. Current disk forensic methods do not adapt to cloud computing well and the forensic research on distributed file system is inadequate. To address the forensic problems, this paper uses the Hadoop distributed file system （HDFS） as a case study and proposes a forensic method for efficient file extraction based on three-level （3L） mapping. First, HDFS is analyzed from overall architecture to local file system. Second, the 3L mapping of an HDFS file from HDFS namespace to data blocks on local file system is established and a recovery method for deleted files based on 3L mapping is presented. Third, a multi-node Hadoop framework via Xen virtualization platform is set up to test the performance of the method. The results indicate that the proposed method could succeed in efficient location of large files stored across data nodes, make selective image of disk data and get high recovery rate of deleted files.

关键词： the Hadoop distributed file system （HDFS） forensics cloud forensics three-level （3L） mapping metadata file extraction file recovery Ext4

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A New Multitasking Malware Classification Model Based on Feature Fusion

A New Multitasking Malware Classification Model Based on Fea...

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IEEE advanced Information Management,Communicates,Electronic and Automation Control Conference (IMCEC)

作者： Wei Shi Xin Zhou Jianmin Pang Guanghui Liang Haoran Gu State Key Laboratory of Mathematics Engineering and Advanced Computing Zhengzhou China Beijing Space Information Relay Transmission Technology Research Center Beijing China

In this paper, In this paper, we achieved a new Multitask Malware Classification Model Based on DNN (Deep Neural Network), we use the API functions extracted from the dynamic analysis as the main features, the static extracted n-gram, entropy, SYM, Metadata, asm-image and grayscale image as the fusion features to construct a classifier that can deal with the dichotomy problem and the family classification problem. The system was trained in 12 malware families, 3,792 malicious samples and 138 benign samples. The accuracy of the dichotomous problem is 99.61%. The proposed malware family classification model also achieves 99.33% malware family classification error rate.

关键词： Feature extraction Malware Data mining Entropy Gray-scale Neural networks Analytical models

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Spatiotemporal Knowledge Distillation for Efficient Estimation of Aerial Video Saliency

arXiv

引用

arXiv 2019年

作者： Li, Jia Fu, Kui Zhao, Shengwei Ge, Shiming State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Institute of Information Engineering Chinese Academy of Sciences Beijing100095 China School of Cyber Security at University of Chinese Academy of Sciences Beijing100095 China

—The performance of video saliency estimation techniques has achieved significant advances along with the rapid development of Convolutional Neural Networks (CNNs). However, devices like cameras and drones may have limited computational capability and storage space so that the direct deployment of complex deep saliency models becomes infeasible. To address this problem, this paper proposes a dynamic saliency estimation approach for aerial videos via spatiotemporal knowledge distillation. In this approach, five components are involved, including two teachers, two students and the desired spatiotemporal model. The knowledge of spatial and temporal saliency is first separately transferred from the two complex and redundant teachers to their simple and compact students, and the input scenes are also degraded from high-resolution to low-resolution to remove the probable data redundancy so as to greatly speed up the feature extraction process. After that, the desired spatiotemporal model is further trained by distilling and encoding the spatial and temporal saliency knowledge of two students into a unified network. In this manner, the inter-model redundancy can be further removed for the effective estimation of dynamic saliency on aerial videos. Experimental results show that the proposed approach outperforms ten state-of-the-art models in estimating visual saliency on aerial videos, while its speed reaches up to 28,738 FPS on the GPU platform. Copyright © 2019, The Authors. All rights reserved.

关键词： Students

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Multi-Variant Execution Research of Software Diversity

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

作者： Zhenwu Liu Zheng Zhang Jiexin Zhang Hao Liu State key Laboratory of Mathematical Engineering and Advanced Computing PLA Strategic Support Force Information Engineering University ZhengZhou HeNan 450001 China

As more and more software products are threatened by malicious reverse analysis, along with software products are pirated, tampered with and so on, it is of great significance to study software security protection technology in depth. As a software security protection technology, software diversification introduces uncertainty into the target program and provides probabilistic protection for the target program. Multi-Variant Execution (MVE) are fine-grained implementation of software diversification that produces functionally identical variants at the system call level. This article first introduces the related concepts of multi-variant execution. Secondly, it expounds the key technologies of multi-variant execution implementation-variant generation, variant monitor, input/output and synchronization, monitor-variant communication. Security of monitors and different variant communication technologies are analyzed and compared, their advantages and defects are pointed out respectively. Several implementation methods of multi-variant execution design are summarized. Finally, the reasons for the false negatives and false positives of attack events are analyzed, and the prospects and challenges of using multi-variant execution techniques to implement security systems are summarized.

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Biological data processing based on bio-processor unit(BPU), a new concept for next generation computational biology

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Science China(Life Sciences) 2018年第5期61卷 597-598页

作者： Di Liu Dongbo Bu Tieliu Shi Jianxiao Quan Depeng Wang Yongyong Shi Xiao-Chen Bo Wenbao Han Computational Virology Group Wuhan Institute of Virology Chinese Academy of Sciences Wuhan 430071 China Key Laboratory of InteUigent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Center for Bioinformatics and Computational Biology and the Institute of Biomedical Sciences School of Life Sciences East China Normal University Shanghai 200214 China State Key Laboratory of Mathematical Engineering and Advanced Computation Wuxi 214122 China Jiangsu Variable Supercomputer Tech Co Ltd. Wuxi 214125 China NextOmics Blosctences Wuhan 430074 China Bio-X Institutes Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders (Ministry of Education) Shanghai Jiao Tong University Shanghai 200030 China Beijing Institute of Radiation Medicine Beifing 100850 China

The revolution of biotechnology has pushed forward life sciences into the Big Data ***,high-throughput bio-techniques have greatly accelerated the integration of biology,computing and informatics,and hence substantially pushed forward the development of bioinformatics and computational *** to the latest report of data deposition within National Centre for Biotechnology Information（NCBI）,the genome sequencing projects have increased 49.94%.

关键词：计算生物学简历处理器数据基信息科学生物信息学生命科学 NCBI

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HPC AI500: A benchmark suite for HPC AI systems

arXiv

引用

arXiv 2019年

作者： Jiang, Zihan Gao, Wanling Wang, Lei Xiong, Xingwang Zhang, Yuchen Wen, Xu Luo, Chunjie Ye, Hainan Lu, Xiaoyi Zhang, Yunquan Feng, Shengzhong Li, Kenli Xu, Weijia Zhan, Jianfeng State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences China BenchCouncil [International Open Benchmarking Council Beijing Academy of Frontier Sciences and Technology State University of New York Buffalo United States Department of Computer Science and Engineering Ohio State University National Supercomputing Center Shenzhen China National Supercomputing Center Changsha China National Supercomputing Center Jinan China Texas Advanced Computing Center Texas University Austin United States

In recent years, with the trend of applying deep learning (DL) in high performance scientific computing, the unique characteristics of emerging DL workloads in HPC raise great challenges in designing, implementing HPC AI systems. The community needs a new yard stick for evaluating the future HPC systems. In this paper, we propose HPC AI500 - a benchmark suite for evaluating HPC systems that running scientific DL workloads. Covering the most representative scientific fields, each workload from HPC AI500 is based on real-world scientific DL applications. Currently, we choose 14 scientific DL benchmarks from perspectives of application scenarios, data sets, and software stack. We propose a set of metrics for comprehensively evaluating the HPC AI systems, considering both accuracy, performance as well as power and cost. We provide a scalable reference implementation of HPC AI500. The specification and source code are publicly available from http://***/HPCAI500/***. Meanwhile, the AI benchmark suites for datacenter, IoT, Edge are also released on the BenchCouncil web site. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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A Demand-Driven Pointer-Range Analysis Technique for Data Transmission Optimization

A Demand-Driven Pointer-Range Analysis Technique for Data Tr...

引用

IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Bo Zhao Xiaoyan Xu Peng Liu Yingying Li Rongcai Zhao Ramin Yahyapour Gesellschaft für wissenschaftliche Datenverarbeitung mbH Göttingen Göettingen Germany State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9781728111421;9781728111414

The goal of range analysis is to determine a program variable's minimum and maximum value at runtime and it becomes more complex to calculate the range space when the variable is a pointer. In this paper, we analyzed the optimization problem of data transmission in parallelization for heterogeneous structure and distributed memory structure. On the basis of symbolic range analysis, we proposed a demand-driven pointer-range analysis technique for data transmission optimization. At first, we introduced the analysis framework of this technique and the representations of pointer range. Then we described the algorithm of the demand-driven pointer-range analysis. The experimental results with various benchmarks demonstrate that our technique can bring about significant performance improvement.

关键词： Optimization Data communication Parallel processing Arrays Data models Software

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Exotic Cooper pairing in multiorbital models of Sr2RuO4

引用

Physical Review B 2019年第13期100卷 134506-134506页

作者： Wen Huang Yi Zhou Hong Yao Shenzhen Institute for Quantum Science and Engineering and Department of Physics Southern University of Science and Technology Shenzhen 518055 China Institute for Advanced Study Tsinghua University Beijing 100084 China Center for Quantum Computing Peng Cheng Laboratory Shenzhen 518005 China Beijing National Laboratory for Condensed Matter Physics & Institute of Physics Chinese Academy of Sciences Beijing 100190 China Kavli Institute for Theoretical Sciences & CAS Center for Excellence in Topological Quantum Computation University of Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Low Dimensional Quantum Physics Tsinghua University Beijing 100084 China

The unconventional superconductivity in Sr2RuO4 continues to defy a unified interpretation. In this paper, we focus on some novel aspects of its superconducting pairing by exploiting the orbital degree of freedom in this material. The multiorbital nature, combined with the symmetry of the orbitals involved, leads to a plethora of exotic Cooper pairings not accessible in single-orbital systems. Essential physics is illustrated first using a two-orbital model with dxz and dyz orbitals. We classify the gap functions according to the underlying lattice symmetries, analyze the effective theories of a few representative pairings, and make connections to Sr2RuO4 in the course. In particular, we show how spin-orbit coupling may entangle spin-triplet and spin-singlet pairings. For completeness, the classification is generalized to the three-orbital model involving the dxy orbital as well. The orbital-basis approach distinguishes from the itinerant-band description for Sr2RuO4 and hence offers an alternative perspective to investigate its enigmatic superconducting state.

关键词： Pairing mechanisms Spin-singlet pairing Spin-triplet pairing Superconducting order parameter Superconductivity Topological materials Topological phases of matter Topological superconductors

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Fine-grained event categorization with heterogeneous graph convolutional networks

arXiv

引用

arXiv 2019年

作者： Peng, Hao Li, Jianxin Gong, Qiran Song, Yangqiu Ning, Yuanxing Lai, Kunfeng Yu, Philip S. Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Software Development Environment Beihang University Department of Computer Science and Engineering Hong Kong University of Science and Technology Platform and Content Group Tencent Institute for Data Science Tsinghua University Department of Computer Science University of Illinois Chicago United States

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text messages containing public events with comprehensive topics. However, mining social events is challenging due to the heterogeneous event elements in texts and explicit and implicit social network structures. In this paper, we design an event meta-schema to characterize the semantic relatedness of social events and build an event-based heterogeneous information network (HIN) integrating information from external knowledge base, and propose a novel Pairwise Popularity Graph Convolutional Network (PP-GCN) based fine-grained social event categorization model. We propose a Knowledgeable meta-paths Instances based social Event Similarity (KIES) between events and build a weighted adjacent matrix as input to the PP-GCN model. Comprehensive experiments on real data collections are conducted to compare various social event detection and clustering tasks. Experimental results demonstrate that our proposed framework outperforms other alternative social event categorization techniques. Copyright © 2019, The Authors. All rights reserved.

关键词： Information services

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