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A BOW-Based Sentence Embedding Method for Chinese Event Identification

Journal of Physics: Conference Series 2020年第4期1486卷

作者： Lizheng Gao Gang Zhou Junyong Luo State key of Laboratory of Mathematical Engineering And Advanced Computing Zhengzhou China

Event extraction is an important branch of information extraction. It involves two challenging issues: event identification and argument identification. Most approaches of event identification are trigger-based, which suffer a lot from sample imbalance, word ambiguity, low scalability, etc. To solve these problems, we attempt to explore sentence-based event identification, whose main issue is sentence representation. With the success of word embedding, it has attracted much attention to generate semantic embeddings of sentences. In this paper, we propose a simple BOW-based sentence embedding method to represent event sentences for Chinese event identification. We compute embeddings on the dependency tree and map different relations to simple arithmetic operations. We evaluate our method with a dataset of Chinese event identification and compare the result with other BOW-based methods. The results show that our approach significantly outperforms other BOW-based methods.

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Software Supply Chain Analysis Based on Function Similarity

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Journal of Physics: Conference Series 2020年第5期1601卷

作者： Wenjie Sun Zheng Shan Fudong Liu Xingwei Li Meng Qiao Chunyan Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

The supply of open source and open source components is growing at an alarming rate, while vulnerabilities in open source components are everywhere. Software supply chain analysis aims to discover third-party components and open source code used in a software, and analyze the software's dependence on components. In this paper, we propose a software component analysis method and a known vulnerabilities detecting method. By scanning the open source components of the binary file and conducting vulnerability analysis, the known vulnerabilities are detected. This paper mainly solves the problem of detecting known vulnerabilities in the supply chain of binary files. We conducted a case analysis and achieved good results.

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A new method for solving the linear recurrence relation with nonhomogeneous constant coefficients in some cases

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Journal of Physics: Conference Series 2021年第1期2113卷

作者： Ben-Chao Yang Xue-Feng Han State Key Laboratory of Mathematical Engineering and advanced computing Zhengzhou Henan China School of mathematics and Information Science Henan Polytechnic University Jiaozuo Henan China

Recursive relation mainly describes the unique law satisfied by a sequence, so it plays an important role in almost all branches of mathematics. It is also one of the main algorithms commonly used in computer programming. This paper first introduces the concept of recursive relation and two common basic forms, then starts with the solution of linear recursive relation with non-homogeneous constant coefficients, gives a new solution idea, and gives a general proof. Finally, through an example, the general method and the new method given in this paper are compared and verified.

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Fine-grained analysis method for Android volatile memory

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IOP Conference Series: Materials Science and engineering 2020年第1期715卷

作者： Peijun Feng Qingbao Li Zhifeng Chen State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

Android dominates the mobile operating system market. Volatile memory analysis of Android devices has been the focus of research on mobile forensics technology. However, due to the semantic gap between the kernel and the volatile memory allocator, existing Android volatile memory analysis methods are coarse-grained. With the volatile memory capacity of Android devices increasing, these methods cannot satisfy the need of Android volatile memory analysis accuracy. In this paper, we first discuss the address space layout of Android processes and the management mechanism of Jemalloc, the default Android volatile memory allocator. Then, we bridge the semantic gap by utilizing the boundary auto alignment feature of the data structure of Jemalloc. Finally, we propose a Fine-grained Analysis Method for Android volatile Memory, called FAMAM. Experimental results shows that FAMAM has an accurate data analysis capability as well as a good robustness. In addition, we successfully use FAMAM to discover new storage patterns for username and password of Wechat.

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Dative epitaxy of commensurate monocrystalline covalent-van der Waals moiré supercrystal

arXiv

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arXiv 2022年

作者： Bian, Mengying Zhu, Liang Wang, Xiao Choi, Junho Chopdekar, Rajesh V. Wei, Sichen Wu, Lishu Huai, Chang Marga, Austin Yang, Qishuo Li, Yuguang C. Yao, Fei Yu, Ting Crooker, Scott A. Cheng, Xuemei M. Sabirianov, Renat F. Zhang, Shengbai Lin, Junhao Hou, Yanglong Zeng, Hao Beijing Key Laboratory for Magnetoelectric Materials and Devices Beijing Innovation Center for Engineering Science and Advanced Technology School of Materials Science and Engineering Peking University Beijing China Department of Physics University at Buffalo State University of New York BuffaloNY United States Department of Physics Shenzhen Key Laboratory of Advanced Quantum Functional Materials and Devices Southern University of Science and Technology Shenzhen China Physics Department Bryn Mawr College Bryn MawrPA United States National High Magnetic Field Laboratory Los Alamos National Laboratory Los AlamosNM United States Advanced Light Source Lawrence Berkeley National Laboratory BerkeleyCA United States Department of Materials Design and Innovation University at Buffalo The State University of New York BuffaloNY United States Division of Physics & Applied Physics School of Physical and Mathematical Sciences Nanyang Technological University Singapore Department of Chemistry University at Buffalo The State University of New York BuffaloNY United States Department of Physics University of Nebraska-Omaha OmahaNE United States Department of Physics Rensselaer Polytechnic Institute TroyNY United States

Realizing van der Waals (vdW) epitaxy in the 80’s represents a breakthrough that circumvents the stringent lattice matching and processing compatibility requirements in conventional covalent heteroepitaxy. However, due to the weak vdW interactions, there is little control over film qualities by the substrate. Typically, discrete domains with a spread of misorientation angles are formed, limiting the applicability of vdW epitaxy. Here we report the epitaxial growth of monocrystalline, covalent Cr5Te8 2D crystals on monolayer vdW WSe2 by chemical vapor deposition, driven by interfacial dative bond formation. The lattice of Cr5Te8, with a lateral dimension of a few ten microns, is fully commensurate with that of WSe2 via 3 × 3 (Cr5Te8)/7 × 7 (WSe2) supercell matching, forming a single-crystalline moiré superlattice. Our work has established a conceptually distinct paradigm of thin film epitaxy termed "dative epitaxy", which takes full advantage of covalent epitaxy with chemical bonding for fixing the atomic registry and crystal orientation, while circumventing its stringent lattice matching and processing compatibility requirements;conversely, it ensures the full flexibility of vdW epitaxy, while avoiding its poor orientation control. Cr5Te8 2D crystals grown by dative epitaxy exhibit square magnetic hysteresis, suggesting minimized interfacial defects that can serve as pinning sites. © 2022, CC BY.

关键词： Van der Waals forces

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General-purpose machine-learned potential for 16 elemental metals and their alloys

arXiv

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arXiv 2023年

作者： Song, Keke Zhao, Rui Liu, Jiahui Wang, Yanzhou Lindgren, Eric Wang, Yong Chen, Shunda Xu, Ke Liang, Ting Ying, Penghua Xu, Nan Zhao, Zhiqiang Shi, Jiuyang Wang, Junjie Lyu, Shuang Zeng, Zezhu Liang, Shirong Dong, Haikuan Sun, Ligang Chen, Yue Zhang, Zhuhua Guo, Wanlin Qian, Ping Sun, Jian Erhart, Paul Ala-Nissila, Tapio Su, Yanjing Fan, Zheyong Beijing Advanced Innovation Center for Materials Genome Engineering University of Science and Technology Beijing Beijing100083 China School of Materials Science and Engineering Hunan University Changsha410082 China MSP group QTF Centre of Excellence Department of Applied Physics Aalto University P.O. Box 15600 Aalto EspooFI-00076 Finland Chalmers University of Technology Department of Physics Gothenburg41926 Sweden National Laboratory of Solid State Microstructures School of Physics Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China Department of Civil and Environmental Engineering George Washington University WashingtonDC20052 United States Department of Electronic Engineering and Materials Science and Technology Research Center The Chinese University of Hong Kong N.T. Shatin999077 Hong Kong Department of Physical Chemistry School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Institute of Zhejiang University-Quzhou Quzhou324000 China College of Chemical and Biological Engineering Zhejiang University Hangzhou310027 China State Key Laboratory of Mechanics and Control of Mechanical Structures Key Laboratory for Intelligent Nano Materials and Devices of Ministry of Education Institute for Frontier Science Nanjing University of Aeronautics and Astronautics Nanjing210016 China Department of Mechanical Engineering The University of Hong Kong Pokfulam Road Hong Kong School of Science Harbin Institute of Technology Shenzhen518055 China College of Physical Science and Technology Bohai University Jinzhou121013 China Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University Leicestershire LoughboroughLE11 3TU United Kingdom

Machine-learned potentials (MLPs) have exhibited remarkable accuracy, yet the lack of general-purpose MLPs for a broad spectrum of elements and their alloys limits their applicability. Here, we present a feasible approach for constructing a unified general-purpose MLP for numerous elements, demonstrated through a model (UNEP-v1) for 16 elemental metals and their alloys. To achieve a complete representation of the chemical space, we show, via principal component analysis and diverse test datasets, that employing one-component and two-component systems suffices. Our unified UNEP-v1 model exhibits superior performance across various physical properties compared to a widely used embedded-atom method potential, while maintaining remarkable efficiency. We demonstrate our approach's effectiveness through reproducing experimentally observed chemical order and stable phases, and large-scale simulations of plasticity and primary radiation damage in MoTaVW alloys. This work represents a significant leap towards a unified general-purpose MLP encompassing the periodic table, with profound implications for materials science. Copyright © 2023, The Authors. All rights reserved.

关键词： Radiation damage

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Convolutional Neural Network Pruning: A Survey

Convolutional Neural Network Pruning: A Survey

引用

第三十九届中国控制会议

作者： Sheng Xu Anran Huang Lei Chen Baochang Zhang School of Automatic Science and Eletrical Engineering Beihang University State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing

Deep convolutional neural networks have enabled remarkable progress over the last years on a variety of visual tasks,such as image recognition, speech recognition, and machine translation. These tasks contribute many to machine ***, developments of deep convolutional neural networks to a machine terminal remains challenging due to massive number of parameters and float operations that a typical model contains. Therefore, there is growing interest in convolutional neural network pruning. Existing work in this field of research can be categorized according to three dimensions: pruning method,training strategy, estimation criterion.

关键词： convolutional neural networks machine intelligence pruning method training strategy estimation criterion

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Security analysis of two unbalancing pairing-free identity-based authenticated key exchange protocols

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International Journal of Network Security 2020年第4期22卷 597-601页

作者： Cheng, Qingfeng Li, Yuting Jiang, Qi Li, Xiong Strategic Support Force Information Engineering University Zhengzhou450001 China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001 China School of Cyber Engineering Xidian University Xi'an710071 China School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan411201 China

The Internet of Things plays an increasingly important role in various fields. However, there are many devices in the Internet of Things that are unbalanced in terms of computing and storage capacity, which should be given full consideration. Recently, Zhang et al. proposed two unbalancing pairing-free identity-based authenticated key exchange (AKE) protocols for disaster scenarios, which was claimed to achieve forward security and imperson-ation attack resilience. In this paper, we show that two proposed AKE protocols are lack of forward security and also cannot resist key compromise impersonation attack. © 2020, Femto Technique Co., Ltd.

关键词： Internet of things

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Non-binary entanglement-assisted quantum stabilizer codes

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Science China(Information Sciences) 2017年第4期60卷 210-223页

作者： Lan LUO Zhi MA Zhengchao WEI Riguang LENG State Key Laboratory of Mathematical Engineering and Advanced Computing

In this paper, we present the pm-ary entanglement-assisted(EA) stabilizer formalism, where p is a prime and m is a positive integer. Given an arbitrary non-abelian "stabilizer", the problem of code construction and encoding is settled perfectly in the case of m = 1. The optimal number of required maximally entangled pairs is discussed and an algorithm to determine the encoding and decoding circuits is proposed. We also generalize several bounds on p-ary EA stabilizer codes, such as the BCH bound, the G-V bound and the linear programming bound. However, the issue becomes tricky when it comes to m > 1, in which case, the former construction method applies only when the non-commuting "stabilizer" satisfies a sophisticated limitation.

关键词： entanglement non-commutativity non-binary stabilizer codes quantum error-correction quantum circuits bounds

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Network representation learning: From preprocessing, feature extraction to node embedding

arXiv

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arXiv 2021年

作者： Zhou, Jingya Liu, Ling Wei, Wenqi Fan, Jianxi Soochow University 1 Shizi street Jiangsu Suzhou215006 China Georgia Institute of Technology during May 2019 - June 2020 AtlantaGA United States State Key Laboratory of Mathematical Engineering and Advanced Computing Jiangsu Wuxi214125 China Georgia Institute of Technology 801 Atlantic Drive AtlantaGA30332 United States Soochow University 1 Shizi street Jiangsu Suzhou215006 China

Network representation learning (NRL) advances the conventional graph mining of social networks, knowledge graphs, and complex biomedical and physics information networks. Over dozens of network representation learning algorithms have been reported in the literature. Most of them focus on learning node embeddings for homogeneous networks, but they differ in the specific encoding schemes and specific types of node semantics captured and used for learning node embedding. This survey paper reviews the design principles and the different node embedding techniques for network representation learning over homogeneous networks. To facilitate the comparison of different node embedding algorithms, we introduce a unified reference framework to divide and generalize the node embedding learning process on a given network into preprocessing steps, node feature extraction steps and node embedding model training for a NRL task such as link prediction and node clustering. With this unifying reference framework, we highlight the representative methods, models, and techniques used at different stages of the node embedding model learning process. This survey not only helps researchers and practitioners to gain an in-depth understanding of different network representation learning techniques but also provides practical guidelines for designing and developing the next generation of network representation learning algorithms and systems. © 2021, CC BY.

关键词： Feature extraction

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