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arXiv 2023年

作者： Zhang, Duo Liu, Xinzijian Zhang, Xiangyu Zhang, Chengqian Cai, Chun Bi, Hangrui Du, Yiming Qin, Xuejian Peng, Anyang Huang, Jiameng Li, Bowen Shan, Yifan Zeng, Jinzhe Zhang, Yuzhi Liu, Siyuan Li, Yifan Chang, Junhan Wang, Xinyan Zhou, Shuo Liu, Jianchuan Luo, Xiaoshan Wang, Zhenyu Jiang, Wanrun Wu, Jing Yang, Yudi Yang, Jiyuan Yang, Manyi Gong, Fu-Qiang Zhang, Linshuang Shi, Mengchao Dai, Fu-Zhi York, Darrin M. Liu, Shi Zhu, Tong Zhong, Zhicheng Lv, Jian Cheng, Jun Jia, Weile Chen, Mohan Ke, Guolin Weinan, E. Zhang, Linfeng Wang, Han AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China CAS Key Laboratory of Magnetic Materials and Devices Zhejiang Province Key Laboratory of Magnetic Materials and Application Technology Chinese Academy of Sciences Ningbo315201 China School of Electronics Engineering and Computer Science Peking University Beijing100871 China Shanghai Engineering Research Center of Molecular Therapeutics & New Drug Development School of Chemistry and Molecular Engineering East China Normal University Shanghai200062 China Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States Department of Chemistry Princeton University PrincetonNJ08540 United States College of Chemistry and Molecular Engineering Peking University Beijing100871 China Yuanpei College Peking University Beijing100871 China School of Electrical Engineering and Electronic Information Xihua University Chengdu610039 China State Key Laboratory of Superhard Materials College of Physics Jilin University Changchun130012 China Key Laboratory of Material Simulation Methods & Software of Ministry of Education College of Physics Jilin University Changchun130012 China International Center of Future Science Jilin University Changchun130012 China Key Laboratory for Quantum Materials of Zhejiang Province Department of Physics School of Science Westlake University Zhejiang Hangzhou310030 China Atomistic Simulations Italia

The rapid advancements in artificial intelligence (AI) are catalyzing transformative changes in atomic modeling, simulation, and design. AI-driven potential energy models have demonstrated the capability to conduct large-scale, long-duration simulations with the accuracy of ab initio electronic structure methods. However, the model generation process remains a bottleneck for large-scale applications. We propose a shift towards a model-centric ecosystem, wherein a large atomic model (LAM), pre-trained across multiple disciplines, can be efficiently fine-tuned and distilled for various downstream tasks, thereby establishing a new framework for molecular modeling. In this study, we introduce the DPA-2 architecture as a prototype for LAMs. Pre-trained on a diverse array of chemical and materials systems using a multi-task approach, DPA-2 demonstrates superior generalization capabilities across multiple downstream tasks compared to the traditional single-task pre-training and fine-tuning methodologies. Our approach sets the stage for the development and broad application of LAMs in molecular and materials simulation research. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi-task learning

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A Non-Stop Double Buffering Mechanism for Dataflow Architecture

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Journal of Computer Science & Technology 2018年第1期33卷 145-157页

作者： Xu Tan Xiao-Wei Shen Xiao-Chun Ye Da Wang Dong-Rui Fan Lunkai Zhang Wen-Ming Li Zhi-Min Zhang Zhi-Min Tang State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing 100049 China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi 214125 China Department of Computer Science The University of Chicago Chicago IL 60637 U.S.A.

Double buffering is an effective mechanism to hide the latency of data transfers between on-chip and off-chip memory. However, in dataflow architecture, the swapping of two buffers during the execution of many tiles decreases the performance because of repetitive filling and draining of the dataflow accelerator. In this work, we propose a non-stop double buffering mechanism for dataflow architecture. The proposed non-stop mechanism assigns tiles to the processing element array without stopping the execution of processing elements through optimizing control logic in dataflow architecture. Moreover, we propose a work-flow program to cooperate with the non-stop double buffering mechanism. After optimizations both on control logic and on work-flow program, the filling and draining of the array needs to be done only once across the execution of all tiles belonging to the same dataflow graph. Experimental results show that the proposed double buffering mechanism for dataftow architecture achieves a 16.2% average efficiency improvement over that without the optimization.

关键词： non-stop double buffering dataflow architecture high-performance computing

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Mobile APP User Attribute Prediction by Heterogeneous Information Network Modeling 5th

Mobile APP User Attribute Prediction by Heterogeneous Inform...

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5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications, DependSys 2019

作者： Zhang, Hekai Gong, Jibing Teng, Zhiyong Wang, Dan Wang, Hongfei Du, Linfeng Bhuiyan, Zakirul Alam School of Information Science and Engineering Yanshan University Qinhuangdao066004 China The Key Lab for Computer Virtual Technology and System Integration Yanshan University Qinhuangdao066004 China School of Computer Science and Engineering Beihang University Beijing100191 China Shenyuan Honors College Beihang University Beijing100191 China State Key Lab of Mathematical Engineering and Advanced Computing Wuxi214000 China Department of Computer and Information Sciences Fordham University New York United States

ISBN: (纸本)9789811513039

User-based attribute information, such as age and gender, is usually considered as user privacy information. It is difficult for enterprises to obtain user-based privacy attribute information. However, user-based privacy attribute information has a wide range of applications in personalized services, user behavior analysis and other aspects. Although many scholars have made achievements in user attribute prediction and other related fields, there are still two main problems that impede further improvement on the accuracy of classification: (1) Traditional machine learning classification merely takes each object as a single individual, ignoring the relationship between them;(2) At present, the popular Heterogeneous Path-Mine Information Network only considers whether the user has a relationship with the attributes of other nodes, rather than the degree of correlation of the attributes. It employs a linear regression model to fit the weight of meta-path, which is highly sensitive to outliers. To solve the above two problems, this paper advances the HetPathMine model and puts forward TPathMine model. With applying the number of clicks of attributes under each node to express the user’s emotional preference information, optimizations of the solution of meta-path weight are also presented. Based on meta-path in heterogeneous information networks, the new model integrates all relationships among objects into isomorphic relationships of classified objects. Matrix is used to realize the knowledge dissemination of category knowledge among isomorphic objects. The experimental results show that: (1) the prediction of user attributes based on heterogeneous information networks can achieve higher accuracy than traditional machine learning classification methods;(2) TPathMine model based on the number of clicks is more accurate in classifying users of different age groups, and the weight of each meta-path is consistent with human intuition or the real world situation. © 2019, Spr

关键词： Forecasting

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REDT: Remote exploitation detection technology for network infrastructure

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

作者： Xiaokang Yin Shengli Liu Fangfang Jia Da Xiao State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou CO 450000 China

An attacker exploits a remote vulnerability to execute arbitrary code on network infrastructure. It seriously threatens the security of network infrastructure. To detect the exploitation attack in a timely and effective way, in this paper, we present REDT, a remote exploitation detection technology based on the effect of exploitation attack. This method determines whether there is an authentication bypass exploitation by detecting the remote authentication process and saves attack traffic to the anomalous traffic database. With the database, we can replay the attack and analyze the exploitation. In the experiment, the detection accuracy rate reached 97.67%. In addition, this method does not rely on the signatures of the vulnerability and shellcode, so it can detect undisclosed vulnerabilities exploitation and provide abnormal traffic for the study of unknown vulnerabilities.

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The Propagation Strategy Model of Taint Analysis

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

作者： Yuzhu Ren Jiangtao Zhao Cao Zhang State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan 450000 China

The taint propagation strategy is the core of the taint analysis technology. When the taint analysis toolsanalyses the target program, it needs to mark the target data according to the formulatedtaint propagation strategy. Formulating a reasonable strategy for taint propagation can effectively improve the accuracy of taint analysis. There are two difficulties in developing the taint propagation strategy, namely, alias analysis in static taint analysis and indirect jump in dynamic taint analysis. In our paper, we propose a taint propagation strategy at the intermediate representation language level, which can effectively improve the accuracy of taint propagation.

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A new network intrusion detection system based on blockchain

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International Journal of Performability engineering 2019年第12期15卷 3187-3195页

作者： Fu, Jinhua Xu, Mixue Huang, Yongzhong Tao, Hongwei State Key Laboratory of Mathematical Engineering and Advanced Computing Information Engineering University Zhengzhou450001 China School of Computer and Communication Engineering Zhengzhou University of Light Industry Zhengzhou450002 China

With the increasing application of computers and networks, several network security problems have emerged, and thus network intrusion detection systems have become the focus of network security research. In order to achieve the purpose of intrusion detection and protection, the traditional network intrusion detection system extracts features from the data of the network data stream according to the feature recognition algorithm and compares the extracted features with those in the training set to recognize the behavior. However, if a user wants to effectively detect malicious behaviors in the network, a large feature library is needed, and it cannot be shared with other users, which makes the quality of single user detection lower than the highest detection quality of the whole network. Blockchain, which is a new network system of decentralization, de-trust, tamper-proof, anti-counterfeiting, and traceability, plays an important role in the transmission and sharing of high-value data. In this paper, a new network intrusion detection system is designed based on blockchain, which can enable users to share feature libraries over the whole network by means of P2P network transmission. Meanwhile, its network structure and consensus algorithm are presented, and its security and performance are analyzed. Analysis results show that this system has lower false negative rates. © 2019 Totem Publisher, Inc. All rights reserved.

关键词： Intrusion detection

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Spiral Field Generation in Smith-Purcell Radiation by Helical Metagratings

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Research 2019年第1期2019卷 1115-1122页

作者： Liqiao Jing Zuojia Wang Xiao Lin Bin Zheng Su Xu Lian Shen Yihao Yang Fei Gao Min Chen Hongsheng Chen Key Lab.of Advanced Micro/Nano Electronic Devices&Smart Systems of Zhejiang College of Information Science and Electronic EngineeringZhejiang UniversityHangzhou 310027China Department of Physics Massachusetts Institute of TechnologyCambridgeMA 02139USA School of Information Science and Engineering Shandong UniversityQingdao 266237China Division of Physics and Applied Physics School of Physical and Mathematical SciencesNanyang Technological UniversitySingapore State Key Laboratory of Integrated Optoelectronics College of Electronic Science and EngineeringJilin UniversityChangchun 130012China

Moving electrons interacting with media can give rise to electromagnetic radiations and has been emerged as a promising platform for particle detection,spectroscopies,and free-electron *** this letter,we investigate the Smith-Purcell radiation from helical metagratings,chiral structures similar to deoxyribonucleic acid(DNA),in order to understand the interplay between electrons,photons,and object *** feld patterns can be generated while introducing a gradient azimuthal phase distribution to the induced electric dipole array at the cylindrical *** measurements show efcient control over angular momentum of the radiated feld at microwave regime,utilizing a phased electromagnetic dipole array to mimic moving charged *** angular momentum of the radiated wave is determined solely by the handedness of the helical structure,and it thus serves as a potential candidate for the detection of chiral *** fndings not only pave a way for design of orbital angular momentum free-electron lasers but also provide a platform to study the interplay between swif electrons with chiral objects.

关键词： momentum chiral angular

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Measurement-Device-Independent Quantum key Distribution with Leaky Sources

arXiv

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arXiv 2020年

作者： Wang, Weilong Tamaki, Kiyoshi Curty, Marcos EI Telecomunicación Department of Signal Theory and Communications University of Vigo VigoE-36310 Spain State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou Henan450001 China Henan Key Laboratory of Network Cryptography Technology ZhengzhouHenan450001 China Faculty of engineering University of Toyama Gofuku 3190 Toyama930-8555 Japan

Measurement-device-independent quantum key distribution (MDI-QKD) can remove all detection side-channels from quantum communication systems. The security proofs require, however, that certain assumptions on the sources are satisfied. This includes, for instance, the requirement that there is no information leakage from the transmitters of the senders, which unfortunately is very difficult to guarantee in practice. In this paper we relax this unrealistic assumption by presenting a general formalism to prove the security of MDI-QKD with leaky sources. With this formalism, we analyze the finite-key security of two prominent MDI-QKD schemes – a symmetric three-intensity decoy-state MDI-QKD protocol and a four-intensity decoy-state MDI-QKD protocol – and determine their robustness against information leakage from both the intensity modulator and the phase modulator of the transmitters. Our work shows that MDI-QKD is feasible within a reasonable time frame of signal transmission given that the sources are sufficiently isolated. Thus, it provides an essential reference for experimentalists to ensure the security of experimental implementations of MDI-QKD in the presence of information leakage. Copyright © 2020, The Authors. All rights reserved.

关键词： Quantum cryptography

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DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

arXiv

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

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Automatic Mining of Security-Sensitive Functions from Source Code

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Computers, Materials & Continua 2018年第8期56卷 199-210页

作者： Lin Chen Chunfang Yang Fenlin Liu Daofu Gong Shichang Ding Zhengzhou Science and Technology Institute Zhengzhou450001China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou450001China University of Göttingen Goldschmidtstr.737077 GöttingenGermany

When dealing with the large-scale program,many automatic vulnerability mining techniques encounter such problems as path explosion,state explosion,and low *** of large-scale programs based on safety-sensitive functions helps solve the above *** manual identification of security-sensitive functions is a tedious task,especially for the large-scale *** study proposes a method to mine security-sensitive functions the arguments of which need to be checked before they are *** argument-checking identification algorithms are proposed based on the analysis of two implementations of argument *** on these algorithms,security-sensitive functions are detected based on the ratio of invocation instances the arguments of which have been protected to the total number of *** results of experiments on three well-known open-source projects show that the proposed method can outperform competing methods in the literature.

关键词： Code mining vulnerabilities static analysis security-sensitive function source code.

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