检索结果-内蒙古大学图书馆

SSRN 2023年

作者： Wu, Min Huang, Hui Wu, Yueqin Xu, Zhiteng Li, Tukun Macleod, Iain Wu, Xiaolei National and Local Joint Engineering Research Center for Intelligent Manufacturing Technology of Brittle Material Products Huaqiao University Xiamen361021 China Institute of Manufacturing Engineering Huaqiao University Xiamen361021 China State Key Laboratory of High-Performance Tools Zhengzhou450001 China EPSRC Future Advanced Metrology Hub Centre for Precision Technologies School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom IMA Ltd 29 Clay Lane CheshireWA15 8PJ United Kingdom Zhengzhou Research Institute for Abrasives & Grinding Co. Ltd Zhengzhou450001 China

This paper proposes a novel method for friction-induced chemical reaction machining of single-crystal 4H-SiC wafer using a pure iron. Material Removal Rate (MRR) and surface quality of single-crystal 4H-SiC were studied, and the results demonstrate that both high surface quality and MRR (Ra: 2.2 nm, MRR: 375 nm/min) were obtained. The mechanism of material removal during friction was investigated by examining the critical roles of friction speed. Solid-state chemical reaction between metal and SiC surfaces can be induced by friction, and the subsequent removal of reactants resulting in a near-damage-free machined surface. A removal mechanism model of friction was established, which reveals that both the rates of solid-state reaction and reactant removal significantly impact the final removal rate of friction. © 2023, The Authors. All rights reserved.

关键词： Silicon carbide

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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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Searching for neutrino signals correlated with LHAASO diffuse Galactic emission 38

Searching for neutrino signals correlated with LHAASO diffus...

引用

38th International Cosmic Ray Conference, ICRC 2023

作者： Li, Wenlian Huang, Tian-Qi Xu, Donglian He, Huihai 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. Tsung-Dao Lee Institute Shanghai Jiao Tong University Shanghai201210 China Key Laboratory of Particle Astrophysics Experimental Physics Division Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China Shanghai Key Laboratory for Particle Physics and Cosmology Shanghai200240 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 Chengdu610000 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 Ya

The diffuse Galactic γ-ray emission is produced from the interaction of cosmic rays and interstellar medium or radiation fields in the Galaxy, where neutrino production is also expected. Recently, the Large High Altitude Air Shower Observatory (LHAASO) reported the measurements of the diffuse γ-ray from the Galactic plane with energies above 10 TeV. In this study, we construct the neutrino emission template based on LHAASO’s observation and search for diffuse neutrinos accompanying the LHAASO diffuse Galactic γ-ray emission using ten-year IceCube track events. No significant signals are found. We set 90% confidence level upper limits, resulting in the neutrino flux of dν/dEν = 1.27 × 10−14(Eν/25TeV)−2.99TeV−1cm−2s−1 for the diffuse γ-ray flux map with a 1.5σ significance cut. © Copyright owned by the author(s) under the terms of the Creative Commons.

关键词： Gamma rays

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Influence of the dissipative topological edge state on quantized transport in MnBi2Te4

引用

Physical Review B 2022年第16期105卷 165411-165411页

作者： Weiyan Lin Yang Feng Yongchao Wang Zichen Lian Hao Li Yang Wu Chang Liu Yihua Wang Jinsong Zhang Yayu Wang Xiaodong Zhou Jian Shen State Key Laboratory of Surface Physics and Institute for Nanoelectronic Devices and Quantum Computing Fudan University Shanghai China Department of Physics Fudan University Shanghai China Beijing Academy of Quantum Information Science Beijing China Beijing Innovation Center for Future Chips Tsinghua University Beijing China State Key Laboratory of Low Dimensional Quantum Physics Department of Physics Tsinghua University Beijing China School of Materials Science and Engineering Tsinghua University Beijing China Tsinghua-Foxconn Nanotechnology Research Center Department of Physics Tsinghua University Beijing China Department of Mechanical Engineering Tsinghua University Beijing China Shanghai Research Center for Quantum Sciences Shanghai China Frontier Science Center for Quantum Information Beijing China Shanghai Qi Zhi Institute Shanghai China Zhangjiang Fudan International Innovation Center Fudan University Shanghai China Collaborative Innovation Center of Advanced Microstructures Nanjing China

The beauty of the quantum Hall (QH) effect is the metrological precision of Hall resistance quantization that originates from the topological edge states. Understanding the factors that lead to quantization breakdown not only provides important insights on the nature of the topological protection of these edge states, but is beneficial for device applications involving such quantized transport. In this work, we combine conventional transport and real space conductivity mapping to investigate whether the quantization breakdown is tied to the disappearance of edge state in the hotly studied MnBi2Te4 system. Our experimental results unambiguously show that topological edge state does exist when quantization breakdown occurs. Such edge state is dissipative in nature and could lead to a quantization breakdown due to its diffusive character causing overlapping with bulk and other edge states in real devices. Our findings bring attention to issues that are generally inaccessible in the transport study of QH, but can play important roles in practical measurements and device applications.

关键词： Quantum anomalous Hall effect Topological insulators Scanning probe microscopy

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A biologically plausible audio-visual integration model for continual learning

arXiv

引用

arXiv 2020年

作者： Chen, Wenjie Du, Fengtong Wang, Ye Cao, Lihong State Key Laboratory of Media Convergence and Communication Communication University of China Beijing China Neuroscience and Intelligent Media Institute Communication University of China Beijing China State Key Laboratory of Mathematical Engineering and Advanced Computing Wuxi China

The problem of catastrophic forgetting has a history of more than 30 years and has not been completely solved yet. Since the human brain has natural ability to perform continual lifelong learning, learning from the brain may provide solutions to this problem. In this paper, we propose a novel biologically plausible audio-visual integration model (AVIM) based on the assumption that the integration of audio and visual perceptual information in the medial temporal lobe during learning is crucial to form concepts and make continual learning possible. Specifically, we use multi-compartment Hodgkin-Huxley neurons to build the model and adopt the calcium-based synaptic tagging and capture as the model’s learning rule. Furthermore, we define a new continual learning paradigm to simulate the possible continual learning process in the human brain. We then test our model under this new paradigm. Our experimental results show that the proposed AVIM can achieve state-of-the-art continual learning performance compared with other advanced methods such as OWM, iCaRL and GEM. Moreover, it can generate stable representations of objects during learning. These results support our assumption that concept formation is essential for continuous lifelong learning and suggest the proposed AVIM is a possible concept formation mechanism. Copyright © 2020, The Authors. All rights reserved.

关键词： Brain

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Recurrent Neural Network Based Binary Code Vulnerability Detection

Recurrent Neural Network Based Binary Code Vulnerability Det...

引用

2019第二届算法、计算和人工智能国际会议

作者： Jianyun Zheng Jianmin Pang Xiaochuan Zhang Xin Zhou MingLiang Li Jun Wang State Key Laboratory of Mathematical Engineering and Advanced Computing

Vulnerability detection, especially vulnerability automatic detection, have long been an area of active research. Considering that RNN can model dependencies automatically, we propose a recurrent neural network(RNN) based method for binary code vulnerability detection. We collected correspond to 4 types of vulnerabilities, including CWE-134, CWE-191, CWE-401 and *** find which RNN is better in vulnerability detection, we evaluated 6 different RNNs, including SRNN, BSRNN, LSTM, BLSTM, GRU and BGRU. The results demonstrated that BSRNN is better than others in vulnerability detection.

关键词： Vulnerability detection Deep Learning Recurrent Neural Network

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Fully secure decentralized ciphertext-policy attribute-based encryption in standard model 14th

Fully secure decentralized ciphertext-policy attribute-based...

引用

14th International Conference on Information Security and Cryptology, Inscrypt 2018

作者： Ma, Chuangui Ge, Aijun Zhang, Jie Army Aviation Institute Beijing China Key Laboratory of Cryptologic Technology and Information Security Ministry of Education Shandong University Jinan China State Key Laboratory of Mathematical Engineering and Advanced Computing Zhengzhou China

ISBN: (纸本)9783030142339

In this paper, we introduce a new multi-authority ciphertext policy attribute-based encryption (MA-CP-ABE) system. In our system, there are multiple central authorities (CAs) and attribute authorities (AAs). The CAs will not need to coordinate or even be aware of each other, and so do the AAs. In particular, we present two constructions that will be proved secure in the standard model. Our first scheme is fully secure under static assumptions in composite-order bilinear group, and can work for any monotone access structure. The second one achieves constant size ciphertexts for AND-gate policy in prime-order group. The security can be proved under the decisional linear (DLIN) assumption. © 2019, Springer Nature Switzerland AG.

关键词： Cryptography

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A Framework for Multi-Variant Execution Environment

A Framework for Multi-Variant Execution Environment

引用

作者： Wang Junchao Liu Zhenwu Liu Hao Pang Jianmin State key Laboratory of Mathematical Engineering and Advanced Computing

Software diversity has been proven to be an effective approach to enhance system *** make the best of the advantage brought by software diversity,a multi-variant execution environment is ***,although some MVEEs have been proposed,most of them are either too simple or only focusing one aspect which limits their widely adoption in *** this paper we propose a framework for multi-variant execution environment to enhance the security of software *** framework addresses different aspects when implementing a MVEE and can help to make the best of software diversity to enhance the system's security.

关键词： A Framework for Multi-Variant Execution Environment Framework

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Summary of Large Scale Graph computing Models

Summary of Large Scale Graph Computing Models

引用

2019 2nd International Conference on Mechanical,Electronic and engineering Technology(MEET 2019)

作者： Sheng Yang State Key Laboratory of Mathematical Engineering and Advanced Computing

Large-scale graph data is a research hotspot in the processing of big data and is generally used in traffic management, analysis of social network and semantic web, etc., resulting in the emergence of a variety of large-scale graph computing platforms. How to select a more suitable graph computing platform for different tasks has become one of the hot topics in the field of graph computing. The related knowledge of graph computing models is systematically reviewed in this paper. First of all, the concept of graph computing model is outlined, including the definition and characteristics of graph computing and the development of graph computing models. Then, three kinds of graph computing models are introduced. Finally, the development process of the graph computing models is summarized, and the future research direction is prospected.

关键词： Large-scale graph computing graph processing distributed computing

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Dynamic Nonreciprocity with a Kerr Nonlinear Resonator

arXiv

引用

arXiv 2022年

作者： Pan, Rui-Kai Tang, Lei Xia, keyu Nori, Franco College of Engineering and Applied Sciences National Laboratory of Solid State Microstructures Collaborative Innovation Center of Advanced Microstructures Nanjing University Nanjing210093 China College of Physics and Electronic Engineering Sichuan Normal University Chengdu610101 China Jiangsu Key Laboratory of Artificial Functional Materials Nanjing University Nanjing210023 China Shishan Laboratory Suzhou Campus of Nanjing University Suzhou215000 China RIKEN Quantum Computing Center RIKEN Cluster for Pioneering Research Saitama Wako-shi351-0198 Japan Physics Department The University of Michigan Ann ArborMI48109-1040 United States

On-chip optical nonreciprocal devices are vital components for integrated photonic systems and scalable quantum information processing. Nonlinear optical isolators and circulators have attracted considerable attention because of their fundamental interest and their important advantages in integrated photonic circuits. However, optical nonreciprocal devices based on Kerr or Kerr-like nonlinearity are subject to dynamical reciprocity when the forward and backward signals coexist simultaneously in a nonlinear system. Here, we theoretically propose a method for realizing on-chip nonlinear isolators and circulators with dynamic nonreciprocity. Dynamic nonreciprocity is achieved via the chiral modulation on the resonance frequency due to coexisting self- and cross-Kerr nonlinearities in an optical ring resonator. This work showing dynamic nonreciprocity with a Kerr nonlinear resonator can be an essential step toward integrated optical isolation. © 2022, CC BY.

关键词： Nonlinear optics

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