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arXiv 2024年

作者： Lian, Haojie Li, Xinhao Qu, Yilin Du, Jing Meng, Zhuxuan Liu, Jie Chen, Leilei Key Laboratory of In-situ Property-improving Mining of Ministry of Education Taiyuan University of Technology Taiyuan100190 China Henan International Joint Laboratory of Structural Mechanics and Computational Simulation College of Architecture and Civil Engineering Huanghuai University Zhumadian463000 China School of Marine Science and Technology Northwestern Polytechnical University Xi’an710072 China Unmanned Vehicle Innovation Center Ningbo Institute of Northwestern Polytechnical University Ningbo315103 China Key Laboratory of Unmanned Underwater Vehicle Technology of Ministry of Industry and Information Technology Northwestern Polytechnical University Xi’an710072 China Center for Strategic Assessment and Consulting Academy of Military Science Beijing100091 China Computer Engineering Department Taiyuan Institute of Technology Taiyuan030008 China

Neural radiance fields (NeRFs) are a deep learning technique that can generate novel views of 3D scenes using sparse 2D images from different viewing directions and camera poses. As an extension of conventional NeRFs in underwater environment, where light can get absorbed and scattered by water, SeaThru-NeRF was proposed to separate the clean appearance and geometric structure of underwater scene from the effects of the scattering medium. Since the quality of the appearance and structure of underwater scenes is crucial for downstream tasks such as underwater infrastructure inspection, the reliability of the 3D reconstruction model should be considered and evaluated. Nonetheless, owing to the lack of ability to quantify uncertainty in 3D reconstruction of underwater scenes under natural ambient illumination, the practical deployment of NeRFs in unmanned autonomous underwater navigation is limited. To address this issue, we introduce a spatial perturbation field Dω based on Bayes’ rays in SeaThru-NeRF and perform Laplace approximation to obtain a Gaussian distribution N(0, Σ) of the parameters ω, where the diagonal elements of Σ correspond to the uncertainty at each spatial location. We also employ a simple thresholding method to remove artifacts from the rendered results of underwater scenes. Numerical experiments are provided to demonstrate the effectiveness of this approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Security-enhanced timed-release encryption in the random oracle model 6th

Security-enhanced timed-release encryption in the random ora...

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6th International Symposium on Security and Privacy in Social Networks and Big Data, SocialSec 2020

作者： Yuan, Ke Wang, Yahui Zeng, Yingming Ouyang, Wenlei Li, Zheng Jia, Chunfu School of Computer and Information Engineering Henan University Kaifeng475004 China International Joint Research Laboratory for Cooperative Vehicular Networks of Henan Kaifeng475004 China Beijing Institute of Computer Technology and Applications Beijing100854 China College of Cyberspace Security Nankai University Tianjin300350 China

ISBN: (纸本)9789811590306

Timed-release encryption (TRE) is a cryptographic primitive that the sender specifies the future decryption time of the receiver. At present, most TRE schemes implement the control of decryption time based on non-interactive time server to publish time trapdoors periodically. However, the generation of a large number of time trapdoors depend on the generation of the fixed private keys of the time server, so a large number of public parameters about the private keys of the time server can then be used for cryptanalysis, which poses a great threat to the security of the private keys of the time server. To solve this problem, a concrete scheme of TRE in the random oracle model are proposed. In our scheme, time trapdoors published by the time server are generated by the private key of the time server and the random number generated in advance. Compared with the most efficient scheme in the random oracle model, our concrete scheme reduces the time consumption by about 10.8%, at the same time it has achieved the one-time-pad of the time trapdoor, which greatly enhances the security of the private key of the time server, and thus enhances the security and effectiveness of the TRE. © Springer Nature Singapore Pte Ltd 2020.

关键词： Random number generation

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Insulating materials for realising carbon neutrality:Opportunities,remaining issues and challenges

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High Voltage 2022年第4期7卷 610-632页

作者： Chuanyang Li Yang Yang Guoqiang Xu Yao Zhou Mengshuo Jia Shaolong Zhong Yu Gao Chanyeop Park Qiang Liu Yalin Wang Shakeel Akram Xiaoliang Zeng Yi Li Fangwei Liang Bin Cui Junpeng Fang Lingling Tang Yulin Zeng Xingtao Hu Jiachen Gao Giovanni Mazzanti Jinliang He Jianxiao Wang Davide Fabiani Gilbert Teyssedre Yang Cao Feipeng Wang Yunlong Zi The State Key Laboratory of Power System Department of Electrical EngineeringTsinghua UniversityBeijingChina Department of Mechanical and Automation Engineering The Chinese University of Hong KongHong KongChina Simpson Querrey Institute Northwestern UniversityEvanstonIllinoisUSA Department of Materials Science and Engineering The Pennsylvania State UniversityUniversity ParkPennsylvaniaUSA Department of Information Technology and Electrical Engineering ETH ZürichZürichSwitzerland School of Electrical and Information Engineering Tianjin UniversityTianjinChina Department of Electrical and Computer Engineering Mississippi State UniversityMississippi StateMississippiUSA Electrical and Electronic Engineering The University of ManchesterManchesterUK Department of Electrical Engineering School of Electronic Information and Electrical EngineeringShanghai Jiao Tong UniversityShanghaiChina Key Laboratory of Control of Power Transmission and Conversion(SJTU) Ministry of EducationShanghaiChina College of Electrical Engineering Sichuan UniversityChengduChina Shenzhen Institute of Advanced Technology Chinese Academy of Sciences ShenzhenShenzhenChina School of Electrical Engineering Wuhan UniversityWuhanChina School of Integrated Circuits Tsinghua UniversityBeijingChina Jiangsu Jinxin Electric Appliance Co.Ltd. YangzhouChina Tai'an Tiancheng Safety Evaluation Co. Ltd.Tai'anChina Department Electrical Electronic and Information EngineeringUniversity of BolognaBolognaItaly National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijingChina Laplace Paul Sabatier Universityand CNRSToulouseFrance Electrical and Computer Engineering University of ConnecticutStorrsConnecticutUSA State Key Laboratory of Power Transmission Equipment&System Security and New Technology School of Electrical EngineeringChongqing UniversityChongqingChina Thrust of Sustainable Energy and Environment The Hong Kong University of Science and Technology(Guangzhou)GuangzhouChina De

The 2050 carbon-neutral vision spawns a novel energy structure revolution,and the construction of the future energy structure is based on equipment *** material,as the core of electrical power equipment and electrified transportation asset,faces unprecedented challenges and *** goal of carbon neutral and the urgent need for innovation in electric power equipment and electrification assets are first *** engineering challenges constrained by the insulation system in future electric power equipment/devices and electrified transportation assets are *** materials,including intelligent insulating material,high thermal conductivity insulating material,high energy storage density insulating material,extreme environment resistant insulating material,and environmental-friendly insulating material,are cat-egorised with their scientific issues,opportunities and challenges under the goal of carbon neutrality being *** the context of carbon neutrality,not only improves the understanding of the insulation problems from a macro level,that is,electrical power equipment and electrified transportation asset,but also offers opportunities,remaining issues and challenges from the insulating material *** is hoped that this paper en-visions the challenges regarding design and reliability of insulations in electrical equipment and electric vehicles in the context of policies towards carbon neutrality *** authors also hope that this paper can be helpful in future development and research of novel insulating materials,which promote the realisation of the carbon-neutral vision.

关键词： neutral insulation extreme

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A Well-Scaling Parallel FEM-DDM Algorithm for Electromagnetic simulation on SW26010 Many-core Processor

A Well-Scaling Parallel FEM-DDM Algorithm for Electromagneti...

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International Conference on Numerical Electromagnetic Modeling and Optimization for RF, Microwave, and Terahertz applications (NEMO)

作者： Jian-Zhong Lai Rui-Qing Liu Wei-Jia He Wu Wang Ming-Lin Yang Xin-Qing Sheng Center of Electromagnatic Simulation Beijing Institute of Technology Beijing China Computer Network Information Center Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728169668

ISBN: (纸本)9781728169675

We present in this paper a well-scaling parallel approach of the finite element-domain decomposition method (FEM-DDM) for solving large scale 3D scattering problems on many-core SW26010 processors. In this parallel approach, the whole solution domain is first decomposed in a two-level hierarchical way to minimize the inter process communication. Then the FEM matrix of each unit-level subdomain to be solved in the iterative solution of the interface problem from FEM-DDM, is solved via another sparse approximate inverse (SPAI) preconditioner accelerated iterative solver. This inner-outer iteration scheme is further accelerated by exploring many-core parallelization, with specially designed sparse matrix vector multiplications to improve the data access efficiency. The performance achievements are investigated in terms of accuracy, speed up, and scalability.

关键词： Program processors Three-dimensional displays Scalability Electromagnetic scattering Finite element analysis Acceleration Sparse matrices

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Content-Augmented Feature Pyramid Network with Light Linear Spatial Transformers for Object Detection

arXiv

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arXiv 2021年

作者： Gu, Yongxiang Qin, Xiaolin Peng, Yuncong Li, Lu Chengdu Institute of Computer Applications Chinese Academy of Sciences Chengdu610041 China Nanchang Institute of Technology Jiangxi Nanchang330044 China Zenseact Gothenburg41756 Sweden University of Chinese Academy of Sciences Beijing100049 China

As one of the prevalent components, Feature Pyramid Network (FPN) is widely used in current object detection models for improving multi-scale object detection performance. However, its feature fusion mode is still in a misaligned and local manner, thus limiting the representation power. To address the inherit defects of FPN, a novel architecture termed Content-Augmented Feature Pyramid Network (CA-FPN) is proposed in this paper. Firstly, a Global Content Extraction Module (GCEM) is proposed to extract multi-scale context information. Secondly, lightweight linear spatial Transformer connections are added in the top-down pathway to augment each feature map with multi-scale features, where a linearized approximate self-attention function is designed for reducing model complexity. By means of the self-attention mechanism in Transformer, there is no longer need to align feature maps during feature fusion, thus solving the misaligned defect. By setting the query scope to the entire feature map, the local defect can also be solved. Extensive experiments on COCO and PASCAL VOC datasets demonstrated that our CA-FPN outperforms other FPN-based detectors without bells and whistles and is robust in different *** Codes 68T45 © 2021, CC BY.

关键词： Object detection

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New insights into the oscillations of the nucleon electromagnetic form factors

arXiv

引用

arXiv 2022年

作者： Yang, Qin-He Guo, Di Dai, Ling-Yun Haidenbauer, Johann Kang, Xian-Wei Meisner, Ulf-G. School of Physics and Electronics Hunan University Changsha410082 China Hunan Provincial Key Laboratory of High-Energy Scale Physics and Applications Hunan University Changsha410082 China Institute for Advanced Simulation Institut für Kernphysik Jülich Center for Hadron Physics Forschungszentrum Jülich JülichD-52425 Germany Key Laboratory of Beam Technology of the Ministry of Education College of Nuclear Science and Technology Beijing Normal University Beijing100875 China Institute of Radiation Technology Beijing Academy of Science and Technology Beijing100875 China Helmholtz Institut für Strahlen- und Kernphysik Bethe Center for Theoretical Physics Universität Bonn BonnD-53115 Germany Tbilisi State University Tbilisi0186 Georgia

The electromagnetic form factors of the proton and the neutron in the timelike region are investigated. Electron-positron annihilation into antinucleon-nucleon (N¯ N) pairs is treated in distorted wave Born approximation, including the final-state interaction in the N¯ N system. The latter is obtained by a Lippmann-Schwinger equation for N¯ N potentials derived within SU(3) chiral effective field theory. By fitting to the phase shifts and (differential) cross section data, a high quality description is achieved. With these amplitudes, the oscillations of the electromagnetic form factors of the proton and the neutron are studied. It is found that each of them can be described by two fractional oscillators. One is characterized as 'overdamped' and dominates near the threshold, while the other is 'underdamped' and plays an important role in the high-energy region. These two oscillators are essential to understand the distributions of polarized electric charges induced by hard photons for the nucleons. Copyright © 2022, The Authors. All rights reserved.

关键词： Born approximation

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Relaxation dynamics in the alternating XY chain following a quantum quench

arXiv

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arXiv 2023年

作者： Cao, Kaiyuan Hu, Yayun Tong, Peiqing Yang, Guangwen Liu, Peng Zhejiang Lab Hangzhou311100 China Department of Physics Institute of Theoretical Physics Nanjing Normal University Nanjing210023 China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems Nanjing Normal University Nanjing210023 China Department of Computer Science and Technology Tsinghua University Haidian District Beijing100084 China College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China

We investigate the relaxation dynamics of the fermion two-point correlation function Cmn(t) = 〈ψ(t)|c†mcn|ψ(t)〉 in the XY chain with alternating nearest-neighbor hopping interaction after a quench. We find that the deviation δCmn(t) = Cmn(t) − Cmn(∞) decays with time following the power law behavior t−µ, where the exponent µ depends on whether the quench is to the commensurate phase (µ = 1) or incommensurate phase (µ = 1/2). This decay of δCmn(t) arises from the transient behavior of the double-excited quasiparticle occupations and the transitions between different excitation spectra. Furthermore, we find that the steady value Cmn(∞) only involves the average fermion occupation numbers (i.e. the average excited single particle) over the time-evolved state, which are different from the ground state expectation values. We also observe nonanalytic singularities in the steady value Cmn(∞) for the quench to the critical points of the quantum phase transitions (QPTs), suggesting its potential use as a signature of QPTs. Copyright © 2023, The Authors. All rights reserved.

关键词： Ground state

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Knot data analysis using multiscale Gauss link integral

arXiv

引用

arXiv 2023年

作者： Shen, Li Feng, Hongsong Li, Fengling Lei, Fengchun Wu, Jie Wei, Guo-Wei Department of Mathematics Michigan State University East LansingMI48824 United States School of Mathematical Sciences Dalian University of Technology Dalian116024 China Yanqi Lake Beijing Institute of Mathematical Sciences and Applications 101408 China Department of Biochemistry and Molecular Biology Michigan State University MI48824 United States Department of Electrical and Computer Engineering Michigan State University MI48824 United States

In the past decade, topological data analysis (TDA) has emerged as a powerful approach in data science. The main technique in TDA is persistent homology, which tracks topological invariants over the filtration of point cloud data using algebraic topology. Although knot theory and related subjects are a focus of study in mathematics, their success in practical applications is quite limited due to the lack of localization and quantization. We address these challenges by introducing knot data analysis (KDA), a new paradigm that incorporating curve segmentation and multiscale analysis into the Gauss link integral. The resulting multiscale Gauss link integral (mGLI) recovers the global topological properties of knots and links at an appropriate scale but offers multiscale feature vectors to capture the local structures and connectivities of each curve segment at various scales. The proposed mGLI significantly outperforms other state-of-the-art methods in benchmark protein flexibility analysis, including earlier persistent homology-based methods. Our approach enables the integration of artificial intelligence (AI) and KDA for general curve-like objects and data. Copyright © 2023, The Authors. All rights reserved.

关键词： Topology

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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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Requirement Consistency and Integrity Verification Method based on Natural Language Processing

引用

Journal of Physics: Conference Series 2021年第1期1756卷

作者： Chao Wu Zhong Hua Huang Yu Ting Yang Yue Liu Beijing Institute of Computer Technology and Applications BeiJing 100854 China

In the iterative process of the update of the information service system, the continuous improvement of the system is restricted due to problems such as non-standard, incomplete, and strong ambiguity in the description of requirements. It makes it difficult to verify the consistency and integrity of requirements. In this paper, we focus on the huge requirements of complex information service system, and propose research on requirement itemized extraction and structured description technology based on natural language processing and requirement item keyword extraction and standardized expression technology. The library management system is used as the verification object to verify the effectiveness of the method proposed in this paper.

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