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Journal of Physics: Conference Series 2021年第1期1757卷

作者： Ni Li Manman Peng Buwen Cao Kenli Li Keqin Li College of Computer Science and Electric Engineering Hunan University Hunan Changsha 410082 China College of Information and Electronic Engineering Hunan Yiyang 413000 China Engineering Research Center of DongTing Lake regional ecological environment intelligent monitoring and disaster prevention and Mitigation Hunan Yiyang 413000 China Key Laboratory of city computing and IoT Hunan City University Hunan Yiyang 413000 China Department of Computer science State University of New York New Paltz NY 12561 USA

Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )

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Performance analysis of parallel replica molecular dynamics

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AIP Conference Proceedings 2018年第1期1978卷

作者： Boyao Zhang Jue Wang Yangde Feng Ji Xiang Lei Wang 1Computer Network Information Center Chinese Academy of Science Department of High Performance Computing Technology and Application Development Beijing China 2University of Chinese Academy of Sciences Beijing China 3State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences China 4School of cybersecurity Chinese Academy of Sciences China

Molecular dynamics simulation is an important tool for studying materials microstructure evolution under radiation effects. It is difficult to simulate atomic diffusion in materials owing to the time-scale limitations of the molecular dynamics method. Accelerated molecular dynamics has been developed as a solution and the parallel replica method is the simplest and most accurate of the accelerated dynamics techniques. The simulation time scale can reach several orders more than the direct molecular dynamics, while retaining the complete atomic detail. In parallel replica method, the entire system is replicated on each of M available parallel or distributed processors, it is in line with the characteristics of high-performance parallel computing and can be optimized from the program level.

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Multi-Agent Reinforcement Learning based Edge Content Caching for Connected Autonomous Vehicles in IoV

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ACM Transactions on Autonomous and Adaptive Systems 1000年

作者： Xiaolong Xu Linjie Gu Muhammad Bilal Maqbool Khan Yiping Wen Guoqiang Liu Yuan Yuan School of Software Jiangsu Province Engineering Research Center of Advanced Computing and Intelligent Services Nanjing University of Information Science and Technology China Changwang School of Honors Nanjing University of Information Science and Technology China Department of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Korea Department of IT and Computer Science Pak-Austria Fachhochschule-Institute of Applied Sciences and Technology Pakistan School of Computer Science and Engineering Hunan University of Science and Technology China School of Software Nanjing University of Information Science and Technology China School of Computer Science and Engineering Beihang University State Key Laboratory of Software Development Environment Zhongguancun Laboratory China

Connected Autonomous Vehicle (CAV) Driving, as a data-driven intelligent driving technology within the Internet of Vehicles (IoV), presents significant challenges to the efficiency and security of real-time data management. The combination of Web3.0 and edge content caching holds promise in providing low-latency data access for CAVs’ real-time applications. Web3.0 enables the reliable pre-migration of frequently requested content from content providers to edge nodes. However, identifying optimal edge node peers for joint content caching and replacement remains challenging due to the dynamic nature of traffic flow in IoV. Addressing these challenges, this article introduces GAMA-Cache, an innovative edge content caching methodology leveraging Graph Attention Networks (GAT) and Multi-Agent Reinforcement Learning (MARL). GAMA-Cache conceptualizes the cooperative edge content caching issue as a constrained Markov decision process. It employs a MARL technique predicated on cooperation effectiveness to discern optimal caching decisions, with GAT augmenting information extracted from adjacent nodes. A distinct collaborator selection mechanism is also developed to streamline communication between agents, filtering out those with minimal correlations in the vector input to the policy network. Experimental results demonstrate that, in terms of service latency and delivery failure, the GAMA-Cache outperforms other state-of-the-art MARL solutions for edge content caching in IoV.

关键词： Content Caching Reinforcement Learning Connected Autonomous Driving

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A coupled excavation and rainfall-induced loess landslide in Liuwanjia Village,Yan'an China: Case study of Middle Pleistocene loess slopes

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IOP Conference Series: Earth and Environmental Science 2019年第1期349卷

作者： Zhongjie Fan Zhenjiang Meng Hongyu Ren Jianbing Peng Xiangjun Pei Linyao Dong Jun Du Hongya Zhang Changjiang River Scientific Research Institute of Changjiang Water Resources Commission Wuhan 430010 Hubei China Research Center on Mountain Torrent and Geologic Disaster Prevention Ministry of Water Resources Wuhan 430010 Hubei China Chengdu University of Technology State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu 610059 China Department of Geological Engineering Chang'an University Xi'an 710054 Shaanxi China

Loess Plateau is one of the natural test sites to carry out the research of hazards induced by human activities. The loess landslide at Liuwanjia, Yan'an City, Shaanxi Province of China was observed to occur after continuous excavation and rainfall. The Liuwanjia landslide was selected as a case study and then detailed field mapping, 3D laser scanning and laboratory tests were carried out to study the formation characteristics and mechanism of this type of landslide. Based on the analysis of field investigation, excavation was a direct causing factor and the rainwater was a significant influencing factor in the formation of the landslide. The unloading fissures caused by excavation were preferential paths for infiltration of rainwater. Soil compression in the slope induced by excavation and the infiltration of rainwater played an significant role in the formation of local failure. Finally, the formation mechanism of Liuwanjia landslide has been discussed.

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Solid‐Oxide Fuel Cells: Recent Progress on Advanced Materials for Solid‐Oxide Fuel Cells Operating Below 500 °C (Adv. Mater. 48/2017)

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Advanced Materials 2017年第48期29卷

作者： Yuan Zhang Ruth Knibbe Jaka Sunarso Yijun Zhong Wei Zhou Zongping Shao Zhonghua Zhu Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM) State Key Laboratory of Materials‐Oriented Chemical Engineering College of Chemical Engineering Nanjing Tech University No. 5 Xin Mofan Road Nanjing 210009 P. R. China School of Mechanical and Mining Engineering The University of Queensland St. Lucia Queensland 4072 Australia Faculty of Engineering Computing and Science Swinburne University of Technology Jalan Simpang Tiga 93350 Kuching Sarawak Malaysia Jiangsu National Synergetic Innovation Center for Advanced Materials (SICAM) State Key Laboratory of Materials‐Oriented Chemical Engineering College of Energy Nanjing Tech University No. 5 Xin Mofan Road Nanjing 210009 P. R. China Department of Chemical Engineering Curtin University Perth Western Australia 6845 Australia School of Chemical Engineering The University of Queensland St. Lucia Queensland 4072 Australia

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

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arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge computing Environment

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ACM Transactions on Sensor Networks 1000年

作者： Yongzhe Jia Bowen Liu Xuyun Zhang Fei Dai Arif Khan Lianyong Qi Wanchun Dou State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University Sydney Australia College of Big Data and Intelligent Engineering Southwest Forestry University Kunming China M3S Empirical Software Engineering Research Unit University of Oulu Oulu Finland College of Computer Science and Technology China University of Petroleum East China - Qingdao Campus Qingdao China

Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.

关键词： Federated learning split learning model pruning edge computing

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Water Splitting: Optical and Electrical Enhancement of Hydrogen Evolution by MoS2@MoO3Core–Shell Nanowires with Designed Tunable Plasmon Resonance (Adv. Funct. Mater. 32/2018)

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Advanced Functional Materials 2018年第32期28卷

作者： Shaohui Guo Xuanhua Li Xingang Ren Lin Yang Jinmeng Zhu Bingqing Wei State Key Laboratory of Solidification Processing Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China Department of Mechanical Engineering University of Delaware Newark DE 19716 USA

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Long-Lived Charge-Transfer state in Spiro Compact Electron Donor–Acceptor Dyads Based on Pyromellitimide-Derived Rhodamine: Charge Transfer Dynamics and Electron Spin Polarization

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Angewandte Chemie 2022年第33期134卷

作者： Xi Chen Dr. Andrey A. Sukhanov Yuxin Yan Damla Bese Cagri Bese Prof. Jianzhang Zhao Prof. Violeta K. Voronkova Dr. Antonio Barbon Prof. Halime Gul Yaglioglu State Key Laboratory of Fine Chemicals School of Chemical Engineering Dalian University of Technology 2 Ling Gong Road Dalian 116024 P. R. China Zavoisky Physical-Technical Institute FRC Kazan Scientific Center of Russian Academy of Sciences Kazan 420029 Russia Department of Engineering Physics Faculty of Engineering Ankara University 06100 Beşevler Ankara Turkey Department of Physics Engineering Hacettepe University 06800 Beytepe Ankara Turkey Dipartimento di Scienze Chimiche Università degli Studi di Padova 35131 Padova Italy

We observed a long-lived charge transfer (CT) state in a novel orthogonal compact electron donor–acceptor dyads, with closed form of rhodamine (Rho) as electron donor and pyromellitimide (PI),or thionated PI, as electron acceptor. The two parts in the dyads are connected via a spiro quaternary carbon atom, thus the torsion between the donor and acceptor is completely inhibited, which is beneficial to reduce the reorganization energy and to exploit the Marcus inverted region effect to prolong the CT state lifetime. Femtosecond transient absorption spectra show that the charge separation is rather fast, while nanosecond transient absorption spectra confirmed the formation of long-lived CT state (2.6 μs). Time-resolved electron paramagnetic resonance (TREPR) spectra determined the spin multiplicity of the long living state and assigned it to a 3 CT state. Replacement of an oxygen atom in the PI part with a sulfur atom favoring classical intersystem crossing processes, causes a consistently shortening of the lifetime of the 3 CT state (0.29 μs).

关键词： Charge Transfer Charge-Separated state Electron Transfer Intersystem Crossing Triplet state

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Folding DNA into a Lipid‐Conjugated Nanobarrel for Controlled Reconstitution of Membrane Proteins

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Angewandte Chemie 2017年第8期130卷

作者： Yuanchen Dong Shuobing Chen Shijian Zhang Joseph Sodroski Zhongqiang Yang Dongsheng Liu Youdong Mao Department of Cancer Immunology and Virology Dana-Farber Cancer Institute Department of Microbiology and Immunobiology Harvard Medical School Boston MA 02115 USA Intel Parallel Computing Center for Structural Biology Dana-Farber Cancer Institute Boston MA 02215 USA State Key Laboratory for Artificial Microstructures and Mesoscopic Physics Institute of Condensed Matter and Material Physics School of Physics and Center for Quantitative Biology Peking University Beijing 100871 China Key Laboratory of Organic Optoelectronics & Molecular Engineering of the Ministry of Education Department of Chemistry Tsinghua University Beijing 100084 China

Building upon DNA origami technology, we introduce a method to reconstitute a single membrane protein into a self‐assembled DNA nanobarrel that scaffolds a nanodisc‐like lipid environment. Compared with the membrane‐scaffolding‐protein nanodisc technique, our approach gives rise to defined stoichiometry, controlled sizes, as well as enhanced stability and homogeneity in membrane protein reconstitution. We further demonstrate potential applications of the DNA nanobarrels in the structural analysis of membrane proteins.

关键词： DNA-Nanotechnologie DNA-Origami Einzelpartikelanalyse Kryo-Elektronenmikroskopie Membranprotein-Rekonstitution

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