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lMFF: Efficient and Scalable layered Materials Force Field on Heterogeneous Many-Core Processors

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lMFF: Efficient and Scalable layered Materials Force Field o...

Supercomputing Conference

作者： Ping Gao Xiaohui Duan Jiaxu Guo Jin Wang Zhenya Song Lizhen Cui Xiangxu Meng Xin Liu Wusheng Zhang Ming Ma Guohui Li Dexun Chen Haohuan Fu Wei Xue Weiguo Liu Guangwen Yang School of Software Shandong University Jinan China National Supercomputing Center in Wuxi Wuxi China Department of Computer Science and Technology Tsinghua University Beijing China College of Computer Science and Technology Jilin University Changchun China Department of Engineering Mechanics Center for Nano and Micro Mechanics Tsinghua University Beijing China Ministry of Natural Resources First Institute of Oceanography and Key Laboratory of Marine Science and Numerical Modeling Qingdao China Joint SDU-NTU Centre for Artificial Intelligence Research (C-FAIR) Jinan China Engineering Research Center of Digital Media Technology Ministry of Education Jinan China Dalian Institute of Chemical Physics Chinese Academy of Sciences Dalian China Department of Earth System Science Ministry of Education Key Lab for Earth System Modeling Tsinghua University Beijing China

ISBN: (数字)9781450384421

ISBN: (纸本)9781665483902

LAMMPS is one of the most popular Molecular Dynamic (MD) packages and is widely used in the field of physics, chemistry and materials simulation. Layered Materials Force Field (LMFF) is our expansion of the LAMMPS potential function based on the Tersoff potential and inter-layer potential (ILP) in LAMMPS. LMFF is designed to study layered materials such as graphene and boron hexanitride. It is universal and does not depend on any platform. We have also carried out a series of optimizations on LMFF and the optimization work is carried out on the new generation of Sunway supercomputer, called SWLMFF. Experiments show that our implementation is efficient, scalable and portable. When generic LMFF is ported to Intel Xeon Gold 6278C, $2\times$ performance improvement is achieved. For the optimized SWLMFF, the overall performance improvement is nearly $200-330\times$ compared to the original ILP and Tersoff potentials. And SWLMFF has good parallel efficiency of 95%-100% under weak scaling with 2.7 million atoms on a single process. The maximum atomic system simulated by SWLMFF is close to $2^{31}$ atoms. And nanosecond simulations in one day can be realized.

关键词： Program processors Atomic layer deposition High performance computing Force Graphene Supercomputers Optimization

An Access Control Mechanism Based on Risk Prediction for the IoV

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An Access Control Mechanism Based on Risk Prediction for the...

IEEE Conference on Vehicular Technology (VTC)

作者： Yuanni Liu Man Xiao Yanyan Zhou Di Zhang Jianhui Zhang Haris Gacanin Jianli Pan Chongqing University of Posts and Telecommunications Chongqing China Zhengzhou University Zhengzhou China National Digital Switching System Engineering & Technological R&D Center China Nokia Bell Labs Antwerp Belgium Department of Mathematics and Computer Science University of Missouri-St.Louis MO USA

ISBN: (数字)9781728152073

ISBN: (纸本)9781728140537

The information sharing among vehicles provides intelligent transport applications in the Internet of Vehicles (IoV), such as self-driving and traffic awareness. However, due to the openness of the wireless communication (e.g., DSRC), the integrity, confidentiality and availability of information resources are easy to be hacked by illegal access, which threatens the security of the related IoV applications. In this paper, we propose a novel Risk Prediction-Based Access Control model, named RPBAC, which assigns the access rights to a node by predicting the risk level. Considering the impact of limited training datasets on prediction accuracy, we first introduce the Generative Adversarial Network (GAN) in our risk prediction module. The GAN increases the items of training sets to train the Neural Network, which is used to predict the risk level of vehicles. In addition, focusing on the problem of pattern collapse and gradient disappearance in the traditional GAN, we develop a combined GAN based on Wasserstein distance, named WCGAN, to improve the convergence time of the training model. The simulation results show that the WCGAN has a faster convergence speed than the traditional GAN, and the datasets generated by WCGAN have a higher similarity with real datasets. Moreover, the Neural Network (NN) trained with the datasets generated by WCGAN and real datasets (NN-WCGAN) performs a faster speed of training, a higher prediction accuracy and a lower false negative rate than the Neural Network trained with the datasets generated by GAN and real datasets (NN-GAN), and the Neural Network trained with the real datasets (NN). Additionally, the RPBAC model can improve the accuracy of access control to a great extent.

关键词： Gallium nitride Generative adversarial networks Generators Access control Training Predictive models Artificial neural networks

TiEV: The tongji intelligent electric vehicle in the intelligent vehicle future challenge of China

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arXiv 2018年

作者： Zhao, Junqiao Ye, Chen Wu, Yan Guan, Linting Cai, Lewen Sun, Lu Yang, Tao He, Xudong Li, Jun Ding, Yongchao Zhang, Xinglian Wang, Xinchen Huang, Jinlin Zhang, Enwei Huang, Yewei Jiang, Wei Zhang, Shaoming Xiong, Lu Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of electrics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

TiEV is an autonomous driving platform implemented by the Tongji University of China. The vehicle is drive-by-wire and is fully powered by electricity. We devised the software system of TiEV from scratch, which is capable of driving the vehicle autonomously in urban paths as well as on fast express roads. We describe our whole system, especially novel modules of probabilistic perception fusion, large-scale mapping and updating, the 1st and the 2nd planning and the overall safety concern. TiEV finished 2016, and 2017 Intelligent Vehicle Future Challenge of China held at Changshu. We show our experiences on the development of autonomous vehicles and discuss the future trends. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

Evaluating the Performance to Detecting Topic Shift of Large Language Models: A Study

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Evaluating the Performance to Detecting Topic Shift of Large...

6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Pingfang Tian Deting Liu Department of Computer Science and Technology Wuhan University of Science and Technology Wuhan China The Key Laboratory of the Rich Media Knowledge Organisation and Service of Digital Publishing Content Institute of Scientific and Technical Information of China Beijing China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan China Big Data Science and Engineering Research Institute Wuhan University of Science and Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Topic shift detection aims to identify whether there is a change in the current topic of conversation or if a change is needed. The study found previous work did not evaluate the performance of large language models like ChatGPT on the task of topic shift. Therefore, this paper's main task and innovation lie in analyzing ChatGPT's performance on topic shift detection. To provide a more comprehensive evaluation, we conducted topic shift detection tasks on ChatGPT from three aspects: single utterances, adjacent utterances, and contextual levels. Additionally, to gauge the performance of large language models, we conducted experiments on multiple small-scale models and compared the results of the two models. Experimental results on the publicly available English TIAGE dataset showed that small-scale models exhibited lower recall in all three aspects, while ChatGPT performed better in the recall. This suggests that compared to small-scale models, large models are more capable of accurately detecting topic shifts. However, large models also exhibited lower precision, indicating that while ChatGPT can recognize content differences in utterances, its judgment on whether these different contents belong to the same topic is poor.

关键词： Technological innovation Large language models Oral communication Signal processing Chatbots Context modeling

Automatic vector-based road structure mapping using multi-beam lidar

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arXiv 2018年

作者： He, Xudong Zhao, Junqiao Sun, Lu Huang, Yewei Zhang, Xinglian Li, Jun Ye, Chen Key Laboratory of Embedded System and Service Computing Tongji University Ministry of Education Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China School of Automotive Studies Tongji University Shanghai China

In this paper, we studied a SLAM method for vector-based road structure mapping using multi-beam LiDAR. We propose to use the polyline as the primary mapping element instead of grid cell or point cloud, because the vector-based representation is precise and lightweight, and it can directly generate vector-based High-Definition (HD) driving map as demanded by autonomous driving systems. We explored: 1) the extraction and vectorization of road structures based on local probabilistic fusion. 2) the efficient vector-based matching between frames of road structures. 3) the loop closure and optimization based on the pose-graph. In this study, we took a specific road structure, the road boundary, as an example. We applied the proposed matching method in three different scenes and achieved the average absolute matching error of 0.07 m. We further applied the mapping system to the urban road with the length of 860 meters and achieved an average global accuracy of 0.466 m without the help of high precision GPS. Copyright © 2018, The Authors. All rights reserved.

关键词： Mapping

DSC: Deep scan context descriptor for large-scale place recognition

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arXiv 2021年

作者： Cui, Jiafeng Huang, Tengfei Cai, Yingfeng Zhao, Junqiao Xiong, Lu Yu, Zhuoping School of Automobile Tongji University Shanghai China Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Institute of Intelligent Vehicles Tongji University Shanghai China

LiDAR-based place recognition is an essential and challenging task both in loop closure detection and global relocalization. We propose Deep Scan Context (DSC), a general and discriminative global descriptor that captures the relationship among segments of a point cloud. Unlike previous methods that utilize either semantics or a sequence of adjacent point clouds for better place recognition, we only use raw point clouds to get competitive results. Concretely, we first segment the point cloud egocentrically to acquire centroids and eigenvalues of the segments. Then, we introduce a graph neural network to aggregate these features into an embedding representation. Extensive experiments conducted on the KITTI dataset show that DSC is robust to scene variants and outperforms existing methods. © 2021, CC BY.

关键词： Eigenvalues and eigenfunctions

The Relationship between Diagnostic Coverage and Proof Test Interval for 1oo2 and 1oo2D Architectures

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IFAC Proceedings Volumes 2012年第24期45卷 181-184页

作者： İlker üstoğlu özgür T. Kaymakçı Josef Börcsök Yıldız Technical University Davutpaşa Campus A209 34220 34220 Esenler İstanbul TURKEY (Tel: +90-535-418-22-64 ) Yıldız Technical University Davutpaşa Campus A210 34220 34220 Esenler İstanbul TURKEY University Kassel Faculty of Electrical Engineering/Computer Science Department of Computer Architecture and System Programming Wilhelmshöher Allee 71 34121 Kassel GERMANY

In order to measure the effectiveness of safety protection systems there are several design parameters. Diagnostic coverage factor is one of the most important parameter which influences all architectures. In this short paper the relationship between PFD avg , DC and T I are presented. 1oo2 and 1oo2D architectures are considered as examples.

关键词： Functional safety 1002 architecture

Design and implimentation of a real-time platform for studying interaction behaviors of scaled-down autonomous vehicles

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Beijing Ligong Daxue Xuebao/Transaction of Beijing Institute of Technology 2015年 35卷 185-189页

作者： Wang, Xiao-Dong He, Wen Liu, Yu-Chao Guo, Mu Zhang, Tian-Lei Han, Peng Department of Computer Science and Technology Tsinghua University Beijing100081 China Institute of China Electronic System Engineering Corporation Beijing100141 China Chongqing Science and Technology Research Institute Chongqing402699 China

Autonomous vehicle was considered to be one of the toughest challenges in the fields of artificial intelligent, pattern recognition, computer vision, sensor fusion and control theory. In the paper, a kind of scaled-down autonomous vehicles was demonstrated and a more human-like environment perception and control system were proposed, including obstacle detection, combined feature-based and model-based lane mark detection algorithm. To verify the efficiency of the proposed perception and control system, a test platform was built. With the platform, the interaction behaviors between autonomous vehicles were studied. © 2015, Editorial department of Transaction of Beijing Institute of Technology. All right reserved.

关键词： Fuzzy control

A PERSONALIZED MOOC LEARNING GROUP AND COURSE RECOMMENDATION METHOD BASED ON GRAPH NEURAL NETWORK AND SOCIAL NETWORK ANALYSIS

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arXiv 2024年

作者： Luo, Zijin Wang, Xu Wang, Yiquan Zhang, Haotian Li, Zhuangzhuang College of Mathematics Jilin University Jilin Changchun130000 China College of Communication Engineering Jilin University Jilin Changchun130000 China College of Mathematics and System Science Xinjiang University Urumqi Xinjiang830046 China College of Computer Science and Technology Jilin University Jilin Changchun130000 China Department of Electronic Engineering Tsinghua University Beijing100084 China Shenzhen X-Institute Shenzhen518055 China

In order to enhance students’ initiative and participation in MOOC learning, this study constructed a multi-level network model based on Social Network Analysis (SNA). The model makes use of data pertaining to nearly 40,000 users and tens of thousands of courses from various higher education MOOC platforms. Furthermore, an AI-based assistant has been developed which utilises the collected data to provide personalised recommendations regarding courses and study groups for students. The objective is to examine the relationship between students’ course selection preferences and their academic interest levels. Based on the results of the relationship analysis, the AI assistant employs technologies such as GNN to recommend suitable courses and study groups to students. This study offers new insights into the potential of personalised teaching on MOOC platforms, demonstrating the value of data-driven and AI-assisted methods in improving the quality of online learning experiences, increasing student engagement, and enhancing learning outcomes. © 2024, CC BY-NC-SA.

关键词： Contrastive Learning