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21st IEEE International Conference on High Performance computing and Communications, 17th IEEE International Conference on Smart City and 5th IEEE International Conference on data Science and Systems, HPCC/SmartCity/DSS 2019

作者： Zhu, Xiaomin Zeng, Yunhui Wei, Yanjie Feng, Shengzhong Liu, Weiguo Balaji, Pavan Joint Engineering Research Center Health Big Data Intelligent Analysis Technology Center for High Performance Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Shandong Computer Science Center National SuperComputer Center in Jinan Jinan China Sclool of Software Shandong University Jinan China Argonne National Laboratory United States

ISBN: (纸本)9781728120584

Stencil is a basic building block widely used in many HPC areas and applications. It generally dominates the time cost and is critical to the overall performance. Given that heterogeneous many-core is frequently adopted to build super computers, porting and optimizing stencil codes on modern accelerator-based architectures is important. Porting is non-trivial, and optimization is more difficult as it requires better understanding of the underlying architecture. As a result, auto tuning targeting on accelerators such as GPU becomes a hot research topic. In this paper, we focus on tuning stencil automatically on SW26010, which is a heterogeneous many-core CPU equipped in Sunway TaihuLight SuperComputer, and mainly work on 2D stencil variants code in ROMS (Regional Ocean Modeling System). The code generator can generate codes running on slave cores for 50+ different FORTRAN stencil-alike loops in ROMS in seconds, and optimization methods are integrated into the generator as well. The performance is better than the directive based method (OpenACC) and the hand-drafted code by a junior programmer, and it is comparable compared with the code written by a senior programmer with several years of programming experience. © 2019 IEEE.

关键词： Supercomputers

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Rely-Guarantee Reasoning about Messaging System for Autonomous Vehicles

Rely-Guarantee Reasoning about Messaging System for Autonomo...

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Theoretical Aspects of Software Engineering (TASE)

作者： Wenjing Xu Yongwang Zhao Dianfu Ma YuXin Zhang Qian Xiao State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang Univerisity Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing China State Key Laboratory of Automotive Simulation and Control College of Automotive Engineering Jilin University Jilin China School of Computer Science and Technology Guizhou University Guiyang China

Messaging system as a communicating infrastructure is a safety-critical component of autonomous vehicles. For the purpose of safety certification of Level 4 autonomous driving systems, its necessary to provide a formally verified specification of messaging systems. This paper presents a realistic case study using the PiCore rely-guarantee framework to formally verify the DGPS (Differential Global Positioning System) of UISEE autonomous driving systems. We first create an axiom model of messaging systems by extending PiCore, which is reusable for concrete applications. The model supports dynamic configuration of message buffers as well as the automatic generation of the rely and guarantee conditions for compositional reasoning. It is instantiated when developing the formal specification of DGPS. Then, we use the rely-guarantee proof system of PiCore to verify the functional correctness and invariant of DGPS. The specification and its proof provide a strong evidence for the ongoing safety certification of DGPS.

关键词： Cognition Safety Formal specifications Vehicle dynamics Autonomous vehicles Certification Global Positioning System

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Adversarial directed graph embedding

arXiv

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arXiv 2020年

作者： Zhu, Shijie Li, Jianxin Peng, Hao Wang, Senzhang He, Lifang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China State Key Laboratory of Software Development Environment Beihang University Beijing100191 China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Department of Computer Science and Engineering Lehigh University BethlehemPA United States

Node representation learning for directed graphs is critically important to facilitate many graph mining tasks. To capture the directed edges between nodes, existing methods mostly learn two embedding vectors for each node, source vector and target vector. However, these methods learn the source and target vectors separately. For the node with very low indegree or outdegree, the corresponding target vector or source vector cannot be effectively learned. In this paper, we propose a novel Directed Graph embedding framework based on Generative Adversarial Network, called DGGAN. The main idea is to use adversarial mechanisms to deploy a discriminator and two generators that jointly learn each node's source and target vectors. For a given node, the two generators are trained to generate its fake target and source neighbor nodes from the same underlying distribution, and the discriminator aims to distinguish whether a neighbor node is real or fake. The two generators are formulated into a unified framework and could mutually reinforce each other to learn more robust source and target vectors. Extensive experiments show that DGGAN consistently and significantly outperforms existing state-of-the-art methods across multiple graph mining tasks on directed graphs. Copyright © 2020, The Authors. All rights reserved.

关键词： Directed graphs

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CrypToth: Cryptic Pocket Detection through Mixed-Solvent Molecular Dynamics Simulations-Based Topological data Analysis

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Journal of chemical information and modeling 2025年第11期65卷 2025 May 22页

作者： Jun Koseki Chie Motono Keisuke Yanagisawa Genki Kudo Ryunosuke Yoshino Takatsugu Hirokawa Kenichiro Imai Cellular and Molecular Biotechnology Research Institute National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan. Integrated Research Center for Self-Care Technology (irc-sct) National Institute of Advanced Industrial Science and Technology (AIST) Tokyo 135-0064 Japan. Computational Bio Big-Data Open Innovation Laboratory (CBBD-OIL) National Institute of Advanced Industrial Science and Technology (AIST) Waseda University Shinjuku-ku Tokyo 169-8555 Japan. Department of Computer Science School of Computing Institute of Science Tokyo Tokyo 152-8550 Japan. Middle Molecule IT-based Drug Discovery Laboratory (MIDL) Institute of Science Tokyo Tokyo 152-8550 Japan. Physics Department Graduate School of Pure and Applied Sciences University of Tsukuba Ibaraki 305-8571 Japan. Division of Biomedical Science Faculty of Medicine University of Tsukuba Ibaraki 305-8575 Japan. Transborder Medical Research Center University of Tsukuba Ibaraki 305-8577 Japan. Global Research and Development Center for Business By Quantum-AI Technology (G-QuAT) National Institute of Advanced Industrial Science and Technology (AIST) Ibaraki 305-8560 Japan.

Some functional proteins undergo conformational changes to expose hidden binding sites when a binding molecule approaches their surface. Such binding sites are called cryptic sites and are important targets for drug discovery. However, it is still difficult to correctly predict cryptic sites. Therefore, we introduce an advanced method, CrypToth, for the precise identification of cryptic sites utilizing the topological data analysis such as persistent homology method. This method integrates topological data analysis and mixed-solvent molecular dynamics (MSMD) simulations. To identify hotspots corresponding to cryptic sites, we conducted MSMD simulations using six probes with different chemical properties: dimethyl ether, benzene, phenol, methyl imidazole, acetonitrile, and ethylene glycol. Subsequently, we applied our topological data analysis method to rank hotspots based on the possibility of harboring cryptic sites. Evaluation of CrypToth using nine target proteins containing well-defined cryptic sites revealed its superior performance compared with recent machine-learning methods. As a result, in seven of nine cases, hotspots associated with cryptic sites were ranked the highest. CrypToth can explore hotspots on the protein surface favorable to ligand binding using MSMD simulations with six different probes and then identify hotspots corresponding to cryptic sites by assessing the protein's conformational variability using the topological data analysis. This synergistic approach facilitates the prediction of cryptic sites with a high accuracy.

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Transferring knowledge distillation for multilingual social event detection

arXiv

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arXiv 2021年

作者： Ren, Jiaqian Peng, Hao Jiang, Lei Wu, Jia Tong, Yongxin Wang, Lihong Bai, Xu Wang, Bo Yang, Qiang Institute of Information Engineering Chinese Academy of Sciences The School of Cyber Security University of Chinese Academy of Sciences Beijing100041 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100083 China The School of Cyber Science and Technology Beihang University Beijing100083 China Institute of Information Engineering Chinese Academy of Sciences Beijing100041 China The Department of Computing Macquarie University Sydney Australia The State Key Laboratory of Software Development Environment The School of Computer Science and Engineering Beihang University Beijing100083 China The National Computer Network Emergency Response Technical Team Coordination Center of China Beijing100029 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong AI Group WeBank Co. Ltd. China

Recently published graph neural networks (GNNs) show promising performance at social event detection tasks. However, most studies are oriented toward monolingual data in languages with abundant training samples. This has left the more common multilingual settings and lesser-spoken languages relatively unexplored. Thus, we present a GNN that incorporates cross-lingual word embeddings for detecting events in multilingual data streams. The first exploit is to make the GNN work with multilingual data. For this, we outline a construction strategy that aligns messages in different languages at both the node and semantic levels. Relationships between messages are established by merging entities that are the same but are referred to in different languages. Non-English message representations are converted into English semantic space via the cross-lingual word embeddings. The resulting message graph is then uniformly encoded by a GNN model. In special cases where a lesser-spoken language needs to be detected, a novel cross-lingual knowledge distillation framework, called CLKD, exploits prior knowledge learned from similar threads in English to make up for the paucity of annotated data. Experiments on both synthetic and real-world datasets show the framework to be highly effective at detection in both multilingual data and in languages where training samples are scarce. © 2021, CC BY.

关键词： Embeddings

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Surface Modification of (001) Facets Dominated TiO2 with Ozone for Adsorption and Photocatalytic Degradation of Gaseous Toluene

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Chinese Journal of Chemical Physics 2019年第5期32卷 611-619页

作者： Yue Yang Zhi-yu Wang Fan Zhang Yi Fan Jing-jing Dong Song Sun Chen Gao Jun Bao National Synchrotron Radiation Laboratory Collaborative Innovation Center of Chemistry for Energy Materials Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutes University of Science and Technology of China Hefei 230029 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Chemistry and Chemical Engineering Anhui University Hefei 230601 China Beijing Advanced Sciences and Innovation center Chinese Academy of Sciences Beijing 101407 China

This study investigated the positive effect of surface modification with ozone on the photocatalytic performance of anatase TiO2 with dominated（001） facets for toluene *** performance of photocatalyst was tested on a home-made volatile organic compounds degradation system. The ozone modification, toluene adsorption and degradation mechanism were established by a combination of various characterization methods, in situ diffuse reflectance infrared fourier transform spectroscopy, and density functional theory *** surface modification with ozone can significantly enhance the photocatalytic degradation performance for toluene. The abundant unsaturated coordinated 5 c-Ti sites on（001）facets act as the adsorption sites for ozone. The formed Ti–O bonds reacted with H2O to generate a large amount of isolated Ti5 c-OH which act as the adsorption sites for toluene,and thus significantly increase the adsorption capacity for toluene. The outstanding photocatalytic performance of ozone-modified TiO2 is due to its high adsorption ability for toluene and the abundant surface hydroxyl groups, which produce very reactive OH·radicals under irradiation. Furthermore, the O2 generated via ozone dissociation could combine with the photogenerated electrons to form superoxide radicals which are also conductive to the toluene degradation.

关键词： Ozone modification (001) Facets Toluene degradation TiO2 In situ diffuse reflectance infrared fourier transform spectroscopy

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Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19

arXiv

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arXiv 2020年

作者： Shi, Feng Wang, Jun Shi, Jun Wu, Ziyan Wang, Qian Tang, Zhenyu He, Kelei Shi, Yinghuan Shen, Dinggang Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 United Imaging Intelligence CambridgeMA02140 United States Institute for Medical Imaging Technology School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing210093 China National Key Laboratory for Novel Software and Technology Nanjing University Nanjing China

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI ca n improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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KE-GAN: Knowledge Embedded Generative Adversarial Networks for Semi-Supervised Scene Parsing

KE-GAN: Knowledge Embedded Generative Adversarial Networks f...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Mengshi Qi Yunhong Wang Jie Qin Annan Li State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Inception Institute of Artificial Intelligence UAE

In recent years, scene parsing has captured increasing attention in computer vision. Previous works have demonstrated promising performance in this task. However, they mainly utilize holistic features, whilst neglecting the rich semantic knowledge and inter-object relationships in the scene. In addition, these methods usually require a large number of pixel-level annotations, which is too expensive in practice. In this paper, we propose a novel Knowledge Embedded Generative Adversarial Networks, dubbed as KE-GAN, to tackle the challenging problem in a semi-supervised fashion. KE-GAN captures semantic consistencies of different categories by devising a Knowledge Graph from the large-scale text corpus. In addition to readily-available unlabeled data, we generate synthetic images to unveil rich structural information underlying the images. Moreover, a pyramid architecture is incorporated into the discriminator to acquire multi-scale contextual information for better parsing results. Extensive experimental results on four standard benchmarks demonstrate that KE-GAN is capable of improving semantic consistencies and learning better representations for scene parsing, resulting in the state-of-the-art performance.

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Distributed State Estimation for Heterogeneous Mobile Sensor Networks with Sensor Faults

Distributed State Estimation for Heterogeneous Mobile Sensor...

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第三十八届中国控制会议

作者： Yingrong Yu Siting Peng Qingdong Li Xiwang Dong Zhang Ren Science and Technology on Aircraft Control Laboratory School of Automation Science and Electrical Engineering Beihang University Beijing Electro-Mechanical Engineering Institute Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Consensus-based distributed state estimation for homogeneous mobile sensor networks has been widely studied currently. However, the sensors used for target detection and tracking in practical applications usually have different physical properties, different measuring range or accuracy. In order to realize data exchange between sensors which are of heterogeneity and to obtain a consistent and precise target state estimation result, an improved Cubature Kalman Filtering(CKF) method based on weighted average consensus theory is put forward herein. Moreover, the adverse impacts caused by sensor faults have been taken into consideration in modeling. The stochastic boundedness of the estimation errors for the discussed sensor networks is proved in the paper. Finally, numerical simulations are provided to demonstrate the validity of the proposed method.

关键词： Cubature Kalman Filter weighted average consensus theory target state estimation mobile sensor networks stochastic boundedness

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Practical adaptive time-varying output formation tracking for high-order nonlinear multi-agent systems using neural networks

Practical adaptive time-varying output formation tracking fo...

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第三十八届中国控制会议

作者： Jianglong Yu Xiwang Dong Qingdong Li Zhang Ren Ming Ma School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beihang University Shananxi Key Laboratory of Integrated and Intelligent Navigation Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Xi'an Research Institute of Navigation Technology

Practical adaptive time-varying output formation tracking problems for high-order nonlinear strict-feedback multiagent systems are investigated in this paper. The time-varying output formation tracking requires the followers' output vectors to track the output vector of the leader while achieving a time-varying formation. The dynamics of the leader and the followers can have uncertain mismatched nonlinearities while the leader's control input is also unknown. To deal with the mismatched nonlinearities, the backstepping techniques are utilized to construct the protocols. Firstly, visual controllers are designed to overcome the effects of the uncertainties while guaranteeing the stability of each layer. The complicated visual controllers are estimated by the neural networks. A full adaptive protocol is constructed with adaptive gains to avoid using the global information of the interaction topology. Then, an algorithm is presented to design the protocol, whose parameters are much less. Besides, the stability of the algorithm is proved by Lyapunov theories. Finally, simulation results are presented to prove the effectiveness.

关键词： Practical time-varying formation tracking Adaptive control High-order nonlinear dynamics Multi-agent systems Neural networks

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