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Engineering 2023年第5期24卷 126-137,I0003,I0004页

作者： Can Leng Zhuo Tang Yi-Ge Zhou Zean Tian Wei-Qing Huang Jie Liu Keqin Li Kenli Li Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense TechnologyChangsha 410073China Laboratory of Software Engineering for Complex Systems National University of Defense TechnologyChangsha 410073China National Supercomputing Center in Changsha Changsha 410082China College of Computer Science and Electronic Engineering Hunan UniversityChangsha 410082China Institute of Chemical Biology and Nanomedicine State Key Laboratory of Chemo/Biosensing and ChemometricsCollege of Chemistry and Chemical EngineeringHunan UniversityChangsha 410082China Department of Applied Physics School of Physics and ElectronicsHunan UniversityChangsha 410082China Department of Computer Science State University of New YorkNew PaltzNY 12561USA

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地... 详细信息

科学正在进入一个新时代——第五范式——它被认为是知识整合到不同领域的主要特征,是基于无所不在的机器学习系统的计算社区中智能驱动的工作。在此,我们通过在天河一号超级计算机系统上构建的催化材料专门设计的典型平台案例,生动地阐明了第五范式的本质,旨在促进第五范式在其他领域的培养。第五范式平台主要包括模型自动构建(原始数据提取)、指纹自动构建(神经网络特征选择)以及跨学科知识串联的重复迭代(“火山图”)。与分解一起进行的是对迭代中实现的体系结构的性能评估。通过讨论,第五范式的智能驱动平台可以极大地简化和改进研究中极其繁琐和具有挑战性的工作,并通过补偿机器学习中缺少样本和替代一些由于计算资源不足而导致的数值计算来实现数值计算与机器学习之间的相互反馈,从而加速探索过程。在数据驱动的学科中,跨学科专家的协同作用和对动态数据需求的急剧增长仍然是一个挑战。我们相信,对第五范式平台的一瞥可以为其在其他领域的应用铺平道路。

关键词：机器学习自动构建天河一号数据驱动神经网络动态数据知识整合科学研究

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A Complexity-Reduced Block-Selective Algebraic Multigrid Method for Implicitly Coupled Velocity-Pressure System on High Performance Computers

SSRN

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SSRN 2023年

作者： Liang, Yuechao Guo, Xiao-Wei Li, Chao Yuan, Fan Song, Min Zhang, Qingyang Chen, Xinhai Liu, Jie Science and Technology on Parallel and Distributed Processing Laboratory National University of Defense Technology Changsha410073 China Laboratory of Digitizing Software for Frontier Equipment National University of Defense Technology Changsha410073 China Institute for Quantum Information State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha410073 China School of Mathematics and Computational Sciences Xiangtan University Xiangtan411105 China College of Computer Science and Technology National University of Defense Technology Changsha410073 China

The implicitly coupled pressure-based algorithm is widely acknowledged for its superior convergence and robustness in solving incompressible flow problems. However, the increased expansion scale of equations and difficulty in solving the resulting equation system has limited the widespread use of the coupling algorithm. This paper presents an optimized block-SAMG method, referred to as Red-SAMG, to significantly reduce computational complexity and memory consumption. Our approach employs a parallel modified independent set algorithm, allowing each process to perform matrix coarsening individually. An aggressive coarsening strategy further reduces complexity to address larger-scale problems. We conducted numerical experiments on a serial two-dimensional backward-facing step case and a parallel three-dimensional tunnel between rod bundles in the reactor core problem. The results demonstrate that the Red-SAMG method with aggressive coarsening reduces solution time by 12\% on the backward-facing step case. For large-scale three-dimensional problems under the same parallel conditions, the Red-SAMG method shortens the solution time by 14\% compared to the existing SAMG method. The Red-SAMG method with aggressive coarsening further reduces solution time by 49\%. Our optimized Red-SAMG method effectively reduces the complexity of the linear system arising from the implicitly coupled algorithm. The aggressive coarsening strategy also represents a significant advancement in improving the computational efficiency of the SAMG method. © 2023, The Authors. All rights reserved.

关键词： Coarsening

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Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

Not All Image Regions Matter: Masked Vector Quantization for...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Mengqi Huang Zhendong Mao Quan Wang Yongdong Zhang University of Science and Technology of China Hefei China Institute of Artificial intelligence Hefei Comprehensive National Science Center Hefei China MOE Key Laboratory of Trustworthy Beijing University of Posts and Telecommunications Distributed Computing and Service Beijing China

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage's autoregressive model's ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQVAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https://***/CrossmodalGroup/MaskedVectorQuantization.

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A New Recursive Approach for Reconstructing Metabolic Networks from KEGG

A New Recursive Approach for Reconstructing Metabolic Networ...

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Japan-China Joint Workshop on Frontier of Computer Science and Technology (FCST)

作者： Tingting Zhou Samuel K.F. Yung Zhenghua Wang Yunping Zhu National Laboratory of Paralleling and Distributed Processing National University of Defense Technology Changsha Hunan China Department of Computing Hong Kong Polytechnic University Hung Hom Hong Kong China State Key Laboratory of Proteomics Beijing Proteome Research Center Beijing Institute of Radiation Medicine Beijing China

The high-quality network reconstruction from metabolites, enzymes and reactions is the first essential step for the studies on the metabolic networks. Previously the approach based on the KEGG/LIGAND database can only reconstruct the organism-level metabolic networks and the network quality is quite doubtful. In this paper we propose a new recursive approach to reconstruct metabolic networks. The data selection mechanism of KEGG/PATHWAY ensures that metabolic networks are parsimonious but reliable. The KO system makes it possible to generate networks hierarchically. The KEGG web service manages to keep the data up-to-date. Results of comparison with the previous approach show our new approach is practical and reliable.

关键词： Biochemistry Organisms Databases Genomics Bioinformatics Laboratories Web services distributed processing Computer networks Biological systems

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A microservice based generic consensus engine for BaaS

A microservice based generic consensus engine for BaaS

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IEEE International Conference on Industrial Technology (ICIT)

作者： Huiyang Li Xu Zhang Jing Li Dongbin Wang Beijing University of Posts and Telecommunications Beijing China School of Cyber Security National Engineering Laboratory for Mobile Network Security Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education Beijing Tongbang Zhuoyi Technology Co. Ltd

ISBN: (纸本)9781728119496

Blockchain-as-a-Service (BaaS) has gradually serve as an infrastructure, providing underlying blockchain services for the rapid deployment and transaction on-chain of large-scale enterprise application scenarios. By hosting different blockchains in its underlying structure layers, BaaS platform satisfies the requirements of consensus algorithms in different scenarios, and still uses its own consensus algorithms in different blockchains, which has limited scalability and is difficult to satisfy the requirements of future applications. To deal with such problems, MGCE, a microservice based generic consensus engine for BaaS is proposed in this paper. It is a functional framework based on microservices architecture which can load the appropriate consensus algorithm and perform the consensus process of transactions for blockchain nodes according the different scenarios. The engine is composed of Consensus Node Group, Consensus Management and four functional modules, namely Consensus Network, Transactions Pool, Consensus Protocol and Consensus Outcome. For microservice purpose, each functional module is deployed into a independently project, running in its own process, which is easy to modify and test, and interacts with other modules through the http APIs to complete the consensus process together. To test the function and performance of this engine, three common consensus algorithms have been implemented including Raft, BFT-SMART and PoW. Through experiments, it is proved that this engine could realize the loading and use of different consensus algorithms. Through the performance comparison between using the engine and with the direct use of each consensus algorithm, the results show that the overhead introducing the engine is acceptable. Through the operation result of three algorithms in different projects, the design of engine architecture could satisfy the microservice objective.

关键词： Costs Scalability Loading Microservice architectures Consensus algorithm Consensus protocol Engines

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Enhancement for Versatile Video Coding in Vehicular Ad Hoc Network Scenarios

Enhancement for Versatile Video Coding in Vehicular Ad Hoc N...

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International Symposium on Communications and Information Technologies (ISCIT)

作者： Jiacheng Wu Jiaqi Zou Songlin Sun School of Information and Communication Engineering Beijing University of Posts and Telecommunications (BUPT) Beijing China Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education China National Engineering Laboratory for Mobile Network Security Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350353952

ISBN: (纸本)9798350353969

Vehicular Ad hoc Network (VANET) allows infor-mation exchange between vehicles. In VANET, video is the most recommended medium for transmitting information. However, the characteristics of VANET and the requirements of video content in terms of quality have resulted in various challenges. Besides, the movements of vehicles introduce complex motions in videos, making them more difficult to compress. As a result, it is a challenging task to transmit as less bits of video streams as possible while maintaining their visual quality. Therefore, we analyze Affine Motion Estimation (AME), a new coding tool in the latest generation video coding standard H.266NV C. Subsequently, a fast algorithm is proposed to make it able to describe complex motions such as rotation, zoom, shear, etc but also with low complexity. Experimental results show that the algorithm reduces the encoding complexity of AME by 20.19% and the overall encoding complexity by 3.25% compared to anchor VTM21.0, with eligible coding performance loss under Random Access (RA) configurations.

关键词： Video coding Visualization Motion estimation Vehicular ad hoc networks Streaming media Feature extraction Encoding Complexity theory Streams Standards

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Not All Image Regions Matter: Masked Vector Quantization for Autoregressive Image Generation

arXiv

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arXiv 2023年

作者： Huang, Mengqi Mao, Zhendong Wang, Quan Zhang, Yongdong University of Science and Technology of China Hefei China MOE Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China Institute of Artificial intelligence Hefei Comprehensive National Science Center Hefei China

Existing autoregressive models follow the two-stage generation paradigm that first learns a codebook in the latent space for image reconstruction and then completes the image generation autoregressively based on the learned codebook. However, existing codebook learning simply models all local region information of images without distinguishing their different perceptual importance, which brings redundancy in the learned codebook that not only limits the next stage’s autoregressive model’s ability to model important structure but also results in high training cost and slow generation speed. In this study, we borrow the idea of importance perception from classical image coding theory and propose a novel two-stage framework, which consists of Masked Quantization VAE (MQ-VAE) and Stackformer, to relieve the model from modeling redundancy. Specifically, MQ-VAE incorporates an adaptive mask module for masking redundant region features before quantization and an adaptive de-mask module for recovering the original grid image feature map to faithfully reconstruct the original images after quantization. Then, Stackformer learns to predict the combination of the next code and its position in the feature map. Comprehensive experiments on various image generation validate our effectiveness and efficiency. Code will be released at https: / / github. com / CrossmodalGroup / MaskedVectorQuantization. Copyright © 2023, The Authors. All rights reserved.

关键词： Redundancy

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Simulation of Nuclear Reactor Accident Scenarios using Physics-Informed Neural Networks and Transfer-Learning

Simulation of Nuclear Reactor Accident Scenarios using Physi...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yang Yu Yufei Xie Wenlin Wang Guohua Wu Haitao Lan Enbo Lin Ping An Zibin Sun Haichuan Zhang Yixian Wu National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China Science and Technology on Reactor System Design Technology Laboratory Nuclear Power Institute of China Chengdu China School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Institute of Automotive Engineers Hubei University of Automotive Technology Shiyan China Nuclear Power Institute of China Chengdu China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In a Loss of Coolant Accident (LOCA), reactor core temperatures can rise rapidly, leading to potential fuel damage and radioactive material release. This research presents a groundbreaking method that combines the power of Monte Carlo Sampling and Physics-Informed Neural Networks (PINNs) to simulate and effectively address the challenging Loss of Coolant Accidents (LOCA) in nuclear reactors. In the event of a LOCA, reactor core temperatures can soar rapidly, posing a significant threat to fuel integrity and potentially leading to the release of radioactive materials. By leveraging the strengths of both Monte Carlo Sampling and PINNs, this approach aims to provide a comprehensive and accurate simulation framework for assessing and mitigating the consequences of such accidents. The method yields high prediction accuracy (MAE: 0.033, RMSE: 0.098, R2: 0.814) and demonstrates robustness through transfer learning, maintaining strong performance (MAE: 0.064, RMSE: 0.163, R 2 : 0.735).

关键词： Radioactive materials Accuracy Monte Carlo methods Transfer learning Neural networks Robustness Data models Fuels Inductors Accidents

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MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Symmetric Encryption for Cloud Storage

MMDSSE: Multi-client and Multi-keyword Dynamic Searchable Sy...

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Annual Conference on Privacy, Security and Trust, PST

作者： Panyu Wu Zhenfu Cao Jiachen Shen Xiaolei Dong Yihao Yang Jun Zhou Liming Fang Zhe Liu Chunpeng Ge Chunhua Su Shanghai Key Laboratory of Trustworthy Computing East China Normal University Shanghai China Research Center for Basic Theories of Intelligent Computing Research Institute of Basic Theories Zhejiang Lab Hangzhou China College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Science and Technology on Parallel and Distributed Processing Laboratory (PDL) Changsha China Shandong University Jinan China University of Aizu Fukushima Japan

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

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Looking for Information Source from Online Social Network with Incomplete Observation

Looking for Information Source from Online Social Network wi...

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International Symposium on Computational Intelligence and Design, ISCID

作者： Peng Shi Lianhong Ding Tianle Zhang National Center for Materials Service Safety University of Science and Technology Beijing Beijing China School of Information Beijing Wuzi University Beijing China Key Laboratory of Trustworthy Distributed Computing and Service Beijing University of Posts and Telecommunications Beijing China

ISBN: (纸本)9781509035595

Online social network has become an important media for information spreading. To target the origin of rumors and well understand the cause of event, information source in social network should be found out. Unfortunately, because of the huge and complex structure of social network, it is nearly impossible to monitor the complete online social network. To solve this problem, the paper gives a method to look for information source with incomplete observation. The basis of the method is the correlation between activated time and network topology structure. Some experiments are executed and the effect of different sampling methods was analyzed as well. Experiments on BA, WS and Facebook show that the method can produce satisfied results with incomplete observation.

关键词： Observers Network topology Diffusion processes Facebook distributed computing

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