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IEEE International Conference on Cluster Computing

作者： Yanlong Zhai Emmanuel Mbarushimana Wei Li Jing Zhang Ying Guo Beijing Engineering Research Center of Massive Language Information Processing Beijing Institute of Technology Beijing China Science and Technology on Complex Systems Simulation Laboratory Beijing China

Big data processing is receiving significant amount of interest as an important technology to reveal the information behind the data, such as trends, characteristics, etc. MapReduce is considered as the most efficient distributed parallel data processing framework. However, some high-end applications, especially some scientific analyses have both data-intensive and computation-intensive features. Current big data processing techniques like Hadoop are not designed for computation-intensive applications, thus have insufficient computation power. In this paper, we presented Lit, a high performance massive data computing framework based on CPU/GPU cluster. Lit integrated GPU with Hadoop to improve the computational power of each node in the cluster. Since the architecture and programming model of GPU is different from CPU, Lit provided an annotation based approach to automatically generate CUDA codes from Hadoop codes. Lit hided the complexity of programming on CPU/GPU cluster by providing extended compiler and optimizer. To utilize the simplified programming, scalability and fault tolerance benefits of Hadoop and combine them with the high performance computation power of GPU, Lit extended the Hadoop by applying a GPUClassloader to detect the GPU, generate and compile CUDA codes, and invoke the shared library. Our experimental results show that Lit can achieve an average speedup of 1x to 3x on three typical applications over Hadoop.

关键词： Graphics processing units Computational modeling Handheld computers Data models Load modeling Semantics

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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A Synchronous Parallel Method with Parameters Communication Prediction for Distributed Machine Learning 19th

A Synchronous Parallel Method with Parameters Communication...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yanguo Xue, Meiting Xu, Peiran Shi, Yukun Zeng, Kaisheng Zhang, Jilin Yue, Lupeng School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China National University of Defense Technology Changsha China School of Cyberspace HangZhou Dianzi University Hangzhou China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031545306

With the development of machine learning technology in various fields, such as medical care, smart manufacturing, etc., the data has exploded. It is a challenge to train a deep learning model for different application domains with large-scale data and limited resources of a single device. The distributed machine-learning technology, which uses a parameter server and multiple clients to train a model collaboratively, is an excellent method to solve this problem. However, it needs much communication between different devices with limited communication resources. The stale synchronous parallel method is a mainstream communication method to solve this problem, but it always leads to high synchronization delay and low computing efficiency as the inappropriate delay threshold value set by the user based on experience. This paper proposes a synchronous parallel method with parameters communication prediction for distributed machine learning. It predicts the optimal timing for synchronization, which can solve the problem of long synchronization waiting time caused by the inappropriate threshold settings in the stale synchronous parallel method. Moreover, it allows fast nodes to continue local training while performing global synchronization, which can improve the resource utilization of work nodes. Experimental results show that compared with the delayed synchronous parallel method, the training time and quality, and resource usage of our method are both significantly improved. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Forecasting

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Large Eddy simulation of Flow and Heat Transfer Performance in Periodically Inward Corrugated Tubes

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SSRN 2023年

作者： Zhong, Yuzhou Song, Yueheng Zhao, Lei Mi, Fei Zhao, Jingquan Zhu, Xiaowei School of Aeronautic Science and Engineering Beihang University Beijing100191 China Science & Technology on Complex Aviation System Simulation Laboratory Beijing100070 China Co. Ltd. Henan453049 China

Corrugated tubes are extensively unitized in enhancing heat transfer within tubular heat exchange devices. The efficiency of these corrugated tubes is closely linked to the geometric characteristics of the surface corrugations. In this study, we conducted an extensive series of Large Eddy simulations on a representative type of enhanced tube featuring periodic inward corrugations (PIC). Our goal was to compile a comprehensive and precise dataset of the thermal and hydraulic properties specific to this design. The key design parameters that define the corrugated patterns are corrugation height, corrugation width, and the length of the straight section. We systematically explored these parameters to understand their impact on heat transfer and fluid dynamics. Among these parameters, corrugation height has been identified as the most influential factor affecting flow and heat transfer properties. As the corrugation height varies from 1/8D to 3/8D, we observed a substantial increase in the friction factor and Nusselt number by a factor of 209 and 5, respectively, at Re = 1000;while the critical Reynolds number decreased roughly from 800 to 100. These findings underscore the significant role of corrugation height in shaping the flow and heat transfer performance of PIC tubes. The other two parameters, corrugation width and straight section length, were also found to be crucial in determining the overall heat transfer coefficient and pressure drop. The results of our study suggest that when utilizing this type of corrugated tubes, careful consideration is required to strike an optimal balance between heat transfer efficiency and pressure loss. The large-sets LES results presented in this paper can serve as valuable inputs for optimizing the design of PIC tubes. © 2023, The Authors. All rights reserved.

关键词： Large eddy simulation

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Evaluation of Colchicine's interaction with the ATP-binding region of mice NLRP3-NACHT domain using molecular docking and dynamics simulation 46

Evaluation of Colchicine's interaction with the ATP-binding ...

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46th Vietnam Conference on Theoretical Physics, VCTP 2021

作者： Lai, Hien T. T. Do, Ha M. Nguyen, Toan T. Key Laboratory for Multiscale Simulation of Complex Systems Department of Theoretical Physics Faculty of Physics VNU University of Science Vietnam National University Ͽ Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi Viet Nam The Key Laboratory of Enzyme and Protein Technology VNU University of Science Vietnam National University Ͽ Hanoi 334 Nguyen Trai street Thanh Xuan district Hanoi Viet Nam

Since the discovery of the role of NLRP3 in microbial infection in 2001, many studies have shown that NLRP3 play a key role in causing many mammal acute and chronic diseases. However, a full understanding of the mechanism of NLRP3 activation is still lacking. Our previous theoretical work and experimental evidence show the role of ATP in interacting with and activating the NATCH region of NLRP3. In this study, we continue to use bioinformatics and molecular dynamic (MD) simulation to evaluate the competitive impact of the interaction the ligand ATP and colchicine (COL) with this NACHT protein. The later ligand is a medication to treat gout attacks. Our results show that COL bind stably to the ATP binding pocket of mice NACHT domain with high numbers of hydrophobic and van der Waals interactions, while hydrogen bond and electrostatic interactions are important types of contact for keeping ATP at its NACHT pocket. Our results assist in building in-silico screening model for natural compounds with pharmacological effects to NLRP3 similar to colchicine with few side effects. In addition, this work helps to better understand the balance between this inflammasome activation and inhibition, which will help in the improvement and development of new therapies for related diseases. © Published under licence by IOP Publishing Ltd.

关键词： Chemical activation

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On Exploring Undetermined Relationships for Visual Relationship Detection

On Exploring Undetermined Relationships for Visual Relations...

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IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Yibing Zhan Jun Yu Ting Yu Dacheng Tao Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University UBTECH Sydney AI Centre School of Computer Science FEIT University of Sydney

ISBN: (纸本)9781728132945

In visual relationship detection, human-notated relationships can be regarded as determinate relationships. However, there are still large amount of unlabeled data, such as object pairs with less significant relationships or even with no relationships. We refer to these unlabeled but potentially useful data as undetermined relationships. Although a vast body of literature exists, few methods exploit these undetermined relationships for visual relationship detection. In this paper, we explore the beneficial effect of undetermined relationships on visual relationship detection. We propose a novel multi-modal feature based undetermined relationship learning network (MF-URLN) and achieve great improvements in relationship detection. In detail, our MF-URLN automatically generates undetermined relationships by comparing object pairs with human-notated data according to a designed criterion. Then, the MF-URLN extracts and fuses features of object pairs from three complementary modals: visual, spatial, and linguistic modals. Further, the MF-URLN proposes two correlated subnetworks: one subnetwork decides the determinate confidence, and the other predicts the relationships. We evaluate the MF-URLN on two datasets: the Visual Relationship Detection (VRD) and the Visual Genome (VG) datasets. The experimental results compared with state-of-the-art methods verify the significant improvements made by the undetermined relationships, e.g., the top-50 relation detection recall improves from 19.5% to 23.9% on the VRD dataset.

关键词： Visualization Fuses Dataset

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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作者： Huawei ZHU Yihao MAO Yiming ZHAO Liyuan SHI Yanli SHAO School of Information and Electrical Engineering Zhejiang University City College Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and TechnologyHangzhou Dianzi University

With the development of urbanization,the number of residents' motor vehicles has increased sharply,and traffic congestion problem has become increasingly *** construction of Intelligent Traffic System(ITS) has become the main means to alleviate traffic ***-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation,and has become one of the research hotspots in intelligent transportation ***,A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction ***,a graph convolutional network(GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow ***,the long and short-term memory(LSTM) neural network is used to process the time ***,the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow *** results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method,and can provide a basis for intelligent traffic control and smart city construction.

关键词： Traffic Flow Prediction Spatio-temporal GCN LSTM

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Topological properties and localization transition in a one-dimensional non-Hermitian lattice with a slowly varying potential

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Physical Review B 2022年第10期105卷 104201-104201页

作者： Siqing Li Mengyao Li Yi Gao Peiqing Tong Center for Quantum Transport and Thermal Energy Science Jiangsu Key Laboratory on Opto-Electronic Technology School of Physics and Technology Nanjing Normal University Nanjing 210023 China Jiangsu Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Physics and Technology Nanjing Normal University Nanjing 210023 China

We explore the eigenenergy distribution, localization transition, and topological property of a one-dimensional non-Hermitian lattice with a slowly varying potential, which does not possess parity-time (PT) symmetry. We found that, in contrast to PT-symmetric systems whose eigenenergies can stay real until the PT-symmetry breaking point, the eigenenergies in the present system are fully complex and their distribution in the complex plane depends on the parameters. As non-Hermiticity is introduced into the system, also depending on the parameters, the eigenstates may show a crossover behavior from fully extended to fully localized as non-Hermiticity is increased, or there will be a sharp localization transition at finite non-Hermiticity. Nevertheless, this non-Hermitian system is proven to be topologically trivial, thus we conclude that in general non-Hermitian systems, the localization transition is not always of a topological nature.

关键词： Anderson localization Topological phase transition Non-Hermitean systems PT-symmetry

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FedECCR: Federated Learning Method with Encoding Comparison and Classification Rectification 19th

FedECCR: Federated Learning Method with Encoding Comparison...

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19th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2023

作者： Zeng, Yan Zheng, Hui Wang, Xin Zhang, Beibei Zhou, Mingyao Zhang, Jilin Ren, YongJian School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou310018 China Data Security Governance Zhejiang Engineering Research Center Hangzhou310018 China Zhejiang Lab Hangzhou311100 China Hangzhou Huawei Communication Technology Co. Ltd. Hangzhou310052 China

ISBN: (纸本)9783031545306

Federated learning is a distributed training method that integrates multi-party data information using privacy-preserving technologies through dispersed client data sets to jointly construct a global model under the coordination of a central server. However, in practical applications, there is a high degree of data distribution skewness among clients, which causes the optimization direction of the client models to diverge, resulting in model bias and reducing the accuracy of the global model. Existing methods require the calculation and transmission of much information to correct the optimization direction of the client models, or only roughly limit the deviation of the client models end-to-end, ignoring targeted processing of the internal structure of the model, resulting in unclear improvement effects. To address these problems, we propose a federated optimization algorithm FedECCR based on encoding contrast and classification correction. This algorithm divides the model into an encoder and a classifier. It utilizes prototype contrastive training of the model encoder and unbiased classification correction of the classifier. This approach notably improves the accuracy of the global model while maintaining low communication costs. We conducted experiments on multiple data sets to evaluate the validity of our method, and the quantified results showed that FedECCR can improve the global model classification accuracy by approximately 1% to 6% compared to FedAvg, FedProx, and MOON. © ICST Institute for Computer sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Classification (of information)

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Effect of Different Migration Strategies on Evolution of Cooperation in Spatial Prisoner's Dilemma Game

Effect of Different Migration Strategies on Evolution of Coo...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Hong Ding Xiangyu Chen Benyun Shi Yanming Ye Yizhi Ren School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi University Hangzhou China Key Lab. of Complex Syst. Modeling & Simulation Dianzi Univ. Hangzhou China

Many migration strategies have been proposed forPrisoner's Dilemma game on square lattice, such as randommigration, success-driven migration and adaptive migration. Inthis paper, we introduce three novel migration strategies: failure driven migration, hate-driven migration and global-scope migration. Based on the length of migration, individual with one of the first two strategies decides when and where to move into empty nearby places based on its performance in current site, while individual with a global-scope migration can move to anywhere. Then we compare them with the cases of no migration, random migration and success-driven migration. The simulations show that the failure-driven migration has the best performance in most circumstances. While defectors dominate in success-driven migration cases.

关键词： High definition video Big data

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