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arXiv 2024年

作者： Luo, Zihan Song, Xiran Huang, Hong Lian, Jianxun Zhang, Chenhao Jiang, Jinqi Xie, Xing Huazhong University of Science and Technology Wuhan China Microsoft Research Asia Beijing China The National Engineering Research Center for Big Data Technology and System Service Computing Technology and Systems Laboratory Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology China

Evaluating and enhancing the general capabilities of large language models (LLMs) has been an important research topic. Graph is a common data structure in the real world, and understanding graph data is a crucial part for advancing general intelligence. To evaluate and enhance the graph understanding abilities of LLMs, in this paper, we propose a benchmark named GraphInstruct, which comprehensively includes 21 classical graph reasoning tasks, providing diverse graph generation pipelines and detailed reasoning steps. Based on GraphInstruct, we further construct GraphLM through efficient instruction-tuning, which shows prominent graph understanding capability. In order to enhance the LLM with graph reasoning capability as well, we propose a step mask training strategy, and construct a model named GraphLM+. As one of the pioneering efforts to enhance the graph understanding and reasoning abilities of LLMs, extensive experiments have demonstrated the superiority of GraphLM and GraphLM+ over other LLMs. We look forward to more researchers exploring the potential of LLMs in the graph data mining domain through GraphInstruct. Our code for generating GraphInstruct is released publicly at: https://***/CGCL-codes/GraphInstruct. Copyright © 2024, The Authors. All rights reserved.

关键词： Computational linguistics

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Genome-wide identification and analysis of MAPK and MAPKK gene families in Brachypodium distachyon

Genome-wide identification and analysis of MAPK and MAPKK ge...

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第十四届全国植物基因组学大会

作者： Lihong Chen Yang Zhang Yuan He Wei Hu Shenglong Tan Min Wang Shiyi Zhou Xiaomin Deng Chao Huang Kexiu Li Junli Chang Mingjie Chen Yuesheng Wang Guangxiao Yang Guangyuan He The Genetic Engineering International Cooperation Base of Chinese Ministry of Science and Technology Key Laboratory of Molecular Biophysics of Chinese Ministry of EducationCollege of Life Science and TechnologyCollege of Life Science and TechnologyHuazhong University of Science & Technology(HUST) Services Computing Technology and System Laboratory Cluster and Grid Computing LaboratorySchool of Computer Science and TechnologyHuazhong University of Science & Technology(HUST)

Mitogen-activated protein kinase(MAPK) cascade,consisting of three Ser/Thr protein kinases,namely,MAPKKK,MAPKK and MAPK,is a universal module in signal transduction pathways in *** proteins have been implicated in diverse cellular processes including cell growth,proliferation,differentiation,survival, development and in responses to a diversity of environmental stimuli including cold, heat,reactive oxygen species,UV,drought and pathogen attack.

关键词： Brachypodium distachyon MAPK MAPKK phylogenetic analysis yeast two hybrid expression profile

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DarkSAM: Fooling Segment Anything Model to Segment Nothing 38

DarkSAM: Fooling Segment Anything Model to Segment Nothing

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Zhou, Ziqi Song, Yufei Li, Minghui Hu, Shengshan Wang, Xianlong Zhang, Leo Yu Yao, Dezhong Jin, Hai National Engineering Research Center for Big Data Technology and System China Services Computing Technology and System Lab China Cluster and Grid Computing Lab China Hubei Engineering Research Center on Big Data Security China Hubei Key Laboratory of Distributed System Security China School of Computer Science and Technology Huazhong University of Science and Technology China School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia

Segment Anything Model (SAM) has recently gained much attention for its outstanding generalization to unseen data and tasks. Despite its promising prospect, the vulnerabilities of SAM, especially to universal adversarial perturbation (UAP) have not been thoroughly investigated yet. In this paper, we propose DarkSAM, the first prompt-free universal attack framework against SAM, including a semantic decoupling-based spatial attack and a texture distortion-based frequency attack. We first divide the output of SAM into foreground and background. Then, we design a shadow target strategy to obtain the semantic blueprint of the image as the attack target. DarkSAM is dedicated to fooling SAM by extracting and destroying crucial object features from images in both spatial and frequency domains. In the spatial domain, we disrupt the semantics of both the foreground and background in the image to confuse SAM. In the frequency domain, we further enhance the attack effectiveness by distorting the high-frequency components (i.e., texture information) of the image. Consequently, with a single UAP, DarkSAM renders SAM incapable of segmenting objects across diverse images with varying prompts. Experimental results on four datasets for SAM and its two variant models demonstrate the powerful attack capability and transferability of DarkSAM. Our codes are available at: https://***/CGCL-codes/DarkSAM. © 2024 Neural information processing systems foundation. All rights reserved.

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New image thresholding method based on graph spectral theory

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Jisuanji Xuebao/Chinese Journal of Computers 2007年第1期30卷 110-119页

作者： Tao, Wen-Bing Jin, Hai Cluster and Grid Computing Laboratory School of Computer Huazhong University of Science and Technology Wuhan 430074 China Service Computing Technology and System Laboratory Huazhong University of Science and Technology Wuhan 430074 China

In this paper, a novel thresholding algorithm is presented to achieve improved image segmentation performance at low computational cost. The proposed algorithm uses the normalized graph cut measure as the thresholding principle to distinguish an object from the background, as such fair treatment of different sets of diversified sizes is ensured. The weight matrices used in evaluating the graph cuts are based on the gray levels of an image, rather than the commonly used image pixels. For most images, the number of gray levels is much smaller than the number of pixels. Therefore, the proposed algorithm occupies much smaller storage space and requires much lower computational costs and implementation complexity than other graph-based image segmentation algorithms. This fact makes the proposed algorithm attractive in various real-time vision applications such as automatic target recognition (ATR). A large number of examples are presented to show the superior performance of the proposed thresholding algorithm compared to existing thresholding algorithms.

关键词： Image processing

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Fast and Scalable Gate-Level Simulation in Massively Parallel Systems

Fast and Scalable Gate-Level Simulation in Massively Paralle...

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IEEE International Conference on Computer-Aided Design

作者： Haichuan Hu Zichen Xu Yuhao Wang Fangming Liu Services Computing Technology and System Lab Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Mathematics and Computer Science Nanchang University Nanchang China Pengcheng Laboratory Shenzhen China

The natural bijection between a proposed circuit design and its graph representation shall allow any graph optimization algorithm deploying into many-core systems efficiently. However, this process suffers from the exponentially growing overhead and heavy memory footprint with the signal propagation. To conquer the unique challenge, we systematically study the simulation with millions of gates, and identify that the processing complexity could grow exponentially from the signal inputs, the skewness of the computational graph stays. Thus, we present ZhouBi, a fast and scalable gate-level simulation framework to fully exploit the parallelism from many-core systems. ZhouBi contributes in threefolds, (I) a graph representation that colors gate-level netlists and identifies skew partitions based on the graph skewness; (II) A set of heuristic algorithms that picks opportunistic and conservative algorithms to accelerate the simulation; (III) A system facility that supports selective mapping between simulation and many-core, providing a tradeoff between the risk of concurrent simulation fail and performance gain. We have prototyped ZhouBi and evaluated it with practical baselines. ZhouBi can achieve a 27.6× performance gain, as compared to the state-of-the-practice Veriwell without compromising any correctness. Our framework supports large graphs enabling scale-out gate-level simulations for chip design.

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Energy Saving Strategy of Power System cluster Based on Container Virtualization

Energy Saving Strategy of Power System Cluster Based on Cont...

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IEEE Asia Power and Energy Engineering Conference (APEEC)

作者： Ran Zheng Hao Wang Hai Jin Dechao Xu Yong Chen Xiaomeng Li Yufei Rao Zhenan Zhang National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China State Key Laboratory of Power Grid Safety and Energy Conservation China Electric Power Research Institute Haidian District Beijing China State Grid Henan Electric Power Company Electric Power Research Institute Zhengzhou China

ISBN: (数字)9781728167824

ISBN: (纸本)9781728167831

With the continuous development of power grids, the scale of supercomputing clusters has also gradually increased to carry a large number of power system simulation calculations, and the problem of high energy consumption has appeared. To solve this problem, we propose a container virtualization-based supercomputing cluster for power system. We analyze the impact of containers on power simulation calculations and compare the energy consumption effects of various container scheduling and migration algorithms on clusters. Experiments show that compared to virtual machines with hypervisor, which consumes massive resources and reduces performances by 28.4%, the performance degradation of container on power simulation calculation is 1.3%, which can be ignored. The energy consumption of load-concentration or resource-and-load-balance container scheduling algorithms is up to 4.0% lower and at least 2.2% lower than other algorithms. In container migration, the method combining autoregressive model with most-correlation and resource-andload-balance algorithms is better than other methods, which not only minimizes energy consumption, but also has lowest number of migrations and SLA violations. Experiments verify the feasibility and advantages of container migration in power system computing clusters.

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Survey on overcoming free riding in peer-to-peer networks

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Jisuanji Xuebao/Chinese Journal of Computers 2008年第1期31卷 1-15页

作者： Yu, Yi-Jiao Jin, Hai Key Laboratory of Services Computing Technology and System Huazhong University of Science and Technology Wuhan 430074 China Key Laboratory of Cluster and Grid Computing of Hubei Province Huazhong University of Science and Technology Wuhan 430074 China

Since the first report of free riding on Gnutella in 2000, large amounts of network measurement results show that free riding is prevalent in almost all Peer-to-Peer (P2P) networks, which reduces the robustness, availability and lifetime of P2P networks. Overcoming free riding becomes a hot research issue both in academic and industrial communities. The traffic measurement mechanisms in P2P networks are overviewed, which include active measurement and passive measurement. Some detailed traffic measurement results of Gnutella overlay network are illustrated, which indicate the development of free riding in P2P network in recent 6 years. The modeling of P2P network is introduced, and the impacts to system performance of free riding are discussed. Moreover, the authors focus on the mechanisms avoiding free riding and categorize the proposed approaches into incentive mechanisms, game theoretical approaches, and social network or economic models based methods. Each category is discussed in detail, and some typical work is introduced. Especially, the features of these three categories of solutions are analyzed and compared from the aspect of engineering implementation, which is helpful to both the P2P software developers and academic researchers. Finally, future works about the mechanisms discouraging free riding in P2P networks are outlined.

关键词： Computer networks

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Query-Centered Temporal Community Search via Time-Constrained Personalized PageRank

arXiv

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arXiv 2023年

作者： Lin, Longlong Yuan, Pingpeng Li, Rong-Hua Zhu, Chunxue Qin, Hongchao Jin, Hai Jia, Tao College of Computer and Information Science Southwest University Chongqing400715 China National Engineering Research Center for Big Data Technology and System Service Computing Technology and System Laboratory Cluster and Grid Computing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China School of Computer Science and Technololgy Beijing Institute of Technology Beijing100081 China

Existing temporal community search suffers from two defects: (i) they ignore the temporal proximity between the query vertex q and other vertices but simply require the result to include q. Thus, they find many temporal irrelevant vertices (these vertices are called query-drifted vertices) to q for satisfying their cohesiveness, resulting in q being marginalized;(ii) their methods are NP-hard, incurring high costs for exact solutions or compromised qualities for approximate/heuristic algorithms. Inspired by these, we propose a novel problem named query-centered temporal community search to circumvent query-drifted vertices. Specifically, we first present a novel concept of Time-Constrained Personalized PageRank to characterize the temporal proximity between q and other vertices. Then, we introduce a model called β-temporal proximity core, which can combine temporal proximity and structural cohesiveness. Subsequently, our problem is formulated as an optimization task that finds a β-temporal proximity core with the largest β. To solve our problem, we first devise an exact and near-linear time greedy removing algorithm that iteratively removes unpromising vertices. To improve efficiency, we then design an approximate two-stage local search algorithm with bound-based pruning techniques. Finally, extensive experiments on eight real-life datasets and nine competitors show the superiority of the proposed solutions. © 2023, CC BY.

关键词： Iterative methods

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Jobgrid: Chinese College Graduates Employment Information grid System

JobGrid: Chinese College Graduates Employment Information Gr...

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Chinagrid Annual Conference (Chinagrid)

作者： Feng Zhao Bin Zhou Xiaoxing Sun Hai Jin Song Wu School of Computer Science and Technology Huazhong University of Science & Technology Wuhan China Cluster and Grid Computing Lab Wuhan China The State Key Laboratory of Virtual Reality Technology and Systems Beijing China School of Computer Science and Engineering Beihang University Beijing China

ISBN: (纸本)9780769533063

This paper proposes a realistic data grid system named Jobgrid to construct information platform for Chinese college graduates employment, which supports centralized transaction processing to ensure consistent, effective operation in data access, control, monitor and analysis, and distributed data management to provide heterogeneous data integration, conversion, exaction and export. Jobgrid has implemented global uniform view and massively heterogeneous data access, and provided efficiently data and transactions processing and cooperative control mechanism for distributed resource over grid.

关键词： Databases Educational institutions Distributed databases Servers Monitoring Computer architecture Employment

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SocialEdge: Socialized Learning-Based Request Scheduling for Edge-Cloud Systems

SocialEdge: Socialized Learning-Based Request Scheduling for...

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International Conference on Distributed computing Systems

作者： Ziwei Wang Yunfeng Zhao Chao Qiu Qiang He Xin Wang Xiaofei Wang Qinghua Hu College of Intelligence and Computing Tianjin University Tianjin China Guangdong Laboratory of Artificial Intelligence and DigitalEconomy (SZ) Shenzhen China School of Computer Science and Technology Huazhong University of Science and Technology China Department of Computing Technologies Swinburne University of Technology Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab Huazhong University of Science and Technology Wuhan China

The ability for cloud data centres and edge data centres to collaborate unleashes the potential of the edge-cloud system. However, its sophistication causes unexpected issues in request scheduling, such as Insufficient intelligence, complicated hierarchy and limited cooperation. Traditional resource optimization methods for the edge-cloud system struggle to accommodate such intricate situations. In this paper, inspired by the behaviors and regulations in human society, we propose a socialized learning-based scheduling approach for the edge-cloud system, namely SocialEdge. In order to adapt to time-varying requests and dynamic service prevalence, we propose a learning-based approach, multi-agent advantage actor-critic (MAA2C) and graph convolutional networks-based MAA2C for two phases, respectively, which can improve individual intelligence to achieve optimal dispatch and orchestration decisions. Then, to make use of hierarchy correlation, we develop the socialized refining inter the layers, where inference results from one layer guide the training process of the other layer so that optimization-critical knowledge can be abstracted. Besides, we apply socialized cooperation to each layer, where federated averaging with MAA2C is implemented to ensure cooperation over private data for achieving optimal global solutions. Experimental evaluations on a proof-of-concept testbed along with two real traces demonstrate that baselines have 105% more delay than SocialEdge while being 76.6% less time efficiency and 21.6% less throughput.

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