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User-level failure detection and auto-recovery of parallel programs in HPC systems

Frontiers of computer Science 2021年第6期15卷 31-42页

作者： Guozhen ZHANG Yi LIU Hailong YANG Jun XU Depei QIAN State Key Laboratory of Software Development Environment Beijing 100191China Sino-German Joint Software Institute Beihang UniversityBeijing 100191China School of Computer Science and Engineering Beihang UniversityBeijing 100191China Science and Technology on Space System Simulation Laboratory Beijing Simulation Center Beijing 100854China

As the mean-time-between-failures(MTBF)continues to decline with the increasing number of components on large-scale high performance computing(HPC)systems,program failures might occur during the execution period with high *** successful execution of the HPC programs has become an issue that the unprivileged users should be *** the user perspective,if the program failure cannot be detected and handled in time,it would waste resources and delay the progress of program ***,the unprivileged users are unable to perform program state checking due to execution control by the job management system as well as the limited ***,automated tools for supporting user-level failure detection and autorecovery of parallel programs in HPC systems are *** paper proposes an innovative method for the unprivileged user to achieve failure detection of job execution and automatic resubmission of failed *** state checker in our method is encapsulated as an independent job to reduce interference with the user *** addition,we propose a dual-checker mechanism to improve the robustness of our *** implement the proposed method as a tool named automatic re-launcher(ARL)and evaluate it on the Tianhe-2 *** results show that ARL can detect the execution failures effectively on Tianhe-2 *** addition,the communication and performance overhead caused by ARL is *** good scalability of ARL makes it applicable for large-scale HPC systems.

关键词： high performance computing parallel program failure detection failure auto-recovery

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Leveraging Interactive Paths for Sequential Recommendation 28th

Leveraging Interactive Paths for Sequential Recommendation

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Li, Aoran Zang, Yalei Wang, Yani Li, Bohan College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing211106 China Key Laboratory of Safety-Critical Software Ministry of Industry and Information Technology Beijing China National Engineering Laboratory for Integrated Aero-Space-Ground Ocean Big Data Application Technology Xi’an China

ISBN: (纸本)9783031306716

Sequential recommendation systems dynamically predict the users’ next behaviors from chronological historical records to provide more accurate recommendations. However, in many real-world scenarios, recommenders find it difficult to make recommendations for cold-start users who have limited interactions with items. To address this issue, existing methods utilize auxiliary information to capture user preferences, which have achieved performance improvement to some extent. But with the gradual awareness of personal privacy, external information becomes difficult to obtain. Therefore, we develop a novel model called Path-based sequential Recommender for cold-start users (PathRec). PathRec exploits the rich sequential dependencies within the interactive paths to give reasonable recommendations for cold-start users without additional external information. Specifically, (i) PathRec constructs the global graph to learn the representations of the items. (ii) Then, PathRec elaborately selects the sampled paths to derive the underlying dependencies of the cold-start sequence. (iii) Finally, PathRec provides rich and complementary information to user-item interactions by strongly coupling the paths to give suitable recommendations. Extensive experiments on three datasets demonstrate the effectiveness of PathRec. In particular, our model can adapt to the cold-start users and has potential interpretability for recommendations. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recommender systems

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3D Multi-Modal Feature Interaction Fusion Network for Early Alzheimer's Disease Diagnosis

3D Multi-Modal Feature Interaction Fusion Network for Early ...

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IEEE International Symposium on Biomedical Imaging

作者： Gege Cai Zhenghua Guan Xiangwen Cai Nina Cheng Wenzheng Hu Tengda Zhang Yi Liu Baiying Lei College of Computer Science and Software Engineering Shenzhen University Shenzhen China Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Shenzhen University Medical School

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Positron Emission Tomography (PET) and Structural Magnetic Resonance Imaging (sMRI) are widely used for early Alzheimer's disease (AD) diagnosis, providing anatomical and metabolic insights. However, current fusion methods, like simple addition or concatenation, fail to leverage their complementary information fully. To address it, we proposed a 3D multi-modal feature interaction fusion network for AD diagnosis. Specifically, we leverage a robust 3D ResNet backbone network as the architecture for feature extraction. To enable the model to dynamically focus on the areas of interest, we added a local attention module at each layer of the network. We further introduced a collaborative attention module to facilitate the interaction and fusion of features between PET and sMRI modalities, thereby enhancing the overall capability of the model to utilize complementary information from both modalities. Finally, an enhanced feature fusion module based on Transformer is integrated into the network to further strengthen the representation of the fused features. The experiment shows that our model performs better than other representative models and validates the effectiveness of the proposed method on the ADNI dataset.

关键词： Solid modeling Three-dimensional displays Magnetic resonance imaging Collaboration Feature extraction Transformers Medical diagnosis Alzheimer's disease Positron emission tomography Biomedical imaging

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Understanding heterophily for graph neural networks 24

Understanding heterophily for graph neural networks

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Proceedings of the 41st International Conference on Machine Learning

作者： Junfu Wang Yuanfang Guo Liang Yang Yunhong Wang State Key Laboratory of Software Development Environment and School of Computer Science and Engineering and Shen Yuan Honors College Beihang University Beijing China State Key Laboratory of Software Development Environment and School of Computer Science and Engineering Beihang University Beijing China School of Artificial Intelligence Hebei University of Technology Tianjin China School of Computer Science and Engineering Beihang University Beijing China

Graphs with heterophily have been regarded as challenging scenarios for Graph Neural Networks (GNNs), where nodes are connected with dissimilar neighbors through various patterns. In this paper, we present theoretical understandings of heterophily for GNNs by incorporating the graph convolution (GC) operations into fully connected networks via the proposed Heterophilous Stochastic Block Models (HSBM), a general random graph model that can accommodate diverse heterophily patterns. Our theoretical investigation comprehensively analyze the impact of heterophily from three critical aspects. Firstly, for the impact of different heterophily patterns, we show that the separability gains are determined by two factors, i.e., the Euclidean distance of the neighborhood distributions and $\sqrt{\mathbb{E}\left[\operatorname{deg}\right]}$, where $\mathbb{E}\left[\operatorname{deg}\right]$ is the averaged node degree. Secondly, we show that the neighborhood inconsistency has a detrimental impact on separability, which is similar to degrading $\mathbb{E}\left[\operatorname{deg}\right]$ by a specific factor. Finally, for the impact of stacking multiple layers, we show that the separability gains are determined by the normalized distance of the l-powered neighborhood distributions, indicating that nodes still possess separability in various regimes, even when over-smoothing occurs. Extensive experiments on both synthetic and realworld data verify the effectiveness of our theory.

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Timo: In-memory temporal query processing for big temporal data

Timo: In-memory temporal query processing for big temporal d...

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作者： Zheng, Xiao Liu, Houkai Wang, Xiujun Wu, Xuangou Yu, Feng School of Computer Science and Technology Anhui University of Technology Maanshan China Institute for Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Engineering Laboratory for Intelligent Applications and Security of Industrial Internet Anhui University of Technology Anhui Maanshan China School of Computer Science and Engineering & School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

Today's internet applications generate massive temporal data anywhere and anytime. Although some disk-based temporal systems are currently available, they suffer from poor I/O performance, especially when applied in intelligent applications deployed in the cloud and edge environments. Therefore, how to process temporal operations with low latency and high throughput becomes a crucial problem for efficient data processing. This paper proposes Timo, a distributed in-memory temporal query and analytic model for big temporal data. Firstly, a space-efficient temporal index is proposed to support more efficiently query performance with less memory space than the state-of-art methods. Secondly, based on the temporal locality feature of temporal queries, Timo proposes a partitioner mechanism, which utilizes forward scan algorithm, to improve query throughput. Thirdly, some optimal strategies are proposed to improve the execution process of temporal query and analysis, which reduce intermediate result data size and improve the throughput much further. Lastly, we implement the Timo system on the Apache Spark platform, which extends Spark dataset API with temporal query and analysis API functions for users. Extensive experimental results show that the Timo system outperforms other Spark-based temporal systems in terms of both query latency and throughput. © 2021 John Wiley & Sons, Ltd.

关键词： Data handling

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A real-time privacy-preserving scheme based on grouping queries for continuous location-based services

A real-time privacy-preserving scheme based on grouping quer...

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作者： Liu, Haoyu Zhang, Shiwen Li, Mengling Liang, Wei Arthur Sandor, Voundi Koe School of Computer Science and Engineering Hunan University of Science and Technology Hunan Xiangtan China Hunan Key Laboratory for Service Computing and Novel Software Technology Hunan Xiangtan China Institute of Artificial Intelligence and Blockchain Guangzhou University Guangdong Guangzhou China

With the advancement of global positioning systems and communication technologies, location-based services (LBS) have become widely used. However, user location privacy is vulnerable during service exchange, and location-related queries may result in serious privacy disclosure issues. In this article, considering that attackers may make use of auxiliary information to track a mobile user, we propose a real-time privacy-preserving scheme based on the grouping of queries in continuous LBS. In this scheme, we first design the probability density functions to select (Figure presented.) virtual locations. Then, we group these virtual locations and the query location of the user into several queries. Simultaneously, we send these grouping queries to the LBS server. Following the steps outlined above, grouping queries can be used to conceal the true query. Moreover, the results of security analyses and performance evaluations show that the proposed scheme is effective and secure. © 2022 John Wiley & Sons, Ltd.

关键词： Location based services

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Local Spectral for Multiresolution Community Search in Attributed Graph

Local Spectral for Multiresolution Community Search in Attri...

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2022 IEEE International Conference on Multimedia and Expo, ICME 2022

作者： Li, Qingqing Ma, Huifang Li, Zhixin Chang, Liang Northwest Normal University College of Computer Science and Engineering China College of Computer Science and Information Technology Guangxi Normal University China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology China

ISBN: (数字)9781665485630

ISBN: (纸本)9781665485630

Community search aims to identify all latent members of a particular community from given nodes. Though previous studies have been proven effective, some of them ignore two insights. First, node attributes provide side information to describe features of nodes. It contributes to optimal results. Second, multiresolution community affords latent information to depict the hierarchical relation of network and ensure that one of them is closest to the real one. These aspects motivate us to propose LSMSA, which utilizes Local Spectral for Multiresolution community Search in Attributed graph. Specifically, inspired by the local modularity and graph wavelets, we propose Multiresolution Local modularity (MLQ) in node-attribute graph. Furthermore, to detect multiscale local communities, a sparse indicator-vector is developed based on MLQ via solving a linear programming problem. Extensive experimental results on real-world attributed graphs have demonstrated the detected community is meaningful and its scale can be changed reasonably. © 2022 IEEE.

关键词： Linear programming

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Dual Cross-Stage Partial Learning for Detecting Objects in Dehazed Images

Dual Cross-Stage Partial Learning for Detecting Objects in D...

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IEEE International Conference on Data Mining (ICDM)

作者： Jinbiao Zhao Zhao Zhang Jiahuan Ren Haijun Zhang Zhongqiu Zhao Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China Department of Computer Science Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Performing an object detection task after the restoration of a hazy image, or rather detecting with the network backbone directly, will result in the inclusion of information mixed with dehazing, which tends to interfere with detection performance. To address these issues, we propose a novel framework for detecting objects in dehazed images via Dual Cross-Stage Partial Learning (DCSP). Specifically, we introduce a Cross-Stage Partial (CSP) module for extracting clean feature information after dehazing. Secondly, to enhance data integrity, we employ a skip-input strategy to supplement information related to object detection features that may be lost during the dehazing task, while avoiding the gradient vanishing problem. In addition, CSP is also introduced to facilitate comprehensive learning of multiple feature representations. Finally, to avoid the inclusion of irrelevant dehazing information in detection, we apply a Ground-Truth Flow at detection network (at dark3), for fine feature information calibration. Additionally, we created a synthetic fog dataset to expand the training data for DCSP. Experimental results on both synthetic and real-world datasets demonstrate the effectiveness and accuracy of the proposed method. The code is available at https://***/zhaojinbiao/DCSP.

关键词： Accuracy Quantization (signal) Codes Data integrity Training data Object detection Feature extraction Image restoration Calibration Data mining

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Propagation and Dispersion Characteristics of Scholte Wave in VTI Media with the Presence of Irregular Seabed

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Journal of Earth Science 2024年第2期35卷 722-725页

作者： Xiaobo Liu Yun Wang Jianlei Zhang Haifeng Chen Hua Zhang School of Ocean Sciences China University of GeosciencesBeijing 100083China Key Laboratory of Polar Geology and Marine Mineral Resources China University of GeosciencesBeijing 100083China School of Geophysics and Information Technology China University of GeosciencesBeijing 100083China Research&Development Center BGP IncCNPCZhuozhou 072751China National Engineering Research Center for Oil and Gas Exploration Computer Software Beijing 100080China

INTRODUCTION Surface waves propagating along the seafloor are general ly called Scholte waves,and were first discovered and studied in the early 1950s(Kugler et al.,2005;Buchen and Ben-Hador,1996).Scholte waves exhibit a dispersion phenomenon,which implies that their velocity varies with ***-frequency Scholte waves can propagate over long distances on the seabed with little attenuation(Bohlen et al.,2004).The particle mo tion of Scholte waves in a solid medium changes from a retro grade to a prograde ellipse(Klein et al.,2005;McMechan and Yedlin,1981).

关键词： seabed Dispersion implies

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High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Video Editing

High-Fidelity Diffusion Editor for Zero-Shot Text-Guided Vid...

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IEEE International Conference on Data Mining (ICDM)

作者： Yan Luo Zhichao Zuo Zhao Zhang Zhongqiu Zhao Haijun Zhang Richang Hong School of Computer Science and Information Engineering Hefei University of Technology Hefei China Yunnan Key Laboratory of Software Engineering Yunan China School of Computer Science Harbin Institute of Technology Shenzhen China

ISBN: (数字)9798331506681

ISBN: (纸本)9798331506698

Text-guided image generative diffusion models achieve fast development on the generation and editing of high-quality images. To extend such success to video editing, some efforts combining image generation with video editing have been made, which however only achieve inferior performance. We attribute it to two challenges: 1) different from the static image generation, it is tricky for dynamic video information to ensure the temporal fidelity of motion consistency across different frames; 2) the randomness of the frame generation process makes it hard to continuously retain the similar spatial fidelity for the original detailed features. In this paper, we propose a new high-fidelity diffusion model-based zero-shot text-guided video editing network, called HiFiVEditor, which aims to conduct effective video editing with high fidelity of the original video's detailed and dynamic information. Specifically, we propose a Spatial-Temporal Fidelity Block (STFB) that enables the model to restore the spatial features by enlarging the spatial perceptual field to avoid loss of important information, and capture more dynamic information between different frames by using all frames for preserving temporal consistency to achieve better temporal fidelity. In addition, we introduce Null-Text Embedding to create a soft text embedding to optimize the noise learning process, so that the latent noise can be aligned with the prompt. Furthermore, to tune the video style and render it more realistic, we employ a Prior-Guided Perceptual Loss to constrain the prediction results to avoid deviating from the original video style. Extensive experiments demonstrate the superior video editing capability compared to existing works.

关键词： Image synthesis Noise Dynamics Diffusion models Data models Image restoration Data mining

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