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Skyway:Accelerate Graph Applications with a Dual-Path Architecture and Fine-Grained Data Management

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Journal of Computer Science & Technology 2024年第4期39卷 871-894页

作者： Mo Zou Ming-Zhe Zhang Ru-Jia Wang Xian-He Sun Xiao-Chun Ye Dong-Rui Fan Zhi-Min Tang institute of computing Technology Chinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China institute of Information Engineering Chinese Academy of SciencesBeijing 100045China Department of Computer Science Illinois Institute of TechnologyChicagoIL 60616U.S.A.

Graph processing is a vital component of many AI and big data ***,due to its poor locality and complex data access patterns,graph processing is also a known performance killer of AI and big data *** this work,we propose to enhance graph processing applications by leveraging fine-grained memory access patterns with a dual-path architecture on top of existing software-based graph *** first identify that memory accesses to the offset,edge,and state array have distinct locality and impact on *** then introduce the Skyway architecture,which consists of two primary components:1)a dedicated direct data path between the core and memory to transfer state array elements efficiently,and 2)a data-type aware fine-grained memory-side row buffer hardware for both the newly designed direct data path and the regular memory hierarchy data *** proposed Skyway architecture is able to improve the overall performance by reducing the memory access interference and improving data access efficiency with a minimal *** evaluate Skyway on a set of diverse algorithms using large real-world *** a simulated fourcore system,Skyway improves the performance by 23%on average over the best-performing graph-specialized hardware optimizations.

关键词： graph application computer architecture memory hierarchy

A Robust Method of Bipolar Mental Illness Detection from Facial Micro Expressions Using Machine Learning Methods

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Intelligent Automation & Soft computing 2024年第1期39卷 57-71页

作者： Ghulam Gilanie Sana Cheema Akkasha Latif AnumSaher Muhammad Ahsan Hafeez Ullah Diya Oommen Department of Artificial Intelligence Faculty of ComputingThe Islamia University of BahawalpurBahawalpur63100Pakistan Biophotonics Imaging Techniques Laboratory Institute of PhysicsThe Islamia University of BahawalpurBahawalpur63100Pakistan West high School Salt Lake CityUT84103USA

Bipolar disorder is a serious mental condition that may be caused by any kind of stress or emotional upset experienced by the *** affects a large percentage of people globally,who fluctuate between depression and mania,or vice versa.A pleasant or unpleasant mood is more than a reflection of a state of ***,it is a difficult task to analyze through physical examination due to a large patient-psychiatrist ratio,so automated procedures are the best options to diagnose and verify the severity of *** this research work,facial microexpressions have been used for bipolar detection using the proposed Convolutional Neural Network(CNN)-based *** Action Coding System(FACS)is used to extract micro-expressions called Action Units(AUs)connected with sad,happy,and angry *** have been conducted on a dataset collected from Bahawal Victoria Hospital,Bahawalpur,Pakistan,Using the Patient Health Questionnaire-15(PHQ-15)to infer a patient’s mental *** experimental results showed a validation accuracy of 98.99%for the proposed CNN modelwhile classification through extracted featuresUsing SupportVectorMachines(SVM),K-NearestNeighbour(KNN),and Decision Tree(DT)obtained 99.9%,98.7%,and 98.9%accuracy,***,the outcomes demonstrated the stated method’s superiority over the current best practices.

关键词： Bipolar mental illness detection facial micro-expressions facial landmarked images

Thermal properties of two-dimensional materials

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Chinese Physics B 2017年第3期26卷 59-70页

作者：张刚张永伟 institute of high performance computing A*STAR Singapore

Two-dimensional （2D） materials, such as graphene, phosphorene, and transition metal dichalcogenides （e.g., MoS2 and WS2）, have attracted a great deal of attention recently due to their extraordinary structural, mechanical, and physical properties. In particular, 2D materials have shown great potential for thermal management and thermoelectric energy generation. In this article, we review the recent advances in the study of thermal properties of 2D materials. We first review some important aspects in thermal conductivity of graphene and discuss the possibility to enhance the ultra-high thermal conductivity of graphene. Next, we discuss thermal conductivity of MoS2 and the new strategy for thermal management of MoS2 device. Subsequently, we discuss the anisotropic thermal properties of phosphorene. Finally, we review the application of 2D materials in thermal devices, including thermal rectifier and thermal modulator.

关键词： thermal conduction two-dimensional materials

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Discriminative explicit instance selection for implicit discourse relation classification

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Frontiers of Computer Science 2024年第4期18卷 129-138页

作者： Wei SONG Hongfei HAN Xu HAN Miaomiao CHENG Jiefu GONG Shijin WANG Ting LIU Information Engineering College Capital Normal UniversityBeijing 100048China iFLYTEK AI Research institute Hefei 230088China Research Center for Social computing and Information Retrieval Harbin Institute of TechnologyHarbin 150006China

Discourse relation classification is a fundamental task for discourse analysis,which is essential for understanding the structure and connection of *** discourse relation classification aims to determine the relationship between adjacent sentences and is very challenging because it lacks explicit discourse connectives as linguistic cues and sufficient annotated training *** this paper,we propose a discriminative instance selection method to construct synthetic implicit discourse relation data from easy-to-collect explicit discourse *** expanded instance consists of an argument pair and its sense *** introduce the argument pair type classification task,which aims to distinguish between implicit and explicit argument pairs and select the explicit argument pairs that are most similar to natural implicit argument pairs for data *** also propose a simple label-smoothing technique to assign robust sense labels for the selected argument *** evaluate our method on PDTB 2.0 and PDTB *** results show that our method can consistently improve the performance of the baseline model,and achieve competitive results with the state-of-the-art models.

关键词： discourse analysis PDTB discourse relation implicit discourse relation classification data expansion

Adaptive joint configuration optimization for collaborative inference in edge-cloud systems

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Science China(Information Sciences) 2024年第4期67卷 335-336页

作者： Zheming YANG Wen JI Zhi WANG institute of computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Peng Cheng Laboratory Tsinghua Shenzhen International Graduate School Tsinghua University

With the rapid development of technologies such as deep learning and the Internet of Things(IoT), the deployment and application of various IoT devices are becoming increasingly widespread, resulting in the inference tasks generated increasing dramatically [1]. Many deep neural network(DNN) models are deployed on servers for performing inference tasks.

关键词： Inference engines

H-hop independently submodular maximization problem with curvature

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High-Confidence Computing

high-Confidence computing 2024年第3期4卷 75-81页

作者： Yang Lv Chenchen Wu Dachuan Xu Ruiqi Yang Beijing institute for Scientific and Engineering computing Beijing University of TechnologyBeijing 100124China College of Science Tianjin University of TechnologyTianjin 300382China

The Connected Sensor Problem(CSP)presents a prevalent challenge in the realms of communication and Internet of Things(IoT)*** primary aim is to maximize the coverage of users while maintaining connectivity among K *** the challenge of managing a large user base alongside a finite number of candidate locations,this paper proposes an extension to the CSP:the h-hop independently submodular maximization problem characterized by curvatureα.We have developed an approximation algorithm that achieves a ratio of 1−e−α/(2h+3)α.The efficacy of this algorithm is demonstrated on the CSP,where it shows superior performance over existing algorithms,marked by an average enhancement of 8.4%.

关键词： Internet of things UAV communication Approximation algorithms

Committed-programming reductions: formalizations,implications and relations

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Science China(Information Sciences) 2024年第10期67卷 151-171页

作者： Jiang ZHANG Yu YU Dengguo FENG Shuqin FAN Zhenfeng ZHANG State Key Laboratory of Cryptology Department of Computer Science and Engineering Shanghai Jiao Tong University Trusted computing and Information Assurance Laboratory Institute of SoftwareChinese Academy of Sciences

In this work, we introduce a class of black-box(BB) reductions called committed-programming reduction(CPRed) in the random oracle model(ROM) and obtain the following interesting results:(1) we demonstrate that some well-known schemes, including the full-domain hash(FDH) signature(Eurocrypt1996) and the Boneh-Franklin identity-based encryption(IBE) scheme(Crypto 2001), are provably secure under CPReds;(2) we prove that a CPRed associated with an instance-extraction algorithm implies a reduction in the quantum ROM(QROM). This unifies several recent results, including the security of the Gentry-Peikert-Vaikuntanathan IBE scheme by Zhandry(Crypto 2012) and the key encapsulation mechanism(KEM) variants using the Fujisaki-Okamoto transform by Jiang et al.(Crypto 2018) in the ***, we show that CPReds are incomparable to non-programming reductions(NPReds) and randomly-programming reductions(RPReds) formalized by Fischlin et al.(Asiacrypt 2010).

关键词： provable security random oracle model quantum random oracle model black-box reduction/separation programmability

Reactor field reconstruction from sparse and movable sensors using Voronoi tessellation-assisted convolutional neural networks

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Nuclear Science and Techniques 2024年第5期35卷 173-185页

作者： He-Lin Gong Han Li Dunhui Xiao Sibo Cheng Paris Elite institute of Technology Shanghai Jiao Tong UniversityShanghai 200240China School of Mathematical Sciences Tongji UniversityShanghai 200092China Department of computing Data Science InstituteImperial College LondonLondon SW72AZUK

The aging of operational reactors leads to increased mechanical vibrations in the reactor *** vibration of the incore sensors near their nominal locations is a new problem for neutronic field *** field-reconstruction methods fail to handle spatially moving *** this study,we propose a Voronoi tessellation technique in combination with convolutional neural networks to handle this *** from movable in-core sensors were projected onto the same global field structure using Voronoi tessellation,holding the magnitude and location information of the *** convolutional neural networks were used to learn maps from observations to the global *** proposed method reconstructed multi-physics fields(including fast flux,thermal flux,and power rate)using observations from a single field(such as thermal flux).Numerical tests based on the IAEA benchmark demonstrated the potential of the proposed method in practical engineering applications,particularly within an amplitude of 5 cm around the nominal locations,which led to average relative errors below 5% and 10% in the L_(2) and L_(∞)norms,respectively.

关键词： Voronoi tessellation Field reconstruction Nuclear reactors Reactor physics On-line monitoring

维普期刊数据库

A comprehensive survey of federated transfer learning: challenges, methods and applications

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Frontiers of Computer Science 2024年第6期18卷 186356-186356页

作者： GUO Wei ZHUANG Fuzhen ZHAN Xiao TONG Yiqi DONG Jin institute of Artificial Intelligence Beihang University SKLSDE School of Computer Science Beihang University School of Computer Science and Technology Shandong University Beijing Academy of Blockchain and Edge computing

Federated learning (FL) is a novel distributed machine learning paradigm that enables participants to collaboratively train a centralized model with privacy preservation by eliminating the requirement of data sharing. In practice, FL often involves multiple participants and requires the third party to aggregate global information to guide the update of the target participant. Therefore, many FL methods do not work well due to the training and test data of each participant may not be sampled from the same feature space and the same underlying distribution. Meanwhile, the differences in their local devices (system heterogeneity), the continuous influx of online data (incremental data), and labeled data scarcity may further influence the performance of these methods. To solve this problem, federated transfer learning (FTL), which integrates transfer learning (TL) into FL, has attracted the attention of numerous researchers. However, since FL enables a continuous share of knowledge among participants with each communication round while not allowing local data to be accessed by other participants, FTL faces many unique challenges that are not present in TL. In this survey, we focus on categorizing and reviewing the current progress on federated transfer learning, and outlining corresponding solutions and applications. Furthermore, the common setting of FTL scenarios, available datasets, and significant related research are summarized in this survey.

关键词： federated transfer learning federated learning transfer learning survey