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计算机科学技术学报（英文版）
计算机科学技术学报（英文版）

李国杰

双月刊

1000-9000

jcst@ict.ac.cn

010-62610746

100080

北京中关村科学院南路6号 《计算机科学技术学报（英）》编辑部

计算机科学技术学报（英文版）/Journal Journal of Computer Science and TechnologyCSCDCSTPCD北大核心EISCI
查看更多>>Journal of Computer Science and Technology(JCST)是中国计算机科学技术领域国际性学术期刊。 JCST于1986 年创刊, 双月刊, 国内外公开发行, 由Springer Science + Business Media代理国际出版发行。 JCST是中国计算机学会会刊, 由中国科学院计算技术研究所承办。JCST由数十位国际计算机界的著名专家和学者联袂编审，把握世界计算机科学技术最新发展趋势。JCST荟萃了国内外计算机科学技术领域中有指导性和开拓性的学术论著，定期组织热点专辑或专题栏目，部分文章邀请了世界著名计算机科学专家撰写。
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    Graph Accelerators—A Case for Sparse Data Processing

    陈文光
    243-244页
      原文链接:
    • 万方数据
    • 维普

    Towards High-Performance Graph Processing:From a Hardware/Software Co-Design Perspective

    廖小飞赵文举金海姚鹏程...
    245-266页
    查看更多>>摘要：Graph processing has been widely used in many scenarios,from scientific computing to artificial intelligence.Graph processing exhibits irregular computational parallelism and random memory accesses,unlike traditional workloads.Therefore,running graph processing workloads on conventional architectures(e.g.,CPUs and GPUs)often shows a signifi-cantly low compute-memory ratio with few performance benefits,which can be,in many cases,even slower than a special-ized single-thread graph algorithm.While domain-specific hardware designs are essential for graph processing,it is still challenging to transform the hardware capability to performance boost without coupled software codesigns.This article presents a graph processing ecosystem from hardware to software.We start by introducing a series of hardware accelera-tors as the foundation of this ecosystem.Subsequently,the codesigned parallel graph systems and their distributed tech-niques are presented to support graph applications.Finally,we introduce our efforts on novel graph applications and hard-ware architectures.Extensive results show that various graph applications can be efficiently accelerated in this graph pro-cessing ecosystem.
      原文链接:
    • 万方数据

    Preface

    吴恩华盛斌
    267-268页
      原文链接:
    • 万方数据
    • 维普

    Computational Approaches for Traditional Chinese Painting:From the"Six Principles of Painting"Perspective

    张玮张建伟黄锦畦王懿芳...
    269-285页
    查看更多>>摘要：Traditional Chinese painting(TCP)is an invaluable cultural heritage resource and a unique visual art style.In recent years,there has been a growing emphasis on the digitalization of TCP for cultural preservation and revitaliza-tion.The resulting digital copies have enabled the advancement of computational methods for a structured and systemat-ic understanding of TCP.To explore this topic,we conduct an in-depth analysis of 94 pieces of literature.We examine the current use of computer technologies on TCP from three perspectives,based on numerous conversations with specialists.First,in light of the"Six Principles of Painting"theory,we categorize the articles according to their research focus on artistic elements.Second,we create a four-stage framework to illustrate the purposes of TCP applications.Third,we sum-marize the popular computational techniques applied to TCP.This work also provides insights into potential applications and prospects,with professional opinion.
      原文链接:
    • 万方数据

    A Transformer-Assisted Cascade Learning Network for Choroidal Vessel Segmentation

    温阳吴依林毕磊石武祯...
    286-304页
    查看更多>>摘要：As a highly vascular eye part,the choroid is crucial in various eye disease diagnoses.However,limited re-search has focused on the inner structure of the choroid due to the challenges in obtaining sufficient accurate label data,particularly for the choroidal vessels.Meanwhile,the existing direct choroidal vessel segmentation methods for the intelli-gent diagnosis of vascular assisted ophthalmic diseases are still unsatisfactory due to noise data,while the synergistic seg-mentation methods compromise vessel segmentation performance for the choroid layer segmentation tasks.Common cas-caded structures grapple with error propagation during training.To address these challenges,we propose a cascade learn-ing segmentation method for the inner vessel structures of the choroid in this paper.Specifically,we propose Transformer-Assisted Cascade Learning Network(TACLNet)for choroidal vessel segmentation,which comprises a two-stage training strategy:pre-training for choroid layer segmentation and joint training for choroid layer and choroidal vessel segmentation.We also enhance the skip connection structures by introducing a multi-scale subtraction connection module designated as MSC,capturing differential and detailed information simultaneously.Additionally,we implement an auxiliary Trans-former branch named ATB to integrate global features into the segmentation process.Experimental results exhibit that our method achieves the state-of-the-art performance for choroidal vessel segmentation.Besides,we further validate the significant superiority of the proposed method for retinal fluid segmentation in optical coherence tomography(OCT)scans on a publicly available dataset.All these fully prove that our TACLNet contributes to the advancement of choroidal ves-sel segmentation and is of great significance for ophthalmic research and clinical application.
      原文链接:
    • 万方数据
    • 维普

    SinGRAV:Learning a Generative Radiance Volume from a Single Natural Scene

    王玉洁陈学霖陈宝权
    305-319页
    查看更多>>摘要：We present SinGRAV,an attempt to learn a generative radiance volume from multi-view observations of a single natural scene,in stark contrast to existing category-level 3D generative models that learn from images of many ob-ject-centric scenes.Inspired by SinGAN,we also learn the internal distribution of the input scene,which necessitates our key designs w.r.t.the scene representation and network architecture.Unlike popular multi-layer perceptrons(MLP)-based architectures,we particularly employ convolutional generators and discriminators,which inherently possess spatial locali-ty bias,to operate over voxelized volumes for learning the internal distribution over a plethora of overlapping regions.On the other hand,localizing the adversarial generators and discriminators over confined areas with limited receptive fields easily leads to highly implausible geometric structures in the spatial.Our remedy is to use spatial inductive bias and joint discrimination on geometric clues in the form of 2D depth maps.This strategy is effective in improving spatial arrange-ment while incurring negligible additional computational cost.Experimental results demonstrate the ability of SinGRAV in generating plausible and diverse variations from a single scene,the merits of SinGRAV over state-of-the-art generative neural scene models,and the versatility of SinGRAV by its use in a variety of applications.Code and data will be released to facilitate further research.
      原文链接:
    • 万方数据

    A Transfer Function Design for Medical Volume Data Using a Knowledge Database Based on Deep Image and Primitive Intensity Profile Features Retrieval

    Younhyun JungJim Kong盛斌Jinman Kim...
    320-335页
    查看更多>>摘要：Direct volume rendering(DVR)is a technique that emphasizes structures of interest(SOIs)within a volume visually,while simultaneously depicting adjacent regional information,e.g.,the spatial location of a structure concerning its neighbors.In DVR,transfer function(TF)plays a key role by enabling accurate identification of SOIs interactively as well as ensuring appropriate visibility of them.TF generation typically involves non-intuitive trial-and-error optimization of rendering parameters,which is time-consuming and inefficient.Attempts at mitigating this manual process have led to approaches that make use of a knowledge database consisting of pre-designed TFs by domain experts.In these approaches,a user navigates the knowledge database to find the most suitable pre-designed TF for their input volume to visualize the SOIs.Although these approaches potentially reduce the workload to generate the TFs,they,however,require manual TF navigation of the knowledge database,as well as the likely fine tuning of the selected TF to suit the input.In this work,we propose a TF design approach,CBR-TF,where we introduce a new content-based retrieval(CBR)method to automat-ically navigate the knowledge database.Instead of pre-designed TFs,our knowledge database contains volumes with SOI labels.Given an input volume,our CBR-TF approach retrieves relevant volumes(with SOI labels)from the knowledge database;the retrieved labels are then used to generate and optimize TFs of the input.This approach largely reduces man-ual TF navigation and fine tuning.For our CBR-TF approach,we introduce a novel volumetric image feature which in-cludes both a local primitive intensity profile along the SOIs and regional spatial semantics available from the co-planar images to the profile.For the regional spatial semantics,we adopt a convolutional neural network to obtain high-level im-age feature representations.For the intensity profile,we extend the dynamic time warping technique to address subtle alignment differences between similar profiles(SOIs).Finally,we propose a two-stage CBR scheme to enable the use of these two different feature representations in a complementary manner,thereby improving SOI retrieval performance.We demonstrate the capabilities of our CBR-TF approach with comparison with a conventional approach in visualization,where an intensity profile matching algorithm is used,and also with potential use-cases in medical volume visualization.
      原文链接:
    • 万方数据
    • 维普

    WavEnhancer:Unifying Wavelet and Transformer for Image Enhancement

    李梓诺陈绪行郭淑娜王书强...
    336-345页
    查看更多>>摘要：Image enhancement is a widely used technique in digital image processing that aims to improve image aes-thetics and visual quality.However,traditional methods of enhancement based on pixel-level or global-level modifications have limited effectiveness.Recently,as learning-based techniques gain popularity,various studies are now focusing on uti-lizing networks for image enhancement.However,these techniques often fail to optimize image frequency domains.This study addresses this gap by introducing a transformer-based model for improving images in the wavelet domain.The pro-posed model refines various frequency bands of an image and prioritizes local details and high-level features.Consequently,the proposed technique produces superior enhancement results.The proposed model's performance was assessed through comprehensive benchmark evaluations,and the results suggest it outperforms the state-of-the-art techniques.
      原文链接:
    • 万方数据

    Enhancing Storage Efficiency and Performance:A Survey of Data Partitioning Techniques

    刘鹏举李翠平陈红
    346-368页
    查看更多>>摘要：Data partitioning techniques are pivotal for optimal data placement across storage devices,thereby enhanc-ing resource utilization and overall system throughput.However,the design of effective partition schemes faces multiple challenges,including considerations of the cluster environment,storage device characteristics,optimization objectives,and the balance between partition quality and computational efficiency.Furthermore,dynamic environments necessitate ro-bust partition detection mechanisms.This paper presents a comprehensive survey structured around partition deployment environments,outlining the distinguishing features and applicability of various partitioning strategies while delving into how these challenges are addressed.We discuss partitioning features pertaining to database schema,table data,workload,and runtime metrics.We then delve into the partition generation process,segmenting it into initialization and optimiza-tion stages.A comparative analysis of partition generation and update algorithms is provided,emphasizing their suitabili-ty for different scenarios and optimization objectives.Additionally,we illustrate the applications of partitioning in preva-lent database products and suggest potential future research directions and solutions.This survey aims to foster the imple-mentation,deployment,and updating of high-quality partitions for specific system scenarios.
      原文链接:
    • 万方数据

    SHA:QoS-Aware Software and Hardware Auto-Tuning for Database Systems

    李进陈全唐晓新过敏意...
    369-383页
    查看更多>>摘要：While databases are widely-used in commercial user-facing services that have stringent quality-of-service(QoS)requirement,it is crucial to ensure their good performance and minimize the hardware usage at the same time.Our investigation shows that the optimal DBMS(database management system)software configuration varies for different us-er request patterns(i.e.,workloads)and hardware configurations.It is challenging to identify the optimal software and hardware configurations for a database workload,because DBMSs have hundreds of tunable knobs,the effect of tuning a knob depends on other knobs,and the dependency relationship changes under different hardware configurations.In this paper,we propose SHA,a software and hardware auto-tuning system for DBMSs.SHA is comprised of a scaling-based per-formance predictor,a reinforcement learning(RL)based software tuner,and a QoS-aware resource reallocator.The perfor-mance predictor predicts its optimal performance with different hardware configurations and identifies the minimum amount of resources for satisfying its performance requirement.The software tuner fine-tunes the DBMS software knobs to optimize the performance of the workload.The resource reallocator assigns the saved resources to other applications to im-prove resource utilization without incurring QoS violation of the database workload.Experimental results show that SHA improves the performance of database workloads by 9.9％on average compared with a state-of-the-art solution when the hardware configuration is fixed,and improves 43.2％of resource utilization while ensuring the QoS.
      原文链接:
    • 万方数据