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science China(Information sciences) 2024年第9期67卷 321-329页

作者： Xiaoyu HE Xiaoming SUN Jialing ZHANG State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

The combination of contextual side information and search is a powerful paradigm in the scope of artificial intelligence. The prior knowledge enables the identification of possible solutions but may be imperfect. Contextual information can arise naturally, for example in game AI where prior knowledge is used to bias move decisions. In this work we investigate the problem of taking quantum advantage of contextual information, especially searching with prior knowledge. We propose a new generalization of Grover's search algorithm that achieves the optimal expected success probability of finding the solution if the number of queries is fixed. Experiments on small-scale quantum circuits verify the advantage of our algorithm. Since contextual information exists widely, our method has wide applications. We take game tree search as an example.

关键词： quantum computing quantum search quantum query algorithm prior knowledge

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Interference mitigation and target detection for automotive FMCW radar with range-Doppler sparse regularization

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science China(Information sciences) 2024年第9期67卷 342-343页

作者： Yan HUANG Yunxuan WANG Xiao ZHOU Hui ZHANG Yuan MAO Guisheng LIAO Wei HONG State Key Lab of Millimeter Waves Southeast University Department of Computer Science University College London National Key Lab of Radar Signal Processing Xidian University

The invention of vehicles has shaped our modern society and accelerated our economy while causing many tragic incidents simultaneously. Some advanced driver-assistance systems(ADAS) based on surrounding environment se... 详细信息

关键词： Radar interference

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Hybrid mesh-neural representation for 3D transparent object reconstruction

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Computational Visual Media 2025年第1期11卷 123-140页

作者： Jiamin Xu Zihan Zhu Hujun Bao Weiwei Xu College of Computer Science Hangzhou Dianzi UniversityHangzhou 310018China State Key Lab of CAD&CG Zhejiang UniversityHangzhou 310058China College of Computer Science ETH ZurichZurich 8092Switzerland.

In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting *** combines the advantages of an explicit mesh and multi-layer perceptron(MLP)network as a hybrid representation to simplify the capture settings used in recent *** obtaining an initial shape through multi-view silhouettes,we introduced surface-based local MLPs to encode the vertex displacement field(VDF)for reconstructing surface *** design of local MLPs allowed representation of the VDF in a piecewise manner using two-layer MLP networks to support the optimization *** local MLPs on the surface instead of on the volume also reduced the search *** a hybrid representation enabled us to relax the ray–pixel correspondences that represent the light path constraint to our designed ray–cell correspondences,which significantly simplified the implementation of a single-image-based environment-matting *** evaluated our representation and reconstruction algorithm on several transparent objects based on ground truth *** experimental results show that our method produces high-quality reconstructions that are superior to those of state-of-the-art methods using a simplified data-acquisition setup.

关键词： transparent object 3D reconstruction environment matting neural rendering

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Understanding Transaction Bugs in Database Systems 24

Understanding Transaction Bugs in Database Systems

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44th ACM/IEEE International Conference on Software Engineering, ICSE 2024

作者： Cui, Ziyu Dou, Wensheng Gao, Yu Wang, Dong Song, Jiansen Zheng, Yingying Wang, Tao Yang, Rui Xu, Kang Hu, Yixin Wei, Jun Huang, Tao State Key Lab of Computer Science at Iscas China University of Cas Beijing China Nanjing Institute of Software Technology China University of Cas Nanjing China Sun Yat-sen University Guangzhou China

ISBN: (纸本)9798400702174

Transactions are used to guarantee data consistency and integrity in Database Management Systems (DBMSs), and have become an indispensable component in DBMSs. However, faulty designs and implementations of DBMSs' transaction processing mechanisms can introduce transaction bugs, and lead to severe consequences, e.g., incorrect database states and DBMS crashes. An in-depth understanding of real-world transaction bugs can significantly promote effective techniques in combating transaction bugs in DBMSs. In this paper, we conduct the first comprehensive study on 140 transaction bugs collected from six widely-used DBMSs, i.e., MySQL, PostgreSQL, SQLite, MariaDB, CockroachDB, and TiDB. We investigate these bugs from their bug manifestations, root causes, bug impacts and bug fixing. Our study reveals many in-teresting findings and provides useful guidance for transaction bug detection, testing, and verification. © 2024 ACM.

关键词： Database systems

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LPW: an efficient data-aware cache replacement strategy for Apache Spark

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science China(Information sciences) 2023年第1期66卷 77-96页

作者： Hui LI Shuping JI Hua ZHONG Wei WANG Lijie XU Zhen TANG Jun WEI Tao HUANG State Key Lab of Computer Science Institute of Software Chinese Academy of Sciences University of Chinese Academy of Sciences Nanjing Institute of Software Technology

Caching is one of the most important techniques for the popular distributed big data processing framework Spark. For this big data parallel computing framework, which is designed to support various applications based on in-memory computing, it is not possible to cache every intermediate result due to the memory size limitation. The arbitrariness of cache application programming interface(API) usage,the diversity of application characteristics, and the variability of memory resources constitute challenges to achieving high system execution performance. Inefficient cache replacement strategies may cause different performance problems such as long application execution time, low memory utilization, high replacement frequency, and even program execution failure resulting from out of memory. The cache replacement strategy currently adopted by Spark is the least recently used(LRU) strategy. Although LRU is a classical algorithm and has been widely used, it lacks consideration for the environment and workloads. As a result, it cannot achieve good performance under many scenarios. In this paper, we propose a novel cache replacement algorithm, least partition weight(LPW). LPW takes comprehensive consideration of different factors affecting system performance, such as partition size, computational cost, and reference count. The LPW algorithm was implemented in Spark and compared against the LRU as well as other state-of-the-art mechanisms. Our detailed experiments indicate that LPW obviously outperforms its counterparts and can reduce the execution time by up to 75% under typical workloads. Furthermore, the decreasing eviction frequency also shows the LPW algorithm can generate more reasonable predictions.

关键词： Spark memory cache replacement least partition weight data-aware

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Grover's search finds new applications in continuous optimization and spectral analysis

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science China(Physics,Mechanics & Astronomy) 2025年第6期Mechanics & Astronomy) .卷 216-217页

作者： Xiaoming Sun State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences School of Computer Science and Technology University of Chinese Academy of Sciences

A novel quantum search algorithm tailored for continuous optimization and spectral problems was proposed recently by a research team from the University of Electronic science and Technology of China to broaden quantum computation frontiers and enrich its application *** computing has traditionally excelled at tackling discrete search challenges, but many important applications from large-scale optimization to advanced physics simulations necessitate searching through continuous domains.

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OpenFed: A Comprehensive and Versatile Open-Source Federated Learning Framework

OpenFed: A Comprehensive and Versatile Open-Source Federated...

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Chen, Dengsheng Tan, Vince Junkai Lu, Zhilin Wu, Enhua Hu, Jie Meituan China Bytedance Inc. China Tsinghua University China University of Macau China State Key Lab of Computer Science Iscas China University of Chinese Academy of Sciences China

ISBN: (纸本)9798350302493

Recent developments in Artificial Intelligence techniques have enabled their successful application across a spectrum of commercial and industrial settings. However, these techniques require large volumes of data to be aggregated in a centralized manner, forestalling their applicability to scenarios wherein the data is sensitive or the cost of data transmission is prohibitive. Federated Learning alleviates these problems by decentralizing model training, thereby removing the need for data transfer and aggregation. To advance the adoption of Federated Learning, more research and development needs to be conducted to address some important open questions. In this work, we propose OpenFed, an open-source software framework for end-to-end Federated Learning. OpenFed reduces the barrier to entry for both researchers and downstream users of Federated Learning by the targeted removal of existing pain points. For researchers, OpenFed provides a framework wherein new methods can be easily implemented and fairly evaluated against an extensive suite of benchmarks. For downstream users, OpenFed allows Federated Learning to be plugged and play within different subject-matter contexts, removing the need for deep expertise in Federated Learning. The source code of OpenFed is publicly available online at https://***/Federallab/OpenFed. © 2023 IEEE.

关键词： Data transfer

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FIFAWC:a dataset with detailed annotation and rich semantics for group activity recognition

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Frontiers of computer science 2024年第6期18卷 271-272页

作者： Duoxuan PEI Di HUANG Yunhong WANG State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Intelligent Recognition and Image Processing Lab. School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

1 *** Activity Recognition(GAR),which aims to identify activities performed collectively in videos,has gained significant attention *** conventional action recognition centered on single individuals,GAR explores the c... 详细信息

关键词： has collective gained

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Learning-Driven Dynamic Multimodal Optimization Algorithm for Real-Time Traceability of Water Pollution

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2472-2481页

作者： Yan, Xuesong Guo, Xing Chen, Jin Hu, Chengyu Gong, Wenyin Gao, Liang China University of Geosciences School of Computer Science Wuhan430074 China Huazhong University of Science and Technology State Key Lab of Digital Manufacturing Equipment and Technology Wuhan430074 China

In recent years, water quality safety problems caused by sudden urban drinking water contamination events have attracted the attention of experts in China and abroad. After an occurrence of urban water pollution, it is challenging to locate the pollution source in real time according to the information collected by water quality sensors and then quickly deduce the injection location, injection concentration quality, and other characteristics of the pollution source. In this article, we propose a learning-driven dynamic multimodal optimization algorithm framework that combines various machine learning algorithms. First, it uses the support vector machine (SVM) model to scale down and perform node probability estimation for a large-scale water supply pipeline network. Second, by predicting the uncertainty parameters of the pipe network when setting the pipe network simulation parameters, the framework can narrow the gap between simulation and real conditions, giving the pollution source characteristics obtained by the algorithm solution a higher confidence level. The experimental results show that the algorithm framework can achieve real-time traceability of water pollution for large-scale, uncertain pipe network environments, and can obtain better accuracy and real-time performance than other dynamic algorithms. © 2020 IEEE.

关键词： Water pollution

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FaultFuzz: A Coverage Guided Fault Injection Tool for Distributed Systems 24

FaultFuzz: A Coverage Guided Fault Injection Tool for Distri...

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46th International Conference on Software Engineering: Companion, ICSE-Companion 2024

作者： Feng, Wenhan Pei, Qiugen Gao, Yu Wang, Dong Dou, Wensheng Wei, Jun Liang, Zheheng Long, Zhenyue State Key Lab of Computer Science at ISCAS China University of CAS Beijing China Nanjing Institute of Software Technology China University of CAS Nanjing China Joint Laboratory on Cyberspace Security China Southern Power Grid GuangDong Power Grid China

ISBN: (纸本)9798400705021

Distributed systems are expected to correctly recover from various faults, e.g., node crash / reboot and network disconnection / reconnection. However, faults that occur under special timing can trigger fault recovery bugs that are rooted in incorrect fault recovery protocols and implementations. Existing random and brute-force fault injection approaches are not effective in revealing fault recovery bugs due to the combinatorial explosion of multiple faults in distributed systems. In this paper, we propose FaultFuzz, a coverage guided fault injection approach that can systematically and effectively test fault recovery behaviors in distributed systems. Based on runtime feedbacks collected from distributed system testing, e.g., code coverage and I/O information, FaultFuzz generates possible combinations of faults, and preferentially selects the combinations that are more likely to trigger new fault recovery behaviors and reveal new fault recovery bugs. We have applied FaultFuzz on three widely-used distributed systems, i.e., Zookeeper, HDFS and HBase and found 5 bugs in them. A video demonstration of FaultFuzz is available at https://***/SMw1ZF1vyXw. © 2024 IEEE computer Society. All rights reserved.

关键词： Software testing

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