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IEEE International Conference on Big Data

作者： Hong Tan Hui Li Xiaopeng Wang Weixiang Huang Runhuai Huang Shaowen Deng Jianping Wu Shenzhen Graduate School Peking University Shenzhen China Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology School of Computer & AI FuYao Univ. of Sci. &Tech Fuzhou China Capability & Platform Business Dept China Mobile Internet Co. Ltd Guangzhou China China Telecom Cloud Computing China Telecom Co. Ltd Beijing China China Unicom Global Limited Hong Kong China China Communications Services Construction Co. Ltd GuangZhou China

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Log-Structured Merge Tree (LSM-Tree) is widely employed in key-value (kv) store owing to its exceptional performance in write-intensive workloads. LSMTree appends kv pairs to memory initially, and only when the memory capacity reaches a threshold does it flush the data to disk. However, in comparison to write, read operations often entail more disk access, resulting in relatively a slower response speed. Although LSM-Tree utilizes an index structure to expedite read operations, its performance enhancement remains limited. In addition, LSM-Tree fails to consider the read hotness of each sorted string table (SSTable) during compaction, leading to significant fluctuations in performance. This paper introduces HL-LSM, a novel LSM-Tree. HL-LSM utilizes learned index to replace original index to save system overhead and improve read performance. Moreover, a lightweight read hotness statistics is devised to optimize the compaction and assess the feasibility of each SSTable in constructing a learned index. In order to evaluate the performance of HL-LSM, we constructed a system prototype, HLDB, based on Wisckey. Experiment demonstrates that in read-intensive scenarios, HLDB is able to reduce the overall system latency by nearly 10% compared to Wisckey. Furthermore, it was observed that the optimization effect of HL-LSM improves as the variance of hotness value distribution increases.

关键词： Fluctuations System performance Prototypes Big Data Compaction Indexes Optimization

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Dual-Protected Metal Halide Perovskite Nanosheets with an Enhanced Set of Stabilities

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Angewandte Chemie 2021年第13期133卷

作者： Dr. Yanjie He Yachao Liang Shuang Liang Dr. Yeu-Wei Harn Dr. Zili Li Mingyue Zhang Dingfeng Shen Zhiwei Li Prof. Yan Yan Prof. Xinchang Pang Prof. Zhiqun Lin School of Materials Science and Engineering Georgia Institute of Technology Atlanta GA 30332 USA Henan Joint International Research Laboratory of Living Polymerizations and Functional Nanomaterials Henan Key Laboratory of Advanced Nylon Materials and Application School of Materials Science and Engineering Zhengzhou University Zhengzhou 450001 China Shenzhen Cloud Computing Center National Supercomputing Center Shenzhen 518055 China

Approaches to achieve stable perovskite nanocrystals (PNCs) of interest, in particular those with large structural anisotropy, through protective coating of the inorganic shell at a single-nanocrystal (NC) level are comparatively few and limited in scope. Reported here is a robust amphiphilic-diblock-copolymer-enabled strategy for crafting highly-stable anisotropic CsPbBr 3 nanosheets (NSs) by in situ formation of a uniform inorganic shell (1st shielding) that is intimately ligated with hydrophobic polymers (2nd shielding). The dual-protected NSs display an array of remarkable stabilities (i.e., thermal, photostability, moisture, polar solvent, aliphatic amine, etc.) and find application in white-light-emitting diodes. In principle, by anchoring other multidentate amphiphilic polymer ligands on the surface of PNCs, followed by templated-growth of shell materials of interest, a rich variety of dual-shelled, multifunctional PNCs with markedly improved stabilities can be created for use in optics, optoelectronics, and sensory devices.

关键词： block copolymers perovskites materials science nanostructures semiconductors

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Improving Deep Assertion Generation via Fine-Tuning Retrieval-Augmented Pre-trained Language Models

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ACM Transactions on Software Engineering and Methodology 1000年

作者： Quanjun Zhang Chunrong Fang Yi Zheng Yaxin Zhang Yuan Zhao Rubing Huang Jianyi Zhou Yun Yang Tao Zheng Zhenyu Chen Department of Computing Technologies Swinburne University of Technology Australia and State Key Laboratory for Novel Software Technology Nanjing University China State Key Laboratory for Novel Software Technology Nanjing University China School of Computer Science and Engineering Macau University of Science and Technology China Huawei Cloud Computing Technologies Co. Ltd. China Department of Computing Technologies Swinburne University of Technology Australia State Key Laboratory for Novel Software Technology Nanjing University China and Shenzhen Research Institute of Nanjing University China

Unit testing validates the correctness of the units of the software system under test and serves as the cornerstone in improving software quality and reliability. To reduce manual efforts in writing unit tests, some techniques have been proposed to generate test assertions automatically, including deep learning (DL)-based, retrieval-based, and integration-based ones. Among them, recent integration-based approaches inherit from both DL-based and retrieval-based approaches and are considered state-of-the-art. Despite being promising, such integration-based approaches suffer from inherent limitations, such as retrieving assertions with lexical matching while ignoring meaningful code semantics, and generating assertions with a limited training *** this paper, we propose a novel Retrieval-Augmented Deep Assertion Generation approach, namely RetriGen, based on a hybrid assertion retriever and a pre-trained language model (PLM)-based assertion generator. Given a focal-test, RetriGen first builds a hybrid assertion retriever to search for the most relevant test-assert pair from external codebases. The retrieval process takes both lexical similarity and semantical similarity into account via a token-based and an embedding-based retriever, respectively. RetriGen then treats assertion generation as a sequence-to-sequence task and designs a PLM-based assertion generator to predict a correct assertion with historical test-assert pairs and the retrieved external assertion. Although our concept is general and can be adapted to various off-the-shelf encoder-decoder PLMs, we implement RetriGen to facilitate assertion generation based on the recent CodeT5 model. We conduct extensive experiments to evaluate RetriGen against six state-of-the-art approaches across two large-scale datasets and two metrics. The experimental results demonstrate that RetriGen achieves 57.66% and 73.24% in terms of accuracy and CodeBLEU, outperforming all baselines with an average improvement of 50.66%

关键词： Unit Testing Assertion Generation Pre-trained Language Models AI4SE

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Corrections to “Cross-Modal Correlation Learning by Adaptive Hierarchical Semantic Aggregation” [Jun 16 1201-1216]

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IEEE Transactions on Multimedia 2016年第10期18卷 2127-2127页

作者： Yan Hua Shuhui Wang Siyuan Liu Anni Cai Qingming Huang School of Information Engineering Communication University of China Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China Smeal College of Business Pennsylvania State University University Park PA USA Center of Cloud Computing Institute of Advanced Computing and Digital Engineering Shenzhen Institutes of Advanced Technology Shenzhen China Department of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intellectual Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

Presents corrections to the paper, "Cross-modal correlation learning by adaptive hierarchical semantic aggregation," (Hua, Y., et al) IEEE Trans. Multimedia, vol. 18, no. 6, pp. 1201-1216, Jun. 2016.

关键词： Semantics Learning (artificial intelligence) Ontologies Query processing

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Inside Cover: Rational Design of Zinc/Zeolite Catalyst: Selective Formation ofp-Xylene from Methanol to Aromatics Reaction (Angew. Chem. Int. Ed. 10/2022)

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Angewandte Chemie International Edition 2022年第10期61卷

作者： Prof. Ning Wang Jing Li Prof. Wenjing Sun Yilin Hou Lan Zhang Xiaomin Hu Yifeng Yang Xiao Chen Congmei Chen Prof. Biaohua Chen Prof. Weizhong Qian Faculty of Environment and Life Beijing University of Technology Beijing 100124 China Department of Chemistry Tsinghua University Beijing 100084 China China-America Cancer Research Institute Key Laboratory for Medical Molecular Diagnostics of Guangdong Province Guangdong Medical University Dongguan Guangdong 523808 China Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing 100084 China National Supercomputing Center in Shenzhen Shenzhen Cloud Computing Center) Guangdong Shenzhen 518055 China

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Rational Design of Zinc/Zeolite Catalyst: Selective Formation ofp-Xylene from Methanol to Aromatics Reaction

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Angewandte Chemie 2022年第10期134卷

The production of p- xylene from the methanol to aromatics (MTA) reaction is challenging. The catalytic stability, which is inversely proportional to the particle size of the zeolite, is not always compatible with p -xylene selectivity, which is inversely proportional to the external acid sites. In this study, based on a nano-sized zeolite, we designed hollow triple-shelled Zn/MFI single crystals using the ultra-dilute liquid-phase growth technique. The obtained composites possessed one ZSM-5 layer (≈30 nm) in the middle and two silicalite-1 layers (≈20 nm) epitaxially grown on two sides of ZSM-5, which exhibited a considerably long lifetime (100 % methanol conversion >40 h) as well as an enhanced shape selectivity of p -xylene (>35 %) with a p -xylene/xylene ratio of ≈90 %. Importantly, using this sandwich-like zeolite structure, we directly imaged the Zn species in the micropores of only the ZSM-5 layer and further determined the specific structure and anchor location of the Zn species.

关键词： Hollow Zeolite Methanol Conversion p-Xylene Triple Shell Zinc

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Innentitelbild: Rational Design of Zinc/Zeolite Catalyst: Selective Formation ofp-Xylene from Methanol to Aromatics Reaction (Angew. Chem. 10/2022)

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Angewandte Chemie 2022年第10期134卷

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面向微博的情感影响最大化模型

面向微博的情感影响最大化模型

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第29届中国数据库学术会议

作者： OU Gaoyan 欧高炎 CHEN Wei 陈薇 WANG Tengjiao 王腾蛟 LEI Kai 雷凯 YANG Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies Ministry of Education Beijing 100871 Chin 高可信软件技术教育部重点实验室北京100871 北京大学信息科学技术学院北京100871 The Shenzhen Key Lab for Cloud Computing Technology and Applications Shenzhen Graduate School Peki 北京大学深圳研究生院深圳市云计算关键技术与应用重点实验室广东深圳518055

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型—... 详细信息

社交网络中影响最大化问题是寻找具有最大影响范围的节点。影响最大化的大部分求解算法仅仅依赖社交网络图。基于微博的转发关系树和微博内容的情感倾向性,以及用户的社交网络图,提出了一个能够刻画用户情感影响的情感影响最大化模型——情感影响分配模型(SID),证明了SID模型下的情感影响最大化问题是一个NP难问题,给出了一个具有精度保证的贪心算法。在真实的微博数据上的实验结果表明,SID模型能够有效地找出情感影响最大化的节点集,同时具有很高的扩展性。

关键词：社交网络情感分析信息传播模型分配算法

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EVOC:一种基于链接和内容信息的动态社区挖掘方法

EVOC:一种基于链接和内容信息的动态社区挖掘方法

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第29届中国数据库学术会议

作者： Yan Qiuling 闫秋玲 Wu Liang 吴良 Chen Wei 陈薇 Wang Tengjiao 王腾蛟 Lei Kai 雷凯 Yang Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies(Peking University) Ministry of Education B 高可信软件技术教育部重点实验室(北京大学) 北京 100871 北京大学信息科学技术学院北京 100871 The Shenzhen Key Laboratory for Cloud Computing Technology and Applications (SPCCTA) Peking Univers 深圳市云计算关键技术与应用重点实验室(SPCCTA)北京大学深圳研究生院广东深圳 518055

社会网络中，不仅包括链接结构，还包括内容信息，内容信息表达了社会网络中个体相关的话题、兴趣等语义层面的信息。只考虑链接结构的社区挖掘方法无疑忽视了内容信息的价值。提出了一种结合链接结构和内容信息进行动态社区挖掘的方法... 详细信息

社会网络中，不仅包括链接结构，还包括内容信息，内容信息表达了社会网络中个体相关的话题、兴趣等语义层面的信息。只考虑链接结构的社区挖掘方法无疑忽视了内容信息的价值。提出了一种结合链接结构和内容信息进行动态社区挖掘的方法，将链接结构和内容表达为矩阵形式，综合历史社区划分结果，使用非负矩阵分解方法进行社区划分，以使社区结果在反映网络的链接特征的同时，还反映话题、兴趣等语义层面的特征。在真实数据集上的实验结果表明，所得到的结果揭示了社区随时间的演化情况、社区在不同时刻所关联的内容信息，并且能应用于其他领域，如专家搜索。

关键词：社会网络数据挖掘搜索算法信息整合

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XML模式匹配:整体连接算法TurboStack

XML模式匹配:整体连接算法TurboStack

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第29届中国数据库学术会议

作者： Guo Shaosong 郭少松 Bao Xiaoyuan 包小源 Chen Wei 陈薇 Wang Tengjiao 王腾蛟 Lei Kai 雷凯 Yang Dongqing 杨冬青 Key Laboratory of High Confidence Software Technologies(Ministry of Education) School of EECS Peki 高可信软件技术教育部重点实验室北京大学信息科学技术学院北京 100871 School of EECS Peking University Beijing 100871 北京大学信息科学技术学院北京 100871 The Shenzhen Key Lab for Cloud Computing Technology and Applications(SPCCTA) Peking University Shen 深圳市云计算关键技术与应用重点实验室(SPCCTA) 北京大学深圳研究生院

含谓词的XPath查询为小枝查询(twig query),它是XQuery最基本的查询模式。小枝查询算法有两大类：小枝分解和整体小枝连接,其中TwigStack系列算法处理小枝查询的效率是最优的,但其缺点在于不支持Twig Query中含“OR”以及通配符“*”的... 详细信息

含谓词的XPath查询为小枝查询(twig query),它是XQuery最基本的查询模式。小枝查询算法有两大类：小枝分解和整体小枝连接,其中TwigStack系列算法处理小枝查询的效率是最优的,但其缺点在于不支持Twig Query中含“OR”以及通配符“*”的查询;在大型商业数据库管理系统中,TurboXPath是DB2用于处理小枝查询的基本算法,它对XPath的支持较全面,但由于TurboXPath是针对XML数据流查询处理设计的,因而对数据库中Native存储的XML数据的查询处理效率较低;针对已有方法的不足,设计了一种纯XML存储方式,以DDE编码为基础,提出了TurboStack算法,并通过大量实验验证了该方法的有效性。

关键词：可扩展标记语言数据模式匹配整体连接算法信息查询

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