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arXiv 2024年

作者： Wang, Tianrui Li, Jin Ma, Ziyang Cao, Rui Chen, Xie Wang, Longbiao Ge, Meng Wang, Xiaobao Wang, Yuguang Dang, Jianwu Tashi, Nyima The Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300350 China Company Ltd. Tianjin300350 China Saw Swee Hock School of Public Health National University of Singapore Singapore MoE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University Shanghai China Co. Ltd. Tianjin China The Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Guangdong China The School of Information Science and Technology Tibet University Lhasa China

Self-supervised learning (SSL) has garnered significant attention in speech processing, excelling in linguistic tasks such as speech recognition. However, jointly improving the performance of pre-trained models on various downstream tasks, each requiring different speech information, poses significant challenges. To this purpose, we propose a progressive residual extraction based self-supervised learning method, named PROGRE. Specifically, we introduce two lightweight and specialized task modules into an encoder-style SSL backbone to enhance its ability to extract pitch variation and speaker information from speech. Furthermore, to prevent the interference of reinforced pitch variation and speaker information with irrelevant content information learning, we residually remove the information extracted by these two modules from the main branch. The main branch is then trained using HuBERT's speech masking prediction to ensure the performance of the Transformer's deep-layer features on content tasks. In this way, we can progressively extract pitch variation, speaker, and content representations from the input speech. Finally, we can combine multiple representations with diverse speech information using different layer weights to obtain task-specific representations for various downstream tasks. Experimental results indicate that our proposed method achieves joint performance improvements on various tasks, such as speaker identification, speech recognition, emotion recognition, speech enhancement, and voice conversion, compared to excellent SSL methods such as wav2vec2.0, HuBERT, and WavLM. © 2024, CC BY.

关键词： Self-supervised learning

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Pan-cancer analysis of somatic mutations across 21 neuroendocrine tumor types

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细胞研究（英文版） 2018年第5期28卷 601-604页

作者： Yanan Cao Weiwei Zhou Lin Li Jiaqian Wang Zhibo Gao Yiran Jiang Xiuli Jiang Aijing Shan Matthew H. Bailey Kuan-lin Huang Sam Q. Sun Michael D. McLellan Beifang Niu Weiqing Wang Li Ding Guang Ning Shanghai Clinical Center for Endocrine and Metabolic Diseases Shanghai Key Laboratory for Endocrine Tumors Rui-Jin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China Laboratory of Endocrinology and Metabolism Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai China Shanghai Clinical Center for Endocrine and Metabolic Diseases Shanghai Key Laboratory for Endocrine Tumors Rui-Jin Hospital Shanghai Jiao Tong University School of Medicine Shanghai China YuceBio Shenzhen China McDonnell Genome Institute Washington University St Louis MO USA Department of Medicine Division of Oncology Washington University St Louis MO USA McDonnell Genome Institute Washington University St Louis MO USA McDonnell Genome Institute Washington University St Louis MO USA Department of High-Performance Computing Technology and Application Development Computer Network Information Center Chinese Academy of Sciences Beijing China McDonnell Genome Institute Washington University St Louis MO USA Department of Medicine Division of Oncology Washington University St Louis MO USA Siteman Cancer Center Washington University St. Louis MO USA

Dear Editor,Neuroendocrine tumors (NETs) comprise a heterogeneous spectrum of neoplasms originating from neuroendocrine cells in various organs — most commonly in the endocrine glands and the gastrointestinal tract.1 The molecular and etiological features of NETs arising from different organs are still far from clarified. Therefore, systematic analysis of genomic alternations and their contribution to core pathways in NETs is urgently needed for the development of novel diagnostic, therapeutic strategies and personalized management of patients. Here, we investigated somatic mutations across 21 NET types through pan-cancer analysis and identified 86 candidate driver genes. Further analysis of druggability and panel sequencing of these genes provide potential diagnostic and therapeutic targets for NETs.

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可持续发展情景下丝绸之路沿线国家土地利用及其对陆地碳库影响

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Science Bulletin 2023年第7期68卷 740-749页

作者：曹敏田雅吴凯陈旻陈玉胡雪孙中昶左丽君林键骆磊朱瑞许振赐 Temenoujka Bandrova Milan Konecny 袁文平郭华东林珲闾国年 Key Laboratory of Virtual Geographic Environment Ministry of EducationNanjing Normal UniversityNanjing 210023China International Research Center of Big Data for Sustainable Development Goals Beijing 100094China Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application Nanjing 210023China School of Geography and Environment Jiangxi Normal UniversityNanchang 330022China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094China The Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Sierra Nevada Research Institute University of CaliforniaMerced CA 95348USA Institute of High Performance Computing Agency for ScienceTechnology and ResearchSingapore 138632Singapore Department of Geography the University of Hong KongHong Kong 999077China Laboratory on Cartography University of ArchitectureCivil Engineering and GeodesySofia 1164Bulgaria Laboratory on Geoinformatics and Cartography Institute of GeographyMasaryk UniversityBrno 60177Czech Republic School of Atmospheric Sciences Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)Sun Yat-sen UniversityZhuhai 519082China Key Laboratory of Poyang Lake Wetland and Watershed Research Ministry of EducationJiangxi Normal UniversityNanchang 330022China

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其... 详细信息

可持续发展目标(SDGs)呼吁全世界共同采取行动,促进繁荣并保护地球.本研究面向SDGs构建了环境、粮食和经济等可持续发展情景,预测2020~2030年丝绸之路沿线国家的土地利用变化,并分析其对陆地碳库的影响。研究表明:未来土地利用变化及其对陆地碳库的影响在不同情景下存在显著差异。在可持续环境情景下,林地得到了有效保护,相较于2020年中国林地碳储量增加了约0.60%;在可持续粮食情景下,耕地流失得到了缓解,尤其在南亚及东南亚地区的耕地面积呈现上升趋势:在可持续经济情景下,城市扩张速度加快,引起的碳损失量最多这项研究有助于推进丝绸之路沿线甚至全球可持续发展进程,为探寻可持续发展路径提供了科学依据。

关键词： Sustainable development goals(SDGs) Land-use projection Carbon pool Scenarios System dynamics model Cellular automata

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The research of springback compensation design method based on reverse modeling

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AIP Conference Proceedings 2017年第1期1864卷

作者： Ru Wang Fenlou Zhai Bing Wu Yutong Ji 1Beijing Computing Center Beijing 100094 China 2Beijing Engineering Laboratory on Cloud Computing Service of Internet Plus Intelligent Equipment Beijing 100094 China 3Beijing Key Laboratory of Cloud Computing Technology and Application Beijing 100094 China 4Gold-Key motor die company droup Hebei 062150 China

To solve the springback problem of automotive covering parts, a springback compensation design method of Stamping die based on reverse modeling is researched. By the Alignment module in Poly Works software, springback analysis is quickly implemented. Then, According to the results of springback analysis, the springback compensation design method which used the smooth displacement adjustment theory is established. At last, Set an inner plate of wheel housing as example, the methods has been applied in springback analysis of the part and springback compensation design of the die-face, which effectively reduces the times of compensation in try pressing stage and improves productivity.

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HL-LSM: A LSM-Tree Combined with Read Hotness and Learned Index

HL-LSM: A LSM-Tree Combined with Read Hotness and Learned In...

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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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