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40th ACM/IEEE International Conference on Software engineering, ICSE 2018

作者： Zhang, Jian Sun, Hailong Tian, Yanfei Liu, Xudong SKLSDE Lab School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing100191 China

ISBN: (纸本)9781450356633

Most software CQAs (e.g. Stack Overflow) mainly rely on users to assign tags for posted questions. This leads to many redundant, inconsistent and inaccurate tags that are detrimental to the communities. Therefore tag quality becomes a critical challenge to deal with. In this work, we propose STR, a deep learning based approach that automatically recommends tags through learning the semantics of both tags and questions in such software CQAs. First, word embedding is employed to convert text information to high-dimension vectors for better representing questions and tags. Second, a Multi-Tasking-like Convolutional Neural Network, the core modules of STR, is designed to capture short and long semantics. Third, the learned semantic vectors are fed into a gradient descent based algorithm for classification. Finally, we evaluate STR on three datasets collected from popular software CQAs, and experimental results show that STR outperforms state-of-The-Art approaches in terms of Precision@k, Recall@k and F1-Measure@k. © 2018 Authors.

关键词： Semantics

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Bioinformatics Commons: The Cornerstone of Life and Health sciences

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Genomics,Proteomics & Bioinformatics 2018年第4期16卷 223-225页

作者： Zhang Zhang Yu Xue Fangqing Zhao BIG Data Center and CAS Key Laboratory of Genome Sciences & Information Beijing Institute of Genomics Chinese Academy of Sciences Department of Bioinformatics & Systems Biology MOE Key Laboratory of Molecular Biophysics College of Life Science and Technology and the Collaborative Innovation Center for Biomedical Engineering Huazhong University of Science and Technology Beijing Institutes of Life Science Chinese Academy of Sciences

Bioinformatics, an interdisciplinary field that combines biology, mathematics, computer science, medicine, and health science, to integrate, analyze, and interpret biological data, is now becoming increasingly data-intensive. To dig out the treasure from big data powered by high-throughput sequencing technologies, it is highly dependent on Bioinformatics Commons that involves a variety of fundamental resources, includ-

关键词： Bioinformatics Commons The Cornerstone of Life and Health sciences

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Fatigue crack fault diagnosis and prognosis based on hidden semi-Markov model

Fatigue crack fault diagnosis and prognosis based on hidden ...

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19th International Conference of Fluid Power and Mechatronic Control engineering 2018

作者： Fan, Lei Wang, Shaoping Duan, Haibin Ran, Hongliang School of Automation Science and Electrical Engineering Beihang University Beijing China 41th Detachment People's Liberation Army Troop Beijing61267 China Beijing Advanced Innovation Center for Big Data-based Precision Medicine Beihang University Beijing China Department of Equipment Support Logistics University of PAP Tianjin China

Aiming at the fault diagnosis and remaining useful life (RUL) prediction of fatigue cracks of a helicopter main gearbox planet carrier, this article proposes a hidden semi-Markov model (HSMM) methodology, which introduces the explicit state durational distribution parameters into the traditional hidden Markov model (HMM), thus overcoming the limitation of exponential distribution in HMM, retaining strong pattern recognition and classification ability, and improving the diagnostic and prognostic accuracy, and the effectiveness of the method was verified through experiments. © 2019 Institution of engineering and Technology. All rights reserved.

关键词： Hidden Markov models

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CoRE MOF DB: A curated experimental metal-organic framework database with machine-learned properties for integrated material-process screening

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Matter 2025年第6期8卷

作者： Zhao, Guobin Brabson, Logan M. Chheda, Saumil Huang, Ju Kim, Haewon Liu, Kunhuan Mochida, Kenji Pham, Thang D. Prerna Terrones, Gianmarco G. Yoon, Sunghyun Zoubritzky, Lionel Coudert, François-Xavier Haranczyk, Maciej Kulik, Heather J. Moosavi, Seyed Mohamad Sholl, David S. Siepmann, J. Ilja Snurr, Randall.Q. Chung, Yongchul G. School of Chemical and Biomolecular Engineering Pusan National University Busan46241 Korea Republic of School of Chemical and Biomolecular Engineering Georgia Institute of Technology AtlantaGA30332 United States Department of Chemistry and Chemical Theory Center University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemical Engineering and Materials Science University of Minnesota 421 Washington Avenue SE MinneapolisMN55455 United States Department of Chemistry Kavli Energy Nanoscience Institute and Bakar Institute of Digital Materials for the Planet College of Computing Data Science and Society University of California Berkeley BerkeleyCA94720 United States Chemical Engineering and Applied Chemistry University of Toronto TorontoONM5S 3E5 Canada Department of Chemical and Biological Engineering Northwestern University EvanstonIL60208 United States Department of Chemical Engineering Massachusetts Institute of Technology CambridgeMA02139 United States Chimie ParisTech PSL University CNRS Institut de Recherche de Chimie Paris Paris75005 France Air Liquide Campus Innovation Paris Les Loges-en-Josas78350 France IMDEA Materials Institute Getafe Madrid28906 Spain Department of Chemistry Massachusetts Institute of Technology CambridgeMA02139 United States Oak Ridge National Laboratory Oak RidgeTN37830 United States of Data Science Pusan National University Busan46241 Korea Republic of

We present an updated version of the Computation-Ready, Experimental (CoRE) Metal-Organic Framework (MOF) database, which includes a curated set of computation-ready MOF crystal structures designed for high-throughput computational materials discovery. data collection and curation procedures were improved from the previous version to enable more frequent updates in the future. Machine-learning-predicted properties, such as stability metrics and heat capacities, are included in the dataset to streamline screening activities. An updated version of MOFid was developed to provide detailed information on metal nodes, organic linkers, and topologies of an MOF structure. DDEC6 partial atomic charges of MOFs were assigned based on a machine-learning model. Gibbs ensemble Monte Carlo simulations were used to classify the hydrophobicity of MOFs. The finalized dataset was subsequently used to perform integrated material-process screening for various carbon-capture conditions using high-fidelity temperature-swing adsorption (TSA) simulations. Our workflow identified multiple MOF candidates that are predicted to outperform CALF-20 for these applications. © 2025 Elsevier Inc.

关键词： Crystal structure

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Impact of temperature and relative humidity on the transmission of COVID-19: A modeling study in China and the United States

arXiv

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arXiv 2020年

作者： Wang, Jingyuan Tang, Ke Feng, Kai Lin, Xin Lv, Weifeng Chen, Kun Wang, Fei School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University China School of Social Sciences Tsinghua University China State Key Laboratory of Software Development Environment Beihang University China Department of Statistics University of Connecticut United States Center for Population Health University of Connecticut Health Center United States Department of Population Health Sciences Weill Cornell Medical College Cornell University United States

Objectives We aim to assess the impact of temperature and relative humidity on the transmission of COVID-19 across communities after accounting for community-level factors such as demographics, socioeconomic status, and human mobility status. Design A retrospective cross-sectional regression analysis via the Fama-MacBeth procedure is adopted. Setting We use the data for COVID-19 daily symptom-onset cases for 100 Chinese cities and COVID-19 daily confirmed cases for 1,005 U.S. counties. Participants A total of 69,498 cases in China and 740,843 cases in the U.S. are used for calculating the effective reproductive numbers. Primary outcome measures Regression analysis of the impact of temperature and relative humidity on the effective reproductive number (R value). Results Statistically significant negative correlations are found between temperature/relative humidity and the effective reproductive number (R value) in both China and the U.S. Conclusions Higher temperature and higher relative humidity potentially suppress the transmission of COVID-19. Specifically, an increase in temperature by 1 degree Celsius is associated with a reduction in the R value of COVID-19 by 0.026 (95% CI [-0.0395,-0.0125]) in China and by 0.020 (95% CI [-0.0311, -0.0096]) in the U.S.;an increase in relative humidity by 1% is associated with a reduction in the R value by 0.0076 (95% CI [-0.0108,-0.0045]) in China and by 0.0080 (95% CI [-0.0150,-0.0010]) in the U.S. Therefore, the potential impact of temperature/relative humidity on the effective reproductive number alone is not strong enough to stop the pandemic. Copyright © 2020, The Authors. All rights reserved.

关键词： Regression analysis

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stClinic dissects clinically relevant niches by integrating spatial multi-slice multi-omics data in dynamic graphs

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Nature communications 2025年第1期16卷 5317页

作者： Chunman Zuo Junjie Xia Yupeng Xu Ying Xu Pingting Gao Jing Zhang Yan Wang Luonan Chen School of Life Sciences Sun Yat-sen University Guangzhou China. zuochm@***. Institute of Artificial Intelligence Donghua University Shanghai China. zuochm@***. Shanghai Collaborative Innovation Center of Endoscopy Fudan University Shanghai China. zuochm@***. Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University Changchun China. zuochm@***. Institute of Artificial Intelligence Donghua University Shanghai China. System Biology Lab for Metabolic Reprogramming Department of Human Genetics and Cell Biology School of Medicine Southern University of Science and Technology Shenzhen China. Shanghai Collaborative Innovation Center of Endoscopy Fudan University Shanghai China. Department of Pathology Changzheng Hospital Secondary Military Medical University Shanghai China. College of Computer Science and Technology Jilin University Changchun China. School of Mathematical Sciences Shanghai Jiao Tong University Shanghai China. lnchen@***. School of AI Shanghai Jiao Tong University Shanghai China. lnchen@***. Key Laboratory of Systems Biology Shanghai Institute of Biochemistry and Cell Biology Center for Excellence in Molecular Cell Science Chinese Academy of Sciences Shanghai China. lnchen@***. Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Chinese Academy of Sciences Hangzhou China. lnchen@***. West China Biomedical Big Data Center Med-X Center for Informatics West China Hospital Sichuan University Chengdu China. lnchen@***.

Spatial multi-slice multi-omics (SMSMO) integration has transformed our understanding of cellular niches, particularly in tumors. However, challenges like data scale and diversity, disease heterogeneity, and limited sample population size, impede the derivation of clinical insights. Here, we propose stClinic, a dynamic graph model that integrates SMSMO and phenotype data to uncover clinically relevant niches. stClinic aggregates information from evolving neighboring nodes with similar-profiles across slices, aided by a Mixture-of-Gaussians prior on latent features. Furthermore, stClinic directly links niches to clinical manifestations by characterizing each slice with attention-based geometric statistical measures, relative to the population. In cancer studies, stClinic uses survival time to assess niche malignancy, identifying aggressive niches enriched with tumor-associated macrophages, alongside favorable prognostic niches abundant in B and plasma cells. Additionally, stClinic identifies a niche abundant in SPP1+ MTRNR2L12+ myeloid cells and cancer-associated fibroblasts driving colorectal cancer cell adaptation and invasion in healthy liver tissue. These findings are supported by independent functional and clinical data. Notably, stClinic excels in label annotation through zero-shot learning and facilitates multi-omics integration by relying on other tools for latent feature initialization.

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SPINBIS: Spintronics based Bayesian Inference System with Stochastic Computing

arXiv

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arXiv 2019年

作者： Jia, Xiaotao Yang, Jianlei Dai, Pengcheng Liu, Runze Chen, Yiran Zhao, Weisheng Advanced Innovation Center for Big Data and Brain Computing Fert Beijing Research Institute School of microelectronics Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Fert Beijing Research Institute School of Computer Science and Engineering Beihang University Beijing100191 China Department of Electrical and Computer Engineering Duke University NC Durham27708 United States

Bayesian inference is an effective approach for solving statistical learning problems, especially with uncertainty and incompleteness. However, Bayesian inference is a computing-intensive task whose efficiency is physically limited by the bottlenecks of conventional computing platforms. In this work, a spintronics based stochastic computing approach is proposed for efficient Bayesian inference. The inherent stochastic switching behaviors of spintronic devices are exploited to build stochastic bitstream generator (SBG) for stochastic computing with hybrid CMOS/MTJ circuits design. Aiming to improve the inference efficiency, an SBG sharing strategy is leveraged to reduce the required SBG array scale by integrating a switch network between SBG array and stochastic computing logic. A device-to-architecture level framework is proposed to evaluate the performance of spintronics based Bayesian inference system (SPINBIS). Experimental results on data fusion applications have shown that SPINBIS could improve the energy efficiency about 12× than MTJ-based approach with 45% design area overhead and about 26× than FPGA-based approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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Low-lying states of 92,93Nb excited in the reactions induced by the weakly-bound nucleus 6Li near the Coulomb barrier

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Chinese Physics C 2019年第10期43卷 69-77页

作者： Yi-Feng Lv Jing-Bin Lu Gao-Long Zhang Yi-Heng Wu Cen-Xi Yuan Guan-Jian Fu Guang-Xin Zhang Zhen Huang Ming-Li Wang Shi-Peng Hu Hui-Bin Sun Huan-Qiao Zhang Cheng-Qian Li Ke-Yan Ma Ying-Jun Ma Yun-Zuo Liu College of physics Jilin UniversityChangChun 130012China INFN Laboratori Nazionali di LegnaroLegnaro (Padova)Italy Irfu/CEA Universite de Paris-SaclayGif-sur-YvetteFrance School of Physics and Nuclear Energy Engineering Beihang UniversityBeijing 100191China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang UniversityBeijing 100083China School of Physics and Electronic Engineering An Qing Normal UniversityAn qing246133China Sino-French Institute of Nuclear Engineering and Technology Sun Yat-Sen UniversityZhuhai 519082China School of Physics Science and Engineering Tongji UniversityShanghai 200092China College of Physics and Optoelectronic Engineering Shenzhen UniversityShenzhen 518060China China Institute of Atomic Energy Beijing 102413China Dipartimento di Fisica and INFN Sezione di PadovaPadovaItaly

Excited states of odd-odd nucleus 92Nb and odd-A nucleus 93Nb were populated in the 6 Li+ 89 Y reaction with an incident energy of 34 MeV. The processes that produce 92,93Nb and can be measured by a combination of light charged particle and gamma ray measurements are discussed. Twenty new transitions are observed and eight new levels are constructed in 92Nb, and in addition two new transitions are added to the level scheme of 93Nb. Using shell2 model calculations, the low-lying structure of 9 Nb is investigated and compared with the experimental results.

关键词： weakly-bound projectile coincidence measurement level structure odd-odd nucleus 92Nb odd-A nucleus 93Nb.

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Review of artificial intelligence techniques in imaging data acquisition, segmentation and diagnosis for COVID-19

arXiv

引用

arXiv 2020年

作者： Shi, Feng Wang, Jun Shi, Jun Wu, Ziyan Wang, Qian Tang, Zhenyu He, Kelei Shi, Yinghuan Shen, Dinggang Department of Research and Development Shanghai United Imaging Intelligence Co. Ltd. Shanghai200232 Key Laboratory of Specialty Fiber Optics and Optical Access Networks Shanghai Institute for Advanced Communication and Data Science School of Communication and Information Engineering Shanghai University Shanghai200444 United Imaging Intelligence CambridgeMA02140 United States Institute for Medical Imaging Technology School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200030 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 Medical School of Nanjing University Nanjing China National Institute of Healthcare Data Science Nanjing University Nanjing210093 China National Key Laboratory for Novel Software and Technology Nanjing University Nanjing China

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Medical imaging such as X-ray and computed tomography (CT) plays an essential role in the global fight against COVID-19, whereas the recently emerging artificial intelligence (AI) technologies further strengthen the power of the imaging tools and help medical specialists. We hereby review the rapid responses in the community of medical imaging (empowered by AI) toward COVID-19. For example, AI-empowered image acquisition can significantly help automate the scanning procedure and also reshape the workflow with minimal contact to patients, providing the best protection to the imaging technicians. Also, AI ca n improve work efficiency by accurate delination of infections in X-ray and CT images, facilitating subsequent quantification. Moreover, the computer-aided platforms help radiologists make clinical decisions, i.e., for disease diagnosis, tracking, and prognosis. In this review paper, we thus cover the entire pipeline of medical imaging and analysis techniques involved with COVID-19, including image acquisition, segmentation, diagnosis, and follow-up. We particularly focus on the integration of AI with X-ray and CT, both of which are widely used in the frontline hospitals, in order to depict the latest progress of medical imaging and radiology fighting against COVID-19. Copyright © 2020, The Authors. All rights reserved.

关键词： Coronavirus

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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

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14th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for Computer sciences, Social Informatics and Telecommunications engineering.

关键词： Adaptive boosting

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