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arXiv 2019年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming SKLOIS Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Lab of Intell. Info. Process. Inst. of Comput. Tech. CAS Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Big Data Mining and Knowledge Management CAS Beijing China

Traditionally, most of the existing attribute learning methods are trained based on the consensus of annotations aggregated from a limited number of annotators. However, the consensus might fail in settings, especially when a wide spectrum of annotators with different interests and comprehension about the attribute words are involved. In this paper, we develop a novel multi-task method to understand and predict personalized attribute annotations. Regarding the attribute preference learning for each annotator as a specific task, we first propose a multi-level task parameter decomposition to capture the evolution from a highly popular opinion of the mass to highly personalized choices that are special for each person. Meanwhile, for personalized learning methods, ranking prediction is much more important than accurate classification. This motivates us to employ an Area Under ROC Curve (AUC) based loss function to improve our model. On top of the AUC-based loss, we propose an efficient method to evaluate the loss and gradients. Theoretically, we propose a novel closed-form solution for one of our non-convex subproblem, which leads to provable convergence behaviors. Furthermore, we also provide a generalization bound to guarantee a reasonable performance. Finally, empirical analysis consistently speaks to the efficacy of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Software Defect Distribution Prediction Model Based on NPE-SVM

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China Communications 2018年第5期15卷 173-182页

作者： Hua Wei Chun Shan Changzhen Hu Huizhong Sun Min Lei School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China China Information Technology Security Evaluation Center Beijing 100085China Beijing Key Laboratory of Software Security Engineering Technology School of SoftwareBeijing Institute of TechnologyBeijing 100081China Information Security Center Beijing University of Posts and TelecommunicationsBeijing 100876China Guizhou University Guizhou Provincial Key Laboratory of Public Big DataGuizhou Guiyang 550025China

During the prediction of software defect distribution, the data redundancy caused by the multi-dimensional measurement will lead to the decrease of prediction accuracy. In order to solve this problem, this paper proposed a novel software defect prediction model based on neighborhood preserving embedded support vector machine(NPESVM) algorithm. The model uses SVM as the basic classifier of software defect distribution prediction model, and the NPE algorithm is combined to keep the local geometric structure of the data unchanged in the process of dimensionality reduction. The problem of precision reduction of SVM caused by data loss after attribute reduction is avoided. Compared with single SVM and LLE-SVM prediction algorithm, the prediction model in this paper improves the F-measure in aspect of software defect distribution prediction by 3%~4%.

关键词： data redundancy SVM NPE algorithm dimensionality reduction

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Particle number conserving BCS approach in the relativistic mean field model and its application to ^32-74Ca

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Chinese Physics C 2018年第11期42卷 63-71页

作者： Rong An Lisheng Geng Shisheng Zhang Lang Liu School of Physics and Nuclear Energy Engineering Beihang UniversityBeijing 100191China School of Physics and Nuclear Energy Engineering ＆ Beijing Key Laboratory of Advanced Nuclear Materials and Physics Beihang University Beijing 100191China Beijing Advanced Innovation Center for Big Data-based Precision MedicineBeihang UniversityBeijing 100191China School of Science Jiangnan UniversityWuxi 214122China

A fixed particle number BCS （FBCS） approach is formulated in the relativistic mean field （RMF） model. It is shown that the RMF＋FBCS model obtained can describe the weak pairing limit. We calculate the ground-state properties of the calcium isotopes ＆32-74Ca and compare the results with those obtained from the usual RMF＋BCS model. Although the results are quite similar to each other, we observe the interesting phenomenon that for ^54Ca, the FBCS approach can enhance the occupation probability of the 2p1/2 single particle level and slightly increases its radius, compared with the RMF＋BCS model. This leads to the unusual scenario that although ^54Ca is more bound with a spherical configuration, the corresponding size is not the most compact. We anticipate that such a phenomenon might happen for other neutron-rich nuclei and should be checked by further more systematic studies.

关键词： relativistic mean field model pairing correlation BCS approach particle number projection

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The dynamical relationship between capital market and macroeconomy: based on dynamic Bayesian network

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Procedia Computer Science 2019年 162卷 46-52页

作者： Yue Liu Yijing Wang Guihuan Zheng Jue Wang Kun Guo School of Economics and Management University of Chinese Academy of Sciences Beijing and 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing and 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing and 100190 China China Institute Of Finance And Capital Markets China Securities Regulatory Commission Beijing and 100032 China The Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing and 100190 China

The stock market volatility will be affected by many economic factors. On the contrary, the stock market will also influence the formulation of the economy and policy. It will be very useful if a qualitative structure can be computed to figure out the corresponding level between the stock market with these factors. The dynamical Bayesian factor graph is a suitable method to solve this problem. We choose a series of economic factors to represent the possible influence from macroeconomy, and macro-policy. We have found that the changes in the stock market during the bull market will affect the macroeconomy factors, and this effect will continue to weaken as the stock market moves to the bear market.

关键词： Dynamical Bayesian factor graph Macroeconomic factors Macro-policy factors Stock market

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Tracking consensus for nonlinear heterogeneous multi-agent systems subject to unknown disturbances via sliding mode control

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Chinese Physics B 2017年第7期26卷 39-48页

作者：张翔王金环杨德东徐勇 School of Sciences Hebei Province Key Laboratory of Big Data Calculation Hebei University of Technology School of Control Science and Engineering Hebei University of Technology

We investigate the tracking control for a class of nonlinear heterogeneous leader-follower multi-agent systems（MAS）with unknown external disturbances. Firstly, the neighbor-based distributed finite-time observers are proposed for the followers to estimate the position and velocity of the leader. Then, two novel distributed adaptive control laws are designed by means of linear sliding mode（LSM） as well as nonsingular terminal sliding mode（NTSM）, respectively. One can prove that the tracking consensus can be achieved asymptotically under LSM and the tracking error can converge to a quite small neighborhood of the origin in finite time by NTSM in spite of uncertainties and disturbances. Finally, a simulation example is given to verify the effectiveness of the obtained theoretical results.

关键词： multi-agent systems tracking consensus distributed adaptive control sliding mode

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Distortion-adaptive salient object detection in 360◦ omnidirectional images

arXiv

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arXiv 2019年

作者： Li, Jia Su, Jinming Xia, Changqun Tian, Yonghong State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing100191 China Beijing Adavanced Innovation Center for Big Data and Brain Computing Beihang University China National Engineering Laboratory for Video Technology School of Electronics Engineering and Computer Science Peking University Beijing China Peng Cheng Laboratory Shenzhen518000 China

Image-based salient object detection (SOD) has been extensively explored in the past decades. However, SOD on 360◦ omnidirectional images is less studied owing to the lack of datasets with pixel-level annotations. Toward this end, this paper proposes a 360◦ image-based SOD dataset that contains 500 high-resolution equirectangular images. We collect the representative equirectangular images from five mainstream 360◦ video datasets and manually annotate all objects and regions over these images with precise masks with a free-viewpoint way. To the best of our knowledge, it is the first public available dataset for salient object detection on 360◦ scenes. By observing this dataset, we find that distortion from projection, large-scale complex scene and small salient objects are the most prominent characteristics. Inspired by these foundings, this paper proposes a baseline model for SOD on equirectangular images. In the proposed approach, we construct a distortion-adaptive module to deal with the distortion caused by the equirectangular projection. In addition, a multiscale contextual integration block is introduced to perceive and distinguish the rich scenes and objects in omnidirectional scenes. The whole network is organized in a progressively manner with deep supervision. Experimental results show the proposed baseline approach outperforms the top-performanced state-of-the-art methods on 360◦ SOD dataset. Moreover, benchmarking results of the proposed baseline approach and other methods on 360◦ SOD dataset show the proposed dataset is very challenging, which also validate the usefulness of the proposed dataset and approach to boost the development of SOD on 360◦ omnidirectional scenes. Copyright © 2019, The Authors. All rights reserved.

关键词： Benchmarking

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Synchronization of Complex Dynamical Networks with Stochastic Coupling via PI Control

Synchronization of Complex Dynamical Networks with Stochasti...

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第三十八届中国控制会议

作者： Haibo Gu Kexin Liu Jinhu Lü Key Laboratory of Systems and Control Academy of Mathematics and Systems Science Chinese Academy of Sciences School of Mathematical Sciences University of Chinese Academy of Sciences School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment and Beijing Advanced Innovation Center for Big Data and Brain Machine Intelligence Beihang University

With the development of sensor networks in the past two decades, synchronization of complex dynamical networks has received an increasing attention and has been widely studied both in theoretical research and in practical applications. This paper concerns the synchronization protocol design problem of nonlinear dynamical complex networks with stochastic ***-integral(PI) control protocols are designed to guarantee that complex dynamical networks with stochastic coupling can achieve global synchronization. Sufficient condition for choice of PI control gains are derived. Numerical example is included to demonstrate the efficiency of the theoretical results.

关键词： Synchronization Complex dynamical network Stochastic coupling PI control

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Study on sensitivity and selectivity of three-stage current protection in distribution network with distributed generation

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IOP Conference Series: Earth and Environmental Science 2020年第1期467卷

作者： Jing Gong Beijing University of Civil Engineering and Architecture Beijing 100044 China Scholarship Council (***) the Scientific Research Fund of BUCEA Beijing Key Laboratory Researching on Intelligent Processing of Building Big Data

The connection of DG (Distributed Generation) changes the topology of distribution network, which will lead to the change of current detected by relay protection, and the sensitivity and scope of protection will be affected. On the basis of introducing the setting calculation principle of three-stage current protection in distribution network, taking a 10kV distribution network with DG as a model, the influence of DG capacity change on the selectivity and sensitivity of three-stage current protection is discussed in detail when short circuit occurs in different locations such as downstream, adjacent and upstream lines. The corresponding restrictive factors are analyzed and the influence degree of DG access on the original protection is given after comprehensive comparison. It is concluded that comparing different short circuit locations, DG has the most serious impact on the stage-I selectivity of downstream protection when the short circuit occurs at DG downstream line. The conclusions in this paper are of great value to ensure the reliability of power supply of distribution network when a large number of DGs are connected to the grid.

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Individual Dairy Cattle Recognition Based on Deep Convolutional Neural Network

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Journal of Donghua University(English Edition) 2018年第2期35卷 107-112页

作者： ZHANG Mandun SHAN Xinyuan YU Jinsu GUO Yingchun LI Ruiwen XU Mingquan School of Computer Science and Engineering Hebei University of Technology Tianjin 300401 China Hebei Province Key Laboratory of Big Data Calculation Tianfin 300401 China Traditional Chinese Veterinary Medicine College Agricultural University of Hebei Baoding 071000 China

Image based individual dairy cattle recognition has gained much attention recently. In order to further improve the accuracy of individual dairy cattle recognition, an algorithm based on deep convolutional neural network( DCNN) is proposed in this paper,which enables automatic feature extraction and classification that outperforms traditional hand craft features. Through making multigroup comparison experiments including different network layers,different sizes of convolution kernel and different feature dimensions in full connection layer,we demonstrate that the proposed method is suitable for dairy cattle classification. The experimental results show that the accuracy is significantly higher compared to two traditional image processing algorithms: scale invariant feature transform( SIFT) algorithm and bag of feature( BOF) model.

关键词： deep learning deep convolutional neural network(DCNN) dairy cattle individual recognition

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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