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arXiv 2019年

作者： Xu, Qianqian Sun, Xinwei Yang, Zhiyong Cao, Xiaochun Huang, Qingming Yao, Yuan Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS Microsoft Research Asia State Key Laboratory of Information Security Institute of Information Engineering CAS School of Cyber Security University of Chinese Academy of Sciences School of Computer Science and Tech. University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management CAS Peng Cheng Laboratory Department of Mathematics Hong Kong University of Science and Technology Hong Kong

Due to the inherent uncertainty of data, the problem of predicting partial ranking from pairwise comparison data with ties has attracted increasing interest in recent years. However, in real-world scenarios, different individuals often hold distinct preferences. It might be misleading to merely look at a global partial ranking while ignoring personal diversity. In this paper, instead of learning a global ranking which is agreed with the consensus, we pursue the tie-aware partial ranking from an individualized perspective. Particularly, we formulate a unified framework which not only can be used for individualized partial ranking prediction, but also be helpful for abnormal user selection. This is realized by a variable splitting-based algorithm called iSplitLBI. Specifically, our algorithm generates a sequence of estimations with a regularization path, where both the hyperparameters and model parameters are updated. At each step of the path, the parameters can be decomposed into three orthogonal parts, namely, abnormal signals, personalized signals and random noise. The abnormal signals can serve the purpose of abnormal user selection, while the abnormal signals and personalized signals together are mainly responsible for individual partial ranking prediction. Extensive experiments on simulated and real-world datasets demonstrate that our new approach significantly outperforms state-of-the-art alternatives. The code is now availiable at https://***/qianqianxu010/ NeurIPS2019-iSplitLBI. Copyright © 2019, The Authors. All rights reserved.

关键词： Forecasting

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Unsupervised keyword Extraction Method Based on Chinese Patent Clustering

Unsupervised Keyword Extraction Method Based on Chinese Pate...

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IEEE International Conference on big knowledge (ICBK)

作者： Yuxin Xie Xuegang Hu Yuhong Zhang Shi Li Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China Intelligent Connected Vehicle Research Institute (JAC Technical Center) Anhui Jianghuai Automobile Group Corp.Ltd Hefei China

Recently, patent data analysis has attracted a lot of attention, and patent keyword extraction is a hot problem. Most existing methods for patent keyword extraction are based on the frequency of words without semantic information. In this paper, we propose an Unsupervised keyword Extraction Method (UKEM) based on Chinese patent clustering. More specifically, we use a Skip-gram model to train word embeddings based on a Chinese patent corpus. Then each patent is represented as a vector called patent vector. These patent vectors are clustered to obtain several cluster centroids. Next, the distance between each word vector in patent abstract and cluster centroid is computed to indicate the semantic importance of this word. The experimental results on several Chinese patent datasets show that the performance of our proposed method is better than several competitive methods.

关键词： Patents Semantics data mining knowledge engineering Neural networks big data Computer science

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Automatic knowledge Graph Construction: A Report on the 2019 ICDM/ICBK Contest

Automatic Knowledge Graph Construction: A Report on the 2019...

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IEEE International Conference on data Mining (ICDM)

作者： Xindong Wu Jia Wu Xiaoyi Fu Jiachen Li Peng Zhou Xu Jiang Mininglamp Academy of Sciences Mininglamp Technology Beijing China Key Laboratory of Knowledge Engineering with Big Data (Hefei Univ. of Technology) Ministry of Education Hefei China Department of Computing Macquarie University Sydney Australia School of Computer Science and Technology Anhui University Hefei China

Automatic knowledge graph construction seeks to build a knowledge graph from unstructured text in a specific domain or cross multiple domains, without human intervention. IEEE ICDM 2019 and ICBK 2019 invited teams from both degree-granting institutions and industrial labs to compete in the 2019 knowledge Graph Contest by automatically constructing knowledge graphs in at least two different domains. This article reports the outcomes of the Contest. The participants were expected to build a model to extract knowledge represented as triplets from text data and develop a web application to visualize the triplets. Awards were given to five teams. Their models and key techniques used to construct knowledge graphs are summarized.

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Genome Sequences Provide Insights into the Reticulate Origin and Unique Traits of Woody Bamboos

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Molecular Plant 2019年第10期12卷 1353-1365页

作者： Zhen-Hua Guo Peng-Fei Ma Guo-Qian Yang Jin-Yong Hu Yun-Long Liu En-Hua Xia Mi-Cai Zhong Lei Zhao Gui-Ling Sun Yu-Xing Xu You-Jie Zhao Yi-Chi Zhang Yu-Xiao Zhang Xue-Mei Zhang Meng-Yuan Zhou Ying Guo Cen Guo Jing-Xia Liu Xia-Ying Ye Yun-Mei Chen Yang Yang Bin Han Choun-Sea Lin Ying Lu De-Zhu Li Germplasm Bank of Wild Species Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China Key Laboratory for Plant Diversity and Biogeography in East Asia Kunming Institute of BotanyChinese Academy of SciencesKunmingYunnan 650201China State Key Laboratory of Tea Plant Biology and Utilization Anhui Agricultural UniversityHefeiAnhui 230036China Kunming College of Life Science University of Chinese Academy of SciencesKunmingYunnan 650201China lnstitute of Plant Stress Biology State Key Laboratory of Cotton BiologyDepartment of BiologyHenan UniversityKaifengHenan 475001China College of Big Data and Intelligent Engineering Southwest Forestry UniversityKunmingYunnan 650224China Yunnan Academy of Biodiversity Southwest Forestry UniversityKunmingYunnan 650224China National Center for Gene Research Shanghai Institute of Plant Physiology and EcologyShanghai Institutes for Biological SciencesChinese Academy of SciencesShanghai 200032China Agricultural Biotechnology Research Center Academia SinicaTaipei College of Fisheries and Life Science Shanghai Ocean UniversityShanghai 201306China

Polyploidization is a major driver of speciation and its importance to plant evolution has been well *** comprise one diploid herbaceous and three polyploid woody lineages,and are members of the only major subfamily in grasses that diversified in forests,with the woody members having a tree-like lignified *** this study,we generated four draft genome assemblies of major bamboo lineages with three different ploidy levels(diploid,tetraploid,and hexaploid).We also constructed a high-density genetic linkage map for a hexaploid species of bamboo,and used a linkage-map-based strategy for genome assembly and identification of subgenomes in *** phylogenomic analyses using a large dataset of syntenic genes with expected copies based on ploidy levels revealed that woody bamboos originated subsequent to the divergence of the herbaceous bamboo lineage,and experienced complex reticulate evolution through three independent allopolyploid events involving four extinct diploid ancestors.A shared but distinct subgenome was identified in all polyploid forms,and the progenitor of this subgenome could have been critical in ancient polyploidizations and the origin of woody *** genetic clues to the unique flowering behavior and woody trait in bamboos were also *** together,our study provides significant insights into ancient reticulate evolution at the subgenome level in the absence of extant donor species,and offers a potential model scenario for broad-scale study of angiosperm origination by allopolyploidization.

关键词： Bambusoideae polyploidization comparative genomics subgenome evolution flowering

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Block-Structured Optimization for Anomalous Pattern Detection in Interdependent Networks

Block-Structured Optimization for Anomalous Pattern Detectio...

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IEEE International Conference on data Mining (ICDM)

作者： Fei Jie Chunpai Wang Feng Chen Lei Li Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education Hefei China School of Computer Science and Information Engineering Hefei University of Technology Hefei China Department of Computer Science University at Albany – SUNY Albany NY USA Erik Jonsson School of Engineering & Computer Science The University of Texas at Dallas Dallas TX USA Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Knowledge Science Hefei University of Technology Hefei China

We propose a generalized optimization framework for detecting anomalous patterns (subgraphs that are interesting or unexpected) in interdependent networks, such as multi-layer networks, temporal networks, networks of networks, and many others. We frame the problem as a non-convex optimization that has a general nonlinear score function and a set of block-structured and non-convex constraints. We develop an effective, efficient, and parallelizable projection-based algorithm, namely Graph Block-structured Gradient Projection (GBGP), to solve the problem. It is proved that our algorithm 1) runs in nearly-linear time on the network size, and 2) enjoys a theoretical approximation guarantee. Moreover, we demonstrate how our framework can be applied to two very practical applications, and we conduct comprehensive experiments to show the effectiveness and efficiency of our proposed algorithm.

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Fault-tolerant coherent H∞ control for linear quantum systems

arXiv

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arXiv 2020年

作者： Liu, Yanan Dong, Daoyi Petersen, Ian R. Gao, Qing Ding, Steven X. Yokoyama, Shota Yonezawa, Hidehiro School of Engineering and Information Technology University of New South Wales CanberraACT2600 Australia Center for Quantum Computation and Communication Technology Australian Research Council CanberraACT2600 Australia University of Duisburg-Essen Duisburg47057 Germany Research School of Electrical Energy and Materials Engineering Australian National University CanberraACT2601 Australia School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

Robustness and reliability are two key requirements for developing practical quantum control systems. The purpose of this paper is to design a coherent feedback controller for a class of linear quantum systems suffering from Markovian jumping faults so that the closed-loop quantum system has both fault tolerance and H∞ disturbance attenuation performance. This paper first extends the physical realization conditions from the time-invariant case to the time-varying case for linear stochastic quantum systems. By relating the fault tolerant H∞ control problem to the dissipation properties and the solutions of Riccati differential equations, an H∞ controller for the quantum system is then designed by solving a set of linear matrix inequalities (LMIs). In particular, an algorithm is employed to introduce additional noises and to construct the corresponding input matrices to ensure the physical realizability of the quantum controller. For real applications of the developed fault-tolerant control strategy, we present a linear quantum system example from quantum optics, where the amplitude of the pumping field randomly jumps among different values. It is demonstrated that a quantum H∞ controller can be designed and implemented using some basic optical components to achieve the desired control goal. Copyright © 2020, The Authors. All rights reserved.

关键词： Controllers

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Corrigendum to “Rational Protocols and Attacks in Blockchain System”

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Security and Communication Networks 2021年第1期2021卷

作者： Tao Li Yuling Chen Yanli Wang Yilei Wang Minghao Zhao Haojia Zhu Youliang Tian Xiaomei Yu Yixian Yang State Key Laboratory of Public Big Data Guizhou University Guiyang Guizhou 550025 *** College of Computer Science and Technology Guizhou University Guiyang Guizhou 550025 *** School of Information Science and Engineering Qufu Normal University Rizhao 276825 *** School of Software Tsinghua University Beijing 100084 *** School of Information Science and Engineering Shandong Normal University Jinan 250014 ***

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Rethinking loss design for large-scale 3D shape retrieval

arXiv

引用

arXiv 2019年

作者： Li, Zhaoqun Xu, Cheng Leng, Biao School of Computer Science and Engineering Beihang University Beijing100191 Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191

Learning discriminative shape representations is a crucial issue for large-scale 3D shape retrieval. In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class. Utilizing simple inner product operation, CIP loss explicitly enforces the features of the same class to be clustered in a linear subspace, while inter-class subspaces are constrained to be at least orthogonal. Compared to previous metric loss functions, CIP loss could provide more clear geometric interpretation for the embedding than Euclidean margin, and is easy to implement without normalization operation referring to cosine margin. Moreover, our proposed loss term can combine with other commonly used loss functions and can be easily plugged into existing off-the-shelf architectures. Extensive experiments conducted on the two public 3D object retrieval datasets, ModelNet and ShapeNetCore 55, demonstrate the effectiveness of our proposal, and our method has achieved state-of-the-art results on both datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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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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Multi-view SVM Classification with Feature Selection

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Procedia Computer Science 2019年 162卷 405-412页

作者： Yuting Niu Yuan Shang Yingjie Tian School of Information Engineering Zhengzhou University Zhengzhou 450001 China Smart City Institute Zhengzhou University Zhengzhou 450001 China Supercomputer Center Smart City Institute Zhengzhou University Henan 450001 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge management Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China

With the rapid development of data mining technology, multi-view learning (MVL) has become a new research field, which has attracted wide attention of scholars at home and abroad. Multi-view learning is to combine multiple view data of the same entity for data classification, thereby improving learning performance. Previous multi-view research methods mainly concentrate on the relationship between different data views for classification problems. However, when the data is in high dimensions, it is necessary to perform feature selection in the multi-view data classification process. In this paper, we proposed a Multi-view Support Vector Machine Classification with Feature Selection (MSVMCFS) algorithm, which can not only classify multi-view data, but also select features for each view data in the process of classification. In the model, feature selection is performed by the l 1 norm sparsity regularization, and consistency and complementarity between the two views are maintained. To achieve the optimization goal, we adopt linear programming to solve the model. The experimental results on 30 binary datasets demonstrate the validity of the model.

关键词： multi-view learning classification feature selection sparsity regularization linear programming

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