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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Leng, Jiaxu Liu, Ying Zhang, Tianlin Quan, Pei Cui, Zhenyu Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 101400 Peoples R China Univ Chinese Acad Sci Data Min & High Performance Comp Lab Beijing 101400 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9781538654880

Biomedical image segmentation is challenging for the lack of training data. U-Net has achieved good performance with small scale datasets through skip connections to merge features of low-level layers and high-level layers. However, it does not fully exploit the valuable contexts, which limits its segmentation performance. To enhance traditional U-Net not fully considering contextual information, we develop a context-aware U-Net, which aims to capture valuable contexts and improves the segmentation performance. Specially, we design a context transfer module that learns the rich contexts and produces context feature maps corresponding to input convolution feature maps. The proposed context transfer module is lightweight and it is easy to embed in any existing networks for image segmentation. In this paper, we present a context-aware U-Net with our context transfer module and conduct experiments to evaluate its performance on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks. Experimental results show that our proposed context-aware U-Net achieves state-of-the-art performance on the ISBI challenge.

关键词： Image segmentation Convolution Computer architecture Biomedical imaging Computer vision Microprocessors Task analysis

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Context Learning Network for Object Detection 18

Context Learning Network for Object Detection

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18th IEEE International Conference on data mining Workshops (ICDMW)

作者： Leng, Jiaxu Liu, Ying Zhang, Tianlin Quan, Pei Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 101400 Peoples R China Chinese Acad Sci Data Min & High Performance Comp Lab Beijing 101400 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9781538692882

Current state-of-the-art detectors typically classify candidate proposals using their interior features. However, the valuable contexts are not fully exploited by the existing detectors yet, which limits the detection performance. In this paper, we propose a Context Learning Network (CLN), which aims to capture the pairwise relation between objects and the global contexts of each object. The proposed CLN consists of two sub-networks: a Multi-Layer Perceptron (MLP) with three layers and a Convolutional neural Network (ConvNet) with two layers. The MLP is firstly designed to capture the pairwise relation context. Pairwise relationship context is then gathered and concatenated to further learn the global contexts by the ConvNet. Finally, we obtain the desired context feature maps with rich contextual information that are useful for accurate object detection. The proposed CLN is lightweight and it is easy to be embedded in any existing network for object detection. Extensive experiments are conducted to evaluate the performance of the context-aware Faster-RCNN with our proposed CLN. Experimental results demonstrate that the context-aware Faster-RCNN achieves state-of-the-art performance with the 82.1%, 80.7% and 38.4% mAPs on VOC 2007, VOC 2012 and COCO datasets, respectively.

关键词： Object detection Proposals Feature extraction Detectors Convolution Neural networks data mining

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Domain specific automatic Chinese multiple-type question generation

Domain specific automatic Chinese multiple-type question gen...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics

作者： Zhang, Tianlin Liu, Ying Quan, Pei Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 101400 Peoples R China Univ Chinese Acad Sci Data Min & High Performance Comp Lab Beijing 101400 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China

ISBN: (纸本)9781538654880

Question Generation is a rising research field of artificial intelligence in education and staff training. It is an effective strategy to conduct knowledge evaluation and performance appraisal. How to generate a variety of good quality questions has been an essential issue. In this paper, we propose an automatic method that generates Chinese multiple-type question by combing diverse knowledge bases and similar language features. Our research is mainly focused on Tradition Chinese Medicine (TCM) domain because it contains many Chinese-based resources and complex relationships. But the method is flexible and can be easily applied to other domains. Experimental results demonstrate that the proposed method obtain a good result.

关键词： Automatic Question Generation Multiple-type question generation Natural language processing knowledge evaluation TCM domain

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How vehicle market is segmented and influenced by subsidy policy: A theoretical study

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2018年 118卷 776-782页

作者： Chen, Xiao Wu, Tian Zheng, Rui Guo, Xiaoxian Tsinghua Univ Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Acad Math & Syst Sci NCMIS Beijing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China Chinese Acad Sci Inst Psychol Key Lab Behav Sci Beijing Peoples R China China Petr Technol & Dev Corp Beijing Peoples R China

This paper studies how vehicle market is segmented among consumers and what impacts subsidy policy may have on segmentation. With constant elasticity of substitution utility function and unit demand, we find positive market share for both high-end and low-end vehicles, but not necessary for middle-end vehicles. Market segmentation may have two regimes according to different levels of subsidy on electric vehicles (EVs), and only a sufficiently high subsidy allows for the existence of low type EV market share welcomed by median income consumers. An EV subsidy cut will definitely crowd out the market share of high type EV and expand that of internal combustion engine vehicle (ICEV). And given in a regime where low type EVs survive, EV subsidy cut's influence on low type EV market share is ambiguous, which depends on the relative consumer density around decision cutoff points.

关键词： Electric vehicles Vehicle consumption behavior Subsidy scheme Market segmentation Prospect theory

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New Discoveries of Domination Between Traffic Matrices

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Acta Mathematicae Applicatae Sinica 2017年第3期33卷 561-566页

作者： Peng-fei LIU Wen-guo YANG Tian-de GUO School of Mathematics Sciences University of Chinese Academy of SciencesBeijing 100049China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100049China

In this paper the definition of domination is generalized to the case that the elements of the traffic matrices may have negative values. It is proved that D3 dominates D3 ＋ λ（D2 - D1） for any λ ≥0 if D1 dominates D2. Let u（D） be the set of all the traffic matrices that are dominated by the traffic matrix D. It is shown that u （ D∞） and u （D ∈） are isomorphic. Besides, similar results are obtained on multi-commodity flow problems. Fhrthermore, the results are the generalized to integral flows.

关键词： robust network design multi-commodity flows domination

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Learning personalized attribute preference via multi-task auc optimization

arXiv

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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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Cost-sensitive multi-label learning with positive and negative label pairwise correlations

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NEURAL NETWORKS 2018年 108卷 411-423页

作者： Wu, Guoqiang Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Comp & Control Engn Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

Multi-label learning is the problem where each instance is associated with multiple labels simultaneously. Binary Relevance (BR) is a representative algorithm for multi-label learning. However, it may suffer the class-imbalance issue especially when the label space is large. Besides, it ignores the label correlations, which is of importance to improve the performance. Moreover, labels might have positive and negative correlations in real applications, but existing methods seldom exploit the negative label correlations. In this paper, we propose a novel Cost-sensitive multi-label learning model with Positive and Negative label pairwise correlations (CPNL), which extends BR to tackle the above issues. The kernel extension of the linear model is also provided to explore complex input-output relationships. Moreover, we adopt two accelerated gradient methods (AGM) to efficiently solve the linear and kernel models. Experimental results show that our approach CPNL achieves a competitive performance to some state-of-the-art approaches for multi-label learning. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Binary Relevance Multi-label learning Cost-sensitive Positive label correlations Negative label correlations

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Fixed-income securities: bibliometric review with network analysis

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SCIENTOMETRICS 2018年第3期116卷 1615-1640页

作者： Yan, Yan Liao, Zhewen Chen, Xiaosong Univ Chinese Acad Sci Sch Econ & Management Beijing 100080 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Chinese Acad Sci Inst Theoret Phys Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Phys Sci Beijing 100080 Peoples R China

In the financial market, many fixed-income analysts focus on trajectories of the hotspots. However, limited attention has been paid to the quantitative and qualitative evolution in a rapidly changing research field of fixed-income securities. This paper identifies intellectual turning points and emerging trends in the fixed-income securities field by using bibliometric methods, visualizing complicated networks including co-keyword networks, journal co-citation networks. Highly cited terms, categories, countries and institutes are detected and discussed. By using all 5915 publications published between 1997 and 2016, recorded in SCI core collection and SSCI in fixed-income securities domain and their references (101911), it can be conducted from this research that 10 top journals in the fixed-income field. The output of this article also shows the pioneer authors, the leading institutes and leading countries and regions, and what's more, the trend of research in fixed-income securities domain. It could possibly be a valuable source for academics and practitioners working in the field of fixed-income securities.

关键词： Bonds Fixed-income securities Bibliometrics Network visualization

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Multi-view learning based on nonparallel support vector machine

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knowledge-BASED SYSTEMS 2018年第Oct.15期158卷 94-108页

作者： Tang, Jingjing Li, Dewei Tian, Yingjie Liu, Dalian Univ Chinese Acad Sci Sch Math Sci Beijing 100049 Peoples R China Chinese Acad Sci Res Ctr Fictitious Econ & Data Sci Beijing 100190 Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing 100190 Peoples R China Beijing Union Univ Dept Basic Course Teaching Beijing 100101 Peoples R China

Multi-view learning (MVL) focuses on the problem of learning from the data represented by multiple distinct feature sets. Various successful SVM-based multi-view learning models have been proposed to improve existing learning tasks performance. Since nonparallel support vector machine (NPSVM) is proposed with several incomparable advantages over the state-of-the-art classifiers, it is potentially beneficial to perform the multi-view classification task using NPSVM. In this paper, we build a new multi-view learning model based on nonparallel support vector machine, termed as MVNPSVM. By combining the large margin mechanism and the consensus principle, MVNPSVM not only inherits the advantages of both NPSVM and multi-view learning, but also brings a new insight of extending NPSVM to the multi-view learning field. To solve MVNPSVM efficiently, we adopt the alternating direction method of multipliers (ADMM) as the solution. We theoretically analyze the performance of MVNPSVM from the viewpoints of the consensus analysis and the comparisons with the other two similar methods SVM-2K and multi-view twin support vector machines. Experimental results on 95 binary data sets confirm the effectiveness of the proposed method.

关键词： Multi-view learning Nonparallel support vector machine Alternating direction method of multipliers

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What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

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ECLINICALMEDICINE 2020年 22卷 100354-100354页

作者： Liu, Yang Gu, Zhonglei Xia, Shang Shi, Benyun Zhou, Xiao-Nong Shi, Yong Liu, Jiming Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China HKBU CSD & NIPD Joint Res Lab Intelligent Dis Sur Hong Kong Peoples R China Chinese Ctr Dis Control & Prevent Natl Inst Parasit Dis Chinese Ctr Trop Dis Res Shanghai Peoples R China Natl Hlth & Commiss Peoples Republ China Key Lab Parasite & Vector Biol Shanghai Peoples R China WHO Collaborating Ctr Trop Dis Shanghai Peoples R China Nanjing Tech Univ Sch Comp Sci & Technol Nanjing Peoples R China Univ Chinese Acad Sci Sch Econ & Management Beijing Peoples R China Chinese Acad Sci Key Lab Big Data Min & Knowledge Management Beijing Peoples R China

Background: COVID-19 has spread to 6 continents. Now is opportune to gain a deeper understanding of what may have happened. The findings can help inform mitigation strategies in the disease-affected countries. Methods: In this work, we examine an essential factor that characterizes the disease transmission patterns: the interactions among people. We develop a computational model to reveal the interactions in terms of the social contact patterns among the population of different age-groups. We divide a city's population into seven age-groups: 0-6 years old (children);7-14 (primary and junior high school students);15-17 (high school students);18-22 (university students);23 -44 (young/middle-aged people);45-64 years old (middle-aged/elderly people);and 65 or above (elderly people). We consider four representative settings of social contacts that may cause the disease spread: (1) individual households;(2) schools, including primary/high schools as well as colleges and universities;(3) various physical workplaces;and (4) public places and communities where people can gather, such as stadiums, markets, squares, and organized tours. A contact matrix is computed to describe the contact intensity between different age-groups in each of the four settings. By integrating the four contact matrices with the next-generation matrix, we quantitatively characterize the underlying transmission patterns of COVID-19 among different populations. Findings: We focus our study on 6 representative cities in China: Wuhan, the epicenter of COVID-19 in China, together with Beijing, Tianjin, Hangzhou, Suzhou, and Shenzhen, which are five major cities from three key economic zones. The results show that the social contact-based analysis can readily explain the underlying disease transmission patterns as well as the associated risks (including both confirmed and unconfirmed cases). In Wuhan, the age-groups involving relatively intensive contacts in households and public/communities are dispersedl

关键词： COVID-19 Underlying transmission patterns Age-specific social contact patterns Retrospective and prospective analysis

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