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Dynamic Shortest Path Monitoring in Spatial Networks

Journal of computer Science & Technology 2016年第4期31卷 637-648页

作者： Shuo Shang Lisi Chen Zhe-Wei Wei Dan-Huai Guo Ji-Rong Wen State Key Laboratory of Software Development Environment Beijing 100191 China Department of Computer Science China University of Petroleum Beijing 102249 China School of Computer Engineering Nanyang Technological University Singapore 639798 Singapore Beijing Key Laboratory of Big-Data Management and Analysis Methods Renmin University of China Beijing 100080 China Computer Network Information Center Chinese Academy of Sciences Beijing 100190 China

With the increasing availability of real-time traffic information, dynamic spatial networks are pervasive nowa- days and path planning in dynamic spatial networks becomes an important issue. In this light, we propose and investigate a novel problem of dynamically monitoring shortest paths in spatial networks （DSPM query）. When a traveler aims to a des- tination, his/her shortest path to the destination may change due to two reasons： 1） the travel costs of some edges have been updated and 2） the traveler deviates from the pre-planned path. Our target is to accelerate the shortest path computing in dynamic spatial networks, and we believe that this study may be useful in many mobile applications, such as route planning and recommendation, car navigation and tracking, and location-based services in general. This problem is challenging due to two reasons： 1） how to maintain and reuse the existing computation results to accelerate the following computations, and 2） how to prune the search space effectively. To overcome these challenges, filter-and-refinement paradigm is adopted. We maintain an expansion tree and define a pair of upper and lower bounds to prune the search space. A series of optimization techniques are developed to accelerate the shortest path computing. The performance of the developed methods is studied in extensive experiments based on real spatial data.

关键词： shortest path dynamic spatial network spatial database location-based service

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Detecting haplotype amplification in cancer sequencing data 9

Detecting haplotype amplification in cancer sequencing data

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9th International Conference on Bioinformatics and Computational Biology, BICOB 2017

作者： Liu, Weiwei Gao, Jingyang Zhao, Zhongmeng Yang, Rongrong Geng, Yu Zheng, Tian Zhang, Xuanping Xiao, Xiao Wang, Jiayin College of Information Science and Technology Beijing University of Chemical Technology 15 East Beisanhuan Road Beijing China Department of Computer Science and Technology Xi'an Jiaotong University 28 West Xianning Road Xi'an China Shaanxi Engineering Research Center of Medical and Health Big Data Xi'an Jiaotong University 28 West Xianning Road Xi'an China School of Management Xi'an Jiaotong University 28 West Xianning Road Xi'an China Institute of Data Science and Information Quality Xi'an Jiaotong University 28 West Xianning Road Xi'an China

ISBN: (纸本)9781943436071

Haplotype amplification on germline variants often implies selective advantage and clonal expansion susceptibility. Efficiently and accurately estimating such amplification provides insights into tumour heterogeneity and clinical implications. Existing approaches on amplification detecting are either limited on array data or focusing on genotype level only, whose accuracy may often be hurt by read depth bias or coverage deviation in clinical sequencing. In this article, we propose HMMLD, a computational approach for haplotype amplification detection. This approach is implemented based on a hidden Markov framework. Both read depth and variant allelic frequency are modeled as observed variables, while the hidden states are mapped to unknown amplifications. As we are considering germline variants, this approach involves linkage disequilibrium and interactions between germline loci, which improves accuracy by maximizing the posterior likelihoods on observed variables. We test HMMLD, on both simulation datasets with different configurations and a set of TCGA paired sequencing samples. The results outperform the existing approaches, especially on low coverage regions.

关键词： Bioinformatics

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Use of information technologies to explore correlations between climatic factors and spontaneous intracerebral hemorrhage in different age groups

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Journal of Computing Science and engineering 2017年第4期11卷 142-151页

作者： Ting, Hsien-Wei Chan, Chien-Lung Pan, Ren-Hao Lai, Robert K. Chien, Ting-Ying Department of Neurosurgery Taipei Hospital Ministry of Health and Welfare New Taipei City Taiwan Department of Computer Science and Engineering Yuan Ze University Taoyuan City Taiwan Department of Information Management Yuan Ze University Taoyuan City Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taoyuan City Taiwan Department of Information Management Tunghai University Taichung City Taiwan

Spontaneous intracerebral hemorrhage (sICH) has a high mortality rate. Research has demonstrated that sICH occurrence is related to weather conditions;therefore, this study used the decision tree method to explore the impact of climatic risk factors on sICH at different ages. The Taiwan National Health Insurance Research database (NHIRD) and other open-access data were used in this study. The inclusion criterion was a first-attack sICH. The decision tree algorithm and random forest were implemented in R programming language. We defined a high risk of sICH as more than the average number of cases daily, and the younger, middle-aged and older groups were calculated as having 0.77, 2.26 and 2.60 cases per day, respectively. In total, 22,684 sICH cases were included in this study;3,102 patients were younger ( 65 years, older group). The risk of sICH in the younger group was not correlated with temperature, wind speed or humidity. Themiddle group had two decision nodes: a higher risk if the maximum temperature was > 19°C (probability = 63.7%), and if the maximum temperature was 23.933°C (probability = 60.7%). This study demonstrated that the sICH incidence in the younger patients was not significantly correlated with weather factors;that in the middle-aged sICH patients was highly-correlated with the apparent temperature;and that in the older sICH patients was highly-correlated with the mean ambient temperature. "Warm" cold ambient temperatures resulted in a higher risk of sICH, especially in the older patients. © 2017. The Korean Institute of Information Scientists and Engineers.

关键词： Decision trees

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Complex network analysis of brain functional connectivity under a multi-step cognitive task

arXiv

引用

arXiv 2017年

作者： Cai, Shi-Min Chen, Wei Liu, Dong-Bai Tang, Ming Chen, Xun Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu610073 China Big Data Research Center University of Electronic Science and Technology of China Chengdu610073 China Department of Neurology Affiliated Jiangyin Hospital of Southeast University of Medical College Jiangyin214400 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Functional brain network has been widely studied to understand the relationship between brain organization and behavior. In this paper, we aim to explore the functional connectivity of brain network under a multi-step cognitive task involving with consecutive behaviors, and further understand the effect of behaviors on the brain organization. The functional brain networks are constructed base on a high spatial and temporal resolution fMRI dataset and analyzed via complex network based approach. We find that at voxel level the functional brain network shows robust small-worldness and scale-free characteristics, while its assortativity and rich-club organization are slightly restricted to order of behaviors performed. More interestingly, the functional connectivity of brain network in activated ROIs strongly correlates with behaviors and behaves obvious differences restricted to order of behaviors performed. These empirical results suggest that the brain organization has the generic properties of small-worldness and scale-free characteristics, and its diverse function connectivity emerging from activated ROIs is strongly driven by these behavioral activities via the plasticity of brain. Copyright © 2017, The Authors. All rights reserved.

关键词： Brain

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YZU-NLP team at SemEval-2016 Task 4: Ordinal Sentiment classification using a recurrent convolutional network 10

YZU-NLP team at SemEval-2016 Task 4: Ordinal Sentiment class...

引用

10th International Workshop on Semantic Evaluation, SemEval 2016

作者： He, Yunchao Yu, Liang-Chih Yang, Chin-Sheng Lai, K. Robert Liu, Weiyi Department of Information Management Yuan Ze University Taiwan Department of Computer Science and Engineering Yuan Ze University Taiwan Innovation Center for Big Data and Digital Convergence Yuan Ze University Taiwan School of Information Science and Engineering Yunnan University Yunnan China

ISBN: (纸本)9781941643952

Sentiment analysis of tweets has attracted considerable attention recently for potential use in commercial and public sector applications. Typical sentiment analysis classifies the sentiment of sentences into several discrete classes (e.g., positive and negative). The aim of Task 4 subtask C of SemEval-2016 is to classify the sentiment of tweets into an ordinal five-point scale. In this paper, we present a system that uses word embeddings and recurrent convolutional networks to complete the competition task. The word embeddings provide a continuous vector representation of words for the recurrent convolutional network to use in building sentence vectors for multipoint classification. The proposed method ranked second among eleven teams in terms of micro-averaged MAE (mean absolute error) and eighth for macro-averaged MAE. © 2016 Association for Computational Linguistics.

关键词： Sentiment analysis

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Network slicing for service-oriented networks under resource constraints

arXiv

引用

arXiv 2017年

作者： Zhang, Nan Liu, Ya-Feng Farmanbar, Hamid Chang, Tsung-Hui Hong, Mingyi Luo, Zhi-Quan School of Mathematical Sciences Peking University State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Huawei Canada Research Center Ottawa Canada Department of Electrical and Computer Engineering University of Minnesota United States Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen China

To support multiple on-demand services over fixed communication networks, network operators must allow flexible customization and fast provision of their network resources. One effective approach to this end is network virtualization, whereby each service is mapped to a virtual subnetwork providing dedicated on-demand support to network users. In practice, each service consists of a prespecified sequence of functions, called a service function chain (SFC), while each service function in a SFC can only be provided by some given network nodes. Thus, to support a given service, we must select network function nodes according to the SFC and determine the routing strategy through the function nodes in a specified order. A crucial network slicing problem that needs to be addressed is how to optimally localize the service functions in a physical network as specified by the SFCs, subject to link and node capacity constraints. In this paper, we formulate the network slicing problem as a mixed binary linear program and establish its strong NP-hardness. Furthermore, we propose efficient penalty successive upper bound minimization (PSUM) and PSUM-R(ounding) algorithms, and two heuristic algorithms to solve the problem. Simulation results are shown to demonstrate the effectiveness of the proposed algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Network function virtualization

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Robust frequent directions with application in online learning

arXiv

引用

arXiv 2017年

作者： Luo, Luo Chen, Cheng Zhang, Zhihua Li, Wu-Jun Zhang, Tong Department of Computer Science and Engineering Shanghai Jiao Tong University 800 Dongchuan Road Shanghai200240 China National Engineering Lab for Big Data Analysis and Applications School of Mathematical Sciences Peking University 5 Yiheyuan Road Beijing100871 China National Key Laboratory for Novel Software Technology Collaborative Innovation Center of Novel Software Technology and Industrialization Department of Computer Science and Technology Nanjing University 163 Xianlin Avenue Nanjing210023 China Computer Science & Mathematics Hong Kong University of Science and Technology Hong Kong

The frequent directions (FD) technique is a deterministic approach for online sketching that has many applications in machine learning. The conventional FD is a heuristic procedure that often outputs rank deficient matrices. To overcome the rank deficiency problem, we propose a new sketching strategy called robust frequent directions (RFD) by introducing a regularization term. RFD can be derived from an optimization problem. It updates the sketch matrix and the regularization term adaptively and jointly. RFD reduces the approximation error of FD without increasing the computational cost. We also apply RFD to online learning and propose an effective hyperparameter-free online Newton algorithm. We derive a regret bound for our online Newton algorithm based on RFD, which guarantees the robustness of the algorithm. The experimental studies demonstrate that the proposed method outperforms state-of-the-art second order online learning algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Heuristic methods

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Who will pass? Analyzing learner behaviors in MOOCs

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Research and Practice in Technology Enhanced Learning 2016年第1期11卷 1-11页

作者： Tseng, Shu-Fen Tsao, Yen-Wei Yu, Liang-Chih Chan, Chien-Lung Lai, K. Robert Department of Information Management and Innovation Center for Big Data and Digital Convergence Yuan Ze University 135 Yuan-Tong Road Tao-Yuan City Chung-Li District Taiwan Department of Information Management Yuan Ze University 135 Yuan-Tong Road Tao-Yuan City Chung-Li District Taiwan Department of Computer Science and Engineering and Innovation Center for Big Data and Digital Convergence Yuan Ze University 135 Yuan-Tong Road Tao-Yuan City Chung-Li District Taiwan

Massive open online courses (MOOCs) have recently gained worldwide attention from educational institutes. MOOCs provide a new option for learning, yet measurable learning benefits of MOOCs still need to be investigated. Collecting data of three MOOCs at Yuan Ze University (YZU), this paper intended to classify learning behaviors among 1489 students on the MOOC platform at YZU. This study further examined learning outcomes in MOOCs by different types of learners. The Ward’s hierarchical and k-means non-hierarchical clustering methods were employed to classify types of learners’ behavior while they engaged in learning activities on the MOOC platform. Three types of MOOC learners were classified—active learner, passive learner, and bystander. Active learners who submitted assignments on time and frequently watched lecture videos showed a higher completion rate and a better grade in the course. MOOC learners who participated in online discussion forum reported a higher rate of passing the course and a better score than those inactive classmates. The finding of this study suggested that the first 2 weeks was a critical point of time to retain students in MOOCs. MOOC instructors need to carefully design course and detect risk behaviors of students in early of the classes to prevent students from dropping out of the course. The feature design of discussion forum is to provide peer interaction and facilitate online learning. Our results suggested that timely feedback by instructors or facilitators on discussion forum could enhance students’ engagement in MOOCs. © 2016, The Author(s).

关键词： Learning analytics Learning behavior Learning engagement MOOCs

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Dynamics of social contagions with local trend imitation

arXiv

引用

arXiv 2017年

作者： Zhu, Xuzhen Wang, Wei Cai, Shimin Eugene Stanley, H. State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China College of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China Big Data Research Center University of Electronic Science and Technology of China Chengdu611731 China Web Sciences Center School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu611731 China Center for Polymer Studies and Department of Physics Boston University BostonMA02215 United States

Research on social contagion dynamics has not yet including a theoretical analysis of the ubiquitous local trend imitation (LTI) characteristic. We propose a social contagion model with a tent-like adoption probability distribution to investigate the effect of this LTI characteristic on behavior spreading. We also propose a generalized edgebased compartmental theory to describe the proposed model. Through extensive numerical simulations and theoretical analyses, we find a crossover in the phase transition: when the LTI capacity is strong, the growth of the final behavior adoption size exhibits a second-order phase transition. When the LTI capacity is weak, we see a first-order phase transition. For a given behavioral information transmission probability, there is an optimal LTI capacity that maximizes the final behavior adoption size. Finally we find that the above phenomena are not qualitatively affected by the heterogeneous degree distribution. Our suggested theory agrees with the simulation results. Copyright © 2017, The Authors. All rights reserved.

关键词： Probability distributions

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Swarm Intelligence Algorithm Inspired by Route Choice Behavior

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Journal of Bionic engineering 2016年第4期13卷 669-678页

作者： Daxin Tian Junjie Hu Zhengguo Sheng Yunpeng Wang Jianming Ma Jian Wang Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing 100191 China School of Transportation Science and Engineering Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control Beihang University Beijing 1 O0191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing 210096 China Department of Engineering and Design University of Sussex Brighton BN1 9RH UK The Texas Department of Transportation Austin TX78750 USA College of Computer Science and Technology Jilin University Changchun 130012 China

Travelers＇ route choice behavior, a dynamical learning process based on their own experience, traffic information, and influence of others, is a type of cooperation optimization and a constant day-to-day evolutionary process. Travelers adjust their route choices to choose the best route, minimizing travel time and distance, or maximizing expressway use. Because route choice behavior is based on human beings, the most intelligent animals in the world, this swarm behavior is expected to in- corporate more intelligence. Unlike existing research in route choice behavior, the influence of other travelers is considered for updating route choices on account of the reality, which makes the route choice behavior from individual to swarm. Anew swarm intelligence algorithm inspired by travelers＇ route choice behavior for solving mathematical optimization problems is introduced in this paper. A comparison of the results of experiments with those of the classical global Particle Swarm Optimization （PSO） algorithm demonstrates the efficacy of the Route Choice Behavior Algorithm （RCBA）. The novel algorithm provides a new approach to solving complex problems and new avenues for the study of route choice behavior.

关键词： swarm intelligence route choice behavior particle swarm optimization mathematical optimization

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