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Predicting essential proteins based on gene expression data, subcellular localization and PPI data 1

12th International Conference on Bio-Inspired Computing: Theories and Applications, BIC-TA 2017

作者： Lei, Xiujuan Wang, Siguo Pan, Linqiang School of Computer Science Shaanxi Normal University Xi’anShaanxi710119 China Key Laboratory of Image Information Processing and Intelligent Control School of Automation Huazhong University of Science and Technology WuhanHubei430074 China School of Electric and Information Engineering Zhengzhou University of Light Industry ZhengzhouHenan450002 China

ISBN: (数字)9789811071799

ISBN: (纸本)9789811071782

Predicting essential proteins is indispensable for understanding the minimal requirements of cellular survival and development. In recent years, many methods combined with the topological features of PPI networks have been proposed. However, most of these approaches ignored the intrinsic characteristics of biological attributes. This paper integrates Gene expression data, Subcellular localization and PPI networks to identify essential proteins, named GSP. We use local average connectivity and edge clustering coefficient unite with gene expression data to measure centralities of nodes. Compared with non-essential proteins, essential proteins appear more frequently in some subcellular localizations such as Nucleus and considering that different compartments play different roles, thus we integrate subcellular localization information to identify essential proteins. The computational experiment results on the yeast PPI networks show that the proposed method GSP outperforms other state-of-art methods including DC, EC, IC, SC, NC, LAC, PeC, WDC and UDoNC. © Springer Nature Singapore Pte Ltd 2017.

关键词： Proteins

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An ensemble classification algorithm based on information entropy for data streams

arXiv

引用

arXiv 2017年

作者： Wang, Junhong Xu, Shuliang Duan, Bingqian Liu, Caifeng Liang, Jiye School of Computer and Information Technology Shanxi University Taiyuan China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan China School of Computer Science and Technology Faculty of Electronic Information and Electrical Engineering Dalian University of Technology Dalian China Key Laboratory of Computational Intelligence and Chinese Information Processing Ministry of Education Taiyuan China

Data stream mining problem has caused widely concerns in the area of machine learning and data mining. In some recent studies, ensemble classification has been widely used in concept drift detection, however, most of them regard classification accuracy as a criterion for judging whether concept drift happening or not. information entropy is an important and effective method for measuring uncertainty. Based on the information entropy theory, a new algorithm using information entropy to evaluate a classification result is developed. It uses ensemble classification techniques, and the weight of each classifier is decided through the entropy of the result produced by an ensemble classifiers system. When the concept in data streams changing, the classifiers' weight below a threshold value will be abandoned to adapt to a new concept in one time. In the experimental analysis section, six databases and four proposed algorithms are executed. The results show that the proposed method can not only handle concept drift effectively, but also have a better classification accuracy and time performance than the contrastive algorithms. Copyright © 2017, The Authors. All rights reserved.

关键词： Data mining

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A novel dictionary based SRC for face recognition

A novel dictionary based SRC for face recognition

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Ying Wen Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University China 200062

ISBN: (纸本)9781509041183

The sparse representation based classification (SRC) performs not very well for small sample data. A discriminative common vector dictionary based SRC is introduced in this paper to address this issue. The contribution of this paper is that the dictionary of the proposed method is constructed by the discriminative common vector per class. The common vector represents the invariant property of each class, which is helpful to improve the performance of the proposed method for small sample database. Furthermore, the new dictionary has much less atoms than the original SRC based scheme, which reduces the computational cost. The experiments implemented on ORL, AR and LFW face databases demonstrate the effectiveness of the proposed method.

关键词： Sparse representation classification discriminative common vector face recognition Facial Recognition Dictionaries as Topic SAFETY REVIEW COMMITTEE sparse representation-based classification sparse representation

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The M-computations induced by accessibility relations in nonstandard models M of Hoare logic

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Frontiers of computer Science 2016年第4期10卷 717-725页

作者： Cungen CAO Yuefei SUI Zaiyue ZHANG Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Department of Computer Science Jiangsu University of Sciences and Technology Zhenjiang 212003 China

Hoare logic is a logic used as a way of specifying semantics of programming languages, which has been extended to be a separation logic to reason about mutable heap structure. In a model M of Hoare logic, each program α induces an M-computable function fα M on the universe of M; and the M-recursive functions are defined on M. It will be proved that the class of all the M-computable functions fα M induced by programs is equal to the class of all the M- recursive functions. Moreover, each M-recursive function is ∑ 1 NM -definable in M, where the universal quantifier is a num- ber quantifier ranging over the standard part of a nonstandard model M.

关键词： Hoare logic recursive ：Function computable function nonstandard model of Peano arithmetic

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Attention-based end-to-end speech recognition on voice search

arXiv

引用

arXiv 2017年

作者： Shan, Changhao Zhang, Junbo Wang, Yujun Xie, Lei Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China Xiaomi Inc. Beijing China

Recently, there has been a growing interest in end-to-end speech recognition that directly transcribes speech to text without any predefined alignments. In this paper, we explore the use of attention-based encoder-decoder model for Mandarin speech recognition on a voice search task. Previous attempts have shown that applying attention-based encoderdecoder to Mandarin speech recognition was quite difficult due to the logographic orthography of Mandarin, the large vocabulary and the conditional dependency of the attention model. In this paper, we use character embedding to deal with the large vocabulary. Several tricks are used for effective model training, including L2 regularization, Gaussian weight noise and frame skipping. We compare two attention mechanisms and use attention smoothing to cover long context in the attention model. Taken together, these tricks allow us to finally achieve a character error rate (CER) of 3.58% and a sentence error rate (SER) of 7.43% on the MiTV voice search dataset. While together with a trigram language model, CER and SER reach 2.81% and 5.77%, respectively. Copyright © 2017, The Authors. All rights reserved.

关键词： Speech

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Complex network analysis of climate change in the Tarim River Basin, Northwest China

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Research in Cold and Arid Regions 2017年第5期9卷 476-487页

作者： ZuHan Liu JianHua Xu WeiHong Li Jiangxi Provincial Key Laboratory of Water Information Cooperative Sensing and Intelligent Processing Nanchang Institute of Technology Nanchang Jiangxi 330099 China Key Laboratory of Geographic Information Science (Ministry of Education) School of Geographic Sciences East China Normal University Shanghai 200241 China Research Center for East-West Cooperation in China East China Normal University Shanghai 200241 China State Key Laboratory of Desert and Oasis Ecology Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi Xinjiang 830011 China

The complex network theory provides an approach for understanding the complexity of climate change from a new *** this study,we used the coarse graining process to convert the data series of daily mean temperature and daily precipitation from 1961 to 2011 into symbol sequences consisting of five characteristic symbols(i.e.,R,r,e,d and D),and created the temperature fluctuation network(TFN)and precipitation fluctuation network(PFN)to discover the complex network characteristics of climate change in the Tarim River Basin of Northwest *** results show that TFN and PEN both present characteristics of scale-free network and small-world network with short average path length and high clustering *** nodes with high degree in TFN are RRR,d RR and Re R while the nodes with high degree in PFN are rre,rrr,eee and err,which indicates that climate change modes represented by these nodes have large probability of *** R and r are mostly included in the important nodes of TFN and PFN,which indicate that the fluctuating variation in temperature and precipitation in the Tarim River Basin mainly are rising over the past 50 *** nodes RRR,DDD,Re R,RRd,DDd and Ree are the hub nodes in TFN,which undertake 19.71%betweenness centrality of the *** nodes rre,rrr,eee and err are the hub nodes in PFN,which undertake 13.64%betweenness centrality of the network.

关键词： climate change complex networks coarse graining process temperature fluctuation network precipitation fluctuation network Northwest China

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Energy efficiency and area spectral efficiency tradeoff for coexisting wireless body sensor networks

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Science China Earth Sciences 2016年第12期59卷 151-165页

作者： Ruixia LIU Yinglong WANG Shangbin WU Cheng-Xiang WANG Wensheng ZHANG College of Information Science and Engineering Shandong University of Science and Technology Shandong Provincial Key Laboratory of Computer Network Shandong Computer Science Center (National Supercomputer Center in Jinan) Institute of Sensors Signals and Systems School of Engineering and Physical Sciences Heriot-Watt University Shandong Provincial Key Laboratory of Wireless Communication Technologies School of Information Science and Engineering Shandong University

The coexistence of wireless body sensor networks(WBSNs) is a very challenging problem, due to strong interference, which seriously affects energy consumption and spectral reuse. The energy efficiency and spectral efficiency are two key performance evaluation metrics for wireless communication networks. In this paper, the fundamental tradeoff between energy efficiency and area spectral efficiency of WBSNs is first investigated under the Poisson point process(PPP) model and Matern hard-core point process(HCPP) model using stochastic geometry. The circuit power consumption is taken into consideration in energy efficiency calculation. The tradeoff judgement coefficient is developed and is shown to serve as a promising complementary measure. In addition, this paper proposes a new nearest neighbour distance power control strategy to improve energy efficiency. We show that there exists an optimal transmit power highly dependant on the density of WBSNs and the nearest neighbour distance. Some important properties are also addressed in the analysis of coexisting WBSNs based on the IEEE 802.15.4 standard, such as the impact of intensity nodes distribution,optimal guard zone, and outage probability. Simulation results show that the proposed power control design can reduce the outage probability and enhance energy efficiency. Energy efficiency and area spectral efficiency of the HCPP model are better than those of the PPP model. In addition, the optimal density of WBSNs coexistence is obtained.

关键词： WBSNs coexistence stochastic geometry energy efficiency area spectral efficiency power control

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Tracking the 3D position and orientation of flying swarms with learned kinematic pattern using LSTM network

Tracking the 3D position and orientation of flying swarms wi...

引用

2017 IEEE International Conference on Multimedia and Expo, ICME 2017

作者： Wang, Shuo Hong Su, Hai Feng Cheng, Xi En Liu, Ye Quo, Aike Chen, Yan Qiu School of Computer Science Shanghai Key Laboratory of Intelligent Information Processing Fudan University Shanghai China Institute of Neuroscience State Key Laboratory of Neuroscience CAS Center for Excellence in Brain Science and Intelligence Technology Shanghai Institutes for Biological Sciences CAS Shanghai China Jingdezhen Ceramic Institute Jindezhen China College of Automation Nanjing University of Posts and Telecommunications China

ISBN: (纸本)9781509060672

Accurately and reliably tracking the 3D position and orientation of individuals in large flying swarms is valuable not only for scientific researches but also practical applications. However, large quantity, frequent occlusions, similar appearance, tiny body size and abrupt motion make it remain an open problem. The 3D flying swarm tracking method proposed in this paper tracks both position and orientation of each individual in the swarm using the particle filter framework. Particles are scattered more pertinently by the dynamic model based on the learned kinematic pattern of a single target with a Long Short-Term Memory (LSTM) network. In addition, the observation model combines the Weighted Occupancy Ratio (WOR) and Temporal Appearance Coherency (TAC) cues in each view to improve the accuracy and robustness of the reconstructed body orientation. Experiments on both simulation and real-world data sets demonstrate the effectiveness and superiority of the proposed method. © 2017 IEEE.

关键词： Long short-term memory

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Movement Tracking from Monocular Video Based on the Particle Filter

Movement Tracking from Monocular Video Based on the Particle...

引用

IEEE International Conference on Multimedia Big Data (BigMM)

作者： Lei Lyu Naiqi Ma Hong Liu School of Information Science and Engineering Shandong Normal University Shandong Provincial Key Laboratory for Distributed Computer Novel Technology Jinan China

ISBN: (纸本)9781509065509

Human motion tracking from monocular video has received increasing attention in recent years due to its broad applications. Among these human motion tracking methods, the particle filter is considered as an effective approach for human motion tracking. However, there are still two limitations of current particle filter approaches such as the prior used for the filtering step is often poor due to relatively large, poorly modeled inter-frame motion and the use of the prior as an importance function results in inefficient sampling of the posterior. In this paper, we present a new approach to track 3D human motion from video clips with the assistance of a pre-captured motion library. We studied the application of particle filter to realize the basic principle of 3d human motion tracking, three-dimensional human body model, the evaluation function of the posture and dynamic model of the movement, and the results of the tracking test is given.

关键词： Image edge detection Tracking Acceleration Particle filters Dynamics Biological system modeling Solid modeling

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Block-Based Projection Matrix Design for Compressed Sensing

引用

Chinese Journal of Electronics 2016年第3期25卷 551-555页

作者： LI Zhetao XIE Jingxiong ZHU Gengming PENG Xin XIE Yanrong CHOI Youngjune The College of Information Engineering Xiangtan University School of Computer National University of Defense Technology Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education Xiangtan University School of Computer Science and Engineering Hunan University of Science and Technology College of Information and Communication Engineering Hunan Institute of Science and Technology Department of Information and Computer Engineering Ajou University

The objective of optimizing a projection matrix is to decrease the mutual coherence between a projection matrix and a basis matrix. In this paper, a novel block-based method is proposed to design a projection matrix in compressed sensing. Here, the projection matrix is divided into two blocks. The relationship between the two blocks was obtained by reasoning and proving. Theoretical analysis demonstrates that the mutual coherence between the whole projection matrix and the whole basis matrix keeps as good as the mutual coherence between the block matrix and blocked basis matrix. Experimental results show that the proposed method obtains better performance compared to existing methods.

关键词： Compressed sensing Projection matrix Block optimization Mutual coherence

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