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2016 8th IEEE International Conference on Communication Software and Networks(ICCSN 2016)

作者： Mengmeng He Yanjun Hu Hui Zhi Quan Yuan Key Lab of Intelligent Computing and Signal Processing Ministry of Education School of Electronics and Information Engineering Anhui University

In this paper,we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(Qo S)*** formulate our original throughput maximization problem into effective capacity maximization problem by applying information theory and the concept of the effective *** our scheme,we focus on full duplex mode and amplify-and-forward(AF)*** particular,our proposed scheme derives closed-form expressions and analyzes the impacts of the SNR of the interference channel on the performance of full duplex relaying *** comparison purpose,we also give the analysis of half duplex relaying *** results show that our proposed power allocation scheme can support diverse Qo S guarantees and achieve better effective capacity than equal power allocation scheme and direct transmission *** analysis also indicate that the perfect full duplex mode can achieve twice optimal effective capacity of the half duplex mode.

关键词： power allocation scheme effective capacity full duplex quality-of-service(Qo S) amplify-and-forward(AF)

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Detecting coordinated regulations of pathways by higher logic analysis

Detecting coordinated regulations of pathways by higher logi...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Yansen Su Xingyi Zhang Linqiang Pan Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781509016129

Non-small cell lung cancer (NSCLC) is a malignant tumor, and contains three major subtypes which are difficult to be distinguished at early stages of NSCLC. Many pathways work together to perform certain functions in cells. One might expect the high level of co-appearance or repression of pathways to distinguish different subtypes of NSCLC. However, it is difficult to detect coordinated regulations of pathways by existing methods. In our work, the coordinated regulations of pathways are detected using modified higher logic analysis of gene expression data. Specifically, we identify the genes whose regulation obeys a logic function by the modified higher logic analysis, which focuses on the relationships among the gene triplets that are not evident when genes are examined in a pairwise fashion. Then, the relationships among genes are mapped to pathways to predict the coordinated regulated relationships among pathways. By comparing coordinated regulations of pathways, we find that the regulation patterns of pathways which are associated with cell death are different in three subtypes of NSCLC. This method allows us to uncover co-appearance or repression of pathways in high level, and it has a potential to distinguish the subtypes for complex diseases.

关键词： Cancer Lungs

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Cross-layer power allocation scheme for wireless full duplex relaying system

Cross-layer power allocation scheme for wireless full duplex...

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International Conference on Communication Software and Networks, ICCSN

作者： Mengmeng He Yanjun Hu Hui Zhi Quan Yuan Ministry of Education Key Lab of Intelligent Computing and Signal Processing China School of Electronics and Information Engineering Anhui University Hefei China

In this paper, we propose a cross-layer power allocation scheme over wireless relay networks for quality-of-service(QoS) guarantees. We formulate our original throughput maximization problem into effective capacity maximization problem by applying information theory and the concept of the effective capacity. In our scheme, we focus on full duplex mode and amplify-and-forward(AF) protocol. In particular, our proposed scheme derives closed-form expressions and analyzes the impacts of the SNR of the interference channel on the performance of full duplex relaying system. For comparison purpose, we also give the analysis of half duplex relaying system. Simulation results show that our proposed power allocation scheme can support diverse QoS guarantees and achieve better effective capacity than equal power allocation scheme and direct transmission scheme. Our analysis also indicate that the perfect full duplex mode can achieve twice optimal effective capacity of the half duplex mode.

关键词： Relays Resource management Quality of service Protocols Wireless communication Physical layer signal to noise ratio

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Multi-view Metric Learning for Multi-view Video Summarization

Multi-view Metric Learning for Multi-view Video Summarizatio...

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International Conference on Cyberworlds

作者： Linbo Wang Xianyong Fang Yanwen Guo Yanwei Fu MOE Key Laboratory of Intelligent Computing and Signal Processing Anhui University Hefei China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Data Science Fudan University China

Traditional methods on video summarization are designed to generate summaries for single-view video records, and thus they cannot fully exploit the mutual information in multi-view video records. In this paper, we present a multiview metric learning framework for multi-view video summarization. It combines the advantages of maximum margin clustering with the disagreement minimization criterion. The learning framework thus has the ability to find a metric that best separates the input data, and meanwhile to force the learned metric to maintain underlying intrinsic structure of data points, for example geometric information. Facilitated by such a framework, a systematic solution to the multi-view video summarization problem is developed from the viewpoint of metric learning. The effectiveness of the proposed method is demonstrated by experiments.

关键词： Extraterrestrial measurements Optimization Manifolds Visualization Minimization Eigenvalues and eigenfunctions

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Perovskite Solar Cells: Unique Seamlessly Bonded CNT@Graphene Hybrid Nanostructure Introduced in an Interlayer for Efficient and Stable Perovskite Solar Cells (Adv. Funct. Mater. 32/2018)

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Advanced Functional Materials 2018年第32期28卷

作者： Xuanhua Li Tengteng Tong Qinjiang Wu Shaohui Guo Qiang Song Jian Han Zhixiang Huang State Key Laboratory of Solidification Processing Research and Development Institute of Northwestern Polytechnical University In Shenzhen Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China

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Water Splitting: Optical and Electrical Enhancement of Hydrogen Evolution by MoS2@MoO3Core–Shell Nanowires with Designed Tunable Plasmon Resonance (Adv. Funct. Mater. 32/2018)

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Advanced Functional Materials 2018年第32期28卷

作者： Shaohui Guo Xuanhua Li Xingang Ren Lin Yang Jinmeng Zhu Bingqing Wei State Key Laboratory of Solidification Processing Center for Nano Energy Materials School of Materials Science and Engineering Northwestern Polytechnical University and Shaanxi Joint Lab of Graphene (NPU) Xi'an 710072 China Northwestern Polytechnical University‐Queen Mary University of London (NPU‐QMUL) Joint Research Institute of Advanced Materials and Structures (JRI‐AMAS) Xi'an 710072 China Key Laboratory of Intelligent Computing and Signal Processing Ministry of Education Anhui University No. 3 Feixi Road Hefei Anhui Province 230039 P. R. China Department of Mechanical Engineering University of Delaware Newark DE 19716 USA

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Robust low-rank tensor factorization by cyclic weighted median

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Science China(Information Sciences) 2015年第5期58卷 145-155页

作者： MENG DeYu ZHANG Biao XU ZongBen ZHANG Lei GAO ChenQiang Institute for Information and System Sciences and Ministry of Education Key Lab for Intelligent Networks and Network Security Xi'an Jiaotong University Department of Computing Hong Kong Polytechnic University Chongqing Key Laboratory of Signal and Information Processing Chongqing University of Posts and Telecommunications

Low-rank tensor factorization(LRTF) provides a useful mathematical tool to reveal and analyze multi-factor structures underlying data in a wide range of practical applications. One challenging issue in LRTF is how to recover a low-rank higher-order representation of the given high dimensional data in the presence of outliers and missing entries, i.e., the so-called robust LRTF problem. The L1-norm LRTF is a popular strategy for robust LRTF due to its intrinsic robustness to heavy-tailed noises and outliers. However, few L1-norm LRTF algorithms have been developed due to its non-convexity and non-smoothness, as well as the high order structure of data. In this paper we propose a novel cyclic weighted median(CWM) method to solve the L1-norm LRTF problem. The main idea is to recursively optimize each coordinate involved in the L1-norm LRTF problem with all the others fixed. Each of these single-scalar-parameter sub-problems is convex and can be easily solved by weighted median filter, and thus an effective algorithm can be readily constructed to tackle the original complex problem. Our extensive experiments on synthetic data and real face data demonstrate that the proposed method performs more robust than previous methods in the presence of outliers and/or missing entries.

关键词： tensor factorization low-rank weighted median filter tensorface

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An improved reference point sampling method on Pareto optimal front

An improved reference point sampling method on Pareto optima...

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Congress on Evolutionary Computation

作者： Cheng He Linqiang Pan Hang Xu Ye Tian Xingyi Zhang Laboratory of Image Information Processing and Intelligent Control Huazhong University of Science and Technology Wuhan Hubei China Huazhong University of Science and Technology Wuhan Hubei CN Department of Computer Science Xiamen University Xiamen Fujian China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei Anhui China

ISBN: (纸本)9781509006243

In this paper, we propose a sampling approach of reference points used for performance metrics of multi-objective evolutionary algorithms. Traditional reference point sampling methods, such as the Das and Dennis method, usually sample the reference points via a set of uniformly distributed weight vectors generated on an ideal hyper-plane in objective space, which however often ignore the geometric shape of a specific Pareto front. Therefore, we propose a novel reference point sampling approach by taking the specific shape of the Pareto optimal front to be tackled into account for measuring the performance of multi-objective evolutionary algorithms. The performance of the proposed reference point sampling method against the other two state-of-the-art sampling methods is tested on six test instances in various conditions, which clearly demonstrate the effectiveness and superiority of the proposed sampling method.

关键词： Sampling methods Shape Convergence Evolutionary computation Optimization Euclidean distance

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

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Application of a multi-factor model in the grain yield prediction

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Metallurgical and Mining Industry 2015年第8期7卷 468-474页

作者： Chen, Lili Guo, Hongjun Laboratory of Intelligent Information Processing Suzhou University Suzhou Anhui China The Key Laboratory of Intelligent Computing and Signal Processing of MOE Anhui University Hefei Anhui China

A multi-factor model for the grain yield prediction based on the previous data and relevant impacting factors is reported. In this model, the weight coefficient of each individual factor that affected the grain yield historically is analyzed by the variation coefficient method and the conversion degree function in the attribute theory. The effects of various factors on the predicted grain yield are then used as standard vectors and subjected to a similarity-based search in the matrix of historical values. The predicted total grain yield is then determined by multiplying the sown area by the unit grain yield which is obtained by the highest similarity between the historical data and those predicted. Compared to the results obtained by the BP nerve network method, this method is simpler, more flexible, less time-consuming, and more accurate.

关键词： Forecasting

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