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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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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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Speech Endpoint Detection Based on EMD and Higher Order Statistics in Noisy Environments

Speech Endpoint Detection Based on EMD and Higher Order Stat...

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2015 5th International Conference on Information Engineering for Mechanics and Materials(ICIMM 2015)

作者： Dexiang Zhang Jiaxing Li Zihong Chen Key Lab.of Intelligent Computing and Signal Processing Anhui University The School of Electrical Engineering and Automation of Anhui University

Accurate endpoint detection is crucial for speech recognition accuracy. This paper presents a new technique for speech endpoint detection in a noisy environment based on the empirical mode decomposition(EMD) algorithm and higher order statistics. With the EMD, the noise speech signals can be decomposed into a sum of the band-limited function called intrinsic mode functions(IMFs), which is a zero-mean AM-FM component. Then higher order statistics of the IMF components can be used to extract the desired feature for endpoint detection. In order to show the effectiveness of the proposed method, we present examples showing that the new measure is more effective than traditional measures. The experimental results show that the performance of the proposed algorithm is noticeable in the real speech signal tests with different SNR.

关键词： Endpoint Detection EMD Higher Order Statistics Noisy Environments

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Diffusion tensor based curvature-driven difiusion model for digital inpainting

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Journal of Computational Information Systems 2015年第1期11卷 185-194页

作者： Qu, Lei Huang, Linsheng Wei, Sui Key Lab of Intelligent Computing and Signal Processing Ministry of Education Anhui University Hefei China

In this paper, we formulate the digital inpainting as a curvature-driven anisotropic concentration difiusing process. The difiusion strength along the gradient directions is defined as a function of curvature, so that the "Connectivity Principle" can be satisfied. Difierent to the classic Curvature-Driven Difiusion (CDD) model, the difiusion along the edge directions can be easily integrated into the difiusion equation in our model by using difiusion tensor. As result, more exibility in designing the local difiusion behavior is allowed, and the CDD model can be generalized as a special case. We show that our model can boost inpainting speed 3fi6 times and at the same time has similar inpainting quality compared to the widely used CDD model. Experimental results on synthetic and real images have verified the stability and eficiency of our model. ©, 2014, Journal of Computational Information Systems. All right reserved.

关键词： Tensors

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Hierarchical Covering Algorithm

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Tsinghua Science and Technology 2014年第1期19卷 76-81页

作者： Jie Chen Shu Zhao Yanping Zhang Department of Computer Science and Technology and Key Lab of Intelligent Computing and Signal Processing Anhui University

The concept of deep learning has been applied to many domains, but the definition of a suitable problem depth has not been sufficiently explored. In this study, we propose a new Hierarchical Covering Algorithm （HCA） method to determine the levels of a hierarchical structure based on the Covering Algorithm （CA）. The CA constructs neural networks based on samples＇ own characteristics, and can effectively handle multi-category classification and large-scale data. Further, we abstract characters based on the CA to automatically embody the feature of a deep structure. We apply CA to construct hidden nodes at the lower level, and define a fuzzy equivalence relation R on upper spaces to form a hierarchical architecture based on fuzzy quotient space theory. The covering tree naturally becomes from R. HCA experiments performed on MNIST dataset show that the covering tree embodies the deep architecture of the problem, and the effects of a deep structure are shown to be better than having a single level.

关键词： deep architecture hierarchy fuzzy equivalence relation covering tree MNIST dataset

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