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Robust low-rank tensor factorization by cyclic weighted median

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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Classification Based on Multilayer Extreme Learning Machine for Motor Imagery Task from EEG Signals

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Procedia Computer Science 2016年 88卷 176-184页

作者： Lijuan Duan Menghu Bao Jun Miao Yanhui Xu Juncheng Chen College of Computer Science and Technology Beijing University of Technology Beijing 100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China Beijing Key Laboratory on Integration and Analysis of Large-scale Stream Data Beijing 100124 China

Classification of motor imagery electroencephalogram (EEG) is one of the most important technologies for BCI. To improve the accuracy, this paper introduces a classification system based on Multilayer Extreme Learning Machine (ML-ELM). In the system, the combination of PCA and LDA is chosen as the method of feature extraction and the ML-ELM is used to classify. The ML-ELM has not only the advantage which ELM has but also better performance than ELM. In the experiment, our method is compared with the methods based on ELM, such as kernel-ELM, Constrained-ELM and V-ELM, and some state-of–the-art methods on the same dataset. The experimental results show that ML-ELM is much more suitable for motor imagery EEG data and has better performance than the others.

关键词： Electroencephalogram Classification Motor Imagery Extreme Learning Machine Multilayer Extreme Learning Machine

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Color Image Segmentation Based on Modified Kuramoto Model

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Procedia Computer Science 2016年 88卷 245-258页

作者： Xiaojie Liu Yuanhua Qiao Xianghui Chen Jun Miao Lijuan Duan College of Applied Sciences Beijing University of Technology Beijing 100124 China Key Lab of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing 100190 China College of Computer Science and Technology Beijing University of Technology Beijing 100124 China

A new approach for color image segmentation is proposed based on Kuramoto model in this paper. Firstly, the classic Kuramoto model which describes a global coupled oscillator network is changed to be one that is locally coupled to simulate the neuron activity in visual cortex and to describe the influence for phase changing by external stimuli. Secondly, a rebuilt method of coupled neuron activities is proposed by introducing and computing instantaneous frequency. Three oscillating curves corresponding to the pixel values of R, G, B for color image are formed by the coupled network and are added up to produce the superposition of oscillation. Finally, color images are segmented according to the synchronization of the oscillating superposition by extracting and checking the frequency of the oscillating curves. The performance is compared with that from other representative segmentation approaches.

关键词： Kuramoto model Neural Network Color image segmentation

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Video saliency prediction with optimized optical flow and gravity center bias

Video saliency prediction with optimized optical flow and gr...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhe Wu Li Su Qingming Huang Bo Wu Jian Li Guorong Li Key Lab on Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology Beijing China Capital Medical University Beijing China Beijing University of Posts and Telecommunications China Beijing

ISBN: (纸本)9781467372596

Dynamic videos are viewed fundamentally different from static images. Besides spatial features, motion feature also plays an important role as a temporal factor. Most existing video saliency models usually employ optical flow to represent the motion feature. However, optical flow often suffers from the discontinuity problem. And we also notice that human fixations in one single video frame are much sparser than that in an identical still picture. However, many spatial saliency models take each video frame as static image independently. In this paper, we predict the dynamic visual saliency by fusing spatial and temporal features. In order to construct the temporal relationships among a set of successive frames, we introduce a smoothness operator in optical flow field to obtain more accurate motion feature. Then, considering the sparse property of video saliency, we adapt the weights of the regions surrounding to the saliency gravity center in the final maps. The experiments show that our model is more consistent with humans eye-tracking benchmarks than the state-of-the-art models.

关键词： Optical imaging Gravity Computational modeling Visualization Biomedical optical imaging Adaptation models Feature extraction

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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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Energy-efficient VM placement algorithms for cloud data center 2nd

Energy-efficient VM placement algorithms for cloud data cent...

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2nd International Conference on cloud computing and Big Data, cloudCom-Asia 2015

作者： Lin, Xiuyan Liu, Zhanghui Guo, Wenzhong Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing College of Mathematics and Computer Science Fuzhou University Fuzhou350116 China

ISBN: (纸本)9783319284293

cloud is the computing paradigm which provides computing resource as a service through network. The client can use computing resource in a convenient and on-demand way, just like the water and the electricity we use daily. The mapping between virtual machine and physical machine is the key of the VM scheduling problem. Nowadays we advocate low-carbon life. It calls for the green cloud computing solutions whether protecting the environment or saving the cost of cloud suppliers. The proposed VM placement algorithm is energy-efficient, and considers the multi-dimentional resource constrains, such as CPU, memory, network bandwidth, and so on. The experimental results show that the proposed algorithms not only contribute a lot to energy saving, but also try best to meet the quality of service (QoS). Therefore, we make significant savings in operating cost and make full use of various resources in the cloud data center. The algorithm has promising prospect in application. © Springer International Publishing Switzerland 2015.

关键词： Virtual machine

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Bayesian localization microscopy based on intensity distribution of fluorophores

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Protein & Cell 2015年第3期6卷 211-220页

作者： Fan Xu Mingshu Zhang Zhiyong Liu Pingyong Xu Fa Zhang Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China University of Chinese Academy of Sciences Beijing 100049 China Laboratory of Non Coding RNA Institute of Biophysics Chinese Academy of Sciences Beijing 100101 China

Super-resolution microscopy techniques have overcome the limit of optical diffraction. Recently, the Bayesian analysis of Bleaching and Blinking data （3B） method has emerged as an important tool to obtain super-resolution fluorescence images. 3B uses the change in information caused by adding or removing fiuorophores in the cell to fit the date. When adding a new fluorophore, 3B selects a random initial position, optimizes this position and then determines its reliability. However, the fluorophores are not evenly distributed in the entire image region, and the fluorescence intensilty at a given position positively correlates with the probability of observing a fluorophore at this position. In this paper, we present a Bayesian analysis of Bleaching and Blinking microscopy method based on fluorescence intensity distribution （FID3B）. We utilize the intensity distribution to select more reliable positions as the initial positions of fluorophores. This approach can improve the reconstruction results and significantly reduce the computational time. We validate the perfor. mance of our method using both simulated date and experimental date from cellular structures. The results confirm the effectiveness of our method.

关键词： super-resolution fluorescence image 3B,intensity distribution

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Memory bandwidth optimization of SpMV on GPGPUs

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Frontiers of Computer Science 2015年第3期9卷 431-441页

作者： Chenggang Clarence YAN Hui YU Weizhi XU Yingping ZHANG Bochuan CHEN Zhu TIAN Yuxuan WANG Jian YIN Key Lab of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China Institute of Microelectronics Tsinghua University Beijing 100084 China Automation Department Tsinghua University Beijing 100084 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China State Grid Information & Communication Company of Hunan EPC Changsha 410007 China Department of Computer Shandong University Weihai 250101 China

It is an important task to improve performance for sparse matrix vector multiplication （SpMV）, and it is a difficult task because of its irregular memory access. Gen- eral purpose GPU （GPGPU） provides high computing abil- ity and substantial bandwidth that cannot be fully exploited by SpMV due to its irregularity. In this paper, we propose two novel methods to optimize the memory bandwidth for SpMV on GPGPU. First, a new storage format is proposed to exploit memory bandwidth of GPU architecture more effi- ciently. The new storage format can ensure that there are as many non-zeros as possible in the format which is suitable to exploit the memory bandwidth of the GPU. Second, we pro- pose a cache blocking method to improve the performance of SpMV on GPU architecture. The sparse matrix is partitioned into sub-blocks that are stored in CSR format. With the block- ing method, the corresponding part of vector x can be reused in the GPU cache, so the time to access the global memory for vector x is reduced heavily. Experiments are carried out on three GPU platforms, GeForce 9800 GX2, GeForce GTX 480, and Tesla K40. Experimental results show that both new methods can efficiently improve the utilization of GPU mem- ory bandwidth and the performance of the GPU.

关键词： GPGPU performance tuning SpMV cacheblocking memory bandwidth

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Real-time task scheduling algorithm for cloud computing based on particle swarm optimization 2nd

Real-time task scheduling algorithm for cloud computing base...

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2nd International Conference on cloud computing and Big Data, cloudCom-Asia 2015

作者： Chen, Huangning Guo, Wenzhong College of Mathematics and Computer Science Fuzhou University Fuzhou China Fujian Provincial Key Lab of the Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China

ISBN: (纸本)9783319284293

As a new computing paradigm, cloud computing is receiving considerable attention in both industry and academia. Task scheduling plays an important role in large-scale distributed systems. However, most previous work only consider cost or makespan as optimized objective for cloud computing. In this paper, we propose a soft real-time task scheduling algorithm based on particle swarm optimization approach for cloud computing. The optimized objectives include not only cost and makespan, but also deadline missing ratio and load balancing degree. In addition, to improve resource utilization and maximize the profit of cloud service provider, a utility function is employed to allocate tasks to machines with high performance. Simulation results show the proposed algorithm can effectively minimize deadline missing ratio, maximize the profit of cloud service provider and achieve better load balancing com- pared with baseline algorithms. © Springer International Publishing Switzerland 2015.

关键词： cloud computing

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