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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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Decision-making strategies for multi-objective community detection in complex networks

Communications in Computer and Information Science

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Communications in computer and Information Science 2014年 472卷 621-628页

作者： Zhang, Yu Zhang, Xingyi Tang, Jin Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230039 China

ISBN: (纸本)9783662450482

Multi-objective optimization algorithms have demonstrated their effectiveness and efficiency in detecting community structure in complex networks, by which a set of trade-off partitions of networks are obtained instead of a single partition. The large number of partitions lead to a challenging problem for decision makers: how to obtain an ideal partition from the set of trade-off partitions of networks. In this paper, we present two decision-making strategies for obtaining the ideal partition, one is based on the knee points and the other is based on the majority voting. Experimental results on random networks and real-world networks illustrate that the presented two strategies are very competitive for obtaining an ideal partition of networks. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Complex networks

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Structural holes study of ICML collaborative network

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Journal of Computational Information Systems 2014年第24期10卷 10715-10722页

作者： Duan, Weiwei Qian, Fulan Shi, Lei Zhang, Yanping School of Computer Science and Technology Anhui University Hefei China Key Lab of Intelligent Computing and Signal Processing Anhui University Hefei China

Collaboration among authors is a pervasive phenomenon currently. Mining useful information from cooperation is a significant study to unravel relationships underneath these papers and predict potential collaborations or future popular academic topics. This paper demonstrates the change of Structural Holes in the whole network and for some individual authors from 2009 to 2012 in ICML. This paper is going to study the biggest subset in 2009, which separates into several smaller subsets with less structural holes in 2010 and 2011, while the number of structural holes in 2012 increases. The graphs of the whole networks from 2009 to 2012 drawn by Ucinet are going to demonstrate the changes of structural holes from 2009 to 2012 and expl the reason why there are more structural holes in 2012 compared to 2010 and 2011. The reason is that the ainnumber of authors in 2012 increases about 50 percent compared to 2011, which results in that authors need to find new collaborations and leaders may become intermediaries to connect new authors and existing authors occupying structural holes. And some authors whose papers are published on ICML continuously from 2009 to 2012 are going to be analysed to indicate the changes of their position in the network. Some authors are increasingly constrained in certain groups, while some other authors become bridges connecting different groups and some other authors changes are correspond to the changes of the whole network. They are constrained in certain subset with less structural holes in 2010 and 2011 but, due to large number of authors joining in ICML, more likely to occupy more structural holes.

关键词： Data mining

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Video Summarization by Robust Low-Rank Subspace Segmentation

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2013年 212卷 929-937页

作者： Tu, Zhengzheng Sun, Dengdi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology Anhui University Hefei 230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei 230039 China

ISBN: (纸本)9783642375019

Video summarization provides condensed and succinct representations of the content of a video stream. A static storyboard summarization approach based on robust low-rank subspace segmentation is proposed in this paper. Firstly, video frames are represented as multi-dimensional vectors, and then embedded into a group of affine subspaces using low-rank representation according to the content similarity of the frames in the same subspace. Secondly, a series of subspaces are segmented based on the Normalized Cuts algorithm. The video summary is finally generated by choosing key frames from the significant subspaces and ranking these key frames in temporal order. The experimental results demonstrate that the proposed summarization algorithm can produce crucial key frames and effectively reduce the visual content redundancy in summary comparing with the conventional approaches. © Springer-Verlag Berlin Heidelberg 2013.

关键词： Vectors

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Video summarization by robust low-rank subspace segmentation

Video summarization by robust low-rank subspace segmentation

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8th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2013

作者： Tu, Zhengzheng Sun, Dengdi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei230039 China School of Computer Science and Technology Anhui University Hefei230601 China Key Lab of Industrial Image Processing and Analysis of Anhui Province Hefei230039 China

ISBN: (纸本)9783642375019

关键词： Vectors

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An Implementation of Membrane Algorithms on GPU

An Implementation of Membrane Algorithms on GPU

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The Second Asian Conference on Membrane computing(ACMC2013)

作者： Xingyi Zhang Dong Qin Zhuanlian Ding Jin Tang Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University

Membrane algorithms are a new class of heuristic algorithms, which attempt to incorporate some components of membrane computing models (also called P systems) for designing efficient optimization algorithms, such as the structure of P systems, the way of communication between cells, etc. Membrane algorithms are a kind of parallel methods, where many operations can be performed in parallel. Although the importance of the parallelism of such algorithms is recognized, membrane algorithms were often implemented on the serial computing device Central processing Unit (CPU), which makes the algorithms cannot work in a more efficient way. In this work, we consider the implementation of membrane algorithms on the parallel computing device Graphics processing Unit (GPU). Under such implementation, all cells of membrane algorithms can work simultaneously. Experiment results on two classical intractable problems, point set matching problem and TSP, show that GPU implementation of membrane algorithms is much more efficient than CPU implementation in terms of runtime, especially for solving the problems with a high complexity.

关键词： Membrane computing Membrane algorithm Parallel computing device GPU

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An efficient implementation of double precision 1-D FFT for GPUs Using CUDA

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Journal of Information and Computational Science 2012年第2期9卷 387-394页

作者： Liu, Yanjun Guo, Licai Luo, Bin Zhang, Xingyi School of Computer Science and Technology Anhui University Hefei 230039 China Key Lab of Intelligent Computing and Signal Processing of Ministry of Education Anhui University Hefei 230039 China School of Computer Science and Technology University of Science and Technology of China Hefei 230027 China

Fast Fourier Transform (FFT) is a well known and widely used tool in many scientific and engineering fields. CUFFT, which is the NVIDIA's FFT library included in the CUDA toolkit, supports double precision FFTs. However, the implementation of CUFFT is not very efficient. In this paper, we implement an efficient double-precision Cooley-tukey algorithm for GPUs using CUDA. Some programming techniques are employed to exploit the hardware characteristics. These techniques include on-chip shared memory utilization, removing redundant computation, and coalescing the global memory access. Experiments show that the performance of our 1-D FFT is as fast as CUFFT. Furthermore, the performance of our FFT implementation is more than twice faster than CUFFT for small input sizes. Copyright © 2012 Binary Information Press.

关键词： Graphics processing unit

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Tissue P systems with cell separation: attacking the partition problem

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Science China(Information Sciences) 2011年第2期54卷 293-304页

作者： ZHANG XingYi1, WANG Shuo2, NIU YunYun2 & PAN LinQiang2 1key lab of intelligent computing and signal processing of Ministry of Education, school of computer Science and technology, Anhui University, Hefei 230039, China 2Image processing and intelligent Control key laboratory of Education Ministry of China, Department of Control Science and Engineering, Huazhong University of Science and technology, Wuhan 430074, China 1. Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China2. Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China

Tissue P systems are distributed parallel and non-deterministic computing models in the framework of membrane computing, which are inspired by intercellular communication and cooperation between neurons. Recently, cell separation is introduced into tissue P systems, which enables systems to generate an exponential workspace in a polynomial time. In this work, the computational power of tissue P systems with cell separation is investigated. Specifically, a uniform family of tissue P systems with cell separation is constructed for effciently solving a well-known NP-complete problem, the partition problem.

关键词： membrane computing tissue P system cell separation partition problem

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Semi-supervised geometric mean of Kullback-Leibler divergences for subspace selection

Semi-supervised geometric mean of Kullback-Leibler divergenc...

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2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, Jointly with the 2011 7th International Conference on Natural Computation, ICNC'11

作者： Chen, Si-Bao Wang, Hai-Xian Zhang, Xing-Yi Luo, Bin Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9781612841816

Subspace selection is widely adopted in many areas of pattern recognition. A recent result, named maximizing the geometric mean of Kullback-Leibler (KL) divergences of class pairs (MGMD), is a successful method for subspace selection, which can significantly reduce the class separation problem. However, in many applications, labeled data are very limited while unlabeled data can be easily obtained. The estimation of divergences of class pairs is unstable using inadequate labeled data. To take advantage of unlabeled data for subspace selection, semi-supervised MGMD (SSMGMD) is proposed using graph Laplacian as normalization. Quasi-Newton method is adopted to solve the optimization problem. Experiments on synthetic data and real image data show the validity of SSMGMD. © 2011 IEEE.

关键词： Newton-Raphson method

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Spiking neural P systems for arithmetic operations

Spiking neural P systems for arithmetic operations

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6th International Conference on Bio-Inspired computing: Theories and Applications, BIC-TA 2011

作者： Zeng, Xiangxiang Song, Tao Pan, Linqiang Zhang, Xingyi Key Laboratory on Image Processing and Intelligent Control Department of Control Science and Engineering Huazhong University of Science and Technology Wuhan 430074 China Key Lab of Intelligent Computing and Signal Processing School of Computer Science and Technology Anhui University Hefei 230039 China

ISBN: (纸本)9780769545141

Recently, Gutiérrez-Naranjo and Leporati considered performing basic arithmetic operations on a new class of bioinspired computing devices - spiking neural P systems (for short, SN P systems). However, the binary encoding mechanism used in their research looks like the encoding approach in electronic circuits, instead of the style of spiking neurons (in usual SN P systems, information are encoded as the time interval between spikes). In this work, three SN P systems are constructed as adder, subtracter and multiplier, respectively. In these devices, a number is inputted to the system as the interval of time elapsed between two spikes received by input neuron, the result of a computation is the time between the moments when the output neuron spikes. © 2011 IEEE.

关键词： Encoding (symbols)

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