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Correction to: Automatic identification of myopic maculopathy related imaging features in optic disc region via machine learning methods

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Journal of translational medicine 2021年第1期19卷 203页

作者： Yuchen Du Qiuying Chen Ying Fan Jianfeng Zhu Jiangnan He Haidong Zou Dazhen Sun Bowen Xin David Feng Michael Fulham Xiuying Wang Lisheng Wang Xun Xu Department of Automation The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University (SJTU) 800 Dongchuan RD. Minhang District Shanghai 200240 People's Republic of China. Department of Preventative Ophthalmology Shanghai Eye Diseases Prevention and Treatment Center Shanghai Eye Hospital No. 380 Kangding Road Shanghai 200040 China. Department of Ophthalmology Shanghai Key Laboratory of Ocular Fundus Diseases Shanghai Engineering Center for Visual Science and Photo Medicine Shanghai General Hospital SJTU School of Medicine Shanghai China. National Clinical Research Center for Eye Diseases Shanghai 20080 China. Biomedical and Multimedia Information Technology Research Group School of Computer Science The University of Sydney Sydney NSW 2006 Australia. Department of Molecular Imaging Royal Prince Alfred Hospital and the University of Sydney Sydney Australia. Department of Automation The Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University (SJTU) 800 Dongchuan RD. Minhang District Shanghai 200240 People's Republic of China. lswang@***. Department of Preventative Ophthalmology Shanghai Eye Diseases Prevention and Treatment Center Shanghai Eye Hospital No. 380 Kangding Road Shanghai 200040 China. drxuxun@***. Department of Ophthalmology Shanghai Key Laboratory of Ocular Fundus Diseases Shanghai Engineering Center for Visual Science and Photo Medicine Shanghai General Hospital SJTU School of Medicine Shanghai China. drxuxun@***. National Clinical Research Center for Eye Diseases Shanghai 20080 China. drxuxun@***.

An amendment to this paper has been published and can be accessed via the original article.

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Preselection via classification: A case study on evolutionary multiobjective optimization

arXiv

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arXiv 2017年

作者： Zhang, Jinyuan Zhou, Aimin Tang, Ke Zhang, Guixu Shanghai Key Laboratory of Multidimensional Information Processing Department of Computer Science and Technology East China Normal University 3663 North Zhongshan Road Shanghai China School of Computer Science and Technology University of Science and Technology of China Hefei Anhui China

In evolutionary algorithms, a preselection operator aims to select the promising offspring solutions from a candidate offspring set. It is usually based on the estimated or real objective values of the candidate offspring solutions. In a sense, the preselection can be treated as a classification procedure, which classifies the candidate offspring solutions into promising ones and unpromising ones. Following this idea, we propose a classification based preselection (CPS) strategy for evolutionary multiobjective optimization. When applying classification based preselection, an evolutionary algorithm maintains two external populations (training data set) that consist of some selected good and bad solutions found so far;then it trains a classifier based on the training data set in each generation. Finally it uses the classifier to filter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution. In such cases, it is not necessary to estimate or evaluate the objective values of the candidate offspring solutions. The classification based preselection is applied to three state-of-the-art multiobjective evolutionary algorithms (MOEAs) and is empirically studied on two sets of test instances. The experimental results suggest that classification based preselection can successfully improve the performance of these MOEAs. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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Nonlinear System Identification Based on Recurrent Neural Networks With Shared and Specialized Memories

Nonlinear System Identification Based on Recurrent Neural Ne...

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Asian Control Conference

作者： Yu Guo Fei Wang James Ting-Ho Lo Shaanxi Provincial Key Laboratory of Digital Technology and Intelligent Systems Xi'an China National Engineering Laboratory for Visual Information Processing and Applications Xi'an Jiaotong University Xi'an China Department of MS University of Maryland Baltimore County Baltimore USA

ISBN: (纸本)9781509015740;9781509015733

Recurrent neural network (RNN) is an important method for nonlinear adaptive filtering in an uncertain environment. In this work, we propose an RNN with shared and specialized memories (SSMs) to improve the accuracy and computational complexity in online tasks. The specialized memory consists of the weights of the linear output layer, and the other nonlinear weights are shared memory. The shared weights are trained in an offline manner by data generated with possible environment parameters. Then they are frozen in online tasks while the specialized memory is adopted sequentially by utilizing the online training data. To improve the generalization capacity of our model, a dropout based recursive least squares algorithm is proposed. We apply our model for nonlinear system identification in both stationary and non-stationary condition. The experiment results show that the performance of our method is superior to the state-of-the-art methods.

关键词： Nonlinear systems identification Recurrent neural networks Shared and specialized memories Drop recursive least squares

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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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CuSAO:Parallel Optimization of Sample Adaptive Offset(SAO) for Video Encoder

CuSAO:Parallel Optimization of Sample Adaptive Offset(SAO) f...

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2017 2nd International Conference on Software, Multimedia and Communication Engineering（SMCE 2017)

作者： Qing-ge JI Ting ZHU Run-yang LIN Fan LIANG School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing School of Electronics and Information Technology Sun Yat-sen University

Conventional SAO module in encoder decides classification mode and offsets based on the statistical data of pixels for *** needs to traverse all pixels several times,which is extremely *** improving works for efficiency either reduce statistical data or concentrate only on SAO *** tackle this,a fast parallel algorithm for standard SAO in encoder named Cu SAO based on Graphics processing Unit(GPU) is *** proposed a pixel grid – thread mapping method and adopted parallel computing of multi-granularity in different stages of SAO *** results demonstrate our method outperforms state-of-the-arts modules and shows impressive results in terms of efficiency,reaching a significant time saving by average 75% and up to more than 85%.

关键词： Sample adaptive offset Encoder parallelization HEVC CUDA

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Design of Distributed Multi-Parameter Measurement Control System Based On CAN Bus

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IOP Conference Series: Materials Science and Engineering 2018年第4期452卷

作者： Hong Guo Wei Hu College of Computer Science and Technology Wuhan University of Science and Technology Wuhan 430065 China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System Wuhan 430065 China

This paper introduces a multi-parameter distributed measurement control system based on CAN bus network and uses C8051F040 MCU as measurement control node, and corresponding hardware and software design to realize simultaneous detection of multiple measurement nodes. This paper described the overall structure of the system, introduced the hardware design and system software design of the data acquisition and control processing module based on C8051F040 microcontroller, and verified the correctness and feasibility of the system design by experimental tests.

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A new chaotic feature for EEG classification based seizure diagnosis

A new chaotic feature for EEG classification based seizure d...

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IEEE International Conference on Acoustics, Speech and Signal processing

作者： Su Yang Anqin Zhang Jiulong Zhang Weishan Zhang Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China School of Computer Science Xi'an University of Technology China Department of Software Engineering China University of Petroleum Qingdao China

ISBN: (纸本)9781509041183

Seeking effective measures to characterize the chaotic patterns of EEG signals for seizure diagnosis is a long-term endeavor in the literature. We propose to count the number of zero-crossing (ZC) points on Poincare surface as a feature when the time series of interest is embedded into the reconstructed state space. The experiments show that Poincare surface can act as a platform to observe the chaotic patterns of EEG signals and the ZC feature on Poincare surface is a promising pattern descriptor to discriminate different categories of EEG signals. When used alone for EEG classification, the ZC feature achieves 100%, 99.27%, and 94.68% accuracy in 2-class, 3-class, and 5-class classification on a widely used benchmark.

关键词： Nonlinear time series analysis feature extraction pattern recognition EEG signal classification Electroencephalography Feature extraction Pattern recognition time series analysis Seizures signal classification signals Phyla state space

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Uniformly locally univalent harmonic mappings associated with the pre-schwarzian norm

arXiv

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arXiv 2017年

作者： Liu, Gang Ponnusamy, Saminathan College of Mathematics and Statistics Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Hengyang Normal University Hengyang Hunan421002 China Department of Mathematics Indian Institute of Technology Madras Chennai600 036 India

In this paper, we consider the class of uniformly locally univalent harmonic mappings in the unit disk and build a relationship between its pre-Schwarzian norm and uniformly hyperbolic radius. Also, we establish eight ways of characterizing uniformly locally univalent sense-preserving harmonic mappings. We also present some sharp distortions and growth estimates and investigate their connections with Hardy spaces. Finally, we study subordination principles of norm *** Codes Primary: 30H10, 30H30, 30H35, 31A05, Secondary 30C55 Copyright © 2017, The Authors. All rights reserved.

关键词： Harmonic analysis

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On the impact of phoneme alignment in DNN-based speech synthesis 9

On the impact of phoneme alignment in DNN-based speech synth...

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9th ISCA Speech Synthesis Workshop, SSW 2016

作者： Li, Mei Wu, Zhizheng Xie, Lei Shaanxi Provincial Key Laboratory of Speech and Image Information Processing School of Computer Science Northwestern Polytechnical University Xi'an China The University of Edinburgh United Kingdom

Recently, deep neural networks (DNNs) have significantly improved the performance of acoustic modeling in statistical parametric speech synthesis (SPSS). However, in current implementations, when training a DNN-based speech synthesis system, phonetic transcripts are required to be aligned with the corresponding speech frames to obtain the phonetic segmentation, called phoneme alignment. Such an alignment is usually obtained by forced alignment based on hidden Markov models (HMMs) since manual alignment is labor-intensive and timeconsuming. In this work, we study the impact of phoneme alignment on the DNN-based speech synthesis system. Specifically, we compare the performances of different DNN-based speech synthesis systems, which use manual alignment and HMM-based forced alignment from three types of labels: HMM mono-phone, tri-phone and full-context. Objective and subjective evaluations are conducted in term of the naturalness of synthesized speech to compare the performances of different alignments. © 2016, 9th ISCA Speech Synthesis Workshop, SSW 2016. All rights reserved.

关键词： Deep neural networks

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Completeness of hoare logic over nonstandard models

arXiv

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arXiv 2017年

作者： Xu, Zhaowei Sui, Yuefei Zhang, Wenhui State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China

The nonstandard approach to program semantics has successfully resolved the completeness problem of Floyd-Hoare logic. The known versions of nonstandard semantics, the Hungary semantics and axiomatic semantics, are so general that they are absent either from mathematical elegance or from practical usefulness. The aim of this paper is to exhibit a not only mathematically elegant but also practically useful nonstandard semantics. A basic property of computable functions in the standard model N of Peano arithmetic PA is Σ1definability. However, the functions induced by the standard interpretation of while-programs S in nonstandard models M of PA are not always arithmetical. The problem consists in that the standard termination of S in M uses the finiteness in N, which is not the finiteness in M. To this end, we shall give a new interpretation of S in M such that the termination of S uses M-finiteness, and the functions produced by S in all models of PA have the uniform Σ1-definability. Then we define, based on the new semantics of while-programs, a new semantics of Hoare logic in nonstandard models of PA, and show that the standard axiom system of Hoare logic is sound and complete w.r.t. the new semantics. It will be established, in PA, that the Hungary semantics and axiomatic semantics coincide with the new semantics of while-programs. Moreover, various comparisons with the previous results, usefulness of the nonstandard semantics, and remarks on the completeness issues are ***: Hoare logic, Peano arithmetic, nonstandard models, nonstandard semantics, logical completeness Copyright © 2017, The Authors. All rights reserved.

关键词： Semantics

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