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A novel generalised meta-heuristic framework for dynamic capacitated arc routing problems

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arXiv 2021年

作者： Tong, Hao Minku, Leandro L. Menzel, Stefan Sendhoff, Bernhard Yao, Xin School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom Honda Research Institute Europe GmbH Offenbach63073 Germany Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The capacitated arc routing problem (CARP) is a challenging combinatorial optimisation problem abstracted from typical real-world applications, like waste collection and mail delivery. However, few studies considered dynamic changes during the vehicles' service, which can make the original schedule infeasible or obsolete. The few existing studies are limited by dynamic scenarios that can suffer single types of dynamic events, and by algorithms that rely on special operators or representations, being unable to benefit from the wealth of contributions provided by the static CARP literature. Here, we provide the first mathematical formulation for dynamic CARP (DCARP) and design a simulation system to execute the CARP solutions and generate DCARP instances with several common dynamic events. We then propose a novel framework able to generalise all existing static CARP optimisation algorithms so that they can cope with DCARP instances. The framework has the option to enhance optimisation performance for DCARP instances based on a restart strategy that makes no use of past history, and a sequence transfer strategy that benefits from past optimisation experience. Empirical studies are conducted on a wide range of DCARP instances. The results highlight the need for tackling dynamic changes and show that the proposed framework significantly improves over existing algorithms. Copyright © 2021, The Authors. All rights reserved.

关键词： Routing algorithms

Reference Vector Adaptation and Mating Selection Strategy via Adaptive Resonance Theory-based Clustering for Many-objective Optimization

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arXiv 2022年

作者： Kinoshita, Takato Masuyama, Naoki Liu, Yiping Nojima, Yusuke Ishibuchi, Hisao Graduate School of Engineering Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan Graduate School of Informatics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan College of Computer Science and Electronic Engineering Hunan University Hunan 410082 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Decomposition-based multiobjective evolutionary algorithms (MOEAs) with clustering-based reference vector adaptation show good optimization performance for many-objective optimization problems (MaOPs). Especially, algorithms that employ a clustering algorithm with a topological structure (i.e., a network composed of nodes and edges) show superior optimization performance to other MOEAs for MaOPs with irregular Pareto optimal fronts (PFs). These algorithms, however, do not effectively utilize information of the topological structure in the search process. Moreover, the clustering algorithms typically used in conventional studies have limited clustering performance, inhibiting the ability to extract useful information for the search process. This paper proposes an adaptive reference vector-guided evolutionary algorithm using an adaptive resonance theory-based clustering with a topological structure. The proposed algorithm utilizes the information of the topological structure not only for reference vector adaptation but also for mating selection. The proposed algorithm is compared with 8 state-of-the-art MOEAs on 78 test problems. Experimental results reveal the outstanding optimization performance of the proposed algorithm over the others on MaOPs with various properties. © 2022, CC BY-NC-ND.

关键词： Clustering algorithms

Multi-label classification via adaptive resonance theory-based clustering

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arXiv 2021年

作者： Masuyama, Naoki Nojima, Yusuke Loo, Chu Kiong Ishibuchi, Hisao Graduate School of Engineering Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

—This paper proposes a multi-label classification algorithm capable of continual learning by applying an Adaptive Resonance Theory (ART)-based clustering algorithm and the Bayesian approach for label probability computation. The ART-based clustering algorithm adaptively and continually generates prototype nodes corresponding to given data, and the generated nodes are used as classifiers. The label probability computation independently counts the number of label appearances for each class and calculates the Bayesian probabilities. Thus, the label probability computation can cope with an increase in the number of labels. Experimental results with synthetic and real-world multi-label datasets show that the proposed algorithm has competitive classification performance to other well-known algorithms while realizing continual learning. © 2021, CC BY-NC-ND.

关键词： Classification (of information)

Glutathione-capped quantum dots for plasma membrane labeling and membrane potential imaging

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Nano Research 2019年第6期12卷 1321-1326页

作者： Guangcun Chen Yejun Zhang Zhao Peng Dehua Huang Chunyan Li Qiangbin Wang CAS Key Laboratory of Nano-Bio Interface Division of Nanobiomedicine and i-LabCAS Center for Excellence in Brain ScienceSuzhou Institute of Nano-Tech and Nano-BionicsChinese Academy of SciencesSuzhou 215123China School of Nano Technology and Nano Bionics University of Science and Technology of ChinaHefei 230026China College of Materials Sciences and Opto-Electronic Technology University of Chinese Academy of SciencesBeijing 100049China

The plasma membrane of cells is a crucial biological membrane that involved in a variety of cellular processes including cell signaling transduction through membrane electrical ***,monitoring membrane electrical activity using fluorescence imaging has attracted numerous attentions for its potential applications in evaluating how the nervous system ***,the development of ideal fluorescent voltage-sensitive probes with both high membrane labeling efficiency and voltage sensitivity is still retain a big ***,glutathionecapped CdSe@ZnS quantum dots (CdSe@ZnS-GSH QDs) with a size of 2.5 nm and an emission peak at 520 nm are synthesized using a facile ligand exchange method for plasma membrane labeling and membrane potential *** as-synthesized CdSe@ZnS-GSH QDs can effectively label cell membrane at neutral pH within 30 min and exhibit excellent optical stability in continuous imaging for up to 60 *** the test concentration up to 200 nM,CdSe@ZnS-GSH QDs show high biocompatibility to cells and do not affect cell proliferation,disturb cell membrane integrity or cause apoptosis and necrosis of ***,a two-component voltage sensor strategy based on fluorescence resonance energy transfer (FRET) between CdSe@ZnS-GSH QDs and the dipicrylamine (DPA) is successfully developed to monitor the membrane potential by the fluorescence of CdSe@ZnS-GSH *** study offers a facile strategy for labeling plasma membrane and monitoring the membrane potential of cells and will hold great potential in the research of signaling within intact neuronal circuits.

关键词： quantum dots membrane labeling membrane potential imaging fluorescence imaging neuron

A knowledge-driven generative model for multi-implication Chinese medical procedure entity normalization

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A knowledge-driven generative model for multi-implication Ch...

2020 Conference on Empirical Methods in Natural Language Processing, EMNLP 2020

作者： Yan, Jinghui Wang, Yining Xiang, Lu Zhou, Yu Zong, Chengqing School of Computer Science and Information Technology Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China National Laboratory of Pattern Recognition Institute of Automation CAS Beijing China University of Chinese Academy of Sciences Beijing China CAS Center for Excellence in Brain Science and Intelligence Technology Beijing China Beijing Fanyu Technology Co. Ltd China

ISBN: (纸本)9781952148606

Medical entity normalization, which links medical mentions in the text to entities in knowledge bases, is an important research topic in medical natural language processing. In this paper, we focus on Chinese medical procedure entity normalization. However, nonstandard Chinese expressions and combined procedures present challenges in our problem. The existing strategies relying on the discriminative model are poorly to cope with normalizing combined procedure mentions. We propose a sequence generative framework to directly generate all the corresponding medical procedure entities. we adopt two strategies: category-based constraint decoding and category-based model refining to avoid unrealistic results. The method is capable of linking entities when a mention contains multiple procedure concepts and our comprehensive experiments demonstrate that the proposed model can achieve remarkable improvements over existing baselines, particularly significant in the case of multi-implication Chinese medical procedures. © 2020 Association for Computational Linguistics

关键词： Natural language processing systems

SYNAPSE: An international roadmap to large brain imaging

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Physics Reports 2023年第1期999卷 1-60页

作者： Stampfl, Anton P.J. Liu, Zhongdong Hu, Jun Sawada, Kei Takano, H. Kohmura, Yoshiki Ishikawa, Tetsuya Lim, Jae-Hong Je, Jung-Ho Low, Chian-Ming Teo, Alvin Tok, Eng Soon Tan, Tin Wee Ban, Kenneth Libedinsky, Camilo Tan, Francis Chee Kuan Chen, Kuan-Peng Yang, An-Cheng Chuang, Chao-Chun Chen, Nan-Yow Shih, Chi-Tin Lee, Ting-Kuo Yang, De-Nian Lai, Hsu-Chao Shuai, Hong-Han Cheng, Chang-Chieh Ching, Yu-Tai Li, Chia-Wei Charng, Ching-Che Lo, Chung-Chuan Chiang, Ann-Shyn Recur, Benoit Petibois, Cyril Cheng, Chia-Liang Chen, Hsiang-Hsin Yang, Shun-Min Hwu, Yeukuang Rojviriya, Catleya Rugmai, Supagorn Rujirawat, Saroj Margaritondo, Giorgio Australian Nuclear Science and Technology Organization Lucas Heights 2234 NSW Australia Henan university of Technology Zhengzhou China Shanghai Institute of Applied Physics Chinese Academy of Science Shanghai China RIKEN SPring-8 Center Hyogo 679-5148 Japan Pohang Accelerator Laboratory Pohang University of Science and Technology Pohang Gyeongbuk 37673 South Korea Department of Materials Science and Engineering Pohang University of Science and Technology Gyeongbuk Pohang 37673 South Korea Department of Pharmacology Department of Anaesthesia and Healthy Longevity Research Programme Yong Loo Lin School of Medicine National University of Singapore Singapore School of Chemical and Life Sciences Nanyang Polytechnic Singapore eMaGIC-Lab Department of Physics National University of Singapore Singapore National Supercomputing Center Singapore Department of Psychology The N1 Institute for Health National University of Singapore Singapore Institute of Molecular and Cell Biology A*STAR Singapore Department of Anaesthesia National University Hospital National University Health System Singapore National Center for High-Performance Computing Hsinchu Taiwan Department of Physics Tunghai University Taichung Taiwan Department of Physics National Tsing Hua University Hsinchu Taiwan Institute of Information Science Academia Sinica Taipei Taiwan Department of Computer Science National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Information Technology Service Center National Yang Ming Chiao Tung University Hsinchu Taiwan National Yang Ming Chiao Tung University Hsinchu Taiwan Department of Life Science National Tsing Hua University Hsinchu Taiwan Institute of Systems Neuroscience and Brain Research Center National Tsing Hua University Hsinchu Taiwan Institute of Physics Academia Sinica Taipei Taiwan Department of Physics National Dong Hwa University

Since 2020, synchrotron radiation facilities in several Asia-Pacific countries have been collaborating in a major project called “SYNAPSE” (Synchrotrons for Neuroscience: an Asia-Pacific Scientific Enterprise). They use x-ray imaging to attack in a coordinated fashion one of the major issues in modern science: the structure of animal and human brains, including neurons and connections. The objective is to develop Google-like maps also including detailed structural and functional information for selected regions of interest. The sheer mass of data needed for the objective poses huge problems for the acquisition, processing, storage and use of images. In order to complete the task within a reasonable time, the key element of the SYNAPSE strategy is the parallel and coordinated work of several facilities on the same specimens. This article reviews different aspects of the enterprise, including the foundations of synchrotron radiation, coherence and of its role in advanced imaging, electron accelerators, x-ray optics and detectors. This will provide the foundation for an extensive presentation of the different components of SYNAPSE, with an overview of results already obtained within the consortium. © 2022

关键词： brain mapping Phase contrast Radiology Supercomputing Synchrotron X-rays

Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation

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Restoration Of Cataract Fundus Images Via Unsupervised Domai...

IEEE International Symposium on Biomedical Imaging

作者： Heng Li Haofeng Liu Yan Hu Risa Higashita Yitian Zhao Hong Qi Jiang Liu School of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Tomey Corporation Nagoya Japan Chinese Academy of Sciences Cixi Institute of Biomedical Engineering Ningbo Institute of Industrial Technology Ningbo China Peking University Third Hospital Beijing China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China

Cataract presents the leading cause of preventable blindness in the world. The degraded image quality of cataract fundus increases the risk of misdiagnosis and the uncertainty in preoperative planning. Unfortunately, the absence of annotated data, which should consist of cataract images and the corresponding clear ones from the same patients after surgery, limits the development of restoration algorithms for cataract images. In this paper, we propose an end-to-end unsupervised restoration method of cataract images to enhance the clinical observation of cataract fundus. The proposed method begins with constructing an annotated source domain through simulating cataract-like images. Then a restoration model for cataract images is designed based on pix2pix framework and trained via unsupervised domain adaptation to generalize the restoration mapping from simulated data to real one. In the experiment, the proposed method is validated in an ablation study and a comparison with previous methods. A favorable performance is presented by the proposed method against the previous methods. The code of of this paper will be released at https://***/liamheng/Restoration-of-Cataract-Images-via-Domain-Adaptation.

关键词： Cataracts Image quality Adaptation models Uncertainty Surgery Blindness Retina

Adaptive Resonance Theory-based Topological Clustering with a Divisive Hierarchical Structure Capable of Continual Learning

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arXiv 2022年

作者： Masuyama, Naoki Amako, Narito Yamada, Yuna Nojima, Yusuke Ishibuchi, Hisao Graduate School of Informatics Osaka Metropolitan University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan Graduate School of Engineering Osaka Prefecture University 1-1 Gakuen-cho Naka-ku Sakai-Shi Osaka599-8531 Japan Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Adaptive Resonance Theory (ART) is considered as an effective approach for realizing continual learning thanks to its ability to handle the plasticity-stability dilemma. In general, however, the clustering performance of ART-based algorithms strongly depends on the specification of a similarity threshold, i.e., a vigilance parameter, which is data-dependent and specified by hand. This paper proposes an ART-based topological clustering algorithm with a mechanism that automatically estimates a similarity threshold from the distribution of data points. In addition, for improving information extraction performance, a divisive hierarchical clustering algorithm capable of continual learning is proposed by introducing a hierarchical structure to the proposed algorithm. Experimental results demonstrate that the proposed algorithm has high clustering performance comparable with recently-proposed state-of-the-art hierarchical clustering algorithms. © 2022, CC BY-NC-ND.

关键词： Clustering algorithms

Atlas of Proteomic signatures of brain structure and its links to brain disorders

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Nature communications 2025年第1期16卷 5092页

作者： Peng Ren Xiao-He Hou Zeyu Li Jia You Yuzhu Li Wei Zhang Weikang Gong Bei Zhang Bangsheng Wu Linbo Wang Chun Shen Yujie Zhao Qing Ma Jujiao Kang Yuchao Jiang Neil Roberts Fan Xu Yong He Jin-Tai Yu Meiyun Wang Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China. Department of Neurology Huashan Hospital Fudan University Shanghai China. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai PR China. Department of Neurology Qingdao Hospital University of Health and Rehabilitation Sciences (Qingdao Municipal Hospital) Qingdao China. School of Data Science Fudan University Shanghai China. Centre for Reproductive Health (CRH) Institute for Regeneration and Repair (IRR) University of Edinburgh 4-5 Little France Drive Edinburgh EH15 4UU UK. Institute of Mechanics and Computational Engineering Department of Aeronautics and Astronautics Fudan University Shanghai China. State Key Laboratory of Cognitive Neuroscience and Learning Beijing Normal University Beijing China. Beijing Key Laboratory of Brain Imaging and Connectomics Beijing Normal University Beijing China. Institute of Science and Technology for Brain-Inspired Intelligence State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China. jintai_yu@***. Department of Neurology Huashan Hospital Fudan University Shanghai China. jintai_yu@***. Department of Medical Imaging Henan Provincial People's Hospital & Zhengzhou University People's Hospital Zhengzhou China. mywang@***. Institute of Science and Technology for Brain-Inspired Intelligence State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Fudan University Shanghai China. wcheng@***. Department of Neurology Huashan Hospital Fudan University Shanghai China. wcheng@***. Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence (Fudan University) Ministry of Education Shanghai PR China. wcheng@fudan.edu.cn. Fudan ISTBI-ZJNU Algorithm Centre for Brain-inspi

Individual variation in brain structure influences deterioration due to disease and comprehensive profiling of the associated proteomic signature advances mechanistic understanding. Here, using data from 4997 UK Biobank participants, we analyzed the associations between 2920 plasma proteins and 272 neuroimaging-derived brain structure measures. We identified 5358 associations between 1143 proteins and 256 brain structure measures, with NCAN and LEP proteins showing the most associations. Functional enrichment implicated these proteins in neurogenesis, immune/apoptotic processes and neurons. Furthermore, bidirectional Mendelian randomization revealed 33 associations between 32 proteins and 23 brain structure measures, and 21 associations between nine brain structure associated proteins and ten brain disorders. Moreover, the significant associations between the identified proteins and mental health were mediated by brain volume and surface area. In summary, this study generates a comprehensive atlas mapping the patterns of association between proteome and brain structure, highlighting their potential value for studying brain disorders.

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