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arXiv 2022年

作者： Jiao, Dongbin Wang, Lingyu Yang, Peng Yang, Weibo Peng, Yu Shang, Zhanhuan Ren, Fengyuan School of Information Science and Engineering Lanzhou University Lanzhou730000 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China School of Automobile Chang'an University Xi’an710064 China State Key Laboratory of Grassland Agro-Ecosystem College of Ecology Lanzhou University Lanzhou730000 China

Grassland restoration is a critical means to safeguard grassland ecological degradation. To alleviate the extensive human labors and boost the restoration efficiency, UAV is promising for its fully automatic capability yet still waits to be exploited. This paper progresses this emerging technology by explicitly considering the realistic constraints of the UAV and the grassland degradation while planning the grassland restoration. To this end, the UAV-enabled restoration process is first mathematically modeled as the maximization of restoration areas of the UAV under the limited battery energy of UAV, the grass seeds weight, the number of restored areas, and the corresponding sizes. Then we analyze that, by considering these constraints, this original problem emerges two conflict objectives, namely the shortest flight path and the optimal areas allocation. As a result, the maximization of restoration areas turns out to be a composite of a trajectory design problem and an areas allocation problem that are highly coupled. From the perspective of optimization, this requires solving two NP-hard problems of both the traveling salesman problem (TSP) and the multidimensional knapsack problem (MKP) at the same time. To tackle this complex problem, we propose a cooperative optimization algorithm, called CHAPBILM, to solve those two problems interlacedly by utilizing the interdependencies between them. Multiple simulations verify the conflicts between the trajectory design and areas allocation. The effectiveness of the cooperative optimization algorithm is also supported by the comparisons with traditional optimization methods which do not utilize the interdependencies between the two problems. As a result, the proposed algorithm successfully solves the multiple simulation instances in a near-optimal way. The model and solution of this work could be also extended to other complication optimization problems in ecological protection and precision agriculture. © 2022, CC BY.

关键词： Energy utilization

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Multi-label classification via adaptive resonance theory-based clustering

arXiv

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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)

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Synergizing domain expertise with self-awareness in software systems: A patternized architecture guideline

arXiv

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arXiv 2020年

作者： Chen, Tao Bahsoon, Rami Yao, Xin Department of Computer Science Loughborough University Epinal Way LoughboroughLE11 3TU United Kingdom School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology ShenzhenGuangdong518055 China

To promote engineering self-aware and self-adaptive software systems in a reusable manner, architectural patterns and the related methodology provide an unified solution to handle the recurring problems in the engineering process. However, in existing patterns and methods, domain knowledge and engineers’ expertise that is built over time are not explicitly linked to the self-aware processes. This linkage is important, as the knowledge is a valuable asset for the related problems and its absence would cause unnecessary overhead, possibly misleading results and unwise waste of the tremendous benefit that could have been brought by the domain expertise. This paper highlights the importance of synergizing domain expertise and the self-awareness to enable better self-adaptation in software systems, relying on well-defined expertise representation, algorithms and techniques. In particular, we present a holistic framework of notions, enriched patterns and methodology, dubbed DBASES, that offers a principled guideline for the engineers to perform difficulty and benefit analysis on possible synergies, in an attempt to keep "engineers-in-the-loop". Through three tutorial case studies, we demonstrate how DBASES can be applied in different domains, within which a carefully selected set of candidates with different synergies can be used for quantitative investigation, providing more informed decisions of the design choices. Copyright © 2020, The Authors. All rights reserved.

关键词： Domain Knowledge

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Cell vibron polariton in the myelin sheath of nerve

arXiv

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arXiv 2019年

作者： Song, Bo Shu, Yousheng School of Optical-Electrical Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research Beijing Normal University 19 Xinjiekou Wai Street Beijing100875 China

Polaritons are arousing tremendous interests in physics and material sciences for their unique and amazing properties, especially including the condensation, lasing without inversion and even room-temperature superfluidity. Herein, we propose a cell vibron polariton (cell-VP): a collectively coherent mode of a photon and all phospholipid molecules in a myelin sheath which is a nervous cell majorly consisting of the phospholipid molecules. Cell-VP can be resonantly self-confined in the myelin sheath under physiological conditions. The observations benefit from the specifically compact, ordered and polar thin-film structure of the sheath, and the relatively strong coupling of the mid-infrared photon with the vibrons of phospholipid tails in the myelin. The underlying physics is revealed to be the collectively coherent superposition of the photon and vibrons, the polariton induced significant enhancement of myelin permittivity, and the resonance of the polariton with the sheath cell. The captured cell-VPs in myelin sheaths may provide a promising way for super-efficient consumption of extra-weak bioenergy and even directly serve for quantum information in the nervous system. These findings further the understanding of neuroscience on the cellular level from the view of quantum mechanics. Copyright © 2019, The Authors. All rights reserved.

关键词： Cell membranes

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A calcium imaging large dataset reveals novel functional organization in macaque V4

arXiv

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arXiv 2023年

作者： Wang, Tianye Yao, Haoxuan Lee, Tai Sing Hong, Jiayi Li, Yang Jiang, Hongfei Andolina, Ian Max Tang, Shiming Peking University School of Life Sciences Beijing China Peking‐Tsinghua Center for Life Sciences Beijing China IDG/McGovern Institute for Brain Research Peking University Beijing China Peking University Beijing China Center for the Neural Basis of Cognition and Computer Science Department Carnegie Mellon University Pittsburgh United States The Center for Excellence in Brain Science and Intelligence Technology State Key Laboratory of Neuroscience Key Laboratory of Primate Neurobiology Institute of Neuroscience Chinese Academy of Sciences Shanghai China

The topological organization and feature preferences of primate visual area V4 have been primarily studied using artificial stimuli. Here, we combined large‐scale calcium imaging with deep learning methods to characterize and understand how V4 processes natural images. By fitting a deep learning model to an unprecedentedly large dataset of columnar scale cortical responses to tens of thousands of natural stimuli and using the model to identify the images preferred by each cortical pixel, we obtained a detailed V4 topographical map of natural stimulus preference. The map contains distinct functional domains preferring a variety of natural image features, ranging from surface‐related features such as color and texture to shape‐related features such as edge, curvature, and facial features. These predicted domains were verified by additional widefield calcium imaging and single‐cell resolution two‐photon imaging. Our study reveals the systematic topological organization of V4 for encoding image features in natural scenes. © 2023, CC BY-NC-SA.

关键词： Large dataset

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Mocpso: A Multi-Objective Cooperative Particle Swarm Optimization Algorithm with Dual Search Strategies

SSRN

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SSRN 2023年

作者： Zhang, Yan Li, Bingdong Hong, Wenjing Zhou, Aimin School of Computer Science and Technology East China Normal University 3663 Zhongshan North Road Shanghai200062 China Shanghai Institute of AI for Education East China Normal University 3663 Zhongshan North Road Shanghai200062 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

Particle swarm optimization (PSO) is a popular meta-heuristic method for solving multi-objective optimization problems (MOPs) due to its simplicity and fast convergence. However, most PSOs suffer from limited local exploration and insufficient randomness on large-scale multi-objective optimization problems (LSMOPs), which can lead to poor results in high-dimensional spaces. To address these issues, this paper proposes MOCPSO, a Multi-Objective Cooperative Particle Swarm Optimization Algorithm with Dual Search Strategies. MOCPSO adopts a diversity search strategy (DSS) to increase disturbance and improve the local search range of particles, and a more convergent search strategy (CSS) to speed up particle convergence. Additionally, MOCPSO uses a three-category framework to effectively utilize both DSS and CSS. Experimental results on benchmark LSMOPs with 500, 1000, and 2000 decision variables demonstrate that MOCPSO outperforms existing state-of-the-art large-scale multi-objective evolutionary algorithms on most test instances. © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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Fun as Moderate Divergence: Evaluating Experience-Driven PCG via RL

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IEEE Transactions on Games 2024年

作者： Wang, Ziqi Li, Yuchen Du, Haocheng Liu, Jialin Yannakakis, Georgios N. Research Institute of Trustworthy Autonomous System Shenzhen518055 China Southern University of Science and Technology Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen518055 China Southern University of Science and Technology Shenzhen518055 China Tsinghua Shenzhen International Graduate School Shenzhen518055 China University of Malta Institute of Digital Games Msida2080 Malta

The computational modelling of player experience is key to the generation of personalised game content. The notion of fun, as one of the most peculiar and core aspects of game experience, has often been modelled and quantified for the purpose of content generation with varying success. Recently, measures of a player's fun have been ad-hoc designed to model moderate levels of in-game divergence in platformer games, inspired by Koster's theory of fun. Such measures have shaped the reward functions of game content generative methods following the experience-driven procedural content generation via reinforcement learning (EDRL) paradigm in Super Mario Bros. In this paper, we present a comprehensive user study involving over 90 participants with a dual purpose: to evaluate the ad-hoc fun metrics introduced in the literature and test the effectiveness of the EDRL framework to generate personalised fun Super Mario Bros experiences in an online fashion. Our key findings suggest that moderate degrees of game level and gameplay divergence are highly consistent with the perceived notion of fun of our participants, cross-verifying the ad-hoc designed fun metrics. On the other hand, it appears that EDRL generators manage to match the preferred (i.e., fun) game experiences of each persona, only in part and for some players. Our findings suggest that the use of multi-faceted in-game data, such as events and actions, will likely enable the modelling of more nuanced gameplay behaviours. Additionally, the verification of player persona modelling and the enhancement of player engagement through dynamic experience modelling are suggested as potential future directions. © 2024 IEEE.

关键词： Digital elevation model

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Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation

Restoration Of Cataract Fundus Images Via Unsupervised Domai...

引用

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

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Enabling surrogate-assisted evolutionary reinforcement learning via policy embedding

arXiv

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arXiv 2023年

作者： Tang, Lan Li, Xiaxi Zhang, Jinyuan Li, Guiying Yang, Peng Tang, Ke Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Faculty of engineering Shenzhen MSU-BIT university Shenzhen518172 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China

Evolutionary Reinforcement Learning (ERL) that applying Evolutionary Algorithms (EAs) to optimize the weight parameters of Deep Neural Network (DNN) based policies has been widely regarded as an alternative to traditional reinforcement learning methods. However, the evaluation of the iteratively generated population usually requires a large amount of computational time and can be prohibitively expensive, which may potentially restrict the applicability of ERL. Surrogate is often used to reduce the computational burden of evaluation in EAs. Unfortunately, in ERL, each individual of policy usually represents millions of weights parameters of DNN. This high-dimensional representation of policy has introduced a great challenge to the application of surrogates into ERL to speed up training. This paper proposes a PE-SAERL Framework to at the first time enable surrogate-assisted evolutionary reinforcement learning via policy embedding (PE). Empirical results on 5 Atari games show that the proposed method can perform more efficiently than the four state-of-the-art algorithms. The training process is accelerated up to 7x on tested games, comparing to its counterpart without the surrogate and PE. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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猕猴脑网络组图谱:包含分区、连接和组织学的多层面全新大脑地图

引用

science Bulletin 2024年第14期69卷 2241-2259页

作者：陆玉恒崔玥曹龙董振伟程禄祺吴雯王昌硕刘新异刘有通张宝贵李德莹赵舶凯王海艳李开心马亮时维阳李雯马亚伟杜宗昌张佳琪熊辉罗娜刘妍妍侯肖逍韩景路孙洪吉蔡涛彭强冯琳清王骄健 George Paxinos 杨正宜樊令仲蒋田仔 Brainnetome Center Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China University of Chinese Academy of Sciences Beijing 100049China Henan Key Laboratory of Imaging and Intelligent Processing PLA Strategic Support Force Information Engineering UniversityZhengzhou 450001China Neuroscience Research Australia and The University of New South Wales Sydney NSW 2031Australia Key Laboratory for NeuroInformation of the Ministry of Education School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengdu 610054China State Key Laboratory of Primate Biomedical Research Institute of Primate Translational MedicineKunming University of Science and TechnologyKunming 650500China School of Mechanical and Power Engineering Harbin University of Science and TechnologyHarbin 150080China School of Life and Environmental Sciences Guilin University of Electronic TechnologyGuilin 541004China Sino-Danish College University of Chinese Academy of ScienceBeijing 100049China Research Center for Augmented Intelligence Zhejiang LabHangzhou 311100China Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital Yongzhou 425000China

The rhesus macaque(Macaca mulatta)is a crucial experimental animal that shares many genetic,brain organizational,and behavioral characteristics with humans.A macaque brain atlas is fundamental to biomedical and evolutionary ***,even though connectivity is vital for understanding brain functions,a connectivity-based whole-brain atlas of the macaque has not previously been *** this study,we created a new whole-brain map,the Macaque brainnetome Atlas(MacBNA),based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI *** new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional *** parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained *** a demonstrative application,the structural connectivity divergence between macaque and human brains was mapped using the brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain *** resulting resource includes:(1)the thoroughly delineated Macaque brainnetome Atlas(MacBNA),(2)regional connectivity profiles,(3)the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset(brainnetome-8),and(4)multi-contrast MRI,neuronal-tracing,and histological images collected from a single *** can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and ***,it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.

关键词： Macaca mulatta brain atlas Connectivity-based parcellation Diffusion MRI Cytoarchitecture Cross-species comparison

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