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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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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

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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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Restoration Of Cataract Fundus Images Via Unsupervised Domain Adaptation

Restoration Of Cataract Fundus Images Via Unsupervised Domai...

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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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Light Radiation Simulation of Micro-LED Array for the Optical Neural interface Targeted Cerebral Cortex in Rhesus Monkey

Light Radiation Simulation of Micro-LED Array for the Optica...

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International Winter Workshop on brain-computer interface (BCI)

作者： Lusheng Liu Wenlei Zhang Bo Wen Minjie Wang Aiping Wang Jing Zhang Yuan Zhang Song Le Haifeng Jiang Rongwei Zhai Lihua Zhang Xiaoyang Kang Shanghai Engineering Research Center of AI & Robotics MOE Frontiers Center for Brain Science State Key Laboratory of Medical Neurobiology Institute of AI & Robotics Laboratory for Neural Interface and Brain Computer Interface Engineering Research Center of AI & Robotics Ministry of Education Institute of Meta-Medical Academy for Engineering & Technology Fudan University Shanghai China Ji Hua Laboratory Foshan Guangdong Province China Yiwu Research Institute of Fudan University Chengbei Road Yiwu City Zhejiang China Research Center for Intelligent Sensing Zhejiang Lab Hangzhou China Shanghai Mental Health Center Shanghai China Jiao Tong University School of Medicine Shanghai China Lingang Laboratory Shanghai China

The advanced technology of optogenetics has greatly promoted the development of neuroscience. To further optimize the optical neural interface, understanding the propagation process of light in neural tissues is essential for neuroscience research on the application of LED in optogenetics. In this work, we use TracePro software to build geometric models and simulate LED devices. The Rhesus monkey brain model is established to simulate the neural tissue to analyze the irradiance distribution at a specific location, to obtain the effective lighting range. In addition, we analyzed the influencing factors of the illumination range of the implanted light source device. We believe that the distance between devices should be $1.1 \sim1.2$ times of the light range of the simulation result when the devices fit the brain.

关键词： Neuroscience Shape Simulation Lighting Implants Light emitting diodes Optogenetics

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Class2Simi: A noise reduction perspective on learning with noisy labels

arXiv

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arXiv 2020年

作者： Wu, Songhua Xia, Xiaobo Liu, Tongliang Han, Bo Gong, Mingming Wang, Nannan Liu, Haifeng Niu, Gang Trustworthy Machine Learning Lab School of Computer Science University of Sydney Department of Computer Science Hong Kong Baptist University School of Mathematics and Statistics University of Melbourne ISN State Key Laboratory School of Telecommunications Engineering Xidian University Brain-Inspired Technology Co. Ltd. RIKEN Center for Advanced Intelligence Project

Learning with noisy labels has attracted a lot of attention in recent years, where the mainstream approaches are in pointwise manners. Meanwhile, pairwise manners have shown great potential in supervised metric learning and unsupervised contrastive learning. Thus, a natural question is raised: does learning in a pairwise manner mitigate label noise? To give an affirmative answer, in this paper, we propose a framework called Class2Simi: it transforms data points with noisy class labels to data pairs with noisy similarity labels, where a similarity label denotes whether a pair shares the class label or not. Through this transformation, the reduction of the noise rate is theoretically guaranteed, and hence it is in principle easier to handle noisy similarity labels. Amazingly, DNNs that predict the clean class labels can be trained from noisy data pairs if they are first pretrained from noisy data points. Class2Simi is computationally efficient because not only this transformation is on-the-fly in mini-batches, but also it just changes loss computation on top of model prediction into a pairwise manner. Its effectiveness is verified by extensive experiments. Copyright © 2020, The Authors. All rights reserved.

关键词： Noise abatement

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Identifying the Effectiveness of Arabica Drip Coffee on Individual Human brainwave

Identifying the Effectiveness of Arabica Drip Coffee on Indi...

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International ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Cui Chenghu Hu Jinna Arnon Visavakitcharoen Punnarumol Temdee Roungsan Chaisricharoen Brain Science and Engineering Innovation Research Group School of Information Technology Mae Fah Luang University Chiang Rai Thailand The State Key Laboratory of Integrated Services Network Xidian Univerisity Shaanxi China Department of Computer Engineering King Mongkut’s University of Technology Thonburi Bangkok Thailand

Caffeine is one of the core substances in coffee. It can directly influence human brain nerve status. Arabica drip coffee is a widely known beverage; drinker often drinks it to keep the brain awake, sensitive, and focus on mental activity. Previous studies have shown that the level of attention in the human brain is controlled by Beta frequency and caffeine can affect the level of Beta frequency. Therefore, this study focuses on the Influence of attention level of local Arabica coffee to the individual human brainwave, thus proving the value of the local Arabica coffee. The research applied twenty participants: ten males and ten females which age between 19 to 22. This research performed human brainwave detection via Electroencephalography (EEG) and interpreted the data through the k-means algorithm. The t-Test study verified that the individual Beta frequency increases significantly after drinking the coffee. Additionally, the classification of k-mean showed that the individual effectiveness could be classified into three groups including low, medium, and high-efficiency level for both before and after drinking the coffee.

关键词： Electroencephalography Sleep Clustering algorithms Digital art Media Telecommunications Classification algorithms

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Towards High-accuracy and Low-latency Spiking Neural Networks with Two-Stage Optimization

arXiv

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arXiv 2022年

作者： Wang, Ziming Zhang, Yuhao Lian, Shuang Cui, Xiaoxin Yan, Rui Tang, Huajin College of Computer Science and Technology Zhejiang University Hangzhou310027 China Research Center for Intelligent Computing Hardware Zhejiang Lab Hangzhou311100 China School of Integrated Circuits Peking University Beijing100871 China College of Computer Science Zhejiang University of Technology Hangzhou310014 China State Key Laboratory of Brain-Machine Intelligence Zhejiang University Hangzhou310027 China

Spiking neural networks (SNNs) operating with asynchronous discrete events show higher energy efficiency with sparse computation. A popular approach for implementing deep SNNs is ANN-SNN conversion combining both efficient training of ANNs and efficient inference of SNNs. However, the accuracy loss is usually non-negligible, especially under few time steps, which restricts the applications of SNN on latencysensitive edge devices greatly. In this paper, we first identify that such performance degradation stems from the misrepresentation of the negative or overflow residual membrane potential in SNNs. Inspired by this, we decompose the conversion error into three parts: quantization error, clipping error, and residual membrane potential representation error. With such insights, we propose a two-stage conversion algorithm to minimize those errors respectively. Besides, We show each stage achieves significant performance gains in a complementary manner. By evaluating on challenging datasets including CIFAR-10, CIFAR- 100 and ImageNet, the proposed method demonstrates the stateof- the-art performance in terms of accuracy, latency and energy preservation. Furthermore, our method is evaluated using a more challenging object detection task, revealing notable gains in regression performance under ultra-low latency when compared to existing spike-based detection algorithms. Codes are available at https://***/Windere/snn-cvt-dual-phase. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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Manipulation of Phase Transitions and Electrical Properties of Ba 1-XSr xTiO 3 Thin Films Through Orientation Engineering

SSRN

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SSRN 2023年

作者： Lin, Jiamin Guo, Xiangwei Li, Cheng Hong, Zijian Wu, Yongjun Huang, Yuhui School of Materials Science and Engineering State Key Laboratory of Silicon and Advanced Semiconductor Materials Cyrus Tang Center for Sensor Materials and Applications Zhejiang University Hangzhou310027 China Zhejiang Brain-computer Interface Institute Hangzhou311100 China Institute of Advanced Semiconductors & Zhejiang Provincial Key Laboratory of Power Semiconductor Materials and Devices Hangzhou Innovation Center Zhejiang University Zhejiang Hangzhou311200 China

This study investigates the influence of epitaxial orientation on the phase structures and electrical properties of Barium Strontium Titanate ( Ba1-XSrxTiO3 , BST) thin films by employing a thermodynamic model. A comprehensive analysis is conducted, including the construction of strain-concentration and temperature-strain phase diagrams for (001)-, (110)-, and (111)-oriented BST thin films. The results demonstrate that BST thin films with high-index (110) and (111) orientations have a greater tendency to stabilize the low-symmetry ferroelectric phase compared to the low-index (001) orientation. Subsequently, the impact of epitaxial orientation on the piezoelectric and electrocaloric properties is explored based on the constructed phase diagrams. Significantly, enhanced piezoelectric and electrocaloric effects closely related to the epitaxial orientation are observed at the different phase boundaries for BST thin films. These findings have profound implications for future experimental investigations and the design of devices incorporating BST thin films with precise epitaxial orientations and innovative functionalities. © 2023, The Authors. All rights reserved.

关键词： Phase diagrams

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