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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Pang, Lie Meng Ishibuchi, Hisao Nan, Yang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635054

To handle constrained multi-objective optimization problems (CMOPs), many constrained multi-objective evolutionary algorithms (CMOEAs) have been proposed. However, a recent study has shown that many of these CMOEAs do not perform well on real-world CMOPs. In contrast, NSGA-II, proposed over 20 years ago with a simple constrained dominance principle, has demonstrated better performance than recent CMOEAs on many real-world CMOPs. Motivated by NSGA-II’s promising results, this paper aims to further enhance its performance. We explore the idea of enhancing NSGA-II with a knee point for solving CMOPs. Specifically, a knee point is identified using the minimum distance from the estimated ideal point, and always included in the next population of NSGA-II. Experimental results show that this simple idea improves the performance of NSGA-II on real-world CMOPs. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Visual Explanations of Some Problematic Search Behaviors of Frequently-Used EMO Algorithms 13th

Visual Explanations of Some Problematic Search Behaviors of ...

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13th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2025

作者： Ishibuchi, Hisao Pang, Lie Meng Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

ISBN: (纸本)9789819635375

Recently, evolutionary multi-objective optimization (EMO) algorithms have been used in various application fields. Whereas many new EMO algorithms are proposed every year, well-known EMO algorithms such as NSGA-II, MOEA/D, SMS-EMOA and NSGA-III have been still frequently used in application papers as shown by their number of citations. Since most EMO algorithms have been designed for and evaluated by artificial test problems such as DTLZ and WFG, they do not always work well on real-world problems. Main difficulties of real-world problems are as follows: (i) their Pareto front shapes are irregular, (ii) some objectives have much larger values than others, (iii) some decision variables have much larger values than others, (iv) some objectives are highly correlated, and (v) solution evaluation is expensive. In this paper, we focus on the first four difficulties (except for expensive solution evaluation). Our goal is to clearly explain problematic search behaviors of NSGA-II, MOEA/D, SMS-EMOA and NSGA-III caused by these difficulties using simple distance minimization problems. We formulate various distance minimization problems with the above-mentioned four difficulties by changing the location of each target point on the map as well as the size and shape of the map. We also suggest simple remedies for performance improvement of each algorithm for real-world applications without changing the algorithm itself. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Optimization algorithms

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Learned distributed image compression with decoder side information

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Digital Communications and Networks 2025年第2期11卷 349-358页

作者： Yin, Yankai Sun, Zhe Ruan, Peiying Li, Ruidong Duan, Feng Tianjin Key Laboratory of Interventional Brain-Computer Interface and Intelligent Rehabilitation Nankai University Tianjin300350 China Institute of Natural Sciences Kanazawa University Ishikawa9201164 Japan Faculty of Health Data Science and Graduate School of Medicine Juntendo University Chiba2790013 Japan NVIDIA AI Technology Center NVIDIA Japan Tokyo1070052 Japan

With the rapid development of digital communication and the widespread use of the Internet of Things, multi-view image compression has attracted increasing attention as a fundamental technology for image data communication. Multi-view image compression aims to improve compression efficiency by leveraging correlations between images. However, the requirement of synchronization and inter-image communication at the encoder side poses significant challenges, especially for constrained devices. In this study, we introduce a novel distributed image compression model based on the attention mechanism to address the challenges associated with the availability of side information only during decoding. Our model integrates an encoder network, a quantization module, and a decoder network, to ensure both high compression performance and high-quality image reconstruction. The encoder uses a deep Convolutional Neural Network (CNN) to extract high-level features from the input image, which then pass through the quantization module for further compression before undergoing lossless entropy coding. The decoder of our model consists of three main components that allow us to fully exploit the information within and between images on the decoder side. Specifically, we first introduce a channel-spatial attention module to capture and refine information within individual image feature maps. Second, we employ a semi-coupled convolution module to extract both shared and specific information in images. Finally, a cross-attention module is employed to fuse mutual information extracted from side information. The effectiveness of our model is validated on various datasets, including KITTI Stereo and Cityscapes. The results highlight the superior compression capabilities of our method, surpassing state-of-the-art techniques. © 2024 Chongqing University of Posts and Telecommunications

关键词： Decoding

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Multimodal brain-computer interfaces: AI-powered Decoding Methodologies

arXiv

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arXiv 2025年

作者： Li, Siyang Wang, Hongbin Chen, Xiaoqing Wu, Dongrui The Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China The Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China

brain-computer interfaces (BCIs) enable direct communication between the brain and external devices. This review highlights the core decoding algorithms that enable multimodal BCIs, including a dissection of the elements, a unified view of diversified approaches, and a comprehensive analysis of the present state of the field. We emphasize algorithmic advancements in cross-modality mapping, sequential modeling, besides classic multi-modality fusion, illustrating how these novel AI approaches enhance decoding of brain data. The current literature of BCI applications on visual, speech, and affective decoding are comprehensively explored. Looking forward, we draw attention on the impact of emerging architectures like multimodal Transformers, and discuss challenges such as brain data heterogeneity and common errors. This review also serves as a bridge in this interdisciplinary field for experts with neuroscience background and experts that study AI, aiming to provide a comprehensive understanding for AI-powered multimodal BCIs. © 2025, CC BY-NC-ND.

关键词： brain computer interface

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Self-Attentive Spatio-Temporal Calibration for Precise Intermediate Layer Matching in ANN-to-SNN Distillation

arXiv

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arXiv 2025年

作者： Hong, Di Wang, Yueming The College of Computer Science and Technology Zhejiang University China Nanhu Brain-computer Interface Institute Hangzhou China The State Key Laboratory of Brain-Machine Intelligence Zhejiang University China

Spiking Neural Networks (SNNs) are promising for low-power computation due to their event-driven mechanism but often suffer from lower accuracy compared to Artificial Neural Networks (ANNs). ANN-to-SNN knowledge distillation can improve SNN performance, but previous methods either focus solely on label information, missing valuable intermediate layer features, or use a layer-wise approach that neglects spatial and temporal semantic inconsistencies, leading to performance degradation. To address these limitations, we propose a novel method called self-attentive spatio-temporal calibration (SASTC). SASTC uses self-attention to identify semantically aligned layer pairs between ANN and SNN, both spatially and temporally. This enables the autonomous transfer of relevant semantic information. Extensive experiments show that SASTC outperforms existing methods, effectively solving the mismatching problem. Superior accuracy results include 95.12% on CIFAR-10, 79.40% on CIFAR-100 with 2 time steps, and 68.69% on ImageNet with 4 time steps for static datasets, and 97.92% on DVS-Gesture and 83.60% on DVS-CIFAR10 for neuromorphic datasets. This marks the first time SNNs have outperformed ANNs on both CIFAR-10 and CIFAR-100, shedding the new light on the potential applications of SNNs. Copyright © 2025, The Authors. All rights reserved.

关键词： Spatio-temporal data

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Enhancing Neurodegenerative Disease Diagnosis through Confidence-Driven Dynamic Spatio-Temporal Convolutional Network

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IEEE Transactions on Neural Systems and Rehabilitation Engineering 2025年 33卷 1715-1728页

作者： Yuan, Ning Guan, Donghai Li, Shengrong Zhang, Li Zhu, Qi Nanjing University of Aeronautics and Astronautics College of Computer Science and Technology Nanjing211106 China Ministry of Education College of Artificial Intelligence Nanjing University of Aeronautics and Astronautics Key Laboratory of Brain-Machine Intelligence Technology Nanjing211106 China Affiliated Brain Hospital of Nanjing Medical University Department of Geriatrics Nanjing210029 China

Dynamic brain networks are more effectivethan static networks in characterizing the evolving pat-terns of brain functional connectivity, making them a morepromising tool for diagnosing neurodegenerative ***, existing classification methods for dynamic brainnetworks often rely on sliding windows to extract multi-window features, leading to suboptimal performance dueto the spatio-temporal coupling on these windows andlimited ability to effectively integrate complex topologi-cal features. To address these limitations, we propose anovel method called Confidence-Driven Dynamic Spatio-Temporal Convolutional Network (CD-DSTCN). First, ourproposed method employs a spatio-temporal convolutionalnetwork integrated with a temporal attention mechanismto extract spatio-temporal features within each *** propagating information across temporal windows dur-ing spatial convolution, the method effectively capturesand integrates complex temporal and spatial dependen-cies. Second, each window generates an output probability,which quantifies prediction confidence based on the trueclass probability (TCP). This confidence score serves as aweight to assess the relative importance of different timewindows. Finally, the confidence-weighted fused featuresare passed through a multilayer perceptron (MLP) for finalclassification. Extensive experiments on Alzheimer’s andParkinson’s datasets show that the proposed method out-performs the state-of-the-art algorithms and can providevaluable biomarkers for brain disease diagnosis. © 2001-2011 IEEE.

关键词： Neurodegenerative diseases

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Artificial sensory neurons and their applications

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Journal of Semiconductors 2025年第1期46卷 108-128页

作者： Jiale Shao Hongwei Ying Peihong Cheng Lingxiang Hu Xianhua Wei Zongxiao Li Huanming Lu Zhizhen Ye Fei Zhuge Faculty of Electrical Engineering and Computer Science Ningbo UniversityNingbo 315211China Ningbo Institute of Materials Technology and Engineering Chinese Academy of SciencesNingbo 315201China School of Electronic and Information Engineering Ningbo University of TechnologyNingbo 315211China State Key Laboratory of Environment-friendly Energy Materials Southwest University of Science and TechnologyMianyang 621010China Institute of Wenzhou Zhejiang UniversityWenzhou 325006China State Key Laboratory of Silicon Materials School of Materials Science and EngineeringZhejiang UniversityHangzhou 310027China Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of SciencesShanghai 200072China

With the rapid development of artificial intelligence(AI)technology,the demand for high-performance and energyefficient computing is increasingly *** limitations of the traditional von Neumann computing architecture have prompted researchers to explore neuromorphic computing as a *** computing mimics the working principles of the human brain,characterized by high efficiency,low energy consumption,and strong fault tolerance,providing a hardware foundation for the development of new generation AI *** neurons and synapses are the two core components of neuromorphic computing *** perception is a crucial aspect of neuromorphic computing,where artificial sensory neurons play an irreplaceable role thus becoming a frontier and hot topic of *** work reviews recent advances in artificial sensory neurons and their ***,biological sensory neurons are briefly ***,different types of artificial neurons,such as transistor neurons and memristive neurons,are discussed in detail,focusing on their device structures and working ***,the research progress of artificial sensory neurons and their applications in artificial perception systems is systematically elaborated,covering various sensory types,including vision,touch,hearing,taste,and ***,challenges faced by artificial sensory neurons at both device and system levels are summarized.

关键词： artificial sensory neurons artificial perception systems neuromorphic computing artificial intelligence

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Right Frontal Gamma Transcranial Alternating Current Stimulation Modulates Optimism Biases

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Neuroscience Bulletin 2025年第1期41卷 172-176页

作者： Ziqing Yao Jinwen Wei Gan Huang Linling Li Zhen Liang Li Zhang Haiyan Wu Tifei Yuan Zhiguo Zhang Xiaoqing Hu Department of Psychology The State Key Laboratory of Brain and Cognitive SciencesThe University of Hong KongHong KongSARChina School of Biomedical Engineering Medical SchoolShenzhen UniversityShenzhen518060China Guangdong Provincial Key Laboratory of Biomedical Measurements and Ultrasound Imaging Shenzhen UniversityShenzhen518060China Centre for Cognitive and Brain Sciences and Department of Psychology University of MacaoMacao SARChina Shanghai Key Laboratory of Psychotic Disorders Brain Health InstituteNational Center for Mental DisordersShanghai Mental Health CenterShanghai Jiaotong University School of MedicineShanghai210109China School of Computer Science and Technology Harbin Institute of TechnologyShenzhen518055China Peng Cheng Laboratory Shenzhen518055China HKU-Shenzhen Institute of Research and Innovation Shenzhen518063China

Dear Editor,People are often optimistic when forecasting the future,such that they believe they are less likely to encounter adverse life *** phenomenon,known as optimism bias,emerges due to preferential encoding and consolidation of desirable over undesirable information[1,2],engaging frontal brain regions such as the anterior cingulate cortex and inferior frontal gyrus[1].An optimism bias is instrumental in supporting mental health:reduction or absence of optimism biases is associated with mood disorders such as depression,characterized by pessimistic thinking about the future[3].This relationship highlights the potential value of interventions that could enhance optimism bias[4].

关键词： ism such likely

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Lack of social interaction advantage: A domain-general cognitive alteration in schizophrenia

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Journal of Psychiatric Research 2025年 186卷 434-444页

作者： Tang, Enze Li, Jian Liu, Huiying Peng, Chihao Zhou, Dongsheng Hu, Shaohua Chen, Hui The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou China Department of Psychology and Behavioral Sciences Zhejiang University Hangzhou China Department of Psychiatry Affiliated Kangning Hospital of Ningbo University (Ningbo Kangning Hospital) Zhejiang Ningbo 315201 China Department of Psychiatry The First Affiliated Hospital Zhejiang University School of Medicine Hangzhou 310003 China Nanhu Brain-computer Interface Institute Hangzhou 311100 China The Zhejiang Key Laboratory of Precision Psychiatry Hangzhou 310003 China MOE Frontier Science Center for Brain Science and Brain-Machine Integration Zhejiang University School of Medicine Hangzhou 310003 China Brain Research Institute of Zhejiang University Hangzhou 310003 China The State Key Lab of Brain-Machine Intelligence Zhejiang University Hangzhou China Zhejiang Engineering Center for Mathematical Mental Health Hangzhou 310003 China

People with schizophrenia (PSZ) showed preserved ability to unconsciously process simple social information (e.g., face and gaze), but not in higher-order cognition (e.g., memory). It is yet unknown how PSZ process social interactions across different cognitive domains. This study systematically investigated the cognitive characteristics of PSZ during social interaction processing from bottom-up perception to top-down memory, and established correlations with schizophrenic symptoms. In two experiments, social interactions were consistently displayed by face-to-face or back-to-back dyads. Experiment 1 enrolled 30 PSZ and 30 healthy control subjects (HCS) with a breaking continuous flash suppression (b-CFS) paradigm. Experiment 2 recruited 36 PSZ and 36 HCS for two memory tasks, wherein participants restored the between-model distance (working memory task) and recalled the socially bound pairs (long-term memory task). Results indicated that HCS showed advantageous processing of socially interactive stimuli against non-interactive stimuli throughout two experiments, including faster access to visual consciousness, closer spatial distance held in working memory and higher recollection accuracy in long-term memory. However, PSZ did not show any of these advantages, with significant interaction effects for all three tasks (task one: p = .018, η_p² = .092;task two: p = .021, η_p² = .074;task three: p = .015, η_p² = .082). Moreover, correlation analyses indicated that PSZ with more severe negative symptoms (r = −.344, p = .040) or higher medication dosages (r = −.334, p = .046) showed fewer advantages in memorizing socially interactive information. Therefore, social interaction is not prioritized in schizophrenia from bottom-up perception to top-down memory, and the magnitude of such a domain-general cognitive alteration is clinically relevant to symptom severity and medication. © 2025 Elsevier Ltd

关键词： Continuous flash suppression Memory Perception Schizophrenia Social interaction

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A survey of motor rehabilitation for hemiplegic upper limbs based on the brain–apparatus interaction

Intelligent Sports and Health

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Intelligent Sports and Health 2025年第1期1卷 4-10页

作者： Yangsong Zhang Xinrui Bian Fali Li Yufeng Tang Ting Zeng Jing Zhao Dezhong Yao Peng Xu School of Computer Science and Technology Laboratory for Brain Science and Artificial Intelligence Southwest University of Science and Technology Mianyang 621010 China Clinical Hospital of Chengdu Brain Science Institute MOE Key Laboratory for Neuroinformation School of Life Science and Technology University of Electronic Scienceand Technology of China Chengdu 611731 China Mianyang Central Hospital Mianyang 621000 China Radiation Oncology Key Laboratory of Sichuan Province Chengdu 610041 China

Stroke remains one of the leading causes of disability among adults, and the demand for stroke rehabilitation services is increasing. Many studies in the literature have provided various rehabilitation training strategies for stroke patients. In this work, we focus on motor rehabilitation for hemiplegic upper limbs with the assistance of a brain–computer interface and an exoskeleton. We reviewed the progress from multiple perspectives, i.e., the rehabilitation training mode, the rehabilitation movement paradigm, decoding algorithms for physiological signals, motion-assisted rehabilitation robots, evaluation strategies and brain mechanisms. Furthermore, we briefly discuss some challenges and list several future research directions. We hope that this paper will provide insights into future studies on upper limb rehabilitation for hemiplegic patients.

关键词： Hemiplegic upper limbs Smart rehabilitation EMG EEG brain–apparatus interaction

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