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2014 International Conference on brain Informatics and Health, BIH 2014

作者： Zhong, Han Chen, Jianhui Han, Jian Zhong, Ning International WIC Institute Beijing University of Technology Beijing 100024 China Department of Computer Science and Technology Tsinghua University Beijing 100084 China Beijing Key Laboratory of MRI and Brain Informatics Beijing China Department of Life Science and Informatics Maebashi Institute of Technology Maebashi-City 371-0816 Japan

ISBN: (纸本)9783319098906

The documents selection related brain information based on the data-brain ontology not only has an important significance in the promotion of data-brain ontology, but also lays the foundation for knowledge integration. However, traditional research of documents selection focuses on the concept, and cannot meet the requirement of the systematic brain Informatics study. This paper analyzes the characteristics of source knowledge firstly with concepts, attributes and relations. Then, we calculate the weight of documents by using the improved method of VSM. Finally, the experiments using real documents associated with brain science are given and calculating the weight of each document achieves a better effect of ranking selection. © 2014 Springer International Publishing.

关键词： Ontology

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Developing brain asymmetry shapes cognitive and psychiatric outcomes in adolescence

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Nature Communications 2025年第1期16卷 1-15页

作者： Wu, Xinran Zhang, Kai Kuang, Nanyu Kong, Xiangzhen Cao, Miao Lian, Zhengxu Liu, Yu Fan, Huanxin Yu, Gechang Liu, Zhaowen Cheng, Wei Jia, Tianye Sahakian, Barbara J. Robbins, Trevor W. Feng, Jianfeng Schumann, Gunter Palaniyappan, Lena Zhang, Jie Institute of Science and Technology for Brain-Inspired Intelligence Fudan University Shanghai China Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence Fudan University Ministry of Education Shanghai China School of Computer Science and Technology East China Normal University Shanghai China Neuroimaging Research Branch National Institute on Drug Abuse National Institutes of Health Baltimore MD United States Department of Psychology and Behavioral Sciences Zhejiang University Zhejiang Hangzhou China Department of Medicine and Therapeutics The Chinese University of Hong Kong Prince of Wales Hospital Hong Kong School of Computer Science of Northwestern Polytechnical University Shanxi Xi’an China Department of Psychiatry Cambridge Biomedical Campus University of Cambridge Cambridge United Kingdom Cambridge shire and Peterborough NHS Trust Elizabeth House Fulbourn Hospital Cambridge United Kingdom Shanghai Center for Mathematical Sciences Shanghai China Department of Computer Science University of Warwick Coventry United Kingdom Collaborative Innovation Center for Brain Science Fudan University Shanghai China Fudan ISTBI—ZJNU Algorithm Centre for Brain-inspired Intelligence Zhejiang Normal University Jinhua China State Key Laboratory of Brain and Cognitive Sciences The University of Hong Kong Hong Kong PONS Centre Charite Mental Health Dept. of Psychiatry and Psychotherapie CCM Charite Universitaetsmedizin Berlin Berlin Germany The Centre for Population Neuroscience and Stratified Medicine (PONS) ISTBI Fudan University Shanghai China Douglas Mental Health University Institute Department of Psychiatry McGill University Montreal QC Canada Department of Psychiatry Schulich School of Medicine and Dentistry Western University London ON Canada Robarts Research Institute University of Western Ontario London ON Canada Department of Medical Biophysica Schulich School of Medicine and Dentistry Western University London ON Canada

Cerebral asymmetry, fundamental to various cognitive functions, is often disrupted in neuropsychiatric disorders. While brain growth has been extensively studied, the maturation of brain asymmetry in children and the factors influencing it in adolescence remain poorly understood. We analyze longitudinal data from 11,270 children aged 10–14 years in the Adolescent brain Cognitive Development (ABCD) study. Our analysis maps the developmental trajectory of structural brain asymmetry. We identify significant age-related, modality-specific development patterns. These patterns link to crystallized intelligence and mental health problems, but with weak correlations. Genetically, structural asymmetry relates to synaptic processes and neuron projections, likely through asymmetric synaptic pruning. At the microstructural level, corpus callosum integrity emerged as a key factor modulating the developing asymmetry. Environmentally, favorable perinatal conditions were associated with prolonged corpus callosum development, which affected future asymmetry patterns and cognitive outcomes. These findings underscore the dynamic yet predictable interactions between brain asymmetry, its structural determinants, and cognitive and psychiatric outcomes during a pivotal developmental stage. Our results provide empirical support for the adaptive plasticity theory in cerebral asymmetry and offer insights into both cognitive maturation and potential risk for early-onset mental health problems. © The Author(s) 2025.

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Sparse representation of global features of visual images in human primary visual cortex: Evidence from fMRI

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Chinese science Bulletin 2008年第14期53卷 2165-2174页

作者： ZHAO SongNian YAO Li JIN Zhen XIONG XiaoYun WU Xia ZOU Qi YAO GuoZheng CAI XiaoHong LIU YiJun State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry Institute of Atmospheric Physics Chinese Academy of Sciences Beijing 100029 China State Key Laboratory of Cognitive Neuroscience and Learning School of Information Science & Technology Beijing Normal University Beijing 100875 China The Magnetic Resonance Imaging Center of Brain's Function Beijing 306 Hospital Chinese People's Liberation Army Beijing 100101 China National Natural Science Foundation of China Beijing 100085 China Department of Computer Science Beijing Jiaotong University Beijing 100044 China College of Information Science Peking University Beijing 100871 China University of Florida McKnight Brain Institute FL 32610 USA

In fMRI experiments on object representation in visual cortex, we designed two types of stimuli: one is the gray face image and its line drawing, and the other is the illusion and its corresponding completed illusion. Both of them have the same global features with different minute details so that the results of fMRI experiments can be compared with each other. The first kind of visual stimuli was used in a block design fMRI experiment, and the second was used in an event-related fMRI experiment. Comparing and analyzing interesting visual cortex activity patterns and blood oxygenation level dependent (BOLD)- fMRI signal, we obtained results to show some invariance of global features of visual images. A plau- sible explanation about the invariant mechanism is related with the cooperation of synchronized re- sponse to the global features of the visual image with a feedback of shape perception from higher cortex to cortex V1, namely the integration of global features and embodiment of sparse representation and distributed population code.

关键词：视皮层视觉成像 fMRI表达生物学

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Ligand modulation of active center to promote lead-free Cs_(2)AgInCl_(6)photocatalytic CO_(2)reduction

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Journal of Energy Chemistry 2024年第8期95卷 660-669,I0015页

作者： Baofei Sun Wei Chen Yanyi Huang Daofu Wu Heng Luo Faguang Kuang Hongmei Ran Yichen Liu Liqin Gao Jinchen Zhou Bo Gao Qiang Huang Xiaosheng Tang Key Laboratory of Human Brain Bank for Functions and Diseases of Department of Education of Guizhou Province College of Basic MedicalGuizhou Medical UniversityGuiyang 550025GuizhouChina School of Electromechanical and Automotive Engineering Yantai UniversityYantai 264005ShandongChina State Key Laboratory of Catalysis Dalian Institute of Chemical PhysicsChinese Academy of SciencesDalian 116023LiaoningChina College of Optoelectronic Engineering Chongqing University of Posts and TelecommunicationsChongqing 400065China Key Laboratory of Optoelectronic Technology&Systems(Ministry of Education) College of Optoelectronic EngineeringChongqing UniversityChongqing 400044China School of Materials Science and Engineering Zhengzhou UniversityZhengzhou 450001HenanChina

Metal halide perovskites(MHP)are potential candidates for the photocatalytic reduction of CO_(2)due to their long photogenerated carrier lifetime and charge diffusion ***,the conventional long-chain ligand impedes the adsorption and activation of CO_(2)molecules in practical ***,a ligand modulation technology is employed to enhance the photocatalytic CO_(2)reduction activity of lead-free Cs_(2)AgInCl_(6)microcrystals(MCs).The Cs_(2)AgInCl_(6)MCs passivated by Oleic acid(OLA)and Octanoic acid(OCA)are used for photocatalytic CO_(2)*** results show that the surface defects and electronic properties of Cs_(2)AgInCl_(6)MCs can be adjusted through ligand *** with the OLA-Cs_(2)AgInCl_(6),the OCA-Cs_(2)AgInCl_(6)catalyst demonstrated a significant improvement in the catalytic yield of CO and CH_(4).The CO and CH_(4)catalytic yields of OCA-Cs_(2)AgInCl_(6)reached 171.88 and34.15μmol g^(-1)h^(-1)which were 2.03 and 12.98 times higher than those of OLA-Cs_(2)AgInCl_(6),and the total electron consumption rate of OCA-Cs_(2)AgInCl_(6)was 615.2μmol g^(-1)h^(-1)which was 3.25 times higher than that of OLA-Cs_(2)AgInCl_(6).Furthermore,in situ diffuse reflectance infrared Fourier transform spectra revealed the enhancement of photocatalytic activity in Cs_(2)AgInCl_(6)MCs induced by ligand *** study illustrates the potential of lead-free Cs_(2)AgInCl_(6)MCs for efficient photocatalytic CO_(2)reduction and provides a ligand modulation strategy for the active promotion of MHP photocatalysts.

关键词： Surface ligand Photocatalysis CO_(2)reduction Lead-free perovskite Reactive mechanism

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T-TIME: Test-Time Information Maximization Ensemble for Plug-and-Play BCIs

arXiv

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arXiv 2024年

作者： Li, Siyang Wang, Ziwei Luo, Hanbin Ding, Lieyun Wu, Dongrui 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 Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen China Henan Key Laboratory of Brain Science and Brain Computer Interface Technology School of Electrical and Information Engineering Zhengzhou University China School of Civil and Hydraulic Engineering Huazhong University of Science and Technology Wuhan430074 China

Objective: An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the human brain and a computer. Due to individual differences and non-stationarity of EEG signals, such BCIs usually require a subject-specific calibration session before each use, which is time-consuming and user-unfriendly. Transfer learning (TL) has been proposed to shorten or eliminate this calibration, but existing TL approaches mainly consider offline settings, where all unlabeled EEG trials from the new user are available. Methods: This paper proposes Test-Time Information Maximization Ensemble (T-TIME) to accommodate the most challenging online TL scenario, where unlabeled EEG data from the new user arrive in a stream, and immediate classification is performed. T-TIME initializes multiple classifiers from the aligned source data. When an unlabeled test EEG trial arrives, T-TIME first predicts its labels using ensemble learning, and then updates each classifier by conditional entropy minimization and adaptive marginal distribution regularization. Our code is publicized. Results: Extensive experiments on three public motor imagery based BCI datasets demonstrated that T-TIME outperformed about 20 classical and state-of-the-art TL approaches. Significance: To our knowledge, this is the first work on test time adaptation for calibration-free EEG-based BCIs, making plug-and-play BCIs possible. © 2024, CC BY.

关键词： Calibration

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Improve the Diversity and Novelty for Open-Ended Neural Text Generation via Inverse Probability Weighting 12th

Improve the Diversity and Novelty for Open-Ended Neural T...

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12th National CCF Conference on Natural Language Processing and Chinese Computing, NLPCC 2023

作者： Zhang, Xinran Sun, Maosong Liu, Jiafeng Li, Xiaobing Department of AI Music and Music Information Technology Central Conservatory of Music Beijing China Key Laboratory of Music and Brain Science Central Conservatory of Music Ministry of Education Beijing China Laboratory of AI Music Central Conservatory of Music Laboratory of Philosophy and Social Sciences Ministry of Education Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (纸本)9783031446924

Stochastic sampling methods for open-ended neural text generation severely affect the diversity and novelty of generated texts. Traditional sampling methods only prune the low-likelihood part of the predicted distribution to trade-off between the fluency and diversity of generated texts. They do not manipulate the high-likelihood part, which leads to the likelihood trap that induces low diversity and boredom. They also do not directly leverage that human does not always favor high-likelihood texts. Inspired by these, we propose a novel sampling method that rescales the high-likelihood part of the distribution with inverse probability weighting. It increases the diversity and novelty of generated texts by rescaling and penalizing the high-likelihood part of the predicted distribution, and preserves the fluency by using multi-filtering truncation on the low-likelihood part. Experimental results show that compared with traditional methods our algorithm can significantly increase the diversity and novelty of generated texts without sacrificing fluency. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Stochastic systems

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Reliability-Aware Network Slicing based on Multi-Objective Optimization 13

Reliability-Aware Network Slicing based on Multi-Objective O...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Xia, Qiqi Wang, Jin Huang, Chengqiang Yao, Xin Southern University of Science and Technology Shenzhen China Southern University of Science and Technology Guangdong Key Laboratory of Brain-inspired Intelligent Computation Department of Computer Science and Engineering Shenzhen China Huawei Technologies Co. Ltd. Reliability Technology Lab Shenzhen China Lingnan University Department of Computing and Decision Sciences Hong Kong

ISBN: (纸本)9798350308365

Network slicing is an essential technology in 5G and the forthcoming 6G networks. It aims to embed multiple virtual networks, i.e., network slices, on top of a shared substrate network to meet diverse service requirements. While a considerable body of existing research strives to maximize overall profits by meeting the resource demands of the network slices, optimizing their reliability is frequently overlooked. In this paper, we formalize the network slicing problem as a multi-objective optimization problem that aims to maximize total profits and reliability of network slices. To tackle this problem, we propose a new multi-objective optimization approach that improves over the state-of-the-art algorithm, which can achieve good approximate Pareto front results balancing total profits and reliability of network slices. The performance of our proposed method is evaluated on both artificial and real-world network topologies. Experimental results demonstrate the superior performance of our proposed method compared to the baseline algorithm, outperforming the latter in 92% of instances in terms of the Hypervolume (HV) metric. © 2024 IEEE.

关键词： Optimization algorithms

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Motion Sickness Alleviation Based on a Mindfulness brain-computer interface

Motion Sickness Alleviation Based on a Mindfulness Brain-Com...

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International Workshop on Advanced Computational Intelligence (IWACI)

作者： Jiawei Zhu Xiaoyu Bao Qiyun Huang Tao Wang Li Huang Yupeng Han Kendi Li Di Chen Kailin Xu Zijian Wang Ya Jiang Yuanqing Li Dept. School of Automation Science and Engineering South China University of Technology Dept. Research Center for Brain–Computer Interface Pazhou Laboratory Guangzhou China Huizhou Desay SV Automotive Co. Ltd. Huizhou China Dept. Research Center for Brain–Computer Interface Pazhou Laboratory South China Brain-Computer Interface Technology Co. Ltd. Guangzhou China

ISBN: (数字)9798350308501

ISBN: (纸本)9798350308518

Motion sickness is a common affliction that affects nearly half of the global population and poses challenges to comfortable travel experiences, necessitating diverse intervention strategies. Pharmacological interventions have shown promise but present limitations due to side effects. To date, there is no non- pharmacological alleviation method for motion sickness that has been proven effective in a large population. In this study, we developed a novel mindfulness brain-computer interface (mindfulness BCI) for alleviation of motion sickness. Thirty-one subjects who were susceptible to motion sickness participated in our experiments in a real-world car motion environment. The experimental results demonstrated that the proposed mindfulness BCI can effectively alleviate motion sickness symptoms, where more than 90% of subjects reported the effectiveness of the mindfulness BCI system and a significant reduction of Motion Sickness Susceptibility Questionnaire (MISC) scores was obtained when comparing the BCI-based meditation state with the resting state for the subjects (t 30 =4.968, P<0.001). To the best of our knowledge, our mindfulness BCI is the first effective non- pharmacological alleviation method for motion sickness as demonstrated by a strict test.

关键词： Training Sociology Motion sickness Motors brain-computer interfaces Automobiles Statistics

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Lying-pose detection with training dataset expansion

Lying-pose detection with training dataset expansion

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作者： Xia, Dao-Xun Su, Song-Zhi Li, Shao-Zi Jodoin, Pierre-Marc School of Information Science and Technology Xiamen University Xiamen361005 China Fujian Key Laboratory of the Brain-like Intelligent Systems Xiamen361005 China School of Mathematics and Computer Science Guizhou Normal University Guiyang550001 China Sherbrooke University SherbrookeQC Canada

ISBN: (纸本)9781479957514

We propose a rotation and scale invariant method to locate people lying on the ground. Unlike conventional human-shape detection methods which assume that all human shapes are in upright position, a person lying on the ground can have arbitrary orientation and pose. Accounting for every possible body configuration would thus require a huge training dataset that would be challenging to gather. In this paper, we propose a method which increases the size of a small training dataset and allows to detect multiple body poses. To do so, our method increases the size of the dataset with a geometric distortion method followed by a rejection sampling method. Then, it automatically identifies K body configurations in the training set, realign it in upright position and trains K SVM classifiers, one for each body configuration. Lying pose detection is then performed by considering a max pooling strategy across all K SVM classifiers. © 2014 IEEE.

关键词： Classification (of information)

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LICO: explainable models with language-image consistency 23

LICO: explainable models with language-image consistency

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Proceedings of the 37th International Conference on Neural Information Processing Systems

作者： Yiming Lei Zilong Li Yangyang Li Junping Zhang Hongming Shan Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Academy of Mathematics and Systems Science Chinese Academy of Sciences Institute of Science and Technology for Brain-Inspired Intelligence and MOE Frontiers Center for Brain Science Fudan University and Shanghai Center for Brain Science and Brain-inspired Technology

Interpreting the decisions of deep learning models has been actively studied since the explosion of deep neural networks. One of the most convincing interpretation approaches is salience-based visual interpretation, such as Grad-CAM, where the generation of attention maps depends merely on categorical labels. Although existing interpretation methods can provide explainable decision clues, they often yield partial correspondence between image and saliency maps due to the limited discriminative information from one-hot labels. This paper develops a Language-Image COnsistency model for explainable image classification, termed LICO, by correlating learnable linguistic prompts with corresponding visual features in a coarse-to-fine manner. Specifically, we first establish a coarse global manifold structure alignment by minimizing the distance between the distributions of image and language features. We then achieve fine-grained saliency maps by applying optimal transport (OT) theory to assign local feature maps with class-specific prompts. Extensive experimental results on eight benchmark datasets demonstrate that the proposed LICO achieves a significant improvement in generating more explainable attention maps in conjunction with existing interpretation methods such as Grad-CAM. Remarkably, LICO improves the classification performance of existing models without introducing any computational overhead during inference. Source code is made available at https://***/ymLeiFDU/LICO.

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