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arXiv 2023年

作者： Wang, Jiquan Zhao, Sha Jiang, Haiteng Li, Shijian Li, Tao Pan, Gang State Key Laboratory of Brain-machine Intelligence Zhejiang University Hangzhou China College of Computer Science and Technology Zhejiang University Hangzhou China Department of Neurobiology Affiliated Mental Health Center Hangzhou Seventh People’s Hospital Zhejiang University School of Medicine Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration Zhejiang University Hangzhou China

Automatic sleep staging is essential for sleep assessment and disorder diagnosis. Most existing methods depend on one specific dataset and are limited to be generalized to other unseen datasets, for which the training data and testing data are from the same dataset. In this paper, we introduce domain generalization into automatic sleep staging and propose the task of generalizable sleep staging which aims to improve the model generalization ability to unseen datasets. Inspired by existing domain generalization methods, we adopt the feature alignment idea and propose a framework called SleepDG to solve it. Considering both of local salient features and sequential features are important for sleep staging, we propose a Multi-level Feature Alignment combining epoch-level and sequence-level feature alignment to learn domain-invariant feature representations. Specifically, we design an Epoch-level Feature Alignment to align the feature distribution of each single sleep epoch among different domains, and a Sequence-level Feature Alignment to minimize the discrepancy of sequential features among different domains. SleepDG is validated on five public datasets, achieving the state-of-the-art performance. © 2023, CC BY-SA.

关键词： Sleep research

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Simultaneously exploring multi-scale and asymmetric EEG features for emotion recognition

arXiv

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arXiv 2021年

作者： Wu, Yihan Xia, Min Nie, Li Zhang, Yangsong Fan, Andong School of Computer Science and Technology Laboratory for Brain Science and Medical Artificial Intelligence Southwest University of Science and Technology Mianyang621010 China College of Mathematics and Physics Chengdu University of Technology Chengdu610059 China MOE Key Lab for Neuroinformation University of Electronic Science and Technology of China Chengdu China

In recent years, emotion recognition based on electroencephalography (EEG) has received growing interests in the brain-computer interaction (BCI) field. The neuroscience researches indicate that the left and right brain hemispheres demonstrate activity differences under different emotional activities, which could be an important principle for designing deep learning (DL) model for emotion recognition. Besides, owing to the nonstationarity of EEG signals, using convolution kernels of a single size may not sufficiently extract the abundant features for EEG classification tasks. Based on these two angles, we proposed a model termed Multi-Scales Bi-hemispheric Asymmetric Model (MSBAM) based on convolutional neural network (CNN) structure. Evaluated on the public DEAP and DREAMER datasets, MSBAM achieved over 99% accuracy for the two-class classification of low-level and high-level states in each of four emotional dimensions, i.e., arousal, valence, dominance and liking, respectively. This study further demonstrated the promising potential to design the DL model from the multi-scale characteristics of the EEG data and the neural mechanisms of the emotion cognition. Copyright © 2021, The Authors. All rights reserved.

关键词： Emotion Recognition

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Simulink comparison of varying-parameter convergent-differential neural-network and gradient neural network for solving online linear time-varying equations

Simulink comparison of varying-parameter convergent-differen...

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World Congress on Intelligent Control and Automation (WCICA)

作者： Zhijun Zhang Siwei Li Xiaoyan Zhang Guangzhou Key Laboratory of Brain Computer Interaction and Applications South China University of Technology Guangzhou P.R. China School of Computer Science and Software Engineering Shenzhen University Shenzhen P.R. China

ISBN: (纸本)9781467384155

A novel kind of recurrent neural network (called varying-parameter convergent-differential neural-network, VP-CDNN) is proposed in this paper for online solving linear time-varying equations. Different from traditional gradient-base neural network (called GNN) with scalar-valued error functions, such new VP-CDNN is designed based on matrix-valued or vector-valued error functions and their coefficients related to convergence is time-varying. In other words, the coefficients are the functions of time t. In addition, this kind of VP-CDNN is depicted in implicit dynamics but not explicit dynamics. To illustrate the effectiveness of the new neural network, the comparative simulations with MATLAB Simulink of the proposed VP-CDNN and GNN for solving online linear time-varying equations are implemented and presented. computer-simulation results verify the fast convergence and good robustness.

关键词： Mathematical model Convergence Computational modeling Biological neural networks MATLAB

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A metamodel based model transformation approach

A metamodel based model transformation approach

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International Conference on Software Engineering Research, Management and Applications (SERA)

作者： Yucong Duan S.C. Cheung Xiaolan Fu Yuqing Gu Institute of Software Chinese Academy and Sciences China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Brain and Cognitive Science Institute of Psychology Chinese Academy and Sciences China

The information transformation problem of model driven software development (MDSD) processes is gaining increasing attentions. In this paper, we present an indepth analysis of the major causes leading to the ambiguities of existing transformation. A metamodel based approach is then proposed to address these causes. A mechanism to generate transformation rules is devised based on a classification of the UML metamodel elements to be used in subsequent transformation processes. Some other critical issues relating to the generated transformation system are also discussed.

关键词： Unified modeling language Programming computer science Laboratories Cognitive science Psychology brain modeling Software systems Automatic control Automation

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Fairer Machine Learning Through the Hybrid of Multi-objective Evolutionary Learning and Adversarial Learning

Fairer Machine Learning Through the Hybrid of Multi-objectiv...

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International Joint Conference on Neural Networks (IJCNN)

作者： Shenhao Gui Qingquan Zhang Changwu Huang Bo Yuan Research Institute of Trustworthy Autonomous Systems (RITAS) Southern University of Science and Technology (SUSTech) Shenzhen China Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology (SUSTech) Shenzhen China

With growing concerns about the unwanted bias or discrimination in machine learning, a number of fairness-aware machine learning algorithms have been developed to mitigate bias in the prediction. Because the objectives of accuracy and fairness are antagonistic, it is hard to balance the trade-off between them. Recently, Multi-objective Evolutionary Learning (MOEL) framework has been proposed to train a set of Pareto models with the consideration of accuracy and fairness simultaneously. However, this framework prefers to train more accurate models rather than fairer models due to the lack of gradient in terms of fairness. In this paper, MOEL is enhanced through introducing Adversarial Learning (AL). The MOEL-AL framework aims to maximize a set of predictors' ability to predict true labels and minimize the ability of an adversarial network to predict the sensitive attributes from the predictors' output. Specifically, the adversarial network can be regarded as a proxy of the undifferentiable fairness metrics, so it is possible to propagate gradients in terms of both accuracy and fairness for the predictors during the back-propagation process. Besides, the adversarial strength for different predictors is adjusted dynamically according to their fairness metric. Compared with the state-of-the-art methods, experimental studies on seven well-known datasets show that our method can provide a set of fairer Pareto models with little drop on accuracy.

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Addressing Intermediate Verification Latency in Online Learning Through Immediate Pseudo-labeling and Oriented Synthetic Correction

Addressing Intermediate Verification Latency in Online Learn...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zixin Zhong Liyan Song Fengzhen Tang Bo Yuan Department of Computer Science and Engineering Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Faculty of Computing Harbin Institute of Technology Harbin China State Key Laboratory of Robotics Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

In non-stationary data streams, the challenges of concept drift are further compounded by the issue of Intermediate Verification Latency (IVL), which can impede timely model adaptation. IVL refers to the finite delay between the arrival of data features and their corresponding labels. This delay could pose a significant challenge in adapting models to new concepts, ultimately hindering predictive performance. However, existing IVL approaches exhibit certain limitations. Some approaches passively wait for delayed labels, thereby overlooking temporarily unlabeled data. Other approaches employ pseudo-labeling for immediate model updates, but may risk losing valuable information when reverting model states to rectify previous pseudo-labeling mistakes. To overcome these limitations, we propose a novel approach called Micro-cluster based Immediate Pseudo-Labeling with Oriented Synthetic Correction (MIPLOSC). MIPLOSC leverages micro-cluster systems to effectively capture data distributions, thus facilitating its two core components: immediate pseudo-labeling and oriented synthetic correction. The immediate pseudo-labeling mechanism facilitates immediate utilization of temporarily unlabeled data, and the oriented synthetic correction mechanism enables finergrained rectification from previous erroneous pseudo-labels and concept drift, minimizing the loss of learned information. Experimental studies validated the effectiveness of MIPLOSC in addressing IVL, demonstrating its superiority over competing methods in both space consumption and predictive performance across varying degrees of label delay.

关键词： Adaptation models Costs Concept drift Noise Neural networks Predictive models Robustness

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Multi-Domain Active Learning: Literature Review and Comparative Study

arXiv

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arXiv 2021年

作者： He, Rui Liu, Shengcai He, Shan 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 School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom

Multi-domain learning (MDL) refers to learning a set of models simultaneously, where each model is specialized to perform a task in a particular domain. Generally, a high labeling effort is required in MDL, as data needs to be labeled by human experts for every domain. Active learning (AL) can be utilized in MDL to reduce the labeling effort by only using the most informative data. The resultant paradigm is termed multi-domain active learning (MDAL). In this work, we provide an exhaustive literature review for MDAL on the relevant fields, including AL, cross-domain information sharing schemes, and cross-domain instance evaluation approaches. It is found that the few studies which have been directly conducted on MDAL cannot serve as off-the-shelf solutions on more general MDAL tasks. To fill this gap, we construct a pipeline of MDAL and present a comprehensive comparative study of thirty different algorithms, which are established by combining six representative MDL models and five commonly used AL strategies. We evaluate the algorithms on six datasets involving textual and visual classification tasks. In most cases, AL brings notable improvements to MDL, and the naive BvSB (best vs. second best) Uncertainty strategy can perform competitively with the state-of-the-art AL strategies. Besides, BvSB with the MAN (multinomial adversarial networks) model can consistently achieve top or above-average performance on all the datasets. Furthermore, we qualitatively analyze the behaviors of the well-performed strategies and models, shedding light on their superior performance in the comparison. Finally, we recommend using BvSB with the MAN model in the application of MDAL due to their good performance in the experiments. Copyright © 2021, The Authors. All rights reserved.

关键词： Classification (of information)

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A Framework Based on Generational and Environmental Response Strategies for Dynamic Multi-objective Optimization

arXiv

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arXiv 2022年

作者： Li, Qingya Liu, Xiangzhi Wang, Fuqiang Wang, Shuai Zhang, Peng Wu, Xiaoming Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Shandong Provincial Key Laboratory of Computer Networks Shandong China Heze Branch Qilu University of Technology Shandong Academy of Sciences Biological Engineering Technology Innovation Center of Shandong Province Shandong China

Due to the dynamics and uncertainty of the dynamic multi-objective optimization problems (DMOPs), it is difficult for algorithms to find a satisfactory solution set before the next environmental change, especially for some complex environments. One reason may be that the information in the environmental static stage can not be used well in the traditional framework. In this paper, a novel framework based on generational and environmental response strategies (FGERS) is proposed, in which response strategies are run both in the environmental change stage and the environmental static stage to obtain population evolution information of those both stages. Unlike in the traditional framework, response strategies are only run in the environmental change stage. For simplicity, the feed-forward center point strategy was chosen to be the response strategy in the novel dynamic framework (FGERS-CPS). FGERS-CPS is not only to predict change trend of the optimum solution set in the environmental change stage, but to predict the evolution trend of the population after several generations in the environmental static stage. Together with the feed-forward center point strategy, a simple memory strategy and adaptive diversity maintenance strategy were used to form the complete FGERS-CPS. On 13 DMOPs with various characteristics, FGERS-CPS was compared with four classical response strategies in the traditional framework. Experimental results show that FGERS-CPS is effective for DMOPs. Copyright © 2022, The Authors. All rights reserved.

关键词： Genetic algorithms

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Realizing Emotional Interactions to Learn User Experience and Guide Energy Optimization for Mobile Architectures 22

Realizing Emotional Interactions to Learn User Experience an...

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Proceedings of the 55th Annual IEEE/ACM International Symposium on Microarchitecture

作者： Xueliang Li Zhuobin Shi Junyang Chen Yepang Liu National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China College of Computer Science and Software Engineering Shenzhen University China Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation and Department of Computer Science and Engineering Southern University of Science and Technology China

ISBN: (纸本)9781665462723

In the age of AI, mobile architectures such as smartphones are still "cold machines"; machines do not feel. If the architecture is able to feel users' feelings and runtime user experience (UX), it will accordingly adapt performance/energy to find the optimal system-operating state that consumes the least energy to satisfy users. In this paper, we will utilize users' facial expressions (FEs) to learn their runtime UX. We know that FEs are the natural and direct way for humans to convey their emotions and feelings. Our study reveals that FEs also reflect UX. Our research for the first time quantifies the link between FEs and UX. Leveraging this link, the architecture will be able to use the front camera to see FEs and feel users' UX. Based on UX, the architecture can appropriately provision computing resources. We propose Vi-energy system to realize the above idea. Our evaluation shows that Vi-energy reduces energy consumption by 52.9% at maximum and secures UX.

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Morphological disruption and visual tuning alterations in the primary visual cortex in glaucoma(DBA/2J)mice

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Neural Regeneration Research 2024年第1期19卷 220-225页

作者： Yin Yang Zhaoxi Yang Maoxia Lv Ang Jia Junjun Li Baitao Liao Jing’an Chen Zhengzheng Wu Yi Shi Yang Xia Dezhong Yao Ke Chen Department of Ophthalmology Sichuan Provincial People’s HospitalMedical SchoolUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for NeuroInformationSchool of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Research Unit of NeuroInformation Chinese Academy of Medical SciencesChengduSichuan ProvinceChina Institute of Basic Medical Sciences Chinese Academy of Medical Sciences School of Basic Medicine Peking Union Medical CollegeBeijingChina Health Management Center Sichuan Provincial Key Laboratory for Human Disease Gene StudySichuan Provincial People’s HospitalUniversity of Electronic Science and Technology of ChinaChengduSichuan ProvinceChina Research Unit for Blindness Prevention of Chinese Academy of Medical Sciences(2019RU026) Sichuan Academy of Medical SciencesChengduSichuan ProvinceChina School of Electrical Engineering Zhengzhou UniversityZhengzhouHenan ProvinceChina

Glaucoma is a leading cause of irreve rsible blindness wo rldwide,and previous studies have shown that,in addition to affecting the eyes,it also causes abnormalities in the ***,it is not yet clear how the primary visual cortex(V1)is altered in *** study used DBA/2J mice as a model for spontaneous secondary *** aim of the study was to compare the electrophysiological and histomorphological chara cteristics of neurons in the V1between 9-month-old DBA/2J mice and age-matched C57BL/6J *** conducted single-unit recordings in the V1 of light-anesthetized mice to measure the visually induced responses,including single-unit spiking and gamma band *** morphology of layerⅡ/Ⅲneurons was determined by neuronal nuclear antigen staining and Nissl staining of brain tissue ***-seven neurons from eight DBA/2J mice and eighty-one neurons from eight C57BL/6J mice were *** with the C57BL/6J group,V1 neurons in the DBA/2J group exhibited weaker visual tuning and impaired spatial *** r,fewer neuro ns were observed in the V1 of DBA/2J mice compared with C57BL/6J *** findings suggest that DBA/2J mice have fewer neurons in the VI compared with C57BL/6J mice,and that these neurons have impaired visual *** findings provide a better understanding of the pathological changes that occur in V1 neuron function and morphology in the DBA/2J mouse *** study might offer some innovative perspectives regarding the treatment of glaucoma.

关键词： DBA/2J degeneration gamma band oscillations glaucoma primary visual cortex(V1) retina single-unit recording tuning curve

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