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arXiv 2021年

作者： Peng, Fu Liu, Shengcai Lu, Ning Tang, Ke Guangdong Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

This work considers a challenging Deep Neural Network (DNN) quantization task that seeks to train quantized DNNs without involving any full-precision operations. Most previous quantization approaches are not applicable to this task since they rely on full-precision gradients to update network weights. To fill this gap, in this work we advocate using Evolutionary Algorithms (EAs) to search for the optimal low-bits weights of DNNs. To efficiently solve the induced large-scale discrete problem, we propose a novel EA based on cooperative coevolution that repeatedly groups the network weights based on the confidence in their values and focuses on optimizing the ones with the least confidence. To the best of our knowledge, this is the first work that applies EAs to train quantized DNNs. Experiments show that our approach surpasses previous quantization approaches and can train a 4-bit ResNet-20 on the Cifar-10 dataset with the same test accuracy as its full-precision counterpart. Copyright © 2021, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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Experience-Based Evolutionary Algorithms for Expensive Optimization

arXiv

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arXiv 2023年

作者： Yu, Xunzhao Wang, Yan Zhu, Ling Filev, Dimitar Yao, Xin The CERCIA School of Computer Science University of Birmingham BirminghamB15 2TT United Kingdom The Research Institute of Trustworthy Autonomous Systems Guangdong Provincial Key Laboratory of Brain-inspired Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China The Ford Motor Company 2101 Village Rd DearbornMI48124 United States

Optimization algorithms are very different from human optimizers. A human being would gain more experiences through problem-solving, which helps her/him in solving a new unseen problem. Yet an optimization algorithm never gains any experiences by solving more problems. In recent years, efforts have been made towards endowing optimization algorithms with some abilities of experience learning, which is regarded as experience-based optimization. In this paper, we argue that hard optimization problems could be tackled efficiently by making better use of experiences gained in related problems. We demonstrate our ideas in the context of expensive optimization, where we aim to find a near-optimal solution to an expensive optimization problem with as few fitness evaluations as possible. To achieve this, we propose an experience-based surrogate-assisted evolutionary algorithm (SAEA) framework to enhance the optimization efficiency of expensive problems, where experiences are gained across related expensive tasks via a novel meta-learning method. These experiences serve as the task-independent parameters of a deep kernel learning surrogate, then the solutions sampled from the target task are used to adapt task-specific parameters for the surrogate. With the help of experience learning, competitive regression-based surrogates can be initialized using only 1d solutions from the target task (d is the dimension of the decision space). Our experimental results on expensive multi-objective and constrained optimization problems demonstrate that experiences gained from related tasks are beneficial for the saving of evaluation budgets on the target problem. Copyright © 2023, The Authors. All rights reserved.

关键词： Evolutionary algorithms

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CEBRA Method: Decoding brain Activity for Advanced brain-computer interface technology

CEBRA Method: Decoding Brain Activity for Advanced Brain-Com...

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Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Jingcheng Yang Frank Kulwa Xuanwei Liu Yiqing Lu Yufa Fu Guanglin Li Yaping Huai Xin Zhang Yongcheng Li CAS Key Laboratory of Human-Machine Intelligence Synergy Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences (CAS) Shenzhen China Faculty of Information Engineering and Automation Kunming University of Science and Technology Kunming China Shenzhen College of Advanced Technology University of Chinese Academy of Sciences Shenzhen China Shenzhen Dapeng New District Nanao People’s Hospital Shenzhen China Department of Rehabilitation Medicine Shenzhen Longhua District Central Hospital Shenzhen China Brain Cognition and Brain-Computer Intelligence Integration Group Kunming University of Science and Technology Kunming China Shandong Zhongke Advanced Technology Co. Ltd. Jinan China

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

The emerging neurorehabilitation technology, brain-computer interface (BCI), provides a novel prospect for stroke recovery. However, decoding brain activity during the movement present substantial challenges, and feature extraction is crutial to build a better decoder. In this study the CEBRA method were employed to extract features first based on electroencephalogram(EEG) data during Motor Execution(ME) and Motor lmagery (Ml) tasks for 20 participants (including 10 stroke patients). The results revealed that, in MI tasks, CEBRA-RF (Random Forests) achieved an average classification accuracy of 91.33%, with an average F1-score of 91.19%, and CEBRA-SVM (Support Vector Machine) achieved an average classification accuracy of 91.32%, with an average F1-score of 90.83%. Compared to other conventional feature extraction methods, CEBRA shows significantly higher accuracy (t-test, p<0.01). However, in ME tasks, CEBRA-RF achieved an average classification accuracy of 75.67%, with an average F1-score of 75.39%, and CEBRA-SVM achieved an average classification accuracy of 76.13%, with an average F1-score of 75.80%. Nevertheless, no significant differences were observed compared to other feature extraction methods. These findings demonstrate the potential of CEBRA in decoding patients’ brain activity. The results of this study hold promise in addressing the current challenges of low decoding accuracy in BCI systems, offering a new approach for designing BCI-assisted rehabilitation systems for stroke patients.

关键词： Support vector machines brain Accuracy Stroke (medical condition) Feature extraction Motors brain-computer interfaces Neurorehabilitation Decoding Paralysis

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Deep Fusion of Shifted MLP and CNN for Medical Image Segmentation

Deep Fusion of Shifted MLP and CNN for Medical Image Segment...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Chengyu Yuan Hao Xiong Guoqing Shangguan Hualei Shen Dong Liu Haojie Zhang Zhonghua Liu Kun Qian Bin Hu Björn W. Schuller Yoshiharu Yamamoto Shlomo Berkovsky College of Computer and Information Engineering Henan Normal University China Key Laboratory of Artificial Intelligence and Personalized Learning in Education Henan China Big Data Engineering Lab of Teaching Resources & Assessment of Education Quality Henan China Centre for Health Informatics Australian Institute of Health Innovation Macquarie University Australia Key Laboratory of Brain Health Intelligent Evaluation and Intervention (BIT) Ministry of Education China School of Medical Technology Beijing Institute of Technology China School of Information Engineering Zhejiang Ocean University China GLAM – Group on Language Audio & Music Imperial College London U.K. Chair of Embedded Intelligence for Health Care and Wellbeing University of Augsburg Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

Medical image segmentation is an important task in modern analysis of medical images. Current methods tend to extract either local features with convolutions or global features with Transformers. However, few of them are able to effectively fuse global and local features to facilitate segmentation. In this work, we propose a novel hybrid network that involves three main branches: the Multi-Layer Perception (MLP) branch, the Convolutional Neural Network (CNN) branch, and a Fusion branch. The MLP and CNN branches aim to learn global and local features, respectively. To fuse these, the fusion branch introduces a novel hierarchical fusion that performs multi-layered fusions that generate high-level representations to enhance segmentation. Our evaluation with two datasets shows strong performance of the proposed method compared to state-of-the-art baselines.

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Single-cell spatial transcriptomic atlas of the whole mouse brain

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Neuron 2025年

作者： Han, Lei Liu, Zhen Jing, Zehua Liu, Yuxuan Peng, Yujie Chang, Huizhong Lei, Junjie Wang, Kexin Xu, Yuanfang Liu, Wei Wu, Zihan Li, Qian Shi, Xiaoxue Zheng, Mingyuan Wang, He Deng, Juan Zhong, Yanqing Pan, Hailin Lin, Junkai Zhang, Ruiyi Chen, Yu Wu, Jinhua Xu, Mingrui Ren, Biyu Cheng, Mengnan Yu, Qian Song, Xinxiang Lu, Yanbing Tang, Yuanchun Yuan, Nini Sun, Suhong An, Yingjie Ding, Wenqun Sun, Xing Wei, Yanrong Zhang, Shuzhen Dou, Yannong Zhao, Yun Han, Luyao Zhu, Qianhua Xu, Junfeng Wang, Shiwen Wang, Dan Bai, Yinqi Liang, Yikai Liu, Yuan Chen, Mengni Xie, Chun Bo, Binshi Li, Mei Zhang, Xinyan Ting, Wang Chen, Zhenhua Fang, Jiao Li, Shuting Jiang, Yujia Tan, Xing Zuo, Guolong Xie, Yue Li, Huanhuan Tao, Quyuan Li, Yan Liu, Jianfeng Liu, Yuyang Hao, Mingkun Wang, Jingjing Wen, Huiying Liu, Jiabing Yan, Yizhen Zhang, Hui Sheng, Yifan Yu, Shui Liao, Xiaoyan Jiang, Xuyin Wang, Guangling Liu, Huanlin Wang, Congcong Feng, Ning Liu, Xin Ma, Kailong Xu, Xiangjie Han, Tianyue Cao, Huateng Zheng, Huiwen Chen, Yadong Lu, Haorong Yu, Zixian Zhang, Jinsong Wang, Bo Wang, Zhifeng Xie, Qing Pan, Shanshan Liu, Chuanyu Xu, Chan Cui, Luman Li, Yuxiang Liu, Shiping Liao, Sha Chen, Ao Wu, Qing-Feng Wang, Jian Liu, Zhiyong Sun, Yidi Mulder, Jan Yang, Huanming Wang, Xiaofei Li, Chao Yao, Jianhua Xu, Xun Liu, Longqi Shen, Zhiming Wei, Wu Sun, Yan-Gang BGI Research Hangzhou 310030 China Lingang Laboratory Shanghai 200031 China CAS Key Laboratory of Computational Biology Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China College of Life Sciences University of Chinese Academy of Sciences Beijing 100049 China Institute of Neuroscience State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology CAS Center for Excellence in Brain Science and Intelligence Technology University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai 200031 China Tencent AI Lab Shenzhen 518057 China BGI Research Shenzhen 518083 China Department of Anesthesiology Huashan Hospital State Key Laboratory of Medical Neurobiology Institute for Translational Brain Research MOE Frontiers Center for Brain Science Fudan University Shanghai 200032 China State Key Laboratory of Molecular Development Biology Institute of Genetics and Developmental Biology Chinese Academy of Sciences Beijing 100101 China BGI College & Henan Institute of Medical and Pharmaceutical Sciences Zhengzhou University Zhengzhou 450000 China School of Biology and Biological Engineering South China University of Technology Guangzhou 510006 China BGI Research Qingdao 266555 China China National GeneBank BGI Research Shenzhen 518120 China Shenzhen Proof-of-Concept Center of Digital Cytopathology BGI Research Shenzhen 518083 China Shanxi Medical University-BGI Collaborative Center for Future Medicine Shanxi Medical University Taiyuan 030001 China BGI Research Wuhan 430074 China BGI Research Chongqing 401329 China JFL-BGI STOmics Center Jinfeng Laboratory Chongqing 401329 China Department of Biology University of Copenhagen Copenhagen 2200 Denmark Shanghai Center for Brain Science and Brain-Inspired Technology Shanghai 201602 China Department of Protein Science Science for Life Laboratory KTH-Royal Institute of Technology Stockholm

A comprehensive atlas of genes, cell types, and their spatial distribution across a whole mammalian brain is fundamental for understanding the function of the brain. Here, using single-nucleus RNA sequencing (snRNA-seq) and Stereo-seq techniques, we generated a mouse brain atlas with spatial information for 308 cell clusters at single-cell resolution, involving over 4 million cells, as well as for 29,655 genes. We have identified cell clusters exhibiting preference for cortical subregions and explored their associations with brain-related diseases. Additionally, we pinpointed 155 genes with distinct regional expression patterns within the brainstem and unveiled 513 long non-coding RNAs showing region-enriched expression in the adult brain. Parcellation of brain regions based on spatial transcriptomic information revealed fine structure for several brain areas. Furthermore, we have uncovered 411 transcription factor regulons showing distinct spatiotemporal dynamics during neurodevelopment. Thus, we have constructed a single-cell-resolution spatial transcriptomic atlas of the mouse brain with genome-wide coverage. © 2025 Elsevier Inc.

关键词： development mouse brain non-coding gene parcellation Stereo-seq transcriptome

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Cross-Domain Animal Pose Estimation with Skeleton Anomaly-Aware Learning

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Han, Le Chen, Kaixuan Zhao, Lei Jiang, Yangbo Wang, Pengfei Zheng, Nenggan Zhejiang Hangzhou310007 China Zhejiang University College of Computer Science and Technology Zhejiang Hangzhou310007 China Zhejiang University State Key Laboratory of Blockchain and Data Security Zhejiang Hangzhou310007 China Institute of Blockchain and Data Security. China Zhejiang University School of Software Technology Ningbo China Zhejiang University State Key Lab of Brain-Machine Intelligence Hangzhou310007 China Zhejiang Provincial Government ZJU Collaborative Innovation Center for Artificial Intelligence by MOE Hangzhou310007 China Bengbu University School of Computer and Information Engineering Bengbu233030 China

Animal pose estimation is often constrained by the scarcity of annotations and the diversity of scenarios and species. The pseudo-label generation based unsupervised domain adaptation paradigm, which discriminates the predicted keypoints of unlabeled data based on the skeleton position consistency, has demonstrated effectiveness for such problems. However, existing methods generate pseudo-labels with massive false positives, because they cannot effectively distinguish sample pairs with the same errors. In this study, we propose a cross-domain animal pose estimation model from a novel perspective of skeleton anomaly learning. We construct a graph contrastive learning mechanism to acquire the skeleton anomaly-aware knowledge, which enables the generation of accurate pseudo-labels for target domain and imposes graph constraint on unlabeled data. And a skeleton anomaly-feedback based domain adaptation framework is designed to facilitate implicit alignment of object-specific features and joint training of cross-domain. Besides, we propose a novel rat pose dataset named UDARP-9.4K to address the gap of small-sized animal pose datasets encompassing diverse experimental scenarios. The related datasets are reviewed and evaluated in detail. Extensive experiments are conducted on UDARP-9.4K and two public datasets to demonstrate the superiority of the proposed model in cross-scenarios and cross-species animal pose estimation tasks. Further analysis reveals the effectiveness of the proposed model for skeleton structure feature learning. The UDARP-9.4K dataset is available here. © 1991-2012 IEEE.

关键词： Invertebrates

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Degradation-invariant Enhancement of Fundus Images via Pyramid Constraint Network

arXiv

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arXiv 2022年

作者： Liu, Haofeng Li, Heng Fu, Huazhu Xiao, Ruoxiu Gao, Yunshu Hu, Yan Liu, Jiang Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Southern University of Science and Technology Shenzhen China Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen China IHPC A*STAR Singapore The School of Computer and Communication Engineering University of Science and Technology Beijing China Singapore Eye Research Institute Singapore National Eye Centre Singapore

As an economical and efficient fundus imaging modality, retinal fundus images have been widely adopted in clinical fundus examination. Unfortunately, fundus images often suffer from quality degradation caused by imaging interferences, leading to misdiagnosis. Despite impressive enhancement performances that state-of-the-art methods have achieved, challenges remain in clinical scenarios. For boosting the clinical deployment of fundus image enhancement, this paper proposes the pyramid constraint to develop a degradation-invariant enhancement network (PCE-Net), which mitigates the demand for clinical data and stably enhances unknown data. Firstly, high-quality images are randomly degraded to form sequences of low-quality ones sharing the same content (SeqLCs). Then individual low-quality images are decomposed to Laplacian pyramid features (LPF) as the multi-level input for the enhancement. Subsequently, a feature pyramid constraint (FPC) for the sequence is introduced to enforce the PCE-Net to learn a degradation-invariant model. Extensive experiments have been conducted under the evaluation metrics of enhancement and segmentation. The effectiveness of the PCE-Net was demonstrated in comparison with state-of-the-art methods and the ablation study. The source code of this study is publicly available at https://***/HeverLaw/PCENet-Image-Enhancement. Copyright © 2022, The Authors. All rights reserved.

关键词： Image enhancement

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Reducing Idleness in Financial Cloud Services via Multi-objective Evolutionary Reinforcement Learning based Load Balancer

arXiv

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arXiv 2023年

作者： Yang, Peng Zhang, Laoming Liu, Haifeng Li, Guiying Department of Statistics and Data Science Southern University of Science and Technology Shenzhen518055 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Academy for Advanced Interdisciplinary Studies Southern University of Science and Technology Shenzhen518055 China Guangdong OPPO Mobile Telecommunications Corp. Ltd Shenzhen518052 China Research Institute of Trustworthy Autonomous Systems Southern University of Science and Technology Shenzhen518055 China

In recent years, various companies have started to shift their data services from traditional data centers to the cloud. One of the major motivations is to save on operational costs with the aid of cloud elasticity. This paper discusses an emerging need from financial services to reduce the incidence of idle servers retaining very few user connections, without disconnecting them from the server side. This paper considers this need as a bi-objective online load balancing problem. A neural network based scalable policy is designed to route user requests to varied numbers of servers for the required elasticity. An evolutionary multi-objective training framework is proposed to optimize the weights of the policy. Not only is the new objective of idleness is reduced by over 130% more than traditional industrial solutions, but the original load balancing objective itself is also slightly improved. Extensive simulations with both synthetic and real-world data help reveal the detailed applicability of the proposed method to the emergent problem of reducing idleness in financial services. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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Weight Light, Hear Right: Heart Sound Classification with a Low-Complexity Model

Weight Light, Hear Right: Heart Sound Classification with a ...

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European Signal Processing Conference (EUSIPCO)

作者： Jiahao Ji Lixian Zhu Haojie Zhang Kun Qian Kele Xu Zikai Song Bin Hu Björn W. Schuller Yoshiharu Yamamoto Key Laboratory of Brain Health Intelligent Evaluation and Intervention (Beijing Institute of Technology) Ministry of Education Beijing China School of Medical Technology Beijing Institute of Technology Beijing China School of Computer Science National University of Defense Technology P. R. China GLAM - the Group on Language Audio & Music Imperial College London UK CHI - Chair of Health Informatics. MRI Technical University of Munich Germany Educational Physiology Laboratory Graduate School of Education The University of Tokyo Japan

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Cardiovascular diseases (CVDs) remain a leading cause of mortality, necessitating early self-diagnosis for effective management. computer-aided CVD diagnosis via computerised auscultation has gained traction, advancing intelligent diagnostics. However, integrating complex neural networks into medical edge devices remains challenging. This paper introduces a novel lightweight model for heart sound classification based on broadcast residual learning, which can be seamlessly deployed in portable health monitoring devices for real-time cardiac auscultation. Comparative experiments validate the model's efficacy, achieving 89.1% accuracy and 89.7% Fl score with just 8.05 K parameters and 10.6 M MACC, showcasing superior performance within constrained complexity.

关键词： Heart Performance evaluation Microcontrollers Computational modeling Neural networks Europe Signal processing Real-time systems Monitoring Medical diagnostic imaging

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Dadm: Enhancing Eeg Artifact Removal by Fusing Artifact Representation into the Time-Frequency Domain

SSRN

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SSRN 2024年

作者： Li, Haoran Feng, Fan Kang, Jiarong Zhang, Jin Gong, Xiaoli Lu, Tingjuan Sun, Zhe Solé-Casals, Jordi College of Computer Science Nankai University Tianjin China Department of Biomedical Engineering School of Medicine Tsinghua University Beijing China Tianjin Key Laboratory of Brain Science and Intelligent Rehabilitation Nankai University Tianjin China State Key Laboratory of High-end Server and Storage Technology China 903rd Hospital of PLA China Faculty of Medicine Faculty of Health Data Science Juntendo University Japan Data and Signal Processing Group University of Vic Central University of Catalonia Catalonia Vic08500 Spain

The electroencephalogram (EEG) provides essential data for analyzing brain activities. However, artifacts such as electrooculography (EOG) and electromyography (EMG) often interleave with the EEG signals, significantly affecting the quality of EEG signal analysis. The heterogeneous distribution of these artifacts in the time-frequency domain makes it challenging to remove multiple artifacts using a unified model. In this paper, we propose an Artifact-Aware EEG Denoising Model, referred to as DADM, to effectively remove various types of artifacts in a unified manner. We first obtain an artifact representation that indicates the type of artifact from a pre-trained artifact classification model. This artifact representation is then used as prior knowledge, which is fused into the denoising model in the frequency-time domain. This enables the model to become aware of the artifact type and precisely remove artifacts based on their type. Due to the heterogeneous distributions of artifacts in the frequency domain, we introduce a frequency enhancement module that can identify specific types of artifacts based on their representation and remove them using a hard attention mechanism. Additionally, we design a time-domain compensation module to enhance the denoising capability of DADM by compensating for potential losses of global information. Comprehensive experiments demonstrate that DADM significantly outperforms the Novel CNN in denoising EEG signals, showing a notable 7.77% improvement in correlation coefficient (CC) metrics. © 2024, The Authors. All rights reserved.

关键词： Electroencephalography

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