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Gridless evolutionary approach for line spectral estimation with unknown model order

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arXiv 2021年

作者： Yan, Bai Zhao, Qi Zhang, Jin Andrew Zhang, J. Yao, Xin 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 and Technology University of Science and Technology of China Hefei230027 China Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China University of Technology SydneyNSW2007 Australia

—Gridless methods show great superiority in line spectral estimation. These methods need to solve an atomic l0 norm (i.e., the continuous analog of l0 norm) minimization problem to estimate frequencies and model order. Since this problem is NP-hard to compute, relaxations of atomic l0 norm, such as nuclear norm and reweighted atomic norm, have been employed for promoting sparsity. However, the relaxations give rise to a resolution limit, subsequently leading to biased model order and convergence error. To overcome the above shortcomings of relaxation, we propose a novel idea of simultaneously estimating the frequencies and model order by means of the atomic l0 norm. To accomplish this idea, we build a multiobjective optimization model. The measurment error and the atomic l0 norm are taken as the two optimization objectives. The proposed model directly exploits the model order via the atomic l0 norm, thus breaking the resolution limit. We further design a variable-length evolutionary algorithm to solve the proposed model, which includes two innovations. One is a variable-length coding and search strategy. It flexibly codes and interactively searches diverse solutions with different model orders. These solutions act as steppingstones that help fully exploring the variable and open-ended frequency search space and provide extensive potentials towards the optima. Another innovation is a model order pruning mechanism, which heuristically prunes less contributive frequencies within the solutions, thus significantly enhancing convergence and diversity. Simulation results confirm the superiority of our approach in both frequency estimation and model order selection. Copyright © 2021, The Authors. All rights reserved.

关键词： Spectrum analysis

Learning Novel Skills from Language-Generated Demonstrations

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arXiv 2024年

作者： Jin, Ao-Qun Xiang, Tian-Yu Zhou, Xiao-Hu Gui, Mei-Jiang Xie, Xiao-Liang Liu, Shi-Qi Wang, Shuang-Yi Cao, Yue Duan, Sheng-Bin Xie, Fu-Chao Hou, Zeng-Guang The State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The School of Computer and Information Engineering Hubei Normal University Huangshi435002 China The School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China The School of Information Shanxi University of Finance and Economics Taiyuan030006 China The CAS Center for Excellence in Brain Science and Intelligence Technology Beijing100190 China The Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology Taipa China

Current robot learning algorithms for acquiring novel skills often rely on demonstration datasets or environment interactions, resulting in high labor costs and potential safety risks. To address these challenges, this study proposes a skill-learning framework that enables robots to acquire novel skills from natural language instructions. The proposed pipeline leverages vision-language models to generate demonstration videos of novel skills, which are processed by an inverse dynamics model to extract actions from the unlabeled demonstrations. These actions are subsequently mapped to environmental contexts via imitation learning, enabling robots to learn new skills effectively. Experimental evaluations in the MetaWorld simulation environments demonstrate the pipeline’s capability to generate high-fidelity and reliable demonstrations. Using the generated demonstrations, various skill learning algorithms achieve an accomplishment rate three times the original on novel tasks. These results highlight a novel approach to robot learning, offering a foundation for the intuitive and intelligent acquisition of novel robotic skills. Copyright © 2024, The Authors. All rights reserved.

关键词： Adversarial machine learning

ViKL: A Mammography Interpretation Framework via Multimodal Aggregation of Visual-knowledge-linguistic Features

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arXiv 2024年

作者： Wei, Xin Tao, Yaling Du, Changde Zhao, Gangming Yu, Yizhou Li, Jinpeng The Ningbo Institute of Life and Health Industry UCAS Ningbo315000 China School of Automation Science and Engineering South China University of Technology Guangdong Guangzhou510641 China The Research Center for Brain-Inspired Intelligence State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China The Department of Computer Science University of Hong Kong Pokfulam Hong Kong

Mammography is the primary imaging tool for breast cancer diagnosis. Despite significant strides in applying deep learning to interpret mammography images, efforts that focus predominantly on visual features often struggle with generalization across datasets. We hypothesize that integrating additional modalities in the radiology practice, notably the linguistic features of reports and manifestation features embodying radiological insights, offers a more powerful, interpretable and generalizable representation. In this paper, we announce MVKL, the first multimodal mammography dataset encompassing multi-view images, detailed manifestations and reports. Based on this dataset, we focus on the challanging task of unsupervised pretraining and propose ViKL, a innovative framework that synergizes Visual, Knowledge, and Linguistic features. This framework relies solely on pairing information without the necessity for pathology labels, which are often challanging to acquire. ViKL employs a triple contrastive learning approach to merge linguistic and knowledge-based insights with visual data, enabling both inter-modality and intra-modality feature enhancement. Our research yields significant findings: 1) Integrating reports and manifestations with unsupervised visual pretraining, ViKL substantially enhances the pathological classification and fosters multimodal interactions. 2) Manifestations can introduce a novel hard negative sample selection mechanism. 3) The multimodal features demonstrate transferability across different datasets. 4) The multimodal pretraining approach curbs miscalibrations and crafts a high-quality representation space. The MVKL dataset and ViKL code are publicly available at https://***/wxwxwwxxx/ViKL to support a broad spectrum of future research. Copyright © 2024, The Authors. All rights reserved.

关键词： Self-supervised learning

Renal Impairment in Wilson's Disease

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Kidney International Reports 2025年

作者： Zheng, Zi-Wei Xu, Meng-Hui Fan, Lu-Na Wang, Rou-Min Xu, Wan-Qing Yang, Guo-Min Guo, Ling-Yu Liu, Chuan Dong, Yi Wu, Zhi-Ying Department of Medical Genetics and Center for Rare Diseases Zhejiang Key Laboratory of Rare Diseases for Precision Medicine and Clinical Translation Second Affiliated Hospital Zhejiang University School of Medicine Zhejiang University Zhejiang Hangzhou China Nanhu Brain-computer Interface Institute Zhejiang Hangzhou China MOE Frontier Science Center for Brain Science and Brain-machine Integration School of Brain Science and Brain Medicine Zhejiang University Zhejiang Hangzhou China

NTIRE 2023 Image Shadow Removal Challenge Report

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NTIRE 2023 Image Shadow Removal Challenge Report

2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Vasluianu, Florin-Alexandru Seizinger, Tim Timofte, Radu Cui, Shuhao Huang, Junshi Tian, Shuman Fan, Mingyuan Zhang, Jiaqi Zhu, Li Wei, Xiaoming Wei, Xiaolin Luo, Ziwei Gustafsson, Fredrik K. Zhao, Zheng Sjölund, Jens Schön, Thomas B. Dong, Xiaoyi Zhang, Xi Sheryl Li, Chenghua Leng, Cong Yeo, Woon-Ha Oh, Wang-Taek Lee, Yeo-Reum Ryu, Han-Cheol Luo, Jinting Jiang, Chengzhi Han, Mingyan Wu, Qi Lin, Wenjie Yu, Lei Li, Xinpeng Jiang, Ting Fan, Haoqiang Liu, Shuaicheng Xu, Shuning Song, Binbin Chen, Xiangyu Zhang, Shile Zhou, Jiantao Zhang, Zhao Zhao, Suiyi Zheng, Huan Gao, Yangcheng Wei, Yanyan Wang, Bo Ren, Jiahuan Luo, Yan Kondo, Yuki Miyata, Riku Yasue, Fuma Naruki, Taito Ukita, Norimichi Chang, Hua-En Yang, Hao-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Huang, Zhi-Kai Chen, Wei-Ting Chen, I-Hsiang Hsieh, Chia-Hsuan Kuo, Sy-Yen Xianwei, Li Fu, Huiyuan Liu, Chunlin Ma, Huadong Fu, Binglan He, Huiming Wang, Mengjia She, Wenxuan Liu, Yu Nathan, Sabari Kansal, Priya Zhang, Zhongjian Yang, Huabin Wang, Yan Zhang, Yanru Phutke, Shruti S. Kulkarni, Ashutosh Khan, Md Raqib Murala, Subrahmanyam Vipparthi, Santosh Kumar Ye, Heng Liu, Zixi Yang, Xingyi Liu, Songhua Wu, Yinwei Jing, Yongcheng Yu, Qianhao Zheng, Naishan Huang, Jie Long, Yuhang Yao, Mingde Zhao, Feng Zhao, Bowen Ye, Nan Shen, Ning Cao, Yanpeng Xiong, Tong Xia, Weiran Li, Dingwen Xia, Shuchen Computer Vision Lab Ifi Caidas University of Würzburg Germany Computer Vision Lab Eth Zürich Switzerland Meituan Group China Department of Information Technology Uppsala University Sweden Institute of Automation Chinese Academy of Sciences Beijing China Nanjing China Maicro Nanjing China Department of Artificial Intelligence Convergence Sahmyook University Seoul Korea Republic of Megvii Technology China University of Electronic Science and Technology of China China University of Macau China China Toyota Technological Institute Japan Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan ServiceNow United States Beijing University of Post and Teleconmunication Beijing China Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education China Couger Inc. Computer Vision and Pattern Recognition Lab Indian Institute of Technology Ropar Punjab Rupnagar India Research Institute Singapore National University of Singapore Singapore Research Institute Singapore University of Sydney Australia Brain-Inspired Vision Laboratory Information Science and Technology Institution University of Science and Technology of China China State Key Laboratory of Fluid Power and Mechatronic Systems School of Mechanical Engineering Zhejiang University Hangzhou310027 China Key Laboratory of Advanced Manufacturing Technology of Zhejiang Province School of Mechanical Engineering Zhejiang University Hangzhou310027 China South China University of Technology China

ISBN: (纸本)9798350302493

This work reviews the results of the NTIRE 2023 Challenge on Image Shadow Removal. The described set of solutions were proposed for a novel dataset, which captures a wide range of object-light interactions. It consists of 1200 roughly pixel aligned pairs of real shadow free and shadow affected images, captured in a controlled environment. The data was captured in a white-box setup, using professional equipment for lights and data acquisition sensors. The challenge had a number of 144 participants registered, out of which 19 teams were compared in the final ranking. The proposed solutions extend the work on shadow removal, improving over the performance level describing state-of-the-art methods. © 2023 IEEE.

关键词： Data acquisition

Language modulates vision: Evidence from neural networks and human brain-lesion models

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arXiv 2025年

作者： Chen, Haoyang Liu, Bo Wang, Shuyue Wang, Xiaosha Han, Wenjuan Zhu, Yixing Wang, Xiaochun Bi, Yanchao State Key Laboratory of Cognitive Neuroscience and Learning IDG/McGovern Institute for Brain Research Beijing Normal University Beijing100875 China Department of Radiology First Hospital of Shanxi Medical University Shanxi Province Taiyuan030001 China College of Medical Imaging Shanxi Medical University Shanxi Province Taiyuan030001 China Shanxi Key Laboratory of Intelligent Imaging First Hospital of Shanxi Medical University Shanxi Province Taiyuan030001 China School of Computer Science & Technology Beijing Jiaotong University Beijing100044 China Institute for Artificial Intelligence Peking University Beijing100871 China State Key Laboratory of General Artificial Intelligence Peking University Beijing100871 China School of Psychological and Cognitive Sciences Beijing Key Laboratory of Behavior and Mental Health Peking University Beijing100871 China IDG/McGovern Institute for Brain Research Peking University Beijing100871 China Chinese Institute for Brain Research Beijing102206 China Peking University Beijing100871 China

Comparing information structures in between deep neural networks (DNNs) and the human brain has become a key method for exploring their similarities and differences. Recent research has shown better alignment of vision-language DNN models, such as CLIP, with the activity of the human ventral occipitotemporal cortex (VOTC) than earlier vision models, supporting the idea that language modulates human visual perception. However, interpreting the results from such comparisons is inherently limited due to the "black box" nature of DNNs. To address this, we combined model–brain fitness analyses with human brain lesion data to examine how disrupting the communication pathway between the visual and language systems causally affects the ability of vision–language DNNs to explain the activity of the VOTC. Across four diverse datasets, CLIP consistently outperformed both label-supervised (ResNet) and unsupervised (MoCo) models in predicting VOTC activity. This advantage was left-lateralized, aligning with the human language network. Analyses of the data of 33 stroke patients revealed that reduced white matter integrity between the VOTC and the language region in the left angular gyrus was correlated with decreased CLIP performance and increased MoCo performance, indicating a dynamic influence of language processing on the activity of the VOTC. These findings support the integration of language modulation in neurocognitive models of human vision, reinforcing concepts from vision–language DNN models. The sensitivity of model–brain similarity to specific brain lesions demonstrates that leveraging manipulation of the human brain is a promising framework for evaluating and developing brain-like computer models. © 2025, CC BY.

关键词： brain models

Unsupervised Spike Sorting Using Local Adaptive Projection and Gaussian Mixture Model

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Unsupervised Spike Sorting Using Local Adaptive Projection a...

Chinese Automation Congress (CAC)

作者： Yuli Huang Mengmeng Li Xinyu Liu Lifang Yang Hong Wan School of Electrical Engineering Zhengzhou University Zhengzhou China Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology Zhengzhou China School of Intelligent Manufacturing Huanghuai University Zhumadian China

ISBN: (数字)9781728176871

ISBN: (纸本)9781728176888

Many researches in neuroscience rely on the analysis of neuronal spike activities recorded under different behavioral conditions, due to the fact that different types of spikes recorded by multi-channel microelectrode arrays may show specific firing in different cognitive tasks. Therefore, accurate and reliable sorting of spikes plays an important role in the related researches in neuroscience. Based on the nonstationarity and local amplitude jump characteristics of spikes, a novel spikes sorting algorithm based on unsupervised local adaptive projection (LAP) feature selection algorithm and Gaussian mixture model (GMM) clustering algorithm (LAGM) was proposed in this work. Firstly, the adaptive similarity matrix and projection matrix were learned by iterative method to select the features of the original spike waveforms using LAP, and the low-dimensional feature set of the spikes was obtained. Then, these low-dimensional features were clustered for spikes sorting based on GMM. Finally, the feasibility and effectiveness of the LAGM was assessed by simulation data from the publish database, and the practicability was validated by the real signals recorded from the hippocamp region of pigeons under the free-foraging with the GMM clustering based on principal component analysis (PCA) dimension reduction and K-means clustering based on LAP feature selection as the algorithms for comparison. Experimental results indicated that the proposed LAGM in this paper shown high accuracy for spike sorting, which provided an effective means for the reliable analysis of spike signals.

关键词： Feature extraction Clustering algorithms Sorting Neurons Reliability Neuroscience Microelectrodes

A new EGO-driven memetic algorithm for solving flexible job shop scheduling problem

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Information sciences 2025年 717卷

作者： Liu, Chanjuan Zhang, Guojing Chen, Bingcai Ishibuchi, Hisao School of Computer Science and Technology Dalian University of Technology Dalian116024 China The Guangdong Provincial Key Laboratory of Brain-Inspired Intelligent Computation Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China

The Flexible Job Shop Scheduling Problem (FJSP) extends the classical job shop problem, posing significant challenges due to its complexity and flexibility. Solving FJSP is vital for improving efficiency, lowering costs, and reducing energy use. However, existing memetic algorithms face slow convergence and low accuracy caused by random neighborhood exploration and repeated local searches, often leading to local optima and reduced solution quality. To address these key issues, we propose an improved memetic algorithm called HMA-HNS, which introduces three key innovations: First, an elite gene extraction operator (EGO) is designed to effectively guide neighborhood search and avoid redundant exploration. Second, the EGO operator is combined with an adaptive genetic operator to form a hybrid neighborhood search strategy to maintain population diversity and get rid of local optima. Third, a local search strategy based on the critical path is integrated to balance solution quality and convergence speed. To evaluate the efficacy of our proposed algorithm, we conducted experiments on three renowned international standard instances and compared the results with state-of-the-art algorithms. The experimental results demonstrate that the HMA-HNS algorithm outperforms existing methods regarding optimality. © 2025 Elsevier Inc.

关键词： Tabu search

AM-ConvBLS: Adaptive Manifold Convolutional Broad Learning System for Cross-Session and Cross-Subject Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Lei, Chunyu Chen, C. L. Philip Zhang, Tong South China University of Technology Guangdong Provincial Key Laboratory of Computational Intelligence and Cyberspace Information The School of Computer Science and Engineering Guangzhou510006 China Pazhou Lab Brain and Affective Cognitive Research Center Guangzhou510335 China Ministry of Education on Health Intelligent Perception and Paralleled Digital-Human Engineering Research Center Guangzhou China

Emotion recognition based on electroencephalography (EEG) data has gained rapid development because of its believability and accuracy. However, most existing EEG emotion recognition methods suffer from three issues: 1) underutilization of multi-scale emotion representations, 2) underexploitation of emotion labels and intrinsic geometric structures, and 3) inherent non-stationarity characteristic and individual variability of EEG signals. To this end, we propose an Adaptive Manifold Convolutional Broad Learning System (AM-ConvBLS) to capture the multi-scale distribution aligned emotion patterns in the geometric structure preserved emotion submanifold space. To begin with, a Multi-Scale Representation Learning (MSRL) module is developed to learn diverse multi-scale emotion representations. The Domain Discrepancy Elimination (DDE) module is then developed to further align feature distributions in the source and target domains. To further utilize EEG emotion labels, we devise a Label Manifold Information Exploration (LMIE) module to retain the sample label consistency. In addition, a global feature importance analysis method for AM-ConvBLS based on Shapley Additive Global importancE (SAGE) is designed to investigate the EEG frequency band importance and brain neural activation patterns. Experiments on SEED, SEED-IV, and SEED-V datasets demonstrate the effectiveness and superiority of our AM-ConvBLS. © 2010-2012 IEEE.

关键词： Convolution