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38th Conference on Neural Information processing Systems, NeurIPS 2024

作者： An, Weixin Liu, Yuanyuan Shang, Fanhua Liu, Hongying Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education School of Artificial Intelligence Xidian University China College of Intelligence and Computing Tianjin University China Medical School Tianjin University China Peng Cheng Lab Shenzhen China

Many zeroth-order (ZO) optimization algorithms have been developed to solve nonconvex minimax problems in machine learning and computer vision areas. However, existing ZO minimax algorithms have high complexity and rely on some strict restrictive conditions for ZO estimations. To address these issues, we design a new unified ZO gradient descent extragradient ascent (ZO-GDEGA) algorithm, which reduces the overall complexity to O(dϵ-6) to find an ϵ-stationary point of the function ψ for nonconvex-concave (NC-C) problems, where d is the variable dimension. To the best of our knowledge, ZO-GDEGA is the first ZO algorithm with complexity guarantees to solve stochastic NC-C problems. Moreover, ZO-GDEGA requires weaker conditions on the ZO estimations and achieves more robust theoretical results. As a by-product, ZO-GDEGA has advantages on the condition number for the NC-strongly concave case. Experimentally, ZO-GDEGA can generate more effective poisoning attack data with an average accuracy reduction of 5%. The improved AUC performance also verifies the robustness of gradient estimations. © 2024 Neural information processing systems foundation. All rights reserved.

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Abnormal EEG Microstates in Post-stroke Patients: A Pilot Study

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IEEE Sensors Journal 2025年第11期25卷 19863-19873页

作者： Chen, Xiaoling Zhai, Jiashun Cai, Yuhao Zhang, Tengyu Wang, Juan Mao, Gengsheng Cheng, Shengcui Zhang, Yuanyuan Xie, Ping Yanshan University Key Laboratory of Intelligent Control and Neural Information Processing Ministry of Education Hebei Province Qinhuangdao066004 China Yanshan University Key Laboratory of Intelligent Rehabilitation and Neuromodulation of Hebei Province Qinhuangdao Hebei China National Research Center for Rehabilitation Technical Aids Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability Beijing China PLA General Hospital Senior Department of Neurosurgery Beijing China Tianjin University Academy of Medical Engineering and Translational Medicine Tianjin300072 China Capital Medical University Xuanwu Hospital Beijing China

Exploring brain function is crucial for unraveling the pathological mechanism underlying stroke. while most studies focus on brain function emphasize dynamic connections and interactions within or between brain regions, they often ignore the global properties of large-scale network topology. In this study, we analyzed resting-state EEG microstates in stroke patients, calculating key parameters such as mean duration (MD), occurrence (OC), time coverage (TC), and transition probability (TP) across different microstate classes. As a result, we identified four microstate classes (A, B, C, and D) in both healthy subjects and stroke patients. Notably, stroke patients showed significant changes in Class B, C, and D, with increased MD, OC, and TC in Class B and C and decreased MD, OC, and TC in Class D. additionally, stroke patients displayed higher TP between Class B and C than healthy controls. Moreover, we observed a positive correlation between the OC of Class D and clinical Fugl-Meyer scores, suggestion a link between microstate dynamic and motor recovery. This study highlights the connection between specific microstates and brain regions, providing evidence that stroke patients demonstrate increased activity in visual and salience networks, but decreased activity in the dorsal attention network. We speculate these results from changes in the occipital lobe, frontal lobe, parietal lobe, and dorsal anterior cingulate cortex. These findings deepen our understanding of stroke pathophysiology and support further research. © 2001-2012 IEEE.

关键词： Electroencephalography

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Multi-objective Optimal Operation of Centralized Battery Swap Charging System with Photovoltaic

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Journal of Modern Power Systems and Clean Energy 2022年第1期10卷 149-162页

作者： Yuanzheng Li Yihan Cai Tianyang Zhao Yun Liu Jian Wang Lei Wu Yong Zhao School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhanChina School of Electrical and Electronic Engineering Nanyang Technological UniversitySingaporeSingapore College of Mechatronics and Control Engineering Shenzhen UniversityShenzhenChina Department of Electrical&Computer Engineering Stevens Institute of TechnologyHobokenUSA

Electric vehicles(EVs)are widely deployed throughout the world,and photovoltaic(PV)charging stations have emerged for satisfying the charging demands of EV *** paper proposes a multi-objective optimal operation method for the centralized battery swap charging system(CBSCS),in order to enhance the economic efficiency while reducing its adverse effects on power *** proposed method involves a multi-objective optimization scheduling model,which minimizes the total operation cost and smoothes load fluctuations,***,we modify a recently proposed multi-objective optimization algorithm of non-sorting genetic algorithm III(NSGA-III)for solving this scheduling ***,simulation studies verify the effectiveness of the proposed multi-objective operation method.

关键词： Multi-objective optimization electric vehicle battery swap charging system scheduling photovoltaic

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Cross-Modality Guided Multi-Scale Feature Representation Based image Compression for Industrial Scene Understanding 22

Cross-Modality Guided Multi-Scale Feature Representation Bas...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Tu, Jingzheng Yan, Yan Lu, Ying Chen, Cailian Zhou, Shiru China Academy of Aerospace Science and Innovation Beijing100088 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Department of Automation Shanghai Jiao Tong University Shanghai200240 China

ISBN: (纸本)9798331527471

Industrial surveillance videos capture subsequent video frames and deliver them to processing servers for video analytics. In such a case, image compression is a promising technology in industrial scene understanding due to its reduced transmission burden. Existing works focus on establishing macroscopic visual feature maps that contain compact image information. In this article, a cross-modality guided multi-scale feature representation based image compression method is proposed for understanding industrial scenes. Firstly, an image compressing framework with multi-grained representation (MGRIC) is proposed for industrial scene understanding, which builds image patch, structure, semantic, and signal levels feature maps. Secondly, an encoder-decoder architecture is designed for image compression with diverse granularities. Thirdly, an interaction policy between different levels is developed to ensure the subsequent level’s decoding. Experiments on the CUB-200-2011 dataset demonstrate that the proposed MGRIC method achieves competitive performance, which verifies its effectiveness. © 2024 IEEE.

关键词： Decoding

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Learning Second-Order Attentive Context for Efficient Correspondence Pruning

arXiv

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arXiv 2023年

作者： Ye, Xinyi Zhao, Weiyue Lu, Hao Cao, Zhiguo Key Laboratory of Image Processing and Intelligent Control Ministry of Education School of Artificial Intelligence and Automation Huazhong University of Science and Technology China

Correspondence pruning aims to search consistent correspondences (inliers) from a set of putative correspondences. It is challenging because of the disorganized spatial distribution of numerous outliers, especially when putative correspondences are largely dominated by outliers. It is more challenging to ensure effectiveness while maintaining efficiency. In this paper, we propose an effective and efficient method for correspondence pruning. Inspired by the success of attentive context in correspondence problems, we first extend the attentive context to the first-order attentive context and then introduce the idea of attention in attention (ANA) to model second-order attentive context for correspondence pruning. Compared with first-order attention that focuses on feature-consistent context, second-order attention dedicates to attention weights itself and provides an additional source to encode consistent context from the attention map. For efficiency, we derive two approximate formulations for the naive implementation of second-order attention to optimize the cubic complexity to linear complexity, such that second-order attention can be used with negligible computational overheads. We further implement our formulations in a second-order context layer and then incorporate the layer in an ANA block. Extensive experiments demonstrate that our method is effective and efficient in pruning outliers, especially in high-outlier-ratio cases. Compared with the state-of-the-art correspondence pruning approach LMCNet, our method runs 14 times faster while maintaining a competitive accuracy. Copyright © 2023, The Authors. All rights reserved.

关键词： Efficiency

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Animatable Human Neural Radiance Field from Sparse images Captured by a Single Camera 43

Animatable Human Neural Radiance Field from Sparse Images Ca...

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43rd Chinese control Conference, CCC 2024

作者： Yang, Qingru Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Recently, the use of neural radiance fields (NeRF) for three-dimensional reconstruction of humans has achieved high-quality view rendering. However, existing methods often rely on multiple or single videos as input, requiring dense images, and the rendering quality tends to be poor with sparse image inputs. To address this issue, this paper proposes a novel method that solely utilizes sparse images captured by a single camera to create an animatable human neural radiance field, enabling the rendering of high-fidelity human images under arbitrary novel viewpoints and poses. Specifically, our method utilizes body poses and multi-view feature to build a deformable neural radiance field, and employs frequency-segmented positional encoding to mitigate overfitting during training process. Extensive quantitative and qualitative experiments demonstrate that our proposed method outperforms prior works. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： image reconstruction

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Ocean archaea PPI prediction with pretraining models 25

Ocean archaea PPI prediction with pretraining models

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Proceedings of the 2025 5th International Conference on Bioinformatics and intelligent Computing

作者： Ying Zhang Yuan Liu Xiaoyong Pan Hongbin Shen Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University Shanghai China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai China

ISBN: (纸本)9798400712203

Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.

关键词： Binary PPI prediction

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Event-Triggered Cooperative Tracking control of Multiagent Systems with a Dynamic Leader via Approximate Dynamic Programming

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2752-2765页

作者： Fu, Hao He, Haodong Chen, Yang Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System The School of Computer Science and Technology Wuhan430081 China Wuhan University of Science and Technology Eng. Res. Center for Metallurgical Automation and Measurement Technology of Ministry of Education Wuhan430081 China

This article investigates the optimal cooperative tracking control problem for unknown nonlinear multiagent systems with a real dynamic leader and uncertainties. Its main difficulty lies in eliminating effect of the unmatched uncertainty and noncooperation in the event-triggered manner. To overcome this difficulty, a distributed robust event-triggered control algorithm is developed with the aid of a disturbance observer and robust approximate dynamic programming (ADP). Specifically, this problem is converted into a cooperative multiplayer game for the distributed auxiliary systems employing observer-based feedforward compensation, which contributes to reducing conservatism;via an adaptive event-triggered mechanism and a new weight tuning law, a distributed robust ADP approach is proposed with hope to obviate judging the existence of the saddle point beforehand. Moreover, the local neighbor tracking error and the weight estimation error are demonstrated to be uniformly ultimately bounded by means of Lyapunov stability theory. We also prove that Zeno behavior is excluded. A comparative simulation example for multiquadrotor systems is carried out to verify the effectiveness and superiority of our algorithm. © 2020 IEEE.

关键词： Dynamic programming

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3D Object Pose Estimation Based on End-to-End Differentiable Perspective Projection 43

3D Object Pose Estimation Based on End-to-End Differentiable...

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43rd Chinese control Conference, CCC 2024

作者： Cai, Yuheng Xu, Chi China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China

ISBN: (纸本)9789887581581

Locating 3D objects from a single RGB image via perspective projection is a long-standing problem in computer vision. Inspired by differentiable geometry methods in the field of camera relocation, we apply this neural network combined with differentiable perspective projection to form an end-to-end architecture to the field of object position estimation through object 3D reconstruction methods. The proposed method uses a differentiable LM algorithm for end-to-end pose estimation to achieve higher accuracy, and the gradient is passed back to the neural network to guide its learning. At the same time, we propose a virtual view generation algorithm for augmenting the data to improve the algorithm's sensitivity to the size of the co-vision region. Through experiments, we prove the effectiveness of our method. © 2024 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Neural networks

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Pedestrian object detection algorithm based on lightweight YOLOv7 in complex street scenarios

Pedestrian object detection algorithm based on lightweight Y...

引用

2023 International Conference on Algorithm, Imaging processing, and Machine Vision, AIPMV 2023

作者： Cheng, Shangqi Niu, Hongxia Key Laboratory of Opt-Electronic Technology and Intelligent Control of Ministry of Education Lanzhou Jiaotong University Lanzhou730070 China Key Laboratory of Plateau Traffic Information Engineering and Control of Gansu Province Lanzhou730070 China School of Automation and Electrical Engineering Lanzhou Jiaotong University Lanzhou730070 China

ISBN: (纸本)9781510672444

In view of the problems of excessive parameter setting and large calculation of YOLOv7 in pedestrian object detection in complex street scenarios, this paper proposes a lightweight method to improve YOLOv7 algorithm. Under the YOLOv7 framework, Partial Convolution (PConv) is integrated into the convolution of the original algorithm, replacing part of the convolution in the original convolution layer, and the SEAttention attention module is introduced to ensure the detection accuracy of the lightweight algorithm. The experimental results on the home-made data set show that, compared with the original YOLOv7 algorithm, the number of model parameters decreased by 11.0% in the improved YOLOv7 algorithm, and the algorithm calculation volume decreased by 19.4%, while ensuring the high accuracy of the original YOLOv7 algorithm. In this paper, the algorithm reduces the number of parameters and calculations, and achieves the balance of lightweight and accuracy. © 2024 SPIE.

关键词： Convolution

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