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arXiv 2025年

作者： Feng, Fuxiang Cong, Runmin Wei, Shoushui Zhang, Yipeng Li, Jun Kwong, Sam Zhang, Wei The School of Control Science and Engineering Shandong University Jinan250061 China The Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China The University of California Los Angeles United States The Hubei Key Laboratory of Intelligent Geo-Information Processing The School of Computer Science China University of Geo-sciences Wuhan430078 China The Lingnan University Hong Kong

Reconstructing Hyperspectral Images (HSI) from RGB images can yield high spatial resolution HSI at a lower cost, demonstrating significant application potential. This paper reveals that local correlation and global continuity of the spectral characteristics are crucial for HSI reconstruction tasks. Therefore, we fully explore these inter-spectral relationships and propose a Correlation and Continuity Network (CCNet) for HSI reconstruction from RGB images. For the correlation of local spectrum, we introduce the Group-wise Spectral Correlation Modeling (GrSCM) module, which efficiently establishes spectral band similarity within a localized range. For the continuity of global spectrum, we design the Neighborhood-wise Spectral Continuity Modeling (NeSCM) module, which employs memory units to recursively model the progressive variation characteristics at the global level. In order to explore the inherent complementarity of these two modules, we design the Patch-wise Adaptive Fusion (PAF) module to efficiently integrate global continuity features into the spectral features in a patch-wise adaptive manner. These innovations enhance the quality of reconstructed HSI. We perform comprehensive comparison and ablation experiments on the mainstream datasets NTIRE2022 and NTIRE2020 for the spectral reconstruction task. Compared to the current advanced spectral reconstruction algorithms, our designed algorithm achieves State-Of-The-Art (SOTA) performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Convolutional neural networks

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Multi-domain fake news detection method based on generative adversarial network and graph network

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Knowledge-Based Systems 2025年 319卷

作者： Li, Xuefeng Wei, Jian Zhao, Chensu Fan, Xiaqiong Wang, Yuhang Key Laboratory of Grain Information Processing and Control Ministry of Education Henan University of Technology Zhengzhou450001 China Henan Key Laboratory of Grain Storage Information Intelligent Perception and Decision Making Henan University of Technology Zhengzhou China School of Artificial Intelligence and Big Data Henan University of Technology Zhengzhou450001 China Shandong Woman's University Jinan250002 China

The proliferation of misinformation in today's digital era poses significant challenges, with fake news detection becoming critical to mitigate economic losses and social instability. Despite extensive research efforts, most existing approaches are tailored for single-domain fake news detection, struggling with data distribution discrepancies and domain shifts when applied to multi-domain scenarios. This limitation underscores the urgent need for solutions that address the complexities of cross-domain detection. Here, we propose a novel framework MFGAG that synergistically integrates adversarial networks and graph neural networks with emotional, stylistic, and semantic features to enable precise domain localization. By leveraging these features, the framework effectively models intricate relationships among news articles within the same temporal context, addressing the challenges posed by multi-domain datasets. Experimental evaluations demonstrate that our approach outperforms state-of-the-art methods, achieving an average accuracy improvement of 3.3 percentage points for single-domain news and nearly 1 percentage point for mixed-domain data, culminating in an overall accuracy of 93.1 %. The code involved in this study is publicly available on website https://***/SWLee777/MFGAG. © 2025 Elsevier B.V.

关键词： Network theory (graphs)

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Joint Masked Reconstruction and Contrastive Learning for Mining Interactions Between Proteins

arXiv

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arXiv 2025年

作者： Li, Jiang Wang, Xiaoping School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Institute of Artificial Intelligence Huazhong University of Science and Technology Wuhan430074 China Hubei Key Laboratory of Brain-Inspired Intelligent Systems Huazhong University of Science and Technology Wuhan430074 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China

Protein-protein interaction (PPI) prediction is an instrumental means in elucidating the mechanisms underlying cellular operations, holding significant practical implications for the realms of pharmaceutical development and clinical treatment. Presently, the majority of research methods primarily concentrate on the analysis of amino acid sequences, while investigations predicated on protein structures remain in the nascent stages of exploration. Despite the emergence of several structure-based algorithms in recent years, these are still confronted with inherent challenges: (1) the extraction of intrinsic structural information of proteins typically necessitates the expenditure of substantial computational resources;(2) these models are overly reliant on seen protein data, struggling to effectively unearth interaction cues between unknown proteins. To further propel advancements in this domain, this paper introduces a novel PPI prediction method jointing masked reconstruction and contrastive learning, termed JmcPPI. This methodology dissects the PPI prediction task into two distinct phases: during the residue structure encoding phase, JmcPPI devises two feature reconstruction tasks and employs graph attention mechanism to capture structural information between residues;during the protein interaction inference phase, JmcPPI perturbs the original PPI graph and employs a multi-graph contrastive learning strategy to thoroughly mine extrinsic interaction information of novel proteins. Extensive experiments conducted on three widely utilized PPI datasets demonstrate that JmcPPI surpasses existing optimal baseline models across various data partition schemes. The associated code can be accessed via https://***/lijfrankopen/JmcPPI. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Adaptive leader-following attitude consensus of multiple rigid body systems with resilience to communication link faults

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Science China(Information Sciences) 2023年第10期66卷 319-320页

作者： Housheng SU Zhuohang LI Key Laboratory of Imaging Processing and Intelligence Control School of Artifcial Intelligence and Automation Huazhong University of Science and Technology

Distributed coordination in multi-agent systems has been widely utilized in attitude alignment of spacecraft and cooperative control of unmanned vehicles, which has attracted extensive attention from biology, physics, and engineering researchers [1]. With the growing complexity of tasks, an increase in space missions and engineering projects needs to be completed by the cooperative operation of multi-spacecraft or multi-robot systems.

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Generation Maintenance Scheduling for Power Systems Considering the Risk Quantification of Hybrid Uncertainty

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IEEE Transactions on Power Systems 2025年

作者： Yang, Xiao Zhao, Yong Li, Yuanzheng Huang, Cheng Ding, Qiang Key Laboratory of Image Information Processing and Intelligent Control of Ministry of Education of China Huazhong University of Science and Technology School of Artificial Intelligence and Automation Wuhan430074 China Power Science Research Institute of State Grid Jiangsu Electric Power Company China China Electric Power Research Institute Company China

The accurate quantification of risk caused by uncertainty forms a crucial foundation for formulating the generation maintenance scheduling (GMS) of power systems. However, the probability distribution functions (PDFs) of uncertain variables such as wind power and load are challenging to model accurately or are unknown, which makes it difficult to measure their economic risk and formulate appropriate GMS for power systems. To address this issue, we consider hybrid uncertainty from wind power and load, and propose a novel interval-probabilistic worst conditional value-at-risk (IP-WCVaR)-based generation maintenance scheduling method. Firstly, a novel IP-WCVaR method is proposed to measure the risk of the interval and probabilistic hybrid uncertainty, and the analytical mathematical model of the IP-WCVaR is derived through typical scenarios of probability correction. On this basis, the positive and negative spinning reserve models are established using the IP-WCVaR and then integrated into the GMS model, which enhances the resilience of the power system. Finally, the new risk-averse GMS model is formulated as the lower and upper boundary optimal models, which are transformed into tractable mixed integer linear programming problems based on the interval extreme value theory. The effectiveness and superiority of the proposed IP-WCVaR method are verified on the modified IEEE 24-bus and IEEE 118-bus power systems. © 1969-2012 IEEE.

关键词： Mixed-integer linear programming

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The role of molecular weight on chitosan and chitosan oligosaccharides in sleep regulation: Integrating network pharmacology and multi-omics analysis in Drosophila

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International Journal of Biological Macromolecules 2025年 308卷 142147页

作者： Liang, Yuxuan Jiang, Kaiyu Liu, Zhengqi Tu, Juncai Gong, Wei Hu, Yuanyuan Li, Jinjin Hardiman, Gary Guo, Xiaoming Zhu, Beiwei Shenzhen Key Laboratory of Food Nutrition and Health GuangDong Engineering Technology Research Center of Aquatic Food Processing and Safety Control College of Chemistry and Environmental Engineering and Institute for Innovative Development of Food Industry Shenzhen University Shenzhen518060 China Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Medical School Shenzhen518060 China State Key Laboratory of Marine Food Processing and Safety Control Dalian Polytechnic University Dalian116034 China National Engineering Research Center of Seafood Collaborative Innovation Center of Seafood Deep Processing School of Food Science and Technology Dalian Polytechnic University Dalian116034 China The Institute for Global Food Security School of Biological Sciences Queen's University Belfast BelfastBT7 1NN United Kingdom

This study aims to investigate the sleep-regulating effects of chitosan (COS) with different molecular weights (1 kDa, 3 kDa and 30 kDa). Network pharmacology predicted the sleep-regulating effects of COS and COS oligosaccharides and identified sleep-related targets. Drosophila sleep analysis demonstrated that COS ameliorated caffeine-induced insomnia, and its efficacy was critically influenced by molecular weight. Specifically, COS30K exhibited superior effects compared to COS1K and COS3K in improving the nighttime sleep time, duration of sleep episode and number of sleep episodes in flies. These differential effects were primarily related to acetate-producing bacteria Acetobacter and polysaccharide-degrading bacteria (Bacteroides and Ruminococcus). Their influence on tryptophan metabolism (serotonin and kynurenine) may contribute to the molecular weight-dependent differences in sleep-regulating effects of COS. Integration of network pharmacology and transcriptomic analyses identified five crucial targets (Insc, Aox1, bgm, Acer and hll). This study provides a strategy for predicting novel biological activity of COS and COS oligosaccharides. © 2025

关键词： Chitosan

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Pre-trained Models Succeed in Medical imaging with Representation Similarity Degradation

arXiv

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arXiv 2025年

作者： Zu, Wenqiang Xie, Shenghao Chen, Hao Ma, Lei Institute of Automation Chinese Academy of Sciences China Academy for Advanced Interdisciplinary Studies National Biomedical Imaging Center National Key Laboratory for Multimedia Information Processing Peking University China School of Chemical Sciences University of Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China

This paper investigates the critical problem of representation similarity evolution during cross-domain transfer learning, with particular focus on understanding why pre-trained models maintain effectiveness when adapted to medical imaging tasks despite significant domain gaps. The study establishes a rigorous problem definition centered on quantifying and analyzing representation similarity trajectories throughout the fine-tuning process, while carefully delineating the scope to encompass both medical image analysis and broader cross-domain adaptation scenarios. Our empirical findings reveal three critical discoveries: the potential existence of high-performance models that preserve both task accuracy and representation similarity to their pre-trained origins;a robust linear correlation between layer-wise similarity metrics and representation quality indicators;and distinct adaptation patterns that differentiate supervised versus self-supervised pre-training paradigms. The proposed similarity space framework not only provides mechanistic insights into knowledge transfer dynamics but also raises fundamental questions about optimal utilization of pre-trained models. These results advance our understanding of neural network adaptation processes while offering practical implications for transfer learning strategies that extend beyond medical imaging applications. The code will be available once accepted. Copyright © 2025, The Authors. All rights reserved.

关键词： Self-supervised learning

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An Integrated Intelligent Autonomous Driving Bulldozer System: Pose Estimation, Object Detection, and Work Planning for Dumping Operations

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Journal of Field Robotics 2025年第0期

作者： Peng, Gang Gao, Qiang Liu, Xingyu Zhou, Yicheng Duan, Hangqi Wu, ZhanGang Hu, Bin Zhu, Xukang Xu, Daosheng School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Engineering Research Center of Autonomous Intelligent Unmanned Systems Ministry of Education Wuhan China Shantui Construction Machinery Co. Ltd. Jining China

In recent years, the rapid advancement of automation control and intelligent sensing technologies has positioned autonomous driving as a focal point of interest for both academia and industry. As core equipment in modern construction and industrial production, engineering machinery urgently requires intelligent transformation. To promote the intelligent development of engineering machinery, we have designed an integrated intelligent autonomous driving bulldozer system, which can be extended to various types of engineering machinery. For the specific mine dumping operation environment, we propose a multisensor fusion pose estimation algorithm framework based on the Global Navigation Satellite System, inertial measurement unit, visual cameras, and light detection and ranging (LiDAR) to address issues arising from sensor failures under harsh construction conditions. Furthermore, to ensure safety during task execution, we design a three-dimensional object detection method based on the optimal observation plane from LiDAR data. By integrating pose estimation and environmental perception results, we develop a comprehensive work planning and path-tracking algorithm to maintain task efficiency. Experimental results demonstrate that our intelligent autonomous driving bulldozer system performs excellently under various working conditions. The accuracy of its pose estimation, object detection, and path tracking meets the requirements of actual construction environments, showcasing its significant potential in engineering applications. © 2025 Wiley Periodicals LLC.

关键词： Construction equipment

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Keeping Representation Similarity in Finetuning for Medical Image Analysis

arXiv

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arXiv 2025年

作者： Zu, Wenqiang Xie, Shenghao Chen, Hao Liang, Yiming Ma, Lei Institute of Automation Chinese Academy of Sciences China Academy for Advanced Interdisciplinary Studies National Biomedical Imaging Center National Key Laboratory for Multimedia Information Processing Peking University China School of Chemical Sciences University of Chinese Academy of Sciences China Beijing Academy of Artificial Intelligence China

Foundation models pretrained on large-scale natural images have been widely used to adapt to medical image analysis through finetuning. This is largely attributed to pretrained representations capturing universal, robust, and generalizable features, which can be reutilized by downstream tasks. However, these representations are later found to gradually vanish during finetuning, accompanied by a degradation of foundation model’s original abilities, e.g., generalizability. In this paper, we argue that pretrained representations can be well preserved while still effectively adapting to downstream tasks. We study this by proposing a new finetuning method RepSim, which minimizes the distance between pretrained and finetuned representations via constraining learnable orthogonal manifold based on similarity invariance. Compared to standard finetuning methods, e.g., full finetuning, our method improves representation similarity by over 30% while maintaining competitive accuracy, and reduces sharpness by 42% across five medical image classification datasets. The code will be released. Copyright © 2025, The Authors. All rights reserved.

关键词： Image representation

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CrossConvPyramid: Deep Multimodal Fusion for Epileptic Magnetoencephalography Spike Detection

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IEEE Journal of Biomedical and Health Informatics 2025年第5期29卷 3194-3205页

作者： Liang Zhang Shurong Sheng Xiongfei Wang Jia-Hong Gao Yi Sun Kuntao Xiao Wanli Yang Pengfei Teng Guoming Luan Zhao Lv AHU-IAI AI Joint Laboratory School of Artificial Intelligence Anhui University Hefei China Anhui Province Key Laboratory of Biomedical Imaging and Intelligent Processing Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Department of Neurosurgery Beijing Key Laboratory of Epilepsy Sanbo Brain Hospital Capital Medical University Beijing China McGovern Institute for Brain Research Peking University Beijing China Center for MRI Research Academy for Advanced Interdisciplinary Studies Peking University Beijing China Beijing City Key Lab for Medical Physics and Engineering Institute of Heavy Ion Physics School of Physics Peking University Beijing China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Anhui Province Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University Hefei China

Magnetoencephalography (MEG) is a vital non-invasive tool for epilepsy analysis, as it captures high-resolution signals that reflect changes in brain activity over time. The automated detection of epileptic spikes within these signals can significantly reduce the labor and time required for manual annotation of MEG recording data, thereby aiding clinicians in identifying epileptogenic foci and evaluating treatment prognosis. Research in this domain often utilizes the raw, multi-channel signals from MEG scans for spike detection, commonly neglecting the multi-channel spiking patterns from spatially adjacent channels. Moreover, epileptic spikes share considerable morphological similarities with artifact signals within the recordings, posing a challenge for models to differentiate between the two. In this paper, we introduce a multimodal fusion framework that addresses these two challenges collectively. Instead of relying solely on the signal recordings, our framework also mines knowledge from their corresponding topography-map images, which encapsulate the spatial context and amplitude distribution of the input signals. To facilitate more effective data fusion, we present a novel multimodal feature fusion technique called CrossConvPyramid, built upon a convolutional pyramid architecture augmented by an attention mechanism. It initially employs cross-attention and a convolutional pyramid to encode inter-modal correlations within the intermediate features extracted by individual unimodal networks. Subsequently, it utilizes a self-attention mechanism to refine and select the most salient features from both inter-modal and unimodal features, specifically tailored for the spike classification task. Our method achieved the average F1 scores of 92.88% and 95.23% across two distinct real-world MEG datasets from separate centers, respectively outperforming the current state-of-the-art by 2.31% and 0.88%. We plan to release the code on GitHub later.

关键词： Feature extraction Recording Brain modeling Attention mechanisms Convolution Bioinformatics Epilepsy Electroencephalography Computational modeling Physics

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