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arXiv 2025年

作者： Chen, Zheyi Huang, Sijin Min, Geyong Ning, Zhaolong Li, Jie Zhang, Yan College of Computer and Data Science Fuzhou University Fuzhou350116 China Engineering Research Center of Big Data Intelligence Ministry of Education Fujian Key Laboratory of Network Fuzhou350002 China Computing and Intelligent Information Processing [Fuzhou University Fuzhou350116 China Department of Computer Science Faculty of Environment Science and Economy University of Exeter ExeterEX4 4QF United Kingdom School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China Department of Informatics University of Oslo Oslo0316 Norway

Mobile Edge computing (MEC) offers low-latency and high-bandwidth support for Internet-of-Vehicles (IoV) applications. However, due to high vehicle mobility and finite communication coverage of base stations, it is hard to maintain uninterrupted and high-quality services without proper service migration among MEC servers. Existing solutions commonly rely on prior knowledge and rarely consider efficient resource allocation during the service migration process, making it hard to reach optimal performance in dynamic IoV environments. To address these important challenges, we propose SR-CL, a novel mobility-aware seamless Service migration and Resource allocation framework via Convex-optimization-enabled deep reinforcement Learning in multi-edge IoV systems. First, we decouple the Mixed Integer Nonlinear Programming (MINLP) problem of service migration and resource allocation into two sub-problems. Next, we design a new actor-critic-based asynchronous-update deep reinforcement learning method to handle service migration, where the delayed-update actor makes migration decisions and the one-step-update critic evaluates the decisions to guide the policy update. Notably, we theoretically derive the optimal resource allocation with convex optimization for each MEC server, thereby further improving system performance. Using the real-world datasets of vehicle trajectories and testbed, extensive experiments are conducted to verify the effectiveness of the proposed SR-CL. Compared to benchmark methods, the SR-CL achieves superior convergence and delay performance under various scenarios. Copyright © 2025, The Authors. All rights reserved.

关键词： Convex optimization

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Context-aware Multimodal AI Reveals Hidden Pathways in Five Centuries of Art Evolution

arXiv

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arXiv 2025年

作者： Kim, Jin Lee, Byunghwee You, Taekho Yun, Jinhyuk Department of Intelligent Semiconductors Soongsil University Seoul06978 Korea Republic of School of Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Luddy School of Informatics Computing and Engineering Indiana University BloomingtonIN47408 United States Institute for Social Data Science Pohang University of Science and Technology Pohang37673 Korea Republic of Center for Digital Humanities & Computational Social Sciences Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of School of AI Convergence Soongsil University Seoul06978 Korea Republic of

The rise of multimodal generative AI is transforming the intersection of technology and art, offering deeper insights into large-scale artwork. Although its creative capabilities have been widely explored, its potential to represent artwork in latent spaces remains underexamined. We use cutting-edge generative AI, specifically Stable Diffusion, to analyze 500 years of Western paintings by extracting two types of latent information with the model: formal aspects (e.g., colors) and contextual aspects (e.g., subject). Our findings reveal that contextual information differentiates between artistic periods, styles, and individual artists more successfully than formal elements. Additionally, using contextual keywords extracted from paintings, we show how artistic expression evolves alongside societal changes. Our generative experiment, infusing prospective contexts into historical artworks, successfully reproduces the evolutionary trajectory of artworks, highlighting the significance of mutual interaction between society and art. This study demonstrates how multimodal AI expands traditional formal analysis by integrating temporal, cultural, and historical contexts. Copyright © 2025, The Authors. All rights reserved.

关键词： Generative adversarial networks

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PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomposition for Single View RGB-D Videos

PD-SDF: Dynamic Surface Reconstruction Based on Plane Decomp...

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

作者： Jinwen Li Weixing Xie Junfeng Yao Shaoqi Wu Youhong Peng Mengyuan Ge Xiao Dong Center for Digital Media Computing School of Film School of Informatics Xiamen University National Institute for Data Science in Health and Medicine Xiamen University Key Laboratory of Digital Protection and Intelligent Processing of Intangible Cultural Heritage of Fujian and Taiwan Ministry of Culture and Tourism Jiujiang Research Institute of Xiamen University Jiujiang China Guangdong Provincial/Zhuhai Key Laboratory of IRADS BNU-HKBU United International College Zhuhai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Surface reconstruction of dynamic scenes from single view videos is a challenging task due to the highly ill-posed and under-constrained nature. Existing single view reconstruction methods suffer from severe quality issues, such as surface distortion and mesh adherison. In this paper, we propose an efficient dynamic representation network, PD-SDF, which consists of a 4D motion field and a 3D geometry field. The explicit disentanglement of motion and geometry based on planar factorization guarantees the mesh consistency across frames. Specifically, to address the mesh adhesion problem, we design a depth-guided sampling strategy to focus on optimizing the SDF field near the object surface. Due to insufficient geometric cues, we design various regularization strategies to constrain smoothness and topological correcness of the scene geometry. Extensive experiments show that our method outperforms existing methods in both appearance and geometry reconstruction. The project page: https://***/

关键词： Geometry Training Surface reconstruction Three-dimensional displays Noise Reconstruction algorithms Stability analysis Speech processing Surface treatment Videos

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A Multi-Stage Deep Learning Pipeline for Crack Detection and Segmentation in High-Speed Rail Brake Discs

A Multi-Stage Deep Learning Pipeline for Crack Detection and...

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Consumer Technology (ICCT-Pacific), International Conference on

作者： Ching-Heng Cheng Yu-Fan Lin Chia-Ming Lee Chih-Chung Hsu Yu-Lin Shih Ting Chin Department of Statistics Institute of Data Science National Cheng Kung Univ. Tainan Taiwan Department of Intelligent Technology Systems Institute of Miin Wu School of Computing National Cheng Kung Univ Tainan Taiwan Institute of Intelligent Systems College of Artificial Intelligence National Yang Ming Chiao Tung Univ. Hsinchu Taiwan Innovation Logistics System Technology Dept. Smart Logistics & Supply Chain Integration Service Division Service Systems Technology Center Industrial Technology Research Institute Hsinchu Taiwan

ISBN: (数字)9798331504120

ISBN: (纸本)9798331504137

The braking system of high-speed rail (HSR) is critical for passenger safety, but prolonged use of brake discs can lead to cracks, posing significant risks. Traditional manual inspection methods are time-consuming and labor-intensive. To address these challenges, we propose a multi-stage crack detection and segmentation system, a cascade mode to progressively identify cracks, leveraging deep learning techniques. The system includes automated circle detection, region cropping, and crack detection and segmentation to accurately identify cracks. Using a real-world dataset collected from HSR brake discs, our approach significantly improves data preprocessing efficiency and model performance. Furthermore, we evaluate the performance on un-seen data and compare our crack detection framework with other deep learning models. The experimental results demonstrate the system's effectiveness which is superior to other deep learning models, highlighting its potential for real-world deployment in HSR maintenance.

关键词： Deep learning Image segmentation Motion segmentation Computational modeling Pipelines Rail transportation data models Maintenance Safety Brakes

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A Causal Adversarial Graph Neural Network for Multi-center Autism Spectrum Disorder Identification

SSRN

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SSRN 2025年

作者： Zhang, Zhuan Wang, Lihui Chen, Qijian Wang, Li Jian, Caiqing Zhu, Yue-Min Wei, Hongjiang Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang550025 China University Lyon INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 Lyon69621 France

Due to the heterogeneous distributions of multi-center rs-fMRI data, currently it is still challenging to identify accurately autism spectrum disorder (ASD) patients from these heterogenous data. To deal with the influence of heterogeneous distributions on multi-center ASD diagnosis, a novel causal adversarial graph neural network (CAG) for multi-center ASD prediction is proposed in this work. CAG mainly consists of a graph filtering (GF) module and a learnable causal graph predictor (CGP). Specifically, the brain connection graph is first constructed from rs-fMRI data, after passing a GF module, the filtered graph is further divided by a causal subgraph and causally irrelevant subgraph. The ground-truth environmental labels and ASD labels are predicted from causal subgraph by a clustering method and a classification block, respectively. In addition, from the filtered graph and causally irrelevant subgraph, the environmental labels are predicted, while from the causally irrelevant subgraph the ASD labels are predicted. Assuming that the predicted environmental labels are independent to causal subgraph, while the predicted ASD labels are independent to causally irrelevant subgraph, accordingly, several adversarial losses are implemented by reversing the gradient. Through this adversarial learning strategy, the derived causal graph can predict multi-center ASD. The comparison experiments on both ABIDE-I and ABIDE-II datasets demonstrate that, the ACC and AUC of CAG has been improved by 3.78\% and 7.38\% at least comparing against the existing methods. Moreover, some abnormal functional connectivities in ASD group are identified from the causal subgraph which may provide useful imaging biomarkers for the early diagnosis of ASD. © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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A Comprehensive Survey on Tiny Machine Learning for Human Behavior Analysis

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IEEE Internet of Things Journal 2025年

作者： Lamaakal, Ismail Essahraui, Siham Maleh, Yassine Makkaoui, Khalid El Ouahbi, Ibrahim Bouami, Mouncef Filali El-Latif, Ahmed A. Abd Almousa, May Peng, Jialiang Sultan Moulay Slimane University Laboratory LaSTI ENSAK Khouribga Morocco Prince Sultan University EIAS Data Science Lab College of Computer and Information Sciences Center of Excellence in Quantum and Intelligent Computing Riyadh11586 Saudi Arabia Menoufia University Faculty of Science Department of Mathematics and Computer Science Shebin El-Koom32511 Egypt Princess Nourah Bint Abdulrahman University College of Computer and Information Sciences Information Technology Department Riyadh Saudi Arabia Heilongjiang University School of Data Science and Technology Harbin150080 China Nanyang Technological University School of Computer Science and Engineering Nanyang Avenue Singapore639798 Singapore

The integration of Tiny Machine Learning (TinyML) with Human Behavior Analysis (HBA) represents a significant advancement in the field of Artificial Intelligence (AI), enabling real-time, efficient, and privacy-preserving analysis on resource-constrained devices. This paper provides the first comprehensive survey exploring this integration, presenting a detailed overview of TinyML, including its definitions, key concepts and advantages. The survey proposes a systematic taxonomy of TinyML applications in HBA, categorizing state-of-the-art implementations based on their use cases and specific methodologies. Furthermore, the challenges and limitations of integrating TinyML in HBA are thoroughly discussed, including technical constraints, data quality issues, and ethical considerations. Finally, future research directions and open issues are outlined, emphasizing the potential advancements and emerging trends in this field. This survey serves as a foundational resource, guiding researchers and practitioners in harnessing the capabilities of TinyML to advance HBA. © 2014 IEEE.

关键词： Embedded systems

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PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implicit Variables 2nd

PreZ-DGGAN: A Drug Graph GAN Based on Pre-Learning of Implic...

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2nd International Conference on Applied Intelligence, ICAI 2024

作者： Liu, Yixin Fan, Yueqin Li, Zhipeng Zhang, Qinhu Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China School of Mechanical Engineering Guangxi University Nanning530004 China Ningbo Institute of Digital Twin Eastern Institute of Technology Ningbo315201 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China College of Advanced Agricultural Sciences Zhejiang Agriculture and Forestry University Hangzhou311300 China

ISBN: (纸本)9789819619061

In the field of drug discovery and development, deep learning techniques have become a powerful tool to accelerate the discovery and development of new drugs. In the design and optimization of lead molecules, generative adversarial network and graph neural network are one of the prime techniques for accelerating research and development, because the former is based on the idea of two-player game to learn drug features to generate effective molecules, while the unstructured inputs of the latter are well-suited for the analysis of molecular data. Therefore, this study develops a new graph generative adversarial model, called PreZ-DGGAN, by inserting the graph neural network layer into the generative adversarial network. Within PreZ-DGGAN, the latent variable input of the generator is pre-learned by an improved graph variational autoencoder, which effectively avoids the mode collapse of the generative adversarial network, whereas the hidden layer of the discriminator is graph neural network layer, which is able to better learn the real molecules and judge the generated molecules as true or false. Experiments on two molecule datasets show that the molecules generated by PreZ-DGGAN have higher validity (100% on average) and uniqueness (91.5% on average), and the generated molecules are more diverse in structure. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Generative adversarial networks

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3D Anatomical Structure-Guided Deep Learning for Accurate Diffusion Microstructure Imaging

3D Anatomical Structure-Guided Deep Learning for Accurate Di...

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IEEE International Symposium on Biomedical Imaging

作者： Xinrui Ma Jian Cheng Wenxin Fan Ruoyou Wu Yongquan Ye Shanshan Wang Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen Guangdong China University of Chinese Academy of Sciences Beijing China State Key Laboratory of Software Development Environment Beihang University Beijing China Key Laboratory of Data Science and Intelligent Computing Institute of International Innovation Beihang University Hangzhou Zhejiang China Pengcheng Laboratory Shenzhen Guangdong China United Imaging Healthcare Houston TX USA

ISBN: (数字)9798331520526

ISBN: (纸本)9798331520533

Diffusion magnetic resonance imaging (dMRI) is a crucial non-invasive technique for exploring the microstructure of the living human brain. Traditional hand-crafted and model-based tissue microstructure reconstruction methods often require extensive diffusion gradient sampling, which can be time-consuming and limits the clinical applicability of tissue microstructure information. Recent advances in deep learning have shown promise in microstructure estimation; however, accurately estimating tissue microstructure from clinically feasible dMRI scans remains challenging without appropriate constraints. This paper introduces a novel framework that achieves high-fidelity and rapid diffusion microstructure imaging by simultaneously leveraging anatomical information from macro-level priors and mutual information across parameters. This approach enhances time efficiency while maintaining accuracy in microstructure estimation. Experimental results demonstrate that our method outperforms four state-of-the-art techniques, achieving a peak signal-to-noise ratio (PSNR) of 30.51±0.58 and a structural similarity index measure (SSIM) of 0.97±0.004 in estimating parametric maps of multiple diffusion models. Notably, our method achieves a 15× acceleration compared to the dense sampling approach, which typically utilizes 270 diffusion gradients.

关键词： Deep learning PSNR Accuracy Three-dimensional displays Magnetic resonance imaging Estimation Reconstruction algorithms Diffusion models Feature extraction Microstructure

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scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell RNA-Seq Clustering 2nd

scMGCAE: A Masked Graph Cluster Autoencoder for Single-Cell ...

引用

2nd International Conference on Applied Intelligence, ICAI 2024

作者： Wu, Zheyu Wang, Yueyue Zhang, Qinhu Ningbo Institute of Digital Twin Eastern Institute of Technology No. 568 Tongxin Road Zhuangshi Street Zhejiang Ningbo315201 China Big Data and Intelligent Computing Research Center Guangxi Academy of Science Nanning530007 China Institute for Regenerative Medicine Medical Innovation Center and State Key Laboratory of Cardiology School of Medicine Shanghai East Hospital Tongji University Shanghai200123 China School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University No. 800 Dongchuan Road Minhang District Shanghai200240 China

ISBN: (纸本)9789819619061

Single-cell RNA sequencing provides high-throughput gene expression information and offers higher resolution of cell differences compared to bulk RNA sequencing, which helps identify cell types and explore cellular heterogeneity at the single-cell level. Although many single-cell clustering methods based on masked autoencoders have been developed recently, they rarely effectively utilize the correlations between cells, resulting in suboptimal clustering results. In this work, we propose a novel clustering method for scRNA-seq data based on a masked graph convolutional autoencoder, which incorporates a graph convolutional network onto the basis of a masked autoencoder to enhance the capture of the correlation between cells and genes by fusing the cell-cell interaction graph and the gene expression matrix. By increasing the relationships between cells, dimensionality reduction is guided, and a clustering-friendly representation is obtained. We conducted extensive comparative experiments using various clustering evaluation metrics on six scRNA-seq datasets from different sequencing platforms. The experimental results demonstrate that scMGCAE outperforms other methods on these datasets. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Dimensionality reduction

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LMP-GAN: Out-Of-Distribution Detection For Non-Control data Malware Attacks

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年 PP卷 PP页

作者： Wood, David Kapp, David Kebede, Temesgen Hirakawa, Keigo Wuhan University School of Computer Science China Wuhan University National Engineering Research Center for Multimedia Software Hubei Key Laboratory of Multimedia and Network Communication Engineering China Zhongguancun Academy China Wuhan University State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing China Sun Yat-sen University School of Geography and Planning China Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Chongqing University College of Computer Science China The University of Tokyo Japan RIKEN Center for Advanced Intelligence Project Japan Intelligent Science & Technology Academy Limited CASIC China iFlytek Company Ltd. National Engineering Research Center of Speech and Language Information Processing China Nanyang Technological University College of Computing & Data Science Singapore Henan Academy of Sciences Aerospace Information Research Institute China

Anomaly detection is a common application of machine learning. Out-of-distribution (OOD) detection in particular is a semi-supervised anomaly detection technique where the detection method is trained only on the inlier (in-distribution) samples - unlike the fully supervised variant, the distribution of the outlier samples are never explicitly modeled in OOD detection tasks. In this work, we design a novel GAN-based OOD detection network specifically designed to protect a cyber-physical signal systems from novel Trojan malware called non-control data (NCD) attack that evades conventional malware detection techniques. Inspired in part by the classical locally most powerful (LMP) test in statistical inferences, the proposed LMP-GAN trains the OOD detector (discriminator) by generating OOD samples that are aimed at making maximal alteration to the inlier samples while evading detection. We experimentally compare the results to the state-of-the-art anomaly detection methods to demonstrate the benefits and the appropriateness of the LMP-GAN OOD detector. © 2025 IEEE.

关键词： Malware

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