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arXiv 2025年

作者： Tang, Rui Yong, Ziyun Jiang, Shuyu Chen, Xingshu Liu, Yaofang Zhang, Yi-Cheng Sun, Gui-Quan Wang, Wei School of Cyber Science and Engineering Sichuan University Chengdu610065 China Cyber Science Research Institute Sichuan University Chengdu610065 China Key Laboratory of Data Protection and Intelligent Management Ministry of Education Sichuan University Chengdu610065 China Department of Reproductive Technology The Affiliated Hospital of Southwest Medical University Luzhou646000 China Physics Department University of Fribourg Chemin du Musée 3 Fribourg1700 Switzerland Sino-Europe Complex Science Center School of Mathematics North University of China Shanxi Taiyuan030051 China Complex Systems Research Center Shanxi University Shanxi Taiyuan030006 China School of Public Health Chongqing Medical University Chongqing400016 China

Complex networks are frequently employed to model physical or virtual complex systems. When certain entities exist across multiple systems simultaneously, unveiling their corresponding relationships across the networks becomes crucial. This problem, known as network alignment, holds significant importance. It enhances our understanding of complex system structures and behaviours, facilitates the validation and extension of theoretical physics research about studying complex systems, and fosters diverse practical applications across various fields. However, due to variations in the structure, characteristics, and properties of complex networks across different fields, the study of network alignment is often isolated within each domain, with even the terminologies and concepts lacking uniformity. This review comprehensively summarizes the latest advance- © 2025, CC BY-NC-ND.

关键词： Large scale systems

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RFAConv: Innovating spatial Attention and Standard Convolutional Operation

arXiv

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arXiv 2023年

作者： Zhang, Xin Liu, Chen Song, Tingting Yang, Degang Ye, Yichen Li, Ke Song, Yingze College of Computer and Information Science Chongqing Normal University China Chongqing Engineering Research Center of Educational Big Data Intelligent Perception and Application China College of Electronic and Information Engineering Southwest University China

spatial attention has been widely used to improve the performance ofconvolutional neural networks. However, it has certain limitations. In thispaper, we propose a new perspective on the effectiveness of spatial attention,which is that the spatial attention mechanism essentially solves the problem ofconvolutional kernel parameter sharing. However, the information contained inthe attention map generated by spatial attention is not sufficient forlarge-size convolutional kernels. Therefore, we propose a novel attentionmechanism called Receptive-Field Attention (RFA). Existing spatial attention,such as Convolutional Block Attention Module (CBAM) and Coordinated Attention(CA) focus only on spatial features, which does not fully address the problemof convolutional kernel parameter sharing. In contrast, RFA not only focuses onthe receptive-field spatial feature but also provides effective attentionweights for large-size convolutional kernels. The Receptive-Field Attentionconvolutional operation (RFAConv), developed by RFA, represents a new approachto replace the standard convolution operation. It offers nearly negligibleincrement of computational cost and parameters, while significantly improvingnetwork performance. We conducted a series of experiments on ImageNet-1k, COCO,and VOC datasets to demonstrate the superiority of our approach. Of particularimportance, we believe that it is time to shift focus from spatial features toreceptive-field spatial features for current spatial attention mechanisms. Inthis way, we can further improve network performance and achieve even betterresults. The code and pre-trained models for the relevant tasks can be found athttps://***/Liuchen1997/RFAConv. © 2023, CC0.

关键词： Convolution

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Adaptive Control for Multi-Agent Systems Based on Dynamic Event-triggered Mechanism: In Different Topologies

Adaptive Control for Multi-Agent Systems Based on Dynamic Ev...

引用

International Symposium on Autonomous Systems (ISAS)

作者： Yu Huang Huiyan Zhang Zixian Chen Jiangyan Zou School of Mechanical Engineering Chongqing Technology and Business University Chongqing China National Research Base of Intelligent Manufacturing Service Chongqing Technology and Business University Chongqing China Chongqing Innovation Center of Industrial Big-Data Co. Ltd. Chongqing China School of Artificial Intelligence Chongqing Technology and Business University Chongqing China

ISBN: (数字)9798350363173

ISBN: (纸本)9798350363180

Under different communication topologies, this paper investigates the consensus problem for multi-agent systems with general linear dynamics under leader following. A distributed dynamic event-triggered control strategy with output feedback adjustment is proposed by using adaptive control technology of auxiliary variables, and it is applied to different follower communication topologies. The simulation results show that the leader-following systems under different communication modes of the followers can solve the completely distributed consensus problem. Moreover, the Zeno behavior of each multi-agent can be excluded. Finally, the effectiveness of the proposed control strategy is verified by taking multi-vehicle cooperative tracking control as an example.

关键词： Trajectory tracking Simulation Consensus control Control systems Topology Vehicle dynamics Adaptive control

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Unifying and Improving Graph Convolutional Neural Networks with Wavelet Denoising Filters 23

Unifying and Improving Graph Convolutional Neural Networks w...

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2023 World Wide Web Conference, WWW 2023

作者： Wan, Liangtian Li, Xiaona Han, Huijin Yan, Xiaoran Sun, Lu Ning, Zhaolong Xia, Feng Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province School of Software Dalian University of Technology Dalian China Research Center of Big Data Intelligence Research Institute of Artificial Intelligence Zhejiang Lab Hangzhou China Department of Communication Engineering Institute of Information Science Technology Dalian Maritime University Dalian China School of Communication and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Computing Technologies Rmit University Melbourne Australia

ISBN: (纸本)9781450394161

Graph convolutional neural network (GCN) is a powerful deep learning framework for network data. However, variants of graph neural architectures can lead to drastically different performance on different tasks. Model comparison calls for a unifying framework with interpretability and principled experimental procedures. Based on the theories from graph signal processing (GSP), we show that GCN's capability is fundamentally limited by the uncertainty principle, and wavelets provide a controllable trade-off between local and global information. We adapt wavelet denoising filters to the graph domain, unifying popular variants of GCN under a common interpretable mathematical framework. Furthermore, we propose WaveThresh and WaveShrink which are novel GCN models based on proven denoising filters from the signal processing literature. Empirically, we evaluate our models and other popular GCNs under a more principled procedure and analyze how trade-offs between local and global graph signals can lead to better performance in different datasets. © 2023 ACM.

关键词： Convolution

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multiPI-TransBTS: A Multi-Path Learning Framework for Brain Tumor Image Segmentation Based on Multi-Physical Information

arXiv

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arXiv 2024年

作者： Zhu, Hongjun Huang, Jiaohang Chen, Kuo Ying, Xuehui Qian, Ying School of Software Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Chongqing Engineering Research Center of Software Quality Assurance Testing and Assessment Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing400065 China

Brain Tumor Segmentation (BraTS) plays a critical role in clinical diagnosis, treatment planning, and monitoring the progression of brain tumors. However, due to the variability in tumor appearance, size, and intensity across different MRI modalities, automated segmentation remains a challenging task. In this study, we propose a novel Transformer-based framework, multiPI-TransBTS, which integrates multi-physical information to enhance segmentation accuracy. The model leverages spatial information, semantic information, and multi-modal imaging data, addressing the inherent heterogeneity in brain tumor characteristics. The multiPI-TransBTS framework consists of an encoder, an Adaptive Feature Fusion (AFF) module, and a multi-source, multi-scale feature decoder. The encoder incorporates a multi-branch architecture to separately extract modality-specific features from different MRI sequences. The AFF module fuses information from multiple sources using channel-wise and element-wise attention, ensuring effective feature recalibration. The decoder combines both common and task-specific features through a Task-Specific Feature Introduction (TSFI) strategy, producing accurate segmentation outputs for Whole Tumor (WT), Tumor Core (TC), and Enhancing Tumor (ET) regions. Comprehensive evaluations on the BraTS2019 and BraTS2020 datasets demonstrate the superiority of multiPI-TransBTS over the state-of-the-art methods. The model consistently achieves better Dice coefficients, Hausdorff distances, and Sensitivity scores, highlighting its effectiveness in addressing the BraTS challenges. Our results also indicate the need for further exploration of the balance between precision and recall in the ET segmentation task. The proposed framework represents a significant advancement in BraTS, with potential implications for improving clinical outcomes for brain tumor patients. Copyright © 2024, The Authors. All rights reserved.

关键词： Decoding

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Blockchain-Based Homomorphic Transaction Framework for Enhanced Consumer Security and Business Scalability

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IEEE Transactions on Consumer Electronics 2024年

作者： Guo, Tao Shang, Fengjun Dai, Xiangguang Liu, Qilie Chongqing University of Posts and Telecommunications School of Computer Science and Technology Chongqing400065 China Key Laboratory of Big Data Intelligent Computing Chongqing400065 China Chongqing Three Gorges University Key Lab. of Intelligent Info. Proc. and Contr. of Chongqing Munic. Institutions of Higher Education Chongqing Engineering Research Center of Internet of Things and Intelligent Control Technology School of Three Gorges Artificial Intelligence Chongqing400044 China Chongqing University of Posts and Telecommunications School of Communications and Information Engineering Chongqing400065 China

Energy trading in distributed microgrids represents an effective means of enhancing the utilization of renewable energy. However, the aggregation of large-scale consumption data may encounter business scalability issues and potential privacy exposure concerns, necessitating the development of novel secure and scalable frameworks for integrating consumption demands between prosumer and microgrid entities. we research focuses on providing a user-friendly transaction model for consumers and microgrid operators. Firstly, a privacy aggregation scheme for user resource requests is designed to ensure the security and decentralization of user data. In addition, the model employs a bidirectional heterogeneous resource allocation mechanism to support the optimal allocation of multiple resources, such as renewable energy, power assets, energy storage devices, and carbon credits, among different microgrids. Finally, a microgrid transaction prototype has been constructed on the Truffle development framework. In this context, smart contracts has been designed to ensure that the transactions are carried out automatically. Experimental results indicate that the scheme provides enhanced security for multi-resource trading in a cluster context relative to existing methodologies. The number of transactions increases linearly with the resource overhead, resulting in more efficient utilization of energy trading. Furthermore, the framework's scalability has been demonstrated through both temporal and spatial considerations. © 1975-2011 IEEE.

关键词： Decentralized finance

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Passenger Waiting Time Prediction Based on Density Spatiotemporal Clustering and Time Series Feature Processing 6

Passenger Waiting Time Prediction Based on Density Spatiotem...

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6th International Conference on Electronic engineering and Informatics, EEI 2024

作者： Huang, Ruixi Xia, Dawen Geng, Jian Zhang, Wenyong Hu, Yang Li, Yantao Li, Huaqing College of Data Science and Information Engineering Guizhou Minzu University Guiyang550025 China Engineering Research Center of Micro-nano and Intelligent Manufacturing Ministry of Education Kaili University Kaili556011 China College of Big Data Engineering College of Microelectronics and Artificial Intelligence Kaili University Kaili556011 China Party School of the Anshun Municipal Committee of C.P.C Anshun561000 China Guizhou Traffic Technician and Transportation College Department of Automotive Engineering Guiyang550008 China College of Computer Science Chongqing University Chongqing400044 China College of Electronic and Information Engineering Southwest University Chongqing400715 China

ISBN: (纸本)9798350353594

Accurate passenger waiting time prediction addresses the taxi supply-demand imbalance in intelligent Transport Systems (ITS). To capture the dependencies of passenger waiting time on urban taxi distribution, we propose a Density Spatiotemporal Clustering and Time Series Feature Processing (DSC-TSFP) method that discovers potential feature relationships of taxi GPS trajectory data. Specifically, a Multilevel Spatiotemporal Density-Based spatial Clustering of Applications with Noise (MST-DBSCAN) is proposed to cluster taxi GPS trajectory data to obtain waiting time series with spatiotemporal correlations. Furthermore, a time series processing module based on the Complete Ensemble Empirical Mode Decomposition with Adaptive Noise Normalization (CEEMDANN) is designed to enhance prediction accuracy by handling non-stationarity and numerical discrepancy, which solves the residual white noise problem in Intrinsic Modal Functions (IMFs) of EEMD and CEEMD. Next, a waiting time prediction module oriented to a Convolutional Long Short-Term Memory Network (ConvLSTM) is incorporated to compensate for the defects of spatially localized dependencies that LSTM cannot capture. Finally, a CEEMDANN-ConvLSTM model is constructed to predict the passenger waiting time by combining the time series processing and waiting time prediction module. Based on real-world taxi GPS trajectory data, experimental results demonstrate that DSC-TSFP outperforms comparable methods regarding prediction accuracy. © 2024 IEEE.

关键词： Global positioning system

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An Investigation of the Probability Distribution Patterns of Parameters in the Well-trained Vision Transformer Model

An Investigation of the Probability Distribution Patterns of...

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Machine Vision, Image Processing and Imaging technology (MVIPIT), International Conference on

作者： Dongmin Deng Bo Zhao Qiyao Sun Ninglei Ouyang Yan Chen School of Green Intelligent Environment Yangtze Normal University Chongqing China School of Earth Sciences and Engineering Hohai University Nanjing China Research Center of Big Data Technology Nanhu Laboratory Jiaxing China

ISBN: (数字)9798350306545

ISBN: (纸本)9798350306552

To the best of our knowledge, this is the first time that the distribution pattern of the big-model (DINO V2) parameters has been investigated. Our findings verified the breakdown of the standard Gaussian distribution pattern in deep learning, and according to this characteristic, we provide some worth-trying advice on how to fine-tune the big-model in RS interpretation tasks.

关键词： Shape Machine vision Sociology Neurons Imaging Transformers Probability distribution

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An accurate cell segmentation method for high-resolution complex brain image

An accurate cell segmentation method for high-resolution com...

引用

International Conference on Image, Vision and Computing (ICIVC)

作者： Xiaofeng Xu Shuai Li Wei Zheng Henan Engineering Research Center of Intelligent Processing for Big Data of Digital Image School of Computer Science and Technology Nanyang Normal University Nanyang China

ISBN: (数字)9798350385991

ISBN: (纸本)9798350386004

Cell segmentation is a key step in medical image analysis and cell biology research, and is important in areas such as understanding cell behaviour, cancer detection and cell cycle analysis. This task is challenging due to the presence of overlap between cells and image noise. With the development of imaging techniques, the amount and quality of acquired cellular image data have increased significantly and even contain diverse information such as neuronal structures and blood vessels, which puts higher demands on segmentation techniques. Real rat brain cell images obtained using micro-optical slice tomography (MOST) contain complex and diverse information such as neuronal structure, cell morphology, and the distribution of blood vessels, demonstrating the characteristics of real biological samples, for which segmentation of the target region is more difficult. In order to solve these challenges, this paper proposes a new cell image segmentation method that combines watershed order differential feature extraction and fuzzy entropy spatial theory. Due to the complexity of image information, we first use traditional image enhancement techniques. Then feature extraction is performed and fuzzy entropy processing is added to achieve accurate identification of cell boundaries. Then distance transform and curvature analysis are added to further segment the cell clusters into individual cells while avoiding over-segmentation and missed detection. Experiments demonstrate that the method not only shows high performance on our dataset, but also validates our method on several publicly available cellular datasets respectively, proving that the segmentation algorithm shows great potential for segmentation on different types of cells. Future research will further optimise the model to improve segmentation performance on complex and irregularly shaped cells and explore the application of these methods to other types of medical image segmentation tasks.

关键词： Image segmentation Accuracy Morphology Clustering algorithms Transforms Feature extraction Entropy Complexity theory data mining Biomedical imaging

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Estimation of 1-Km All-Weather Land Surface Temperature Over the Tibetan Plateau

Estimation of 1-Km All-Weather Land Surface Temperature Over...

引用

IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Xiaodong Zhang Ji Zhou Weichen Dong Lisheng Song School of Resources and Environment University of Electronic Science and Technology of China Chengdu China Chongqing Engineering Research Center for Remote Sensing Big Data Application Southwestern University Chongqing China

Land surface temperature (LST) immensely affects the energy balance and water cycle on the earth's surface. Merging thermal infrared (TIR) and passive microwave (MW) remote sensing provides the possibility to obtain all-weather LST with moderate resolutions. However, due to difficulties in downscaling MW LST, current methods merging TIR LST and MW LST into such an all-weather LST are limited over large areas with very complicated land surfaces (e.g. the Tibetan Plateau). By fully considering the influence of the topography on estimation of merged LSTs, this study revises the recently-developed physical method for generating the 1-km all-weather LST and applies it over the Tibetan Plateau to merge MODIS (1 km) and AMSR2 (10 km) observations. Results show that the merged LST has accuracy of 0.99 K-3.22 K when validated against insitu LSTs from five ground stations with various land cover types. This study would be beneficial for continuously monitoring LST and improving spatio-temporal resolutions for associated land surface process studies requiring high-quality all-weather LST over large scales.

关键词： Land surface temperature MODIS Surface topography Microwave imaging Microwave theory and techniques Land surface Temperature sensors

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