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Replace2Self: Self-Supervised Denoising based on Voxel Replacing and Image Mixing for Diffusion MRI

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Wu, Linhai Wang, Lihui Deng, Zeyu Zhu, Yuemin Wei, Hongjiang Guizhou University Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology 550025 China Univ Lyon INSA Lyon CNRS Inserm CREATIS UMR 5220 LyonF-69621 France Shanghai Jiao Tong University School of Biomedical Engineering Shanghai200030 China

Low signal to noise ratio (SNR) remains one of the limitations of diffusion weighted (DW) imaging. How to suppress the influence of noise on the subsequent analysis about the tissue microstructure is still challenging. This work proposed a novel self-supervised learning model, Replace2Self, to effectively reduce spatial correlated noise in DW images. Specifically, a voxel replacement strategy based on similar block matching in Q-space was proposed to destroy the correlations of noise in DW image along one diffusion gradient direction. To alleviate the signal gap caused by the voxel replacement, an image mixing strategy based on complementary mask was designed to generate two different noisy DW images. After that, these two noisy DW images were taken as input, and the non-correlated noisy DWimage after voxel replacement was taken as learning target, a denoising network was trained for denoising. To promote the denoising performance, a complementary mask mixing consistency loss and an inverse replacement regularization loss were also proposed. Through the comparisons against several existing DW image denoising methods on extensive simulation data with different noise distributions, noise levels and b-values, as well as the acquisition datasets and the ablation experiments, we verified the effectiveness of the proposed method. Regardless of the noise distribution and noise level, the proposed method achieved the highest PSNR, which was at least 1.9% higher than the suboptimal method when the noise level reaches 10%. Furthermore, our method has superior generalization ability due to the use of the proposed strategies. © 2025 IEEE. All rights reserved.

关键词： Signal to noise ratio

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Auction Theory and Game Theory Based Pricing of Edge Computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Remote sensing image scene classification based on gated pyramidal convolution 7

Remote sensing image scene classification based on gated pyr...

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7th IEEE International Conference on Progress in Informatics and Computing, PIC 2020

作者： Liu, Hang Bian, Shuai Wang, Zhengang Liu, Bing Hu, Qiang Guo, Dong-En Chongqing MeiAnSen Technology Co. Ltd Chongqing China Chongqing University of Posts and Telecommunications Chongqing Spatial Big Data Intelligent Engineering Research Center Chongqing China

ISBN: (纸本)9781728170862

Deep learning technology has demonstrated superior performance in remote sensing image scene classification. However, the complex background and diverse target objects in remote sensing images have always been a major challenge in scene classification. To solve this problem, we propose a novel remote sensing scene classification method based on Gated Pyramidal Convolution (GPC). Specifically, pyramidal convolution (PyConv) is first introduced into the residual block to capture different levels of detailed features. Then, the Gated Unit (GU) is placed behind the last three residual units to filter useless background information. Finally, a multi-level feature fusion structure is introduced to fuse the features of the last three residual units to further enhance the feature discriminant ability. Extensive experiments on AID and NWPU-RESISC45 datasets demonstrate that the proposed GPC model outperforms the state-of-the-art baselines. © 2020 IEEE.

关键词： Convolution

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A Geographically Weighted Durbin Model for spatial Downscaling of Land Surface Temperatures 2

A Geographically Weighted Durbin Model for Spatial Downscali...

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2023 2nd International Conference on Remote Sensing, Surveying, and Mapping, RSSM 2023

作者： Xiaoqun, Shen Xiaobo, Luo Xingang, Sang Chongqing Engineering Research Center for Spatial Big Data Intelligence Technology Chongqing University of Posts and Telecommunications Chongqing400065 China

ISBN: (纸本)9781510666436

To obtain land surface temperatures data with high spatiotemporal resolution, A Geographically Weighted Durbin Model(GWDM) for spatial Downscaling of Land Surface Temperatures is newly proposed in this study. The normalized difference water index (NDWI), the normalized difference built-up index (NDBI), and the normalized difference vegetation index (NDVI) were selected as scale factors to conduct downscaling experiments. Beijing and Zhangye were taken as the study area. Compared with the thermal data sharpening (TsHARP), the geographically weighted regression (GWR), the geographically weighted autoregressive (GWAR). The results indicate that the GWDM-based algorithm has better spatial texture and is closer to the real image. The determination coefficient (Beijing: 0.88, Zhangye: 0.91), mean absolute error (Beijing: 0.85 °C, Zhangye: 1.06 °C ) and root mean square error (Beijing: 1.22 °C, Zhangye: 1.57 °C ) are better than the other three methods. © 2023 SPIE · 0277-786X.

关键词： Surface temperature

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Efect of Pre‑stretch Strain at High Temperatures on the Formability of AZ31 Magnesium Alloy Sheets

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Acta Metallurgica Sinica(English Letters) 2023年第1期36卷 48-60页

作者： Yongqiao Li Lifei Wang Xiaohuan Pan Qiang Zhang Guangsheng Huang Bin Xing Weili Cheng Hongxia Wang Kwang Seon Shin College of Materials Science and Engineering Taiyuan University of TechnologyTaiyuan 030024China Chongqing Innovation Center of Industrial Big-Data Co.Ltd. National Engineering Laboratory for Industrial Big-Data Application TechnologyChongqing 400707China School of Materials Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China National Engineering Research Center for Magnesium Alloys Chongqing UniversityChongqing 400044China Magnesium Technology Innovation Center School of Materials Science and EngineeringSeoul National UniversitySeoul 881416Republic of Korea

High-temperature pre-stretching experiments were carried out on the AZ31 Mg alloy at 723 K with strain levels of 2.54%,6.48%,10.92%,and 19.2%to alter the microstructure and texture for improving room-temperature *** results showed that the strain-hardening coefcient increased,while the Lankford value *** addition,the Erichsen values of all pre-stretch sheets were enhanced compared with that of the as-received *** maximum Erichsen value increased from 2.38 mm for the as-received sample to 4.03 mm for the 10.92%-stretched sample,corresponding to an improvement of 69.32%.This improvement was mainly attributed to the gradual increase in grain size,and the(0001)basal texture was weakened due to the activated non-basal slip as the high-temperature pre-stretching strain levels *** visco-plastic self-consistent analysis was performed on the as-received and high-temperature pre-stretched *** confrmed the higher activity of the prismatic slip in 10.92%-stretched sample,leading to divergence and weakening of basal texture *** results in an augmentation of the Schmid factor under diferent slip ***,it can be concluded that high-temperature pre-stretching technology provided an efective method to enhance the formability of Mg alloy sheets.

关键词： High-temperature deformation Grain growth Magnesium alloys Texture Visco-plastic self-consistent modeling Formability

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Hyperspectral Image Classification of Convolutional Neural Network Combined with Valuable Samples

Hyperspectral Image Classification of Convolutional Neural N...

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2020 International Conference on Environment Science and Advanced Energy Technologies, ESAET 2020

作者： Hu, Lixin Luo, Xiaobo Wei, Yufan Institute of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Engineering Research Center for Spatial Big Data Intelligent Technology Chongqing University of Posts and Telecommunications Chongqing China Chongqing Institute of Meteorological Sciences Chongqing China

Aiming at the problem that the manual labeling of samples in the hyperspectral image classification is expensive and laborious, a large number of unlabeled samples are not effectively utilized and the classification results are not ideal. A method which can provide valuable samples and employ convolutional neural network to extract spectral spatial features for classification is proposed. Active learning method is used to construct a valuable training sample set by iteratively selecting the most uncertain samples through support vector machine which performs well in small sample classification, and labeling them. Then the 3D convolutional neural network is used to extract the spectral spatial features of hyperspectral image. The experimental results of the hyperspectral classification on Indian Pines and PaviaU datasets show that the proposed method (3D VS-CNN) is better than traditional classification methods. © Published under licence by IOP Publishing Ltd.

关键词： Iterative methods

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Extracting hierarchical relationships of aspects from reviews

引用

Applied Soft Computing 2025年 178卷

作者： Jiangtao Qiu Ling Lin Siyu Wang Chongqing Engineering Research Center for Intelligent Applications of Financial Big Data Chongqing Institute of Engineering Chongqing China School of Computing and Artificial Intelligence Southwestern University of Finance and Economics Chengdu China College of Artificial Intelligence (CUIT Shuangliu Industrial College) Chengdu University of Information Technology Chengdu China Gusu Laboratory of Materials Suzhou China

Aspect Based Sentiment Analysis (ABSA) attracts significant attention in recent years. Three elements of ABSA including aspect term, aspect, and domain (or entity) present a hierarchical relationships in e-commerce reviews. Extracting the hierarchical relationships can significantly enhance various applications, such as creating user profiles, identifying hierarchical topics, and visualizing review data. In this study, we proposed a framework to tackle this task, consisting of two primary components: a text adversarial autoencoder that efficiently encodes review content, and a deep network that extracts the clusters of aspect terms from review dataset and organizes them to a hierarchical structure using the Student-Teacher paradigm. Our framework also addresses the challenge of acquiring labeled training data by utilizing self-supervised learning. We evaluated the proposed framework on three public datasets and observed that it outperforms baseline models, indicating the feasibility and effectiveness of our approach.

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A Distributed Framework for Large-scale Protein-protein Interaction data Analysis and Prediction Using MapReduce

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IEEE/CAA Journal of Automatica Sinica 2022年第1期9卷 160-172页

作者： Lun Hu Shicheng Yang Xin Luo Huaqiang Yuan Khaled Sedraoui MengChu Zhou School of Computer Science and Technology Dongguan University of TechnologyDongguan 523808China School of Computer Science and Technology Wuhan University of TechnologyWuhan 430070China Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent ComputingChongqing Institute of Green and Intelligent TechnologyChinese Academy of SciencesChongqing 400714China Xinjiang Technical Institute of Physics and Chemistry Chinese Academy of SciencesUrumqi 830000China Center of Research Excellence in Renewable Energy and Power Systems and the Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

Protein-protein interactions are of great significance for human to understand the functional mechanisms of *** the rapid development of high-throughput genomic technologies,massive protein-protein interaction(PPI)data have been generated,making it very difficult to analyze them *** address this problem,this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms,i.e.,CoFex,using *** do so,an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and *** solutions are then devised to overcome these *** particular,we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of *** that,its procedure is modified by following the MapReduce framework to take the prediction task distributively.A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and *** results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.

关键词： Distributed computing large-scale prediction machine learning MapReduce protein-protein interaction(PPI)

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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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Urban subsidence zones prone to flooding: mitigated deformation trends post-2024 Guilin megaflood

npj Natural Hazards

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npj Natural Hazards 2025年第1期2卷 1-9页

作者： Pinglang Kou Xu Dong Yuxiang Tao Ruikun Chen Zhengwu Yuan Ying Xia Qiang Xu Ali P. Yunus Rui Liu Qin Ji Chongqing Engineering Research Center of Spatial Big Data Intelligent Technology Chongqing University of Posts and Telecommunications Chongqing 400065 China Key Laboratory of Tourism Multisource Data Perception and Decision Ministry of Culture and Tourism (TMDPD MCT) Chongqing University of Posts and Telecommunications Chongqing 400065 China State Key Laboratory of Geohazard Prevention and Geoenvironment Protection Chengdu University of Technology Chengdu Sichuan 610059 China Department of Earth and Environmental Sciences Indian Institute of Science Education and Research Mohali Sahibzada Ajit Singh Nagar Punjab 140 306 India Chongqing Key Laboratory of GIS Application School of Geography and Tourism Chongqing Normal University Chongqing 401331 China

Climate change intensifies urban flood hazards, yet existing research often overlooks the complex dynamic relationships between surface deformation, soil properties, and flooding. This study uses the 2024 Guilin flood event as a case study, integrating SBAS-InSAR, DInSAR techniques, and various machine learning methods to explore the complex interactions between surface deformation, soil characteristics, and flooding. The results show that the flood caused significant water expansion, with ground subsidence mainly concentrated in the southern and eastern parts of Guilin, highly coinciding with the severely flooded areas. The flood-inundated areas exhibited opposite deformation trends before and after the flood, shifting from subsidence to uplift, while road subsidence also showed a dynamic process. Different machine learning methods showed varying performance in predicting surface deformation, with the ERT model performing relatively well. Soil thickness was positively correlated with surface subsidence within a certain range, and this relationship exhibited noticeable nonlinear characteristics post-flood. The findings of this study have important practical implications for urban flood mitigation, aiding urban planners in more accurately identifying flood-prone areas, especially those experiencing subsidence.

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