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A proximal algorithm for solving split monotone variational inclusions

UPB Scientific Bulletin, Series A: Applied Mathematics and Physics 2020年第3期82卷 43-52页

作者： Zhao, Xiaopeng Yao, Jen-Chih Yao, Yonghong School of Mathematical Sciences Tianjin Polytechnic University Tianjin300387 China Center for General Education China Medical University Taichung Taiwan The Key Laboratory of Intelligent Information and Big Data Processing of NingXia Province North Minzu University Yinchuan750021 China

In this paper, we study the split monotone variational inclusion problem in Hilbert spaces. We suggest a proximal algorithm for finding a solution of the split monotone variational inclusion problem. Strong convergenc... 详细信息

关键词： Variational techniques

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The role of 4D flow MRI in deep vein thrombosis research

Meta-Radiology

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Meta-Radiology 2025年第1期3卷

作者： Li, Meizhi Wu, Shangjie Liang, Xiao Gao, Chuanqi Hu, Muhua Chen, Zhu He, Pei Jia, Tingting Xiong, Li Department of Pulmonary and Critical Care Medicine The Second Xiangya Hospital of Central South University Changsha China Department of Medical Administration The Second Xiangya Hospital of Central South University Changsha China Clinical Medical Research Center for Pulmonary and Critical Care Medicine in Hunan Province Changsha China Hunan Research Centre for Evidence-based Medicine The Second Xiangya Hospital of Central South University Changsha China Department of Biomedical Engineering School of Basic Medical Science Central South University Changsha China Department of Radiology Second Xiangya Hospital Central South University Changsha China Hunan Key Laboratory for Super Microstructure and Ultrafast Process School of Physics and Electronics Central South University Changsha China National Engineering Research Center for Medical Big Data Application Technology and Big Data Institute Central South University Changsha China General Surgery Department of Second Xiangya Hospital Central South University Changsha China General Surgery Intelligent Healthcare Division of Clinical Medical Research Center of Hunan Province Changsha China

Four-dimensional (4D) flow Magnetic Resonance Imaging (MRI) technology has emerged as a valuable tool in angiography, offering unique insights into the hemodynamics and flow patterns. This research aims to explore the role of 4D flow MRI in advancing our understanding of Deep Vein Thrombosis (DVT), covering its applications in diagnosing, characterizing and mechanism of DVT, as well as its potential for guiding treatment strategies. The qualitative and quantitative information provided by 4D flow MRI enables a comprehensive assessment of blood flow in different vascular regions, shedding light on the relationship between hemodynamic changes and the onset and progression of vascular diseases. Nevertheless, most quantitative research findings for 4D hemodynamic indicators are lacking, and their use is mainly limited to examining arterial conditions. More exploration will be necessary to determine their applicability in studying venous vessels. © 2025 The Authors

关键词： 4D flow MRI Deep vein thrombosis Image analysis algorithms Noninvasive imaging

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FeatureBand: A feature selection method by combining early stopping and genetic local search 3rd

FeatureBand: A feature selection method by combining early s...

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3rd APWeb and WAIM Joint Conference on Web and big data, APWeb-WAIM 2019

作者： Xue, Huanran Jiang, Jiawei Shao, Yingxia Cui, Bin Center for Data Science National Engineering Laboratory for Big Data Analysis and Applications Peking University Beijing China Peking University Beijing China Tencent Inc. Shenzhen China Beijing Key Lab of Intelligent Telecommunications Software and Multimedia BUPT Beijing China

ISBN: (纸本)9783030260743

Feature selection is an important problem in machine learning and data mining. In reality, the wrapper methods are broadly used in feature selection. It treats feature selection as a search problem using a predictor as a black-box. However, most wrapper methods are time-consuming due to the large search space. In this paper, we propose a novel wrapper method, called FeatureBand, for feature selection. We use the early stopping strategy to terminate bad candidate feature subsets and avoid wasting of training time. Further, we use a genetic local search to generate new subsets based on previous ones. These two techniques are combined under an iterative framework in which we gradually allocate more resources for more promising candidate feature subsets. The experimental result shows that FeatureBand achieves a better trade-off between search time and search accuracy. It is 1.45 × to 17.6 × faster than the state-of-the-art wrapper-based methods without accuracy loss. © 2019, Springer Nature Switzerland AG.

关键词： Feature Selection

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A Nonlinear Proportional Integral Derivative-Incorporated Stochastic Gradient Descent-based Latent Factor Model

A Nonlinear Proportional Integral Derivative-Incorporated St...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jinli Li Ye Yuan Computer School China West Normal University Nanchong China Chongqing Institute of Green and Intelligent Technology Chongqing China Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China University of Chinese Academy of Sciences Beijing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Recommender system (RS) commonly describes its user-item preferences with a high-dimensional and sparse (HiDS) matrix. A latent factor (LF) model relying on stochastic gradient descent (SGD) is frequently adopted to extract useful information from such an HiDS matrix. In spite of its efficiency, an SGD-based LF model commonly takes many iterations to converge. When processing a large-scale HiDS matrix, its computational efficiency should be further improved by further accelerating its convergence rate as well as maintaining its learning ability. To address this issue, this paper innovatively proposes novel SGD algorithm which incorporates a nonlinear proportional integral derivative (NPID) controller into its learning scheme for building an LF model. The main idea is to adopt an NPID controller to model the learning residual achieved in the past iterations, thereby adjusting the learning direction and step size of the current iteration, thereby making a resultant model converge fast. With the NPID-incorporated SGD algorithm, this study proposes an NPID-SGD-based LF (NSLF) model. Experimental results on two HiDS matrices demonstrate that compared with a standard SGD-based LF model, the proposed model achieves higher computational efficiency and prediction accuracy for missing data of an HiDS matrix.

关键词： Computational modeling Stochastic processes Predictive models data models Computational efficiency Sparse matrices Convergence

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Nonlinear Longitudinal Control for Connected Vehicle Platoon Considering the Acceleration Difference

Nonlinear Longitudinal Control for Connected Vehicle Platoon...

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第三十九届中国控制会议

作者： Ao Jiao Yongfu Li Shuyou Yu Zhongwei Cui Key Laboratory of Intelligent Air-Ground Cooperative Control for Universities in Chongqing College of Automation Chongqing University of Posts and Telecommunications Department of Control Science & Engineering Jilin University School of Mathematics and Big Data Guizhou Education University

This study proposes a distributed nonlinear control algorithm for the heterogeneous connected vehicle(CV) *** particular, in order to capture the nonlinear behavior of vehicle such as stop-and-go phenomena, the effects of the car-following interactions are considered. Besides, the vehicle dynamics of heterogeneous CVs is characterized by using the third-order vehicle dynamics model, and the controller is designed by considering the acceleration difference. In addition, the stability and consensus of the proposed control algorithm is analyzed by using Routh-Hurwitz stability criterion, hence, the negative velocity, negative spacing error and unreasonable acceleration/deceleration can be avoided based on the proposed control algorithm. Finally, the effectiveness and robustness of proposed algorithm is verified by conducting multiple numerical simulation experiments in presence of external disturbances and multiple real vehicle confirmatory experiments.

关键词： Connected Vehicle Platoon Acceleration Difference Car-Following Interaction Convergence Analysis

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Discriminative locality-constrained sparse representation for robust face recognition

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Journal of Physics: Conference Series 2021年第1期1780卷

作者： Meng Huang Guifang Shao Keqi Wang Tundong Liu Hao Lu Department of Automation Xiamen University Xiamen Fujian China Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision-making Fujian China

In this paper, a new joint sparse representation method called discriminative locality-constrained sparse representation (DLSR) is proposed for robust face recognition. DLSR incorporates locality and label information of training samples into the framework of sparse representation. Locality information can distinguish dissimilarity between samples and plays an important role in image classification. Compared with the existing methods, DLSR contains more discriminative information of samples and can obtain more discriminative recognition results. Due to the use of l2-norm regularization, DLSR can obtain a closed-form solution. This makes it computationally very efficient. Experimental results based on the benchmark face databases ORL have shown that DLSR can achieve more promising performance than some state-of-the-art methods.

关键词：

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Few-Shot Semantic Segmentation with Democratic Attention Networks 16th

Few-Shot Semantic Segmentation with Democratic Attention Net...

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16th European Conference on Computer Vision, ECCV 2020

作者： Wang, Haochen Zhang, Xudong Hu, Yutao Yang, Yandan Cao, Xianbin Zhen, Xiantong Beihang University Beijing China Key Laboratory of Advanced Technology of Near Space Information System Ministry of Industry and Information Technology of China Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beijing China AIM Lab University of Amsterdam Amsterdam Netherlands Inception Institute of Artificial Intelligence Abu Dhabi United Arab Emirates YouKu Cognitive and Intelligent Lab Alibaba Group Hangzhou China

ISBN: (纸本)9783030586003

Few-shot segmentation has recently generated great popularity, addressing the challenging yet important problem of segmenting objects from unseen categories with scarce annotated support images. The crux of few-shot segmentation is to extract object information from the support image and then propagate it to guide the segmentation of query images. In this paper, we propose the Democratic Attention Network (DAN) for few-shot semantic segmentation. We introduce the democratized graph attention mechanism, which can activate more pixels on the object to establish a robust correspondence between support and query images. Thus, the network is able to propagate more guiding information of foreground objects from support to query images, enhancing its robustness and generalizability to new objects. Furthermore, we propose multi-scale guidance by designing a refinement fusion unit to fuse features from intermediate layers for the segmentation of the query image. This offers an efficient way of leveraging multi-level semantic information to achieve more accurate segmentation. Extensive experiments on three benchmarks demonstrate that the proposed DAN achieves the new state-of-the-art performance, surpassing the previous methods by large margins. The thorough ablation studies further reveal its great effectiveness for few-shot semantic segmentation. © 2020, Springer Nature Switzerland AG.

关键词： Semantic Segmentation

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A new method of three-dimensional green volume retrieval and its applications in urban greenery evaluation

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Ecological Indicators 2025年 176卷

作者： Zehu Hong Yun Liu Weiheng Xu Leiguang Wang Ning Lu Guanglong Ou Weili Kou College of Big Data and Intelligent Engineering Southwest Forestry University Kunming 650233 China Key Laboratory for Environment and Disaster Monitoring and Evaluation Hubei Innovation Academy for Precision Measurement Science and Technology Chinese Academy of Sciences Wuhan 430071 China College of Forestry Southwest Forestry University Kunming Yunnan 650233 China College of Landscape Architecture and Horticulture Southwest Forestry University Kunming 650224 China

Urban greening benefits plays a crucial role in promoting public health and aligns with regional development strategies. Three-Dimensional Green Volume (3DGV) has emerged as a precise metric for evaluating greening benefits, yet its large-scale application remains limited. This study developed a satellite-derived 3DGV retrieval model using multi-source remote sensing data to assess urban greenery in central Yunnan, China. Field measurements were collected to establish UAV-derived 3DGV retrieval models, integrating Canopy Height Model (CHM), Leaf Area Index (LAI), and Fractional Vegetation Cover (FVC) from RGB images. We established a power regression model in large scale retrieval linking these UAV-derived parameters to Sentinel-1 and Sentinel-2 images, achieving strong performance (R 2 = 0.72; RMSE = 139.71 m 3 /pixel; AE = 120.69 m 3 /pixel; MAE = 10.54 %). Spatial analysis was used to revealed the distribution of retrieved 3DGV, and it showed a pronounced west-to-east gradient (Moran’s I = 0.772) and an obviously increase trend from 2018 to 2022. This study demonstrated that Sentinel-1 and Sentinel-2 images enable accurate large-scale 3DGV mapping and reveal 3DGV dynamics to evaluate the greening benefits, providing a feasible and effectiveness approach for sustainable urban greenery evaluation and ecological management.

关键词： Greening benefits Three-dimensional green volume Multi-source remote sensing Green visible index The central Yunnan

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Fashion Editing With Adversarial Parsing Learning

Fashion Editing With Adversarial Parsing Learning

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Haoye Dong Xiaodan Liang Yixuan Zhang Xujie Zhang Xiaohui Shen Zhenyu Xie Bowen Wu Jian Yin School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou P.R. China School of Intelligent Systems Engineering Sun Yat-sen University Petuum Inc. ByteDance AI Lab

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Interactive fashion image manipulation, which enables users to edit images with sketches and color strokes, is an interesting research problem with great application value. Existing works often treat it as a general inpainting task and do not fully leverage the semantic structural information in fashion images. Moreover, they directly utilize conventional convolution and normalization layers to restore the incomplete image, which tends to wash away the sketch and color information. In this paper, we propose a novel Fashion Editing Generative Adversarial Network (FE-GAN), which is capable of manipulating fashion images by free-form sketches and sparse color strokes. FE-GAN consists of two modules: 1) a free-form parsing network that learns to control the human parsing generation by manipulating sketch and color; 2) a parsing-aware inpainting network that renders detailed textures with semantic guidance from the human parsing map. A new attention normalization layer is further applied at multiple scales in the decoder of the inpainting network to enhance the quality of the synthesized image. Extensive experiments on high-resolution fashion image datasets demonstrate that the proposed FE-GAN significantly outperforms the state-of-the-art methods on fashion image manipulation.

关键词： Image color analysis Semantics Convolution Colored noise Feature extraction Task analysis Neural networks

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Dictionary Pair-based data-Free Fast Deep Neural Network Compression

Dictionary Pair-based Data-Free Fast Deep Neural Network Com...

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IEEE International Conference on data Mining (ICDM)

作者： Yangcheng Gao Zhao Zhang Haijun Zhang Mingbo Zhao Yi Yang Meng Wang School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) & Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China Harbin Institute of Technology (Shenzhen) Xili University Town Shenzhen China City University of Hong Kong Hong Kong SAR Centre for Artificial Intelligence University of Technology Sydney Sydney NSW Australia

ISBN: (纸本)9781665423991

Deep neural network (DNN) compression can reduce the memory footprint of deep networks effectively, so that the deep model can be deployed on the portable devices. However, most of the existing model compression methods cost lots of time, e.g., vector quantization or pruning, which makes them inept to the real-world applications that need fast online computation. In this paper, we therefore explore how to accelerate the model compression process by reducing the computation cost. Then, we propose a new deep model compression method, termed Dictionary Pair-based data-Free Fast DNN Compression, which aims at reducing the memory consumption of DNNs without extra training and can greatly improve the compression efficiency. Specifically, our proposed method performs tensor decomposition on the DNN model with a fast dictionary pair learning-based reconstruction approach, which can be deployed on different layers (e.g., convolution and fully-connection layers). Given a pre-trained DNN model, we first divide the parameters (i.e., weights) of each layer into a series of partitions for dictionary pair-based fast reconstruction, which can potentially discover more fine-grained information and provide the possibility for parallel model compression. Then, dictionaries of less memory occupation are learned to reconstruct the weights. Extensive experiments on popular DNNs (i.e., VGG-16, ResNet-18 and ResNet-50) showed that our proposed weight compression method can significantly reduce the memory footprint and speed up the compression process, with less performance loss.

关键词： Deep learning Training Performance evaluation Dictionaries Tensors Costs Computational modeling

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