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Information Science, Parallel and Distributed Systems (ISPDS), International Conference on

作者： Zhijun Luo Zhibin Zhu School of Mathematics and Finance Hunan University of Humanities Science and Technology Loudi China School of Mathematics and Computing Science and Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin China

Magnetic resonance imaging is an essential tool for clinical diagnosis, but acquisition time and reconstruction errors have always been bottleneck issues limiting the development of this field. In this paper, we use SCAD nonconvex function to construct a new MRI reconstruction nonconvex model, which can overcome the shortcomings of the traditional convex model. In model solving, considering the symmetry ADMM method improves the image reconstruction performance while increasing the reconstruction speed. The experiment shows that the method proposed in this paper has significant improvements over traditional TV methods.

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Embedding and Duality of Matrix-Weighted Modulation Spaces

arXiv

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arXiv 2024年

作者： Wang, Shengrong Guo, Pengfei Xu, Jingshi School of Mathematics and Statistics Hainan Normal University Haikou571158 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin541004 China

关键词： Embeddings

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Precompact Sets in Matrix Weighted Lebesgue Spaces with Variable Exponent

arXiv

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arXiv 2024年

作者： Wang, Shengrong Guo, Pengfei Xu, Jingshi School of Mathematics and Statistics Hainan Normal University Haikou571158 China School of Mathematics and Computing Science Guilin University of Electronic Technology Guilin541004 China Guilin541004 China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin541004 China

In this paper, we first give a sufficiently condition for precompactness in the matrix-weighted Lebesgue spaces with variable exponent by translation operator. Then we obtain a criterion for precompactness in the matrix-weighted Lebesgue space with variable exponent by average operator. Next, we give a criterion for precompactness in the matrix-weighted Lebesgue space with variable exponent by approximate identity. Finally, precompactness in the matrix-weighted Sobolev space with variable exponent is also considered. Copyright © 2024, The Authors. All rights reserved.

关键词： Sobolev spaces

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Random Projection Ensemble Conformal Prediction for High-Dimensional Classification

SSRN

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SSRN 2024年

作者： Qian, Xiaoyu Wu, Jinru Wei, Ligong Lin, Youwu School of Mathematics and Computing Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541002 China Guilin541002 China Guangxi Academy of Sciences High-tech Group Co. LTD Nanning530007 China

In classification problems, many models with superior performance fail to provide confidence estimates or intervals for each prediction. This lack of reliability poses risks in real-world applications, making these models difficult to trust. Conformal prediction, as distribution-free and model-free approaches with finite-sample coverage guarantee, have recently been widely used to construct prediction sets for classification models. However, traditional conformal prediction methods only produce set-valued results without specifying a definitive predicted class. Particularly in complex settings, these methods fail to assist models in effectively addressing challenges such as high dimensionality, resulting in ambiguous prediction sets with low statistical efficiency, i.e. the prediction sets contain many false classes. In this study, a novel Ensemble Conformal Prediction algorithm based on Random Projection and a designed voting strategy, RPECP, is developed to tackle these challenges. Initially, a procedure for selecting the approximately oracle random projections and classifiers is executed to best leverage the internal information and structure of the data. Subsequently, based on the approximately oracle random projections and underlying classifiers, conformal prediction is performed on new test samples in a lower-dimensional space, resulting in multiple independent prediction sets. Finally, an accurate predicted class and a prediction set with high coverage guarantee and statistical efficiency are produced through a designed voting strategy. Compared to several base classifiers, RPECP obtain higher classification accuracy;against other conformal prediction algorithms, it achieves less ambiguous prediction sets with fewer false classes while ensuring coverage guarantee. For illustration, this paper demonstrates RPECP's superiority over other methods in four cases: two high-dimensional settings and two real-world datasets. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Multipliers of Dirichlet-Type Subspaces of Weighted Bloch Spaces

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数学研究 2022年第3期55卷 229-241页

作者： Conghui Shen Jingshi Xu College of Mathematics and Data Science Minjiang UniversityFuzhou 350108China Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation School of Mathematics and Computing ScienceGuilin University of Electronic TechnologyGuilin 541004China.

In this paper,we characterize the multipliers on the intersection of the Diri-chlet-type space and the logarithmic Bloch space,and the intersection of the Dirichlet-type space and the logarithmic space of analytic fun... 详细信息

关键词： Multiplier Carleson measure Dirichlet-type space weighted Bloch space

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Iterative two-level algorithm for nonsymmetric or indefinite elliptic problems

arXiv

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arXiv 2023年

作者： Tang, Ming Xing, Xiaoqing Yang, Ying Zhong, Liuqiang School of Mathematical Sciences South China Normal University Guangzhou510631 China School of Mathematics and Computational Science Guangxi Colleges Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China

In this paper, a new iterative two-level algorithm is presented for solving the finite element discretization for nonsymmetric or indefinite elliptic problems. The iterative two-level algorithm uses the same coarse space as the traditional two-grid algorithm, but its "fine space" uses the higher oder finite element space under the coarse grid. Therefore, the iterative two-level algorithm only needs one grid, and the computational cost is much lower than the traditional iterative two-grid algorithm. Finally, compared with the traditional two-grid algorithm, numerical experiments show that the computational cost is lower to achieve the same convergence order. Copyright © 2023, The Authors. All rights reserved.

关键词： Iterative methods

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Hyperspectral Image Restoration via a New Symmetric ADMM Approach

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Mathematical Problems in Engineering 2023年第1期2023卷

作者： Wang, Shuo Zhu, Zhibin Zhao, Ruwen Zhang, Benxin School of Electronic Engineering and Automation Key Laboratory of Automatic Detecting Technology and Instruments Guilin University of Electronic Technology Guilin541004 China School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China Guilin541004 China

For the hyperspectral image (HSI) denoising problem, a symmetric proximal alternating direction method multiplier (spADMM) is proposed to solve the sparse optimization problem which cannot be solved accurately by traditional ADMM. The proposed method finds a high-quality recovery method using the traditional low-rank Tucker decomposition method, which can fully take into account the overall spatial and spectral correlation between HSI bands by using the Tucker decomposition. By choosing appropriate steps to update the Lagrange multipliers twice, it makes the selection and use of variables more flexible and better for solving sparsity problems. To maintain stability, we also add appropriate proximity terms to solve the problem during the computation. Experiments have shown that the spADMM has better results than the traditional ADMM. The final experimental results on the dataset demonstrate the effectiveness of the method. © 2023 Shuo Wang et al.

关键词： Lagrange multipliers

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A Novel Grey Multivariate Convolution Model Based on the Improved Marine Predators Algorithm for Predicting Fossil Co2 Emissions in China

SSRN

引用

SSRN 2023年

作者： Cang, Haoze Zeng, Xiangyan Yan, Shuli School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin541004 China School of Management Science and Engineering Nanjing University of Information Science and Technology Nanjing210044 China

The effective prediction of fossil CO2 emissions provides a foundation for environmental protection authorities to make relevant management and decisions. CO2 emissions are influenced by various factors and exhibit a nonlinear and complex trend. First, the weighted conformable fractional accumulation (WCFA) method is proposed. The weight of old and new information can be regulated by adjusting the parameters. A nonlinear discrete grey Bernoulli multivariate convolution model is introduced to model the sequences processed by WCFA. Then, five parameters in the model need to be optimized. An improved marine predators algorithm is proposed through the tent map and an improved control parameter. The feasibility and predictive effectiveness of the model is demonstrated by comparing it with four improved grey multivariate convolution models. Finally, the total fossil CO2 emissions in China from 2022 to 2026 are predicted and analyzed. The results indicate a gradual decrease in fossil CO2 emissions over the next five years. Some reasonable suggestions are made based on the prediction results and sources of fossil CO2 emissions. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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Prediction Intervals of Principal Component Regression with Applications to Molecular Descriptors datasets 1st

Prediction Intervals of Principal Component Regression with ...

引用

1st International Conference on Applied Intelligence, ICAI 2023

作者： Fu, Yuling Bin, Zixin Wei, Ligong Lin, Youwu School of Mathematics and Computational Sciences Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guangxi Guilin541002 China Guilin541002 China Guangxi Academy of Sciences High-Tech Group Co. LTD. Guangxi Nanning530007 China

ISBN: (纸本)9789819708260

The prediction interval of the Principal Component Regression (PCR) model is usually based on some strong distributional assumptions. To overcome this drawback, this study extends a prediction interval estimation method by considering a condition of distribution-free. Six different prediction interval estimation methods are developed for constructing prediction intervals for PCR models. The simulated and real data experiment results show that the developed methods perform better than some state-of-the-art methods. This study can enrich the tools of PCR model prediction and statistical inference and is significant for data analysis. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Forecasting

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The Chebyshev Collocation Least Squares Approximation Method for the Audio data 2021

The Chebyshev Collocation Least Squares Approximation Method...

引用

2nd International Conference on Computing and data Science, CONF-CDS 2021

作者： Zhang, Qian Qin, Yonghui Chen, Ziqiang Wen, Nengxiang Guilin University of Electronic Technology Guilin541004 China College of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guangxi Key Laboratory of Cryptography and Information Security Guilin University of Electronic Technology Guilin541004 China Cognitive Radio and Information Processing Key Laboratory of Ministry of Education Guilin University of Electronic Technology Guilin541004 China

The Chebyshev collocation least squares configuration fitting method is investigated for the fitting of audio data. This method couples the least squares method with the Chebyshev-Gauss points. Numerical examples are ... 详细信息

ISBN: (纸本)9781450389570

关键词： Least squares approximations

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