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Akademický článek

A study of preschool integration of deep learning to optimize the content of personalized education for young children

Autor: Ding Yanyun, Li Yongxia

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

With the reform and development of modern education curriculum, the focus of preschool education has changed from focusing on the “learning results” to the “learning process” of young children, and deep learning, as a hot trend of education r

Externí odkaz: https://doaj.org/article/45028f81becf437e9d1c69bdee3cc0d6

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Akademický článek

Research on Informatization Construction of Educational Management Innovation Path in Colleges and Universities in the Intelligent Era

Autor: Li Yongxia, Ding Yanyun

Publikováno v: Applied Mathematics and Nonlinear Sciences, Vol 9, Iss 1 (2024)

The prevailing education management systems in colleges and universities are antiquated, adversely impacting not only the administrative efficiency but also the formulation of faculty teaching plans and class schedules, as well as the management of s

Externí odkaz: https://doaj.org/article/ea409ff09dcd4e05874c4f21aa0edcde

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Iterative Reweighted Framework Based Algorithms for Sparse Linear Regression with Generalized Elastic Net Penalty

Autor: Ding, Yanyun, Yao, Zhenghua, Li, Peili, Xiao, Yunhai

The elastic net penalty is frequently employed in high-dimensional statistics for parameter regression and variable selection. It is particularly beneficial compared to lasso when the number of predictors greatly surpasses the number of observations.

Externí odkaz: http://arxiv.org/abs/2411.14875

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Akademický článek

Few-shot meta-learning applied to whole brain activity maps improves systems neuropharmacology and drug discovery

Autor: Luo, Xuan, Ding, Yanyun, Cao, Yi, Liu, Zhen, Zhang, Wenchong, Zeng, Shangzhi, Cheng, Shuk Han, Li, Honglin, Haggarty, Stephen J., Wang, Xin, Zhang, Jin, Shi, Peng

Publikováno v: In iScience 18 October 2024 27(10)

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Efficient Dual ADMMs for Sparse Compressive Sensing MRI Reconstruction

Autor: Ding, Yanyun, Li, Peili, Xiao, Yunhai, Zhang, Haibin

Magnetic Resonance Imaging (MRI) is a kind of medical imaging technology used for diagnostic imaging of diseases, but its image quality may be suffered by the long acquisition time. The compressive sensing (CS) based strategy may decrease the reconst

Externí odkaz: http://arxiv.org/abs/2111.12298

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An Efficient Semismooth Newton Method for Adaptive Sparse Signal Recovery Problems

Autor: Ding, Yanyun, Zhang, Haibin, Li, Peili, Xiao, Yunhai

We know that compressive sensing can establish stable sparse recovery results from highly undersampled data under a restricted isometry property condition. In reality, however, numerous problems are coherent, and vast majority conventional methods mi

Externí odkaz: http://arxiv.org/abs/2111.12252

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Symmetric Gauss-Seidel Technique Based Alternating Direction Methods of Multipliers for Transform Invariant Low-Rank Textures Problem

Autor: Ding, Yanyun, Xiao, Yunhai

Transform Invariant Low-Rank Textures, referred to as TILT, can accurately and robustly extract textural or geometric information in a 3D from user-specified windows in 2D in spite of significant corruptions and warping. It was discovered that the ta

Externí odkaz: http://arxiv.org/abs/1710.07473

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