16 citations · 57 across the 20 of their papers we have counts for
20 papers
Source-Free Domain Adaptation for Medical Image Segmentation via Prototype-Anchored Feature Alignment and Contrastive Learning
Qinji Yu, Nan Xi, Junsong Yuan +3
Unsupervised domain adaptation (UDA) has increasingly gained interests for its capacity to transfer the knowledge learned from a labeled source domain to an unlabeled target domain…
A Smart Switch Configuration and Reliability Assessment Method for Large-Scale Offshore Wind Farm Electrical Collector System
Xiaochi Ding, Xinwei Shen, Qiuwei Wu +2
With the development of offshore wind farms (OWFs) in far-offshore and deep-sea areas, each OWF could contain more and more wind turbines and cables, making it imperative to study…
Region and Spatial Aware Anomaly Detection for Fundus Images
Jingqi Niu, Shiwen Dong, Qinji Yu +2
Recently anomaly detection has drawn much attention in diagnosing ocular diseases. Most existing anomaly detection research in fundus images has relatively large anomaly scores in…
DualStreamFoveaNet: A Dual Stream Fusion Architecture with Anatomical Awareness for Robust Fovea Localization
Sifan Song, Jinfeng Wang, Zilong Wang +4
Accurate fovea localization is essential for analyzing retinal diseases to prevent irreversible vision loss. While current deep learning-based methods outperform traditional ones,…
Coarse Retinal Lesion Annotations Refinement via Prototypical Learning
Qinji Yu, Kang Dang, Ziyu Zhou +2
Deep-learning-based approaches for retinal lesion segmentation often require an abundant amount of precise pixel-wise annotated data. However, coarse annotations such as circles or…
Learning Low-Dimensional Nonlinear Structures from High-Dimensional Noisy Data: An Integral Operator Approach
Xiucai Ding, Rong Ma
We propose a kernel-spectral embedding algorithm for learning low-dimensional nonlinear structures from high-dimensional and noisy observations, where the datasets are assumed to b…
A Riemann--Hilbert approach to the perturbation theory for orthogonal polynomials: Applications to numerical linear algebra and random matrix theory
Xiucai Ding, Thomas Trogdon
We establish a new perturbation theory for orthogonal polynomials using a Riemann--Hilbert approach and consider applications in numerical linear algebra and random matrix theory.…
How do kernel-based sensor fusion algorithms behave under high dimensional noise?
Xiucai Ding, Hau-Tieng Wu
We study the behavior of two kernel based sensor fusion algorithms, nonparametric canonical correlation analysis (NCCA) and alternating diffusion (AD), under the nonnull setting th…
Impact of signal-to-noise ratio and bandwidth on graph Laplacian spectrum from high-dimensional noisy point cloud
Xiucai Ding, Hau-Tieng Wu
We systematically study the spectrum of kernel-based graph Laplacian (GL) constructed from high-dimensional and noisy random point cloud in the nonnull setup. The problem is motive…
Multivariate functional responses low rank regression with an application to brain imaging data
Xiucai Ding, Dengdeng Yu, Zhengwu Zhang +1
We propose a multivariate functional responses low rank regression model with possible high dimensional functional responses and scalar covariates. By expanding the slope functions…
Statistical inference for principal components of spiked covariance matrices
Zhigang Bao, Xiucai Ding, Jingming Wang +1
In this paper, we study the asymptotic behavior of the extreme eigenvalues and eigenvectors of the high dimensional spiked sample covariance matrices, in the supercritical case whe…
Globally Optimal And Adaptive Short-Term Forecast of Locally Stationary Time Series And A Test for Its Stability
Xiucai Ding, Zhou Zhou
Forecasting the evolution of complex systems is one of the grand challenges of modern data science. The fundamental difficulty lies in understanding the structure of the observed s…
On the spectral property of kernel-based sensor fusion algorithms of high dimensional data
Xiucai Ding, Hau-Tieng Wu
We apply local laws of random matrices and free probability theory to study the spectral properties of two kernel-based sensor fusion algorithms, nonparametric canonical correlatio…
Principal components of spiked covariance matrices in the supercritical regime
Zhigang Bao, Xiucai Ding, Jingming Wang +1
In this paper, we study the asymptotic behavior of the extreme eigenvalues and eigenvectors of the spiked covariance matrices, in the supercritical regime. Specifically, we derive…
Modified Multidimensional Scaling and High Dimensional Clustering
Xiucai Ding, Qiang Sun
Multidimensional scaling is an important dimension reduction tool in statistics and machine learning. Yet few theoretical results characterizing its statistical performance exist,…
Singular vector and singular subspace distribution for the matrix denoising model
Zhigang Bao, Xiucai Ding, Ke Wang
In this paper, we study the matrix denosing model , where is a low-rank deterministic signal matrix and is a random noise matrix, and both are . In the sc…
Estimation and inference for precision matrices of non-stationary time series
Xiucai Ding, Zhou Zhou
In this paper, we consider the estimation and inference of precision matrices of a rich class of locally stationary and nonlinear time series assuming that only one realization of…
Spiked sample covariance matrices with possibly multiple bulk components
Xiucai Ding
In this paper, we study the convergent limits and rates of the eigenvalues and eigenvectors for spiked sample covariance matrices whose spectrum can have multiple bulk components.…
High dimensional deformed rectangular matrices with applications in matrix denoising
Xiucai Ding
We consider the recovery of a low rank matrix from its noisy observation in two different regimes. Under the assumption that is comparable to ,…
Singular vector distribution of sample covariance matrices
Xiucai Ding
We consider a class of sample covariance matrices of the form where is an rectangular matrix consisting of i.i.d entries and is a dete…