Existing subspace clustering methods typically employ shallow models to estimate underlying
subspaces of unlabeled data points and cluster them into corresponding groups. However…

… SL} and Xi = [x1,..., xi−1, 0, xi+1,..., xn],(i = 1,..., n). In the following, we use the data set Xi as
the dictionary of xi, ie, D = Xi for the specific xi. The proposed objective function is as follows: …

We review the use of luminescent nanoparticles in super-resolution imaging and single-molecule
tracking, and showcase novel approaches to super-resolution imaging that leverage …

The advent of single-cell RNA sequencing (scRNA-seq) has provided insight into the tumour
immune microenvironment (TIME). This review focuses on the application of scRNA-seq in …

Structured illumination microscopy (SIM) has become the standard for next-generation wide-field
microscopy, offering ultrahigh imaging speed, superresolution, a large field-of-view, …

In this paper, we propose an online clustering method called Contrastive Clustering (CC)
which explicitly performs the instance-and cluster-level contrastive learning. To be specific, for …

This paper proposes a deep learning approach for accelerating magnetic resonance imaging
(MRI) using a large number of existing high quality MR images as the training datasets. An …

Optical multiplexing plays an important role in applications such as optical data storage 1 ,
document security 2 , molecular probes 3 , 4 and bead assays for personalized medicine 5 . …

In this paper, we study a challenging problem in image restoration, namely, how to develop
an all-in-one method that could recover images from a variety of unknown corruption types …

Lanthanide-doped glasses and crystals are attractive for laser applications because the
metastable energy levels of the trivalent lanthanide ions facilitate the establishment of …