OBJECTIVE: This study aimed to identify sleep disturbance subtypes ("phenotypes") among Latinx adults based on objective sleep data using a flexible unsupervised machine learning technique.
Our study aims to improve the signal-to-noise ratio of positron emission tomography (PET) imaging using conditional unsupervised learning. The proposed method does not require low- and high-quality pairs for network training which can be easily appli...
Spatial co-location detection is the task of inferring the co-location of two or more objects in the geographic space. Mobile devices, especially a smartphone, are commonly employed to accomplish this task with the human object. Previous work focused...
Laboratory investigation; a journal of technical methods and pathology
Jul 9, 2021
Single-cell RNA sequencing (scRNA-seq) data has been widely used to profile cellular heterogeneities with a high-resolution picture. Clustering analysis is a crucial step of scRNA-seq data analysis because it provides a chance to identify and uncover...
Recently, advanced informatics and sensing techniques show promise of enabling a new generation of smart stormwater systems, where real-time sensors are deployed to detect flooding hotspots. Existing stormwater design criteria assume that historical ...
Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support...
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jun 9, 2021
In this paper, we quest the capability of transferring the quality of natural scene images to the images that are not acquired by optical cameras (e.g., screen content images, SCIs), rooted in the widely accepted view that the human visual system has...
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
Jun 2, 2021
In recent years, person re-identification (re-ID) has achieved relatively good performance, benefiting from the revival of deep neural networks. However, due to the existence of domain bias which refers to the different data distributions between two...
Although there is a rapidly growing literature on dynamic connectivity methods, the primary focus has been on separate network estimation for each individual, which fails to leverage common patterns of information. We propose novel graph-theoretic ap...