Cluster analysis plays an indispensable role in machine learning and data mining. Learning
a good data representation is crucial for clustering algorithms. Recently, deep clustering …

Human activity recognition (HAR) technology that analyzes data acquired from various types
of sensing devices, including vision sensors and embedded sensors, has motivated the …

Single-cell technologies have revealed the complexity of the tumour immune
microenvironment with unparalleled resolution,,,,,,,–. Most clinical strategies rely on …

Large Language Models (LLMs) have drawn a lot of attention due to their strong
performance on a wide range of natural language tasks, since the release of ChatGPT in …

Latent profile analysis (LPA) is a categorical latent variable approach that focuses on
identifying latent subpopulations within a population based on a certain set of variables. LPA …

Latent class analysis (LCA) is a statistical procedure used to identify qualitatively different
subgroups within populations who often share certain outward characteristics. The …

People with late-stage Parkinson's disease (PD) often suffer from debilitating locomotor
deficits that are resistant to currently available therapies. To alleviate these deficits, we …

Latent class analysis is a probabilistic modeling algorithm that allows clustering of data and
statistical inference. There has been a recent upsurge in the application of latent class …

With the increasing prominence of partial least squares structural equation modeling (PLS-
SEM) in business research, the use of latent class analyses for identifying and treating …

Estimates of the social cost of carbon are the yardstick for climate policy targets. However,
there is great uncertainty and we do not know how estimates have evolved over time. Here I …