DynIMTS replaces static graphs with instance-attention that updates edge weights on the fly, delivering SOTA imputation and P12 classification ...

Researchers at Oak Ridge National Laboratory have used specialized tools to study materials at the atomic scale and analyze ...

The GC–MS dataset was integrated with the sensory data using a series of exploratory and predictive multivariate statistical ...

A research team introduces a fully automated, non-destructive phenotyping platform that combines X-ray fluorescence microscopy with computer vision and machine learning.

Background The relationship of social determinants of health (SDOH), environmental exposures and medical history to lung function trajectories is underexplored. A better understanding of these ...

By modeling the single-trial electroencephalogram of participants performing perceptual decisions, and building on predictions from two century-old psychological laws, we estimate the times of ...

Professor Klaus Nordhausen develops modern multivariate statistical methods to analyze high-dimensional and large datasets in different fields.

Objective Patients with atrial fibrillation (AF) frequently have multiple comorbidities that increase the risk of hospitalisation and contribute to higher mortality. However, studies examining the ...

An Osaka Metropolitan University researcher examined whether abdominal obesity predicts frailty progression in 2,962 ...

Discover how multivariate models use multiple variables for investment forecasting, risk analysis, and decision-making in finance. Ideal for portfolio management.

Researchers have identified specific coupled patterns of brain activity and gene expression that help explain impulsive behavior in children with attention deficit hyperactivity disorder. By analyzing ...