Emotion plays a vital role in human daily life, and EEG signals are widely used in emotion
recognition. Due to individual variability, training a generic emotion recognition model …

Covariance matrices have attracted attention for machine learning applications due to their
capacity to capture interesting structure in the data. The main challenge is that one needs to …

This paper explores the development and implementation of advanced load balancing
algorithms aimed at minimizing latency while maximizing throughput in distributed systems …

Despite the progress recently made towards automatic sleep staging for adults, children
have complicated sleep structures that require attention to the pediatric sleep staging. Semi …

The classification of six types of white blood cells (WBCs) is considered essential for
leukemia diagnosis, while the classification is labor-intensive and strict with the clinical …

Neural network on Riemannian symmetric space such as hyperbolic space and the manifold
of symmetric positive definite (SPD) matrices is an emerging subject of research in …

Recent research has shown that alignment between the structure of graph data and the
geometry of an embedding space is crucial for learning high-quality representations of the …

We propose the use of the vector-valued distance to compute distances and extract
geometric information from the manifold of symmetric positive definite matrices (SPD), and …

Covariance matrix learning methods have become popular for many classification tasks
owing to their ability to capture interesting structures in non-linear data while respecting the …

Illegal, malicious or dangerous uses of drones, require developing systems capable of
detecting, tracking and recognizing them in a non-collaborative way, and with enough …