Node outlier detection in attributed graphs is a challenging problem for which there is no
method that would work well across different datasets. Motivated by the state-of-the-art results …

This paper presents the computational challenge on topological deep learning that was
hosted within the ICML 2023 Workshop on Topology and Geometry in Machine Learning. The …

Geodesic convexity in networks is an intrinsic property of graphs. It aids in distinguishing
between real-world networks and random graphs. One possible application is recommending …

Noninvasive methods of near-infrared, short-wave infrared, and thermographic inspection of
artwork are described in this article and compared in terms of their ability to reveal both …

The aim of the study was to determine the association between ACE I/D, ACTN3 R577X and
PPARA intron 7 G/C gene polymorphisms and strength-related traits in 457 middle school-…

The data transition has become one of the most actual problems nowadays. Classical
methods of video and image compression give great results but refers huge computational …

Barriers and bridges to implement policies about sustainable development and sustainability
commonly depend on the past development of social–ecological systems. Production of …

Prediction of protein-ligand (PL) binding affinity remains the key to drug discovery. Popular
approaches in recent years involve graph neural networks (GNNs), which are used to learn …

Composites of synthetic bone mineral substitutes (BMS) and biodegradable polyesters are
of particular interest for bone surgery and orthopedics. Manufacturing of composite scaffolds …

This is a brief review of our recent work on memristor-based spiking neuromorphic networks.
We first describe the recent experimental demonstration of several most biology-plausible …