Abstract Spectral Graph Neural Network is a kind of Graph Neural Network (GNN) based on
graph signal filters. Some models able to learn arbitrary spectral filters have emerged …

Recently, neural network based approaches have achieved significant progress for solving
large, complex, graph-structured problems. Nevertheless, the advantages of multi-scale …

Common explanations for shortcut learning assume that the shortcut improves prediction
only under the training distribution. Thus, models trained in the typical way by minimizing log …

Ultra-dense low-Earth-orbit (LEO) satellite communication networks have significant
potential for providing high-speed data services. To compensate the severe path loss in …

Reconfigurable Intelligent Surfaces (RISs) are planner surfaces that include a large number
of passively radiating elements. By changing the phase shifts of these passive elements …

In this work, a data-driven fault detection, isolation, and estimation (FDI&E) methodology is
proposed and developed specifically for monitoring the aircraft gas turbine engine actuator …

Even after decades of quantum computing development, examples of generally useful
quantum algorithms with exponential speedups over classical counterparts are scarce …

We propose a novel privacy-preserving support vector machine (SVM) classifier for a data
matrix A whose input feature columns are divided into groups belonging to different entities …

Many experiments in the field of quantum foundations seek to adjudicate between quantum
theory and speculative alternatives to it. This requires one to analyze the experimental data …

Low-Power Wide-Area Networks (LP-WANs) are seeing wide-spread deployments
connecting millions of sensors, each powered by a ten-year AA battery to radio …