High-fidelity fracture mechanics simulations of multiple microcracks interaction via physics-
based models can become computationally demanding as the number of microcracks …

Graph drawing techniques have been developed in the last few years with the purpose of
producing esthetically pleasing node-link layouts. Recently, the employment of differentiable …

Many organizations are migrating their on-premise artificial intelligence workloads to the
cloud due to the availability of cost-effective and highly scalable infrastructure, software and …

The popularity of deep learning techniques renewed the interest in neural architectures able
to process complex structures that can be represented using graphs, inspired by Graph …

Neural network models are based on a distributed computational scheme in which signals
are propagated among neurons through weighted connections. The network topology …

Cloud services have an auto-scaling feature for load balancing to meet the performance
requirements of an application. Existing auto-scaling techniques are based on upscaling …

Effectively learning from sequential data is a longstanding goal of Artificial Intelligence,
especially in the case of long sequences. From the dawn of Machine Learning, several …

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 …

Ensuring the reliability of software through early-stage defect prevention and prediction is of
utmost importance in the face of today's increasingly complex software systems. Automated …