Forecasting infections with spatio-temporal graph neural networks: a case study of the Dutch SARS-CoV-2 spread

The spread of an epidemic over a population is influenced by a multitude of factors having both spatial and temporal nature, which are hard to completely capture using first principle methods. This paper concerns regional forecasting of SARS-Cov-2 infections 1 week ahead using machine learning. We especially focus on the Dutch case study for...

Understanding Human Manipulation with the Environment: A Novel Taxonomy for Video Labelling

In recent years, the spread of data-driven approaches for robotic grasp synthesis has come with the increasing need for reliable datasets, which can be built e.g. through video labelling. To this goal, it is important to define suitable rules to characterize the main human grasp types, for easily identifying them in video streams. In this...