Neue Zürcher Zeitung | A data lake, not a data swamp [In German]

RAvaFcast is a data-driven model pipeline developed for automated regional avalanche danger forecasting in Switzerland. It combines a recently proposed classifier for avalanche danger prediction at weather stations with a spatial interpolation model and a novel aggregation strategy to estimate the danger levels in predefined wider warning regions, ultimately assembled as an avalanche bulletin.

The goal of CarboSense4D is to produce an accurate map of the evolution of carbon dioxide over Switzerland by applying machine learning methods from a network of low-cost sensors.

Can we extend the concept of word embeddings to any collection of items, possibly unordered? More precisely, can we learn representations from item sets, such as the product baskets in online retail or music playlists on streaming platforms?