The reconstruction of geographical data over a century, allows to figuring out the evolution of the French railway landscape, and how it has been impacted by major events (eg.: WWII), or longer time span processes : industry outsourcing, metropolization, public transport policies or absence of them. This work is resulting from the fusion of several public geographical data (SNCF, IGN), enriched with the computer-assisted addition of multiple data gathered on the Internet (Wikipedia, volunteer geographic information). The dataset compounds almost every rail stations (even simple stops) and railway branch nodes, whose link to their respective rail lines allows to build the underlying consistent graph of the network. Every rail line has a "valid to" date (or approx) so that time evolution can be displayed. The present progress of that reconstruction sums up to roughly 90% of what is expected (exact total unknown). This allows to consider temporal demographic analysis (how many cities and towns served by the railway since 1925 up on today), and environmental simulations as well (CO2 cost by given destination ).

From Picasso's The Young Ladies of Avignon to Munch's The Scream, what was it about these paintings that arrested people's attention upon viewing them, that cemented them in the canon of art history as iconic works? In many cases, it's because the artist incorporated a technique, form or style that had never been used before. They exhibited a creative and innovative flair that would go on to be mimicked by artists for years to come. Throughout human history, experts have often highlighted these artistic innovations, using them to judge a painting's relative worth. But can a painting's level of creativity be quantified by Artificial Intelligence (AI)? At Rutgers' Art and Artificial Intelligence Laboratory, my colleagues and I proposed a novel algorithm that assessed the creativity of any given painting, while taking into account the painting's context within the scope of art history.