Deep Reinforcement Learning framework for Autonomous Driving

Reinforcement learning is considered to be a strong AI paradigm which can be used to teach machines through interaction with the environment and learning from their mistakes. Despite its perceived utility, it has not yet been successfully applied in automotive applications. Motivated by the successful demonstrations of learning of Atari games and Go by Google DeepMind, we propose a framework for autonomous driving using deep reinforcement learning. This is of particular relevance as it is difficult to pose autonomous driving as a supervised learning problem due to strong interactions with the environment including other vehicles, pedestrians and roadworks. As it is a relatively new area of research for autonomous driving, we provide a short overview of deep reinforcement learning and then describe our proposed framework. It incorporates Recurrent Neural Networks for information integration, enabling the car to handle partially observable scenarios. It also integrates the recent work on attention models to focus on relevant information, thereby reducing the computational complexity for deployment on embedded hardware. The framework was tested in an open source 3D car racing simulator called TORCS. Our simulation results demonstrate learning of autonomous maneuvering in a scenario of complex road curvatures and simple interaction of other vehicles.Comment: Reprinted with permission of IS&T: The Society for Imaging Science and Technology, sole copyright owners of Electronic Imaging, Autonomous Vehicles and Machines 201

" La prise en charge des proverbes en discours "

Disponible sur http://www.paremia.org/wp-content/uploads/14.FOURNET.pdfInternational audienc

Evidence that natural selection maintains genetic variation for sleep in Drosophila melanogaster.

BackgroundDrosophila melanogaster often shows correlations between latitude and phenotypic or genetic variation on different continents, which suggests local adaptation with respect to a heterogeneous environment. Previous phenotypic analyses of latitudinal clines have investigated mainly physiological, morphological, or life-history traits. Here, we studied latitudinal variation in sleep in D. melanogaster populations from North and Central America. In parallel, we used RNA-seq to identify interpopulation gene expression differences.ResultsWe found that in D. melanogaster the average nighttime sleep bout duration exhibits a latitudinal cline such that sleep bouts of equatorial populations are roughly twice as long as those of temperate populations. Interestingly, this pattern of latitudinal variation is not observed for any daytime measure of activity or sleep. We also found evidence for geographic variation for sunrise anticipation. Our RNA-seq experiment carried out on heads from a low and high latitude population identified a large number of gene expression differences, most of which were time dependent. Differentially expressed genes were enriched in circadian regulated genes and enriched in genes potentially under spatially varying selection.ConclusionOur results are consistent with a mechanistic and selective decoupling of nighttime and daytime activity. Furthermore, the present study suggests that natural selection plays a major role in generating transcriptomic variation associated with circadian behaviors. Finally, we identified genomic variants plausibly causally associated with the observed behavioral and transcriptomic variation

The Aachen Mutual Defence Clause: A Closer Look at the Franco-German Treat. Egmont Security Policy Brief No. 105

On 22 January 2019, Emmanuel Macron and Angela Merkel signed a new treaty on “Franco-German cooperation and integration” in Aachen. Complementing the 1963 Elysée Treaty which symbolized the reconciliation between Germany and France in the post-war period, the Aachen Treaty aims to further strengthen the ties between the two countries in the domains of economy, culture, administration, environment, diplomacy and defence. Although the Treaty has been criticised for its lack of ambition, a closer reading of its text reveals some hidden gems, including its mutual defence clause. What does this new clause mean for the Franco-German tandem and for collective defence in Europe

Mixture enhances productivity in a two-species forest: evidence from a modeling approach

The effect of mixture on productivity has been widely studied for applications related to agriculture but results in forestry are scarce due to the difficulty of conducting experiments. Using a modeling approach, we analyzed the effect of mixture on the productivity of forest stands composed of sessile oak and Scots pine. To determine whether mixture had a positive effect on productivity and if there was an optimum mixing proportion, we used an aggregation technique involving a mean-field approximation to analyze a distance-dependent individual-based model. We conducted a local sensitivity analysis to identify the factors that influenced the results the most. Our model made it possible to predict the species proportion where productivity peaks. This indicates that transgressive over-yielding can occur in these stands and suggests that the two species are complementary. For the studied growth period, mixture does have a positive effect on the productivity of oakpine stands. Depending on the plot, the optimum species proportion ranges from 38 to 74% of oak and the gain in productivity compared to the current mixture is 2.2% on average. The optimum mixing proportion mainly depends on parameters concerning intra-specific oak competition and yet, intra-specific competition higher than inter-specific competition was not sufficient to ensure over-yielding in these stands. Our work also shows how results obtained for individual tree growth may provide information on the productivity of the whole stand. This approach could help us to better understand the link between productivity, stand characteristics, and species growth parameters in mixed forests