École Polytechnique

About: École Polytechnique is a education organization based out in Palaiseau, France. It is known for research contribution in the topics: Laser & Plasma. The organization has 18995 authors who have published 39265 publications receiving 1225163 citations. The organization is also known as: Ecole Polytechnique & Polytechnique.

Dark Energy Survey year 1 results: a precise H0 estimate from DES Y1, BAO, and D/H data

Abstract: We combine Dark Energy Survey Year 1 clustering and weak lensing data with baryon acoustic oscillations and Big Bang nucleosynthesis experiments to constrain the Hubble constant. Assuming a flat ΛCDM model with minimal neutrino mass (∑m_ν = 0.06 eV), we find |$H_0=67.4^{+1.1}_{-1.2}\ \rm {km\,\rm s^{-1}\,\rm Mpc^{-1}}$| (68 per cent CL). This result is completely independent of Hubble constant measurements based on the distance ladder, cosmic microwave background anisotropies (both temperature and polarization), and strong lensing constraints. There are now five data sets that: (a) have no shared observational systematics; and (b) each constrains the Hubble constant with fractional uncertainty at the few-per cent level. We compare these five independent estimates, and find that, as a set, the differences between them are significant at the 2.5σ level (χ^2/dof = 24/11, probability to exceed = 1.1 per cent). Having set the threshold for consistency at 3σ, we combine all five data sets to arrive at |$H_0=69.3^{+0.4}_{-0.6}\ \rm {km\,\mathrm{ s}^{-1}\,\mathrm{ Mpc}^{-1}}$|⁠.

Optofluidics for energy applications

Abstract: Since its emergence as a field, optofluidics has developed unique tools and techniques for enabling the simultaneous delivery of light and fluids with microscopic precision. In this Review, we describe the possibilities for applying these same capabilities to the field of energy. We focus in particular on optofluidic opportunities in sunlight-based fuel production in photobioreactors and photocatalytic systems, as well as optofluidically enabled solar energy collection and control. We then provide a series of physical and scaling arguments that demonstrate the potential benefits of incorporating optofluidic elements into energy systems. Throughout the Review we draw attention to the ways in which optofluidics must evolve to enable the up-scaling required to impact the energy field.

Bayesian interactions and collective dynamics of opinion: Herd behavior and mimetic contagion

Abstract: Much recent work has been devoted to the analysis of herd behavior within sequential decision models. The present article generalizes their results to non-sequential contexts. We will show that, as soon as the hypothesis of sequentiality is dropped, a large variety of situations can be observed. Our model has been designed to study the collective learning process through which a group of interacting agents deals with environmental uncertainty. The crucial question revolves around the relative weight given by each individual to the different sources of information: his private information and his observation of the group opinion.

Distinguishing computer graphics from natural images using convolution neural networks

Abstract: This paper presents a deep-learning method for distinguishing computer generated graphics from real photographic images The proposed method uses a Convolutional Neural Network (CNN) with a custom pooling layer to optimize current best-performing algorithms feature extraction scheme Local estimates of class probabilities are computed and aggregated to predict the label of the whole picture We evaluate our work on recent photo-realistic computer graphics and show that it outperforms state of the art methods for both local and full image classification