State representation learning with recurrent capsule networks

Unsupervised learning of compact and relevant state representations has beenproved very useful at solving complex reinforcement learning tasks Ha and Schmid-huber (2018). In this paper, we propose a recurrent capsule network Hinton et al.(2011) that learns such representations by trying to predict the future observationsin an agent’s trajector

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Flatlandis a simple, lightweight environment for fastprototyping and testing of reinforcement learning agents. It is oflower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of thereal world, such as continuity, multi-modal partially-observablestates with first-person view and coherent physics. We proposeto use it as an intermediary benchmark for problems related toLifelong Learning.Flatlandis highly customizable and offers awide range of task difficulty to extensively evaluate the propertiesof artificial agents. We experiment with three reinforcementlearning baseline agents and show that they can rapidly solvea navigation task inFlatland. A video of an agent acting inFlatlandis available here: https://youtu.be/I5y6Y2ZypdA

Using Wavelets to reject background in Dark Matter experiments

A method based on wavelet techniques has been developed and applied to background rejection in the data of the IGEX dark matter experiment. The method is presented and described in some detail to show how it efficiently rejects events coming from noise and microphonism through a mathematical inspection of their recorded pulse shape. The result of the application of the method to the last data of IGEX is presented.Comment: 14 pages, 8 figures. Submitted to Astrop. Phy

Preliminary Measurements of Be-10/Be-7 Ratio in Rainwater for Atmospheric Transport Analysis

The meteoric cosmogenic beryllium has been used as an essential geophysical tracer in the analysis of atmospheric flows and erosion soils since 1960. The first measurements Be-7 and Be-10 concentrations in rainwater from Mexico, have been carried out by using gamma decay spectroscopy and AMS techniques, respectively for each isotope. With this it was possible to report a preliminar value for the Be-10/Be-7 isotopic ratio in such environmental samples. The present work described preliminary results related to rainwater collected at mountain and metropolitan areas. Results are compared with predictions and previous measurements for both radioisotopes, observing a very sensible behavior particularly for the case of Be-7 activities

Neutron background at the Canfranc Underground Laboratory and its contribution to the IGEX-DM dark matter experiment

A quantitative study of the neutron environment in the Canfranc Underground Laboratory has been performed. The analysis is based on a complete set of simulations and, particularly, it is focused on the IGEX-DM dark matter experiment. The simulations are compared to the IGEX-DM low energy data obtained with different shielding conditions. The results of the study allow us to conclude, with respect to the IGEX-DM background, that the main neutron population, coming from radioactivity from the surrounding rock, is practically eliminated after the implementation of a suitable neutron shielding. The remaining neutron background (muon-induced neutrons in the shielding and in the rock) is substantially below the present background level thanks to the muon veto system. In addition, the present analysis gives us a further insight on the effect of neutrons in other current and future experiments at the Canfranc Underground Laboratory. The comparison of simulations with the body of data available has allowed to set the flux of neutrons from radioactivity of the Canfranc rock, (3.82 +- 0.44) x 10^{-6} cm^{-2} s^{-1}, as well as the flux of muon-induced neutrons in the rock, (1.73 +- 0.22(stat) \+- 0.69(syst)) x 10^{-9} cm^{-2} s^{-1}, or the rate of neutron production by muons in the lead shielding, (4.8 +- 0.6 (stat) +- 1.9 (syst)) x 10^{-9} cm^{-3} s^{-1}.Comment: 17 pages, 8 figures, elsart document class; final version to appear in Astroparticle Physic

Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning

Flatlandis a simple, lightweight environment for fastprototyping and testing of reinforcement learning agents. It is oflower complexity compared to similar 3D platforms (e.g. Deep-Mind Lab or VizDoom), but emulates physical properties of thereal world, such as continuity, multi-modal partially-observablestates with first-person view and coherent physics. We proposeto use it as an intermediary benchmark for problems related toLifelong Learning.Flatlandis highly customizable and offers awide range of task difficulty to extensively evaluate the propertiesof artificial agents. We experiment with three reinforcementlearning baseline agents and show that they can rapidly solvea navigation task inFlatland. A video of an agent acting inFlatlandis available here: https://youtu.be/I5y6Y2ZypdA

Numerical Methods for the Stochastic Landau-Lifshitz Navier-Stokes Equations

The Landau-Lifshitz Navier-Stokes (LLNS) equations incorporate thermal fluctuations into macroscopic hydrodynamics by using stochastic fluxes. This paper examines explicit Eulerian discretizations of the full LLNS equations. Several CFD approaches are considered (including MacCormack's two-step Lax-Wendroff scheme and the Piecewise Parabolic Method) and are found to give good results (about 10% error) for the variances of momentum and energy fluctuations. However, neither of these schemes accurately reproduces the density fluctuations. We introduce a conservative centered scheme with a third-order Runge-Kutta temporal integrator that does accurately produce density fluctuations. A variety of numerical tests, including the random walk of a standing shock wave, are considered and results from the stochastic LLNS PDE solver are compared with theory, when available, and with molecular simulations using a Direct Simulation Monte Carlo (DSMC) algorithm

Educational Benefits and Cognitive Health Life Expectancies: Racial/Ethnic, Nativity, and Gender Disparities

Background and Objectives: To examine racial/ethnic, nativity, and gender differences in the benefits of educational attainment on cognitive life expectancies among older adults in the United States. Design and Methods: We used data from the Health and Retirement Study (1998-2014) to estimate Sullivan-based life tables of cognitively healthy, cognitively impaired/no dementia (CIND), and dementia life expectancies by gender for older White, Black, U.S.-born and foreign-born Hispanic adults with less than high school, high school, and some college or more. Results: White respondents lived a greater percentage of their remaining lives cognitive healthy than their minority Black or Hispanic counterparts, regardless of level of education. Among respondents with some college or more, versus less than high school, Black and U.S.-born Hispanic women exhibited the greatest increase (both 37 percentage points higher) in the proportion of total life expectancy spent cognitively healthy; whereas White women had the smallest increase (17 percentage points higher). For men, the difference between respondents with some college or more, versus less than high school, was greatest for Black men (35 percentage points higher) and was lowest for U.S.-born Hispanic men (21 percentage points higher). Discussion and Implications: Our results provide evidence that the benefits of education on cognitive life expectancies are largest for Black men and women, and U.S.-born Hispanic women. The combination of extended longevity and rising prevalence of Alzheimer’s disease point to the need for understanding why certain individuals spend an extended period of their lives with poor cognitive health