4,614 research outputs found
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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
Strategies for the Integration of quantum networks for a future quantum internet
The great scientific and technological advances that are being carried out in
the field of quantum communications, accompanied by large investment programs
such as EuroQCI, are driving the deployment of quantum network throughout the
world. One of the final long-term objectives is to achieve the development of a
quantum internet that provides greater security in its services and new
functionalities that the current internet does not have. This article analyzes
the possible integration strategies of already deployed networks or in the
process of being deployed in order to reach a future global quantum network.
Two strategies based on the SDN paradigm are proposed, based on a hierarchical
controller scheme and on a distributed model. Each of these approaches shows
pros and cons and could be applicable in different use cases. To define these
strategies, the most relevant deployments of quantum communications networks
carried out to date has been analyzed, as well as the different approaches for
a quantum network architecture and topology, and the various proposed
definitions of what quantum internet is and what are the components that would
make it up in an ideal scenario. Finally, several detected opportunities and
challenges regarding security and technological aspects are presented
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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
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
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
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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
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