Density estimates on a parabolic spde

We consider a general class of parabolic spde's [formula] with (t, x) [member of] [0, T]×[0, 1] and [epsilon]Wt,x, [epsilon] > 0, a perturbed Gaussian space-time white noise. For (t, x) [member of] (0, T]×(0, 1) we prove the called Davies and Varadhan-Léandre estimates of the density p[epsilon]t,x of the solution u[epsilon]t,x

Non-diffusive transport in plasma turbulence: a fractional diffusion approach

Numerical evidence of non-diffusive transport in three-dimensional, resistive pressure-gradient-driven plasma turbulence is presented. It is shown that the probability density function (pdf) of test particles' radial displacements is strongly non-Gaussian and exhibits algebraic decaying tails. To model these results we propose a macroscopic transport model for the pdf based on the use of fractional derivatives in space and time, that incorporate in a unified way space-time non-locality (non-Fickian transport), non-Gaussianity, and non-diffusive scaling. The fractional diffusion model reproduces the shape, and space-time scaling of the non-Gaussian pdf of turbulent transport calculations. The model also reproduces the observed super-diffusive scaling

Does size matter?

Failures of the complex infrastructures society depends on having enormous human and economic cost that poses the question: Are there ways to optimize these systems to reduce the risks of failure? A dynamic model of one such system, the power transmission grid, is used to investigate the risk from failure as a function of the system size. It is found that there appears to be optimal sizes for such networks where the risk of failure is balanced by the benefit given by the size

Kinetic description of avalanching systems

Avalanching systems are treated analytically using the renormalization group (in the self-organized-criticality regime) or mean-field approximation, respectively. The latter describes the state in terms of the mean number of active and passive sites, without addressing the inhomogeneity in their distribution. This paper goes one step further by proposing a kinetic description of avalanching systems making use of the distribution function for clusters of active sites. We illustrate application of the kinetic formalism to a model proposed for the description of the avalanching processes in the reconnecting current sheet of the Earth magnetosphere.Comment: 9 page

Towards a method to anticipate dark matter signals with deep learning at the LHC

We study several simplified dark matter (DM) models and their signatures at the LHC using neural networks. We focus on the usual monojet plus missing transverse energy channel, but to train the algorithms we organize the data in 2D histograms instead of event-by-event arrays. This results in a large performance boost to distinguish between standard model (SM) only and SM plus new physics signals. We use the kinematic monojet features as input data which allow us to describe families of models with a single data sample. We found that the neural network performance does not depend on the simulated number of background events if they are presented as a function of S/pB, for reasonably large B, where S and B are the number of signal and background events per histogram, respectively. This provides flexibility to the method, since testing a particular model in that case only requires knowing the new physics monojet cross section. Furthermore, we also discuss the network performance under incorrect assumptions about the true DM nature. Finally, we propose multimodel classifiers to search and identify new signals in a more general way, for the next LHC runThe work of EA is partially supported by the “Atracción de Talento” program (Modalidad 1) of the Comunidad de Madrid (Spain) under the grant number 2019-T1/TIC-14019 and by the Spanish Research Agency (Agencia Estatal de Investigación) through the grant IFT Centro de Excelencia Severo Ochoa SEV-2016-0597. This work has been also partially supported by CONICET and ANPCyT under projects PICT 2016-0164, PICT 2017-0802, PICT 2017-2751, PICT 2017- 2765, and PICT 2018-0368

An Intervention-AUV learns how to perform an underwater valve turning

Intervention autonomous underwater vehicles (I-AUVs) are a promising platform to perform intervention task in underwater environments, replacing current methods like remotely operate underwater vehicles (ROVs) and manned sub-mersibles that are more expensive. This article proposes a complete system including all the necessary elements to perform a valve turning task using an I-AUV. The knowledge of an operator to perform the task is transmitted to an I-AUV by a learning by demonstration (LbD) algorithm. The algorithm learns the trajectory of the vehicle and the end-effector to accomplish the valve turning. The method has shown its feasibility in a controlled environment repeating the learned task with different valves and configurations

Asymptotic behaviour of the density in a parabolic SPDE

Consider the density of the solution $X(t,x)$ of a stochastic heat equation with small noise at a fixed $t\in [0,T]$, $x \in [0,1]$. In the paper we study the asymptotics of this density as the noise is vanishing. A kind of Taylor expansion in powers of the noise parameter is obtained. The coefficients and the residue of the expansion are explicitly calculated. In order to obtain this result some type of exponential estimates of tail probabilities of the difference between the approximating process and the limit one is proved. Also a suitable local integration by parts formula is developped.Malliavin Calculus, parabolic SPDE, large deviations, Taylor expansion of a density, exponential estimates of the tail probabilities, stochastic integration by parts formula