A LES-Langevin model for turbulence

We propose a new model of turbulence for use in large-eddy simulations (LES). The turbulent force, represented here by the turbulent Lamb vector, is divided in two contributions. The contribution including only subfilter fields is deterministically modeled through a classical eddy-viscosity. The other contribution including both filtered and subfilter scales is dynamically computed as solution of a generalized (stochastic) Langevin equation. This equation is derived using Rapid Distortion Theory (RDT) applied to the subfilter scales. The general friction operator therefore includes both advection and stretching by the resolved scale. The stochastic noise is derived as the sum of a contribution from the energy cascade and a contribution from the pressure. The LES model is thus made of an equation for the resolved scale, including the turbulent force, and a generalized Langevin equation integrated on a twice-finer grid. The model is validated by comparison to DNS and is tested against classical LES models for isotropic homogeneous turbulence, based on eddy viscosity. We show that even in this situation, where no walls are present, our inclusion of backscatter through the Langevin equation results in a better description of the flow.Comment: 18 pages, 14 figures, to appear in Eur. Phys. J.

The blinking spotlight of attention

Increasing evidence suggests that attention can concurrently select multiple locations; yet it is not clear whether this ability relies on continuous allocation of attention to the different targets (a "parallel" strategy) or whether attention switches rapidly between the targets (a periodic "sampling" strategy). Here, we propose a method to distinguish between these two alternatives. The human psychometric function for detection of a single target as a function of its duration can be used to predict the corresponding function for two or more attended targets. Importantly, the predicted curves differ, depending on whether a parallel or sampling strategy is assumed. For a challenging detection task, we found that human performance was best reflected by a sampling model, indicating that multiple items of interest were processed in series at a rate of approximately seven items per second. Surprisingly, the data suggested that attention operated in this periodic regime, even when it was focused on a single target. That is, attention might rely on an intrinsically periodic process

Geometrical Expression for the Angular Resolution of a Network of Gravitational-Wave Detectors

We report for the first time general geometrical expressions for the angular resolution of an arbitrary network of interferometric gravitational-wave (GW) detectors when the arrival-time of a GW is unknown. We show explicitly elements that decide the angular resolution of a GW detector network. In particular, we show the dependence of the angular resolution on areas formed by projections of pairs of detectors and how they are weighted by sensitivities of individual detectors. Numerical simulations are used to demonstrate the capabilities of the current GW detector network. We confirm that the angular resolution is poor along the plane formed by current LIGO-Virgo detectors. A factor of a few to more than ten fold improvement of the angular resolution can be achieved if the proposed new GW detectors LCGT or AIGO are added to the network. We also discuss the implications of our results for the design of a GW detector network, optimal localization methods for a given network, and electromagnetic follow-up observations.Comment: 13 pages, for Phys. Rev.

A study of course deviations during cross-country soaring

Several models are developed for studying the impact of deviations from course during cross country soaring flights. Analyses are performed at the microstrategy and macrostrategy levels. Two types of lift sources are considered: concentrated thermals and thermal streets. The sensitivity of the optimum speed solutions to various model, piloting and performance parameters is evaluated. Guides are presented to provide the pilot with criterions for making in-flight decisions. In general, course deviations are warranted during weak lift conditions, but are less justifiable with moderate to strong lift conditions

Object Detection Through Exploration With A Foveated Visual Field

We present a foveated object detector (FOD) as a biologically-inspired alternative to the sliding window (SW) approach which is the dominant method of search in computer vision object detection. Similar to the human visual system, the FOD has higher resolution at the fovea and lower resolution at the visual periphery. Consequently, more computational resources are allocated at the fovea and relatively fewer at the periphery. The FOD processes the entire scene, uses retino-specific object detection classifiers to guide eye movements, aligns its fovea with regions of interest in the input image and integrates observations across multiple fixations. Our approach combines modern object detectors from computer vision with a recent model of peripheral pooling regions found at the V1 layer of the human visual system. We assessed various eye movement strategies on the PASCAL VOC 2007 dataset and show that the FOD performs on par with the SW detector while bringing significant computational cost savings.Comment: An extended version of this manuscript was published in PLOS Computational Biology (October 2017) at https://doi.org/10.1371/journal.pcbi.100574

Policy Search: Any Local Optimum Enjoys a Global Performance Guarantee

Local Policy Search is a popular reinforcement learning approach for handling large state spaces. Formally, it searches locally in a paramet erized policy space in order to maximize the associated value function averaged over some predefined distribution. It is probably commonly b elieved that the best one can hope in general from such an approach is to get a local optimum of this criterion. In this article, we show th e following surprising result: \emph{any} (approximate) \emph{local optimum} enjoys a \emph{global performance guarantee}. We compare this g uarantee with the one that is satisfied by Direct Policy Iteration, an approximate dynamic programming algorithm that does some form of Poli cy Search: if the approximation error of Local Policy Search may generally be bigger (because local search requires to consider a space of s tochastic policies), we argue that the concentrability coefficient that appears in the performance bound is much nicer. Finally, we discuss several practical and theoretical consequences of our analysis