Simple vs complex temporal recurrences for video saliency prediction

This paper investigates modifying an existing neural network architecture for static saliency prediction using two types of recurrences that integrate information from the temporal domain. The first modification is the addition of a ConvLSTM within the architecture, while the second is a conceptually simple exponential moving average of an internal convolutional state. We use weights pre-trained on the SALICON dataset and fine-tune our model on DHF1K. Our results show that both modifications achieve state-of-the-art results and produce similar saliency maps. Source code is available at https://git.io/fjPiB

Losses for microwave transmission in metamaterials for producing left-handed materials: The strip wires

This paper shows that the effective dielectric permitivity for the metamaterials used so far to obtain left-handed materials, with strip wires 0.003cm thick, is dominated by the imaginary part at 10.6- 11.5 GHz frequencies, where the band pass filter is, and therefore there is not propagation and the wave is inhomogeneous inside the medium. This is shown from finite-differences time-domain calculations using the real permitivity values for the Cu wires. For thicker wires the losses are reduced and the negative part of the permitivity dominates. As the thickness of the wires is critical for the realization of a good transparent left- handed material we propose that the strip wires should have thickness of 0.07-0.1cm and the split ring resonators 0.015-0.03c

Rydberg Wave Packets are Squeezed States

We point out that Rydberg wave packets (and similar ``coherent" molecular packets) are, in general, squeezed states, rather than the more elementary coherent states. This observation allows a more intuitive understanding of their properties; e.g., their revivals.Comment: 7 pages of text plus one figure available in the literature, LA-UR 93-2804, to be published in Quantum Optics, LaTe

Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures

In this article we propose novel Bayesian nonparametric methods using Dirichlet Process Mixture (DPM) models for detecting pairwise dependence between random variables while accounting for uncertainty in the form of the underlying distributions. A key criteria is that the procedures should scale to large data sets. In this regard we find that the formal calculation of the Bayes factor for a dependent-vs.-independent DPM joint probability measure is not feasible computationally. To address this we present Bayesian diagnostic measures for characterising evidence against a "null model" of pairwise independence. In simulation studies, as well as for a real data analysis, we show that our approach provides a useful tool for the exploratory nonparametric Bayesian analysis of large multivariate data sets

Exchangeable Claims Sizes in a Compound Poisson Type Proces

When dealing with risk models the typical assumption of independence among claim size distributions is not always satisfied. Here we consider the case when the claim sizes are exchangeable and study the implications when constructing aggregated claims through compound Poisson type processes. In par- ticular, exchangeability is achieved through conditional independence and using parametric and nonparametric measures for the conditioning distribution. A full Bayesian analysis of the proposed model is carried out to illustrate.Bayes nonparametrics, compound Poisson process, exchangeable claim process, exchangeable sequence, risk model.