Employment and Unemployment Transitions in Spain from 1996 to 2005

In this paper, we have studied the employment and nonemployment transitions in Spain from 1996 to 2005. To do so, we have used a multi-state multiepisode duration model and a censored continuous-time Markovian matrix. By using the censored Markovian matrix, we have been able to balance the negative effect that censore has on the estimated parameters. The results obtained suggest that women have a probability of employment six percent lower than men. In addition, we have been able to show that Spanish employees experience three different stages of employment during their first decade in the labor market.Employment and Nonemployment Transitions; Multi-state Multi-episode Duration Model; Hazard Rate; Censored Continuous-time Markovian Matrix

Performing Deep Recurrent Double Q-Learning for Atari Games

International audienceCurrently, many applications in Machine Learning are based on define new models to extract more information about data, In this case Deep Reinforcement Learning with the most common application in video games like Atari, Mario, and others causes an impact in how to computers can learning by himself with only information called rewards obtained from any action. There is a lot of algorithms modeled and implemented based on Deep Recurrent Q-Learning proposed by DeepMind used in AlphaZero and Go. In this document, We proposed Deep Recurrent Double Q-Learning which is an implementation of Deep Reinforcement Learning using Double Q-Learning algorithms and Recurrent Networks like LSTM and DRQN

Detection of emission in the Si i 1082.7 nm line core in sunspot umbrae

We analyze spectropolarimetric sunspot umbra observations taken in the near-infrared Si i 1082.7 nm line taking NLTE effects into account. The data were obtained with the GRIS instrument installed at the German GREGOR telescope. A point spread function (PSF) was constructed using prior Mercury observations with GRIS and the information provided by the adaptive optics system of the GREGOR telescope. The data were then deconvolved from the PSF using a principal component analysis deconvolution method and were analyzed via the NICOLE inversion code. The Si i 1082.7 nm line seems to be in emission in the umbra of the observed sunspot after the effects of scattered light are removed. We show how the spectral line shape of umbral profiles changes dramatically with the amount of scattered light. Indeed, the continuum levels range, on average, from 44% of the quiet Sun continuum intensity to about 20%. The inferred levels are in line with current model predictions and empirical umbral models. Current umbral empirical models are not able to reproduce the emission in the deconvolved umbral Stokes profiles. The results of the NLTE inversions suggests that to obtain the emission in the Si i 1082.7 nm line, the temperature stratification should first have a hump located at about log tau -2 and start rising at lower heights when moving into the transition region. This is, to our knowledge, the first time the Si i 1082.7 nm line is seen in emission in sunspot umbrae. The results show that the temperature stratification of current umbral models may be more complex than expected with the transition region located at lower heights above sunspot umbrae. Our finding might provide insights into understanding why the sunspot umbra emission in the millimeter spectral range is less than that predicted by current empirical umbral models

Solving second-order conic systems with variable precision

We describe and analyze an interior-point method to decide feasibility problems of second-order conic systems. A main feature of our algorithm is that arithmetic operations are performed with finite precision. Bounds for both the number of arithmetic operations and the finest precision required are exhibited

WSAM: Visual Explanations from Style Augmentation as Adversarial Attacker and Their Influence in Image Classification

Currently, style augmentation is capturing attention due to convolutional neural networks (CNN) being strongly biased toward recognizing textures rather than shapes. Most existing styling methods either perform a low-fidelity style transfer or a weak style representation in the embedding vector. This paper outlines a style augmentation algorithm using stochastic-based sampling with noise addition to improving randomization on a general linear transformation for style transfer. With our augmentation strategy, all models not only present incredible robustness against image stylizing but also outperform all previous methods and surpass the state-of-the-art performance for the STL-10 dataset. In addition, we present an analysis of the model interpretations under different style variations. At the same time, we compare comprehensive experiments demonstrating the performance when applied to deep neural architectures in training settings.Comment: 8 pages, 10 figure