Un paisaje de la literatura carcelaria: San Marcos de León en la obra de Quevedo

Francisco de Quevedo se hizo eco en parte de su epistolario de su experiencia carcelaria vivida en el otoño de sus días en San Marcos de León. En dicho epistolario mostró su visión más íntima y personal del fatídico episodio. El resultado fue una literatura ubicada en las lábiles fronteras entre la verdad y la ficción biográficas; una literatura surgida como una especie de conjura contra la cárcel. El epistolario de Quevedo —como la literatura carcelaria y la literatura del exilio— es una rebeldía contra el olvido y una forma de rebeldía contra el poder político. Del mismo modo que Ovidio hiciera con su experiencia del exilio, Quevedo supo transformar su peripecia vital en San Marcos en una poética de literatura carcelaria

Frequentist versus Bayesian analyses: Cross-correlation as an approximate sufficient statistic for LIGO-Virgo stochastic background searches

Sufficient statistics are combinations of data in terms of which the likelihood function can be rewritten without loss of information. Depending on the data volume reduction, the use of sufficient statistics as a preliminary step in a Bayesian analysis can lead to significant increases in efficiency when sampling from posterior distributions of model parameters. Here we show that the frequency integrand of the cross-correlation statistic and its variance are approximate sufficient statistics for ground-based searches for stochastic gravitational-wave backgrounds. The sufficient statistics are approximate because one works in the weak-signal approximation and uses measured estimates of the auto-correlated power in each detector. Using analytic and numerical calculations, we prove that LIGO-Virgo's hybrid frequentist-Bayesian parameter estimation analysis is equivalent to a fully Bayesian analysis. This work closes a gap in the LIGO-Virgo literature, and suggests directions for additional searches

Progressive Probabilistic Hough Transform for line detection

We present a novel Hough Transform algorithm referred to as Progressive Probabilistic Hough Transform (PPHT). Unlike the Probabilistic HT where Standard HT is performed on a pre-selected fraction of input points, PPHT minimises the amount of computation needed to detect lines by exploiting the difference an the fraction of votes needed to detect reliably lines with different numbers of supporting points. The fraction of points used for voting need not be specified ad hoc or using a priori knowledge, as in the probabilistic HT; it is a function of the inherent complexity of the input data. The algorithm is ideally suited for real-time applications with a fixed amount of available processing time, since voting and line detection is interleaved. The most salient features are likely to be detected first. Experiments show that in many circumstances PPHT has advantages over the Standard HT

Robust symmetric multiplication for programmable analog VLSI array processing

This paper presents an electrically programmable analog multiplier. The circuit performs the multiplication between an input variable and an electrically selectable scaling factor. The multiplier is divided in several blocks: a linearized transconductor, binary weighted current mirrors and a differential to single-ended current adder. This paper shows the advantages introduced using a linearized OTA-based multiplier. The circuit presented renders higher linearity and symmetry in the output current than a previously reported single-transistor multiplier. Its inclusion in an array processor based on CNN allows for a more accurate implementation of the processing model and a more robust weight distribution scheme than those found in previous designs.Office of Naval Research (USA) N-00014- 02-1-0884Ministerio de Ciencia y Tecnología TIC2003-09817-C02-0

RPNet: an End-to-End Network for Relative Camera Pose Estimation

This paper addresses the task of relative camera pose estimation from raw image pixels, by means of deep neural networks. The proposed RPNet network takes pairs of images as input and directly infers the relative poses, without the need of camera intrinsic/extrinsic. While state-of-the-art systems based on SIFT + RANSAC, are able to recover the translation vector only up to scale, RPNet is trained to produce the full translation vector, in an end-to-end way. Experimental results on the Cambridge Landmark dataset show very promising results regarding the recovery of the full translation vector. They also show that RPNet produces more accurate and more stable results than traditional approaches, especially for hard images (repetitive textures, textureless images, etc). To the best of our knowledge, RPNet is the first attempt to recover full translation vectors in relative pose estimation