Nested Sampling with Constrained Hamiltonian Monte Carlo

Nested sampling is a powerful approach to Bayesian inference ultimately limited by the computationally demanding task of sampling from a heavily constrained probability distribution. An effective algorithm in its own right, Hamiltonian Monte Carlo is readily adapted to efficiently sample from any smooth, constrained distribution. Utilizing this constrained Hamiltonian Monte Carlo, I introduce a general implementation of the nested sampling algorithm.Comment: 15 pages, 4 figure

Cruising The Simplex: Hamiltonian Monte Carlo and the Dirichlet Distribution

Due to its constrained support, the Dirichlet distribution is uniquely suited to many applications. The constraints that make it powerful, however, can also hinder practical implementations, particularly those utilizing Markov Chain Monte Carlo (MCMC) techniques such as Hamiltonian Monte Carlo. I demonstrate a series of transformations that reshape the canonical Dirichlet distribution into a form much more amenable to MCMC algorithms.Comment: 5 pages, 0 figure

Alteraciones morfológicas en riñones de ratas sometidos a isquemia reperfusión

Fundamento: La isquemia reperfusión renal es un proceso en donde se involucra una serie de eventos que pueden conducir al daño y muerte celular. Objetivo: Determinar las modificaciones morfológicas que se producen en el riñón a medida que progresa el tiempo en el proceso de isquemia reperfusión. Métodos: Se utilizó como muestra un grupo de 35 ratas Wistar, machos, que fueron divididos en cinco grupos de siete ratas cada uno. Al subgrupo que no se le provocó isquemia se le denominó control (C) y al resto se les nombró experimentales (E). El subgrupo experimental E- I fue sometido a 15 minutos de isquemia, el E-II a 30 minutos, el E-III a 45 minutos y el E-IV a una hora. Luego se tomaron muestras de tejido renal para estudio morfométrico. Resultados: Se produjo una reducción significativa del número de glomérulos y de la fracción de área ocupada por estos tras isquemia reperfusión temporal de 15 minutos (EI), la que parece ser reversible por la ligera mejoría observada tras 30 minutos (EII) a partir del cual sigue recayendo hasta después de una hora de isquemia reperfusión (EIV). Conclusiones: La combinación de isquemia con reperfusión supera el tiempo crítico para provocar lesión irreversible celular del tejido renal

Detecting Hands in Egocentric Videos: Towards Action Recognition

Recently, there has been a growing interest in analyzing human daily activities from data collected by wearable cameras. Since the hands are involved in a vast set of daily tasks, detecting hands in egocentric images is an important step towards the recognition of a variety of egocentric actions. However, besides extreme illumination changes in egocentric images, hand detection is not a trivial task because of the intrinsic large variability of hand appearance. We propose a hand detector that exploits skin modeling for fast hand proposal generation and Convolutional Neural Networks for hand recognition. We tested our method on UNIGE-HANDS dataset and we showed that the proposed approach achieves competitive hand detection results

Summarizing First-Person Videos from Third Persons' Points of Views

Video highlight or summarization is among interesting topics in computer vision, which benefits a variety of applications like viewing, searching, or storage. However, most existing studies rely on training data of third-person videos, which cannot easily generalize to highlight the first-person ones. With the goal of deriving an effective model to summarize first-person videos, we propose a novel deep neural network architecture for describing and discriminating vital spatiotemporal information across videos with different points of view. Our proposed model is realized in a semi-supervised setting, in which fully annotated third-person videos, unlabeled first-person videos, and a small number of annotated first-person ones are presented during training. In our experiments, qualitative and quantitative evaluations on both benchmarks and our collected first-person video datasets are presented.Comment: 16+10 pages, ECCV 201