Efficient learning of decomposable models with a bounded clique size

The learning of probability distributions from data is a ubiquitous problem in the fields of Statistics and Artificial Intelligence. During the last decades several learning algorithms have been proposed to learn probability distributions based on decomposable models due to their advantageous theoretical properties. Some of these algorithms can be used to search for a maximum likelihood decomposable model with a given maximum clique size, k, which controls the complexity of the model. Unfortunately, the problem of learning a maximum likelihood decomposable model given a maximum clique size is NP-hard for k > 2. In this work, we propose a family of algorithms which approximates this problem with a computational complexity of O(k · n^2 log n) in the worst case, where n is the number of implied random variables. The structures of the decomposable models that solve the maximum likelihood problem are called maximal k-order decomposable graphs. Our proposals, called fractal trees, construct a sequence of maximal i-order decomposable graphs, for i = 2, ..., k, in k − 1 steps. At each step, the algorithms follow a divide-and-conquer strategy based on the particular features of this type of structures. Additionally, we propose a prune-and-graft procedure which transforms a maximal k-order decomposable graph into another one, increasing its likelihood. We have implemented two particular fractal tree algorithms called parallel fractal tree and sequential fractal tree. These algorithms can be considered a natural extension of Chow and Liu’s algorithm, from k = 2 to arbitrary values of k. Both algorithms have been compared against other efficient approaches in artificial and real domains, and they have shown a competitive behavior to deal with the maximum likelihood problem. Due to their low computational complexity they are especially recommended to deal with high dimensional domains.

Proyecto de instalaciones del Polígono Agote II en Zestoa

El presente proyecto tiene como objetivo la definición y cálculo de las Instalaciones de abastecimiento, saneamiento, electricidad y telecomunicaciones del sector AG.I.2 Agote II del polígono Agote, en el término municipal de Zestoa. La ejecución de las instalaciones en el polígono industrial AG.I.2 Agote II supone beneficios de índole urbanística para el municipio y en especial para el polígono industrial Agote. En este documento se definen todos los detalles necesarios para definir a nivel constructivo todos los elementos que constituyen el proyecto de instalación del polígono Agote.

Combining rimonabant and fentanyl in a single entity: preparation and pharmacological results

Based on numerous pharmacological studies that have revealed an interaction between cannabinoid and opioid systems at the molecular, neurochemical, and behavioral levels, a new series of hybrid molecules has been prepared by coupling the molecular features of two well-known drugs, ie, rimonabant and fentanyl. The new compounds have been tested for their affinity and functionality regarding CB1 and CB2 cannabinoid and mu opioid receptors. In [S-35]-GTP.S (guanosine 5'-O-[gamma-thio] triphosphate) binding assays from the post-mortem human frontal cortex, they proved to be CB1 cannabinoid antagonists and mu opioid antagonists. Interestingly, in vivo, the new compounds exhibited a significant dual antagonist action on the endocannabinoid and opioid systems.