An R package for permutations, Mallows and Generalized Mallows models

[EN]Probability models on permutations associate a probability value to each of the permutations on n items. This paper considers two popular probability models, the Mallows model and the Generalized Mallows model. We describe methods for making inference, sampling and learning such distributions, some of which are novel in the literature. This paper also describes operations for permutations, with special attention in those related with the Kendall and Cayley distances and the random generation of permutations. These operations are of key importance for the efficient computation of the operations on distributions. These algorithms are implemented in the associated R package. Moreover, the internal code is written in C++.

Effective and Robust Generalized Predictive Speed Control of Induction Motor

14 p.

Kazetaritza infografiaren garapena espainiako prentsan. "El Correo Español-El Pueblo Vasco"-ren kasua

383 p. : il.; gráf.

Mateda-2.0: Estimation of Distribution Algorithms in MATLAB

This paper describes Mateda-2.0, a MATLAB package for estimation of distribution algorithms (EDAs). This package can be used to solve single and multi-objective discrete and continuous optimization problems using EDAs based on undirected and directed probabilistic graphical models. The implementation contains several methods commonly employed by EDAs. It is also conceived as an open package to allow users to incorporate different combinations of selection, learning, sampling, and local search procedures. Additionally, it includes methods to extract, process and visualize the structures learned by the probabilistic models. This way, it can unveil previously unknown information about the optimization problem domain. Mateda-2.0 also incorporates a module for creating and validating function models based on the probabilistic models learned by EDAs.

Prioritization of candidate cancer genes-an aid to oncogenomic studies

The development of techniques for oncogenomic analyses such as array comparative genomic hybridization, messenger RNA expression arrays and mutational screens have come to the fore in modern cancer research. Studies utilizing these techniques are able to highlight panels of genes that are altered in cancer. However, these candidate cancer genes must then be scrutinized to reveal whether they contribute to oncogenesis or are coincidental and non-causative. We present a computational method for the prioritization of candidate (i) proto-oncogenes and (ii) tumour suppressor genes from oncogenomic experiments. We constructed computational classifiers using different combinations of sequence and functional data including sequence conservation, protein domains and interactions, and regulatory data. We found that these classifiers are able to distinguish between known cancer genes and other human genes. Furthermore, the classifiers also discriminate candidate cancer genes from a recent mutational screen from other human genes. We provide a web-based facility through which cancer biologists may access our results and we propose computational cancer gene classification as a useful method of prioritizing candidate cancer genes identified in oncogenomic studies.

Functional analysis of cancer-associated EGFR mutants using a cellular assay with YFP-tagged EGFR intracellular domain

Background: The presence of EGFR kinase domain mutations in a subset of NSCLC patients correlates with the response to treatment with the EGFR tyrosine kinase inhibitors gefitinib and erlotinib. Although most EGFR mutations detected are short deletions in exon 19 or the L858R point mutation in exon 21, more than 75 different EGFR kinase domain residues have been reported to be altered in NSCLC patients. The phenotypical consequences of different EGFR mutations may vary dramatically, but the majority of uncommon EGFR mutations have never been functionally evaluated. Results: We demonstrate that the relative kinase activity and erlotinib sensitivity of different EGFR mutants can be readily evaluated using transfection of an YFP-tagged fragment of the EGFR intracellular domain (YFP-EGFR-ICD), followed by immunofluorescence microscopy analysis. Using this assay, we show that the exon 20 insertions Ins770SVD and Ins774HV confer increased kinase activity, but no erlotinib sensitivity. We also show that, in contrast to the common L858R mutation, the uncommon exon 21 point mutations P848L and A859T appear to behave like functionally silent polymorphisms. Conclusion: The ability to rapidly obtain functional information on EGFR variants of unknown relevance using the YFP-EGFR-ICD assay might prove important in the future for the management of NSCLC patients bearing uncommon EGFR mutations. In addition, our assay may be used to determine the response of resistant EGFR mutants to novel second-generation TKIs.

Notas sobre fascismo y mundo antiguo en España

Comunicación presentada en el II Congresso Peninsular de Historia Antigua (Coimbra, 1990)

Origen y evolución de la relación entre periodistas y militares en operaciones. El sistema de empotrados Irak 2003

344 p.

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.

Extending Distance-based Ranking Models In Estimation of Distribution Algorithms

Recently, probability models on rankings have been proposed in the field of estimation of distribution algorithms in order to solve permutation-based combinatorial optimisation problems. Particularly, distance-based ranking models, such as Mallows and Generalized Mallows under the Kendall’s-t distance, have demonstrated their validity when solving this type of problems. Nevertheless, there are still many trends that deserve further study. In this paper, we extend the use of distance-based ranking models in the framework of EDAs by introducing new distance metrics such as Cayley and Ulam. In order to analyse the performance of the Mallows and Generalized Mallows EDAs under the Kendall, Cayley and Ulam distances, we run them on a benchmark of 120 instances from four well known permutation problems. The conducted experiments showed that there is not just one metric that performs the best in all the problems. However, the statistical test pointed out that Mallows-Ulam EDA is the most stable algorithm among the studied proposals.

Cogeneración de procesos en el desarrollo de una práctica escultórica

96 p. : il.

Fabricación de Blisks aeronáuticos mediante electroerosión (EDM)

[ES]En el mundo de la aeronáutica cada vez más se utilizan materiales de alta dureza y baja maquinabilidad. Por ello la necesidad de utilización de métodos de fabricación no convencionales de mecanizado. Para la fabricación de los Blisks, la electroerosión resulta un proceso ventajoso frente a otros procesos. En este trabajo se va llevar a cabo las etapas de puesta a punto de utillajes, mecanizado y control dimensional de los álabes mecanizados.

Análisis y estudio técnico de una instalación industrial para la fabricación de tubos de acero sin costura

[ES]En este trabajo se analiza el proceso de fabricación de tubos de acero sin soldadura en la planta de Tubos Reunido Industrial (en lo sucesivo TRI) en la localidad alavesa de Amurrio. Además, se acompaña el análisis con puntualizaciones sobre las alternativas aplicables en cada paso del proceso y la conveniencia de aplicarlas en la planta estudiada. De esta manera se realiza un estudio de los procesos y equipos requeridos para los distintos pasos que se dan para fabricar tubos de acero sin soldadura de gran calidad a partir de chatarra de acero. Así mismo, dada la importancia de la calidad y el medio ambiente en la estrategia de TRI, se aporta también en este trabajo un resumen de los métodos utilizados en estas áreas.

Estudio técnico sobre la sistematización de herramientas informáticas en procesos de troquelería para automoción.

[ES]El presente estudio técnico tiene por objeto desarrollar y exponer la metodología empleada en el análisis CAD/CAE de piezas de chapa conformada en troquelería así como poner de manifiesto las ventajas de dicha técnica frente a las técnicas clásicas. Para ello, en él deben figurar una descripción general de dicha metodología, los objetivos de este estudio, los pasos para alcanzarlos y una distribución en forma de cronograma de las tareas que incluye el mismo. El estudio abarca dos partes claramente diferenciadas:  Descripción de un software CAD/CAE y sus múltiples beneficios en el diseño y desarrollo del producto  Desarrollo y exposición de un ejemplo sencillo Los contenidos del documento se estructuran de la siguiente forma: Inicialmente, se exponen los beneficios que reporta el uso de un programa CAE en empresas de conformado de chapa. A continuación, se describen las características generales de un software de elementos finitos. Finalmente se concluye con un caso ejemplo básico que ilustra las características expuestas anteriormente.

On the optimal usage of labelled examples in semi-supervised multi-class classification problems

In recent years, the performance of semi-supervised learning has been theoretically investigated. However, most of this theoretical development has focussed on binary classification problems. In this paper, we take it a step further by extending the work of Castelli and Cover [1] [2] to the multi-class paradigm. Particularly, we consider the key problem in semi-supervised learning of classifying an unseen instance x into one of K different classes, using a training dataset sampled from a mixture density distribution and composed of l labelled records and u unlabelled examples. Even under the assumption of identifiability of the mixture and having infinite unlabelled examples, labelled records are needed to determine the K decision regions. Therefore, in this paper, we first investigate the minimum number of labelled examples needed to accomplish that task. Then, we propose an optimal multi-class learning algorithm which is a generalisation of the optimal procedure proposed in the literature for binary problems. Finally, we make use of this generalisation to study the probability of error when the binary class constraint is relaxed.