Kernels semàntics per a clustering de patrons

Memòria elaborada a partir d’una estada al projecte Proteus de la New York University entre abril i juny del 2007. Les tècniques de clustering poden ajudar a reduir la supervisió en processos d’obtenció de patrons per a Extracció d’Informació. Tanmateix, és necessari disposar d’algorismes adequats a documents, i aquests algorismes requereixen mesures adequades de similitud entre patrons. Els kernels poden oferir una solució a aquests problemes, però l’aprenentatge no supervisat requereix d’estrat`egies m´es astutes que l’aprenentatge supervisat per a incorporar major quantitat d’informació. En aquesta memòria, fruit de la meva estada de mes d’Abril al de Juny de 2007 al projecte. Proteus de la New York University, es proposen i avaluen diversos kernels sobre patrons. Ini- cialment s’estudien kernels amb una família de patrons restringits, i a continuació s’apliquen kernels ja usats en tasques supervisades d’Extracció d’Informació. Degut a la degradació del rendiment que experimenta el clustering a l’afegir informació irrellevant, els kernels se simpli- fiquen i es busquen estratègies per a incorporar-hi semàntica de forma selectiva. Finalment, s’estudia quin efecte té aplicar clustering sobre el coneixement semàntic com a pas previ al clustering de patrons. Les diverses estratègies s’avaluen en tasques de clustering de documents i patrons usant dades reals.

6

The use of the Internet now has a specific purpose: to find information. Unfortunately, the amount of data available on the Internet is growing exponentially, creating what can be considered a nearly infinite and ever-evolving network with no discernable structure. This rapid growth has raised the question of how to find the most relevant information. Many different techniques have been introduced to address the information overload, including search engines, Semantic Web, and recommender systems, among others. Recommender systems are computer-based techniques that are used to reduce information overload and recommend products likely to interest a user when given some information about the user's profile. This technique is mainly used in e-Commerce to suggest items that fit a customer's purchasing tendencies. The use of recommender systems for e-Government is a research topic that is intended to improve the interaction among public administrations, citizens, and the private sector through reducing information overload on e-Government services. More specifically, e-Democracy aims to increase citizens' participation in democratic processes through the use of information and communication technologies. In this chapter, an architecture of a recommender system that uses fuzzy clustering methods for e-Elections is introduced. In addition, a comparison with the smartvote system, a Web-based Voting Assistance Application (VAA) used to aid voters in finding the party or candidate that is most in line with their preferences, is presented.

Alineamiento múltiple de secuencias con T-Coffee: una aproximación paralela

Las aplicaciones de alineamiento múltiple de secuencias son prototipos de aplicaciones que requieren elevada potencia de cómputo y memoria. Se destacan por la relevancia científica que tienen los resultados que brindan a investigaciones científicas en el campo de la biomedicina, genética y farmacología. Las aplicaciones de alineamiento múltiple tienen la limitante de que no son capaces de procesar miles de secuencias, por lo que se hace necesario crear un modelo para resolver la problemática. Analizando el volumen de datos que se manipulan en el área de las ciencias biológica y la complejidad de los algoritmos de alineamiento de secuencias, la única vía de solución del problema es a través de la utilización de entornos de cómputo paralelos y la computación de altas prestaciones. La investigación realizada por nosotros tiene como objetivo la creación de un modelo paralelo que le permita a los algoritmos de alineamiento múltiple aumentar el número de secuencias a procesar, tratando de mantener la calidad en los resultados para garantizar la precisión científica. El modelo que proponemos emplea como base la clusterización de las secuencias de entrada utilizando criterios biológicos que permiten mantener la calidad de los resultados. Además, el modelo se enfoca en la disminución del tiempo de cómputo y consumo de memoria. Para presentar y validar el modelo utilizamos T-Coffee, como plataforma de desarrollo e investigación. El modelo propuesto pudiera ser aplicado a cualquier otro algoritmo de alineamiento múltiple de secuencias.

An objective anisotropic elastic plastic model and Algorithm applicable to bone mechanics

The implicit projection algorithm of isotropic plasticity is extended to an objective anisotropic elastic perfectly plastic model. The recursion formula developed to project the trial stress on the yield surface, is applicable to any non linear elastic law and any plastic yield function.A curvilinear transverse isotropic model based on a quadratic elastic potential and on Hill's quadratic yield criterion is then developed and implemented in a computer program for bone mechanics perspectives. The paper concludes with a numerical study of a schematic bone-prosthesis system to illustrate the potential of the model.

A numerical algorithm for finding solutions of a generalized Nash equilibrium problem

A family of nonempty closed convex sets is built by using the data of the Generalized Nash equilibrium problem (GNEP). The sets are selected iteratively such that the intersection of the selected sets contains solutions of the GNEP. The algorithm introduced by Iusem-Sosa (2003) is adapted to obtain solutions of the GNEP. Finally some numerical experiments are given to illustrate the numerical behavior of the algorithm.

Why do creative industries cluster? An analysis of the determinants of clustering of creative industries

Creative industries tend to concentrate mainly around large- and medium-sized cities, forming creative local production systems. The text analyses the forces behind clustering of creative industries to provide the first empirical explanation of the determinants of creative employment clustering following a multidisciplinary approach based on cultural and creative economics, evolutionary geography and urban economics. A comparative analysis has been performed for Italy and Spain. The results show different patterns of creative employment clustering in both countries. The small role of historical and cultural endowments, the size of the place, the average size of creative industries, the productive diversity and the concentration of human capital and creative class have been found as common factors of clustering in both countries.

Clustering or scattering: the underlying reason for regulating distance among retail outlets

Concerns on the clustering of retail industries and professional services in main streets had traditionally been the public interest rationale for supporting distance regulations. Although many geographic restrictions have been suppressed, deregulation has hinged mostly upon the theory results on the natural tendency of outlets to differentiate spatially. Empirical evidence has so far offered mixed results. Using the case of deregulation of pharmacy establishment in a region of Spain, we empirically show how pharmacy locations scatter, and that there is not rationale for distance regulation apart from the underlying private interest of very few incumbents.

An algorithm for semi-infinite polynomial optimization

"Vegeu el resum a l'inici del document del fitxer adjunt."

An approximation algorithm for #k-SAT

"Vegeu el resum a l'inici del document del fitxer adjunt"

Comparing Multilevel Clustering Methods on Weighted Graphs : The Case of Worldwide Air Passenger Traffic 2000-2004

Specific properties emerge from the structure of large networks, such as that of worldwide air traffic, including a highly hierarchical node structure and multi-level small world sub-groups that strongly influence future dynamics. We have developed clustering methods to understand the form of these structures, to identify structural properties, and to evaluate the effects of these properties. Graph clustering methods are often constructed from different components: a metric, a clustering index, and a modularity measure to assess the quality of a clustering method. To understand the impact of each of these components on the clustering method, we explore and compare different combinations. These different combinations are used to compare multilevel clustering methods to delineate the effects of geographical distance, hubs, network densities, and bridges on worldwide air passenger traffic. The ultimate goal of this methodological research is to demonstrate evidence of combined effects in the development of an air traffic network. In fact, the network can be divided into different levels of âeurooecohesionâeuro, which can be qualified and measured by comparative studies (Newman, 2002; Guimera et al., 2005; Sales-Pardo et al., 2007).

Clustering of Schistosoma mansoni mRNA sequences and analysis of the most transcribed genes: implications in metabolism and biology of different developmental stages

The study of the Schistosoma mansoni genome, one of the etiologic agents of human schistosomiasis, is essential for a better understanding of the biology and development of this parasite. In order to get an overview of all S. mansoni catalogued gene sequences, we performed a clustering analysis of the parasite mRNA sequences available in public databases. This was made using softwares PHRAP and CAP3. The consensus sequences, generated after the alignment of cluster constituent sequences, allowed the identification by database homology searches of the most expressed genes in the worm. We analyzed these genes and looked for a correlation between their high expression and parasite metabolism and biology. We observed that the majority of these genes is related to the maintenance of basic cell functions, encoding genes whose products are related to the cytoskeleton, intracellular transport and energy metabolism. Evidences are presented here that genes for aerobic energy metabolism are expressed in all the developmental stages analyzed. Some of the most expressed genes could not be identified by homology searches and may have some specific functions in the parasite.

Clustering and hot spot detection in socio-economic spatio-temporal data

Distribution of socio-economic features in urban space is an important source of information for land and transportation planning. The metropolization phenomenon has changed the distribution of types of professions in space and has given birth to different spatial patterns that the urban planner must know in order to plan a sustainable city. Such distributions can be discovered by statistical and learning algorithms through different methods. In this paper, an unsupervised classification method and a cluster detection method are discussed and applied to analyze the socio-economic structure of Switzerland. The unsupervised classification method, based on Ward's classification and self-organized maps, is used to classify the municipalities of the country and allows to reduce a highly-dimensional input information to interpret the socio-economic landscape. The cluster detection method, the spatial scan statistics, is used in a more specific manner in order to detect hot spots of certain types of service activities. The method is applied to the distribution services in the agglomeration of Lausanne. Results show the emergence of new centralities and can be analyzed in both transportation and social terms.