Phenomenology Tools on Cloud Infrastructures using OpenStack

We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage "virtual" machines on which the phenomenology codes/tools can be deployed easily in an automated way. We analyze the performance of this environment based on "virtual" machines versus the utilization of "real" physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.Comment: 25 pages, 12 figures; information on memory usage included, as well as minor modifications. Version to appear in EPJ

La Garza Real en España. I. Población reproductora (1950-2000)

En este trabajo se analiza la evolución de la población reproductora de Garza Real, Ardea cinerea, en España durante el período 1950-2000. El territorio español se ha dividido en siete grandes zonas: Norte, Levante, y las cuencas de los cinco grandes ríos (Guadalquivir, Guadiana, Tajo, Duero y Ebro). Los datos sobre colonias y número de parejas nidificantes fueron obtenidos a partir de prospecciones de los autores, de citas bibliográficas y de comunicaciones de organismos y personas conocedoras de las colonias. Es probable que ya antes de 1950 hubiera colonias ocupadas, pero se carece de datos publicados sobre ellas. En 1950 pudo haber 168 parejas reproductoras en 4 colonias, mientras que en 2000 hubo 4790 parejas en 75 colonias. Las subpoblaciones de garza real asentadas de las cuencas del Duero y Tajo parecen estar próximas al equilibrio numérico, mientras que las del Ebro, Guadalquivir y Levante han aumentado notablemente su tamaño en la década 1990-2000. La cuenca del Guadiana contenía relativamente pocas parejas en el período de estudio y en el Norte de España la población reproductora de la garza real es aún poco significativa

Use of topical and temporal profiles and their hybridisation for content-based recommendation

In the context of content-based recommender systems, the aim of this paper is to determine how better profiles can be built and how these affect the recommendation process based on the incorporation of temporality, i.e. the inclusion of time in the recommendation process, and topicality, i.e. the representation of texts associated with users and items using topics and their combination. The main contribution of the paper is to present two different ways of hybridising these two dimensions and to evaluate and compare them with other alternatives

Publication venue recommendation using profiles based on clustering

In this paper we study the venue recommendation problem in order to help researchers to identify a journal or conference to submit a given paper. A common approach to tackle this problem is to build profiles defining the scope of each venue. Then, these profiles are compared against the target paper. In our approach we will study how clustering techniques can be used to construct topic-based profiles and use an Information Retrieval based approach to obtain the final recommendations. Additionally, we will explore how the use of authorship, representing a complementary piece of information, helps to improve the recommendations

In vivo absorption behaviour of theophylline from starch-methyl methacrylate matrix tablets in beagle dogs

This study evaluates in vivo the drug absorption profiles from potato starch-methyl methacrylate matrices∗ using theophylline as a model drug. Healthy beagle dogs under fasting conditions were used for in vivo studies and plasma samples were analyzed by a fluorescence polarization immunoassay analysis (FPIA method). Non-compartmental and compartmental (population approach) analysis was performed to determine the pharmacokinetic parameters. The principle of superposition was applied to predict multiple dose plasma concentrations from experimental single dose data. An in vitro-in vivo correlation (IVIVC) was also assessed. The sustained absorption kinetics of theophylline from these formulations was demonstrated by comparison with two commercially available oral sustained-release theophylline products (Theo-Dur® and Theolair®). A one-compartment model with first order kinetics without lag-time best describes the absorption/disposition of theophylline from the formulations. Results revealed a theophylline absorption rate in the order FD-HSMMA Theo-Dur®OD-CSMMA > Theolair®FD-CSMMA. On the basis of simulated plasma theophylline levels, a twice daily dosage (every 12 h) with the FD-CSMMA tablets should be recommended. A Level C IVIVC was found between the in vitro t50% and the in vivo AUC/D, although further optimization of the in vitro dissolution test would be needed to adequately correlate with in vivo data.Ministerio de Educación y Ciencia MAT2004-0159

Hot and repulsive traffic flow

We study a message passing model, applicable also to traffic problems. The model is implemented in a discrete lattice, where particles move towards their destination, with fluctuations around the minimal distance path. A repulsive interaction between particles is introduced in order to avoid the appearance of traffic jam. We have studied the parameter space finding regions of fluid traffic, and saturated ones, being separated by abrupt changes. The improvement of the system performance is also explored, by the introduction of a non-constant potential acting on the particles. Finally, we deal with the behavior of the system when temporary failures in the transmission occurs.Comment: 22 pages, uuencoded gzipped postscript file. 11 figures include

Predicting IR Personalization Performance using Pre-retrieval Query Predictors

Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements

LDA-based Term Profiles for Expert Finding in a Political Setting

A common task in many political institutions (i.e. Parliament) is to find politicians who are experts in a particular field. In order to tackle this problem, the first step is to obtain politician profiles which include their interests, and these can be automatically learned from their speeches. As a politician may have various areas of expertise, one alternative is to use a set of subprofiles, each of which covers a different subject. In this study, we propose a novel approach for this task by using latent Dirichlet allocation (LDA) to determine the main underlying topics of each political speech, and to distribute the related terms among the different topic-based subprofiles. With this objective, we propose the use of fifteen distance and similarity measures to automatically determine the optimal number of topics discussed in a document, and to demonstrate that every measure converges into five strategies: Euclidean, Dice, Sorensen, Cosine and Overlap. Our experimental results showed that the scores of the different accuracy metrics of the proposed strategies tended to be higher than those of the baselines for expert recommendation tasks, and that the use of an appropriate number of topics has proved relevant

Predicting IR Personalization Performance using Pre-retrieval Query Predictors

Personalization generally improves the performance of queries but in a few cases it may also harms it. If we are able to predict and therefore to disable personalization for those situations, the overall performance will be higher and users will be more satisfied with personalized systems. We use some state-of-the-art pre-retrieval query performance predictors and propose some others including the user profile information for the previous purpose. We study the correlations among these predictors and the difference between the personalized and the original queries. We also use classification and regression techniques to improve the results and finally reach a bit more than one third of the maximum ideal performance. We think this is a good starting point within this research line, which certainly needs more effort and improvements.This work has been supported by the Spanish Andalusian “Consejerı́a de Innovación, Ciencia y Empresa” postdoctoral phase of project P09-TIC-4526, the Spanish “Ministerio de Economı́a y Competitividad” projects TIN2013-42741-P and TIN2016-77902-C3-2-P, and the European Regional Development Fund (ERDF-FEDER)