Influencia del nivel socioeconómico en la adquisición de la Competencia Traductora

This longitudinal study (2006-2013), which falls within the field of empirical-experimental translation studies, was conducted in the Language School of the Autonomous University of Baja California, Mexico. The search for paradigms to explain the mechanisms by which students translate, and how to evaluate students’ development as they become experts, led me to select the holistic model proposed by the PACTE research group (Process of Acquisition of Translation Competence and Evaluation; PACTE, 2000, 2001, 2003, 2005, 2008, 2009, 2011), which breaks translation competence down into five sub-competences and a psychophysiological component. My working hypothesis was that the higher parents’ socio-economic level, the better their children’s academic performance. The object of study was the fifth semester of the bachelor’s degree in translation, and these variables were measured and analyzed with the aid of a pre-TOEFL exam and the Translog2000 software program. As part of this process, I isolated transfer sub-competence and linguistic sub-competence in L2, which were cross-tabulated with a socio-economic study that recorded monthly family income. The results showed that people with greater purchasing power have a more balanced development in their sub-competences (mainly in their transfer and linguistic sub-competence in L2) and a better holistic translation competence.El presente estudio longitudinal (2006-2013), que se ubica dentro de los estudios de traducción empírico-experimentales, se realizó en la Facultad de Idiomas de la Universidad Autónoma de Baja California, México. La búsqueda de paradigmas para explicar los mecanismos por los cuales los estudiantes traducen, y cómo evaluar su desarrollo a medida que se convierten en expertos, me llevó a seleccionar el modelo holístico propuesto por el grupo de investigación PACTE (Proceso de Adquisición de la Competencia Traductora y Evaluación; PACTE, 2000, 2001, 2003, 2005, 2008, 2009, 2011), que divide la competencia traductora en cinco subcompetencias y un componente psicofisiológico. La hipótesis de trabajo planteó que: a mayor nivel socioeconómico de los padres, mejor era el rendimiento académico de sus hijos. El objeto de estudio fue el quinto semestre de la licenciatura en traducción, cuyas variables se midieron y analizaron con la ayuda de un examen pre-TOEFL y el programa informático Translog2000. Como parte de este proceso, aislé los resultados arrojados por la sub-competencia de transferencia y la sub-competencia lingüística en L2, que se cruzaron con un estudio socioeconómico, el cual registró el ingreso familiar mensual. Los resultados mostraron que las personas con mayor poder adquisitivo tienen un desarrollo más equilibrado en sus subcompetencias (sobre todo, de la subcompetencia de transferencia y lingüística en L2) y una mejor competencia traductora holística

On the Uniqueness of the Fock Quantization of the Dirac Field in the Closed FRW Cosmology

The Fock quantization of free fields propagating in cosmological backgrounds is in general not unambiguously defined due to the non-stationarity of the spacetime. For the case of a scalar field in cosmological scenarios it is known that the criterion of unitary implementation of the dynamics serves to remove the ambiguity in the choice of Fock representation (up to unitary equivalence). Here, applying the same type of arguments and methods previously used for the scalar field case, we discuss the issue of the uniqueness of the Fock quantization of the Dirac field in the closed FRW spacetime proposed by D'Eath and Halliwell.Comment: 11 page

Artificial intelligence in knowledge management for Time Series Forecasting

Knowledge Management (KM) is a keen topic for an organization, in particular to those that have to deal with knowledge acquired from different sources, either from its own experiences or from that of others, to decide on the effective use of that knowledge to fulfill the goals of the organization. As representative examples of KM, one may have the object-oriented data bases, hypermedia or concept maps. On the other hand, techniques developed in Artificial Intelligence for knowledge representation and discovery may be of great use in KM; in particular, it seems natural to explore the potential of the organization past data to deal with management decisions of the present. One way is to use Time Series Forecasting (TSF), where forecasts are based on pattern recognition of past observations ordered in time. Traditional TSF methods, such as the Holt-Winters and the Box-Jenkins ones, are based on particular characteristics of the Time Series (TS), such as trend or seasonal effects. These methods work with well behaved TS, but present some drawbacks on TS with noise or some unknown nonlinear relations among the TS data. An alternative approach is the use of Artificial Neural Networks (ANNs), which present two main advantages: ANNs can extrapolate patterns from past data, even in TS with noise, and may adapt their behavior as new data comes in. A problem with the use of this approach is the search time for the best ANN architecture, which involves a large searching space, demanding a huge computational effort. Other aspect of concern is that of TS data filtering. Not all lags of the TS have the same influence over the forecast. Feeding the ANN with a big time window will slow the ANN forecasting efficiency. To solve these pitfalls, one may use random search, hill climbing or genetic procedures. The last ones are known to work well on problems of combinatorial nature, obtaining good solutions where other methods seem to fail. This paper presents an integrated approach for TSF: a set of rules will create the training cases, based on some lags of the TS; these rules and the ANN parameters will be encoded on the genetic chromosomes; finally, each ANN will be trained, leading to competition

A uniqueness criterion for the Fock quantization of scalar fields with time dependent mass

A major problem in the quantization of fields in curved spacetimes is the ambiguity in the choice of a Fock representation for the canonical commutation relations. There exists an infinite number of choices leading to different physical predictions. In stationary scenarios, a common strategy is to select a vacuum (or a family of unitarily equivalent vacua) by requiring invariance under the spacetime symmetries. When stationarity is lost, a natural generalization consists in replacing time invariance by unitarity in the evolution. We prove that, when the spatial sections are compact, the criterion of a unitary dynamics, together with the invariance under the spatial isometries, suffices to select a unique family of Fock quantizations for a scalar field with time dependent mass.Comment: 11 pages, version accepted for publication in Classical and Quantum Gravit

Criteria for the determination of time dependent scalings in the Fock quantization of scalar fields with a time dependent mass in ultrastatic spacetimes

For Klein-Gordon fields, it is well known that there exist an infinite number of nonequivalent Fock representations of the canonical commutation relations and, therefore, of inequivalent quantum theories. A context in which this kind of ambiguities arises and prevents the derivation of robust results is, e.g., in the quantum analysis of cosmological perturbations. In these situations, typically, a suitable scaling of the field by a time dependent function leads to a description in an auxiliary static background, though the nonstationarity still shows up in a time dependent mass. For such a field description, and assuming the compactness of the spatial sections, we recently proved in three or less spatial dimensions that the criteria of a natural implementation of the spatial symmetries and of a unitary time evolution are able to select a unique class of unitarily equivalent vacua, and hence of Fock representations. In this work, we succeed to extend our uniqueness result to the consideration of all possible field descriptions that can be reached by a time dependent canonical transformation which, in particular, involves a scaling of the field by a function of time. This kind of canonical transformations modify the dynamics of the system and introduce a further ambiguity in its quantum description, exceeding the choice of a Fock representation. Remarkably, for any compact spatial manifold in less than four dimensions, we show that our criteria eliminate any possible nontrivial scaling of the field other than that leading to the description in an auxiliary static background. Besides, we show that either no time dependent redefinition of the field momentum is allowed or, if this may happen, the redefinition does not introduce any Fock representation that cannot be obtained by a unitary transformation.Comment: 37 pages. Modified title. Improved discussion concerning the spatial symmetry group. New section (section VI

Algoritmos eficientes de búsqueda de códigos cíclicos y cíclicos acortados correctores de ráfagas de errores

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería del Software e Inteligencia Artificial, leída el 30/01/2013Depto. de Ingeniería de Software e Inteligencia Artificial (ISIA)Fac. de InformáticaTRUEUniversidad Complutense de MadridAgencia Española de Cooperación Internacional para el Desarrollo (AECID)unpu

An evolutionary artificial neural network time series forecasting system

Artificial Neural Networks (ANNs) have the ability of learning and to adapt to new situations by recognizing patterns in previous data. Time Series (TS) (observations ordered in time) often present a high degree of noise which difficults forecasting. Using ANNs for Time Series Forecasting (TSF) may be appealing. However, the main problem with this approach is on the search for the best ANN architecture. Genetic Algorithms (GAs) are suited for problems of combinatorial nature, where other methods seem to fail. Therefore, an integration of ANNs and GAs for TSF, taking the advantages of both methods, may be appealing. ANNs will learn to forecast by back-propagation. Different ANNs architectures will give different forecasts, leading to competition. At the end of the evolutionary process the resulting ANN is expected to return the best possible forecast. It is asserted that the combined strategy exceeded conventional TSF methods on TS of high non-linear degree, particularly for long term forecasts

Nepeta coerulea Aiton subsp. sanabrensis (Losa) Ubera & Valdés: uma labiada nova para a flora de Portugal

A Nepeta coerulea subsp. sanabrensis é uma subespécie nova para Portugal cujo tipo nomenclatural [basión. N. sanabrensis Losa, Contribuición al Estúdio de la Flora y Vegetación de la Província de Zamora, Inst. A.J. Cavanilles: 117] provém da vizinha região de Puebla de Sanábria. Além da Serra de Nogueira e da localidade clássica, estão publicadas na bibliografia apenas mais duas localidades para esta espécie por F. NAVARRO et al. (Stud. Bot. 10: 17-24, 1992), ambas localizadas não muito longe da Serra de Nogueira, na província espanhola de Zamora