Reproducción del certificado extendido por el Consul Español en Hendaya a Carlos Solano.

Fecha: 20-4-1943 (>1970 reproducción) / Unidad de instalación: Carpeta 25 - Expediente 23-3 / Nº de pág.: 1 (mecanografiada)

[R_Clavijo_SanPrudencio] Intensive Program ERASMUS: TOPCART. Geometric Documentation of the Heritage (report of activities 2010-2011)

[EN] Data contained in this record come from the following accademic activity (from which it is possible to locate additional records related with the Monastery):

A Model of Ischemia-Induced Neuroblast Activation in the Adult Subventricular Zone

12 p.

Intensive Program ERASMUS: TOPCART. Geometric Documentation of the Heritage (administrative and academic documentation)

[EN] This academic activity has been the origin of other work that are also located in this repository. The first one is the dataset of information about the geometry of the Monastery recorded during the two years of fieldwork, then some bachelor thesis and papers are listed:

Cartas enviadas por Roberto Alejandro Apra a Justo Garate entre 1988 y 1993

1 carta y 2 tarjetas postales (manuscritas y mecanografiadas) ; entre 215x235mm y 151x130mm. Ubicación: Caja 1 - Carpeta 32

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.