Ciudad de Vacaciones 1933. El concurso internacional para Playa de San Juan (Alicante)

En 1933, por iniciativa municipal y con el apoyo del Gobierno, con la intención de captar un turismo extranjero y nacional de élite generando una nueva ‘industria’, se convoca el concurso de anteproyectos para la construcción de una ciudad satélite (a modo de ciudad jardín) para destinarla a ciudad de vacaciones en la Playa de San Juan (cvPSJ), Alicante, al que se presentan tres propuestas. Aquí se estudia el anteproyecto ganador (de P. Muguruza), que resulta pionero por las técnicas urbanísticas empleadas (información y zonificación), por la aplicación de la fotografía para la inserción de arquitecturas y equipamientos y por la sensibilidad desplegada en la protección del patrimonio cultural (medioambiental e histórico). Los referentes para este macro complejo turístico (de casi 10 km2), coetáneo a la Ciutat del Repós i Vacances (CRV) de Castelldefels, no proceden tanto de Europa como de EUA. Se realiza un análisis pormenorizado de la ordenación urbanística en atención a cómo el territorio existente la condiciona y se entrelaza con estrategias de promoción turística, donde se combinan la tríada: hotel, deporte y naturaleza (alojamiento, ocio y salud). Pero toda la ciudad está enfocada a un turismo burgués, para el que se prevé una arquitectura comercial que pronto envejecería en su repertorio. Veinticinco años después, en 1958, cuando las condiciones económicas y sociales fueron favorables al desarrollo de la zona, el mundo sería ya otro y el proyecto quedó obsoleto.

Feature selection by multi-objective optimisation: Application to network anomaly detection by hierarchical self-organising maps

Feature selection is an important and active issue in clustering and classification problems. By choosing an adequate feature subset, a dataset dimensionality reduction is allowed, thus contributing to decreasing the classification computational complexity, and to improving the classifier performance by avoiding redundant or irrelevant features. Although feature selection can be formally defined as an optimisation problem with only one objective, that is, the classification accuracy obtained by using the selected feature subset, in recent years, some multi-objective approaches to this problem have been proposed. These either select features that not only improve the classification accuracy, but also the generalisation capability in case of supervised classifiers, or counterbalance the bias toward lower or higher numbers of features that present some methods used to validate the clustering/classification in case of unsupervised classifiers. The main contribution of this paper is a multi-objective approach for feature selection and its application to an unsupervised clustering procedure based on Growing Hierarchical Self-Organising Maps (GHSOMs) that includes a new method for unit labelling and efficient determination of the winning unit. In the network anomaly detection problem here considered, this multi-objective approach makes it possible not only to differentiate between normal and anomalous traffic but also among different anomalies. The efficiency of our proposals has been evaluated by using the well-known DARPA/NSL-KDD datasets that contain extracted features and labelled attacks from around 2 million connections. The selected feature sets computed in our experiments provide detection rates up to 99.8% with normal traffic and up to 99.6% with anomalous traffic, as well as accuracy values up to 99.12%.

Quantum theory of spin waves in finite chiral spin chains

We calculate the effect of spin waves on the properties of finite-size spin chains with a chiral spin ground state observed on biatomic Fe chains deposited on iridium(001). The system is described with a Heisenberg model supplemented with a Dzyaloshinskii-Moriya coupling and a uniaxial single ion anisotropy that presents a chiral spin ground state. Spin waves are studied using the Holstein-Primakoff boson representation of spin operators. Both the renormalized ground state and the elementary excitations are found by means of Bogoliubov transformation, as a function of the two variables that can be controlled experimentally, the applied magnetic field and the chain length. Three main results are found. First, because of the noncollinear nature of the classical ground state, there is a significant zero-point reduction of the ground-state magnetization of the spin spiral. Second, there is a critical external field from which the ground state changes from chiral spin ground state to collinear ferromagnetic order. The character of the two lowest-energy spin waves changes from edge modes to confined bulk modes over this critical field. Third, in the spin-spiral state, the spin-wave spectrum exhibits oscillatory behavior as function of the chain length with the same period of the spin helix.

A connectionist computational method for face recognition

In this work, a modified version of the elastic bunch graph matching (EBGM) algorithm for face recognition is introduced. First, faces are detected by using a fuzzy skin detector based on the RGB color space. Then, the fiducial points for the facial graph are extracted automatically by adjusting a grid of points to the result of an edge detector. After that, the position of the nodes, their relation with their neighbors and their Gabor jets are calculated in order to obtain the feature vector defining each face. A self-organizing map (SOM) framework is shown afterwards. Thus, the calculation of the winning neuron and the recognition process are performed by using a similarity function that takes into account both the geometric and texture information of the facial graph. The set of experiments carried out for our SOM-EBGM method shows the accuracy of our proposal when compared with other state-of the-art methods.