259 research outputs found

    Astrophysical Data Analytics based on Neural Gas Models, using the Classification of Globular Clusters as Playground

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    In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band HST images, is a typical data analytics problem, where methods based on Machine Learning have revealed a high efficiency and reliability, demonstrating the capability to improve the traditional approaches. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning, on the classification of Globular Clusters, extracted from the NGC1399 HST data. Main focus of this work was to use a well-tested playground to scientifically validate such kind of models for further extended experiments in astrophysics and using other standard Machine Learning methods (for instance Random Forest and Multi Layer Perceptron neural network) for a comparison of performances in terms of purity and completeness.Comment: Proceedings of the XIX International Conference "Data Analytics and Management in Data Intensive Domains" (DAMDID/RCDL 2017), Moscow, Russia, October 10-13, 2017, 8 pages, 4 figure

    A Note on Bank Capital Buffer: Does Bank Heterogeneity matter?

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    International audienceThe objective of this paper is to extend the literature on bank capital buffer by considering the role of bank heterogeneity. Using a sample of European commercial banks over 1992-2006, we show that four key determinants – risk, business cycle, market and peer discipline – have different impact on capital buffer depending on banks' financing mode, activity or size. Our results offer a framework for discussing the appropriateness of the still on-going suggestions on bank capital regulation. Whereas they support the differentiating measures undertaken in Basel 3 such as specific capital surcharges for SIFIs, they disagree with the adoption of uniform countercyclical buffers

    Necrosis avascular secundaria al tratamiento de la luxación congénita de cadera: relación entre factores terapéuticos y secuelas radiológicas

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    Se presentan 108 casos de Luxación Congénita de Cadera (LCC) unilateral tratados con la misma metódica terapéutica: tracción más reducción abierta o cerrada dependiendo de los hallazgos artrográficos. Tras un seguimiento medio de 7 años (Rango: 5-13), 5 (5%) tenían una coxa magna, 14 (13%) mostraban disminución de la altura epifisaria, 22 (20%) tenían una coxa magna con disminución de la altura epifisaria 10 (9%) presentaban lesión fisaria residual. El análisis estadístico demostró asociación significativa (p<0,05 ) entre el desarrollo de coxa magna con disminución de la altura epifisaria y la ausencia de descenso cefálico al terminar la tracción, así como con la reducción abierta. La lesión fisaria residual, se encontró asociada significativamente a LCC Tipo IV de Tönnis, caderas que estuvieron más de 5 semanas en tracción, fallo en el descenso cefálico al finalizar la tracción y reducción abierta. En conclusión, se recomienda la tracción preoperatoria «efectiva», que desciende la cabeza femoral a nivel del cotilo, para disminuir las alteraciones radiológicas finales, secuelas de necrosis avascular.A total of 108 patients with unilateral congenital dislocation of the hip treated by the same therapeutic approach, are reviewed. The protocol for treatment consisted in traction and open or closed reduction, depending of the arthrographic findings. After 7-year follow-up (range, 5-13), 5 (5%) had coxa magna, 14 (13%) showed a decrease in epiphyseal height, 22 (20%) exhibited both coxa magna and decreased epiphyseal height, and 10 (9%) showed physeal damage. The statistical analysis revealed a significant relationship (p < 0,05) between the development of coxa magna with decreased epiphyseal height and both an absence of femoral head descent after traction and an open reduction of the hip. Physeal damage was found to be associated to Tönnis type-IV congenital dislocation, to hips undergoing more than 5 weeks traction, to failed cephalic descent following traction and an open reduction procedure. In conclusion, a effective preoperative hip traction allowing an appropriate descent of the femoral head to the acetabulum is recommended in order to prevent radiological alterations induced by avascular necrosis

    Quiebra psicológica y conducta autolítica en los movimientos migratorios forzados

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    Los movimientos de población forzada se han incrementado notablemente en los últimos años. Quienes forman parte de ellos pueden llegar a sufrir estrés, trauma y realizar conductas autolíticas durante las rutas de desplazamiento. En este artículo se describe el modelo de atención en salud mental y apoyo psicosocial a estas personas por parte de los equipos de Médicos del Mundo en el terreno. Se pretende proporcionar una respuesta precoz para evitar la cronificación de estos procesos derivados de la quiebra psicológica.Forced population movements have increased markedly in recent years. Those who are part of them can suffer stress, trauma and perform autolytic behavior during the routes of displacement. This article describes the model of mental health care and psychosocial support to these people by the World Medical teams in the field. It is intended to provide an early response to avoid the chronification of these processes derived from psychological bankruptcy

    Influencia de algunos factores demográficos en el modelo dietético de los españoles

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    Tesis de la Universidad Complutense de Madrid, Facultad de Farmacia, Departamento de Nutrición y Bromatología I (Nutrición), leída el 9-04-1996El objeto de este trabajo fue estudiar el consumo de alimentos de la población española, su contenido en energía y nutrientes, así como su evolución durante los últimos treinta años y la influencia que ejercen sobre los mismos algunos factores demográficos (Comunidad Autonoma y tamaño del municipio de residencia). Para ello se utilizan los datos procedentes de la última epf realizada por el instituto nacional de estadística en 1991 y se comparan con los existentes en 1964 y 1981. La calidad de la dieta juzgada por la energía procedente de los macronutrientes ha cambiado. En 1964, los porcentajes (12% de proteina, 32% de grasa, 53% de hidratos de carbono) coincidían con los niveles recomendados. Posteriormente, la contribución de los hidratos de carbono a la energía total ha disminuido siendo sustituidos por una cada vez mayor ingesta de grasa y proteína. Este cambio es debido al considerable descenso en la ingesta absoluta de hidratos de carbono consecuencia de la importante disminución del consumo de pan y patatas. A pesar de esto, las verduras (excepto patatas) y las frutas con 478 g/dia han aumentado junto a los cereales (233 g/dia) representando aproximadamente un 50% de la dieta total, un aspecto tradicional y saludable de la dieta española que debe mantenerse. La calidad de la grasa es satisfactoria debido al alto contenido en agm del aceite de oliva. Sin embargo, la relación agp+agm/ags aunque aún buena, continúa disminuyendo. Así, el consumo de frutas es el que se muestra mas homogéneo entre todas las comunidades, seguido por el de cereales, mayoritariamente pan, aceites y grasas, lácteos, carnes y verduras. Las mayores diferencias entre autonomías, se observan en bebidas alcohólicas, principalmente cerveza, patatas; leguminosas y bebidas no alcohólicas. La mayor ingesta calórica corresponde a Galicia (3270 kcal) y la menor a la Comunidad Valenciana (2309 kcal). El perfil calórico de Canarias, con 2423 kcal, es el que se acerca más al recomendado y el más desfavorable corresponde a La Rioja con 2744 kcal. A medida que aumenta el tamaño del municipio de residencia se produce una disminución en la ingesta de alimentos de carácter básico (pan, patatas, aceites y leguminosas) y de otros como pollo, cerdo, embutidos, sardinas, pescados congelados y vino. Por el contrario aumenta el consumo de verduras, frutas, carne de vacuno y algunos pescados como merluza y gallosDepto. de Nutrición y Ciencia de los AlimentosFac. de FarmaciaTRUEpu

    A novel approach to the classification of terrestrial drainage networks based on deep learning and preliminary results on solar system bodies

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    Several approaches were proposed to describe the geomorphology of drainage networks and the abiotic/biotic factors determining their morphology. There is an intrinsic complexity of the explicit qualification of the morphological variations in response to various types of control factors and the difficulty of expressing the cause-effect links. Traditional methods of drainage network classification are based on the manual extraction of key characteristics, then applied as pattern recognition schemes. These approaches, however, have low predictive and uniform ability. We present a different approach, based on the data-driven supervised learning by images, extended also to extraterrestrial cases. With deep learning models, the extraction and classification phase is integrated within a more objective, analytical, and automatic framework. Despite the initial difficulties, due to the small number of training images available, and the similarity between the different shapes of the drainage samples, we obtained successful results, concluding that deep learning is a valid way for data exploration in geomorphology and related fields

    Neural Gas based classification of Globular Clusters

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    Within scientific and real life problems, classification is a typical case of extremely complex tasks in data-driven scenarios, especially if approached with traditional techniques. Machine Learning supervised and unsupervised paradigms, providing self-adaptive and semi-automatic methods, are able to navigate into large volumes of data characterized by a multi-dimensional parameter space, thus representing an ideal method to disentangle classes of objects in a reliable and efficient way. In Astrophysics, the identification of candidate Globular Clusters through deep, wide-field, single band images, is one of such cases where self-adaptive methods demonstrated a high performance and reliability. Here we experimented some variants of the known Neural Gas model, exploring both supervised and unsupervised paradigms of Machine Learning for the classification of Globular Clusters. Main scope of this work was to verify the possibility to improve the computational efficiency of the methods to solve complex data-driven problems, by exploiting the parallel programming with GPU framework. By using the astrophysical playground, the goal was to scientifically validate such kind of models for further applications extended to other contexts.Comment: 15 pages, 3 figures, to appear in the Volume of Springer Communications in Computer and Information Science (CCIS). arXiv admin note: substantial text overlap with arXiv:1710.0390
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