A search for energy deposition by neutrinos in matter

An exploratory search for an anomalous energy deposition by neutrinos in a germanium crystal was performed in the CERN high energy neutrino beam. No signal was found and a limit is set at a level of about 10^-12 of the normal dE/dx for a minimum ionizing particle.Comment: 7 pages, 3 figures Paper accepted by Physics Letters B on March 4th, 199

Development of new photonic devices based on barium titanate in silicon

Integration of complex optical functionalities with high performance will lead to a huge development in the field of nanophotonics for a broad range of applications. Silicon photonics is currently the leading technology for the implementation of low-cost photonic integrated devices. The great potential of this technology relies on its compatibility with the mature silicon integrated circuits manufacturing based on complementary metal-oxide semiconductor (CMOS) processes widely used in microelectronic industry and the availability of high quality silicon-on-insulator wafers, an ideal platform for creating planar waveguide circuits that offers strong optical confinement due to the high index contrast between silicon (n=3.45) and silicon dioxide (n=1.45). In order to keep improving the performance of photonic devices on silicon, the integration of CMOS compatible materials with unique properties shows up as an excellent opportunity to overcome the current limitations in silicon while offering unprecedented and novel capabilities to the silicon platform. In this way, barium titantate (BaTiO3) stands out as one of the most disruptive candidates. The work developed in this thesis is essentially focused on the design, fabrication and characterization of an electro-optic modulator based on a hybrid BaTiO3 on silicon structure for the implementation of high performance electro-optic functionalities with beyond state-of-the art performance that currently cannot be afforded in silicon photonics technology.La integración de funcionalidades ópticas con alto rendimiento llevará a un gran desarrollo en el campo de la nanofotónica para un amplio abanico de aplicaciones. Actualmente, la fotónica de silicio es la tecnología líder para la implementación de dispositivos fotónicos integrados a bajo coste. El gran potencial de esta tecnología reside en su compatibilidad con las maduras técnicas de fabricación de circuitos integrados de silicio basadas en los procesos "complementary metal-oxide semiconductor" (CMOS) ampliamente utilizados en la industria microelectrónica y la disponibilidad de disponer de obleas de silicio sobre aislante de alta calidad, una plataforma ideal para crear circuitos de guía de ondas planas que ofrecen un fuerte confinamiento óptico debido al alto contraste índices entre el silicio (n=3,45) y el dióxido de silicio (n=1,45). Para poder mejorar el rendimiento de dispositivos fotónicos en silicio, la integración de materiales con propiedades excepcionales y compatibles con los procesos de fabricación CMOS surge como una excelente oportunidad para superar las actuales limitaciones de la tecnología de silicio al mismo tiempo que ofrece oportunidades novedosas y sin precedentes en la plataforma de silicio. En este sentido, el material titanato de bario (BaTiO3) se postula como uno de los candidatos más prometedores. El trabajo desarrollado en esta tesis está esencialmente enfocado en el diseño, fabricación y caracterización de un modulador electro-óptico basado en una estructura híbrida de BaTiO3 en silicio para la implementación de funcionalidades electro-ópticas de alto rendimiento más allá del estado del arte de las que no se puede disponer actualmente en la tecnología de fotónica de silicio.La integració de funcionalitats òptiques amb alt rendiment portarà a un gran desenvolupament en el camp de la nanofotònica per a un ampli ventall d'aplicacions. Actualment, la fotònica de silici és la tecnologia capdavantera per a la implementació de dispositius fotònics integrats a baix cost. El gran potencial d'aquesta tecnologia resideix en la seva compatibilitat amb les madures tècniques de fabricació de circuits integrats de silici basades en els processos "complementary metal-oxide semiconductor" (CMOS) amplament utilitzats en la indústria microelectrònica i la disponibilitat de disposar d'hòsties de silici sobre aïllant d'alta qualitat, una plataforma ideal per crear circuits de guia d'ones planes que ofereixen un fort confinament òptic a causa de l'alt contrast d'índexs entre el silici (n=3,45) i el diòxid de silici (n=1,45). Per poder millorar el rendiment de dispositius fotònics en silici, la integració de materials amb propietats excepcionals i compatibles amb els processos de fabricació CMOS sorgeix com una excel·lent oportunitat per superar les actuals limitacions de la tecnologia de silici al mateix temps que ofereix oportunitats noves i sense precedents en la plataforma de silici. En aquest sentit, el material titanat de bari (BaTiO3) es postula com un dels candidats més prometedors. El treball desenvolupat en aquesta tesi està essencialment enfocat en el disseny, fabricació i caracterització d'un modulador electro-òptic basat en una estructura híbrida de BaTiO3 en silici per a la implementació de funcionalitats electro-òptiques d'alt rendiment més enllà de l'estat de l'art de les quals no es pot disposar actualment a la tecnologia de fotònica de silici.Castera Molada, P. (2017). Development of new photonic devices based on barium titanate in silicon [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/86197TESI

Les matemàtiques a pre-escolar

Es el propósito del presente artículo estudiar la evolución de los conceptos básicos matemáticos en relación con la evolución madurativa del niño, con la finalidad de adecuar los contenidos matemáticos de la enseñanza para la etapa preescolar. Las matemáticas que se proponen no se definen ni como "tradicionales" ni como "modernas" La propuesta es, en cambio, de una didáctica que busca situaciones matemáticamente relacionables. El contenido hace referencia tanto a la formación del número natural (cardinal y ordinal) como a la formación de clases.It is the aim of the present article to study the evolution of the basic mathematical concepts in relation to the maturative evolution of the child, with the intention of adapting the mathematical contents of education for the pre-school stage. The mathematics that is proposed does not define itself neither as "traditional " not as "modern" The proposal is, on the other hand, of a kind of didactics that pursues situations mathematically related. The content refers to the formation of cardinal and ordinal number as well as the formation of classes

Non invasive evaluation of portal hypertension using transient elastography

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Mechanical grading of structural timber and species conservation in the forest of the Congo Basin

The aim of the present work is to propose a non-destructive experimental approach, organizing the species of the Congo Basin in four; according to the likeness of their main mechanical properties, and is also to promote the subsistence of over-consumed species in our biodiversity. The assignment of a given specie to one of the four groups takes place in return for a homogeneity test of comparison of the random variables of species to the random variable of the group. The present grading shows that, some species can be interchangeably consumed in the construction industry, without altering the 5th percentile characteristic value of mechanical properties. For a lasting management of its forest, the application of the findings of this work promotes the exploitation of less-consumed species and the conservation of exotic biodiversity of the Congo Basin.Key words: Vibratory method, characteristic value, test of homogeneity, group of species, grading, conservation

La qualité du bois de pin maritime.

Cet article nous présente les principales caractéristiques de la qualité du bois de pin maritime. Bien que les études aient été réalisées sur des pins d'origine landaise, les résultats sont pour l'essentiel transposable au pin mésogéen

Inertial and Second-order Optimization Algorithms for Training Neural Networks

Neural network models became highly popular during the last decade due to their efficiency in various applications. These are very large parametric models whose parameters must be set for each specific task. This crucial process of choosing the parameters, known as training, is done using large datasets. Due to the large amount of data and the size of the neural networks, the training phase is very expensive in terms of computational time and resources. From a mathematical point of view, training a neural network means solving a large-scale optimization problem. More specifically it involves the minimization of a sum of functions. The large-scale nature of the optimization problem highly restrains the types of algorithms available to minimize this sum of functions. In this context, standard algorithms almost exclusively rely on inexact gradients through the backpropagation method and mini-batch sub-sampling. As a result, firstorder methods such as stochastic gradient descent (SGD) remain the most used ones to train neural networks. Additionally, the function to minimize is non-convex and possibly nondifferentiable, resulting in limited convergence guarantees for these methods. In this thesis, we focus on building new algorithms exploiting second-order information only by means of noisy firstorder automatic differentiation. Starting from a dynamical system (an ordinary differential equation), we build INNA, an inertial and Newtonian algorithm. By analyzing together the dynamical system and INNA, we prove the convergence of the algorithm to the critical points of the function to minimize. Then, we show that the limit is actually a local minimum with overwhelming probability. Finally, we introduce Step-Tuned SGD that automatically adjusts the step-sizes of SGD. It does so by cleverly modifying the mini-batch sub-sampling allowing for an efficient discretization of second-order information. We prove the almost sure convergence of Step-Tuned SGD to critical points and provide rates of convergence. All the theoretical results are backed by promising numerical experiments on deep learning problems