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Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference

The 6th ECCOMAS Young Investigators Conference YIC2021 will take place from July 7th through 9th, 2021 at Universitat Politècnica de València, Spain. The main objective is to bring together in a relaxed environment young students, researchers and professors from all areas related with computational science and engineering, as in the previous YIC conferences series organized under the auspices of the European Community on Computational Methods in Applied Sciences (ECCOMAS). Participation of senior scientists sharing their knowledge and experience is thus critical for this event.YIC 2021 is organized at Universitat Politécnica de València by the Sociedad Española de Métodos Numéricos en Ingeniería (SEMNI) and the Sociedad Española de Matemática Aplicada (SEMA). It is promoted by the ECCOMAS.The main goal of the YIC 2021 conference is to provide a forum for presenting and discussing the current state-of-the-art achievements on Computational Methods and Applied Sciences,including theoretical models, numerical methods, algorithmic strategies and challenging engineering applications.Nadal Soriano, E.; Rodrigo Cardiel, C.; Martínez Casas, J. (2022). Proceedings of the YIC 2021 - VI ECCOMAS Young Investigators Conference. Editorial Universitat Politècnica de València. https://doi.org/10.4995/YIC2021.2021.15320EDITORIA

Microwave medical imaging using level set techniques

El cáncer de mama es una de las enfermedades que causan una mayor mortalidad entre las mujeres. Se estima que, sólo en Europa, una mujer es diagnosticada de esta enfermedad cada 2 minutos y medio, y que una muere cada 7 minutos y medio. Para su cura es fundamental la deteccin temprana de los pequeños tumores. Si éstos son detectados a tiempo, los tratamientos que existen hoy en da son mucho más efectivos. En consecuencia, es de fundamental disponer de tecnologías especializadas que puedan llevar a cabo esta tarea con seguridad y, al ser posible, a un costo bajo. La técnica de referencia hoy en día sigue siendo la mamografa, una imagen de rayos X de la mama comprimida. Sin embargo, éstas siguen teniendo inconvenientes bien conocidos: no detectan un 15 % de los tumores malignos, al mismo tiempo que el resultado de los falsos positivos es muy elevado (sólo un 13 % de las manchas encontradas resultan finalmente ser un tumor maligno). Además, exponen a las pacientes a radiación potencialmente peligrosa y el procedimiento es, a veces, poco confortable. Otras técnicas, como la resonancia magnética, dan buenos resultados pero son muy caros y no pueden utilizarse como medio de prevención a una escala general. Por ello, otras técnicas alternativas se están estudiando en la actualidad para el diagnóstico no invasivo de esta enfermedad. Entre ellas, destacan la tomografía de óptica difusa, la tomografía de impedancia eléctrica y las imágenes de microondas. En esta tesis se propone un algoritmo numérico especialmente diseñado para la detección y caracterización de pequeos tumores usando microondas. La idea consiste en iluminar la mama con radiación de frecuencias del orden de unos pocos GHz, y reconstruir las imágenes del interior a partir de las señales que se recogen en la superficie de la mama. La reconstrucción de estas imagenes supone la resolución de un problema inverso en donde se minimiza la diferencia de las señales medidas y las simuladas con el modelo de mama propuesto (que incluye el posible tumor). Para ello aplicamos técnicas novedosas de conjunto de nivel que permiten la representación implícita de las estructuras del interior de la mama, y suponen además una regularización implicita que estabiliza la resolución del problema inverso. Los resultados de nuestros experimentos numéricos demuestran que el algoritmo es capaz de localizar los tumores y reconstruir las distrubuciones de los parámetros dieléctricos dentro del mama de una manera eficiente. El algoritmo no sólo detecta el posible tumor y aproxima correctamente su tamaño, sino que además es capaz de caracterizar el tejido sano por su contenido en fibra y grasa y aproximar las propiedades dieléctricas del tumor, que pueden ser reflejo de su grado de malignidad

The Relation Between Classical and Quantum Mechanics

This thesis examines the relation between classical and quantum mechanics from philosophical, mathematical and physical standpoints. It first presents arguments in support of “conjectural realism” in scientific theories distinguished by explicit contextual structure and empirical testability; and it analyses intertheoretic reduction in terms of weakly equivalent theories over a domain of applicability. Familiar formulations of classical and quantum mechanics are shown to follow from a general theory of mechanics based on pure states with an intrinsic prob- ability structure. This theory is developed to the stage where theorems from quantum logic enable expression of the state geometry in Hilbert space. Quan- tum and classical mechanics are then elaborated and applied to subsystems and the measurement process. Consideration is also given to space-time geometry and the constraints this places on the dynamics. Physics and Mathematics, it is argued, are growing apart; the inadequate treatment of approximations in general and localisation in quantum mechanics in particular are seen as contributing factors. In the description of systems, the link between localisation and lack of knowledge shows that quantum mechanics should reflect the domain of applicability. Restricting the class of states provides a means of achieving this goal. Localisation is then shown to have a mathematical expression in terms of compactness, which in turn is applied to yield a topological theory of bound and scattering states. Finally, the thesis questions the validity of “classical limits” and “quantisations” in intertheoretic reduction, and demonstrates that a widely accepted classical limit does not constitute a proof of reduction. It proposes a procedure for determining whether classical and quantum mechanics are weakly equivalent over a domain of applicability, and concludes that, in this restricted sense, classical mechanics reduces to quantum mechanics

Sequential Machine learning Approaches for Portfolio Management

Cette thèse envisage un ensemble de méthodes permettant aux algorithmes d'apprentissage statistique de mieux traiter la nature séquentielle des problèmes de gestion de portefeuilles financiers. Nous débutons par une considération du problème général de la composition d'algorithmes d'apprentissage devant gérer des tâches séquentielles, en particulier celui de la mise-à-jour efficace des ensembles d'apprentissage dans un cadre de validation séquentielle. Nous énumérons les desiderata que des primitives de composition doivent satisfaire, et faisons ressortir la difficulté de les atteindre de façon rigoureuse et efficace. Nous poursuivons en présentant un ensemble d'algorithmes qui atteignent ces objectifs et présentons une étude de cas d'un système complexe de prise de décision financière utilisant ces techniques. Nous décrivons ensuite une méthode générale permettant de transformer un problème de décision séquentielle non-Markovien en un problème d'apprentissage supervisé en employant un algorithme de recherche basé sur les K meilleurs chemins. Nous traitons d'une application en gestion de portefeuille où nous entraînons un algorithme d'apprentissage à optimiser directement un ratio de Sharpe (ou autre critère non-additif incorporant une aversion au risque). Nous illustrons l'approche par une étude expérimentale approfondie, proposant une architecture de réseaux de neurones spécialisée à la gestion de portefeuille et la comparant à plusieurs alternatives. Finalement, nous introduisons une représentation fonctionnelle de séries chronologiques permettant à des prévisions d'être effectuées sur un horizon variable, tout en utilisant un ensemble informationnel révélé de manière progressive. L'approche est basée sur l'utilisation des processus Gaussiens, lesquels fournissent une matrice de covariance complète entre tous les points pour lesquels une prévision est demandée. Cette information est utilisée à bon escient par un algorithme qui transige activement des écarts de cours (price spreads) entre des contrats à terme sur commodités. L'approche proposée produit, hors échantillon, un rendement ajusté pour le risque significatif, après frais de transactions, sur un portefeuille de 30 actifs.This thesis considers a number of approaches to make machine learning algorithms better suited to the sequential nature of financial portfolio management tasks. We start by considering the problem of the general composition of learning algorithms that must handle temporal learning tasks, in particular that of creating and efficiently updating the training sets in a sequential simulation framework. We enumerate the desiderata that composition primitives should satisfy, and underscore the difficulty of rigorously and efficiently reaching them. We follow by introducing a set of algorithms that accomplish the desired objectives, presenting a case-study of a real-world complex learning system for financial decision-making that uses those techniques. We then describe a general method to transform a non-Markovian sequential decision problem into a supervised learning problem using a K-best paths search algorithm. We consider an application in financial portfolio management where we train a learning algorithm to directly optimize a Sharpe Ratio (or other risk-averse non-additive) utility function. We illustrate the approach by demonstrating extensive experimental results using a neural network architecture specialized for portfolio management and compare against well-known alternatives. Finally, we introduce a functional representation of time series which allows forecasts to be performed over an unspecified horizon with progressively-revealed information sets. By virtue of using Gaussian processes, a complete covariance matrix between forecasts at several time-steps is available. This information is put to use in an application to actively trade price spreads between commodity futures contracts. The approach delivers impressive out-of-sample risk-adjusted returns after transaction costs on a portfolio of 30 spreads

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Investigation of hadron matter using lattice QCD and implementation of lattice QCD applications on heterogeneous multicore acceleration processors

Observables relevant for the understanding of the structure of baryons were determined by means of Monte Carlo simulations of Lattice Quantum Chromodynamics (QCD) using 2+1 dynamical quark flavours. Especial emphasis was placed on how these observables change when flavour symmetry is broken in comparison to choosing equal masses for the two light and the strange quark. The first two moments of unpolarised, longitudinally, and transversely polarised parton distribution functions were calculated for the nucleon and hyperons. The latter are baryons which comprise a strange quark. Lattice QCD simulations tend to be extremely expensive, reaching the need for petaflop computing and beyond, a regime of computing power we just reach today. Heterogeneous multicore computing is getting increasingly important in high performance scientific computing. The strategy of deploying multiple types of processing elements within a single workflow, and allowing each to perform the tasks to which it is best suited is likely to be part of the roadmap to exascale. In this work new design concepts were developed for an active library (QDP++) harnessing the compute power of a heterogeneous multicore processor (IBM PowerXCell 8i processor). Not only a proof-of-concept is given furthermore it was possible to run a QDP++ based physics application (Chroma) achieving a reasonable performance on the IBM BladeCenter QS22