Nonlinear Exchange Rate Predictability

We study whether the nonlinear behavior of the real exchange rate can help us account for the lack of predictability of the nominal exchange rate. We construct a smooth nonlinear error-correction model that allows us to test the hypotheses of nonlinear predictability of the nominal exchange rate and nonlinear behavior on the real exchange rate in the context of a fully specified cointegrated system. Using a panel of 19 countries and three numeraires, we find evidence of nonlinear predictability of the nominal exchange rate and of nonlinear mean reversion of the real exchange rate. Out-of-sample Theil's U-statistics show a higher forecast precision of the nonlinear model than the one obtained with a random walk specification. Although the robustness of the out-of-sample results over different forecast windows is somewhat limited, we are able to obtain significant predictability gains--from a parsimonious structural model with PPP fundamentals--even at short-run horizons.Exchange rates; Predictability; Nonlinearities; Purchasing power parity

Phase-field simulation of core-annular pipe flow

Phase-field methods have long been used to model the flow of immiscible fluids. Their ability to naturally capture interface topological changes is widely recognized, but their accuracy in simulating flows of real fluids in practical geometries is not established. We here quantitatively investigate the convergence of the phase-field method to the sharp-interface limit with simulations of two-phase pipe flow. We focus on core-annular flows, in which a highly viscous fluid is lubricated by a less viscous fluid, and validate our simulations with an analytic laminar solution, a formal linear stability analysis and also in the fully nonlinear regime. We demonstrate the ability of the phase-field method to accurately deal with non-rectangular geometry, strong advection, unsteady fluctuations and large viscosity contrast. We argue that phase-field methods are very promising for quantitatively studying moderately turbulent flows, especially at high concentrations of the disperse phase.Comment: Paper accepted for publication in International Journal of Multiphase Flo

A comparison of general-purpose optimization algorithms forfinding optimal approximate experimental designs

Several common general purpose optimization algorithms are compared for findingA- and D-optimal designs for different types of statistical models of varying complexity,including high dimensional models with five and more factors. The algorithms of interestinclude exact methods, such as the interior point method, the Nelder–Mead method, theactive set method, the sequential quadratic programming, and metaheuristic algorithms,such as particle swarm optimization, simulated annealing and genetic algorithms.Several simulations are performed, which provide general recommendations on theutility and performance of each method, including hybridized versions of metaheuristicalgorithms for finding optimal experimental designs. A key result is that general-purposeoptimization algorithms, both exact methods and metaheuristic algorithms, perform wellfor finding optimal approximate experimental designs

Diluting the Scalability Boundaries: Exploring the Use of Disaggregated Architectures for High-Level Network Data Analysis

Traditional data centers are designed with a rigid architecture of fit-for-purpose servers that provision resources beyond the average workload in order to deal with occasional peaks of data. Heterogeneous data centers are pushing towards more cost-efficient architectures with better resource provisioning. In this paper we study the feasibility of using disaggregated architectures for intensive data applications, in contrast to the monolithic approach of server-oriented architectures. Particularly, we have tested a proactive network analysis system in which the workload demands are highly variable. In the context of the dReDBox disaggregated architecture, the results show that the overhead caused by using remote memory resources is significant, between 66\% and 80\%, but we have also observed that the memory usage is one order of magnitude higher for the stress case with respect to average workloads. Therefore, dimensioning memory for the worst case in conventional systems will result in a notable waste of resources. Finally, we found that, for the selected use case, parallelism is limited by memory. Therefore, using a disaggregated architecture will allow for increased parallelism, which, at the same time, will mitigate the overhead caused by remote memory.Comment: 8 pages, 6 figures, 2 tables, 32 references. Pre-print. The paper will be presented during the IEEE International Conference on High Performance Computing and Communications in Bangkok, Thailand. 18 - 20 December, 2017. To be published in the conference proceeding

Stratified regression Monte-Carlo scheme for semilinear PDEs and BSDEs with large scale parallelization on GPUs

In this paper, we design a novel algorithm based on Least-Squares Monte Carlo (LSMC) in order to approximate the solution of discrete time Backward Stochastic Differential Equations (BSDEs). Our algorithm allows massive parallelization of the computations on multicore devices such as graphics processing units (GPUs). Our approach consists of a novel method of stratification which appears to be crucial for large scale parallelization

Aplicación web progresiva para el proceso de control de inventario en la empresa Mega Security Solutions S.A.

La presentextesis detallaxel desarrollo de una Aplicación Web Progresiva (PWA) para el Proceso dexControl de Inventarioxen la empresa Mega Security Solutions S.A., debido axquexlaxsituación empresarialxprevia axla aplicaciónxdel sistemaxpresentaba deficienciasxen cuanto a la gestión de las entradas/salidas de productos, así como el conteo de exactitud de estos mismos. El objetivo de estaxinvestigación xfue determinarxla influenciaxde una Aplicación Web Progresiva para el Proceso de Control Logístico en la empresa Mega Security Solutions, Los Olivos, 2018. En el desarrollo de esta investigación se describen previamente aspectos teóricos en referencia al proceso de control logístico, así como las metodologías que se utilizaron para el desarrollo de la aplicación web progresiva. Para la gestión del proyecto y sus entregables se empleó la metodología SCRUM, por ser la que más se acomodaba a las necesidades y etapas del proyecto, así mismo, para la construcción de este aplicativo se utilizó una arquitectura Front-end/Back-end. Es así que para la construcción del Back-end se utilizó se utilizó el lenguaje de programación PHP con el framework Symfony, para la construcción del Front-end y así mismo de la aplicación web progresiva se utilizó el Framework Angular en su versión 8. Para el motor de base de datos se utilizó MariaDb. Este proyecto involucra un tipo xde xinvestigaciónxaplicada, el diseñoxde laxinvestigación xes pre-experimentalxy el enfoquexesxcuantitativo. La técnicaxdexrecolección dexdatos fue elx fichaje xyxel instrumento fuexla ficha de registro, los cuales fueron validados por expertos. Después de realizarse las pruebas de pre-test y post-test, con respecto al indicador índice de rotación de inventario en el plazo establecido se obtuvo un incremento del 11,94%, teniendo inicialmente un 72.25,48% y posteriormente un 84,19% y con respecto al indicador exactitud de inventario se obtuvo un aumento de 9.61%, teniendo inicialmente un 87,01% y posteriormente un 96.62%. Se concluye que la aplicación web progresiva influyóxpositivamentexen elxProceso de Control de inventario en la empresa Mega Security Solutions, Los Olivos, 2018

Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes

Crohn's disease; Microbiome; Ulcerative colitisEnfermedad de Crohn; Microbioma; Colitis ulcerosaMalaltia de Crohn; Microbioma; Colitis ulcerosaInflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple omics data, it is possible to develop predictive models that are able to prognosticate the course and development of the disease. The interpretability of these models, and the study of the variables used, allows the identification of biological aspects of great importance in the development of the disease. In this work we generated a metagenomic signature with predictive capacity to identify IBD from fecal samples. Different Machine Learning models were trained, obtaining high performance measures. The predictive capacity of the identified signature was validated in two external cohorts. More precisely a cohort containing samples from patients suffering Ulcerative Colitis and another from patients suffering Crohn's Disease, the two major subtypes of IBD. The results obtained in this validation (AUC 0.74 and AUC = 0.76, respectively) show that our signature presents a generalization capacity in both subtypes. The study of the variables within the model, and a correlation study based on text mining, identified different genera that play an important and common role in the development of these two subtypes.CF-L's work was supported by the Collaborative Project in Genomic Data Integration (CICLOGEN) PI17/01826 funded by the Carlos III Health Institute from the Spanish National plan for Scientific and Technical Research and Innovation 2013-2016 and the European Regional Development Funds (FEDER)–A way to build Europe. JS's work was funded by the Ramón y Cajal grant (RYC2019-026576-I) funded by Ministry of Science and Innovation of the Spanish government. GL-C's work was supported by a grant from the Biotechnology and Biological Sciences Research Council (BBSRC grant BB/S006281/1) and open access publication fees were supported by Queen's University of Belfast UKRI block grant