An Experimental Study in Adaptive Kernel Selection for Bayesian Optimization

Bayesian Optimization has been widely used along with Gaussian Processes for solving expensive-to-evaluate black-box optimization problems. Overall, this approach has shown good results, and particularly for parameter tuning of machine learning algorithms. Nonetheless, Bayesian Optimization has to be also configured to achieve the best possible performance, being the selection of the kernel function a crucial choice. This paper investigates the convenience of adaptively changing the kernel function during the optimization process, instead of fixing it a priori. Six adaptive kernel selection strategies are introduced and tested in well-known synthetic and real-world optimization problems. In order to provide a more complete evaluation of the proposed kernel selection variants, two major kernel parameter setting approaches have been tested. According to our results, apart from having the advantage of removing the selection of the kernel out of the equation, adaptive kernel selection criteria show a better performance than fixed-kernel approaches

in-depth analysis of SVM kernel learning and its components

The performance of support vector machines in non-linearly-separable classification problems strongly relies on the kernel function. Towards an automatic machine learning approach for this technique, many research outputs have been produced dealing with the challenge of automatic learn- ing of good-performing kernels for support vector machines. However, these works have been carried out without a thorough analysis of the set of components that influence the behavior of support vector machines and their interaction with the kernel. These components are related in an in- tricate way and it is difficult to provide a comprehensible analysis of their joint effect. In this paper we try to fill this gap introducing the necessary steps in order to understand these interactions and provide clues for the research community to know where to place the emphasis. First of all, we identify all the factors that affect the final performance of support vector machines in relation to the elicitation of kernels. Next, we analyze the factors independently or in pairs and study the influence each component has on the final classification performance, providing recommendations and insights into the kernel setting for support vector machines.IT1244-19 PID2019-104966GB-I0

Observatorio de geografía de la salud del Estado de México: Mortalidad Infantil

En el presente libro se abordan algunos resultados del proyecto de investigación denominado: “Observatorio de Geografía de la salud del Estado de México: mortalidad infantil, una perspectiva del pasado, situación actual y del futuro”, financiado por la Universidad Autónoma del Estado de México. Los objetivos del presente libro son: -Implementar una base de datos de mortalidad infantil por municipio en el período de 1990 a 2013 y generar la cartografía correspondiente. -Analizar la distribución espacial de la mortalidad infantil en un corte transversal para los años 1990, 2000 y 2010, así como su relación con los índices de marginación. -Establecer modelos matemáticos de las tendencias de mortalidad infantil por municipio y proyectar escenarios para los años 2015, 2020 y 2025.La mortalidad infantil constituye un problema fundamental para la salud pública en el mundo y en México en particular, representa un tema de agenda internacional y nacional, se considera como un indicador que refleja la situación de la salud y el nivel de desarrollo de la población y debe ser un tema prioritario en las políticas y estrategias de nivel multisectorial, lo que constituye un reto para la educación en salud, para la accesibilidad geográfica a los servicios de salud, para la alimentación de la población como factores que inciden en la mejoría de la salud infantil principalmente y la continua reducción de sus niveles de mortalidad. La importancia de un observatorio de geografía de la salud del Estado de México: mortalidad infantil, radica en la generación de reportes no solamente de un momento actual, sino del pasado como base para determinar tendencias y escenarios a corto, mediano y largo plazo, que permitan formular estrategias enfocadas a la promoción y prevención de la salud y se inserten en los planes de desarrollo municipal y/o planes de desarrollo urbano, en donde los diferentes actores se sumen en el trabajo intersectorial con el propósito de reducir las tasas de mortalidad infantil y garantizar la salud de la población.Universidad Autónoma del Estado de Méxic

Propagação vegetativa de manjericão (Ocimum basilicum L.) por estacas de diferentes posições cultivadas sob diferentes substratos

O manjericão (Ocimum basilicum L), é um arbusto herbáceo pertencente à família Lamiaceae. No Brasil é cultivado para fins comerciais, como planta ornamental, medicinal e aromática. Sendo assim, o trabalho objetivou avaliar a melhor forma de propagação vegetativa de manjericão (O. basilicum L) por estacas de diferentes posições cultivadas sob diferentes substratos. Foram testados cinco tipos de substratos (areia, solo, solo+areia, solo+esterco com serragem, solo+esterco) e três partes vegetativas da planta (apical, medial, basal) com aproximadamente 12,5cm de comprimento em tubetes 55cm³. O delineamento utilizado foi de inteiramente casualizados, em esquema de parcelas subsubdivididas e mantidas em sombrite (50% de sombreamento). Após cinquenta e oito dias foram avaliados os comprimentos e o número das raízes e altura de planta. Com base nos resultados apresentados no presente trabalho pode se afirmar que as melhores mudas foram feitas a partir da parte apical da planta e com o substrato composto apenas de solo

Selective Synthesis of α-, β-, and γ-Ag2WO4 Polymorphs: Promising Platforms for Photocatalytic and Antibacterial Materials

Silver tungstate (Ag2WO4) shows structural polymorphism with different crystalline phases, namely, orthorhombic, hexagonal, and cubic structures that are commonly known as α, β, and γ, respectively. In this work, these Ag2WO4 polymorphs were selectively and successfully synthesized through a simple precipitation route at ambient temperature. The polymorph-controlled synthesis was conducted by means of the volumetric ratios of the silver nitrate/tungstate sodium dehydrate precursors in solution. The structural and electronic properties of the as-synthesized Ag2WO4 polymorphs were investigated by using a combination of X-ray diffraction and Rietveld refinements, X-ray absorption spectroscopy, X-ray absorption near-edge structure spectroscopy, field-emission scanning electron microscopy images, and photoluminescence. To complement and rationalize the experimental results, first-principles calculations, at the density functional theory level, were carried out, leading to an unprecedented glimpse into the atomic-level properties of the morphology and the exposed surfaces of Ag2WO4 polymorphs. Following the analysis of the local coordination of Ag and W cations (clusters) at each exposed surface of the three polymorphs, the structure–property relationship between the morphology and the photocatalytic and antibacterial activities against amiloride degradation under ultraviolet light irradiation and methicillin-resistant Staphylococcus aureus, respectively, was investigated. A possible mechanism of the photocatalytic and antibacterial activity as well the formation process and growth of the polymorphs is also explored and proposed

Comparative Analysis of Molecular Allergy Features of Seed Proteins from Soybean (Glycine max) and Other Legumes Extensively Used for Food

Food allergies due to eating habits, pollution, and other factors are a growing problem in Western nations as well as developing countries. Symptoms of food allergies include changes in the respiratory and digestive systems. Legumes are a potential solution to the enormous demands for healthy, nutritive, and sustainable food. However, legumes also contain families of proteins that can cause food allergies. Some of these legumes include peanut, pea, chickpea, soy, and lupine. It has been shown that processing can alter the allergenicity of legumes since thermic and enzymatic resistance can affect these properties. Cross-reactivity (CR) is an allergy feature of some allergen proteins when the immune system recognizes part of the common share sequences (epitopes) in these allergic proteins. The research about molecular allergy includes comparisons of immunoglobulin E (IgE) and T-cell epitopes, assessment of three-dimensional structure and comparison of secondary structure elements, post-transduction modifications analysis by bioinformatic approach, and post-transduction modifications affecting epitopes properties may facilitate molecular tools to predict protein allergic behavior establishing prevention measurements that could promote the use of legumes and other seeds. This chapter provides an overview of the structural features of the main allergen proteins from legumes and their allergenic potential

Application of Tensor Neural Networks to Pricing Bermudan Swaptions

The Cheyette model is a quasi-Gaussian volatility interest rate model widely used to price interest rate derivatives such as European and Bermudan Swaptions for which Monte Carlo simulation has become the industry standard. In low dimensions, these approaches provide accurate and robust prices for European Swaptions but, even in this computationally simple setting, they are known to underestimate the value of Bermudan Swaptions when using the state variables as regressors. This is mainly due to the use of a finite number of predetermined basis functions in the regression. Moreover, in high-dimensional settings, these approaches succumb to the Curse of Dimensionality. To address these issues, Deep-learning techniques have been used to solve the backward Stochastic Differential Equation associated with the value process for European and Bermudan Swaptions; however, these methods are constrained by training time and memory. To overcome these limitations, we propose leveraging Tensor Neural Networks as they can provide significant parameter savings while attaining the same accuracy as classical Dense Neural Networks. In this paper we rigorously benchmark the performance of Tensor Neural Networks and Dense Neural Networks for pricing European and Bermudan Swaptions, and we show that Tensor Neural Networks can be trained faster than Dense Neural Networks and provide more accurate and robust prices than their Dense counterparts.Comment: 15 pages, 9 figures, 2 table

Target Selection for the SDSS-IV APOGEE-2 Survey

APOGEE-2 is a high-resolution, near-infrared spectroscopic survey observing roughly 300,000 stars across the entire sky. It is the successor to APOGEE and is part of the Sloan Digital Sky Survey IV (SDSS-IV). APOGEE-2 is expanding upon APOGEE's goals of addressing critical questions of stellar astrophysics, stellar populations, and Galactic chemodynamical evolution using (1) an enhanced set of target types and (2) a second spectrograph at Las Campanas Observatory in Chile. APOGEE-2 is targeting red giant branch (RGB) and red clump (RC) stars, RR Lyrae, low-mass dwarf stars, young stellar objects, and numerous other Milky Way and Local Group sources across the entire sky from both hemispheres. In this paper, we describe the APOGEE-2 observational design, target selection catalogs and algorithms, and the targeting-related documentation included in the SDSS data releases.Comment: 19 pages, 6 figures. Accepted to A