Nonparametric estimation of structural breakpoints

This paper proposes point and interval estimates of location and size of jumps in multiple regression curves or its derivatives. We are mainly concerned with time series models where structural breaks occur at a given period of time or they are explained by the value taken by some predictor (e.g. threshold models). No previous knowledge of the underlying regression function is required. Left and right limits of the function, with respect to the regressor explaining the break, are estimated at each data point using multivariate multiplicative kernels. The univariate kernel corresponding to the regressor explaining the break is one-sided, with all its mass at the right or left of zero. Since left and right limits are the same, except at the break point, the location of the jump is estimated as the observed regressor value maximizing the difference between left and right limit estimates. This difference, evaluated at the estimated location point, is the estimation of the jump size. A small Monte Carlo study and an empirical application to USA macroecomic data illustrates the performance of the procedure in small samples. The paper also discusses some extensions, in particular the identification of the coordinate explaining the break, the application of the procedure to the estimation of parametric models, and robustification of the method for the influence of outliers

Bootstrap goodness-of-fit tests for farima models

This paper proposes goodness-of-fit tests for the class of covariance stationary FARIMA processes. They are based on functionals of weighted empirical processes, say Sn C.), where the weights are the relative error between the periodogram and the fitted spectral density function under the null specification of the data. Two examples of such functionals are the Tp - Barlett and the Cramer-Von Mises standardized ro - statistics. We show that the tests are able to detect contiguous alternatives converging to the null at the rate n-JI2 • However, because the cumbersome covariance structure of the limiting process of Sn C.), tests based on its asymptotic distribution are difficult to implement in practice_ To circumvent this problem, we propose a bootstrap test, showing its consistency, and studying its small sample performance by a Monte Carlo experiment. _________________________________________________

Distribution Free Goodness-of-Fit Tests for Linear Processes

This article proposes a class of goodness-of-fit tests for the autocorrelation function of a time series process, including those exhibiting long-range dependence. Test statistics for composite hypotheses are functionals of a (approximated) martingale transformation of the Bartlett's Tp-process with estimated parameters, which converges in distribution to the standard Brownian Motion under the null hypothesis. We discuss tests of different nature such as omnibus, directional and Portmanteau-type tests. A Monte Carlo study illustrates the performance of the different tests in practice.Nonparametric model checking, spectral distribution, linear processes, martingale decomposition, local alternatives, omnibus, smooth and directional tests, long-range alternatives

Open tools for dendrochronology. Advances in sample digitization and deep learning methods for image segmentation

Dendrochronological techniques are paramount in forest research. The current climate change scenario and the central role of forests in biogeophysical cycles enforce the importance of novel techniques to get accurate data from trees and their relationship with the environment in faster ways. Recent technological advances and the place of open source software and hardware are making free, user-developed tools for forest research available to the research community. The aim of this Ph.D. thesis is the development of tools for image acquisition and data collection in dendrochronology based on open source software and hardware. Thus, four different tools for dendrochronological research are presented in five different chapters. The first chapter focuses on the development of a do-it-yourself tool based on open source hardware for image acquisition and wood sample digitization at high resolution. We used open hardware equipment from Arduino and Python programming to develop CaptuRING and published the entire free open source tool as: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" in Methods in Ecology and Evolution, 2022; 13:1185-1191. Furthermore, the original software was registered in the Registro General de Propiedad Intelectual (00/2022/737) of Ministerio de Cultura y Deporte (Spain). The second chapter presents "How to build and install your own CaptuRING". This contribution presents a series of videos with a step-by-step guide to promote the use of CaptuRING in the research community. The manuscript and related videos have been submitted for publication. The third chapter describes ρ-MtreeRing. This free and open-source software, which is written in R, analyzes X-ray films from dendrochronological samples to get microdensity values automatically segmented through a graphical user interface. The open source tool and manuscript are published as: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" in Forests. 2021; 12(10):1405. The fourth chapter describes the potential of deep learning methods to automatically segment xylem vessels. We trained three different convolutional neural networks to segment vessels in stained wood microsections using the Keras framework in Python. Our results demonstrate the potential of these techniques to automatically segment xylem vessels and overcome derived problems from image illumination, which hamper segmentation using classical image segmentation methods. The manuscript is published as "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" in: Neural Computing & Applications, 2020; 32:17927-17939. The fifth chapter develops an algorithm to delineate annual ring limits in stained wood microsections of a species with diffuse porous wood using convolutional neural networks. We used Python for image processing and the Keras framework for the algorithm training. The results show the ability of this techniques to obtain accurate tree ring segmentation for quantitative wood anatomy, reaching similar or even outperforming conventional manual delimitation in most of the evaluated cases. The results of this chapter will be presented in the manuscript "Deep Learning for ring bordering in stained cross-sectional images". This PhD Thesis presents four open source tools to get accurate information from wood features to unveil how trees respond to the environment. From digitization at macroscopic perspective, automatic data collection and the development of feature segmentation on microscopic samples. The presented four novel dendrochronological tools based on open source software facilitates forest research in the current climate change scenario.Las técnicas dendrocronológicas son fundamentales en la investigación forestal. El escenario actual de cambio climático y el papel central de los bosques en los ciclos biogeofísicos subrayan la necesidad de nuevas técnicas para obtener de un modo ágil datos precisos de los árboles y de su relación con el medio ambiente. Los recientes avances tecnológicos, además de la disponibilidad actual del software y el hardware de código abierto están poniendo a disposición de la comunidad investigadora herramientas gratuitas desarrolladas por los usuarios para la investigación forestal. El objetivo de esta tesis doctoral es el desarrollo de herramientas para la adquisición de imágenes y la recogida de datos basadas en software y hardware de código abierto para el estudio dendrocronológico. Esta tesis presenta cuatro herramientas diferentes para esta rama científica en cinco capítulos diferentes. El primer capítulo se centra en el desarrollo de una herramienta "hágalo usted mismo" basada en hardware de código abierto para la adquisición de imágenes y la digitalización de muestras de madera a alta resolución. Usamos equipos de hardware abierto de Arduino y programación de Python para desarrollar CaptuRING y publicamos la herramienta completa de código abierto como: "CaptuRING: A Do-It-Yourself tool for wood sample digitization" en Methods in Ecology and Evolution, 2022; 13:1185-1191. Además, el software original fue registrado en el Registro General de Propiedad Intelectual (00/2022/737) del Ministerio de Cultura y Deporte (España). El segundo capítulo presenta "Cómo construir e instalar su propio CaptuRING" ("How to build and install your own CaptuRING"). Esta contribución presenta una serie de vídeos con una guía paso a paso para promover el uso de CaptuRING en la comunidad investigadora. El manuscrito y los vídeos relacionados se han enviado para su publicación. El tercer capítulo describe ρ-MtreeRing. Este software gratuito y de código abierto, que está escrito en R, analiza imágenes de rayos X de muestras dendrocronológicas para obtener valores de microdensidad automáticamente segmentados a través de una sencilla interfaz gráfica de usuario. La herramienta de código abierto y el manuscrito se publicaron como: "ρ-MtreeRing. A graphical user interface for X-ray microdensity analysis" en Forests. 2021; 12(10):1405. El cuarto capítulo describe el potencial de los métodos de aprendizaje profundo para segmentar automáticamente los vasos del xilema. Entrenamos tres redes neuronales convolucionales diferentes para segmentar vasos en cortes histológicos de madera utilizando el marco Keras en Python. Nuestros resultados demuestran el potencial de estas técnicas para segmentar automáticamente los vasos del xilema y superar los problemas derivados de la iluminación de la imagen, que dificultan la labor de métodos clásicos de segmentación de imágenes. El manuscrito se publicó como "Convolutional neural networks for segmenting xylem vessels in stained cross-sectional images" en: Neural Computing & Applications. 2020; 32:17927-17939. El quinto capítulo desarrolla un algoritmo para delinear los límites anuales de los anillos en cortes histológicos de una especie con madera difuso-porosa utilizando redes neuronales convolucionales. Se utilizó Python para el procesamiento de imágenes y el marco Keras para el entrenamiento del algoritmo. Los resultados muestran la capacidad de estas técnicas para obtener una segmentación precisa de los anillos de los árboles para la anatomía cuantitativa de la madera alcanzando, en la mayoría de los casos evaluados, un rendimiento similar o incluso superior a la delimitación manual convencional. Los resultados de este capítulo se presentarán en el manuscrito "Deep Learning for ring bordering in stained cross-sectional images". Esta Tesis Doctoral presenta cuatro herramientas de código abierto para obtener información precisa de las características de la madera investigar cómo los árboles responden al entorno facilitando la investigación en el actual escenario de cambio climático.Escuela de DoctoradoDoctorado en Conservación y Uso Sostenible de Sistemas Forestale

Particles adsorbed at various non-aqueous liquid-liquid interfaces

Particles adsorbed at liquid interfaces are commonly used to stabilise water-oil Pickering emulsions and water-air foams. The fundamental understanding of the physics of particles adsorbed at water-air and water-oil interfaces is improving significantly due to novel techniques that enable the measurement of the contact angle of individual particles at a given interface. The case of non-aqueous interfaces and emulsions is less studied in the literature. Non-aqueous liquid-liquid interfaces in which water is replaced by other polar solvents have properties similar to those of water-oil interfaces. Nanocomposites of non-aqueous immiscible polymer blends containing inorganic particles at the interface are of great interest industrially and consequently more work has been devoted to them. By contrast, the behaviour of particles adsorbed at oil-oil interfaces in which both oils are immiscible and of low dielectric constant (ε < 3) is scarcely studied. Hydrophobic particles are required to stabilise these oil-oil emulsions due to their irreversible adsorption, high interfacial activity and elastic shell behaviour

Zc(3900)Z_c(3900): what has been really seen?

The $Z^\pm_c(3900)/Z^\pm_c(3885)$ resonant structure has been experimentally observed in the $Y(4260) \to J/\psi \pi\pi$ and $Y(4260) \to \bar{D}^\ast D \pi$ decays. This structure is intriguing since it is a prominent candidate of an exotic hadron. Yet, its nature is unclear so far. In this work, we simultaneously describe the $\bar{D}^\ast D$ and $J/\psi \pi$ invariant mass distributions in which the $Z_c$ peak is seen using amplitudes with exact unitarity. Two different scenarios are statistically acceptable, where the origin of the $Z_c$ state is different. They correspond to using energy dependent or independent $\bar D^* D$ $S$-wave interaction. In the first one, the $Z_c$ peak is due to a resonance with a mass around the $D\bar D^*$ threshold. In the second one, the $Z_c$ peak is produced by a virtual state which must have a hadronic molecular nature. In both cases the two observations, $Z^\pm_c(3900)$ and $Z^\pm_c(3885)$, are shown to have the same common origin, and a $\bar D^* D$ bound state solution is not allowed. Precise measurements of the line shapes around the $D\bar D^*$ threshold are called for in order to understand the nature of this state.Comment: 6 pages, 6 figure