MINLP in Chemical Reaction Networks

Universidad de Sevilla. Máster Universitario en Matemática

Classification and regression with functional data: a mathematical optimization approach.

El objetivo de esta tesis doctoral es desarrollar nuevos métodos para la clasificación y regresión supervisada en el Análisis de Datos Funcionales. En particular, las herramientas de Optimización Matemática analizadas en esta tesis explotan la naturaleza funcional de los datos, dando lugar a nuevas técnicas que pueden mejorar los métodos clásicos y que conectan las matemáticas con las aplicaciones. El Capítulo 1 presenta las ideas generales, los retos y la notación usada a lo largo de la tesis. El Capítulo 2 trata el problema de seleccionar el conjunto finito de instantes de tiempo que mejor clasifica datos funcionales multivariados en dos clases predefinidas. El uso, no sólo de la información proporcionada por la propia función, sino también por sus derivadas será decisivo para mejorar la predicción, como se pondrá de manifiesto posteriormente. Para ello se formula un problema de optimización binivel continuo. Dicho problema combina la aplicación de la conocida técnica SVM (Support Vector Machine) con la maximización de la correlación entre la etiqueta de la clase y la denominada función score, vinculada a dicha técnica. El Capítulo 3 también se centra en la clasificación binaria de datos funcionales usando SVM. Sin embargo, en lugar de buscar los instantes de tiempo más relevantes, aquí se define un ancho de banda funcional para la denominada función kernel. De esta forma, se puede mejorar el rendimiento del clasificador, a la vez que se identifican los diferentes intervalos del dominio de la función, de acuerdo a su capacidad predictiva, mejorando además la interpretabilidad del modelo resultante. La obtención de tales intervalos se lleva a cabo mediante la resolución de un problema de optimización binivel por medio de un algoritmo alternante. El Capítulo 4 se centra en la clasificación de los llamados datos funcionales híbridos, es decir, datos que están formados por variables funcionales y estáticas (constantes a lo largo del tiempo). El objetivo es seleccionar las variables, funcionales o estáticas, que mejor clasifiquen. Para ello, se define un kernel no isotrópico que asocia un parámetro ancho de banda escalar a cada una de las variables. De forma análoga a como se ha hecho en los capítulos anteriores, se propone un algoritmo alternante para resolver el problema de optimización binivel, que permite resolver los parámetros del kernel. El problema de selección de variables presentado en el Capítulo 2 se generaliza al campo de la regresión en el Capítulo 5. El método de resolución combina la técnica denominada SVR (Support Vector Regression) con la minimización de la suma de los cuadrados de los residuos entre la verdadera variable respuesta y la prevista. Todos los algoritmos propuestos a lo largo de esta tesis han sido aplicados a bases de datos sintéticas y reales, quedando probada su efectividad.The goal of this PhD dissertation is to develop new approaches for supervised classification and regression in Functional Data Analysis. articularly, the Mathematical optimization tools analyzed in this thesis exploit the functional nature of the data, leading to novel strategies which may outperform the standard methodologies and link mathematics with real-life applications. Chapter 1 presents the main ideas, challenges and the notation used in this thesis. Chapter 2 addresses the problem of selecting a finite set of time instants which best classify multivariate functional data into two predefined classes. Using, not only the information provided by the function itself but also its high-order derivatives will be crucial to improve the accuracy. To do this, a continuous bilevel optimization problem is solved. Such problem combines the resolution of the well-known technique SVM (Support Vector Machine) with the maximization of the correlation between the class label and the score. Chapter 3 also focuses on the binary classification problem using SVM. However, instead of finding the most important time instants, here we define a functional bandwidth in the so-called kernel function. In this way, accuracy may be improved and the most relevant intervals of the domain of the function, according to their classification ability, are identified, enhancing the interpretability. A bilevel optimization problem is formulated and solved by means of an alternating procedure. Chapter 4 is focused on classifying the so-called hybrid functional data, i.e., data which are formed by functional and static (constant over time) covariates. The goal is to select the features, functional or static, which best classify. An anisotropic kernel which associates a scalar bandwidth to each feature is defined. As in previous chapters, an alternating approach is proposed to solve a bilevel optimization problem. Chapter 5 generalizes the variable selection problem presented in Chapter 2 to regression. The solution approach combines the SVR (Support Vector Regression) problem with the minimization of sum of the squared residuals between the actual and predicted responses. An alternating heuristic is developed to handle such model. All the methodologies presented along this dissertation are tested in synthetic and real data sets, showing their applicability.Premio Extraordinario de Doctorado U

Extending FuzAtAnalyzer to approach the management of classical negation

FuzAtAnalyzer was conceived as a Java framework which goes beyond of classical tools in formal concept analysis. Specifically, it successfully incorporated the management of uncertainty by means of methods and tools from the area of fuzzy formal concept analysis. One limitation of formal concept analysis is that they only consider the presence of properties in the objects (positive attributes) as much in fuzzy as in crisp case. In this paper, a first step in the incorporation of negations is presented. Our aim is the treatment of the absence of properties (negative attributes). Specifically, we extend the framework by including specific tools for mining knowledge combining crisp positive and negative attributes.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

The flavoproteins CryD and VvdA cooperate with the white collar protein WcoA in the Control of photocarotenogenesis in Fusarium fujikuroi

Light stimulates carotenoid biosynthesis in the ascomycete fungus Fusarium fujikuroi through transcriptional activation of the structural genes of the pathway carRA, carB, and cart, but the molecular basis of this photoresponse is unknown. The F. fujikuroi genome contains genes for different predicted photoreceptors, including the WC protein WcoA, the DASH cryptochrome CryD and the Vivid-like flavoprotein VvdA. We formerly found that null mutants of wcoA, cryD or vvdA exhibit carotenoid photoinduction under continuous illumina- tion. Here we show that the wild type exhibits a biphasic response in light induction kinetics experiments, with a rapid increase in carotenoid content in the first hours, a transient arrest and a subsequent slower increase. The mutants of the three photoreceptors show different kinetic responses: the wcoA mutants are defective in the rapid response, the cryD mutants are affected in the slower response, while the fast and slow responses were respectively enhanced and attenuated in the vvdA mutants. Transcriptional analyses of the car genes re- vealed a strong reduction of dark and light-induced transcript levels in the wcoA mutants, while minor or no reductions were found in the cryD mutants. Formerly, we found no change on carRA and carB photoinduction in vvdA mutants. Taken together, our data suggest a co- operative participation of WcoA and CryD in early and late stages of photoinduction of carot- enoid biosynthesis in F. fujikuroi, and a possible modulation of WcoA activity by VvdA. An unexpected transcriptional induction by red light of vvdA, cryD and carRA genes suggest the participation of an additional red light-absorbing photoreceptor

Turismo gay: Análisis de una modalidad turística emergente en la ciudad de Sevilla

El trabajo hace una aproximación a una modalidad turística emergente: el turismo gay. El cambio antropológico experimentado por la sociedad española en relación con esta orientación sexual repercute directamente en el despegue del turismo gay, a través de una mayor visibilidad, y en la creación de destinos especializados hacia esta demanda. Primeramente se profundiza sobre el concepto “Homosexualidad”, considerando de qué manera afecta a la sociedad, y de forma más concreta a España. En segundo lugar, se estudia Sevilla como destino turístico gay incipiente, mediante el trabajo de campo etnográfico. Concluye el trabajo con el análisis de las prácticas turísticas de este colectivo denominado LGBT (Lesbianas, Gais, Bisexual, Transexual) y la oferta disponible actual en la ciudad de Sevilla, objeto de estudio.The investigation makes an approach to an emerging form of tourism: the gay tourism. The anthropological change experienced by the Spanish society regarding this sexual orientation has a direct impact on takeoff gay tourism, through greater visibility, and the creation of specialized destinations to this demand. Firstly, deepens on "Homosexuality" concept, considering how it affects society, and more specifically to Spain form. Secondly, Seville is studied as incipient gay tourist destination, through ethnographic fieldwork. The investigation ends with an analysis of the tourism practices of this group called LGBT (Lesbian, Gay, Bisexual and Transgender) and the current available supply in the city of Seville, under study

Machine-learning-aided warm-start of constraint generation methods for online mixed-integer optimization

Mixed Integer Linear Programs (MILP) are well known to be NP-hard problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to provide an optimal solution if enough time is given, their use in online applications is still a great challenge due to their usual excessive time requirements. To alleviate their computational burden, some machine learning techniques have been proposed in the literature, using the information provided by previously solved MILP instances. Unfortunately, these techniques report a non-negligible percentage of infeasible or suboptimal instances. By linking mathematical optimization and machine learning, this paper proposes a novel approach that speeds up the traditional constraint generation method, preserving feasibility and optimality guarantees. In particular, we first identify offline the so-called invariant constraint set of past MILP instances. We then train (also offline) a machine learning method to learn an invariant constraint set as a function of the problem parameters of each instance. Next, we predict online an invariant constraint set of the new unseen MILP application and use it to initialize the constraint generation method. This warm-started strategy significantly reduces the number of iterations to reach optimality, and therefore, the computational burden to solve online each MILP problem is significantly reduced. Very importantly, the proposed methodology inherits the feasibility and optimality guarantees of the traditional constraint generation method. The computational performance of the proposed approach is quantified through synthetic and real-life MILP applications

Warm-starting constraint generation for mixed-integer optimization: A Machine Learning approach

Mixed Integer Linear Programs (MILP) are well known to be NP-hard (Non-deterministic Polynomial-time hard) problems in general. Even though pure optimization-based methods, such as constraint generation, are guaranteed to provide an optimal solution if enough time is given, their use in online applications remains a great challenge due to their usual excessive time requirements. To alleviate their computational burden, some machine learning techniques (ML) have been proposed in the literature, using the information provided by previously solved MILP instances. Unfortunately, these techniques report a non-negligible percentage of infeasible or suboptimal instances. By linking mathematical optimization and machine learning, this paper proposes a novel approach that speeds up the traditional constraint generation method, preserving feasibility and optimality guarantees. In particular, we first identify offline the so-called invariant constraint set of past MILP instances. We then train (also offline) a machine learning method to learn an invariant constraint set as a function of the problem parameters of each instance. Next, we predict online an invariant constraint set of the new unseen MILP application and use it to initialize the constraint generation method. This warm-started strategy significantly reduces the number of iterations to reach optimality, and therefore, the computational burden to solve online each MILP problem is significantly reduced. Very importantly, all the feasibility and optimality theoretical guarantees of the traditional constraint generation method are inherited by our proposed methodology. The computational performance of the proposed approach is quantified through synthetic and real-life MILP applications.This work was supported in part by the Spanish Ministry of Science and Innovation through project PID2020-115460GB-I00, in part by the European Research Council (ERC) under the EU Horizon 2020 research and innovation program (grant agreement No. 755705) in part, by the Junta de Andalucía (JA), the Universidad de Málaga (UMA), and the European Regional Development Fund (FEDER) through the research projects P20_00153 and UMA2018-FEDERJA-001, and in part by the Research Program for Young Talented Researchers of the University of Málaga under Project B1-2020-15. The authors thankfully acknowledge the computer resources, technical expertise, and assistance provided by the SCBI (Supercomputing and Bioinformatics) center of the University of Málaga. Funding for open access charge: Universidad de Málaga / CBUA

Granular activated carbons from residues by activation cycles of oxidation-desorption

Tesis doctoral inédita. Universidad Autónoma de Madrid, Facultad de Ciencias, Departamento de Química Física Aplicada. Fecha de lectura: 12-07-201