Estudos computacionais de esfingomielinases D: docking, dinâmica molecular e métodos híbridos QM/MM

Pós-graduação em Biofísica Molecular - IBILCE

Estudos computacionais de esfingomielinases D: docking, dinâmica molecular e métodos híbridos QM/MM

Pós-graduação em Biofísica Molecular - IBILCE

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.

El problema del rozamiento en el análisis de estructuras de fábrica mediante modelos de sólidos rígidos

La evaluación de la seguridad de estructuras antiguas de fábrica es un problema abierto.El material es heterogéneo y anisótropo, el estado previo de tensiones difícil de conocer y las condiciones de contorno inciertas. A comienzos de los años 50 se demostró que el análisis límite era aplicable a este tipo de estructuras, considerándose desde entonces como una herramienta adecuada. En los casos en los que no se produce deslizamiento la aplicación de los teoremas del análisis límite estándar constituye una herramienta formidable por su simplicidad y robustez. No es necesario conocer el estado real de tensiones. Basta con encontrar cualquier solución de equilibrio, y que satisfaga las condiciones de límite del material, en la seguridad de que su carga será igual o inferior a la carga real de inicio de colapso. Además esta carga de inicio de colapso es única (teorema de la unicidad) y se puede obtener como el óptimo de uno cualquiera entre un par de programas matemáticos convexos duales. Sin embargo, cuando puedan existir mecanismos de inicio de colapso que impliquen deslizamientos, cualquier solución debe satisfacer tanto las restricciones estáticas como las cinemáticas, así como un tipo especial de restricciones disyuntivas que ligan las anteriores y que pueden plantearse como de complementariedad. En este último caso no está asegurada la existencia de una solución única, por lo que es necesaria la búsqueda de otros métodos para tratar la incertidumbre asociada a su multiplicidad. En los últimos años, la investigación se ha centrado en la búsqueda de un mínimo absoluto por debajo del cual el colapso sea imposible. Este método es fácil de plantear desde el punto de vista matemático, pero intratable computacionalmente, debido a las restricciones de complementariedad 0 y z 0 que no son ni convexas ni suaves. El problema de decisión resultante es de complejidad computacional No determinista Polinomial (NP)- completo y el problema de optimización global NP-difícil. A pesar de ello, obtener una solución (sin garantía de exito) es un problema asequible. La presente tesis propone resolver el problema mediante Programación Lineal Secuencial, aprovechando las especiales características de las restricciones de complementariedad, que escritas en forma bilineal son del tipo y z = 0; y 0; z 0 , y aprovechando que el error de complementariedad (en forma bilineal) es una función de penalización exacta. Pero cuando se trata de encontrar la peor solución, el problema de optimización global equivalente es intratable (NP-difícil). Además, en tanto no se demuestre la existencia de un principio de máximo o mínimo, existe la duda de que el esfuerzo empleado en aproximar este mínimo esté justificado. En el capítulo 5, se propone hallar la distribución de frecuencias del factor de carga, para todas las soluciones de inicio de colapso posibles, sobre un sencillo ejemplo. Para ello, se realiza un muestreo de soluciones mediante el método de Monte Carlo, utilizando como contraste un método exacto de computación de politopos. El objetivo final es plantear hasta que punto está justificada la busqueda del mínimo absoluto y proponer un método alternativo de evaluación de la seguridad basado en probabilidades. Las distribuciones de frecuencias, de los factores de carga correspondientes a las soluciones de inicio de colapso obtenidas para el caso estudiado, muestran que tanto el valor máximo como el mínimo de los factores de carga son muy infrecuentes, y tanto más, cuanto más perfecto y contínuo es el contacto. Los resultados obtenidos confirman el interés de desarrollar nuevos métodos probabilistas. En el capítulo 6, se propone un método de este tipo basado en la obtención de múltiples soluciones, desde puntos de partida aleatorios y calificando los resultados mediante la Estadística de Orden. El propósito es determinar la probabilidad de inicio de colapso para cada solución.El método se aplica (de acuerdo a la reducción de expectativas propuesta por la Optimización Ordinal) para obtener una solución que se encuentre en un porcentaje determinado de las peores. Finalmente, en el capítulo 7, se proponen métodos híbridos, incorporando metaheurísticas, para los casos en que la búsqueda del mínimo global esté justificada. Abstract Safety assessment of the historic masonry structures is an open problem. The material is heterogeneous and anisotropic, the previous state of stress is hard to know and the boundary conditions are uncertain. In the early 50's it was proven that limit analysis was applicable to this kind of structures, being considered a suitable tool since then. In cases where no slip occurs, the application of the standard limit analysis theorems constitutes an excellent tool due to its simplicity and robustness. It is enough find any equilibrium solution which satisfy the limit constraints of the material. As we are certain that this load will be equal to or less than the actual load of the onset of collapse, it is not necessary to know the actual stresses state. Furthermore this load for the onset of collapse is unique (uniqueness theorem), and it can be obtained as the optimal from any of two mathematical convex duals programs However, if the mechanisms of the onset of collapse involve sliding, any solution must satisfy both static and kinematic constraints, and also a special kind of disjunctive constraints linking the previous ones, which can be formulated as complementarity constraints. In the latter case, it is not guaranted the existence of a single solution, so it is necessary to look for other ways to treat the uncertainty associated with its multiplicity. In recent years, research has been focused on finding an absolute minimum below which collapse is impossible. This method is easy to set from a mathematical point of view, but computationally intractable. This is due to the complementarity constraints 0 y z 0 , which are neither convex nor smooth. The computational complexity of the resulting decision problem is "Not-deterministic Polynomialcomplete" (NP-complete), and the corresponding global optimization problem is NP-hard. However, obtaining a solution (success is not guaranteed) is an affordable problem. This thesis proposes solve that problem through Successive Linear Programming: taking advantage of the special characteristics of complementarity constraints, which written in bilinear form are y z = 0; y 0; z 0 ; and taking advantage of the fact that the complementarity error (bilinear form) is an exact penalty function. But when it comes to finding the worst solution, the (equivalent) global optimization problem is intractable (NP-hard). Furthermore, until a minimum or maximum principle is not demonstrated, it is questionable that the effort expended in approximating this minimum is justified. XIV In chapter 5, it is proposed find the frequency distribution of the load factor, for all possible solutions of the onset of collapse, on a simple example. For this purpose, a Monte Carlo sampling of solutions is performed using a contrast method "exact computation of polytopes". The ultimate goal is to determine to which extent the search of the global minimum is justified, and to propose an alternative approach to safety assessment based on probabilities. The frequency distributions for the case study show that both the maximum and the minimum load factors are very infrequent, especially when the contact gets more perfect and more continuous. The results indicates the interest of developing new probabilistic methods. In Chapter 6, is proposed a method based on multiple solutions obtained from random starting points, and qualifying the results through Order Statistics. The purpose is to determine the probability for each solution of the onset of collapse. The method is applied (according to expectations reduction given by the Ordinal Optimization) to obtain a solution that is in a certain percentage of the worst. Finally, in Chapter 7, hybrid methods incorporating metaheuristics are proposed for cases in which the search for the global minimum is justified.

Targeting cyclin-dependent kinases in Drosophila with peptide aptamers

Two-hybrid technology provides a simple way to isolate small peptide aptamers that specifically recognize and strongly bind to a protein of interest. These aptamers have the potential to dominantly interfere with specific activities of their target proteins and, therefore, could be used as in vivo inhibitors. Here we explore the ability to use peptide aptamers as in vivo inhibitors by expressing aptamers directed against cell cycle regulators in Drosophila. We expressed two peptide aptamers, each of which specifically recognizes one of the two essential cyclin-dependent kinases (Cdks), DmCdk1 and DmCdk2, in Drosophila. Expression of each Cdk aptamer during organogenesis caused adult eye defects typical of those caused by cell cycle inhibition. Co-overexpression of DmCdk1 or DmCdk2 resulted in suppression of the eye phenotypes, indicating that each aptamer interacts with a Cdk target in vivo and suggesting that these peptides disrupt normal eye development by inhibiting Cdk function. Moreover, the specificity of each aptamer for one of the two Cdks as determined in two-hybrid assays was retained in Drosophila. Combined, our results demonstrate that peptide aptamers generated by yeast two-hybrid methods can serve as inhibitory reagents to target specific proteins in vivo.

Cells that register logical relationships among proteins

Two-hybrid methods have augmented the classical genetic techniques biologists use to assign function to genes. Here, we describe construction of a two-bait interaction trap that uses yeast cells to register more complex protein relationships than those detected in existing two-hybrid systems. We show that such cells can identify bridge or connecting proteins and peptide aptamers that discriminate between closely related allelic variants. The protein relationships detected by these cells are analogous to classical genetic relationships, but lend themselves to systematic application to the products of entire genomes and combinatorial libraries. We show that, by performing logical operations on the phenotypic outputs of these complex cells and existing two-hybrid cells, we can make inferences about the topology and order of protein interactions. Finally, we show that cells that register such relationships can perform logical operations on protein inputs. Thus these cells will be useful for analysis of gene and allele function, and may also define a path for construction of biological computational devices.

New trends in natural language processing: statistical natural language processing.

The field of natural language processing (NLP) has seen a dramatic shift in both research direction and methodology in the past several years. In the past, most work in computational linguistics tended to focus on purely symbolic methods. Recently, more and more work is shifting toward hybrid methods that combine new empirical corpus-based methods, including the use of probabilistic and information-theoretic techniques, with traditional symbolic methods. This work is made possible by the recent availability of linguistic databases that add rich linguistic annotation to corpora of natural language text. Already, these methods have led to a dramatic improvement in the performance of a variety of NLP systems with similar improvement likely in the coming years. This paper focuses on these trends, surveying in particular three areas of recent progress: part-of-speech tagging, stochastic parsing, and lexical semantics.

Avaliação do desempenho de fundações em edificação no DF com a utilização de técnicas de interação solo-estrutura

Dissertação (mestrado)—Universidade de Brasília, Faculdade de Tecnologia, Departamento de Engenharia Civil e Ambiental, 2015.

Analysis of the claims data of a life insurance portfolio

Mestrado em Ciências Actuariais

La investigación científico-social: Un debate epistemológico desde la educación

Recibido 27 de julio de 2011 • Aceptado 26 de agosto de 2011 • Corregido 28 de octubre de 2011  El debate epistemológico de la investigación científico-social durante años ha consistido en confrontar los métodos, tratando de hacer valer más uno que el otro a partir de la gran teoría que lo soporta. Aunque propuestas más recientes recuperan métodos híbridos o planteamientos epistemológicos más diversos, la postura de la dualidad ha prevalecido. Descriptiva o explicativa; fenomenológica o positivista; clásica o emergente; los paradigmas de investigación son la manera en cómo tenemos acceso a la realidad investigada y así obtener principios y respuestas. Ello define los métodos. Aún cuando es posible agrupar y categorizar estos métodos en cuantitativos y cualitativos, el entramado epistemológico que establecen las estructuras de cualquier metodología permitirá el abordaje de un fenómeno mejor que otro, sin que esto pondere necesariamente alguno de manera definitoria. Dentro de la educación, tanto métodos cuantitativos como cualitativos han ayudado a abordar fenómenos de tal manera que han producido resultados favorables para el bienestar del quehacer educativo. Este ensayo, producto de una revisión bibliográfica y una trayectoria investigativa y educativa, pretende esclarecer el complejo panorama inicial de los investigadores educativos novatos antes de revisar y recuperar una postura epistemológica.

Derivation methods for hybrid knowledge bases with rules and ontologies

Trabalho apresentado no âmbito do Mestrado em Engenharia Informática, como requisito parcial para obtenção do grau de Mestre em Engenharia Informática

Novel Methods for Error Modeling and Parameter Identification of a Redundant Serial-Parallel Hybrid Robot

To obtain the desirable accuracy of a robot, there are two techniques available. The first option would be to make the robot match the nominal mathematic model. In other words, the manufacturing and assembling tolerances of every part would be extremely tight so that all of the various parameters would match the “design” or “nominal” values as closely as possible. This method can satisfy most of the accuracy requirements， but the cost would increase dramatically as the accuracy requirement increases. Alternatively, a more cost-effective solution is to build a manipulator with relaxed manufacturing and assembling tolerances. By modifying the mathematical model in the controller, the actual errors of the robot can be compensated. This is the essence of robot calibration. Simply put, robot calibration is the process of defining an appropriate error model and then identifying the various parameter errors that make the error model match the robot as closely as possible. This work focuses on kinematic calibration of a 10 degree-of-freedom (DOF) redundant serial-parallel hybrid robot. The robot consists of a 4-DOF serial mechanism and a 6-DOF hexapod parallel manipulator. The redundant 4-DOF serial structure is used to enlarge workspace and the 6-DOF hexapod manipulator is used to provide high load capabilities and stiffness for the whole structure. The main objective of the study is to develop a suitable calibration method to improve the accuracy of the redundant serial-parallel hybrid robot. To this end, a Denavit–Hartenberg (DH) hybrid error model and a Product-of-Exponential (POE) error model are developed for error modeling of the proposed robot. Furthermore, two kinds of global optimization methods, i.e. the differential-evolution (DE) algorithm and the Markov Chain Monte Carlo (MCMC) algorithm, are employed to identify the parameter errors of the derived error model. A measurement method based on a 3-2-1 wire-based pose estimation system is proposed and implemented in a Solidworks environment to simulate the real experimental validations. Numerical simulations and Solidworks prototype-model validations are carried out on the hybrid robot to verify the effectiveness, accuracy and robustness of the calibration algorithms.

Hybrid constraint programming and metaheuristic methods for large scale optimization problems

This work presents hybrid Constraint Programming (CP) and metaheuristic methods for the solution of Large Scale Optimization Problems; it aims at integrating concepts and mechanisms from the metaheuristic methods to a CP-based tree search environment in order to exploit the advantages of both approaches. The modeling and solution of large scale combinatorial optimization problem is a topic which has arisen the interest of many researcherers in the Operations Research field; combinatorial optimization problems are widely spread in everyday life and the need of solving difficult problems is more and more urgent. Metaheuristic techniques have been developed in the last decades to effectively handle the approximate solution of combinatorial optimization problems; we will examine metaheuristics in detail, focusing on the common aspects of different techniques. Each metaheuristic approach possesses its own peculiarities in designing and guiding the solution process; our work aims at recognizing components which can be extracted from metaheuristic methods and re-used in different contexts. In particular we focus on the possibility of porting metaheuristic elements to constraint programming based environments, as constraint programming is able to deal with feasibility issues of optimization problems in a very effective manner. Moreover, CP offers a general paradigm which allows to easily model any type of problem and solve it with a problem-independent framework, differently from local search and metaheuristic methods which are highly problem specific. In this work we describe the implementation of the Local Branching framework, originally developed for Mixed Integer Programming, in a CP-based environment. Constraint programming specific features are used to ease the search process, still mantaining an absolute generality of the approach. We also propose a search strategy called Sliced Neighborhood Search, SNS, that iteratively explores slices of large neighborhoods of an incumbent solution by performing CP-based tree search and encloses concepts from metaheuristic techniques. SNS can be used as a stand alone search strategy, but it can alternatively be embedded in existing strategies as intensification and diversification mechanism. In particular we show its integration within the CP-based local branching. We provide an extensive experimental evaluation of the proposed approaches on instances of the Asymmetric Traveling Salesman Problem and of the Asymmetric Traveling Salesman Problem with Time Windows. The proposed approaches achieve good results on practical size problem, thus demonstrating the benefit of integrating metaheuristic concepts in CP-based frameworks.