Made available in DSpace on 2015-03-03T15:22:32Z (GMT). No. of bitstreams: 1 FranciscoMCM.pdf: 1391370 bytes, checksum: 2bdc2511202e3397ea85e69a321f5847 (MD5) Previous issue date: 2010-02-10 / Coordena??o de Aperfei?oamento de Pessoal de N?vel Superior / In this work we study the Hidden Markov Models with finite as well as general state space. In the finite case, the forward and backward algorithms are considered and the probability of a given observed sequence is computed. Next, we use the EM algorithm to estimate the model parameters. In the general case, the kernel estimators are used and to built a sequence of estimators that converge in L1-norm to the density function of the observable process / Neste trabalho estudamos os modelos de Markov ocultos tanto em espa?o de estados finito quanto em espa?o de estados geral. No caso discreto, estudamos os algoritmos para frente e para tr?s para determinar a probabilidade da sequ?ncia observada e, em seguida, estimamos os par?metros do modelo via algoritmo EM. No caso geral, estudamos os estimadores do tipo n?cleo e os utilizamos para conseguir uma sequ?ncia de estimadores que converge na norma L1 para a fun??o densidade do processo observado

<p>Transcriptional regulation has been studied intensively in recent decades. One important aspect of this regulation is the interaction between regulatory proteins, such as transcription factors (TF) and nucleosomes, and the genome. Different high-throughput techniques have been invented to map these interactions genome-wide, including ChIP-based methods (ChIP-chip, ChIP-seq, etc.), nuclease digestion methods (DNase-seq, MNase-seq, etc.), and others. However, a single experimental technique often only provides partial and noisy information about the whole picture of protein-DNA interactions. Therefore, the overarching goal of this dissertation is to provide computational developments for jointly modeling different experimental datasets to achieve a holistic inference on the protein-DNA interaction landscape. </p><p>We first present a computational framework that can incorporate the protein binding information in MNase-seq data into a thermodynamic model of protein-DNA interaction. We use a correlation-based objective function to model the MNase-seq data and a Markov chain Monte Carlo method to maximize the function. Our results show that the inferred protein-DNA interaction landscape is concordant with the MNase-seq data and provides a mechanistic explanation for the experimentally collected MNase-seq fragments. Our framework is flexible and can easily incorporate other data sources. To demonstrate this flexibility, we use prior distributions to integrate experimentally measured protein concentrations. </p><p>We also study the ability of DNase-seq data to position nucleosomes. Traditionally, DNase-seq has only been widely used to identify DNase hypersensitive sites, which tend to be open chromatin regulatory regions devoid of nucleosomes. We reveal for the first time that DNase-seq datasets also contain substantial information about nucleosome translational positioning, and that existing DNase-seq data can be used to infer nucleosome positions with high accuracy. We develop a Bayes-factor-based nucleosome scoring method to position nucleosomes using DNase-seq data. Our approach utilizes several effective strategies to extract nucleosome positioning signals from the noisy DNase-seq data, including jointly modeling data points across the nucleosome body and explicitly modeling the quadratic and oscillatory DNase I digestion pattern on nucleosomes. We show that our DNase-seq-based nucleosome map is highly consistent with previous high-resolution maps. We also show that the oscillatory DNase I digestion pattern is useful in revealing the nucleosome rotational context around TF binding sites. </p><p>Finally, we present a state-space model (SSM) for jointly modeling different kinds of genomic data to provide an accurate view of the protein-DNA interaction landscape. We also provide an efficient expectation-maximization algorithm to learn model parameters from data. We first show in simulation studies that the SSM can effectively recover underlying true protein binding configurations. We then apply the SSM to model real genomic data (both DNase-seq and MNase-seq data). Through incrementally increasing the types of genomic data in the SSM, we show that different data types can contribute complementary information for the inference of protein binding landscape and that the most accurate inference comes from modeling all available datasets. </p><p>This dissertation provides a foundation for future research by taking a step toward the genome-wide inference of protein-DNA interaction landscape through data integration.</p> / Dissertation

Automating the generation of test cases for software is an active area of research. Specification based test generation is an approach in which a formal representation of a method is analyzed to generate valid test cases. Constraint solving and state space exploration are important aspects of the specification based test generation. One problem with specification based testing is that the size of the state space explodes when we apply this approach to a code of practical size. Hence finding ways to reduce the number of candidates to explore within the state space is important to make this approach practical in industry. Korat is a tool which generates test cases for Java programs based on predicates that validate the inputs to the method. Various ongoing researches intend to increase the tools effectiveness in handling large state space. Parallelizing Korat and minimizing the exploration of invalid candidates are the active research directions. This report surveys the basic algorithms of Korat, PKorat, and Fast Korat. PKorat is a parallel version of Korat and aims to take advantage of multi-processor and multicore systems available. Fast Korat implements four optimizations which reduce the number of candidate explored to generate validate candidates and reduce the amount of time required to explore each candidate. This report also presents the execution time results for generating test candidates for binary tree, doubly linked list, and sorted singly linked list, from their respective predicates. / text

Mark-recapture methods use repeated captures of individually identifiable animals to provide estimates of properties of populations. Different models allow estimates to be obtained for population size and rates of processes governing population dynamics. State-space models consist of two linked processes evolving simultaneously over time. The state process models the evolution of the true, but unknown, states of the population. The observation process relates observations on the population to these true states. Mark-recapture models specified within a state-space framework allow population dynamics models to be embedded in inference ensuring that estimated changes in the population are consistent with assumptions regarding the biology of the modelled population. This overcomes a limitation of current mark-recapture methods. Two alternative approaches are considered. The "conditional" approach conditions on known numbers of animals possessing capture history patterns including capture in the current time period. An animal's capture history determines its state; consequently, capture parameters appear in the state process rather than the observation process. There is no observation error in the model. Uncertainty occurs only through the numbers of animals not captured in the current time period. An "unconditional" approach is considered in which the capture histories are regarded as observations. Consequently, capture histories do not influence an animal's state and capture probability parameters appear in the observation process. Capture histories are considered a random realization of the stochastic observation process. This is more consistent with traditional mark-recapture methods. Development and implementation of particle filtering techniques for fitting these models under each approach are discussed. Simulation studies show reasonable performance for the unconditional approach and highlight problems with the conditional approach. Strengths and limitations of each approach are outlined, with reference to Soay sheep data analysis, and suggestions are presented for future analyses.

State space modeling represents a statistical framework for exponential smoo- thing methods and it is often used in time series modeling. This thesis descri- bes seasonal innovations state space models and focuses on recently suggested TBATS model. This model includes Box-Cox transformation, ARMA model for residuals and trigonometric representation of seasonality and it was designed to handle a broad spectrum of time series with complex types of seasonality inclu- ding multiple seasonality, high frequency of data, non-integer periods of seasonal components, and dual-calendar effects. The estimation of the parameters based on maximum likelihood and trigonometric representation of seasonality greatly reduce computational burden in this model. The universatility of TBATS model is demonstrated by four real data time series.

Quel impératif pousse Guilloux à se tourner vers les autres? Provient-il d'un appel au sentiment ? D'un appel à la raison ? Est-il la réponse à une éthique ? Pourquoi cette nécessité de rendre la vie valable ? La valeur d'un homme se juge d'aprés ses actes semble dire Guilloux. Qu'est-ce qu'écrire en regard de l'action ? Comment écrire dans un monde en guerre ? L'écriture peut-elle être agissante ? Guilloux a voulu partager ses interrogations avec ses lecteurs, esquisser tous les cheminements possibles jusqu'à la contradiction. D'abord, montrer le processus de mythification entourant les actions des hommes qui ont précédé Guilloux dans cette vie. Ceux d'avant savaient agir ensemble et pour le bien de tous, ce savoir s'est perdu laissant l'individu seul face à ses doutes. Les tourments qui agitent le début du XXème siècle n'ont fait qu'exacerber le questionnement existentiel. Les divers degrés de responsabilité des hommes se dévoilent ainsi dans leurs manières de se conduire vis-à-vis d'autrui, autant d'engagements concrets ou de retraits qui attestent de l'humanité ou de l'inhumanité: ? Trop et pas assez d'intelligence ? trop et pas assez d'amour ?. Ni la famille, ni la société, jaugée au travers de ses institutions, l'école, la justice, le clergé, ne répondent plus à l'exigence d'équité. Alors c'est aux hommes de s'unir. Agir, pour Guilloux, semble une forme de révolte et d'indignation contre la souffrance et l'injustice. Lutter, c'est aussi veiller sur la vie des autres. En refusant toutes compromissions, les personnages que Guilloux met en scène voient leur champ d'action se réduire à l'action humanitaire, quant à l'auteur lui revient le devoir de témoigner. Il se lance alors dans une écriture labyrinthique, un incessant jeu de miroir qui rapproche les hommes les plus différents : l'homme d'action, l'homme de lettres, l'homme militant, l'homme pas cru, l'homme en difficulté, l'homme perdu? chacun explore, estime, selon son itinéraire, la valeur accordée à sa vie et à celles des autres, hanté toujours par le devoir et la volonté de changer le monde / What is writing compared to action ? How to write in a state of war ? Guilloux wanted to share his ethical questioning with his readers, try every possible ways to the point of contradiction. First, he tried to show the mythification process of the people who lived before Guilloux's lifetime. The people who lived in days of old knew how to act together, this knowledge is now lost and individuals remain all alone facing their doubts. The troubles of the beginning of the 20th century could but magnify the existencial questioning. The different degrees of responsibility of the people highlight their commitments or retreats and give evidence of the humanity or the inhumanity. It is now up to men to unite against injustice. Acting to Guilloux is a form of revolt. Guilloux's characters refuse to compromise themselves and as a consequence their field of action becomes barely humaniatrian. The author has the duty to give his own account

This master thesis treats the modelling of a common-rail direct fuel injection system where pressure generation is decoupled from the injection process. It has been shown that the fuel pressure plays a vital role for the general performance of the engine, affecting both emissions and efficiency, and it is carefully regulated to achieve optimal performance at different operating points. In an attempt to facilitate the development of the responsible control algorithms, a simulation framework has been requested. A model describing the complete work cycle of the high-pressure fuel system is developed and implemented in a Simulink environment. It is to a large extent based on the underlying physics and constructed in a modular manner, which allows for different engine configurations to be simulated. The modelled pressure signal is compared to experimental data at different operating points with promising results in capturing the transient behaviour from a low-level perspective. Additionally, it manages to replicate some of the pressure oscillations which has been observed in the real system and it shows good response to changes in the input signals. However, there are some areas which are subject to improvement since capturing the static pressure levels over longer drive cycles has proved to be a difficult task. Overall, the developed model serves as a starting point for future development and validation of control algorithms.

National security needs dictate the development of new radar systems capable of identifying and tracking exoatmospheric threats to aid our defense. These new radar systems feature reduced noise floors, electronic beam steering, and ultra-wide bandwidths, all of which facilitate threat discrimination. However, in order to identify missile attributes such as RF reflectivity, distance, and velocity, many existing processing algorithms rely upon narrow bandwidth assumptions that break down with increased signal bandwidth. We present a fresh investigation into these algorithms for removing bandwidth limitations and propose novel state-space and direct-data factoring formulations such as * the multidimensional extension to the Eigensystem Realization Algorithm, * employing state-space models in place of interpolation to obtain a form which admits a separation and isolation of solution components, * and side-stepping the joint diagonalization of state transition matrices, which commonly plagues methods like multidimensional ESPRIT. We then benchmark our approaches and relate the outcomes to the Cramer-Rao bound for the case of one and two adjacent reflectors to validate their conceptual design and identify those techniques that compare favorably to or improve upon existing practices.

Este trabalho analisa e modela a Estrutura a Termo das Taxas de Juros objetivando ao teste da Hipótese das Expectativas(HE) na ponta curta da curva de juros e a uma aplicação da teoria de Markowitz (1952) no mercado de renda fixa utilizando a estrutura proposta por Caldeira, Moura e Santos (2015). Para estes fins foram utilizados dados dos contratos futuros de 1 dia dos depósito interbancários (DI1) negociados na BMF interpolados em maturidades fixas, sendo utilizados em base semanal quando do teste da HE e em base diária para a construção dos portfólios de mínima variância. Os resultados encontrados para o teste da HE sugerem a invalidade da teoria, uma vez que o prêmio de risco é se mostra ajustável a um modelo GARCH-M e, portanto, variante no tempo. Os portfólios de mínima variância ajustados nas versões irrestrita e restrita (duration máxima de 1 ano) se mostraram consistentes, tendo superado quase a totalidade dos fundos analisados. O portfólio de mínima variância irrestrito obteve o maior Índice de Sharpe no período analisado. / This work analyzes and model the Term Structure of Interest Rates seeking testing Expectation Hypothesis in the short end of the Yield Curve and to apply the portfolio theory to the fixed income context using the framework proposed by Caldeira, Moura e Santos (2015). We used a database of constant maturities interbank deposits’s future contracts. The results suggest Expectation Hypothesis doesn’t hold and risk premium could be modeled by a GARCH-M framework, being time variant. The bond portfolio optimized were, in general, consistent with high sharpe ratio relative to other funds and beated the chosen benchmark during the period analyzed.

Improving agricultural water management is important for conserving water during dry seasons, using limited water resources in the most efficient way, and minimizing environmental risks (e.g., leaching, surface runoff). The understanding of water movement in different zones of agricultural production fields is crucial to developing an effective irrigation strategy. This work centered on optimizing field water management by characterizing the spatial patterns of soil hydraulic properties. Soil hydraulic conductivity was measured across different zones in a farmer’s field, and its spatial variability was investigated by using geostatistical techniques. Since direct measurement of hydraulic conductivity is time-consuming and arduous, pedo-transfer functions (PTFs) have been developed to estimate hydraulic conductivity indirectly through more easily measurable soil properties. Due to ignoring soil structural information and spatial covariance between soil variables, PTFs often perform unsatisfactorily when field-scale estimations of hydraulic conductivity are needed. The performance of PTFs in estimating hydraulic conductivity in the field was therefore critically evaluated. Due to the presence of structural macro-pores, saturated hydraulic conductivity (Ks) showed high spatial heterogeneity, and this variability was not captured by texture-dominated PTF estimates. However, the general spatial pattern of near-saturated hydraulic conductivity can still be reasonably generated by PTF estimates. Therefore, the hydraulic conductivity maps based on PTF estimates should be evaluated carefully and handled with caution. Recognizing the significant contribution of macro-pores to saturated water flow, PTFs were further improved by including soil macro-porosity and were proven to perform much better in estimating Ks compared with established PTFs tested in this study. Additionally, the spatial relationship between hydraulic conductivity and its potential influencing factors were further quantified by the state-space approach. State-space models outperformed current PTFs and effectively described the spatial characteristics of hydraulic conductivity in the studied field. These findings provided a basis for modeling water/solute transport in the vadose zone, and sitespecific water management.