This thesis studies bubbles in the U.S. stock market and how they are influenced by monetary policy pursued by the FED. Using Kalman filtering, the log-real price of S&P 500 is decomposed into a market-fundamentals component and a bubble component. The market-fundamentals component depends on the expected future dividends and the required rate of return, while the bubble component is treated as an unobserved state vector in the state-space model. The results suggest that, mainly in recent decades, the bubble has accounted for a substantial portion of S&P 500 price dynamics and might have played a significant role during major bull and bear markets. The innovation of this thesis is that it goes one step further and investigates the effects of monetary policy on both estimated components of S&P 500. For this purpose, the block- restriction VAR model is employed. The findings indicate that the decreasing interest rates have a significant short-term positive effect on the market-fundamentals component but not on the bubble. On the other hand, quantitative easing seems to have a positive effect on the bubble but not on the market-fundamentals component. Finally, the results suggest that the FED has not been successful at distinguishing between stock price movements due to fundamentals or the price misalignment.

Regression testing of an evolving software system may involve significant challenges. While, there would be a requirement of maximising the probability of finding out if the latest changes to the system has broken some existing feature, it needs to be done as economically as possible. A particularly important class of software systems are API libraries. Such libraries would typically constitute a very important component of many software systems. High quality requirements make it imperative to continually optimise the internal implementation of such libraries without affecting the external interface. Therefore, it is preferred to guide the regression testing by some kind of formal specification of the library. The testing problem comprises of three parts: computation of test data, execution of test, and analysis of test results. Current research mostly focuses on the first part. The objective of test data computation is to maximise the probability of uncovering bugs, and to do it with as few test cases as possible. The problem of test data computation for regression testing is to select a subset of the original test suite running which would suffice to test for bugs probably inserted in the modifications done after the last round of testing. A variant of this problem is that of regression testing of API libraries. The regression testing of an API is usually done by making function calls in such a way that the sequence of function calls thus made suffices a test specification. The test specification in turn embodies some concept of completeness. In this thesis, we focus on the problem of test sequence computation for the regression testing of API libraries. At the heart of this method lies the creation of a state space model of the API library by reverse engineering it by executing the system, with guidance from an formal API specification. Once the state space graph is obtained, it is used to compute test sequences for satisfying some test specification. We analyse the theoretical complexity of the problem of test sequence computation and provide various heuristic algorithms for the same. State space explosion is a classical problem encountered whenever there is an attempt of creating a finite state model of a program. Our method also faces this limitation. We explore a simple and intuitive method of ameliorating this problem – by simply reducing the size of the state vector. We develop the theoretical insights into this method. Also, we present experimental results indicating the practical effectiveness of this method. Finally, we bring all this together into the design and implementation of a tool called Modest.

Submitted by Maike Costa (maiksebas@gmail.com) on 2016-03-23T11:16:42Z No. of bitstreams: 1 arquivototal.pdf: 1343904 bytes, checksum: dfca90c2164204a1513fc4a55eca4527 (MD5) / Made available in DSpace on 2016-03-23T11:16:43Z (GMT). No. of bitstreams: 1 arquivototal.pdf: 1343904 bytes, checksum: dfca90c2164204a1513fc4a55eca4527 (MD5) Previous issue date: 2015-05-26 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / Our purpose is to study the geometry of Riemannian immersions in certain semi- Riemannian manifolds. Initially, considering linearWeingarten hypersurfaces immersed in locally symmetric manifolds and, imposing suitable constraints on the scalar curvature, we guarantee that such a hypersurface is either totally umbilical or isometric to a isoparametric hypersurface with two distinct principal curvatures, one of them being simple. In higher codimension, we use a Simons type formula to obtain new characterizations of hyperbolic cylinders through the study of submanifolds having parallel normalized mean curvature vector field in a semi-Riemannian space form. Finally, we investigate the rigidity of complete spacelike hypersurfaces immersed in the steady state space via applications of some maximum principles. / Nos propomos estudar a geometria de imersões Riemannianas em certas variedades semi-Riemannianas. Inicialmente, consideramos hipersuperfícies Weingarten lineares imersas em variedades localmente simétricas e, impondo restrições apropriadas à curvatura escalar, garantimos que uma tal hipersuperfície é totalmente umbílica ou isométrica a uma hipersuperfície isoparamétrica com duas curvaturas principais distintas, sendo uma destas simples. Em codimensão alta, usamos uma fórmula do tipo Simons para obter novas caracterizações de cilindros hiperbólicos a partir do estudo de subvariedades com vetor curvatura média normalizado paralelo em uma forma espacial semi-Riemanniana. Finalmente, investigamos a rigidez de hipersuperfícies tipo-espaço completas imersas no steady state space via aplicações de alguns princípios do máximo.

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.