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.

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.

The linkage between barrier island morphologies and dune topographies, vegetation, and biogeomorphic feedbacks, has been examined. The two-fold stability domain (i.e., overwash-resisting and overwash-reinforcing stability domains) model from case studies in a couple of islands along the Georgia Bight and Virginia coast has been proposed to examine the resilience properties in the barrier dune systems. Thus, there is a need to examine geographic variations in the dune topography among and within islands. Meanwhile, previous studies just analyzed and compared dune topographies based on transect-based point elevations or dune crest elevations; therefore, it is necessary to further examine dune topography in terms of multiple patterns and processes across scales. In this dissertation, I develop and deploy a cross-scale data model developed from resilience theory to represent and compare dune topographies across twelve islands over approximately 2,050 kilometers of the US southeastern Atlantic coast. Three sets of topographic variables were employed to summarize the cross-scale structure of topography (elevational statistics, patch indices, and the continuous surface properties). These metrics differed in their degree of spatial explicitness, their level of measurement, and association with patch or gradient paradigms. Topographic metrics were derived from digital elevation models (DEMs) of dune topographies constructed from airborne Light Detection and Ranging (LiDAR). These topographic metrics were used to construct dune topographic state space to investigate and visualize the cross-scale structure of dune topography. This study investigated (1) dune topography and landscape similarity among barrier islands in different barrier island morphologic contexts, (2) the differences in barrier island dune topographies and their resilience properties across large geographic extents, and (3) how geomorphic and biogeomorphic processes are related to resilience prosperities. The findings are summarized below. First, dune topography varies according to island morphologies of the Virginia coast; however, local controls (such as human modification of the shore or shoreline accretion and erosion) also play an important role in shaping dune topographies. Compared with tide-dominated islands, wave-dominated islands exhibited more convergence in dune topographies. Second, the dune landscapes of the Virginia Barrier Islands have a poorly consistent spatial structure, along with strong collinearity among elevational variables and landscape indices, which reflects the rapid retreat and erosion along the coast. The dune landscapes of the Georgia Bight have a more consistent spatial structure and a greater dimensionality in state space. Thus, the weaker multicollinearity and higher dimensionality in the dataset reflect their potential for resilience. Last, islands of different elevations may have similar dune topography characteristics due to the difference in resistance and resilience. Notwithstanding the geographic variability in geomorphic and biogeomorphic processes, convergence in dune topography exists, which is evidenced by the response curves of the topographic metrics that are correlated with both axes. This work demonstrates the usefulness of different representations of dune topography by cross-scale data modeling. Also, the two existing models of barrier island dune states were integrated to form a conceptual model that illuminates different, but complementary, resilience properties in the barrier dune system. The differences in dune topographies and resilience properties were detected in state space, and this information offers guidance for future study’s field site selections.

This manuscript consists of two papers that formulate novel methodologies pertaining to time series analysis in the state-space framework. In Chapter 1, we introduce an innovative time series forecasting procedure that relies on model-based clustering and model averaging. The clustering algorithm employs a state-space model comprised of three latent structures: a long-term trend component; a seasonal component, to capture recurring global patterns; and an anomaly component, to reflect local perturbations. A two-step clustering algorithm is applied to identify series that are both globally and locally correlated, based on the corresponding smoothed latent structures. For each series in a particular cluster, a set of forecasting models is fit, using covariate series from the same cluster. To fully utilize the cluster information and to improve forecasting for a series of interest, multi-model averaging is employed. We illustrate the proposed technique in an application that involves a collection of monthly disease incidence series. In Chapter 2, to effectively characterize a count time series that arises from a zero-inflated binomial (ZIB) distribution, we propose two classes of statistical models: a class of observation-driven ZIB (ODZIB) models, and a class of parameter-driven ZIB (PDZIB) models. The ODZIB model is formulated in the partial likelihood framework. Common iterative algorithms (Newton-Raphson, Fisher Scoring, and Expectation Maximization) can be used to obtain the maximum partial likelihood estimators (MPLEs). The PDZIB model is formulated in the state-space framework. For parameter estimation, we devise a Monte Carlo Expectation Maximization (MCEM) algorithm, using particle methods to approximate the intractable conditional expectations in the E-step of the algorithm. We investigate the efficacy of the proposed methodology in a simulation study, and illustrate its utility in a practical application pertaining to disease coding.

Due to its generality and flexibility, the state-space model has become one of the most popular models in modern time domain analysis for the description and prediction of time series data. The model is often used to characterize processes that can be conceptualized as "signal plus noise," where the realized series is viewed as the manifestation of a latent signal that has been corrupted by observation noise. In the state-space framework, parameter estimation is generally accomplished by maximizing the innovations Gaussian log-likelihood. The maximum likelihood estimator (MLE) is efficient when the normality assumption is satisfied. However, in the presence of contamination, the MLE suffers from a lack of robustness. Basu, Harris, Hjort, and Jones (1998) introduced a discrepancy measure (BHHJ) with a non-negative tuning parameter that regulates the trade-off between robustness and efficiency. In this manuscript, we propose a new parameter estimation procedure based on the BHHJ discrepancy for fitting state-space models. As the tuning parameter is increased, the estimation procedure becomes more robust but less efficient. We investigate the performance of the procedure in an illustrative simulation study. In addition, we propose a numerical method to approximate the asymptotic variance of the estimator, and we provide an approach for choosing an appropriate tuning parameter in practice. We justify these procedures theoretically and investigate their efficacy in simulation studies. Based on the proposed parameter estimation procedure, we then develop a new model selection criterion in the state-space framework. The traditional Akaike information criterion (AIC), where the goodness-of-fit is assessed by the empirical log-likelihood, is not robust to outliers. Our new criterion is comprised of a goodness-of-fit term based on the empirical BHHJ discrepancy, and a penalty term based on both the tuning parameter and the dimension of the candidate model. We present a comprehensive simulation study to investigate the performance of the new criterion. In instances where the time series data is contaminated, our proposed model selection criterion is shown to perform favorably relative to AIC. Lastly, using the BHHJ discrepancy based on the chosen tuning parameter, we propose two versions of an influence diagnostic in the state-space framework. Specifically, our diagnostics help to identify cases that influence the recovery of the latent signal, thereby providing initial guidance and insight for further exploration. We illustrate the behavior of these measures in a simulation study.