Dendrocolaptes platyrostris é uma espécie de ave florestal associada às matas de galeria do corredor de vegetação aberta da América do sul (D. p. intermedius) e à Floresta Atlântica (D. p. platyrostris). Em um trabalho anterior, foi observada estrutura genética populacional associada às subespécies, além de dois clados dentro da Floresta Atlântica e evidências de expansão na população do sul, o que é compatível com o modelo Carnaval-Moritz. Utilizando approximate Bayesian computation, o presente trabalho avaliou a diversidade genética de dois marcadores nucleares e um marcador mitocondrial dessa espécie com o objetivo de comparar os resultados obtidos anteriormente com os obtidos utilizando uma estratégia multi-locus e considerando variação coalescente. Os resultados obtidos sugerem uma relação de politomia entre as populações que se separaram durante o último período interglacial, mas expandiram após o último máximo glacial. Este resultado é consistente com o modelo de Carnaval-Moritz, o qual sugere que as populações sofreram alterações demográficas devido às alterações climáticas ocorridas nestes períodos. Trabalhos futuros incluindo outros marcadores e modelos que incluam estabilidade em algumas populações e expansão em outras são necessários para avaliar o presente resultado / Dendrocolaptes platyrostris is a forest specialist bird associated to gallery forests of the open vegetation corridor of South America (D. p. intermedius) and to the Atlantic forest (D. p. platyrostris). A previous study showed a population genetic structure associated with the subspecies, two clades within the Atlantic forest, and evidence of population expansion in the south, which is compatible with Carnaval- Moritz\'s model. The present study evaluated the genetic diversity of two nuclear and one mitochondrial markers of this species using approximate Bayesian computation, in order to compare the results previously obtained with those based on a multi-locus strategy and considering the coalescent variation. The results suggest a polytomic relationship among the populations that split during the last interglacial period and expanded after the last glacial maximum. This result is consistent with the model of Carnaval-Moritz, which suggests that populations have undergone demographic changes due to climatic changes that occurred in these periods. Future studies including other markers and models that include stability in some populations and expansion in others are needed to evaluate the present result

Reinforcement learning algorithms hold promise in many complex domains, such as resource management and planning under uncertainty. Most reinforcement learning algorithms are iterative - they successively approximate the solution based on a set of samples and features. Although these iterative algorithms can achieve impressive results in some domains, they are not sufficiently reliable for wide applicability; they often require extensive parameter tweaking to work well and provide only weak guarantees of solution quality. Some of the most interesting reinforcement learning algorithms are based on approximate dynamic programming (ADP). ADP, also known as value function approximation, approximates the value of being in each state. This thesis presents new reliable algorithms for ADP that use optimization instead of iterative improvement. Because these optimization-based algorithms explicitly seek solutions with favorable properties, they are easy to analyze, offer much stronger guarantees than iterative algorithms, and have few or no parameters to tweak. In particular, we improve on approximate linear programming - an existing method - and derive approximate bilinear programming - a new robust approximate method. The strong guarantees of optimization-based algorithms not only increase confidence in the solution quality, but also make it easier to combine the algorithms with other ADP components. The other components of ADP are samples and features used to approximate the value function. Relying on the simplified analysis of optimization-based methods, we derive new bounds on the error due to missing samples. These bounds are simpler, tighter, and more practical than the existing bounds for iterative algorithms and can be used to evaluate solution quality in practical settings. Finally, we propose homotopy methods that use the sampling bounds to automatically select good approximation features for optimization-based algorithms. Automatic feature selection significantly increases the flexibility and applicability of the proposed ADP methods. The methods presented in this thesis can potentially be used in many practical applications in artificial intelligence, operations research, and engineering. Our experimental results show that optimization-based methods may perform well on resource-management problems and standard benchmark problems and therefore represent an attractive alternative to traditional iterative methods.

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Population genetic analyses can provide information about both neutral and selective evolutionary processes shaping genetic variation. In this thesis, extensive population genetic methods were used to make inferences about genetic drift and selection in spruce species. In paper I we studied four species from the Qinghai-Tibetan Plateau (QTP): Picea likiangensis, P. purpurea, P. wilsonii and P. schrenkiana. Big differences in estimates of genetic diversity and Ne were observed in the more restricted species, P. schrenkiana, and the other more widely distributed species. Furthermore, P. purpurea appears to be a hybrid between P. likiangensis and P. wilsonii. In paper II we used Approximate Bayesian Computation (ABC) to find that the data support a drastic reduction of Ne in Taiwan spruce around 300-500 kya, in line with evidence from the pollen records. The split from P. wilsonii was dated to between 4-8 mya, around the time that Taiwan was formed. These analyses relied on a small sample size, and so in Paper III we investigated the impact of small datasets on the power to distinguish between models in ABC. We found that when genetic diversity is low there is little power to distinguish between simple coalescent models and this can determine the number of samples and loci required. In paper IV we studied the relative importance of genetic drift and selection in four spruce species with differing Ne: P. abies, P. glauca, P. jezoensis and P. breweriana. P. breweriana, which has a low Ne, exhibits a low fraction of adaptive substitutions, while P. abies has high Ne and a high fraction of adaptive substitutions. The other two spruce, however, do not support this suggesting other factors a more important. In paper V we find that several SNPs correlate with both a key adaptive trait (budset) and latitude. The expression of one in particular (PoFTL2) correlates with budset and was previously indentified in P. abies. These studies have helped characterise the importance of different population genetic processes in shaping genetic variation in spruce species and has laid some solid groundwork for future studies of spruce.

We propose a new approach to optimize operations of hydro storage systems with multiple connected reservoirs whose operators participate in wholesale electricity markets. Our formulation integrates short-term intraday with long-term interday decisions. The intraday problem considers bidding decisions as well as storage operation during the day and is formulated as a stochastic program. The interday problem is modeled as a Markov decision process of managing storage operation over time, for which we propose integrating stochastic dual dynamic programming with approximate dynamic programming. We show that the approximate solution converges towards an upper bound of the optimal solution. To demonstrate the efficiency of the solution approach, we fit an econometric model to actual price and in inflow data and apply the approach to a case study of an existing hydro storage system. Our results indicate that the approach is tractable for a real-world application and that the gap between theoretical upper and a simulated lower bound decreases sufficiently fast. (authors' abstract)

Master of Science / Department of Industrial and Manufacturing Systems Engineering / Todd Easton / Integer programs (IPs) are mathematical models that can provide an optimal solution to a variety of different problems. They have the ability to maximize profitability and decrease wasteful spending, but IPs are NP-complete resulting in many IPs that cannot be solved in reasonable periods of time. Cutting planes or valid inequalities have been used to decrease the time required to solve IPs. These valid inequalities are commonly created using a procedure called lifting. Lifting is a technique that strengthens existing valid inequalities without cutting off feasible solutions. Lifting inequalities can result in facet defining inequalities, the theoretically strongest valid inequalities. Because of these properties, lifting procedures are used in software to reduce the time required to solve an IP. This thesis introduces a new algorithm for synchronized simultaneous approximate lifting for multiple knapsack problems. Synchronized Simultaneous Approximate Lifting (SSAL) requires O(|E1|SLP_|E1|+|E2|,m + |E1|2) effort, where |E1| and |E2| are the sizes of sets used in the algorithm and SLP is the time to solve a linear program. It approximately uplifts two sets simultaneously to creates multiple inequalities of a particular form. These new valid inequalities generated by SSAL can be facet defining. A small computational study shows that SSAL is quick to execute, requiring fractions of a second. Additionally, applying SSAL inequalities to large knapsack problem enabled commercial software to solve faster and also eliminate off the initial linear relaxation point.

The problem of constructing data-based, predictive, reduced models for the Kuramoto–Sivashinsky equation is considered, under circumstances where one has observation data only for a small subset of the dynamical variables. Accurate prediction is achieved by developing a discrete-time stochastic reduced system, based on a NARMAX (Nonlinear Autoregressive Moving Average with eXogenous input) representation. The practical issue, with the NARMAX representation as with any other, is to identify an efficient structure, i.e., one with a small number of terms and coefficients. This is accomplished here by estimating coefficients for an approximate inertial form. The broader significance of the results is discussed.

We give a common frame for different estimates in survival models. For models with nuisance parameters we approximate the profile likelihood and find estimates especially for the proportional hazard model.

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The thesis focuses on a model that seeks to address patient scheduling step of the surgical scheduling process to determine the number of surgeries to perform in a given day. Specifically, provided a master schedule that provides a cyclic breakdown of total OR availability into specific daily allocations to each surgical specialty, we look to provide a scheduling policy for all surgeries that minimizes a combination of the lead time between patient request and surgery date, overtime in the ORs and congestion in the wards. We cast the problem of generating optimal control strategies into the framework of Markov Decision Process (MDP). The Approximate Dynamic Programming (ADP) approach has been employed to solving the model which would otherwise be intractable due to the size of the state space. We assess performance of resulting policy and quality of the driven policy through simulation and we provide our policy insights and conclusions.