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Local governments are often willing to offer companies generous tax incentives to attract businesses to their region. In the United States, many states have tried to attract film productions and establish local film industries by offering different forms of state film tax incentives. As a prominent example of this, the state of Georgia offers a film tax credit which has no annual maximum compensation cap, creating an attractive tax environment for large film productions. The purpose of this study is to investigate if the Georgia state “Film, Television and Digital Entertainment Tax Credit” significantly affected film jobs in the state, and if any other industries were also affected through cross-industry employment spillovers. A difference in differences approach was used by way of the synthetic control case study method. This method estimates the counterfactual development of the outcome variable by creating a synthetic Georgia consisting of a weighted combination of untreated states. The results show a large and highly significant effect of the tax credit on film production jobs. However, little evidence of employment spillovers from the film industry is found, with only a select few affected industries being insurance and interior design. These results imply that tax incentives can establish a local film industry, but likely only if the annual maximum compensation is high or uncapped, making for a significantly more generous incentive than the average.

Decreasing gas prices and the pressing need for fast-responding electric power generators are currently transforming natural gas networks. The intermittent operation of gas-fired plants to balance wind generation introduces spatiotemporal fluctuations of increasing gas demand. At the heart of modeling, monitoring, and control of gas networks is a set of nonlinear equations relating nodal gas injections and pressures to flows over pipelines. Given gas demands at all points of the network, the gas flow task aims at finding the rest of the physical quantities. For a tree network, the problem enjoys a closed-form solution; yet solving the equations for practical meshed networks is non-trivial. This problem is posed here as a feasibility problem involving quadratic equalities and inequalities, and is further relaxed to a convex semidefinite program (SDP) minimization. Drawing parallels to the power flow problem, the relaxation is shown to be exact if the cost function is judiciously designed using a representative set of network states. Numerical tests on a Belgian gas network corroborate the superiority of the novel method in recovering the actual gas network state over a Newton-Raphson solver. This thesis also considers the coupled infrastructures of natural gas and electric power systems. The gas and electric networks are coupled through gas-fired generators, which serve as shoulder and peaking plants for the electric power system. The optimal dispatch of coupled natural gas and electric power systems is posed as a relaxed convex minimization problem, which is solved using the feasible point pursuit (FPP) algorithm. For a decentralized solution, the alternating direction method of multipliers (ADMM) is used in collaboration with the FPP. Numerical experiments conducted on a Belgian gas network connected to the IEEE 14 bus benchmark system corroborate significant enhancements on computational efficiency compared with the centralized FPP-based approach. / Master of Science / The increase in penetration of renewable energy in the electric power grid has led to increased fluctuations in the power. The conventional coal based generators are inept to handle these fluctuations and thus, natural gas generators, which have fast response times are used to handle the intermittency caused by renewable energy sources. This manuscript solves the problem of finding the optimal dispatch of coupled natural gas and electric power systems. First, the optimal dispatch problem is framed as a optimization problem and then mathematical solvers are developed. Using the mathematical tools of Feasible point pursuit and Alternating direction method of multipliers, a distributed solver is developed, which can solve the optimal dispatch for large power and natural gas networks. The proposed algorithm is tested on a part of a Belgian gas network and the IEEE 14 bus power system. The algorithm is shown to converge to a feasible point.

L'intérêt de l'échantillonnage compressé dans l'imagerie ultrasonore a été récemment évalué largement par plusieurs équipes de recherche. Suite aux différentes configurations d'application, il a été démontré que les données RF peuvent être reconstituées à partir d'un faible nombre de mesures et / ou en utilisant un nombre réduit d'émission d'impulsions ultrasonores. Selon le modèle de l'échantillonnage compressé, la résolution des images ultrasonores reconstruites à partir des mesures compressées dépend principalement de trois aspects: la configuration d'acquisition, c.à.d. l'incohérence de la matrice d'échantillonnage, la régularisation de l'image, c.à.d. l'a priori de parcimonie et la technique d'optimisation. Nous nous sommes concentrés principalement sur les deux derniers aspects dans cette thèse. Néanmoins, la résolution spatiale d'image RF, le contraste et le rapport signal sur bruit dépendent de la bande passante limitée du transducteur d'imagerie et du phénomène physique lié à la propagation des ondes ultrasonores. Pour surmonter ces limitations, plusieurs techniques de traitement d'image en fonction de déconvolution ont été proposées pour améliorer les images ultrasonores. Dans cette thèse, nous proposons d'abord un nouveau cadre de travail pour l'imagerie ultrasonore, nommé déconvolution compressée, pour combiner l'échantillonnage compressé et la déconvolution. Exploitant une formulation unifiée du modèle d'acquisition directe, combinant des projections aléatoires et une convolution 2D avec une réponse impulsionnelle spatialement invariante, l'avantage de ce cadre de travail est la réduction du volume de données et l'amélioration de la qualité de l'image. Une méthode d'optimisation basée sur l'algorithme des directions alternées est ensuite proposée pour inverser le modèle linéaire, en incluant deux termes de régularisation exprimant la parcimonie des images RF dans une base donnée et l'hypothèse statistique gaussienne généralisée sur les fonctions de réflectivité des tissus. Nous améliorons les résultats ensuite par la méthode basée sur l'algorithme des directions simultanées. Les deux algorithmes sont évalués sur des données simulées et des données in vivo. Avec les techniques de régularisation, une nouvelle approche basée sur la minimisation alternée est finalement développée pour estimer conjointement les fonctions de réflectivité des tissus et la réponse impulsionnelle. Une investigation préliminaire est effectuée sur des données simulées. / The interest of compressive sampling in ultrasound imaging has been recently extensively evaluated by several research teams. Following the different application setups, it has been shown that the RF data may be reconstructed from a small number of measurements and/or using a reduced number of ultrasound pulse emissions. According to the model of compressive sampling, the resolution of reconstructed ultrasound images from compressed measurements mainly depends on three aspects: the acquisition setup, i.e. the incoherence of the sampling matrix, the image regularization, i.e. the sparsity prior, and the optimization technique. We mainly focused on the last two aspects in this thesis. Nevertheless, RF image spatial resolution, contrast and signal to noise ratio are affected by the limited bandwidth of the imaging transducer and the physical phenomenon related to Ultrasound wave propagation. To overcome these limitations, several deconvolution-based image processing techniques have been proposed to enhance the ultrasound images. In this thesis, we first propose a novel framework for Ultrasound imaging, named compressive deconvolution, to combine the compressive sampling and deconvolution. Exploiting an unified formulation of the direct acquisition model, combining random projections and 2D convolution with a spatially invariant point spread function, the benefit of this framework is the joint data volume reduction and image quality improvement. An optimization method based on the Alternating Direction Method of Multipliers is then proposed to invert the linear model, including two regularization terms expressing the sparsity of the RF images in a given basis and the generalized Gaussian statistical assumption on tissue reflectivity functions. It is improved afterwards by the method based on the Simultaneous Direction Method of Multipliers. Both algorithms are evaluated on simulated and in vivo data. With regularization techniques, a novel approach based on Alternating Minimization is finally developed to jointly estimate the tissue reflectivity function and the point spread function. A preliminary investigation is made on simulated data.

Distributed computing is an approach where computationally heavy problems are broken down into more manageable sub-tasks, which can then be distributed across a number of different computers or servers, allowing for increased efficiency through parallelization. This thesis explores an established distributed computing setting, in which the computationally heavy task involves a number of users requesting a linearly separable function to be computed across several servers. This setting results in a condition for feasible computation and communication that can be described by a matrix factorization problem. Moreover, the associated costs with computation and communication are directly related to the number of nonzero elements of the matrix factors, making sparse factors desirable for minimal costs. The Alternating Direction Method of Multipliers (ADMM) is explored as a possible method of solving the sparse matrix factorization problem. To obtain convergence results, extensive convex analysis is conducted on the ADMM iterates, resulting in a theorem that characterizes the limiting points of the iterates as KKT points for the sparse matrix factorization problem. Using the results of the analysis, an algorithm is devised from the ADMM iterates, which can be applied to the sparse matrix factorization problem. Furthermore, an additional implementation is considered for a noisy scenario, in which existing theoretical results are used to justify convergence. Finally, numerical implementations of the devised algorithms are used to perform sparse matrix factorization. / Distribuerad beräkning är en metod där beräkningstunga problem bryts ner i hanterbara deluppgifter, som sedan kan distribueras över ett antal olika beräkningsenheter eller servrar, vilket möjliggör ökad effektivitet genom parallelisering. Denna avhandling undersöker en etablerad distribuerad beräkningssmiljö, där den beräkningstunga uppgiften involverar ett antal användare som begär en linjärt separabel funktion som beräknas över flera servrar. Denna miljö resulterar i ett villkor för tillåten beräkning och kommunikation som kan beskrivas genom ett matrisfaktoriseringsproblem. Dessutom är det möjligt att relatera kostanderna associerade med beräkning och kommunikation till antalet nollskilda element i matrisfaktorerna, vilket gör glesa matrisfaktorer önskvärda. Alternating Direction Method of Multipliers (ADMM) undersöks som en möjlig metod för att lösa det glesa matrisfaktoriseringsproblemet. För att erhålla konvergensresultat genomförs omfattande konvex analys på ADMM-iterationerna, vilket resulterar i ett teorem som karakteriserar de begränsande punkterna för iterationerna som KKT-punkter för det glesa matrisfaktoriseringsproblemet. Med hjälp av resultaten från analysen utformas en algoritm från ADMM-iterationerna, vilken kan appliceras på det glesa matrisfaktoriseringsproblemet. Dessutom övervägs en ytterligare implementering för ett brusigt scenario, där befintliga teoretiska resultat används för att motivera konvergens. Slutligen används numeriska implementeringar av de framtagna algoritmerna för att utföra gles matrisfaktorisering.

La segmentation hiérarchique est une méthode pour produire des partitions qui représentent une même image de manière de moins en moins fine. En même temps, elle sert d'entrée à la recherche d'une partition optimale, qui combine des extraits des diverses partitions en divers endroits. Le traitement hiérarchique des images est un domaine émergent en vision par ordinateur, et en particulier dans la communauté qui étudie les images hyperspectrales et les SIG, du fait de son capacité à structurer des données hyper-dimensionnelles. Le chapitre 1 porte sur les deux concepts fondamentaux de tresse et de treillis énergétique. La tresse est une notion plus riche que celle de hiérarchie de partitions, en ce qu'elle incorpore, en plus, des partitions qui ne sont pas emboîtées les unes dans les autres, tout en s'appuyant globalement sur une hiérarchie. Le treillis énergétique est une structure mixte qui regroupe une tresse avec une énergie, et permet d'y définir des éléments maximaux et minimaux. Lorsqu'on se donne une énergie, trouver la partition formée de classes de la tresse (ou de la hiérarchie) qui minimise cette énergie est un problème insoluble, de par sa complexité combinatoriale. Nous donnons les deux conditions de h-croissance et de croissance d'échelle, qui garantissent l'existence, l'unicité et la monotonie des solutions, et conduisent à un algorithme qui les détermine en deux passes de lecture des données. Le chapitre 2 reste dans le cadre précédent, mais étudie plus spécifiquement l'optimisation sous contrainte. Il débouche sur trois généralisations du modèle Lagrangien. Le chapitre 3 applique l'optimisation par treillis énergétique au cas de figure où l'énergie est introduite par une « vérité terrain », c'est à dire par un jeu de dessins manuel, que les partitions optimales doivent serrer au plus près. Enfin, le chapitre 4 passe des treillis énergétiques à ceux des courbes de Jordan dans le plan euclidien, qui définissent un modèle continu de segmentations hiérarchiques. Il permet entre autres de composer les hiérarchies avec diverses fonctions numériques / Hierarchical segmentation has been a model which both identifies with the construct of extracting a tree structured model of the image, while also interpreting it as an optimization problem of the optimal scale selection. Hierarchical processing is an emerging field of problems in computer vision and hyper-spectral image processing community, on account of its ability to structure high-dimensional data. Chapter 1 discusses two important concepts of Braids and Energetic lattices. Braids of partitions is a richer hierarchical partition model that provides multiple locally non-nested partitioning, while being globally a hierarchical partitioning of the space. The problem of optimization on hierarchies and further braids are non-tractable due the combinatorial nature of the problem. We provide conditions, of h-increasingness, scale-increasingness on the energy defined on partitions, to extract unique and monotonically ordered minimal partitions. Furthermore these conditions are found to be coherent with the Braid structure to perform constrained optimization on hierarchies, and more generally Braids. Chapter 2 demonstrates the Energetic lattice, and how it generalizes the Lagrangian formulation of the constrained optimization problem on hierarchies. Finally in Chapter 3 we apply the method of optimization using energetic lattices to the problem of extraction of segmentations from a hierarchy, that are proximal to a ground truth set. Chapter 4 we show how one moves from the energetic lattice on hierarchies and braids, to a numerical lattice of Jordan Curves which define a continous model of hierarchical segmentation. This model enables also to compose different functions and hierarchies

O objetivo deste trabalho é investigar se os multiplicadores de gastos do governo diferem de acordo com o estado do ciclo de negócios para o período 1999: I- 2016: II. Para tanto é utilizado o Método de Projeção Local de Jordà para estimar as funções resposta ao impulso e os multiplicadores fiscais sob dois regimes diferentes: recessão e expansão. Para definir os diferentes regimes foram utilizadas as variáveis comumente usadas na literatura (o hiato do produto, o nível de utilização da capacidade instalada, a taxa de crescimento do PIB, a taxa de desemprego), além da datação oficial de ciclos do CODACE. A estimação do modelo não linear resulta em multiplicadores de gastos do governo, após um e dois anos, maiores nos períodos de recessão do que nos períodos de expansão, independentemente da variável escolhida para diferenciar os regimes. Porém, os multiplicadores obtidos não parecem ser diferentes estatisticamente entre os regimes. Infelizmente, como observado por Ramey e Zubairy (2017) a existência de séries históricas é fundamental para a estimação dos multiplicadores fiscais e sua ausência para a economia brasileira limita muito o que é possível dizer sobre o assunto / This paper aims to investigate whether government spending multipliers are different according to the state of the business cycle for the Brazilian economy during the period 1999:I-2016:II. In order to do so we use Jordà\'s Local Projection Method to estimate impulse response functions and fiscal multipliers under two different regimes: recession and expansion. To define the different regimes we use several variables commonly used in the literature: the output gap, the capacity utilization level, the GDP growth rate, the unemployment rate and CODACE. The nonlinear model estimations result in larger multipliers, after one and two years, in periods of economic recession than in periods of economic expansion, regardless of the variable chosen to differentiate regimes. However, the multipliers do not seem to be statistically different between regimes. Unfortunately, as observed by Ramey and Zubairy (2017), long historical series are fundamental for the adequate estimation of fiscal multipliers and their absence for the Brazilian economy does not allow anyone to say much about the subject.

Made available in DSpace on 2016-03-22T17:26:45Z (GMT). No. of bitstreams: 1 monica.pdf: 3913704 bytes, checksum: 391eb8287c6e4e8d928a93819e3828ee (MD5) Previous issue date: 2012-03-25 / This work proposes the implementation of dedicated hardware architecture for the Least Mean Square (LMS) adaptive filtering algorithm by using Hybrid encoding, whose main goal is to cancel the interferences in the signal of interest. In the used scheme, from a 60Hz reference signal, the algorithm is able to estimate the superior harmonics, using after these results for the cancelling of interferences related to the signal of interest. One of the techniques that is widely used for the switching activity reduction uses signal encoding. In this work, the proposed adaptive filtering architecture uses the Hybrid encoding in its data buses, whose main idea is to split the operands in group of m-bits, encode each group using the Gray code (that potentially enables reduction of the switching activity into each group) and propagate the carry between the groups as in the Binary encoding. We developed new Hybrid multipliers for signed multiplication, which uses radix-2m encoding. The multipliers are applied to the adaptive filtering architecture. We have implemented 18, 23 and 36 bit-width radix-4 Hybrid array multipliers, as well as a particular case for the radix-8 (m=3) operation. The main results showed that the Hybrid multipliers are more efficient than the Binary ones, by presenting less power consumption in some cases. Moreover, the implemented adaptive filtering architectures were validated and compared in both Binary and Hybrid encoding. The efficiency of the implemented filters for the cancelling of interferences was proved by using both encoding scheme. By the presented results, we conclude that it could be practicable to implement an adaptive filtering architecture operating on Hybrid encoding / Este trabalho tem como proposta a implementação de uma arquitetura de hardware dedicada para o algoritmo LMS (Least Mean Square) de filtragem adaptativa, para o cancelamento de interferências em codificação Híbrida. No esquema utilizado, a partir de um sinal de referência de 60Hz, o algoritmo estima as harmônicas superiores, utilizando esses resultados para o cancelamento da interferência associada ao sinal de interesse. Um dos métodos para a redução da atividade de chaveamento em barramentos de dados que tem sido amplamente utilizado é a codificação de dados. Neste trabalho, a arquitetura de filtragem adaptativa proposta utiliza em seus barramentos de dados a codificação Híbrida, cuja idéia é dividir os operandos em grupos de m bits, codificar cada grupo utilizando o código Gray (que habilita reduções na atividade de chaveamento dentro de cada grupo) e utilizar o comportamento do código Binário para propagar o carry entre os grupos. Dessa forma, são desenvolvidas arquiteturas otimizadas de circuitos multiplicadores array base 2m na codificação Híbrida para a aplicação na arquitetura dedicada de filtro adaptativo. São implementados circuitos multiplicadores array de 18, 23 e 36 bits na codificação Híbrida na base 4 (m=2), bem como um caso particular para a base 8 (m=3). Essas arquiteturas são implementadas em linguagem de descrição de hardware. Os principais resultados mostraram que os multiplicadores Híbridos apresentaram, em alguns casos, menor consumo de potência em relação aos multiplicadores binários. Além disso, foi possível validar e comparar as arquiteturas de filtro adaptativo nas codificações Binária e Híbrida, onde se pôde verificar a eficiência dos filtros para o cancelamento de interferências em ambas as codificações, mostrando-se possível a implementação de um filtro adaptativo em codificação Híbrida

Bibliography: leaves 217-238. / ix, 238 leaves ; 30 cm. / Title page, contents and abstract only. The complete thesis in print form is available from the University Library. / Thesis (Ph.D.)--University of Adelaide, Dept. of Applied Mathematics, 1995?