No description available.

Parsimonious voice leading is a term, first used by Richard Cohn, to describe non-diatonic motion among triads that will preserve as many common tones as possible, while limiting the distance traveled by the voice that does move to a tone or, better yet, a semitone. Some scholars have applied these principles to seventh chords, laying the groundwork for this study, which strives toward a reasonably comprehensive, usable model for musical analysis. Rather than emphasizing mathematical proofs, as a number of approaches have done, this study relies on two- and three-dimensional geometric visualizations and spatial analogies to describe pitch-class and harmonic relationships. These geometric realizations are based on the organization of the neo-Riemannian Tonnetz, but they expand and apply the organizational principles of the Tonnetz to seventh sonorities. It allows for the descriptive “mapping” or prescriptive “navigation” of harmonic paths through a defined space. The viability of the theoretical model is examined in analyses of passages from the repertoire of Frédéric Chopin. These passages exhibit a harmonic syntax that is often difficult to analyze as anything other than “tonally unstable” or “transitional.” This study seeks to analyze these passages in terms of what they are, rather than what they are not.

A large portion of Earth¿s terrestrial surface is subject to arid climatic water stress. As in these regions the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modeling of these two systems is needed to fully reproduce the ecosystems¿ behavior over time and to predict possible future responses to climate change. In this thesis, the performance of three parsimonious dynamic vegetation models, suitable for inclusion in an operational ecohydrological model, are tested in a semi-arid Aleppo pine forest area in the south-east of Spain. The first model considered, HORAS (Quevedo & Francés, 2008), simulates growth as a function of plant transpiration (T), evaluating environmental restraints through the transpiration-reference evapotranspiration ratio. The state variable related to vegetation is R, relative foliar biomass, which is equivalent to FAO crop coefficient (Allen et al., 1998), but not fixed in time. The HORAS model was then abandoned because of its unsatisfactory results, probably due to a poor simulation of evaporation and transpiration processes. As for the other two models, WUE-model and LUE-model, the state variable is the leaf biomass (Bl, kg dry mass m-2 vegetation cover). Both models simulate gross primary production (GPP), in the first case as a function of transpiration and water use efficiency (WUE), in the second case as a function of absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE). Net primary production (NPP) is then calculated taking into account respiration. The modelling is focused particularly on simulating foliar biomass, which is obtained from NPP through an allocation equation based on the maximum leaf area index (LAI) sustainable by the system, and considering turnover. An analysis of the information offered by MODIS EVI, NDVI, and LAI products was also performed, in order to investigate vegetation dynamics in the study site and to select the best indices to be used as observational verification for models. MODIS EVI is reported in literature (Huete et al., 2002) to be highly correlated with leaf biomass. In accordance with the phenological cycle timing described for the Aleppo pine in similar climates (Muñoz et al., 2003), the EVI showed maximum values in spring and minimum values in winter. Similar results were found applying the aforementioned WUE- and LUE- models to the study area. Contrasting simulated LAI with the EVI series, the correlation coefficients rWUE = 0.45 and rLUE = 0.57 were found for the WUE-model and LUE-model respectively. Concerning NDVI, its own definition links this index to the ¿greenness¿ of the target, so that it appears highly linked to chlorophyll content and vegetation condition, but only indirectly related to LAI. Photosynthetic pigment concentrations are reported to be sensitive to water stress in Aleppo pine (Baquedano and Castillo, 2006) so, to compare the models¿ results with NDVI, the simulated LAI was corrected by plant water-stress. The resulting correlation coefficients were rWUE = 0.62 and rLUE = 0.59. Lastly, MODIS LAI and ET were found to be unreliable in the study area because very low compared to field data and to values reported in literature (e.g. Molina & del Campo, 2012) for the same species in similar climatic conditions. The performance of both WUE- and LUE- models in this semi-arid region is found to be reasonable. However, the LUE-model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature. / Pasquato, M. (2013). Comparison of parsimonious dynamic vegetation modelling approaches for semiarid climates [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34326

Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classification automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de la matrice de covariance et qui offre un cadre flexible notamment pour les problèmes de classification en grande dimension. Ensuite, nous investiguons les mélanges Bayésiens non-paramétriques qui se basent sur des processus généraux flexibles comme le processus de Dirichlet et le Processus du Restaurant Chinois. Cette formulation non-paramétrique des modèles est pertinente aussi bien pour l’apprentissage du modèle, que pour la question difficile du choix de modèle. Nous proposons de nouveaux modèles de mélanges Bayésiens non-paramétriques parcimonieux et dérivons une technique d’échantillonnage par Monte Carlo dans laquelle le modèle de mélange et son nombre de composantes sont appris simultanément à partir des données. La sélection de la structure du modèle est effectuée en utilisant le facteur de Bayes. Ces modèles, par leur formulation non-paramétrique et parcimonieuse, sont utiles pour les problèmes d’analyse de masses de données lorsque le nombre de classe est indéterminé et augmente avec les données, et lorsque la dimension est grande. Les modèles proposés validés sur des données simulées et des jeux de données réelles standard. Ensuite, ils sont appliqués sur un problème réel difficile de structuration automatique de données bioacoustiques complexes issues de signaux de chant de baleine. Enfin, nous ouvrons des perspectives Markoviennes via les processus de Dirichlet hiérarchiques pour les modèles Markov cachés. / This thesis focuses on statistical learning and multi-dimensional data analysis. It particularly focuses on unsupervised learning of generative models for model-based clustering. We study the Gaussians mixture models, in the context of maximum likelihood estimation via the EM algorithm, as well as in the Bayesian estimation context by maximum a posteriori via Markov Chain Monte Carlo (MCMC) sampling techniques. We mainly consider the parsimonious mixture models which are based on a spectral decomposition of the covariance matrix and provide a flexible framework particularly for the analysis of high-dimensional data. Then, we investigate non-parametric Bayesian mixtures which are based on general flexible processes such as the Dirichlet process and the Chinese Restaurant Process. This non-parametric model formulation is relevant for both learning the model, as well for dealing with the issue of model selection. We propose new Bayesian non-parametric parsimonious mixtures and derive a MCMC sampling technique where the mixture model and the number of mixture components are simultaneously learned from the data. The selection of the model structure is performed by using Bayes Factors. These models, by their non-parametric and sparse formulation, are useful for the analysis of large data sets when the number of classes is undetermined and increases with the data, and when the dimension is high. The models are validated on simulated data and standard real data sets. Then, they are applied to a real difficult problem of automatic structuring of complex bioacoustic data issued from whale song signals. Finally, we open Markovian perspectives via hierarchical Dirichlet processes hidden Markov models.

<p>Development of complex aerospace systems often takes decades of research and testing. High  performing propellants are important to the success of rocket propulsion systems. Development  and testing of new propellants can be expensive and dangerous. Full scale tests are often required  to understand the performance of new propellants. Many industries have started using data science  tools to learn from previous work and conduct smarter tests. Material scientists have started using  these tools to speed up the development of new materials. These data science tools can be used to  speed up the development and design better propellants. I approach the development of new solid  propellants through two steps: Prediction of delivered performance from available literature tests,  prediction of ideal performance using physics-based models. Random Forest models are used to  correlate the ideal performance to delivered performance of a propellant based on the composition  and motor properties. I use Parsimonious Neural Networks (PNNs) to learn interpretable models  for the ideal performance of propellants. I find that the available open literature data is too biased  for the models to learn from and discover families of interpretable models to predict the ideal  performance of propellants. </p>

No description available.

Several ready-to-use supervised classifiers perform predictively well in large-sample cases, but generally, the same cannot be expected when transitioning to high-dimensional settings. This can be explained by the classical supervised theory that has not been developed within high-dimensional spaces, giving several classifiers a hard combat against the curse of dimensionality. A rise in parsimonious classification procedures, particularly techniques incorporating feature selectors, can be observed. It can be interpreted as a two-step procedure: allowing an arbitrary selector to obtain a feature subset independent of a ready-to-use model and subsequently classify unlabelled instances within the selected subset. Modeling the two-step procedure is often heavy in motivation, and theoretical and algorithmic descriptions are frequently overlooked. In this thesis, we aim to describe the theoretical and algorithmic framework when employing a feature selector as a pre-processing step for Support Vector Machine and assess its validity in high-dimensional settings. The validity of the proposed classifier is evaluated based on predictive performance through a comparative study with a state-of-the-art algorithm designed for advanced learning tasks. The chosen algorithm effectively employs feature relevance during training, making it suitable for high-dimensional settings. The results suggest that the proposed classifier performs predicatively superior to the Support Vector Machine in lower input dimensions; however, a high rate of convergence towards a performance comparable to the Support Vector Machine tends to emerge for input dimensions beyond a certain threshold. Additionally, the thesis could not conclude any strict superior performance between the chosen state-of-the-art algorithm and the proposed classifier. Nonetheless, the state-of-the-art algorithm imposes a more balanced performance across both labels.

A escassez de dados hidrológicos no Brasil é um problema recorrente em muitas regiões, principalmente em se tratando de dados hidrométricos, produção de sedimentos e qualidade da água. A pesquisa por modelos de bacias hidrográficas tem aumentado nas últimas décadas, porém, a estimativa de dados hidrossedimentológicos a partir de modelos mais sofisticados demanda de grande número de variáveis, que devem ser ajustadas para cada sistema natural, o que dificulta a sua aplicação. O objetivo principal desta tese foi avaliar diferentes ferramentas de modelagem utilizadas para a estimativa da vazão, produção de sedimentos e qualidade da água e, em particular, comparar os resultados obtidos de um modelo hidrológico físico semi-distribuído, o Soil Water Assessment Tool (SWAT) com os resultados obtidos a partir de modelos hidrológicos concentrados, com base na metodologia do número da curva de escoamento do Soil Conservation Service (SCS-CN) e no modelo Generalized Watershed Loading Function (GWLF). Buscou-se avaliar e apresentar em quais condições o uso de cada modelo deve ser recomendado, ou seja, quando o esforço necessário para executar o modelo semi-distribuído leva a melhores resultados efetivos. Em relação à simulação da vazão, os resultados dos dois modelos foram altamente influenciados pelos dados de precipitação, indicando que existem, possivelmente, falhas ou erros de medição que poderiam ter influenciado negativamente os resultados. Portanto, foi proposto aplicar o modelo semi-distribuído com dados de precipitação interpolados (DPI) de alta resolução para verificar a eficiência de seus resultados em comparação com os resultados obtidos com a utilização dos dados de precipitação observados (DPO). Para simulação da produção de sedimentos, e das concentrações de nitrogênio e fósforo, o SWAT realiza uma simulação hidrológica mais detalhada, portanto, fornece resultados ligeiramente melhores para parâmetros de qualidade da água. O uso do modelo semi-distribuído também foi ampliado para simular uma bacia hidrográfica sob a influência do reservatório, a fim de verificar a potencialidade do modelo para esse propósito. Os modelos também foram aplicados para identificar quais os impactos potenciais das mudanças no uso do solo previstas e em andamento. Os cenários estudados foram: I &#8211; cenário atual, II &#8211; cenário tendencial, com o aumento da mancha urbana e substituição do solo exposto e de parte da mata nativa por uso agrícola; III &#8211; cenário desejável, complementa o crescimento urbano tendencial com aumento de áreas de reflorestamento. As metodologias foram aplicadas em duas bacias hidrográficas localizadas no Sudeste do Brasil. A primeira é a bacia do rio Jacaré-Guaçu, incluída na Unidade de Gerenciamento de Recursos Hídricos 13 (UGRHI-13), a montante da confluência do rio das Cruzes, com uma área de 1934 km2. O segundo caso de estudo, é a bacia do rio Atibaia, inserida na UGRHI-5, tem uma área de 2817,88 km2 e abrange municípios dos estados de São Paulo e Minas Gerais. Como principal conclusão, o desempenho do modelo semi-distribuído para estimar a produção de sedimentos, e as concentrações de nitrogênio e fósforo foi ligeiramente melhor do que as simulações do modelo concentrado SCS-CN e GWLF, mas essa vantagem pode não compensar o esforço adicional de calibrá-lo e validá-lo. / The lack of hydrological data in Brazil is a recurrent problem in many regions, especially in hydrometric data, sediment yield and water quality. The research by simplified models has increased in the last decades, however, the estimation of hydrossedimentological data from these more sophisticated models demands many variables, which must be adjusted for each natural system, which makes it difficult to apply. At times it is necessary to respond quickly without much precision in the results, in these situations, simpler models with few parameters can be the solution. The objective of this research is to evaluate different modelling tools used estimate streamflow, sediments yield and nutrients loads values, and namely to compare the results obtained from a physically-based distributed hydrological model (SWAT) with the results from a lumped hydrological, the Soil Conservation Service (SCS-CN) and the Generalized Watershed Loading Function (GWLF) model. Both models use the curve number (CN) concept, determined from land use, soil hydrologic group and antecedent soil moisture conditions and were run with a daily time step. We are particularly interested in understanding under which conditions the use of each model is to be recommended, namely when does the addition effort required to run the distributed model leads to effective better results. The input variables and parameters of the lumped model are assumed constant throughout the watershed, while the SWAT model performs the hydrological analysis at a small unit level, designated as hydrological response units (HRUs), and integrates the results at a sub-basin level. In relation to the flow simulation, the results of the two models were highly influenced by the rainfall data, indicating that, possibly, faults or measurement errors could have negatively influenced the results. Therefore, it was proposed to apply the distributed model with high-resolution grids of daily precipitation to verify the efficiency of its results when compared to rainfall data. For simulation of sediment, nitrogen and phosphorus, SWAT performs a more detailed simulation and thus provides slightly better results. The use of the SWAT was also extended to simulate the influence of reservoir, in order to verify the potentiality of the model, in relation to the simulation. The models also were used to identify which are potential impacts of the ongoing land use changes. The scenarios were: I - Current scenario, II - trend scenario, with the increase of urban land and replacement of the exposed soil and part of the native forest by agricultural use; III - desirable scenario complements the trend urban growth with the replacement of exposed soil and part of the agricultural use by reforestation. The methodologies were applied on two watersheds located in the Southeast of Brazil. The first one is the Jacaré-Guaçu river basin, included in the Water Resources Management Unit 13 (UGRHI-13), upstream of Cruzes river confluence, with an area of 1934 km2. The second watershed is the Atibaia River Basin, a part of Water Resources Management Unit 5 (UGRHI-5). It has an area of 2817.88 km2 and covers municipalities of the states of São Paulo and Minas Gerais.

Nesta tese apresentamos alguns resultados teóricos e práticos da modelagem de seqüências simbólicas com cadeias estocásticas parcimoniosas. As cadeias estocásticas parcimoniosas, que incluem as cadeias estocásticas de memória variável, constituem uma generalização das cadeias de Markov de alcance fixo. As seqüências simbólicas às quais foram aplicadas as ferramentas desenvolvidas são as cadeias de aminoácidos. Primeiramente, introduzimos um novo algoritmo, chamado de SPST, para selecionar o modelo de cadeia estocástica parcimoniosa mais ajustado a uma amostra de seqüências. Em seguida, utilizamos esse algoritmo para estudar dois importantes problemas da genômica; a saber, a classificação de proteínas em famílias e o estudo da evolução das seqüências biológicas. Finalmente, estudamos a velocidade de convergência de algoritmos relacionados com a estimação de uma subclasse das cadeias estocásticas parcimoniosas, as cadeias estocásticas de memória variável. Assim, generalizamos um resultado prévio de velocidade exponencial de convergência para o algoritmo PST, no caso de cadeias de memória ilimitada. Além disso, obtemos um resultado de velocidade de convergência para uma versão generalizada do Critério da Informação Bayesiana (BIC), também conhecido como Critério de Schwarz. / In this thesis we present some theoretical and practical results, concerning symbolic sequence modeling with parsimonious stochastic chains. Parsimonious stochastic chains, which include variable memory stochastic chains, constitute a generalization of fixed order Markov chains. The symbolic sequences modeled with parsimonious stochastic chains were the sequences of amino acids. First, we introduce a new algorithm, called SPST, to select the model of parsimonious stochastic chain that fits better to a sample of sequences. Then, we use the SPST algorithm to study two important problems of genomics. These problems are the classification of proteins into families and the study of the evolution of biological sequences. Finally, we find upper bounds for the rate of convergence of some algorithms related with the estimation of a subclass of parsimonious stochastic chains; namely, the variable memory stochastic chains. In consequence, we generalize a previous result about the exponential rate of convergence of the PST algorithm, in the case of unbounded variable memory stochastic chains. On the other hand, we prove a result about the rate of convergence of a generalized version of the Bayesian Information Criterion (BIC), also known as Schwarz\' Criterion.

<p>In this thesis, a parsimoniously parameterized digital predistorter is derived for linearization of the IQ modulation mismatch and the amplifier imperfection in the signal generator [1]. It is shown that the resulting predistorter is linear in its parameters, and thus they may be estimated by the method of least-squares. Spectrally pure signals are an indispensable requirement when the signal generator is to be used as part of a test bed. Due to the non-linear characteristic of the IQ modulator and power amplifier, distortion will be present at the output of the signal generator. The device under test was the IQ modulation mismatch and power amplifier deficiencies in the signal generator.</p><p>In [2], the dynamic range of low-cost signal generators are improved by employing model based digital pre-distortion and the designed predistorter seems to give some improvement of the dynamic range of the signal generator.</p><p>The goal of this project is to implement and verify the theory parts [1] using data program (Matlab) to improve the dynamic range of the signal generator. The design digital pre-distortion that is implemented in software so that the dynamic range of the signal generator output after predistortion is superior to that of the output prior to it. In this project, we have observed numerical<strong> </strong>problems in the proposed theory and we have found other methods to solve the problem.</p><p>The polynomial model is commonly used in power amplifier modeling and predistorter design. However, the conventional polynomial model exhibits numerical instabilities when higher order terms are included, we have used the conventional and orthogonal polynomial models. The result shows that the orthogonal polynomial model generally yield better power amplifier modeling accuracy as well as predistortion linearization performance then the conventional polynomial model.</p>