The use of molecular markers to predict non-tested materials in field trials has been extensively employed in breeding programs. The genomic prediction of single crosses is a promising approach in maize breeding programs as it reduces selection cycle and permits the selection of promising crosses. Accounting for non-additive effects on genomic prediction can increase prediction accuracy of models depending on the traits genetic architecture. Genomic prediction was first developed for single environments andrecently extended to exploit the genotype by environment interactions for prediction of non-evaluated individuals. The employment of multi-environment genomic models is advantageous in several aspects and has enabled significant higher prediction accuracies than single environment models. However, only a small number of studies regarding the inclusion of non-additive effects in these models are reported. Moreover, the genotype by environment interaction complexity can largely impact the prediction accuracyof these models. Thus, the objectives were to i)evaluate the contribution of additive and non-additive (dominance and epistasis) effects for the prediction of agronomical traits with different genetic architecture in tropical maize single-crosses grown under two nitrogen regimes (ideal and stressing), and ii)verify the impact of the genotype by environment interaction complexity, and the inclusion of dominance deviations, on the prediction accuracy of hybrids grain yield using a multi-environment prediction model. For this, we used phenotypic and genotypic data of 906 single-crosses evaluated during two years, at two locations, under two nitrogen regimes, totaling eight contrasting environments (combination of year x locations x nitrogen regimes). The traits considered in the study were grain yield, ear, and plant height. The results regarding the inclusion of additive and non-additive effects (dominance and epistasis) in genomic prediction models suggest that non-additive effects play an important role instressing conditions, having a high, medium and low contribution for phenotypic expression of grain yield, plant height, and ear height, respectively. The inclusion of dominance deviations in multi-environment prediction model increases the prediction accuracy. Furthermore, a linear relationship between genotype by environment complexity and prediction accuracywas found. / O uso de marcadores moleculares para a predição do fénotipo de materiais não testados em campo tem sido amplamente utilizado em programas de melhoramento genético de plantas. A predição genômica de hibridos simples é uma ferramenta promissora no melhoramento genético do milho, pois além da redução do tempo necessário para cada ciclo de seleção, ela pode ser utilizada para a identificação de cruzamentos promissores. Dependendo da característica em estudo, a inclusão de efeitos não aditivos em modelos de predição genômica pode aumentar significativamente sua acurácia de predição. Além disso, estes modelos foram inicialmente propostos para a predição de materiais em apenas um único ambiente. Atualmente, foram expandidos para considerarem os efeitos da interação genótipos por ambiente. O uso de tais modelos têm se mostrado vantajoso em vários aspectos, um deles é o considerável aumento da acurácia de predição de novos materiais. Contudo, ainda são escassos estudos envolvendoa inclusão de efeitos não aditivos nesses modelos. Ademais, fatores como a complexidade da interação genótipo por ambiente pode influenciar de maneira significativa a acurácia preditiva de modelos considerando múltiplos ambientes. Portanto, os objetivos foram: i)avaliar a contribuição de efeitos aditivos e não aditivos (dominância e epistasia) para a predição de caracteres agronômicos com diferentes arquiteturas genéticas em cruzamentos simples de milho tropical cultivados sob dois níveis de disponibilidade de nitrogênio (ideal e estressado), e ii)verificar o impacto da complexidade da interação genótipo por ambiente, e da inclusão de desvios de dominância na acurácia de predição de modelos multi-ambientes para a predição da produtividade grãos de híbridos simples de milho. Para isto, foram utilizados os dados fenótipicos e genotípicos de 906 híbridos simples de milho avaliados durante dois anos, em dois locais, sob dois níveis de adubação nitrogenada, totalizando oito ambientes distintos (combinação ano xlocal x nivel de adubação nitrogenada). Os caracteres estudados foram produtividade de grãos, altura de espiga, e plantas. Os resultados acerca da inclusão de efeitos aditivos e não aditivos (dominancia e epistasia) sugerem que, efeitos não aditivos são mais importantes sob condições de estresse, contribuem de maneira significativa para produtividade grãos, de modo intermediário para altura de plantas e possuem pouca importância para altura de espiga. A inclusão de desvios de dominância em modelos de predição multi-ambientes aumentou de forma significativa a acurácia de predição. Além disto, observou-se uma relação linear entre complexidade da interação genótipos por ambientes e acurácia preditiva do modelo.

Esta dissertação descreve a aplicação do KIII, um modelo de rede neural biologicamente mais plausível, para a previsão de séries temporais econômicas. Os conjuntos K são modelos conexionistas baseados em populações de neurônios e foram usados em muitas aplicações de aprendizado de máquina, incluindo previsões de séries temporais. Nesta dissertação, este método foi aplicado ao IPCA, um índice de preços ao consumidor brasileiro pesquisado pelo IBGE em 13 regiões metropolitanas. Os valores abrangem o período de agosto de 1994 a junho de 2017. Os experimentos foram realizados utilizando quatro modelos não-paramétricos (KIII, kNN contínuo, RNAs clássicas e SVM) e seis métodos paramétricos: ARIMA, SARIMA, Médias Móveis, SES, Holt, Holt-Winters Aditivo e Holt-Winters Multiplicativo. A médida estatística RMSE foi utilizada para comparar o desempenho dos métodos. Os conjuntos KIII de Freeman funcionaram bem como um filtro, melhorando o desempenho do método, mas não foram um bom método de previsão, sendo superado, na maior parte dos experimentos, por outros métodos de previsão de séries temporais. Esta dissertação contribui com o uso de modelos não paramétricos para prever a inflação em um país em desenvolvimento. / This thesis describes the application of KIII, a biologically more plausible neural network model, for forecasting economic time series. K-sets are connectionist models based on neural populations and have been used in many machine learning applications, including time series prediction. In this thesis, this method was applied to IPCA, a Brazilian consumer price index surveyed by IBGE in 13 metropolitan areas. The values ranged from August 1994 to June 2017. Experiments were performed using four non-parametric models (KIII, continuous kNN, classical ANN, and SVM) and four parametric methods: ARIMA, SARIMA, Moving Average, SES, Holt, Additive HoltWinters, and Multiplicative HoltWinters. The statistical metric RMSE was used to compare methods performance. Freemans KIII sets worked well as filter, improving method performance, but it was not a good prediction method, and was overcome in most experiments by other time series prediction methods. This thesis contributes with the use of non-parametrics models for forecasting inflation in a developing country.

Material and structure engineering are increasingly employed in active optical media,in this context defined as media capable of providing laser or/and optical parametric gain. For laser materials, the main aim of the engineering is to tailor the absorption and emission cross sections in order to optimise the laser performance. At the same time, the engineering also results in a collateral modification of the material’s nonlinear response. In the first part of this work, the nonlinear index of refraction is characterised for two crystallographic forms of laser-ion doped and undoped double-tungstate crystals. These laser crystals have broad gain bandwidths, in particular when doped with Yb3+. As shown in this work, the crystals also have large Kerr nonlinearities, where the values vary significantly for different chemical compositions of the crystals. The combination of a broad gain bandwidthand a high Kerr nonlinearity makes the laser-ion doped double tungstates excellent candidates to employ for the generation of ultrashort laser pulses by Kerr-lens modelocking. The second part of the work relates to the applications of engineered second-order nonlinear media, which here in particular are periodically-poled KTiOPO4 crystals. Periodic structure engineering of second-order nonlinear crystals on a submicrometre scale opens up for the realisation of novel nonlinear devices. By the use of quasi-phase matching in these structures, it is possible to efficiently downconvert a pump wave into two counterpropagating parametric waves, which leads to a device called a mirrorless optical parametric oscillator. The nonlinear response in these engineered submicrometre structures is such that the parametric wave that propagates in the opposite direction of the pump automatically has a narrow bandwidth, whereas the parametric wave that propagates with the pump essentially is a frequency-shifted replica of the pump wave. The unusual spectral properties andthe tunabilities of mirrorless optical parametric oscillators are investigated. / QC 20120330

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The objective of this research is to develop, characterize, and demonstrate novel parametric architectures capable of wideband operation while maintaining high gain and stability. To begin the study, phase-incoherent upconverting parametric amplifiers will be explored by first developing a set of analytical models describing their achievable gain and efficiency. These models will provide a set of design tools to optimize and evaluate prototype circuit boards. The prototype boards will then be used to demonstrate their achievable gain, bandwidth, efficiency, and stability. Further investigation of the analytical models and data collected from the prototype boards will conclude bandwidth and gain limitations and end the investigation into phase-incoherent upconverting parametric amplifiers in lieu of negative-resistance parametric amplifiers. Traditionally, there were two versions of negative-resistance parametric amplifiers available: degenerate and non-degenerate. Both modes of operation are considered single-frequency amplifiers because both the input and output frequencies occur at the source frequency. Degenerate parametric amplifiers offer more power gain than their non-degenerate counterpart and do not require additional circuitry for idler currents. As a result, a phase-coherent degenerate parametric amplifier printed circuit board prototype will be built to investigate achievable gain, bandwidth, and stability. Analytical models will be developed to describe the gain and efficiency of phase-coherent degenerate parametric amplifiers. The presence of a negative resistance suggests the possibility of instability under certain operating conditions, therefore, an in-depth stability study of phase-coherent degenerate parametric amplifiers will be performed. The observation of upconversion gain in phase-coherent degenerate parametric amplifiers will spark investigation into a previously unknown parametric architecture: phase-coherent upconverting parametric amplifiers. Using the phase-coherent degenerate parametric amplifier prototype board, stable phase-coherent upconversion with gain will be demonstrated from the source input frequency to its third harmonic. An analytical model describing the large-signal transducer gain of phase-coherent upconverting parametric amplifiers from the first to the third harmonic of the source input will be derived and validated using the prototype board and simulations.

We consider the problem of estimating the failure stresses of bundles (i.e. the tensile forces that destroy the bundles), constructed of several statisti-cally similar fibres, given a particular kind of censored data. Each bundle consists of several fibres which have their own independent identically dis-tributed failure stresses, and where the force applied on a bundle at any moment is distributed equally between the unbroken fibres in the bundle. A bundle with these properties is an example of an equal load-sharing sys-tem, often referred to as the Daniels failure model. The testing of several bundles generates a special kind of censored data, which is complexly struc-tured. Strongly consistent non-parametric estimators of the distribution laws of bundles are obtained by applying the theory of martingales, and by using the observed data. It is proved that random sampling, with replace-ment from the statistical data related to each tested bundle, can be used to obtain asymptotically correct estimators for the distribution functions of deviations of non-parametric estimators from true values. In the case when the failure stresses of the fibres are described by a Weibull distribution, we obtain strongly consistent parametric maximum likelihood estimators of the distribution functions of failure stresses of bundles, by using the complexly structured data. Numerical examples illustrate the behavior of the obtained estimators.

Unemployment in South Africa has continued to be consistently high as indicated by the various reports published by Statistics South Africa. Unemployment is a global problem where in Organisation for Economic Co-operation and Development (OECD) countries it is related to economic condition. The economic conditions are not solely responsible for the problem of unemployment in South Africa. Consistently high unemployment rates are observed irrespective of the level of economic growth, where unemployment responds marginally to changes Gross Domestic Product (GDP). To understand factors that influence unemployment in South Africa, we need to understand the dynamics of the unemployed population. This study aims at providing a statistical tool useful in improving the understanding of the labour market and enhancing of the labour market policy relevancy. Survival techniques are applied to determine duration dependence, probabilities of exiting unemployment, and the association between socio-demographic factors and unemployment duration. A labour force panel data from Statistic South Africa is used to analyse the time it takes an unemployed person to find employment. The dataset has 4.9 million people who were unemployed during the third quarter of 2013. The data is analysed by computing non-parametric and semi-parametric estimates to avoid making assumption about the functional form of the hazard. The results indicate that the hazard of finding employment is reduced as people spend more time in unemployment (negative duration dependence). People who are unemployed for less than six months have higher hazard functions. The hazards of leaving unemployment at any given duration are significantly lower for people in the following categories - females, adults, education level of lower than tertiary, single or divorced, attending school or doing other activities prior to job search and no work experience. The findings suggest an existence of association between demographics and the length of stay in unemployment; which reflect the nature of the labour market. Due to lower exit probabilities young people spent more time unemployed thus growing out of the age group which is more likely to be employed. Seasonal jobs are not convenient for pregnant women and for those with young kids at their care thus decreasing their employment probabilities. Analysis of factors that affect employment probabilities should be based on datasets which have no seasonal components. The findings suggest that the seasonal components on the labour force panel impacted on the results. According to the findings analysis of unemployment durations can be improved by analysing men and women separately. Men and women have different challenges in the labour market, which influence the association between other demographic factors and unemployment duration / Statistics / M. Sc. (Statistics)

The use of molecular markers to predict non-tested materials in field trials has been extensively employed in breeding programs. The genomic prediction of single crosses is a promising approach in maize breeding programs as it reduces selection cycle and permits the selection of promising crosses. Accounting for non-additive effects on genomic prediction can increase prediction accuracy of models depending on the traits genetic architecture. Genomic prediction was first developed for single environments andrecently extended to exploit the genotype by environment interactions for prediction of non-evaluated individuals. The employment of multi-environment genomic models is advantageous in several aspects and has enabled significant higher prediction accuracies than single environment models. However, only a small number of studies regarding the inclusion of non-additive effects in these models are reported. Moreover, the genotype by environment interaction complexity can largely impact the prediction accuracyof these models. Thus, the objectives were to i)evaluate the contribution of additive and non-additive (dominance and epistasis) effects for the prediction of agronomical traits with different genetic architecture in tropical maize single-crosses grown under two nitrogen regimes (ideal and stressing), and ii)verify the impact of the genotype by environment interaction complexity, and the inclusion of dominance deviations, on the prediction accuracy of hybrids grain yield using a multi-environment prediction model. For this, we used phenotypic and genotypic data of 906 single-crosses evaluated during two years, at two locations, under two nitrogen regimes, totaling eight contrasting environments (combination of year x locations x nitrogen regimes). The traits considered in the study were grain yield, ear, and plant height. The results regarding the inclusion of additive and non-additive effects (dominance and epistasis) in genomic prediction models suggest that non-additive effects play an important role instressing conditions, having a high, medium and low contribution for phenotypic expression of grain yield, plant height, and ear height, respectively. The inclusion of dominance deviations in multi-environment prediction model increases the prediction accuracy. Furthermore, a linear relationship between genotype by environment complexity and prediction accuracywas found. / O uso de marcadores moleculares para a predição do fénotipo de materiais não testados em campo tem sido amplamente utilizado em programas de melhoramento genético de plantas. A predição genômica de hibridos simples é uma ferramenta promissora no melhoramento genético do milho, pois além da redução do tempo necessário para cada ciclo de seleção, ela pode ser utilizada para a identificação de cruzamentos promissores. Dependendo da característica em estudo, a inclusão de efeitos não aditivos em modelos de predição genômica pode aumentar significativamente sua acurácia de predição. Além disso, estes modelos foram inicialmente propostos para a predição de materiais em apenas um único ambiente. Atualmente, foram expandidos para considerarem os efeitos da interação genótipos por ambiente. O uso de tais modelos têm se mostrado vantajoso em vários aspectos, um deles é o considerável aumento da acurácia de predição de novos materiais. Contudo, ainda são escassos estudos envolvendoa inclusão de efeitos não aditivos nesses modelos. Ademais, fatores como a complexidade da interação genótipo por ambiente pode influenciar de maneira significativa a acurácia preditiva de modelos considerando múltiplos ambientes. Portanto, os objetivos foram: i)avaliar a contribuição de efeitos aditivos e não aditivos (dominância e epistasia) para a predição de caracteres agronômicos com diferentes arquiteturas genéticas em cruzamentos simples de milho tropical cultivados sob dois níveis de disponibilidade de nitrogênio (ideal e estressado), e ii)verificar o impacto da complexidade da interação genótipo por ambiente, e da inclusão de desvios de dominância na acurácia de predição de modelos multi-ambientes para a predição da produtividade grãos de híbridos simples de milho. Para isto, foram utilizados os dados fenótipicos e genotípicos de 906 híbridos simples de milho avaliados durante dois anos, em dois locais, sob dois níveis de adubação nitrogenada, totalizando oito ambientes distintos (combinação ano xlocal x nivel de adubação nitrogenada). Os caracteres estudados foram produtividade de grãos, altura de espiga, e plantas. Os resultados acerca da inclusão de efeitos aditivos e não aditivos (dominancia e epistasia) sugerem que, efeitos não aditivos são mais importantes sob condições de estresse, contribuem de maneira significativa para produtividade grãos, de modo intermediário para altura de plantas e possuem pouca importância para altura de espiga. A inclusão de desvios de dominância em modelos de predição multi-ambientes aumentou de forma significativa a acurácia de predição. Além disto, observou-se uma relação linear entre complexidade da interação genótipos por ambientes e acurácia preditiva do modelo.

1 Title: High-average power picosecond mid-IR source Author: Michal Vyvlečka Department: Department of Chemical Physics and Optics Supervisor: Ing. Ondřej Novák, Ph.D., Hilase centre, Institute of Physics of CAS Abstract: High average power wavelength tunable picosecond mid-IR source based on optical parametric generation (OPG) and optical parametric amplification (OPA) is being developed. The conversion system is pumped by an Yb:YAG thin-disk laser delivering 100 W of average power at 100 kHz repetition rate, 1030 nm wavelength, and 2-3 ps pulse width. Part of this fundamental beam pumps an OPG process in a PPLN crystal. The generated wavelength is determined by PPLN's poling period and temperature. Tunability of the signal wavelength between 1.46 µm and 1.95 µm was achieved, the signal beam of 20 mW was generated at 2 W of pump power, when double pass of the beams through PPLN crystal was used. The corresponding idler wavelengths were in range 2.18-3.50 μm. The signal beam was further amplified by OPA process in two KTP crystals, which was pumped by the fundamental beam. The signal beam was amplified up to 2 W at pumping of 38 W. Tuning of the output wavelength was realized by change of the phase-matching angle in KTP crystals. Tunability between 1.70-1.95 µm for signal and 2.18-2.62 µm for idler was...

Esta dissertação descreve a aplicação do KIII, um modelo de rede neural biologicamente mais plausível, para a previsão de séries temporais econômicas. Os conjuntos K são modelos conexionistas baseados em populações de neurônios e foram usados em muitas aplicações de aprendizado de máquina, incluindo previsões de séries temporais. Nesta dissertação, este método foi aplicado ao IPCA, um índice de preços ao consumidor brasileiro pesquisado pelo IBGE em 13 regiões metropolitanas. Os valores abrangem o período de agosto de 1994 a junho de 2017. Os experimentos foram realizados utilizando quatro modelos não-paramétricos (KIII, kNN contínuo, RNAs clássicas e SVM) e seis métodos paramétricos: ARIMA, SARIMA, Médias Móveis, SES, Holt, Holt-Winters Aditivo e Holt-Winters Multiplicativo. A médida estatística RMSE foi utilizada para comparar o desempenho dos métodos. Os conjuntos KIII de Freeman funcionaram bem como um filtro, melhorando o desempenho do método, mas não foram um bom método de previsão, sendo superado, na maior parte dos experimentos, por outros métodos de previsão de séries temporais. Esta dissertação contribui com o uso de modelos não paramétricos para prever a inflação em um país em desenvolvimento. / This thesis describes the application of KIII, a biologically more plausible neural network model, for forecasting economic time series. K-sets are connectionist models based on neural populations and have been used in many machine learning applications, including time series prediction. In this thesis, this method was applied to IPCA, a Brazilian consumer price index surveyed by IBGE in 13 metropolitan areas. The values ranged from August 1994 to June 2017. Experiments were performed using four non-parametric models (KIII, continuous kNN, classical ANN, and SVM) and four parametric methods: ARIMA, SARIMA, Moving Average, SES, Holt, Additive HoltWinters, and Multiplicative HoltWinters. The statistical metric RMSE was used to compare methods performance. Freemans KIII sets worked well as filter, improving method performance, but it was not a good prediction method, and was overcome in most experiments by other time series prediction methods. This thesis contributes with the use of non-parametrics models for forecasting inflation in a developing country.