This dissertation explores the neural coding and mechanisms associated with consciousness by analyzing electrical rhythms of the brain under altered states of consciousness, namely epilepsy and anesthesia. First, transformation of neural coding under epileptogenic conditions is examined by computing the Volterra kernels in a rodent epilepsy model, where the epileptogenic condition is induced by altering the concentrations of Mg2+ and K+ of the perfusate for different levels of excitability. Principal dynamic modes (PDMs) are further deduced from the Volterra kernels to compare the changes in neural dynamics under epileptogenic conditions. The integrating PDMs are shown to dominate at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs shift to higher ranges under epileptogenic conditions, from ripple activities (75 - 200 Hz) to fast ripple activities (200 - 500 Hz). Second, markers of anesthetic states are explored by analyzing amplitude and phase of brain rhythms as well as their interaction and modulation, utilizing electroencephalogram (EEG) recorded from patients undergoing anesthesia. Anesthesia shifts the power to low frequency rhythms, especially alpha rhythms. Additionally anesthesia increases the coupling between alpha rhythms and gamma rhythms while disrupting the coupling between alpha rhythms and ripples (70 - 200 Hz). The results also indicate that the dose responses (i.e. depth of anesthesia) are not necessarily monophasic or linear. The commonality and differences of the changes in brain rhythms associated with these conditions are discussed to elucidate on the possible underlying mechanisms involved in producing consciousness.

This dissertation explores the neural coding and mechanisms associated with consciousness by analyzing electrical rhythms of the brain under altered states of consciousness, namely epilepsy and anesthesia. First, transformation of neural coding under epileptogenic conditions is examined by computing the Volterra kernels in a rodent epilepsy model, where the epileptogenic condition is induced by altering the concentrations of Mg2+ and K+ of the perfusate for different levels of excitability. Principal dynamic modes (PDMs) are further deduced from the Volterra kernels to compare the changes in neural dynamics under epileptogenic conditions. The integrating PDMs are shown to dominate at all levels of excitability in terms of their relative contributions to the overall response, whereas the dominant frequency responses of the differentiating PDMs shift to higher ranges under epileptogenic conditions, from ripple activities (75 - 200 Hz) to fast ripple activities (200 - 500 Hz). Second, markers of anesthetic states are explored by analyzing amplitude and phase of brain rhythms as well as their interaction and modulation, utilizing electroencephalogram (EEG) recorded from patients undergoing anesthesia. Anesthesia shifts the power to low frequency rhythms, especially alpha rhythms. Additionally anesthesia increases the coupling between alpha rhythms and gamma rhythms while disrupting the coupling between alpha rhythms and ripples (70 - 200 Hz). The results also indicate that the dose responses (i.e. depth of anesthesia) are not necessarily monophasic or linear. The commonality and differences of the changes in brain rhythms associated with these conditions are discussed to elucidate on the possible underlying mechanisms involved in producing consciousness.

Habitat loss is the greatest contributor to the decline of species globally and thus understanding habitat use and the consequences fragmentation has on biodiversity is a fundamental step towards management and recovery. I conducted a radio-telemetry study to examine the spatial ecology and the effects of roads on Eastern Hognose Snakes (Heterodon platirhinos), a species at risk, in the Long Point Region of Ontario, Canada. I tested habitat selection at multiple spatial scales and I found that within the home range, snakes avoided agricultural land and selected open sand barrens, particularly for nesting. At the local scale, snakes avoided mature overstory trees and used younger patches of forest. Used locations had more woody debris, logs and low-vegetative coverage than locations selected at random. Eastern Hognose Snakes also showed avoidance of paved road crossings in their seasonal movements, but readily crossed unpaved roads. Management efforts for this species at risk should be placed on the conservation of sand barrens and on the construction of road underpasses to prevent genetic isolation of populations.

Habitat loss is the greatest contributor to the decline of species globally and thus understanding habitat use and the consequences fragmentation has on biodiversity is a fundamental step towards management and recovery. I conducted a radio-telemetry study to examine the spatial ecology and the effects of roads on Eastern Hognose Snakes (Heterodon platirhinos), a species at risk, in the Long Point Region of Ontario, Canada. I tested habitat selection at multiple spatial scales and I found that within the home range, snakes avoided agricultural land and selected open sand barrens, particularly for nesting. At the local scale, snakes avoided mature overstory trees and used younger patches of forest. Used locations had more woody debris, logs and low-vegetative coverage than locations selected at random. Eastern Hognose Snakes also showed avoidance of paved road crossings in their seasonal movements, but readily crossed unpaved roads. Management efforts for this species at risk should be placed on the conservation of sand barrens and on the construction of road underpasses to prevent genetic isolation of populations.

Magíster en Ciencias de la Ingeniería, Mención Matemáticas Aplicadas. Ingeniero Civil Matemático / Multi-Output Gaussian Processes (MOGPs) are the multivariate extension of Gaussian processes (GPs \cite{Rasmussen:2006}), a Bayesian nonparametric method for univariate regression. MOGPs address the multi-channel regression problem by modeling the correlation in time and/or space (as scalar GPs do), but also across channels and thus revealing statistical dependencies among different sources of data. This is crucial in a number of real-world applications such as fault detection, data imputation and financial time-series analysis. Analogously to the univariate case, MOGPs are entirely determined by a multivariate covariance function, which in this case is matrix valued. The design of this matrix-valued covariance function is challenging, since we have to deal with the trade off between (i) choosing a broad class of cross-covariances and auto-covariances, while at the same time (ii) ensuring positive definiteness of the symmetric matrix containing these scalar-valued covariance functions. In the stationary univariate case, these difficulties can be bypassed by virtue of Bochner's theorem, that is, by building the covariance function in the spectral (Fourier) domain to then transform it to the time and/or space domain, thus yielding the (single-output) Spectral Mixture kernel \cite{Wilson:2013}. A classical approach to define multivariate covariance functions for MOGPs is through linear combinations of independent (latent) GPs; this is the case of the Linear Model of Coregionalization (LMC \cite{goo1997}) and the Convolution Model \cite{Alvarez:2008}. In these cases, the resulting multivariate covariance function is a function of both the latent-GP covariances and the linear operator considered, which usually results in symmetric cross-covariances that do not admit lags across channels. Due to their simplicity, these approaches fail to provide interpretability of the dependencies learnt and force the auto-covariances to have similar structure. The main purpose of this work is to extend the spectral mixture concept to MOGPs: We rely on Cram\'er's theorem \cite, the multivariate version of Bochner's theorem, to propose an expressive family of complex-valued square-exponential cross-spectral densities, which, through the Fourier transform yields the Multi-Output Spectral Mixture kernel (MOSM). The proposed MOSM model provides clear interpretation of all the parameters in spectral terms. Besides the theoretical presentation and interpretation of the proposed multi-output covariance kernel based on square-exponential spectral densities, we inquiry the plausibility of complex-valued t-Student cross-spectral densities. We validate our contribution experimentally through an illustrative example using a tri-variate synthetic signal, and then compare it against all the aforementioned methods on two real-world datasets.

High performance computing (HPC) platforms are evolving to more heterogeneous configurations to support the workloads of various applications. The current hardware landscape is composed of traditional multicore CPUs equipped with hardware accelerators that can handle high levels of parallelism. Graphical Processing Units (GPUs) are popular high performance hardware accelerators in modern supercomputers. GPU programming has a different model than that for CPUs, which means that many numerical kernels have to be redesigned and optimized specifically for this architecture. GPUs usually outperform multicore CPUs in some compute intensive and massively parallel applications that have regular processing patterns. However, most scientific applications rely on crucial memory-bound kernels and may witness bottlenecks due to the overhead of the memory bus latency. They can still take advantage of the GPU compute power capabilities, provided that an efficient architecture-aware design is achieved. This dissertation presents a uniform design strategy for optimizing critical memory-bound kernels on GPUs. Based on hierarchical register blocking, double buffering and latency hiding techniques, this strategy leverages the performance of a wide range of standard numerical kernels found in dense and sparse linear algebra libraries. The work presented here focuses on matrix-vector multiplication kernels (MVM) as repre- sentative and most important memory-bound operations in this context. Each kernel inherits the benefits of the proposed strategies. By exposing a proper set of tuning parameters, the strategy is flexible enough to suit different types of matrices, ranging from large dense matrices, to sparse matrices with dense block structures, while high performance is maintained. Furthermore, the tuning parameters are used to maintain the relative performance across different GPU architectures. Multi-GPU acceleration is proposed to scale the performance on several devices. The performance experiments show improvements ranging from 10% and up to more than fourfold speedup against competitive GPU MVM approaches. Performance impacts on high-level numerical libraries and a computational astronomy application are highlighted, since such memory-bound kernels are often located in innermost levels of the software chain. The excellent performance obtained in this work has led to the adoption of code in NVIDIAs widely distributed cuBLAS library.

Modern computer hardware platforms are moving towards high core-count and heterogeneous Instruction Set Architecture (ISA) processors to achieve improved performance as single core performance has reached its performance limit. These trends put the current monolithic SMP operating system (OS) under scrutiny in terms of scalability and portability. Proper pairing of computing workloads with computing resources has become increasingly arduous with traditional software architecture. One of the most promising emerging operating system architectures is the Multi-kernel. Multi-kernels not only address scalability issues, but also inherently support heterogeneity. Furthermore, provide an easy way to properly map computing workloads to the correct type of processing resources in presence of heterogeneity. Multi-kernels do so by partitioning the resources and running independent kernel instances and co-operating amongst themselves to present a unified view of the system to the application. Popcorn is one the most prominent multi-kernels today, which is unique in the sense that it runs multiple Linux instances on different cores or group of cores, and provides a unified view of the system i.e., Single System Image (SSI). This thesis presents four contributions. First, it introduces a filesystem for Popcorn, which is a vital part to provide a SSI. Popcorn supports thread/process migration that requires migration of file descriptors which is not provided by traditional filesystems as well as popular distributed file systems, this work proposes a scalable messaging based file descriptor migration and consistency protocol for Popcorn. Second, multi-kernel OSs rely heavily on a fast low latency messaging layer to be scalable. Messaging is even more important in heterogeneous systems where different types of cores are on different islands with no shared memory. Thus, another contribution proposes a fast-low latency messaging layer to enable communication among heterogeneous processor islands for Heterogeneous Popcorn. With advances in networking technology, newest Ethernet technologies are able to support up to 40 Gbps bandwidth, but due to scalability issues in monolithic kernels, the number of connections served per second does not scale with this increase in speed.Therefore, the third and fourth contributions try to address this problem with Snap Bean, a virtual network device and Angel, an opportunistic load balancer for Popcorn's network system. With the messaging layer Popcorn gets over 30% performance benefit over OpenCL and Intel Offloading technique (LEO). And with NetPopcorn we achieve over 7 to 8 times better performance over vanilla Linux and 2 to 5 times over state-of-the-art Affinity Accept. / Master of Science

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.

In this report we survey some recent developments of relationship between Hausdorff moment sequences and subnormality of an unilateral weighted shift operator. Although discrete convolution of two Haudorff moment sequences may not be a Hausdorff moment sequence, but Hausdorff convolution of two moment sequences is always a moment sequence. Observing from the Berg and Dur´an result that the multiplication operator on Is subnormal, we discuss further work on the subnormality of the multiplication operator on a reproducing kernel Hilbert space, whose kernel is a point-wise product of two diagonal positive kernels. The relationship between infinitely divisible matrices and moment sequence is discussed and some open problems are listed.

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.