Sequential learning refers to the ability to learn the temporal and ordinal patterns of one’s environment. The current study examines the effects of synchronous and asynchronous temporal patterns on sequential learning. Twenty healthy adults participants (11 females, 18–34 years old) performed two versions of a visual sequential learning paradigm while event-related potentials (ERPs) were recorded. Reaction times to the targets following two predictor types were also recorded. Reaction time data revealed that learning occurred in both temporal conditions, although overall the synchronous condition was responded to faster. On the other hand, the mean ERP amplitudes between 300 and 700ms post-predictor onset revealed an interaction between timing condition and predictability in the posterior regions of interest. Specifically, the ERP results indicated that learning of the statistical contingencies between items was more pronounced for the synchronous temporal condition compared to the asynchronous condition.

The ability of spatial transfer to occur in movement sequences is reflected upon in theoretical perspectives, but limited research has been done to verify to what extent spatial characteristics of a sequential learning task occur. Three experiments were designed to determine participants’ ability to transfer a learned movement sequence to new spatial locations. A 16-element dynamic arm movement sequence was used in all experiments. The task required participants to move a horizontal lever to sequentially projected targets. Experiment 1 included 2 groups. One group practiced a pattern in which targets were located at 20, 40, 60, and 80° from the start position. The other group practiced a pattern with targets at 20, 26.67, 60, and 80°. The results indicated that participants could effectively transfer to new target configurations regardless of whether they required proportional or non-proportional spatial changes to the movement pattern. Experiment 2 assessed the effects of extended practice on proportional and non-proportional spatial transfer. The data indicated that while participants can effectively transfer to both proportional and non-proportional spatial transfer conditions after one day of practice, they are only effective at transferring to proportional transfer conditions after 4 days of practice. The results are discussed in terms of the mechanism by which response sequences become increasingly specific over extended practice in an attempt to optimize movement production. Just as response sequences became more fluent and thus more specific with extended practice in Experiment 2, Experiment 3 tested whether this stage of specificity may occur sooner in an easier task than in a more difficult task. The 2 groups in Experiment 3 included a less difficult sequential pattern practiced over either 1 or 4 days. The results support the existence of practice improvement limitations based upon simplicity versus complexity of the task.

The ability of spatial transfer to occur in movement sequences is reflected upon in theoretical perspectives, but limited research has been done to verify to what extent spatial characteristics of a sequential learning task occur. Three experiments were designed to determine participants’ ability to transfer a learned movement sequence to new spatial locations. A 16-element dynamic arm movement sequence was used in all experiments. The task required participants to move a horizontal lever to sequentially projected targets. Experiment 1 included 2 groups. One group practiced a pattern in which targets were located at 20, 40, 60, and 80° from the start position. The other group practiced a pattern with targets at 20, 26.67, 60, and 80°. The results indicated that participants could effectively transfer to new target configurations regardless of whether they required proportional or non-proportional spatial changes to the movement pattern. Experiment 2 assessed the effects of extended practice on proportional and non-proportional spatial transfer. The data indicated that while participants can effectively transfer to both proportional and non-proportional spatial transfer conditions after one day of practice, they are only effective at transferring to proportional transfer conditions after 4 days of practice. The results are discussed in terms of the mechanism by which response sequences become increasingly specific over extended practice in an attempt to optimize movement production. Just as response sequences became more fluent and thus more specific with extended practice in Experiment 2, Experiment 3 tested whether this stage of specificity may occur sooner in an easier task than in a more difficult task. The 2 groups in Experiment 3 included a less difficult sequential pattern practiced over either 1 or 4 days. The results support the existence of practice improvement limitations based upon simplicity versus complexity of the task.

Cognitive radio had been proposed as a methodology for overcoming the inefficiency of the conventional static allocation of the available spectrum in wireless communication networks. The majority of opportunistic spectrum access schemes in cognitive radio networks (CRNs) rely on the Listen-Before-Talk (LBT) model due to the half-duplex nature of conventional wireless radios. However, LBT su ers from the problem of high collision rates and low secondary user throughput if time is misaligned among the secondary users (SUs) and the primary users (PUs). This problem can be mitigated by leveraging full-duplex (FD) communications that facilitate concurrent sensing and transmission. This thesis considers the problem of optimal opportunistic multi-channel spectrum sensing and access using FD radios in the presence of uncertain primary user (PU) activity statistics. A joint learningand spectrum access scheme is proposed. To optimize its throughput, the SU sensing period has to be carefully tuned. However, in absence of exact knowledge of the PU activity statistics, the PU's performance may be adversely a ected. To address this problem, a robust optimization problem is formulated. Analysis shows that under some non-restrictive simplifying assumptions, the robust optimization problem is convex. The impact of sensing periods on the PU collision probability and the SU throughput are analyzed, and the optimal sensing period is found via convex optimization. An "\epsilon-greedy algorithm is proposed for use by the SU to learn the PUs' activity statistics in multichannel networks. It is shown that sublinear regrets can be attained by the proposed estimation and robust optimization strategy. Simulation studies demonstrate that the resulting robust solution achieves a good trade-o between optimizing the SU's throughput and protecting the PU. / Thesis / Master of Applied Science (MASc)

With remarkable advances in computing power, computer experiments continue to expand the boundaries and drive down the cost of various scientific discoveries. New challenges keep arising from designing, analyzing, modeling, calibrating, optimizing, and predicting in computer experiments. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For heteroskedastic computer experiments, an optimal lookahead based sequential learning strategy is presented, balancing replication and exploration to facilitate separating signal from input-dependent noise. Motivated by challenges in both large data size and model fidelity arising from ever larger modern computer experiments, highly accurate and computationally efficient divide-and-conquer calibration methods based on on-site experimental design and surrogate modeling for large-scale computer models are developed in this dissertation. The proposed methodology is applied to calibrate a real computer experiment from the gas and oil industry. This on-site surrogate calibration method is further extended to multiple output calibration problems. / Doctor of Philosophy / With remarkable advances in computing power, complex physical systems today can be simulated comparatively cheaply and to high accuracy through computer experiments. Computer experiments continue to expand the boundaries and drive down the cost of various scientific investigations, including biological, business, engineering, industrial, management, health-related, physical, and social sciences. This dissertation consists of six chapters, exploring statistical methodologies in sequential learning, model calibration, and uncertainty quantification for heteroskedastic computer experiments and large-scale computer experiments. For computer experiments with changing signal-to-noise ratio, an optimal lookahead based sequential learning strategy is presented, balancing replication and exploration to facilitate separating signal from complex noise structure. In order to effectively extract key information from massive amount of simulation and make better prediction for the real world, highly accurate and computationally efficient divide-and-conquer calibration methods for large-scale computer models are developed in this dissertation, addressing challenges in both large data size and model fidelity arising from ever larger modern computer experiments. The proposed methodology is applied to calibrate a real computer experiment from the gas and oil industry. This large-scale calibration method is further extended to solve multiple output calibration problems.

We develop in this thesis a computational model of music expectation, which may be one of the most important aspects in music listening. Many phenomenons related to music listening such as preference, surprise or emo- tions are linked to the anticipatory behaviour of listeners. In this thesis, we concentrate on a statistical account to music expectation, by modelling the processes of learning and predicting spectro-temporal regularities in a causal fashion. The principle of statistical modelling of expectation can be applied to several music representations, from symbolic notation to audio signals. We first show that computational learning architectures can be used and evaluated to account behavioral data concerning auditory perception and learning. We then propose a what/when representation of musical events which enables to sequentially describe and learn the structure of acoustic units in musical audio signals. The proposed representation is applied to describe and anticipate timbre features and musical rhythms. We suggest ways to exploit the properties of the expectation model in music analysis tasks such as structural segmentation. We finally explore the implications of our model for interactive music applications in the context of real-time transcription, concatenative synthesis, and visualization. / Esta tesis presenta un modelo computacional de expectativa musical, que es un aspecto muy importante de como procesamos la música que oímos. Muchos fenómenos relacionados con el procesamiento de la música están vinculados a una capacidad para anticipar la continuación de una pieza de música. Nos enfocaremos en un acercamiento estadístico de la expectativa musical, modelando los procesos de aprendizaje y de predicción de las regularidades espectro-temporales de forma causal. El principio de modelado estadístico de la expectativa se puede aplicar a varias representaciones de estructuras musicales, desde las notaciones simbólicas a la señales de audio. Primero demostramos que ciertos algoritmos de aprendizaje de secuencias se pueden usar y evaluar en el contexto de la percepción y el aprendizaje de secuencias auditivas. Luego, proponemos una representación, denominada qué/cuándo, para representar eventos musicales de una forma que permite describir y aprender la estructura secuencial de unidades acústicas en señales de audio musical. Aplicamos esta representación para describir y anticipar características tímbricas y ritmos. Sugerimos que se pueden explotar las propiedades del modelo de expectativa para resolver tareas de análisis como la segmentación estructural de piezas musicales. Finalmente, exploramos las implicaciones de nuestro modelo a la hora de definir nuevas aplicaciones en el contexto de la transcripción en tiempo real, la síntesis concatenativa y la visualización.

Through the years, educational theorists and researchers have been interested in a possible relationship between teachers' effectiveness and their beliefs and expectations. Three concepts underpinned this work: teacher effectiveness, cultural diversity, and teachers' beliefs and expectations. The premise of the study was that the beliefs and expectations of effective secondary choral teachers are related to the social-cultural contexts in which they teach. The study implemented critical discourse analysis as the theoretical framework and the in-depth phenomenological long interview for data collection. Three secondary choral teachers were selected to participate in the study based on the researcher's criteria. The study revealed how each teacher conceptualized student cultural diversity during the teaching experience. Teacher beliefs about effective teaching in culturally diverse settings were described as developing over time in phases along a continuum. The study also confirmed that teachers' beliefs about students can be changed through experiences and reflection. The study revealed effective teachers focused on three different types of expectations in the teaching and learning context and affirmed diverse cultural identities and backgrounds. Recommendations included the development of stronger mentorship programs to increase effective teaching strategies for the secondary choral classroom. The findings of this study support my previous work, which introduces a sequential learning framework for teaching music in culturally diverse schools.

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Cette thèse se place dans le contexte de la fiabilité structurale associée à des modèles numériques représentant un phénomène physique. On considère que la fiabilité est représentée par des indicateurs qui prennent la forme d'une probabilité et d'un quantile. Les modèles numériques étudiés sont considérés déterministes et de type boîte-noire. La connaissance du phénomène physique modélisé permet néanmoins de faire des hypothèses de forme sur ce modèle. La prise en compte des propriétés de monotonie dans l'établissement des indicateurs de risques constitue l'originalité de ce travail de thèse. Le principal intérêt de cette hypothèse est de pouvoir contrôler de façon certaine ces indicateurs. Ce contrôle prend la forme de bornes obtenues par le choix d'un plan d'expériences approprié. Les travaux de cette thèse se concentrent sur deux thématiques associées à cette hypothèse de monotonie. La première est l'étude de ces bornes pour l'estimation de probabilité. L'influence de la dimension et du plan d'expériences utilisé sur la qualité de l'encadrement pouvant mener à la dégradation d'un composant ou d'une structure industrielle sont étudiées. La seconde est de tirer parti de l'information de ces bornes pour estimer au mieux une probabilité ou un quantile. Pour l'estimation de probabilité, l'objectif est d'améliorer les méthodes existantes spécifiques à l'estimation de probabilité sous des contraintes de monotonie. Les principales étapes d'estimation de probabilité ont ensuite été adaptées à l'encadrement et l'estimation d'un quantile. Ces méthodes ont ensuite été mises en pratique sur un cas industriel. / This thesis takes place in a structural reliability context which involves numerical model implementing a physical phenomenon. The reliability of an industrial component is summarised by two indicators of failure,a probability and a quantile. The studied numerical models are considered deterministic and black-box. Nonetheless, the knowledge of the studied physical phenomenon allows to make some hypothesis on this model. The original work of this thesis comes from considering monotonicity properties of the phenomenon for computing these indicators. The main interest of this hypothesis is to provide a sure control on these indicators. This control takes the form of bounds obtained by an appropriate design of numerical experiments. This thesis focuses on two themes associated to this monotonicity hypothesis. The first one is the study of these bounds for probability estimation. The influence of the dimension and the chosen design of experiments on the bounds are studied. The second one takes into account the information provided by these bounds to estimate as best as possible a probability or a quantile. For probability estimation, the aim is to improve the existing methods devoted to probability estimation under monotonicity constraints. The main steps built for probability estimation are then adapted to bound and estimate a quantile. These methods have then been applied on an industrial case.

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