The Effects of the Word of Wisdom Meditation Technique on the Development of Executive Function Skills in Early Childhood Education

This theory-based paper examines the definition of Executive Functioning (EF) skills, their importance in the early childhood classroom and how to aid in their natural development. The Word of Wisdom meditation technique is considered as a viable alternative to increase the natural development of EF skills in early childhood.

The possible selves method in evaluating youth development interventions: The use of Integrated Qualitative /Quantitative Data Analytic Strategies (IQ -DAS)

This study investigated the feasibility of using qualitative methods to provide empirical documentation of the long-term qualitative change in the life course trajectories of “at risk” youth in a school based positive youth development program (the Changing Lives Program—CLP). This work draws from life course theory for a developmental framework and from recent advances in the use of qualitative methods in general and a grounded theory approach in particular. Grounded theory provided a methodological framework for conceptualizing the use of qualitative methods for assessing qualitative life change. The study investigated the feasibility of using the Possible Selves Questionnaire-Qualitative Extension (PSQ-QE) for evaluating the impact of the program on qualitative change in participants' life trajectory relative to a non-intervention control group. Integrated Qualitative/Quantitative Data Analytic Strategies (IQ-DAS) that we have been developing a part of our program of research provided the data analytic framework for the study. ^ Change was evaluated in 85 at risk high school students in CLP high school counseling groups over three assessment periods (pre, post, and follow-up), and a non-intervention control group of 23 students over two assessment periods (pre and post). Intervention gains and maintenance and the extent to which these patterns of change were moderated by gender and ethnicity were evaluated using a mixed design Repeated Measures Multivariate Analysis of Variance (RMANOVA) in which Time (pre, post) was the within (repeated) factor and Condition, Gender, and Ethnicity the between group factors. The trends for the direction of qualitative change were positive from pre to post and maintained at the year-end follow-up. More important, the 3-way interaction for Time x Gender x Ethnicity was significant, Roy's Θ =. 205, F(2, 37) = 3.80, p <.032, indicating that the overall pattern of positive change was significantly moderated by gender and ethnicity. Thus, the findings also provided preliminary evidence for a positive impact of the youth development program on long-term change in life course trajectory, and were suggestive with respect to the issue of amenability to treatment, i.e., the identification of subgroups of individuals in a target population who are likely to be the most amenable or responsive to a treatment. ^

The utility of an Integrated Qualitative /Quantitative Data Analytic Strategy (IQ -DAS) to evaluate the impact of youth development interventions to positive qualitative change in the life course

This study reports one of the first controlled studies to examine the impact of a school based positive youth development program (Lerner, Fisher, & Weinberg, 2000) on promoting qualitative change in life course experiences as a positive intervention outcome. The study built on a recently proposed relational developmental methodological metanarrative (Overton, 1998) and advances in use of qualitative research methods (Denzin & Lincoln, 2000). The study investigated the use the Life Course Interview (Clausen, 1998) and an integrated qualitative and quantitative data analytic strategy (IQ-DAS) to provide empirical documentation of the impact the Changing Lives Program on qualitative change in positive identity in a multicultural population of troubled youth in an alternative public high school. The psychosocial life course intervention approach used in this study draws its developmental framework from both psychosocial developmental theory (Erikson, 1968) and life course theory (Elder, 1998) and its intervention strategies from the transformative pedagogy of Freire's (1983/1970). ^ Using the 22 participants in the Intervention Condition and the 10 participants in the Control Condition, RMANOVAs found significantly more positive qualitative change in personal identity for program participants relative to the non-intervention control condition. In addition, the 2X2X2X3 mixed design RMANOVA in which Time (pre, post) was the repeated factor and Condition (Intervention versus Control), Gender, and Ethnicity the between group factors, also found significant interactions for the Time by Gender and Time by Ethnicity. ^ Moreover, the directionality of the basic pattern of change was positive for participants of both genders and all three ethnic groups. The pattern of the moderation effects also indicated a marked tendency for participants in the intervention group to characterize their sense of self as more secure and less negative at the end of the their first semester in the intervention, that was stable across both genders and all three ethnicities. The basic differential pattern of an increase in the intervention condition of a positive characterization of sense of self relative to both pre test and relative to the directionality of the movement of the non-intervention controls, was stable across both genders and all three ethnic groups. ^

Evaluating the Effectiveness of Discrete Trial Procedures for Teaching Receptive Discrimination to Children with Autism Spectrum Disorders

Research has found that children with autism spectrum disorders (ASD) show significant deficits in receptive language skills (Wiesmer, Lord, & Esler, 2010). One of the primary goals of applied behavior analytic intervention is to improve the communication skills of children with autism by teaching receptive discriminations. Both receptive discriminations and receptive language entail matching spoken words with corresponding objects, symbols (e.g., pictures or words), actions, people, and so on (Green, 2001). In order to develop receptive language skills, children with autism often undergo discrimination training within the context of discrete trial training. This training entails teaching the learner how to respond differentially to different stimuli (Green, 2001). It is through discrimination training that individuals with autism learn and develop language (Lovaas, 2003). The present study compares three procedures for teaching receptive discriminations: (1) simple/conditional (Procedure A), (2) conditional only (Procedure B), and (3) conditional discrimination of two target cards (Procedure C). Six children, ranging in age from 2-years-old to 5-years-old, with an autism diagnosis were taught how to receptively discriminate nine sets of stimuli. Results suggest that the extra training steps included in the simple/conditional and conditional only procedures may not be necessary to teach children with autism how to receptively discriminate. For all participants, Procedure C appeared to be the most efficient and effective procedure for teaching young children with autism receptive discriminations. Response maintenance and generalization probes conducted one-month following the end of training indicate that even though Procedure C resulted in less training sessions overall, no one procedure resulted in better maintenance and generalization than the others. In other words, more training sessions, as evident with the simple/conditional and conditional only procedures, did not facilitate participants’ ability to accurately respond or generalize one-month following training. The present study contributes to the literature on what is the most efficient and effective way to teach receptive discrimination during discrete trial training to children with ASD. These findings are critical as research shows that receptive language skills are predictive of better outcomes and adaptive behaviors in the future.

Association between mean residual life (MRL) and failure rate functions for continuous and discrete lifetime distributions

The purpose of this study was to correct some mistakes in the literature and derive a necessary and sufficient condition for the MRL to follow the roller-coaster pattern of the corresponding failure rate function. It was also desired to find the conditions under which the discrete failure rate function has an upside-down bathtub shape if corresponding MRL function has a bathtub shape. The study showed that if discrete MRL has a bathtub shape, then under some conditions the corresponding failure rate function has an upside-down bathtub shape. Also the study corrected some mistakes in proofs of Tang, Lu and Chew (1999) and established a necessary and sufficient condition for the MRL to follow the roller-coaster pattern of the corresponding failure rate function. Similarly, some mistakes in Gupta and Gupta (2000) are corrected, with the ensuing results being expanded and proved thoroughly to establish the relationship between the crossing points of the failure rate and associated MRL functions. The new results derived in this study will be useful to model various lifetime data that occur in environmental studies, medical research, electronics engineering, and in many other areas of science and technology.

Emotional Intelligence and Leadership in Organization: A Meta-analytic Test of Process Mechanisms

The present study – employing psychometric meta-analysis of 92 independent studies with sample sizes ranging from 26 to 322 leaders – examined the relationship between EI and leadership effectiveness. Overall, the results supported a linkage between leader EI and effectiveness that was moderate in nature (ρ = .25). In addition, the positive manifold of the effect sizes presented in this study, ranging from .10 to .44, indicate that emotional intelligence has meaningful relations with myriad leadership outcomes including effectiveness, transformational leadership, LMX, follower job satisfaction, and others. Furthermore, this paper examined potential process mechanisms that may account for the EI-leadership effectiveness relationship and showed that both transformational leadership and LMX partially mediate this relationship. However, while the predictive validities of EI were moderate in nature, path analysis and hierarchical regression suggests that EI contributes less than or equal to 1% of explained variance in leadership effectiveness once personality and intelligence are accounted for.

The utility of an integrated qualitative/quantitative data analytic strategy (IQ-DAS) to evaluate the impact of youth development interventions on positive qualitative change in the life course

This study reports one of the first controlled studies to examine the impact of a school based positive youth development program (Lerner, Fisher, & Weinberg, 2000) on promoting qualitative change in life course experiences as a positive intervention outcome. The study built on a recently proposed relational developmental methodological metanarrative (Overton, 1998) and advances in use of qualitative research methods (Denzin & Lincoln, 2000). The study investigated the use the Life Course Interview (Clausen, 1998) and an integrated qualitative and quantitative data analytic strategy (IQDAS) to provide empirical documentation of the impact the Changing Lives Program on qualitative change in positive identity in a multicultural population of troubled youth in an alternative public high school. The psychosocial life course intervention approach used in this study draws its developmental framework from both psychosocial developmental theory (Erikson, 1968) and life course theory (Elder, 1998) and its intervention strategies from the transformative pedagogy of Freire's (1983/1970). Using the 22 participants in the Intervention Condition and the 10 participants in the Control Condition, RMANOVAs found significantly more positive qualitative change in personal identity for program participants relative to the non-intervention control condition. In addition, the 2X2X2X3 mixed design RMANOVA in which Time (pre, post) was the repeated factor and Condition (Intervention versus Control), Gender, and Ethnicity the between group factors, also found significant interactions for the Time by Gender and Time by Ethnicity. Moreover, the directionality of the basic pattern of change was positive for participants of both genders and all three ethnic groups. The pattern of the moderation effects also indicated a marked tendency for participants in the intervention group to characterize their sense of self as more secure and less negative at the end of the their first semester in the intervention, that was stable across both genders and all three ethnicities. The basic differential pattern of an increase in the intervention condition of a positive characterization of sense of self relative to both pre test and relative to the directionality of the movement of the non-intervention controls, was stable across both genders and all three ethnic groups.

Estimation of Inequality Indices of the Cumulative Distribution Function

Date of Acceptance: 02/03/2015

Estimation of Inequality Indices of the Cumulative Distribution Function

Date of Acceptance: 02/03/2015

A Measurement of the Proton Structure Function g2p at Low Q2

Experiments at Jefferson Lab have been conducted to extract the nucleon spin-dependent structure functions over a wide kinematic range. Higher moments of these quantities provide tests of QCD sum rules and predictions of chiral perturbation theory ($\chi$PT). While precise measurements of $g_{1}^n$, $g_{2}^n$, and $g_1^p$ have been extensively performed, the data of $g_2^p$ remain scarce. Discrepancies were found between existing data related to $g_2$ and theoretical predictions. Results on the proton at large $Q^2$ show a significant deviation from the Burkhardt-Cottingham sum rule, while results for the neutron generally follow this sum rule. The next-to-leading order $\chi$PT calculations exhibit discrepancy with data on the longitudinal-transverse polarizability $\delta_{LT}^n$. Further measurements of the proton spin structure function $g_2^p$ are desired to understand these discrepancies.

Experiment E08-027 (g2p) was conducted at Jefferson Lab in experimental Hall A in 2012. Inclusive measurements were performed with polarized electron beam and a polarized ammonia target to obtain the proton spin-dependent structure function $g_2^p$ at low Q$^2$ region (0.02$<$Q$^2$$<$0.2 GeV$^2$) for the first time. The results can be used to test the Burkhardt-Cottingham sum rule, and also allow us to extract the longitudinal-transverse spin polarizability of the proton, which will provide a benchmark test of $\chi$PT calculations. This thesis will present and discuss the very preliminary results of the transverse asymmetry and the spin-dependent structure functions $g_1^p$ and $g_2^p$ from the data analysis of the g2p experiment .

Bayesian Inference in Large-scale Problems

Many modern applications fall into the category of "large-scale" statistical problems, in which both the number of observations n and the number of features or parameters p may be large. Many existing methods focus on point estimation, despite the continued relevance of uncertainty quantification in the sciences, where the number of parameters to estimate often exceeds the sample size, despite huge increases in the value of n typically seen in many fields. Thus, the tendency in some areas of industry to dispense with traditional statistical analysis on the basis that "n=all" is of little relevance outside of certain narrow applications. The main result of the Big Data revolution in most fields has instead been to make computation much harder without reducing the importance of uncertainty quantification. Bayesian methods excel at uncertainty quantification, but often scale poorly relative to alternatives. This conflict between the statistical advantages of Bayesian procedures and their substantial computational disadvantages is perhaps the greatest challenge facing modern Bayesian statistics, and is the primary motivation for the work presented here.

Two general strategies for scaling Bayesian inference are considered. The first is the development of methods that lend themselves to faster computation, and the second is design and characterization of computational algorithms that scale better in n or p. In the first instance, the focus is on joint inference outside of the standard problem of multivariate continuous data that has been a major focus of previous theoretical work in this area. In the second area, we pursue strategies for improving the speed of Markov chain Monte Carlo algorithms, and characterizing their performance in large-scale settings. Throughout, the focus is on rigorous theoretical evaluation combined with empirical demonstrations of performance and concordance with the theory.

One topic we consider is modeling the joint distribution of multivariate categorical data, often summarized in a contingency table. Contingency table analysis routinely relies on log-linear models, with latent structure analysis providing a common alternative. Latent structure models lead to a reduced rank tensor factorization of the probability mass function for multivariate categorical data, while log-linear models achieve dimensionality reduction through sparsity. Little is known about the relationship between these notions of dimensionality reduction in the two paradigms. In Chapter 2, we derive several results relating the support of a log-linear model to nonnegative ranks of the associated probability tensor. Motivated by these findings, we propose a new collapsed Tucker class of tensor decompositions, which bridge existing PARAFAC and Tucker decompositions, providing a more flexible framework for parsimoniously characterizing multivariate categorical data. Taking a Bayesian approach to inference, we illustrate empirical advantages of the new decompositions.

Latent class models for the joint distribution of multivariate categorical, such as the PARAFAC decomposition, data play an important role in the analysis of population structure. In this context, the number of latent classes is interpreted as the number of genetically distinct subpopulations of an organism, an important factor in the analysis of evolutionary processes and conservation status. Existing methods focus on point estimates of the number of subpopulations, and lack robust uncertainty quantification. Moreover, whether the number of latent classes in these models is even an identified parameter is an open question. In Chapter 3, we show that when the model is properly specified, the correct number of subpopulations can be recovered almost surely. We then propose an alternative method for estimating the number of latent subpopulations that provides good quantification of uncertainty, and provide a simple procedure for verifying that the proposed method is consistent for the number of subpopulations. The performance of the model in estimating the number of subpopulations and other common population structure inference problems is assessed in simulations and a real data application.

In contingency table analysis, sparse data is frequently encountered for even modest numbers of variables, resulting in non-existence of maximum likelihood estimates. A common solution is to obtain regularized estimates of the parameters of a log-linear model. Bayesian methods provide a coherent approach to regularization, but are often computationally intensive. Conjugate priors ease computational demands, but the conjugate Diaconis--Ylvisaker priors for the parameters of log-linear models do not give rise to closed form credible regions, complicating posterior inference. In Chapter 4 we derive the optimal Gaussian approximation to the posterior for log-linear models with Diaconis--Ylvisaker priors, and provide convergence rate and finite-sample bounds for the Kullback-Leibler divergence between the exact posterior and the optimal Gaussian approximation. We demonstrate empirically in simulations and a real data application that the approximation is highly accurate, even in relatively small samples. The proposed approximation provides a computationally scalable and principled approach to regularized estimation and approximate Bayesian inference for log-linear models.

Another challenging and somewhat non-standard joint modeling problem is inference on tail dependence in stochastic processes. In applications where extreme dependence is of interest, data are almost always time-indexed. Existing methods for inference and modeling in this setting often cluster extreme events or choose window sizes with the goal of preserving temporal information. In Chapter 5, we propose an alternative paradigm for inference on tail dependence in stochastic processes with arbitrary temporal dependence structure in the extremes, based on the idea that the information on strength of tail dependence and the temporal structure in this dependence are both encoded in waiting times between exceedances of high thresholds. We construct a class of time-indexed stochastic processes with tail dependence obtained by endowing the support points in de Haan's spectral representation of max-stable processes with velocities and lifetimes. We extend Smith's model to these max-stable velocity processes and obtain the distribution of waiting times between extreme events at multiple locations. Motivated by this result, a new definition of tail dependence is proposed that is a function of the distribution of waiting times between threshold exceedances, and an inferential framework is constructed for estimating the strength of extremal dependence and quantifying uncertainty in this paradigm. The method is applied to climatological, financial, and electrophysiology data.

The remainder of this thesis focuses on posterior computation by Markov chain Monte Carlo. The Markov Chain Monte Carlo method is the dominant paradigm for posterior computation in Bayesian analysis. It has long been common to control computation time by making approximations to the Markov transition kernel. Comparatively little attention has been paid to convergence and estimation error in these approximating Markov Chains. In Chapter 6, we propose a framework for assessing when to use approximations in MCMC algorithms, and how much error in the transition kernel should be tolerated to obtain optimal estimation performance with respect to a specified loss function and computational budget. The results require only ergodicity of the exact kernel and control of the kernel approximation accuracy. The theoretical framework is applied to approximations based on random subsets of data, low-rank approximations of Gaussian processes, and a novel approximating Markov chain for discrete mixture models.

Data augmentation Gibbs samplers are arguably the most popular class of algorithm for approximately sampling from the posterior distribution for the parameters of generalized linear models. The truncated Normal and Polya-Gamma data augmentation samplers are standard examples for probit and logit links, respectively. Motivated by an important problem in quantitative advertising, in Chapter 7 we consider the application of these algorithms to modeling rare events. We show that when the sample size is large but the observed number of successes is small, these data augmentation samplers mix very slowly, with a spectral gap that converges to zero at a rate at least proportional to the reciprocal of the square root of the sample size up to a log factor. In simulation studies, moderate sample sizes result in high autocorrelations and small effective sample sizes. Similar empirical results are observed for related data augmentation samplers for multinomial logit and probit models. When applied to a real quantitative advertising dataset, the data augmentation samplers mix very poorly. Conversely, Hamiltonian Monte Carlo and a type of independence chain Metropolis algorithm show good mixing on the same dataset.

The Market for Apples: A Theory of Identity and Consumption

This paper presents an economic model of the effects of identity and social norms on consumption patterns. By incorporating qualitative studies in psychology and sociology, I propose a utility function that features two components – economic (functional) and identity elements. This setup is extended to analyze a market comprising a continuum of consumers, whose identity distribution along a spectrum of binary identities is described by a Beta distribution. I also introduce the notion of salience in the context of identity and consumption decisions. The key result of the model suggests that fundamental economic parameters, such as price elasticity and market demand, can be altered by identity elements. In addition, it predicts that firms in perfectly competitive markets may associate their products with certain types of identities, in order to reduce product substitutability and attain price-setting power.

Theory and Practice in Sustainability Science: Influence of Urban Form on the Urban Heat Island and Implications for Urban Systems

As the world population continues to grow past seven billion people and global challenges continue to persist including resource availability, biodiversity loss, climate change and human well-being, a new science is required that can address the integrated nature of these challenges and the multiple scales on which they are manifest. Sustainability science has emerged to fill this role. In the fifteen years since it was first called for in the pages of Science, it has rapidly matured, however its place in the history of science and the way it is practiced today must be continually evaluated. In Part I, two chapters address this theoretical and practical grounding. Part II transitions to the applied practice of sustainability science in addressing the urban heat island (UHI) challenge wherein the climate of urban areas are warmer than their surrounding rural environs. The UHI has become increasingly important within the study of earth sciences given the increased focus on climate change and as the balance of humans now live in urban areas.

In Chapter 2 a novel contribution to the historical context of sustainability is argued. Sustainability as a concept characterizing the relationship between humans and nature emerged in the mid to late 20th century as a response to findings used to also characterize the Anthropocene. Emerging from the human-nature relationships that came before it, evidence is provided that suggests Sustainability was enabled by technology and a reorientation of world-view and is unique in its global boundary, systematic approach and ambition for both well being and the continued availability of resources and Earth system function. Sustainability is further an ambition that has wide appeal, making it one of the first normative concepts of the Anthropocene.

Despite its widespread emergence and adoption, sustainability science continues to suffer from definitional ambiguity within the academe. In Chapter 3, a review of efforts to provide direction and structure to the science reveals a continuum of approaches anchored at either end by differing visions of how the science interfaces with practice (solutions). At one end, basic science of societally defined problems informs decisions about possible solutions and their application. At the other end, applied research directly affects the options available to decision makers. While clear from the literature, survey data further suggests that the dichotomy does not appear to be as apparent in the minds of practitioners.

In Chapter 4, the UHI is first addressed at the synoptic, mesoscale. Urban climate is the most immediate manifestation of the warming global climate for the majority of people on earth. Nearly half of those people live in small to medium sized cities, an understudied scale in urban climate research. Widespread characterization would be useful to decision makers in planning and design. Using a multi-method approach, the mesoscale UHI in the study region is characterized and the secular trend over the last sixty years evaluated. Under isolated ideal conditions the findings indicate a UHI of 5.3 ± 0.97 °C to be present in the study area, the magnitude of which is growing over time.

Although urban heat islands (UHI) are well studied, there remain no panaceas for local scale mitigation and adaptation methods, therefore continued attention to characterization of the phenomenon in urban centers of different scales around the globe is required. In Chapter 5, a local scale analysis of the canopy layer and surface UHI in a medium sized city in North Carolina, USA is conducted using multiple methods including stationary urban sensors, mobile transects and remote sensing. Focusing on the ideal conditions for UHI development during an anticyclonic summer heat event, the study observes a range of UHI intensity depending on the method of observation: 8.7 °C from the stationary urban sensors; 6.9 °C from mobile transects; and, 2.2 °C from remote sensing. Additional attention is paid to the diurnal dynamics of the UHI and its correlation with vegetation indices, dewpoint and albedo. Evapotranspiration is shown to drive dynamics in the study region.

Finally, recognizing that a bridge must be established between the physical science community studying the Urban Heat Island (UHI) effect, and the planning community and decision makers implementing urban form and development policies, Chapter 6 evaluates multiple urban form characterization methods. Methods evaluated include local climate zones (LCZ), national land cover database (NCLD) classes and urban cluster analysis (UCA) to determine their utility in describing the distribution of the UHI based on three standard observation types 1) fixed urban temperature sensors, 2) mobile transects and, 3) remote sensing. Bivariate, regression and ANOVA tests are used to conduct the analyses. Findings indicate that the NLCD classes are best correlated to the UHI intensity and distribution in the study area. Further, while the UCA method is not useful directly, the variables included in the method are predictive based on regression analysis so the potential for better model design exists. Land cover variables including albedo, impervious surface fraction and pervious surface fraction are found to dominate the distribution of the UHI in the study area regardless of observation method.

Chapter 7 provides a summary of findings, and offers a brief analysis of their implications for both the scientific discourse generally, and the study area specifically. In general, the work undertaken does not achieve the full ambition of sustainability science, additional work is required to translate findings to practice and more fully evaluate adoption. The implications for planning and development in the local region are addressed in the context of a major light-rail infrastructure project including several systems level considerations like human health and development. Finally, several avenues for future work are outlined. Within the theoretical development of sustainability science, these pathways include more robust evaluations of the theoretical and actual practice. Within the UHI context, these include development of an integrated urban form characterization model, application of study methodology in other geographic areas and at different scales, and use of novel experimental methods including distributed sensor networks and citizen science.

On the Advancement of Probabilistic Models of Decompression Sickness

The work presented in this dissertation is focused on applying engineering methods to develop and explore probabilistic survival models for the prediction of decompression sickness in US NAVY divers. Mathematical modeling, computational model development, and numerical optimization techniques were employed to formulate and evaluate the predictive quality of models fitted to empirical data. In Chapters 1 and 2 we present general background information relevant to the development of probabilistic models applied to predicting the incidence of decompression sickness. The remainder of the dissertation introduces techniques developed in an effort to improve the predictive quality of probabilistic decompression models and to reduce the difficulty of model parameter optimization.

The first project explored seventeen variations of the hazard function using a well-perfused parallel compartment model. Models were parametrically optimized using the maximum likelihood technique. Model performance was evaluated using both classical statistical methods and model selection techniques based on information theory. Optimized model parameters were overall similar to those of previously published Results indicated that a novel hazard function definition that included both ambient pressure scaling and individually fitted compartment exponent scaling terms.

We developed ten pharmacokinetic compartmental models that included explicit delay mechanics to determine if predictive quality could be improved through the inclusion of material transfer lags. A fitted discrete delay parameter augmented the inflow to the compartment systems from the environment. Based on the observation that symptoms are often reported after risk accumulation begins for many of our models, we hypothesized that the inclusion of delays might improve correlation between the model predictions and observed data. Model selection techniques identified two models as having the best overall performance, but comparison to the best performing model without delay and model selection using our best identified no delay pharmacokinetic model both indicated that the delay mechanism was not statistically justified and did not substantially improve model predictions.

Our final investigation explored parameter bounding techniques to identify parameter regions for which statistical model failure will not occur. When a model predicts a no probability of a diver experiencing decompression sickness for an exposure that is known to produce symptoms, statistical model failure occurs. Using a metric related to the instantaneous risk, we successfully identify regions where model failure will not occur and identify the boundaries of the region using a root bounding technique. Several models are used to demonstrate the techniques, which may be employed to reduce the difficulty of model optimization for future investigations.

Formal function and phrase structure in contemporary music : Pierre Boulez’s late solo works and Sean Clarke’s "Lucretia Overture" and "4 Impromptus"

Cette thèse présente une théorie de la fonction formelle et de la structure des phrases dans la musique contemporaine, théorie qui peut être utilisée aussi bien comme outil analytique que pour créer de nouvelles œuvres. Deux concepts théoriques actuels aident à clarifier la structure des phrases : les projections temporelles de Christopher Hasty et la théorie des fonctions formelles de William Caplin, qui inclut le concept de l’organisation formelle soudée versus lâche (tight-knit vs. loose). Les projections temporelles sont perceptibles grâce à l’accent mis sur les paramètres secondaires, comme le style du jeu, l’articulation et le timbre. Des sections avec une organisation formelle soudée ont des projections temporelles claires, qui sont créées par la juxtaposition des motifs distincts, généralement sous la forme d'une idée de base en deux parties. Ces projections organisent la musique en phrases de présentation, en phrases de continuité et finalement, à des moments formels charnières, en phrases cadentielles. Les sections pourvues d’une organisation plus lâche tendent à présenter des projections et mouvements harmoniques moins clairs et moins d’uniformité motivique. La structure des phrases de trois pièces tardives pour instrument soliste de Pierre Boulez est analysée : Anthèmes I pour violon (1991-1992) et deux pièces pour piano, Incises (2001) et une page d’éphéméride (2005). Les idées proposées dans le présent document font suite à une analyse de ces œuvres et ont eu une forte influence sur mes propres compositions, en particulier Lucretia Overture pour orchestre et 4 Impromptus pour flûte, saxophone soprano et piano, qui sont également analysés en détail. Plusieurs techniques de composition supplémentaires peuvent être discernés dans ces deux œuvres, y compris l'utilisation de séquence mélodiques pour contrôler le rythme harmonique; des passages composés de plusieurs couches musicales chacun avec un structure de phrase distinct; et le relâchement de l'organisation formelle de matériels récurrents. Enfin, la composition de plusieurs autres travaux antérieurs a donné lieu à des techniques utilisées dans ces deux œuvres et ils sont brièvement abordés dans la section finale.