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Preterm birth is the leading cause of neonatal mortality and long-term morbidity. Neonatologists can adjust nutrition to preterm neonates to control their weight gain so that the possibility of long-term morbidity can be minimized. This optimization of growth trajectories of preterm infants can be achieved by studying a cohort of selected healthy preterm infants with weights observed during day 1 to day 21. However, missing values in such a data poses a big challenge in this case. In fact, missing data is a common problem faced by most applied researchers. Most statistical softwares deal with missing data by simply deleting subjects with missing items. Analyses carried out on such incomplete data result in biased estimates of the parameters of interest and consequently lead to misleading or invalid inference. Even though many statistical methods may provide robust analysis, it will be better to handle missing data by imputing them with plausible values and then carry out a suitable analysis on the full data. In this thesis, several imputation methods are first introduced and discussed. Once the data get completed by the use of any of these methods, the growth trajectories for this cohort of preterm infants can be presented in the form of percentile growth curves. These growth trajectories can now serve as references for the population of preterm babies. To find out the explicit growth rate, we are interested in establishing predictive models for weights at days 7, 14 and 21. I have used both univariate and multivariate linear models on the completed data. The resulting predictive models can then be used to calculate the target weight at days 7, 14 and 21 for any other infant given the information at birth. Then, neonatologists can adjust the amount of nutrition given in order to preterm infants to control their growth so that they will not grow too fast or too slow, thus avoiding later-life complications. / Thesis / Master of Science (MSc)

In children, diarrheal disease can lead to malnutrition, poor growth, and increased risk of developing other infectious disease. With multiple cholera outbreaks, natural disasters damaging vital infrastructure, and political unrest, the health and safety of Haitians, especially children, is at risk. The purpose of this cross-sectional study was to examine the relationships between the outcome variable, diarrheal disease reporting in the last two weeks and predictor variable, source of drinking water, while accounting for additional characteristics such as region, type of place of residence, education level of the child’s mother, and toilet facility in the home. Demographic and Health Survey data from 2016-2017, collected from the mothers of households in Haiti, (n=10654) was requested and analyzed. To be included in the current analysis, respondents had to have answered whether they had any household diarrheal illness in the last two weeks and the type of water source they used, leaving an analytic sample size of 3,599 individuals. Characteristics of the population were described using weighted percentages and unweighted frequencies. Bivariate and multivariate logistic regressions were used to examine factors associated with diarrheal disease in the last two weeks (yes/no). Unadjusted and adjusted odds ratios and corresponding 95% confidence intervals were reported. Nearly one quarter of households (24.40%) reported any diarrheal illness in the last two weeks. In the study’s final multivariate model, only region had a significant relationship with diarrheal illness. The regions of Nippes, Nord-Est, Nord-Ouest, Rest-Ouest, Sud, and Sud-Est all had lower odds of contracting diarrheal disease as compared to Aire-Metropolitaine. Children living in Sud-Est were 52.9% less likely to develop diarrheal disease in two weeks than those in Aire-Metropolitaine (OR=0.471, p=0.0061). In Rest-Ouest (OR=0.561, p=0.201), Nippes(OR=0.576, p=0.0420), and Nord-Est(OR=0.585, p=0.0327), children were 43.9%, 42.4%, and 41.5% less likely to develop diarrheal disease in the last two weeks, respectively, when compared to the Aire-Metropolitane region. Lastly, Nord-Ouest (OR=0.600 p=0.0371) and Sud (OR=0.605, p=0.0463). were both approximately 40% less likely than the reference group, Aire-Metropolitan. The statistical analyses conducted calls for multiple interventions, one of which being an assessment of infrastructure and health in the region of Aire-Metropolitaine. The capital city of Haiti, Port-au-Prince, is located in this region and has experienced many natural diseases as of late, especially earthquakes. As a result, the damaged infrastructure associated with septic lines and water sources could be impacting the quality of sanitation of water in the city. Despite the results of this study, there were limitations. The most pertinent limitation involves the cross-sectional nature of this study, which means that causality between variables cannot be established. Furthermore, only about one third of respondents answered questions related to household diarrheal illness and water source, limiting the sample size. Future research should involve inspecting prevalence of diarrheal disease over time, especially in Port-au-Prince, in order to better understand potential relationships between causative factors and diarrheal disease. In addition to this, geographic analyses examining specific areas of Port-au-Prince could be conducted in an attempt to identify areas needing additional support and infrastructure within the Aire-Metropolitaine region.

On temporal, spatial and spectral scales which are small enough, all fields are fully polarized. In the optical regime, however, instantaneous fields can rarely be examined, and, instead, only average quantities are accessible. The study of polarimetry is concerned with both the description of electromagnetic fields and the characterization of media a field has interacted with. The polarimetric information is conventionally presented in terms of second order field correlations which are averaged over the ensemble of field realizations. Motivated by the deficiencies of classical polarimetry in dealing with specific practical situations, this dissertation expands the traditional polarimetric approaches to include higher order field correlations and the description of fields fluctuating in three dimensions. In relation to characterization of depolarizing media, a number of fourth-order correlations are introduced in this dissertation. Measurements of full polarization distributions, and the subsequent evaluation of Stokes vector element correlations and Complex Degree of Mutual Polarization demonstrate the use of these quantities for material discrimination and characterization. Recent advancements in detection capabilities allow access to fields near their sources and close to material boundaries, where a unique direction of propagation is not evident. Similarly, there exist classical situations such as overlapping beams, focusing, or diffusive scattering in which there is no unique transverse direction. In this dissertation, the correlation matrix formalism is expanded to describe three dimensional electromagnetic fields, providing a definition for the degree of polarization of such a field. It is also shown that, because of the dimensionality of the problem, a second parameter is necessary to fully describe the polarimetric properties of three dimensional fields. Measurements of second-order correlations of a three dimensional field are demonstrated, allowing the determination of both the degree of polarization and the state of polarization. These new theoretical concepts and innovative experimental approaches introduced in thiss dissertation are expected to impact scientific areas as diverse as near field optics, remote sensing, high energy laser physics, fluorescence microscopy, and imaging.

Yes / A workshop was recently held at Nagoya University (31 October – 02 November 2017), sponsored by the Center for International Collaborative Research, at the Institute for Space-Earth Environmental Research, Nagoya University, Japan, to quantitatively compare the performance of today’s operational solar flare forecasting facilities. Building upon Paper I of this series (Barnes et al. 2016), in Paper II (Leka et al. 2019) we described the participating methods for this latest comparison effort, the evaluation methodology, and presented quantitative comparisons. In this paper we focus on the behavior and performance of the methods when evaluated in the context of broad implementation differences. Acknowledging the short testing interval available and the small number of methods available, we do find that forecast performance: 1) appears to improve by including persistence or prior flare activity, region evolution, and a human “forecaster in the loop”; 2) is hurt by restricting data to disk-center observations; 3) may benefit from long-term statistics, but mostly when then combined with modern data sources and statistical approaches. These trends are arguably weak and must be viewed with numerous caveats, as discussed both here and in Paper II. Following this present work, we present in Paper IV a novel analysis method to evaluate temporal patterns of forecasting errors of both types (i.e., misses and false alarms; Park et al. 2019). Hence, most importantly, with this series of papers we demonstrate the techniques for facilitating comparisons in the interest of establishing performance-positive methodologies.

This dissertation presents an approach to robot programming by demonstration based on two key concepts: demonstrator intent is the most meaningful signal that the robot can observe, and the robot should have a basic level of behavioral competency from which to interpret observed actions. Intent is a teleological, robust teaching signal invariant to many common sources of noise in training. The robot can use the knowledge encapsulated in sensorimotor schemas to interpret the demonstration. Furthermore, knowledge gained in prior demonstrations can be applied to future sessions. I argue that programming by demonstration be organized into declarative and pro-cedural components. The declarative component represents a reusable outline of underlying behavior that can be applied to many different contexts. The procedural component represents the dynamic portion of the task that is based on features observed at run time. I describe how statistical models, and Bayesian methods in particular, can be used to model these components. These models have many features that are beneficial for learning in this domain, such as tolerance for uncertainty, and the ability to incorporate prior knowledge into inferences. I demonstrate this architecture through experiments on a bimanual humanoid robot using tasks from the pick and place domain. Additionally, I develop and experimentally validate a model for generating grasp candidates using visual features that is learned from demonstration data. This model is especially useful in the context of pick and place tasks.