In the last few years, engagement in medical care among individuals living with HIV has become a major priority among HIV medical providers and public health researchers. Engagement in medical care is an important concept as it involves the process of linking newly diagnosed individuals into medical care and retaining those individuals in care throughout the course of their infection. Although there have been major advances in the management of HIV, like the advent of Highly Active Antiretroviral Therapy, morbidity and mortality due to HIV cannot be fully reduced if the individual does not optimally retain in care. Retention in HIV medical care has become an emerging topic in HIV research, but there still remains a scarce amount of research on how to properly define retention, understand its predictors, and how it impacts HIV outcomes. The purpose of this dissertation was to evaluate retention in HIV medical care among individuals diagnosed with HIV and seeking care at an urban infectious disease clinic in Kentucky. The three specific aims of this dissertation were to: (1) compare methods in measuring retention in HIV medical care; (2) determine the predictors of poor retention in care and assess the effect of non-HIV related comorbidities have on retention over time; and (3) determine the impact early retention to medical care has on time to viral load suppression and rebound among individuals initiating Highly Active Antiretroviral Therapy. A retrospective cohort study was conducted employing a medical chart review, and patients who sought HIV care at the Bluegrass Care Clinic between January 1st 2003 and May 1st 2011 were eligible for the study. There were 1,358 patients included in the study and these individuals were followed until December 31st, 2011. The results suggested that individuals living with HIV should seek care at least once every six months (visit constancy) and that only 48.6% of the study population obtained optimal retention over time. Over time the rate of retention decreased among the study sample and those with optimal retention were more likely to suppress their viral loads compared to poor retainers.

Classical experiments on honey bee time-memory showed that foragers trained to collect food at a fixed time of day return the following day with remarkable time-accuracy. Previous field experiments revealed that not all foragers return to a food source on unrewarded test days. Rather, there exist 2 subgroups: “persistent” foragers reconnoiter the source; “reticent” foragers wait in the hive for confirmation of source availability. To examine how these foragers contribute to a colony’s ability to reallocate foragers across sources with rapidly changing availabilities, foragers were trained to collect sucrose during a restricted window for several days and observed over 3 days throughout which the feeder was empty. In 2 separate trials, activity monitoring revealed a high level of activity apparently directed at other food sources. This “extracurricular” activity showed extensive temporal overlap with visits to the feeder, indicating that honey bees can manage at least 2 different overlapping time memories.

The thesis contains the four chapters: Testing parameter constancy in unit root autoregressive models against continuous change; Dickey-Fuller type of tests against nonlinear dynamic models; Inference for unit roots in a panel smooth transition autoregressive model where the time dimension is fixed; Testing unit roots in nonlinear dynamic heterogeneous panels. In Chapter  1 we derive tests for parameter constancy when the data generating process is non-stationary against the hypothesis that the parameters of the model change smoothly over time. To obtain the asymptotic distributions of the tests we generalize many theoretical results, as well as new are introduced, in the area of unit roots . The results are derived under the assumption that the error term is a strong mixing. Small sample properties of the tests are investigated, and in particular, the power performances are satisfactory. In Chapter 2 we introduce several test statistics of testing the null hypotheses of a random walk (with or without drift) against models that accommodate a smooth nonlinear shift in the level, the dynamic structure, and the trend. We derive analytical limiting distributions for all tests. Finite sample properties are examined. The performance of the tests is compared to that of the classical unit root tests by Dickey-Fuller and Phillips and Perron, and is found to be superior in terms of power. In Chapter 3 we derive a unit root test against a Panel Logistic Smooth Transition Autoregressive (PLSTAR). The analysis is concentrated on the case where the time dimension is fixed and the cross section dimension tends to infinity. Under the null hypothesis of a unit root, we show that the LSDV estimator of the autoregressive parameter in the linear component of the model is inconsistent due to the inclusion of fixed effects. The test statistic, adjusted for the inconsistency, has an asymptotic normal distribution whose first two moments are calculated analytically. To complete the analysis, finite sample properties of the test are examined. We highlight scenarios under which the traditional panel unit root tests by Harris and Tzavalis have inferior or reasonable power compared to our test. In Chapter 4 we present a unit root test against a non-linear dynamic heterogeneous panel with each country modelled as an LSTAR model. All parameters are viewed as country specific. We allow for serially correlated residuals over time and heterogeneous variance among countries. The test is derived under three special cases: (i) the number of countries and observations over time are fixed, (ii) observations over time are fixed and the number of countries tend to infinity, and (iii) first letting the number of observations over time tend to infinity and thereafter the number of countries. Small sample properties of the test  show modest size distortions and satisfactory power being superior to the Im, Pesaran and Shin t-type of test. We also show clear improvements in power compared to a univariate unit root test allowing for non-linearities under the alternative hypothesis. / Diss. Stockholm : Handelshögskolan, 2004

The use of grading scales is common in clinical practice and research settings. A number of grading scales are available to the practitioner, however, despite their frequent use, they are only poorly understood and may be criticised for a number of things such as the variability of the assessments or the inequality of scale steps within or between scales. Hence, the global aim of this thesis was to study the McMonnies/Chapman-Davies (MC-D), Institute for Eye Research (IER), Efron, and validated bulbar redness (VBR) grading scales in order to (1) get a better understanding and (2) attempt a cross-calibration of the scales. After verifying the accuracy and precision of the objective and subjective techniques to be used (chapter 3), a series of experiments was conducted. The specific aims of this thesis were as follows: • Chapter 4: To use physical attributes of redness to determine the accuracy of the four bulbar redness grading scales. • Chapter 5: To use psychophysical scaling to estimate the perceived redness of the four bulbar redness grading scales. • Chapter 6: To investigate the effect of using reference anchors when scaling the grading scale images, and to convert grades between scales. • Chapter 7: To grade bulbar redness using cross-calibrated versions of the MC-D, IER, Efron, and VBR grading scales. Methods: • Chapter 4: Two image processing metrics, fractal dimension (D) and % pixel coverage (% PC), as well as photometric chromaticity (u’) were selected as physical measures to describe and compare redness in the four bulbar redness grading scales. Pearson correlation coefficients were calculated between each set of image metrics and the reference image grades to determine the accuracy of the scales. • Chapter 5: Ten naïve observers were asked to arrange printed copies of modified versions of the reference images (showing vascular detail only) across a distance of 1.5m for which only start and end point were indicated by 0 and 100, respectively (non-anchored scaling). After completion of scaling, the position of each image was hypothesised to reflect its perceived bulbar redness. The averaged perceived redness (across observers) for each image was used for comparison to the physical attributes of redness as determined in chapter 4. • Chapter 6: The experimental setup from chapter 5 was modified by providing the reference images of the VBR scale as additional, unlabelled anchors for psychophysical scaling (anchored scaling). Averaged perceived redness from anchored scaling was compared to non-anchored scaling, and perceived redness from anchored scaling was used to cross-calibrate grades between scales. • Chapter 7: The modified reference images of each grading scale were positioned within the 0 to 100 range according to their averaged perceived redness from anchored scaling, one scale at a time. The same 10 observers who had participated in the scaling experiments were asked to represent perceived bulbar redness of 16 sample images by placing them, one at a time, relative to the reference images of each scale. Perceived redness was taken as the measured position of the placed image from 0 and was averaged across observers. Results: • Chapter 4: Correlations were high between reference image grades and all sets of objective metrics (all Pearson’s r’s≥0.88, p≤0.05); each physical attribute pointed to a different scale as being most accurate. Independent of the physical attribute used, there were wide discrepancies between scale grades, with sometimes little overlap of equivalent levels when comparing the scales. • Chapter 5: The perceived redness of the reference images within each scale was ordered as expected, but not all consecutive within-scale levels were rated as having different redness. Perceived redness of the reference images varied between scales, with different ranges of severity being covered by the images. The perceived redness was strongly associated with the physical attributes of the reference images. • Chapter 6: There were differences in perceived redness range and when comparing reference levels between scales. Anchored scaling resulted in an apparent shift to lower perceived redness for all but one reference image compared to non-anchored scaling, with the rank order of the 20 images for both procedures remaining fairly constant (Spearman’s ρ=0.99). • Chapter 7: Overall, perceived redness depended on the sample image and the reference scale used (RM ANOVA; p=0.0008); 6 of the 16 images had a perceived redness that was significantly different between at least two of the scales. Between-scale correlation coefficients of concordance (CCC) ranged from 0.93 (IER vs. Efron) to 0.98 (VBR vs. Efron). Between-scale coefficients of repeatability (COR) ranged from 5 units (IER vs. VBR) to 8 units (IER vs. Efron) for the 0 to 100 range. Conclusions: • Chapter 4: Despite the generally strong linear associations between the physical characteristics of reference images in each scale, the scales themselves are not inherently accurate and are too different to allow for cross-calibration based on physical redness attributes. • Chapter 5: Subjective estimates of redness are based on a combination of chromaticity and vessel-based components. Psychophysical scaling of perceived redness lends itself to being used to cross calibrate the four clinical scales. • Chapter 6: The re-scaling of the reference images with anchored scaling suggests that redness was assessed based on within-scale characteristics and not using absolute redness scores, a mechanism that may be referred to as clinical scale constancy. The perceived redness data allow practitioners to modify the grades of the scale they commonly use so that comparisons of grading estimates between calibrated scales may be made. • Chapter 7: The use of the newly calibrated reference grades showed close agreement between grading estimates of all scales. The between-scale variability was similar to the variability typically observed when a single scale is repeatedly used. Perceived redness appears to be dependent upon the dynamic range of the reference images of the scale. In conclusion, this research showed that there are physical and perceptual differences between the reference images of all scales. A cross-calibration of the scales based on the perceived redness of the reference images provides practitioners with an opportunity to compare grades across scales, which is of particular value in research settings or if the same patient is seen by multiple practitioners who are familiar with using different scales.

The use of grading scales is common in clinical practice and research settings. A number of grading scales are available to the practitioner, however, despite their frequent use, they are only poorly understood and may be criticised for a number of things such as the variability of the assessments or the inequality of scale steps within or between scales. Hence, the global aim of this thesis was to study the McMonnies/Chapman-Davies (MC-D), Institute for Eye Research (IER), Efron, and validated bulbar redness (VBR) grading scales in order to (1) get a better understanding and (2) attempt a cross-calibration of the scales. After verifying the accuracy and precision of the objective and subjective techniques to be used (chapter 3), a series of experiments was conducted. The specific aims of this thesis were as follows: • Chapter 4: To use physical attributes of redness to determine the accuracy of the four bulbar redness grading scales. • Chapter 5: To use psychophysical scaling to estimate the perceived redness of the four bulbar redness grading scales. • Chapter 6: To investigate the effect of using reference anchors when scaling the grading scale images, and to convert grades between scales. • Chapter 7: To grade bulbar redness using cross-calibrated versions of the MC-D, IER, Efron, and VBR grading scales. Methods: • Chapter 4: Two image processing metrics, fractal dimension (D) and % pixel coverage (% PC), as well as photometric chromaticity (u’) were selected as physical measures to describe and compare redness in the four bulbar redness grading scales. Pearson correlation coefficients were calculated between each set of image metrics and the reference image grades to determine the accuracy of the scales. • Chapter 5: Ten naïve observers were asked to arrange printed copies of modified versions of the reference images (showing vascular detail only) across a distance of 1.5m for which only start and end point were indicated by 0 and 100, respectively (non-anchored scaling). After completion of scaling, the position of each image was hypothesised to reflect its perceived bulbar redness. The averaged perceived redness (across observers) for each image was used for comparison to the physical attributes of redness as determined in chapter 4. • Chapter 6: The experimental setup from chapter 5 was modified by providing the reference images of the VBR scale as additional, unlabelled anchors for psychophysical scaling (anchored scaling). Averaged perceived redness from anchored scaling was compared to non-anchored scaling, and perceived redness from anchored scaling was used to cross-calibrate grades between scales. • Chapter 7: The modified reference images of each grading scale were positioned within the 0 to 100 range according to their averaged perceived redness from anchored scaling, one scale at a time. The same 10 observers who had participated in the scaling experiments were asked to represent perceived bulbar redness of 16 sample images by placing them, one at a time, relative to the reference images of each scale. Perceived redness was taken as the measured position of the placed image from 0 and was averaged across observers. Results: • Chapter 4: Correlations were high between reference image grades and all sets of objective metrics (all Pearson’s r’s≥0.88, p≤0.05); each physical attribute pointed to a different scale as being most accurate. Independent of the physical attribute used, there were wide discrepancies between scale grades, with sometimes little overlap of equivalent levels when comparing the scales. • Chapter 5: The perceived redness of the reference images within each scale was ordered as expected, but not all consecutive within-scale levels were rated as having different redness. Perceived redness of the reference images varied between scales, with different ranges of severity being covered by the images. The perceived redness was strongly associated with the physical attributes of the reference images. • Chapter 6: There were differences in perceived redness range and when comparing reference levels between scales. Anchored scaling resulted in an apparent shift to lower perceived redness for all but one reference image compared to non-anchored scaling, with the rank order of the 20 images for both procedures remaining fairly constant (Spearman’s ρ=0.99). • Chapter 7: Overall, perceived redness depended on the sample image and the reference scale used (RM ANOVA; p=0.0008); 6 of the 16 images had a perceived redness that was significantly different between at least two of the scales. Between-scale correlation coefficients of concordance (CCC) ranged from 0.93 (IER vs. Efron) to 0.98 (VBR vs. Efron). Between-scale coefficients of repeatability (COR) ranged from 5 units (IER vs. VBR) to 8 units (IER vs. Efron) for the 0 to 100 range. Conclusions: • Chapter 4: Despite the generally strong linear associations between the physical characteristics of reference images in each scale, the scales themselves are not inherently accurate and are too different to allow for cross-calibration based on physical redness attributes. • Chapter 5: Subjective estimates of redness are based on a combination of chromaticity and vessel-based components. Psychophysical scaling of perceived redness lends itself to being used to cross calibrate the four clinical scales. • Chapter 6: The re-scaling of the reference images with anchored scaling suggests that redness was assessed based on within-scale characteristics and not using absolute redness scores, a mechanism that may be referred to as clinical scale constancy. The perceived redness data allow practitioners to modify the grades of the scale they commonly use so that comparisons of grading estimates between calibrated scales may be made. • Chapter 7: The use of the newly calibrated reference grades showed close agreement between grading estimates of all scales. The between-scale variability was similar to the variability typically observed when a single scale is repeatedly used. Perceived redness appears to be dependent upon the dynamic range of the reference images of the scale. In conclusion, this research showed that there are physical and perceptual differences between the reference images of all scales. A cross-calibration of the scales based on the perceived redness of the reference images provides practitioners with an opportunity to compare grades across scales, which is of particular value in research settings or if the same patient is seen by multiple practitioners who are familiar with using different scales.

Children’s understanding of race constancy and their subsequent use of race as a means of drawing inductive inferences were investigated. Race constancy was determined by children’s tendency to say that people could change category membership by changing their outside appearance. A second phase of the study measured how many race-based inferences children made relative to other social categories such as age or sex. The results indicated that children who had a better understanding of race constancy were also more likely to use race as a means of drawing inductive inferences. These findings support a developmental progression of race constancy and give insight to the development of potential bias and stereotypes.

Η παρούσα διπλωματική εργασία ασχολείται με την εκτίμηση του χρώματος αντικειμένων τα οποία απεικονίζονται σε φωτογραφίες, μέσω χρωματικής ισοστάθμισης και κατά συνέπεια μέσω διαχωρισμού των συνιστωσών ανάκλασης του φωτός. Συγκεκριμένα, εφαρμόσθηκε μια μέθοδος εκτίμησης του χρώματος αντικειμένων χρησιμοποιώντας μια βάση δεδομένων πολλαπλών εικόνων οι οποίες επεξεργάστηκαν ψηφιακά. Οι φωτογραφίες της βάσης δεδομένων απεικονίζουν την ίδια σκηνή και λήφθηκαν τοποθετώντας τη φωτεινή πηγή σε διαφορετική θέση στην περίπτωση κάθε φωτογραφίας. Η εργασία βασίστηκε κατά ένα μεγάλο μέρος σε άρθρα και πληροφορίες που συλλέχθηκαν από το διαδίκτυο. Τα πειραματικά αποτελέσματα ήταν ικανοποιητικά και έδειξαν πως ο συγκεκριμένος αλγόριθμος επιτυγχάνει χρωματική ισοστάθμιση και απόδοση χρωμάτων με καλή ακρίβεια και μικρή υπολογιστική πολυπλοκότητα. / The present Diploma Thesis deals with color estimation of objects which are depicted in photos, through color constancy and thus by separating the reflection components of light. We applied a method for estimating the color of objects using a database of multiple images that were digitally processed. The photos of the database represent the same scene and were obtained by placing the light source in a different location while capturing each photo. The present work was largely based on articles and information gathered from the internet. The experimental results were satisfactory and showed that the specific algorithm achieves color constancy and color estimation with good accuracy and low computational complexity.

This thesis is comprised of three independent essays. One essay is in the field of macroeconomics and the other two are in time-series econometrics. The first essay, "Productivity and Business Investment over the Business Cycle", is co-authored with my co-supervisor Hashmat Khan. This essay documents a new stylized fact: the correlation between labour productivity and real business investment in the U.S. data switching from 0.54 to -0.1 in 1990. With the assistance of a bivariate VAR, we find that the response of investment to identified technology shocks has changed signs from positive to negative across two sub-periods: ranging from the time of the post-WWII era to the end of 1980s and from 1990 onwards, whereas the response to non-technology shocks has remained relatively unchanged. Also, the volatility of technology shocks declined less relative to the non-technology shocks. This raises the question of whether relatively more volatile technology shocks and the negative response of investment can together account for the decreased correlation. To answer this question, we consider a canonical DSGE model and simulate data under a variety of assumptions about the parameters representing structural features and volatility of shocks. The second and third essays are in time series econometrics and solely authored by myself. The second essay, however, focuses on the impact of ignoring structural breaks in the conditional volatility parameters on time-varying volatility parameters. The focal point of the third essay is on empirical relevance of structural breaks in time-varying volatility models and the forecasting gains of accommodating structural breaks in the unconditional variance. There are several ways in modeling time-varying volatility. One way is to use the autoregressive conditional heteroskedasticity (ARCH)/generalized ARCH (GARCH) class first introduced by Engle (1982) and Bollerslev (1986). One prominent model is Bollerslev (1986) GARCH model in which the conditional volatility is updated by its own residuals and its lags. This class of models is popular amongst practitioners in finance because they are able to capture stylized facts about asset returns such as fat tails and volatility clustering (Engle and Patton, 2001; Zivot, 2009) and require maximum likelihood methods for estimation. They also perform well in forecasting volatility. For example, Hansen and Lunde (2005) find that it is difficult to beat a simple GARCH(1,1) model in forecasting exchange rate volatility. Another way of modeling time-varying volatility is to use the class of stochastic volatility (SV) models including Taylor's (1986) autoregressive stochastic volatility (ARSV) model. With SV models, the conditional volatility is updated only by its own lags and increasingly used in macroeconomic modeling (i.e.Justiniano and Primiceri (2010)). Fernandez-Villaverde and Rubio-Ramirez (2010) claim that the stochastic volatility model fits better than the GARCH model and is easier to incorporate into DSGE models. However, Creal et al. (2013) recently introduced a new class of models called the generalized autoregressive score (GAS) models. With the GAS volatility framework, the conditional variance is updated by the scaled score of the model's density function instead of the squared residuals. According to Creal et al. (2013), GAS models are advantageous to use because updating the conditional variance using the score of the log-density instead of the second moments can improve a model's fit to data. They are also found to be less sensitive to other forms of misspecification such as outliers. As mentioned by Maddala and Kim (1998), structural breaks are considered to be one form of outliers. This raises the question about whether GAS volatility models are less sensitive to parameter non-constancy. This issue of ignoring structural breaks in the volatility parameters is important because neglecting breaks can cause the conditional variance to exhibit unit root behaviour in which the unconditional variance is undefined, implying that any shock to the variance will not gradually decline (Lamoureux and Lastrapes, 1990). The impact of ignoring parameter non-constancy is found in GARCH literature (see Lamoureux and Lastrapes, 1990; Hillebrand, 2005) and in SV literature (Psaradakis and Tzavalis, 1999; Kramer and Messow, 2012) in which the estimated persistence parameter overestimates its true value and approaches one. However, it has never been addressed in GAS literature until now. The second essay uses a simple Monte-Carlo simulation study to examine the impact of neglecting parameter non-constancy on the estimated persistence parameter of several GAS and non-GAS models of volatility. Five different volatility models are examined. Of these models, three --the GARCH(1,1), t-GAS(1,1), and Beta-t-EGARCH(1,1) models -- are GAS models, while the other two -- the t-GARCH(1,1) and EGARCH(1,1) models -- are not. Following Hillebrand (2005) who studied only the GARCH model, this essay examines the extent of how biased the estimated persistence parameter are by assessing impact of ignoring breaks on the mean value of the estimated persistence parameter. The impact of neglecting parameter non-constancy on the empirical sampling distributions and coverage probabilities for the estimated persistence parameters are also studied in this essay. For the latter, studying the effect on the coverage probabilities is important because a decrease in coverage probabilities is associated with an increase in Type I error. This study has implications for forecasting. If the size of an ignored break in parameters is small, then there may not be any gains in using forecast methods that accommodate breaks. Empirical evidence suggests that structural breaks are present in data on macro-financial variables such as oil prices and exchange rates. The potentially serious consequences of ignoring a break in GARCH parameters motivated Rapach and Strauss (2008) and Arouri et al. (2012) to study the empirical relevance of structural breaks in the context of GARCH models. However, the literature does not address the empirical relevance of structural breaks in the context of GAS models. The third and final essay contributes to this literature by extending Rapach and Strauss (2008) to include the t-GAS model and by comparing its performance to that of two non-GAS models, the t-GARCH and SV models. The empirical relevance of structural breaks in the models of volatility is assessed using a formal test by Dufour and Torres (1998) to determine how much the estimated parameters change over sub-periods. The in-sample performance of all the models is analyzed using both the weekly USD trade-weighted index between January 1973 and October 2016 and spot oil prices based on West Texas Intermediate between January 1986 and October 2016. The full sample is split into smaller subsamples by break dates chosen based on historical events and policy changes rather than formal tests. This is because commonly-used tests such as CUSUM suffer from low power (Smith, 2008; Xu, 2013). For each sub-period, all models are estimated using either oil or USD returns. The confidence intervals are constructed for the constant of the conditional parameter and the score parameter (or ARCH parameter in GARCH and t-GARCH models). Then Dufour and Torres's union-intersection test is applied to these confidence intervals to determine how much the estimated parameter change over sub-periods. If there is a set of values that intersects the confidence intervals of all sub-periods, then one can conclude that the parameters do not change that much. The out-of-sample performance of all time-varying volatility models are also assessed in the ability to forecast the mean and variance of oil and USD returns. Through this analysis, this essay also addresses whether using models that accommodate structural breaks in the unconditional variance of both GAS and non-GAS models will improve forecasts.

This master's thesis emerges from the research in the field of uniform marker fields for augmented reality and broadens it by the possibility of generating colored marker fields based on arbitrary pictures. The thesis describes basics of augmented reality and explains the ideas from the Uniform Marker Fields paper. Presented is a program for generating colored marker fields as well as a tool for detecting markers and evaluating success rate of the detection. In addition, the work proposes a study of color constancy in the process of printing and recording. This study exceeds the scope of the requirements for this master's thesis; the knowledge can be used in arbitrary applications that use colors to code information. Finally, the integration of the knowledge into the marker field generating algorithm and resulting improvement of detection success rate are discussed.

New materials used at construction gauge they are though expensive, but shall they meaningful influence over making and metrological characteristics. Work evaluation influence these material on increasing quality concrete gauge.