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Evaluating the effectiveness of an intervention can pose challenges if there is not an adequate control group. The effects of the intervention can be distorted by observable differences in the characteristics of the control and treatment groups. Propensity score matching can be used to confirm the outcomes of an intervention are due to the treatment and not other characteristics that may also explain the intervention effects. Propensity score matching is an advanced statistical technique that uses background information on the characteristics of the study population to establish matched pairs of treated participants and controls. This technique improves the quality of control groups and allowing for a better evaluation of the true effects of an intervention. The purpose of this study was to implement this technique to derive county-level matches across the southeastern United States for existing counties within a single state where future statewide initiatives are planned. Statistical analysis was performed using SAS 9.4 (Cary, NC, USA). A select set of key county-level socio-demographic measures theoretically relevant for deriving appropriate matches was examined. These include the proportion of African Americans in population, population density, and proportion of the female population below poverty level. To derive the propensity-matched counties, a logistic regression model with the state of primary interest as the outcome was conducted. The baseline covariates of interest were included in the model and used to predict the probability of a county being in the state of primary interest; this acts as the propensity score used to derive matched controls. A caliper of 0.2 was used to ensure the ratio of the variance of the linear propensity score in the control group to the variance of the linear propensity score in the treatment group is close to 1. The balance of covariates before and after the propensity score matching were assessed to determine if significant differences in each respective covariate persisted after the propensity score matching. Before matching, a significant difference was found in the proportion of African Americans in control group (21.08%, n=3,450) and treatment group (36.95%, n=230) using the t-test (P<0.0001). The percent of females below poverty level showed significant difference between control and treatment group (P=0.0264). The t-test of population density also showed significant differences between the groups (P=0.0424). After matching, the mean differences for the treated-control groups were all zero for these three covariates and the characteristics were no longer showing any significant differences between the two groups. This study found that the use of propensity score matching methods improved the accuracy of matched controls. Ensuring that the control and treatment counties have statistically similar characteristics is important for improving the rigor of future studies examining county-level outcomes. Propensity score matching does not account for unobserved differences between the treatment and control groups that may affect the observed outcomes; however, it does ensure that the observable characteristics between the groups are statistically similar.This method reduces the threat to internal validity that observable characteristics pose on interventions by matching for these potentially confounding characteristics.

In a widely cited 2003 report, the U.S. Surgeon General criticized mental health and social services within the United States for failing to adequately serve the needs of clients of color. The report highlighted the fact that therapists often do not adequately account for cultural variables in their evaluations or interventions. Clients of color are rarely seen by therapists who adequately understand their cultural values and backgrounds. To address this discrepancy, researchers have explored a variety of therapy process and outcome variables across clients seen by therapists of their same race vs. another race (often called "ethnic matching"). Over 200 of these studies have appeared in the literature, but few conclusions have been drawn due to the large disparity across findings. To more accurately summarize these studies, three rigorous quantitative reviews using meta-analytical methods were conducted. Forty-nine studies met inclusion criteria for the first meta-analysis (client preference studies), with the average effect size across studies being d = .65, indicating a strong preference for a therapist of the same ethnicity or race. Seventy-seven studies met inclusion criteria for the second meta-analysis (client perception studies), with the average effect size across studies being d = .33, indicating that ethnically matched clients tend to perceive their therapists moderately better than they perceived ethnically mismatched therapists. Fifty-two studies met inclusion criteria for the third meta-analysis (client outcome studies), with the average effect size across studies being d = .09, indicating that ethnic matching had minimal impact on client outcome. The effects of potential moderator variables, including age, gender, and ethnicity were also investigated. The results of this meta-analysis help inform current practice and future research efforts to promote multiculturally competent mental health interventions.

Research has shown that by 7-months of age infants demonstrate recognition of emotion by successfully matching faces and voices based on affect in an intermodal matching procedure. It is often assumed that once an ability is present the development of that ability has "ceased." Therefore, no research has examined if and how the ability to match faces and voices based on affect develops after the first 7-months. This study examined how the ability to match faces and voices based on affect changes from 7- to 12-months. Looking at infant's proportion of total looking time (PTLT) results showed that, consistent with previous research, 7-month-old infants looked significantly longer at the affectively congruent facial expression. However, 12-month- olds showed no matching of faces and voices. Further analyses showed that 7-month-olds also increased their looking to facial expressions while being presented with the affectively congruent vocal expression. Once again, 12-month-olds failed to show significant matching. That 7-month- olds were able to demonstrate matching while 12-month-olds failed to do so is possibly a result of 12-month-olds attending to other information. More research is needed to better understand how infants' recognition of affect and overall perceptual abilities change as they develop.

The pattern matching is the problem of locating one string, a pattern, inside another, a text, which is required in for example databases, search engines, and text editors. Thus, several algorithms have been created to tackle this problem and this thesis evaluates whether a parallel version of the Naïve algorithm, given a reasonable amount of threads for a personal computer, could become more efficient than some state-of-the-art algorithms used today. Therefore, an algorithm from the Deadzone family, the Horspool algorithm, and a parallel Naïve algorithm was implemented and evaluated on two different sized alphabets. The results show that a parallel Naïve implementation is to be favoured over the Deadzone and Horspool on a alphabet of size 4 for patterns larger than 2 up to 20. Furthermore, for alphabet of size 256 the parallel Naïve should also be used for patterns of lengths 1 to 20.

This study analyses empirically what patent characteristics matter for collateralization. In accordance with the finance literature, loan collateral is determined by the liquidation value of the asset which in turn depends on the three factors "physical attributes of the asset", "number of alternative users" and "financial strength of alternative users". Hence, the study is focusing on patent characteristics influencing the three factors of the liquidation value. To control for firm effects of the patent pledging firms, a treatment group of pledged patents and a comparison group of unpledged patents have been matched based on firm characteristics of the patent owner. The subsequent empirical analysis revealed that patent characteristics related to the physical attributes of patents enhancing their redeployability matter for collateralization. Patent characteristics related to the market liquidity measuring the financial strength of alternative users, are insignificant. Furthermore, the study confirms the additional function of patents as source of finance by offering them for loan collateral. Especially small and young firms, scare of tangible assets pledge patents for receiving debt finance.

The positional Burrows-Wheeler transform (PBWT) is a foundational data structure for representing haplotype matches of biobank scale. Once the PBWT panel of a set of haplotypes are constructed, efficient algorithms are available for “All vs. All” positional substring matching, finding exact matches of substrings in pre-aligned strings, for haplotypes within the panel, and “One vs. All” positional substring match query for an out-of-panel haplotype against all haplotypes in the panel. While the original PBWT was designed from linear reference genomes, GBWT was proposed to extend PBWT to genome graphs that allow large insertions and deletions. However, there are no GBWT algorithms for haplotype matching. In this work, we develop the efficient algorithms for “All vs. All” and “One vs. All” haplotype set-maximal and long matching algorithms for GBWT. For a GBWT containing a panel of paths P, we show algorithms similar to the matching algorithms of PBWT. Our algorithms achieves theoretically optimal time complexity to output all “All vs. All” matches in time linear to the size of the input panel (O(∑|Pi| + |out put|)), and quasilinear time to the length of the query path for “One vs. All” path match queries (O(|Q| log σ + |out put| log σ ), where σ is the maximum out- degree in the GBWT and out put is the set of discovered path matches). Under the constant σ assumption made by gPBWT and GBWT, these algorithms are in fact linear. Our algorithms open the possibilities for applications of efficient positional substring matching in pangenome references such as identical-by-descent (IBD) segment identification and genotype imputation.

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<p>Conventional image compression algorithms use transform coding to achieve a compact representation of the image. Most transforms used in image compression algorithms map image data to a complete set of transform basis functions which can decorrelate image information and represent data in a more compact form. This technique has proven to be very efficient and is used in most state of the art compression algorithms. However, if an over-complete set of basis functions is available, the image information can be captured by fewer basis functions. This results in a more compact image representation and can potentially yield a better compression performance. In this thesis, we study the use of over-complete image representation as an alternative to transform coding techniques used in image compression. The matching pursuit (MP) algorithm is used to map the image to an over-complete dictionary. We develop new quantization and encoding algorithms for matching pursuit image coding and compare the proposed MP image encoder with state of the art image codecs that use transform coding techniques. Additionally, the iterative nature of the matching pursuit algorithm can be used to design progressive encoders. We also study progressive coding by matching pursuit and design new progressive MP encoders and show how they outperform existing solutions.</p> <p>We start by study of progressive coding by matching pursuit and design a progressive encoder for i.i.d. Gaussian sources. The choice of Gaussian sources is motivated by the fact that theoretical bounds on progressive coding of Gaussian sources are known and therefore can be used to determine the efficiency of matching pursuit in progressive coding. Our proposed MP progressive encoder outperforms all existing progressive encoders designed for Gaussian sources. However, redundancies in the MP algorithm prevents us from closing the gap that exists between progressive and non-progressive Gaussian source coding. Therefore, we design another progressive encoder based on lattice quantization and address some of the issues associated with our proposed MP encoder.</p> <p>In the second part of this thesis we study the application of matching pursuit m image compression. We start our study by developing a new adaptive quantization technique that can outperform existing quantization techniques designed for matching pursuit image coding. We continue our study by designing an optimal encoding algorithm for encoding MP coefficients and atom positions. The proposed encoding algorithm results in significant rate distortion improvement over existing encoding techniques. The use of our proposed encoding technique enables comparison of matching pursuit image coding with state of the art compression algorithms that use transform coding such as JPEG2000. Our proposed MP image encoder outperforms JPEG2000 at low bit rates and results in better visual quality at moderate bit rates. We show that the flexibility offered by the over-complete dictionary can result in superior performance compared to image compression using transform coding techniques.</p> / Thesis / Doctor of Philosophy (PhD)

A globular cluster is a collection of stars that orbits the center of its galaxy as a single satellite. Understanding what influences the formations of these clusters provides understanding of galaxy structure and insight into their early development. We continue the work of Harris et al. (2013), who identified a set of predictors that accurately determined the number of clusters Ngc, through analysis of an incomplete dataset. We aimed to improve upon these results through imputation of the missing data. A small amount of precision was gained for the slope of Ngc~ R_e*sigma_ e, while the intercept suffered a small loss of precision. Estimates of intrinsic variance also increased with the addition of imputed data. We also found galaxy morphological type to be a significant predictor of Ngc in a model with R_e*sigma_ e. Although it increased precision of the slope and reduced the residual variance, its overall contribution was negligible. / Thesis / Master of Science (MSc)