Genetic Programming for Non-Photorealistic Rendering

This thesis focuses on developing an evolutionary art system using genetic programming. The main goal is to produce new forms of evolutionary art that filter existing images into new non-photorealistic (NPR) styles, by obtaining images that look like traditional media such as watercolor or pencil, as well as brand new effects. The approach permits GP to generate creative forms of NPR results. The GP language is extended with different techniques and methods inspired from NPR research such as colour mixing expressions, image processing filters and painting algorithm. Colour mixing is a major new contribution, as it enables many familiar and innovative NPR effects to arise. Another major innovation is that many GP functions process the canvas (rendered image), while is dynamically changing. Automatic fitness scoring uses aesthetic evaluation models and statistical analysis, and multi-objective fitness evaluation is used. Results showed a variety of NPR effects, as well as new, creative possibilities.

Dynamic Performance Evaluation of Canadian Fixed-Income Funds using Markov Regime Switching Models

This thesis examines the performance of Canadian fixed-income mutual funds in the context of an unobservable market factor that affects mutual fund returns. We use various selection and timing models augmented with univariate and multivariate regime-switching structures. These models assume a joint distribution of an unobservable latent variable and fund returns. The fund sample comprises six Canadian value-weighted portfolios with different investing objectives from 1980 to 2011. These are the Canadian fixed-income funds, the Canadian inflation protected fixed-income funds, the Canadian long-term fixed-income funds, the Canadian money market funds, the Canadian short-term fixed-income funds and the high yield fixed-income funds. We find strong evidence that more than one state variable is necessary to explain the dynamics of the returns on Canadian fixed-income funds. For instance, Canadian fixed-income funds clearly show that there are two regimes that can be identified with a turning point during the mid-eighties. This structural break corresponds to an increase in the Canadian bond index from its low values in the early 1980s to its current high values. Other fixed-income funds results show latent state variables that mimic the behaviour of the general economic activity. Generally, we report that Canadian bond fund alphas are negative. In other words, fund managers do not add value through their selection abilities. We find evidence that Canadian fixed-income fund portfolio managers are successful market timers who shift portfolio weights between risky and riskless financial assets according to expected market conditions. Conversely, Canadian inflation protected funds, Canadian long-term fixed-income funds and Canadian money market funds have no market timing ability. We conclude that these managers generally do not have positive performance by actively managing their portfolios. We also report that the Canadian fixed-income fund portfolios perform asymmetrically under different economic regimes. In particular, these portfolio managers demonstrate poorer selection skills during recessions. Finally, we demonstrate that the multivariate regime-switching model is superior to univariate models given the dynamic market conditions and the correlation between fund portfolios.

The evaluation of a predialysis class on client knowledge and attitude toward compliant behaviours

In this quasi-experimental study, the theory of reasoned action was used as a conceptual framework to assess the outcome effect of a predialysis class. A pretest, posttest design was used to determine changes in client knowledge about their condition and its treatment, and their intention, attitudes and social norm towards compliant behaviours. The related compliant behaviours were following a low-salt diet and taking medications as proscribed. Thirty-eight End Stage Renal Diseases (ESRD) clients were self-selected into the treatment or control groups. Both groups received the standard predialysis education from members of the multidisciplinary renal team. In addition, the treatment group also attended the predialysis class. Subjects' health locus of control, anxiety and demographic variables were measured as possible extraneous variables. Study subjects from both groups demonstrated a high internal and powerful others health locus of control and a normal range of anxiety. Although not statistically significant ill = .64), the experimental group demonstrated higher knowledge level and greater intention to follow a low salt diet UL= .73). They developed more significantly positive attitudes towards following a low salt diet and increased subjective norm influence after attending the predialysis class. Attending the predialysis class did not have an effect on subjects' intentions, attitudes or subjective norm towards taking medications as prescribed. Conclusion: The predialysis class was only marginally effective in increasing client knowledge, but influenced clients' attitudes towards following a low-salt diet. Based on the results, recommendations for improvements to the class have been suggested.

Canadian curling coaches' use of psychological skills training

Hom's (2008) model of coaching effectiveness proposes a series of direct relationships between the beliefs and values of coaches, their behaviours, and the perceptions of their athletes. One specific area of coaching behaviour that is in need of more research is their use of psychological skills training (PSn. The purpose of this study was to examine the relationship between the beliefs and behaviours of curling coaches with respect to PST, and the perceptions of their athletes. In collaboration with the Canadian Curling Association, data was collected from a national sample of 115 curling teams with varying levels of competition and experience. One hundred and fifteen coaches completed PST attitude (SPA-RC-revised) and behaviour (MSQ-revised) measures, while 403 athletes completed two perception measures (CCS and S-CI). Interclass correlation coefficients (ICC) were calculated to ensure intra-team consistency. All ICCs were positive, ranging from r =.39 to .56, and significant at the p < .01 level. A series of multiple regressions were performed. Three of the four regression models were significant, with coaches' PST behaviours accounting for 16% of the variance in athletes' evaluation of their coaches' competencies (GeC). The models for athletes' PhysicalSport Confidence (P-SC) and Cognitive-Sport Confidence (C-SC) accounted for 15% and 36% of the variation, with GCC and coaches' PST behaviours both being significant predictors of the models. After statistically controlling the influence of GCC, coaches' PST behaviours accounted for 3% and 26% of the variation in athletes P-SC and C-SC. These results provide partial support for Hom's (2008) model of coaching effectiveness, and offer new insight into the benefits of coaches' use of sport psychology-related training behaviours.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.