John W. Fisher Fonds, 1937-1961, 1964-1989, 1993, n.d.

Norah and Fred Fisher welcomed John Fisher into the world on November 29, 1912, not knowing what an influential role he would play in shaping Canada's history. John Fisher grew up as the middle child of five brothers and sisters in Frosty Hollow, New Brunswick, close to today’s town of Sackville. Sackville’s main industry was the Enterprise Foundry which the Fisher family owned and operated; however, Fisher had no plans of going into the family business. He was more inspired by his maternal grandfather, Dr. Cecil Wiggins, who lived with the family after retiring from the Anglican ministry. Wiggins encouraged all his grandchildren to be well read and to take part in discussions on current events. There were often visitors in the Fisher household taking part in discussions about politics, religion, and daily life. Fisher forced himself to take part in these conversations to help overcome his shyness in social settings. These conversations did help with his shyness and also in forming many opinions and observations about Canada. It put Fisher on the road to becoming Mr. Canada and delivering the many eloquent speeches for which he was known. Fisher did not venture far from home to complete his first degree. In 1934 he graduated from Mount Allison University in Sackville, NB with an Arts degree. The same year Fisher enrolled in Dalhousie’s law school. During his time at Dalhousie, Fisher discovered radio through Hugh Mills. Mills or “Uncle Mel” was on CHNS, Halifax’s only radio station at the time. Fisher began by making appearences on the radio drama show. By 1941 he had begun writing and broadcasting his own works and joined the staff as an announcer and continuity writer. In 1936 the Canadian Broadcasting Corporation was formed, the first National radio station. Fisher joined the CBC shortly after it’s beginning and remained with them, as well as the Halifax Herald newspaper, even after his law school graduation in 1937. By 1943 Fisher’s talks became a part of the CBC’s programming for a group of maritime radio stations. Fisher once described his talks as follows “my talks weren’t meant to be objective. . . they were meant to be favourable. They were ‘pride builders’” He began his famed John Fisher Reports at CBC Toronto when he transfered there shortly after the war. This program brought emmence pride to the fellow Canadians he spoke about leading to approximately 3500 requests per year to speak at banquets and meeting throughout Canada and the United States. Fisher was a well travelled indivdual who would draw on personal experiences to connect with his audience. His stories were told in simple, straight forward language for anyone to enjoy. He became a smooth, dynamic and passionate speaker who sold Canada to Canadians. He became a renowned journalist, folk historian, writer and broadcaster. Fisher was able to reach a vast array of people through his radio work and build Canadian pride, but he did not stop there. Other ways Fisher has contributed to Canada and the Canadian people include: Honoured by five Canadian Universities. 1956, became the Director of the Canadian Tourist Association. 1961, was appointed Special Assistant to the Prime Minister of Canada. 1963, Commissioner of the Centennial Commission (the Federal Agency Responsible for Canada’s 100th birthday) 1968, received the Service Medal , a coveted Order of Canada. President of John Fisher Enterprises Ltd., private consultant work, specializing in Centennial planning, broadcasts, lectures and promotion. John Fisher continued recording radio broadcasts even after his diagnosis with cancer. He would record 3 or 4 at a time so he was free to travel across Canada, the U.S., Europe and Mexico in search of treatments. Fisher passed away from the disease on February 15, 1981 and he is buried at Mount Pleasant Cemetery in Toronto.

THE INFLUENCE OF LENGTH CHANGE SPEED AND DIRECTION ON DYNAMIC FUNCTION POTENTIATION IN FAST MOUSE MUSCLE

The purpose of this study was to test the hypothesis that the potentiation of dynamic function was dependent upon both length change speed and direction. Mouse EDL was cycled in vitro (25º C) about optimal length (Lo) with constant peak strain (± 2.5% Lo) at 1.5, 3.3 and 6.9 Hz before and after a conditioning stimulus. A single pulse was applied during shortening or lengthening and peak dynamic (concentric or eccentric) forces were assessed at Lo. Stimulation increased peak concentric force at all frequencies (range: 19 ± 1 to 30 ± 2%) but this increase was proportional to shortening speed, as were the related changes to concentric work/power (range: -15 ± 1 to 39 ± 1 %). In contrast, stimulation did not increase eccentric force, work or power at any frequency. Thus, results reveal a unique hysteresis like effect for the potentiation of dynamic output wherein concentric and eccentric forces increase and decrease, respectively, with work cycle frequency.

Automatic Structure Generation using Genetic Programming and Fractal Geometry

Three dimensional model design is a well-known and studied field, with numerous real-world applications. However, the manual construction of these models can often be time-consuming to the average user, despite the advantages o ffered through computational advances. This thesis presents an approach to the design of 3D structures using evolutionary computation and L-systems, which involves the automated production of such designs using a strict set of fitness functions. These functions focus on the geometric properties of the models produced, as well as their quantifiable aesthetic value - a topic which has not been widely investigated with respect to 3D models. New extensions to existing aesthetic measures are discussed and implemented in the presented system in order to produce designs which are visually pleasing. The system itself facilitates the construction of models requiring minimal user initialization and no user-based feedback throughout the evolutionary cycle. The genetic programming evolved models are shown to satisfy multiple criteria, conveying a relationship between their assigned aesthetic value and their perceived aesthetic value. Exploration into the applicability and e ffectiveness of a multi-objective approach to the problem is also presented, with a focus on both performance and visual results. Although subjective, these results o er insight into future applications and study in the fi eld of computational aesthetics and automated structure design.

Dynamic Upper Leg Strength and Neuromuscular Function in Children, Adolescents and Adults

This study examined muscle strength, muscle performance, and neuromuscular function during contractions at different velocities across maturation stages and between sexes. Participants included pre-pubertal, late-pubertal and adult males and females. All completed 8 isometric and 8 isokinetic leg extensions at two different velocities. Peak torque (PT), rate of torque development (PrTD), electromechanical-day (EMD), rate of muscle activation (Q30), muscle activation efficiency and coactivation were determined. Sex, maturity, and velocity main effects were found in PT and PrTD, reflecting greater values in men, adults, and isometric contractions respectively. When values were normalized to quadriceps cross-sectional area (qCSA), there was still an increase with maturity. EMD decreased with maturity. Adults had greater activation efficiency than children. Overall, differences in muscle size and neuromuscular function failed to explain group differences in PT or PrTD. More research is needed to investigate why adults may be affected to a greater extent by increasing movement velocity.

THE INFLUENCE OF LENGTH CHANGE SPEED AND DIRECTION ON DYNAMIC FUNCTION POTENTIATION IN FAST MOUSE MUSCLE

The purpose of this study was to test the hypothesis that the potentiation of dynamic function was dependent upon both length change speed and direction. Mouse EDL was cycled in vitro (250 C) about optimal length (Lo) with constant peak strain (± 2.5% Lo) at 1.5,3.3 and 6.9 Hz before and after a conditioning stimulus. A single pulse was applied during shortening or lengthening and peak dynamic (concentric or eccentric) forces were assessed at Lo. Stimulation increased peak concentric force at all frequencies (range: 19±1 to 30 ± 2%) but this increase was proportional to shortening speed, as were the related changes to concentric work/power (range: -15 ± 1 to 39 ± 1 %). In contrast, stimulation did not increase eccentric force, work or power at any frequency. Thus, results reveal a unique hysteresis like effect for the potentiation of dynamic output wherein concentric and eccentric forces increase and decrease, respectively, with work cycle frequency.

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.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

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.

Passive Solar Building Design Using Genetic Programming

Passive solar building design is the process of designing a building while considering sunlight exposure for receiving heat in winter and rejecting heat in summer. The main goal of a passive solar building design is to remove or reduce the need of mechanical and electrical systems for cooling and heating, and therefore saving energy costs and reducing environmental impact. This research will use evolutionary computation to design passive solar buildings. Evolutionary design is used in many research projects to build 3D models for structures automatically. In this research, we use a mixture of split grammar and string-rewriting for generating new 3D structures. To evaluate energy costs, the EnergyPlus system is used. This is a comprehensive building energy simulation system, which will be used alongside the genetic programming system. In addition, genetic programming will also consider other design and geometry characteristics of the building as search objectives, for example, window placement, building shape, size, and complexity. In passive solar designs, reducing energy that is needed for cooling and heating are two objectives of interest. Experiments show that smaller buildings with no windows and skylights are the most energy efficient models. Window heat gain is another objective used to encourage models to have windows. In addition, window and volume based objectives are tried. To examine the impact of environment on designs, experiments are run on five different geographic locations. Also, both single floor models and multi-floor models are examined in this research. According to the experiments, solutions from the experiments were consistent with respect to materials, sizes, and appearance, and satisfied problem constraints in all instances.

A DYNAMIC FRAMEWORK FOR THE RELATIONS BETWEEN FOREIGN EXCHANGE RATES, SAVINGS AND INVESTMENTS

The Meese-Rogoff forecasting puzzle states that foreign exchange (FX) rates are unpredictable. Since one country’s macroeconomic conditions could affect the price of its national currency, we study the dynamic relations between the FX rates and some macroeconomic accounts. Our research tests whether the predictability of the FX rates could be improved through the advanced econometrics. Improving the predictability of the FX rates has important implications for various groups including investors, business entities and the government. The present thesis examines the dynamic relations between the FX rates, savings and investments for a sample of 25 countries from the Organization for Economic Cooperation and Development. We apply quarterly data of FX rates, macroeconomic indices and accounts including the savings and the investments over three decades. Through preliminary Augmented Dickey-Fuller unit root tests and Johansen cointegration tests, we found that the savings rate and the investment rate are cointegrated with the vector (1,-1). This result is consistent with many previous studies on the savings-investment relations and therefore confirms the validity of the Feldstein-Horioka puzzle. Because of the special cointegrating relation between the savings rate and investment rate, we introduce the savings-investment rate differential (SID). Investigating each country through a vector autoregression (VAR) model, we observe extremely insignificant coefficient estimates of the historical SIDs upon the present FX rates. We also report similar findings through the panel VAR approach. We thus conclude that the historical SIDs are useless in forecasting the FX rate. Nonetheless, the coefficients of the past FX rates upon the current SIDs for both the country-specific and the panel VAR models are statistically significant. Therefore, we conclude that the historical FX rates can conversely predict the SID to some degree. Specifically, depreciation in the domestic currency would cause the increase in the SID.

Real-Time Automatic Object Classification and Tracking using Genetic Programming and NVIDIA R CUDA TM

Genetic Programming (GP) is a widely used methodology for solving various computational problems. GP's problem solving ability is usually hindered by its long execution times. In this thesis, GP is applied toward real-time computer vision. In particular, object classification and tracking using a parallel GP system is discussed. First, a study of suitable GP languages for object classification is presented. Two main GP approaches for visual pattern classification, namely the block-classifiers and the pixel-classifiers, were studied. Results showed that the pixel-classifiers generally performed better. Using these results, a suitable language was selected for the real-time implementation. Synthetic video data was used in the experiments. The goal of the experiments was to evolve a unique classifier for each texture pattern that existed in the video. The experiments revealed that the system was capable of correctly tracking the textures in the video. The performance of the system was on-par with real-time requirements.

A Study of Ordered Gene Problems Featuring DNA Error Correction and DNA Fragment Assembly with a Variety of Heuristics, Genetic Algorithm Variations, and Dynamic Representations

Ordered gene problems are a very common classification of optimization problems. Because of their popularity countless algorithms have been developed in an attempt to find high quality solutions to the problems. It is also common to see many different types of problems reduced to ordered gene style problems as there are many popular heuristics and metaheuristics for them due to their popularity. Multiple ordered gene problems are studied, namely, the travelling salesman problem, bin packing problem, and graph colouring problem. In addition, two bioinformatics problems not traditionally seen as ordered gene problems are studied: DNA error correction and DNA fragment assembly. These problems are studied with multiple variations and combinations of heuristics and metaheuristics with two distinct types or representations. The majority of the algorithms are built around the Recentering- Restarting Genetic Algorithm. The algorithm variations were successful on all problems studied, and particularly for the two bioinformatics problems. For DNA Error Correction multiple cases were found with 100% of the codes being corrected. The algorithm variations were also able to beat all other state-of-the-art DNA Fragment Assemblers on 13 out of 16 benchmark problem instances.

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.

The Dynamic Interplay of Attachment and Temperament in Relation to Dimensions of Aggression

The current study examined whether overt and relational forms of reactive and proactive aggression were differentially related to adolescents’ temperament and attachment security. Measures of adolescents’ temperament, attachment security, and aggression were completed by 211 adolescents, ages 10–14, and their caregivers. Attachment security was consistently associated with all four dimensions of aggression, whereas proneness to frustration was found to be uniquely associated with reactive-overt aggression. Additionally, it was found that at lower levels of effortful control more secure attachment was related to lower levels of reactive-relational aggression. Results also indicated that, for girls, the relation between attachment and proactive-overt and proactive-relational aggression was only significant when effortful control was low. Conversely, for boys, the relation between attachment and proactive-overt aggression and proactive-relational aggression was significant when effortful control was high. Implications of these findings and limitations to the current study are discussed.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.