Response curves of deterministic and probabilistic cellular automata in one and two dimensions

One of the most important problems in the theory of cellular automata (CA) is determining the proportion of cells in a specific state after a given number of time iterations. We approach this problem using patterns in preimage sets - that is, the set of blocks which iterate to the desired output. This allows us to construct a response curve - a relationship between the proportion of cells in state 1 after niterations as a function of the initial proportion. We derive response curve formulae for many two-dimensional deterministic CA rules with L-neighbourhood. For all remaining rules, we find experimental response curves. We also use preimage sets to classify surjective rules. In the last part of the thesis, we consider a special class of one-dimensional probabilistic CA rules. We find response surface formula for these rules and experimental response surfaces for all remaining rules.

A Multi-Objective Genetic Algorithm with Side Effect Machines for Motif Discovery

Understanding the machinery of gene regulation to control gene expression has been one of the main focuses of bioinformaticians for years. We use a multi-objective genetic algorithm to evolve a specialized version of side effect machines for degenerate motif discovery. We compare some suggested objectives for the motifs they find, test different multi-objective scoring schemes and probabilistic models for the background sequence models and report our results on a synthetic dataset and some biological benchmarking suites. We conclude with a comparison of our algorithm with some widely used motif discovery algorithms in the literature and suggest future directions for research in this area.

'To do justice and judgement' : the place of passion in public life

Please consult the paper edition of this thesis to read. It is available on the 5th Floor of the Library at Call Number: Z 9999 P65 F47 2003

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.

When the Beautiful Game Turns Ugly: A Study of Fan Experiences of Perceived Match Fixing in Soccer

Limited academic attention has been given to the nexus between corruption in soccer and its impact on fandom. Consequently, the purpose of this qualitative study was to better understand the lived experiences of highly identified soccer fanatics living through this era of match fixing in the sport. Social networking site Twitter was utilized to recruit participants from three continents – Africa, Europe, and North America – based on submissions to the site in response to a perceived fix from a high-profile March, 2013 match. A total of 12 semi-structured interviews were conducted with highly identified soccer fans in accordance with Funk and James’ (2001) Psychological Continuum Model (PCM). Despite the majority of participants feeling skepticism about the purity of soccer today, half of the participants’ fandom remained unchanged in the face of perceived match fixing. Directions for future research and recommendations are considered and discussed.

New Teacher Perceptions of Inclusive Pedagogies: Designing New Future for the Changing Classroom

Our perceptions of knowledge attainment have changed (Bezemer & Kress, 2010). The type of students our teachers once were is vastly different from the students they currently teach. We need our next generation to thrive in a dynamically, interactive world saturated with opportunities for meaning making (Kress & Selander, 2012). Our current students are responsible for continuing our society, but that does not mean we need them to become us (Gee, 2009). Rather desperately, we need them to be thinkers and expressive in a variety of modes. The world will be different when they take their rightful place as the next generation of leaders, and so too must their thinking be different (Cope & Kalantzis, 2000). This explanatory mixed-method study (Creswell, 2013; Mertens, 2014) involved an investigation into perceptions of new teachers regarding inclusive pedagogies like Universal Design for Learning (CAST, 2011). It specifically discusses the contemporary thinking of 44 new Ontario teachers regarding inclusive pedagogies in their teacher education as well as their relative intent to utilize them in their practice. This study reveals a distinct tone of skepticism and provides suggestions for the continued improvement of teacher education programs in this province.