The thesis provides a political analysis of a position paper on government programming recently adopted by the Ukrainian Canadian Congress (UCC) a national ethno-cultural organisation that ostensibly represents over one million Canadians of Ukrainian heritage and a historically important player in the development of multiculturalism in Canada. The impetus for such an analysis is to explore whether there are alternative policy directions available to the UCC that could satisfy its mandate developing and enhancing the Ukrainian-Canadian community while taking into account the reality that Ukrainian-Canadians culturally resemble more and more the broader Canadian society. <p>In a wide-ranging analysis that criticizes both, official Canadian multiculturalism for falling short in meeting its commitment to cultural pluralism and the UCC for upholding a position that relies on a static or retrograde version of culture, the thesis makes the case for a multiculturalism that can recognize cultural differences while allowing for change. <p>The thesis is significant because it asks relevant questions concerning how multiculturalism in Canada takes into account an increasingly heterogeneous citizenship characterized by cultural change. In this regard, the thesis is of particular importance to Canadians who claim a multiplicity of cultures rather than a single ethnicity and yet still express a desire to be included in the discourse on Canadian national identity.

Class numbers of algebraic number fields are central invariants. Once the underlying field has an infinite unit group they behave very irregularly due to a non-trivial regulator. This phenomenon occurs already in the simplest case of real quadratic number fields of which very little is known. Hooley derived a conjectural formula for the average of class numbers of real quadratic fields. In this thesis we extend his methods to obtain conjectural formulae and bounds for any moment, i.e., the average of an arbitrary real power of class numbers. Our formulae and bounds are based on similar (quite reasonable) assumptions of Hooley's work. In the final chapter we consider the case of the -1 power from a numerical point of view and develop an efficient algorithm to compute the average for the -1 class number power without computing class numbers.

Automated facial expression analysis for Emotion Recognition (ER) is an active research area towards creating socially intelligent systems. The eye region, often considered integral for ER by psychologists and neuroscientists, has received very little attention in engineering and computer sciences. Using eye region as an input signal presents several bene ts for low-cost, non-intrusive ER applications. This work proposes two frameworks towards ER from eye region images. The first framework uses Local Binary Patterns (LBP) as the feature extractor on grayscale eye region images. The results validate the eye region as a signi cant contributor towards communicating the emotion in the face by achieving high person-dependent accuracy. The system is also able to generalize well across di erent environment conditions. In the second proposed framework, a color-based approach to ER from the eye region is explored using Local Color Vector Binary Patterns (LCVBP). LCVBP extend the traditional LBP by incorporating color information extracting a rich and a highly discriminative feature set, thereby providing promising results.

Class numbers of algebraic number fields are central invariants. Once the underlying field has an infinite unit group they behave very irregularly due to a non-trivial regulator. This phenomenon occurs already in the simplest case of real quadratic number fields of which very little is known. Hooley derived a conjectural formula for the average of class numbers of real quadratic fields. In this thesis we extend his methods to obtain conjectural formulae and bounds for any moment, i.e., the average of an arbitrary real power of class numbers. Our formulae and bounds are based on similar (quite reasonable) assumptions of Hooley's work. In the final chapter we consider the case of the -1 power from a numerical point of view and develop an efficient algorithm to compute the average for the -1 class number power without computing class numbers.

Automated facial expression analysis for Emotion Recognition (ER) is an active research area towards creating socially intelligent systems. The eye region, often considered integral for ER by psychologists and neuroscientists, has received very little attention in engineering and computer sciences. Using eye region as an input signal presents several bene ts for low-cost, non-intrusive ER applications. This work proposes two frameworks towards ER from eye region images. The first framework uses Local Binary Patterns (LBP) as the feature extractor on grayscale eye region images. The results validate the eye region as a signi cant contributor towards communicating the emotion in the face by achieving high person-dependent accuracy. The system is also able to generalize well across di erent environment conditions. In the second proposed framework, a color-based approach to ER from the eye region is explored using Local Color Vector Binary Patterns (LCVBP). LCVBP extend the traditional LBP by incorporating color information extracting a rich and a highly discriminative feature set, thereby providing promising results.

Bioinformatics techniques to protein secondary structure prediction mostly depend on the information available in amino acid sequence. Support vector machines (SVM) have shown strong generalization ability in a number of application areas, including protein structure prediction. In this study, a new sliding window scheme is introduced with multiple windows to form the protein data for training and testing SVM. Orthogonal encoding scheme coupled with BLOSUM62 matrix is used to make the prediction. First the prediction of binary classifiers using multiple windows is compared with single window scheme, the results shows single window not to be good in all cases. Two new classifiers are introduced for effective tertiary classification. This new classifiers use neural networks and genetic algorithms to optimize the accuracy of the tertiary classifier. The accuracy level of the new architectures are determined and compared with other studies. The tertiary architecture is better than most available techniques.

The knowledge of the phase behaviour of heavy oils and bitumen is important in order to understand the phenomenon of coke formation. Computation of their phase behaviour, using an equation of state, faces problems due to their complex composition. Hence n-alkane binaries of polyaromatic hydrocarbons are used to approximate the phase behaviour of heavy oils and bitumen. Appropriate values of binary interaction parameters are required for an equation of state to predict the correct phase behaviour of these model binary fluids. This thesis deals with fitting of the binary interaction parameter for the Peng-Robinson equation of state using landmarks in the binary phase space such as K- and L-points. A K- or an L-point is a point in the phase space where two phases become critical in the presence of another phase in equilibrium. An algorithm to calculate K- and L-points using an equation of state was developed. The variation of calculated K- and L-points with respect to the binary interaction parameter was studied and the results were compared with the experimental data in the literature. The interaction parameter was then fitted using the best match of experimental results with the computed ones. The binary interaction parameter fitted using a K- or an L-point was then used to predict the P-T projection of the binary system in phase space. Also, the qualitative effect of the binary interaction parameter on the P-T projection was studied. A numerical and thermodynamic study of the algorithm was done. Numerical issues like the initial guesses, convergence criterion and numerical techniques were studied and the thermodynamic constraints in the generalization of the algorithm are discussed. It was observed that the binary interaction parameter not only affects the location of K- and L-points in the phase space but also affects the calculation procedure of K- and L-points. Along with the propane binaries of polyaromatic hydrocarbons, K- and L-points were also calculated for systems like methane binaries of higher n-alkanes and the ethane + ethanol binary. In the case of the ethane + ethanol system, K- and L-points, matching the experimental results were calculated with different values of the binary interaction parameter. But the Peng-Robinson equation of state was unable to predict the correct type of phase behaviour using any value of the binary interaction parameter. The Peng-Robinson equation of state was able to predict the correct type of phase behaviour with the binary interaction parameter, fitted using K- and/or L-points for methane + n-alkane systems. The systems studied were the methane binaries of n-pentane, n-hexane and n-heptane. For the propane binaries of polyaromatic hydrocarbons, no value of the binary interaction parameter was able to predict the K-point with a good accuracy. The binary interaction parameter which gave the best possible results for a K-point failed to predict the correct type of phase behaviour. The binary interaction parameter fitted using the P-T projection enabled the Peng-Robinson equation of state to give a qualitative match for the high pressure complex phase behaviour of these systems. Solid phase equilibria were not taken into consideration.

High Mass X-Ray Binary Stars (HMXBs) are stars that contain one early-type main sequence or giant star and one of a black hole, neutron star or white dwarf. HMXBs in the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) are instructive to study because both galaxies are metal poor in compari- son to the Milky Way and they are fairly transparent to both optical and X-ray radiation. This allows a more complete study of the whole population, without the biasing effects of gas and dust that occur in our own Galaxy. The objective of this study was to find the periods of HMXBs in the LMC and SMC with known optical counterparts in the dataset acquired by the Robotic Optical Transient Search Ex- periment telescope. Two possible orbital periods were found for the objects XTE J0055-724 and RX J0101.3-7211 of 1724 days and 478 days, respectively. Continued observations are recommended to conrm the two periods.

High Mass X-Ray Binary Stars (HMXBs) are stars that contain one early-type main sequence or giant star and one of a black hole, neutron star or white dwarf. HMXBs in the Large Magellanic Cloud (LMC) and the Small Magellanic Cloud (SMC) are instructive to study because both galaxies are metal poor in compari- son to the Milky Way and they are fairly transparent to both optical and X-ray radiation. This allows a more complete study of the whole population, without the biasing effects of gas and dust that occur in our own Galaxy. The objective of this study was to find the periods of HMXBs in the LMC and SMC with known optical counterparts in the dataset acquired by the Robotic Optical Transient Search Ex- periment telescope. Two possible orbital periods were found for the objects XTE J0055-724 and RX J0101.3-7211 of 1724 days and 478 days, respectively. Continued observations are recommended to conrm the two periods.

As Field-Programmable Gate Array (FPGA) capacity can now support several processors on a single device, the scalability of FPGA design tools and methods has emerged as a major obstacle for the wider use of FPGAs. For example, logic synthesis, which has traditionally been the fastest step in the FPGA Computer-Aided Design (CAD) flow, now takes several hours to complete in a typical FPGA compile. In this work, we address this problem by focusing on two areas. First, we revisit FPGA logic synthesis and attempt to improve its scalability. Specifically, we look at a binary decision diagram (BDD) based logic synthesis flow, referred to as FBDD, where we improve its runtime by several fold with a marginal impact to the resulting circuit area. We do so by speeding up the classical cut generation problem by an order-of-magnitude which enables its application directly at the logic synthesis level. Following this, we introduce a guided partitioning technique using a fast global budgeting formulation, which enables us to optimize individual “pockets” within the circuit without degrading the overall circuit performance. By using partitioning we can significantly reduce the solution space of the logic synthesis problem and, furthermore, open up the possibility of parallelizing the logic synthesis step. The second area we look at is the area of Engineering Change Orders (ECOs). ECOs are incremental modifications to a design late in the design flow. This is beneficial since it is minimally disruptive to the existing circuit which preserves much of the engineering effort invested previously in the design. In a design flow where most of the steps are fully automated, ECOs still remain largely a manual process. This can often tie up a designer for weeks leading to missed project deadlines which is very detrimental to products whose life-cycle can span only a few months. As a solution to this, we show how we can leverage existing logic synthesis techniques to automatically modify a circuit in a minimally disruptive manner. This can significantly reduce the turn-around time when applying ECOs.