Because of limited tester time and memory, a primary goal of digital circuit manufacture test generation is to create compact test sets. Test generation programs that use Ordered Binary Decision Diagrams (OBDDs) as their primary functional representation excel at this task. Unfortunately, the use of OBDDs limits the application of these test generation programs to small circuits. This is because the size of the OBDD used to represent a function can be exponential in the number of the function's switching variables. Working with these functions can cause OBDD-based programs to exceed acceptable time and memory limits. This research proposes using Ordered Partial Decision Diagrams (OPDDs) instead as the primary functional representation for test generation systems. By limiting the number of vertices allowed in a single OPDD, complex functions can be partially represented in order to save time and memory. An OPDD-based test generation system is developed and techniques which improve its performance are evaluated on a small benchmark circuit. The new system is then demonstrated on larger and more complex circuits than its OBDD-based counterpart allows.

This thesis develops a conceptual framework to determine the probability of adopting HT canola when producers are assumed heterogeneous. The model is based on the framework developed by Fulton and Keyowski (1999), but is modified from a deterministic model to a probabilistic model. The study also considers the gross returns from adopting HT canola. Canola production in Manitoba, Canada is chosen as the region of analysis for the empirical component of the study. In 2002, 74 per cent of total canola acres in Manitoba were devoted to HT canola production. Factors such as soil type, producer risk profile, experience, productivity, and management ability are considered as potential determining factors which distinguish adopters of HT technology from non-adopters. Based on an initial assessment of Manitoba canola data, which shows the incomplete adoption of HT technology in Manitoba, a model is developed which considers adoption of a new technology as a function of the characteristics of the adopters. The conceptual model is tested empirically in two-stages. The first stage employs Ordinary Least Squares analysis to estimate the expected yield of different canola varieties to determine whether producers realize a benefit from the adoption of HT varieties. A logit analysis is conducted in the second stage, and considers different attributes of producers such as risk aversion, management ability, productivity and expected yields to determine the probability of producers adopting HT technology. The results show two primary findings. First, certain HT varieties can be shown to give producers higher returns. Second, differentiating characteristics of producers are key in determining the likely adoption of HT canola.

Since William Gibson includes a Rastafarian Enclave and a theological compass in <i>Neuromancer</i>, this can be used to examine the troublesome natures of the two Artificial Intelligences, Wintermute and Neuromancer. The Rastafarian's beliefs and interpretations of Babylon and Zion, the oppressors and the liberated, add political significance to the Tessier-Ashpool's and their enslaved Artificial Intelligences. Since the Artificial Intelligences are both created of Babylon the Tessier-Ashpools and also wanting to be free of them, they are something outside of both. In the Artificial Intelligences, then, Gibson collapses the straightforward dichotomy of Babylon and Zion.

This thesis develops a conceptual framework to determine the probability of adopting HT canola when producers are assumed heterogeneous. The model is based on the framework developed by Fulton and Keyowski (1999), but is modified from a deterministic model to a probabilistic model. The study also considers the gross returns from adopting HT canola. Canola production in Manitoba, Canada is chosen as the region of analysis for the empirical component of the study. In 2002, 74 per cent of total canola acres in Manitoba were devoted to HT canola production. Factors such as soil type, producer risk profile, experience, productivity, and management ability are considered as potential determining factors which distinguish adopters of HT technology from non-adopters. Based on an initial assessment of Manitoba canola data, which shows the incomplete adoption of HT technology in Manitoba, a model is developed which considers adoption of a new technology as a function of the characteristics of the adopters. The conceptual model is tested empirically in two-stages. The first stage employs Ordinary Least Squares analysis to estimate the expected yield of different canola varieties to determine whether producers realize a benefit from the adoption of HT varieties. A logit analysis is conducted in the second stage, and considers different attributes of producers such as risk aversion, management ability, productivity and expected yields to determine the probability of producers adopting HT technology. The results show two primary findings. First, certain HT varieties can be shown to give producers higher returns. Second, differentiating characteristics of producers are key in determining the likely adoption of HT canola.

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

Ultrasonography is widely used in medical diagnosis with the advantages of being low cost, non-invasive and capable of real time imaging. When interpreting ultrasonographic images of mammalian ovaries, the structures of interest are follicles, corpora lutea (CL) and stroma. This thesis presents an approach to perform CL texture analysis, including detection and segmentation, based on the classiers trained by genetic programming (GP). The objective of CL detection is to determine whether there is a CL in the ovarian images, while the goal of segmentation is to localize the CL within the image. Genetic programming (GP) oers a solution through the evolution of computer programs by methods inspired by the mechanisms of natural selection. Herein, we use rotationally invariant local binary patterns (LBP) to encode the local texture features. These are used by the programs which are manipulated by GP to obtain highly t CL classiers. Grayscale standardization was performed on all images in our data set based on the reference grayscale in each image. CL classication programs were evolved by genetic programming and tested on ultrasonographic images of ovaries. On the bovine dataset, our CL detection algorithm is reliable and robust. The detection algorithm correctly determined the presence or absence of a CL in 93.3% of 60 test images. The segmentation algorithm achieved a mean ( standard deviation) sensitivity and specicity of 0.87 (0.14) and 0.91 (0.05), respectively, over the 30 CL images. Our CL segmentation algorithm is an improvement over the only previously published algorithm, since our method is fully automatic and does not require the placement of an initial contour. The success of these algorithms demonstrates that similar algorithms designed for analysis of in vivo human ovaries are likely viable.

Globular clusters are extreme stellar populations. They have the highest stellar density, and host both the oldest and most metal-poor stellar populations in the Galaxy. Their densities make them excellent testbeds for stellar dynamics, while the properties of their stars allows us to test our understanding of old and metal-poor stellar evolution. This thesis is comprised of three projects studying the two nearest globular clusters, NGC 6397 and Messier 4. By examining high-quality HST photometry of NGC 6397, we have constrained the binary fraction in both the central regions, and beyond the half-light radius. We find a binary fraction of ~0.05 in the core and ~0.015 in the outskirts. In the context of recent N-body simulations by Hurley et al., we interpret the observed binary fraction in the outer field as the primordial binary fraction. This value is lower than typically assumed, and has implications for cluster dynamics and N-body modeling. We report the discovery that young white dwarfs are dynamically hotter than their progenitors. Using the same photometry as mentioned above, and archival HST photometry of Messier 4, we have found that young white dwarfs have an extended radial distribution, and therefore a higher velocity dispersion, compared with older white dwarfs and their progenitors. This implies the existence of a ``natal kick''. Implications for cluster dynamics and stellar evolution are discussed. Finally, we present the spectra of 23 white dwarfs in Messier 4 obtained with the Keck/LRIS and Gemini/GMOS spectrographs. We find that all white dwarfs are of type DA. Assuming the same DA/DB ratio as is observed in the field, the chance of finding no DBs in our sample due to statistical fluctuations is 0.006. This suggests DB formation is suppressed in the cluster environment. Furthermore, we constrain the mass of these white dwarfs by fitting models to the spectral lines. Our best estimate of the masses of the white dwarfs currently forming in Messier 4 is 0.51+/-0.02 M_sun.This extends the empirical constraint on the initial-final mass relation over the entire range of initial masses that could have formed white dwarfs in a Hubble time.

The behavioral properties shown by people when they make selections between different choices will be studied. Based on empirical and logical data a mathematical axiomatic model is built. D. Luce is a major contributor in this area. This thesis is based on his works and those of his many co-authors. Three approaches will be considered that lead to the rank-dependent utility representation of binary gambles composed only of gains (losses) relative to a status quo. The proofs involve the theory of functional equations which is a very powerful tool giving the precise numerical representations.

In this thesis, I demonstrate how the numerous forms of oppression are grounded in a hierarchical and binary thinking that permeates racism and sexism and that is present throughout the feminist movement. It is this biased thinking that creates further divide among diverse social groups resulting in a foundation for justifying oppressive practices. I argue that the human rights framework is the best by which to defeat this problematic thinking, fostering a collectivity among disparate people and establishing a more appropriate footing upon which to face the problems of feminism. In the end, I claim that there must be a global commitment to end oppression that begins with educating people as to the unjustified harm created by biased and binary thinking and to the effectiveness of a human rights approach in eliminating any validation of oppression.

This is a spectroscopic and photometric study of suspected close binary systems among the massive stars. The stars studied here include stars with temperatures ranging from 45,000 Kelvin (K) to 15,000 K, corresponding to spectral types ranging from O3 V to B5 III, masses between 47 Solar Masses and 5 Solar Masses, and absolute V magnitudes from -6.28 to -2.0. I categorize 30 targets according to my spectroscopic observations into groups with no radial velocity variability, single-lined, and double-lined variability. My analysis of the 18 constant velocity stars results in estimates of stellar effective temperature, Teff, gravity, log g, projected rotational velocity, v sin i, and spectral classification. Analyzing single-lined systems, I find the same stellar parameters for five more systems, and also present the first orbits for these systems. I also explore the probable characteristics of the unseen companions in these systems. Three double-lined systems, two eclipsing and one with an ellipsoidal variation in the light curve, are fully analyzed, and I present important astrophysical parameters for each of these systems, including stellar masses, radii, ages, and distances to each system. The masses are accurate to 4.3% and 3.6% for the primary and secondary for LH 54-425, 2.1% and 1.6% for HI Mon, and 1.1% and 0.6% for HD 42401. Two more double-lined systems are studied, and preliminary results are presented. Photometric observations are analyzed for 56 targets from the All Sky Automated Survey in order to facilitate spectroscopic observations at key points in the binary orbit where spectral features of both components will be well separated. New spectroscopic observations of these eclipsing binaries with my computed ephemerides will allow us to obtain double-lined orbital elements and determine their masses, radii, ages, and distances. These computed parameters will then allow for comparison with theoretical stellar models, and a better understanding of the evolution of massive stars.