Title: Pattern matching in compilers Author: Ondřej Bílka Department: Department of Applied Mathematics Supervisor: Jan Hubička, Department of Applied Mathematics Abstract: In this thesis we develop tools for effective and flexible pattern matching. We introduce a new pattern matching system called amethyst. Amethyst is not only a generator of parsers of programming languages, but can also serve as an alternative to tools for matching regular expressions. Our framework also produces dynamic parsers. Its intended use is in the context of IDE (accurate syntax highlighting and error detection on the fly). Amethyst offers pattern matching of general data structures. This makes it a useful tool for implement- ing compiler optimizations such as constant folding, instruction scheduling, and dataflow analysis in general. The parsers produced are essentially top-down parsers. Linear time complexity is obtained by introducing the novel notion of structured grammars and reg- ularized regular expressions. Amethyst uses techniques known from compiler optimizations to produce effective parsers. Keywords: Packrat parsing, dynamic parsing, structured grammars, functional programming 1

Abstract The primary purpose of this study was to determine if applicants who had an associate degree in the health sciences prior to acceptance to a physician assistant program would do better than those applicants without an associate degree in the health sciences on three measures of success of physician assistant education. The three measures of success used were graduation rates, scores on the Physician Assistant Knowledge Rating and Assessment Tool (PACKRAT), and performance on the national certifying exam, the Physician Assistant National Certification Examination (PANCE). Data used for this dissertation were taken from original source documents and raw data sent to Nova Southeastern University by the PACKRAT and PANCE testing services. The study population was the three classes graduating in 2007 to 2009. Correlations between the groups and their measures of success showed that there were no statistically significant difference in the graduation rates or PACKRAT scores (p-value was 0.328 and 0.095 respectively). The variable having a statistical significance was PANCE scores. The mean scores between the groups were significantly different (p-value 0.012) with the group without an associate degree in the health sciences having higher mean scores. Coincidental findings showed that older students and students with higher graduate records examination (GRE) scores did better on the PANCE. Following this, further data analysis showed that the group with an associate degree in the health sciences were older (p-value 0.06) and scored statistically lower on the GRE (p-value 0.012). Findings showed that many of the considerations used to select students for physician assistant programs did not make a difference in outcomes. The two that did were age and GRE scores. The study group with associate degrees in the health sciences was, on average, older, had lower mean GRE scores and demonstrated the most gender and ethnic diversity. Programs using admission data to select students for the best chance of success should consider student educational experience and GRE scores, especially when some schools are looking to increase diversity in the students entering their programs.

abstract: This work investigates the effects of non-random sampling on our understanding of species distributions and their niches. In its most general form, bias is systematic error that can obscure interpretation of analytical results by skewing samples away from the average condition of the system they represent. Here I use species distribution modelling (SDM), virtual species, and multiscale geographically weighted regression (MGWR) to explore how sampling bias can alter our perception of broad patterns of biodiversity by distorting spatial predictions of habitat, a key characteristic in biogeographic studies. I use three separate case studies to explore: 1) How methods to account for sampling bias in species distribution modeling may alter estimates of species distributions and species-environment relationships, 2) How accounting for sampling bias in fossil data may change our understanding of paleo-distributions and interpretation of niche stability through time (i.e. niche conservation), and 3) How a novel use of MGWR can account for environmental sampling bias to reveal landscape patterns of local niche differences among proximal, but non-overlapping sister taxa. Broadly, my work shows that sampling bias present in commonly used federated global biodiversity observations is more than enough to degrade model performance of spatial predictions and niche characteristics. Measures commonly used to account for this bias can negate much loss, but only in certain conditions, and did not improve the ability to correctly identify explanatory variables or recreate species-environment relationships. Paleo-distributions calibrated on biased fossil records were improved with the use of a novel method to directly estimate the biased sampling distribution, which can be generalized to finer time slices for further paleontological studies. Finally, I show how a novel coupling of SDM and MGWR can illuminate local differences in niche separation that more closely match landscape genotypic variability in the two North American desert tortoise species than does their current taxonomic delineation. / Dissertation/Thesis / Doctoral Dissertation Geography 2018