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Skip to Search Results- 7Frei, Christoph (Mathematical and Statistical Sciences)
- 7Kong, Linglong (Mathematical and Statistical Sciences)
- 7Lewis, Mark (Mathematical and Statistical Sciences)
- 6Han, Bin (Mathematical and Statistical Sciences)
- 6Hillen, Thomas (Mathematical and Statistical Sciences)
- 6Mizera, Ivan (Mathematical and Statistical Sciences)
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Leveraging Natural language Processing and Machine Learning Techniques to find Frailty Deficits from Clinical Dataset
DownloadSpring 2023
Introduction Frailty is a syndrome that is often associated with aging. It can be identified through specific frailty scales or a comprehensive assessment by a healthcare provider. In Alberta, it appears that there are no specific billing or diagnostic codes for frailty. So, healthcare providers...
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Fall 2023
This dissertation establishes various structural and representation theoretic results in super Yangian theory. In its first part, this dissertation details the algebraic structure and representation theory for the Yangians of orthosymplectic Lie superalgebras. Addressing these Yangians via the...
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Fall 2023
$G$-structures on fusion categories have been shown to be an important tool to understand orbifolds of vertex operator algebras \cite{Kirillov}\cite{Gcrossedmuger}\cite{Orbifold_Paper}. We continue to develop this idea by generalizing Eilenberg-Maclane's notion of an Abelian $3$-cocycle to...
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Fall 2023
Determinantal point processes (DPPs) arise as important tools in various aspects of mathematics, such as stochastic processes, random matrices, and combinatorics. Over the last decade, DPPs have also been widely used in ma- chine learning community; they are especially popular in subset selection...
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Fall 2023
The aim of this thesis is to provide an exposition to Mochizuki and Hoshi's approach to birational anabelian geometry of mixed characteristic local fields. In the introductory chapter, we begin by recalling the relevant backgrounds on the Grothendieck conjectures on the étale fundamental groups...
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Fall 2023
Bayesian nonparametric models have gained increasing attention due to their flexibility in modelling natural and social phenomena and have been widely applied in machine learning, biology, social science and so on. Unlike traditional Bayesian parametric models, Bayesian nonparametric models place...
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Fall 2023
Many standard approaches for conducting statistical inference on regression parameters rely heavily on parametric assumptions and asymptotic results. The wild bootstrap (Mammen, 1993) was developed as a nonparametric means to estimate a sampling distribution and is particularly useful when...
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Fall 2023
This thesis presents a comprehensive study of Gaussian Differential Privacy (GDP) and Local Differential Privacy (LDP), exploring their properties, relationships, and applications in developing novel algorithms and optimization methods for efficient and accurate privacy-preserving data analysis....
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Fall 2023
In this thesis, we perform a systematic study of the Allee effect in cancer stem cell (CSC) models with an application to non-small cell lung cancer (NSCLC). Previously, it was shown that an Allee effect exists in mathematical tumor growth models incorporating cancer stem cell (CSC) dynamics....