Physical Sciences and Mathematics Commons^™

Evaluating Cranial Nonmetric Traits In Mummies From Pachacamac, Peru: The Utility Of Semi-Automated Image Segmentation In Paleoradiology, Cameron J. Beason

Electronic Thesis and Dissertation Repository

Anthropologists employ biodistance analysis to understand past population interactions and relatedness. The objectives of this thesis are twofold: to determine whether a sample of five mummies from the pilgrimage centre, Pachacamac, on the Central Coast of Peru comprised local or non-local individuals through an analysis of cranial nonmetric traits using comparative samples from the North and Central Coasts of Peru and Chile; and to test the utility of machine-learning-based image segmentation in the image analysis software, Dragonfly, to automatically segment CT scans of the mummies such that the cranial nonmetric traits are visible. Results show that while fully automated segmentation …

A Generative-Discriminative Approach To Human Brain Mapping, Deepanshu Wadhwa

Electronic Thesis and Dissertation Repository

During everyday behaviours, the brain shows complex spatial patterns of activity. These activity maps are very replicable within an individual, but vary significantly across individuals, even though they are evoked by the same behaviour. It is unknown how differences in these spatial patterns relate to differences in behavior or function. More fundamentally, the structural, developmental, and genetic factors that determine the spatial organisation of these brain maps in each individual are unclear. Here we propose a new quantitative approach for uncovering the basic principles by which functional brain maps are organized. We propose to take an generative-discriminative approach to human …

Generating Effective Sentence Representations: Deep Learning And Reinforcement Learning Approaches, Mahtab Ahmed

Electronic Thesis and Dissertation Repository

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. Many Natural Language applications are powered by machine learning models performing a large variety of underlying tasks. Recently, deep learning approaches have obtained very high performance across many NLP tasks. In order to achieve this high level of performance, it is crucial for computers to have an appropriate representation of sentences. The tasks addressed in the thesis are best approached having shallow semantic representations. These representations are vectors that are then embedded in …

Protein Interaction Sites Prediction Using Deep Learning, Sourajit Basak

Electronic Thesis and Dissertation Repository

The accurate prediction of protein-protein interaction (PPI) binding sites is a fundamental problem in bioinformatics, since most of the time proteins perform their functions by interacting with some other proteins. Experimental methods are slow, expensive and not very accurate, hence the need for efficient computational methods.

In this thesis, we perform a study aiming to improve the performance of the currently best program for binding site prediction, DELPHI. We have employed some of the currently best techniques from machine learning, including attention and various embedding techniques, such as BERT and ELMo. This is the first time such tools are being …

A Deep Topical N-Gram Model And Topic Discovery On Covid-19 News And Research Manuscripts, Yuan Du

Electronic Thesis and Dissertation Repository

Topic modeling with the latent semantic analysis (LSA), the latent Dirichlet allocation (LDA) and the biterm topic model (BTM) has been successfully implemented and used in many areas, including movie reviews, recommender systems, and text summarization, etc. However, these models may become computationally intensive if tested on a humongous corpus. Considering the wide acceptance of machine learning based on deep neural networks, this research proposes two deep neural network (NN) variants, 2-layer NN and 3-layer NN of the LDA modeling techniques. The primary goal is to deal with problems with a large corpus using manageable computational resources.

This thesis analyze …