Computer Sciences Commons^™

Better Understanding Genomic Architecture With The Use Of Applied Statistics And Explainable Artificial Intelligence, Jonathon C. Romero

Doctoral Dissertations

With the continuous improvements in biological data collection, new techniques are needed to better understand the complex relationships in genomic and other biological data sets. Explainable Artificial Intelligence (X-AI) techniques like Iterative Random Forest (iRF) excel at finding interactions within data, such as genomic epistasis. Here, the introduction of new methods to mine for these complex interactions is shown in a variety of scenarios. The application of iRF as a method for Genomic Wide Epistasis Studies shows that the method is robust in finding interacting sets of features in synthetic data, without requiring the exponentially increasing computation time of many …

Multimodal Neuroscience Data Modeling And Inference, Sima Azizi

Doctoral Dissertations

“Mathematical models can be combined with deep learning and machine learning methods to provide new insights in neuroscience. The field of neuroscience is characterized by rich datasets that include fluid biomarkers, EEG signals, and advanced neuroimages. Recent advances in natural language processing have led to the opportunity to gain additional insights from rapidly growing text databases as well as electronic health records. In this research, we focus on applying computational intelligence methods to the analysis of three different complex data sources: blood levels of disease biomarkers, EEG signals from schizophrenic patients, and disease phenotypes encoded in electronic health records. First, …

Exposure Assessment Of Emerging Contaminants: Rapid Screening And Modeling Of Plant Uptake, Majid Bagheri

Doctoral Dissertations

"With the advent of new chemicals and their increasing uses in every aspect of our life, considerable number of emerging contaminants are introduced to environment yearly. Emerging contaminants in forms of pharmaceuticals, detergents, biosolids, and reclaimed wastewater can cross plant roots and translocate to various parts of the plants. Long-term human exposure to emerging contaminants through food consumption is assumed to be a pathway of interest. Thus, uptake and translocation of emerging contaminants in plants are important for the assessment of health risks associated with human exposure to emerging contaminants. To have a better understanding over fate of emerging contaminants …

Function And Dissipation In Finite State Automata - From Computing To Intelligence And Back, Natesh Ganesh

Doctoral Dissertations

Society has benefited from the technological revolution and the tremendous growth in computing powered by Moore's law. However, we are fast approaching the ultimate physical limits in terms of both device sizes and the associated energy dissipation. It is important to characterize these limits in a physically grounded and implementation-agnostic manner, in order to capture the fundamental energy dissipation costs associated with performing computing operations with classical information in nano-scale quantum systems. It is also necessary to identify and understand the effect of quantum in-distinguishability, noise, and device variability on these dissipation limits. Identifying these parameters is crucial to designing …

Game-Assisted Rehabilitation For Post-Stroke Survivors, Hee-Tae Jung

Doctoral Dissertations

Stroke is a leading cause of permanent impairments among its survivors. Although patients need to go through intensive, longitudinal rehabilitation to regain function before the stroke, patients show poor engagement and adherence to rehabilitation therapies which hampers their recovery. As a means to enhance stroke survivors' motivation, engagement, and adherence to intensive and longitudinal rehabilitation, the use of games in stroke rehabilitation has received attention from research and clinical communities. In order to realize this, it is important to take a holistic, end-to-end research approach that encompasses 1) the development of game technologies that are not only entertaining but also …

Integration Of Robotic Perception, Action, And Memory, Li Yang Ku

Doctoral Dissertations

In the book "On Intelligence", Hawkins states that intelligence should be measured by the capacity to memorize and predict patterns. I further suggest that the ability to predict action consequences based on perception and memory is essential for robots to demonstrate intelligent behaviors in unstructured environments. However, traditional approaches generally represent action and perception separately---as computer vision modules that recognize objects and as planners that execute actions based on labels and poses. I propose here a more integrated approach where action and perception are combined in a memory model, in which a sequence of actions can be planned based on …

Machine Learning Techniques Implementation In Power Optimization, Data Processing, And Bio-Medical Applications, Khalid Khairullah Mezied Al-Jabery

Doctoral Dissertations

"The rapid progress and development in machine-learning algorithms becomes a key factor in determining the future of humanity. These algorithms and techniques were utilized to solve a wide spectrum of problems extended from data mining and knowledge discovery to unsupervised learning and optimization. This dissertation consists of two study areas. The first area investigates the use of reinforcement learning and adaptive critic design algorithms in the field of power grid control. The second area in this dissertation, consisting of three papers, focuses on developing and applying clustering algorithms on biomedical data. The first paper presents a novel modelling approach for …

Towards A Unification Of Supercomputing, Molecular Dynamics Simulation And Experimental Neutron And X-Ray Scattering Techniques, Benjamin Lindner

Doctoral Dissertations

Molecular dynamics simulation has become an essential tool for scientific discovery and investigation. The ability to evaluate every atomic coordinate for each time instant sets it apart from other methodologies, which can only access experimental observables as an outcome of the atomic coordinates. Here, the utility of molecular dynamics is illustrated by investigating the structure and dynamics of fundamental models of cellulose fibers. For that, a highly parallel code has been developed to compute static and dynamical scattering functions efficiently on modern supercomputing architectures. Using state of the art supercomputing facilities, molecular dynamics code and parallelization strategies, this work also …

A Novel Approach To Assess Environmental Changes In Marine Ecosystems Via Spectroscopic Analyses Of Microalgae, Rebecca Burke Horton

Doctoral Dissertations

Chemical analyses for environmental monitoring encounter many challenges which are imposed by a multitude of chemically complex and interrelated processes. For such investigations, innovative analytical methodologies must be developed which characterize chemical shifts of key environmental parameters in order to deduce insights into their ecological relevance. This dissertation is driven by an analytical chemistry perspective to develop chemical sensing techniques with the ultimate goal of gaining a deeper understanding of environmental changes and their chemical origins.

In order to overcome limitations inherent to any chemical sensor designed for a specific task, new paths are pursued which are based on the …

A Geospatial Based Decision Framework For Extending Marssim Regulatory Principles Into The Subsurface, Robert Nathan Stewart

Doctoral Dissertations

The Multi-Agency Radiological Site Survey Investigation Manual (MARSSIM) is a regulatory guidance document regarding compliance evaluation of radiologically contaminated soils and buildings (USNRC, 2000). Compliance is determined by comparing radiological measurements to established limits using a combination of hypothesis testing and scanning measurements. Scanning allows investigators to identify localized pockets of contamination missed during sampling and allows investigators to assess radiological exposure at different spatial scales. Scale is important in radiological dose assessment as regulatory limits can vary with the size of the contaminated area and sites are often evaluated at more than one scale (USNRC, 2000). Unfortunately, scanning is …

A Time-And-Space Parallelized Algorithm For The Cable Equation, Chuan Li

Doctoral Dissertations

Electrical propagation in excitable tissue, such as nerve fibers and heart muscle, is described by a nonlinear diffusion-reaction parabolic partial differential equation for the transmembrane voltage $V(x,t)$, known as the cable equation. This equation involves a highly nonlinear source term, representing the total ionic current across the membrane, governed by a Hodgkin-Huxley type ionic model, and requires the solution of a system of ordinary differential equations. Thus, the model consists of a PDE (in 1-, 2- or 3-dimensions) coupled to a system of ODEs, and it is very expensive to solve, especially in 2 and 3 dimensions.

In order to …

The Maximum Clique Problem: Algorithms, Applications, And Implementations, John David Eblen

Doctoral Dissertations

Computationally hard problems are routinely encountered during the course of solving practical problems. This is commonly dealt with by settling for less than optimal solutions, through the use of heuristics or approximation algorithms. This dissertation examines the alternate possibility of solving such problems exactly, through a detailed study of one particular problem, the maximum clique problem. It discusses algorithms, implementations, and the application of maximum clique results to real-world problems. First, the theoretical roots of the algorithmic method employed are discussed. Then a practical approach is described, which separates out important algorithmic decisions so that the algorithm can be easily …

Associative Pattern Mining For Supervised Learning, Harpreet Singh

Doctoral Dissertations

The Internet era has revolutionized computational sciences and automated data collection techniques, made large amounts of previously inaccessible data available and, consequently, broadened the scope of exploratory computing research. As a result, data mining, which is still an emerging field of research, has gained importance because of its ability to analyze and discover previously unknown, hidden, and useful knowledge from these large amounts of data. One aspect of data mining, known as frequent pattern mining, has recently gained importance due to its ability to find associative relationships among the parts of data, thereby aiding a type of supervised learning known …

Biological Simulations And Biologically Inspired Adaptive Systems, Edgar Alfredo Duenez-Guzman

Doctoral Dissertations

Many of the most challenging problems in modern science lie at the interface of several fields. To study these problems, there is a pressing need for trans-disciplinary research incorporating computational and mathematical models. This dissertation presents a selection of new computational and mathematical techniques applied to biological simulations and problem solving: (i) The dynamics of alliance formation in primates are studied using a continuous time individual-based model. It is observed that increasing the cognitive abilities of individuals stabilizes alliances in a phase transition-like manner. Moreover, with strong cultural transmission an egalitarian regime is established in a few generations. (ii) A …

Discrete Nondeterministic Modeling Of Biochemical Networks, John R. Jack

Doctoral Dissertations

The ideas expressed in this work pertain to biochemical modeling. We explore our technique, the Nondeterministic Waiting Time algorithm, for modeling molecular signaling cascades. The algorithm is presented with pseudocode along with an explanation of its implementation. The entire source code can be found in the Appendices. This algorithm builds on earlier work from the lab of Dr. Andrei Nun, the advisor for this dissertation. We discuss several important extensions including: (i) a heap with special maintenance functions for sorting reaction waiting times, (ii) a nondeterministic component for handling reaction competition, and (iii) a memory enhancement allowing slower reactions to …

Integrated Mining Of Feature Spaces For Bioinformatics Domain Discovery, Pradeep Chowriappa

Doctoral Dissertations

One of the major challenges in the field of bioinformatics is the elucidation of protein folding for the functional annotation of proteins. The factors that govern protein folding include the chemical, physical, and environmental conditions of the protein's surroundings, which can be measured and exploited for computational discovery purposes. These conditions enable the protein to transform from a sequence of amino acids to a globular three-dimensional structure. Information concerning the folded state of a protein has significant potential to explain biochemical pathways and their involvement in disorders and diseases. This information impacts the ways in which genetic diseases are characterized …

Membrane Systems With Limited Parallelism, Bianca Daniela Popa

Doctoral Dissertations

Membrane computing is an emerging research field that belongs to the more general area of molecular computing, which deals with computational models inspired from bio-molecular processes. Membrane computing aims at defining models, called membrane systems or P systems, which abstract the functioning and structure of the cell. A membrane system consists of a hierarchical arrangement of membranes delimiting regions, which represent various compartments of a cell, and with each region containing bio-chemical elements of various types and having associated evolution rules, which represent bio-chemical processes taking place inside the cell.

This work is a continuation of the investigations aiming to …

Modeling Of The Inverse Heat -Conduction Problem With Application To Laser Chemical Vapor Deposition And Bioheat Transfer, Peng Zhen

Doctoral Dissertations

This dissertation consists of two parts. Part one deals with three-dimensional laser induced chemical vapor deposition (3D-LCVD), whereas part two deals with a Pennes model of a 3D skin structure. LCVD is an important technique in manufacturing complex micro-structures with high aspect ratio. In part one, a numerical model was developed for simulating kinetically-limited growth of an axisymmetric cylindrical rod by pre-specifying the surface temperature distribution required for growing the rod and then by obtaining optimized laser power that gives rise to the pre-specified temperature distribution. The temperature distribution at the surface of the rod was assumed to be at …