Treasures @ UT Dallas
Not a member yet
7697 research outputs found
Sort by
Investigation Into Higher Dimensional Rotations
No full textAxis-angle representations provides efficient methods to study three dimensional rotations.
The representation imparts visualization and thus aids the analysis of a three-dimensional
proper rotation by reducing its study to that of a two dimensional one. In this dissertation,
we accomplish a similar result for five dimensional proper rotation by reducing its study to
that of either two or four dimensional proper rotations.
For a matrix in SO(5, R), we complete a closed from formula for the axis which is the fixed
point set of the matrix as well as the formula for the angle which is the complementary proper
rotation that the matrix performs in the orthogonal complement to the axis. In fact, two
such derivations are provided. The first is based on the properties of a matrix in SO(5, R)
such as the special structure of its characteristic polynomial being skew palindromic while the
second utilizes the structure of the Lie algebra of the covering group. Closed form formula
for the logarithm in the covering group of SO(5, R) is also derived as it is essential for the
second method.
Further, we study indefinite rotations with signature (1,9) and come close to establish that
the group of such rotations is isomorphic to 2x2 octonion matrices with determinant 1
Uncertain Inputs for Convex Hulls and Clustering
No full textGeometric algorithms and inputs have received an increasing amount of attention with the
explosion of data and computing challenges that arise from real world applications. This
real world data is often uncertain in nature, either in the location or the existence of the
data points. However, many classical computational geometry algorithms assume inputs to
be precise. Thus the inherent presence of uncertainty in real data motivates the further
exploration of classical geometric problems, though modeled to include uncertain inputs.
This dissertation considers two of the most fundamental computational geometry problems,
namely convex hulls and clustering, when the inputs are uncertain. We consider two different
ways to model uncertainty: (i) uncertainty on location, where an uncertain point set is a
collection of compact regions in the plane, and (ii) a probabilistic framework to model the
existence of each point from the input point set. First, we study the complexity of the convex
hull when the uncertain input points are modeled as a set of compact subsets, namely line
segments. Here we seek the realization of the points whose convex hull has the fewest number
of vertices. Next, we explore the classic k-center clustering problem for when the uncertain
input points are a set of convex objects, for which we present several results. Finally, the last
part of this dissertation concerns the k-center clustering problem with probabilistic centers,
where each cluster center has a probability of failure. In presenting geometric properties,
algorithms, and hardness results for convex hulls and clustering, this dissertation aims to
give a better understanding to fundamental geometric problems with uncertain inputs
Hemodynamic Response Variability and its Relationship to the BOLD signal in Younger and Older Adults
No full textStudies have shown age-related differences in blood-oxygen-level-dependent signal (BOLD)
variability, specifically amplitude variability. However, results have been mixed. Little remains
known about the sources contributing to this variability. Identifying these sources would have
implications for underlying mechanisms contributing to BOLD measurement. Changes in BOLD
yield a characteristic hemodynamic response function (HRF) that reflects a combination of blood
flow and oxygenation changes that follow neural activity. In healthy aging, multiple components
of the HRF (e.g., time-to-peak, rise slope, peak amplitude, full-width half-maximum, peak-to-
trough, time-to-trough, fall slope, and trough amplitude) are susceptible to the mediating effects
of age-related cerebrovascular alterations and underlying processes. Additionally, several studies
have demonstrated that neuro-vascular coupling (NVC) differences in older adults are mirrored
in HRF differences. To further explore these phenomena, the current study utilized the publicly
available Cambridge Center for Aging and Neuroscience (CamCAN) dataset to estimate HRF
variability in a visual-auditory task in 80 younger (18-30 years old; 44 Female/36 Male) and 212
older adults (54-74 years old; 100 Female/112 Male). The proposed study was carried out
according to three aims: (1) examine intra-individual HRF variability in younger and older
adults, (2) examine inter-individual HRF variability in younger and older adults, and (3)
determine the relationship between HRF variability and cognitive performance in younger and
older adults. Linear mixed models were used to assess individual and age-related differences in
HRF features. I hypothesized that individuals, regardless of age, would have increased HRF
variability in higher frequency task conditions compared to lower frequency conditions. For age-
related differences, I hypothesized that older adults would have increased HRF feature
variability, and that their HRF variability would be inversely related to canonical-derived BOLD
voxel extent. Finally, I hypothesized that there would be an interaction between HRF variability,
age, and cognitive performance such that low-performing older adults would have increased
HRF variability compared to high-performing older and younger adults. For group differences in
HRF feature variability, I found that increased/decreased HRF feature variability was associated
with increasing auditory frequencies depending on the region examined. For group differences in
mean HRF features, I found that increased mean HRF features were associated with increasing
auditory frequencies, with the exception of fall slope which exhibited an inverse relationship.
Older adults had increased HRF feature variability and mean HRF features, primarily in the
precentral and temporal ROIs, compared to younger adults. Older adults’ increased voxel extent
was associated with decreased variance of their rise slopes, full-width half-maxima, peak-to-
troughs, and time-to-troughs. Finally, younger adults exhibited a significant relationship between
their reaction times and mean HRF features in the highest frequency condition while the older
adults did not. My results showed that HRF feature variability exhibits region- and task-
dependent differences that need to be accounted for when performing age-group comparisons,
the latter-half of the HRF evolution and underlying mechanisms are potential sources of
additional variability in older adults, and the difference between HRF features in the precentral
cortex and other sensory cortices may serve a mediatory role between age and processing speed
ability. This study assessed features of BOLD HRF shape as a proxy of NVC to identify
potential sources of altered age-related variability and their relationships to behavior
Modeling Integrated Cortical Learning: Explorations of Cortical Map Development, Unit Selectivity, and Object Recognition
No full textOne of the most formative theories in neuroscience is the Hierarchical Theory of Cortex
(HTC), which postulates a hierarchy of simple and complex cells within each cortical visual
area. The Deep Convolutional Neural Networks (DCNN) architecture is the most computationally successful implementations of HTC, and has been adopted as a tool for linking
cognition to neural processes. However, DCNNs are exceedingly abstract models of cortical
learning. First, DCNNs use fixed connectivity, whereas cortical connectivity is plastic. Second, DCNNs use convolutional weight-sharing, whereas simple cells in visual cortex learn
using local competition rules. Third, DCNNs use fixed pools, whereas complex cells in visual
cortex may learn their pooling structure. This means that DCNNs do not develop an analogue to the cortical maps developed by cortex. In addition, differences in feature learning
may mean that DCNNs learn very different high-level unit representations compared to the
high-level visual cortex. In this dissertation, I introduce a biologically inspired framework
for understanding unsupervised visual category learning, called the Temporal Relation Manifold TRM framework, which extends the object manifold framework of vision. With this
new framework, I develop a model of hierarchical cortical learning that integrates biologically plausible models of axon development, simple cell learning, and complex cell learning, into a single model called the Integrated Cortical Learning Model (ICL). As part of these
efforts I also introduce novel methods for incorporating axonal learning and development
into artificial neural networks called the Axon Game and the Arbor Layer. I examined the
utility of this new cortical model in three main sets of simulation studies. First, I explored
its ability to develop high-level cortical maps organized by semantic categories. Second, I
explored whether the ICL model would develop functionally specialized unit representations
or unspecialized unit representations. Third, I tested the performance of several versions of
the model on two image recognition benchmarks (Fashion-MNIST & ImageNette). These
simulation studies showed three main results. First, that the ICL model developed continuous topological maps in its upper layers, but these maps were not substantially different
from the maps developed in its lower layers in key ways. Second, the ICL model developed
unspecialized unit representations similar to those of DCNNs, though this result may be due
to propagation of shallow representations. Third, the ICL model performed at a comparable
level to similarly-sized DCNNs with very modest tuning (91% accuracy for Fashion-MNIST,
and 40% accuracy for ImageNette). Post-hoc analyses suggested that the proposed complex
cell model may have been a limiting factor, highlighting an area for future study. The deep
ICL model built for this dissertation showed the novel ability to learn hierarchical cortical
maps, agreement with DCNN work on unit-level representations, and promising performance,
all while using more biologically motivated unsupervised learning rules. In summary, this
dissertation introduces a framework (TRM) and bio-inspired model (ICL) as an alternative to DCNNs, and evaluates this new model in terms of cortical map development, unit
representations, and classification performance
Light-emitting Electrochemical Cells: Temperature Dependence and Host-guest-systems
No full textLight-emitting electrochemical cells (LECs) yield high efficiency and long-lasting performance in
a simple device architecture. Due to the ionic nature of these devices, electric double layer
formation occurs at the electrodes in response to an applied field, producing efficient charge
injection and recombination for light emission. LECs from perovskites, nanoparticles, or organic
small molecules have potential as thin, conformable, lost-cost solutions for seamless integration
in light-emitting applications. Understanding the interplay of electronic and ionic processes of
LECs is necessary to improve the luminance, efficiency, stability, and lifetimes for this potential
to be actualized.
This dissertation focuses on temperature-dependent studies of ionic, electronic, and optical
properties of LECs and host-guest systems to improve stability, increase efficiency, and control
color. We established that iridium LECs show superior temperature stability to their ubiquitous
ruthenium counterparts, resisting radiant flux loss until 67 °C (152 °F). To understand the
mechanistic origin of this superior stability, the temperature-dependence of the photoluminescence
of iridium and ruthenium complexes was measured. Although textbooks have asserted that iridium
complex stability superiority is due to energetic suppression of non-luminescent and antibonding
states, it was alternatively found that this superiority was due to favorable radiative recombination
rates relative to nonradiative transitions.
We implemented novel small-molecule ionic hosts based on carbazole derivatives with ionic
transitional metal complex (iTMC) guestsfor use in LECs. These hosts demonstrated wide, tunable
bandgaps and accessible oxidation and reduction features, consistent with our design parameters.
LECs with a PBI-CzH host demonstrated superior performance, where PBI is 4-
bromophenylbenzimidazole, and CzH is an unsubstituted carbazole unit. These LECs achieved
624 cd/m2
luminance at 3.80% external quantum efficiency, competitive in the field of iTMCs. Xray diffraction suggested that host packing caused the superior performance of the PBI-CzH host
and offered a key design insight for future LEC hosts.
A novel ionic iridium complex guest was prepared to be used in conjunction with a CsPbBr3
perovskite host and a polyelectrolyte to achieve high performance in a perovskite light-emitting
device with a single-layer structure. Maximum luminance (10600 cd/m2
), current efficiency (11.6
cd/A), and power efficiency (9.04 Lm/W) were achieved at a 14% weight fraction of the guest,
and voltage-tunable color was demonstrated. These results show improvement for all reported
metrics for perovskite host devices, demonstrating the potential for a host-guest approach with
radiationally designed ionic emitters in perovskite LECs.
Finally, we followed the low-temperature performance of current, electroluminescence, and
photoluminescence of perovskite LECs to reveal the effects on ionic transport, electronic transport,
and optical properties. Initially, lowering the temperature increased device efficiency. However,
the efficiency was found to decrease as the temperature decreased from 240 K and below.
Differential scanning calorimetry was used to assess morphological changes induced by the
polyelectrolyte. Ultimately, it was revealed that the interplay of these factors greatly depends on
the mechanical properties of the polyethylene oxide electrolyte, and the suppression of the glass
transition could substantially improve low-temperature device performance
Forbidden Games: Representations of the Holocaust in Videogames
No full textIn the contemporary world, the Holocaust is remembered and represented not just in classrooms
and memorial sites but increasingly in virtual spaces: digital exhibits, simulations, and
videogames. This is a shift for the commercial videogame industry, which largely avoided
referencing the Holocaust despite the prevalence of World War II as a setting until the 2017
release of Call of Duty: WWII incorporated Holocaust imagery into both the game and its
promotional trailers. This project investigates how videogames represent the Holocaust through
both embedded and emergent narratives alongside game mechanics and aesthetics, as well as
how players respond to these games on the levels of entertainment and history. It is beyond the
scope of this study to judge whether it is tasteful or ethical to create videogames about the
Holocaust—these videogames are already being made, and we must study how they shape player
perspectives on historical events.
Due to the relative youth of videogames as an expressive medium, few researchers have explored
how videogames might represent or distort memory of the Holocaust. However, as Holocaust
education becomes steadily more involved with digital and new media, it is important to bring
areas like Holocaust representation, digital rhetoric, and game studies into closer communication.
The literature review seeks to bridge these gaps in discourse, starting with a background on the
history of Holocaust representation and thinkers like Alvin Rosenfeld, Saul Friedlander, and
Marianne Hirsch before exploring how videogames function as persuasive texts, drawing from
rhetoricians like Ian Bogost, Kenneth Burke, and Chaim Perelman. Lastly, I explore how
videogames create meaning through the relationship between a game’s preprogrammed worlds
and narratives and the game itself as an interactive system of rules and procedures. This section
also introduces some of the unique conventions and grammar of videogames such as the different
platforms and genres, elements like the user interface and graphical perspective, and stylistic
devices like cutscenes.
This review is followed by the rhetorical analysis of three videogames as case studies: KZ
Manager, Call of Duty: WWII, and World of Warcraft. These chapters explore how each game
represents the Holocaust through its storylines, mechanics, and aesthetic design, in addition to
other factors like a game’s accessibility and community of players. These representations are
also shaped by the game’s genre and the underlying philosophies associated with each, from the
resource-focused simulation game to the action-oriented first-person shooter (FPS) and more
narrative-driven massively multiplayer online role-playing game (MMORPG). These games
provide examples of historical inaccuracy and developer bias, as well as a tendency towards
reducing the Holocaust to fit narratives of individual heroism or a mythic struggle against evil.
However, the latter two games also demonstrate how well-designed mechanics, quests, and
characters can impact individual players, inspiring some to study history in more depth or simply
showing them the humanity of the Other
Variant Influence Maximization: Approximation Algorithm and Deep Solution
No full textIn recent two decades, online social platforms have become more and more popular, and the
dissemination of information on social networks has attracted wide attention of the industries
and academia. The users of these social media platforms and the relationships between them
can be characterized as a social network. A large number of works have been focused on
the diffusion phenomenon on social networks, including diffusion of ideas, news, adoptions of
new products, etc. Influence maximization problem is one of the well-studied topics, which
seeks for a small subset of nodes as seeds to maximize the expected number of influenced
users under some diffusion model. However, there are some impractical assumptions of this
problem, such as the uniform cost of activating nodes as seeds and profit obtained from
influenced users. In this dissertation, we propose several practical and novel variants of
influence maximization problem: continuous activity maximization problem, budget profit
maximization with coupon advertisement problem, adaptive multi-feature budgeted profit
maximization problem, and learning-based influence maximization problem. Due to their
NP-hardness, we focused on designing approximation algorithms and the deep reinforcement
learning model. Reverse influence sampling and deep Q-networks techniques are utilized to
overcome the #P-hardness of computing the objective functions and to solve the problems
more efficiently
True Self-love in Eighteenth-century New England: a Case Study of Roger Sherman and the Edwardsean Theological Tradition
No full textThis dissertation charts the history of the idea of true self-love (i.e., the pleasure of the self-
approbation of Christian love) as a form of moral motivation and a model for others-centeredness
in the second half of the eighteenth century. It questions the solidity of Albert Hirschman’s
famous thesis that religious self-love collapsed into economic self-interest by the century’s end.
Conversely, I will show through the lens of Roger Sherman and his connection to the
Edwardsean theological tradition (the group that through Jonathan Edwards synthesized the
doctrine as sourced originally in Augustine) how evangelicals were shown a paradoxically self-
denying form of self-seeking (i.e., ordering one’s ambition by seeking the welfare of another) as
a way to satisfy the entrepreneurial impulse of the more mobile and acquisitive era of the 1750s
and onward in ways that reconstituted the communalistic ethics of love, mutual obligation, and
reciprocity. This dissertation highlights the persistence and the development of the idea. Doing
so will alter our broader understanding of early American sociability, suggest a new framework
for American secularization, and reveal an ideological source for nineteenth-century American
sentimental literature
Study of Intrinsic Alignment of Galaxies in Recent Galaxy Surveys
No full textWeak lensing is one of the most promising new probes of cosmological parameters. However, it also comes with its own series of systematics and challenges to overcome. In this dissertation we will focus on our work done to isolate the astrophysical systematic effect known as intrinsic alignment of galaxies. We first introduce the methodology and underlying theory that gave rise to the field of weak gravitational lensing, explaining how minuscule changes in the shapes of distant galaxies can be used to obtain better constraints on the matter distribution and the makeup of our entire universe. Then we introduce the idea that galaxies are not isolated objects, but rather are located inside clusters of galaxies or along gas-rich filaments of the large scale structure of the universe. This leads to a systematic effect in the form of the galaxy shapes being distorted prior to lensing, because they intrinsically align with the large scale structure around them. This effect causes either false negatives or false positives when interpreted as weak lensing distortions in observations. Intrinsic alignment of galaxies was first detected about a decade and a half ago, and since then a few different approaches have been suggested to mitigate this. Our work is focused on the idea of using the extra information like unused correlations available within modern cosmological surveys to isolate these intrinsic alignment signals. This provides us with the dual advantages of getting rid of a systematic effect but also gives the possibility of studying the intrinsic alignment itself in more detail. Our work has focused on the development of analysis tools for this separation and the subsequent application of these tools to detect of intrinsic alignment of galaxies. This is done in anticipation of the upcoming Vera Rubin Observatory’s Legacy Survey of Space and Time (LSST). The tools described in this dissertation were made specifically to be ready for this survey and were developed as extensions to the analysis tools being developed for the LSST by its Dark Energy Science Collaboration (DESC). Since LSST has not yet begun, we have applied these tools to two earlier surveys: The Kilo Degree Survey (KiDS)’s data release of approximately 450 square degrees and the Dark Energy Survey (DES)’s year one data release. In both cases we have found strong signs of intrinsic alignment using our self-calibration analysis. We also showed how we can use this method to handle both intrinsic alignment in galaxy-galaxy lensing and in cosmic shear correlations. This has strong implications for constraining the physics of galaxy evolution with LSST
The Effect of Tolerance to Ambiguity on Risk Perception and Preventive Behavioral Intents With Ambiguously Uncertain Health Information
No full textThe study investigated how precision and novelty in health information affect judgement and
decision-making in people with different levels of tolerance to ambiguity. A three-way mixed
design was used with precision (low vs. high; within subject), novelty (low vs. high; within
subject), and tolerance to ambiguity (low vs. high; between subject) as independent variables,
and perception of ambiguity, risk perception, preventive behavioral intents, and thinking mode as
dependent variables. A sample of 320 healthy adults (age Mean = 30.67, SD = 7.35; Female% =
47.81%) were recruited via Prolific and took part in the experiment on Qualtrics. Participants
read vignettes on 8 disease outbreaks in which levels of novelty and precision were manipulated,
and rated statements regarding the dependent variables. Afterwards, they completed measures of
individual difference factors, including tolerance to ambiguity, health literacy, numeracy, trust in
public health authorities, and cognitive reflection. Results showed that high novelty and high
precision increased perception of ambiguity, decreased risk perception and preventive behavioral
intents, and led to more controlled/systematic inferential processing. No moderating effect of the
individual difference factors were found. These findings had important implications for real-
world communications on health crises, such as COVID-19 pandemic