VictorJuiz/thesis huggingface dataset:
Updating and Developing Risk Prediction Models for Incident Cardiovascular Disease in Ontario, Canada
Keywords: Atherosclerotic Cardiovascular Disease
Primary Prevention
Risk Prediction
Statins

The Framingham Risk Score and Pooled Cohort Equations are used to predict cardiovascular disease risk and select individuals for preventative therapy. Studies have suggested these scores overestimate risk, may be underutilized because they are time consuming, and may predict narrower outcomes that may not be the most relevant endpoint for primary prevention. The objectives of this dissertation were: (1) recalibrate and validate the Framingham Risk Score and Pooled Cohort Equations; (2) develop and validate new sex-specific models for atherosclerotic cardiovascular disease using common serum laboratory tests; and (3) estimate the rate of nine cardiovascular disease manifestations in a contemporary primary prevention population.In the first study, the Framingham Risk Score was recalibrated in 6,938,971 individuals and validated in 71,450 primary care patients. Recalibration reduced overestimation in women (109% to 49%) and men (131% to 32%). However, recalibration of the Pooled Cohort Equations did not improve risk estimates. These results suggest recalibration is feasible but dependent on the model used. In the second study, sex-specific laboratory models were developed and internally validated for atherosclerotic cardiovascular disease in 3,995,218 individuals. The C-statistic was 0.77 in women and 0.72 in men with less than 2% relative difference between mean predicted and observed risks. External validation in 31,707 primary care patients demonstrated C-statistics of 0.72 in both sexes and less than 15% relative difference between mean predicted and observed risks. This suggests cardiovascular risk can be predicted without clinical risk factors which may simplify risk estimation in routine clinical practice. In the final study, among 7,496,165 individuals followed for 11 years, the rate of incident atherosclerotic cardiovascular disease defined by a myocardial infarction, stroke, or circulatory death event was 3.95 per 1000 person-years. When including unstable angina, transient ischemic attack, heart failure, peripheral arterial disease, coronary revascularization, and out-of-hospital cardiac arrest events, the rate was almost doubled at 6.67 per 1000 person-years. The most common additional manifestations were heart failure (12.0%) and coronary revascularization (12.7%). Therefore, atherosclerotic cardiovascular disease accounts for just over half of all incident cardiovascular disease manifestations. This suggests broader endpoints may enhance risk-discussions with patients and improve informed decision-making.
Quantum State and Unitary Complexity
Keywords: circuit complexity
interactive proofs
quantum complexity
quantum state
query complexity
unitary transformation

Many natural problems in quantum computing involve constructing a quantum state or implementing a unitary transformation. However, relatively little is known about the computational complexity of these problems compared to that of computing boolean functions. In this thesis we do the following:• We prove upper bounds on the complexity of constructing arbitrary states and implementing arbitrary unitaries with the help of a classical oracle. • We prove bounds on the complexity of computing parity in QAC0, a quantum analogue of AC0, by way of a reduction to the task of constructing a certain type of state. • We prove upper bounds on the circuit size and depth required to construct arbitrary states and implement arbitrary unitaries, in various quantum circuit models. Many of these bounds are tight. • We define models of interactive proofs for constructing states and implementing unitaries with the help of an untrusted prover. We prove a quantum state analogue of the inclusion PSPACE ⊆ QIP, and obtain somewhat analogous results for unitaries and with multiple entangled provers. • We present barriers to improving several of the above results.
Device Placement Optimization with Deep Reinforcement Learning
Keywords: Deep Reinforcament Learning
Distributed Machine Learning

With the proliferation of machine learning, deep neural networks (DNNs) have become ubiquitous in various real-world applications, and their sizes have become increasingly massive. In order to train DNNs with hundreds of millions of parameters, it is customary to employ a cluster of accelerators, such as GPUs and TPUs, to expedite the training process. As such, there is a pressing need to coordinate the accelerators for efficient DNN training, which gives rise to the device placement problem. Recently, deep reinforcement learning (DRL) has been proposed as an approach to fully automated and effective device placement optimization. In this dissertation, we examine critical research problems concerning the utilization of DRL for discovering optimal device placements for training complex and large DNNs. We begin by reviewing some key concepts, unique properties, and principles of distributed machine learning and DRL. Then we present EAGLE to generate device placement for large deep neural networks. Specifically, EAGLE is an end-to-end framework that groups and places the operations of a deep neural network via two jointly trained policy networks. An advanced DRL algorithm proximal policy optimization (PPO) is applied to achieve better sample efficiency. In addition to EAGLE, we propose Mars, to further improve the granularity and generalizability of the DRL agent. Mars pre-trains a graph neural network with contrastive learning, to generate comprehensive embeddings for each operation. Then, the placer can easily decide the placement for each operation based on its embedding. Lastly, we study the device placement problem in a pipeline parallelism setting, where model parallelism and data parallelism are combined to speed up the DNN training. Specifically, we carefully redesigned the action space of the DRL agent for pipelined device placement, especially for the placement of multi-branching DNNs. We conducted extensive experiments on different DNN training workloads, and our experimental results demonstrated that our approach effectively achieves the highest speedup for various DNN training workloads.
Leaving Long-Term Care during a Global Pandemic: Decision-Making and Experiences of Residents, Care Partners, and Health Professionals in Ontario
Keywords: Aging
Care Transition
COVID-19
Crisis
Decision-Making
Long-Term Care

The COVID-19 pandemic exposed pre-existing vulnerabilities in long-term care (LTC) sectors globally, prompting a change in the decision-making and care trajectories of many residents. In Ontario, some care partners responded to the crisis in LTC by moving residents home with them for the duration or a portion of the pandemic. This type of care transition, from LTC to home care, was previously uncommon and complex to arrange due to the multiple stakeholders involved. This thesis aims to explore the nature of individual and group decision-making during periods of crisis and change. The research comprises a scoping review and an exploratory qualitative study. Findings are presented across three interconnected papers. Paper 1 is a scoping review that synthesizes existing knowledge on institutional care transition planning from the perspectives of the care triad (i.e., older adults, care partners and health professionals). 39 articles were included and stakeholders’ perspectives on, and involvement in, care transition planning is discussed. Seven factors appeared to guide decision-making: (a) institutional priorities and requirements; (b) resources; (c) knowledge; (d) risk; (e) group structure and dynamic; (f) health and support needs; and (g) personality, preferences and beliefs. Papers 2 and 3 present a qualitative description of the care triad’s transition decision-making in Ontario’s LTC settings during the COVID-19 pandemic. Interviews were conducted with 3 residents, 18 care partners, and 11 health professionals. Paper 2 discusses stakeholder involvement in care transition planning during the pandemic and the factors that guided their decision-making. Decisions to move a resident out of LTC during the pandemic were largely made by care partners, and stakeholders considered each of the seven factors identified in Paper 1. Paper 3 analyzes stakeholders’ decision-making through the lens of Crisis Decision Theory. Individual and group differences in how participants assessed the severity of the crisis and evaluated response options were identified. Overall, this thesis demonstrates the complexity of care transition planning from multiple perspectives. The findings provide insights towards individual and group decision-making in crisis situations, and can be used to better support Ontarians through LTC to community transitions, particularly in event of future pandemics.
Comparing Canada's Antigonish Movement and Farm Radio Forum: Economic, Technological, and Social Factors
Keywords: adult education in Canada
Antigonish movement
economic factors
Farm Radio Forum
philosophy of education
technological factors

The Antigonish movement and Canada’s Farm Radio Forum were two acclaimed adult education initiatives that helped thousands of Canadians address rural problems in the mid-20th century by promoting self-help initiatives such as cooperatives. The Antigonish movement operated through the St. Francis Xavier University Extension Department in Nova Scotia, during the 1930s and 1940s. Farm Radio Forum began broadcasts in 1941 under the joint sponsorship of the Canadian Association for Adult Education, the Canadian Federation of Agriculture, and the Canadian Broadcasting Corporation, and ran until 1965. Typically, literature presents the two movements as similar examples of a critical Canadian adult education tradition. However, scholars have not, to date, compared them, particularly in terms of how they were impacted by economic, technological, and social factors. Considering the postmodern turn taken by some historians of adult education in recent decades, this dissertation takes a more nuanced historical materialist approach that accepts that many factors interact to produce the historical phenomena we study. The study asks: What can a comparison of the and Farm Radio Forum in terms of how they were impacted by economic, technological, and social factors tell us about the similarities—and differences—between these two adult education movements? iii The comparison was carried out by exploring the economic, technological, and social backgrounds of each movement. Archival and secondary source materials were then examined and analyzed to identify reflections of background factors in the education the movements provided. A focus on education about cooperatives was used to set parameters for the study. The research found that economic, technological, and social factors significantly impacted the purposes of each movement, the choice of topics for discussion, and the tone adopted for presenting information. Most telling among the findings was that the Antigonish movement provided answers to discussion questions, but Farm Radio Forum did not, preferring to let participants arrive at their own conclusions. Many of the differences can be traced back to constraints imposed by the Farm Radio Forum three-party sponsorship arrangement and the often conflicting economic, technological, and social perspectives it embodied. More generally, the dissertation demonstrates the value of a materialist approach to understanding historical developments in adult education. The study supports future research into Canadian adult education history that focuses on economic, technological, and social factors to return the field to its critical tradition.
On The Modification of Flow Hydrodynamics for Complex Fluids Experiencing Confinement
Keywords: coalescence-induced wetting
concentrated suspensions
emulsion wetting
low Reynolds number hydrodynamics
spinodal wetting
surface forces apparatus

This thesis studies two complex fluids (emulsions and suspensions), and how hydrodynamic confinement modifies their flow. Emulsion wetting is investigated, and a novel hydrodynamic theory proposed to model experimental spatiotemporal drainage dynamics for a small axisymmetric drop approaching a rigid glass/pristine mica surface. Dimensionless drainage equations are evaluated (within a 95% confidence interval), to verify the theory. Building on this framework, construction of an integrated surface forces apparatus is attempted, to simultaneously track the force F and the height profile h of the drop –the two physical parameters exclusively contributing towards drainage. Coalescence-induced wetting is studied using this apparatus, to track island growth/disappearance kinetics. Next, the last stage of the emulsion wetting mechanism is investigated, where spontaneous bridge formation, followed by its subsequent growth, is believed to trigger wetting. Contrary to classical spinodal wetting theory prediction, experiments demonstrate that emulsion wetting always prefers a coalescence-induced wetting pathway. From deVries’ thermodynamic criterion for the activation energy, the calculated heights of the drop fluid bridge are found to always exceed 1μm, so the bridge always forms between the parent drop and one of several islands. Bridge formation is observed to be a necessary but insufficient criterion to guarantee wetting. Bridge formation is also found to be a non-unique event, and multiple bridges are observed to form. The bridge’s radial velocity of propagation follows a unidirectional flow model, and warrants further studies, especially since this step may potentially become rate determining, thereby increasing the wetting time significantly. Classical spinodal theory appears to require a revamp, to describe emulsion wetting. Finally, the flow of concentrated suspensions across a Hele-Shaw geometry is investigated, and depth-averaged macrotransport equations are obtained, by implementing a two-timescale expansion on the suspension balance model of Nott and Brady, coupled with the constitutive equations of Zarraga et al. A perturbation analysis predicts a Saffmann-Taylor instability to arise, for the case of a high-volume fraction suspension pushing a low-volume fraction suspension. Contribution of individual transport terms towards the dispersion relationship, and stability characteristics are studied for different aspect ratios, which follow the scaling laws of Tan & Homsy for porous media flows.
Spoken Word Poetry with Multilingual Students in an English for Academic Purposes Class: Translanguaging and Emotion for Critical Practice
Keywords: emotion
english as a second language
english for academic purposes
language teaching
spoken word poetry
translanguaging

Combining theories of translanguaging (García, 2011) and emotion (Ahmed, 2014), this qualitative study asks: (1) what are multilingual learners’ impressions of and experiences with learning and using language in spoken word poetry activities in an EAP class? (2) how does engaging in spoken word poetry curricular activities shape relationships between and among students and instructors? Data for this study was gathered from two teachers and 15 multilingual EAP students with diverse backgrounds. Data sources included curriculum planning and debriefing sessions with instructor Nancy, tutorial sessions and post-semester interviews with the students, classroom observations, and student’s online journal and artifacts. By employing both a systematic and slow intensity (MacClure, 2013) approach to data engagement, I present what I term sticky moments that surfaced from the data because of their affect. By positioning students’ lives and languages at the epicenter of critical language learning, spoken word poetry cultivated learner agency in meaning making and provided dialogic opportunities for self-reflection and co-learning. The findings supported students’ expanding repertoires in a critical practice that underscored the affective processes of language learning as culturally and relationally situated, rather than a neutral or universal set of skills. Bearing witness to stories actualized in between the self and other, spoken word poetry united the listeners and speakers in dynamic ways, encouraging learners to examine the emotional attachments that bound them to people, memories, and experiences. Results indicate that some students developed an awareness of how structures of power reproduce social hierarchies. However, not all students were interested in challenging these hierarchies, nor were all participants willing to examine their role in perpetuating them. Based on the findings of this study and supported by literature, I propose a framework for brave spacs that integrates critical practice, emotions as centripetal to humanizing interaction, and language-inclusive teaching based on translanguaging pedagogy. Practically, this research has direct implications for second language pedagogy in supporting efforts to design equitable and diverse second language curricular materials rooted in the languages and emotional lives of students. Theoretically, the spoken word poetry activities illuminated the significance of the relationship between language and emotions.
Scalable and Interpretable Machine Learning for Scientific Discovery
Keywords: chemistry
generative models
language models
machine learning
molecules
proteins

Machine learning has the potential to accelerate scientific discovery across the physical, biological, and chemical sciences. A major component of scientific discovery is the search for structured data. Molecules and Quantum optics experiments are important examples of structured data in the sciences that can be represented as both graphs and sequences. For machine learning models to be effective tools for scientific discovery they must be able to account for the range in size and structure of scientific data. Furthermore, for scientists to effectively use these models for fundamental research, they need to be interpretable and have explainable representations. In this thesis, work is presented that addresses these challenges. First, we describe a variational autoencoder for the design of quantum experiments that is interpretable for physicists. Next, we study chemical language models, which are generative models for molecules– we analyze their ability to learn larger, more complex molecular distributions than standard benchmarks that contain small drug-like molecules. We then test how well chemical language models perform in the challenging task of atom-level protein generation. Lastly, we focus on molecules represented as three-dimensionalstructures. Here, we first demonstrate a reinforcement learning framework, based on fragments, that can generate 3D molecules from a range of distributions. We then describe a graph neural network architecture that can distinguish molecules based on the relative positions of their atoms in 3D space.
Searching for Beyond the Standard Model Phenomena in Dijet Events with at Least One Lepton with the ATLAS Detector
Keywords: ATLAS
BSM
HEP
LHC
resonance search

A search for new resonances in events with at least one isolated charged lepton ($e$ or $\mu$) and two jets is performed using 139 fb$^{−1}$ of $\sqrt{s}$=13 TeV proton-proton collision data recorded by the ATLAS detector at the LHC. Deviations from a smoothly falling background hypothesis are tested in three- and four-body invariant mass distributions constructed from leptons and jets. The inclusion of leptons in dijet invariant masses provides two main benefits. Firstly, lepton triggers can be used to search for resonance masses starting from 500 GeV, as the transverse momentum thresholds on lepton triggers can be as low as 24 GeV, while inclusive dijet searches are limited to resonance masses greater than 1 TeV due to jet-based triggers which have a minimum jet transverse momentum of about 400 GeV. Secondly, this search in new final states provides sensitivity to new signal models. The search presented in this thesis considers four different search channels in invariant mass distributions composed of two jets and two leptons, as well as two jets plus one lepton where either zero, one or both jets are identified as originating from bottom quarks. The largest deviation between the observed data and the estimated Standard Model background was found in the two jet plus one lepton channel at a invariant mass of 1.3 TeV with a global statistical significance of 1.5 standard deviations. In the absence of a statistically significant deviation from the background estimate, upper exclusion limits are set at a 95\% confidence level in each signal channel on generic Gaussian-shaped resonances with different widths and masses, as well as in the context of four new signal models.
Exploring Exemplar Bias Effects: Category Influences on Visual Attention
Keywords: attention
categorization
cognition
memory
vision

To successfully perform visual search for object categories, observers are required to draw from prior knowledge to instantiate the appropriate attentional templates. Through encountering category exemplars, long-term categorical representations are developed over time. One account for the way in which exemplars are selected among competitors during encoding is the biased competition theory. Previous research has shown that categorical search templates are biased towards recently encountered perceptual information, but does not acknowledge the potentially separate roles that attentional facilitation and suppression play. In this thesis, I test the idea that competition between exemplars is a necessity when forming attentional templates for categorical search, and that these templates are biased towards information that is attended and away from information that is suppressed. Results from the present experiments suggest that attentional facilitation and suppression both influence how categorical search templates are formed. Furthermore, and contrary to prior work, the process of template formation may rely on non-competitive mechanisms. I discuss how these findings may be situated within a conceptual framework combining attention, categories, and memory.
Tendon-Driven Parallel Continuum Robots: Modeling, Characterization and State Estimation
Keywords: Continuum Robots
Parallel Robots
Robot Kinematics
Robot Statics
State Estimation

Continuum robots are slender and flexible manipulators, that are mainly characterized by their inherent compliance, easy miniaturization and ability to bend into highly non-linear shapes. Thanks to their inherent structural properties, continuum robots are predestined for applications in confined environments and hard-to-reach locations with limited access along narrow insertion paths, such as minimal-invasive surgery or industrial in-situ inspection and maintenance. One recent development in continuum robotics is the introduction of parallel continuum robots. Through the parallel arrangement of multiple continuously bending links, these structures aim to overcome some of the inherent drawbacks of continuum robots, such as their low accuracies and strength when compared to conventional rigid-link robots. In this thesis, a novel type of parallel continuum robot is studied, which features actively bending continuum segments that are driven by tendons. Planar and spatial designs of such tendon-driven parallel continuum robots are introduced and modeling frameworks are derived to compute their forward, inverse and differential kinematics and statics. Based on these models, the kinematic properties, such as the reachable workspace, manipulability, compliance and singularity occurrences are evaluated and studied, characterizing tendon-driven parallel continuum robots on a general level. Lastly, this work focuses on an efficient state estimation approach for both conventional and parallel continuum robots that allows to estimate their state given noisy measurements.