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Recent Submissions
Losing One’s self: A Case Study of Disaster Recovery Processes Through an Identity Approach
(University of Waterloo, 2025-08-15) Willis, Duncan
Climate disasters are characterized by the changes delivered in their wake. Despite the mass disruption brought on by such events, it can only ever be experienced by agents. Ensued by the larger rapid environmental transition, individual agents experience en masse changes to their subjective environments. Through a Symbolic Interactionist lens, these jarring alterations to individuals’ object environments threaten their sense of self as interactional patterned continuity is ruptured. Although such micro-macro relations mark disaster processes’ innate complexity, the interplay of ‘self’ in disaster scenarios is significantly under-researched. Thus, this case study focuses on this interplay within the context of Beira, Mozambique, following Cyclone Idai (2019). Five years post-cyclone landfall, 37 semi-structured interviews were conducted among informal market vendors who experienced temporary or permanent loss of economic livelihood activities. A qualitative thematic analysis, informed by a literature review, was used to analyze the transcribed interviews and to develop a theoretical framework. The theoretical framework proposes multiple propositional relationships that inform mechanisms underpinning the disaster recovery process. To summarize, the vast changes to individuals’ object environments (composed of animate beings, inanimate things, and intangible processes) brought on through the disaster-driven rapid environmental transition may result in identity continuity challenges, leading to agency loss and past-oriented recovery trajectories, resulting in a perpetually liminal recovery process. Findings from this thesis work suggest that the proposed theoretical framework is helpful in identifying and explaining these relations. First, through a Symbolic Interactionist lens, participants’ changes to their livelihood activities, social networks, and time allocation following Cyclone Idai demonstrate significant object environment shifts, insinuating that identity continuity challenges have occurred. Second, participants expressed a positive association of normalcy with their pre-cyclone states, and a desire to return to such a state, implying a past-oriented recovery trajectory. Finally, participants demonstrated low levels of human agency which may have been brought on by failed goal attainment. These findings in combination may illuminate previously untheorized causal mechanisms that impede post-disaster recovery processes. As such, this work contributes to ongoing disaster management research and the emerging space of disaster-induced identity loss.
Optimal Graph Streaming Algorithms and Further Advances in Modern Models of Computation
(University of Waterloo, 2025-08-15) Shah, Vihan
The rise of large-scale datasets across domains such as social networks, biological systems, and the web has made it increasingly important to understand how core graph problems can be solved under tight resource constraints. As these datasets grow, traditional algorithms that assume random access to the input become increasingly infeasible. This thesis explores how to process massive graphs efficiently under modern models of computation that address these limitations. The primary focus is on the streaming model, and this thesis also explores other modern models, including sublinear-time, fully dynamic, and oracle-based models.
The first part of the thesis develops space-optimal algorithms for fundamental graph problems in the streaming setting. We study approximate minimum cut, k-vertex connectivity, maximum matching, minimum vertex cover, and correlation clustering across different streaming models—including insertion-only, dynamic, and random-order streams. By establishing tight upper and lower bounds—often matching up to constant or polylogarithmic factors—these results resolve several open questions in the streaming literature and characterize entire space-approximation trade-offs for some of these problems.
The second part of the thesis expands the exploration to other modern models of computation, including sublinear-time algorithms, fully dynamic algorithms, and oracle-based models, such as learning-augmented algorithms and streaming verification. We begin by studying the 4-cycle counting problem in the fully dynamic model and show an improvement over the previous best algorithm, demonstrating that the previously assumed natural bound was not tight. In the sublinear setting, we examine the problem of estimating matching size and prove strong lower bounds against non-adaptive algorithms. For the maximum independent set problem in the learning-augmented model, we develop a new algorithm that achieves a significantly improved approximation factor in polynomial time. Lastly, we explore the streaming verification model, focusing primarily on connectivity problems. Together, these contributions deepen our understanding of the fundamental limits and possibilities of algorithm design for massive data under constrained computational resources.
Actualizing High-Dimensional Qudits on a Trapped-Ion Quantum Computer
(University of Waterloo, 2025-08-15) Tathed, Gaurav Ashish
Trapped-ion processors offer unrivalled qubit fidelities but encounter heating and spectral-crowding limits as chain lengths increase. This thesis mitigates those bottlenecks by encoding high-dimensional qudits in the 25 hyperfine–Zeeman sublevels of a single ¹³⁷Ba⁺ ion, thereby expanding the Hilbert space without adding additional ions. The work presented spans the key challenges of preparing arbitrary qudit basis states, efficiently maintaining calibration of the 80 transitions used to manipulate the ion, and benchmarking coherent control, culminating in performing a full quantum algorithm on a qudit.
A narrow-band optical-pumping sequence was devised to prepare any of the five 6S₁/₂ (F = 2) sublevels and then selectively shelve population into the long-lived 5D₅/₂ manifold, achieving an average state-preparation-and-measurement (SPAM) fidelity of 99.51% across 25 levels. Two-point Ramsey interferometry, supported by analytic calculations of magnetic-field sensitivities and laser-polarisation geometries, allows for sub-100 Hz transition-frequency calibration and Rabi-frequency normalization.
Coherence was benchmarked with multilevel Ramsey interferometry on registers of dimension d = 2–24. Star-topology encodings retained high contrast up to d = 17; beyond this, the bus-state architecture required for larger d was limited chiefly by 60 Hz and 180 Hz line noise, as confirmed by a noise-resolved Monte Carlo model that predicted measured contrasts to within ±3% with no free parameters.
Using the same physical toolbox, qudit registers were mapped onto virtual-qubit subspaces and algorithmic primitives executed: the Bernstein–Vazirani algorithm succeeded with probabilities of 95% (two virtual qubits) and 84% (three virtual qubits), while a four-virtual-qubit controlled-Toffoli gate reached 99.5% success—currently limited by SPAM error—using a single Givens rotation.
Collectively, these results establish ¹³⁷Ba⁺ as a versatile 25-level qudit platform and demonstrate that high-fidelity, noise-aware control of large qudit Hilbert spaces is already practical, opening a path toward resource-efficient, fault-tolerant quantum computing with far fewer ions than traditional qubit-based architectures require.
Predicting Cardiovascular Events or Death in People with Dysglycemia Using Machine Learning Methods
(University of Waterloo, 2025-08-15) Zhang, Yuanhong
Cox regression is commonly used to analyze time-to-event for patients in tabular medical data. Hazard ratio can then be calculated to show how much riskier an event may occur for a patient in one group versus the other. Nonetheless, the hazard ratio and Cox regression rely on the proportional hazards assumption, which is not guaranteed. In this paper, we investigate the use of machine learning models to predict patients’ outcomes and identify key factors that may influence the outcomes. We focused on the ORIGIN Trial dataset because it has undergone extensive analysis using Cox regression, allowing us to compare the machine learning model results with previous findings. Three outcomes, major adverse cardiovascular events (MACE), expanded composite outcome (COPRIM2), and all-cause death (ALLDTH), were analyzed in this thesis. The machine learning models we used are Neural Network (NN), Random Forest (RF) and Gradient Boosted Trees (GBT), which were trained with nested cross-validation to tune their hyperparameters. When testing the trained models for all three outcomes, we found that machine learning models had higher Area-Under-the-Curve scores (AUCs) than Cox regression (0.91-0.95 vs 0.63-0.65), and Random Forest and Gradient Boosted Trees had excellent recall scores (0.80 - 0.88). Subsequently, we used SHAP values, mean decrease in AUC, and partial dependency plots (PDPs) to further examine variable importance for RF and GBT. For MACE and COPRIM2, prior cardiovascular events (priorcv), cancer, and blood lipid measures are the most important variables, while for ALLDTH, cancer and kidney functions related measures are the most important variables. The PDPs are harder to analyze than hazard ratio due to having no assumptions and fewer restrictions, but it is useful to estimate the non-linear relation between an explanatory variable and the average probability of the outcome occurring to patients in the dataset.
Functional Social Support as a Mediator of the Association Between Anxiety and Executive Function: A Moderated Mediation Analysis of the Canadian Longitudinal Study on Aging
(University of Waterloo, 2025-08-15) Wang, Cindy
Background: Anxiety in older adulthood may adversely affect executive function, a cognitive domain essential for adaptability and independence. Functional social support (FSS), the perception that others will provide help, care, or comfort when needed, may partially explain the link between anxiety and executive function. This link may differ by age or sex.
Objective: To examine whether FSS mediates the association between anxiety (self-reported clinical diagnosis of anxiety or anxiety symptoms) and executive function in middle-aged and older adults, stratified by age and sex.
Methods: Analyses included 6,719 community-dwelling adults aged 45 to 85 years at baseline, drawn from the Comprehensive cohort of the Canadian Longitudinal Study on Aging. Data were collected over three waves spanning six years. Clinical history of an anxiety disorder (yes/no) and anxiety symptoms (four items from the Kessler Psychological Distress Scale) were self-reported at baseline (T0). Three-year (T1) FSS was self-reported using the Medical Outcomes Study-Social Support Survey. Six-year (T2) executive function was obtained by standardizing and combining scores from five neuropsychological tests. Conditional process analysis with percentile bootstrapping was used to estimate mediation across levels of age and sex, adjusted for relevant covariates and antecedent measures of FSS (T0) and executive function (T0, T1).
Results: FSS did not significantly mediate the association between either anxiety measure (clinical anxiety or anxiety symptoms) and executive function for any age or sex subgroup (bs = -0.0043 to 0.0103, p > .05).
Discussion: While social support has known benefits for cognition, the results suggest that the provision of FSS as a strategy to mitigate the impact of anxiety on executive function may not be needed in healthy middle-aged and older men and women. To promote the cognitive health of aging Canadians, interventions may be better directed to targeting other pathways linking anxiety to cognitive decline.