UWSpace

UWSpace is the University of Waterloo’s institutional repository for the free, secure, and long-term home of research produced by faculty, students, and staff.

Depositing Theses/Dissertations or Research to UWSpace

Are you a Graduate Student depositing your thesis to UWSpace? See our Thesis Deposit Help and UWSpace Thesis FAQ pages to learn more.

Are you a Faculty or Staff member depositing research to UWSpace? See our Waterloo Research Deposit Help and Self-Archiving pages to learn more.

Photo by Waterloo staff

Communities in UWSpace

Select a community to browse its collections.

Recent Submissions

  • Thumbnail Image
    Item type: Item ,
    Effect of Modifying Visual and Sensory Feedback on Neural Error Processing During Sensorimotor Tasks
    (University of Waterloo, 2026-07-02) Du, Rachel
    The error-related negativity (ERN) is a frontocentral EEG component reflecting neural error monitoring during motor tasks, and has been identified as a promising input signal for brain-computer interface (BCI) systems. Although the ERN has been reliably observed across a range of motor and cognitive tasks, significant gaps remain in characterizing how sensory signals contribute to its generation and modulation. This thesis investigates how the central nervous system (CNS) integrates visual and proprioceptive feedback during motor error monitoring, as reflected in the ERN, across three experiments in which participants performed upper limb reaching tasks while EEG was recorded. The first experiment examined how the availability and fidelity of visual feedback influenced the ERN during a reaching task performed in a velocity-dependent curl force field. Three visual feedback conditions were applied in alternating blocks: a veridical cursor, a hidden cursor in which visual feedback was removed during the reach, and a cursor cloud that replaced the true cursor with a diffuse cluster of cursors to obscure its precise location. Trials were binned by three kinematic error metrics — integral error at 100 ms, signed maximum perpendicular deviation, and absolute maximum perpendicular deviation — and ERPs were generated for each bin. An ERN was elicited across all visual feedback conditions when trials were binned by integral error at 100 ms, and no significant difference in ERN amplitude was found between conditions. This result suggests that the ERN during reaching is driven primarily by early proprioceptive feedback rather than online visual feedback, consistent with the theory that the ERN arises from an internal error prediction mechanism using the efferent copy of the motor command. Binning by integral error at 100 ms was also found to be a more reliable method for eliciting consistent ERNs compared to maximum perpendicular deviation, highlighting the importance of selecting kinematic metrics that are temporally aligned with the ERN effect window. Additionally, ERNs were found to be elicited by trajectory deviations in both directions relative to the force field, with no significant difference in amplitude between error directions. The second experiment investigated how perturbing proprioceptive feedback through tendon vibration affected the ERN during a reaching task. Vibration was applied to the biceps brachii tendon at 90 Hz to induce an illusory perception of elbow extension, and participants performed reaches under conditions of full visual feedback, hidden cursor with no task feedback, and a transitional condition in which the vibration state was switched during hidden cursor trials. Kinematic results confirmed that the vibration successfully induced a proprioceptive shift: endpoint reach displacement differed significantly between vibration conditions when visual feedback was removed, with participants overextending when vibration was turned off following adapted reaches under vibration. When visual feedback was available, participants corrected for the proprioceptive shift, consistent with visual dominance over proprioception in multisensory integration. An ERN was elicited only when participants performed reaches with full visual feedback and without applied vibration. The absence of the ERN under vibration, despite comparable task performance, suggests that the ERN depends on the integrity of the proprioceptive re-afferent signal rather than task outcome, providing causal evidence that reliable proprioceptive feedback is a necessary condition for the error prediction process underlying the ERN. The third experiment examined how introducing a shift to the visuomotor mapping affected the ERN during reaching, using pilot data collected from five participants. A 2 cm spatial shift was introduced gradually to the on-screen cursor position along the direction of reach, creating a mismatch between the perceived and true end-effector positions that fell below the threshold of conscious detection. Participants adapted rapidly following each mapping swap, with endpoint reach displacement returning to within target bounds within the first few trials of each new mapping. Preliminary inspection of ERN amplitudes suggested that a negative deflection consistent with an ERN was present across all cursor feedback and visual shift conditions, with the largest mean amplitude observed in the first trials following a mapping swap to the shifted cursor condition. This is consistent with the finding from the second experiment that the ERN is preserved when proprioceptive feedback remains intact, and further suggests that the error prediction mechanism is sensitive to the introduction of a novel visuomotor mismatch. These trends also confirm that the absence of the ERN under tendon vibration in the second experiment was a consequence of the corrupted proprioceptive signal rather than the visuomotor mismatch it produced. Full statistical analysis awaits data collection from a sufficient number of participants. Taken together, the findings of this thesis support a model in which the ERN during reaching is generated through an internal predictive mechanism that depends on reliable proprioceptive re-afferent feedback, and in which visual feedback plays a reinforcing rather than primary role. These results advance our understanding of how the CNS integrates multisensory feedback for motor error monitoring, with implications for the development of more robust ERN-based BCI systems.
  • Thumbnail Image
    Item type: Item ,
    Adaptive Reuse of Federal Office Buildings into Housing: Leveraging Federal Office Downsizing Initiatives
    (University of Waterloo, 2026-07-02) Lai, Julian Jeun Yin
    As part of the August 2024 budget announcement, Canada’s federal government outlined a plan to optimize their real estate holdings by downsizing its office portfolio by 50%. In Ottawa, home to 42.2% of the federal workforce, this move would release 8.8 million sqft. The downtown core would be hit the hardest as widespread vacancies would force local businesses to close. This would harm not only the vitality of the area, but also the overall health and vibrancy of the city. This projected oversupply of office buildings lies in contrast to the Canada’s ongoing housing crisis. The country is desperately in need of affordable purpose-built rental housing. If it was to set a needs-based target for affordable rental housing it is estimated that the country would need 2 million affordable rental housing units at and below a minimum-wage income, which would take 30 years to build. Despite the federal government’s repeated commitments to addressing the issue, it has failed to dismantle the systemic profit-driven structures perpetuating residential alienation. This thesis builds upon existing research suggesting that converting federal offices into housing can address the housing crisis and repurpose soon-to-be vacant federal properties. It critiques current housing and adaptive reuse practices which are driven by profit and approach the crisis as a supply issue which perpetuates profit-driven commodification of housing which fails to disalienate housing. Ottawa presents opportunity to challenge the status quo of for-profit housing through federal-office-to-residential conversions. It imagines utilizing existing public assets - federal public land and vacant federal offices - for the public’s benefit by converting vacant federal offices in Ottawa into non-market housing. This can simultaneously lower the environmental impact of construction and construction costs while also creating decommodified, affordable, and good quality housing. Aside from revitalizing vacant buildings and building nonmarket housing, this strategy also has the added benefit of being environmentally beneficial too. Repurposing existing surplus buildings presents significant opportunity to mitigate building emissions through limiting construction and improving building performance which lowers operational energy-use and emissions. Research is grounded in axiological thought and seeks to critically examine, challenge, and redefine the underlying capitalist values embedded within Canada’s housing to support an expanded spectrum of what housing, living, and domesticity should and could be. Research methodologies consist of literature review, case studies, and speculative design as research. A literature review will act as the thesis’ theoretical framework by examining critical housing research challenging existing capitalist understandings of housing and propose nonmarket housing as the solution. Building on this will be a series of case studies, literature review, and speculative design to develop a successful and implementable nonmarket housing strategy. Case studies will examine successful nonmarket housing programs locally and internationally and measure both their successes and ease of implementation to outline an Ottawa-specific nonmarket housing strategy. This will be followed by a literature review and further case studies examining adaptive reuse strategies and office-to-residential conversions architectural, technical, and financial considerations to inform proper adaptive reuse practices. Finally, all research findings will be synthesized into a speculative design proposal for the conversion of the L’Esplanade Laurier federal office building in downtown Ottawa. The goal of the design proposal is to illustrate the viability and potential for impact of non-profit federal office-to-residential conversions. This thesis is intended to function both as a document demonstrating the potential for change within Canada’s current housing system and as a guide to federal office-to-residential conversions. It will demonstrate what is possible if all levels of government, housing developers, stakeholders, and community groups commit to systemic changes for housing disalienation. Housing within Canada can get better. Federal office-to-residential conversions can be part of the solution to the Canada’s housing system if the right steps are taken.
  • Thumbnail Image
    Item type: Item ,
    Multistroke Character Recognition Using Orthogonal Polynomial Representations
    (University of Waterloo, 2026-06-30) Cheriakara Joseph, Arun
    This thesis studies stroke grouping for online word-level handwriting recognition of Latin letters and digits using orthogonal polynomial representations of pen strokes. A word arrives as an ordered sequence of pen-down strokes, and the system has to decide which strokes belong to which character before it can decide what each character is. At the word level the problem is harder than for isolated characters: the right grouping of strokes depends on what the characters turn out to be, and the right characters depend on how the strokes are grouped. Most existing systems commit to one segmentation and use whatever that segmentation outputs, which can lead to wrong results. The difficulty is sharpened by characters drawn with multiple strokes, by variation in stroke order between writers, and by several letter pairs and letter/digit pairs that share the same shape. This thesis describes an online word-level recognition pipeline built on orthogonal polynomial representations of multistroke characters. Each pen stroke is re-parameterized by arc length, and its coefficients are projected onto an orthogonal Legendre basis of degree eleven, giving a fixed-length coefficient vector per stroke. For multistroke characters, the per-stroke vectors are concatenated into a single feature vector. Because all strokes in a character are normalized together against a shared bounding box, this block-concatenated representation captures the relative position and scale of the strokes within the character, but it does not directly encode every pairwise relationship between strokes. A probabilistic gap model generates up to six candidate groupings per word, and each candidate character group is normalized in a common bounding box before projection. The resulting vectors are matched against a reference database of 76{,}428 samples across 62 character labels, organized into 3{,}237 classes. Classification runs in two stages: a centroid-and-radius heuristic prunes the candidate pool to fifty classes, and a label-pooled $k$-nearest-neighbour stage then ranks the seven closest samples per label by distance to the convex hull of those samples. The pipeline is evaluated on the UniPen word collection drawn from the 62-character Latin-plus-digits alphabet.
  • Thumbnail Image
    Item type: Item ,
    Risk Sharing with Distortion Risk Measures Beyond Risk Aversion
    (University of Waterloo, 2026-06-30) Ren, Qinghua
    This thesis studies optimal risk sharing among multiple agents whose preferences are represented by distortion risk measures, equivalently Yaari dual utilities. The central question is how to characterize Pareto-optimal risk allocations when agents may have heterogeneous risk preferences, including risk-averse, risk-seeking, and behavioral attitudes toward risk. Particular attention is paid to the dependence structure of optimal allocations and to the geometry of the Pareto frontier. The first part of the thesis develops counter-monotonic risk sharing as a counterpart to the classical comonotonic theory. Comonotonicity represents positive dependence and is fundamental in risk sharing among risk-averse agents, while counter-monotonicity represents an extreme form of negative dependence and arises naturally for risk-seeking agents. Chapters 2 and 3 analyze this counter-monotonic structure for distortion risk measures, moving from a homogeneous setting with a common distortion function to a heterogeneous setting with different distortion functions. Inf-convolution is an important tool for studying Pareto optimality. Using this tool, Chapters 2 and 3 compare the usual formulation with variants that restrict allocations to be comonotonic or counter-monotonic, derive explicit formulas for risk-seeking agents, and illustrate the formulas through a portfolio manager’s problem. Chapter 4 studies markets with mixed risk attitudes, where risk-averse and risk-seeking agents coexist. This setting is more challenging because neither the usual comonotonic arguments for risk-averse agents nor the counter-monotonic arguments for risk-seeking agents apply directly to the whole market. The chapter establishes a reduction theorem showing that the general multi-agent problem can be reduced to a two-agent problem between representative risk-averse and risk-seeking agents. Based on this reduction, the chapter further studies the existence of optimal allocations, identifies cases in which the inf-convolution is unbounded, and derives explicit solutions for piecewise linear distortion functions and Bernoulli-type aggregate risks. Chapter 5 studies Pareto optimality beyond universal risk aversion, with emphasis on constrained allocation problems. Feasibility constraints, such as nonnegative allocations, are natural in insurance and reinsurance, but they change the geometry of the risk-sharing problem and may limit the applicability of weighted-sum methods. The chapter reduces the two-agent Pareto problem to a one-parameter family of constrained optimization problems. For Bernoulli aggregate risks, the Pareto frontier admits a convex-envelope characterization and can be attained by three-atom allocations. For two-point aggregate risks, finite-atom structural results are developed, showing that efficient allocations can be represented by a bounded number of payment levels. These results provide both theoretical insight into non-risk-averse risk sharing and a tractable framework for numerical computation.
  • Thumbnail Image
    Item type: Item ,
    LLM-Based Frameworks for Information Retrieval Evaluation
    (University of Waterloo, 2026-06-29) Upadhyay, Shivani Jayantkumar
    Evaluating information retrieval (IR) systems requires a reference that captures what correct or relevant output looks like, as well as a mechanism for determining whether a system’s output matches that reference. For lexical retrieval systems, both requirements are relatively straightforward. Systems rank documents by term overlap, pooling produces a judgment file that covers most documents any system is likely to return, and determining relevance reduces to a simple membership test against that file. This evaluation paradigm relies on the assumption that relevance can be detected through surface-form overlap. When retrieval moves beyond that assumption, the framework begins to break down. Retrieval-augmented generation (RAG) systems strain this setup by synthesising free-form natural language responses from retrieved evidence. A gold answer set constructed before system execution cannot anticipate every correct phrasing, so even semantically correct outputs can fail under lexical matching. Dense retrieval systems encode queries and documents as vectors, retrieving relevant documents that might not share vocabulary with the query. Under pooling-based evaluation, these documents never receive human judgments and are instead assigned a default relevance grade of zero. Together, these failures highlight the limits of surface-form evaluation and point to the need for judgment mechanisms that reason directly about meaning. This thesis investigates whether large language models (LLMs) can fill this gap by contributing three frameworks across successive layers of the evaluation pipeline. The first contribution is an open-source QA evaluation framework that combines chain-of-thought (CoT) prompting with self-consistency decoding using instruction-tuned LLMs. When evaluated across 12 systems on NQ-open, it matches zero-shot GPT‑4 in rank correlation with human judgments while using a model more than an order of magnitude smaller, demonstrating that prompting strategy can matter as much as scale. The second contribution is a framework for patching incomplete relevance judgment sets by assigning four-level TREC-style labels to unjudged query-passage pairs via few-shot prompting. When evaluated across five TREC Deep Learning Track collections at removal rates varying from 10 to 90%, it substantially improves system ranking fidelity over the standard practice of treating unjudged documents as non-relevant. The third contribution is UMBRELA, which is a fully automated open-source relevance assessment framework deployed in the TREC 2024 RAG Track across 301 topics, achieving run-level Kendall's tau >= 0.86 against fully manual assessment. All frameworks are released as open-source tools to support reproducible and scalable IR evaluation.