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Recent Submissions
Cybersickness: Linking Postural Control to User Discomfort in a Virtual Roller Coaster
(University of Waterloo, 2025-10-23) Gulifeire, Alimu
Cybersickness (CS) remains a major obstacle to the widespread use of Virtual Reality (VR), with leading explanations emphasizing sensory conflict, sensory reweighting, and postural instability. Prior research has shown that individuals who flexibly reweight visual, vestibular, and body cues report lower CS, particularly in interactive VR tasks where users can move freely. Whether this relationship generalizes to more passive, visually intense VR experiences is less clear. This thesis examined sensory cue reweighting and postural control as predictors of CS during an immersive roller coaster simulation. Nineteen younger adults completed the Oriented Character Recognition Task (OCHART) before and after VR exposure to estimate perceptual upright and quantify cue weightings. During VR exposure, postural movement was recorded using markerless motion capture, and participants reported symptoms using the Fast Motion Sickness (FMS) scale after each trial. Contrary to findings from interactive VR contexts, sensory reweighting was not significantly associated with CS in this passive roller coaster environment. In contrast, measures of postural control, particularly total path length, were robust predictors of sickness severity, with greater displacement linked to higher FMS scores. These findings suggest that in visually dominant VR tasks with limited bodily engagement, postural instability provides a more reliable marker of CS than sensory reweighting. This work clarifies that the predictive value of sensory reweighting is context dependent, emerging more clearly in interactive than passive VR tasks. It further points toward movement-based strategies for mitigating discomfort in VR experiences where movement is restricted but visual conflict is high.
Evaluating a priori and data-driven weighting of the Healthy Eating Food Index-2019 for assessing diet quality and gastrointestinal and aerodigestive cancer risk in Canadian adults
(University of Waterloo, 2025-10-23) Singh, Navreet
Background: Diet is a modifiable exposure implicated in gastrointestinal and aerodigestive cancers. Because foods are consucmed in combination, diet quality indices are used to summarize overall dietary patterns. The Healthy Eating Food Index-2019 (HEFI-2019) measures adherence to Canada’s Food Guide 2019, and its component scores are nearly equally weighted, reflecting the importance of all foods in a healthful dietary pattern. Its discriminatory capacity for measuring diet-disease associations, and the influence of the weighting schema of the index, remains uncertain.
Objective: To assess whether associations between diet quality and gastrointestinal and aerodigestive cancer risk differ among adults in Canada based on the a priori Healthy Eating Food Index-2019 (HEFI-2019) versus a novel modified version with components reweighted using a data-driven approach.
Methods: A prospective cohort analysis was conducted using the Canadian Community Health Survey 2004 Nutrition (CCHS 2004) linked with the Canadian Cancer Registry (CCR) through 2016. After exclusions, 10,530 adults were included, representing approximately 23.5 million Canadians. Diet was assessed using interviewer-administered 24-hour recalls. HEFI-2019 total scores were computed using standard weights and using data-driven weights derived from ridge-penalized Cox models in 10 iterations of 80/20 training–test splits with cross-validated penalty selection. Weighted Cox proportional hazards models, adjusted for age, sex, education, income, marital status, smoking status, body mass index, and alcohol consumption, estimated associations with incident gastrointestinal and aerodigestive cancers (ICD-9 140–149, 150–159, 160–161). Discrimination was assessed with Harrell’s C-index.
Results: The data-driven approach altered component weights substantially (e.g., protein foods increased from 5 to 16.4; vegetables and fruits decreased from 20 to 3.73). No associations with cancer risk were observed for either the a priori (adjusted HR per unit increase 1.01; 95% CI: 0.99, 1.04) or reweighted HEFI-2019 scores (adjusted HR: 1.00; 95% CI: 0.98, 1.02). Model discrimination was similar (Harrell’s C-index: 0.81 [95% CI: 0.77, 0.85] for a priori; 0.87 [95% CI: 0.80, 0.93] for reweighted).
Discussion: Neither the a priori nor reweighted HEFI-2019 was associated with gastrointestinal and aerodigestive cancer risk. Data-driven reweighting did not meaningfully improve associations or discriminatory capacity. These findings suggest challenges in using diet quality indices for complex diet-disease relationships and highlight the need for further research on index construction and application in cancer epidemiology.
Multi-Outcome Trajectories in Traumatic Brain Injury
(University of Waterloo, 2025-10-23) Shein, Vladyslav
Traumatic Brain Injury (TBI) presents a global health challenge, affecting millions of individuals annually, resulting in diverse outcome trajectories that complicate patient management. The heterogeneity in TBI outcomes, influenced by varied clinical presentations and injury responses, requires advanced analytical approaches. The analysis of trajectories using single metrics, such as the Glasgow Outcome Scale Extended Global (GOSE), falls short of capturing the multi-faceted nature of TBI progression, often overlooking the complexity of individual patient experiences.
This thesis reports on two studies. First, a systematic scoping review was conducted to synthesize the current research on trajectory analysis in TBI, followed by a modeling study. This work identifies 6 distinct multi-outcome trajectories in TBI patients by employing Latent Class Mixed Models (LCMM) and clustering approaches. Utilizing longitudinal data from the Transforming Research and Clinical Knowledge in Traumatic Brain Injury study (TRACK-TBI), a prospective multicenter observational cohort study conducted at 18 level 1 trauma centers across the United States, which includes 17 selected outcome measures collected at four time points post-injury, provides a comprehensive understanding of the heterogeneous progression of TBI. By addressing the limitations of single outcome analyses, this research contributes to a better understanding of TBI progression that can lead to the optimization of TBI management and treatment. The future integration of these trajectories will facilitate the development of personalized treatment strategies, ultimately improving patients’ recovery.
Learning-Based Stability Certification and System Identification of Nonlinear Dynamical Systems
(University of Waterloo, 2025-10-23) Zhou, Ruikun
In recent decades, by taking advantage of the abundance of sensory measurements, learning-based methods have been prevalent and shown their effectiveness in tackling challenging or intractable problems for classical approaches in systems and control. For instance, many systems with complex nonlinearities, high-dimensional state spaces, or unknown dynamics cannot be effectively handled by classical mathematical tools, and computing stability certifications for such systems is often intractable. This thesis aims to construct systematic approaches to perform system identification tasks and learning-based Lyapunov functions for nonlinear dynamical systems, with some extensions to optimal control.
The first aspect of this thesis is to develop an efficient method based on a special feedforward neural network structure, an extreme learning machine, to compute stability certificates for nonlinear systems by solving linear PDEs when the dynamics are accessible. Differing from the typical neural network-based approaches that require training on high-performance computing platforms, one only needs to solve a convex optimization problem. On top of that, the proposed method can also be used to efficiently solve the notable HJB equation via policy iteration to obtain optimal control policies for nonlinear systems. The second aspect of this research is to tackle these issues for nonlinear systems with (partially) unknown dynamics. We first show that with two feedforward neural networks, the unknown system and a Lyapunov-based stability certificate can be learned simultaneously. With the help of satisfiability modulo theories (SMT) solvers, the resulting Lyapunov function can be formally verified to provide stability certificates for the unknown nonlinear system.
Alternatively, in the past two decades, the Koopman operator and its generator have demonstrated advantages in identifying discrete-time systems and continuous-time systems, respectively, requiring significantly less data while achieving better performance than most existing classical methods. For unknown continuous-time dynamical systems, we propose a novel resolvent operator-based learning framework to learn the Koopman generator, which is a linear operator that describes the infinitesimal evolution of the Koopman operator. The learned generator, thereafter, can be used to identify the vector field of the nonlinear systems. Moreover, with the learned high-accuracy Koopman generator, we can also construct a Lyapunov-based stability certificate for the unknown nonlinear system in the same function space. By formulating the linear PDEs as a linear least squares problem, Lyapunov functions can be computed efficiently. The learned Lyapunov functions can be formally verified using an SMT solver and provide less conservative estimates of the region of attraction, compared to existing methods.
Taken together, these contributions provide a coherent pathway that begins with model-based stability certification computation and continues to fully data-driven system identification and thereafter computing Lyapunov-based stability certificates.
Polymorphic Type Qualifiers
(University of Waterloo, 2025-10-21) Edward, Lee
Type qualifiers offer a simple yet effective mechanism for extending existing type systems to deal with additional constraints or safety requirements. For example, the const qualifier is a popular mechanism for annotating existing types to signify that the value in question is read-only in addition. A variable of type const int is both an integer and also cannot be written to.
While type qualifiers themselves are well-studied, polymorphism over type qualifiers remains an area less well examined. This has led to a number of ill-desired outcomes. For one, many practical systems implementing type qualifiers in their type systems simply ignore their interaction with generic types. Other systems implement polymorphism with seemingly unique and ad-hoc rules for dealing with qualifiers.
In this thesis, we show that this does not need to be the case. We start by examining three well-known qualifier systems: systems for tracking immutability, function colour, and captured variables, and show that despite their differences that they share surprising common structure. We then give a design recipe, inspired by that structure, using the mathematical structure of free lattices for modelling polymorphism over type qualifiers, to give a framework for polymorphic type qualifiers. We then show that our design recipe precisely captures this structure by recasting those three existing systems in our framework for qualifier polymorphism by free lattices. Finally, we extend type qualifiers from ranging over lattices to type qualifiers ranging over Boolean algebras, which we then use to extend an existing effect system with effect exclusion to support subeffecting as well via subqualification and subtyping.