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    « Words are not simple play things! » : L’hétérolinguisme théâtral chez Louis Patrick Leroux
    (Linguistica Antverpiensia, New Series – Themes in Translation Studies, 2014-09-16) Nolette, Nicole
    This article explores how, in the 1990s, Canadian playwright Patrick Leroux broke away from previously prevalent representations of bilingualism in minority Franco-Ontarian drama and made multilingualism and translation into theatrical “play things”. His most playful performance text, Le Rêve totalitaire de dieu l’amibe, features as many games as issues at stake for staging experimental minority theatre. Substractive ideologies around bilingualism are torn apart, heterolingualism is raised and deconstructed like a strange tower of Babel and translation becomes BabelFish-like. L’ombre du lecteur anglais (The shadow of the English reader) and an Anglophone commentator are added to a production that is constantly reworked, retranslated and surtitled. The trajectory of the production from Ottawa to Sudbury (in Ontario) and Saint-Lambert (in Quebec), and then on to Montreal and to Hull, delineates a playground for translation riddled with layers of address to spectators, depending on their level of comprehension of the languages spoken on (and off) stage.
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    Polyelectrolyte templated synthesis and formation behavior of high entropy alloys
    (University of Waterloo, 2025-12-18) Li, Alexander
    High entropy alloys have recently received significant attention in electrocatalysis because their unique compositional complexity can enhance both catalytic activity and long term stability. Despite this promise, there remains a lack of scalable synthesis methods that can produce nanoscale high entropy alloys with controlled and more complex morphologies. One promising strategy is to leverage the electrical double layer that forms when polyelectrolytes interact with metal salts. Polyelectrolytes can serve as effective templates by creating a locally high ion concentration along their surface, which promotes initial mixing during nanoparticle nucleation. In particular, polystyrene sulfonate can also bridge nucleating particles, allowing for the formation of more intricate, networked morphologies. The goal of this work is to investigate how polyelectrolyte concentration, polymer chain length, and different reducing agents influence the resulting catalyst composition and morphology. In addition, this study aims to provide insight into the mechanisms of nanoscale high entropy alloy formation.
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    Robust and Hierarchy-Aware Classification
    (University of Waterloo, 2025-12-18) Pellegrino, Nicholas
    The BIOSCAN project, led by the International Barcode of Life (iBOL) Consortium, is an international, multi-year, and multidisciplinary effort seeking to catalogue all multicellular life on Earth by 2045 to enable the global-scale study of changes in biodiversity, species interactions, and species dynamics. Access to this information has the potential to inform strategies to mitigate the damaging ecological effects of climate change. In the near term, the goal is to catalogue all insects. Each sample is imaged, genetically barcoded, and taxonomically classified by domain experts, a time- and resource-intensive process that is becoming increasingly impractical as collection rates surpass five million samples annually. Addressing such needs is among the foundational motivations for the research of this thesis. This thesis presents several contributions motivated by the challenges of the BIOSCAN project. Over five million insect samples were organized into a machine-learning-ready dataset, and a deep neural network classifier was developed to establish a baseline for image-to-taxonomy classification performance. To mitigate the harmful impacts of mislabelled samples in training data, a study of neural network architecture robustness was conducted alongside the development of two novel loss functions: Blurry and Piecewise-zero loss. Blurry loss de-weights and reverses the gradient of samples likely to be mislabelled, while Piecewise-zero loss disregards these samples. These improvements strengthen model robustness and enhance label error detection, enabling the referral of suspicious samples for expert review and correction. Additional work investigates the hierarchical structure of biological data and its integration into classification models, specifically through Hyperbolic neural networks, and measures the benefits of doing so in comparison to using conventional architectures. Finally, this thesis explores aligning image, genetic, and taxonomic representations in a hierarchy-aware manner to improve retrieval across modalities. The contributions of this thesis advance the application of machine learning to facilitate the ongoing global-scale cataloguing of insect life. As challenges such as label errors, hierarchical structures in data, and incomplete annotations are present across many domains, the contributions are valuable to both the machine learning community and the global network of BIOSCAN collaborators.
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    Predicting ACL Injuries Using Machine Learning Models and Tibial Anatomical Predictors
    (University of Waterloo, 2025-12-18) Cheng-Hao, Kao
    The tibial slope and the tibial depth are well-established risk factors for Anterior Cru- ciate Ligament (ACL) injury. As ML continues to progress, it has become an increasingly reliable tool for clinical screening and risk factor analysis. This thesis aims to develop and validate an explainable prognostic ML model to predict ACL injury outcomes from these Tibial Anatomical Feature (TAF), and identify the most predictive features among these parameters. A dataset comprising Coronal Tibial Slope (CTS), Medial Tibial Slope (MTS), Lat- eral Tibial Slope (LTS), Medial Tibial Depth (MTD), and sex was constructed using MRI scans taken from 104 subjects (44 males: 22 injured, 22 uninjured; 60 females: 27 in- jured, 33 uninjured). Two distinct ML pipelines were developed: a self-developed pipeline (including K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Random Forest (RF), XGBoost, CATBoost, Multi-Layer Perceptron (MLP), and TabNet) and an advanced AutoGluon pipeline (including XGBoost, LightGBM, CatBoost, TabPFN, TabM, TabICL, MITRA, and their weighted ensembles). Both were designed as end-to-end pipelines to pro- cess the dataset and output predictions with integrated feature importance explanations. Empirically, the AutoGluon Pipeline demonstrated superior performance and training-time efficiency. The recommended F2-tuned standard ensemble achieved an F2-score of 0.736 on the validation set. On the test set, it demonstrated a test balanced accuracy of 0.955, F1-score of 0.952, F2-score of 0.980, ROC AUC of 1.000, precision of 0.909, and recall of 1.000. A full-dataset model, the F2-tuned full-dataset ensemble refitted on the entire dataset for clinical deployment achieved a validation F2-score of 0.813. The global feature importance analyses performed via SHapley Additive exPlanations (SHAP), established the descending order of influences as MTD, LTS, MTS, CTS, and sex. In summary, the study recommends two versions of the F2-tuned prognostic models, one being a standard ensemble model and the other a full-dataset ensemble. The former, which demonstrated moderately high predictive power, was designed for subsequent research comparison. The latter, without access to the original held-out test set, is constructed for maximum robustness and generalization in real-life clinical deployment. Global feature importance analyses elucidated from the standard ensemble decreased MTD along with increased LTS and MTS as most contributive features for ACL injury. These models serve as both feature attribution tools as well as clinical screening tools. These models are intended to be integrated into clinical practice as explainable machines to assist clinicians in predicting the likelihood of ACL injury.
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    Identifying the Relative Contribution of Motoric and Cognitive Engagement on Spatial Memory
    (University of Waterloo, 2025-12-18) Sivashankar, Yadurshana
    I investigated the cognitive mechanisms underlying spatial memory, with the aim of differentiating the contributions of motor engagement and decision-making. In Experiment 1, I examined whether volitional motor control or decision-making, during initial exploration of a map within virtual reality, better supported retention of routes travelled. Participants explored virtual cities under three navigation conditions that varied in terms of motor and decision-making demands. During Active navigation, participants had volitional control over their movement using hand-held controllers, allowing head and body rotation in a swivel chair, and made independent decisions about which route to take to reach a target location. During Guided navigation, participants still controlled their movement, but followed a visually guided path overlaid onto the road, eliminating the need for decisionmaking. In the Passive condition participants observed a pre-defined route without having to make any decisions or engage motorically. Following exploration of each environment, participants were asked to “re-trace their steps” using the exact route they had just traveled, from the same starting point. Route memory was significantly better following Active and Guided encoding relative to Passive, suggesting that volitional movement during navigation underlay the benefit. Notably, the complexity of the path chosen by participant at encoding did not predict accuracy of route memory. Experiment 2 assessed the necessity of motor engagement and decision-making by comparing memory benefits following two types of VR implementation: Desktop-VR, in which movement was limited to keyboard input (lower motor engagement), and Headset-VR, in which participants navigated using a steering wheel (higher motor engagement). An effect of navigation strategy emerged only in the Headset-VR group: Active and Guided navigation at encoding led to significantly better route memory relative to Passive. No significant differences emerged between Active and Guided trials, suggesting that motoric engagement, rather than decision-making, is the driver of memory performance. Interestingly, in Headset-VR, a stronger personal preference for Active exploration predicted better route memory, whereas in Desktop-VR, personal motivation predicted route memory accuracy. However, neither motivation nor preference mediated performance, indicating that these factors did not account for the effect of navigation condition on memory. If motor engagement contributes to the formation of route memories, as suggested by experiments 1 and 2, then reduced mobility in older adults may influence performance, and the components underlying it. At the same time, reliance on landmark memory to guide memory may be heightened, as landmarks provide salient external cues that could compensate for reduced motor-based encoding. To test these predictions, in Experiment 3 I examined route and landmark memory in younger and older adults as they explored virtual environments. In younger adults, both Active and Guided navigation equally enhanced memory for routes compared to Passive, replicating experiments 1 and 2. However, in older adults only Active navigation, which engaged both movement and decision-making, resulted in improved route memory. Further, landmark memory in older adults benefitted the most from Active relative to Passive and Guided navigation. Simply put, active encoding eliminated age-related deficits in route memory, suggesting that decision-making (present only in this condition) during navigation may be particularly important for supporting spatial memory in aging populations. This benefit may reflect increased recruitment of frontal lobe-based resources during active navigation, which can compensate for reductions in motor engagement. There were some differences in motivation and preference ratings across conditions in both age groups. However, these subjective measures did not emerge as significant predictors of memory performance. Overall, my findings suggest that motor engagement plays a more critical role than decision-making in enhancing subsequent route memory in younger adults, whereas conditions that require decision-making benefit memory in older adults. These findings have important implications for the design of navigational tools and cognitive interventions aimed at promoting spatial independence, particularly among older adults.