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Recent Submissions

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    A Sensor Fusion Platform for Semantic Segmentation of Outdoor Ice Scenes
    (University of Waterloo, 2026-04-29) Duan, Richard Rui Jia
    Due to climate change and global warming, Arctic sea ice coverage has been on a downwards trend that is expected to continue late into this century, opening up new shipping routes through the Arctic. These routes provide opportunities for much shorter transoceanic journeys compared to currently used routes, offering significant savings in transportation time and resource cost. However, travelling through ice-covered waters still provides a constant source of risk from collisions with ice damaging or even sinking the vessel. Thus, modern tools such as ice charts, satellite imagery, and near-field observations from on-board experts are still used to avoid dangerous collisions with ice. In adverse weather and lighting conditions, near-field human observations can be uncertain, causing inaccuracies in determining nearby ice conditions. This thesis introduces a system composed of an optical RGB camera, a thermal infrared camera, and a polarization camera to aid in such near-field observations for the purposes of determining nearby ice conditions in a variety of outdoor conditions. The novel sensor suite was tested during field trials conducted in February 2025, collecting river ice data across multiple days and at different locations along the Ottawa and St. Lawrence rivers. After image registration and post processing, a first-of-its-kind, fully labelled semantic segmentation dataset of 118 images across 5 scenes was created to test different methods of sensor fusion. For testing, the test set was further split into easy and hard subsets based on perceived clarity to the human eye. Early, middle, and late fusion networks using different combinations of sensor inputs were created based on modifying the widely used fully convolutional neural network U-Net architecture with a pre-trained ResNet-18 backbone. Experimental results show that early fusion methods performed consistently worse than the baseline case of only using the optical RGB data on both the easy and hard test sets, regardless of the other input sources used, most likely due to the higher levels of noise being introduced by the additional sources. Middle fusion with polarization and thermal managed to outperform the baseline on the hard test set, and late fusion with polarization managed to outperform on the easy test set. While both middle and late fusion methods show improvements over the baseline through extracting useful information even from noisy sources, late fusion with polarization had the highest overall mIoU improvement on the full test set due to an enhanced ability to differentiate between brash ice and water. Ultimately, sensor fusion shows potential for improving sea ice classification accuracy while being robust to a variety of different environmental and lighting conditions. These preliminary results serve to support continued development of sensor fusion platforms as tools to aid in the tracking and identification of nearby ice conditions.
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    Three Essays on “Good” and “Bad” Volatility: Modeling, Dynamics, and Classification
    (University of Waterloo, 2026-04-29) Ghosh, Sudipto
    This thesis investigates the role of positive ("good") and negative ("bad") realized semivariances in modeling and forecasting financial market volatility. The central contribution is the development and empirical evaluation of threshold-based realized volatility frameworks that allow for regime dependence, asymmetry, and time variation in the influence of volatility components. By combining high-frequency information with flexible nonlinear volatility dynamics, the thesis provides new insights into how economically meaningful downside and upside risks affect future volatility. The first part of the thesis develops a threshold realized GARCH framework that explicitly distinguishes between positive and negative realized semivariances. Closed-form expressions for the cross-moment conditions are derived, yielding a computationally feasible setting for regime-specific volatility dynamics. Monte Carlo simulations demonstrate favorable finite-sample properties of the proposed model. Empirical applications to 26 Dow Jones Industrial Average constituents and the S&P 500 index from 1997–2013, as well as an extended sample from 2014–2024, show that future volatility is driven predominantly by negative realized semivariance. This dominance is especially pronounced when regimes are selected from the left tail of the return distribution. The proposed model delivers superior out-of-sample volatility forecasts relative to standard realized GARCH specifications. The second part extends the framework to allow for smooth, time-varying regime shifts, addressing parameter instability in long financial time series. Model parameters evolve gradually over time, capturing extended periods of structural change. Simulation results confirm the reliability of the estimation approach in finite samples. Empirical findings reveal pronounced regime-dependent asymmetries between positive and negative volatility components. While negative realized semivariance dominates during the Global Financial Crisis, the COVID-19 period exhibits a relatively stronger contribution from positive realized semivariance, underscoring important differences in volatility dynamics across crisis episodes. The final part of the thesis examines volatility forecasting under alternative, threshold-based definitions of realized semivariance that extend beyond the conventional zero threshold decomposition. Using high-frequency data for large-cap U.S. equities, forecasting performance is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Likelihood Ratio (LR) tests. The results show that forecast accuracy remains robust even as increasing thresholds exclude a substantial fraction of low-magnitude intraday returns, indicating that predictive information is concentrated in relatively few large price movements. Further extensions incorporating intraday segmentation and multiscale averaging reveal that negative semivariance during the market closing period contains the strongest predictive content. Overall, the thesis demonstrates that modeling volatility as distinct positive and negative components within threshold-based, regime-dependent frameworks yields substantial gains in interpretability and forecasting performance. The findings highlight the central role of downside and upside risk in volatility dynamics while showing that the informational content of realized semivariance is robust to alternative threshold definitions and market conditions.
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    Prophecy Variables and Invariants in the Move Prover
    (University of Waterloo, 2026-04-29) Yang, Dao Bo
    formal verification, Move Prover, prophecy variables, borrow analysis, ownership, invariant injections, automated reasoning, specifications
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    Assessing the hidden burden and costs of COVID-19 pandemic in South Asia: Implications for health and well-being of women, children and adolescents
    (Public Library of Science, 2023-04-12) Owais, Aatekah; Rizvi, Arjumand; Jawwad, Muhammed; Horton, Susan; Das, Jai K.; Merritt, Catherine; Moreno, Ralfh; Asfaw, Atnafu G.; Rutter, Paul; Nguyen, Phuong H.; Menon, Purnima; Bhutta, Zulfiqar A.
    The COVID-19 pandemic has disproportionately affected vulnerable populations. With its intensity expected to be cyclical over the foreseeable future, and much of the impact estimates still modeled, it is imperative that we accurately assess the impact to date, to help with the process of targeted rebuilding of services. We collected data from administrative health information systems in six South Asian countries (Afghanistan, Bangladesh, Nepal, India, Pakistan and Sri Lanka), to determine essential health services coverage disruptions between January–December 2020, and January–June 2021, compared to the same calendar months in 2019, and estimated the impact of this disruption on maternal and child mortality using the Lives Saved Tool. We also modelled impact of prolonged school closures on continued enrollment, as well as potential sequelae for the cohort of girls who have likely dropped out. Coverage of key maternal and child health interventions, including antenatal care and immunizations, decreased by up to 60%, with the largest disruptions observed between April and June 2020. This was followed by a period of recovery from July 2020 to March 2021, but a reversal of most of these gains in April/May 2021, likely due to the delta variant-fueled surge in South Asia at the same time. We estimated that disruption of essential health services between January 2020 and June 2021 potentially resulted in an additional 19,000 maternal and 317,000 child deaths, an increase of 19% and 13% respectively, compared to 2019. Extended school closures likely resulted in 9 million adolescents dropping out permanently, with 40% likely being from poorest households, resulting in decreased lifetime earnings. A projected increase in early marriages for girls who dropped out could result in an additional 500,000 adolescent pregnancies, 153,000 low birthweight births, and 27,000 additional children becoming stunted by age two years. To date, the increase in maternal and child mortality due to health services disruption has likely exceeded the overall number of COVID-19 deaths in South Asia. The indirect effects of the pandemic were disproportionately borne by the most vulnerable populations, and effects are likely to be long-lasting, permanent and in some cases inter-generational, unless policies aimed at alleviating these impacts are instituted at scale and targeted to reach the poorest of the poor. There are also implications for future pandemic preparedness.
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    Immune defense in Drosophila melanogaster depends on diet, sex, and mating status
    (Public Library of Science, 2023-04-13) Rai, Kshama Ekanath; Yin, Han; Bengo, Arnie Lynn C.; Cheek, Madison; Courville, Robert; Bagheri, Elnaz; Ramezan, Reza; Behseta, Sam; Shahrestani, Parvin
    Immune defense is a complex trait that affects and is affected by many other host factors, including sex, mating, and dietary environment. We used the agriculturally relevant fungal emtomopathogen, Beauveria bassiana, and the model host organism Drosophila melanogaster to examine how the impacts of sex, mating, and dietary environment on immunity are interrelated. We showed that the direction of sexual dimorphism in immune defense depends on mating status and mating frequency. We also showed that post-infection dimorphism in immune defense changes over time and is affected by dietary condition both before and after infection. Supplementing the diet with protein-rich yeast improved post-infection survival but more so when supplementation was done after infection instead of before. The multi-directional impacts among immune defense, sex, mating, and diet are clearly complex, and while our study shines light on some of these relationships, further study is warranted. Such studies have potential downstream applications in agriculture and medicine.