VictorJuiz/thesis huggingface dataset:
Neurodegenerative Diseases in Two Ontario Mining Cohorts

Neurodegenerative diseases are a group of progressive neurological disorders with continuously rising incidence rates. Aluminum, diesel engine exhaust, and radon have been studied as risk factors for neurodegenerative diseases, but no research has been performed on populations of miners with high levels of exposure. This dissertation used one mining cohort and one occupational disease surveillance cohort from Ontario, Canada, to understand the patterns of neurodegenerative diseases among miners and to estimate the association of mine type, aluminum dust, diesel engine exhaust, and radon with risk of neurodegenerative diseases (Alzheimer’s disease, Alzheimer’s with other dementias, Parkinson’s disease, parkinsonism, and motor neuron disease). Two Ontario cohorts were used for my analysis, one subset of over 1.2 million workers from a linked surveillance cohort, the Occupational Disease Surveillance System (ODSS), and the other linked cohort of 36,836 Ontario miners, the Mining Master File (MMF). Poisson regression models were used to examine incidence rate ratios of different neurodegenerative outcomes. McIntyre Powder exposure was assessed using both cleaned self-reports and reconstruction of powder use from historical records. Radon exposure assessed using job-exposure matrices that were developed using results of radon measurements carried out in Ontario mines. Different exposure assessment approaches were explored for diesel exhaust exposure, including a mine-based diesel equipment use indicator and reconstructions of diesel use for underground mines through historical records and expert assessments. In the ODSS cohort, an elevated incidence rate of motor neuron disease was suggested among workers in metal mines, as well as an indicative elevation of Alzheimer's or Parkinson's disease rates among workers of gold and miscellaneous metal (primarily nickel-copper ore) mines. In the MMF cohort, I observed a 30% increased rate of Parkinson's disease and 10% increased rate of Alzheimer's with other dementia in association with respirable aluminum dust exposure. However, my findings do not support positive associations between cumulative radon exposure level and diesel exhaust exposure duration and risk of neurodegenerative outcomes. My findings support aluminum as a risk factor for Parkinson’s disease, yet more epidemiological research is needed to understand the role of aluminum, radon, and diesel exhaust in the development of neurodegenerative outcomes.

Keywords: brain
metals
mining
occupational epidemiology
radon
vehicle emissions
Advisor: Demers, Paul A. PAD
Arrandale, Victoria H. VHA
Shifting Pedagogies during COVID-19: The Professional Learning of Teacher Educators regarding Digital Technology in a Time of Crisis and Change

The COVID-19 crisis that began in March 2020 presented teacher education with many challenges, among them the urgent need to support teacher educators with the sudden move to remote teaching. Teacher educators immediately started rethinking their pedagogy for a new digital classroom environment as universities rushed to provide overnight supports to help with this transition. Empirical research on how teacher educators have navigated their professional roles during this period of uncertainty is limited. There is also limited research on the professional learning of teacher educators, particularly in the area of digital technology. The aim of this study was to explore teacher educators’ shifts in views and pedagogy using digital technology as they adapted to teaching in an online environment. Investigating those shifts also required examining teacher educators’ backgrounds and experiences with digital technology and their professional learning during the COVID-19 pandemic. In this qualitative multi-case study, I explored the experiences of six teacher educators within a graduate teacher education program in a large urban university in Canada. Each participant took part in two semi-structured interviews and described teaching and learning artifacts that reflected a change in their pedagogy. Three key findings emerged from this study. First, the COVID-19 pandemic served as a catalyst for change in teacher educators’ enacted pedagogies. Second, teacher educators displayed heightened responsivity in two ways. They placed teacher candidate learning at the center of the online classroom and offered flexible and accessible learning experiences. Also, teacher educators’ changes in pedagogy were frequently shaped by inequities exacerbated by the pandemic. Lastly, study participants discussed different possibilities for teacher educator pedagogies, with an emphasis on flexible approaches to teaching and learning, a changed role in an online setting, and digital technology integration. Implications for teacher educators include the need to engage in ongoing learning, understand the role and uses of digital technology in the teacher education classroom, and recognize their ongoing responsibility to foreground equity and social justice in their teaching. Thus, teacher education programs must include formal and informal structures and resources that support the individualized and diverse professional learning of teacher educators.

Keywords: Digital Technology
Pandemic Pedagogies
Professional Learning
Teacher education
Teacher educator
Teaching during COVID-19
Advisor: Broad, Kathy
Land Reform and Local Agents: The Grassroots Origins of State Capacity in Communist China and Beyond

How do dictatorships morph from weak ‘paper leviathans’ into powerful institutional machines that command monopolized authority throughout its realm? While previous scholarship has shown how strong central state capacity can contribute to the endurance of authoritarian rule, less attention has been paid to how autocratic regimes construct local state capacity through the recruitment, deployment, and management of agents to enforce their rule at the grassroots level. Yet the selection of local agents for policy implementation also requires regimes to make strategic tradeoffs regarding the centralization and decentralization of their political authority. Using the National Land Reform Campaign (1950-53) under the early years of the Chinese Communist regime as a case study, I address two puzzles: under what conditions do regimes seek to prioritize different types of local agents, and which types of agents deliver better policy performance for regime leaders? The dissertation utilizes a mixed-method approach, by leveraging rare primary archival materials (e.g. internal party communications, work reports, memoirs) and novel quantitative historical datasets at the subnational level to answer these questions. I argue that the Chinese Communist Party (CCP) post-1949 faced a critical centralization dilemma during the Land Reform Campaign: it could select either centrally-deployed agents, who are more likely to be faithful representatives of central political will but are unfamiliar with local governing communities, or locally-embedded agents, who are more likely to adapt to local particularities but are prone to societal capture. However, I also find that the CCP regime was sometimes able to harness a ‘hybrid’ model of local agent selection by maintaining both agent loyalty and governing familiarity, under conditions of deliberate central intervention and the presence of organizational legacies left behind by revolutionary mobilization – which consequently allowed for more effective implementation of land redistribution targets. By revisiting this massive project of socioeconomic reform in 1950s China, which was one of the largest redistributions of land ownership in human history, this dissertation sheds new light on the study of state-led land reform beyond China (including Taiwan and Asia), and offers new insight into the study of principle-agent problems and state capacity in authoritarian regimes.

Advisor: Ong, Lynette H
Optimization and Loss Landscape Geometry of Deep Learning

The impressive success of deep learning is powered by models that are rapidly growing in size along with the computational resources that are used to train them. Despite these growing demands, the dominant tools used to train these networks have not evolved significantly to match these needs. One natural hypothesis for this limitation is our lack of understanding of the training dynamics of deep neural networks. In this thesis, I present our research on understanding and improving the optimization of deep learning models. The thesis begins by presenting two first-order optimization algorithms for deep learning: the Aggregated Momentum and Lookahead optimizers. We demonstrate their success on modern deep learning optimization problems and provide theoretical analyses of both optimizers in convex settings. However, our theoretical understanding of optimization for practical training of deep neural networks is severely limited. Following this, we turn towards building a better understanding of deep learning optimization. We achieve this by studying the loss landscape geometry of deep neural networks. This is extremely challenging due to non-convex objective functions and extremely high-dimensional parameter spaces. We address this by first studying a simple class of neural networks: two-layer linear networks. Despite their simplicity, these models capture some core challenges of deep learning optimization effectively. Within this class, we investigate regularized linear autoencoders and linear variational autoencoders and carefully characterize their loss landscape geometry theoretically. We then move beyond the simple class of two-layer linear networks to investigate a phenomenon that arises across a vast set of deep learning optimization problems. This phenomenon, which we term the Monotonic Linear Interpolation (MLI) property, describes a global property of the loss landscape geometry of deep learning models. We provide the first theoretical explanation of this phenomenon and conduct a thorough empirical investigation to better understand the pervasiveness and limitations of the MLI property. In the final chapter, the thesis is discussed as a whole and promising directions for future research are presented.

Keywords: deep learning
machine learning
optimization
Advisor: Grosse, Roger B
Zemel, Richard
The Ubiquitous Acceptance of an Exterminatory Legality: Rights, Framing, and Legal Opposition to Animal Farming

Animal farming is a seemingly intractable problem, but it can and should be resisted and opposed. Animal advocates can provide significantly more potent opposition than current reform strategies do. The main reform strategy is the pursuit of legal rights for animals. Yet, animal’s rights are rendered ineffective by background social conditions. The first part of this thesis explores these issues with reference to legal rights animals already possess. Considering the significance of background conditions, the second part deploys insights from social movement framing literature to propose how animal advocates can seek to alter those conditions, producing a social environment more conducive to meaningful protection for animals. This involves countering the scientific and legal narratives (scientific animal welfare and the legality of farming) which obfuscate the wrongfulness of animal farming. The third part justifies why the legality of farming can be denied, with legal theoretical, doctrinal, and public/constitutional law arguments.

Keywords: Animal law
Animal rights
Constitutional law
Framing
Public law
Social movements
Advisor: Fernandez, Angela AF
An Exploration of Eating Disorders in Canada: Towards Equitable Access to Care

At any given time, approximately 1.5 million Canadians meet the diagnostic criteria for anorexia or bulimia, not including individuals experiencing other eating disorder subtypes or those who are in recovery. Eating disorders are serious mental health conditions linked to severe distress, deterioration of physical health, mental health comorbidities, and loss of life. Individuals of different cultural backgrounds, genders, ages, and socio-economic status are all at risk of developing eating disorders, yet not all are able to access eating disorder diagnoses and treatment equally. The goal of this dissertation is to contribute towards expanding eating disorder care options for care-seekers, with particular attention to those left out of formal treatment options, such as Latinx individuals. Towards this goal, my work research focuses on eating disorders in Latinx individuals in Canada and care options available to people who may not be able to access clinical care. Research in these areas in a Canadian context is sparse. Thus, this project uses a variety of research methods and methodologies to address gaps in understanding eating disorder care options outside of clinical treatment and the case of eating disorders among Latinx individuals in Canada. I first present quantitative work on correlates of eating disorders to provide data on the prevalence of eating disorders in racialized population groups in Canada. I then present findings on barriers to eating disorder care identified by non-profit eating disorder organizations in Canada. I also show that these eating disorder organizations play an important role in expanding access to care by helping individuals access clinical treatment and by providing non-clinical care alternatives. Finally, I present work produced through a community-based project, in partnership with a Latinx organization in Toronto. Together, this body of work demonstrates a continued need for research on assessing the extent to which certain individuals are unable to access timely, appropriate, and affordable eating disorder treatment. Additionally, my findings highlight an urgent need to provide reliable and robust funding for eating disorder–related work, and in particular expand funding for eating disorder organizations in Canada, given their critical role in improving access to eating disorder care.

Keywords: Access to Care
Barriers to Care
Eating Disorders
Latinx
Mental Health
Advisor: Wakefield, Sarah
Three Pictures of Lusztig’s Asymptotic Hecke Algebra

Let $\Waff$ be an extended affine Weyl group, $\HH$ be its Hecke algebra over the ring $\Z[\bq, \bq^{-1}]$ with standard basis $\{T_w\}_{w\in\Waff}$, and $J$ be Lusztig's asymptotic Hecke algebra, viewed as a based ring with basis $\{t_w\}_{w\in\Waff}$. This thesis studies the algebra $J$ from several perspectives, proves theorems about various incarnations of $J$, and provides tools to be applied for future work. We prove three types of results. In the second and third chapters, we investigate $J$ as a subalgebra of the $(\bq^{-1})$-adic completion of $\HH$ via Lusztig's map $\phi$. In the second chapter, we use Harish-Chandra's Plancherel formula for $p$-adic groups to show that the coefficient of $T_x$ in $t_w$ is a rational function of $\bq$, depending only on the two-sided cell containing $w$, with no poles outside of a finite set of roots of unity that depends only on $\Waff$. In type $\tilde{A}_n$ and type $\tilde{C}_2$, we show that the denominators all divide a power of the Poincar\'{e} polynomial of the finite Weyl group. As an application, we conjecture that these denominators encode more detailed information about the failure of the Kazhdan-Lusztig classification of $\HH$-modules at roots of the Poincar\'{e} polynomial than is currently known. In the third chapter, we reprove the results of the second chapter without using any tools from harmonic analysis in the special case $\G=\SL_2$. In this case we also prove a positivity property for the coefficients of $T_x$ in $t_w$, that we conjecture holds in general. We also produce explicit formulas for the action of $J$ on the Iwahori invariants $\mathcal{S}^I$ of the Schwartz space of the basic affine space. In the fourth chapter, we give a triangulated monoidal category of coherent sheaves whose Grothendieck group surjects onto $J_0\subset J$, the based ring of the lowest two sided cell of $\Waff$, equipped with a monoidal functor from the category of coherent sheaves on the derived Steinberg variety. We show that this partial categorification acts on natural coherent categorifications of $\mathcal{S}^I$. In low rank cases, we construct complexes lifting the basis elements $t_w$ of $J_0$ and their structure constants.

Keywords: affine Hecke algebra
asymptotic Hecke algebra
Hecke algebra
Iwahori-Hecke algebra
Schwartz space of basic affine space
Advisor: Braverman, Alexander
Cognitive and Mental Health in Aging Sexual Minority Populations: Diversifying the Minority Stress Model

The aging population is continuously growing in North America, yet limited efforts have been made to understand the role of aging in the sexual minority research. Guided by the minority stress model (Meyer, 2003), the current study examined cognitive and mental health outcomes of aging sexual minority adults in Canada by using a population data from the Canadian Longitudinal Study on Aging. To understand the role of social factors in sexual minority aging, the study first compared social participation rates between different sexual orientation groups by using the analysis of variance. The second part of the study investigated the relationship between sexual orientation and cognition by conducting multiple regression analyses while considering the role of social participation and social support. The third part of the study explored the relationship between sexual orientation and depression while considering the influence of neighborhood deprivation levels. Findings from the first part of the study indicate that social participation levels differ by sexual orientation in both aging men and women. Results from the second part of the study found that aging gay men perform better in cognitive flexibility and memory related tasks compared to their heterosexual peers even after considering the effects of social support and social participation. Bisexual women also performed significantly better in memory tasks than their lesbian and heterosexual counterparts, while reporting no significant effects of social support or social participation in the model. The third part of the study that investigated the role of neighborhood deprivation levels in the relationship between sexual orientation and depression in aging populations indicated that material deprivation levels contribute to the relationship between lesbian identity and depression in aging women as well as the relationship between bisexual identity and depression in aging men. The results of this study suggest that cognition, social participation, and neighborhood material deprivation are important constructs for studies that explore the aging process of sexual minority populations. In particular, this dissertation’s results related to depression point to the need for further support for aging sexual minority adults through behavioral and policy interventions.

Keywords: CLSA
Cognition
Deprivation
Gerontology
Mental Health
Minority Health
Advisor: Craig, Shelley L
A School-based Cognitive Behavioural Therapy Skills Intervention for Suicide Prevention and Wellbeing in Youth

This dissertation aimed to evaluate the impact of a Cognitive Behavioural Therapy Skills Intervention (CBTSI) on suicidality in middle schoolers. The primary objective was to determine whether the CBTSI produced a meaningful reduction in suicidality (4-item composite) in middle schoolers who endorsed at least one item on the Life Problems Inventory (LPI) questionnaire. Exploratory objectives were to determine if the CBTSI produced a meaningful improvement in depression, anxiety, and wellbeing. Following a feasibility study, I conducted a randomized control trial (RCT) with 430 students (46 classes) in total, to evaluate the causal effect of the CBTSI on outcome measures of interest in a large, urban, diverse school board. In Study 3, I evaluated the potential differences in the impact of the intervention on youth with and without mental health symptoms that may be clinically relevant. A subset of these students participated in Study 4; there were 8 groups of 60 students in total. I utilized qualitative methods to explore students' experiences of the intervention during the RCT and to better understand barriers and facilitators to engagement. Students were 12 to 14 years of age or in grades 7 or 8 for all studies. One of the most reliable risk factors for youth suicide is suicidal ideation and most notably, suicide attempts. Non-suicidal injury’s (NSSI) role in youth suicidality remains debated and is not fully understood. For these reasons, measures of suicidality consisted of a composite score reflective of 1 suicide attempt item, 2 suicidal ideation items and 1 NSSI item. A decision was made to rerun the analyses and introduce the NSSI back into the results of the RCT following a Chronbach’s alpha reliability estimate calculation. Overall results of the quantitative studies indicate inconsistent findings in suicidality, but improved wellbeing. Results across the studies for depression and anxiety outcomes were mixed. When discussing the CBTSI, students expressed that the intervention needs to be adaptable to enhance the personal dignity of youth, respecting their wishes and individual goals. Maximizing the opportunities for involvement and self-determination created an atmosphere for confidence and self-compassion to flourish, the highlighted core themes.

Keywords: Depression and Anxiety
Population Based
School-Based Intervention
Suicide Prevention
Wellbeing
Advisor: Levitt, Anthony J.
Towards Deep Learning for Long-term Visual Localization in Visual Teach and Repeat

Visual path following can facilitate autonomous operation of robots in a large variety of environments while solely relying on an inexpensive camera sensor. Visual Teach and Repeat (VT&R) achieves accurate metric and long-range autonomous path following in unstructured environments. The user teaches a path by driving the robot manually, after which the path can be repeated autonomously. During long-term operation, the camera is affected by lighting changes, and the appearance of the environment changes. This poses a challenge for localizing to the visual map that was built when teaching the path. VT&R can operate across large appearance change by using multi-experience localization. Each time the robot repeats a path, the run is stored as a new experience in the map. When localizing, VT&R can rely on intermediate experiences to bridge the appearance gap. This requires collecting experiences continuously as the environment changes and from scratch whenever a new path is taught. In this thesis, we tackle long-term localization using deep learning techniques with the aim of removing the need for intermediate bridging experiences. We first show that we can train a neural network to regress relative poses for localization directly from pairs of images. With this method, we localize across large lighting and seasonal change but fail to generalize to new and unfamiliar paths. We improve on this work by learning visual features that can be used in the VT&R pipeline. With these deep learned features, we perform closed-loop path following across a full range of lighting change and also show that the learned features generalize to new areas. We further test the learned features’ robustness to seasonal change in an experiment from late summer through autumn. The experiments cover a total of 87.7 km of driving.

Advisor: Barfoot, Timothy