VictorJuiz/thesis huggingface dataset:
Aging Alone, Together: Older Adult ICT Use, Health, and Place during the COVID-19 Pandemic
Keywords: COVID
ICT
Mental health
Migrants
Older adults
Social isolation

As adults progress into later life, they are likely to experience age-related mobility decline and social isolation. Social isolation has deleterious effects on mental and physical health, including increased anxiety, mortality rates, and cognitive aging. During the COVID-19 pandemic, older adults faced new obstacles to socializing: the closure of third places (e.g., libraries, coffee shops) and increased risk of serious illness. Information and communication technology (ICT) was widely adopted during this time to facilitate daily activities and social connection, but the extent to which it was able to replace the emotional satisfaction of in-person socializing (e.g., reducing feelings of loneliness) and support health is unclear. In this dissertation, I employ multiple methods, including qualitative interviews, structural equation modeling, and time-use cluster analysis, to investigate the relationship between ICT, mobility, and health. Ultimately, the aim of this dissertation is to explore how older adults socialized during the pandemic and the relationship between different modes of socializing (i.e., online or in-person) and health. In Chapter 2, I present qualitative evidence that older adults adopted ICT in a variety of ways to preserve their pre-pandemic routine and modify their homes into activity places. In Chapter 3, I provide details about data collection with an older Chinese migrant community during the pandemic. In Chapter 4, I test a theoretical framework using a sample of older Chinese migrants during the extended COVID-19 lockdown. This social isolation framework breaks the concept into two levels: spatial and technological isolation (conceptualized as community mobility and ICT use). Using a structural equation model, I find that higher community mobility is related to decreased feelings of loneliness, while higher ICT use is associated with better physical health. In Chapter 5, I use older Chinese migrants’ single-day activity diaries to explore socializing behavior, finding that a majority of older migrants were socializing online while relatively few were socializing in person during the extended lockdown. I also find that better physical health is associated with socializing in person. Taken together, the results of this work demonstrate the importance of considering ICT in tandem with spatial measures of social isolation. Findings also suggest that in-person socializing has a greater impact on mental and emotional health while ICT use has the potential to support physical health.
Assessing the Potential of Oral Opioid Agonist Treatment to Prevent Injection Drug Use Initiation Assistance Provision in People Who Inject Drugs
Keywords: Injection drug use
Injection initiation
Opioid agonist therapy
Opioid agonist treatment

Injection drug use is a significant risk factor for morbidity and mortality from overdose and injection-related infections. Most first-time injections involve assistance from experienced people who inject drugs (PWID), with frequent injecting and withdrawal associated with assistance provision. Initial cross-sectional data suggests that first-line oral opioid agonist treatment (OAT) with methadone or buprenorphine/naloxone for opioid use disorder may reduce the likelihood that PWID facilitate first injections. This thesis evaluates whether available oral OAT medications in Canada can prevent injection initiation assistance provision in PWID. I first validated common questionnaire-based measures of current and recent first-line OAT enrollment in a sample of PWID in Toronto, relative to prescription dispensation claims data. Both self-reported measures were relatively accurate, though participants tended to underreport recent treatment and overreport current treatment. Using marginal structural models, I then analyzed questionnaire data from December 2014-May 2018 to assess whether current first-line OAT reduces the short-term risk of providing injection initiation assistance in a cohort of Vancouver-based PWID with frequent non-medical opioid use. Participants on first-line OAT (versus no OAT) were 50% less likely, on average, to help someone initiate injecting in the following six months. However, the corresponding 95% confidence interval (0.23-1.11) was imprecise due to small sample size, outcome rarity, and a weaker effect in people with daily baseline opioid injecting. Adjusting for self-reported treatment misclassification yielded comparable results. Lastly, I conducted a prevalent new-user cohort study using questionnaire data from June 2017-March 2020 on Vancouver-based PWID to evaluate the effectiveness of OAT with slow-release oral morphine (SROM) on non-medical opioid injecting and injection initiation assistance provision versus first-line OAT. After matching to reduce confounding, recent SROM initiation was not associated with a differential likelihood of daily non-medical opioid injecting or providing injection initiation assistance over the same six-month period in new users. Collectively, these findings provide additional support for oral OAT as prevention against injection initiation assistance provision in PWID, while reinforcing a need for alternative medications for those who have not adequately benefitted from previous oral OAT. Further research could explore the impact of higher-intensity injectable OAT on injection initiation assistance provision.
Ecomorphology of the Avian Sternum and the Evolution of Powered Flight
Keywords: Ecomorphology
Morphometrics
Ornithology
Paleoecology
Phylogenetic comparative methods

The evolution of powered flight was a key locomotor innovation that expanded the ecological potential of maniraptoran dinosaurs and led to remarkable variation in crown-group birds (Neornithes). Despite extensive research on this major evolutionary transition, several aspects of the morphology of flight evolution remain overlooked or contentious, primarily in elements of the pectoral girdle such as the sternum. While sternum morphology varies considerably across Neornithes, its relationship with flight abilities has never been rigorously analyzed. The objectives of this dissertation are to test hypotheses on the ecological correlates of sternum morphology in avian locomotion, with a specific focus on sternum-driven powered flight, and to infer the evolution of flight abilities in the avian fossil record. I document several significant correlations between sternum shape and locomotor mode in extant birds, notably that an enlarged sternum with a tall or anteriorly projected keel is necessary for powered flight. I then use this relationship to estimate locomotor abilities in one of the most crownward stem birds
Interactive Machine Learning in Cybersecurity: Using Human Expertise More Effectively
Keywords: Active Learning
Anomaly Detection
Cybersecurity
Human Factors
Human-Computer Interaction
Machine Learning

Cybersecurity is increasingly important in a world where malicious actors seek to profit from various forms of data exfiltration. In this dissertation I examine the potential for improving the detection of anomalies related to potential data exfiltration in a large financial service company through better use of human expertise. The overall approach that guided the research reported below is interactive Machine Learning (iML) where humans work together with Machine Learning to solve prediction and classification problems. After reviewing work on data exfiltration and ML, I carried out a series of three case studies with the overall goal of using iML to improve defence against data exfiltration. I first demonstrated that the best known synthetic data set did not provide credible results that would generalize to real world settings. I then did two further case studies on real world data (from a financial services company). In the first of those further studies I looked at how the organization of human labelers affects outcomes with active learning (AL), comparing model training performance between individuals, groups, and situations where pairs of labelers exchanged places halfway through the training process so that different people trained the model on the second half of the training process. I found that a single individual performed best and the role of expertise was further reinforced in my final case study where a group of skilled (vs. unskilled) labelers was shown to produce higher model performance, as well as producing confidence ratings in their labels that were better calibrated with resulting model accuracy. I also showed that an information gain maximizing approach was a viable method of AL in this application area, with better F1 scores and labeler confidence than a more traditional model uncertainty approach. One limitation of this research was that due to privacy concerns I was using redacted email data that did not contain the email body.
Picosecond InfraRed Laser-Mass Spectrometry for Rapid Detection of Skin Cancers
Keywords: Cancer
Cancer Detection
Mass Spectrometry
Oncology
PIRL-MS
Skin Cancer

Skin cancer is the most common cancer known to humans, worldwide. Current detection and treatment of skin cancers remains challenging and time-consuming, as detection relies on stepwise protocols involving visual inspection, optical inspection by a device known as a dermoscopy, and subsequent excision or punch biopsy for histopathological analysis. Furthermore, cutaneous melanoma (melanoma of the skin) can visually mimic other cutaneous lesions, leading to some degree of difficulty in classification of melanoma subtypes, necessitating a large number of biopsies to be taken. Various studies have indicated 24 out of every 25 biopsies performed for a suspected melanoma are confirmed to be benign post-biopsy, leading to a large amount of unnecessary biopsies. There thus exists a need for development of novel detection technologies for more rapid, less subjective, yet highly specific characterization of skin cancers at the pre-biopsy stage.Mass spectrometry (MS) is an analytical technique becoming increasingly important in clinical research. MS allows for tissue identification and disease site categorization based on molecular characterization of tissues. Through addition of an ambient ionization laser source known as a Picosecond InfraRed Laser (PIRL), rapid and accurate classifications of skin cancers may be realized. Throughout the research presented in this thesis on banked ex vivo human skin cancers as well as in murine models, once a mass spectrometry molecular signature model has been built from a library of known samples, PIRL-MS may be used to diagnose unknown skin cancers rapidly, within just 5-10 seconds of sampling and analysis time with high sensitivity and specificity. In this thesis, I apply PIRL-MS to skin cancers to enable for rapid, sensitive and specific detection of skin cancers prior to the biopsy stage.
Scale in Acoustic Modelling
Keywords: Machine learning
Scale
Speech processing
Speech recognition

Speech signals encode information across multiple, often overlapping, scales in time. I argue that, by designing acoustic models faithful to that multi-scale encoding, speech recognition accuracy can sometimes be improved. I do so by partitioning the acoustic model into a three-component pipeline and design interventions for each. In the first stage, feature extraction, the acoustic model transforms an audio signal into a filter-bank representation. We modify the traditional recipe, experimenting with different filters with better resolution in both frequency and scale. We also rearrange the spectral windowing equation to capture overlapping temporal information with higher resolution. With these modifications, we find significant gains in phone recognition performance on already-competitive models. For the second stage in which machine learning takes place, we propose a novel neural layer which is scale equivariant, similar to how a convolutional layer is shift equivariant. We pit those layers against one another in a phone recognition task and find that, although the former does outperform the latter, it uses twice the parameters to do so. When fixing the number of parameters, performance is commensurate between models. In the final stage, the acoustic model must transduce the learned representation into a transcription. We design a statistical framework for transducing time series events compatible with multi-scale phenomena by modelling event locations and types separately. Determining that marginalizing out locations as a latent variable would be combinatorically infeasible in its full generality, we propose either making simplifying assumptions until computation is tractable or performing stochastic estimation. We offer algorithms for stochastic estimation with nicer theoretical properties and better empirical performance (in terms of phone recognition) than some of the prior techniques we generalize.
Synthesizing Observations and Models to Improve Estimates of Snow on Arctic Sea Ice
Keywords: Arctic
sea ice
snow
snow depth
snow on sea ice
snowfall

Snow on Arctic sea ice plays multiple, sometimes contrasting roles in several feedbacks between sea ice and the global climate system. Furthermore, estimates of snow depth and density are necessary for deriving sea ice thickness from satellite altimetry measurements, such as those from the NASA Ice, Cloud, and land Elevation Satellite 2 (ICESat-2). In situ measurements of snow depth and density on Arctic sea ice are sparse and infrequent, and model-based reconstructions are difficult to validate, and thus, highly uncertain. This thesis presents a synthesis of model and observation products to observationally constrain estimates of Arctic Ocean snowfall, and to improve estimates of derived snow depth and density on Arctic sea ice produced from the NASA Eulerian Snow on Sea Ice Model (NESOSIM). The question of the representativeness of reanalysis snowfall over the Arctic Ocean is addressed by comparing snowfall rates retrieved from CloudSat, which provides frequent high-latitude snowfall observations, to reanalysis snowfall products. To reconcile discrepancies between the products, the reanalysis snowfall rates are calibrated to CloudSat snowfall observations, bringing the monthly climatologies into agreement while preserving year-to-year variations between the products. An application of this calibration is demonstrated, wherein calibrated reanalysis snowfall products are used as snow input to NESOSIM version 1.0, and the calibration is found to decrease the inter-product spread in modelled snow depth on Arctic sea ice. The CloudSat-based snowfall calibration is incorporated into the subsequent version (1.1) of NESOSIM, and updates to the model motivate the re-calibration of model free parameters. An automated observation-based calibration of the wind packing (densification) and blowing snow (loss) parameters using a Markov chain Monte Carlo (MCMC) method is performed; the first use of such an approach for a snow-on-sea-ice model. This calibration is used to further reconcile differences in NESOSIM snow depth output generated using different reanalysis snowfall input products, although snow densities are less well-constrained following the calibration. Posterior parameter values, and consequently, the representation of physics in the model, are found to be strongly dependent on the choice of reanalysis snowfall product used as input to NESOSIM. Uncertainties in model output snow depth and density due to snowfall input and posterior parameter uncertainty from MCMC are quantified. The MCMC-calibrated NESOSIM outputs produced using ERA5, MERRA-2, and JRA-55 snowfall are combined, yielding a blended snow-on-sea-ice reconstruction product with basin-wide snow depth and density, and associated uncertainty estimates.
Hidden Order and Spin Lquids in Correlated D-orbital Oxides
Keywords: hidden order
magnetism
multipolar magnetism
spin liquid
spin orbit coupling
strongly correlated systems

Correlated materials provide condensed matter physicists with opportunities to explore a wide zoo of exotic quantum phases of matter. Transition metal oxides (TMOs) are ideal platforms to explore such physics, since spin-orbit coupling (SOC) and electron-electron interactions are comparable in strength. The present thesis will present two systems of TMOs which realize unconventional forms of magnetism. Multipolar magnetism, first studied in f-electron heavy fermion systems has been at the center of condensed matter research for a number of decades. This form of magnetic ordering has been termed ``hidden order" due to its very weak coupling with conventional probes of magnetism. Recently, such physics has been explored also in spin-orbit entangled d-orbital Mott insulators. The first part of this thesis presents the theory of multipolar order in the 5d2 Osmate double perovskites. These systems' ionic ground state is a non-Kramers doublet where physical dipoles vanish, allowing one to study the effects purely from higher order multipoles. The microscopic origins of the ferro-octupolar ordered phase in these materials are elucidated, and in doing so, a novel method for the extraction of pseudospin exchange interactions is developed, based on an exact Schrieffer-Wolff transformation. Additionally, a new method of detection of the ferro-octupolar order via doping in NMR experiments is then proposed. This work highlights intimate connections between the physics in the TMOs and that of the heavy fermion systems, and carries valuable lessons for the general study of multipolar magnetism. The second part of the thesis will turn to the study of quantum spin liquids (QSLs). Since Anderson's initial proposal for such a state in 1973, physicists have been trying to realize these exotic states of matter, both in theoretical models as well as in experiments. The viability of the honeycomb cobaltates to host a QSL state is explored. Initially proposed to be able to realize a Kitaev spin liquid, numerical evidence that the low-energy spin Hamiltonians are better described as frustrated J1-J3 XXZ models is presented. It is shown that this frustration in fact allows for the realization of a U(1) spin liquid at intermediate values of J3, and that this state shows response and transport properties that are in remarkable agreement with experimental data.
Genetic Determinants of Micronutrient Metabolism and Premenstrual Symptoms
Keywords: mendelian randomization
micronutrients
nutrigenomics
premenstrual disorders
premenstrual symptoms
women's health

Background: Premenstrual symptoms are a set of psychological and somatic symptoms experienced during the luteal phase of the menstrual cycle by up to 90% of females of reproductive age. The evidence on the efficacy of dietary supplements for premenstrual symptoms has been inconsistent. These inconsistencies may be due to unaccounted genetic differences in micronutrient metabolism. Objective: To determine the association between iron, vitamin C, folate and magnesium and premenstrual symptoms by assessing dietary intakes, genetic determinants of metabolism, and diet-gene interactions for each micronutrient. Methods: Subjects were around 678 young females from the Toronto Nutrigenomics and Health Study. HFE (rs1800562 and rs1799945), TMPRSS6 (rs482026), TFR2 (rs3811647), TF (rs738584), GSTT1 (Ins/Del), MTHFR (rs1801133), and TRPM6 (rs11144134) were genotyped. Fifteen premenstrual symptoms were self-reported by all female participants. Serum ascorbic acid was assessed from fasting blood. Dietary intake for iron, vitamin C, folate and magnesium was captured using a 196-item validated Toronto modified Harvard food frequency questionnaire. Logistic regression was used to assess all associations. Results: Women with an elevated risk of iron overload were less likely to experience premenstrual confusion, headaches, and nausea. Increased vitamin C intake was associated with premenstrual appetite changes. Compared to deficient ascorbic acid levels, suboptimal levels were associated with premenstrual appetite changes and bloating/swelling. Women with the GSTT1 functional variant (Ins*Ins) had an increased risk of premenstrual bloating/swelling. We also found that among women with lower folate intake and those with the TC genotype of MTHFR had increased odds of reporting premenstrual confusion/difficulty concentrating/forgetfulness and depression, compared to those with the CC genotype. Those with the TT genotype of MTHFR also had a higher risk of reporting premenstrual depression. Magnesium intake was associated with lower premenstrual depression and confusion. Conclusions: We found that iron overload was inversely associated with premenstrual nausea, headaches, and confusion and vitamin C biomarkers were positively associated with increased appetite. We also found that genetic markers of low folate by MTHFR genotype and magnesium intake was associated with increased premenstrual depression and confusion, and magnesium intake was inversely associated with premenstrual depression and confusion.
Essays on Social Responsibility and Marketing Strategy
Keywords: experiment
game theory
innovation
product quality
social comparison
social responsibility

Consumer preferences for social responsibility have both intrinsic and extrinsic components. The first essay of this thesis investigates whether extrinsic preferences, referred to as extrinsic warm-glow and cold-prickle, generate network effects among individuals when choosing eco-friendly versus regular products. The essay employs an experimental design that manipulates extrinsic preferences in sustainable purchase behaviour, by creating a scenario where participants' choices are observed by others. The findings suggest that among socially conscious participants, higher expectations of eco-friendly purchases result in weaker warm-glow feelings, indicating a negative network effect. The study does not find strong evidence of a network effect from experiencing extrinsic cold-prickle when choosing the regular product, suggesting that extrinsic warm-glow and cold-prickle can be asymmetric. Additionally, individuals who are more socially conscious demonstrate a more significant negative network effect induced by extrinsic warm-glow, compared to those who are intrinsically less concerned about social responsibility. Importantly, firms across various industries invest considerable resources in developing innovations to improve the social and environmental responsibility of their products, capitalizing on consumers' intrinsic and extrinsic preferences for social responsibility. In this context, the second essay examines how a firm's incentive to invest in environmental responsibility (ER) innovation is influenced by the quality of its products in a competitive market. More precisely, the essay investigates whether product quality and product ER are complements or substitutes, by utilizing a game-theoretic framework. The analysis reveals that when the magnitude of ER innovation is sufficiently large to grant a high-quality firm monopoly power over environmentally conscious buyers, quality and ER are complementary. This obtains because higher quality amplifies the benefits of having ER-induced monopoly power over environmentally conscious buyers. Conversely, when the expected ER advantage is medium, such that competition for environmentally conscious buyers is reduced but not eliminated, quality and ER become substitutes because they both act as levers to reduce competition. Finally, when the expected ER advantage has little effect on competition intensity, quality and ER are complements when the extrinsic benefits of ER are strong and are substitutes otherwise.