VictorJuiz/thesis huggingface dataset:
Robust Statistical Arbitrage: Trading Foreign Exchange Triplets and Trading Assets with Short Term Alphas
Keywords: Algorithmic Trading
Ambiguity Aversion
Foreign Exchange
Short Term Alpha

Decision making in a stochastic environment depends on the decision makers' models of the environment. To reach their goal, decision makers consider not only the randomness of the outcomes described by a certain probability law, but also their uncertainty of the probability law itself. We call such decision makers ambiguity averse. In the area of algorithmic trading, financial agents often trade multiple assets with some inherent price structure. The main goal of this work is to investigate how these agents behave when facing model uncertainty by considering two real-world algorithmic trading problems: trading foreign exchange triplets and trading short term alpha. In the trading foreign exchange (FX) triplets problem, an FX broker accepts trades from her clients, and hence needs to manage positions in multiple currencies by acquisition and liquidation. She admits that her model for FX rates may be misspecified and we explore the optimal strategy for her to liquidate a large position in an illiquid currency pair. We find that the ambiguity averse broker can improve her liquidation profit by trading a currency triplet consisting of the illiquid currency pair and two relatively liquid currency pairs. In the trading short term alpha problem, our trader can predict prices of N assets with M alpha signals. She is uncertain about the models of asset prices and alpha signals and wants to maximize her final trading wealth. We formulate the trader's stochastic control problem and solve it in closed form. We also calibrate our model to real market data and numerically demonstrate that our robust strategy can increase the mean and reduce the standard deviation of the trader's Profit and Loss (P).
Neutron Scattering Study of Magnetic Excitations in Quantum Magnets Bi2CuO4 and CoTiO3
Keywords: Magnetism
Neutron Scattering

Anisotropic spin interactions beyond simple Heisenberg models have been intensively studied in recent years as new routes for realizing exotic magnetic phases. Although theories of anisotropic interactions have been developed extensively, careful experimental studies that determine the forms and magnitude of these interactions in actual magnetic systems have been scarce. In this thesis, I present inelastic neutron scattering (INS) studies of magnetic excitations in two ordered antiferromagnets, Bi2CuO4 and CoTiO3, to elucidate their microscopic spin interactions. Bi2CuO4 is an easy-plane antiferromagnet with a tetragonal crystal structure. Despite many earlier studies using INS and other techniques, nature of low energy magnons and hence the form of anisotropic interactions in Bi2CuO4 have been controversial. Using higher resolution than all previous INS work, we determined the dispersions of the low energy magnons, which we used to construct an anisotropic spin model based on lattice symmetry. Remarkably, the anisotropic interactions in our model were shown to play a fundamental role by producing an in-plane anisotropy through a quantum order by disorder mechanism. This anisotropy was directly observed in a field-dependent neutron scattering measurement. CoTiO3 has an ilmenite structure with a stacked honeycomb lattice of Co2+ ions. We obtained its full energy-momentum resolved magnon spectrum, which we showed to be consistent with a simple XXZ model. A Dirac-cone like dispersion was discovered in CoTiO3, making it a promising candidate for realizing topological magnons. A high-resolution measurement revealed important features of the magnon dispersion, namely a zone center and a Dirac magnon gap, that can only be explained by anisotropic interactions beyond an XXZ model. Field dependence of these features was studied and used to constrain the forms of the anisotropic interactions. Higher energy spin-orbit exciton in CoTiO3 was studied as a function of temperature across the magnetic ordering transition. A strong temperature dependence was observed, which we attributed to a mixing between the ground and excited multiplets using a multi-level model. By comparing the theoretically determined dispersion of the spin-orbit exciton to our data at different temperatures, we found indirect evidence for higher order spin interactions beyond a bilinear spin model.
Anthranilate Analogs as a Druggable Target for the Proteopathy and Immunopathy of Alzheimer's Disease
Keywords: Alzheimer's disease
autophagy
ER-phagy
microglia
neuroinflammation
β-amyloid

β-Amyloid (Aβ) triggered proteopathic and immunopathic processes are a postulated cause of Alzheimer’s disease (AD). Monomeric Aβ is derived from amyloid precursor protein, whereupon it aggregates into various assemblies, including oligomers and fibrils, which disrupt neuronal membrane integrity and induce cellular damage. Aβ is directly neurotoxic, but may also induce neuroinflammation through activation of microglia. This project aims to identify drug candidates that may concomitantly inhibit Aβ aggregation and neuroinflammation, and further investigate their mechanism of actions. Previous work has demonstrated that 3-hydroxyanthranilic acid, an endogenous tryptophan metabolite, is a bifunctional inhibitor against Aβ aggregation and neuroinflammation. This study therefor sought known drugs with an anthranilate backbone, hypothesizing that anthranilate analogs may provide a platform for drug design targeting both the proteopathy and immunopathy of AD. Furosemide was initially identified as a lead compound. The results demonstrate that furosemide is a probe molecule for the treatment of neuroinflammation in AD. It inhibits proinflammatory responses, enhances anti-inflammatory responses, and promotes phagocytic activity. Mechanism studies further demonstrate that furosemide suppresses upregulation of endoplasmic reticulum (ER)-stress marker genes during inflammation, suggesting an interplay between ER-stress and inflammation. To further explore the pharmacologic effects of furosemide, this study devised and synthesized a series of furosemide analogs that target both Aβ aggregation and neuroinflammation. Forty compounds were synthesized and evaluated. Compounds 3c, 3g (SI-W052), and 20 inhibit Aβ oligomerization; 33 and 34 inhibit Aβ fibrilization; 3g and 34 inhibit the production of tumor necrosis factor-α, interleukin-6, and nitric oxide while downregulating the expression of cyclooxygenase-2 and inducible nitric oxide synthase and promoting microglial phagocytosis, addressing the combined proteopathic-immunopathic pathogenesis of AD. This study further investigates the mechanism of compound 3g. Based on the potent activity and its structure similarity with furosemide, the mechanism study focuses on the downstream signaling of ER-stress, autophagy. The data identify a beneficial anti-neuroinflammatory role for mild ER-stress mediated autophagy. This protective mechanism is impaired during prolonged neuroinflammation. Compound 3g is capable of restoring the protective effects of autophagy through mTOR. In addition, 3g attenuates the downregulation of ER-phagy receptor TEX264 during neuroinflammation, suggesting promoted ER self-renovation.
Applications and Methods for Energy-based Models at Scale
Keywords: Energy-Based Models
Generative Models
Machine Learning
MCMC Sampling
Semi-supervised Learning
Unsupervised Learning

Energy-Based Models (EBMs) are a class of generative models like Variational Autoencoders, Normalizing Flows, and Autoregressive Models. It is a commonly held belief that generative models like these can help to improve downstream discriminative machine learning applications. Generative models present an avenue for learning about underlying low-dimensional structure hidden within high-dimensional datasets and they can be trained on unlabeled data, enabling a pathway for building more label-efficient learning systems. Unfortunately, this dream has not been fully realized as most classes of generative models perform poorly at discriminative applications. EBMs parameterize probability distributions in a fundamentally different way than other generative models which allows them to be more expressive and have more architectural flexibility. We demonstrate that we can take advantage of this additional freedom to apply EBMs successfully to downstream discriminative tasks, notably improving performance over alternative classes of generative models and many other baselines. Unfortunately, this freedom comes at a price, and in practice, EBMs are notoriously difficult to work with, train, scale, and evaluate. The remainder of the thesis covers my work to address these issues. In particular, we explore the use of alternative (non-KL) divergences for EBM training and evaluation. Next, we explore the use of generators to improve EBM training and reduce their dependency on MCMC sampling. Finally, we present a new approach to sampling from discrete distributions which enables recently developed methods for training EBMs on continuous data to be applied to discrete data as well.
Urban Biotope Development for Green Infrastructure and Urban Forest Planning in Mexico City
Keywords: Mexico City
Predictive modelling
Spatial planning
Urban biotopes
Urban forest
Urban planning

As cities continue to expand and their environmental problems intensify, there is a growing need to incorporate urban forests into land-use planning to enhance and preserve the many environmental services they provide. The environmental problems and demands for greener and more sustainable urban areas are magnified in Mexico City, the largest city in North America. Here, the urban biotope approach was used to understand the extent and structure of the urban forest to support urban land use and planning. First, the urban forest was measured in 500 fixed-area plots (400 m2) and then field data were collected from a subset of plots. A total of 25 urban forest structural and compositional variables were generated from these plots, and then Principal Component Analysis was used to reduce the number of forest variables to five. Hierarchical clustering was used to derive three broader-level biotope groups defined by the dominant land cover and eight finer-level biotope classes characterized by the urban forest. Statistical modelling, based on a random forest algorithm, yielded high classification errors suggesting relatively low accuracy and poor model fit. Alternatively, mapping of four landscape classes based on canopy and environmental variables across the study area provided a much-improved classification system. This work is the first to develop urban biotopes for the entire area of Mexico City and demonstrates that urban biotope information analyzed at different spatial and operational scales is relevant for planning at the neighbourhood, borough, or land use levels. Land use plans at the city-wide level could be developed by incorporating biotope information into planning instruments operating at the neighbourhood and borough levels. Urban biotopes have many practical applications, from informing land-use planning, defining tree planting and stewardship priority areas, setting tree diversity targets, and supporting the development of comprehensive urban forest management and green infrastructure spatial plans.
Symmetry-broken Phases and Transport in Multiband Systems
Keywords: Electronic transport
Monte Carlo
Multiband and multiorbital systems
Nematicity
Superconductivity
Symmetry-breaking

In this thesis, we study the interplay between electronic, orbital, structural, and dipolar degrees of freedom in systems with different unit cells and crystal symmetries. The first project was motivated by experiments revealing transport anisotropies in 2D electron gas systems formed at the surface of certain oxide heterostructures. We propose that multiorbital electronic correlations leading to orbital order can drive nematicity and account for the observed anisotropy. In the second project, we suggest an alternative explanation for the reported large in-plane anisotropic magnetoresistance by considering small momentum transfer scattering, spin-orbit coupling, and structural distortions. In the third project, we move away from transport and explore an old question that has recently resurfaced in the community -- how dilute superconductors can emerge and lead to dome signatures in Tc. We provide a geometric picture for how long-range attractive interactions naturally predict the presence of a peak of Tc and prove its robustness against fluctuations from competing channels in the low-density regime. Lastly, we consider a system of electric dipole moments packed in a triangular array, inducing a restricted motion of the surface electrons along an effective honeycomb lattice and giving rise to relativistic Dirac dispersions. We show that the dipolar interactions lead to stripe and vortex ground states and that the coupling to fermions can embed them with mass as well as cause nematicity via an order-by-disorder mechanism.
Employment and Education Pathways: Exploring Traditional Knowledges Supports for Transitions Out of Homelessness for Indigenous Peoples
Keywords: Indigenous homelessness
Indigenous mental health
Indigenous traditional knowledges
Indigenous youth education
employment
Indigenous youth life transitions

Indigenous Peoples are the fastest growing population in Canada, yet face persistent housing and homelessness crises. While Indigenous Peoples often move to cities in search of employment and education, they are overrepresented among the homeless population. There is little known about what current life transitions out of homelessness look like for Indigenous Peoples. This study asked: “How do traditional knowledge/Elders support the mental health and wellbeing of Indigenous homeless individuals transitioning to housing and seeking employment/education?” This study interviewed 13 Indigenous participants using a narrative inquiry methodology, in which their perspectives, experiences, and insights on housing transitions, employment barriers, and education supports were shared. Results included five metathemes: Housing Shelter; Indigenous Knowledges Community Connections; Education Employment Pathways; Social Services Programming, and Indigenous Existence in Society. Limitations, future directions, and implications for policy are included in this work.
Automating Log-based Software Debugging on Distributed Server Stacks
Keywords: Debugging
Distributed server stacks
Failure diagnosis
Logging

Debugging is one of the oldest yet hardest problems in the computer engineering field. People have been spending billions of dollars each year fighting with software bugs, yet debugging is still a highly inefficient process that requires a lot of manual efforts. While computer systems are getting more complicated, manual debugging has become extremely time-consuming. This thesis aims to automate debugging from a log-based perspective. Log-based debugging,or printf debugging, is important for three reasons. First, logs contain rich debugging information. They are printed by log printing statements that are manually inserted by software developers. Second, logs are cheap. The cost of printing logs is often lower comparing with other tools. Finally, logs are sometimes the only source of runtime information in production environments. However, current log-based debugging is largely manual, which has severe limitations. First, it is hard for manual logging to control the log quality. In practice, people don’t spend enough time on writing log printing statements. Second, logs are often outputted in high volumes,making it hard for manual analysis. This thesis aims to solve these two problems. First, it solves the problem of “where to log” based on the following observation: the decision of where to print log messages is a trade-off between their usefulness in debugging and the cost to print them. We further define the logs’ usefulness by their abilities to differentiate execution paths. On top of this, we build a tool, Log20, that can automatically insert the optimal log printing statements under a given performance threshold. Second, it solves the problem of “how to correlate the logs”. Correlating log messages from the same execution flow is a critical step in debugging, where an execution flow is a sequence of computer instructions triggered by a user request. We solve this problem by proposing the Flow Reconstruction Principle: programmers will insert sufficient information in the logs in order to reconstruct the execution in post-mortem diagnosis. We further apply this principle in the Stitch tool, and demonstrate its usefulness by performing real world system diagnosis tasks in a user study.
Psychological Distress Among LGBTQ People of Colour: Understanding the Role of Intersectional Microaggressions, Social Identity, and Community Connectedness
Keywords: Emerging adults
Intersectionality
LGBTQ people of colour
Microaggressions
Psychological distress

Lesbian, gay, bisexual, transgender, and queer people of colour (LGBTQ-POC) are confronted with daily, subtle racist and heterosexist microaggressions, which have been linked to stress and increased likelihood of mental health problems. The current study applied an intersectional framework to understand the role that intersectional microaggressions play in predicting psychological distress among LGBTQ-POC. Furthermore, rather than merely investigating risk, the current study integrated a resiliency perspective to understand how individual- and community-level promotive factors compensate for and protect against psychological distress in the face of intersectional microaggressions. A racially diverse sample of 200 LGBTQ emerging adults of colour (ages 18-29) participated in an online study that assessed psychological distress, three types of microaggressions (racial, heterosexist, and intersectional), social identity, and community connectedness. Results indicated that although heterosexist and racial microaggressions predicted psychological distress, when considered together, intersectional microaggressions accounted for the majority of the relationship and were a better predictor of psychological distress. Additionally, although social identity was not protective, connectedness with each of the three communities (LGBTQ, POC, LGBTQ-POC) had a direct compensatory effect on psychological distress. The clinical implications of these findings are discussed.
Air Pollution Measurements near Roadways: Verifying Measurements and Discerning Traffic-related Signal from Background
Keywords: Aerosols
Background
Near-road
Pollution
Traffic
Urban

The work in this thesis examined multiple years of air quality data collected in near-roadway and urban background environments as part of an air quality monitoring pilot study, the first of its kind in Canada. In addition to validation strategies involved in assuring accuracy of these data, near-road pollutant concentrations were compared with urban background concentrations (between 2-10 km away from each near-road receptor) in order to isolate excess pollution arising from vehicle emissions, understood to be approximately equal to the difference between the two locations. One major hypothesis in this work was that these excess pollutant quantities can be estimated using the near-road data alone, an important research question as oftentimes studies do not have concurrent background measurements, and two strategies for doing this were compared with the measured site differences. Namely, utilizing differences between measurements made downwind and upwind of the roadway, and applying a background-subtraction algorithm to the near-road time series data. It was found that, relative to the explicitly measured site differences, downwind/upwind differences were 40% larger on average and not universally applicable, whereas an optimized background-subtraction algorithm replicated the site differences well and was robust across all sites. After isolating these local vehicle-related concentrations, the role of meteorological variability on vehicular plume dispersion in the near-road environment was quantified. It was found that the relationship between normalized local concentrations and wind speed was well-described with a simple exponential equation. Wind direction had the effect of enhancing this by a factor of 1.5-2.0 when downwind of the roadway, and suppressing it by a factor of 0.25-0.50 when upwind at these sites. Lastly, 15 years of continuous ambient near-road ultrafine particulate matter data were analyzed utilizing this background-subtraction methodology. It was found that local concentrations contributed ~45% to total ambient concentrations, and while these total concentrations have been steadily declining, this local fraction has remained relatively consistent. Future utilization of the methodologies developed and validated in this thesis will be useful for continued monitoring of vehicular fleet emissions.
Cardiovascular Disease Risk and Cardio-oncology Rehabilitation (CORE) Referral in Breast Cancer Survivors
Keywords: Anthracyclines
Breast Cancer
Cardiac Rehabilitation
Cardiovascular Disease
Referral
Trastuzumab

Breast cancer is the most prevalent cancer among Canadian women. Due to advancements in cancer prevention, treatment and screening, more breast cancer survivors are living longer, for whom, survivorship issues have become a significant concern. Cardiovascular disease (CVD) is recognized as the leading non-cancer cause of death in this population, and is the number one cause of death in older cancer survivors (≥65 years of age). The American Heart Association recently endorsed the use of multi-modal exercise-rehabilitation programs, similar to cardiac rehabilitation (CR), to help mitigate CVD risk in breast cancer survivors. This approach has been coined as cardio-oncology rehabilitation (CORE). Despite this, there are no clinical guidelines on who should be prioritized for CORE among people with cancer and referral of all patients would quickly overwhelm current CR programs. Against this background, we aimed to identify which early-stage breast cancer survivors should be prioritized for CORE referral until greater program capacity is available. Novel measures that were explored included, cardiac biomarkers, echocardiography derived global longitudinal strain, cardiopulmonary fitness, measures of vascular function and structure, and clinical data that is prognostic and diagnostic for CVD events in breast cancer survivors. Our research revealed that women treated with potentially cardiotoxic therapies and the presence of ≥2 CVD risk factors, vascular impairments, history of cardiotoxicity, diastolic impairment and impaired GLS (