VictorJuiz/thesis huggingface dataset:
On Surgeon Stress: An Exploration of Distress and Eustress

Rationale: High rates of distress among surgeons, and physicians more broadly, are alarming and negatively impacting provider wellness, performance, and healthcare system functioning. Yet despite increased attention, we continue to struggle in addressing stress in practice. One challenge is the lack of a shared vocabulary for understanding stress, which is a complex and idiosyncratic phenomenon that can be experienced both positively (as eustress) and negatively (as distress). While traditional reductionist approaches focusing on individual facets –such as physiology or cognition– have greatly advanced scientific knowledge, stepping back to examine the composite, subjective experience of stress may provide new, complementary insights for mitigating distress and supporting more favorable states in practice. Objectives: This research program explored the multidimensional, integrated experience of stress among academic surgeons in order to establish conceptual frameworks for understanding surgeon distress and eustress in practice. Design: Qualitative data were collected over three phases, guided by a constructivist grounded theory methodology. Semi-structured interviews were conducted with staff surgeons affiliated with the University of Toronto, purposively sampled to capture a broad range of perspectives including specialty and experience level. Results: Eustress and distress were reconceptualized as multidimensional experiences, comprising physiologic, physical, cognitive, affective, social, cultural, and environmental facets. Subjective control was identified as central to the stress experience. In contrast to traditional dichotomous views placing eustress and distress on two polar ends of a spectrum, findings illustrated how experiences of distress and eustress could overlap and coexist. Conceptual frameworks were established for understanding eustress and distress, and how physicians might reflect on their own experiences in practice. Conclusions: Stress is not simply positive or negative, cognitive or physiologic, or individual- versus system-based; understanding the points of intersection and seeking to embrace inherent complexities in the subjective human experience may provide deeper understandings and allow us to approach stress in a more authentic way.

Keywords: Distress
Eustress
Flow
Medical Education
Surgeon Stress
Surgical Education
Advisor: Moulton, Carol-anne
Optimal Attention

In many environments it is costly for decision makers to determine which option is best for them because learning about different options takes time, and time is valuable to decision makers. To accurately predict changes in behavior and estimate preferences in such settings it is crucial to understand what information agents choose to acquire. In the following three chapters I study costly information acquisition both theoretically and experimentally. In the first chapter I characterize the choice patterns that are consistent with two major models of costly learning when price changes and then test these theoretical results in a lab setting. The experiment shows that the heterogeneity of subjects is key for understanding aggregate behavior and that subjects are sophisticated when choosing both if and what to learn. In the second chapter I use an axiomatic foundation to create a new measure for the cost of information that allows for multiple perceptual distances in a single choice environment so that some events can be harder to differentiate between than others. In the third chapter I show that the new measure of uncertainty produced in the second chapter maintains the tractability of Shannon's classic measure but produces richer choice predictions, identifying a new form of informational bias significant for both welfare and counterfactual analysis. The third chapter also establishes a new foundation for `non-compensatory' behavior anddemonstrates novel predictions for the formation of consideration sets.

Keywords: Costly Learning
Inattention
Posterior Separable
Shannon Entropy
Advisor: Deb, Rahul
Stewart, Colin
My Heart is in Sepharad: Writing Medieval Spain in the Modern Sephardic Diaspora

This dissertation investigates the cultural construction of a modern global diaspora by foregrounding the historical, linguistic and nationalist contexts embedded in literary works produced over the last two centuries, written by a diverse collection of writers who imagine a preserved ethnoreligious community. Sephardim—descendants of medieval Jews expelled from Spain in the fifteenth century—have sustained an ethnic heritage by drawing on the figures, texts, and experiences of medieval Iberia, specifically the Golden Age of al-Andalus (ca. 950- 1140) and the anti-Jewish measures in Christian Spain that led to the Inquisition and expulsion (ca. 1391-1492). I argue that by reading, translating, and rewriting the rich literature of medieval Iberian Jews, from Andalusian liturgical poetry to Kabbalistic commentaries to converso epistles, modern Sephardim creatively interpret the historical and political significance of their ancestral homeland. To conceptualize such diasporic identities, I compare temporalities, regions, and genres, consistently juxtaposing major forms of literature written by Iberian Jews throughout the Middle Ages with those written by their descendants.This study of a modern diaspora also compares the role of contemporaneous historiography, from Romantic historicism to the Wissenschaft des Judenthums to archival research of the Cairo Geniza, in accessing the history and texts of a former homeland. I furtherexplore the intertextual strategies through which modern Sephardim imagine the medieval Jewish experience, constructing palimpsests that bring influential texts into relief by writing around and about them in order to situate artistic expressions, such as those in homoerotic or Romantic poetry, as a medieval inheritance. By adopting genres from al-Andalus, where quantified meters of poetry and conventions of form reflected Jewish acculturation in a predominantly Arab society, Sephardim writing in Hebrew, in particular, produce experimental, actively archaic forms of poetry and narrative. Finally, this study addresses how Sephardim deploy medieval tropes, such as the intolerance of the Inquisition or the heroic pilgrimage of an Andalusi poet, to project an ethnicity onto modern nationalist identities.

Keywords: al-Andalus
Diaspora
Inquisition
Jewish
Sephardi
Spain
Advisor: Ross, Jill
New Elemental Mass Tags for Mass Cytometry Applications

This thesis describes the synthesis, characterization, and application of four new elemental mass tags for mass cytometry immunoassays. Mass cytometry is an emerging bioanalytical technique that uses metal-tagged antibodies (Abs) for high-dimensional single-cell analysis. Currently, this technique can measure ~ 50 parameters per cell with metal-chelating polymer (MCP)-based reagents. My research focused on developing new elemental mass tags with the goal to further expand the multiplexity of current immunoassays. The first mass tag (Chapter 3) was based on the azide-functionalized tantalum oxide nanoparticles (NPs). These NPs could react with DBCO-functionalized Abs to generate the metal-tagged Abs for detection of both high-abundance and low-abundance cellular biomarkers. This tantalum oxide NP-based mass tag opened the 181Ta mass channel for mass cytometry immunoassays. The second mass tag (Chapter 4) was a rhenium-containing polymer. The polymer had ca. 20 pendant dipicolylamine (DPA)-containing chelators for complexing with the rhenium tricarbonyl core ([Re(CO)3]+) and a maleimide end group for Ab attachment. Ab-polymer conjugates prepared by thiol-maleimide reaction were employed in multiplexing immunoassays to identify cell subsets within PBMCs. This rhenium-containing polymer allowed the 185Re and 187Re mass channels to be used in mass cytometry immunoassays. The third mass tag (Chapter 5) was a clickable platinum-chelating polymer. This polymer shared a similar chemical structure to that of the rhenium-containing polymer but with some side chains of the polymer being modified with azide functional groups. Platinum (II) was loaded to the polymer by treating the polymer with platinum salt (K2PtCl4) in methanol. The Ab-polymer conjugate prepared by a DBCO-azide click reaction gave promising results in mass cytometry immunoassays. The development of this platinum-chelating polymer added the six Pt isotopes (m/z, 190, 192, 194, 195, 196, 198) to the repertoire of MCPs for biomarker detection. The last mass tag (Chapter 6) was a tellurium-containing polymer with a poly-l-lysine backbone. This polymer carried ca. 48 tellurophene units per chain and had a terminal azide group for antibody conjugation. Antibody-polymer conjugates prepared via the DBCO-azide click reaction were tested in mass cytometry immunoassays and gave curious results.

Keywords: Elemental mass tags
Mass cytometry
Advisor: Winnik, Mitchell A
Language-based Lifting of Analyses to Software Product Lines

Software Product Lines (SPLs) allow software engineering teams to develop families of products together using a common set of artifacts. The goal is to maximize reuse of artifacts (requirements, design models, source code, test cases, etc...) across related products. Many of the analysis tools (e.g., static analysis, model checking, test-case generation) used throughout the engineering process are designed to analyze one product at a time. Several attempts have been made to \emph{lift} some of those analysis tools to analyze the whole product line at once, leveraging the amount of commonality between individual products to avoid the intractability of analyzing each product instance by itself in a brute-force fashion. In this thesis, we address the problem from a different angle. Instead of lifting an analysis, we lift languages used to implement analyses. Since a given analysis is a program written in some programming language, extending that language with variability-aware constructs would allow for reinterpreting the original analysis program to process a product line instead of a single product. We presented the lifting of two languages: Datalog, and an extended version of Programming Computable Functions (PCF). Datalog combines relational data definition and manipulation, together with logical inference. It has been used to implement different categories of declarative, succinct, and easy to understand software analyses. We present syntactic extensions to Datalog to allow variability annotations of facts, and a variability-aware inference algorithm. In addition, we present two approaches to automatic variability-aware lifting of software analyses written in a functional language (PCF+). A light-weight, black-box approach (shallow lifting) wraps an analysis in its variability-aware version. The second approach (deep lifting) is an automatic program-rewriting mechanism that translates an analysis program into a semantically equivalent variability-aware analysis. We evaluated the lifted versions of Datalog and PCF+ on a set of Java and C-language benchmarks, comparing their performance against brute-force analysis. Our evaluation results validate our assumption that variability-aware analysis is in general orders of magnitude faster than brute-force analysis. Also in the case of Datalog analyses, variability-aware analysis resulted in similar savings in fact database size.

Keywords: Datalog
Functional Programming
Lifting
Software Analysis
Software Product Lines
Advisor: Chechik, Marsha
Air Quality and Energy Implications of Filtration System and Operation Strategies in Residential Buildings

Central forced-air HVAC systems with filters are commonly used in North American homes to reduce particle concentrations and occupant exposure. The filtration performance of these systems is strongly influenced by many system-, filter-, and building-specific parameters including system runtime, cycle on-time, recirculation rate, in-situ filter efficiency, and building air change rate. These parameters vary considerably across homes, and in the same home over time. Thus, characterization of these parameters is essential for filtration system performance evaluation. The goal of this dissertation is to improve the performance of residential filtration systems by advancing the understanding of their influencing parameters. This work first assesses the runtimes in both heating- and cooling-dominated climates. The results show that there were large variations in runtimes and the mean daily runtime was less than 20% in the studied homes. Secondly, this work evaluates the in-situ efficiency of four types of filters in 21 residences in Toronto, Ontario. The results show that the variations for the same type of filters across homes were greater than the differences between filter types. Comparisons between in-situ and lab-tested filter performance further confirm that the lab-tested results of a filter may not well represent its in-situ performance – they generally overestimate the efficiency and underestimate the pressure drops. Lastly, this work examines the range of effectiveness of the HVAC filtration systems with varying influencing parameters through a time-varying mass balance model. The results show that in addition to runtimes, when and how long (i.e., cycle on-time) the system operates also influences its performance. Based on this finding, four filtration operation strategies (concentration, source, pulsed, and concentration pulsed) are evaluated, and the results show that operating the system during periods with high indoor concentrations could achieve a comparable level of effectiveness as continuous operation but at runtimes as low as 80%. This strategy provides opportunities to reduce indoor particle concentration while conserving fan energy use. Overall, this dissertation provides a framework with tools and models to characterize the influencing parameters of residential filtration, evaluate filtration performance, and provide guidance on operation strategies for performance improvement.

Keywords: Exposure reduction
Filter efficiency
Filtration effectiveness
HVAC system
Indoor air
System runtime
Advisor: Siegel, Jeffrey A
Aerial Robots for Wireless Coverage, Traffic Monitoring, and Transport Applications: A Path Planning and Fleet Management Perspective

Unmanned aerial vehicles (UAVs) have recently attracted significant attention in a wide range of commercial and civilian applications. Wireless coverage, traffic monitoring, and transport are among the emerging applications of UAVs for future smart cities. This study focuses on developing efficient path planning and fleet management algorithms for the UAVs in the mentioned applications. We begin by modeling a UAV-assisted communication network where the UAVs are used as the aerial base stations. We solve the trajectory optimization and resource allocation problem for the UAVs to improve the coverage of ground users. We extend this study to the Internet of Things (IoT) networks where UAVs are utilized to collect data of IoT devices. We propose an efficient algorithm that generates multi-channel representations from the data collection problem and uses deep reinforcement learning (RL) to solve it. Next, we study the path planning problem under connectivity constraints, which is important for applications such as traffic monitoring and drone delivery. We propose a model-aided path planning algorithm that uses federated learning to create a model of the connectivity in the environment. Using this model and rapidly exploring random trees, our algorithm optimizes the UAV path to guarantee connectivity. We extend our study to a traffic monitoring system where a team of UAVs monitors the traffic condition in a road network. To address the stochastic nature of traffic events, we define an uncertainty metric for the road network. We develop a scalable algorithm based on distributed RL to solve the multi-UAV traffic monitoring problem. Our goal is to minimize the network’s average uncertainty even in scenarios where the UAVs’ observations are partial, and the information exchange between the UAVs and the traffic management center is limited. Finally, we study the dynamic UAV-assignment problem for a drone delivery system. We adopt a queuing theoretic approach to model the customer-service nature of the problem and use deep RL to relocate the UAVs in the system. Our RL-based method guarantees a probabilistic upper bound on the queue length of the packages waiting in each package distribution center. Moreover, it outperforms state-of-the-art schemes for different arrival classes.

Keywords: Aerial Robots
Drone Delivery
Fleet Management
Path Planning
Traffic Management
Advisor: Sousa, Elvino Silveira
Approximate Ramsey Methods in Functional Analysis

We study various aspects of approximate Ramsey theory and its interactions with functional analysis. In particular, we consider approximate versions of the structural Ramsey property and the amalgamation property within the context of multi-seminormed spaces, Fréchet spaces and other related structures from functional analysis. Along the way, we develop the theory of Fraïssé limits of classes of finite-dimensional Fréchet spaces, and we prove a version of the Kechris-Pestov-Todorcevic correspondence relating the approximate Ramsey property to the topological dynamics of the isometry groups of certain infinite-dimensional Fréchet spaces. Motivated by problems regarding the structural Ramsey theory of Banach spaces, we study various generalizations of the Dual Ramsey Theorem of Carlson and Simpson. Specifically, using techniques from the theory of topological Ramsey spaces we obtain versions of the Dual Ramsey Theorem where ω is replaced by an arbitrary countable ordinal. Moving toward block Ramsey theory, we prove an infinite-dimensional version of Gowers’ approximate Ramsey theorem concerning the oscillation stability of S(c0), the unit sphere of the Banach space c0. We then show that results of this form can be parametrized by products of infinitely many perfect sets of reals, and we use this result toobtain a parametrized version of Gowers’ c0 theorem.

Advisor: Todorcevic, Stevo
Lopez-Abad, Jordi
Ambiguity Aversion in Commodity Markets

This thesis explores the impact of model uncertainty on commodity market models. The commodity markets include various idiosyncratic uncertainties that are not present in other markets, and this thesis is the first to provide a thorough and detailed analysis of their effects. We assume that agents acknowledge the possibility of model misspecification, adopt the framework of ambiguity aversion to protect themselves, and utilize a robust indifference pricing framework for valuation and hedging. Most exotic financial options written on commodities are valued by discretizing continuous-time and state models. Thus, in the first part of this thesis, we study the impact of ambiguity aversion in discrete-time and state using trinomial trees. We develop a general mathematical framework for incorporating model uncertainty, compare it with existing methodologies, and look at practical examples, including the valuation of swing options with model uncertainty. Next, we consider the continuous-time formulation of model uncertainty by investigating option pricing with both stochastic volatility and Levy jumps. We provide analytical characterization of ambiguity, robust prices, and resulting hedging strategies and provide perturbative approximations when analytical methods fail. A key insight is that the effect of ambiguity aversion is the strongest at-the-money and the weakest deep-in- or deep out-of-the-money. Finally, we study electricity interconnectors from the perspective of statistical arbitrage with model uncertainty. We provide closed-form optimal strategies when there is no ambiguity and obtain perturbative approximations of the robust optimal strategies. We illustrate the efficacy of the resulting strategies on simulated data. We propose a calibration algorithm on jump-diffusion data to bridge the abstract mathematical theory and real-world dynamics.

Keywords: Ambiguity Aversion
Commodity Markets
Model Uncertainty
Stochastic Control
Advisor: Jaimungal, Sebastian SJ
An Exploration of How Age and Aging Shape Caregiving Experiences in Oldest-old Spousal Caregivers of People with Dementia

Oldest-old spousal caregivers (CGs) of people with dementia are a fast growing, highly vulnerable, yet poorly understood population. As oldest-old individuals, these CGs have a high likelihood of experiencing aging-related changes (e.g., frailty, multimorbidities) that result in unique caregiving experiences and support needs. Specialized interventions, sensitive to CGs' age- and aging-related experiences and needs, may be required to provide adequate support to this group. To date, this group has received limited attention in the literature. The purpose of my dissertation was to elucidate how age and aging shape caregiving experiences in oldest-old spousal CGs of people with dementia. I conducted two studies. In the first, I used a meta-study methodology to explore existing qualitative research on the experiences of this caregiving subgroup. The meta-study revealed a critical lack of research exploring issues of age and aging in the caregiving role, and illuminated numerous limitations to be considered in future research. In the second study, I used a narrative gerontology approach to explore issues of age and aging from CGs' perspectives. Data were analyzed to address two separate objectives. The first analysis explored CGs' perceptions of how their experiences are shaped by age and aging and caregiving. Results illustrated how age and aging can shape CGs' experiences through a multitude of age- and aging-related factors such as physical decline, changes in social networks, life experiences, socio-cultural norms, and stereotypes. The second analysis explored how CGs manage age-related caregiving challenges. CGs recounted the use of a myriad of strategies to master their caregiving goals and to reduce stress, and reported on the facilitators and barriers to using different strategies. Together the studies illustrated many ways in which age and aging matter in caregiving. This research can ultimately help inform the tailoring of caregiving supports to the unique age- and aging-related experiences and needs of oldest-old spousal CGs of people with dementia.

Keywords: Aging
Caregivers
Dementia
Oldest-old
Advisor: Dawson, Deirdre R.
High Precision Real World Perception for Autonomous Driving

The captivating hopes for a future with autonomous vehicles promises to free us from one of the most laborious, monotonous, and dangerous of our daily tasks. This thesis presents several works that I have undertaken to improve the perception capabilities of autonomous vehicles, with a particular attention to high precision. The first part of this thesis describes a technique to exploit the rich, high resolution camera information and the unambiguous 3D measurements of LiDAR to detect lane boundaries in 3D in real time with centimeter-level accuracy. We show that our approach significantly outperforms the baselines, particularly at longer distances. Next, we leverage the implicit boundary parameterization used in the previous work in a multi-task neural network for panoptic segmentation in the image space, with the goal of categorizing each pixel in an image into various semantic classes and delineating different instances of countable objects. We compare with the state-of-the-art technique and show that our approach can produce more accurate object boundaries. Finally, we bring temporal information into the segmentation task. Here, we leverage the full history of observations and reasoning results encoded into a non-parametric memory structure and intelligently incorporate new information at each instant in time to perceive small, safety critical objects such as construction elements in 3D.

Keywords: Autonomous Driving
Computer Vision
Machine Learning
Advisor: Urtasun, Raquel