VictorJuiz/thesis huggingface dataset:
On the Structure of Information Cohomology

We study the cohomology of information structure. Specifically, we formulate and prove analogues to results from other cohomology theories, and use order theory (specifically lattice theory) to obtain information-specific results. We then apply those results. The homological nature of Shannon entropy has been a subject of interest to some for well over fifty years, and yet new approaches are still being suggested and studied. Most recently, P. Baudot and D. Bennequin, and later J. P. Vigneaux have approached this using topoi whose cohomology of Ext functors contain the entropy function as a 1-cocycle. Moreover, both sets of authors have proven, under some conditions on their respective constructions, that the entropy in fact generates all of H^1. This leaves open questions regarding the general structure (algebraic and categorical) of information structures and the possible higher order cocycles. This thesis is aimed at extending the theory behind the constructions of J. P. Vigneaux. In particular, we produce new results arising as analogues to results from other cohomology theories, namely: - The Mayer-Vietoris long exact sequence, to allow for decomposition of information structures into closed information structures; - Shapiro's lemma, to allow for some novel elementary computations, and lastly; - The Hochschild-Serre spectral sequence, to allow for cohomology computations by means of sub- and quotient structures. We also provide structural results of algebraic and order-theoretic nature: we provide means to produce useful projective and injective sheaves over information structures, and furthermore prove a general structural result for projective sheaves. This enables easy computations of projective and injective resolutions that provide novel bounds on the cohomological dimension of information structures. Additionally, by taking into account the order-theoretic nature of information structures, we are able to produce an improved bound based on combinatorial invariants. We finally utilise our results to study the second cohomology groups of some information structures and, in one particular case of interest, find new families of 2-cocycles. Lastly, we provide the alternative perspective in which we can view entropy function as a 1-cocycle by investigating it through the lens of operads rather than topoi. We prove that, in this new theory also, the Shannon entropy generates H^1.

Keywords: Entropy
Homological Algebra
Information Theory
Lattice
Operads
Order Theory
Advisor: Murty, Kumar K.M.
Increasing the Efficiency of Electrochemical Nitrogen Fixation by Using a Direct Nitrate-to-Urea Pathway

With the increasing demand and application of nitrogen fertilizers, electrochemical urea synthesis from CO2 and NO3- is seen as a great alternative to bypass heavily fossil fuel-based production and remediate agricultural leaching. However, urea synthesis remains underdeveloped with a handful of existing literature due to the lack of understanding of reaction conditions. This thesis summarizes my efforts to understand nitrogen electrochemical reduction (Chapter 2), carbon dioxide reduction (Chapter 3), and the combination of two reactions to synthesize urea (Chapter 4). Chapter 2 describes a strategy to improve nitrogen conversion to ammonia catalytic performance via catalyst facet design. We challenged conventional wisdom and found that smooth low-energy facets outcompete irregular high-energy facets on nickel sulfide catalysts. Chapter 3 describes a carbon dioxide conversion project with molecular catalysts to understand CO2 reduction in aqueous systems. We showed adjusting terminal functional groups on metalloporphyrins could affect the electron density on the metal center, thus further altering the reaction pathway. Chapter 4 initially describes our experience in reproducing early studies results, the electrolyte impacts on urea electrosynthesis, and limitations on previous urea detection methods. Initially, we concluded urea synthesis is achievable on a copper catalyst with small current densities and proper electrolyte conditions. NO3- concentration is the most impactful factor and can vary urea selectivity from 0% to 50%. NO2- is observed as the major competing product under small bias and shares a scaling relationship with urea. In the second part of Chapter 4, we demonstrate that limitations in the current colorimetric and 13C NMR methods can lead to false conclusions, which overthrow our previous results. We provide guidelines for reliable urea detection and advocate liquid chromatography-mass spectrometry (LC-MS) as the ultimate detection method due to its high sensitivity for ppm-level urea and isotope-coupled resolving ability. By using a simple LC-MS protocol, ppm-level urea concentrations can be firmly proven with/without isotope labeling reactants.

Keywords: ammonia synthesis
C-N coupling
CO2 reduction
electrocatalysis
nitrate reduction
urea synthesis
Advisor: Voznyy, Oleksandr OV
Inverse Optimization and its Applications in Measuring Clinical Pathway Concordance

Inverse optimization aims to infer the parameters of an optimization problem given a set of observed decisions that are assumed to be optimal or minimally suboptimal. In this thesis, we propose novel inverse optimization techniques and present new applications. In particular, we use inverse optimization in developing a metric for measuring clinical pathway concordance. The resulting metric can help monitor variations in the healthcare system, identify bottlenecks, and provide data-driven evidence to inform health policy decisions. We first propose an inverse optimization-based framework for measuring clinical pathway concordance in diseases with simple patient journeys. We construct a network where the nodes represent the clinical activities, and patients accumulate costs as they visit the nodes and move through arcs. We use linear programming duality to formulate a two-phased inverse optimization problem to obtain arc costs that make the clinical pathways optimal for a shortest path problem on the network. The arc costs are the weights used in the concordance metric. We then provide an in-depth case study applying our inverse optimization-based model and metric to real patient data from stage III colon cancer patients. We also develop a rigorous framework to identify the sources of discordance in the patient population. Next, we propose an approach for learning arc costs in a hierarchical network that can be used to measure clinical pathway concordance for diseases that have complex patient journeys. The overall network comprises nested subnetworks defined over multiple levels in a hierarchical network structure. The hierarchical structure facilitates the modelling of complex pathways by dividing them into shorter sections and allowing us to incorporate crucial contextual information. We then apply our methodology to a large dataset of breast cancer patients. Finally, we extend the inverse optimization methodology by quantifying the uncertainty around the point estimates for unknown parameters of an optimization problem. Even though uncertainty is inherent in inverse optimization with noisy data, the quantification of this uncertainty has been treated very little in the literature. We propose one parametric and one non-parametric approach to estimate the unknown parameters and construct confidence regions for the point estimates.

Advisor: Chan, Timothy T
Achievement and Mental Well-Being in Post-Secondary Education (PSE) Students with Attention Deficit Hyperactivity Disorder (ADHD)

Disproportionately few young adults with Attention Deficit Hyperactivity Disorder (ADHD) are admitted to post-secondary education. If admitted, they are more likely to withdraw from classes or fail to graduate, relative to post-secondary education (PSE) students without ADHD. They are reported to have lower levels of academic self-efficacy, confidence, and competence levels, which ultimately affect their overall achievement and Mental Well-being. Recommended treatments for people with ADHD typically include medication, accommodations to their learning environment, cognitive behavioural therapy, and social supports. In this study, 96 PSE students attending undergraduate and graduate programs at a Canadian university completed surveys to gauge their Mental Well-being, to estimate their current achievement (GPA), and the extent of their ADHD symptoms. They also completed open-ended questions about their physical activity levels, leisure activities, social supports, and coping strategies. Of the 96 participants, 75 responded to the surveys after the start of the COVID-19 pandemic lockdowns. The results showed the effects of physical activity, social supports and coping strategies, and a negative correlation of self-reported symptomology, on the Mental Well-being and achievement scores of the participants. The responses of the majority of those who completed the survey during the COVID-19 pandemic lockdown also demonstrated adaptations and coping strategies that were strength-based, and that indicated resilience and self-determination. The findings suggest that more emphasis should be placed on supporting the self-determination of this population.

Keywords: Achievement
Attention Deficit Hyperactivity Disorder (ADHD)
Leisure activities
Mental Well being
Physical Activity
Resilience
Advisor: Jordan, Anne
Moral Conflict and Socialization

Growing polarization in civil society has motivated scholars to explore the sources of conflict. While top-down explanations emphasizing opportunistic political leaders and changes to media structures have been instructive, micro foundations from the bottom-up remain comparatively less understood. This dissertation addresses the topic by bringing socialization into the fold. I argue that understanding the cleavages and dynamics of political conflict in post-industrial society requires examining reproduction of those moral orientations that underlie it. The dissertation comprises of three studies that elaborate this theme in different ways. Collectively, they focus on a cohort of US Millennials born between 1986-1990 and apply longitudinal data analysis to nationally representative sample following their transition from adolescence into early adulthood (2002-2013). Each chapter takes a staple topic in political sociology as its entry point. Chapter two revisits earlier collective behaviour theories on alienation and social movements. Against current wisdom, the analysis finds that alienation in the forms of meaninglessness and social isolation are among the strongest predictors of participation in populist movements of both left-wing and right-wing persuasions. Chapter three examines moral socialization in higher education with an eye to ‘culture war’ conflict. It challenges the self-selection hypothesis by finding evidence of substantial moral change. Students mostly move in a liberal direction, prioritizing individual harm reduction over traditionalism and come to believe morality ought to be reformed to reflect social changes. They depart from the typical liberal profile on moral relativism, however, by becoming increasingly assured of definite rights and wrongs. Chapter four explores social reproduction of expressive individualism – a moral orientation valorizing change over conservation. While modernization theory treats expressivism as an aggregate phenomenon, the study finds that diffusion proceeds more unevenly, affecting the population at differential rates. Reproduction involves both continuity and conflict: expressive orientations can be transmitted through liberal aesthetic upbringings but are also associated with parent-child conflict where explicit parental transmission isn’t required.Taken together, the studies unpack the micro foundations of polarization by situating moral orientations underlying conflict and change at the tail-end of early-life enculturation processes.

Keywords: Millennial
Moral conflict
Moral development
Polarization
Social Movements
Socialization
Advisor: Silver, Daniel A
The Helping Paradox: Understanding the Psychological and Emotional Effects of Bystander Intervention in Situations of Sexual and Dating Violence on Campus

Prevalence of gender-based violence (GBV) on postsecondary school campuses is a major public and institutional concern. Effective prevention includes bystander intervention training, which fosters a culture of responsibility among community members for each other’s safety. Bystanders’ prosocial response is critical for reducing harm, though little is known about the mental health impacts of intervention on student bystanders. This study’s purpose was to: 1) examine the psychological and emotional effects student bystanders to GBV experienced; 2) identify individual and situation-specific factors influencing these outcomes; and 3) determine the role of effects on intentions to help in the future and to participate in intervention training. Participants were students at Canadian postsecondary schools who indirectly encountered sexual or dating violence during their tenure (N = 126). The study relied on a cross-sectional design and was conducted via online survey. Measures assessed psychological and emotional experiences and reflected underlying theoretical constructs of vicarious trauma (VT), secondary traumatic stress (STS), and vicarious posttraumatic growth (VPTG). More bystanders intervened (n = 100) than not (n = 26), often in situations perceived as serious and when closely connected to the victim involved. Participants reported positive and negative psychological and emotional effects. Multivariate analyses investigating differences in impacts between groups revealed a mediation model; variables related to incident seriousness, validation, and confusion explained the relationship between intervention behaviour and outcomes. Upon further exploration of predictors, regression analyses also identified bystanders’ personal GBV history and self-mastery influenced outcomes. Positive feelings and perceived ineffectiveness in bystander action emerged as significant positive predictors of most outcomes, though the latter had greater influence on negative psychological and emotional effects. Moreover, bystanders’ willingness to help in the future and to partake in training were affected by their intervention behaviour and some psychological and emotional experiences. This study contributes to a deeper understanding of the ripple effect of trauma on students indirectly exposed to GBV, including the factors associated with their experiences of VT, STS, and VPTG. Findings underscore the importance of intervention training to improve bystanders’ knowledge and skills to respond effectively when confronted with an incident and perhaps lessen potential adverse impacts.

Keywords: Bystander Intervention
Dating Violence
Intimate Partner Violence
Mental Health
Sexual Violence
Student mental health
Advisor: Stermac, Lana
Real-time Hybrid Simulation with Higher-fidelity Numerical Substructure: Methodology, Implementation, and Application

Real-time hybrid simulation (RTHS) is an efficient experimental testing technique proposed for the seismic evaluation of structures. In RTHS, structural components that are challenging to characterize numerically are physically tested using actuators, known as the experimental substructures, while the remaining part is numerically modeled, referred to as the numerical substructure. The real-time execution mode of RTHS enables the capture of rate-dependent behaviors, but it also presents challenges in actuator control for the experimental substructure and computational efficiency for the numerical substructure. To address these limitations, this study proposes outer-loop controllers to minimize tracking errors while applying target displacement to the experimental substructure during testing. An adaptive phase-lead compensator (APLC) supplemented with a proportional-integral-derivative controller has been introduced to enhance the performance of the actuators in trajectory tracking and applied to study a benchmark control problem defined for RTHS. Following the adaption logic of APLC, a model-based adaptive outer-loop controller, namely an adaptive discrete feedforward controller (ADFC), formulated as the inverse of the transfer function model of the servo-hydraulic loading system in RTHS, has been proposed. Additionally, a computational tool, namely Hybrid2D, optimized for real-time execution, has been developed to enable a concentrated plasticity model in RTHS, improving the fidelity of the numerical substructure. Modeling theory, along with the procedure to interface the Hybrid2D with real-time targets, is elaborated. To address the challenge when the complexity and the number of the computational degrees of freedom (DOFs) of the numerical substructure exceed the limit of computational hardware, multi-rate RTHS (mrRTHS) has been introduced, where the actuator control with experimental substructure and the computation with numerical substructure are executed at different rates. After investigating the data transmission within mrRTHS, the proposed ADFC is applied to compensate for the experimental errors in mrRTHS. In the end, an mrRTHS example, which investigates the seismic performance of a nine-story five-bay base-isolated structure controlled by gyromass dampers, is conducted to demonstrate the effectiveness and potential of the proposed ADFC, Hybrid2D, and mrRTHS implementation strategy in facilitating the application of RTHS in complex structural systems.

Keywords: adaptive controller
concentrated plasticity
gyromass damper
multi-rate
real-time hybrid simulation
Advisor: Mercan, Oya O.
Transformation of Hippocampal Memory Engram Cells and the Role of Adult Hippocampal Neurogenesis

The hippocampus is a 'plastic' neural structure where divergent sensory information from upstream cortices converges and is allocated into a subset of hippocampal neurons during learning. The reactivation of these neuronal ensembles elicits memory retrieval, and are referred to as memory engram cells. It is well understood that the hippocampus and hippocampal engrams play a crucial role in memory formation. However, there is a discrepancy regarding the role of the hippocampus in remote memory storage and retrieval. While some research suggests that the involvement of the hippocampus in remote memory retrieval is transient, other studies indicate that the hippocampus is involved in functions such as the retrieval of autobiographical memory and memory-guided higher cognitive functions, even at remote time points. To gain insight into the role of the hippocampus in remote memory retrieval, I investigated the reactivity of hippocampal memory engram cells during the retrieval process. I demonstrated that remote hippocampal engrams are not only reactivated in the specific environment where mice initially formed the memory, but also in similar environments, and this generalized engram reactivity is essential for remote memory-guided generalized behavior. Moreover, I investigated whether modifications in the structure of the engram circuits affect the change in functional reactivity of the remote engrams. I found that while the learning-dependent enhancement of recent engram circuit structures is correlated with episodic-like retrieval, the reduced but still maintained synaptic connectivity of remote engrams is associated with generalized engram reactivity and behavior. Lastly, I examined whether adult hippocampal neurogenesis plays a role in driving the rewiring of remote hippocampal engram structure. I demonstrated that newly generated neurons are integrated into the existing engram circuitry, thereby contributing to the transformation of both the structure and function of remote hippocampal engrams. My research provides evidence for the enduring role of hippocampal memory engram cells in memory-guided cognitive processes and behavior.

Advisor: Frankland, Paul W
Fair and Efficient Social Decision Making

Algorithms have had a remarkable impact on human. Consequently, it is more important than ever to design decision-making algorithms that treat people fairly, use limited resources efficiently, and foster social good. We contribute toward this direction by optimizing decision-making for social good, analyzing and recommending improvements to methods of civic participation, and suggesting novel notions of fairness for domains like machine learning and peer review. In Part I, we focus on optimizing decision-making for social good in two real-life applications. First, we consider the problem of allocating pooled diagnostic tests during an epidemic. We design novel algorithms that approximately maximize the social welfare and complement our theoretical results with experiments on real data. Second, we analyze the effect of different grading systems on student performance and identify conditions under which certain grading systems are optimal. In Part II, we study three approaches used to increase civic participation. The first approach, called sortition, is based on the idea of choosing representatives for decision-making by lot. We suggest selection algorithms that satisfy fairness and efficiency guarantees. The second approach, called participatory budgeting, engages citizens in decision-making by having them vote on the allocation of limited public resources. We formalize the idea of individual fairness in participatory budgeting and study to what extent it can be guaranteed. The last approach is to conduct polls for an election to betterengage citizens, and we study the effectiveness of polls for predicting the outcome of ranked-choice elections. In Part III, we leverage the economic literature to propose meaningful notions of fairness for novel application domains. First, inspired by the idea of core in economics, we provide approximate fairness guarantees for clustering. Second, we utilize Nash welfare from welfare economics to design fair algorithms for a multi-agent variant of the classical multi-armed bandits problem. Third, we adopt a popular fairness notion from the fair division literature, namely envy-freeness, to two-sided matching markets. Fourth, we also import core, to the problem of reviewer assignment and to the problem of teamformation.

Keywords: algorithmic fairness
computational social choice
Advisor: Shah, Nisarg