VictorJuiz/thesis huggingface dataset:
Toward an Understanding of the Novelistic Dimension of Jean-Jacques Rousseau’s Emile
Keywords: eighteenth century
Enlightenment
politics and literature
Rousseau
state of nature
theories of education

The multi-genre combination of philosophic and literary expression in Rousseau’s Emile provides an opportunity to explore the relationship between the novelistic structure of this work and the substance of its philosophical teachings. This dissertation explores this matter through a textual analysis of the role of the novelistic dimension of the Emile. Despite the vast literature on Rousseau’s manner of writing, critical aspects of the novelistic form of the Emile remain either misunderstood or overlooked. This study challenges the prevailing image in the existing scholarship by arguing that Rousseau’s Emile is a prime example of how form and content can fortify each other. The novelistic structure of the Emile is inseparable from Rousseau’s conception and communication of his philosophy. That is, the novelistic form of the Emile is not simply harmonious with the substance of its philosophical content, but its form and content also merge to reinforce Rousseau’s capacity to express his teachings. This dissertation thus proposes to demonstrate how and why the novelistic dimension of the Emile belongs to it not mechanistically, but integrally. To illustrate this central idea, this dissertation is divided into three sub-themes, each of which is motivated by a corresponding major research question: (1) Rousseau as a writer of a novel: What is the function of the novelistic structure of the Emile? (2) Rousseau as an appropriator of a novel: To what end does Rousseau use (and abuse) novels in the Emile as part of Emile’s and Sophie’s pedagogical programs? (3) Rousseau as a fictional character in a novel: What is the function of Rousseau’s fictionalized self-representation in the Emile as Jean-Jacques the tutor? The findings of each chapter prompt a re-thinking of Rousseau’s novelistic technique in the Emile as it relates to the substance of his broader philosophical teachings.
Continuous-time Latent-variable Models for Time Series
Keywords: Cancer evolution
Latent-variable models
Neural ODE
Time series

Time series data play a crucial role in many applications, such as biology, medicine, economics, engineering and others. One of the most powerful approaches for time series is latent-variable models, thanks to their ability to handle multi-dimensional data with complex interactions. Typically, these models represent a timeline as a sequence of discrete states and therefore assume that observations occur at regular intervals. However, this assumption does not always hold. An illustrative example is medical records, where a patient is screened only when the need arises, resulting in the irregularly-spaced and possibly sparse time series. In this type of time series, the time intervals between the observations can provide valuable information about the time series, such as patient's health condition. To bridge the gap between the data and the available models, a common approach is to convert a continuous timeline into a discrete one by aggregating observations into a sequence of discrete clusters. However, this transformation erases a lot of the temporal structure of the data and prevents us from utilizing the data to its full potential. In this thesis, I present latent-variable models for continuous-time data across several application domains. First, I present a method for ordering cancer mutations on a linear timeline and use a mixture model to summarize them into a set of trajectories over time. Thanks to this ordering, I perform a more fine-grained discretization of the cancer timeline in comparison to the previous methods and can more accurately detect the changes in cancer dynamics. Next, I introduce Latent Ordinary Differential Equations (Latent ODE) -- a framework that allows to model time series as a solution of a differential equation, in other words, as a continuous function over time. Unlike the previous models, this approach does not require any discretization of the data and can naturally handle irregularly-spaced time points. I showcase the potential of the model on the interpretable dataset. I demonstrate that the Latent ODE model has better extrapolation properties and is more robust to noise compared to existing sequential models. Finally, I improve the Latent ODE model by proposing an ODE-based recognition model. I demonstrate that, by preserving the observations on a real-values timeline and modelling them as a continuous function, we can get improvement in a variety of tasks, such as forecasting, imputation and classification.
Managing the Surface of Infrared Colloidal Quantum Dots for Photovoltaics
Keywords: Infrared
Nanotechnology
Photovoltaics
Quantum Dot
Solar Cell

Colloidal quantum dots (CQDs) are a thin film technology that has been used in optoelectronic devices such as lasers, light emitting diodes, photodetectors, and solar cells. CQDs exhibit size-dependent absorption and are capable of harvesting light beyond the bandedge of many traditional semiconducting materials, making them of interest as the back cell in multijunction solar cells. Specifically, using CQDs with an exciton peak positioned beyond 1100 nm – IR-CQDs – allows short-wave infrared (SWIR) absorption beyond silicon’s bandgap. The aim of this thesis is to understand how passivation is achieved in IR-CQDs; and to use these insights to create IR-CQDs that advance IR harvesting performance. The crucial performance metric to be advanced is IR power conversion efficiency (IR-PCE) – the power points added by harvesting light transmitted by a silicon solar cell. I began by tackling the problem of improving CQD colloidal stability, an advance necessary to the fabrication of high-quality films. I developed a halide re-shelling exchange that stabilizes the IR-CQDs in polar solvents. The resulting solar cells increased the IR-PCE to 0.76%, greater than the IR-PCE of 0.4% achieved in the best previously-reported ink-based IR-CQD solar cells. Different halide passivants were the focus of my next effort. Using a mixed lead halide (MPbX) PbS ligand exchange, I improved both passivation and transport in CQD solar cells. MPbX-PbS solar cells reached an external quantum efficiency (EQE) of 80% at their exciton at 1210 nm and an IR-PCE of 1.17%. I then went on to study how smaller-bandgap (0.7 eV) IR-CQDs could be used to fabricate solar cells. A blade coating strategy was developed to fabricate micrometer thick films capable of harvesting 60% of available solar photons. The resulting IR-CQD solar cell reached an EQE exciton peak of 80% at 1670 nm, resulting in a record IR-PCE of 1.57%. This thesis presents a suite of strategies to passivate IR-CQDs. The techniques developed in this work offer routes to liquid-processed optoelectronic devices in solar energy and beyond.
Morphology and Connectivity Impairments in Rett Syndrome Human Stem Cell-Derived Neurons
Keywords: neuroscience
Rett syndrome
stem cells

Rett syndrome (RTT) is a rare neurodevelopmental disorder affecting 1 in 10,000 females born, leading to early progressive loss of developed language, cognition and motor skills. RTT is primarily caused by heterozygous loss-of-function mutations in the X linked gene encoding the global transcriptional regulator methyl-CpG-binding protein 2 (MECP2). MECP2 undergoes alternative splicing of its exon 2 resulting in the MECP2e1 and MECP2e2 isoforms, whose functions in the brain remain unclear. MECP2 is also alternatively polyadenylated to yield MECP2 short and long 3’-UTR isoforms that can undergo post-transcriptional regulation during neurodevelopment. In this thesis, I modelled RTT using human neurons differentiated from embryonic and induced pluripotent stem cells (ESCs/iPSCs) to study perturbations of MECP2. The overall hypothesis is that MECP2 mutations or changes of its isoform levels in human cortical neurons cause impairments in morphology and electrophysiology. In the first part of this thesis, I studied how modulation of MECP2 level can alter cell size and established a platform for evaluating the functional connectivity of neurons. I demonstrated that iPSC-derived neurons from an RTT patient with an MECP2e1 isoform-specific mutation have soma area defects that were rescued by expression of MECP2e1, while remaining unaffected by MECP2e2. I also showed in control ESC-derived neurons that over-expressed PUM1 consistent with destabilization of the MECP2 long 3’-UTR isoform led to phenocopy of the smaller soma area observed in RTT. Although an automated morphology-based approach for phenotyping neurons was not robust, I established a micro-electrode array (MEA) platform for assessing the network activity of control excitatory neurons. In the latter part of this thesis, I used isogenic gene edited and RTT patient reprogrammed stem cell-derived neurons to evaluate a range of phenotypes associated with different types of MECP2 mutations. I investigated the consequences of a functionally mild MECP2 L124W mutation in RTT patient neurons and used MEAs to identify novel network circuitry impairments in MECP2 null excitatory neuronal cultures. In summary, this work provides insights into RTT using human stem cell-derived neurons to reveal a wide spectrum of severe and core shared morphological and circuitry changes associated with MECP2 dysfunction.
The Effect of Empagliflozin on Left Ventricular Mass and Myocardial Extracellular Volume in Patients with Type 2 Diabetes and Cardiovascular Disease
Keywords: cardiac magnetic resonance
cardiac structure
empagliflozin
left ventricular mass
myocardial extracellular volume
sodium glucose cotransporter 2 inhibitors

Background: The sodium glucose co-transporter 2 (SGLT2) inhibitors improve cardiovascular (CV) outcomes in patients with type 2 diabetes (T2DM) and established CV disease (CVD) but the mechanism driving their cardio-benefit remains poorly understood. Specifically, whether SGLT2 inhibitors have direct cardiac effects remains unknown. Objective: To evaluate the effects of the SGLT2 inhibitor empagliflozin on left ventricular (LV) mass index (LVMi) and myocardial extracellular volume (ECV) in individuals with T2DM and CVD. Method: We randomized 97 participants with T2DM and CVD to receive empagliflozin 10 mg daily or placebo for 6 months in addition to standard of care therapy. The primary outcome was change in LVMi from baseline to 6 months determined by cardiac magnetic resonance. Other outcomes included change in indexed LV end diastolic volume (LVEDVi), end systolic volume (LVESVi) or ejection fraction (LVEF) and in circulating N-terminal pro B-type natriuretic peptide (NT-proBNP) and troponin I. For our sub-study with 74 participants we assessed ECV, indexed intracellular and extracellular compartment volume (iICV, iECV), the fibrosis biomarkers soluble suppressor of tumorgenicity (sST2) and matrix metalloproteinase-2 (MMP-2). Results: In the primary study, change in LVMi from baseline to 6-months was -2·6 (7·8) g/m2 for the empagliflozin group and -0·01 (5·7) g/m2 for placebo (adjusted difference between groups -3·4 g/m2; 95% Confidence Interval (CI), -5·9 to -0·8; P=0·01). There were no significant differences between groups for LVEDVi, LVESVi, LVEF nor in NT-proBNP and troponin I. In our sub-study, empagliflozin reduced ECV compared to placebo (adjusted difference: -1.4%; 95% CI, -2.6 to -0.14; P=0.03). This reduction in ECV in the empagliflozin group was observed after 6 months, alongside the reduction in LVMi. Empagliflozin therapy compared with placebo, also reduced iECV (adjusted difference: -1.5 mL/m2; 95% CI, -2.6 to -0.5; P=0.006) with a trend towards reduction in iICV (adjusted difference: -1.7 mL/m2; 95% CI, -3.8 to 0.3; P=0.09). Empagliflozin had no difference in impact upon circulating levels of MMP-2 or sST2 when compared to placebo. Conclusions: In patients with T2DM and CVD, empagliflozin therapy was associated with a reduction in ECV in parallel to a reduction in LVMi which may contribute to its CV benefits.
Recreational Sport Programs for Emerging Adults with a Disability: Exploring Quality Experiences and the Social Environment
Keywords: Disability
Participation
Peers
Qualitative
Recreational Sport
Social Environment

Recreational sport provides leisure and community opportunities to many individuals. Specifically, recreational sport can provide continuity during the transition from adolescence to adulthood when many life changes occur (e.g., school, work). For individuals with a disability, this transitional period (ages 18 – 25 years) may be extremely important as there is an abrupt ending to community programming after adolescence. This doctoral research program explored the social and transitionary experiences of emerging adults with a disability in recreational sport programs. Study 1, a scoping review of the peer-reviewed literature, identified limited research and a lack of explicit discussion of peer interactions in recreational sport programs for emerging adults with a disability. Study 2 used a mixed methods approach to observe and explore the peer interactions that occur for emerging adults with a disability within recreational sport programs. Conducted in communities across Ontario (two women, six men, and three sport programs), Study 2 combined dynamic systems and qualitative approaches to highlight the non-normative ways individuals with a disability may interact and achieve a sense of belonging on a recreational sports team. Finally, Study 3 presents a case study of a recreational paddling program for individuals with an intellectual disability. All persons involved in the program (i.e., administration, coaches, athletes, and parents) engaged in a multi-method, two-month case study of the social environment, development, and experiences of participating in such a program with attention focused on the role peers may (not) have. Study 3 highlights issues of disablism – socially-imposed barriers to full participation – that occurred within the paddling program and the necessity of coach training regulation to ensure coaches understand foundational coaching strategies along with disability-relevant knowledge (e.g., communication strategies). Overall, this doctoral research program highlights the individuality in social experiences within recreational sport contexts, the extended role of parents in their adult children’s lives, and the role that recreational sport can play on the transitionary experiences of emerging adults with a disability.
Uncoupled Base Rocking and Shear Mechanisms for Controlling Higher-mode Effects in High-rise Buildings
Keywords: High-rise buildings
Higher-mode effects
RC walls
Rocking mechanism
Seismic resilience
Shear mechanism

While modern seismic design philosophies prioritize life safety under major earthquakes, structural damage is not precluded. As for high-rise buildings, earthquake-induced damage can be rather extensive and exacerbated as a result of higher-mode effects. This may lead to repair and replacement being infeasible or uneconomical. A large body of research has been conducted on low-damage systems for high-rise buildings. Concepts involved in these systems essentially fall into three categories including rocking mechanisms, seismic isolation, and the combination thereof. While rocking systems have limited efficiency in limiting higher-mode effects, , base isolation can also be challenging for high-rise buildings due to base isolators being overloaded axially while undergoing significant lateral deformations. Combining both concepts by allowing base-isolated structures to rock at the base as well cannot fully resolve all these problems, leading to design and implementation challenges. This dissertation proposes a novel system consisting of uncoupled rocking and shear mechanisms incorporated at the base of RC core-wall buildings. Acting in parallel, the dual mechanism allows for an independent control of the flexural and shear responses of structures and an effective mitigation of the higher-mode response. A physical implementation is developed for the proposed system after the fundamental kinematics defining the system are understood. This physical embodiment is then designed, detailed, and numerically validated using a reference 42-storey benchmark building. Results of extensive nonlinear dynamic analyses indicate that the proposed system is efficient in mitigating higher-mode effects and minimizing damage to RC core-wall high-rise buildings. To generalize the design of the proposed system, closed-form analytical studies and parametric nonlinear time history analyses are conducted. Based on these analyses, general procedures are developed for the preliminary design of the proposed system.
Effects of Transient Hydro-mechanical Cemented Paste Tailings Properties on One-dimensional Deposition Behaviour
Keywords: Cemented Paste Backfill
Column Experiments
Consolidation
Hydraulic Conductivity
Mine Tailings
Numerical Analyses

When underground mining voids are filled with Cemented Paste Backfill (CPB) a dynamic interaction occurs between the hydrating CPB's engineering properties (hydraulic conductivity, stiffness and strength) and the fill rise rate, drainage conditions, and underground atmospheric conditions. These interactions are particularly important in the first 24 hours after a fill layer's placement in order to support continuous filling. Unfortunately, most previous studies started taking measurements at one day or more, missing this critical initial period. The work presented in this thesis obtains representative material properties at curing times as soon as 30 minutes. Four stages of hydraulic conductivity evolution are linked to corresponding stages of binder hydration at times from 30 minutes to 5 days, and it is shown conventional measurement methods could under-estimate initial hydraulic conductivity by an order of magnitude. Consolidation tests with virtually continuous loading rates simulated effective stress development onsets between 4 and 48 hours, at rates between 5 and 20 kPa/hr and final level of 400 kPa. In virtually continuous loading it is impossible to deconvolve the parameters controlling the ultimately achieved void ratio and stiffness, but a graphical technique is developed to aid preliminary design and determine likely void ratios that should be used in other laboratory tests for engineering properties. The hydraulic conductivity and consolidation element test results are then used to interpret results from four mesoscale laboratory experiments that simulate continuous backfilling. These tests demonstrate the importance of properly simulating the underground atmospheric conditions, and in placing the fill in the most continuous manner possible. For placement conditions that most closely simulate the actual placement conditions at the studied mine, it is found that virtually no self-weight consolidation occurs and thus the void ratios in the field and in the meso-scale experiments are essentially identical. Therefore, the effective stress path realized in the field will not lead to significant self-weight consolidation and the attendant strength gains that might otherwise occur.
Approximate Inference in Variational Autoencoders
Keywords: Approximate Inference
Generative Modelling
Machine Learning
Variational Autoencoder

A deep latent variable model is a powerful tool for modelling complex distributions. However, in order to train this model, we must perform approximate inference of the latent variable. A variational autoencoder (VAE) is a framework for learning both the generative and inference models for a latent variable model. This thesis provides novel analyses, applications, and interpretations of approximate inference in VAEs. This thesis reviews many of the recent developments made to improve VAEs. One such improvement is the importance-weighted autoencoder. The standard interpretation of importance-weighted autoencoders is that they maximize a tighter lower bound on the marginal likelihood than the standard evidence lower bound. The first contribution of this thesis is to provide an alternative interpretation: that it optimizes the standard variational lower bound, but using a stochastic importance-weighted variational distribution. In order to improve approximate inference in VAEs, it is important to understand what causes inference to be suboptimal. Two important factors that determine the quality of approximate inference in VAEs are: a) whether the variational distribution is expressive enough to match the true posterior and b) the ability of the recognition network to produce good variational parameters for each datapoint. This thesis contributes to our understanding of approximate inference in VAEs by using these factors to diagnose suboptimal inference across a number of settings. Finally, we investigate the task of using a latent variable to learn a joint distribution over two data modalities: images and text. The contribution of this work is to develop a model for this task and analyze how to perform approximate inference in this model. Specifically, we identify a problem arising from the mismatch between the posteriors of each modality and we demonstrate how the problem can be largely addressed by modelling the aggregate of the image posteriors.
The Electrophysiological Characterization of Duchenne Muscular Dystrophy Using 3D Human Bioengineered Skeletal Muscle Tissue and Neuromuscular Junctions
Keywords: bioengineered
Duchenne muscular dystrophy
electrophysiology
Muscle
Neuromuscular junction

Duchenne muscular dystrophy (DMD) is the most severe and common form of inherited muscular disease, affecting 1 in 3,500 boys. Patients with DMD experience progressive muscle weakness, where most children with DMD are subject to a wheelchair by age 13 and eventual fatality occurs during their third decade of life. More than 25 years ago, mutations in the Dystrophin gene were found to cause DMD. Dystrophin is an important cytoplasmic protein in the Dystrophin-associated glycoprotein complex (DGC) that underlies the plasma membrane of skeletal muscle. The lack of Dystrophin in the DGC causes muscle cells to be susceptible to muscle membrane damage. My view is that there is a problem with skeletal muscle homeostasis in DMD, where the balance between muscle damage and muscle repair strongly favours damage. One area of DMD research which appears understudied is the pathophysiology of the DMD neuromuscular junction (NMJ). In this thesis, I use a 3D culture system to model skeletal muscle and the neuromuscular junction, using immortalized muscle cell lines from healthy and DMD patient biopsies, as well as human embryonic stem cell (hESC) derived motor neurons. The findings of my thesis provide a comprehensive analysis of muscle cell physiological of healthy and DMD skeletal muscle tissue, and provide preliminary synaptic physiological data for in vitro NMJ. The results lead me to conclude that DMD muscle alone-constructs as well as DMD NMJ co-cultures have a more positive resting membrane potential, and a lower muscle membrane resistance when compared to healthy muscle and healthy muscle co-cultures. DMD muscle alone-constructs were unable to achieve muscle excitability, and all DMD co-cultures had varying recordings of spontaneous muscle excitability. Furthermore, DMD muscle membrane fragility is an aspect that can be studied further using my 3D muscle culture system. All together my thesis provides the first comprehensive analysis of muscle cell physiology of engineered muscle, and I provide the first report of a human NMJ that shows successful innervation seen through synaptic physiological recordings. The results of my research contribute to the basic biological role of dystrophin, and provides a characterized 3D muscle culture system that could be used for disease modeling and drug screening.