VictorJuiz/thesis huggingface dataset:
On The Modification of Flow Hydrodynamics for Complex Fluids Experiencing Confinement

This thesis studies two complex fluids (emulsions and suspensions), and how hydrodynamic confinement modifies their flow. Emulsion wetting is investigated, and a novel hydrodynamic theory proposed to model experimental spatiotemporal drainage dynamics for a small axisymmetric drop approaching a rigid glass/pristine mica surface. Dimensionless drainage equations are evaluated (within a 95% confidence interval), to verify the theory. Building on this framework, construction of an integrated surface forces apparatus is attempted, to simultaneously track the force F and the height profile h of the drop –the two physical parameters exclusively contributing towards drainage. Coalescence-induced wetting is studied using this apparatus, to track island growth/disappearance kinetics. Next, the last stage of the emulsion wetting mechanism is investigated, where spontaneous bridge formation, followed by its subsequent growth, is believed to trigger wetting. Contrary to classical spinodal wetting theory prediction, experiments demonstrate that emulsion wetting always prefers a coalescence-induced wetting pathway. From deVries’ thermodynamic criterion for the activation energy, the calculated heights of the drop fluid bridge are found to always exceed 1μm, so the bridge always forms between the parent drop and one of several islands. Bridge formation is observed to be a necessary but insufficient criterion to guarantee wetting. Bridge formation is also found to be a non-unique event, and multiple bridges are observed to form. The bridge’s radial velocity of propagation follows a unidirectional flow model, and warrants further studies, especially since this step may potentially become rate determining, thereby increasing the wetting time significantly. Classical spinodal theory appears to require a revamp, to describe emulsion wetting. Finally, the flow of concentrated suspensions across a Hele-Shaw geometry is investigated, and depth-averaged macrotransport equations are obtained, by implementing a two-timescale expansion on the suspension balance model of Nott and Brady, coupled with the constitutive equations of Zarraga et al. A perturbation analysis predicts a Saffmann-Taylor instability to arise, for the case of a high-volume fraction suspension pushing a low-volume fraction suspension. Contribution of individual transport terms towards the dispersion relationship, and stability characteristics are studied for different aspect ratios, which follow the scaling laws of Tan & Homsy for porous media flows.

Keywords: coalescence-induced wetting
concentrated suspensions
emulsion wetting
low Reynolds number hydrodynamics
spinodal wetting
surface forces apparatus
Advisor: Ramachandran, Arun
The Causes and Impact of Trauma Overtriage in Ontario

Introduction: Given the mortality benefit related to trauma centre care among severely injured patients, multiple initiatives have been undertaken to minimize the admission of these patients to non-trauma centres (undertriage). An unintended consequence of these initiatives is the transfer of patients without severe injuries to trauma centres (overtriage). To better understand the impact of overtriage on the healthcare system, this thesis aimed to evaluate hospital characteristics associated with, and quantify healthcare costs attributable to, overtriage. Methods: We identify all injured adults who presented to a non-trauma centre in Ontario using population-based administrative data (2009–2020). Bivariate generalized mixed effects models were used to estimate the accuracy of triage decisions for patients transferred from non-trauma to trauma centres. Rates of under- and overtriage were compared between centres with high and low triage accuracy using negative binomial regression. Hierarchical logistic regression was then used to investigate the impact of hospital resources on overtriage, adjusting for patient and injury characteristics. Finally, we performed a propensity-matched cohort analysis to estimate differences in 30-day healthcare costs between overtriaged patients and matched controls. Results: Triage accuracy varied significantly across non-trauma centres (diagnostic odds ratios ranging from 0.20 to 21.1). After adjusting for case-mix and resources, rates of overtriage were 75% lower at highly accurate centres relative to those with low accuracy (RR 0.27, 95% CI: 0.20–0.36), whereas there were no differences in undertriage (RR 1.06, 95% CI: 1.00–1.12). Resources at the hospital of presentation, including CT scanners, surgical support, and intensive care units did not impact a patient’s likelihood of overtriage. However, hospital of presentation itself had a greater impact on a patient’s odd of overtriage than any patient, injury, or hospital characteristic (MOR 3.76). Thirty-day healthcare costs were 6.5% greater among overtriaged patients relative to matched controls (RR 1.06, 95% CI: 1.04–1.09). Conclusion: Overtriage represents a key contributor to the accuracy of trauma transfers. Our findings suggest there are high performing centres that have developed processes to ensure patients without severe injuries are cared for locally. Dissemination of these processes represents an opportunity to improve trauma systems and potentially reduce healthcare spending.

Advisor: Nathens, Avery B
Spoken Word Poetry with Multilingual Students in an English for Academic Purposes Class: Translanguaging and Emotion for Critical Practice

Combining theories of translanguaging (García, 2011) and emotion (Ahmed, 2014), this qualitative study asks: (1) what are multilingual learners’ impressions of and experiences with learning and using language in spoken word poetry activities in an EAP class? (2) how does engaging in spoken word poetry curricular activities shape relationships between and among students and instructors? Data for this study was gathered from two teachers and 15 multilingual EAP students with diverse backgrounds. Data sources included curriculum planning and debriefing sessions with instructor Nancy, tutorial sessions and post-semester interviews with the students, classroom observations, and student’s online journal and artifacts. By employing both a systematic and slow intensity (MacClure, 2013) approach to data engagement, I present what I term sticky moments that surfaced from the data because of their affect. By positioning students’ lives and languages at the epicenter of critical language learning, spoken word poetry cultivated learner agency in meaning making and provided dialogic opportunities for self-reflection and co-learning. The findings supported students’ expanding repertoires in a critical practice that underscored the affective processes of language learning as culturally and relationally situated, rather than a neutral or universal set of skills. Bearing witness to stories actualized in between the self and other, spoken word poetry united the listeners and speakers in dynamic ways, encouraging learners to examine the emotional attachments that bound them to people, memories, and experiences. Results indicate that some students developed an awareness of how structures of power reproduce social hierarchies. However, not all students were interested in challenging these hierarchies, nor were all participants willing to examine their role in perpetuating them. Based on the findings of this study and supported by literature, I propose a framework for brave spacs that integrates critical practice, emotions as centripetal to humanizing interaction, and language-inclusive teaching based on translanguaging pedagogy. Practically, this research has direct implications for second language pedagogy in supporting efforts to design equitable and diverse second language curricular materials rooted in the languages and emotional lives of students. Theoretically, the spoken word poetry activities illuminated the significance of the relationship between language and emotions.

Keywords: emotion
english as a second language
english for academic purposes
language teaching
spoken word poetry
translanguaging
Advisor: Bale, Jeff
Exploring Poisoning Effects on Deep Learning

Deep learning is leading the way of numerous ongoing advances. With sufficient high-quality training data, correct implementations, and benign training environments, deep learning can achieve superior accuracy than traditional machine learning techniques. However, in practice, the massive training data may come from untrusted sources, where some adversaries probably hide behind to inject manipulated data. The implementations and training environments are not always trustworthy as well, due to the existence of malicious code packages, servers, and clients. All of the above training-stage threats, referred to as poisoning attacks, can mislead the trained models to make wrong predictions.To assess the risks posed by poisoning attacks, the community proposed varied attack methods, which demonstrate effectiveness but encounter issues regarding stealthiness, efficiency, and generalizability. With the aim of addressing the problems of existing attacks and raising awareness about poisoning effects, we explore poisoning attacks against deep learning through three attack vectors, i.e., data, code, and training environments, in this dissertation. We first explore the risk of training data and propose FC poisoning---a First-order Clean-label data poisoning attack. FC poisoning can mislead the trained models to make adversary-defined predictions on targeted data with a subset of poisoned training data. In contrast to conceptually similar second-order attacks, FC poisoning only uses first-order information to craft the clean-label poisoned data. In terms of the code supply chain, most machine learning developers overlook the vulnerability of their installed packages, according to our investigation. Leveraging this unconscious manner, we design a malicious PyPI package to insert backdoors into the developers' trained models by manipulating their local packages. Finally, we investigate the risks in the training environments. Orthogonal to the previous literature, we focus on an under-explored direction, i.e., deep learning based time-series forecasting, for which we propose practical attack algorithms and apply them to wireless traffic prediction in centralized and decentralized training environments. For the environment of federated learning, we demonstrate that the server can act as an adversary and use targeted poisoning to enhance membership inference attacks. Extensive evaluations on multiple datasets and networks verify the effectiveness of our proposed attack methods and countermeasures.

Advisor: Li, Baochun
Archaeological data work as continuous and collaborative practice

This dissertation critically examines the sociotechnical structures that archaeologists rely on to coordinate their research and manage their data. I frame data as discursive media that communicate archaeological encounters, which enable archaeologists to form productive collaboration relationships. All archaeological activities involve data work, as archaeologists simultaneously account for the decisions and circumstances that framed the information they rely on to perform their own practices, while anticipating how their information outputs will be used by others in the future. All archaeological activities are therefore loci of practical epistemic convergence, where meanings are negotiated in relation to communally-held objectives. Through observations of and interviews with archaeologists at work, and analysis of the documents they produce, I articulate how data sharing relates distributed work experiences as part of a continuum of practice. I highlight the assumptions and value regimes that underlie the social and technical structures that support productive archaeological work, and draw attention to the inseparable relationship between the management of labour and data. I also relate this discursive view of data sharing to the open data movement, and suggest that it is necessary to develop new collaborative commitments pertaining to data publication and reuse that are more in line with disciplinary norms, expectations, and value regimes.

Advisor: Dallas, Costis
Scalable and Interpretable Machine Learning for Scientific Discovery

Machine learning has the potential to accelerate scientific discovery across the physical, biological, and chemical sciences. A major component of scientific discovery is the search for structured data. Molecules and Quantum optics experiments are important examples of structured data in the sciences that can be represented as both graphs and sequences. For machine learning models to be effective tools for scientific discovery they must be able to account for the range in size and structure of scientific data. Furthermore, for scientists to effectively use these models for fundamental research, they need to be interpretable and have explainable representations. In this thesis, work is presented that addresses these challenges. First, we describe a variational autoencoder for the design of quantum experiments that is interpretable for physicists. Next, we study chemical language models, which are generative models for molecules– we analyze their ability to learn larger, more complex molecular distributions than standard benchmarks that contain small drug-like molecules. We then test how well chemical language models perform in the challenging task of atom-level protein generation. Lastly, we focus on molecules represented as three-dimensionalstructures. Here, we first demonstrate a reinforcement learning framework, based on fragments, that can generate 3D molecules from a range of distributions. We then describe a graph neural network architecture that can distinguish molecules based on the relative positions of their atoms in 3D space.

Keywords: chemistry
generative models
language models
machine learning
molecules
proteins
Advisor: Aspuru-Guzik, Alan
Searching for Beyond the Standard Model Phenomena in Dijet Events with at Least One Lepton with the ATLAS Detector

A search for new resonances in events with at least one isolated charged lepton ($e$ or $\mu$) and two jets is performed using 139 fb$^{−1}$ of $\sqrt{s}$=13 TeV proton-proton collision data recorded by the ATLAS detector at the LHC. Deviations from a smoothly falling background hypothesis are tested in three- and four-body invariant mass distributions constructed from leptons and jets. The inclusion of leptons in dijet invariant masses provides two main benefits. Firstly, lepton triggers can be used to search for resonance masses starting from 500 GeV, as the transverse momentum thresholds on lepton triggers can be as low as 24 GeV, while inclusive dijet searches are limited to resonance masses greater than 1 TeV due to jet-based triggers which have a minimum jet transverse momentum of about 400 GeV. Secondly, this search in new final states provides sensitivity to new signal models. The search presented in this thesis considers four different search channels in invariant mass distributions composed of two jets and two leptons, as well as two jets plus one lepton where either zero, one or both jets are identified as originating from bottom quarks. The largest deviation between the observed data and the estimated Standard Model background was found in the two jet plus one lepton channel at a invariant mass of 1.3 TeV with a global statistical significance of 1.5 standard deviations. In the absence of a statistically significant deviation from the background estimate, upper exclusion limits are set at a 95\% confidence level in each signal channel on generic Gaussian-shaped resonances with different widths and masses, as well as in the context of four new signal models.

Keywords: ATLAS
BSM
HEP
LHC
resonance search
Advisor: Trischuk, William WT
Exploring Exemplar Bias Effects: Category Influences on Visual Attention

To successfully perform visual search for object categories, observers are required to draw from prior knowledge to instantiate the appropriate attentional templates. Through encountering category exemplars, long-term categorical representations are developed over time. One account for the way in which exemplars are selected among competitors during encoding is the biased competition theory. Previous research has shown that categorical search templates are biased towards recently encountered perceptual information, but does not acknowledge the potentially separate roles that attentional facilitation and suppression play. In this thesis, I test the idea that competition between exemplars is a necessity when forming attentional templates for categorical search, and that these templates are biased towards information that is attended and away from information that is suppressed. Results from the present experiments suggest that attentional facilitation and suppression both influence how categorical search templates are formed. Furthermore, and contrary to prior work, the process of template formation may rely on non-competitive mechanisms. I discuss how these findings may be situated within a conceptual framework combining attention, categories, and memory.

Keywords: attention
categorization
cognition
memory
vision
Advisor: Pratt, Jay
Tendon-Driven Parallel Continuum Robots: Modeling, Characterization and State Estimation

Continuum robots are slender and flexible manipulators, that are mainly characterized by their inherent compliance, easy miniaturization and ability to bend into highly non-linear shapes. Thanks to their inherent structural properties, continuum robots are predestined for applications in confined environments and hard-to-reach locations with limited access along narrow insertion paths, such as minimal-invasive surgery or industrial in-situ inspection and maintenance. One recent development in continuum robotics is the introduction of parallel continuum robots. Through the parallel arrangement of multiple continuously bending links, these structures aim to overcome some of the inherent drawbacks of continuum robots, such as their low accuracies and strength when compared to conventional rigid-link robots. In this thesis, a novel type of parallel continuum robot is studied, which features actively bending continuum segments that are driven by tendons. Planar and spatial designs of such tendon-driven parallel continuum robots are introduced and modeling frameworks are derived to compute their forward, inverse and differential kinematics and statics. Based on these models, the kinematic properties, such as the reachable workspace, manipulability, compliance and singularity occurrences are evaluated and studied, characterizing tendon-driven parallel continuum robots on a general level. Lastly, this work focuses on an efficient state estimation approach for both conventional and parallel continuum robots that allows to estimate their state given noisy measurements.

Keywords: Continuum Robots
Parallel Robots
Robot Kinematics
Robot Statics
State Estimation
Advisor: Burgner-Kahrs, Jessica
A Geometric Framework for Conservation Laws Along Null Hypersurfaces and their Relation to Huygens Principle

In the present thesis we examine two main topics. In the first part, we use the general theory of local conservation laws for arbitrary partial differential equations to provide a geometric framework for conservation laws on characteristic null hypersurfaces. The operator of interest is the wave operator on general four-dimensional Lorentzian manifolds restrictedon a null hypersurface. In the second part of the thesis, we investigate relations between the geometric conditions that lead to the validity of Huygens’ principle and those that give rise to conservation laws along null hypersurfaces for the wave operator. We apply our results in spacetimessuch as the Minkowski, Schwarzschild, and Reissner-Nordström, as well as in general spherically symmetric spacetimes.

Advisor: Aretakis, Stefanos
Exploring the Association Between Models of Addiction and Stigma

How we understand the etiology, development, and chronicity of addictive disorders lays the foundation for how they are stigmatized. Advancements in neurobiological research on addictions has led to the development of a brain disease model of addiction (BDMA) and experts have suggested that understanding addiction with this model may reduce stigma associated with addiction. Reducing stigma has important implications for treatment, as stigma has been identified as a significant barrier to treatment in this population. Other experts in the field have suggested that other models of addiction (MOAs) may be important to consider for stigma reduction. The current research work has one main aim which is to provide the field with clarity on how different MOAs relate to different forms of stigma directed toward individuals with addictive disorders. Three studies were conducted in pursuit of this aim. Study 1 revealed that there is some support regarding the relationship between greater BDMA beliefs and lower stigma. Study 2 revealed that other MOAs such as the psychological MOA is related to lower stigma ratings in a public sample. Study 3 also indicated that greater beliefs in a nature MOA related to lower stigma scores for a sample of clients who had received treatment for an alcohol use disorder and/or opioid use disorder. The current work reinforces extant research indicating that a moral MOA is related to greater public and self-stigma. Ultimately, this work furthers the understanding of the relationships between different MOAs and addiction stigma and the findings herein may be used to invigorate future research into investigating their impact on the many factors associated with stigmatizing attitudes.

Keywords: addiction
brain disease
models of addiction
public stigma
self-stigma
Advisor: Goldstein, Abby