VictorJuiz/thesis huggingface dataset:
Towards Practical Machine Perception: Data, Representations and Models

Machine perception aims to enable AI systems to interpret sensory data and reason about the world. Despite significant strides in computer vision, current machine perception systems still fall short of the robustness and generalizability inherent in human perception. This thesis addresses this discrepancy from multiple aspects, concentrating on data, representations, and models. From the model perspective, we propose a Unified Panoptic Segmentation Network (UPSNet) to handle the panoptic segmentation task. UPSNet employs a single backbone for joint processing of semantic and instance segmentation, integrating the results in an end-to-end fashion, thereby enhancing speed and performance. In terms of representation learning, we develop two self-supervised learning algorithms, LoCo and FlowE, for efficient representation learning. LoCo introduces a local contrastive objective and overlapping local blocks for effective self-supervised representation learning. FlowE, on the other hand, employs a flow equivariance objective to learn representations directly from high-resolution video data with complex scenes, a task where previous methods have struggled. From the data perspective, we present UltraLiDAR for LiDAR data completion and generation. UltraLiDAR learns a compact, noise-resistant, and easily manipulable discrete representation of LiDAR point clouds. It can densify sparse point clouds by aligning them to dense ones and generate realistic LiDAR point clouds by learning a prior over the discrete codebook. UltraLiDAR significantly enhances 3D perception systems and outperforms previous LiDAR generation methods in terms of realism. In conclusion, this thesis offers a thorough investigation of machine perception, proposing innovative solutions to advance the field from multiple angles. The methods developed herein push the frontiers of machine perception, edging us closer to achieving human-level perception.

Advisor: Urtasun, Raquel
Addressing Challenges for Reliable Machine Learning Model Updates

The dynamic nature of data and user expectations requires machine learning models to be updated throughout deployment for maximal performance. Updating models presents a set of challenges that must be handled carefully to ensure that performance improves.The non-convex nature of neural network training makes it challenging to update a model without introducing new errors relative to the original model, regardless if accuracy increases overall. Flipping previously correct predictions to incorrect is known as predictive churn and decreases user trust. I expand the understanding of how churn happens by explaining it through the lens of incompatible parameter updates. I use this analysis to motivate the need for a churn reduction method which does not suffer from a stability-plasticity tradeoff. Finally, I propose a solution for reducing predictive churn called accumulated model combination (AMC) that achieves state-of-the-art churn reduction performance. In some cases, the quality of newly gathered data may be lower than the initial training data. This leads to model updates decreasing performance; i.e., deterioration. To understand how deterioration occurs, I use model-dependent noise (MDN) where samples of a particular difficulty are corrupted. I show that depending on the sample difficulty targeted (easy, intermediate, hard), deterioration occurs mainly through two mechanisms: forgetting or learning prevention. Given that hard samples are more likely to be corrupted than easy samples, I demonstrate the potential societal implications as difficulty-based subgroups can coincide with demographic subgroups. I also provide various insights which explain why continual learning is particularly susceptible to deterioration. Lastly, I introduce the feedback loop problem which occurs in settings where data is confounded by model predictions. I investigate the role that clinician trust plays in limiting feedback loops, and also show the feasibility of detecting this phenomenon. I then present a variety of design choices which need to be adjusted to account for feedback loops in order to limit their effect. As part of a first-of-its-kind study on machine learning deployment in healthcare, I investigate to what extent clinician impressions are influenced by an early warning system, and how these predictions subsequently change behaviour and outcomes.

Keywords: Machine Learning
Model Updates
Performance Stability
Advisor: Goldenberg, Anna
Haibe-Kains, Benjamin
Profiling plant circRNAs as a guide for expression of plant genes involved in viral infection.

In this study, we explore the changes in the expression of plant circRNAs as a guide to trace plant gene expression during viral/viral component infections, focusing on the circRNAs dysregulation and their role in triggering plant cell responses. Our investigation distinguishes host-virus (TRoV) determinants versus non-host virus (RYMV) in Arabidopsis thaliana (A. thalian) plants. We identified 760 plant circRNAs with varying abundances, primarily encoded in exonic regions, and further analyzed their distribution across all A. thaliana chromosomes. Notably, the chloroplast chromosome exhibits the highest number of circRNAs compared to other chromosomes, suggesting the chloroplast as a circRNA hotspot in response to viruses and/or viral genes. Gene ontology (GO) analysis indicates that most host genes encoding these circR-NAs are primarily associated with plant development and protein binding activity, suggesting a potential role for circRNAs in the regulation of their host genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis highlights photosynthesis as the most enriched pathway among the identified circRNAs. While some circRNAs are anticipated to function as decoys for miRNA binding, such as circRNA AT1G01520 targeting ath-miR396b-5p, it is noteworthy that the major-ity of identified circRNAs do not correspond with miRNA sequences. Additionally, the non-coding exogenous circular satellite RNA (scLTSV) (virusoid) is shown to affect specific genes. Our results demonstrate that numerous cellular circRNAs (e.g., ATCG00030 (ciRNA106) and ATCG00130 (ciRNA11)) are induced solely by scLTSV. One circRNA (circRNA2) with an open reading frame (ORF) codes for a protein similar to a described wheat protein with monooxy-genase activity. CircRNA205, displaying significant expression changes between non-host and host viruses, also contains a translatable ORF. RYMV genomic RNA is transcribed in transgenic A. thaliana by the 35S promoter but is unable to replicate (non-host), impacting host eIF4G ex-pression (AT3G60240). Intriguingly, expression of transgenic scLTSV (satellite RNA) in A. thali-ana enhances plant resistance to TRoV infection. These findings contribute to our understand-ing of circRNA-mediated mechanisms in plant-virus interactions, shedding light on potential av-enues for improving plant resistance to viral infections.

Advisor: AbouHaidar, Mounir MA
A Framework for Automated, Unsupervised Control Synthesis in Noiseless Subspaces of Open Quantum Systems

In this thesis, we develop an automated framework for open-loop quantum control design, called the Generalized Control of Noiseless Subspaces (GCNS) framework, that targets a broad class of open quantum systems subject to the Born-Markov approximation. The framework is automated, highly generalized, computationally efficient, and extensible. It rests on an eleven-point Formal Framework Scope that specifies the model classes, control standards, information preservation standards, and control input classes it supports. It additionally rests on a nine-point set of Software Implementation Guidelines (SIG) that specifies how any software implementation of the framework should ideally perform with respect to efficiency, automaticity, and scalability. In constructing the framework, we develop novel theorems for controllability in Quantum Noiseless Subsystems, novel algorithms for the efficient, automated detection and parameterization of subsystem codes, and novel algorithms for the systematic selection and synthesis of control Hamiltonians from large candidate sets of feasible controls. Additionally, we develop the theory of Transition-Effort-based Control Input Synthesis: a versatile approach to generating control input signals, subject to a wide potential range of constraints, for the purpose of noiselessly effecting quantum operations on encoded quantum states. The thesis provides a comprehensive examination of a MATLAB- and Java-based implementation of the framework called the QBox library. The examination covers supporting algorithms critical for meeting the efficiency and extensibility guidelines in the SIG. It additionally covers all major aspects of the software library's anatomy, its application programming interface, and its suite of supplementary tools assisting control design. To validate the GCNS framework, its principal workflow is applied to two open quantum system models selected from the quantum information processing literature, based on trapped ions and superconducting transmon qutrits. The models and workflow parameters are selected to demonstrate a broad cross-section of the algorithms, subroutines, and software tools developed in the thesis. The examples further establish the exceptional scope and configurability of the framework, highlighting its importance to rapid design iteration in the noisy intermediate-scale quantum computing era.

Keywords: decoherence-free subspaces
noiseless subspaces
noiseless subsystems
open quantum systems
quantum control
Advisor: Kwong, Raymond HS
The Meaning of Equality

The subject of this dissertation is the foundations of human equality. It investigates what exactly we mean when we claim we are one another’s equals. Why do we believe that no one is naturally the subject of another? While many assume that we are equals because we are all rational beings, my project defends an alternative conception of equality. We are all equals because we are equally dangerous. This dissertation turns to the Early Modern period to uncover forgotten ways of articulating what we mean by natural equality. Contrary to common assumptions that equality must be based on our shared rational capacities, this dissertation finds that the early modern period offers multiple ways of articulating our belief in equality, which were not predicated on the similarity of our rational minds. I identify three different theories of equality, each of which is associated with a different school of thought that emerged in the Early Modern period: skepticism, empiricism, and materialism. The skepticist conception of equality suggests that we are equal because of our inability to judge the relative value of our differences. Empiricists’ theories of equality hold that we are all born blank slates, and hence we cannot know who is superior a priori. Materialist theories on equality base equality on our bodies, and particularly our capacities to kill. This project is by no means unique in turning to this period to think about equality, however, it is distinctive in placing the ‘woman question’ at the center of this inquiry. This dissertation considers, not just canonical articulations of equality, but how these ideas were taken up and transformed by women writers to defend the equality of the sexes. Each chapter stages a dialogue between canonical male thinkers—Montaigne, Hobbes, and Locke and their female interlocutors—Marie de Gournay, Margaret Cavendish, and Judith Drake. By turning to women writers, my project spurs a reevaluation of the apparently ‘brutal’ materialist theory of equality. When considering the debate through a feminist lens, I conclude that focusing on dangers equals pose, rather than their shared rational capacities, might have more emancipatory potential than previously imagined.

Keywords: Early Modern
Equality
Feminism
Political Theory
Advisor: Shanks, Torrey
Towards Equity in Science, Technology, Engineering and Math (STEM), in Kindergarten to Grade 12 Education

In their quest to improve student outcomes and address the needs of all learners, school districts are now increasingly including a focus on Science, Technology, Engineering and Math (STEM) programming among their core commitments. Within school districts’ Multi-Year Strategic Plans (see definition on p. 18) are often included a number of equity-related commitments and core priorities, such as: providing equity of access to learning opportunities for all students; providing all students with equitable access to deep learning experiences enabled by technology; striving to close the opportunity gap so that students who have been historically and contemporarily underserved can achieve their full potential; and eliminating disproportionate outcomes for students. Yet my professional experience in the field and analyses of student learning data indicate persistent achievement gaps and underrepresentation for certain groups of students in STEM. In order to develop a framework for addressing disproportionate student outcomes in STEM, this study examined K-12 educator perceptions about the factors associated with maximizing learning conditions for students across classroom, teachers’ professional learning, school, and board contexts. In addition to collecting perceptual data from educators, this mixed methods research study conducted in-depth interviews with study participants to gain further insight on strategies that address the barriers encountered by students from equity deserving groups within STEM-focused classrooms. Grounded in a critical perspective, the conceptual framework used in this study leveraged educator perceptions about how features across four contexts impacted their professional capacity to influence student learning in STEM. This set the stage for developing a clearer understanding of the disproportionate student outcomes problem. The study identified barriers to equitable student outcomes and how they might be addressed to attain equitable outcomes for all students in STEM. Additionally, this study considered how learning conditions might be improved for all students and how historically stubborn disproportionate student outcomes might be mitigated by paying closer attention to a range of features across all four contexts, including teacher content and pedagogical knowledge in the implementation of STEM programming. The study also highlighted the most impactful strategies across all four contexts to address disproportionate student outcomes in STEM. The findings of this study and the resulting policy implications hold the potential to raise awareness among policy actors and lead to better-informed decision making processes, where the presence of barriers to equitable student outcomes in STEM are more readily identified; and historically and contemporarily persistent opportunity and achievement gaps are mitigated with greater intentionality.

Keywords: Education
Engineering
Math
Science
STEM
Technology
Advisor: Bascia, Nina
Resurgent Babylon: A Cultural, Political and Intellectual History of the Second Dynasty of Isin

By the middle of the twelfth century B.C., the final upheavals of the Assyrian-Elamite phase were playing out on the stage of Babylonian geopolitics, and the long-reigning Kassite dynasty was brought to an end. In short order, a dynasty of kings hailing from nearby Isin would claim the Babylonian throne with the distinction of being the first native Babylonians to do so. The kings of the Second Dynasty of Isin would reign for one hundred and thirty-two years, a tumultuous time in which old foes would fall and new eminently formidable foes would rise, a time in which occurred a recasting of the local religio-political culture according to a new and distinctly Babylonian mold. Owing to the fact that the post-Kassite period happens to be the least archaeologically established as well as (concomitantly) the least documented Babylonian era, the task of assembling enough data to bring these events incrementally into a sharper focus has been ongoing for well over a century. Leaning on the evidence of kudurru texts, royal inscriptions and chronicles that have come to light in recent decades, the current study is able to draw on a corpus of texts roughly 32.5% larger than that available in previous studies of the period. By integrating the study of Isin II political history with the study of Babylonian religion and the rise of Marduk, complimentary topics which have typically been dealt with in isolation, the study to follow will take a path of inquiry that is well-established yet seldom trodden. Included within this study will be an updated examination of the royal ideology, dynastic succession and state formation of the Isin II period, together with a new interpretation of the timeline and political context of the first six kings, as well as a novel take on Assyro-Babylonian relations in the time of Adad-apla-iddina. The study concludes with an examination of the literary-theological developments of the period which places an emphasis on the Lambertian model of the rise of Marduk, together with fresh perspectives and new challenges to this model.

Keywords: Babylonian History
Babylonian Religion
Isin II
Marduk
Mesopotamian Chronology
Nebuchadnezzar I
Advisor: Beaulieu, Paul-Alain
Essays in Technology and Market Power

This dissertation consists of three chapters. Chapter 1 is solo-authored, while Chapters 2 and 3 are coauthored with researchers at the University of Toronto and other institutions. In “Wiring Growth” I investigate the effect of communications technology improvements on the level and composition of economic activity. Between 1840 and 1860 the United States experienced a dramatic shift in economic geography, as population and output moved west and Northeastern counties shifted production away from agriculture. At the same time, the country saw the rapid growth of a new communications technology, the electric telegraph. I investigate how the telegraph facilitated the geographical transformation, using an instrumental variables approach and cross- county variation in telegraph growth from 1840 to 1850. I find persistent positive effects of the telegraph on the growth of county-level goods output per capita. This finding is consistent with historical accounts of the telegraph’s effects on production and distribution. I also find evidence that the telegraph facilitated the transition away from home production in nonagricultural sectors. In “VancUber,” we investigate the long-run effect of ride-hailing on public transit ridership, traffic congestion, and traffic fatalities. We estimate the long-run effect of ride-hailing by exploiting British Columbia’s use of a pre-existing regulation in 2013 to ban ride-hailing from Vancouver and using the synthetic control method to construct a counterfactual Vancouver. We do not find a statistically significant effect of ride-hailing on our outcomes. Our results for fatalities are imprecise, but our confidence intervals for the effect on transit ridership and congestion provide bounds on the likely effect that are smaller in magnitude than many existing estimates. In “Multi-Product Markups,” we examine the identification of firm-product markups in multi- product firms using production-side data. Identifying within-firm markup differences relies on identifying the marginal rates of transformation across goods. Since marginal rates of trans- formation are generally functions of (i) the degree of joint production and (ii) the magnitude of within-firm productivity differences, we explore whether markup estimates are sensitive to misspecification in these factors. Monte Carlo exercises indicate that misspecification of within-firm productivity differences is particularly likely to generate bias. However, a weighted average of 2 firm-product level markups (the “firm markup”) can be identified without information on these factors, and nests the firm markup of De Loecker and Warzynski (2012). The firm markup can be estimated using standard empirical methods. Standard parametric restrictions on the marginal rates of transformation across goods often deliver implausible estimates of plant-product markups. Firm markups, which do not require these restrictions, are more well behaved. We also discuss the welfare properties of the firm markup.

Keywords: Infrastructure
Market power
Markups
Telegraph
U.S. economic history
Uber
Advisor: Morrow, Peter M
Attentional Templates in Context: Understanding the Tuning of Target Features During High- Versus Low-Similarity Search

In this dissertation, I investigate the phenomenon of attentional sharpening, wherein, difficult target-nontarget discriminations are more efficient when frequently made, compared to when they are infrequently made. In the past, this sharpening effect has been attributed to feature-based tuning processes, however, such claims have largely been assumed in the absence of explicit proof. I thus evaluate the proposal that expectations regarding target-nontarget similarity modulate the underlying tuning of mnemonic target representations. For my general method, I rely on a series of tasks where I bias individuals to expect either high- or low-similarity distractors while searching for targets defined by a relevant feature. I additionally apply several novel manipulations and analyses that better allow for meaningful interpretation of the underlying mechanisms that subserve the attentional sharpening effect. For example, by evaluating the extent to which attentional sharpening transfers across feature sets, in Chapter 2, I show that the sharpening effect is better accounted for by differences in the underlying gain of target and nontarget features than by global differences in attentional control that could be engaged to meet task-based demands more generally. Further, in Chapter 3, I demonstrate that the sharpening effect is reflective of how information is maintained in visual working memory. That is, while I do not find that the specificity of target feature maintenance is affected by the number of maintained features (which should be a limiting factor of visual working memory), I nonetheless show that the specificity of target feature maintenance is detrimentally affected when access to visual working memory is restricted. Finally, using a multivariate EEG analysis, in Chapter 4, I directly measure the tuning of target features in visual working memory. In doing so, I demonstrate that, when targets and nontargets are expected to resemble one another, target-specific activity at occipital sites becomes more selective than is the case when targets and nontargets are expected to be dissimilar; offering the most decisive proof to date that target-nontarget expectations directly alter mnemonic target representations. These results are discussed in relation to current theories of attention and memory and future avenues of investigation are offered.

Keywords: EEG
Feature-Based Attention
Selective Attention
Advisor: Ferber, Susanne
Pratt, Jay