VictorJuiz/thesis huggingface dataset:
The Space Complexity of Asynchronous Algorithms from Bounded Size Base Objects

In this thesis, we consider distributed algorithms that use base objects with bounded domain sizes. Since real distributed systems have base objects with bounded domain sizes, such algorithms are more practical. Considering systems with bounded size base objects also gives us more power to prove new space complexity lower bounds. We devise a new technique for obtaining space complexity lower bounds for obstruction-free algorithms that use bounded size base objects. First, we consider obstruction-free implementations of scannable objects, which consist of a sequence of components that can be read simultaneously. More specifically, we consider scannable objects whose components are fully reusable, which means that, for every pair of values v, v' of a component, there is a sequence of operations which changes the value of that component from v to v'. When k >= 1 and 2^k - 2^(k-r) <= n-1 < 2^k - 2^(k-r-1), we show that any obstruction-free, n-process implementation of a scannable object with k fully reusable components that have domain {0, 1} requires at least n+k-r-2 base objects with domain {0, 1}. We also generalize our technique to obtain new space complexity lower bounds for scannable objects that have fully reusable components with possibly different bounded domain sizes. Next, we obtain new lower bounds for obstruction-free consensus algorithms from readable swap objects with bounded domain sizes. We show that at least n-2 readable swap objects with domain {0, 1} are needed to solve obstruction-free consensus among n processes, asymptotically matching the best known upper bounds. When using readable swap objects with domain size b, we show that at least (n-2)/(3b+1) objects are needed. Finally, we investigate the space complexity of solving obstruction-free k-set agreement from swap objects. For all n > k >= 1, we show that ceil(n/k) - 1 swap objects are needed to solve obstruction-free k-set agreement among n processes. We also give an obstruction-free k-set agreement algorithm that uses n-k swap objects, which exactly matches our lower bound for k = 1.

Keywords: consensus
distributed computing
lower bound
set agreement
snapshot
space complexity
Advisor: Ellen, Faith
A Pragmatic Approach to Metaethics

Metaethics is ordinarily thought of as the investigation of the metaphysics, semantics, psychology, and epistemology of normative judgment. However, metaethicists have recently started asking normative questions about our normative judgments as well. Instead of asking how normative judgment does work, or what normative properties are like, we might also ask how normative judgment should work, or which (if any) normative properties we should think and talk about. More broadly, we can ask what governs our normative practices in general, and whether they are serving us well. My aim in this dissertation is to defend the importance of these normative questions for metaethics and to advocate for a pragmatic approach to answering them.

Advisor: Sepielli, Andrew
Thresholds for Initiating Invasive Ventilation in Hypoxemic Respiratory Failure

Background: Invasive ventilation is a potentially life-saving intervention for people with severe hypoxemic respiratory failure, however the optimal thresholds for its initiation are unknown. Objectives: In people with hypoxemic respiratory failure, (1) measure the probability of invasive ventilation after meeting physiologic thresholds, (2) compare 28-day survival associated with lower versus higher hypoxemia severity thresholds for initiating invasive ventilation, and (3) estimate costs, quality-adjusted life-years, and the value of a randomized controlled trial for different thresholds used to initiate invasive ventilation. Methods: We studied people from the Medical Information Mart for Intensive Care version IV (MIMIC-IV) and Amsterdam University Medical Centres databases who were receiving an inspired oxygen fraction of 0.4 or more via non-rebreather mask, high flow nasal cannula, or noninvasive ventilation. We used thresholds based on prior research, Gaussian processes for imputation, G-computation for causal inference, and Monte Carlo simulation for health economic modelling. Results: We studied 4,726 patients. Invasive ventilation occurred in 28% (1,320). The probability of invasive ventilation within 3 hours of meeting physiologic thresholds was consistently 25% or less, even for severe thresholds such as a saturation-to-inspired oxygen fraction ratio (SF) of less than 90. In the MIMIC-IV cohort, for invasive ventilation initiation thresholds SF < 110, SF < 98, and SF < 88, the predicted 28-day probabilities of invasive ventilation were 72%, 47%, and 19%. Predicted 28-day mortality was lowest with threshold SF < 110 (22.2%), compared to SF < 98 (absolute riskincrease 1.6%) or SF < 88 (absolute risk increase 3.5%). A threshold of SF < 110, compared to usual care, resulted in more quality-adjusted life years (8.48 vs 8.34) and lifetime costs (86,700 Canadian dollars (CAD) vs 75,600 CAD). The expected value to Canadian society over the next 10 years of a 400-person randomized trial was at least 2.64 billion CAD. Conclusion: For people with hypoxemic respiratory failure, there is variation in the physiologic states that prompt invasive ventilation and the predicted mortality that results from different initiation thresholds. The estimated value to society of a corresponding randomized trial is large.

Keywords: Bayesian analysis
Causal inference
Hypoxemic respiratory failure
Invasive ventilation
Value of information analysis
Advisor: Tomlinson, George
A Design Methodology for Achieving Near Nyquist Continuous Time Pipelined ADCs

Wireless applications such as base stations for 5G, 6G, and radars are imposing stringent requirementson analog-to-digital converters (ADC) to digitize small Multi-GHz bandwidth signals with the presence of large out-of-band blockers arising the need for high dynamic range Multi-GHz ADC. Those systems usually require sharp high-order power-consuming anti-aliasing filters to reduce blockers and on-chip buffers to drive the ADC. Providing high bandwidth ADC with inherent anti-aliasing and easy-to-drive resistive input with benign current requirements is very appealing. Continuous-time delta-sigma (CTDS) modulators are good candidates for medium to high-resolution.However, the high oversampling ratio (OSR) limits the bandwidth, and CTDS modulators need highorder complicated loops that are complicated to tune, stabilize, and time/frequency interleave. Continuous-time pipelined (CTP) ADCs are an excellent candidate for achieving wide bandwidthwith medium to high resolution. CTP ADCs have shown the potential to alleviate the challenges of discrete-time (DT) pipelined converters with Multi-GHz bandwidth, such as having a resistive input, high amplifier efficiency, inherent anti-aliasing, blocker tolerance, and lower OSR than CTDS ADCs, but have so far required an OSR of at least 4. This thesis elucidates the factors that limit CTP ADCs bandwidth and presents a design methodologyto achieve near Nyquist continuous-time pipelined ADCs. The delay circuit is optimized to match the ADC/DAC path in both its broadband magnitude response and phase response. The residue filter response is optimized to prevent image signals from overloading the subsequent stage. We show that practical circuits can realize these optimized responses and extend the bandwidth of the CTP architecture to OSRs of 1.5-2.0, essentially equivalent to the OSR of DT converters when allowing for practical antialiasing. A 3 GHz bandwidth 5-stage CTP ADC is fabricated, and the direct path (i.e. delay) and the residue-amplifying filter circuits required for 3 GHz bandwidth are incorporated into a complete 9-bit 10 GS/s CTP ADC in a 28nm CMOS prototype. Simulation and measurement results confirm that CTP ADCs can operate at a near-Nyquist sampling rate with an OSR of 1.7. A 5-stage CT pipeline ADC prototype is introduced. Moreover, this thesis presents an analysis and design of a system-level novel tunable multi-band inductor-less bandpass CTP ADC with a third-order residue-amplifying filter, including two case studies, achieving the lowest OSR and the highest system-level dynamic range reported.

Keywords: Analog-to-Digital Converters
Continuous-Time Circuits
Data Converters
Delay Lines
Nyquist-rate ADCs
Pipelined ADC
Advisor: Chan Carusone, Anthony
Mouse Model of a Novel Molecular Subtype of Atrial Fibrillation: Physiologic Characterization and Therapeutic Drug Targeting

The mechanisms underlying atrial fibrillation (AF), the most common cardiac arrhythmia, are not fully understood, hindering the development of novel drugs. Elucidating the genetic causes of familial forms of AF can provide significant mechanistic insights and reveal novel drug targets. Our research group studied a large family with AF and identified a single rare variant shared exclusively by affected individuals, a heterozygous missense mutation in the CaV3.2 T-type calcium channel (TTCC) gene, CACNA1H (p.R1823H). Electrophysiological studies revealed that this mutation increases CaV3.2 current density, an effect that could be reversed by selective blockade of the affected channel. These data identify CACNA1H as a novel gene associated with the development of AF, but the underlying mechanisms are unknown. Moreover, TTCCs, though known to regulate atrial automaticity, have never been implicated in the pathogenesis of AF. Using a knock-in (KI/+) mouse model expressing the murine ortholog of the human variant, p.R1828H-CaV3.2, this thesis demonstrates direct causation of AF by the mutation and implicates enhanced atrial triggered activity as the major underlying electrophysiological mechanism. KI/+ mice had frequently prolonged episodes of AF and significantly enhanced vulnerability to AF induction compared to wild-type (WT) littermates. Characterization of the novel mouse model in vivo and at the cellular level revealed electrophysiological functional abnormalities known to provoke triggered activity and AF, including an increased frequency of spontaneous atrial beats in ambulatory animals and a significant burden of DADs in isolated atrial myocytes. AF vulnerability in KI/+ mice appears to be the direct result of the primary electrophysiologic abnormality, as KI/+ mice had structurally normal hearts noted by the absence of excess fibrosis, and HRV analysis revealed no evidence of autonomic tone imbalance. Selective blockade of CaV3.2 channels suppressed the increased DADs seen in mutant atrial myocytes and abolished AF inducibility in vulnerable KI/+ mice, suggesting that the CaV3.2-R1828H variant is directly responsible for the AF phenotype of KI/+ mice. The successful suppression of AF vulnerability and DADs by selective CaV3.2 blockade suggests TTCC modulation may be a potential therapeutic strategy in the treatment of AF.

Keywords: arrhythmias
atrial fibrillation
electrophysiology
genetics
Advisor: Gollob, Michael H
Machine Learning for Improved Weather and Climate Predictions

Numerical prediction models that simulate the complex and chaotic atmospheric system have successfully transformed the field of weather and climate prediction in recent decades. However, there are several real-world scenarios in which numerical models prove to be slow, uncertain or biased. These models are characterized by immense computational costs due to the large amounts of data they regularly process. This raises the question: how can we use machine learning to efficiently improve the accuracy of weather and climate predictions? In this thesis, we introduce novel algorithms for tackling the stubbornly challenging task of subseasonal forecasting.We start by exploring whether we can train neural networks to emulate the full dynamics of a numerical weather prediction (NWP) model and predict the global atmospheric flow in the medium-range. Creating such a forecast requires understanding complex atmospheric dynamics as well as the interplay between several variables across a range of scales. We show that neural networks can indeed perform this task, closely emulating the one performed by physical NWP models. We then use the task and dataset defined in this work, to establish the first benchmark for medium-range data-driven weather forecasting. An even more challenging forecasting task is sub-seasonal to seasonal climate prediction. While the accuracy of short-term forecasting is sustained by physics-based dynamical models, these models, due to their deterministic nature, suffer from a decrease in skill on subseasonal to seasonal timescales. We tackle this problem using a hybrid dynamical-learning approach and introduce an adaptive bias correction algorithm that outperforms both de-biased NWP models as well as state-of-the-art learning models. We conclude this thesis by discussing the future directions and challenges in data-driven weather and climate prediction, with the aim of identifying our approaches’ shortcomings and limiting assumptions. The studies presented in this thesis illustrate how large and varied data, an increase in computing resources, and advances in deep learning intersect, meet and collide to create a fertile playground for a promising field of research.

Keywords: adaptive bias correction
deep learning
statistical post-processing
subseasonal forecasting
weather and climate prediction
windows of forecasting opportunity
Advisor: Easterbrook, Stephen M.
Bridging the Gap Between Artificial and Human Intelligence Using Chess as a Model System

In the past half century Artificial Intelligence (AI) agents have grown to become embedded in modern life. Understanding why these AI agents make decisions has become increasingly difficult as they become more capable. These agents are components of larger sociotechnical systems making increasingly more significant decisions and are often doing so in collaboration with humans, for example, by providing evaluations to a judge or by making recommendations on the next movie to watch. As such, we need to build AI agents that are understandable to humans and that can understand humans with whom they interact. In this thesis we will present research using a model system, chess. Chess has a long history of use at the forefront of AI research, from the mechanical Turk, to Deep Blue and AlphaZero. Moreover, chess allows access to large and diverse datasets, powerful chess engines that can act as oracles, and chess can be considered benign, i.e., the potential negative consequences of its research are low, making it an ideal model system for AI research. These characteristics led to our first work on building a new style of AI chess engines, ones that attempt to mimic human play---instead of ones that achieve higher performance. These models, called Maia Chess, are able to predict the actions of a player at a given skill level with higher accuracy than any previous methods. Building on the methods for building human-like chess AI we will show how they can be individualized, increasing their accuracy by targeting a specific person. This individualization leads to the models better understanding the mistakes that player makes and incorporates their play style allowing for their identification from a set of many players. We will also present a method that generalized the identification techniques. First we formalize the task into a novel type of stylometry, behavioural stylometry - the task of identifying chess players based solely on their actions in game. We then demonstrate a novel deep learning method that allows us to identify players from a set of over 40,000 candidates without any model fine-tuning. These then lead to the final discussion of the implications of these models, specifically the ethical implications of building AI systems that imitate specific people. This work seeks to use the chess results as a starting point, and describe the implications of these models in the near future. To do this we define a new term mimetic models to describe models that imitate specific player. Then we discuss the ethical concerns that may arise from their use. This discussion has become more grounded in reality in the months since its first publication, some parts described as near future developments and concerns have become real.

Keywords: Chess
Computational Social Science
Deep Learning
Human Centric AI
Machine Learning
Reinforcement Learning
Advisor: Anderson, Ashton
Large-Scale Experimental Validation and Design of Resilient EBFs with Cast Steel Replaceable Modular Yielding Links

Shear-critical replaceable links, which were proposed about a decade ago for eccentrically braced frames (EBFs), require stringent detailing efforts for their connections and web stiffeners. They are also susceptible to undesirable failure modes such as weld fracture, web fracture, lateral torsional buckling, and local buckling. This thesis is focused on the experimental validation of a new generation of yielding links, referred to as cast steel replaceable modular yielding links (CMLs). The proposed links rely on the freedom of geometry offered by steel casting to promote simultaneous yielding to achieve an improved ultra-low cycle fatigue life and prevent undesirable failure modes. In addition, CMLs eliminate detailing efforts, simplify the design and post-earthquake repair process, and promote modular construction in EBFs. In this study, the limitations of EBFs are discussed and the need for an improved yielding link in EBFs is highlighted. Two designs of CMLs are experimentally validated through eight component- and five system-level experiments and their response parameters are characterized. The CMLs demonstrated excellent performance reaching rotations of up to 0.21 radians for small to medium size links, and a rotation of 0.11 radians for the largest links. Following this, the response of two prototype EBFs with CMLs is evaluated under earthquake excitations using pseudo-dynamic hybrid simulations (PsDHSs). A framework for performing PsDHSs on EBFs is proposed where the response of the yielding link is captured in a single degree of freedom system. Using these experimental results, a numerical model for CMLs is developed. A comprehensive numerical study is then performed on EBF structures ranging from 2 to 12 stories. The results from the PsDHSs and the numerical studies demonstrated that the seismic response of EBFs is often accompanied by large residual deformations. Therefore, a low-cost system for mitigation of residual deformations in EBFs is developed and validated through PsDHSs. Recognizing that the response of CMLs is affected by axial loads, the effects of axial loads on the response of CMLs is critically studied next. The study is concluded by presenting a set of guidelines for the design of EBFs with CMLs and for performing qualification tests on CMLs.

Keywords: Eccentrically Braced Frames
Energy Dissipative Fuses
Hybrid Simulation
Large-Scale Testing
Steel Casting
Steel Structures
Advisor: Christopoulos, Constantin
Kwon, Oh-Sung
Pursuing a Just Energy Transition in the Canadian Legal System

The transition from fossil fuels to low-carbon energies has sparked debates regarding justice and equity. Renewable energies hold the potential to significantly reduce greenhouse gas emissions and mitigate climate change. However, the trade-offs associated with these cleaner alternatives seem to disproportionately affect the rights of certain communities. The lands required for renewable energy infrastructure might lead to conflicts over land rights, resulting in the displacement of specific communities, particularly indigenous people, and exposing them to health issues and environmental impacts. Additionally, climate change mitigation policies, such as carbon pricing and cap-and-trade regulations, should be carefully designed to ensure justice and equity for all individuals. This research aims to first identify situations in which the rights of certain communities may be affected by the development of clean energies, whether related to clean infrastructure or policies. Subsequently, the study proposes solutions to enhance justice and equity in the energy sector.

Advisor: Iacobucci, Edward
Creativity on Demand: Cognition, Materiality, and Sociality in Improvisation

This dissertation examines the situated character of creativity via a qualitative analysis of improvisational theatre performances. Across these papers I demonstrate how creativity emerges out of the relations between actors, audiences, and material settings. Chapter two analyzes the cognitive processes through which improvisers generate new ideas during live performances. In this chapter I highlight limitations in contemporary sociological research using dual-process models, demonstrating how improvised interactions feature elements of order and disruption, how improvisers develop propensities in their reliance on automatic and deliberate cognition, and how creativity emerges in large part through automatic responses to the social and material contingencies of a live performance. Chapter three analyzes the role of the material environment in the creativity of improvisers. In this chapter I develop a novel account of how affordances are perceived and enacted, one that can be used to bridge the gap between cognition and materiality in cultural analysis. Chapter four analyzes the social-psychological skills employed by improvisers to tailor their creativity to live audiences. Specifically, I find that, although improvisation is an inherently uncertain process, improvisers are able to put on successful shows consistently through their ability to generate expressive enthusiasm (“work the room”) make inferences about the mental and emotional states of audiences (“read the room”), orient their performance style to the inferred preferences of audiences (“feed the room”), and make effective use of theatre space (“play to the room”). Chapters 2-4 of this dissertation are presented as stand-alone articles. While each paper has a distinct conceptual and analytical focus, taken together they establish creativity as a situated phenomenon.

Keywords: Action
Cognition
Creativity
Culture
Advisor: Leschziner, Vanina