VictorJuiz/thesis huggingface dataset:
Applying Clinician Macrocognition in Designing a Clinical Decision Support Tool for Congenital Heart Disease
Keywords: Artificial Intelligence
Congenital Heart Disease
Decision Centered Design
Decision Support
Human Factors Engineering
Macrocognition

Introduction: When presenting to the emergency department (ED), children with congenital heart disease (CHD) experience higher rates of hospital admission, morbidity, and mortality compared to children without CHD. The burden of residual lesions and the natural history of many CHDs predisposes these children to hemodynamic fragility and altered response to traditional resuscitative measures. Their optimal outcome requires CHD-expertise which is limited in local EDs. Hence, there is an urgent need for a sociotechnical solution to support the evaluation, diagnosis, and management of these patients in ED. Three studies address this need by contributing to the development and evaluation of a clinical decision support system (CDSS) based on the cognitive work of CHD-experts and ED physicians. Methods: In study 1, using the critical decision method, a cognitive task analysis (CTA) of CHD-experts and ED physicians was conducted to understand differences in their macrocognition when managing acutely ill pediatric CHD patients. In study 2, decision centered design was used to identify key decision requirements and design concepts to inform the prototype CDSS. In study 3, scenario-based simulation was used to test the effect of the CDSS on ED physicians’ decision making. Results: The most pertinent differences in decision making between CHD-experts and ED physicians were in sensemaking, anticipation, and managing complexity. Accordingly, the key decision requirements and design concepts incorporated into the prototype CDSS included appreciating the CHD physiology, identifying CHD-specific diagnoses, and selecting appropriate CHD interventions. The use of the CDSS significantly improved ED physicians’ CHD-specific decision-making (M=5.43, 95%CI 3.7-7.2) compared to usual care. Conclusion: These studies reveal differences in CHD-expert and ED physicians’ macrocognition and decision making while also proposing a design approach to develop and evaluate a CHD-specific CDSS. Results and methods presented here can be used by clinicians, developers, and researchers to design sociotechnical solutions beyond pediatric CHD.
Aid and Development at the Thailand-Myanmar Border: Mapping humanitarianism as a settler colonial construct
Keywords: Black feminist geographies
Critical humanitarian studies
Indigenous theory
International development studies
Land and place
Mobilities and borders

This study maps Western humanitarianism as an imaginary that positions the Western world as superior to other places. Humanitarian and international development activities work to recruit local populations into Western worldviews and economic practices, instead of meeting them on their own terms. In placing the focus on the supposed deficiencies of the peoples and places they work in, Western humanitarianism evades and actively obscures how global economic forces–originating in and supported by the West–are the reason why people face challenges of conflict, poverty and displacement. I learn from the context on the Thailand-Myanmar border, site of some of the world’s longest civil wars and land sovereignty struggles, where exploitative development projects are accelerating. I conducted interviews with 33 humanitarian workers in that region to learn how they understand humanitarianism, and what their organizations are doing or not doing about land theft. I combine the results with a review of historical and current literature, analysis of INGO annual reports, and theorizations from across the fields of critical humanitarian studies and Black and Indigenous Feminist Studies. I observe that the problem of land confiscation is largely ignored by INGOs in the Thailand-Myanmar border region, and this is also true of humanitarian practices globally. I trace how INGOs self-define their work, constructing concepts of an international humanitarian community that is separate from communities in the places they work, that is accountable not to those communities but to Western audiences: humanitarians report. I consider how humanitarianism idealizes a certain type of human: civilized, developed and progressive: humanitarians are human. Humanitarian projects exoticize and pathologize places that Westerners travel to, conceiving of those places as open to exploration: humanitarians travel. When they arrive in places, they reinforce models of extractive capitalism imported from the West: humanitarians create economies. Finally, they ignore local relations to land, avoiding support for land sovereignty struggles, and in doing so reproduce settler colonial ways of being: humanitarians settle. This work also considers questions of research ethics and suggests practices of refusal in research. The final chapter incorporates suggestions from participants on how academic research might be more useful.
Improving Multi-threaded Application Performance on Modern Chip Multiprocessors
Keywords: criticality
learning-to-rank
multi-threaded

Multi-threading is one way of leveraging the increasing number of cores in today's processors to speed up applications by dividing the workload, that would otherwise be executed sequentially, across multiple threads that run in parallel. However, if one thread lags behind the rest of the threads, it will limit the overall achievable benefit from parallelization. To unlock the full potential of multi-threading such critical threads have to be predicted and optimized during runtime in a way that minimizes the difference in performance between them and the remaining threads. The large number of factors that can affect a thread's performance make critical thread prediction a very challenging task. Moreover, these factors are not completely independent of each other and can be indirectly related in ways that are difficult to model accurately. Therefore, previous works that addressed the issue of thread criticality only focused on a limited set of factors when predicting which threads are critical. However, the results presented in this thesis show that none of these factors strongly correlates to thread criticality when considered separately resulting in suboptimal thread criticality predictions. Instead of focusing on a limited set of factors, this thesis leverages machine learning to facilitate the study of a wide variety of factors that can potentially affect a thread's progress. Specifically, this thesis formulates the problem of thread criticality as a Learning-to-Rank problem. Each thread is represented by a set of runtime statistics representing the input features to the ranking model and the predicted scores reflect how critical a thread is. Using a ranking model puts more emphasis on threads' criticality scores relative to each other rather than exactly matching the criticality score for each thread. This work shows how the proposed thread criticality prediction methodology can be applied to two types of run environments. The first runs the benchmarks on a real system and uses Linux Trace Toolkit: next generation (LTTng) for data collection while the second uses Gem5 to simulate the required multi-core system. Collected results show that single features are not very accurate predictors for thread criticality compared to a model that considers several features. The obtained model performs 17% better than a single-metric based predictor in predicting threads' criticality. Finally, a case study where critical threads are prioritized by doubling the corresponding core frequency shows the potential benefit the proposed ranking model offers compared to a predictor that is based on a single feature.
Numerical PDE Methods for Pricing Bilateral XVA
Keywords: asymptotic approximation
Black-Scholes
Crank-Nicolson finite differences
Partial Differential Equations
penalty iteration method
stochastic default intensity

Adjusting derivative prices to take into account default risk has attracted the attention of several researchers and practitioners, especially after the 2007-2008 financial crisis. The thesis is a study, via numerical Partial Differential Equation (PDE) approaches, of the modeling and computation of valuation adjustment problems in financial derivative pricing if we consider the bilateral counterparty default intensities.Under some assumptions, the valuation of financial derivatives, including a value adjustment to account for bilateral default risk (XVA), assuming constant default inten- sities gives rise to a nonlinear PDE. We formulate a penalty iteration method to handle the nonlinearity in this PDE, study its convergence, and extend it to American derivatives, resulting in a double-penalty iteration. We also propose boundary conditions and their discretization for the XVA PDE problem in the cases of call, put options, and forward contracts. Numerical results demonstrate the effectiveness of our methods. Then, we derive a novel PDE for derivative pricing including XVA, assuming that the default risk of one of the counterparties follows a Cox-Ingersoll-Ross (CIR) process, while the other party has constant default risk. The time-dependent PDE derived is of Black- Scholes type and involves two “space” variables, namely the asset price and the buyer default intensity, as well as a nonlinear source term. We formulate boundary conditions appropriate for the default intensity variable. The numerical solution of the PDE uses a penalty-like iteration for handling the nonlinearity. We also develop a novel asymptotic approximation formula for the adjusted price of derivatives, resulting in a very efficient, accurate, and convenient for practitioners formula. Numerical results indicate stable ii second order convergence for the 2D PDE solution in terms of the discretization size and convergence of order at least 1.5 for the asymptotic approximation in terms of inverse of the mean-reversion rate. We extend the PDE model we developed to price European derivatives including XVA, considering stochastic counterparty default intensity, to American derivatives. We also extend the asymptotic approximation to the American put option problem. A key step is to derive the asymptotic approximation to the free boundary. We present nu- merical experiments in order to study the accuracy and effectiveness of the 2D PDE and asymptotic approximations.
Investigating Teachers’ Assessment Practices of Students’ Mathematical Thinking
Keywords: assessment
education
elementary
instruction
mathematics
thinking

Mathematics educators and researchers describe mathematical thinking as a set of specific mental processes or habits of minds. While research has identified a wide range of effective instructional strategies for enhancing students’ understanding of mathematics by learning through mathematical thinking or thinking classrooms; however, little is known on how to assess students’ mathematical thinking or habits of mind that are integral to teaching and learning mathematics. This study investigated teachers’ practices and perceptions about teaching and learning through mathematical thinking to help students construct a deep understanding of mathematical concepts and skills. Using a qualitative multiple-case study approach, I investigated the ways in which four elementary school teachers approach the assessment of students’ mathematical thinking in their classrooms. The major findings are: (1) participants reported that instructional practices and the assessment of students’ mathematical thinking occur simultaneously; however, the participants struggled to describe mathematical thinking; (2) participants felt that explicitly embedding the skills that underpin mathematical thinking in the learning goals shifted the focus of the assessment; (3) participants relied on students’ reflective thought processes to support their assessment practices of students’ mathematical thinking; (4) participants reported that they could more readily assess students’ mathematical thinking when students are given opportunities to apply their understanding of mathematics concepts and skills; (5) participants reported that students need to be given ample opportunities to make their thinking visible to enhance their assessment practices; (6) participants reported that students who are comfortable with learning mathematics and exhibit positive attitudes toward learning mathematics are more likely to share their mathematical thinking; and (7) participants reported that their comfort and attitude towards mathematics enhanced their assessment practices as part of their daily practices. Eliciting students’ mathematical thinking through purposefully selected student tasks is an integral component of a successful mathematics program; however, eliciting student thinking on its own does not imply students’ thinking is being attended to for assessment purposes. This study provides insight about the assessment practices of teachers and their perceptions of mathematical thinking. It is hoped that the findings of this study will ultimately enhance teachers’ formative assessment practices of students’ mathematical thinking.
Managing, Profiling, and Optimizing Heterogeneous GPU Workloads
Keywords: GPU
Optimization
Passthrough
Profiling
Reinforcement Learning
Virtual machine

The popularity of machine learning (ML) workloads have made GPU instance offerings ubiquitous in the cloud, introducing new challenges in managing, profiling, and optimizing GPU workloads. Cloud providers assign passthrough GPUs directly to virtual machines (VMs) for high performance, but doing so renders VM migration non-functional, limiting cloud operator ability to manage hardware resources. Existing general purpose GPU (GPGPU) and deep neural network (DNN) profiling tools are ineffective for heterogeneous CPU/GPU workloads like reinforcement learning (RL) since they only provide information about GPU kernel and DNN layer execution, and ignore CPU-side bottlenecks such as simulation. The lack of adequate profiling tools has led ML researchers to rely on naive costly cluster scale-up solutions to optimize RL training time, which can cost millions of dollars and are inaccessible to most ML researchers. In this dissertation, we build systems software for addressing these challenges. For management, we build Crane, a GPU virtualization middleware that achieves within 5% of passthrough GPU performance, requires no OS/application/hypervisor modifications, and can even enable migration between heterogeneous GPU targets. For profiling, we build RL-Scope, a cross-stack profiling tool tailored to RL workloads that breaks down low-level CPU/GPU training time scoped to high-level algorithmic operations (i.e., inference, simulation, backpropagation). We survey RL workloads across major workload dimensions (i.e., simulator, RL algorithm, DNN framework) and demonstrate that RL workloads suffer universally from low GPU utilization, and that na ̈ıve attempts to increase GPU utilization by parallelizing GPU inference requests are unsuccessful. For optimization, we propose two optimizations targeting the time-consuming data collection phase of RL training. First, GPU vectorization moves simulation from the CPU to the GPU to benefit from increased hardware parallelism, achieving a 1024× speedup over CPU implementations. Second, Simulator kernel fusion fuses multiple steps of simulation into a single GPU kernel launch to benefit from caching simulator state in fast GPU registers, obtaining a 11.3× speedup over an unfused GPU kernel. Both optimizations are orthogonal and can be combined for massive multiplicative speedups, and are more accessible to ML researchers since they do not rely on costly cluster scale-up
Fast and Energy-Efficient Inference for Attention-Based Natural Language Processing Models
Keywords: Attention-Based Models
Deep Learning
Natural Language Processing
Post-Training Quantization
Transformer Models

Creating machines that can ``understand’’ our language and ``interact’’ with us as we interact with each other has been a dream that motivated many and captured the imaginations of even more. Attention-Based Transformer models have demonstrated remarkable success in getting us closer to this dream by tackling many natural language understanding tasks.However, for these impressive algorithmic developments to be of practical use, they need to be matched by capable hardware devices. Unfortunately, these models' storage, data transfer, and computation demands are already taxing even the most powerful computing hardware available today. This thesis' goal is to reduce the cost of inference for Transformer models in terms of latency and energy consumption to help enabling widespread application in our daily lives. Since in these models, the primary performance and energy efficiency bottleneck is memory transactions, this dissertation develops model compression techniques to reduce the model's memory footprint. The model compression techniques are post-training and require no fine-tuning. To reach this goal, this thesis investigates the underlying distribution of values in Attention-Based models and shows most of the weights and activations throughout the model follow a bell-shaped distribution. This motivated us to develop GOBO and Mokey, two quantization techniques for Transformer models that focus on the bulk of values to map FP32 or FP16 numbers into 3- or 4-bit indexes to dictionaries of representative values. This means forcing most of the values in a layer to be one of 8 or 16 unique values (instead of 2^32 in single-precision floating-point, FP32, format) while maintaining the models' accuracy. To exploit the quantized model even during inference computations, we designed custom hardware accelerators for GOBO and Mokey to enable computation directly on dictionary indexes instead of representative values, eliminating the need to build look-up tables for index-value conversion. GOBO and Mokey hardware accelerators improve the performance and energy efficiency of such models by one to two orders of magnitude.
An Engineered Paper-Based 3D Co-Culture Model of Pancreatic Cancer as a Platform for Systems Tissue Engineering
Keywords: cancer organoid
hypoxia
in vitro co-culture
microenvironment
pancreatic cancer
tissue engineering

Pancreatic ductal adenocarcinoma (PDAC) is a deadly form of pancreatic cancer and is projected to be the third leading cause of cancer-related deaths in Canada this year. With a 5-year survival rate that has barely improved over the last decade, new therapies are sorely needed to treat this disease. PDAC tumours are characterized by a stroma-rich and hypoxic tumour microenvironment (TME) that drives disease progression and results in a highly aggressive disease. Furthermore, the spatial configuration of cells in the TME affects cell phenotypes, which contributes to the clinical challenge of tumour heterogeneity and therapeutic resistance. Yet, pre-clinical studies typically rely on growing cancer cells deprived of their native complex and heterogeneous TME. Patient-derived organoids (PDOs) are emerging as powerful tools to model cancer, but their self-assembling nature and lack of stromal cells results in microenvironments that are not controllable, nor physiological. The principles of tissue engineering provide a powerful toolbox to elevate PDO cultures to better mimic the complex TME of PDAC. In this work, I present a versatile engineered 3D in vitro stroma-cancer co-culture model of PDAC based on the Tissue Roll for Analysis of Cellular Environment and Response (TRACER). By leveraging patterned polymer infiltration to reduce the number of cells required for TRACER, we incorporate human PDOs into our system. PDOs establish oxygen gradients across TRACER and in response exhibit graded cell viability, proliferation, hypoxia-response gene expression, and response to chemotherapy. We then incorporate primary pancreatic stellate cells (PSCs) to produce co-culture tissues with user-defined architectures that were inferred from clinical PDAC samples. By performing high dimensional characterization using CyTOF, we demonstrate that tissue architecture leads to distinct hypoxia and proliferation gradients. Furthermore, phenotypic markers for both cell types are also graded in ways that cannot be explained by either hypoxia or co-culture alone. I anticipate that our engineered tissue model will enable systems biology studies not possible with classic in vitro models. Specifically, our model will help decipher how tissue architecture and cell interactions regulate cell phenotype and, in turn, tissue-scale phenotypes that can be targeted for therapeutic purposes.
Neural Mechanisms Underlying Speech-in-Noise Perception in Aging
Keywords: aging
binding
GABA
gamma oscillations
perception
speech-in-noise loss

Everyday conversations take place in environments with some level of background noise. An aging-related decrease in the ability to understand speech in the presence of noise, called speech-in-noise loss, is a common experience causing major communication problems. Unfortunately, it is not only a matter of audibility. Aging-related changes in the central auditory system cause noise to interfere with speech further along the processing hierarchy, at the level of perception. This thesis investigated how some of those changes may impair older listeners’ ability to segregate and integrate speech sounds and background noise into separate perceptual objects, a process called feature binding. Binding is achieved by synchronizing 40-Hz gamma oscillations in spatially overlapping sensory and higher-order thalamocortical networks, which crucially depends on inhibition mediated by the neurotransmitter γ-aminobutyric acid (GABA). For this thesis, auditory cortex GABA levels in young and older adults were measured with magnetic resonance spectroscopy (MRS). The total amount of auditory cortex GABA declined in aging, and this decline was associated with reduced speech-in-noise understanding. Thorough analysis allowed the GABA decline to be teased apart from aging-related changes in brain structure and MRS reference signals, demonstrating an additional decline in the brain matter GABA concentration. In the same participants, the 40-Hz auditory steady-state response (ASSR) in quiet and with concurrent multi-talker background noise was recorded using magnetoencephalography (MEG). Careful analysis indicated multiple components of the ASSR. In noise, it was dominated by an early sensory component, and in quiet by a later higher-order binding component. For both stimulus conditions, 40-Hz gamma synchrony increased in aging. The gamma synchrony increase in the noise condition was associated with reduced speech-in-noise understanding, indicating heightened synchrony in sensory networks may cause stronger interference between speech and noise. This makes it more difficult to bind the sounds into separate perceptual objects for further processing. The grey matter GABA concentration was positively associated with gamma synchrony in the quiet condition, supporting GABA’s role in the internal generation of gamma oscillations. These results provide insight into how aging-related changes in the central auditory system affect feature binding and contribute to speech-in-noise loss.