VictorJuiz/thesis huggingface dataset:
Compositional representation learning : from reasoning to synthesis
Keywords: Engineering.

The world we live in is inherently compositional: Just like a sentence is built upon phrases and words, a visual scene comprises a collection of interacting objects and entities, which in turn are derived from the sum of their parts. This compositionality plays a pivotal role in our ability to understand the world, organize the acquired knowledge through a rich set of concepts, and readily adapt them to novel situations and environments. Essentially, it is considered one of the fundamental building blocks of human intelligence. What does compositionality mean in the context of machine intelligence? How can we encourage neural networks to develop structured understanding of our surroundings? And how can we apply this knowledge to improve in downstream tasks of vision and language? These are the key questions that are explored in the dissertation. We discuss manners and mechanisms to encourage multimodal neural networks to learn compositional scene representations, and in turn operate over them in a compositional manner, which we leverage for two downstream goals: multimodal reasoning, where we introduce models that can draw a sequence of inferences about visual scenes so to answer textual questions about them; and visual synthesis, where a model can inversely generate pictures depicting multi-object scenes from scratch. We fulfill these aims by incorporating a graphical structure into neural networks, which consists of nodes and edges, respectively meant to capture the objects within the scene and the relations among them. We demonstrate how these graph-based structural priors endow neural networks with multiple desirable properties, including: data efficiency, achieved by decomposing a given task into a series of subtasks, each of which could be learned more easily; generalization, where a model can recombine known concepts together in novel ways; controllability, where modifying respective components of the model's latent representation can selectively induce intended modifications within its output; and interpretability of the computational process the model follows, either to create an image or to reason over it. Throughout this work, we study the interplay and analogies between synthesis and reasoning, and show how, with the right inductive biases incorporated, the former capability could foster the latter.
A compression-enabled approach to analyze seizures for people with refractory epilepsy
Keywords: Engineering.
Electrical Engineering.

This dissertation presents an information theoretic measure called the inverse compression ratio (ICR)—an unbiased estimate of joint entropy (measure of signal complexity)—and demonstrates its potential for seizure detection. First, a minimally disruptive, scalable platform to acquire 10 kHz research-quality, intracranial EEG (iEEG) in a hospital setting was deployed to build the Brain Interfacing Laboratory iEEG repository. Second, ICR was implemented using the DEFLATE algorithm (a time independent and lossless encoder), and it outperformed other conventional, model-free algorithms. Third, leveraging video compressors, it was determined that some degrees of time dependence and lossiness in the compression encoding can improve ICR performance for seizure detection. Finally, when quantifying seizure detection performance, positive-centric performance metrics—those that ignore true negatives (e.g., area under the precision-recall curve)—were found to be more informative than others. Compression-enabled measures, without linear assumptions or parametric modeling, provide a promising direction for qEEG analyses
Computation of wall-bounded flows using a new universal velocity profile
Keywords: Engineering.
Aeronautics and Astronautics.

Turbulence has applications in countless engineering, energy and natural systems. As such, it is crucial to be able to understand and predict how flows with turbulence behave as this could lead to enhanced understanding of applications such as atmospheric circulation patterns, blood flow, pollution dispersion in the atmosphere and ocean, combustion and vehicle drag to list only a few. At high Reynolds numbers, solutions of turbulent flows, especially those that are bounded by a wall, remain a computational challenge due to the range of scales that need to be accounted for. As such, it is common to use models to alleviate the need to account for the full range of scales near the wall and help bring about wall-bounded turbulent computational solutions at a much more feasible cost. This work focuses on a new wall model that can be used in both the wall and wake layers of a boundary layer. A boundary layer develops over a wall due to the no-slip at the wall. The wall layer of a turbulent boundary layer includes the viscous sublayer, buffer layer and logarithmic layer. This near-wall region is universal to all smooth wall turbulent flows and is where the most dissipative scales of turbulence lie. The wake layer, however, is where the flow is governed far more by the overall forces that govern the flow and is extremely flow dependent. To date, there is no single theory that unifies all these regions. We seek to provide such a theory with the universal velocity profile. The universal velocity profile is uniformly valid from the wall to the edge of a boundary layer. We use direct numerical simulations, large eddy simulations and experimental data to fit it to channel flow and boundary layers with favorable, zero and adverse pressure gradients and provide a new friction law and a shape function that can be used for high Reynolds number computations. The main focus of the universal velocity profile in this work is as a computational wall model in large eddy simulations. Since it is valid in the wake layer, it can be used freely throughout a boundary layer with assurance that it will give an accurate flow prediction. Large eddy simulations for channel flow with and without a periodic hill and external flow around an airfoil are shown and the universal velocity profile is shown to be robust in its modeling capability.
Computational and experimental approaches to understanding mammalian gene regulation with synthetic biology
Keywords: Humanities and Sciences.

Transcriptional regulation is a key pathway cells use to regulate gene expression in response to temporal signaling, and is becoming widely used as a platform for synthetic biology applications. Transcription in mammalian cells is in large part driven by the actions of transcription factors, which consist of DNA-binding domains that recognize and bind to specific genomic locations and effector domains that recruit transcriptional machinery and chromatin regulators to activate or repress target genes. This work uses mathematical modeling and high-throughput experimental approaches to dissect the temporal and combinatorial logic of gene regulation in mammalian cells. First, we build a mathematical framework for analyzing the response of genetic circuits containing chromatin regulators to temporal signals in mammalian cell populations, elaborating on prior models in which individual cells stochastically transitioning between active, reversibly silent, and irreversibly silent gene states at constant rates over time. We analyze classical gene regulatory motifs such as feedforward and autoregulatory loops in the context of duration-dependent signaling, and find that repressive regulators with epigenetic memory can sum up and encode the total duration of their recruitment in the fraction of cells irreversibly silenced. Last, we use an information theoretic approach to show that all-or-none stochastic silencing can be used by populations to transmit information reliably and with high fidelity even in very simple genetic circuits. Second, we use a high-throughput approach to dissecting how distinct effector domains within a single transcription factor can be combined to regulate gene expression. We measure transcriptional activity for 8,400 effector domain combinations by recruiting them to reporter genes in human cells. We find that weak and moderate activation domains synergize to drive strong gene expression, while combining strong activators often results in weaker activation. In contrast, weaker repressor domains tend to average each other out, and moderate to strong repressor domains often overpower activation domains. We use this information to build a synthetic transcription factor whose function can be tuned between repression and activation independent of recruitment to target genes by using a small molecule drug. Altogether, this work helps to advance our understanding of how to build synthetic transcription factors, and how to design and model chromatin regulation-based circuits. Our hope is that these efforts will help advance mammalian synthetic biology as a tool for biological research and a reliable strategy for developing medical therapies
Computational and functional studies on the genomic basis of vertebrate traits
Keywords: Medicine.
Developmental Biology.

One of the most profound discoveries of modern science thus far is that the breathtakingly beautiful diversity of form and function in life is encoded by the genome. Despite major advances in genome sequencing and functional testing, there is yet much to learn about how traits are specified by underlying DNA sequence. For vertebrate genomes, linking genotypic variation to phenotypic outcome is especially challenging given that their typical size spans hundreds of megabases to several gigabases in length. In my graduate work, I contributed to efforts addressing this challenge. Each study described in this thesis involved computational analysis of vertebrate genome sequence, either in the form of whole nuclear genome assemblies or of high-throughput short-read sequencing data, to identify candidate regions associated with and hypothesized to control traits of interest. For two of the studies, including my main thesis project described in chapter 6, I also performed functional testing to evaluate the extent to which candidate regions actually contribute to the associated phenotype. Together, the efforts described in this thesis represents genotype-phenotype mapping methods to identify the protein-coding and cis-regulatory sequence basis of disease-related and naturally evolved traits that arose over the last 10s to 10s of millions of years in humans, other placental mammals, and fishes
Computational applications towards 1D van der Waals and phase change materials
Keywords: Humanities and Sciences.
Applied Physics.

This thesis focuses on the intersection of materials screening, machine learning, and ab-initio calculations, with regards to low dimensional materials - mainly 1D dimensional van der Waals wires and materials with 1D and 2D structures that are particularly suitable for phase change applications. 1D van der Waals materials are a less studied counterpart to their 2D dimensional cousins, but there exists a wide range of such 1D prototypes, including conducting wires that may be suitable as consideration for interconnect replacement candidates. We find exfoliation energies can be reasonable and of the same magnitude as their 2D layers, and evaluate their electronic property changes as we thin these wires to the 1D limit. Due to the fact there is a limited number of such materials, only a few hundred, we machine-learn to expand this composition space, training using the known compositions and using input features based on the elemental-makeup. We particularly target conductive and magnetic materials, as well as those containing transition metals and members of the chalcogen, pnictogen, and halogen families, as those are often particularly suited for chemical vapour decomposition (CVD) and chemical vapour transport (CVT) synthesis mechanisms. Previous work in the group has examined the phase changes in the transition metal dichalcogenides (TMD), and we extend this to a wider range of materials as well as changes where only the relative stacking between low-dimensional components differ. The multiple phases present can display unique dynamics, including switching mechanisms potentially relevant for phase change memory applications. We present a bottom-up screening across all crystalline material to evaluate their phase change potential. For the stacking changes, we study the 1T' and the $T_d$ transition present in select members of the transition metal dichalcogenide family. Past research has focused on the 1T, 1T' and the 2H phase transitions, where the individual 2D layers support differing structures. In contrast, the 1T' and the $T_d$ phases have the same individual layers, but a differing relative orientation that is present between the two. This type of sliding phase transition offers the potential to further reduce switching timescales. We present an analysis on the few-layer behavior of the transition metal dichalcogenide structures of 1T' and T$_d$ WTe$_2$ and MoTe$_2$ with the observation that the energy differences are far more subtle
Computational methods and mathematical measures for population relationships
Keywords: Engineering.

A population is the most central unit of study in population genetics. Patterns revealed by data on variation, both genetic and cultural, are important in understanding the composition and dynamics of populations. These patterns help unravel the driving forces behind evolutionary processes. Numerous statistics, mathematical models, and computational approaches have been developed to quantitatively analyze relationships within and between populations. To build upon these efforts, this dissertation investigates and devises computational methods and mathematical measures for studying various aspects of populations. Chapters 2, 3, and 4 investigate the mathematical properties of genetic diversity measures used to quantify the variation within and between populations. Chapters 5 and 6 study the challenge of cluster alignment in analyzing and visualizing population structure analysis results. Chapters 7 and 8 adapt population-genetic approaches to study the patterns of composition and change in cultural traits. Collectively, the studies conducted in the dissertation introduce tools to improve existing approaches, to devise new frameworks, and to resolve previously unaddressed issues in analyzing population relationships. The advancements also demonstrate potential applications in fields beyond population genetics.
Computational modeling of axon mechanics : viscoelasticity, growth, and damage
Keywords: Engineering.
Mechanical Engineering.

Axons play a vital role in the function of the nervous system by carrying electrical signals between neurons and other cells. Their long and thin structure, however, makes them especially vulnerable to damage. In fact, axons are a frequent lesion site in traumatic brain injury, and diffuse axonal injury has been linked to coma, cognitive impairment, and the development of neurodegenerative disease. While loading applied at high rates and magnitudes can damage the axon, the axon exhibits different responses to mechanical loading at other loading rates and magnitudes. At physiological levels of loading, the axon deforms as a viscoelastic solid, and at intermediate levels of loading, mechanical forces can even induce growth and promote regeneration. In fact, the gradual application of tension is being explored as an avenue to promote axon regeneration in peripheral nerve repair. The success of approaches like this is dependent on knowledge of the mechanical responses of the axon - viscoelasticity, growth, and damage - as well as the thresholds separating these responses. Here, computational modeling can aid the generalization from in vitro measurements to engineering applications. In this thesis, I introduce computational models describing the complex mechanical response of the axon. First, I present a mathematical framework modeling axon viscoelasticity, growth, and damage and discuss its application in informing nerve repair strategies. Then, focusing in on damage, I introduce an image-based axon model used to pinpoint factors influencing susceptibility to injury. Finally, I discuss the application of mechanics-informed neural networks to model viscoelastic response. These computational models assemble myriad experimental measurements into frameworks that provide a comprehensive representation of axon mechanical response.
Computational modeling of tissue-engineered vascular grafts
Keywords: Engineering.
Bioengineering.

Tissue-engineered vascular grafts (TEVGs) have the potential to improve patient outcomes in the field of pediatric cardiology given their ability to grow and remodel over time. However, the widespread clinical use of TEVGs has historically been limited by complications such as stenosis and occlusion, as well as variability in outcomes. This thesis focuses on leveraging computational models to enhance our understanding of TEVGs and aid their translation to clinical use. In this work, we analyzed outcomes from the first U.S. clinical trial of TEVGs in single-ventricle patients with completed Fontan circulation. The study identified two distinct phases of graft remodeling, characterized by rapid geometry changes followed by sustained growth and reduced stiffness. Clinically-informed and patient-specific computational fluid dynamics (CFD) simulations were employed to examine the impact of TEVG geometry. Using virtual surgery, we evaluated the outcomes under mild and severe stenosis. Particular focus was placed on evaluating exercise tolerance in these patients, and, to this end, we developed a model of the cardiovascular behavior of Fontan patients during both rest and exercise. We determined that the Fontan circulation is robust to a wide variety of TEVG geometries and that TEVGs demonstrate favorable hemodynamics compared to the standard-of-care synthetic grafts. These insights support ongoing clinical evaluation of TEVGs. We extend the capabilities of growth and remodeling frameworks to predict TEVG behavior by incorporating constrained mixture theory into a full, three-dimensional fluid-structure interaction solver using a novel coupling framework. We validate this framework's behavior against past work and demonstrate its robustness across a variety of vascular settings, timescales, and pathologies. This "fluid-solid-growth" solver enables long-term, predictions of changing hemodynamics, vessel wall morphology, tissue composition, and material properties and provides one of the first patient-specific growth and remodeling frameworks that accounts for complex hemodynamics. Using these computational tools, we next evaluated treatment options for single-ventricle physiology, specifically examining the design of flow restrictors for hybrid palliation. We used patient-specific CFD simulations to compare the performance of internal flow restrictors with traditional external pulmonary bands. Our findings demonstrate that the geometric resistance of flow restrictors, crucial for regulating systemic-to-pulmonary flow ratios, is influenced by patient-specific geometry. Additionally, we note that device design does not disrupt the load balance in the distal pulmonary vasculature compared to the preoperative or externally banded geometry. By leveraging and expanding computational models, we show how computational tools can aid the path from it in-silico experiments to it in-vivo applications. Together, this work has the potential to significantly improve the diagnosis, classification, and treatment of pediatric CHD patients
Computational validity
Keywords: Education.

As online learning platforms and computerized testing become more common, an increasing amount of data are collected about users. These data include, but are not limited to, response time, keystroke logs, and raw text. The desire to observe these features of the response process reflect an underly- ing interest in the cognitive processes and behaviors respondents engage in while taking tests and navigating learning platforms. Alongside this interest in response processes is an increased desire to use computational methods, artificial intelligence, and machine learning to combine this process data with traditional item response data. This dissertation attempts to advance a notion of computational validity, defined as arguments for the use and interpretation of test scores from evidence collected or analyzed using computational methods. Chapter one looks at item position effects as a threat to comparability across test forms. The presence of position effects is especially threatening to computer adaptive tests. Here a mixture item response theory model that decomposes item and person components of position effects is developed and applied to a large Brazilian college admissions exam to demonstrate its ability to both recover information about position effects in items and automatically produce comparable ability distributions across forms. Chapter two looks at data from a browser-based reading assessment and proposes drift diffusion models as a theoretically grounded way to combine response time and response accuracy data. The individual parameters estimated from the drift diffusion models are combined with scores on the browser-based assessment and used to predict scores on more expensive and time consuming measures of reading ability. This provides a line of evidence justifying the clinical and classroom use of the tool for diagnostic purposes. Chapter three looks at the issue of model selection. To make decisions from a model requires justification that the model is appropriate for the data. Current methods of model selection are well suited to make decisions on which model fits better, but not to communicate about how much better or if the degree of fit is appropriate. This paper uses a tool called the InterModel Vigorish that aims to provide measures of increases in predictive performance on an interpretable scale and extends it to polytomous item response theory models. Simulations are performed to explore what can be learned about data generating processes from this tool and then it is applied to a selection of empirical datasets. Combined, this work aims to highlight ways that modern computational methodologies can be used to improve not only assessments, but the decisions we make from them and our ability to communicate about them
Computer vision and robotics enable automated experimentation on drosophila behavior
Keywords: Engineering.
Electrical Engineering.

The fruit fly, Drosophila melanogaster, is a widely used model animal in neuroscience and diverse fields of biological and medical domains. Increased automation of assays would greatly facilitate many aspects of research, but the implementation of automated assays has been hindered by the difficulty of handling individual flies in a mechanized way. To address this hurdle, we introduce FlyMAX (Fly Manipulation and Automated eXperimentation), a system capable of collecting adult flies without the use of anesthesia and inspecting them with machine vision, yielding a throughput over 1,000 flies daily for high-throughput assays. Our study validates that the robotic handling of flies has no adverse effects on fly health. Furthermore, the integration of deep learning-based machine vision enables real-time inspection of fly quality and phenotypes. Notably, the system allows for automated assessments of stimuli-response behavior assays on individual flies. FlyMAX offers a promising solution for enhancing the efficiency and reproducibility of insect research in various fields such as genetics, neuroscience, and drug testing.