VictorJuiz/thesis huggingface dataset:
Random constraint satisfaction problems and high-dimensional estimation
Keywords: Statistics.

In this thesis, we consider random constraint satisfaction problems(rCSP's) that exhibit sharp phase transitions. In the first part, we study a rCSP with discrete variables, namely the random regular NAE-SAT model. In a broad class of discrete rCSP's, one-step replica symmetry breaking cavity heuristics from statistical physics predict that there is the condensation threshold before the satisfiability threshold where most of the solutions lie inside a bounded number of solution clusters and the overlap between two independently drawn solutions concentrates precisely at two values. We give the first rigorous confirmation of this prediction for the random regular NAE-SAT problem. The second part of the thesis studies the binary linear classification problem in high dimensions, which can be viewed as a rCSP with continuous variables. We determine the satisfiability threshold for the data to achieve a positive margin assuming the covariates are Gaussian and the labels are drawn from a generalized linear model. Moreover, we characterize the limiting test error of the max-margin estimator
Randomized methods in optimization : fast algorithms and fundamental limits
Keywords: Electrical Engineering.

High-dimensional machine learning models trained on massive datasets have shown exceptional predictive ability over the last decade. Their adoption has created novel challenges and paradigms for practitioners and researchers. Typically, stochastic first-order methods have become the method of choice for training these models, due to their small per-iteration computational cost. Yet, these are known to be sensitive to hyperparameters' choices with weak or even no convergence guarantees. Alternative (e.g., second-order) optimization methods have not received the same amount of attention nor proved to be as effective in practice. A key bottleneck usually lies in their computational requirements, way more intensive than first-order methods. In this thesis, we are interested in random projections -- also known as (a.k.a.) sketching or subsampling methods -- in the context of large-scale optimization problems, which either involve massive datasets or high-dimensional optimization variables. The generic purpose of using random projections here is to reduce the dimensionality of the data matrix and/or the optimization variable, to obtain both faster convergence and smaller memory requirements. Naturally, random projections incur a loss of information, whence a loss in statistical performance. We consider the fundamental questions that arise when compressing information for faster computational procedures. What is the minimal embedding dimension one can use to project the data that guarantees convergence of an optimization solver? In fact, how to optimally couple random projections of the data with an optimization solver? And how to achieve optimal trade-offs between statistical and computational performance in terms of the choice of the random projection and the embedding dimension (a.k.a. sketch size)? To answer these questions, we will first focus on unconstrained convex quadratic objectives, and consider a broad class of first-order optimization solvers that involve a sketching-based preconditioner. We will explore and compare different random projections and classical first-order methods. We gradually extend our analysis and methodology to regularized convex quadratic optimization, and then to generic convex optimization problems
Rational preparation of broadband-light-emitting lead-halide materials
Keywords: Chemistry.

Among the various properties that two-dimensional lead-halide perovskites exhibit, their ability to emit strong, tunable photoluminescence at room temperature is particularly attractive for solid-state lighting applications. These materials' emission originates from the recombination of excitons (bound electron-hole pairs). For the majority of lead-bromide and iodide perovskites, excitonic recombination results in a narrow emission feature with a small Stokes' shift. However, in a small subset of perovskites, an additional broadband emission feature is observed which covers most of the visible light spectrum. This behavior has been attributed to exciton self-trapping, a process by which an exciton produces a lattice distortion around itself and, subsequently, becomes trapped in that distortion. The combination of the narrow free excitonic emission and broad self-trapped emission results in a photoluminescence spectrum which appears white to the human eye. While the ability for perovskites to emit broadband white light is attractive, it is currently not possible to reliably prepare perovskites which exhibit white-light emission at room temperature. In an effort to develop rational routes to obtaining white-light emission in lead-halide materials at room temperature, I studied the emission properties of lead-chloride perovskites and non-perovskite lead-bromide materials. Through observation of the lead-chloride perovskites, I find that all the materials examined exhibit broadband emission at room temperature. Further, I demonstrate that the (001) perovskites, the most common structure type, can reliably emit white light at room temperature when prepared as mixed chloride-bromide materials. I then sought to expand the search for broadband-light emission to octahedral lead-bromide materials in general. After surveying a wide variety of lead-bromide hybrid materials with varying dimensionality and interoctahedral connectivity, I find that broadband emission is common across this large and diverse family despite the differences in material dimensionality or interoctahedral connectivity. Additionally, I also find that there is no correlation between any ground state structural parameters and the broadband emission's position or breadth. These data contradict ideas which have previously been proposed for perovskites and non-perovskite materials and suggest that more detailed study of these materials is required
Reclaiming Indigenous Women’s Reproductive Health: The Impacts of Colonization on Indigenous Motherhood and the Revitalization of Traditional Birthing Practices
Keywords:  Department of Science
Indigenous women
reproductive health
tradition birthing practices
traditional medicinal plants
Science
Technology
and Society
STS
Thesis

Prior to the Allotment and Assimilation Era in 1887 and the introduction of Western medicine, Indigenous women’s reproductive health was immersed with cultural practices, medicinal plant remedies, and ultimately handled by women of the community. However, with time and assimilation, these practices began to fade and be forgotten. This thesis explores the following questions: How has assimilation and western expansion impacted the reproductive health and rights of Indigenous women and how do these continue to impact them in today’s current society? Looking into the oral histories of traditional birthing practices and the herbal medicines used for women’s health, how can these teachings be integrated or reintegrated back into the culture and potentially into Western medicine? What steps can be taken to reclaim this traditional knowledge and reverse the current state of Native women’s reproductive health and rights? I examine the impact of colonization and assimilation on the current state of Indigenous Women’s Reproductive Health to provide insight as to why there is a loss of Indigenous traditions. Looking at Navajo, Lakota, and Dakota Sioux birthing knowledge specifically, I explain their traditional birthing practices based on examinations of ethnographies from 1920-1940, interviews I conducted with elder women affiliated with these tribes, along with other research interviews on the topic. With this knowledge of the birthing practices, I explore the medicinal plants that aided in the births, as well as in serving as a contraceptive, abortifacient, gynecological aid, and reproductive aid to women in these tribes. By understanding the history of Indigenous women’s reproductive health, this paper aims to help in revitalizing Indigenous knowledge around birth and women’s medicine and possibly integrating these practices into Western medicine.
Recommendations for algorithmic fairness assessments of predictive models in healthcare : evidence from large-scale empirical analyses
Keywords: Biomedical Informatics.

The use of machine learning to develop predictive models that inform clinical decision making has the potential to introduce and exacerbate health inequity. A growing body of work has framed these issues as ones of algorithmic fairness, seeking to develop techniques to anticipate and proactively mitigate harms. The central aim of my work is to provide and justify practical recommendations for the development and evaluation of clinical predictive models in alignment with these principles. Using evidence derived from large-scale empirical studies, I demonstrate that, when it is assumed that the predicted outcome is not subject to differential measurement error across groups and threshold selection is unconstrained, approaches that aim to incorporate fairness considerations into the learning objective used for model development typically do not improve model performance or confer greater net benefit for any of the studied patient populations compared to standard learning paradigms. For evaluation in this setting, I advocate for the use of criteria that assess the calibration properties of predictive models across groups at clinically-relevant decision thresholds. To contextualize the interplay between measures of model performance, fairness, and benefit, I present a case study for models that estimate the ten-year risk of atherosclerotic cardiovascular disease to inform statin initiation. Finally, I caution that standard observational analyses of algorithmic fairness in healthcare lack the contextual grounding and causal awareness necessary to reason about the mechanisms that lead to health disparities, as well as about the potential for technical approaches to counteract those mechanisms, and argue for refocusing algorithmic fairness efforts in healthcare on participatory design, transparent model reporting, auditing, and reasoning about the impact of model-enabled interventions in context
Reduced order model predictive control of high-dimensional systems
Keywords: Aeronautics and Astronautics.

Many physical systems are modeled by ﬁnite-dimensional sets of ordinary diﬀerential equations (ODEs). Others have dynamics that evolve over a continuum (i.e. are inﬁnite-dimensional) and are best modeled by partial diﬀerential equations (PDEs), including systems with ﬂuid ﬂows, deformable/ﬂexible structures, or ﬂuid-structure interaction. In practice, PDE models are generally semi-discretized to produce high-ﬁdelity ﬁnite-dimensional ODE models. Since the dimension of these models can range from thousands to millions, computational challenges severely limit the use of standard approaches to model-based controller design. In this thesis, we propose an approach for efficiently designing high-performing controllers based on high-dimensional models. Specifically, we develop a model predictive control (MPC) algorithm for solving constrained optimal control problems that leverages high-fidelity, but low-dimensional, reduced order approximations of the original model to satisfy practical computational requirements. In the linear setting, we combine existing ideas from tube MPC with novel approaches for controller synthesis and analysis to develop a reduced order MPC (ROMPC) scheme for solving robust, output feedback control problems, and we provide theoretical closed-loop performance guarantees that explicitly account for model reduction error. We also extend the ROMPC scheme to the nonlinear setting by exploiting piecewise-affine reduced order models. We motivate and validate the proposed approach through two case studies. First, we use a linear, coupled rigid-body/fluid dynamics model for aircraft control, where the high-dimensional computational fluid dynamics (CFD) model has over one million dimensions. Second, we use a nonlinear finite element model (FEM) with over ten thousand dimensions to control a soft robot. Simulation and hardware experiments are used in both studies to demonstrate the practicality and performance of ROMPC
Re-engineering Digital Privacy for Domestic Violence Survivors
Keywords: digital privacy
domestic violence
cybersecurity
privacy
human-computer interaction
design thinking
computer science
Text
Thesis

In recent decades, technology has increasingly been used as a tool for domestic violence perpetrators to harass, stalk, and surveil victims. Subsequently, survivors of domestic violence face critical digital privacy needs that are inadequately addressed by modern digital privacy practices and tools. Drawing from interviews with survivors and domain experts, this paper first presents a graphical framework to model digital privacy attacks in domestic violence. It then outlines five key digital privacy needs of domestic violence survivors— awareness, coverage, convenience, maintenance, and alerts. Next, it introduces the Berrier system, a single control tower for cross-platform privacy settings designed specifically for survivors’ privacy needs. Finally, the paper presents a 10-participant user study for Berrier, which finds that using Berrier sharply increases the average total accuracy (+10.0%), speed (+39.5%), and perceived ease (+51.0%) of changing one’s online privacy settings. These findings present a crucial step forward in re-engineering digital privacy for domestic violence survivors.
Refraction of Dirac Fermions across Molecular Graphene Junctions
Keywords:  Program in Engineering Physics
graphene
molecular graphene
condensed matter physics
scanning tunneling microscopy
individual atomic manipulation
Veselago lens
Dirac fermions
refraction
tachyonic behavior
Text
Thesis

Graphene has been a subject of great interest in condensed matter physics due to its exotic topological properties. We have used an STM capable of individual atomic manipulation to engineer molecular graphene nanostructures which host relativistic massless Dirac fermions and constitute a fully-tunable electronic system. Motivated by this light-like behavior, our ultimate goal is to create a Veselago lens out of flakes of doped molecular graphene. This work explores the refraction of Dirac fermions across various types of molecular graphene junctions, including neutral-p, neutral-n, n-n, and neutral graphene - 2 dimensional electron system junctions. Principle to our analysis is a new technique combining the traditional quasi-particle interference method used in scanning tunneling microscopy with individual atomic manipulation, which allows us to investigate the scattering of electron wave functions in 2D systems with great precision. Finally, we also explore the possibility of ’tachyonic’ behavior of Dirac fermions as they refract across junctions from areas with higher to lower group velocities. We find significant evidence of refraction of Dirac fermions across various junctions, as well as propose a theoretical explanation for the angles of refraction based off of band structure. We also find evidence of ’tachyonic behavior’ in Dirac fermion refraction.
The regulatory landscape of CAR T cell dysfunction
Keywords: Genetics.

Chimeric Antigen Receptor (CAR) T cells have emerged as a promising immunotherapy strategy to target cancer in a subset of patients. T cell exhaustion presents a significant barrier to the progress of CAR T cell therapy development and efficacy. We use a human CAR T cell model system, utilizing the non-exhausting CD19-targeting CD19-28z CAR T cells and the in vitro exhausting GD2-targeting HA-28z CAR T cells, to investigate the genetic interactions and regulation underlying CAR T cell exhaustion and dysfunction. We nominate regulatory transcription factors and enhancer loci that control the expression of exhaustion-associated and -promoting genes and perturb these regulatory networks to modulate the exhaustion phenotype and prevent T cells from exhibiting the hallmarks of exhaustion
Regulatory roles of long non-coding RNA genes in cellular identity and complex traits
Keywords: Genetics.

Genomic research primarily focuses on protein-coding genes, due to their obvious functional relevance and the well-defined relationships between DNA variation and protein sequence. However, most disease-associated variants are not in protein-coding sequences. This has led to much investigation of non-coding variation as it relates to protein-coding gene processing, but there is also a need to examine non-coding transcripts, and their roles in cellular function and trait outcomes. This dissertation contributes to the latter goal through studies of long non-coding RNA (lncRNA) genes, which are genes that produce RNAs > 200 nucleotides long that are not translated into proteins. There are several examples of lncRNAs with key regulatory roles in processes such as dosage compensation (XIST), embryonic development (HOTAIR), and the immune response (NEAT1). However, it is still unclear how many lncRNA genes are functional, how many are byproducts of surrounding active gene expression, and how many are just transcriptional noise. The objective of this dissertation is to progress our understanding of non-coding genes through both broad analyses of the interplay between genetic variation and lncRNA expression patterns, followed by focused analyses of the involvement of candidate lncRNAs in immune response and other complex traits. In Chapter 2, I use the Genotype-Tissue Expression (GTEx) version 8 data to establish hallmarks of lncRNA gene expression and regulation and identify lncRNAs with robust complex trait associations. In Chapter 3, I examine the effects of the anti-inflammatory mouse lncRNA NeST on gene expression and chromatin accessibility in murine T cells. Finally, in Chapter 4, I profile lncRNA expression patterns in bulk purified immune cell populations, explore the regulatory patterns of the human NEST gene in these cell types, and evaluate this gene's possible relevance to autoimmune diseases. The findings and approaches described in this dissertation add to the existing annotation of functional lncRNA genes, and can help frame future studies of non-coding gene expression
Reimagining state-society relations through the works of China’s New Documentary movement: West of the Tracks, 24 City, and We the Workers
Keywords:  Department of History
Documentary films
Post-communism
Cinéma vérité
New wave films > History
China
Labor
Text
Thesis

Through analysis of three “New Documentary” films about labor in post-socialist China, this thesis investigates how Philip Huang’s concept of the third realm appears in both the filmmakers' negotiations with the state and the state-society interactions embedded in the films themselves. Though the Chinese state is frequently seen as authoritarian and its citizens as repressed individuals, in Wang Bing’s West of the Tracks (2003), Jia Zhangke’s 24 City (2008), and Huang Wenhai’s We the Workers (2017), we witness how their relationship can be one of negotiation, rather than simply one of command.
Replication of RNA by transcription polymerases
Keywords: Bioengineering.

While both DNA and RNA can serve as hereditary material in viruses, DNA is considered the carrier of genetic information in cellular life. For RNA to serve as a genetic material in cells, enzymatic machinery to replicate RNA (i.e., synthesize full-length copies of RNA) would be required. However, RNA replicase enzymes that are well-known to replicate RNA have so far only been found in RNA viruses such as influenza and dengue but not in cellular organisms. Interestingly, numerous lines of evidence suggest that two classes of pathogenic RNAs (hepatitis delta virus in animals and viroids in plants) replicate their RNA genetic material by redirecting cellular DNA-dependent RNA polymerases (also called transcription polymerases) to serve as RNA-dependent RNA polymerases. This noncanonical RNA replication activity of transcription polymerases could provide a general means for propagating RNA as hereditary material in cellular contexts because transcription polymerases are found across the tree of cellular life. But a molecular-level understanding of the diversity, mechanisms, and origins of RNAs that can be replicated by transcription polymerases was lacking. By integrating next-generation sequencing, microfluidics, bioinformatics, and biochemistry approaches, I have investigated in vitro RNA replication by a model transcription polymerase enzyme from T7 bacteriophage called T7 RNAP. There were three previous papers on RNA replication by T7 RNAP, all published more than two decades ago, which together described five replicating RNA sequences. The origins of the RNAs replicated by T7 RNAP and requirements for RNA replication by T7 RNAP were unclear. I identified hundreds of new replicating RNAs, defined three mechanisms involved in replication (subterminal de novo initiation, RNA shape-shifting, and interrupted rolling circle synthesis), and described emergence from DNA seeds as a mechanism for the origin of novel RNA replicons. These results inform models for the replication of naturally occurring RNA genetic elements and demonstrate how replicating RNAs can originate from non-replicating materials
Representation learning and algorithms in hyperbolic spaces
Keywords: Computational and Mathematical Engineering.

Graph embedding methods aim at learning representations of nodes that preserve graph properties (e.g., graph distances). These embeddings can then be used in downstream applications such as clustering, visualization, nearest neighbor search and classification tasks. Most machine learning algorithms learn embeddings in standard Euclidean spaces. Recent research shows promise for more faithful embeddings by leveraging non-Euclidean geometries, such as hyperbolic or spherical geometries. In particular, trees can be embedded almost perfectly into two-dimensional hyperbolic spaces, while this is not possible in Euclidean spaces of any dimension. In this thesis, we develop machine learning models that operate in hyperbolic spaces. We start by introducing hyperbolic representation learning methods for hierarchical graphs, including methods for multi relational graphs or graphs with node features. We demonstrate the benefits of these hyperbolic representations compared to their Euclidean counterparts on a variety of link prediction benchmark datasets. We then design algorithms that operate on these hyperbolic representations. We first propose a method for hierarchical clustering in hyperbolic space, which can be used to solve any similarity-based hierarchical clustering problem with gradient-descent. We then propose a generalization of Principal Component Analysis for hyperbolic inputs, and show that it yields improved low-dimensional representations and data visualizations compared to previous manifold dimensionality reduction methods
Resolving structural changes in photoinduced reactions in solution with X rays and electrons
Keywords: Physics.

The need for efficient, low-cost solar energy capture has driven interest in mechanistic understanding of light-induced chemical reactions in solution, for instance, natural and artificial photosynthesis. Resolving the structural changes of a molecule during a photoinduced reaction in solution can be challenging, however, with many interdependent intra- and inter-molecular degrees of freedom. This thesis reports experiments using three techniques that can be used to disentangle structural changes during photoinduced reactions: x-ray absorption spectroscopy, valence-to-core x-ray emission spectroscopy, and ultrafast electron diffraction