VictorJuiz/thesis huggingface dataset:
Flexible electronics as a platform for neuroscience : exploring the dynamics of aging, neurodegeneration, and novel animal models

Fundamental knowledge about the brain is primarily acquired through two distinct groups of methods. Techniques with low spatiotemporal resolution, such as functional magnetic resonance imaging (fMRI) and electroencephalography (EEG), have provided insights into meso- and macroscopic neural activity. Owing to their noninvasiveness and growing availability to the general population, these techniques enable longitudinal studies of changes in brain states in the same individuals. Conversely, high spatiotemporal resolution techniques, such as single-neuron electrophysiology and imaging, have facilitated a deep understanding of the basic building blocks of the brain, from individual ion channels to single-neuron activity, and to region-wide dynamics of neuronal circuitry. However, high spatiotemporal techniques have largely been precluded from translating to long-term studies because of their invasiveness and a mismatch of properties with neural tissue. This spatiotemporal span (cross-sectional vs. longitudinal) and resolution gap has led to a significant division in our exploration of the brain's dynamics. This dissertation aims to explore flexible electronics, a recently developed electrophysiology tool, as a platform for bridging this very gap to enable a new longitudinal understanding of the brain at the single-neuron resolution. In Chapter 1, I provide background on the electric basis of neuronal communication, a historical perspective on methods used to electrically interface with the nervous system, and present an argument for developing more compliant, brain-like neural probes. Chapter 2 introduces flexible electronics as a competitive class of neural probes for interfacing with the brain as compared with conventional neurotechnologies and reviews the properties and previous applications of bioinspired, brain-like mesh electronics. In Chapter 3, I present key hardware improvements made to the mesh electronics platform to enable chronic interfacing with the brain. Chapter 4 reviews work conducted to facilitate simultaneous electrophysiology and behavioral neuroscience through the integration of a virtual reality (VR) system. Chapter 5 is centered around work measuring the longitudinal neural correlates of aging and neurodegeneration in wildtype (WT) and 3xTg Alzheimer's Disease (AD) mouse models, respectively. In Chapter 6, I introduce a novel application of mesh electronics to studying spatial cognition in a nonstandard amphibian model, the cane toad (Rhinella marina). Finally, I present a broad outlook for the future of flexible electronics and its applications to neuroscience.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Ganguli Surya
Hong Guosong
Good Benjamin H.
Flow physics of radiatively heated particle-laden channel flow and simulation methods for shock-driven problems in materials with strength

Simulations of multiphase flows are used to understand and predict a wide variety of natural and industrial phenomena. This thesis concerns simulations of two different multiphase flows. The first part of this thesis uses high fidelity simulations to study particle-laden turbulent channel flow, subjected to strong radiation heating. Chapter 2 describes the physics of isothermal particle- laden channel flow, characterizing the effects of particles on turbulence across wide parametric variation in Stokes number and mass loading. Ideas from other branches of turbulence study, in particular compressible turbulence and rough-wall turbulence, are applied to make progress toward a model for two-way coupled particle-laden channel flow. Chapter 3 considers the dynamics of particle clouds in isothermal particle-laden channel flow using new tessellation based approaches to characterize particle clustering, rotation, and swirling motions. The dependence of the particle dynamics is characterized as function of Stokes number and mass loading, but also flow region, and comparisons with previous results from homogeneous isotropic turbulence are made in the logarithmic layer. Finally, the effect of strong radiation heating is shown on particle-laden channel flow in chapter 4. The strong heat transfer drives expansion and acceleration, and the flow tends towards re-laminarization from acceleration and viscosity increase. The second part of this thesis describes the development of an Eulerian finite difference method for simulating multi-phase mixtures of elastic-plastic materials subjected to shock waves and undergoing strain hardening. The new method uses localized artificial diffusivity (LAD) to regularize discontinuities, including discontinuities in strain, which are important in shocks in solids. New LAD terms are added to stabilize the kinematic evolution equations in strong shear deformation. The new method is tested on a variety of 1D and 2D problems to demonstrate its convergence properties, ability to simulate impacts, and shock capturing ability. The 2D problems considered are a Taylor impact with realistic equation of state and plasticity models, and a solid-solid Richtmyer-Meshkov instability, which would be challenging for more traditional Lagrangian methods. Lastly, three interface sharpening techniques for multiphase flows are extended to elastic-plastic materials, and their performance is evaluated on a variety of test problems. The methods are comparable when simulations are well-resolved, but show different deficiencies when flow topologies become under-resolved

Keywords: Engineering.
Mechanical Engineering.
Advisor: Lele Sanjiva K. (Sanjiva Keshava)
Farhat Charbel
Mani Ali (Professor of mechanical engineering)
Fluctuating orders in strongly correlated systems : a computational study

Strongly correlated electronic systems are at the forefront of research in materials and condensed matter physics, because of their many exotic properties and potential applications. Cuprate high temperature superconductors are the quintessential strongly correlated materials, characterized by an unconventional superconducting phase, the mechanism of which is still unclear, and an equally enigmatic normal state with many poorly understood properties, such as intertwined orders, spectroscopic pseudogap, and strange metallicity. In this thesis, I will present theoretical studies on fluctuating intertwined orders by investigating the Hubbard model, which is the canonical model Hamiltonian for describing electron dynamics in cuprates. First, I discuss tendencies of electronic nematicity in the Hubbard model and compare it with electronic Raman scattering in cuprates. The results show an enhancement of electronic nematicity, consistent with experimental observations, and suggest the capability of the Hubbard model to capture ordering tendencies relevant in cuprates. Next, I discuss fluctuating charge- and spin-density waves in the Hubbard model. Calculations show that robust fluctuating charge and spin orders are present in a wide parameter regime in the strange metal phase, consistent with recent experiments. The results point to the importance of fluctuating orders to all electronic properties of cuprates, as opposed to just the pseudogap regime as previously believed. Finally, I summarize the findings presented in this thesis and briefly survey the outlook for theoretical studies on fluctuating intertwined orders relevant for cuprates.

Keywords: Humanities and Sciences.
Chemistry.
Advisor: Devereaux Thomas
Markland Thomas
Martinez Todd
Foregone-conclusions

The argument of this document is oriented around a general constraint on partial explanations of why an event where an agent concludes happened. The kind of partial explanations for why an event where an agent concludes happened of interest are routine. For example, while playing a game of chess an agent concludes moving one of their rooks to e4 secures checkmate. And, at least in part, this conclusion is explained by observing the agent identified the checkmate by applying their understanding of chess. In more detail, the agent may have performed an exhaustive search of some fixed depth over the remaining pieces. Or, perhaps the agent had finished cornering their opponent with a particular strategy. In contrast, some things are not partial explanations for why the event where the agent concludes to move one of their rooks to e4 happened. For example, consider the agent's conclusion to eat chocolate rather than vanilla ice cream while playing, or whatever the agent concludes to do in the opening of the next game they play. Neither seems relevant. Very roughly, the constraint holds that partial explanations are limited by what happens when the agent concludes or prior to the agent concluding. I argue the constraint does not hold. The contribution of this document may be split into four connected parts: First, a fairly theory-neutral framework to talk about the way events where an agent concludes happen. Second, a statement of the relevant constraint within the framework. Third, a deductive argument which shows the constraint fails when certain conditions obtain. Fourth, supporting argument to establish the relevant conditions (often) obtain, and the constraint (often) fails. Though this document is orientated around the constraint, the focus of the document is the details of the argument against the constraint. The argument highlights the way a handful of observations combine. And, as such, I have worked to ensure the observations lead to general ideas which may be applied or adapted to projects.

Keywords: Humanities and Sciences.
Philosophy.
Advisor: Briggs Ray
Condoravdi Cleo
Lawlor Krista
Peacocke Antonia
Foundation models for robust machine learning

Machine learning systems are not robust to distribution shifts---they suffer large drops in accuracy when deployed in different environments from what they were trained on. For example when satellite remote sensing models are deployed in new countries, tumor detection models are deployed in new hospitals, or wildlife conservation models are deployed in new forests, they face large drops in accuracy. In this thesis, we show that the foundation model paradigm is a principled solution that leads to state-of-the-art robustness. The foundation model paradigm consists of three steps: pretraining a model on diverse unlabeled data (e.g., satellite images from around the world) to learn general-purpose representations, adapting these models to downstream tasks that we care about, and then deploying these models in the real world. This thesis will focus on understanding and improving each of these steps for robustness. (1) First, we show that pretraining on unlabeled data learns transferable representations that improves accuracy even on domains where we had no labels. We explain why pretraining can work in a very different way from some classical intuitions of collapsing representations (domain invariance). Our theory predicts phenomena on real datasets, and leads to improved pretraining methods. (2) Next, we will show that the standard approach of adaptation (updating all the model's parameters) can distort pretrained representations and perform poorly out-of-distribution. Our theoretical analysis leads to better methods for adaptation and state-of-the-art accuracies on ImageNet and in applications such as satellite remote sensing, wildlife conservation, and radiology. (3) Finally, when we deploy models in the real world, the data distribution evolves over time which leads to a drop in model performance. We show that self-training on a model's own predictions can improve robustness to distribution shift, and explain when and why self-training works

Keywords: Engineering.
Advisor: Liang Percy
Ma Tengyu
Finn Chelsea
Foundation models for the real world

Foundation models are quickly moving from their origins in the lab into real world deployment and use. In this thesis, I discuss two connected lines of research that work towards bridging this gap, so that foundation models can be fruitfully used in real-world settings, e.g. in engineering, medicine, or the sciences. The first is making models more domain-agnostic: while techniques for training foundation models were developed for language and vision domains, we show that simple techniques can generalize these approaches to work across at least twelve different domains. The second is making models more useful in cases of task ambiguity, where the user's desired task may be vague or not-perfectly specified, as is often the case in real-world settings. Here we show how to measure and improve the performance of foundation models under task ambiguity, and explore how models themselves can aid in the process of disambiguating user intent. We close by discussing future directions and the broader outlook of challenges and opportunities ahead

Keywords: Engineering.
Advisor: Goodman Noah (Noah D.)
Hashimoto Tatsunori
Jurafsky Dan
A framework for signal decomposition with applications to solar energy generation

We consider the well-studied problem of decomposing a vector time series signal into components with different characteristics, such as smooth, periodic, nonnegative, or sparse. We describe a simple and general framework in which the components are defined by loss functions (which include constraints), and the signal decomposition is carried out by minimizing the sum of losses of the components (subject to the constraints). When each loss function is the negative log-likelihood of a density for the signal component, this framework coincides with maximum a posteriori probability (MAP) estimation; but it also includes many other interesting cases. Summarizing and clarifying prior results, we give three distributed optimization methods for computing the decomposition. The signal decomposition (SD) framework has applications across many fields, but we have been motivated by problems related to large-scale data analysis for the photovoltaic (PV) power generation industry. We will demonstrate a typical example of loss-factor analysis for PV systems using the SD framework. In addition, we will discuss software implementations of both the SD modeling framework and the PV data analysis applications, both of which are published as open-source Python packages

Keywords: Engineering.
Electrical Engineering.
Advisor: Boyd Stephen P.
Brandt Adam (Adam R.)
Eglash Stephen J.
Pilanci Mert
Framing the Frontlines: Great Power Narratives on Facebook in India about the Russian Invasion of Ukraine

Russia's 2022 invasion of Ukraine not only initiated a devastating ground war, it also ignited a competition on social media over foreign audiences' perceptions of the frontlines. India, which is officially neutral and has the world’s largest number of Facebook users, is a prime battleground for this competition. This thesis examines how actors from Russia, the United States, and China influenced narratives on Facebook in India leading up to and immediately after the invasion. By analyzing roughly 300,000 Facebook posts in India, it uses thematic analysis to identify dominant narratives from actors from each state and a topic classifier built on a state-of-the-art language model to measure the alignment of narratives between states. Actors from India overwhelmingly drove Facebook conversations, and, despite India’s neutral stance, discussed narratives that frequently aligned with those propagated by US-based actors while rarely aligning with narratives from China- or Russia-based actors. Chinese state-controlled media targeted India with narratives that closely resembled those from users in Russia. However, this thesis finds no evidence of state-controlled media from Russia or the United States targeting India on Facebook. Policymakers should be cautious about overstating the role of Russian or Chinese state-controlled media on Facebook and should focus on understanding the diverse foreign policy interests of the Indian public in shaping narratives around international conflicts such as the 2022 invasion of Ukraine.

Keywords: Center for International Security and Cooperation
India
Propaganda
Machine learning
Russia
Ukraine
Facebook
Natural language processing
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Thesis
Advisor: Schultz Kenneth
DiResta Renee
From BET to Beyond: Exploring Black Family Culture and Black Media Politics

Media and popular culture have long played a significant role in forming perceptions of and social attitudes about race, including the differences between Black, Indigenous, Asian, Latinx, and White American family culture and structure. This has led to the historical portrayal of minority families, especially Black families, as poor and dysfunctional. Over time, the American media and entertainment industry has become more inclusive. Recent shifts in technology have accelerated the democratization of media production and cultural commentary, increasing the availability of opportunities for Black people within the entertainment industry.
In the status quo, major networks, independent media companies, and individual creators simultaneously contribute to the ever-expanding volume of Black American media, visual art, and popular culture. Black media and popular culture comprise a variety of genres and innovative multimedia formats including but not limited to web series, music videos, movies, television series, and short films. The increasing prevalence of Black media and cultural productions has led to a more diverse representation of Black family structures and social identities within the Black community. Black artists, critics, and creators are producing work that reflects a beautiful mosaic of perspectives on the Black living experience and the universal human condition.
Black American media has become (hyper)visible in American culture and across the world. This allows for unprecedented social discourse on identity politics and political discourse on Black social issues in government, academia, and beyond. Contemporary Black media engages in critical conversations about a range of intersectional issues of race, gender, class, and disability including sexuality, religiosity, addiction, mental health, and more. Despite the growing volume of Black media and improved media representation within the United States, certain Black characters, figures, films, and series maintain reliance on stereotypes and archetypes. This increases the importance of robust and nuanced engagement in terms of media commentary, analysis of the media production process, and consideration of additional other issues such as economic conditions or socio-political factors that influence media and popular culture.

Keywords: African and African American Studies
Black Studies
Media Studies
Black Family Culture
Black Identity Politics
Black Feminist Studies
Intersectionality
Queer Studies
Quare Studies
Cultural Anthropology
Media History
Black Music Studies
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Thesis
From falcons to robots : design principles and systems analysis for autonomous bio-inspired visually guided aerial grasping robots

The peregrine falcon's ability to pursue and grasp flying pigeons at high speed is a feat that is unparalleled in its demonstration of its supermaneuverable capabilities. It is thought that the falcon's pursuit and grasping ability is enabled by a fast, long-range visual tracking system, dynamic pursuit trajectories modeled by established guidance laws, as well as legs and claws that enable in-flight grasp and recovery following the capture of prey. The falcon's agility and the systems that enable it can therefore inspire the design of capable novel aerial grasping robots that capture rogue drones in flight in sensitive airspaces. Juxtaposed with the practical application is the opportunity to use bio-inspired aerial grasping robots to further understand the falcon's ability. This inspired the research direction for FalconQuad, an aerial grasping robot that visually tracks, pursues, and captures target quad-rotors in flight. In this thesis, I show the engineering considerations essential to implementing falcon-inspired pursuit and grasping for FalconQuad. This includes hardware development of the robot, a bio-inspired pursuit planning algorithm that considers hardware limitations, and a grasping system that minimizes disturbance on the pursuer throughout the pursuit flight. Using this robot, we understand the critical flight conditions for successful pursuit and capture by developing simulations and experiments that model the pursuit scenario using aerial robots as scale models of the biological example. The experiments and hardware implementation consequently informed a model that explains how the falcon might prioritize different factors in stages of aerial pursuit and grasping. Specifically at a distance, visual state estimation of the target is degraded and cannot be fully relied upon by the planning system. Therefore, the pursuer's objective is to minimize distance most effectively. Nearing interception, the pursuer must rely primarily on the mechanical redundancy of its gripper system rather than precise flight control algorithms. Following the experiments, a systems-level analysis shows that better visual tracking and pursuit logic could improve the performance of FalconQuad by precisely predicting target motion and informing when to attempt pursuit and interception. This exploration provokes future research to elucidate how depth to target and biological visual sensing limitations play a role in the falcon's ability to capture aerial prey. I hope this work will inspire novel multifunctional aerial robots enabled by developments in smart materials on platforms that could, in-turn, be used to evaluate new technologies for future aerospace vehicle concepts.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Cutkosky Mark R.
Follmer Sean
Lentink David
From genomes to populations : measuring the microbiome at small and large scales

A comprehensive understanding of the presence, function, and variation of gut mi- crobiomes across populations is important for relating the gut microbiome and hu- man health. However, most efforts in the microbiome field have been grounded in fragmented microbial reference genomes derived from a non-representative sub- set of the world's population. These limitations can constrain associations between genome-level and population-level microbial variation and downstream effects on hu- man health and disease. This dissertation focuses on leveraging cutting-edge metage- nomics approaches to more accurately and comprehensively measure human gut mi- crobes. I present five projects that collectively detail the characterization of the gut microbiome across biological and experimental scales, from building complete mi- crobial genomes to measuring microbiome variation across global cohorts. Chapter 1 provides an introduction to the current state and recent advances in microbiome research. Chapter 2 details molecular and computational pipelines to recover com- plete, contiguous microbial genomes from metagenomes, which represents a significant technical advance. Chapter 3 further describes these protocols in a detailed and ac- cessible manner to enable broader research in this space. Chapter 4 presents the first application of a new synthetic long-read sequencing assay to the problem of metagenomic assembly. Transitioning more broadly to accurate microbial measure- ment on experimental and methodological scales, Chapter 5 compares how sample preservation methods introduce experimental bias and affect microbial concentration quantification. Finally, widening scope to the measurement of microbiome variation across global populations, Chapter 6 presents a study of the gut microbiome of two South African populations and places their microbiome composition in global con- text. Together, these studies advance our ability to measure microbiomes, through building accurate microbial genome representations, thoughtfully considering study design and the importance of measured variables, and providing a concrete framework for conducting community-engaged, large-scale global studies

Keywords: Medicine.
Genetics.
Advisor: Bhatt Ami (Ami Siddharth)
Good Benjamin H.
Sherlock Gavin
Sidow Arend