VictorJuiz/thesis huggingface dataset:
Chaining Superparamagnetic Iron Oxide Nanoparticles and their effect on High-Resolution Magnetic Particle Imaging

Magnetic Particle Imaging (MPI) is a noninvasive imaging modality that exploits the saturation properties of superparamagnetic iron oxide particles (SPIOs). A major thrust of MPI research aims to sharpen the magnetic resolution of biocompatible SPIOs, which will be crucial for affordable and safe clinical translation. We recently reported on a new class of MPI tracers —called superferromagnetic iron oxide nanoparticles (SFMIOs) — which offer much sharper magnetic saturation curves. SFMIOs experimentally demonstrate 5-13x improvement in both resolution and sensitivity. However, superferromagnetism is a relatively unexplored branch of physics and the nanoscale physics and dynamics of SFMIOs remain a mystery. Here we show experimentally that chaining of SPIOs can explain SFMIO’s boost in SNR and resolution. We show how concentration, viscosity, transmit amplitude, and prepolarization time can all affect SPIO chain formation and SFMIO behavior. These experiments will inform strategies on SFMIO chemical synthesis as well as SFMIO data acquisition pulse sequences.

Advisor: Conolly, Steven M
Mobiles

The mobiles by the sculptor Alexander Calder are built from delicate separate pieces connected together firmly, but in a way to ensure they can move from slightest breeze of air. Hanging in the air, they turn slowly and silently revealing new shapes as they move. At the same time their shadow is changing almost organically creating endless variations on the surrounding surfaces./nThe chamber ensemble piece Mobiles draws from the idea of endless variation and change of view point. At times the piece seems to be hanging delicately in the air, at times it grows firm roots into the ground. The first movement Timbre, with its gently fluctuating textures of colour change abruptly into a section of expressive instrumental solos titled Movement. The music is dense with attacks and sound. Gradually changing by omitting the sound tones, the texture morphs into delicate rattling noises and whispers. In the third movement Whisperings the different sections of the ensemble appear to be in a dialogue with each other. For this movement, and with the aid of computer assisted orchestration, I used the sound of a recorded human voice as a timbral model for the ensemble arrangements. From these fragments I gradually built a dense but delicate asymmetrical texture./nIn the heart of the piece, the fourth movement Calm, the music stays still almost as if floating in the air. Two crystal singing bowls tuned slightly differently form a mesmerising fluctuating surface on which the other instrumental sounds float. Slowly turning and forming different shapes in the air the piece returns to its original shape. Timbre comes back with its gentle textures of colour with slight change of view point from the beginning. The piece ends with round and soothing multiphonics by the bass clarinet.

Advisor: Campion, Edmund
The Material and Industrial Ecologies of Excavated Sediment: Insights for Climate Change Adaptation Planning

Global warming is producing countless changes in the biogeophysical world, and willincreasingly force human responses to them. These challenges are evident everywhere: in all manner of resource use, growth and development processes; in the intensification of climate risks, hazards and natural disasters; and in the sociopolitical and socioeconomic systems that must confront this paradigm shift in earnest during the 21st century. This dissertation seeks to situate prominent and emergent issues of how this confrontation and the changes induced by it—adaptation--function and matter in the context of environmental design and planning. In particular, and because of the global scale and widespread socioenvironmental issues involved, the work focuses on sea level rise (SLR) and issues stemming from its impacts on developed shorelines and the ecological complexes and structures evident therein. Human beings have deep roots in the manipulation of landscapes, especially as they concern the role and resources that coastal waterbodies and waterways represent, through the active design and physical defining of topography: how the shape and elevational contours of land affect flows and functions of water. Accordingly, the introduction herein frames the roles of landform as an elemental aspect of the construction and spatial planning of urban coastal and shoreline zones, and focuses on the physical materials, including actively-gathered geomaterial resources called sediment that compose the basic building blocks of constructed landforms. In chapter one, the interplay of various terms and concepts involved in climate change adaptation that matter in the context of spatial planning are articulated and clarified to frame ways in which challenges and opportunities of the era may be described. Chapter two then works to establish areas of much-needed consideration for the fields of landscape architecture and environmental planning to enfold into its professional practice milieu: namely tools and techniques from industrial ecology, which has been traditionally applied almost exclusively to climate mitigation (as opposed to adaptation). The third and final chapter discusses the application of modeling methods to an excavated sediment budget in an approach designed to assess aspects of the climate change future of a case study region. The work helps illustrate several insights and critical questions that are discussed in the final conclusion section.

Advisor: Hill, Kristina Z
Structure, Properties and Reactions of Solid-Liquid Interfaces

“God made the bulk; surfaces were invented by the devil.” - Wolfgang Pauli. Solid−liquid interfaces are ubiquitous and involved in numerous natural processes and practical applications, such as corrosion, mineralization, self-assembly, electrochemical energy conversion and storage, etc. Determining the composition, structure, and chemical state of interfaces, in particular the structure of liquids/soft materials near solids, is crucial to obtain a molecular/atomic understanding of the interfacial processes needed to further improve their applications. Currently, this is hampered by the shortage of appropriate characterization tools. In this dissertation a new methodology will be introduced, which is based on the combination of many surface-sensitive techniques and liquid cells capped with ultra-thin membranes. Using this platform, the structure, and properties of the electrical double layers, as well as the nanoscale structure of soft materials at interfaces have been explored. The nanoscale chemical information at solid-liquid interfaces reveals unprecedent details and offers new knowledge of many interfacial properties and processes. This platform opens the way for spectroscopic study of solid-liquid interfaces with less radiation damage with applications in biology, plastic processing, and inorganic materials for electrocatalysis, batteries, etc.

Advisor: Salmeron, Miquel
Yao, Jie
Improved Algorithms and Upper Bounds in Differential Privacy

Differential privacy is the now de facto industry standard for ensuring privacy while publicly releasing statistics about a sensitive database. At a high level, differentially private algorithms add noise to the statistics they compute, so an adversary cannot with high confidence guess if any individual is in the database as any individual's effect on the statistics can be replicated by the noise./nThe fundamental paradigm in differential privacy is the privacy-accuracy trade-off: A differentially private algorithm's output can be made more accurate by reducing the amount of noise added, but in doing so the privacy guarantee decays. Current state-of-the-art algorithms often require practitioners to choose between a large drop in accuracy compared to non-private algorithms, or very weak privacy guarantees. Improving the trade-off between privacy, accuracy, would ideally allow practitioners to get the ``best of both worlds.'' Sometimes, efficiency is also traded off with privacy and accuracy. That is, despite differential privacy being an information-theoretic guarantee, an inefficient (and thus impractical to implement) algorithm may still obtain a better privacy-accuracy trade-off than the best known efficient algorithm. Designing efficient algorithms that match the privacy-accuracy trade-off of known inefficient algorithms thus is also of practical importance./nIn this thesis, we consider counting queries, private log-strongly concave sampling, and private convex optimization, all fundamental problems in sampling and optimization, and give algorithms for each problem improving the privacy-accuracy trade-off or efficiency when compared to the previous state of the art algorithms. For counting queries, we show that adding noise from a ``Generalized Gaussian'' gives better worst-case accuracy compared to Gaussian noise. For private log-strongly concave sampling, we show that the discrete Langevin dynamics allows one to efficiently approximately sample from a target distribution while preserving privacy, a commonly needed primitive in private optimization. For private convex optimization, we show that in some settings (including e.g. private linear regression), if we are given a sufficient amount of public data, we can obtain a better dependence on the dimensionality of the problem than differentially private gradient descent.

Advisor: Rao, Satish
Exploring the Structural Scope of Enantiopure Supramolecular Catalysts and Investigating Their Function in a Highly Selective Aza-Darzens Condensation

Chapter 1. An assessment of the current state of the field of supramolecular catalysis is presented. This chapter has a specific focus on catalysis mediated by self-assembled container molecules, highlighting the most relevant literature from the last six years. This chapter provides context for this thesis and highlights the further need for reaction development and in-depth structure activity relationship studies with supramolecular hosts./nChapter 2. It has been previously demonstrated that nanovessels can be highly competent catalysts providing large rate accelerations and unique selectivity to the organic transformations they mediate. However, for supramolecular assemblies to be considered a standard reagent in organic synthesis, they must first demonstrate the ability to catalyze increasingly complex transformations. This chapter reports a three-component aza-Darzens reaction that generates N-phenylaziridines, catalyzed by a supramolecular host, that provides the stereoisomer opposite to the one generated in bulk solution (trans vs. cis). This transformation constitutes a rare catalytic three-component coupling within a supramolecular assembly, providing a supramolecular solution to a synthetically challenging transformation./nChapter 3. A highly enantioselective aza-Darzens reaction (up to 99% ee) catalyzed by an enantiopure supramolecular host has been discovered. To understand the role of host structure on reaction outcome, nine new gallium(III)-based enantiopure supramolecular assemblies were prepared via substitution of the external chiral amide. Despite the distal nature of the substitution in these catalysts, changes in enantioselectivity (61% ee to 90% ee) in the aziridine product were observed. The enantioselectivities were correlated to the flexibility of the supramolecular host scaffold, as measured by the kinetics of exchange of a model cationic guest. This correlation led to the development of a best-in-class catalyst by substituting the gallium(III)-based host with one based on indium(III), which generated the most flexible and selective catalyst.

Advisor: Toste, F. Dean
Raymond, Kenneth N.
Investigating Diffusion Using Video Scanning Tunneling Microscopy and Deep Learning

Diffusion is a ubiquitous phenomenon encompassing a wide range of physical systems from atomic scale motion to cellular locomotion. Diffusion plays a central role in practical applications such as electrochemistry, batteries, and thin film growth. The external control of diffusion properties presents new possibilities for understanding fundamental physical phenomena as well as a basis for next generation technological advances. The contributions of this thesis include the fabrication, modeling, and characterization of diffusion on gate-tunable nanodevices. We first derive the fundamental results of diffusion which form the basis of our experimental analysis. Afterwards, we describe the procedures for fabrication and algorithms for characterization of diffusion in video scanning tunneling microscopy (STM). The physical platform upon which most of these results are based on is a single-layer graphene on hexagonal boron nitride (h-BN) field-effect transistor (FET) with fluorinated tetracyanoquinodimethane (F4TCNQ) adsorbates, which were chosen due to favorable gate-tunable electronic properties. This device architecture enables control of diffusion through external temperature control, application of lateral and vertical electric fields, and substrate engineering. The analysis presented in this work provides rich sandbox for locally probing diffusion behavior in different system regimes. In particular, we find that device level control allows us to tune the density of adsorbates on the device due to the proximity of molecular orbitals to the device Fermi level and the device classical capacitance. Charged molecules form uniform arrays in response to the gate voltage to screen the applied field. In addition, we see that gate voltage can directly control diffusion through the modification of transition state energies as a result of molecule charging. We show that substrate level control in the form of moiré superlattice engineering can suppress diffusion by inducing anomalous subdiffusion in short time scales. Such behavior is explained using thermal equilibrium statistics for diffusion which show that the introduction of the moiré superlattice increases the number of unique energy states available to the diffusing particle. As a result, the size of the state space increases which in turn increases the time required to visit all available states. For short times, the entirety of the state space is not visited with high likelihood, a violation of the thermodynamic hypothesis that all accessible states are equiprobable, a phenomenon known as ergodicity breaking. Despite the challenges inherent in uncovering this behavior under experimental constraints of data sparsity using canonical statistical methods, we demonstrate that it is possible to observe this phenomenon by using deep learning.

Advisor: Crommie, Michael F
Creating and Collecting Meaningful Musical Material with Machine Learning

This dissertation explores how machine learning and artificial intelligence can be applied within music composition and production. My approach in this research stems from an underlying perspective that these technologies are deeply intertwined with the people who use them or are affected by them: we can’t hope to understand one side of the picture without looking at the other. From this vantage point, I explore the following questions: How do we design algorithms, datasets, and models to support the processes of composers, producers, and other creators? How can we design meaningful interactions with these algorithms? And finally, how do music creators and listeners experience interacting with algorithms for creating music in situations when they have reasons to be emotionally invested in the music?First, I explore new approaches to music creation technology with machine learning, focusing on two musical settings: beat-making and orchestration. I find that creative tools can benefit from incorporating machine learning if we introduce models in specific contexts motivated by well-defined musical goals. For beat-making, I use machine learning to expand the possibilities for groove in drum machines by modeling the timing and dynamics of professional drummers, and for orchestration, I use machine learning to predict the timbral characteristics that result when mixing many different instruments together. In both cases, I find that careful data collection and management are key components. Next, I investigate some of the main technical choices that need to be made when using machine learning in creative musical contexts: data representations and controls for guiding models. Here, I find that allowing users to provide more than one demonstration at a time can allow for more diverse and more precisely controlled model outputs. I also find that using data representations designed to capture musical gestures can provide benefits in settings with limited musical data. Finally, in the last part of the dissertation, I conduct a series of qualitative studies to investigate the individual experiences of listeners and musicians over the course of interactions with algorithms that generate personalized music samples using machine learning. I find the musical “quality” of algorithmically generated music to be comparatively unimportant to participants; instead, the degree to which music fits with the participants’ existing narratives or creative intentions matters more to them. These findings highlight the need to understand the contexts in which algorithms are deployed as well as the artistic choices that listeners may or may not feel comfortable turning over to an automated process when working with emotionally sensitive materials.

Advisor: Bamman, David
Hindered Hooves and the Wires that Bind Them

Long-distance animal movements have long fascinated humans. However, such movements necessarily translate to a requirement for extensive habitat, rendering the animals susceptible to natural and anthropogenic environmental changes, especially landscape fragmentation induced by linear infrastructure. In particular, fencing might be the most pervasive yet the least understood linear infrastructure that can extensively alter animal movement behavior, space use, and population dynamics. Importantly, fencing is often situated in social-ecological systems where humans and wide-ranging animals co- exist, hence altered wildlife spatial ecology subsequently affects human communities at the same time. In this dissertation, I examined wildlife responses to environmental changes, in particular fencing-induced fragmentation, and the complex social-ecological contexts of fenced landscapes. Working with collaborators, I first developed a guiding framework that synthesizes the pathways through which migratory ungulates respond to natural and anthropogenic environmental changes on a global scale. We then zoomed into two wide ranging sympatric ungulates, pronghorn (Antilocapra americana) and mule deer (Odocoileus hemionus) in southeast Wyoming, and examined their behavioral and spatial responses to fences. Specifically, we developed a spatial- and temporal-explicit approach, Barrier Behavior Analysis (BaBA), with which we quantified the within- and among-individual variations in barrier behaviors along a fence density gradient. Finally, we situated fences in social-ecological systems to understand why and how fences became wide-spread around the world, as well as their coupled social and ecological impacts. Combing theories and methods in movement ecology, behavior ecology, rangeland ecology, and landscape ecology, this work provides new insights into ungulate movement in a changing world. By elucidating the impacts of fences across social and ecological boundaries, this research also marks a step in developing effective strategies to make the landscape connected for both humans and wildlife.

Advisor: Huntsinger, Lynn LH
Middleton, Arthur ADM
The Role of Professional Relationships in Early Childhood Special Education Practitioners’ Resilience: A Phenomenological Investigation

The field of early childhood special education faces a significant teacher shortage that is in need of solutions (Bruder, 2010; Carver-Thomas & Darling-Hammond, 2017). An unaddressed area of examination in resilience research is the role that professional relationships play in enhancing teacher resilience, career longevity, and job satisfaction. This dissertation details a phenomenological qualitative study that explores relational resilience in early childhood special education practitioners. Qualitative methods included a demographics and resilience questionnaire and in-depth interviews. These methods offered a better understanding of the phenomenon of relational resilience in early childhood special education practitioners. Implications for research, theory, and practice are discussed.

Advisor: Friesen, Amber
Holloway, Susan D
Random Walks and Delocalization through Graph Eigenvector Structure

In this thesis we prove the following results./n1. We show that the multiplicity of the second normalized adjacency matrix eigenvalueof any connected graph of maximum degree Δ is bounded by nΔ^(7/5)/polylog(n) for any Δ, and n*polylog(d)/polylog(n) for simple d-regular graphs when d is sufficiently large./n2. Let G be a random d-regular graph. We prove that for every constant α > 0, withhigh probability every eigenvector of the adjacency matrix of G with eigenvalue less than −2√(d − 2) − α has Ω(n/polylog(n)) nodal domains./n3. For every d = p + 1 for prime p and infinitely many n, we exhibit an n-vertexd-regular graph with girth Ω(log_(d−1) n) and vertex expansion of sublinear sized sets upper bounded by (d+1)/2 whose nontrivial eigenvalues are bounded in magnitude by 2√(d − 1) + O(1/log n). This gives a high-girth version of Kahale’s example showing Ramanujan graphs can have poor vertex expansion./n4. Anantharaman and Le Masson proved that any family of eigenbases of the adjacencyoperators of a family of graphs is quantum ergodic, assuming the graphs satisfy conditions of expansion and high girth. We show that neither of these two conditions is sufficient by itself to imply quantum ergodicity (which is a form of delocalization)./nThese results although different in nature, all exhibit the utility of the structure of eigenvectors.The main ingredient in the first result is a polynomial (in k) lower bound on the typical support of a closed random walk of length 2k in any connected graph, which in turn relies on new lower bounds for the entries of the Perron eigenvector of submatrices of the normalized adjacency matrix. The second result suggests Gaussian behavior of eigenvectors of random regular graphs conjectured by Elon, a discrete analog of Berry’s conjecture. The third result shows that properties that are sufficient to imply eigenvector delocalization are not strong enough to imply vertex expansion. The theorems and examples in the fourth result show why Anantharaman and Le Masson’s quantum ergodicity result requires expansion both at a global scale (spectral expansion) and a local scale (high girth).

Advisor: Srivastava, Nikhil
Trevisan, Luca