VictorJuiz/thesis huggingface dataset:
Nanoconfined liquid dynamics in aqueous and nonaqueous systems investigated with nonlinear infrared spectroscopies
Keywords: Humanities and Sciences.
Chemistry.

Nanoconfined liquids are found in settings ranging from nature to industry, from intracellular fluids to reagents in mesoporous nanoreactors. Their behavior is often distinct from their bulk counterparts, providing unique opportunities to design new materials and processes that harness the differences. Despite the growing applications of materials such as mesoporous silica as nanoconfinement frameworks, the challenge remains to derive molecular-level details from the experimental observables and understand the underlying mechanisms that govern confined behavior. This work aims to contribute to the efforts to predict the dynamical and structural properties of liquids confined in mesoporous silica based on the nature of the liquid. Using ultrafast infrared pulses with durations of ~160 fs and vibrational probes with vibrational lifetimes of 20-200 ps, both very fast time scales, like the subpicosecond bulk water dynamics, and dramatically slower time scales upwards of 100 ps for confined liquid dynamics can be studied. Polarization selective pump-probe (PSPP) and two-dimensional infrared (2D IR) vibrational echo spectroscopies are the two nonlinear third-order techniques used to measure the rotational and vibrational frequency dynamics, respectively, of the chemical systems. Ultrafast studies of confined liquids other than water are scarce, so the understanding of confinement effects benefits from studies on liquids with different molecular dimensions and interactions with surface moieties. A nonaqueous solvent and a concentration series of an aqueous salt solution were used as non-water model systems for comparing the confinement effects in the same hydrophilic silica pores
Nanophotonics enhanced optoelectronic devices : from photovoltaics to imaging
Keywords: Engineering.
Materials Science and Engineering.

Next-generation optoelectronic devices are required to be more compact and light-weight, and to achieve new optical functionalities at the same time. While these are challenging for current hardware designs, nanophotonics can be a key strategy to realize an optoelectronic device with desired functions in a smaller form factor. In this work, we will explore how nanophotonics can be leveraged to enhance the performance of optoelectronic devices, focusing on photovoltaics and imaging application. In the first part, we will see how we can design a thin, single layer of nanostructures that can function as both antireflection coating and light-trapping layer. We will experimentally demonstrate the effectiveness of these light-trapping antireflection coatings (LARCs) on 2.8-µm-thick c-Si solar cells and notably improve the efficiencies. In the second part, we will present a new type of imaging technology that can discern surface textures by utilizing a set of metasurface-driven pixels (i.e. meta-pixels). The metasurfaces in each type of pixel are engineered to achieve desired angular sensitivities, which enable efficient perception of texture. Furthermore, we will experimentally demonstrate the meta-pixels and show their efficacy through reconstructed texture images that effectively visualize differences in textures
Nanophotonics with 2D semiconductors : enhanced and tunable light-matter interactions
Keywords: Engineering.
Materials Science and Engineering.

Among the many advances in nanotechnology, optical antennas have emerged as one of the most important and versatile technologies with applications in sensing, displays, photovoltaics, and imaging. The broad definition of an antenna is an element that acts as an interface between freely propagating radiation and localized energy. Our everyday experience with antennas involves using them for communication purposes such as in mobile phones and television. The advent of optical antennas was driven by the advances in nanofabrication, with resulting antenna nanostructures typically on the order of 100 nm. The main advantages of metallic/plasmonic antennas in particular are their ability to concentrate light into highly confined fields below the diffraction limit, and engineer the properties of light scattering and radiative decay. However, these antennas have weak intrinsic electrical tunability. Typical approaches to achieve tunability involve coupling the antennas with an active medium. In this thesis, I highlight transition metal dichalcogenide (TMDC) monolayers as a materials platform to integrate with nanophotonic structures due to their strong and tunable optical responses in the visible spectral range. I will first introduce the properties of optical nanoantennas and TMDC monolayers. Next, I investigate the electromagnetic coupling of TMDCs with nanophotonic structures using both full-field numerical simulations and theoretical modeling. I then present an experiment demonstration of this tunable coupling through the design, fabrication, and characterization of a coupled plasmonic antenna-TMDC system that is actively tuned by electrostatic gating. As an outlook, I discuss several other methods of active tuning, highlighting a particularly promising method of mechanically straining TMDC monolayers that we have investigated
National interest in international relations : domestic distributional consequences and constraints to conflict
Keywords: Humanities and Sciences.
Political Science.

Issue value, commonly known as the "v-term, " plays a central role in many international theories. The value of an issue influences whether states choose to escalate conflict, which states prevail in war, and whether leaders are rewarded for escalating conflict or punished for backing down. Yet we know surprisingly little about why issues take on the value that they do in the first place. In fact, states have often displayed puzzling behaviors when it comes to issue value—they commonly fight over barren lands and uninhabitable islands while not fighting over other issue areas or lands with more evident benefits. So what becomes understood by states and their citizens as important national interests worth fighting for and why? Why do these perceptions of national interest often fail to reflect an issue's objective material value? I argue that domestic distributional consequences play an important role in determining what becomes understood as important national interests worth fighting for. Issues without clear economic value, such as barren lands, are more likely to be perceived as national interests because they do not benefit any single domestic group. Since who benefits is unclear, politicians have an easier time framing such issues as benefiting the entire nation. In contrast, issues with specific economic benefits are more likely to be seen as helping select domestic groups, which makes it harder for leaders to persuasively frame them as representing broad national interests. I support my argument using three original survey experiments (Chapter 3), newly geocoded data of territorial claims spanning 172 years (Chapter 4), qualitative case studies (Chapter 5), and text analysis of political rhetoric (Chapter 6)
The natural/non-natural distinction in metaethics
Keywords: Humanities and Sciences.
Philosophy.

Realists in metaethics typically divide into two camps: naturalists and non-naturalists. But beyond this, there is very little agreement on what the natural/non-natural distinction is supposed to be. Naturalists see themselves as assimilating the normative into a scientific worldview, while non-naturalists typically either see realism as incompatible with such a worldview or they simply don't find the relationship between science and the normative particularly pressing. This is an unfortunate state of affairs. Naturalists and non-naturalists see themselves as involved in a deep disagreement about the nature of the normative, but it is not clear why the views espoused by non-naturalists stand in contrast to the views espoused by scientists (or by naturalists). Nor is it obvious why certain naturalists can lay claim to being in keeping with a scientific worldview. If metaethicists are to continue to debate the question of naturalism versus non-naturalism, the distinction will need to be clarified. This dissertation attempts to do just that. Chapter 1 provides an introduction to the issue. Chapters 2-4 examine prominent proposals for drawing the distinction in the literature (respectively, reduction, spatiotemporality, and causation), finding them all wanting. Chapter 5 contains a positive proposal in which I argue that the distinction can helpfully be drawn in terms of the kind of evidence naturalists ought to require for normative facts. In particular I argue that the publicity and spatiotemporality of evidence for natural facts provides a useful way of drawing the distinction.
Nature of learning and learning of nature
Keywords: Engineering.

This thesis explores questions surrounding the foundations of intelligence, both artificial and natural. The first part focuses on the algorithmic and statistical underpinnings of modern machine learning systems. First, we discuss a clean framework for investigating the surprising ability of large language models to learn in-context: the apparent ability to solve new tasks given just a text prompt that provides examples. Further, motivated by concerns around the insatiable data appetite of modern machine learning systems, we discuss the problem of "sample amplification", where we formalize the seemingly naive question of how hard it is to create new data and contrast the hardness of this task to that of learning the data-generating distribution. The second part considers the algorithmic basis of intelligence in nature, specifically in ant colonies and the brain. We examine how arboreal turtle ants solve variants of the shortest path problem without any central control and with minimal computational resources. In the context of the brain, we study how it manages to train its neural network despite its structural limitations. Specifically, we investigate a biologically plausible learning algorithm and contrast it with gradient descent, arguably the only known algorithm for training large-scale artificial neural networks.
Navigating Place, Gender, and Desire: A Study of Strangers in Bupyeong District, Incheon, South Korea
Keywords: Korea (South), Damunhwa, Hallyu
Text
Thesis

This thesis explores the current discourse on damunhwa (literally “multiculturalism,” but also describing any non-Korean presence) in a local, everyday context. I specifically examine how a narrow but changing definition of Koreanness has become a justifiable reason for exclusion and hostility against minority bodies in contemporary Korean society. I also look at how minorities in Korean society make sense of this phenomenon. This thesis is composed of two chapters. The first looks at how Korean residents of Bupyeong perceive damunhwa, separated into two sections. The second chapter explores how the Koreanness at the core of Hallyu products such as K-dramas, K-movies, and K-pop can project cultural imperialistic traits onto some groups of minorities in Korea.
Near-THz CMOS camera for automotive applications
Keywords: Engineering.
Electrical Engineering.

The near-THz band, which combines advantages from both radio and optical bands, is relatively less recognized primarily because of the absence of established commercial applications. In this project, we analyze the limitations of existing automotive sensors and conclude that near-THz sensor offers distinctive advantages for next-generation automotive applications. We propose a novel near-THz camera architecture for automotive sensing by leveraging the advancement of CMOS technology. The implementation details of the system are presented , especially the design of a plastic engraving lens and a CMOS imaging array. To validate the feasibility of the proposed system, we develop a prototype operating at 220 GHz. This prototype includes a code-modulated transmitter, a low-cost polyethylene Fresnel lens, and an integrated receiver array in 65 nm CMOS process. The measurement results of the prototype are presented, following a discussion of future work.
Network methods for inferring effects of complex genetic variation : development and application in SARS-COV-2 entry
Keywords: Medicine.
Medicine. Department of Biomedical Data Science

Many human diseases depend on complex genetic architectures, whereby genotypes or expression changes at multiple loci contribute cooperatively to the observed phenotype. High-throughput targeted mutagenesis techniques enable systematic genotype-phenotype mapping that encompasses near-complete first-order mutational landscapes at the level of both amino acids and genes. However, identifying the central patterns of variation that underlie a phenotype remains challenging due to the sparsity of experimental data relative to the combinatorial variant space. To address this limitation, we develop a hierarchical framework for interpreting large-scale screening data through biological network topologies at the level of amino acids, genes, and drugs. We apply this approach to variant screening datasets for critical SARS-CoV-2 entry proteins to identify key patterns of genetic variation that contribute to viral entry. Specifically, we first develop a framework for inferring epistatic relationships from deep mutational scanning data and use it to identify combinatorial Spike mutations conferring high activity. We next develop an improved pathway analysis algorithm that enables more accurate interpretation of functional genomics datasets and apply it to a collection of SARS-CoV-2 entry-factor screens. Finally, we perform mechanistic prioritization of drug repurposing opportunities in COVID-19 based on functional genomics datasets. We perform both experimental and large-scale, retrospective clinical validation of top candidate drugs, identifying a protective effect for spironolactone against severe COVID-19 consistent with an androgen-dependent mechanism. Our work provides broadly applicable bioinformatic tools that enable identification of conserved SARS-CoV-2 entry pathways, culminating in supporting evidence for a protective role for spironolactone in severe COVID-19.
Neural bases of emotion reactivity and emotion regulation
Keywords: Humanities and Sciences.
Psychology.

Both our emotions and our efforts to regulate our emotions play a crucial role in our everyday lives. However, much remains to be learned about the brain processes that underlie emotion and emotion regulation. In this research, we seek to better understand the neural bases of emotion and emotion regulation by conducting theory- and data-driven analyses on human functional magnetic resonance imaging (fMRI) data recorded during emotion reactivity and emotion regulation. In the first study, using multiple samples and both univariate and multivariate analyses, we tested the commonly held the neural separability hypothesis, which holds that emotion reactivity and emotion regulation are associated with spatially separable macro-scale neural processes. Our fMRI results did not support the neural separability hypothesis. Both presumptive "reactivity" and "regulation" brain regions demonstrated equal or greater activation associated with the reactivity condition than the regulation condition, and both their multivoxel data could be used to decode reactivity trials more accurately than regulation trials. In the second study, we explored the predictions of individual differences in emotion reactivity and emotion regulation from whole-brain functional connectivity using a machine-learning approach. With exploratory analyses, we found that brain functional connectivity could predict unobserved individuals' emotion regulation but not emotion reactivity. Particularly, functional connectivity within and with the subcortical-cerebellum brain network was found to be most predictive of emotion regulation compared to other brain networks. Taken together, our studies advance our understanding of the neural bases of emotion reactivity and emotion regulation, moving us away from a spatially separated locationist account and towards a more integrated network-based multivariate model
A neural circuit for sexual behavior and reward
Keywords: Medicine.

Male sexual behavior is innate and rewarding. While execution of this behavior is central to an animal's reproductive success, a molecularly-specified neural circuit governing male mat-ing and reward remains to be identified. In this thesis document, I report the discovery and characterization of a neural circuit for male sexual behavior emanating from chemosensory pathways to BNSTprTac1 neurons, which innervate POATacr1 neurons that project to centers that regulate motor output and reward. We show that this pathway is necessary and suffi-cient for mating, and Substance P released from the BNSTTac1 neurons transforms a sex recognition signal to a mating initiation signal in POATacr1 neurons. Experimental activation of POATacr1 neurons triggers mating regardless of a male's sexual satiety state, and animals perform arbitrary behaviors in order to receive optogenetic stimulation of POATacr1 neurons. Furthermore, optogenetic activation of POATacr1 neurons triggers robust release of dopamine in the core of the nucleus accumbens, indicating that the male neural circuit that we have identified has powerful access to the mesolimbic reward system.
Neural networks as models of the brain : insights on biological learning rules, limiting dynamics of training and model-brain comparison
Keywords: Engineering.

This dissertation explores the intersection of Neuroscience and Artificial Intelligence, focusing on the mutual understanding that can be derived between the study of the brain and the development of Artificial Neural Networks (ANNs). The models studied in this work are derived in the context of goal-driven modeling, which describes ANNs by specifying four components: the objective function to optimize, the dataset used, the architecture of the ANN, and the learning rule used to iteratively update its parameters. The first chapters of this dissertation focus on the learning rule component. In Chapter 2, we explore the biological plausibility of backpropagation and propose performant learning rules that could be implemented in the brain while performing approximate backpropagation. Chapter 3 theoretically studies the limiting dynamics of weights in ANNs trained with SGD using stochastic differential equations and draws connections between the hyperparameters of the learning algorithm and the characteristics of the limiting solution. Still, if we build a goal-driven model of the brain, a crucial question remains: How can we effectively evaluate it as an accurate representation of the brain? Existing approaches based on behavioral decoding or comparing population-level representations are informative but have limitations. In the final chapter, we will argue for an approach based on single-unit response predictivity using a generalized linear model (GLM). We arrive at this particular GLM following a principled approach that seeks to predict brain responses from ANN responses using the same class of functions one would use to predict one specimen's brain responses from another specimen's. The ideal function class should exhibit high predictive accuracy when mapping between equivalent brain areas in a pair of specimens and low predictive accuracy when mapping between different brain areas. To measure these criteria, we define similarity and separability scores. We simulate a population of models of the mouse visual system and evaluate a variety of function classes on these two metrics. By comparing our results to neural data from mouse visual cortex electrophysiology, we refine the candidate function class and provide preliminary evidence that a single-unit predictivity metric based on this function class could better differentiate brain models
Neurons alter endoplasmic reticulum exit sites to accommodate dendritic arbor size
Keywords: Medicine.

Nervous systems consist of neurons with a wide range of form and function. This neural diversity is critical for nervous system function. How neurons achieve such diverse morphologies has been a central question in neuroscience since its inception. Neurite outgrowth is regulated on several levels. First, a neurite's particular shape is determined by guidance receptors on the neurite surface interacting with environmental ligands to inform neurite pathfinding. Second, intracellular cytoskeleton is coordinated by these guidance receptors to generate the force required for neurite progress through the sub-strate. Third, a neuron must coordinately scale its biological processes, such as biosyn-thesis and secretion, to supply sufficient building blocks, including lipids and surface proteins, for its expanding surface area. Considerable work has been done to elucidate mechanisms of neurite pathfinding and force generation. However, relatively little is known about how neurons scale biosynthetic and secretory machinery to accommodate cell membranes of vastly different size. Using the nematode Caenorhabditis elegans (worm), we observed that neurons with larg-er plasma membranes displayed higher numbers of the early secretory organelles, Endo-plasmic Reticulum Exit Sites (ERESs). ERES function is required for dendrite growth and ERES size and number are intimately linked with the biosynthetic and secretory flux of non-neuronal cells. However, how neurons of different size scale secretory machinery, such as ERESs, to meet vastly differing growth requirements is not known. Using timelapse microscopy, we show that the worm's largest neuron, PVD, has espe-cially high ERES numbers, which are initially established during neuron birth via asym-metric cell division. We show that maintenance of PVD's high ERES number during neu-ron development does not require normal dendrite outgrowth, but depends on cell fate transcription factors, nutrient availability, and master growth regulator ceTOR/LET-363. Together these factors coordinately maintain ERES number and somato-dendritic growth throughout PVD development. Our data suggest a model in which cell fate transcription factors drive both asymmetric cell division and subsequent nutrient sensor levels to set the biosynthetic output that specifies ERES number and cell size. This work takes a first step toward identifying the mechanisms regulating neuronal bio-synthetic and secretory output, and highlights several advantages and challenges of pur-suing novel mechanisms for cell-specific size and organelle scaling in multicellular or-ganisms
A new approach to the diffusive limit of the random schrodinger equation
Keywords: Humanities and Sciences.
Mathematics.

In this thesis we introduce a new approach to the diffusive limit of the weakly random Schrodinger equation, first studied by L. Erdos, M. Salmhofer, and H.T. Yau. Our approach is based on a wavepacket decomposition of the evolution operator, which allows us to interpret the Duhamel series as an integral over piecewise linear paths. We relate the geometry of these paths to combinatorial features of a diagrammatic expansion which allows us to express the error terms in the expansion as an integral over paths that are exceptional in some way. These error terms are bounded using geometric arguments. The main term is then shown to have a semigroup property, which allows us to iteratively increase the timescale of validity of an effective diffusion. This is the first derivation of an effective diffusion equation from the random Schrodinger equation that is valid in two and more dimensions.