VictorJuiz/thesis huggingface dataset:
Two experiments using brain imaging to answer questions about the neural basis of Human Vision

Anatomical, physiological, and psychophysical approaches have revealed a great deal about the structure and function of the visual system. Non-invasive techniques based on nuclear magnetic resonance supplement these approaches by allowing the possibility of recording from the awake, behaving human brain. Here, we present two experiments using different non-invasive techniques to answer questions about the neural basis of human vision. In Chapter 2, we examine the relationship between γ-aminobutyric acid (GABA), perceptual suppression, and amblyopia (“lazy eye”). In amblyopia, abnormal visual experience during development leads to an enduring loss of visual acuity in adulthood. Physiological studies in animal models suggest that intracortical GABAergic inhibition may mediate visual deficits in amblyopia. To better understand the relationship between visual cortical GABA and perceptual suppression in persons with amblyopia (PWA), we employed magnetic resonance spectroscopy (MRS) to quantify GABA levels in both PWA and normally-sighted persons (NSP). In the same individuals, we obtained psychophysical measures of perceptual suppression for a variety of ocular configurations. In PWA, we found a robust negative correlation between the depth of amblyopia (the difference in visual acuity between the amblyopic and non-amblyopic eyes) and GABA concentration that was specific to visual cortex and was not observed in a sensorimotor cortical control region. Moreover, lower levels of visual cortical GABA were associated with weaker perceptual suppression of the fellow eye by the amblyopic eye and stronger suppression of the amblyopic eye by the fellow eye. Taken together, our findings provide evidence that intracortical GABAergic inhibition is an important component of the pathology of human amblyopia and suggest possible therapeutic interventions to restore vision in the amblyopic eye through enhancement of visual cortical GABAergic signaling in PWA. In Chapter 3, we use functional magnetic resonance imaging (fMRI) to identify cortical and subcortical components of the visual system. In particular, we localized many topographically-organized visual cortical areas as well as the lateral geniculate nucleus (LGN) and the magnocellular and parvocellular subdivisions thereof. We report the extension of this M/P localization procedure to fMRI data acquired with multiband acceleration. We also examined thalamocortical coupling between the LGN subdivisions and visual cortical areas but found no effects of stimulus type or spatial attention.

Advisor: Silver, Michael A
Degenerations of Negative Kahler-Einstein Surfaces

Aubin and Yau proved that every compact Kahler manifold with negative first Chern class admits a unique metric g such that Ric(g) =-g. Understanding how families of these metrics degenerate gives insight into their geometry and is important for understanding the compactification of the moduli space of negative Kahler-Einstein metrics. I study a special class of such families in complex dimension two. Following the work of Sun and Zhang in the Calabi-Yau case, I construct a Kahler-Einstein neck region interpolating between canonical metrics on components of the central fiber. This provides a model for the limiting geometry of metrics in the family.

Advisor: Sun, Song
Economic Determinants and Consequences of Corporate Disclosures

Corporate disclosures are important means for managers to communicate firm performance and are essential information sources for investors. This dissertation examines corporate disclosures from two distinct angles: economic determinants and consequences of corporate disclosures. I investigate these two topics separately in Chapters 1 and 2./nIn Chapter 1, The Jobs Act Did Not Raise IPO Underpricing, my co-authors and I examine how the amount of pre-IPO disclosures affects IPO pricing. While the intended goal of the 2012 JOBS Act was to ease access to capital for Emerging Growth Companies (EGCs), prior studies, notably Barth et al. (2017), find evidence of an increase in IPO underpricing and a higher cost of equity capital for EGC issuers. Using a difference-in-differences design, we find that changes in overall IPO market conditions explain the seeming increase in IPO underpricing. In fact, EGC issuers that take advantage of the accounting disclosure relief afforded by the Act raise capital at higher pre-IPO multiples. These reduced-accounting disclosure EGCs have more speculative valuation profiles and lower institutional ownership and are more likely to destroy long-term shareholder value in the IPO aftermarket. Overall, this chapter offers an alternative perspective on the effect of the JOBS Act on IPO pricing./nIn Chapter 2, I examine whether and how the recognition of operating leases on balance sheets influences managerial leasing decisions. In 2019 the FASB implemented ASC 842, which requires companies to capitalize operating leases. Given prior operating lease disclosure in footnotes, it was unclear if capitalization would affect financial statement users or managers. I find that firms' use of operating leases decreases upon the new standard's adoption and that this decline is driven by lease-intensive and less-levered firms. I also document a systematic substitution of operating leases with short-term and variable leases, both of which can still be left off balance sheet. The latter finding suggests that the FASB has not entirely succeeded in preventing firms from opportunistically omitting liabilities from balance sheets. My study responds directly to the FASB's call for research that examines the new standard's unintended consequences on managerial leasing decisions. Overall, I provide evidence that disclosure location (footnote vs. balance sheet) exerts a real effect on managerial behavior.

Advisor: Patatoukas, Panos
Even-Tov, Omri
Dynamical Diffraction: Friend or Foe? 4D-STEM Measurements Robust To Multiple Scattering

Four-dimensional scanning transmission electron microscopy (4D--STEM) is a modern operating mode of a transmission electron microscope in which a focused electron probe is rastered across the sample and the diffraction pattern is recorded at each position.The resulting diffraction patterns can be analyzed to obtain a wealth of local structural information, such as deformation or strain, changes in symmetry or lattice distortions, orientation of a crystal lattice, as well as to measure electric and magnetic fields. More advanced analyses, i.e. ptychography, can also extract structural information at a spatial resolution finer than the size of the electron probe./nSeveral challenges arise in realizing these measurements: First, the sheer number of diffraction patterns recorded in a 4D-STEM experiment leads to computational challenges and puts demands on the complexity of the algorithms used to recover the structural information. Second, experimental considerations often strictly limit the number of electrons in each of the diffraction patterns, which can be mitigated through robust analysis approaches or by de-noising that takes advantage of the high dimensionality of the data. Most critically, all of the structural measurements described above are effectively trivial in the limit of thin and weakly scattering materials but become rather challenging when analyzing diffraction from a thick sample where multiple scattering effects are present./nIn this work, we will explore several means to mitigate these challenges. First, to handle the large quantities of data and the low number of electrons recorded by modern detectors operated at their full speed, we will show a hyperspectral denoising method based on total variation denoising and show its application to 4D--STEM datasets./nThe bulk of this work, however, will focus on the latter challenge: dynamical scattering. In 4D--STEM measurements of local strain or deformation, dynamical scattering causes unwanted contrast inside of diffraction disks which hinders accurate determination of the lattice. To mitigate this, we demonstrate a method for imprinting known contrast into the diffraction disks to improve the precision of the measured lattice. In measurements of the local orientation of the crystal, multiple scattering causes the diffraction disk intensities to vary in a highly nonlinear way as the crystal tilts, and as a function of the thickness of the crystal. We present a hybrid pattern-matching and simulation-matching algorithm for precisely determining both the orientation and thickness of a crystalline sample from 4D--STEM measurements. Finally, many polar structures of technological interest exist only under exacting electrical and mechanical boundary conditions and so can only be studied as a thick and heterogeneous sample. To measure polarization structures in such materials, we construct a dynamical scattering model for the system and demonstrate an optimization procedure which recovers local polar order from large-area scans of a thick multilayer sample.

Advisor: Minor, Andrew
Imaging Synaptic Scale Dopamine Disruption in Huntington’s Disease Model Mice With Near Infrared Catecholamine Nanosensors

Dopamine neuromodulation is a critical process that facilitates learning, motivation, and movement. Disruption of these processes has been implicated in several neurological and psychiatric disorders including Huntington’s Disease (HD). While many treatments for physical and psychiatric HD symptoms target dopaminergic neuromodulation, the mechanism by which dopaminergic dysfunction occurs during HD is unknown. This is partly due to limited capability to visualize dopamine release at the spatiotemporal resolution of both neuromodulator release (ms) and dopaminergic boutons (µm). We have designed a synthetic, optical probe for catecholamines that utilizes near-infrared (nIR) fluorescent, polymer-functionalized single wall carbon nanotubes to report dopamine dynamics within striatal brain tissue. These nIR catecholamine sensors (nIRCats) show a strong response to dopamine within the near-infrared wavelengths ideal for imaging in optically scattering brain tissue. Furthermore, the chemically synthetic molecular recognition elements of nIRCats allow for expression-free imaging of dopamine in the presence of dopamine receptor pharmacology. These characteristics of nIRCat imaging make it a powerful tool uniquely suited for the study of dopamine release in diseased tissues./nIn this dissertation I develop methods to utilize nIRCat imaging for the study of dopamine release within R6/2 Huntington’s Disease Model mice (R6/2). Using nIRCat’s high spatial resolution, I show that dopamine release in R6/2 HD mice decreases with progressive motor degeneration and that these decreases are primarily driven by a decrease in the number of dopamine hotspots combined with decreased release intensity. I adapt this analytical framework towards elucidating how dopamine release sensitivity to extracellular calcium concentration and D2-autoreceptor modulation is affected over the course of HD. In contrast to findings from spatially diffuse dopamine recordings, nIRCat imaging in ex vivo R6/2 HD slices indicates that calcium signaling within dopamine hotspots is altered late in disease and D2-autoreceptor signaling is altered early in disease. Lastly, I utilize nIRCat's ability to track individual dopamine hotspots over repeated stimulations and pharmacological washes to measure the release fidelity of dopamine hotspots in late disease. Compellingly, I demonstrate that antagonism of D2-autoreceptors using Sulpiride and direct blocking of Kv1.2 channels using 4-Aminopyradine (4-AP) increases the fidelity of dopamine hotspot activity in the striatum of WT slices, but not in the striatum of late HD slices. Altogether, these findings --- enabled by nIRCats --- provide a deeper look into how dopamine release is disrupted and dysregulated during Huntington’s Disease to alter the coverage of dopamine modulation across the dorsal striatum.

Advisor: Landry, Markita P
Schaffer, David V
Searching for the Process Linking Emotion-Related Impulsivity to Internalizing Symptoms: Arousal-Induced Decays in Working Memory

Emotion-related impulsivity, the trait-based tendency to respond impulsively to heightened emotions, is a transdiagnostic phenomenon associated with diverse forms of psychopathology and problematic behavior. Theory and research on the process of emotion-related impulsivity have identified deficits in cognitive control. Emotion-related impulsivity leads to problems during heightened emotions, which suggests that increased arousal may be core to problematic behavior. Arousal is also known to contribute to decays in some facets of cognitive control, especially working memory. A key need is to understand how arousal-induced decays in working memory relate to emotion-related impulsivity and whether arousal-induced decays in working memory mediate the relation between emotion-related impulsivity and internalizing symptoms./nParticipants (N = 100) were recruited as part of a larger two-site study including people who experience a full range of internalizing and externalizing symptoms. Participants completed measures of internalizing symptoms, impulsivity, and potential confounds and then completed a novel N-back task including a within-task stressor to induce high arousal. Skin conductance and pupil diameter were collected at baseline and throughout the task to index arousal. Preliminary cleaning and analyses were conducted to exclude unusable data to yield a final sample (N=82)./nImpulsivity was positively related to internalizing symptoms with significant large effects. Contrary to hypotheses, neither higher emotion-related impulsivity nor higher internalizing symptoms were related to poorer working memory performance, and neither interacted with arousal in relation to working memory performance. Arousal did not induce decays in working memory, and arousal-induced decay in working memory was not a putative mediator for the relationship between emotion-related impulsivity and internalizing symptoms./nFindings inform the emotional and neurocognitive processes involved in emotion-related impulsivity. Results did not support the role of arousal-induced decays in working memory in impulsivity or internalizing symptoms, however, future research is warranted to evaluate this model with other methods given the present findings contradict large and growing literatures on the role of arousal and cognition. Further pursuit of this model could identify the process underlying emotion-related impulsivity which may point to potential targets for intervention.

Advisor: Johnson, Sheri L
Can We Discern Authenticity in Others?

A burgeoning research literature has documented that authenticity has important benefits for a broad range of outcomes, among them well-being, intimate relationships, and leader effectiveness. Most past research on the construct has focused on either the consequences of experiencing subjective feelings of authenticity or being perceived as authentic. Such findings make salient the question of whether people can accurately discern others’ subjective experience of authenticity – a question that has not yet been explored. In the five studies reported here, we began tackling this question by examining the accuracy of perceivers’ judgments of others’ felt authenticity. In Studies 1-3 we investigated correspondence between ratings of perceived and felt authenticity within acquainted dyads (romantic couples) who interacted with each other and discussed a particular set of topics. In Study 1, we examined couples in a naturalistic, daily life setting and found that members of romantic couples were able to accurately discern each other’s felt authenticity. In Study 2, with a different sample of romantic couples, we introduced experimental control over couples’ interactions by bringing them into the lab to discuss two topics to assess situation-specific authenticity. We found that across two different conversation topics, one self-focused and one related to the relationship, people were able to accurately discern their romantic partners’ felt authenticity during the conversations. In Study 3, with yet another set of romantic couples, we tested boundary conditions to determine if there was evidence of authenticity accuracy even when couples were in an emotionally-charged, antagonistic situation (conflict). We did not find accuracy in partners’ ratings of each other’s felt authenticity after a conflict conversation, raising questions about possible moderating variables (e.g., nature of the social interaction in which authenticity is being judged), as well as calling for further examination of perceivers’ accuracy in discerning others’ authenticity. Studies 1-3 relied on authenticity manifested and judged in close relationships in which both individuals knew each other. Thus, in our final two studies, we tested whether the evidence we found in Studies 1 and 2 for accurate discernment of others’ felt authenticity was limited to dyads in which members areacquainted with one another in some manner (e.g., romantic couples). Specifically, in Study 4A, and then replicated in pre-registered Study 4B, we found that outside observers could accurately judge strangers’ self-reported authenticity from video clips of a social interaction. We discuss the importance of authenticity perceptions to impression formation and close relationships, and point to critical unanswered questions regarding such perceptions.

Advisor: Chen, Serena
Learning-Based Characterization and Control of Colloidal Self-Assembly Systems

Colloidal self-assembly (colloidal SA) is the process by which particles in solution spontaneously organize into an ordered structure. The spontaneous self-organization central to colloidal SA enables "bottom-up" materials synthesis, which would allow for manufacturing advanced, highly ordered crystalline structures in an inherently parallelizable and cost-effective manner. Thus, colloidal SA can create new avenues for highly scalable, economical manufacturing of novel metamaterials with unique optical, electrical, or mechanical properties. Colloidal SA is an inherently stochastic (i.e., random) process prone to kinetic arrest due to particle Brownian motion. This leads to variability in materials synthesis and possibly high defect rates, which can severely compromise the viability of using SA to manufacture advanced materials reproducibly. Successful implementation of colloidal SA thus critically hinges on the ability to avoid defective, kinetically arrested configurations and consistently reach highly-ordered, often defect-free states that tend to exist within global minima on the free energy landscape./nThe thermodynamic and kinetic driving forces that govern colloidal SA thus need to be precisely modulated -- by actively exploiting intermolecular forces, selective template or surface geometries, and/or external fields such as temperature and pressure -- to direct colloidal SA systems consistently and efficiently towards mass-producible structures and materials. Two major strategies for this precise modulation are particle design, which involves designing the colloidal SA system such that specific inter-particle interactions ensure the high probability realization of a desired configuration, and control, which seeks to modulate external actuators systematically based on real-time measurements in order to induce global colloidal SA configuration changes. The primary objective of this thesis is to enable more effective particle design and control of colloidal SA systems. To this end, this thesis investigates strategies based on machine learning and optimal control for quantifying and classifying colloidal SA system states, learning tractable stochastic dynamical models of colloidal SA dynamics, and learning control policies that dynamically change external actuators to guide colloidal SA. The insights gained from these methods provide a deeper mechanistic understanding of colloidal SA and contribute to an ever-developing archive of methods that can be used or expanded upon to achieve reproducible colloidal SA.

Advisor: Mesbah, Ali
Radke, Clayton
Surfactant Properties of Per- and Polyfluoroalkyl Substances (PFAS): Implications for Detection, Fate, and Transport in Environmental and Treatment Matrices

‘Forever chemicals’ is on its way to becoming a household name. Yet as researchers, we arestill identifying the numerous data gaps in our understanding of bio- and chemical transformation, toxicity, transport processes, and physicochemical behavior of the poly- and perfluorinated alkyl substances (PFAS) that have earned this colloquial title. This dissertation addresses how the unusual surfactant properties of PFAS derived from fluorine chemistry impact their behavior in the context of environmental management and treatment technologies./nThe physicochemical properties investigated in this work are aggregation, surface activity,and sorptive behavior, all of which have the potential to cause artifacts of removal in treatment systems, as well as unexpected behavior in the environment. The aggregation of PFOS with specific groundwater relevant salts is investigated in order to understand an artifact of PFOS removal in high ionic strength treatment-like matrix. Additionally, the surface activity and interfacial accumulation activity of PFOS is evaluated using the Langmuir-Szyszkowski adsorption model (Chapter 2). This work is expanded upon by investigation of the aggregation and surface accumulation properties of an industrial AFFF, which is more representative of the complex PFAS contamination matrices that may be subject to high salinity conditions in the environment (Chapter 3). Lastly, sorption processes related to PFAS removal in a biobased treatment system are uncovered through a mechanistic investigation of a multi-step oxidative treatment mechanism (Chapter 4)./nOverall, this works serves to provide researchers with a more complete picture of the complexitiesassociated with PFAS treatment in relation to their unique surfactant properties.

Advisor: Alvarez-Cohen, Lisa
Efficient Learning Methods in Mixed Autonomy Traffic

Automated driving systems are expected to play a critical role in the future of transportation. With fast reaction times, vehicle-to-vehicle communication, and the potential for socially optimal driving behaviors, automated vehicles may serve a central role in improving driving conditions within existing road networks, reducing the prevalence of traffic congestion and enabling fast and energy-efficient driving. Generating behaviors that produce such effects in real world settings, however, is no trivial task. In particular, when coupled with human drivers in mixed-autonomy settings, coordination between human-driven and automated vehicles becomes increasingly delicate, and motivates the need for new and advanced tools for solving these tasks./nThrough the research outlined in this document, we aim to identify efficient methods for learning congestion-mitigating control strategies that can be employed by automated vehicles in partially automated road networks. Recent advances in deep reinforcement learning have highlighted the potential of said techniques in producing control strategies that match or outperform classical approaches on a variety of decision making and control tasks. The applicability of similar approaches to mixed-autonomy traffic control, however, is hindered by a number of challenges. For one, exploration in these settings is difficult, as individual actions may not influence the flow of traffic until multiple timesteps in the future. In addition, the process of modeling and executing simulations of realistic traffic flow networks at the level of individual vehicles is a difficult and computationally costly endeavor. Through techniques such as hierarchical learning, imitation from experts, and robust learning in simplified tasks, we hope to design data-efficient methods for generating control strategies for automated vehicles that are transferable to the real world.

Advisor: Bayen, Alexandre M