VictorJuiz/thesis huggingface dataset:
Exploring How the Human Brain Learns to Recognize New Visual Symbols
Keywords:  Department of Biology
Neurobiology
electrophysiology
behavior
epilepsy
learning
visual symbols
Thesis

The process of associating meaning with a visual symbol, such as a number or a letter, is established through learning that involves interactions of specific regions of the brain. In this study a visual learning task was created through which subjects learned to first categorize previously unknown symbols as letters or numbers (categorical learning) and then associate each symbol with a specific letter or number (translation learning). The protocol was tested in three consecutive stages: 1) behavioral analysis in a healthy control group (n=21), 2) behavioral analysis in an epileptic nonsurgical group (n=5), 3) and a combined behavioral and electrophysiological study in an epileptic surgical group implanted with intracranial recording electrodes (n=3). Efficacy of the protocol was proven with behavioral data collected among the three groups that tested response time to stimuli, accuracy of response, and confidence interval. Groups all had similar upward learning trends towards 80% accuracy in categorical learning phase before moving to the translation learning phase. This study validated that the protocol could be completed within a one-hour time frame. Intracranial electrophysiological recordings were also used to capture the spatial and temporal dynamics of brain activity during the testing protocol. As indicated by the power of high-gamma activity recorded from individual electrodes over the posterior superior temporal region, there was a medial to lateral build-up of neural activity over the course of the testing phases of the protocol. Our findings validated the feasibility of a novel experimental paradigm for studying the human brain during learning of new symbols. Future empirical knowledge obtained with this paradigm will provide valuable mechanistic information about the interactions among different brain regions when human subjects learn new visual symbols.
Exploring shared structure among vehicle trajectories [electronic resource]
Keywords: Electrical Engineering.

Trajectory data, which record the evolution of objects in space and time, are everywhere. Examples are GPS trajectories recording the moving traces of a group of people, video sequences of moving cars, etc. Such trajectory collections contain rich and diverse information about their surrounding environment, as well as the collective behavior of their human agents. A common characteristic of trajectory data collected from mobile platforms is data uncertainty. The uncertainty comes from sensor noise, low sampling rate due to power, storage, or data transmission constraints. However, large amounts of trajectories traversing the same spatial-temporal space imply that the trajectories share a lot of common structures. A good understanding of this shared structure can benefit many applications, including route planning, autonomous driving, and ride sharing. In this work, we explore shared structure among trajectory collections and show their usefulness in various applications. We begin with the physical shared structure, i.e., the road network, and show that trajectory collections, such as GPS trajectories, or driving video sequences, have great value in creating and maintaining up-to-date, information-rich maps of the underlying road network. We then explore shared mobility patterns among trajectories from a unique and knowledgeable group of people, specifically taxi drivers. We describe common mobility patterns in such groups using pathlets, which represent popular sub-trajectories shared by many trajectories. We present an unsupervised pathlet learning framework that extracts a compact pathlet dictionary from a collection of GPS traces and demonstrate various applications.
Exploring the Process of Critical Reflection in Photovoice with Youth in the Cape Flats
Keywords:  Graduate School of Education
Graduate School of Education
Stanford University
Photovoice
Cape Town
Cape Flats
Critical Pedagogy
Critical Consciousness
Critical Reflection
Visual Research
Participatory Research
Youth
Urban Youth
Thesis

Critical consciousness is theorized to be an “antidote” to structural oppression for marginalized youth. Though founded on principles of critical pedagogy, Photovoice has largely been used as a needs assessment tool founded in community-based participatory research. There has only been limited study of the potential for Photovoice to be used as a tool to develop critical consciousness, particularly among youth. This study explores how youth in a Photovoice program participate in the process of critical reflection, which has been identified as one of the core and most understudied components of critical consciousness. Working in the Coloured Cape Flats context in Cape Town, South Africa, this phenomenological case study recruited 7 participants, ages 13 - 17, to participate in a 10-week Photovoice program that asked them to identify strengths and opportunities for improvement in their communities, participate in dialogue, and present their photos at a photo exhibition for community members and policy makers. Methods from visual sociology were used to analyze the primary source of data, 35 photos presented by participants and their matched captions, along with field notes, recordings of classroom discussions, interviews, and other photos that helped contextualize these photos. This analysis showed that participants display levels of critical consciousness matching the stages as Paulo Freire described – magic, naïve, and critical. This analysis also found that intentionality and forethought before and during photo-taking, using the imagination during photo interpretation and captioning, and group dialogue that facilitated collective introspection and helped push participants towards the critical level of consciousness. These results are aimed to be a guide for educators and community researchers who want to engage youth in critical pedagogy and emancipatory practice through Photovoice, and hope to inform previous frameworks created to conceptualize critical reflection.
Extrinsic stabilizers of proteins: their roles in acclimatizing to fluctuating temperatures in the rocky intertidal environment
Keywords:  Department of Biology
Biology
intertidal
heat stress
acclimatization
Mytilus californianus
proteins
osmolytes
extrinsic stability
Thesis

Organisms living in rocky intertidal habitats endure extremely variable conditions. Body temperatures of sessile invertebrates can vary by over 25°C in concert with the tidal cycle. Proteins are temperature-sensitive, and many denature at the highest temperatures intertidal species encounter. This study sought to elucidate whether and how the adjustment of organic cellular solutes known as “osmolytes” helps protect proteins in the California ribbed mussel Mytilus californianus against heat-denaturation. I approached these questions by directly measuring the extrinsic stability, i.e. stability afforded by co-solutes, of M. californianus malate dehydrogenase, and by measuring osmolyte concentrations after heat stress in the laboratory and in the field.
Though malate dehydrogenase was much more stable in cytosol than in buffer as expected, prior heat stress did not strongly alter its stability in the cell (p=0.14). Changes in the osmolyte composition of gill tissue were observed after twenty-three days of heat stress in the intertidal zone, but not after one eight-hour heating-cooling cycle in the laboratory. Thus, induction of a putative osmolyte response requires multiple thermal stress episodes. Cellular concentrations of alanine, taurine, and glycine varied significantly (p<0.05) with mean daily maximum body temperature measured over twenty-three days in the field. Taurine, by far the most abundant osmolyte and a strong protein stabilizer, showed a significant increase with acclimatization to higher temperatures, consistent with an increase in extrinsic stabilization under heat stress. A weak trend (p=0.14) in the concentration of the stabilizer glycine betaine also supports a model in which osmolyte synthesis pathways are regulated in a way that protects proteins. I discuss the plausible role of osmolytes in mussels’ acclimatization to repetitive heat stress, and suggest further experiments to isolate and quantify this function.
Factor analysis for high dimensional data [electronic resource]
Keywords: Statistics.

Factor analysis is a powerful dimension reduction tool with explicit statistical model and assumptions. It assumes that the data matrix can be decomposed as linear combinations of latent factors plus independent noise which has heteroscedastic variances for each variable. This dissertation provides two approaches estimating the factor analysis model for high-dimensional data and an application of factor analysis to confounder adjustment in high-throughput genetics experiments. One approach, BCV-ESA, uses an early stopping iterative algorithm to estimate the model and a bi-cross-validation technique to select the number of factors to retain. The other approach, POT-S, starts with a joint convex optimization using perspective transformation and nuclear penalty and estimates the tuning parameter by a Wold-style cross-validation. Both methods are superior to previous methods in the accuracy of estimating the weak factors and the noise variances. For the confounder adjustment application, the mathematical properties of a two-step algorithm estimating the individual effects of the primary variable after confounding factors adjustment has been carefully studied. It is shown that under regularity conditions, the estimators of the individual effects can asymptotically achieve an oracle efficiency and eliminate bias or correlations in the p-values of individual effects.
Faster fidelity for better design [electronic resource] : a scalable model order reduction framework for steady aerodynamic design applications
Keywords: Aeronautics and Astronautics.

This thesis proposes a framework for building and leveraging Reduced Order Models (ROMs) for industrial-scale steady-state nonlinear Computational Fluid Dynamics (CFD) applications involving shape parameters. This framework is enabled by four distinct contributions. First, a local ROM framework is matured for steady applications, with an emphasis on consistent affine approximations. Second, a suitable approach for handling parametric geometry is proposed, utilizing volumetric deformation techniques to morph a body-fitted mesh. Third, a robust solution strategy is proposed for predictive steady-state CFD applications. Finally, the full framework is implemented in a high-performance computing environment, and can scale to large applications. The numerical experiments in this work represent the first demonstrations of predictive ROMs for realistic nonlinear CFD applications. In these experiments, the proposed ROM framework is shown to predict integrated forces with accuracy similar to simple data-fit approaches, and is shown to predict distributed quantities with better accuracy than simple data-fit approaches.
Fear Conditioning in Siberian Hamsters: Circadian Effects and Sex Differences
Keywords:  Department of Biology
Biology
circadian
rhythm
fear
conditioning
hippocampus
amygdala
learning
memory
neurobiology
locomotor
activity
tone
Siberian
hamster
Phodopus
sungorus
Thesis

Circadian modulation of contextual and cued fear conditioning (FC) has been investigated in mice, but not in the Siberian hamster, Phodopus sungorus. This study tested whether measuring locomotor activity suppression, a validated index of fear in rodents, during FC reveals a circadian rhythm to FC performance, both in hippocampal and non-hippocampal dependent memory tasks. Animals were exposed to a series of tones followed by footshocks during the first two days of training, then tested for their cued recollection of the tone in a novel environment two days later. Locomotion and activity state were continuously measured via video during testing. Our data suggest that both contextual and cued memory ability are stronger during the light phase than the dark, as indicated by a greater increase in inactivity measurements during the light. Therefore, there exists a significant circadian rhythm in P. sungorus contextual and cued FC, although the latter’s rhythm only manifests itself after several exposures to the tone. On the whole, there were few appreciable sex differences, with the exception of the Inactive Events and Bouts measurements for contextual FC. These results verify the robustness of FC and the inactivity measurement as an evaluation of learning and memory in P. sungorus.
Financial crises, firms' innovation and the macroeconomy [electronic resource]
Keywords: Economics.

This dissertation analyzes the interaction between financial crises and firms' innovation, its macroeconomic consequences and the implications for economic policy. The first chapter, "The Financing of Ideas and the Great Deviation", studies the macroeconomic impact of financial shocks through their negative effect on intangible capital investment, using a panel dataset of Spanish manufacturing firms. The second chapter, "How Destructive is Innovation?", is co-authored with Chang-Tai Hsieh and Peter J. Klenow. It quantifies the contribution of different innovation channels to aggregate economic growth, matching a growth model to statistical moments on establishment dynamics in the U.S. The final chapter, "Macroprudential Policy with Liquidity Panics", is co-authored with Alonso Villacorta. It shows how financial panics can appear endogenously due to precautionary cash accumulation by firms, and how this changes the optimal macroprudential policy.
Financial reporting quality and voluntary disclosure [electronic resource]
Keywords: Business.

This thesis is comprised of two essays that explore how investors' uncertainty over financial reporting quality influences firms' voluntary disclosures. I consider two shocks that cause investors to assign a higher likelihood of restatement and examine how managers respond using voluntary disclosures. Managers inform stakeholders of the firm through mandatory disclosures (e.g. financial statements) and voluntary disclosures (e.g. earnings forecasts, conference calls, press releases). Financial reporting quality represents the extent to which financial statements faithfully reflect the underlying economics of the firm, and therefore, how much stakeholders can learn from these mandatory disclosures alone. The focus of this thesis is on how managers use voluntary channels to inform stakeholders following shocks to investors' expectations of financial reporting quality.
Finding needles in a haystack [electronic resource] : molecular similarity and machine learning for drug discovery applications
Keywords: Structural Biology.

We are in the midst of a machine learning revolution. From self-driving cars to clinical diagnostics, machine learning promises to change the way we live our lives and make decisions. Applied to drug discovery, machine learning enables us to build upon existing experimental data and more effectively explore the vastness of chemical space for new therapeutics. In particular, virtual screening allows us to evaluate many more compounds and biological targets than we can test experimentally, helping to identify starting points for further development. In this dissertation, I present several applications of machine learning to drug discovery. Much of the work presented here focuses on multitask neural networks---variants of the models that have transformed computer vision and beaten some of the world's best Go players. Applications of these models to ligand-based virtual screening demonstrate improvements over standard machine learning methods such as random forest and logistic regression. I also describe neural network models built on simple encodings of the molecular graph, moving beyond traditional fingerprint-based screening methods to a richer and more flexible input representation.
Firm strategy in early-stage ecosystems [electronic resource]
Keywords: Management Science and Engineering.

Many industries consist of networks of interdependent firms that offer discrete products or services that together comprise a valuable solution. Within these networks, often termed ecosystems, firms depend on one another to create value, but simultaneously compete with one another to capture value. Prior research on ecosystems has generally examined firm strategy in established ecosystems, in which the identity and relationships between firms and components are known. Less is known about firm strategy in early-stage ecosystems, which are those that are in an early state of emergence or evolution. This dissertation addresses this gap through three linked studies. The first is an inductive multiple-case study of five firms in the nascent US residential solar ecosystem as it emerged from 2007 to 2014. The second is a formal mathematical model that examines how the level of competition (ecosystem vs. component) affects firms' ability to create and capture value in nascent ecosystems. The third moves from nascent to evolving ecosystems, and presents a cooperative game theory model that examines how and when firms can collaboratively resolve technological constraints that inhibit their ability to jointly create and capture value. Together, the studies in this dissertation offer rich theory regarding how firms can succeed in early-stage ecosystems, despite the uncertainty and ambiguity that characterizes these settings. Overall, this research contributes to the literatures on strategy, organizations theory, and entrepreneurship.
Flat optics based on dielectric gradient metasurfaces [electronic resource]
Keywords: Electrical Engineering.

Metasurfaces provide unprecedented control over light propagation by imparting local, space-variant phase changes on incident electromagnetic waves. They have contributed to the replacement of bulky optical components with ultrathin planar elements. They can also improve the performance of conventional optical elements and facilitate the creation of optical components with new functionalities. We first describe the operating principles and the experimental demonstration of dielectric gradient metasurface optical elements (DGMOEs) that capitalize on the development of high-index dielectric optical antenna and Pancharatnam-Berry phase optical elements. In particular, we illustrate how ultrathin gratings, lenses, and axicons, operating in transmission mode in the visible spectrum, can be realized by patterning a 100-nm-thin Si layer into a dense arrangement of judiciously arranged nanostructures. Additionally, we build on the knowledge obtained from the development of shared-aperture phased array antennas and Si-based gradient metasurfaces to fabricate various multifunctional metasurfaces capable of achieving multiple distinct functions within a single surface. In particular, we demonstrate that interleaving of multiple optical elements can be accomplished without reducing the aperture of each sub-element. Multifunctional optical elements constructed from a Si-based gradient metasurface are realized, including polarization-independent metasurfaces and axial and color separating metasurface lenses. We also demonstrate multi-wavelength color imaging with high spatial resolution using an axial multifunctional metasurface. Finally, an optical imaging functionality with simultaneous color separation is obtained using a color separating metasurface. Lastly, we develop a multiplexed metasurface lens array based on multi-functional dielectric gradient metasurfaces to replace the conventional microlens array in light-field imaging applications. The multiplexed metasurface lens arrays allow simultaneous 3D imaging at a high spatial resolution, which overcomes the trade-off between spatial and angular resolutions when using a conventional microlens array constructed from a series of lenslets with a small aperture size. These Si-based gradient metasurfaces offer facile integration with electronics and can be manufactured using mature semiconductor fabrication techniques. They open up a myriad of opportunities in the fields of optical communications, advanced computational imaging and display systems.
The formation and evolution of a neural code for rewarding memories [electronic resource]
Keywords: Applied Physics.

Memories of rewarding episodes can be vivid and contain detail that lasts a lifetime. The mammalian hippocampus is essential for the formation of episodic memory, but how it supports the privileged encoding and long-term retention of memories for rewarding events is poorly understood. Hippocampal CA1 pyramidal cells exhibit place fields at a higher prevalence around reward locations, however, it has been unknown how CA1 representations of rewarding experiences form and evolve over the timescales of long-term memory formation. We used a head-mounted miniature fluorescence microscope to track the calcium dynamics of > 10,000 CA1 pyramidal cells expressing the calcium indicator GCaMP6m as freely behaving mice learned to perform a reward-motivated spatial task over 9 consecutive days. The CA1 ensemble showed progressive refinement as the mice learned the association between place and reward. We observed the formation of a place field over-representation of the rewarded location, the magnitude of which increased with learning. Selective recruitment, remapping and stabilization of cells at the reward location supported the formation and maintenance of this over-representation. Ensemble decoding of the mouse's position from the population of imaged neural activity suggested that both the preponderance of place fields and narrower field widths contributed to better representational accuracy of the reward location. These results suggest that long-term refinements to the over-representation of rewarded locations may support reward-associated spatial memory.