VictorJuiz/thesis huggingface dataset:
Development of Video Tracking Techniques to Study the Mechanics of Head Injuries and Their Motor Effects

TBI is the leading cause of disability, chronic disease, and death among the under-40 age group in the developed world. From 2002-2006, the reported incidence of TBI in the United States was almost 600 per 100,000 persons according to the CDC. Up to 80% of these injuries involve closed-head, blunt-force impacts (CDC), however many of our animal models involve open-head injury techniques. Recent models have moved to inducing an injury with a weight-drop device allowing unrestricted motion of the head following impact. This mimics head injuries seen in sports and motor vehicle accidents more accurately, but also leads to variation in the injury induction itself. The added motion of these models requires quantification of mechanical variation in the animal's head motion post-impact to determine how much mechanical forces play a role in injury outcomes compared to physiological injury mechanisms. Using a high-speed video capture system and tracking software, a technique was developed to analyze the kinematics of animal head motion immediately post impact. Those kinematic variables can be compared to behavioral test data to determine the effects of mechanical forces on this type of injury. The development of this technique also led to the construction and development of a gait analysis system that can give increased resolution to motor deficits experienced by animals post-injury. These types of methods work towards bridging our understanding of the brain’s mechanical response to loading and our knowledge of the physiological mechanisms that underlie damage to the tissue itself into mechanical modeling of clinically relevant functional measures. This research is a step towards merging the fields of engineering and neuroscience to take on one of the most complex issues in medicine: traumatic brain injury.

Advisor: Pruitt, Lisa
Kaufer, Daniela
El sonido y la cultura aural en la narrativa del fin de siglo diecinueve en México.

This dissertation questions the analytic categories that have been used to study nineteenth century Mexican and Latin American literatures and culture. My dissertation explores the way aural and sonic culture impacts fiction and non-fiction narratives during a period of high economic liberalization and State consolidation. I draw upon the fields of sound studies and literature, to analyze the Mexican fin-de-siècle marked by the emergence of a sonic excess, which I define as the proliferation of sounds (technological and human) that modified arts and politics during a period of nation dependent integration into a global economy./nI work on the concept of aural regimes and performative listening, where I explore the ways in which gender and modernista aesthetics are projected and heard in the noises of modern cities such as Mexico and Paris. I analyze how gender is learned, represented, reinforced, or contested in the aural regime. Here, categories of the feminine (and their intersectionality with race and class) are constituted within spatial sound environments where power, inequality and agency are manifested in the sonic field. Sound becomes a signifier that helps us to understand how power relations, gender, and aesthetics are constructed. I also study a war narrative, Tomochic, an historical novel that gives account of a military campaign to reduce and silence a popular upheaval in northern Mexico as an audionarrative, this is a narrative informed, mainly, through different points of auditions, voices, and war sounds, shifting the naturalist and realist narratological conceptualization of nineteenth century approaches. Additionally, I analyze, for instance, sound representations in a non-fiction travel narrative from the perspective of intellectual Justo Sierra who travels to the United States from Mexico City, noticing that in his geographical displacement there is also an epistemological one that takes him from the lettered city to the audible city, in which the power of writing assimilated by the political order, is affected not only by the hegemonic discourses of elites, but by the noises and sounds of multicultural North American cities./nIn this sense, war, material and popular culture, and fiction and non-fiction narratives allow me to reinterpret the turn of the 19th century archive in terms of sound ecologies informed by gender, economics, technologies, and nation building and social movement processes.

Advisor: Dominguez, Daylet
Learning, Control and Optimization for Electricity Distribution Networks

Traditionally, utility distribution companies have treated electricity distribution networks as passive loads, assuming that the possibility of network constraint violations cannot be managed by controlling distributed energy resources (DERs) such as electric vehicle charging stations. The emergence of edge computing has made it possible for DER management systems (DERMS) to control the power injected/extracted by DERs in real time. By actively managing DERs when necessary, DERMS both increase interconnection availability and reduce the need for infrastructure upgrades. This dissertation presents several tools that can either make power-injection decisions for a DERMS or that support the system’s decision-making. These tools include Unsupervised Impedance and Topology Estimation, Multiple Model Adaptive Power System State Estimation, Voltage Phasor Control, Linearized Output Projected Gradient Descent Feedback Optimization, and Nullspace-Based Power Flow Linearization. The focus of the work is to develop theory which can explain when and how to use these tools on real distribution networks. A large portion of this dissertation develops the theory behind Nullspace-Based Power Flow Linearization, a novel interpretation of power flow linearization that explicitly considers constant-voltage buses, phasor angle-reference ambiguity, and power balance. This dissertation also includes a comment that was submitted in August 2022 on CPUC Proceeding R2207005. With Proceeding R2207005, the California Public Utilities Commission is investigating new rules and price structures for utilities which will change the future of DER management in California.

Advisor: Tomlin, Claire
von Meier, Alexandra
Optical Voltage Sensing: Micro- to Kilo-Scale

Detection of electrical neural activity has been possible for almost a century. Despite the progress in the last century, neural recording technologies remain fundamentally constrained in their scale by the power they are able to dissipate. As channel counts have increased, and electrodes have grown flexible and microscale, connectorization to neural recording circuits has become ever more challenging. All-optical neural recording technologies hold the promise to eliminate these barriers. However, until this point, it has been impossible to quantitatively compare existing electrical recording technology with emerging optical solutions. I introduce a novel figure of merit $E_Q$, which overcomes this challenge, and allows direct and quantitative comparison between arbitrary technologies. I propose a novel neural recording architecture which can achieve a lower $E_Q$ than existing electronic methods, consisting of free-floating passive optical voltage sensors. These sensors have no active circuitry and require no stored power, and can be interrogated wirelessly at will. I describe progress towards the realization of these sensors, including their design, fabrication, and characterization, as well as their use for digital communication through highly scattering tissue. Additionally, I show that the same sensors and the same framework can be applied to solve some outstanding problems in these sensors use in microgrid applications for kV-range sensing.

Advisor: Maharbiz, Michel
Galectin-mediated immune responses to infection with intracellular bacteria

Macrophages employ an array of receptors to detect and eliminate intracellular pathogens that access the cytosol. Galectins represent one such class of receptors, and activate membrane repair or autophagy-mediated degradation of ruptured organelles. In macrophages, Gal-3, -8, and -9 promote innate immune signaling following recognition of luminal host glycoproteins in damaged pathogen-containing phagosomes, but their contributions to immunity during infection in vivo remain unclear. We found that Gal-9 interacts directly with the intracellular pathogens, Mycobacterium tuberculosis (Mtb) and Salmonella typhimurium. This interaction was dependent upon Gal-9 carbohydrate-binding and occurred both in vitro and in vivo. To determine the contribution of the galectin system to host resistance in vivo, we generated Gal-3, -8, -9 triple knockout mice (TKO) and challenged these mice with a panel of intracellular bacteria. We found that galectin deficiency leads to impairments in bacterial growth restriction and CD4 T cell activation during chronic Mtb infection. However, during acute infection with Mtb, S. typhimurium, or L. monocytogenes, TKO mice exhibited normal resistance. In addition, we found that galectin deficiency in macrophages leads to inefficient clearance of Mtb from the cytosol, despite TKO macrophages exhibiting normal bacterial growth restriction. Collectively, our findings indicate that the galectin system is dispensable for protection against acute bacterial infection but necessary for full resistance to chronic tuberculosis infection.

Advisor: Cox, Jeffery S
Traffic Conflict Identification and Validation using Deep Unsupervised Learning

Surrogate safety measures can provide fast and pro-active safety analysis and give insights on the pre-crash process and crash failure mechanism by studying traffic conflicts. Traffic conflict can be identified by the presence of evasive actions or the amount of temporal (spatial) proximity measures like time-to-collision (TTC). However, it is not enough to use only one kind of measures in some scenarios and it is hard to set a threshold for those measures. Moreover, validating surrogate safety measures by connecting them to crashes is still an open question./nWe first study the problem of traffic conflict identification. We proposed a method to identify traffic conflict by learning the representation of TTC and driver maneuver profiles with deep unsupervised learning and clustering the representations into traffic conflict and non-conflict clusters. We first trained a transformer encoder to encode sequences of surrogate safety measures into some latent space with unsupervised pre-training. Second, we identified informative clusters in the latent space by calculating the statistic summaries and visualizing trajectory pairs of each cluster. Some clusters are interpreted as traffic conflict clusters because they have small TTC, large deceleration rate and intertwining trajectories and they can be further interpreted as rear-end or angle conflicts. Moreover, the identified traffic conflicts contain critical conditions from the two vehicles in an interaction and one vehicle perceives them as abnormal and takes evasive action to avoid crashes./nSecondly, we study the problem of traffic conflict validation. We proposed a method to connect surrogate safety measures to crash probability using probabilistic time series prediction. The method used sequences of speed, acceleration and time-to-collision to estimate the probability density functions of those variables with transformer masked autoregressive flow (transformer-MAF). The autoregressive structure mimicked the causal relationship between condition, action and crash outcome and the probability density functions are used to calculate the conditional action probability, crash probability and conditional crash probability. The predicted sequence is accurate and the estimated probability is reasonable under both traffic conflict context and normal interaction context and the conditional crash probability shows the effectiveness of evasive action to avoid crashes in a counterfactual experiment.

Advisor: Hansen, Mark
Grembek, Offer
High Throughput Computational Search for Lead-Free Piezoelectric Materials

Ab initio methods provide a powerful tool in the search for novel polar materials. In particular, there have been efforts to identify lead-free piezoelectric material systems to re- place PbZr0.52Ti0.48O3. This work first utilizes the Materials Project database of piezoelectric tensors to develop a design rule for disorder-tolerant piezoelectric materials based on the presence of multiple stable optical phonon modes which contribute to the polar response. This criteria is used to produce novel materials with large computationally predicted response. A methodology for exploring alloy systems utilizing a Vegard’s law-like linear interpolation of properties is then developed to capitalize on these identified disorder-tolerant materials. A parent ferroelectric Sr2Nb2O7 compound is chosen as a parent material for which all reasonable isovalent cation substitutions are considered and explored. Based on our approximations, thermodynamic analysis and density-functional theory (DFT) validation of the large scale system, Sr2Nb2−2xV2xO7 arises as a promising polar system. Sr2Nb2−2xV2xO7 is synthesized as single-crystalline thin-film heterostructures using pulsed-laser deposition and an enhanced dielectric response is observed at x = 0.05 and x = 0.1. This alloy and methodology are presented to aid the search for additional lead-free piezoelectric systems to replace PbZr0.52Ti0.48O3 (PZT). Finally we present a software package, Automated X-Ray Diffraction to Structure (AXS), to aid in the materials discovery bottleneck of characterization. The algorithm is based solely on symmetry considerations, and an efficient structure generation algorithm coupled with DFT. AXS is benchmarked against a diverse dataset of structures for which is solves 92% of cases and is also demonstrated to provide ground state structures for showcased experimental systems.

Advisor: Persson, Kristin A
Leveraging Speaker Context for Natural Language Processing

Neural networks have allowed for a host of advances in Natural Language Processing (NLP), from text classification to machine translation. These applications have demonstrated the ability to capture the effects of properties such as sentiment and politeness on language usage through computational means. However, using NLP to examine the effects of contextual information in relation to the intrinsic features of one's identity or the extrinsic features of one's conversational role is still an active area of research. This thesis focuses on modeling the effects of speaker attributes on language, looking at applications that are designed to help improve the safety of users in the digital world. Gender is a personal characteristic that people might not wish to share online but that can be determined by one's language use. We first examine how intrinsic speaker attributes affect language by attempting to obfuscate the gender of users on Reddit. Detecting deceptive actors in online interactions is also important for user security. We next explore the effect of extrinsic speaker attributes on language through the game of Mafia, in which participants may take on either an honest or a deceptive role. Through these analyses, we demonstrate that there are linguistic differences based on a person's role or identity, indicating that these aspects of an entity might be identified through their linguistic behavior. In addition to providing insight on how such entities use language in accordance with these features, these applications have implications for real-life communication paradigms, providing possible avenues for hiding aspects of one's identity or discovering aspects of another's.

Advisor: DeNero, John
Gating of Sec61 in Posttranslational Translocation Across the Endoplasmic Reticulum

The endoplasmic reticulum (ER) is a major site for protein biosynthesis, required for production of about a third of all proteins in eukaryotic cells. The heterotrimeric Sec61 complex is a protein-conducting channel which mediates the transport or membrane integration of a majority of the ER-targeted proteins, such as secretory proteins and membrane proteins. Using a sophisticated gating mechanism, Sec61 transports soluble amino acid segments through its vertical water filled pore, and integrates hydrophobic transmembrane helices laterally into the membrane. The Sec61 channel translocates client polypeptides either co- or post-translationally. The posttranslational mode requires the formation of a larger complex with the membrane proteins Sec62 and Sec63. Substrate translocation further requires the essential ER resident Hsp70, BiP. The post translational complex in fungal species is additionally associated with the non-essential proteins Sec71 and Sec72. The structure and mechanism by which these proteins activate Sec61 for transport of client proteins has been poorly understood./nUsing cryo-electron microscopy (cryo-EM), we determined the structures of several variants of the Sec61–Sec62–Sec63–Sec71–Sec72 complex (shortly, the Sec complex) from Saccharomyces cerevisiae and Thermomyces lanuginosus and showed that Sec62 and Sec63 induce opening of the Sec61 channel. The structures reveal that Sec63 is positioned in the back of Sec61 and interacts with Sec61 and Sec71-Sec72, while Sec62 is flexibly associated with the complex in front of the lateral gate. The J-domain of Sec63 is optimally positioned under the channel pore to bind BiP and enable efficient polypeptide translocation. Sec63 associates with Sec61 tightly through interactions in cytosolic, transmembrane, and ER-luminal domains. These extensive interactions pry open Sec61’s lateral gate. Association of Sec62 further opens the luminal end of the lateral gate causing dislocation of the small helix blocking the pore called the plug domain. A simultaneous disruption of the cytosolic and luminal interactions of Sec63 with Sec61 completely closes the channel and abolishes its binding with Sec62. The structures and molecular dynamics simulations suggest that Sec62 may also prevent lipids from invading the channel through the open lateral gate. Our studies show how Sec63 and Sec62 work together in a hierarchical manner to activate Sec61 for post-translational protein translocation.

Advisor: Park, Eunyong
Data-Driven Information-Optimal Computational Microscopy

Optical microscopes have been an indispensable tool in biology and medicine for over three centuries. Unlike their simple predecessors, contemporary microscopes often employ complex robotic automation and customized algorithms. In the past decade, advances in high-performance computer processors, the ease of collecting massive datasets, and machine learning have created many new possibilities for data-driven approaches to microscope control and image analysis.This dissertation covers the challenges and opportunities in modern microscopy. First, it shows how neural networks can be used to create microscopes that adapt to the samples they are imaging in real time. For example, this paradigm can be used to quickly focus microscopes using inexpensive hardware or visualize developing immune responses at large scales. Next, new open-source software that facilitates development of these and other microscopy techniques is presented. Next, it turns to how microscopes can make measurements of the intrinsic optical properties of cells, from which their biological function can be inferred. Development of techniques that do so requires comparing approaches on standardized datasets, and the creation of such a dataset containing hundreds of thousands of images of single cells is described. Finally, a new theoretical framework for modeling the information transmission of both microscopes and image-processing algorithms is introduced. This perspective provides a new set of engineering principles for microscopes and opens a range of new research questions.

Advisor: Waller, Laura
Listgarten, Jennifer
Designing Exercises to Teach Programming Patterns

As digital technologies continue to grow in importance and influence, the need for strong programmers continues to grow. ``Programming,'' though captured by a single word, actually requires the acquisition of many different skills. One such skill is identifying where and how to apply programming patterns --- reusable abstractions and organizations of code. This skill has been shown to be a distinguishing characteristic between experts and novices./nHowever, our current Computer Science classrooms offer scarce opportunities for students to deliberately practice patterns through exercises. The most common code practice tool is the Code Writing exercise; the goal it presents to students is to construct a program that matches certain outputs from given inputs. Code Writing exercises offer minimal affordances to guide students towards particular solutions, making it difficult for instructors to ensure students will practice a specific pattern./nThis work introduces three new exercises designed to support students in acquiring new patterns. In Faded Parsons Problems, students rearrange and complete lines of code to reconstruct a program. In Subgoal Decomposition exercises, students reconstruct the execution flow of subgoals across multiple files and languages, specifying where each subgoal fits in an architectural framework. In Data Flow exercises, students further concretize each subgoal by using code snippets to specify the data processing for which each subgoal is responsible. Each of these exercises were designed to be easy to adopt into existing curricula without significant changes, complementing existing Code Writing exercises. Studies with students revealed that each of these exercises addresses currently unmet needs in CS courses, that these exercises support students practicing patterns by allowing students to focus on the full solution, and that students want these exercises to be integrated into their courses. Additionally, a classroom-based study with 237 students found that Faded Parsons Problems are more effective than Code Tracing and Code Writing exercises at helping students acquire patterns while also improving their general code writing abilities. Finally, from the lessons learned in developing these exercises, five design goals are presented to motivate the creation of more exercises for new contexts.

Advisor: Fox, Armando
Hearst, Marti A