VictorJuiz/thesis huggingface dataset:
Reliability and stability in statistical and machine learning problems
Keywords: Statistics.

The increasing application of statistical machine learning techniques has substantial promise in diverse fields such as medicine, policy-making, and finance. Hence, very complex machine learning models are being built for use in automated decision-making protocols. However, with the growing sophistication of these models, they are often impossible to analyze statistically, and hence, the predictions made by such models often come with no guarantees. Furthermore, we often have data from heterogeneous sources, shifting distributions, or only have access to noisy or weakly supervised data (e.g., it may be expensive to obtain properly labeled data for a classification task). With such additional challenges, how can we trust the results we achieve when we apply machine-learned methods particularly in critical areas? Further, a shift in data generating distributions makes it challenging for statistical knowledge to generalize well. For instance, the performance of predictive models may drastically deteriorate when deployed on a new test set or a statistical finding from an experiment may not replicate in future experiments. Is it possible to identify situations where models or statistical findings are sensitive to changes in the data generating distributions? Can we suggest methods to transfer statistical knowledge more accurately in such situations? To address the above challenges, this dissertation focuses on developing methods that leverage conformal inference, distributional robustness, and causal inference. The efficacy of these methods are further demonstrated via extensive experiments including applications to real-world machine learning and statistical problems
Resolving Conformational Changes Within the Mu-opioid receptor via smFRET and its Correlation with G-Protein Activation
Keywords:  Department of Biology
Biology
single molecule Fluorescent Resonance Energy Transfer (smFRET)
Mu-opioid receptor (µOR)
G-protein Coupled Receptor (GPCR)
Nanopore Electroporation
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Thesis

The Mu-opioid receptor is a therapeutic target of great importance and underlies the site of action for opiates. These opiates can have adverse effects that are related to the ability of the Mu-opioid receptor to signal with a defined level of activity. Alternatively, opiates may cause a particular bias to produce favorable therapeutic goals such as analgesia and avoid side-effects such as respiratory depression that may lead to death. Here, we have purified and labelled the Mu-opioid receptor to study the change in conformation it undergoes when binding opiates. Conformational changes were measured via single molecule FRET (smFRET), a biophysical technique that reveals the functions, kinetics, and conformation dynamics of individual molecules by monitoring the energy transfer between two probes (i.e., a donor and acceptor probe). We identified ligand dependent changes in conformational states in real time and directly measured the conformational changes that the receptor undergoes when it interacts with its cognate G-protein. Additionally, we attempted to reinsert purified Mu-opioid receptor by nanopore electroporation into live cells to confirm its functionality in the cell membrane. This attempt laid the groundwork for an in vivo model required for future evaluation of ligand driven conformational effects. Continued work will use more sophisticated methods of single-molecule FRET to resolve the conformational states of the receptor and to characterize the effects that ligands of varying efficacies have on G-protein interactions.
Resonant photoelastic modulation for time-of-flight imaging with standard image sensors
Keywords: Electrical Engineering.

Imaging has played a pivotal role throughout human history, with various optical imaging systems invented over the last several centuries. Some of these inventions include the pinhole camera, the photographic film, charge coupled device sensors, and most recently CMOS image sensors. CMOS image sensors are the dominant image sensor in the present day, offering high performance at a low cost. Standard CMOS image sensors are designed to mimic human vision; they are 2D sensors capable of capturing the brightness and color per pixel. These sensors were primarily designed for humans to capture images, but there is a fundamental change recently in users of imaging systems. Significant developments in robotics and artificial intelligence in the last decade have enabled intelligent autonomous platforms. These platforms, similar to humans, require sophisticated imaging systems to interact effectively with their environment. The existing standard image sensors (2D sensors) are not the most effective sensors for these platforms; depth (3D) sensing provides significant value. Unfortunately, existing standard CMOS image sensors are unable to measure the depth dimension. Although custom-built 3D sensors are able to measure the depth axis, it would be a significant advance to adapt the already mature and high-performance standard image sensors to function as high-resolution depth sensors. In this work, we describe a method to convert a standard CMOS image sensor into a depth sensor by integrating it with a fundamentally new kind of free-space intensity modulator. We explain the design, fabrication, and working principle of this new free-space intensity modulator that we refer to as a longitudinal piezoelectric resonant photoelastic modulator. The optical modulator described in this work uses a simple fabrication process while exhibiting record-setting energy efficiency among resonant free-space intensity modulators operating in the megahertz frequency regime. The simple fabrication process and low-drive power required to operate the modulator allow for easy integration with any standard image sensor. We demonstrate proof-of-concept time-of-flight imaging experiments by integrating the fabricated modulator with a standard CMOS image sensor
Revealing the unforeseen role and sensitivity of anoxic protection in soil carbon cycling
Keywords: Earth System Science.

Increasing soil carbon (C) content promises to mitigate climate change and enhance soil fertility. Soil C content is determined, in part, by microbial respiration, which converts soil C into carbon dioxide. Carbon protection mechanisms represent processes and conditions that limit microbial respiration of soil C. Anoxic microsites, zones of oxygen depletion in otherwise oxic soils, are a recently recognized and under-studied soil C protection mechanism. In this dissertation, I use field and laboratory methods to determine the extent and contribution of anoxic microsites to soil C protection within natural and agricultural systems. In Chapter 2, I measure the dissolved oxygen content of soil porewater from California agricultural soils. I show that physical disturbance destroys anoxic microsites through enhancing oxygen supply to the smallest soil pores. In Chapter 3, I show that oxygen limitations constrain OM turnover in a Hawaiian rainforest soil, an environment where mineral content is presumed to be the dominant soil C protection mechanism. In Chapter 4, I examine a soil textural gradient at the Stanford Dish and demonstrate that anoxic microsites are particularly important for protecting C in coarsely textured soils. Finally, in Chapter 5, I use droplet digital PCR to quantify anaerobe DNA (a proxy for anoxic microsites) in soils from four long-term agricultural experiments across the continental United States. I show that anoxic microsites vary with soil properties, respond to management, and uniquely contribute to soil C stabilization within cropland soils. In sum, this dissertation reveals that anoxic microsites represent a vulnerability in the soil C stock but also an opportunity to enhance soil C storage
Revisiting the role of long-term potentiation in learning and memory
Keywords:  Department of Biology
Biology, Neurobiology, Long-term potentiation, Memory, Learning, Stx3
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Thesis

A central function of the nervous system is its ability to enable an animal to learn, defined as the process by which an animal’s behavior changes in response to experience. Long term potentiation (LTP) is a candidate mechanism for learning and memory in the nervous system because LTP, as a form of synaptic plasticity, has the hallmarks expected for a cellular process that enables an animal to learn. Prior studies have established strong correlations between LTP in the hippocampus, a critical center for learning and memory, and spatial learning in rodents. However, causally linking LTP with learning has proven to be difficult. The present study aims to test the relationship between LTP and learning and memory using a novel mouse line with a conditional knock-out (cKO) of Stx3. The Stx3 gene encodes a SNARE protein that was shown in prior studies to be essential for LTP. The mouse line used deletes Stx3 conditionally in excitatory forebrain neurons by crossing Stx3 cKO mice with CamKIIa-Cre driver mice (CamKIIa-Cre;Syntaxin-3fl/fl). Thus, the goal of the present study is to answer the question: Is LTP necessary for learning and memory? To determine the ability of Stx3 cKO mice to perform learning and memory tasks, this study uses spatial and associative learning behavioral assays. Additionally, to confirm that cKO of Stx3 does not have negative effects on neuronal health, this study quantitatively evaluates neuronal morphology. Analyses of neuronal morphology indicate that the cKO of Stx3 did not cause major effects on the architecture of the brain or the morphology of neurons. Furthermore, results from behavioral assays show that Stx3 cKO mice can perform learning and memory tasks similarly to wild-type mice, with no significant differences between the two groups. These findings suggest that LTP may not be necessary for learning and memory in mice. This disrupts the long-standing hypothesis that LTP is causally linked to learning and memory, indicating that another mechanism may be responsible. This work will contribute to our understanding of how the brain learns and offers the first step towards finding solutions for disorders of learning and memory.
Revoked Refuge: How Deported Cambodian American Refugees Negotiate the Contradictions Between Citizenship and Belonging
Keywords: Cambodian Americans
Refugees
Deportation
Return migration
Imprisonment
Immigrants
Southeast Asia
Text
Thesis

Since 2002, the United States has deported over 1,000 Cambodian American refugees back to Cambodia, with many of these refugees being from the 1.5 generation, having arrived in the US in the 1980s as children with few memories of Cambodia. Southeast Asian American studies scholars and immigration scholars have primarily focused on 1st and 2nd generation Southeast Asian refugees’ resettlement, incorporation into the US, and remembrance of historical trauma. This project seeks to understand the experiences of the 1.5 generation who called the US home but have now been deported back to Cambodia, the country they once fled from. How does this group understand their political and social membership to the US and to Cambodia given their deportation? Between June 2021 and January 2022, I conducted 8 one-time virtual interviews, 7 with deported Cambodian American refugees and 1 with a Laotian American refugee awaiting deportation. I asked questions about growing up in the US, life in Cambodia now, and thoughts on the criminal legal and immigrant control systems. I find themes of refugees’ continued movement between countries and internally within the US, statelessness and community exclusion, and hope in the ongoing search for a refuge. These results further our understandings of how the war in Southeast Asia, US policy, and Cambodian policy continues to impact the lives of these refugees and demonstrates the divide between political-legal and social membership to the US and Cambodia.
Rewiring of food webs in the anthropocene : measuring the effects of novel species interactions with environmental DNA
Keywords: Biology.

Global biodiversity is threatened by the anthropogenic restructuring of animal communities, rewiring species interaction networks in real-time as individual species are extirpated or introduced. Biodiversity and functionality are not only declining, but human-induced hybridization of wildlife and the shuffling of biomes are also becoming more common. Conservation science and adaptive ecosystem management demand more rapid, quantitative, and non-invasive technologies for robustly capturing changing biodiversity, quantifying species interactions, and measuring ecosystem function to protect remaining systems. Using both non-invasive DNA metabarcoding of diet DNA (dDNA) and network theory, the aim of this dissertation was to develop molecular ecological network analysis (MENA) as an ecosystem assessment tool to address these needs. These methods were first tested in a well-studied biological preserve (Jasper Ridge Biological Preserve, Stanford, CA), where they more rapidly, accurately, and effectively captured the broader biodiversity of the area in comparison to other methodologies (camera trap and soil eDNA), while reconstructing and unveiling the hidden complexity in trophic structure and interaction networks within the community. I then applied MENA in a hybrid ecotone in Garamba National Park, Democratic Republic of Congo to understand the consequences of two African elephant species hybridizing, the critically endangered Forest (Loxodonta cyclotis) and endangered Savanna elephant (L. africana). The aim was to determine how hybrid introgression may currently be shaping the ecology (diet selection and habitat use) and health of the individual elephant (gut endoparasites), while assessing a suite of demographic (age, sex), environmental (habitat type), and social (family vs bull groups) characteristics. A third of this population are hybrids and the admixed genetics influenced helminth composition and significantly reduced host-specific parasite infection. Garamba forest elephants and hybrid elephants have very similar ecologies (dietary composition and habitat use), however, this population of elephants is unique in diet and habitat use compared to other typically frugivorous forest elephant populations and might be fulfilling a vacant niche within the grasslands, maintaining ecosystem function. This research will hopefully equip conservation ecologists and managers with a powerful tool to measure biodiversity and species interactions to assess the integration and impact of novel species in the ecosystem, unveil hidden multitrophic interactions and community structure, and identify key and vulnerable species within a terrestrial system
Robot in-hand manipulation using roller graspers
Keywords: Mechanical Engineering.

This work describes the development of a class of dexterous robot hands that use steerable, continuously rotating fingertips to perform complex in-hand manipulation of grasped objects, a task that has alluded many widely used robot graspers. While a typical laboratory robot with a parallel-jaw gripper can perform basic pick-and-place tasks, they lack the fine manipulation proficiency needed for more demanding, precise and varied tasks. Inspired by the human hand, many research efforts are focused on creating and controlling human-like (anthropomorphic) hands in the hopes of duplicating human dexterity. One goal of these designs has been to enable robotic in-hand manipulation. Except for small motions, in-hand manipulation with these hands requires grasp-gaiting, a complex process where the fingers "walk" across the object, making and breaking contacts in order to propel the object to a desired pose, which can be an inefficient and difficult approach for a variety of tasks. So far, the fragility, cost, and complexity of these devices have precluded use outside of a laboratory setting. While we share with other researchers the goal of enabling in-hand manipulation, we achieve it by completely different means. We have developed a series of highly non-anthropomorphic grasping devices - the Roller Graspers - that use rotating fingertips to perform full six-degrees-of-freedom (DoF) in-hand manipulation on grasped objects. We do this by intelligently driving the fingertips across the object. The first Roller Grasper used steerable cylindrical fingertip rollers mounted on a pivoting linkage, for a total of three DoF for each of its three fingers. Using scripted motion control, it demonstrated the feasibility of our in-hand manipulation concept. The second and third versions used spherical fingertips which afforded better grasp stability and range of motion. These designs also supported our development of a hierarchical manipulation architecture that allowed the roller graspers to achieve autonomous in-hand manipulation. The manipulation architecture consisted of a sample-based high-level planner and a heuristic low-level policy that allowed the grasper to perform full 6-DoF manipulation of objects with a variety of shapes and sizes. The final version of our hand, called Tactile-Enabled Roller Grasper (TERG), incorporated a novel tactile sensing system that could extract the surface contour of a grasped object as well as the shear force applied at the contact location, even while the fingertips were rotating. This enabled more diverse and robust in-hand manipulation that was not possible in the previous generations
The role of hyaluronic acid in regulating inflammation and the immune response
Keywords: Microbiology and Immunology.

Hyaluronan (HA) is a disaccharide polymer consisting of glucuronic acid and N-acetyl glucosamine that dominates the extracellular space in the eukaryotic body. As part of the pericellular matrix (PCM), HA is extruded outward from the cell membrane in the mesh of proteins and sugars that make up the glycocalyx. This thick coat, consisting predominantly of HA, dominates the interface between a cell and its environment. HA plays an important role in cell communication within the extracellular space as well as tissue structure. In this thesis I will discuss the biological role of HA in chronic and acute disease. I focus on how dysregulation of HA production potentiates pathophysiology of three different inflammatory states: chronic asthma, type II diabetes, and solid organ transplantation. Moreover, I present investigations into 4-MU treatment to protect against inflammatory pathologies. Lastly, I present investigations looking at developing new interventions that can add to our repertoire of HA targeting therapies
The role of response modes in consumer judgment and choice
Keywords: Business.

Marketing research has predominantly focused on how and what consumers choose. However, most options considered by consumers are rejected (i.e., not chosen); yet, the study of how consumers make rejections has not received much attention. While choosing a good option and rejecting a bad option had long been regarded as nominally the same decision, a growing body of research has uncovered that these two response modes differ in their psychological processes and behavioral consequences. Recognizing this discrepancy between choice and rejection, my dissertation explores the impact of response modes on product evaluations and decisions, with a focus on contexts in which consumers make rejections
RRAM compute-in-memory hardware for efficient, versatile, and accurate AI inference
Keywords: Electrical Engineering.

Performing ever-demanding artificial intelligence (AI) tasks directly on the resource-constrained edge devices calls for unprecedented energy-efficiency of edge AI hardware. AI hardware today consumes most energy through data movement between separate compute and memory units. Compute-in-memory (CIM) architectures using resistive random-access memory (RRAM) integrated on the CMOS logic platform alleviate this challenge by storing AI model weights in dense and analog RRAM devices, and performing AI computation directly within RRAM, thus eliminating explicit weight memory access. To date, AI application-level benchmarks on fully integrated RRAM-CIM hardware have been limited in diversity and complexity. Meanwhile, the energy-efficiency benefit of CIM usually comes at the cost of functional flexibility and computational accuracy, hampering its practical use at the edge. Such trade-offs between efficiency, versatility and accuracy cannot be addressed by isolated improvements on any single layer of the design. As our attempts to ameliorate this fundamental trade-off, this thesis will present two RRAM-CIM chips that I led in design, tape-out, and test. The first chip contains a single RRAM-CIM core with 65K RRAM devices; the second chip, named NeuRRAM, integrates 48 RRAM-CIM cores with a total of 3M RRAM devices. The two chips integrate multiple innovations including a voltage-mode sensing scheme, a transposable neurosynaptic array architecture, and non-ideality-aware model training and fine-tuning techniques. By co-optimizing across all hierarchies of the design from algorithms and architecture to circuits and devices, the two chips simultaneously deliver a high degree of versatility in reconfiguring CIM cores for diverse model architectures, higher energy- and area-efficiency than prior arts across various computational bit-precisions, and fully hardware measured accuracy comparable to software models across diverse AI benchmarks, including MNIST and CIFAR-10 image classifications using convolutional neural networks, Google speech command recognition using a long short-term memory, and MNIST image recovery using a Restricted Boltzmann Machine
Rural and urban board members' identities and their attitudes toward representation in school policy and decision making
Keywords: Education Honors Program
School board members
School boards
Representation
Rural
Urban
Decision Making
Text
Thesis
Book

With over 90,000 school board members in the United States, school boards have been influential in the desegregation of schools. More recently, have influenced mask mandates in schools, banning of books, and policies regarding equitable education. Previous research has addressed the impact of direct representation and the positive influence that it may have on communities and students. Additionally, research shows how responsive and sensitive communities can be to school boards. Communities respond in elections, re-elections, and attitudes towards schools. My research asks about how rural and urban board members align their identities with their decisions on the board. Using qualitative data, I find that the most significant differences between urban and rural board members are the values they call upon to enact. These values are experiences as people of color and being a conservative religious person.
Sandlin Honors Thesis: Urban Indigenous Self-Expressionism from 1960s to Present and its Impact on Indigenous Identity
Keywords: Indigeneity, photography
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Thesis

This paper aims to study how urban North American Indigenous communities represent their identities and cultures; specifically, it will analyze how these practices have evolved since the rise of Native activism in the mid-20th century, catalyzed by the creation of the American Indian Movement (AIM) in the late 1960s. As there is little existing research in this field, especially by Indigenous scholars, my argument will be primarily circumstantial and will advocate for further development of the analysis. Furthermore, as my research is interdisciplinary, I will utilize multiple theoretical frameworks and contextual analyses to develop my argument and subsequent close visual analysis. More specifically, I will utilize the frameworks from the fields of visual studies and Native studies in formulating my work. I assert that AIM and the rise of Native representation has forced the American public to reconcile with contemporary self-representation which has subsequently shaped modern Native and urban Native identity.