VictorJuiz/thesis huggingface dataset:
Understanding and learning robotic manipulation skills from humans
Keywords: Mechanical Engineering.

Humans are constantly learning new skills and improving upon their existing abilities. In particular, when it comes to manipulating objects, humans are extremely effective at generalizing to new scenarios and using physical compliance to our advantage. Compliance is key to generating robust behaviors by reducing the need to rely on precise trajectories. Inspired by humans, we propose to program robots at a higher level of abstraction by using primitives that leverage contact information and compliant strategies. Compliance increases robustness to uncertainty in the environment and primitives provide us with atomic actions that can be reused to avoid coding new tasks from scratch. We have developed a framework that allows us to: (i) collect and segment human data from multiple contact-rich tasks through direct or haptic demonstrations, (ii) analyze this data and extract the human's compliant strategy, and (iii) encode the strategy into robot primitives using task-level controllers. During autonomous task execution, haptic interfaces enable human real-time intervention and additional data collection for recovery from failures. The framework was extensively validated through simulation and hardware experiments, including five real-world construction tasks
Understanding feature use divergences between human and machine vision
Keywords: Psychology.

Recent work has highlighted a seemingly sharp divergence between human and machine vision: studies have argued that, whereas people exhibit a shape bias, preferring to classify objects according to their shape (Landau et al., 1988; Geirhos et al., 2018; Kucker et al., 2019), standard ImageNet-trained CNNs privilege texture (Geirhos et al., 2018). How prevalent is this texture bias, and where does it come from? I will present evidence that, while both model architecture and training objective affect a model's level of texture bias, the statistics of the training data are the most important factor, and that naturalistic data augmentation schemes can ameliorate texture bias and improve generalization to out-of-distribution images. On the human side, existing studies have tested people under conditions different than those faced by a feedforward CNN; does the human—machine divergence remain when testing conditions are more fairly aligned? In experiments using brief stimulus presentations, we find that people do still privilege shape over texture. Even so, texture information plays more of a role than previously reported. This work establishes a new benchmark for assessing how "human-like" feedforward vision models are in their shape bias. Shape and texture are two features that are both useful in predicting an object's class. Zooming out, I will study models' treatment of such redundant features in a more general setting. Using synthetic data to explore which input features models learn as a function of their task relevance and difficulty of extraction, we find that CNNs are vulnerable to "feature blindness", privileging a single useful feature even when two features perfectly and redundantly predict image labels. Which of the features is privileged is predictable from the untrained model. Finally, I will discuss challenges and open questions that remain in the quest to build models with human-like visual representations
Understanding microbial landscapes : biogeography of plant-fungal interactions in the face of warming and wildfire
Keywords: Biology.

Understanding the distribution and abundances of species across the globe has long been a focus of the ecological field. While these patterns have been easily observed for plants, animals, and macrofungi, recently developed molecular tools have revolutionized our ability to characterize the biogeographies of microorganisms. In this dissertation, I use field surveys, greenhouse experiments, and modeling to explore spatial patterns of plant-fungal interactions, attempting to understand the unseen microbial landscape and its response to global change stressors like warming and wildfire. In the first chapter, I illustrate how microbial traits like uni- vs multicellularity affect regional patterns of fungal and bacterial biogeography across many biome types in Minnesota. In the second chapter, I explore changes in the composition of the mycobiome hosted by trees in the genus Populus across a continental range of environments and how these distributions might change in future climate regimes. In the third and fourth chapters, I show how wildfire changes the composition of soil microbial communities in coast redwood forests, and how these shifts in microbial composition affect seedling growth and competitive ability with potential for impacts on long term forest composition. Taken together, these chapters use multiple approaches to investigate the spatial heterogeneity of plant-microbe interactions and what this could mean for forest composition and function
Understanding Parler: Exploring the Evolution of an Unmoderated Platform and the Effect of Deplatforming on Extremist Communities
Keywords: Law (CDDRL)
parler
Radicalism
Social media
Right-wing extremists
Social media > Influence
Trump, Donald, 1946-
QAnon conspiracy theory
Text
Thesis

Extremist and violent content on social media poses an existential threat to the fabric of our democracy. Social media has democratized information dissemination, enabling a single message to reach millions of individuals in an instant. Individualized news feeds contribute to growing polarization, delegitimizing verified sources of news, and threatening fundamental democratic processes. Yet not all social media platforms propagate such harmful information in the same way.
Parler, a social media platform, differentiates itself from others due to its lack of content moderation. Drawing a base of right-wing users, Parler rose to national prominence throughout 2020, boasting nearly 20 million users by December 2020. Parler’s success reflected a dangerous and growing trend of unease with Trump’s loss. The platform was partially blamed for the insurrection on January 6th and was taken offline shortly after by Amazon Web Services, with Google and Apple removing it from app stores. Parler represents a unique case study of a harmful online community growing at a rapid pace. In addition, Parler’s deplatforming sparked a national debate concerning best practices to ameliorate the hate extremist groups spread while simultaneously protecting free speech. Investigating both the evolution of Parler and the effectiveness of its deplatforming allows for important insights into this radical community and the connection between social media and democracy at large.
QAnon, a dangerous and hateful conspiracy theory, was able to grow exponentially on Parler. This content found a home on Parler and skyrocketed in popularity as more users flowed to the platform. Parler has provided a home for radical and hateful rhetoric, allowing for content banned on other platforms. As more and more users joined Parler, the content reflects rising hatred and violence. Studying Parler’s evolution makes it inextricably clear that it is inherently linked with Trump. Trump remains popular on the platform throughout its lifespan, heavily influencing the content and rhetoric.
Deplatforming as a potential solution to limiting the power of radical communities is an intricate balance. While deplatforming succeeded in reducing the number of users and the volume of content on the platform, core users with a greater number of posts returned in higher numbers. While hosting services may have succeeded in reducing the general appeal of Parler, dedicated right-wing users were not dissuaded and returned to regularly post. Web hosting services hold tremendous power in the arena of controlling hate. In order to properly exercise this power, they must proceed with caution and a principled approach.
Understanding plant drought adaptation through genetic trade-offs with Arabidopsis thaliana
Keywords:  Department of Biology
Biology
Adaptation (Biology)
Plants, Flowering of > Flowering time
Avoidance
Escape
Water Use Efficiency
Climatic changes > Research
Text
Thesis

With consistently rising global temperatures and lower rainfall, drought conditions are becoming increasingly severe, and plants are compelled to either migrate, adapt, or face extinction. Two well-known adaptation strategies to drought are escape, where plants shift their phenology (e.g. earlier flowering) to have important life history events occur before the drought, and avoidance, where plants utilize mechanisms to improve their water use efficiency (WUE) (e.g. stomatal regulation). Previous studies have found that early flowering plants have a lower WUE than late-flowering plants, and plants under drought conditions typically select for escape. However, unpublished data from Expósito-Alonso et al. 2019 found that Arabidopsis thaliana from a dry climate of origin and grown in drought conditions trends towards having a higher fitness with both early flowering and high WUE. To expand on this study, we used a CRISPR-mediated knockout of the early flowering Flowering Locus C (FLC) gene. We grew 47 A. thaliana and 47 mutated lines in three different drought conditions. To evaluate the escape-avoidance trade-off, we took several phenology and fitness measurements as well as using WUE and flowering time data from previous studies done on the same ecotypes of A. thaliana. Our results show that, in well-watered conditions, earlier-flowering founder plants had a higher fitness than late-flowering plants, and late-flowering plants had greater WUE, as expected. In well-watered conditions, plants with the flc KO had a significant positive correlation between fitness and WUE, as well as between fitness and flowering time. Therefore, we found that there could be selection for both early flowering and high WUE when flowering time is held constant, breaking the escape-avoidance trade-off. Ultimately, understanding drought strategies and their trade-offs in the natural world will help inform predictions in plant adaptations and distributions under worsening climate change.
Understanding structure-property relationships in thermoelectric materials
Keywords: Mechanical Engineering.

Thermoelectric materials have the ability to convert thermal energy into electrical energy. Successful development of thermoelectric technologies could have a significant impact on the current energy landscape, where much of the energy consumed is wasted as low-grade heat. However, progress in increasing the efficiency of thermoelectric materials has been hampered by the difficulty of optimizing competing material properties, such as thermal conductivity, electrical conductivity, and carrier concentration. Each of these properties is fundamentally driven by the underlying material structure, with defects and dopants playing an especially important role. So in order to optimize a material's thermoelectric properties, it is first necessary to understand the material's crystal structure. In this work, I focus on understanding the structure-property relationships in a specific thermoelectric alloy material, Cu2xHg2−xGeTe4, which has proven to be an interesting case study in the field of thermoelectrics. To analyze the structure side of the structure-property relationship, I have primarily relied on the use of a technique known as resonant energy X-ray diffraction to provide a holistic view of the material structure and to quantify the presence of anti-site defects. Cu-Hg anti-site defects are found to be the dominating force in controlling the material's carrier concentration. The effect of these Cu-Hg anti-site defects is also explored using inelastic neutron scattering, which provides insight on the phonon density of states of a material. The results indicate that Cu-Hg anti-site defects not only control the carrier concentration of Cu2xHg2−xGeTe4, but also impact the behavior of phonon dynamics within the system. In turn, this has implications on other relevant thermoelectric properties, such as the lattice thermal conductivity. The findings and processes established in this dissertation can contribute to future efforts for characterizing structure-property relationships, not only in the field of thermoelectrics, but for semiconductors in general
Understanding the role of catalyst structure in facilitating sustainable chemical production
Keywords: Chemical Engineering.

The catalytic transformation of carbon dioxide into syngas and subsequently into higher oxygenate products is one route for meeting the rising global demand for energy and carbon-based consumer products. However, even the most optimized materials have insufficient performance for industrially scaling this process. Because catalyst performance is closely related to its structure, identifying the relationships between the structure of promising catalysts and their behavior under reaction conditions can make it possible to improve and optimize materials that achieve a sustainable future. This dissertation builds this fundamental understanding of catalyst surfaces across several systems of interest. We first bridge catalysis science subdisciplines by investigating a Ni-N-C catalyst, which is known as promising material for electrochemical carbon dioxide reduction to carbon monoxide. We explore the same catalyst for the analogous, thermally driven reverse water-gas shift reaction to generate novel insights on the active site for carbon dioxide reaction in this cross-compatible material. We then apply atomic layer deposition (ALD) of ZnO to uniformly promote Co nanoparticles supported on different materials to induce Co2C formation and understand the effect the support has on cobalt carbide performance. Across three of the four tested supports, we observe different extents of cobalt carbide formation and, subsequently, different oxygenate selectivities during syngas conversion. In contrast, interactions between the ZnO and an aluminum oxide support prevent the ZnO from modifying the Co active phase, highlighting the importance of competing promoter-support interactions that can inhibit the desired catalytic behavior. We finally apply a similar ALD procedure to modify a Rh catalyst with iron oxide ALD and discover that the addition of iron oxide improves methanol and ethanol formation during syngas conversion. In-situ experiments reveal that this change in reactivity is due to the formation of clusters of Fe atoms on the surface of the Rh nanoparticles during catalyst activation, which enhance the intrinsic activity of non-dissociative CO adsorption sites. The insights uncovered through each of the projects were made possible by relying on precise synthesis methods to minimize the potential variables that can impact catalyst performance and on in-situ characterization to fully understand the properties of each material. These tools enable us to develop a stronger fundamental understanding of the intrinsic characteristics of catalysts and can advance our ability to make materials that have sufficient performance for widespread implementation
Universal analog computation on programmable nanophotonic integrated circuits
Keywords: Electrical Engineering.

Universal programmable nanophotonic meshes, or networks of Mach-Zehnder interferometers, are an exciting new avenue to arbitrarily shape light beams for energy-efficient chip-scale linear optical matrix multiplication. Applications include sensing, imaging, LIDAR, quantum computing, cryptography, and machine learning. Such devices can be scalably manufactured commercially in semiconductor foundries but can suffer from manufacturing and other systematic errors. In this thesis, I discuss both theoretical and experimental contributions for programmable optical meshes to address these concerns. First, I introduce a new class of binary tree meshes that are more error tolerant than currently proposed architectures. To prove this error tolerance formally, I explain my architecture-dependent error sensitivity theory relating individual component errors to overall system performance. I furthermore discuss several calibration and configuration algorithms aimed at reducing these errors, including self-configuration and "parallel nullification" that minimizes programming time of any feedforward photonic mesh network. Next, I present my design and implementation of a fully packaged 6 x 6 programmable "triangular mesh" outfitted with an experimental optical rig setup in our lab and explore its use as a matrix multiply accelerator for machine learning. As a key application, I demonstrate in situ backpropagation training (the most popular and widely used gradient-based training algorithm for machine learning) directly through optical measurement for the first time on our chip, which agrees well with digital simulations of the training process. Finally, I apply the same chip to study new photonic cryptocurrency and blockchain applications by exploring a paradigm shift: implementing discrete-valued matrix multiplication in a photonic mesh for robust and energy-efficient digitally verifiable computation. In this vein, I propose LightHash, a hash function that incorporates photonic computation into the Bitcoin protocol to secure cryptocurrency transactions. I analyze component error scaling to overall LightHash error rates with size and bit depth of our photonic matrix multiplier and demonstrate new error correction protocols to minimize such error. These experimental achievements coupled with my new theoretical framework could have significant and lasting implications on photonic integrated circuits, programmable optics, major tech sectors for artificial intelligence and cryptography, and beyond
Universality for some random growth models
Keywords: Mathematics.

This thesis studies the problem of deriving fluctuations of hydrodynamic limits for general time-dependent microscopic systems. To this end, we develop a derivation of the Boltzmann-Gibbs principle depending only on the dynamics of the system at hand. This provides an answer to conjectures of Spohn and Jensen-Yau. As an application of the method, we derive the Kardar-Parisi-Zhang equation as a continuum limit of the current fluctuations in some non-integrable and non-stationary asymmetric exclusion processes
Unmapped or untapped? Leveraging the unmapped read space of whole genome sequences to understand the human genome, virome, and contaminome
Keywords: Bioengineering.

Whole Genome Sequencing (WGS) has become an incredibly popular method to understand the genetic contributors to human health and disease. In a typical WGS pipeline, millions of sequencing reads are aligned to the human reference genome, in order to identify potential variants linked to disease. However, in most WGS pipelines between 1-10% of these reads will not align to the reference genome, because they come either from non-human genomes or from diverse human sequences not well-represented on the current single reference genome. These reads, known as the unmapped read space, are often discarded in downstream analysis, despite their potential role in human health. Using WGS from over 1,000 nuclear families, I interrogate the three contributors of the unmapped read space - bacteria, viruses, and non-reference human sequences - in order to better understand these important components of human genomic and metagenomic diversity. In this thesis, I present my findings from studying these contributors to the unmapped read space. (1) I identify common blood DNA viruses and their transmission patterns, including never-before-seen integration and latency mechanisms of Human Herpesvirus 6B and 7 in lymphocytes. (2) I characterize signatures of experimental and computational contamination that may pervade WGS pipelines. Of interest to the field of metagenomics, the ladder includes Y chromosome fragments that frequently mismap to bacterial references. (3) Finally, in order to identify non-reference human DNA sequences and localize them to their appropriate location on the human genome, I present a maximum-likelihood model algorithm, ASLAN - an Algorithm for Sequence Location Approximation using Nuclear families. After validating ASLAN to show its high accuracy and resolution, I use ASLAN to localize unmapped sequences from WGS to the human genome. I compare these localizations to alignments to the recently released CHM13 genome (the first assembly of a full human genome done by the Telomere-to-Telomere consortium). I identify potentially problematic regions on this assembly set to become the new human reference genome, as well as characterize hotspots for genetic diversity
Unraveling fundamental questions of polyketide synthases through biochemical and structural studies
Keywords: Chemical Engineering.

A good test of one's understanding of an enzyme's structure-mechanism relationships is the ability to engineer the enzyme to exhibit a new property. Through a deeper understanding of active site structures, several enzymes have been engineered however, assembly-line polyketide synthases (PKSs) have been relatively resistant to improvement via engineering. These PKSs are large multifunctional and multimodular enzymes that are responsible for the biosynthesis of many medicinally relevant compounds. The co-linear biosynthetic mechanisms of assembly-line PKSs have inspired researchers to "mix-and-match" domains and modules to make novel natural products. Unfortunately, in most cases, the yields of these new products are often very low. It is generally believed that improvements in the catalytic activities of these hybrid enzymes would depend on an improved understanding of critical protein-protein and protein-substrate interfaces and interactions. In this thesis, two PKSs (NOCAP and DEBS) were studied through biochemical and structural analysis to gain insight into fundamental questions of how these enzymes operate
Unraveling the immunological ecosystem of human tuberculosis
Keywords: Immunology.

Outcomes in tuberculosis (TB) infection are thought to be driven in part by underlying differences in the phenotype and structure of cells in granulomas, the histological hallmark of TB. However, high-dimensional phenotypic and spatial analysis of TB granulomas has not been conducted. Here, we utilize multiplexed ion beam imaging by time of flight (MIBI-TOF) to construct a map of the composition and architecture of human TB granulomas. MIBI-TOF employs mass spectrometry to image metal-conjugated antibodies, allowing multiplexed single-cell phenotyping that retains the spatial organization of complex tissues. We applied MIBI-TOF to a cohort of clinical specimens from patients with TB disease, revealing the complex single cell composition of granulomas, including a spectrum of myeloid subsets and a highly ordered structure of protein expression. We then used a spatial adaptation of the machine learning algorithm, Latent Dirichlet Allocation (spatial-LDA), to identify cellular microenvironments indicative of spatially coordinated immune responses in TB. This revealed an immunosuppressed microenvironment with spatially coordinated co-expression of IDO1 and PD-L1 by myeloid cells, proliferating regulatory T cells, and high levels of TGFB along with a near absence of IFNg and markers consistent with T-cell activation, supporting a myeloid-mediated mechanism of immune suppression. We observed similar trends in gene expression of an immunoregulatory program in a confirmatory transcriptomic analysis of peripheral blood collected from over 1500 individuals with TB infection and healthy controls across 29 cohorts spanning 14 countries. Overall, this first-of-its-kind systems analysis of human granulomas raises the possibility of myeloid-mediated immune suppression within the granuloma and serves as a framework for leveraging independent cohorts and complementary methodologies to understand how local and systemic immune responses are linked in human health and disease