VictorJuiz/thesis huggingface dataset:
Extensions to Stimulated Raman Scattering Microscopy For The Study of Dynamic Metabolic Processes in Live Cells

The fundamental properties of the stimluated Raman scattering (SRS) interaction make it attractive for application in biological microscopy. The signal is linear in both the concentration of scatterers, as well as the power of both excitation sources used. In turn, this allows for quantitative concentration measurements to be made within a sample. Furthermore, the SRS spectrum, an exact match to the spontaneous Raman spectrum, is chemically specific. I.e., individual molecules can be specifically and uniquely identified by their spectra. As a result, in principle, SRS microscopy allows for quantitative label-free measurements to be made of biological molecules./nIn practice, however, outside of a few applications, SRS microscopy is neither widely adopted nor applied in biology. It is often thought that the specificity of fluorescence cannot be achieved in a label-free manner with SRS due to the complexity of the biological systems under investigation. Additionally, as the standard implementation of SRS microscopy requires laser scanning for the formation of an image, fundamental limits on acquisition speed are thought to make the application of SRS microscopy to the study of dynamic processes challenging, if not infeasible./nThe following dissertation presents new experimental and computational methods to broaden the applications of SRS imaging within the life sciences. An overview of the fabrication of the standard SRS imaging system is provided, with attention to the implications of certain design choices. This helps establish the limits within which the SRS microscopy technique currently operates, as well as make the technique more accessible to a general audience. Best practices for performing measurements and analyzing the results are presented. The application of SRS microscopy to the imaging of small molecule neurotransmitters, in a specific manner, is demonstrated, highlighting the capability of SRS microscopy to achieve of the specificity of fluorescence imaging. Finally, extensions to the standard imaging system are presented which lay the groundwork for future applications with faster acquisition speeds. In aggregate, the work presented in this dissertation serves to extend the range of problems to which SRS microscopy can be applied in biology.

Advisor: Landry, Markita
Streets, Aaron
The Discovery of Novel Bioactive Small Molecule Natural Products Through Metabolomic Method Development and Genome Mining

Natural products have been utilized by humans since the beginning of recorded history. Some of the first documented uses of these compounds comes in the form of plant-based extracts and mixtures of various poultices for a wide range of medicinal uses. Natural product small molecule discovery serves as an essential pipeline for novel therapeutics, improves our understanding of human disease, and supplies innovative chemistries for the synthesis and derivatization of compounds. This work will be split amongst the development and application of natural product discovery methodologies (Chapters 2 and 3) and the combination of existing methodologies to discover molecules pertinent to disease (Chapters 4 and 5). Each of these chapters will deal with the discovery, identification, purification, and characterization of small molecules with a key focus on metabolomics as a focal tool.

Advisor: Zhang, Wenjun
Design of Impact-Resistant Tensegrity Landers

Modern emergency response operations increasingly use remote ground-based sensors to provide real-time situational awareness for first responders. However, human users often must personally enter a dangerous situation to place the sensors. The seamless deployment of such sensors without human intervention allows for faster, wider distribution at minimal risk to human lives. This work presents a systematic approach to the design of impact-resistant tensegrity landers capable of autonomous high-altitude aerial deployment of critical sensor payloads in disaster scenarios./nTensegrity structures are derived from the concept of “tensile integrity” and are composed of rigid rods suspended by elastic cables in a tension network. Their lightweight and flexible properties protect the sensor payload from landing impacts at high velocities. Design equations and simulation models are first used to inform the hardware design of the tensegrity structure and provide upper-bound estimates of viable tensegrity lander designs. A comprehensive range of drop test experiments are conducted to validate the impact behavior and demonstrate the impact-resistance in the highest-altitude drop test experiments of tensegrity landers to date. The experimental results are leveraged to propose empirical scaling laws for the rapid and efficient design of future generations of tensegrity landers. Furthermore, two new tensegrity lander designs, an asymmetrically-weighted structure and a badminton-inspired structure, augment the tensegrity with self-stabilizing functionality to control for the landing orientation of optimal impact mitigation as indicated by the drop test experiment data. The results reveal further opportunities to optimize the design of the structure by aligning the stiffness of the structure with the desired landing orientation. Such a tailored structure can have motorized cables decoupled from the impact event that can then be used to provide locomotion for an ideal hybrid lander-rover tensegrity system./nOverall, the work contributes design methods, reorientation capabilities, and validated hardware prototypes to the grand challenge of tensegrity robotics: demonstrating a fully autonomous tensegrity robot capable of safely navigating through unstructured terrain.

Advisor: Agogino, Alice
Isolated photon-jet correlations in 5.02 TeV Pb-Pb collisions with ALICE

Jets correlated with isolated photons are a promising channel to study jet quenching in heavy-ion collisions, as photons do not participate in the strong interaction and therefore constrain the Q^2 of the initial hard scattering. We present isolated photon-jet correlations measured in Pb–Pb collisions at sqrt(s_NN) = 5.02 TeV by the ALICE collaboration. We study correlations of isolated photons with 28 < pT < 40 GeV/c with charged-particle jets with pT > 10 GeV/c reconstructed with the anti-kT algorithm and report the azimuthal correlation and pT asymmetry. The correlations probe the lowest jet pT range ever measured at LHC energies, and larger modifications due to the QGP are expected in the lower pT regime. We see no trend in the away-side yield with centrality, but do see a change in the distribution of the pT asymmetry consistent with increasing jet energy loss in more central collisions.

Advisor: Jacak, Barbara
Common Era records of Santa Barbara Basin benthic foraminifera reveal nineteenth and twentieth century shifts in reproductive life history, body size, and community structure

Past ecological information is critical for contextualizing the rapid, decadal to centennial-scale climate changes characteristic of novel human regimes—such as colonialism, industrialization, and urbanization. Yet these changes occur over 1-100 year “invisible timescales,” making them difficult to resolve with traditional biological and paleontological methods. In this dissertation, I use a highly temporally-resolved fossil record from an extraordinary system, benthic foraminifer fossils preserved in the marine varves of the Santa Barbara Basin (SBB), to examine the short-term impacts of environmental shifts on individuals and communities over the Common Era, an interval that includes both environmental stasis and rapid change./nHere I use the largest dataset of benthic foraminifera images and morphometric measurements generated to date to assess trends in life history reproduction, intra- and interspecific body size, and community-level diversity and abundance. In Chapter 1, I introduce benthic foraminifera from the SBB as an extraordinary system for high-resolution paleoecology. In Chapter 2, I detail the high-throughput imaging method I employ to produce a dataset of over 20,000 foraminifer images and measurements. In Chapter 3, I use these data to document the reproductive life history of biserial Bolivina foraminifera from ~50 CE to 2008 CE to examine the range of natural variation in reproductive mode and how reproduction is correlated with environmental variables. In Chapter 4, I apply a ~760-year-long dataset of individual measurements to characterize connections between intraspecific and community body size and whether size is modulated by life history and environmental variation. In Chapter 5, I undertake a multivariate analysis of diversity, biomass, and environmental data to assess how benthic foraminifer communities are structured from ~1834-2008 CE. Finally, I synthesize my findings in Chapter 6 and outline a vision for how paleoecology and the historical sciences more broadly can be in conversation with other disciplines to better understand the impacts of social and ecological change on the ocean./nI find that all aspects of ecology examined—life history, body size, abundance, and community structure—undergo state changes in the 19th and 20th centuries. The timing of change corresponds to major shifts in human-environment interactions that accompanied the colonization and industrialization of California. Taken together, these findings suggest that not only do SBB benthic foraminifera communities change towards the present day, but that modern communities are more changeable than those of the past, demonstrating heightened variability in individual characteristics that have species- and community-scale ecological consequences.

Advisor: Finnegan, Seth
A Gram-Scale low-Tc Low-Surface-Coverage Athermal-Phonon Sensitive Dark Matter Detector

In recent years, the dark matter direct detection community has become increasingly interested in dark matter below the mass scale of the WIMP. Often called `low mass dark matter' or `light dark matter', this refers to a collection of models for fermionic dark matter with masses typically in the range eVGeV. Due to the slow relative velocity of the dark matter in the local halo, the dark matter is poorly kinematically matched to typical detector targets, leaving a detectable amount of energy that is orders of magnitude below the current state-of-the-art detector technology./nIn this thesis, I will discuss the details of the athermal phonon sensor mediated detector technology used by the SuperCDMS and SPICE/HeRALD collaborations. I will motivate how this technology can be used to reach detector baseline energy thresholds of order meV. I then use these concepts in the design of the SPICE MELANGE detectors - the initial prototype dark matter detectors for the SPICE/HeRALD collaboration, with baseline energy resolutions expected to be sub-100 meV. I present the characterization from the testing of the initial fabrication on Si substrates. These Si versions are expected to be able to explore nuclear recoil dark matter parameter space for masses of order MeV - GeV. A future fabrication on Sapphire is planned, which will extend this mass range down easily into the keV range./nA major hurdle in the realization of ultra-low noise detector technology is the fact that the sensitivity to noise and backgrounds also increases. As such, much of this thesis is also dedicated to the characterization of ultra-sensitive cryogenic calorimeters and analysis of noise - from both intrinsic and environmental sources.

Advisor: Pyle, Matt C
From sim-to-real: learning and deploying autonomous vehicle controllers that improve transportation metrics

The recent wide availability of semi-autonomous vehicles with distance and lane keep capabilities have created an exciting opportunity to improve the throughput and energy efficiency of the highway by deploying modified control strategies. However, even at current penetration rates, the optimal mechanism for the design of these decentralized, cooperative strategies is an open problem. In this work, we use Multi-Agent Reinforcement Learning (MARL) to investigate, design, and deploy cooperative autonomous vehicles (CAVs) to achieve these goals and demonstrate a field deployment of an RL-based traffic smoothing controller. We focus on multi-agent reinforcement learning as a mechanism for handling the complexity and non-linearity of large-scale traffic. We start by constructing a standardized suite of benchmark tasks for evaluating the efficacy of learning algorithms in designing controllers for CAVs; we evaluate these algorithms in the centralized setting where all CAVs are actuated by a single controller. We then extend one of these benchmarks, regulation of the inflow to a bottleneck via decentralized CAVs, to the multi-agent setting. We demonstrate that from both low to high penetration rates, CAVs are capable of improving the throughput of a scaled model of the San Francisco-Oakland Bay Bridge and investigate challenges in scaling our methods in open-network settings where vehicles can enter and exit the system. In preparation for a road test intended to demonstrate stop-and-go wave smoothing on large scale networks, we next study energy optimization of a full-scale model of a section of the I-210 in Los Angeles. Using Proximal Policy Optimization with an augmented value function we demonstrate that we are able to sharply improve the miles-per-gallon of the system and that the resultant controller is robust to likely variations of the system such as system speed and CAV penetration rate. However, we observe that the resultant waves are very unrealistic and additional calibration using higher resolution data is needed. With the goal of designing a more calibrated simulator, we pursue two approaches: one approach focuses on designing new driver models using available data-sets from Waymo and another approach focused on the use of collected data from the field deployment site. In the first approach, we design a new simulator that 1) efficiently represents the partially observable view-cone of human drivers and investigate whether learning safe driving policies in the simulator yields human-like behavior 2) serves as a challenging MARL benchmark. We observe promising signs of human-similarity from agents trained in the simulator. In the more direct approach, we collect data from the deployment site and use it to design a new, simplified simulator capable of using the collected data while maintaining a high simulation speed. We design energy-improving CAVs in this simulator and demonstrate that these CAVs can be successfully and safely used in a field deployment test.

Advisor: Bayen, Alexandre
Opportunities in the labor market: the role of apprenticeships and entrepreneurship

This dissertation studies studies issues related to opportunities in the labor market, with a focus on the Brazilian context. The first chapter studies apprenticeships during high school, which are labor market contracts that are comprised of vocational education and on-the-job training. The second chapter examines how the race of firm owners matters for the sorting, wages, and entrepreneurship opportunities of white and nonwhite workers. The third chapter documents disparities in startup capital between female and male-founded businesses, relating these disparities to firms' outcomes and hiring practices.

Advisor: Finan, Frederico
Multipartite Entanglement in Rabi Driven Superconducting Qubits

In harnessing quantum advantages for computation, there is a need for developing high fidelity operations on qubits. An algorithm can be broken down into single qubit operations and multi-qubit entangling gates. However, as the leading quantum processors today are limited to 50-100 qubits and each qubit is sensitive to decoherence noise (often referred to as NISQ era devices), running algorithms with long gate depth is difficult. Understanding the errors that plague existing gates and also expanding the dictionary of available gates is an important part of building a quantum computer. In this thesis we demonstrate two multiqubit gate experiments./nIn the first experiment we demonstrate a multiqubit entangling gate for superconducting qubits on an all-to-all connected processor that draws upon the advantages of Rabi driven qubits. We also take inspiration from the ion qubit community by using a Mølmer-Sørensen-like interaction through the use of a shared coplanar waveguide (CPW) resonator driven superconducting qubits. We perform sensitivity analysis to understand the parameters that limit our gate fidelities./nIn the second experiment we introduce and demonstrate a technique for scalable RB of many universal and continuously parameterized gate sets, using a class of circuits called randomized mirror circuits. The technique can be applied to a gate set containing an entangling Clifford gate and the set of arbitrary single-qubit gates, as well as gate sets containing controlled rotations about the Pauli axes. We use our technique to benchmark universal gate sets on four qubits, including a gate set containing a controlled-S gate and its inverse, and we investigate how the observed error rate is impacted by the inclusion of non-Clifford gates. We also show that our technique scales to many qubits with experiments on a 27-qubit IBM Q processor. We use our technique to quantify the impact of crosstalk on this 27-qubit device, and we find that it contributes approximately 2/3 of the total error per gate in random many-qubit circuit layers.

Advisor: Siddiqi, Irfan
Unpacking the evolution of thermotolerance in Saccharomyces yeast

A key goal of evolutionary biology is understanding how organisms build new traits. Many adaptations that distinguish taxa evolved so long ago that they are separated across species barriers, and many of these traits also have a complex genetic basis. In this thesis, I have leveraged advances in tools for interspecies genetics to illuminate the genetic underpinnings of thermotolerance evolution in Saccharomyces yeast, as a case study. We sought to improve the statistical power of screens to detect genetic determinants of this interspecific trait, both through computational advances in data processing and further development of the screening methodology. We wanted to know what traces of the putative ancient selective sweep(s) on thermotolerance factors might linger in the genomes of modern yeasts. We were also curious how initial conditions the evolutionary landscape might have differed from those further along during the evolutionary acquisition of thermotolerance. In Chapter 1, we introduce the field, and the questions within the field that we studied specifically. In Chapter 2, we investigate the population and comparative genetics of thermotolerance loci, as well as the mechanisms by which one key allele contributes to the phenotype. In Chapter 3, we use a barcoded version of the reciprocal hemizygosity analysis via sequencing screening technique to identify dozens of candidate genes involved in the thermotolerance phenotype. In Chapter 4, we delve into thermotolerance across a range of temperatures and discuss models for the early evolutionary landscape of the thermotolerance divergence. Lastly, in Chapter 5, we use directed evolution to gain insight into adaptations that allow a thermosensitive Saccharomyces yeast to acquire thermotolerance.

Advisor: Brem, Rachel B
Dueber, John E
Learning Transferable Representations across Domains

Deep neural networks have achieved great success in learning representations on a given dataset. However, in many cases, the learned representations are dataset-dependent and cannot be transferred to datasets with different distributions, even for the same task. How to deal with domain shift is crucial to improve the generalization capability of models. Domain adaptation offers a potential solution, allowing us to transfer networks from a source domain with abundant labels onto target domains with only limited or no labels./nIn this dissertation, I will present the many ways that we can learn transferable representations under different scenarios, including 1) when the source domain has only limited labels, even only one label per class, 2) when there are multiple labeled source domains, 3) when there are multiple unseen unlabeled target domains. These approaches are general across different data modalities (e.g. vision and language) and can be easily combined to solve other similar domain transfer settings (e.g. adapting from multiple sources with limited labels), enabling models to generalize beyond the source domains. Many of the works transfer knowledge from simulation data to real-world data in order to alleviate the need for expensive manual annotations. Finally, I present our pioneering work on building a LiDAR point cloud simulator, which has further enabled a large amount of domain adaptation work on LiDAR point cloud segmentation adaptation.

Advisor: Sangiovanni-Vincentelli, Alberto
Printed Biodegradable Wireless Soil Nitrate Sensor Nodes

Precision agriculture is a set of practices that seek to measure and understand different conditions in space and time across a field, in order to manage water and fertilizer efficiently. Precision agriculture requires high spatial and temporal resolution data, which comes from sensors. However, lack of simple, inexpensive, unobtrusive, low- maintenance sensors that can be widely distributed across a field is a challenge for precision agriculture producers. This thesis describes work towards a network of printed, biodegradable, wireless soil nitrate sensors to meet this need. Sensor nodes consist of potentiometric nitrate sensors, passive UHF-RFID antennas, and a silicon chip to manage power and data communication. Over 95% of the mass of the node is biodegradable.The thesis includes a discussion of each part of the sensor stake. Fabrication techniques for biodegradable substrates, conductors, and encapsulants are discussed. A room-temperature process for printing highly conductive zinc on wood using beeswax encapsulants is a key enabling process. Nitrate sensor operation and development are described, and impacts of using biodegradable materials in nitrate sensors are explored. I show the design, fabrication, and testing of compact RF antennas, and discuss modifications which are required when using biodegradable materials. The printed antennas are integrated with silicon integrated circuits to form passive RFID tags. Requirements and challenges for integrating printed potentiometric sensors with these tags are also discussed. Finally, the impacts of field-deployment are explored. This includes materials lifetime characterization, measuring nitrate sensors in field soils, and validating passive RFID communication in growing crops.

Advisor: Arias, Ana C