VictorJuiz/thesis huggingface dataset:
The Good, the Bad, the Algae: Using High Resolution Imagery to Detect Freshwater Algal Blooms in California

Algae are an essential component of aquatic ecosystems. They provide food, habitat,structural support, and oxygen to marine, freshwater, and brackishwater environments alike. However, algae in excess can be problematic. Harmful algal blooms (HABs) are proliferations of both toxic and non-toxic algal species that can cause ecological and environmental damage in lakes, reservoirs, and rivers. As global temperatures rise, coupled with increasing nutrient inputs from eutrophication and atmospheric deposition, many predict HABs will also increase in frequency and intensity. There is a need to advance methods in algal bloom monitoring to keep pace with these global trends. As in situ techniques such as water quality samples, swimming, and laboratory assessments can be time-consuming and expensive, remote sensing may offer a faster, more cost-effective method to investigate blooms at greater spatial and temporal extents in freshwater ecosystems. Rivers, reservoirs, and lakes are particularly important because of their environmental, economic, cultural, and recreational roles in nature and society, and advancing remote sensing methods could improve our ability to monitor and mitigate blooms in these settings across the world. This dissertation explores the spatial and temporal dynamics of algal blooms, both “good” (non-toxic filamentous algae) and “bad” (toxic cyanobacteria), throughout freshwater environments of California. In Chapter 1, I introduce the importance and risks of algae, as well as a background of in situ and remote sensing methods to monitor algae. Chapter 2 examines the use of unoccupied aerial vehicle (UAV) imagery over the Klamath River to understand the distribution of aquatic plants and filamentous algae prior to the largest dam removal in history. Chapter 3 moves upstream in the Klamath River to two reservoirs, Iron Gate and Copco, and uses high-resolution Sentinel-2 satellite imagery to detect the spatial distribution and timing of potentially toxic blooms in a five-year time series. Chapter 4 integrates two statewide harmful algal bloom datasets (one of crowdsourced reports and another of 300-meter Sentinel-3 satellite imagery) with higher-resolution Sentinel-2 imagery in order to monitor a greater number of lakes and reservoirs in California with higher resolution data. Finally, in Chapter 5, I review the major findings, lessons learned, and challenges of this research and posit new directions of future research to improve remote sensing techniques of algal bloom detection in freshwater environments.

Advisor: Kelly, Maggi
Dynamics and Methods of Manipulating Ferroic Order in BiFeO3 and Related Materials

This dissertation details methods of manipulating and dynamic studies of ferroic order in the multiferroic material, BiFeO3, and related material systems. The primary advances made as a result of the work described herein are understanding of intrinsic and extrinsic limits on ferroelectric switching (in particular free- and bound-charge dynamics, the role of, and pathways to manipulate the Landau free energy landscape and lattice dynamics) and implications for switching of magnetic order, mechanisms and factors impacting depolarization in ferroelectric materials, and a novel manifestation of magnetoelectric coupling enabling nonvolatile electric field control of magnon spin transport.

Advisor: Ramesh, Ramamoorthy
Applications of Deep Learning to Medical Image Analysis in Ophthalmology

Medical image is an important information source to understand the patient’s condition. As a result, interpreting the medical images is a critical part of the clinical procedure. However, physicians’ visual evaluation of medical images in clinics has a few challenges such as the limited human resources, the unavailability of appropriate experts, the increasing number ofmedical images, and so on./nDeep learning is a subarea of machine learning that uses deep neural networks to learn the patterns behind the given data set. Deep learning showed excellent performances in image analysis problems including medical image analysis. In this dissertation, I propose and evaluate new methods for evaluating the images of three different vision problems inophthalmology./nThe first problem is the early detection of retinopathy of prematurity (ROP). ROP is a leading cause of childhood blindness globally, and early detection is a key to preventing ROP to progress to severe conditions. We developed two convolutional neural networks with different depths. The deeper model showed an excellent performance including bettermetrics than an experienced human expert./nThe second problem is about transfer learning in retinal vascular diseases. We propose a transfer learning method that uses the detection of a well-studied retinal vascular disease as a source problem and uses the knowledge to the detection of an under-studied retinal vascular disease. Our proposed method showed better performance with more robustness tothe stochasticity in the training process and the reduction of sample size./nThe final problem is to predict the treatment response to a drug from the baseline characteristics. Both symbolic features like clinical measurements and medical images are considered for the modeling. To merge the two types of input, we proposed two approaches. The results showed the potential of the proposed method to the problem./nDeep learning is successfully applied to three medical image analysis problems in ophthalmology in this dissertation. These results offer key evidence for further development of deep learning-based medical image analysis systems in the future.

Advisor: Guo, Xin
Cavity quantum electrodynamics with tweezer-trapped single atoms

The development of quantum technology demands systems of many interacting quantum particles, each individually controlled. Neutral atom arrays are a leading platform in this pursuit, boasting scalable, configurable arrays of single atoms trapped in optical tweezer traps, with demonstrated mechanisms for generating local interactions. Neutral atoms coupled to an optical cavity provide complementary capabilities, enabled by cavity quantum electrodynamics: the control of single atoms with single photons, photon-mediated interactions between distant atoms, and cavity-assisted measurement./nThis thesis presents a novel platform for cavity quantum electrodynamics with a neutral atom tweezer array. After motivating and describing the experimental apparatus, I present the details of a quantum sensing result, using single atoms as scanning probes to characterize optical fields of our cavity. I then discuss our study of single-atom cavity state detection, used to demonstrate a mid-circuit measurement within a coherent neutral atom array. The integration of the tweezer array and cavity QED platforms represents a small step toward quantum information processing with neutral atoms.

Advisor: Stamper-Kurn, Dan M
Computational Methods for Higher Accuracy Nanopore Sequencing

Nanopore sequencing is a versatile technology that can generate long, single-molecule reads on a portable device. This presents strong advantages in areas such as metagenomics and de novo genome assembly. These advantages have traditionally been tempered by an error rate higher than other sequencing technologies, though advancements in both sequencing chemistry and basecalling models have yielded steady increases in sequencing accuracy. This work presents multiple computational techniques for further reducing this error rate, primarily by combining information from multiple noisy reads into a single, higher accuracy consensus sequence. We present algorithms for using the probabilistic output of existing basecallers to find the consensus of two (Chapter 2) or more (Chapter 3) reads. We also introduce a neural network polisher for multi-read consensus at higher read depths (Chapter 4). Finally we explore the application of policy gradients, a technique developed for reinforcement learning, to train nanopore basecallers (Chapter 5). These methods are implemented in a variety of software tools, all of which are freely available on GitHub (https://github.com/jordisr/). Our software PoreOver is designed to work with the output of multiple basecallers and implements two main consensus algorithms. We also present PoreOverNet, a simple standalone basecaller. In addition to the standard maximum likelihood loss generally used to train most basecallers, PoreOverNet also implements a multi-objective loss designed to reduce the expected number of errors through a policy gradient approach. Finally, Semapore is a neural network tool for consensus and polishing of genome assemblies.

Advisor: Holmes, Ian
Molecular Orbitals and Where to Find Them

In this thesis, new ways of generating orbitals to accelerate quantum mechanical calculations or gain chemical insight from such calculations are presented. While antibonding orbitals are integral to our understanding of chemistry, finding them is not trivial because they cannot be uniquely defined. A new way to find them is demonstrated along with many uses they have in valence space methods such as complete active space self-consistent field (CASSCF) theory. New molecule-adapted atomic orbitals can also be obtained from the antibonding orbitals which is used for atomic charge analysis. Furthermore, new oxidation state localized orbitals (OSLOs) for assigning oxidation states are also presented and assessed across a wide range of systems to demonstrate improved performance on challenging systems. Lastly, an approach to decompose the intermolecular forces based on the absolutely localized molecular orbital energy decomposition analysis (ALMO-EDA) is described. This force decomposition analysis (FDA) separates different physically distinct contributions (such as those from permanent electrostatics and Pauli repulsion, those from the effect of induced electrostatics, and those from charge transfer or dative interactions). A series of chemical examples ranging from ion water interactions to activation of carbon dioxide by gold (and silver) anions are studied to explore the chemical insight that can be gained. The FDA may also be useful for the development and testing of molecular mechanics force fields. As an example, we use force decomposition to further validate the promising polarizable MB-UCB force field for water.

Advisor: Head-Gordon, Martin
Sirtuins in Mitochondrial Protein Folding Stress and Neuroinflammation

Systemic aging and neurodegeneration are characterized by the progressive accumulation of aberrant molecules and cellular damage. Tissue homeostasis deteriorates due to stem cell exhaustion, chronic inflammation, and metabolic dysfunction, among other causes. In the following chapters, I present evidence of the beneficial roles that sirtuins (SIRT1-7), NAD+-dependent deacetylases, play in alleviating these conditions. Further understanding of the molecular mechanisms associated with these conditions would provide potential targets of therapeutic intervention for the alleviation of debilitating diseases, such as hematopoietic stem cell dysfunction and multiple sclerosis./nIn Chapter Two, we uncover the importance of the mitochondrial unfolded protein response (UPRmt) in hematopoietic stem cell maintenance. The UPRmt is a cellular protective program that ensures proteostasis in the mitochondria, and it is known to be regulated by SIRT7. We devised three experimental approaches that enabled us to monitor quiescent and proliferating hematopoietic stem cells (HSCs) and provided direct evidence that the UPRmt is activated upon HSC transition from quiescence to proliferation, and more broadly, mitochondrial integrity is actively monitored at the restriction point to ensure metabolic fitness before stem cells are committed to proliferation./nIn Chapter Three, I use a mouse model of progressive multiple sclerosis (MS), a chronic autoimmune disease of the central nervous system (CNS), and single-nuclei RNA seq data from MS patients to demonstrate that SIRT2 deficiency may drive an inflammatory phenotype within progressive MS lesions. More importantly, I show that NAD+ boosting may alleviate MS pathology./nThese findings further emphasize the importance of understanding sirtuin biology for the control of homeostasis across multiple systems, such as the hematopoietic and central nervous systems.

Advisor: Garriga, Gian
Physics-Informed Machine Learning for Computational Imaging

A key aspect of many computational imaging systems, from compressive cameras to low light photography, are the algorithms used to uncover the signal from encoded or noisy measurements. Some computational cameras encode higher-dimensional information (e.g.\ different wavelengths of light, 3D, time) onto a 2-dimensional sensor, then use algorithms to decode and recover this high-dimensional information. Others capture measurements that are extremely noisy, or degraded, and require algorithms to extract the signal and make the images usable by people, or by higher-level downstream algorithms. In each case, the algorithms used to decode and extract information from raw measurements are critical and necessary to make computational cameras function. Over the years the predominant methods, classic methods, to recover information from computational cameras have been based on minimizing an optimization problem consisting of a data term and hand-picked prior term. More recently, deep learning has been applied to these problems, but often has no way to incorporate known optical characteristics, requires large training datasets, and results in black-box models that cannot easily be interpreted. In this dissertation, we present physics-informed machine learning for computational imaging, which is a middle ground approach that combines elements of classic methods with deep learning. We show how to incorporate knowledge of the imaging system physics into neural networks to improve image quality and performance beyond what is feasible with either classic or deep methods for several computational cameras. We show several different ways to incorporate imaging physics into neural networks, including algorithm unrolling, differentiable optical models, unsupervised methods, and through generative adversarial networks. For each of these methods, we focus on a different computational camera with unique challenges and modeling considerations. First, we introduce an unrolled, physics-informed network that improves the quality and reconstruction time of lensless cameras, improving these cameras and showing photorealistic image quality on a variety of scenes. Building up on this, we demonstrate a new reconstruction network that can improve the reconstruction time for compressive, single-shot 3D microscopy with spatially-varying blur by 1,600X, enabling interactive previewing of the scene. In cases where training data is hard to acquire, we show that an untrained physics-informed network can improve image quality for compressive single-shot video and hyperspectral imaging without the need for training data. Finally, we design a physics-informed noise generator that can realistically synthesize noise at extremely high-gain, low-light settings. Using this learned noise model, we show how we can push a camera past its typical limit and take photorealistic videos at starlight levels of illumination for the first time. Each case highlights how using physics-informed machine learning can improve computational cameras and push them to their limits.

Advisor: Waller, Laura
Situated Literacy Practices of Chilean Families to Support Early Development in their Pre-reading Children

While the study of family literacy in Latino communities has grown in the U.S. since the 1990s, this is a nascent research area in Latin America, with Chile being no exception. Limited information is available about the impact of parents’ literacy practice on child literacy outcomes at different developmental stages, the shared reading practices implemented by Chilean caregivers with their young children, and the distribution of said practices by family members. To address this gap, this dissertation seeks to explore the situated literacy activities conducted by Chilean parents with their pre-reading children./nUsing a mix-method approach, this dissertation explores family literacy practices from three different dimensions, each represented by a study. In the first study, using a nationally representative longitudinal database from Chile, it is explored how mothers and fathers share the responsibility of conducting practices that support child language and cognitive development. Random effects at the child level and fixed effects at the time level were used to identify the causal impact of mother-child and father-child activities on child development. In the second study, utilizing the same database and a similar methodology, it is analyzed how the frequency of parents' literacy practices change across early childhood and how changes in these practices are associated with child language and literacy outcomes. Finally, in the last study, caregivers' behaviors during a shared reading event with their toddlers using a video data set from 120 Chilean families are examined. The goal of this dissertation is to highlight the assets and possible needs of Chilean parents when supporting their young children's literacy acquisition process.

Advisor: Cunningham, Anne
Entropy in Unsupervised Machine Learning

Entropy is a central concept in physics and has deep connections with Information theory, which is one of the foundations of modern machine learning. Specifically, energy-based models are unsupervised machine learning models that adopt a simple yet general formulation based on the principle of maximum entropy. Three Chapters in my thesis are related to energy-based models, and one Chapter uses a Gaussian Coding rate function, which is also related to entropy./nThe Boltzmann machine is an energy-based model with strong connections to spin systems in Physics. Boltzmann machines were conceived with bipolar real-valued spin states (up and down) and later generalized to complex valued spin states with unit length. Building on the previous work on complex Boltzmann machines, here we study a generalization where the complex spin states can vary in both, phase and amplitude. Complex Boltzmann machines are closely related to networks of coupled stochastic oscillators and thus can be efficiently implemented in coupled oscillator and also neuromorphic hardwares./nNeural Network Energy-based model (EBM) provide a unified framework for a diverse set of functions, such as sample synthesis, denoising, outlier detection, and Bayesian reasoning. However, the downside of EBMs is that their standard training method based on maximum-likelihood requires expensive sampling and is therefore extremely slow. Denoising score matching is an attractive alternative. Inspired by [143], we study a new method of training EBM in high-dimensional space using multiple scales with denoising score matching. The resulting model exhibits strong performance on data generation and inpainting./nAnother approach that could make training of EBMs efficient is developing an efficient MCMC (Markov Chain Monte Carlo) sampler. The entropy of the proposa distribution of a sampler is an effective measure of the efficiency of the sampler with-out reference to any detail of the target distributions, thus has the potential to be applied to neural networks energy functions. We developed a new neural network augmented MCMC sampler that can be trained to exactly maximize its proposal entropy. The resulting sampler can adapt to very difficult target distribution geometry, and is shown to improve the training of an EBM./nSelf-supervised learning, another important type of unsupervised learning, learns a useful data representation for downstream tasks (for example classification), instead of learning a generative model that fully recreates the dataset. For this, an objective function based on the Gaussian coding rate function called MCR2[178] shows promise. Using this objective, we build a framework that unifies neural networks based non-linear subspace clustering and data-augmentation based self-supervised learning. The resulting algorithm shows strong performance in various subspace clustering tasks.

Advisor: Sommer, Friedrich T
DeWeese, Michael R
Chemical Engineering of Nanomaterials for the Delivery of Biomolecular Cargo

Nanomaterials have the potential to play a significant role in the development of new biotechnology for diverse applications, including agricultural genetic engineering, biological sensing, nanomedicine, optics, and more. Therefore, a robust, fundamental understanding of their material properties and the techniques to engineer these properties is a key area of scientific interest./nIn this dissertation, we explore several key areas where the chemical engineering of nanotechnology, particularly single walled carbon nanotubes (SWNTs), can have a significant impact. We first broadly explore emerging trends in some of the most promising near-infrared luminescent materials and applications. In particular, we focus on how a more comprehensive understanding of their intrinsic material properties might allow researchers to better leverage these traits for innovative and robust applications./nFurther, we focus on the ways in which nanomaterials can address some of the most critical challenges of CRISPR genome editing in plants through improvements in cargo delivery, species independence, germline transformation and gene editing efficiency. We identify major barriers preventing CRISPR-mediated plant genetic engineering from reaching its full potential, and discuss ways that nanoparticle technologies can lower or eliminate these barriers./nNext, we present mechanisms to optimize DNA loading on SWNTs with a library of polymer- SWNT constructs and assess DNA loading ability, polydispersity, and both chemical and colloidal stability. We demonstrate that polymer hydrolysis from nanomaterial surfaces can occur depending on polymer properties and attachment chemistries, and we describe mitigation strategies against construct degradation. Given the growing interest in delivery applications in plant systems, we also assess the stress response of plants to polymer-based nanomaterials and provide recommendations for future design of nanomaterial-based polynucleotide delivery strategies./nWe then adapt our findings from the development of polymer-SWNTs towards the synthesis of protein-functionalized SWNTs and the covalent attachment of DNA cargo. Specifically, we explore the development of streptavidin-biotin chemistry as a potential alternative to other existing nanoparticle-biomolecule delivery systems and offer exciting opportunities for future research./nFinally, we explore the federal regulatory challenges in the United States that limit scientific innovation and unduly hinder the widespread production of genetically engineered crops, like those we propose to develop throughout this thesis. To address these shortcomings, we propose policy recommendations including the consolidation of federal regulatory communication and a unified web platform for commercial approval applications.

Advisor: Landry, Markita