VictorJuiz/thesis huggingface dataset:
On Building Generalizable Learning Agents

It has been a long-standing goal in Artificial Intelligence (AI) to build machines that can solve tasks that humans can. Thanks to the recent rapid progress in data-driven methods, which train agents to solve tasks by learning from massive training data, there have been many successes in applying such learning approaches to handle and even solve a number of extremely challenging tasks, including image classification, language generation, robotics control, and several multi-player games. The key factor for all these data-driven successes is that the trained agents can generalize to test scenarios that are unseen during training. This generalization capability is the foundation for building any practical AI system./nThis thesis studies generalization, the fundamental challenge in AI, and proposes solutions to improve the generalization performances of learning agents in a variety of problems. We start by providing a formal formulation of the generalization problem in the context of reinforcement learning and proposing 4 principles within this formulation to guide the design of training techniques for improved generalization. We validate the effectiveness of our proposed principles by considering 4 different domains, from simple to complex, and developing domain-specific techniques following these principles. Particularly, we begin with the simplest domain, i.e., path-finding on graphs (Part I), and then consider visual navigation in a 3D world (Part II) and competition in complex multi-agent games (Part III), and lastly tackle some natural language processing tasks (Part IV). Empirical evidences demonstrate that the proposed principles can generally lead to much improved generalization performances in a wide range of problems.

Advisor: Russell, Stuart
Early Mars Geodynamics: Giant Impacts, Super-plumes, and Vast Oceans

The ancient crust of Mars contains a remarkable record of the planet's early history. With 45% of the martian crust dated to the Noachian (>3.7 Ga) and another 30% of the crust dated to the Hesperian (>3 Ga), Mars provides a unique opportunity to examine processes that can influence the early evolution of terrestrial planets. Observations of Mars' surface motivate hypotheses as to what processes controlled the planet's early evolution and how the surface developed into its present form. Key features to explain for early Mars are the formation of the crustal dichotomy, the massive Tharsis volcanic province, and the prevalence of features indicating widespread early water. Hypotheses for the formation of these features can be tested using geodynamic models. For the origin of the crustal dichotomy, both endogenic (mantle convection) and exogenic (giant impact) mechanisms have been proposed. We examine if a hybrid model in which a giant impact can influence the global pattern of mantle convection. We find that a superplume can form on Mars following an early giant impact, and examine the subsequent superplume dynamics and migration in relation to the formation of Tharsis and remanent crustal magnetism. For a purely giant impact origin of the crustal dichotomy, we find that the crustal structure of the dichotomy is difficult to reproduce with high-resolution impact models. We also examine evidence for early oceans on Mars, finding that deviations in proposed paleoshoreline elevations from present-day equipotential surfaces can be explained by deformation due to the emplacement of Tharsis and other surface loads. Our results suggest that Mars paleoshorelines follow paleo-equipotentials, supporting the ocean hypothesis. Overall, our results provide insight into the early processes that may have occurred on Mars, and their effect on the subsequent evolution of the planet.

Advisor: Manga, Michael
In vivo Proximity labeling of translation initiation complexes and repressive RNA granules by APEX-Seq

RNA localization is a fundamentally important regulatory mechanism for the control of gene expression. I developed an in vivo subcellular RNA proximity labeling technique called APEX-Seq to monitor RNA localization and organization in the cell. In my approach, a proximity labeling enzyme is fused to a query protein and rapidly (<1 minute) biotinylates nearby RNAs in vivo upon addition of a labeling substrate, allowing their subsequent purification and analysis. We show first that APEX-Seq can distinguish the localization of transcripts to different regions of the cell. Further, we can detect the enrichment of specific transcripts in proximity to translation initiation proteins, showing that our system can capture more subtle, RNA-protein localization patterns. Finally, we present a high-resolution time course of protein and RNA condensation into stress induced RNA granules. A powerful aspect of APEX-Seq is the ability to match the spatial transcriptome with quantitative spatial proteomics, allowing for a more complete picture of the spatial landscape of the cell. Additionally, I rationally engineered a split APEX enzyme for conditional spatial proteomics and transcriptomics. I found evidence of unique translation initiation complexes through the use of split APEX on the initiation factors eIF1A/eIF4H, and eIF4A/eIF4B. Overall, these tools open the door for comprehensive and high throughout spatial transcriptomics, and proteomics with subcellular resolution.

Advisor: Ingolia, Nicholas T
On Super Heat Kernel and One Loop Divergence in Conformal Supergravity

We study the one-loop effective action of a quantum theory in $N=1$ superspace using the heat kernel method, with the super Yang-Mills(SYM) theory as our main example. We shall develop two methods for calculating the super heat kernel coefficients. The first method is a supersymmetric generalization of a non-recursive technique. It is applicable to some second order operators with constraints satisfied. The other method we develop is a Fourier integration technique that applies to any second order operators. We also demonstrate how to use perturbation theory to calculate perturbative corrections systematically, which allows us to apply the methods developed to analyze operators higher than second order./nAs an application, we present the one-loop divergences of SYM in conformal supergravity. We use the former method for the constant coupling case, and the latter one for a more general setup, with the coupling strength being determined by a string dilaton $S$. The result with a dilaton is the first superfield calculation for SYM with a non-trivial gauge kinetic function. We shall work the conformal superspace with a conformal supergravity background, but one can obtain the results in K\"ahler $U(1)$[$U(1)_K$] or Poincar\'e supergravity by properly gauge-fixing the conformal symmetry.

Advisor: Gaillard, Mary K.
Understanding the Contribution of Cellular Factors to Heterologous Type III Secretion in Salmonella enterica

Type III secretion systems (T3SS) are complex, membrane-embedded macromolecular machines found in Gram-negative bacteria. These bacteria utilize T3SSs to facilitate microbe-host interactions by translocating specific proteins out of their cytosol in a single step. This provides an interesting system for engineering the selective translocation of proteins out of the bacterial cell. Secretion strategies have been useful in exporting proteins of biotechnological interest in systems such as yeast, insect cells and mammalian cells. Bacteria fermentations are faster and cheaper than other systems./nIn our lab, we use the Salmonella Pathogenicity Island 1 (SPI-1) T3SS in Salmonella enterica serovar Typhimurium for heterologous protein export. As the Salmonella SPI-1 T3SS is not an essential metabolic pathway, we can repurpose it for heterologous protein secretion without affecting cell viability under laboratory conditions. The availability of numerous genetic tools for S. enterica makes it an attractive platform for engineering purposes. Different heterologous proteins of interest can be directed to the T3SS using known native secretion signals. We were able to achieve high secretion titer greater than 400mg/mL through extensive engineering efforts./nMy work focuses on the contribution of cellular factors to heterologous secretion by the SPI-1 T3SS through the use of comparative genomics and transcriptomics. Despite a deep understanding of the regulation of SPI-1 and its regulatory inputs, this complex circuit has yet to be successfully expressed heterologously in another host such as E. coli. This suggests that SPI-1 relies on cellular factors specific to S. enterica, despite the high genetic conservation of homologs between S. enterica and E. coli. The difference in the molecular makeup of the cellular environment could be a result of differential regulation of genes or genes unique to each species. Through my work presented here, I identified numerous cellular factors important for SPI-1 activation and engineered strains capable of high secretion of heterologous proteins. I also showed how insights from systems biology can be used to guide precise strain engineering and process engineering.

Advisor: Ryan, Kathleen R
Tullman-Ercek, Danielle
A constrained optimization problem for the Fourier transform

Among functions f majorized by indicator functions of sets E of measure 1, which functions have maximal Fourier transform in L^p? We investigate the existence of maximizers, using a concentration compactness approach and ingredients from additive combinatorics to establish properties of maximizing sequences. For exponents q sufficiently close to even integers, we exploit variational techniques and combinatorial results to identify all maximizers. This follows from establishing a sharper version of an associated inequality: if the input f, where |f| is less than or equal to the indicator function of a measure 1 set E, has a certain structure, then the Fourier transform of f in L^q is at least a certain quantitative distance from being optimal.

Advisor: Christ, Michael
Multilinear and Sharpened Inequalities

Multilinear integral inequalities, such as H¨older’s inequality and Young’s convolution inequality, play a large role in analysis. In [6] and [5], Bennett, Carbery, Christ, and Tao provide a classification of such inequalities of the form/n → R. Rd/nn j=1/nfj(Lj(x))dx ≤ C/nn j=1/n||fj||pj/nfor constant C > 0, exponents pj ∈ [1,∞], and surjective linear maps Lj : Rd → Rdj with fj : Rdj/n, In Chapter 2, we discuss a generalization of the above related to work by Ivanisvili/nand Volberg [23]. Specifically, we provide a classification of functions B : Rn → [0,∞) such that/n B(f1(L1(x)), ..., fn(Ln(x))dx ≤ CB Rd  f1, ...,/n  fn/n./nIn some cases, it will be shown that maximizers of the above inequality exist. Tuples of Gaussians are not always maximizers, differing from the usual multilinear theory. Chapter 3 focuses its attention on the trilinear form for twisted convolution:/n f(x)g(y)h(x + y)eiσ(x,y)dxdy. Rd×Rd While existence of maximizers can shed light on the structure of an operator,/nsometimes it is useful to establish more refined information. For twisted convolution, we show a quantitative version of the statement that if a triple of functions nearly maximizes the form, then it must be close to a maximizing triple. Such a statement2/nmay be referred to as a sharpened inequality. Here, the proof of a sharpened inequality is complicated by the fact that no maximizers exist for twisted convolution; however, one may vary the amount of oscillation and compare to the case in which there is zero oscillation. In Chapter 4, we establish a sharpened version of the following inequality due to/nBaernstein and Taylor [3]: /nSd×Sd f(x)g(y)h(x · y)dσ(x)dσ(y) ≤  f∗(x)g∗(y)h(x · y)dσ(x)dσ(y) Sd×Sd/nwhere f, g, h are restricted to the class of indicator functions and h is monotonic on [−1, 1]. In the above, f∗ refers to the symmetric decreasing rearrangement of f, and likewise for g and g∗.

Advisor: Christ, Michael
Computational Investigation of Nanoporous Materials for Clean Energy Applications

Nanoporous materials such as metal-organic frameworks (MOFs) are promising for applications in clean energy thanks to their diverse and extraordinary gas adsorption properties. Additionally, these materials' high degree of tunability means that nearly infinite distinct materials can be envisioned with different combinations of compositions, topologies, and surface properties. Each combination could yield a material with its own unique gas adsorption phenomena and resulting real-world applications. While many materials with outstanding properties have been studied, it remains a challenge to find or design an optimal material for a given application and to understand the complex molecular-level interactions that give rise to macroscopic behavior./nIn this dissertation, we take three different computational approaches towards advancing the discovery and understanding of nanoporous materials for various clean energy applications./nIn Chapters 2 and 3, we screen large databases of materials for carbon capture and hydrogen storage, using molecular simulations. We develop accurate atomistic models for the relevant adsorption phenomena and identify appropriate criteria for predicting performance. In this way, we identify optimal materials for carbon capture and hydrogen storage, as well as characteristics common among top performers. Ultimately, such findings can help guide the design of optimal materials for these applications./nIn Chapter 4, we study in depth a novel biporous MOF for natural gas processing, using molecular simulations in conjunction with experiments. This MOF's rare feature of biporosity, or coexistence of two chemically distinct pores within the same MOF, gives rise to unique stepwise activation that can be controlled to consequently tune the MOF's CO2/CH4 separation performance. This phenomenon is explained in terms of CO2's and CH4's differing interactions with the pore surfaces. In this way, we contribute to the understanding of complex behavior in biporous materials that affects their gas separation performance./nFinally, in Chapter 5, we investigate the assembly process of MOFs using a statistical mechanical lattice model. In particular, we map out the thermodynamic assembly pathway of MOF-74 and explore the effect of surface tension energies on the shapes of nucleating clusters. With this study we work towards understanding the assembly pathway of MOFs with the ultimate goal of being able to control the synthesis process and obtain crystals of the desired morphology.

Advisor: Smit, Berend
Optoelectronics for refrigeration and analog circuits for combinatorial optimization

The increasing efficiency and adoption rate of light-emitting diodes (LEDs) for lighting is forecasted to lead to large energy savings in that sector. Intrinsic self-refrigeration, which surprisingly accompanies the emission of light from an LED, might allow the same technology to be harnessed for new applications. In the first half of this dissertation, I will discuss whether refrigeration and cooling, which also account for a large share of world energy consumption, comprise a realistic future energy application for optoelectronics. To date, electroluminescent cooling has eluded direct observation at practical power densities, since a pre-requisite for net cooling is the near-complete elimination of LED internal losses that dissipate heat. I will propose a near-ideal-efficiency LED structure, realizable with existing optoelectronic material quality and device processing capabilities, to predict the technological limits of electroluminescent refrigeration. I will show that LED-based cooling can realistically be more efficient than solid-state alternatives like thermoelectric cooling, particularly for cryogenic applications and at moderate power densities./nIn the second half of this dissertation, I will discuss a new hardware accelerator for difficult combinatorial optimization problems. These problems are abundant in modern society, in the fields of operations research, artificial intelligence, chip design, financial optimization, medicine, and many others. They are difficult in that no algorithm has yet been found that solves them efficiently, but the increasing computational power of digital computers has allowed these problems to be routinely tackled. As conventional computers reach their scaling limits, however, alternative hardware architectures are being explored to accelerate computationally intensive tasks like machine learning and combinatorial optimization. Here, I will discuss the design of a new optimization machine, implemented using a network of coupled analog electrical oscillators. The machine uses an unconventional search mechanism to discover the global minimum of the Ising problem, a difficult problem that maps quickly onto other hard optimization problems. I will present the simulated performance of this machine for moderately sized problems with all-to-all connectivity and discuss its potential to scale to larger problems.

Advisor: Yablonovitch, Eli
A Compact, Ultrastable, Operando X-ray Ptychography Microscope at the Advanced Light Source

X-ray imaging traces its roots back to the discovery of x-rays by Ro ̈ntgen in 1901, in the form of simple projection imaging. Several decades later, fabrication of x-ray optics enabled image formation at higher resolutions, leading to microscopy. Nevertheless, microscopy at resolutions on the order of the x-ray wavelength has only become possible in recent years, after a host of enabling technologies, including high-brightness coherent sources, advanced detectors, high-performance computers and algorithms, and precision instrumentation were brought together by multidisciplinary teams of scientists to form one coherent imaging sys- tem. Today, x-ray ptychography microscopes operating at modern synchrotrons can image thick samples at unprecedented resolutions, and yield chemical information about the sam- ple, enabling novel material science studies. The field is rapidly developing, and the advent of new Diffraction Limited Storage Ring (DLSR) sources around the world promises to provide new opportunities with increased coherent brightness./nThis dissertation presents new advances in instrumentation and analysis at the Advanced Light Source at Lawrence Berkeley National Lab. A new x-ray ptychography microscope is described, compact in size and ultrastable in both vibrations and drift, enabling better than 3nm spatial resolution, and integrating a TEM-compatible in-situ sample holder to enable multimodal, operando, and cryogenic experiments. A new approach to the analysis of spectro-microscopic data is presented, combining low-resolution STXM and high-resolution ptychography images to produce chemical maps at high resolution without a priori reference spectra. This is achieved by collecting STXM data at fine x-ray energy increments, from which spectra are extracted by PCA and clustering. The ptychography data, collected at only a few energies, is then fitted to these extracted spectra. Finally, new ideas are explored to speed up scanning in ptychography, leading to a reduction in scan overhead, dose, and data collection times. This is done in two ways: firstly, a simple, yet effective, scanning algorithm is introduced to adjust feedback parameters for the piezo stage scanning the zone plate, reducing the scan overhead time. Secondly, lower density scan patterns and their effects on quality and resolution of reconstructions are investigated; based on these, ideas are proposed to intelligently reduce the total number of diffraction patterns collected, while only minimally affecting results. Combined with the promise of increased coherent flux with upcoming DLSRs, leading to much shorter exposure times, these speedups will enable much faster ptychography scans, greatly facilitating tomography or in-situ experiments, currently taking many hours or days.

Advisor: Falcone, Roger
A Measurement of the Degree Scale B-mode CMB Angular Power Spectrum from the Polarbear Experiment

Measurements of the polarization anisotropies in the cosmic microwave background (CMB) provide powerful experimental constraints on the contents and history of the universe. These anisotropies are conventionally split by spatial parity, with even (odd) parity modes called E-modes (B-modes). The degree-scale (multipole moment l ≈ 100) B-mode polarization is expected to receive a contribution from a stochastic gravitational wave background in the early universe. This feature is a generic prediction of theories of cosmic inflation, however its overall amplitude r is a free parameter. Detecting this gravitational wave signal presents a formidable experimental challenge./nThis analysis is based on three years of data from the Polarbear experiment using a continuously rotating half-wave plate to modulate the CMB polarization. This dissertation describes the analysis pipeline used to transform the raw detector time ordered data (TOD) into a CMB power spectrum and likelihood on r. This analysis is the tightest constraint on the degree scale B-mode power spectrum yet achieved from a mid-lattitude site or with a polarization modulator. As such, it serves as a pathfinder for future instruments including the Simons Array and Simons Observatory./nThis dissertation is structured as follows. Chapter 1 presents an overview of the science case for precise measurements of the CMB B-mode power spectrum. Chapter 2 gives a brief description of the Polarbear instrument and the observation strategy used in this dataset. Chapter 3 describes the data pre-processing and calibration steps used in this analysis. Chapter 4 describes the pipeline used to turn the TOD into maps and subsequently power spectra. Chapter 5 describes the sensitivity degradation seen at large angular scales in this analysis and how it may be mitigated in future experiments. Chapter 6 describes the final calibrations and consistency of this data with existing E-mode measurements. Chapters 7 and 8 describe the detailed internal consistency checks preformed and simulations of a number of known sources of systematic contamination. Chapter 9 shows the recovered B- mode angular power spectrum. Chapter 10 shows the cross correlation with data from the Planck satellite to control contamination due to galactic foregrounds and the likelihood model for the constraint on r. Chapter 11 summarizes these results and describes future experiments.

Advisor: Lee, Adrian