VictorJuiz/thesis huggingface dataset:
Precision navigation of miniaturized distributed space systems using GNSS

The way humans conduct spaceflight is being revolutionized by two key trends. The first trend is the distribution of payload tasks among multiple coordinated units, referred to as Distributed Space Systems (DSS), which allow for advances in planetary science, astronomy and astrophysics, and space infrastructure and development. The second is spacecraft miniaturization, where micro- and nanosatellites are transitioning from being merely educational tools to a viable scientific platform. Together, these pushes call for strict Guidance, Navigation, and Control (GNC) requirements while adhering to on-board constraints. In particular, advanced DSS require precise real-time knowledge of the relative orbits of each spacecraft in the system. Centimeter-level relative positioning precision can be obtained from Global Navigation Satellite Systems (GNSS), but this has only been demonstrated on ground through post-processing and only between two spacecraft. This research presents a methodology for nanosatellite swarms to provide on-board navigation solutions in real time using differential GNSS, combining the precision from ground-operated navigation systems with the timeliness of on-board payloads. To accomplish this task, a large swarm is divided into smaller subsets, within which differential GNSS provides centimeter-level relative positioning through successful carrier-phase integer ambiguity resolution. These precise subset estimates are then fused together to form a full swarm orbit estimate on each spacecraft. This system is validated in a new GNSS testbed at Stanford, designed to characterize and profile both hardware and software for GNC payloads. A hardware-in-the-loop experiment demonstrates that the new navigation system can provide precise navigation solutions to a swarm of six spacecraft, including the first demonstration of integer ambiguity resolution on CubeSat avionics. The payload is then applied to two upcoming science missions for which it is considered a mission-enabling technology: the Virtual Super-resolution Optics with Reconfigurable Swarms (VISORS) and the Miniaturized Distributed Occulter/Telescope (mDOT). These missions both present unique challenges, showing that the navigation system can meet strict mission requirements in the presence of frequent control maneuvers and large inter-spacecraft separations. In the latter case, a novel hybrid extended/unscented Kalman filter (KF) estimates the differential ionospheric path delay between GNSS receivers in the swarm, using an unscented KF measurement update to better handle nonlinearities while reducing computational load through an extended KF time update

Keywords: Aeronautics and Astronautics.
Advisor: D'Amico Simone
Gao Grace X. (Grace Xingxin)
Schwager Mac
Predicting Scalar Inference and Alternativehood With Neural Sentence Encoders

Scalar inference is the interpretation of a less informative term to also mean the negation of a more informative alternative. The strength of these inferences is often sensitive to context and the presence of linguistic cues. Neural sentence encoders have been shown to successfully predict the strength of certain scalar inferences (in particular, from some to not all). In this work, we explore two topics: First, the extent of variability in scalar inference strength from or to not both, and whether this inference strength can be predicted by a neural sentence encoder; second, whether a BERT-based framing of lexical expectation can be used to model the availability of scalar alternatives to or. We find that a BERT-based LSTM predicts inference strength from or to not both with limited success (r = 0.45), due to the relative pragmatic complexity of or. We also find that a BERT- based language model successfully predicts the categorical availability of alternatives for or, indicating that BERT captures qualitative information about alternativehood, and that lexical expectation measures of alternativehood weakly predict inference strength.

Keywords: neural network
BERT
alternatives
scalar inference
pragmatics
machine learning
NLP
Symbolic Systems
Thesis
Prediction and mechanistic dissection of transcriptional activation domains using deep learning and pooled screening

Transcription factors (TFs) comprise a DNA-binding domain and an effector domain that regulates nearby genes. Activation domains (ADs) -- effector domains that increase transcription -- have long been of particular interest due to their roles as oncogenic drivers and use as scientific tools. We combined quantitative, high-throughput measurements of in vivo activation and in vitro interaction with computational modeling to characterize ADs. Using a domain-tiling in vivo screen, we identified all ADs in budding yeast. We trained a neural network named PADDLE (Predictor of Activation Domains using Deep Learning in Eukaryotes) that accurately predicts ADs across species, and experimentally confirmed predictions of 23 new ADs in human TFs. Surprisingly, and suggesting an expanded role, ADs were also predicted and confirmed in all major coactivator complexes, including Mediator, TFIID, SAGA, cohesin, and condensin. Guided by PADDLE predictions, we designed and measured activation of thousands of AD mutants to derive a deeper understanding of the principles underlying activation. ADs shared no common sequence motifs. Acidic and bulky hydrophobic residues were each necessary for activation, but excess hydrophobicity was strongly inhibiting. While a few ADs required alpha helical folding, most activated based on biochemical features and did not need any specific sequence or secondary structure. We then used mRNA display and pull-down experiments to measure in vitro binding of coactivator proteins to all TF domains. Remarkably, 73% of ADs bound Mediator and binding strength was strongly predictive of activation strength. In contrast, TFIID bound only 18% of ADs, all of which also bound Mediator. Structural modeling revealed that ADs interact with Mediator without shape complementarity ("fuzzy" binding). Kinetic measurements showed that multivalent Mediator-AD interactions were high affinity yet allowed dynamic exchange of bound molecules. We propose that this dynamic exchange is a consequence of fuzzy binding, enables rapid recruitment and release of individual Mediator complexes during activation, and drives phase separation when gene-regulating proteins are at high concentrations

Advisor: Dror Ron
Kornberg Roger D.
Kundaje Anshul
The presence of Copley : animacy, magic, and afterlife in American painting, 1765-1925

Between 1753 and 1774, John Singleton Copley (1738-1815) painted some 350 portraits depicting men, women, and children of the British-American colonies, where he lived. Though his portraits were painted two and a half centuries ago, still they strike contemporary viewers as compellingly immediate and eerily modern, somehow charged with energy despite the passing of time. While many have commented fleetingly on the uncanny realism of Copley's colonial canvases, the origins and implications of these aesthetic effects have never been questioned. This dissertation argues that the painter's portraits were made, viewed, and experienced as near-magical repositories of human presence, believed to carry some vital relic of their sitters into the future. Accordingly, the project explores the rich afterlives of Copley's portraits—canvases that have "lived on" because their pulsating lifelikeness anticipates a future in which the sitter has passed on but the painting has not. Contributing to a growing body of scholarship on the afterlives of art and the history of enchantment, I demonstrate how these portraits and their reception during the long nineteenth century are evidence of the widespread cultural inclination to attribute inanimate portraits with the force of animation

Keywords: Art and Art History.
Advisor: Nemerov Alexander
Kwon Marci
Lubin David M.
Troy Nancy J.
Principles of circular RNA translation and immunity

The rapid development of Covid-19 mRNA therapeutics was enabled by an upfront investment in mRNA platform technology, built on a fundamental understanding of mRNA biology. Decades of mechanistic work have uncovered key drivers of mRNA translation such as the cap, untranslated regions, and poly(A) tail. Additionally, the use of nucleoside modifications such as N1-methylpseudouridine enabled in vivo translation by suppressing innate immune responses to mRNA. Circular RNAs (circRNAs) are single-stranded covalently-closed RNAs whose translation is driven by internal ribosome entry sites (IRESs) and whose structure confers stability advantages over mRNAs. My dissertation defense will describe the circRNA assembly platform I built, its use in uncovering principals behind circRNA translation, and the study of the mechanism of immune sensing of nucleoside modification N6-methyladenosine (m6A) on circRNAs. In order to elucidate principles for driving strong circRNA translation, I built a platform for rapid and robust assembly of vectors and subsequent synthesis of circRNAs. Using this modular system, I identified an optimum format for circRNAs and systematically tested sequence elements that enhanced circRNA translation. I screened 100 viral IRESs, discovered a set with greatly improved translation across cell types, then created novel synthetic IRESs through both rational engineering and random mutagenesis. Further, I characterized a set of nucleoside modifications that improve circRNA translation. We decided to focus on one such modification, m6A, the most abundant post-transcriptional RNA modification on endogenous RNAs. Investigating the interaction behind m6A and anti-circRNA innate immunity, I found that circRNAs without nucleoside modifications activate the antiviral RNA sensor RIG-I in the presence of lysine-63-linked polyubiquitin chains to trigger a downstream interferon response. m6A blunts this response in an m6A reader YTHDF2-dependent manner. Viewed broadly, my findings characterize a mechanism behind self versus non-self recognition of circRNAs. Taken together, my circRNA development platform and the numerous discoveries made with it have pushed circRNAs forward as a promising class of RNA therapeutics

Keywords: Bioengineering.
Advisor: Chang Howard Y. (Howard Yuan-Hao)
Qi Lei (Professor of Bioengineering)
Smolke Christina D.
Private Made Public: Women in Cinema in Post-Famine North Korea

This paper examines the evolution of the public and private sphere in North Korea before and after the paradigmatic social change caused by the famine of 1994-1998, also known as the Arduous March. Of particular interest is how depiction of the public and private spheres in state-produced films has adapted to respond to this social shift. Analysis draws upon six post-famine films with female leads: “A Mother’s Happiness” (2003), “A Schoolgirl’s Diary” (2006), “Kites Flying in the Sky” (2008), “A Flower in the Snow” (2011), “Comrade Kim Goes Flying” (2012), and “ The Story of Our Home” (2016). Compared with prior films, depictions of the public and private sphere appear to be intentionally blurred through two common narrative devices: the reframing of private aspirations as public affairs and the portrayal of motherhood and reproductive labor as service to the state. Two of the six films include the famine in their narratives, offering a revisionist interpretation of the famine that implies the markets were not necessary for survival. Taken together, these six films represent a propaganda campaign targeting the agents of the private sphere - North Korea’s housewives - with the end goal of convincing women to forego private market activity and dedicate their labor to serving the needs of the state.

Keywords:  Stanford Global Studies
east asian studies
Korea (North)
Women
Propaganda
private sphere
Public sphere
stanford global studies
Text
Thesis
Advisor: Zur Dafna
Probabilistic analysis of grid reliability with renewables and storage

Can the California grid support 100% renewables and 100% EVs? Rigorous analysis in this area says the answer is yes, but this would require large energy storage and charging EVs during the day. Doubling green energy imports, such as Canadian hydro, will be also required. This thesis presents new probabilistic analysis tools that allow evaluating risk and cost of the grid with storage, variable generation, and EVs at scales beyond anything seen before. First, we demonstrate a new method of probabilistic analysis of grid reliability based on Machine Learning models built using convex optimization from historical data. To analyze the impact of large-scale storage and high penetration of EVs on the grid, we introduce a probabilistic energy balancing analysis. The developed rigorous methods allow to analyze capacity allocation scenarios quickly and accurately. Second, this thesis presents the analysis of capacity planning scenarios for a Green California grid using the developed methods. Third, the developed methods are applied to practical reliability analysis of major power grids including California ISO, ISO New England, and Horizon (Western Australia). Grid reliability analysis is to assess the grid outage risk forgiven the recourse capacity portfolios. North American Electric Reliability Corporation (NERC) defined '1-in-10' reliability rule, which is no more than one-day outage in 10 years. This rule is currently used in the capacity analysis for major regional grids in North America. However, the rapidly increasing growth of renewable generation challenges traditional grid structure and started to threaten grid reliability gradually as the penetration increases. Further, the fast development of EVs boosts the increase in demand. Both supply and demand of electricity grid become complicated. Storage can be a tool to balance the risk. Analyzing the grid reliability with these variable components is complicated and calls for the development of new analysis. Many regions and countries have very ambitious plans to reduce carbon emissions. California plans to go carbon-free by 2045, Massachusetts considers similar legislation. This requires all generations to be renewable and most cars EV near future. This thesis established the new methods of reliability analysis for the grid with extremely high penetration of renewables, EVs, and storage. A New Machine Learning method is proposed to capture the randomness and uncertainty among grid components based on historical data. This question is whether the grid can have both 100% renewables and 100% EVs without losing the reliability of the power supply. This thesis uses California ISO data to explore the path to achieve a completely green scenario

Advisor: Lepech Michael
Gorinevskiĭ D. M. 
Lall Sanjay
The probabilistic method in combinatorics

The probabilistic method is the art of introducing probability to prove results that often do not involve randomness to begin with. In this thesis, we present four applications of this powerful technique, which has become one of the cornerstones of modern combinatorics. First, we prove a new lower bound for online Ramsey numbers, giving for the first time an exponential separation between the lower bounds for classical and online Ramsey numbers. Informally, this means that it is quite difficult to adaptively find a monochromatic clique in an edge-coloring of a large complete graph. Next, we determine the growth rate of a certain off-diagonal hypergraph Ramsey number, answering a question of Erdős and Hajnal from 1972. This is the first nontrivial hypergraph Ramsey number whose exponential order has been determined. The proof introduces a new random model for hypergraphs and relies heavily on the entropy method. Third, we apply the entropy method to extremal graph theory, proving Tomescu's graph-coloring conjecture from 1971. This determines the maximum number of proper k-colorings of any graph with chromatic number k and n vertices. In the proof we use an entropy inequality related to sequential importance sampling, an estimation technique from statistics. Finally, we present a result in probabilistic combinatorics outside graph theory. An n-permutation is called k-universal if it contains every k-permutation as a pattern, and it is known that the shortest k-universal permutation has length O(k^2). It was suggested by Alon that actually almost all n-permutations are k-universal for some n=O(k^2), and he proved that a random permutation of length O(k^2 logk) is k-universal with high probability. Using a structure-versus-randomness approach, we improve this bound to O(k^2 loglogk), almost closing the gap to the conjecture

Keywords: Mathematics.
Advisor: Fox Jacob
Diaconis Persi
Vondrak Ján (Mathematician)
Probability on groups : random walks and limit theorems

This thesis studies three problems in probability on groups and related structures. A variety of techniques are used, with the common thread connecting the various works being the interplay between the algebraic structure of the group and the probability within the problem being studied. The first problem concerns the mixing time of random walk on finite fields given by applying an almost rational bijection between steps of a random walk. It is shown that the mixing time is much faster than the random walk alone, using Cheeger's inequality and the Weil bounds. The second problem concerns a bi-invariant random walk on the symmetric space of symplectic forms over a finite field. Cutoff is established using spectral theory. An elementary argument is given for the eigenvalue computation, along with a proof through constructing a characteristic map relating the spherical functions to Macdonald symmetric functions. Other applications of this characteristic map are also given. The final problem concerns the distribution of descents in the symmetric group under the Mallows measure. A joint central limit theorem is established for descents of both the permutation and its inverse, as well as stronger results for their sum. The key tool is Stein's method with size-bias coupling

Keywords: Mathematics.
Advisor: Diaconis Persi
Bump Daniel
Chatterjee Sourav
Probing force-dependent interactions between aE-catenin, vinculin and F-actin

Classical cadherins are transmembrane proteins whose extracellular domains link neighboring cells, and whose intracellular domains connect to the actin cytoskeleton via β-catenin and α-catenin. The cadherin-catenin complex transmits forces that drive tissue morphogenesis and wound healing. αE-catenin binds directly to filamentous actin (F-actin) and displays catch bond behavior, such that force applied to the cadherin-catenin complex increases its lifetime on actin. Tension-dependent changes in αE-catenin conformation enable it to recruit the actin-binding protein vinculin to cell-cell junctions, where it contributes to junctional strengthening. Vinculin also plays a similar role at cell-matrix adhesions, where it is recruited to talin in a tension-dependent manner. I used single-molecule optical trap assays to probe the force-dependence of vinculin's interaction with F-actin. I found that vinculin's interaction with F-actin displays directional catch bond behavior -- that is, force directed towards the pointed (-) end of the F-actin results in mean binding lifetimes 3-fold longer than when load was applied towards the barbed (+) end. In a second set of experiments, I sought to assess whether vinculin might regulate the binding interaction between αE-catenin and F-actin. To do this, I measured force-dependent actin binding by a quaternary complex comprising the cadherin-catenin complex and the vinculin head region, which cannot itself bind actin. Binding lifetimes of this quaternary complex increased as additional complexes bound F-actin, but only when load was oriented toward the (-) end of the filament. In contrast, the cadherin-catenin complex alone did not show this form of cooperativity. Together, my findings reveal molecular mechanisms which may facilitate the higher-order cytoskeletal organization necessary for cellular functions such as cell-cell adhesion and cell mobility

Keywords: Structural Biology.
Advisor: Dunn Alexander Robert
Weis William I.
Brünger Axel T.
Bryant Zev David
Probing gut bacterial physiology and antibiotic action in complex environments

For decades, research on bacterial physiology has been conducted in standardized conditions in monocultures, ignoring the natural community context of most species. As a result, predicting the effect of perturbations such as antibiotics on gut microbial communities remains highly challenging despite its paramount importance. Mechanistic understanding of human gut microbiota ecology has also been hampered by limited throughput and the fact that key parameters often cannot be precisely tuned or measured in animal models. During this PhD, I developed tools and systems to interrogate bacterial physiology in complex communities and within animal hosts, and used these tools to make seminal discoveries regarding how life in a community alters the behaviors of individuals. The gut microbiota of the laboratory fruit fly is a low diversity consortium with 5 bacterial species from the Lactobacillus and Acetobacter genera, all of which can be cultured in vitro. I leveraged the simplicity and manipulability of this microbiota to study the role of metabolic cross feeding on growth and antibiotic action. Using in vitro synthetic communities composed of Lactobacillus and Acetobacter species, I discovered non-canonical tolerance to rifampin and erythromycin that is induced by interspecies interactions and mediated by changes in pH due to metabolic cross-feeding. Additionally, I developed imaging techniques to visualize individual bacterial cells within live fruit flies, opening the door to studying single-cell bacterial physiology within intact hosts. To enable quantitative, systematic experimentation on the human gut microbiota, I developed culturing techniques to directly compare responses in vitro and in humanized mice. I compared the sensitivity of gut commensals in vitro and in humanized mice to explore the role of heterogeneous environments and community composition in antibiotic action. I then cultured hundreds of communities that resemble the human gut microbiota using fecal samples as inocula for high-throughput liquid culturing. In vitro antibiotic treatment of these communities recapitulated changes observed in vivo in mice, and uncovered different modes of recovery, highlighting the capabilities of this methodology.

Keywords: Bioengineering.
Advisor: Huang Kerwyn Casey
Fischbach Michael
Monack Denise M.
Sonnenburg Justin
Probing quantum criticality in an iron-based superconductor with symmetry-breaking strain

In this thesis, I study the effect of the symmetry-preserving strain and the orthogonal antisymmetric strain to the critical temperature of the nematic phase transition (Ts) in a prototypical iron-based superconductor, BaFe2As2. The result establishes both strains as effective tuning parameters. This perspective is the utilized to systematically study the effect of these strain to Ts in the cobalt-doping series of BaFe2As2. A power-law behavior of the critical temperature was observed as a function of the combined tuning parameters, which is a linear combination the strains and the cobalt concentration. This provides the first clear evidence for power-law behavior of the critical temperature as a function of non-thermal tuning parameters. Lastly, I proposed an experiment that permits the measurement of the nematic susceptibility while continuously tuning the critical temperature with strain

Keywords: Applied Physics.
Advisor: Fisher Ian R. (Ian Randal)
Hwang Harold Yoonsung
Kivelson Steven