VictorJuiz/thesis huggingface dataset:
Robotic path planning with sparse environment representations

This thesis explores different aspects of robot resiliency in path planning problems. Our goal is to endow autonomous robots with the ability to be robust to uncertainty and adaptable so that they can be entrusted in challenging real-world applications. To do so, we focus on four application areas, and in each we carefully co-design the robot's environment representation and path planning algorithms so that they jointly capture the key properties of each problem. We first focus on unknown environment exploration, which is a problem where model uncertainty is so high that the planners proposed in the literature are myopic and greedy. We propose an environment representation and coverage planning algorithms that strategically balance greedy information gain and long-horizon planning so that the robot is able make strategic planning decisions over its full time budget. By doing so, we outperform methods that either ignore model uncertainty, or compensate with a myopic planning strategy, while remaining robust to perceptual challenges. We used the method in practice to explore a variety of urban, tunnel, and mine-like environments with autonomous Spot and Husky robotic platforms, and we integrated the solution in Team COSTAR's global planning framework for participating in the DARPA SubTerranean Challenge in 2021. The second application area in this thesis is the problem of searching for radio signal sources in an unknown environment. We find a similar gap in the literature where to overcome the high model uncertainty, solutions proposed unfortunately limit the robot's action space to the extent of its explored regions. We propose a formulation where the robot looks ahead into the unknown space to search for radio emitters that may be located far away from its visited locations. In the third application area, we focus on the robot's ability to adapt to human indications, which is a key aspect of making a robot resilient and trustworthy in the real world. We propose an encoding of a human's intention that enables the robot to infer a human's path preference from indications and adapt to these preferences online. Finally, we explore the Multi-Agent Path Finding problem, and propose a stochastic travel time model for robots to compute solutions optimizing their expected performance, making them robust to unexpected delays in travel time.

Keywords: Engineering.
Mechanical Engineering.
Advisor: Cutkosky Mark
Kochenderfer Mykel
Schwager Mac
The role of Caspr2 in autism spectrum disorder-related gastrointestinal function

Gastrointestinal (GI) symptoms are highly prevalent among individuals with autism spectrum disorder (ASD), but the molecular link between ASD and GI dysfunction remains poorly understood. The enteric nervous system (ENS) is critical for normal GI motility and has been shown to be altered in mouse models of ASD and other neurological disorders. Contactin-associated protein-like 2 (Caspr2) is an ASD-related synaptic cell-adhesion molecule necessary for regulating sensory function in the central and peripheral nervous system. In this study, we examine the role of Caspr2 in GI motility by characterizing Caspr2's expression in the ENS and assessing ENS organization and GI function in Caspr2 mutant mice. We find that Caspr2 is predominantly expressed in enteric sensory neurons in both the small intestine and colon. We further assess colonic motility in Caspr2 mutants using an ex-vivo motility monitor and show altered colonic contractions and faster expulsion of artificial pellets. The organization of neurons within the myenteric plexus remains unchanged. Our results suggest a role for enteric sensory neurons in ASD-related GI dysmotility, a finding relevant to consider in the treatment of ASD-related GI symptoms

Keywords: Medicine.
Advisor: Kaltschmidt Julia
Becker Laren 
Chen Lu (Professor of neurosurgery)
Clandinin Thomas R. (Thomas Robert)
Südhof Thomas C.
Role of co-receptors LRP6 and Tspan12 in Norrin/ß-catenin signaling

The Wnt/β-catenin signaling pathway co-evolved with multicellularity and dictates embryogenesis and tissue renewal across metazoans. In this pathway, the secreted growth factor Wnt binds and heterodimerizes the receptors Frizzled (Fzd1-10) and Low-density lipoprotein receptor-related protein 5 or 6 (LRP5/6) in the plasma membrane; this ultimately leads to the stabilization of transcriptional co-activator β-catenin, which directs the transcription of target genes. This pathway is also activated by the atypical ligand Norrin. In response to Wnt or Norrin binding to Fzd and LRP5/6, Dishevelled (Dvl1-3) is recruited from the cytoplasm to the plasma membrane. Dvl is essential to Wnt/β-catenin signal transduction because it bridges the receptors with downstream signaling partners, yet how Dvl is recruited to the membrane in response to ligand binding is unclear. Wnt ligands generate Fzd-LRP5/6 heterodimers and may also homodimerize Fzd, but it is not known whether or how these receptor complexes work together to transduce the ligand-binding signal to Dvl. Further, although Fzd and LRP5/6 are sufficient for Wnt-stimulated signaling, and Norrin simultaneously binds these two receptors, Norrin-stimulated signaling additionally requires the membrane protein Tetraspanin-12 (Tspan12) through an unknown mechanism. Tspan12 coimmunoprecipitates with Fzd4 and Norrin, suggesting that it acts as a co-receptor. In this dissertation, I develop biochemical tools to investigate whether the Fzd-Dvl interaction is directly strengthened either by ligand binding to Fzd or by Fzd homo- or heterodimerization with LRP5/6 or Tspan12. Using purified protein, I find that Fzd-Dvl affinity is agnostic to ligand binding or Fzd homo- or heterodimerization. Instead, we show that Fzd-Dvl binding is highly dependent on the lipid phosphatidylinositol 4,5-bisphosphate. I also find that Tspan12 directly binds Norrin with nanomolar affinity, and provide data consistent with a model in which Tspan12 captures Norrin on the cell surface, then hands it off to Fzd4 for association with LRP5/6 and subsequent signaling. This work builds on our understanding of the direct physical interactions that underlie signal initiation in Wnt- and Norrin/β-catenin signaling.

Keywords: Medicine.
Molecular and Cellular Physiology.
Advisor: Kobilka Brian K.
Feng Liang
Huttenhain Ruth
Nusse Roel
The role of monounsaturated fatty acids in ferroptosis

Ferroptosis is an iron-dependent, oxidative form of cell death that is a result of toxic accumulation of polyunsaturated fatty acid phospholipid hydroperoxides (PUFA-PL-OOH). This death can occur upon inhibition of the phospholipid glutathione peroxidase, GPX4. I find here that cancer cell lines are not uniformly reliant on GPX4 for cell survival. A compendium of factors drives GPX4 essentiality including PUFA content within cells but also reliance on de novo MUFA biogenesis. This work reveals novel MUFAs that can inhibit ferroptosis. Protective MUFAs lead to decreased abundance of toxic PUFA-PLs which reduces ferroptotic stress in cancer cells. I also find that ASCL3, which is required for oleic acid to protect from ferroptosis, is required for some but not all MUFAs to protect from ferroptosis. I identify a novel player in MUFA metabolism, MBOAT2, which prevents cells from using MUFAs to prevent ferroptosis. Finally, I discover that oleic acid can become less protective against ferroptosis when FSP1 is inhibited, depending on the cell line context. Overall, these findings suggest there is a complex machinery within cells that metabolizes MUFAs and complicates our view of how ferroptosis pathways interact

Keywords: Humanities and Sciences.
Biology.
Advisor: Dixon Scott James
Bassik Michael
Long Jonathan Z.
Morrison Ashby J.
Room : drawing presence and absence

Room is a shared aural experience. In Room, the performer sits in a four-foot cubed wooden box, drawing circles on its walls. This sound is captured by a system of contact microphones relayed into a Max/MSP patch that directs, filters, and spatializes these signals onto a dome of speakers surrounding the audience. Encircling the listener in the center of creation, the hidden performer explodes the interiority of the box outside, and brings the presence of performance inside, like a hypercube. Room explores the continuous sound and gesture of the circle, innate and timeless

Keywords: Humanities and Sciences.
Music.
Advisor: Kapuscinski Jaroslaw
Alessandrini Patricia
Chafe Chris
Ulman Erik
Safe machine learning-based perception via closed-loop analysis

While machine learning enables new capabilities for the automation of safety-critical systems such as aircraft and cars, its introduction to these systems will require a significant validation effort. Machine learning presents new challenges in safe design and validation due to its complex nature. For this reason, the initial use of machine learning in these systems is likely to be limited to tasks that cannot be performed solely by traditional automation techniques. This thesis focuses on perceptual tasks that require processing high-dimensional image data from cameras to replace visual tasks typically performed by human operators. Specifically, we outline methods that rely on closed-loop analysis to encourage safety in machine learning-based perception systems. Our first two contributions relate to the safe design of neural network-based perception systems. Once designed, these systems should be put through a thorough verification process. The final two contributions of this thesis develop methods for the formal verification of these systems

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Kochenderfer Mykel J.
Barrett Clark
Schwager Mac
Sample-efficient uncertainty calibration for reliable autonomous systems

As the capabilities of autonomous systems continue to improve, they are increasingly deployed in unstructured environments for safety-critical tasks where the cost of failure is high. These systems take as input observations from the external environment, rely on intermediate learning-based components, and output actions that govern system behavior. To operate safely and reliably in high-stakes environments, these systems must have a meaningful and well-calibrated notion of uncertainty over the inputs, over the outputs, and for the internal components. In the first part of this thesis, we address the problem of designing a meaningful and calibrated notion of uncertainty over the system inputs. Knowing when the underlying data distribution has changed and the system may be operating outside its domain of competency is a critical form of uncertainty, as such distribution shifts may lead to catastrophic failures for learning-based systems. To address this problem, we present a warning system that issues alerts when the data distribution shifts, and show that these alerts are calibrated in the sense that there are rarely false alarms (backed with statistical guarantees). This alert system issues alerts an order of magnitude faster than prior work. In the second part of this thesis, we address the problem of calibrating the uncertainty representations used by intermediate components, enabling improved real-time decision-making. In the third part of this thesis, we address the problem of designing a meaningful and calibrated notion of uncertainty over the system outputs. Recognizing when the outputs of the system may lead to a dangerous mistake is a critical form of uncertainty; thus, we present a warning system that issues alerts when a dangerous situation is imminent, and show that these alerts are calibrated by providing a statistical guarantee on their false negative rate. This calibration is achieved with limited data using techniques from conformal prediction.

Keywords: Engineering.
Electrical Engineering.
Advisor: Pavone Marco
Savarese Silvio
Guibas Leonidas J.
Sadigh Dorsa
Sarah Diane Olmstead's NSC ePortfolio

This document is an archive of an electronic portfolio (ePortfolio) produced as a capstone requirement for the Notation in Science Communication. In their ePortfolios, NSC students demonstrate how they have met the NSC learning outcomes. These learning portfolios detail students’ growth and development as scientists and science communicators rather than showcasing only their best work. ePortfolios are reviewed and approved by faculty, earning students a special designation on their official Stanford transcript that reflects their science communication acumen.

Keywords: Communication in science
Human biology
Cancer
Pediatrics
Genetics
Mixed materials
Image
Portfolio
Text
Book
Essay
Poster
Thesis
Student project report
Portfolios
Essays
Posters
Advisor: Pittock Sarah
Fisher Paul
Scalable hierarchical high-order CFD solvers for future exascale architectures

With the public launch of supercomputer Frontier at Oak Ridge National Laboratory in May 2022, we are officially entering the exascale era of computing, characterized by systems capable of executing one exaFLOPS (10^18 floating-point operations per second). With the anticipated advent of additional exascale supercomputers in the forthcoming years, the extraordinary leap in computational power promises to revolutionize various fields for scientific discovery. For the Computational Fluid Dynamics (CFD) community to harness the power of these future supercomputers for complex Navier-Stokes computations, we must rethink and redesign numerical algorithms for extreme parallelism, paying particular attention to data placement and movement. In addition, advances in turbulence simulation technology will play a critical role in the analysis and design of aerospace systems. The accurate prediction of turbulence is essential for determining aerodynamic characteristics of aircrafts. High-order numerical methods have recently emerged as a promising approach to address various challenges of scale-resolving simulations. Yet, efficient preconditioners and linear solvers, which are often the most computationally intensive parts of implicit high-order codes, are still largely elusive. In this work, we aim to design a high-order CFD solver capable of providing high-fidelity solutions for scale-resolving simulations while simultaneously showcasing efficient scalability on prospective exascale supercomputers. To this end, this dissertation presents the development and implementation of a hierarchical scalable high-order CFD solver framework. The proposed framework integrates a promising hierarchical preconditioner based on Sparsified Nested Dissection and a communication-avoiding iterative Krylov solver with guaranteed numerical stability. The performance of the framework is evaluated in an implicit Discontinuous Galerkin high-order solver. With scaling studies, we demonstrate the algorithmic scalability and robustness of the overall framework for solving real-world aerospace engineering problems

Keywords: Engineering.
Aeronautics and Astronautics.
Advisor: Alonso Juan José
Darve Eric
Saunders Michael A.
Scalable infrastructure for digital currencies

Recent years have seen a significant increase in interest among policymakers and financial institutions in digital currencies and a new generation of financial infrastructure. This thesis investigates the design of computational infrastructure and economic mechanisms for these new systems. Specifically, this thesis develops infrastructure that, unlike prior state-of-the-art systems, performs with computational scalability that is nearly-linear on both best-case and worst-case workloads, and that does not significantly limit the range of operations that a user can express. Building this infrastructure requires instantiating and therefore demonstrating the practical feasibility of previously-unused types of economic mechanisms for exchanging digital currencies, with efficiency and fairness properties of independent interest. This thesis then studies the economic tradeoffs encoded in mechanisms for exchanging assets, the tradeoffs involved when combining different mechanisms together, and ways in which these mechanisms interact with the underlying computational infrastructure.

Keywords: Engineering.
Advisor: Mazieres David
Boneh Dan
Goel Ashish
Scaling deep robotic learning to broad real-world data

From general object grasping to in-hand manipulation, deep learning has enabled a number of exciting robotic manipulation capabilities in recent years. Despite this, the quintessential home robot that can enter a previously unseen home environment and complete a wide range of tasks like humans can is far from a reality. While there are many problems to solve in accomplishing this goal, one of the central bottlenecks lies in learning control policies from the robot's sensor inputs that can generalize to new tasks, objects, and environments. For example, a robot cooking in a home cannot afford to re-learn from scratch for each new dish, nor is it feasible to hard-code state features for every new kitchen a robot might encounter. One potential route to accomplishing this generalization is to train the robot on a wide distribution of data that contains many tasks, objects, and environments. Indeed, this recipe of large, diverse datasets combined with scalable offline learning algorithms (e.g. self-supervised or cheaply supervised learning) has been the key behind recent successes in natural language processing (NLP) and vision. However, directly extending this recipe to robotics is nontrivial, as we neither have sufficiently large and diverse datasets of robot interaction nor is it obvious what types of learning algorithms or sources of supervision can enable us to scalably learn skills from these datasets. The goal of this thesis lies in tackling these challenges, and replicating the recipe of large-scale data and learning in the context of robotic manipulation. The first part of this thesis will discuss how we can scalably collect large and diverse datasets of robots interacting in the physical world and how we can effectively pre-train self-supervised world models on such offline robot datasets. We'll then explore how we might use these pre-trained world models to solve tasks by combining them with planning, first for solving long-horizon manipulation tasks, and second for completing tasks specified by natural language. Finally, we'll discuss how we might go beyond robot data and unlock the broad sources of data that exist on the web, like videos of humans, to enable more effective learning in our robots, specifically through reward learning and visual pre-training. The thesis will conclude by discussing open challenges, particularly how we might unify the paradigms of simulation, real-world data collection, and videos of humans to realize the vision of a general-purpose household robot

Keywords: Engineering.
Advisor: Finn Chelsea
Sadigh Dorsa
Scaling human feedback

Human-generated data has been pivotal for significant advancements in artificial intelligence (AI). As AI models scale and are applied to a wider range of tasks, the demand for more and increasingly specialized human data will grow. However, current methods of acquiring human feedback, such as learning from demonstrations or preferences, and designing objective functions or prompts, are becoming unsustainable due to their high cost and the extensive effort or domain knowledge they require from users. We addresses this challenge by developing algorithms that reduce the cost and effort of providing human feedback. We leverage Foundation models to aid users in offering feedback. Users initially define their objectives (through language or a small dataset), and Foundation models expand this into more detailed feedback. A key contribution is an algorithm, based on a large language model, that allows users to cheaply define their objectives and train a reinforcement learning agent without needing to develop a complex reward function or provide extensive data. For situations where initial objectives are poorly defined or biased, we introduce an algorithm that efficiently queries humans for more information, reducing the number of needed queries. Finally, we conclude by proposing an information-gathering algorithm that eliminates the requirement for human intervention altogether, streamlining the feedback process. By making it cheaper for users to give feedback, either during training or when queried for more information, we hope to make learning from human feedback more scalable.

Keywords: Engineering.
Advisor: Sadigh Dorsa
Goodman Noah (Noah D.)
Yang Diyi
Scaling single cell genomics analysis to millions of cells

Improved experimental methods in single cell genomics have increased dataset sizes by two orders of magnitude in the last five years, such that software scalability is quickly becoming a key bottleneck in our ability to analyze and understand multi-million cell atlases. Most analysis methods load full datasets in memory, resulting in excessive memory usage that scales one-to-one with dataset size. Furthermore, existing compressed storage formats for single cell datasets are so slow to read that practical analysis must be performed on uncompressed data. This work describes BPCells, a software package for scalable analysis of massive single cell RNA-seq and ATAC-seq datasets. BPCells provides lossless, seekable bitpacking compression for scATAC-seq fragment alignments and sparse single cell counts matrices. These compression formats are so fast that a single thread can decompress a dataset faster than loading an uncompressed version from a hard drive. Additionally, BPCells implements disk-backed streaming computations that can reduce memory requirements by two orders of magnitude compared to popular tools like Scanpy and Seurat, while incurring little or no speed penalty. Notably, BPCells can reproduce the results of existing software packages to within numerical precision, making it a drop-in replacement for existing tools. This work covers the design and implementation of BPCells, along with applications of single cell analysis

Keywords: Engineering.
Advisor: Greenleaf William James
Kundaje Anshul
Dror Ron
Scavenged state : archives and the making of modern China

Scavenged State reconstructs the socioeconomic and political history of modern Chinese archives. A key concept for this project is scavenging, a term that is not usually applied to state archive creation. Often considered a desperate survival tactic that occurs in the wake of ruin, I found that archival scavenging in China was surprisingly systematic, strategic, and creatively optimistic. Over a century of civil war, imperial invasion, and revolution, certain caches of documents were privileged and then reintroduced as foundational for a variety of states, including Japanese-aligned, warlord, republican, and communist. When pressured to conform to international standards (such as those favored by France, the Soviet Union, or the United Nations), record keepers advocated for "Chinese-style" systems, deliberately ambiguous phrasing that reached to imperial precedent for authority while embedding contemporary political concerns about state survival in reforms of archival material processes. These processes—which included everything from document accession and indexing guidelines to exhibition strategies, destruction protocols, and security audits—reveal fascinating shifts in strategic conservation. My research on the development and implementation of these seemingly mundane curatorial procedures shaped the source-base that scholars and policymakers rely on today. Documentary protocols turn out to have been highly political. Recognizing the role of scavenging in the creation of state archives calls for us to decenter the party-state and make room for local and individual agency in summoning forth visions for modern China

Keywords: Humanities and Sciences.
History.
Advisor: Mullaney Thomas S. (Thomas Shawn)
Chang Gordon H.
Sommer Matthew Harvey
Wigen Kären