VictorJuiz/thesis huggingface dataset:
Learned motion models for the perception and generation of dynamic humans and objects

Understanding the motion of humans and objects is key for intelligent systems. Motions are the result of physics, but non-physical dynamics also play an important role, for example, social norms and traffic laws determine how pedestrians and vehicles behave. The ability to perceive and generate these motions enables important applications, such as autonomous robots that operate in the real world, mixed reality that augments the real world, and animation and simulation that imitate the real world. Despite often being approached as separate problems, the perception and generation of motion both fundamentally rely on having an accurate model of dynamics for humans and objects in a scene. Perception problems like pose estimation, tracking, and shape estimation require motion understanding to reason about occlusions and noise from partial and ambiguous inputs. Generation problems such as forecasting future motion rely entirely on being able to predict motion. A promising avenue to solve these problems is learning models of motion, however, it is challenging to develop models that accurately reflect the real world, capture the diversity of motion due to inherent uncertainty, and robustly generalize to many possible scenarios. This thesis explores how to effectively learn models of motion to solve important perception and generation problems. We propose several data-driven methods to accurately capture the dynamics of humans, objects, and how they interact with each other and their environment. In the first part of the thesis, we introduce two methods for perceiving 3D human pose and 3D object shape, respectively. The first uses a robust generative model of 3D human pose transitions, while the second learns a continuous motion representation entirely from point cloud observations. The second part of the thesis focuses on motion models for synthesizing high-level human behavior in the form of 2D top-down trajectories. In these works, we introduce two new generative models that handle complex multi-agent interactions and can be controlled by a user to produce trajectories with desirable properties. We show this is useful to create rare scenarios for testing autonomous vehicles and to animate crowds of pedestrians. Finally, the thesis ends with a discussion of important future directions to continue improving learned models of motion for humans and objects

Keywords: Engineering.
Advisor: Guibas Leonidas J.
Bohg Jeannette
Liu Cheng-Yun Karen
Learned Syntax Aware Embeddings Using Equivariant Graph Neural Networks

In this paper, we aim to improve foundational model embeddings by explicitly leveraging syntactic information. Present foundational models do not explicitly model structural information, which can be problematic in certain domains such as biochemistry. We model this syntactic information as a graph to auto-encode domain-specific information into the large model embeddings. Particularly, we implement an Equivariant GNN version of the SIWR model to further improve downstream performance by considering a given sequence as a vector backbone. We pose a generalized learning framework for Syntactically Aware Embeddings (SAEs) that extend across learning domains. We pretrain SAE models using a small amount of data (~ 30,000 samples) and test downstream performance on a variety of learning domains and tasks. We test SAEs on the NLP (GLUE and CoNLL benchmarks) and biochemical (SMP) domains. Across the NLP domain, the SAEs outperform baseline embeddings on 10/11 tasks. In the biochemical domain, we observe improvements in 11/20 tasks. SAEs show particular promise in settings with limited fine-tuning when compared to baselines.

Keywords: Embeddings
graph neural networks
Natural language processing (Computer science)
Biochemical engineering > Computer simulation
Text
Thesis
Advisor: Dror Ron
Learning on the Job: How Teachers Learn to Adapt Their Approaches to Conversations About Racism

In recent years, amplified by the unjust murders of George Floyd, Breonna Taylor, and countless other Black lives, there has been a national push to better educate our citizens about race and racism. In response, there have also been attempts to limit these discussions, with some states passing bills that prohibit teachers from talking about racism, white supremacy, and Critical Race Theory. However, in order to help our students develop into empathetic and informed citizens, discussions of race and racism must occur in classrooms. Due to the flexibility of their subject and the focus on identity development, California high school English teachers are uniquely situated to highlight these discussions in their classrooms, yet many teachers do not feel prepared or comfortable to do so early in their careers. In order to better understand how teachers’ level of comfort with and approaches to these conversations change throughout their career, I conducted semi structured interviews with four California Bay Area high school English teachers, all of whom work at the same school and have been teaching for over a decade. My findings revealed most of the teachers felt uncomfortable having these discussions with their students at the beginning of their career, but they all viewed English teachers as having some level of responsibility to facilitate these discussions and struggled to engage different student beliefs. They all gained comfort with these discussions throughout their career and were able to adapt their approaches to these conversations accordingly by taking into account how they frame and model these conversations, learning from their mistakes, and establishing a strong classroom culture. Importantly, through working with colleagues who were equally open-minded and committed to this work and learning from their students, especially their Students of Color, they were able to gain comfort with their discomfort in these conversations by acknowledging that they have a great extent to learn from others’ lived experiences and commitment to this work.

Keywords: Education
Race
Racism
Education
Learning
Teachers
Text
Thesis
Advisor: Kelman Ari Y
Garcia Antero
Learning priors for neural scene representations

Neural 3D scene representations have recently emerged as a novel way to store information about 3D environments, their properties, and behavior, with learned features. Their key distinction from classical computer graphics representations is that the parameters of a representation can be obtained through optimization with the objective to be consistent with observations. As such, they have become a transformative tool for combining techniques from computer graphics and machine learning to represent 3D scenes. These representations have found use in applications ranging from robotics and remote sensing to cinematography and video editing. However, these representations are limited by the fact that they are only able to leverage captured information in a specific single scene. Specifically, only observations of a single individual scene can be used to create and improve the quality of the neural representation of this scene. This leads to undesirable properties, like slow creation of neural scene representations from observations or inability to generate realistic entirely new neural scene representations. In this thesis, Learning Priors for Neural Scene Representations, I propose novel methods for creating neural 3D scene representations which leverage information learned from data beyond a single individual scene. I look into using 3D, images, and pre-trained models as sources of information from which neural 3D scene representation priors can be learned. Moreover, I show how building standard computer graphics methodology into our neural scene representation architecture can ease the learning of priors from data which does not capture full 3D geometry.

Keywords: Engineering.
Electrical Engineering.
Advisor: Wetzstein Gordon
Van Roy Benjamin
Wu Jiajun
Learning to design protein structure and sequence

Over the last ten years the field of de novo protein design has been revolutionized by new computational methods. Here, I discuss work which illustrates applications of the "traditional" methods of de novo protein design, sampling with MCMC using semi-physical energy functions and heavy manual intervention to design a novel TIM barrel and a stabler and more functional IL-2 cytokine. I will also discuss my work on developing "new" methods based on deep learning which are much more efficient and accurate than traditional methods, and abstract away the need for expert curation. This includes some of the first works to generate NTF2 structures conditioned on secondary and tertiary structure labels, and to co-design protein structures and sequences for all-atom protein modeling. Finally, I briefly conclude and suggest possible future directions for the field to continue improving how we are able to design novel proteins.

Keywords: Humanities and Sciences.
Advisor: Huang Possu
Altman Russ
Dror Ron
Fordyce Polly
Leveraging a cancer drug's off-target effects

Red blood cells (erythrocytes, RBCs) are common to almost all vertebrate life on earth. In humans, RBCs circulate for approximately 120 days prior to their phago-cytic destruction by macrophages. RBCs are protected from premature destruction by their expression of the surface protein CD47, which interacts with the macrophage surface receptor SIRPa to inhibit phagocytosis. CD47 is expressed by all cells, and is overexpressed in many cancers. Antibody-mediated blockade of the CD47-SIRPa interaction overcomes this resistance, promoting macrophage-mediated destruction of cancer cells. This strategy works both by disrupting the anti-phagocytic CD47-SIRPa interaction and by engaging pro-phagocytic macrophage-expressed Fc gamma receptors (FcgRs). While a number of CD47-blocking antibodies are now in clinical trials for cancer immunotherapy, one question remains: how are patients able to tolerate CD47 antibody, given that it sensitizes their RBCs to phagocytic destruction? We find that CD47 antibody therapy induces myeloid FcgR-mediated pruning of RBC CD47 in mice, with global concomitant myeloid FcgR loss and splenic myeloid phagocytic inhibition, which protect antibody-bound and CD47-deficient RBCs from phagocytosis. Our findings provide a mechanism for the surprising tolerability of CD47 blocking antibodies and a mechanism by which RBC-binding antibodies may protect against autoimmune destruction of circulating cells like RBCs and platelets.

Keywords: Medicine.
Advisor: Nolan Garry P.
Weissman Irving L.
Angelo Michael (Pathologist)
Jaiswal Siddhartha
Leveraging azides in the synthesis of cyclobutenes and the conversion of arenes to pyridines

Four-membered rings are rapidly becoming sought-after scaffolds in pharmaceuticals due to their rigid structure and well-defined exit vectors. Towards this goal, we developed a method to enantioselectively form cyclobutenes from simple olefins and N-sulfonyl-1,2,3-triazoles. While these triazoles are known to act as diazo precursors via ring-chain tautomerization, we recognized them as vicinal dicarbene equivalents. Thus, alkynes are reacted in [3+2] cycloadditions with azides to form triazoles, which are then reacted with alkenes in a formal [2+2] cycloaddition. A host of enantioenriched cyclobutenes were synthesized, several of which were carried on to assemble the carbon skeletons of several natural products. The ability to selectively delete, insert, or exchange atoms in the core scaffolds of molecules is of fundamental interest to synthetic chemists and could be of great use to medicinal chemists seeking to rapidly modulate the parameters of lead compounds. Though atom deletions and insertions have garnered much interest in the form of ring contractions and expansions, atom exchanges have seen considerably less development. One notable exchange that has eluded chemists is the conversion of benzene to pyridine, which is of interest due to the "necessary nitrogen atom" effect, which describes the enhancement of key pharmacological properties when an arene in a lead compound was replaced with a pyridine. To this end, we found that azides serve as effective nitrene precursors to engage arenes in a C to N atom exchange sequence featuring nitrogen atom insertion and carbon atom deletion

Keywords: Chemistry.
Advisor: Burns Noah
Du Bois Justin
Xia Yan
Leveraging game structure in modern optimization

First-order algorithms have risen in prominence in modern optimization, partly due to their successful application to problems in data science and machine learning, especially in settings with large dimensionality and dataset size. In this thesis, we focus on large-scale optimization problems with game structure, either explicitly or implicitly. By uncovering and leveraging such game structure, we design accelerated or randomized first-order algorithms which obtain nearly-linear or sublinear time for a wide range of fundamental decision making tasks. Towards achieving our results we take a modern twist on two classic optimization techniques, acceleration and randomization, demonstrating their versatile utility in modern algorithm design for game-structured optimization tasks.

Keywords: Engineering.
Management Science and Engineering.
Advisor: Sidford Aaron
Blanchet Mancilla Jose
Ye Yinyu
Leveraging text analytics and large-scale collections of identity artifacts for the culturally-informed classroom : creating teacher resources, gaining student insights, and designing learning experiences

Despite widespread consensus in education research that incorporating students' lived experiences in the classroom can improve teaching and learning outcomes, opportunities for students to benefit from such instruction remain scarce. Constraints on teachers, such as lack of time, resources, and peer support, often hinder teachers' ability to design learning environments that are responsive to students' lived experiences. This dissertation proposes and investigates a novel approach to addressing these limitations. Drawing from theoretical and pedagogical frameworks such as Funds of Knowledge and Funds of Identity, a 21-hour online workshop for teachers was developed and piloted. The workshop leverages the analysis of large-scale textual data from a culturally-relevant corpus of student productions to identify recurrent issues that matter to students. This dissertation contends that outputs of such analyses, including lists of topics and associated essays, can serve as an accessible source for teachers to incorporate students' experiences in the classroom. This dissertation further explores how teachers learn from these cultural artifacts and the ways in which they make connections to learners' lived experiences in curriculum units and lesson plans after engaging with them during the workshop. Twelve Brazilian public-school teachers participated in the study, providing valuable insights into the applicability of the proposed approach. The results highlight the potential of such an innovative approach for enhancing culturally-informed teaching and learning environments and justify further research in this direction

Keywords: Education.
Advisor: Pea Roy D.
Banks Adam J. (Adam Joel)
Blikstein Paulo
Loyalka Prashant
Lexi Straube'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
Mind and body > Health aspects
Mixed materials
Image
Portfolio
Software/code
Sound
Text
Video
Book
Essay
Photograph
Podcast
Poster
Sound recording
Story
Thesis
Student project report
Portfolios
Essays
Photographs
Podcasts
Posters
Fiction
Advisor: Brawn Shay
LIN ZEXU’S SECOND ACT: SETTLEMENT AND DEVELOPMENT OF THE TARIM BASIN 1844–1847

The Qing empire’s early development of its western frontier remains an under-studied area in English and Chinese scholarship. Shortly after the First Opium War (1839–1842), the emperor turned his attention to the looming overpopulation crisis facing the empire. The Taklamakan oases of the Tarim Basin seemed to provide a ready solution: the emperor resolved to settle the landless farmers from the interior provinces in this western borderland, also known as the “Muslim Region.” In time, he argued, the migrants would become “natives,” i.e. people born of the land. When his local administrators rejected the idea, he sent the cashiered official-in-exile Lin Zexu to conduct a fact-finding trip. Drawing on the diaries, letters, and field reports of Lin Zexu, this project uses the history of an individual actor to illuminate Qing’s attempt to assimilate the Taklamakan oases via a settlement policy focused on social structure, water management, and frontier security. The project provides a useful comparison to other empires’ expansion efforts and China’s current development programs in the region.

Keywords:  Stanford School of Humanities and Sciences
Lin, Zexu, 1785-1850
林則徐
Daoguang, Emperor of China, 1782-1850
道光帝
Bu-yan-tai, 1791–1880
布彥泰
Mujangga, 1782–1856
穆彰阿
Tarim Basin
Tarim River
Taklamakan Desert
Borderlands
Xinjiang
settlement
migration
oasis
Qing Empire
Text
Thesis
Advisor: Sommer Matthew
Lineage & freedom : a quantum computational framework for improvisation

Musical improvisation is the spontaneous act of musically communicating and composing with one or more musicians in the moment. However, in the current field of music information retrieval (MIR) there has not been a focus on developing algorithms to better understand the inner workings of improvisation, especially between musicians of different cultures and backgrounds. It is in part, due to the vast and complex space in which improvisation operates. A space with infinite outcomes that could possibly unfold. Because of this, classical computers are not efficient at modeling these types of interactions. Musical improvisation is more aligned with quantum computers, their properties allow us to simultaneously consider and handle this large number of musical possibilities. This dissertation proposes and outlines a new framework for developing music improvisation applications on quantum computers.

Keywords: Humanities and Sciences.
Music.
Advisor: Chafe Chris
Alessandrini Patricia
Bosi Marina
Smith Julius
Linguistic Indicators of the Growth Mindset

We use data from the synergistic mindsets intervention to find how language in student essays might correlate with self-reported stress levels n= 2000. We use word count based approaches, topic modeling, and word embedding based methods to find the linguistic patterns that correlate with stress levels. Our results show that in comparison to students who cope poorly with stress, those that cope well make greater use of negative over positive emotions, past and present tense over future tense, higher cognitive processing words, and more action over bodily sensation words in their writing. Our text classification models predict stress appraisals with an accuracy of 68 ± 0.4%. However, the length of the text students write is a major confounder in all our results. We discuss the text length as a confounder along with our other qualitative findings in relation to previous linguistic studies of mindfulness and stress.

Keywords: Language and education
Computational linguistics
Essays
growth mindset
Mindset (Dweck, Carol S.)
liwc
Text
Capstone
Report
Technical report
Thesis
Student project report
Advisor: Demszky Dora
Literary Reasoning in Large Language Models

Humans are capable of recognizing similarities not only between single concepts but also between intricate real-world situations that involve multiple entities and relationships. Such analogical reasoning has been considered the hallmark of human cognition. Humans are also able to draw affective inferences from such real-world situations, such as which situations inspire attention and empathy, and to what degree. Large Language Models (LLMs) have shown a variety of human-like content effects in logical reasoning and human-like performance in analogical reasoning in zero-shot and few-shot conditions. This study examines the interaction of LLMs with narratives, with a focus on inferred affect, fictionality, and embedding. In particular, I investigate LLMs’ estimations of affective impact and empathy in response to narrative stimuli presented in one of four different conditions: a fictional story with no embedding,  a fictional story with embedding, a non-fictional story with no embedding, and a non-fictional story with embedding. This study builds upon prior work in narrative psychology and natural language understanding, proposing that LLMs as few-shot and zero-shot learners can differentiate the affective impact of narratives presented in different ways, and demonstrate literary reasoning, in particular, the abilities to 1) draw affective inferences from a story and 2) generate stories analogous to the story in question in mood, form and content.

Keywords: Large Language Models
Reasoning
Fiction
Text
Thesis
Lithium ferrite for spin-based spintronics

The realization of more energy efficient nanomagnetic information devices in the field of spintronics relies on the existence of magnetic insulators capable of supporting pure spin currents in the absence of a dissipative charge current. Currently, there is a limited number of thin-film magnetic insulators with low magnetic damping. Li0.5Fe2.5O4 (LFO) is well known to possess the lowest damping among the bulk spinel structure oxides, but, thus far, LFO thin films have not lived up to these expectations. In this work I establish LFO as a promising low damping material in the field of spintronics. First, I demonstrate the first successful synthesis of LFO thin films with bulk-like magnetic properties. With chemical substitution, perpendicular magnetic anisotropy (PMA) was engineered into these LFO films - an additional desirable characteristic for spintronics devices. As a proof-of-concept application I fabricate an LFO-based device and demonstrate spin-orbit-torque switching in LFO films with current densities up to an order of magnitude lower than those observed in similar systems. Furthermore, I demonstrate that LFO can support the transport of pure spin currents over micron length scales, which is moderately large compared to other spinel ferrite insulators. These properties make LFO ideal for efficient spin current applications and highlight LFO's potential to be an all-star material in the field of spintronics.

Keywords: Humanities and Sciences.
Applied Physics.
Advisor: Suzuki Yuri
Feldman Ben
Hwang Harold