VictorJuiz/thesis huggingface dataset:
Category-level object perception for physical interaction

Perceiving and performing physical interaction is a crucial ability of humans and therefore an important topic for machine intelligence. Human object interaction and robot object interaction, both of which involve interactions with objects, have been intensively studied for decades, leading to many research problems and applications in both computer vision and robotics community. Therefore, perceiving objects under physical interactions or for the sake of performing interaction becomes a key problem in this field. This thesis covers works on learning interaction-oriented object-centric visual representations for the sake of perceiving and performing physical interactions. The key idea is to perceive objects in a category manner and extract actionable information (e.g. pose, affordance, articulation) that is shared across different object instances from the same object category. The goal of such a vision system is to allow machines to perceive objects from a physical interaction perspective and in a generalizable, interpretable and annotation-efficient way. Here being generalizable means the vision system can well handle many object instances that may have never been seen before; being interpretable means the learned visual representation should be explainable and understandable by humans; and being annotation-efficient means we want to minimize the amount of labels required for learning such visual representations. The thesis starts with three works on estimating and tracking category-level object pose for rigid and articulated objects, which generalize the problem of pose estimation from instance level to category level.Following that, we introduce a novel semi-supervised 3D object detection framework to allow annotation-efficient learning of category-level object information. Lastly, we present a work on multi-step interaction generation, where the built system learns to perceive category-level object states and their changes in human object interaction videos and build a generative model that can be used for generating new interaction sequences as well as robotic motion planning. The thesis concludes by summarizing the projects and discusses potential future directions in the field

Keywords: Electrical Engineering.
Advisor: Guibas Leonidas J.
Finn Chelsea
Savarese Silvio
Caught On Camera, But Not Convicted: Understanding The Embeddedness of Racial Bias In Police Body Cameras

Police body cameras have been popularized as a tool that will be used to hold officers accountable after unlawfully engaging in excessive and deadly force with civilians. However, after being widely adopted by police departments, instances of excessive force and deadly force have not drastically declined. In fact, several instances of excessive and deadly force have been caught on police body camera and have shown victims that were unarmed, compliant, or nonviolent. Though footage of the interactions may exist, offending officers are often not charged with criminal behavior or are not convicted. These occurrences disproportionately affect people of marginalized backgrounds. This thesis seeks to explore the way that police body cameras can create biased, pro-police narratives and the surrounding social and technical environment that supports this bias. The environment that supports police body cameras is explored by analyzing legal cases, news reports, academic studies, and historical records of the relationship between police, technologies, and marginalized communities. Due to the environment in which they exist, an environment that has historically allowed police technologies to criminalize marginalized people and justify the actions of law enforcement personnel, it is argued that police body cameras cannot capture objective insights into violent officer-civilian interactions. This thesis argues that police technologies, even ones designed to increase transparency and social good, fall short due to protections in place for officers, technical limitations of the body camera, bias embedded into police data, and historical criminalization of marginalized people. This understanding of the police body camera’s social environment is used to directly examine visual and auditory elements of body camera footage that displays violent officer-civilian interactions. This thesis concludes with paths forward for decreasing violent officer-civilian interactions, including structural reform, increased consideration of social contexts when policing, and defunding the police.

Keywords:  Department of Science
Science Technology and Society
Stanford University
Honors program
policing
social constructivism
equity
racial justice
race
police body cameras
body cameras
marginalized communities
defund police
abolish police
racial inequity
police technology
surveillance
film analysis
case analysis
Thesis
Advisor: Li Xiaochang
Causal inference : methodological advances with an application to climate impact

Causal inference lies at the heart of leveraging data to evaluate interventions and to inform policy decisions. This thesis makes three methodology contributions. The first two introduce methods in leveraging flexible machine learning models for causal estimators, in the context of estimating heterogeneous treatment effects and learning dynamic treatment policies respectively. The third contribution provides a framework to generalize results from randomized trials across locations. I will conclude with an application of applying the state-of-the-art causal methodology to studying the impact of wildfire risk perception on the California residential real estate market

Advisor: Brunskill Emma
Wager Stefan
Liang Percy
Causes, measurement, and mitigation of loss discrepancy

Machine learning models influence people's lives profoundly. In spite of their great performance, it has been observed that their predictions are often discriminatory against protected groups (e.g., women). One way to analyze the discrimination in machine learning models is to measure the difference in model performance across groups or individuals known as loss discrepancy. There are two flavors of loss discrepancies: statistical and counterfactual loss discrepancy. Statistical loss discrepancy measures how much-protected groups are impacted differently. Counterfactual loss discrepancy measures how much similar individuals are treated differently because of their group membership. Recent studies usually attribute this loss discrepancy to an information deficiency for one group (e.g., one group has less data). In this thesis, I show that: 1) Even when there is no information deficiency specific to one group (e.g., both groups have infinite data), adding the same amount of feature noise to all individuals leads to loss discrepancy; 2) Even when features are noiseless and perfectly determine the prediction target, the inductive bias in the overparameterized regime leads to reliance on spurious features and loss discrepancy. Understanding the source of loss discrepancies helps to come up with mitigation methods. I explain two methods to mitigate loss discrepancy. First, I explain how to leverage unlabeled data to reduce counterfactual loss discrepancy without affecting accuracy. Next, I propose another mitigation method, the unanimity principle: only predict when all models consistent with the training data predict the same output. I operationalize this principle for semantic parsing, the task of mapping utterances to logical forms. I develop a simple, efficient method that reasons over the infinite set of consistent models by only checking two models. I prove that this method obtains 100% precision, thus no loss discrepancy. Finally, I investigate the methods to measure of loss discrepancy when there is no information about protected attributes, or there are exponentially many protected groups. I introduce and study a notion which I call maximum weighted loss discrepancy (MWLD), the maximum (weighted) difference between the loss of a group and the loss of the population. I show that it is statistically impossible to estimate MWLD when all groups have equal weights, but for a particular family of weighting functions, I show how to estimate MWLD efficiently. Finally, I draw a relation between MWLD and loss variance, a quantity that arises in generalization bounds

Advisor: Liang Percy
Hashimoto Tatsunori 
Reingold Omer
Celebrating Twenty-Five Years of Cardcaptor Sakura: A Print and Digital Publishing History of Manga from 1996 to 2021

Manga’s materiality significantly alters the reading experience, and by studying these properties it becomes possible to document how changes in a fan-reader’s personal or shared reading experience inform a type of humanistic manga publishing history for Japan and North America as well as globally. What does this rise in manga’s popularity which took it from a national to a global product look like? CLAMP’s Cardcaptor Sakura and Cardcaptor Sakura Clear Card tell us thanks to extensive republishing efforts by four different publishers over a span of twenty-five years. While this thesis only touched one aspect of this phenomenon by trying to address how manga publishing transformed from one where the most esteemed manga artist said it would not become global to one where multiple editions of a series are present outside its country of origin, it grew to comment on manga as a global product with origins in Japan due to the remarkable spreadability of digital manga.

Keywords:  Stanford Global Studies
Stanford Global Studies
East Asian Studies
Manga Studies
Cardcaptor Sakura
CLAMP (Mangaka group)
TokyoPop (Firm)
Kōdansha
Mixx Entertainment
Dark Horse Comics
Comic books, strips, etc.
Comic books, strips, etc. > Collectors and collecting
Comic books, strips, etc. > Publishing > History
Graphic novels
Japan
Japanese comic books, strips, etc.
Text
Thesis
Advisor: Inoue Miyako
A cellular roadmap of ribosome heterogeneity that controls stem cell fate and organismal development

The regulation of translation is fundamental to the diversification and control of gene expression across cell types and tissues. The processes of stem cell renewal and differentiation have been reported to be particularly sensitive to changes in protein synthesis, suggesting that finely tailored translation is necessary for the proper regulation of gene expression during cell fate specification, but the mechanisms underlying this translational control remain poorly understood. A source for this regulation may be the ribosome itself, which has recently been shown to be heterogeneous in composition within mouse embryonic stem cells, with distinct ribosomal populations specialized for the translation of particular sets of mRNA transcripts. However, as ribosome heterogeneity has previously been measured only within individual cell types, we lack a systematic view of how ribosome composition changes across development and whether particular ribosome compositions have tissue-specific functions. To this end, I present work delineating changes in ribosome composition across cell differentiation and identifying specialized functions for ribosomal components in the translation of core developmental signaling pathways needed for organismal patterning

Keywords: Biology.
Advisor: Barna Maria (Professor of developmental biology)
Puglisi Joseph D.
Red-Horse Kristy
Stearns Tim
Centriole and cilium formation in the development of mouse olfactory sensory neurons

Olfaction, the sense of smell, is mediated by olfactory sensory neurons (OSNs) and the multiple cilia which protrude from the dendrite into the nasal cavity. A cilium (plural: cilia) is a microtubule-based structure which arises from a centriole, located at its base. The centriole, itself, is also made up of microtubules as well as hundreds of associated proteins. Many eukaryotic cells, including most cell types in the vertebrate body, maintain a strict count of two centrioles and one cilium per cell. Although OSNs have long been known to have multiple cilia, how they arrive at this state has remained largely unknown. Here, we characterize centriole-related events during the OSN differentiation in the olfactory epithelium of mice. Using electron microscopy and fluorescence microscopy, we build upon work by Cuschieri and Bannister (Cuschieri and Bannister, 1975) by demonstrating that cells in the olfactory epithelium of both adult and embryonic mice amplify centrioles via rosette structures, and that free centrioles are also present in the rosette-bearing cells. Intriguingly, cells amplify centrioles during S phase and go on to divide, rather than amplifying centrioles after cell cycle exit, as canonical multiciliated cells are known to do. Furthermore, we identify the early immediate neuronal precursor stage as the developmental time during which centrioles are likely to be amplified. In addition to traditional fluorescence microscopy, we also developed a procedure for expansion microscopy. We use these techniques to characterize what happens to centrioles after they are amplified in progenitors near the basal lamina, building on earlier work (Heist and Mulvaney, 1968; Mulvaney and Heist, 1971). We observed migrating centrioles in the mouse olfactory epithelium, and although they migrate in one or a few groups, the cohesion protein rootletin is missing from migrating centrioles. Migrating centrioles also have less abundant γ tubulin than do centrioles in mature OSNs, and all but one centriole lack distal appendages during migration. These data suggest that centrioles in immature OSNs migrate in a reduced state and proteins associated with mature OSN centrioles only accumulate once centrioles reach the apical surface, where olfactory cilia ultimately form. Together, our findings describe major events and characteristics associated with centrioles during the development of OSNs

Keywords: Biology.
Advisor: Stearns Tim
Luo Liqun
Shen Kang
Changes of geometry and properties of porous media

This work enables the computation of material properties, both static and dynamic, from 2D images, and of course, from 3D images. For example, we may compute the porosity of a rock from a microscopy image. Moreover, from that single microscopy image, we can produce a 3D geometry that changes as a function of time, subsequently obtaining permeability changes as a function of some processes (such as compaction). We highlight that only a single 2D image of a material may be used to get the results and predictions that we explain. We introduce a method based on geometry that increases the dimension of a single 2D image to a 3D image, where computational simulations provide reasonable estimates of the reality. Then we move on to increase the dimension even more by including time, where we simulate physical and chemical processes that change the material structure. Consequently, getting the history of changes in the material properties. We provide powerful tools for any industry working with porous materials. This work is the first step towards explaining natural and man-made: diagenesis, cementation, dissolution, pressure solution, filter degradation, and any other process that changes the underlying structure of a material

Keywords: Geophysics.
Advisor: Mukerji Tapan
Mavko Gary
Nur Amos
Characterization and linking of imaging and molecular patterns in hepatocellular carcinoma

A wealth of cancer imaging data is becoming available due to routine clinical care. Similarly, public research efforts are unprecedently making large genomic cancer data sets more accessible. These data present unique opportunities: with the right statistical tools we can improve our clinical decision making and biological knowledge of disease. One instance of disease where computational models can be used to improve medical treatment is hepatocellular carcinoma (HCC). HCC is the third most common cause of cancer-related death in men, accounting for three billion dollars in direct and indirect costs. In this dissertation, we present the application and development of methods that leverage clinical radiological image data and genomic data of cancer with a focus on hepatocellular carcinoma as an example. This work comprises applications of statistical tools and machine learning algorithms to create surrogate cancer biomarkers, and the development of statistical methods that improve clinical diagnosis and discovery of cancer mechanisms. First, (I) we evaluate inter-reader reliability in annotating semantic features on preoperative computed tomography and investigate the potential of semantic features of radiological images to provide a surrogate imaging biomarker to microvascular invasion. We then show the overall superiority of consensus models over single-reader semantic-based models. Second, (II) we employ a radiomic-based approach to develop an imaging signature and develop a radomics model with superior performance over previously reported semantic signatures. Third, (III) we develop a new model for gene regulatory inference and learning. Specifically, our framework is the first to combine Bayesian and module-based approaches. It tackles an important limitation in module-based methods by allowing probabilistic assignments of target genes to modules. Our method outperforms current methods on sparsity metrics, with similar performance in modeling module expression. We show that our method can recover a range of diverse liver functions from metabolic and immune to protein synthesis

Keywords: Electrical Engineering.
Advisor: Napel Sandy
Gentles Andrew J.
Gevaert Olivier Michel Simonne
Nishimura Dwight George
Pauly John (John M.)
Characterization of centromere assembly machinery interactions in Xenopus laevis

Centromeres are specialized chromosomal regions that mediate chromosome segregation during mitosis by providing the assembly site for the mitotic kinetochore. Centromeres are epigenetically defined by the incorporation of the histone H3 variant, centromere protein A (CENP-A), into centromeric nucleosomes. In vertebrates, CENP-A nucleosome assembly is coupled to the exit from mitosis and requires the targeting of soluble CENP-A to the centromere in complex with its histone chaperone the holliday junction recognition protein (HJURP). Two centromere specific factors, centromere protein C (CENP-C) and the Mis18 complex (Mis18α, Mis18β, and M18BP1) are required for recruiting HJURP to centromeres. However, how these factors coordinate the cell cycle specific assembly of CENP-A at centromeres is poorly understood. Using a cell free system for centromere assembly in Xenopus laevis frog egg extracts, I discovered a mechanism for regulating the cell cycle dependent interaction between HJURP and CENP-C. Non-phosphorylatable mutations in HJURP serine 220 result in constitutive interaction between HJURP and CENP-C in metaphase yet do not trigger new CENP-A assembly, while phosphomimetic mutations prevent HJURP binding to CENP-C and inhibit new CENP-A assembly. I show that the inhibitory activity that prevents HJURP localization and new CENP-A assembly in metaphase is due to competition for HJURP's binding site on CENP-C by M18BP1.S. My results identify two important mechanisms for coupling CENP-A assembly to the cell cycle; negative regulation of HJURP-CENP-C interaction by mitotic phosphorylation and competitive inhibition of metaphase HJURP-CENP-C interaction by M18BP1.S.

Keywords: Biology.
Advisor: Straight Aaron
Feldman Jessica L.
Gozani Or Pinchas
Rohatgi Rajat
Characterization of mouse Bmp5 regulatory injury element in zebrafish wound models

The body plan and tissues of an organism are built during development through a series of repeatable and ordered events. Restoring the body plan or the integrity of a tissue following injury is more complex, since no two injuries are exactly alike. Zebrafish, Danio rerio, are champion tissue regenerators, and I begin this dissertation with a review of regeneration biology, specifically through the lens of zebrafish tail regeneration. One emergent theme is that key signaling molecules that specify patterning are used in both development and following injury. However, little is known about how these molecules are re-activated. I focus on understanding the re-activation of one of a classic group of signaling molecules: Bone Morphogenetic Proteins (BMPs). Previous studies have mapped a large "injury response element" (IRE) in the mouse Bmp5 gene that drives expression in bone fractures and other types of injury. Here I show that the large mouse IRE regulatory region is also activated in both zebrafish tail resection and mechanosensory hair cell injury models. Using the ability to test multiple constructs and image temporal and spatial dynamics following injury responses in fish, I have narrowed the original size of the mouse IRE region by over 100-fold and identified a small 142 bp minimal enhancer that is rapidly induced in both mesenchymal and epithelial tissues after injury. These studies identify a small mouse sequence that responds to evolutionarily conserved local signals in wounded tissues and suggest candidate pathways that contribute to BMP reactivation after injury. Mammals have lost much of their capacity to regenerate; reclaiming some of this ability would be of great significance to human health. I discuss potential therapeutic applications suggested by this work, as well as possible roles for the IRE in human diseases beyond acute injuries. I also discuss the tradeoffs that guide evolutionary modifications to the IRE and injury enhancers generally. Together, this work adds new detail to our understanding of how organisms meet the challenge of growing tissue outside the predictable events of development

Keywords: Developmental Biology.
Advisor: Kingsley David M. (David Mark)
Fuller Margaret T.
Jarosz Daniel
Talbot William S.