Doctor of Philosophy in Computing

dissertationPhysics-based animation has proven to be a powerful tool for creating compelling animations for film and games. Most techniques in graphics are based on methods developed for predictive simulation for engineering applications; however, the goals for graphics applications are dramatically different than the goals of engineering applications. As a result, most physics-based animation tools are difficult for artists to work with, providing little direct control over simulation results. In this thesis, we describe tools for physics-based animation designed with artist needs and expertise in mind. Most materials can be modeled as elastoplastic: they recover from small deformations, but large deformations permanently alter their rest shape. Unfortunately, large plastic deformations, common in graphical applications, cause simulation instabilities if not addressed. Most elastoplastic simulation techniques in graphics rely on a finite-element approach where objects are discretized into a tetrahedral mesh. Using these approaches, maintaining simulation stability during large plastic flows requires remeshing, a complex and computationally expensive process. We introduce a new point-based approach that does not rely on an explicit mesh and avoids the expense of remeshing. Our approach produces comparable results with much lower implementation complexity. Points are a ubiquitous primitive for many effects, so our approach also integrates well with existing artist pipelines. Next, we introduce a new technique for animating stylized images which we call Dynamic Sprites. Artists can use our tool to create digital assets that interact in a natural, but stylized, way in virtual environments. In order to support the types of nonphysical, exaggerated motions often desired by artists, our approach relies on a heavily modified deformable body simulator, equipped with a set of new intuitive controls and an example-based deformation model. Our approach allows artists to specify how the shape of the object should change as it moves and collides in interactive virtual environments. Finally, we introduce a new technique for animating destructive scenes. Our approach is built on the insight that the most important visual aspects of destruction are plastic deformation and fracture. Like with Dynamic Sprites, we use an example-based model of deformation for intuitive artist control. Our simulator treats objects as rigid when computing dynamics but allows them to deform plastically and fracture in between timesteps based on interactions with the other objects. We demonstrate that our approach can efficiently animate the types of destructive scenes common in film and games. These animation techniques are designed to exploit artist expertise to ease creation of complex animations. By using artist-friendly primitives and allowing artists to provide characteristic deformations as input, our techniques enable artists to create more compelling animations, more easily

Do Large Language Models know what humans know?

Humans can attribute beliefs to others. However, it is unknown to what extent this ability results from an innate biological endowment or from experience accrued through child development, particularly exposure to language describing others' mental states. We test the viability of the language exposure hypothesis by assessing whether models exposed to large quantities of human language display sensitivity to the implied knowledge states of characters in written passages. In pre-registered analyses, we present a linguistic version of the False Belief Task to both human participants and a Large Language Model, GPT-3. Both are sensitive to others' beliefs, but while the language model significantly exceeds chance behavior, it does not perform as well as the humans, nor does it explain the full extent of their behavior -- despite being exposed to more language than a human would in a lifetime. This suggests that while statistical learning from language exposure may in part explain how humans develop the ability to reason about the mental states of others, other mechanisms are also responsible

A role for metabolism in determining neonatal immune function

Immune responses of neonates differ markedly to those of adults, with skewed cytokine phenotypes, reduced inflammatory properties and drastically diminished memory function. Recent research efforts have started to unravel the role of cellular metabolism in determining immune cell fate and function. For studies in humans, much of the work on metabolic mechanisms underpinning innate and adaptive immune responses by different haematopoietic cell types is in adults. Studies investigating the contribution of metabolic adaptation in the unique setting of early life are just emerging, and much more work is needed to elucidate the contribution of metabolism to neonatal immune responses. Here, we discuss our current understanding of neonatal immune responses, examine some of the latest developments in neonatal immunometabolism and consider the possible role of altered metabolism to the distinctive immune phenotype of the neonate. Understanding the role of metabolism in regulating immune function at this critical stage in life has direct benefit for the child by affording opportunities to maximize immediate and long-term health. Additionally, gaining insight into the diversity of human immune function and naturally evolved immunometabolic strategies that modulate immune function could be harnessed for a wide range of opportunities including new therapeutic approaches

Mates2Motion: Learning How Mechanical CAD Assemblies Work

We describe our work on inferring the degrees of freedom between mated parts in mechanical assemblies using deep learning on CAD representations. We train our model using a large dataset of real-world mechanical assemblies consisting of CAD parts and mates joining them together. We present methods for re-defining these mates to make them better reflect the motion of the assembly, as well as narrowing down the possible axes of motion. We also conduct a user study to create a motion-annotated test set with more reliable labels.Comment: Contains 5 pages, 2 figures. Presented at the ICML 2022 Workshop on Machine Learning in Computational Desig

The photomultiplier tube calibration system of the MicroBooNE experiment

We report on the design and construction of a LED-based fiber calibration system for large liquid argon time projection detectors. This system was developed to calibrate the optical systems of the MicroBooNE experiment. As well as detailing the materials and installation procedure, we provide technical drawings and specifications so that the system may be easily replicated in future LArTPC detectors.National Science Foundation (U.S.) (Grant PHY-1205175

Canagliflozin impairs T cell effector function via metabolic suppression in autoimmunity

Augmented T cell function leading to host damage in autoimmunity is supported by metabolic dysregulation, making targeting immunometabolism an attractive therapeutic avenue. Canagliflozin, a type 2 diabetes drug, is a sodium glucose co-transporter 2 (SGLT2) inhibitor with known off-target effects on glutamate dehydrogenase and complex I. However, the effects of SGLT2 inhibitors on human T cell function have not been extensively explored. Here, we show that canagliflozin-treated T cells are compromised in their ability to activate, proliferate, and initiate effector functions. Canagliflozin inhibits T cell receptor signaling, impacting on ERK and mTORC1 activity, concomitantly associated with reduced c-Myc. Compromised c-Myc levels were encapsulated by a failure to engage translational machinery resulting in impaired metabolic protein and solute carrier production among others. Importantly, canagliflozin-treated T cells derived from patients with autoimmune disorders impaired their effector function. Taken together, our work highlights a potential therapeutic avenue for repurposing canagliflozin as an intervention for T cell-mediated autoimmunity

Convolutional neural networks applied to neutrino events in a liquid argon time projection chamber

We present several studies of convolutional neural networks applied to data coming from the MicroBooNE detector, a liquid argon time projection chamber (LArTPC). The algorithms studied include the classification of single particle images, the localization of single particle and neutrino interactions in an image, and the detection of a simulated neutrino event overlaid with cosmic ray backgrounds taken from real detector data. These studies demonstrate the potential of convolutional neural networks for particle identification or event detection on simulated neutrino interactions. We also address technical issues that arise when applying this technique to data from a large LArTPC at or near ground level

Validation of Measured Damping Trends for Flight-Like Vehicle Panel/Equipment including a Range of Cable Harness Assemblies

This validation study examines the effect on vibroacoustic response resulting from the installation of cable bundles on a curved orthogrid panel. Of interest is the level of damping provided by the installation of the cable bundles and whether this damping could be potentially leveraged in launch vehicle design. The results of this test are compared with baseline acoustic response tests without cables. Damping estimates from the measured response data are made using a new software tool that leverages a finite element model of the panel in conjunction with advanced optimization techniques. While the full test series is not yet complete, the first configuration of cable bundles that was assessed effectively increased the viscous critical damping fraction of the system by as much as 0.02 in certain frequency ranges