VictorJuiz/thesis huggingface dataset:
Process-Sensitive Creativity Support Tools

Technology is deeply entwined with creativity. In creative work today, computational tools provide unique capabilities, new materials, and shape how we perform tasks. Yet many modern computer systems have roots in productivity and industrial contexts, the needs and values of which do not always align with those of creative work. In this dissertation, I seek to understand the values of creative process, and how these values can inform the design of creativity support tools that embrace the messy, complicated, and human aspects of creative work./nThis dissertation contributes two concepts for organizing our approach to creative process. Value inversions define moments when the values in creative process seem at odds with traditional computational values; these inversions reveal opportunities for changing our expectations of the role of tools in our work and perceptions about effective workflows. Process-sensitive creativity support tools embrace the values of creative process, foregrounding the actions and mindsets necessary to creative work rather than solely focusing on output. To develop these concepts, this dissertation combines qualitative research on professional creative practice with the design and evaluation of novel computational tools in the areas of creative writing, documentation tools, and design education./nAs we integrate existing tools into our workflows and develop novel creativity support tools, it is important to consider how these tools are shaping our process and workflows. The expectations and values embedded in our tools have concrete effects on how we work, how we evaluate success, and how we learn and grow as creative practitioners.

Advisor: Paulos, Eric
Fault Tolerance in Distributed Systems

Modern datacenter applications need to provide resiliency to mask failures. These applications widely use techniques like Replicated State Machines to provide fault tolerance. Replicated State Machines typically rely on consensus protocols to provide availability and consistency. These applications also require high throughput and low latency from the underlying consensus protocols. Furthermore, in an effort to further reduce latency experienced by clients, we are seeing the emergence of edge computing; storage and computational resources are placed in between the clients and servers in datacenters (typically closer to the client). This placement provides many benefits: lower-latency responses to clients, lower bandwidth demands on the backbone and increased privacy. Stateful applications running on the edge pose a problem of losing state when an edge node fails. This dissertation looks at fault tolerance for datacenters and edge computing. First, RingWorld looks at datacenter fault tolerance for highly available lock services. RingWorld adapts ring-based consensus protocols to leverage programmable switches and datacenter topology. This allows RingWorld to provide higher throughput and comparable latency to existing lock services. Second, to provide fault tolerance for edge computing, CESSNA provides a mechanism to recover from edge failures for strongly stateful applications that ensures correctness and good performance. To do this, CESSNA defines the consistency guarantee for correctness in the face of edge failures, and recovers from failures by adapting techniques like log-replay.

Advisor: Shenker, Scott
Ratnasamy, Sylvia
Safe Reinforcement Learning Using Learned Safe Sets

Reinforcement learning is an increasingly popular framework that enables robots to learn to perform tasks from prior experience in environments where dynamics or shaped reward functions are challenging to model. However, because this requires robots to sample trajectories under significant dynamical uncertainty, the robot may perform unsafe maneuvers during online exploration. This is particularly problematic in real-world robotics, where unsafe behaviors can lead to damage to surroundings. As a result, many impressive reinforcement learning results are in simulation only. Safe reinforcement learning is a field with a rich history that studies how to reduce the number and magnitude of unsafe behaviors during learning, particularly in the real world. Safe reinforcement learning is challenging, because it requires limiting exploration to provide safety, but enabling sufficient exploration to maximize the task reward function. Algorithms frequently draw inspiration from methods in control theory, constrained optimization, and online learning to adaptively balance task-driven exploration and safety based on prior experience./nThis thesis presents a set of novel safe reinforcement learning algorithms that maintain subsets of the state space where safety is highly probable under the current policy. The algorithms leverage these safe sets in different ways to promote safety during online exploration in the real world. The first part of the thesis covers a class of algorithms that requires the robot to maintain a conservative safe set of states from which it has already completed the task. As long as the robot approximately maintains the ability to return to the safe set, the robot can explore outside the safe set and iteratively expand it. This thesis also presents strong theoretical guarantees for this class of algorithms under known but stochastic, nonlinear dynamics. The second part presents another class of algorithms that maintains a much larger safe set based on the probability of the robot committing unsafe behaviors. The robot uses the boundary of this set to determine whether it should focus on task-driven exploration or safety recovery maneuvers. The final part of this thesis covers an algorithm that uses policy uncertainty to implicitly model safety and request human interventions for corrective feedback. This thesis concludes with a commentary on lessons learned and future endeavors.

Advisor: Goldberg, Ken
Gonzalez, Joseph E
Some models for dependence in stochastic processes

This thesis is composed of five chapters, regarding several models for dependence in stochastic processes. We first discuss the class $L$ of selfdecomposable laws, which is a subclass of the class of infinitely divisible laws and contains all stable laws. We show an example of selfdecomposable law whose selfdecomposability is related to path decomposition of planar Brownian motions. Then we introduce the family of self-similar additive processes, which is known to have a close relationship with the class $L$ of selfdecomposable laws. The discussion is suggested by the scale invariant Poisson spacings theorem, which arose in various contexts including records, extremal processes and random permutations. We are able to show that the range of a self-similar gamma process is a scale invariant Poisson point process $(\theta x^{-1} dx)$ and also conversely, this distribution of the range characterizes the gamma process among all self-similar additive processes. We then turn to a discussion of counting processes in discrete times. In particular, when the counting process is stationary $1$-dependent, its distribution is determined by the bivariate probability generating function in terms of run probability generating functions. A probabilistic explanation is provided, alongside with comparison to other known encodings including the determinantal representation and a combinatorial enumeration formula. We also compare the bivariate generating function for $1$-dependent sequences with similar generating functions derived from other dependence structures. Lastly, we discuss a positivity problem related to a bivariate probability generating function for renewal processes, allowing signed measures. Fascinating graphs and qualitative observational results are provided, as well as natural but challenging open problems to explain these facts.

Advisor: Pitman, James
Many-Body Quantum Information Dynamics

Quantum information has emerged as a unifying force in our understanding of large-scale quantum mechanical systems. In quantum technologies, the precise dispersal of quantum information enables computational advantages beyond those of any classical computer. In quantum materials, spatial patterns in the quantum information of electrons give rise to topological phases, with exotic properties when these patterns are "cut" by a material boundary. In more remarkable systems still, quantum information appears central to the unification of quantum mechanics and gravity, one of the frontier open questions in our understanding of the universe. At the same time, the emergence of quantum technologies and accompanying theoretical developments have motivated a widespread interest in dynamical quantum phenomena. In this thesis, I explore several questions at the interface of quantum information and many-body quantum dynamics. Can dynamical many-body quantum systems realize stable coherent quantum phenomena---for example, topological phases---beyond those of static systems? How does quantum information move under many-body dynamics? How can we characterize this movement, and what are its physical implications? And finally, what are the best strategies to learn properties of unknown quantum dynamics from measurements involving them? These inquiries are united by their pursuit to realize and understand large-scale quantum mechanical phenomena in the world around us.

Advisor: Yao, Norman Y
Designing Explainable Autonomous Driving System for Trustworthy Interaction

The past decade has witnessed significant breakthroughs in autonomous driving technologies. We are heading toward an intelligent and efficient transportation system where human errors are eliminated. While excited about the emergence of autonomous vehicles with increasing intelligence, the public has also raised concerns about their reliability. Modern autonomous driving systems usually adopt black-box deep-learning models for multiple function modules (e.g., perception, behavior prediction, behavior generation). The opaque nature of neural networks and their complex system architecture make it extremely difficult to understand the behavior of the overall system, which prevents humans from confidingly sharing the road and interacting with autonomous vehicles. This motivates the design of a more transparent system to build a foundation for trustworthy interaction between humans and autonomous vehicles./nThis dissertation is concerned with the design of an explainable autonomous driving system, leveraging the strengths of explainable artificial intelligence, control, and causality. In particular, we focus on the behavior system of an autonomous vehicle, which plays a crucial role in its interaction with human road participants. The work consists of two parts. In Part I, we explore methods to improve model interpretability. The goal is to ensure that the model is more intelligible for humans in the design stage, which is achieved by introducing hard or soft constraints formulated from domain knowledge. We demonstrate how to formulate domain knowledge of social interaction into structured reward functions (Chapter 2) and pseudo labels (Chapter 3) as well as how to utilize them to induce interpretable driving behavior models. We also introduce an interpretable and transferable hierarchical driving policy that combines deep learning with robust model-based control (Chapter 4). In Part II, we explore the usage of post hoc explanation techniques in diagnosing model behavior. We introduce two case studies, in which we utilize sparse graph attention to diagnose interaction modeling in behavior prediction (Chapter 5) and develop a Shapley-value-based method to study the inherent causality issue in conditional behavior prediction (Chapter 6).

Advisor: Tomizuka, Masayoshi
High-precision in vivo imaging of the mouse visual pathway by adaptive optical two-photon fluorescence microscopy

Two-photon fluorescence microscopy (2PFM) is a superior alternative to conventional fluorescence microscopy with its higher contrast and deeper tissue penetration. It has become a method of choice for biological imaging of complex samples, such as the mammalian brain. For high-resolution imaging, however, 2PFM suffers from optical aberrations induced by the sample and imaging system, which lead to degraded image quality. Adaptive optics (AO) has been applied to 2PFM to remove these aberrations, hence improving image resolution and enhancing image contrast./nThis thesis explores two applications of AO in 2PFM to achieve high-precision imaging of neurons in the mouse brain./nThe first application is divergence-based remote focusing (Chapter 2). Capable of imaging through scattering and opaque tissues, 2PFM has been widely applied to three-dimensional (3D) imaging of biological samples. Remote focusing by controlling the divergence of excitation light is a common approach to scan the focus axially. However, changing the beam divergence induces distortion to the excitation wavefront, which leads to an aberrated focus and degraded image quality away from the natural focal plane. To maintain the focal quality, we used indirect-wavefront-sensing-based AO to measure and correct divergence-induced aberrations and achieved diffraction-limited resolution during remote focusing./nThe second application is in vivo mouse retinal imaging (Chapter 3). The retina, behind the transparent optics of the eye, is the only neural tissue whose physiology and pathology can be non-invasively probed by optical microscopy. The aberrations intrinsic to the mouse eye, however, prevent high-resolution investigation of retinal structure and function in vivo. To characterize and correct mouse ocular aberration, we applied direct-wavefront-sensing-based AO to in vivo two-photon imaging of the mouse retina. We demonstrated the capability of AO-2PFM in longitudinally tracking structural and functional changes of the mouse retina with high accuracy. Our method represents an important advancement in microscopic investigation of retinal pathology and pharmacology in vivo for disease diagnosis and treatment./nBesides technological developments of AO, this thesis also presents the applications of 2PFM in functional studies of mouse primary visual cortex (V1) and visual thalamus (Chapter 4) with in vivo calcium imaging. Measuring the neuronal activities of V1 neurons and their thalamocortical afferents in response to visual stimuli, we investigated the spatial organization of orientation tuning in V1 and found significant tuning similarities among nearby neurons in both L2/3 and L4 of V1.

Advisor: Ji, Na
Automated Model Construction for Image-Based Cardiac Computational Simulations

This dissertation seeks to develop novel data-driven algorithms to automatically construct simulation-suitable meshes from volumetric medical image data to enable high-throughput, large-cohort analyses of patient cardiac function from medical image data. Image-based cardiac modeling derives geometries from patient medical image data, and can simulate a variety of aspects of cardiac function, including electrophysiology, hemodynamics, and tissue mechanics to explore improvements in cardiovascular diagnoses and treatments, and the biomechanical underpinnings of diseases. However, creating accurate models of the heart from patient image data requires significant time and human efforts and is the primary challenge of image-based modeling./nDeforming-domain computational fluid dynamics (CFD) simulations of the intracardiac hemodynamics, in particular, require both the geometry and the deformation of the heart from a sequence of image snapshots throughout the cardiac cycle. In the first part of the thesis, we present a two-stage approach to automatically generate CFD-ready left ventricle (LV) models from patient image data. This approach first uses deep-learning-based automatic segmentation and then uses geometry processing algorithms to robustly create CFD-suitable LV models from image data./nIn the remainder of this dissertation, we developed end-to-end deep-learning algorithms to directly construct the surface meshes of the whole heart from volumetric medical image data without the need for a multistage approach. These algorithms leverage shape templates, shape deformation methods, and regularization functions during optimization to generate meshes that are suitable for computational simulations. Namely, we trained a graph convolutional neural network to deform a pre-defined mesh template to fit the whole heart in a target 3D image volume. Various mesh deformation methods were combined with deep learning to create whole heart surfaces, including direct displacements on meshes, free-form deformation by control lattices as well as control-handle-based deformation using bi-harmonic coordinates. We demonstrated the application of our method in constructing a dynamic whole heart mesh from time-series CT image data to simulate the cardiac flow driven by the cardiac motion. We also demonstrated the capability to switch template meshes to accommodate different modeling requirements./nThe algorithms developed in this thesis were able to construct a 4D dynamic simulation-suitable mesh of the heart within a minute on a standard desktop computer whereas prior methods require hours of human efforts and multiple programs. The code was implemented in Python, fully open-sourced, and can be conveniently executed from the command line.

Advisor: Shadden, Shawn
Towards Privacy-Preserving and Regulation-Compliant Data Analysis

Data analysis has become an indispensable part of today’s society and is greatly impacting our everyday life. At the same time, a variety of privacy attacks are threatening data sovereignty and safety in each step of data analysis from data collection to result release. Preservation of data privacy has been studied through various lens, and can be roughly classified into two categories: the top-down approach (e.g. General Data Protection Regulation) attempts to design a set of universal rules to regulate sensitive data, and the bottom-up approach (e.g. differential privacy) targets concrete privacy challenges and solves them from an algorithmic perspective./nThese two approaches, although have achieved great success separately, suffer from their intrinsic defects as well. Specifically, 1) the effective enforcement of the top-down regulations and 2) the design of the bottom-up algorithms for various applications with different trade-offs have been critical problems to solve. Fortunately, these two approaches are complementary and can become more powerful once used together. The top-down approach can be used for guidance when designing bottom-up solutions and the bottom-up methods can be leveraged to enforce the top-down regulations./nIn this dissertation, the researcher presents an end-to-end framework, namely Aegis. Aegis comprises two main components, a sub-system verifying the compliance between a privacy regulation and a data analysis task, and a library of standardized privacy-preserving algorithms to implement the data analysis tasks. These two components respectively address challenges 1) and 2) mentioned above to some extent. Furthermore, by gluing the two approaches, \aegis magnifies their advantages and promotes a new privacy-preserving data analysis paradigm.

Advisor: Song, Dawn
Multiplicity and Metaform: Late Russian Romanticism as Literary Laboratory

This dissertation traces the development of new literary forms in Russia between the failure of the Decembrist uprising in 1825 and the ascendancy of proto-realism in the 1840s. I argue that this period of intense experimentation with literary form—“form” here understood broadly, as encompassing genre, device, and mode—was motivated by the late Romantics’ desire to expand literature’s capacity for embodiment, to push past material form’s perceived limits to represent nonmaterial, ideal truths. I trace the aesthetic strategies devised to reconcile with and/or provide alternatives to inherited Romantic dualisms (not only materiality/ideality, but also presence/absence, wholeness/fragmentation, self/non-self) in texts of key prose writers of the 1830s – Vladimir Odoevskii, Nikolai Gogol, and Mikhail Lermontov. Central to each of these three writers’ experimental approach to form is their sense of lateness, of engaging in literary creation at the temporal boundary line between Romanticism and an as-yet undefined future epoch in Russian letters. I demonstrate that all three writers employ metafictive interarts devices—what I call “metaforms”—that draw on the realms of painting, music, and theatrical performance to encode new formal possibilities in their texts. The three chapters examine major prose cycles and collections—Odoevskii’s Russian Nights [Russkie nochi] (1844), Gogol’s Arabesques [Arabeski] (1835) and Dead Souls [Mertvye dushi] (1842), and Lermontov’s A Hero of Our Time [Geroi nashego vremeni] (1840)—alongside a range of theoretical texts central to Romantic aesthetics in Russia and Germany, including works by Vissarion Belinskii, E.T.A. Hoffmann, and Friedrich Schiller. While my analysis is grounded in close reading and literary theory, I also draw on art history, theater and performance studies, and music criticism to demonstrate that the Russian late Romantics consistently appealed to non-literary artforms to organize or inspire their own literary experiments./nResisting the realist teleology dominant in Slavic studies to this day, my dissertation redescribes the 1830s and 40s, not as merely “transitional,” but rather as a distinct moment whose formal innovations lie outside standard accounts of either Romanticism or realism.

Advisor: Golburt, Lyubov
Spatially Confined Reactions: Stochastic Effects in Signaling Reactions at the Membrane

Signaling reactions often originate at the membrane, where competing membrane reactions decide if and how the signal will be passed on to downstream signaling cascades. In some cases, membrane reactions can even lead to polarization of signaling molecules that convey spatial information to downstream pathways. Due to the microscopic size of cellular structures, reactions at the membrane are confined and limited to low molecular copy numbers. Signaling decisions are therefore subject to strong stochastic effects. Understanding how membrane reactions can be influenced by stochastic effects is an imperative step toward deciphering the molecular logic of cellular signaling. However, the space of possibilities remains sparsely mapped. In this dissertation, I aim to expand our understanding of this topic by studying two-dimensional confinement effects on phosphatidylinositol signaling and small GTPase signaling reactions, both of which are ubiquitous membrane signaling mediators. Reducing the size of the membrane can lead to two distinct types of stochastic effects: size-dependent reaction speed and stochastic bistability. The underlying physical mechanism is intrinsically stochastic and cannot be predicted by classical deterministic kinetics. The biochemical requirements and potential cellular regulation of these stochastic effects are further discussed. In membranes with asymmetric shapes, the difference in local stochastic effects that the reaction experiences could emerge as stochastic polarization. This stochastic polarization has no analog in continuum reaction-diffusion systems and exhibits a striking ability to polarize in a consistent direction when constrained to cellular shapes. The requirements of size-dependent reaction speed, stochastic bistability, and stochastic polarization are very basic, and largely met in cellular environments, such that many membrane signaling reactions may be subject to these effects. Overall, the discussions here provide a basis to understand how the size and shape of the membrane can be a regulating factor of biochemical reactions and further influence living phenomena.

Advisor: Groves, Jay T
Kuriyan, John