Theses and Dissertations from UMD
Permanent URI for this communityhttp://hdl.handle.net/1903/2
New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
More information is available at Theses and Dissertations at University of Maryland Libraries.
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Item Learning and Composing Primitives for the Visual World(2023) Gupta, Kamal; Shrivastava, Abhinav; Davis, Larry; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Compositionality is at the core of how humans understand and create visual data. In order for the computational approaches to assist humans in creative tasks, it is crucial for them to understand and perform composition. The recent advances in deep generative models have enabled us to convert noise to highly realistic scenes. However, in order to harness these models for building real-world applications, I argue that we need to be able to represent and control the generation process with the composition of interpretable primitives. In the first half of this talk, I’ll discuss how deep models can discover such primitives from visual data. By playing a cooperative referential game between two neural network agents, we can represent images with discrete meaningful concepts without supervision. I further extend this work for applications in image and video editing by learning a dense correspondence of primitives across images. In the second half, I’ll focus on learning how to compose primitives for both 2D and 3D visual data. By expressing the scenes as an assembly of smaller parts, we can easily perform generation from scratch or from partial scenes as input. I’ll conclude the talk with a discussion of possible future directions and applications of generative models, and how we can better enable users to guide the creative process.Item INFERENCE-BASED MODELING, MONITORING, AND CONTROL ALGORITHMS FOR AUTONOMOUS MEDICAL CARE(2022) Tivay, Ali; Hahn, Jin-Oh; Mechanical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Autonomous medical care systems are relatively recent developments in biomedical research that aim to leverage the vigilance, precision, and processing power of computers to assist (or replace) humans in providing medical care to patients. Indeed, past research has demonstrated initial promise for autonomous medical care in applications related to anesthesia, hemodynamic management, and diabetes management, to name a few. However, many of these technologies yet do not exhibit the maturity necessary for widespread real-world adoption and regulatory approval. This can be attributed, in part, to several outstanding challenges associated with the design and development of algorithms that interact with physiological processes. Ideally, an autonomous medical care system should be equipped to exhibit (i) transparent behavior, where the system’s perceptions, reasoning, and decisions are human-interpretable; (ii) context-aware behavior, where the system is capable of remaining mindful of contextual and peripheral information in addition to its primary goal; (iii) coordinated behavior, where the system can coordinate multiple actions in synergistic ways to best achieve multiple objectives; (iv) adaptable behavior, where the system is equipped to identify and adapt to variabilities that exist within and across different patients; and (v) uncertainty-aware behavior, where the system can handle imperfect measurements, quantify the uncertainties that arise as a result, and incorporate them into its decisions. As these desires and challenges are specific to autonomous medical care applications and not fully explored in past research in this area, this dissertation presents a sequence of methodologies to model, monitor, and control a physiological process with special emphasis on addressing these challenges. For this purpose, first, a collective variational inference (C-VI) method is presented that facilitates the creation of personalized and generative physiological models from low-information and heterogeneous datasets. The generative physiological model is of special importance for the purposes of this work, as it encodes physiological knowledge by reproducing the patterned randomness that is observed in physiological datasets. Second, a population-informed particle filtering (PIPF) method is presented that fuses the information encoded in the generative model with real-time clinical data to form perceptions of a patient’s states, characteristics, and events. Third, a population-informed variational control (PIVC) method is presented that leverages the generative model, the perceptions of the PIPF algorithm, and user-defined definitions of actions and rewards in order to search for optimal courses of treatment for a patient. These methods together form a physiological decision-support and closed-loop control (PCLC) framework that is intended to facilitate the desirable behaviors sought in the motivations of this work. The performance, merits, and limitations of this framework are analyzed and discussed based on clinically-important case studies on fluid resuscitation for hemodynamic management.