UMD Theses and Dissertations

Permanent URI for this collectionhttp://hdl.handle.net/1903/3

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 given thesis/dissertation in DRUM.

More information is available at Theses and Dissertations at University of Maryland Libraries.

Browse

Subject: search.filters.subject.Computational Modeling

Search Results

Now showing 1 - 5 of 5
  • Thumbnail Image
    Item
    The Learning and Usage of Second Language Speech Sounds: A Computational and Neural Approach
    (2023) Thorburn, Craig Adam; Feldman, Naomi H; Linguistics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Language learners need to map a continuous, multidimensional acoustic signal to discrete abstract speech categories. The complexity of this mapping poses a difficult learning problem, particularly for second language learners who struggle to acquire the speech sounds of a non-native language, and almost never reach native-like ability. A common example used to illustrate this phenomenon is the distinction between /r/ and /l/ (Goto, 1971). While these sounds are distinct in English and native English speakers easily distinguish the two sounds, native Japanese speakers find this difficult, as the sounds are not contrastive in their language. Even with much explicit training, Japanese speakers do not seem to be able to reach native-like ability (Logan, Lively, & Pisoni, 1991; Lively, Logan & Pisoni, 1993) In this dissertation, I closely explore the mechanisms and computations that underlie effective second-language speech sound learning. I study a case of particularly effective learning--- a video game paradigm where non-native speech sounds have functional significance (Lim & Holt, 2011). I discuss the relationship with a Dual Systems Model of auditory category learning and extend this model, bringing it together with the idea of perceptual space learning from infant phonetic learning. In doing this, I describe why different category types are better learned in different experimental paradigms and when different neural circuits are engaged. I propose a novel split where different learning systems are able to update different stages of the acoustic-phonetic mapping from speech to abstract categories. To do this I formalize the video game paradigm computationally and implement a deep reinforcement learning network to map between environmental input and actions. In addition, I study how these categories could be used during online processing through an MEG study where second-language learners of English listen to continuous naturalistic speech. I show that despite the challenges of speech sound learning, second language listeners are able to predict upcoming material integrating different levels of contextual information and show similar responses to native English speakers. I discuss the implications of these findings and how the could be integrated with literature on the nature of speech representation in a second language.
  • Thumbnail Image
    Item
    CHARACTERIZATION OF SURVIVAL ASSOCIATED GENE INTERACTIONS AND LYMPHOCYTE HETEROGENEITY IN CANCER
    (2019) Magen, Assaf; Hannenhalli, Sridhar; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    Cancer is the second leading cause of death globally. Tumors form intricate ecosystems in which malignant and immune cells interact to shape disease progression. Yet, the molecular underpinnings of tumorigenesis and immunological responses to tumors are poorly understood, limiting their manipulation to elicit favorable clinical outcomes. This thesis lays conceptual frameworks for investigating the molecular interactions taking place in tumors as well as the diversity of the immune response to cancer. In the molecular level of individual cancer cells, the phenotypic effect of perturbing a gene’s activity depends on the activity level of other genes, reflecting the notion that phenotypes are emergent properties of a network of functionally interacting genes. In the context of cancer, contemporary investigations have primarily focused on just one type of functional genetic interaction (GI) – synthetic lethality (SL). However, there may be additional types of GIs whose systematic identification would enrich the molecular and functional characterization of cancer. This thesis describes a novel data-driven approach called EnGIne, that applied to large-scale cancer data identifies 71,946 GIs spanning 12 distinct types, only a small minority of which are SLs. The detected GIs explain cancer driver genes’ tissue- specificity and differences in patients’ response to drugs, and stratify breast cancer tumors into refined subtypes. These results expand the scope of cancer GIs and lay a conceptual and computational basis for future studies of additional types of GIs and their translational applications. Furthermore, tumor growth is continuously shaped by the immune response. However, T cells typically adopt a dysfunctional phenotype may be reversed using immunotherapy strategies. Most current tumor immunotherapies leverage cytotoxic CD8+ T cells to elicit an effective anti-tumor response. Despite evidence for clinical potential of CD4+ tumor-infiltrating lymphocytes (TILs), their functional diversity has limited our ability to harness their anti-tumor activity. To address this issue, we have used single-cell mRNA sequencing (scRNAseq) to analyze the response of CD4+ T cells specific for a defined recombinant tumor antigen, both in the tumor microenvironment and draining lymph nodes (dLN). New computational approaches to characterize subpopulations identified TIL transcriptomic patterns strikingly distinct from those elicited by responses to infection, and dominated by diversity among T-bet-expressing T helper type 1 (Th1)-like cells. In contrast, the dLN response includes Follicular helper (Tfh)-like cells but lacks Th1 cells. We identify an interferon-driven signature in Th1-like TILs, and show that it is found in human liver cancer and melanoma, in which it is negatively associated with response to checkpoint therapy. Our study unveils unsuspected differences between tumor and virus CD4+ T cell responses, and provides a proof-of-concept methodology to characterize tumor- control CD4+ T cell effector programs. Targeting these programs should help improve immunotherapy strategies.
  • Thumbnail Image
    Item
    An Environmental Anthropology of Modeling and Management on the Chesapeake Bay Watershed
    (2017) Trombley, Jeremy M.; Paolisso, Michael; Anthropology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    In the last few decades, computational models have become an essential component of our understanding of complex environmental processes. In addition, they are increasingly used as tools for the management of large-scale environmental problems like climate change. As a result, understanding the role that these models play in the socioecological process of environmental management is an important area of inquiry for an environmental anthropology concerned with understanding human-environment interactions. In this dissertation, I examine these roles through an ethnographic study of computational environmental modeling in the Chesapeake Bay watershed. The Chesapeake Bay region is an excellent place to investigate modeling and management because, for over thirty years, it has been the site of a watershed-scale effort to reduce nutrient pollution (nitrogen, phosphorous, and sediment) to the Chesapeake Bay. In order to carry out this management process, the Chesapeake Bay Program (CBP) was created as a partnership between the federal government and seven watershed jurisdictions. In addition, modelers at the CBP have been developing a complex computational model of the watershed known as the Chesapeake Bay Modeling System (CBMS) in order to identify and track the sources and effects of nutrient pollution on the estuary. In this dissertation, I explore the role of the CBMS and other models in our understanding and management of nutrient pollution in the region through three articles written for publication in peer-reviewed journals, each of which addresses the question in a different way. The first discusses the ways that the process of building and implementing a computational model is affected by its inclusion in a management institution. The second describes the ways that the computational models themselves are affected by the management contexts in which they are developed and deployed. The third examines the various roles that they play in building and maintaining the relationships that underlie the management process. Together, these articles shed light on the ways that computational models mediate human-environment interactions by way of environmental management, and will help to plan more inclusive and effective modeling and management approaches in the future.
  • Thumbnail Image
    Item
    EFFECTS OF 3D PRINTED VASCULAR NETWORKS ON HUMAN MESENCHYMAL STEM CELL VIABILITY IN LARGE BONE TISSUE CONSTRUCTS
    (2015) Ball, Owen Matthew; Fisher, John P; Bioengineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    There is a significant clinical need for engineered bone graft substitutes that can quickly, effectively, and safely repair segmental bone defects. One emerging field of interest involves the growth of engineered bone tissue in vitro within bioreactors, the most promising of which, are perfusion bioreactors. Utilizing a tubular perfusion system bioreactor, which allows media to perfuse freely around alginate scaffolds laden with human mesenchymal stem cells, large-scale bone constructs can be created by simply aggregating these beads together in the desired shape. However, these engineered constructs lack inherent vasculature and quickly develop a necrotic core, where no nutrient exchange occurs. Through the use of 3D printed vascular structures, used in conjunction with a TPS bioreactor, cell viability after just one day of aggregation was found to increase by as much as 50 percent in the core of these constructs, with in silico modeling predicting construct viability at steady state.
  • Thumbnail Image
    Item
    DEVELOPMENT OF A LAGRANGIAN-LAGRANGIAN METHODOLOGY TO PREDICT BROWNOUT DUST CLOUDS
    (2012) Syal, Monica; Leishman, J. Gordon; Aerospace Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)
    A Lagrangian-Lagrangian dust cloud simulation methodology has been developed to help better understand the complicated two-phase nature of the rotorcraft brownout problem. Brownout conditions occur when rotorcraft land or take off from ground surfaces covered with loose sediment such as sand and dust, which decreases the pilot's visibility of the ground and poses a serious safety of flight risk. The present work involved the development of a comprehensive, computationally efficient three-dimensional sediment tracking method for dilute, low Reynolds number Stokes-type flows. The flow field generated by a helicopter rotor in ground effect operations over a mobile sediment bed was modeled by using an inviscid, incompressible, Lagrangian free-vortex method, coupled to a viscous semi-empirical approximation for the boundary layer flow near the ground. A new threshold model for the onset of sediment mobility was developed by including the effects of unsteady pressure forces that are induced in vortically dominated rotor flows, which can significantly alter the threshold conditions for particle motion. Other important aspects of particle mobility and uplift in such vortically driven dust flows were also modeled, including bombardment effects when previously suspended particles impact the bed and eject new particles. Bombardment effects were shown to be a particularly significant contributor to the mobilization and eventual suspension of large quantities of smaller-sized dust particles, which tend to remain suspended. A numerically efficient Lagrangian particle tracking methodology was developed where individual particle or clusters of particles were tracked in the flow. To this end, a multi-step, second-order accurate time-marching scheme was developed to solve the numerically stiff equations that govern the dynamics of particle motion. The stability and accuracy of this scheme was examined and matched to the characteristics of free-vortex method. One-way coupling of the flow and the particle motion was assumed. Particle collisions were not considered. To help reduce numerical costs, the methodology was implemented on graphic processing units, which gave over an order of magnitude reduction in simulation time without any loss in accuracy. Validation of the methodology was performed against available measurements, including flow field measurements that have been made with laboratory-scale and full-scale rotors in ground effect operations. The predicted dust clouds were also compared against measurements of developing dust clouds produced by a helicopter during taxi-pass and approach-to-touchdown flight maneuvers. The results showed that the problem of brownout is mostly driven by the local action of the rotor wake vortices and the grouping or bundling of vortex filaments near the sediment bed. The possibilities of mitigating the intensity of brownout conditions by diffusing the blade tip vortices was also explored. While other means of brownout mitigation may be possible, enhancing the diffusion of the tip vortices was shown to drastically reduce the quantity of mobilized particles and the overall severity of the brownout dust cloud.