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.
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Item Optimization of Signal Routing in Disruption-Tolerant Networks(2021) Singam, Caitlyn; Ephremides, Anthony; Systems Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Communication networks are prone to disruption due to inherent uncertainties such as environmental conditions, system outages, and other factors. However, current state-of-the-art communication protocols are not yet optimized for communication in highly disruption-prone environments, such as deep space, where the risk of such uncertainties is not negligible. This work involves the development of a novel protocol for disruption-tolerant communication across space-based networks that avoids idealized assumptions and is consistent with system limitations. The proposed solution is grounded in an approach to information as a time-based commodity, and on reframing the problem of efficient signal routing as a problem of value optimization. The efficacy of the novel protocol was evaluated via a custom Monte Carlo simulation against other state-of-the-art protocols in terms of maintaining both data integrity and transmission speed, and was found to provide a consistent advantage across both metrics of interest.Item EXPLORATORY GRAPH BASED BOT DETECTION APPLICATION ON REDDIT SUBNETWORKS(2021) Cruz, Gabriel; Golbeck, Jennifer; Library & Information Services; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Methods for detecting bots have traditionally focused on implementing machine learning systems to classify abnormal behavior. We focus on abnormal term usage as a markerof bot behavior around politically charged language regarding the Covid-19 pandemic on Reddit. We look at multiplex networks abstracted from different subreddits around six terms. We then use novel measures to quantify the differences between the layers in all of these multiplex networks to detect abnormalities in term usage over time and to quantify the differences between subreddit aggregated networks. We conclude that there is not enough evidence to declare that any one term investigated demonstrated an abnormal rate of usage over time. Additionally, none of the aggregated networks demonstrated differences between them indicating that the usage of the terms themselves is not different. We hope to demonstrate the efficacy of this graph-based technique to potentially detect botnet structures on social media.Item DEVELOPMENT AND OPTIMIZATION OF TOOLS FOR CO-EXPRESSION NETWORK ANALYSES OF HOST-PATHOGEN SYSTEMS(2017) Hughitt, Vincent Keith; El-Sayed, Najib M; Cell Biology & Molecular Genetics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)High-throughput transcriptomics has provided a powerful new approach for studying host-pathogen interactions. While popular techniques such as differential expression and gene set enrichment analysis can yield informative results, they do not always make full use of information available in multi-condition experiments. Co-expression networks provide a novel way of analyzing these datasets which can lead to new discoveries that are not readily detectable using the more popular approaches. While significant work has been done in recent years on the construction of coexpression networks, less is known about how to measure the quality of such networks. Here, I describe an approach for evaluating the quality of a co-expression network, based on enrichment of biological function across the network. The approach is used to measure the influence of various data transformations and algorithmic parameters on the resulting network quality, leading to several unexpected findings regarding commonly-used techniques, as well as to the development of a novel similarity metric used to assess the degree of co-expression between two genes. Next, I describe a simple approach for aggregating information across multiple network parameterizations, in order to arrive at a robust “consensus” co-expression network. This approach is used to generate independent host and parasite networks for two host-trypanosomatid transcriptomics datasets, resulting in the detection of both previously known disease pathways and novel gene networks potentially related to infection. Finally, a differential network analysis approach is developed and used to explore the impact of infection on the host co-expression network, and to elucidate shared transcriptional signatures of infection by different intracellular pathogens. The approaches developed in this work provide a powerful set of tools and techniques for the rigorous generation and evaluation of co-expression networks, and have significant implications for co-expression network-based research. The application of these approaches to several host-pathogen systems demonstrates their utility for host-pathogen transcriptomics research, and has resulted in the creation of a number of valuable resources for understanding systems-levels processes that occur during the process of infection.Item Cumulative impacts of stream burial on network structure and functional connectivity in headwater stream systems(2015) Weitzell, Jr, Roy Everett; Elmore, Andrew J; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Stream burial is common during urbanization, and disproportionately affects headwater streams. Burial undermines the physical, chemical, and spatial processes governing aquatic life, with consequences for water quality and biodiversity, both within headwaters and in downstream waters. Network changes associated with headwater burial have not been explored, limiting our understanding of changes in biotic composition with urbanization of these critical ecosystems. To address this need, I predicted stream burial across the Potomac River Basin (PRB) from impervious cover data and training observations from high-resolution aerial photography. Results across the PRB urban gradient reveal consistent burial patterns related to catchment area and topographic slope. I discuss these results in the context of physiographic constraints on stream location and urban development, including implications for management of aquatic resources. Second, I examined burial-related changes to headwater network structure and habitat connectivity, using a series of topological and distance measures, and a novel application of circuit-theoretical modeling to stream networks. Results show stream burial significantly affects both the number and size of remnant stream segments and their spatial orientation. Significant decreases in landscape connectivity were observed with burial, around ecologically important features such as confluences, and for urbanized headwater systems as a whole. Third, I used biological data to compare environmental and spatial controls on species turnover in fish and insect communities across headwater systems. Turnover was analyzed using generalized dissimilarity modeling, which accommodates variation in rates of species turnover along and between gradients, and two novel measures of resistance distance, which combine aspects of space and environment, specifically the spatial extent, orientation, and relative favorability of habitat across the landscape. Results show headwater species are more sensitive to environmental parameters, with less mobile species more sensitive to habitat fragmentation and required dispersal distances. Rapid compositional turnover occurred within short distances from the sampled reaches, suggesting headwater taxa disperse only short distances, with even small obstructions or habitat loss having potential to impact diversity within headwater systems. Knowledge gained from this research is critical for understanding the cumulative impact to stream networks, and for future decision-making allowing for urban development while protecting stream ecosystem function.Item HISTORICAL GRAPH DATA MANAGEMENT(2015) Khurana, Udayan; Deshpande, Amol; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Over the last decade, we have witnessed an increasing interest in temporal analysis of information networks such as social networks or citation networks. Finding temporal interaction patterns, visualizing the evolution of graph properties, or even simply comparing them across time, has proven to add significant value in reasoning over networks. However, because of the lack of underlying data management support, much of the work on large-scale graph analytics to date has largely focused on the study of static properties of graph snapshots. Unfortunately, a static view of interactions between entities is often an oversimplification of several complex phenomena like the spread of epidemics, information diffusion, formation of online communities, and so on. In the absence of appropriate support, an analyst today has to manually navigate the added temporal complexity of large evolving graphs, making the process cumbersome and ineffective. In this dissertation, I address the key challenges in storing, retrieving, and analyzing large historical graphs. In the first part, I present DeltaGraph, a novel, extensible, highly tunable, and distributed hierarchical index structure that enables compact recording of the historical information, and that supports efficient retrieval of historical graph snapshots. I present analytical models for estimating required storage space and snapshot retrieval times which aid in choosing the right parameters for a specific scenario. I also present optimizations such as partial materialization and columnar storage to speed up snapshot retrieval. In the second part, I present Temporal Graph Index that builds upon DeltaGraph to support version-centric retrieval such as a node’s 1-hop neighborhood history, along with snapshot reconstruction. It provides high scalability, employing careful partitioning, distribution, and replication strategies that effectively deal with temporal and topological skew, typical of temporal graph datasets. In the last part of the dissertation, I present Temporal Graph Analysis Framework that enables analysts to effectively express a variety of complex historical graph analysis tasks using a set of novel temporal graph operators and to execute them in an efficient and scalable manner on a cloud. My proposed solutions are engineered in the form of a framework called the Historical Graph Store, designed to facilitate a wide variety of large-scale historical graph analysis.Item Network Algorithms for Complex Systems with Applications to Non-linear Oscillators and Genome Assembly(2013) Schmitt, Karl Robert Bruce; Girvan, Michelle; Zimin, Aleksey; Applied Mathematics and Scientific Computation; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Network and complex system models are useful for studying a wide range of phenomena, from disease spread to traffic flow. Because of the broad applicability of the framework it is important to develop effective simulations and algorithms for complex networks. This dissertation presents contributions to two applied problems in this area First, we study an electro-optical, nonlinear, and time-delayed feedback loop commonly used in applications that require a broad range of chaotic behavior. For this system we detail a discrete-time simulation model, exploring the model's synchronization behavior under specific coupling conditions. Expanding upon already published results that investigated changes in feedback strength, we explore how both time-delay and nonlinear sensitivity impact synchronization. We also relax the requirement of strictly identical systems components to study how synchronization regions are affected when coupled systems have non-identical components (parameters). Last, we allow wider variance in coupling strengths, including unique strengths to each system, to identify a rich synchronization region not previously seen. In our second application, we take a complex networks approach to improving genome assembly algorithms. One key part of sequencing a genome is solving the orientation problem. The orientation problem is finding the relative orientations for each data fragment generated during sequencing. By viewing the genomic data as a network we can apply standard analysis techniques for community finding and utilize the significantly modular structure of the data. This structure informs development and application of two new heuristics based on (A) genetic algorithms and (B) hierarchical clustering for solving the orientation problem. Genetic algorithms allow us to preserve some internal structure while quickly exploring a large solution space. We present studies using a multi-scale genetic algorithm to solve the orientation problem. We show that this approach can be used in conjunction with currently used methods to identify a better solution to the orientation problem. Our hierarchical algorithm further utilizes the modular structure of the data. By progressively solving and merging sub-problems together we pick optimal `local' solutions while allowing more global corrections to occur later. Our results show significant improvements over current techniques for both generated data and real assembly data.Item Patterns and Complexity in Biological Systems: A Study of Sequence Structure and Ontology-based Networks(2010) Glass, Kimberly; Girvan, Michelle; Physics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Biological information can be explored at many different levels, with the most basic information encoded in patterns within the DNA sequence. Through molecular level processes, these patterns are capable of controlling the states of genes, resulting in a complex network of interactions between genes. Key features of biological systems can be determined by evaluating properties of this gene regulatory network. More specifically, a network-based approach helps us to understand how the collective behavior of genes corresponds to patterns in genetic function. We combine Chromatin-Immunoprecipitation microarray (ChIP-chip) data with genomic sequence data to determine how DNA sequence works to recruit various proteins. We quantify this information using a value termed "nmer-association.'' "Nmer-association'' measures how strongly individual DNA sequences are associated with a protein in a given ChIP-chip experiment. We also develop the "split-motif'' algorithm to study the underlying structural properties of DNA sequence independent of wet-lab data. The "split-motif'' algorithm finds pairs of DNA motifs which preferentially localize relative to one another. These pairs are primarily composed of known transcription factor binding sites and their co-occurrence is indicative of higher-order structure. This kind of structure has largely been missed in standard motif-finding algorithms despite emerging evidence of the importance of complex regulation. In both simple and complex regulation, two genes that are connected in a regulatory fashion are likely to have shared functions. The Gene Ontology (GO) provides biologists with a controlled terminology with which to describe how genes are associated with function and how those functional terms are related to each other. We introduce a method for processing functional information in GO to produce a gene network. We find that the edges in this network are correlated with known regulatory interactions and that the strength of the functional relationship between two genes can be used as an indicator of how informationally important that link is in the regulatory network. We also investigate the network structure of gene-term annotations found in GO and use these associations to establish an alternate natural way to group the functional terms. These groups of terms are drastically different from the hierarchical structure established by the Gene Ontology and provide an alternative framework with which to describe and predict the functions of experimentally identified groups of genes.Item Using Internet Geometry to Improve End-to-End Communication Performance(2009) Lumezanu, Cristian; Spring, Neil; Computer Science; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The Internet has been designed as a best-effort communication medium between its users, providing connectivity but optimizing little else. It does not guarantee good paths between two users: packets may take longer or more congested routes than necessary, they may be delayed by slow reaction to failures, there may even be no path between users. To obtain better paths, users can form routing overlay networks, which improve the performance of packet delivery by forwarding packets along links in self-constructed graphs. Routing overlays delegate the task of selecting paths to users, who can choose among a diversity of routes which are more reliable, less loaded, shorter or have higher bandwidth than those chosen by the underlying infrastructure. Although they offer improved communication performance, existing routing overlay networks are neither scalable nor fair: the cost of measuring and computing path performance metrics between participants is high (which limits the number of participants) and they lack robustness to misbehavior and selfishness (which could discourage the participation of nodes that are more likely to offer than to receive service). In this dissertation, I focus on finding low-latency paths using routing overlay networks. I support the following thesis: it is possible to make end-to-end communication between Internet users simultaneously faster, scalable, and fair, by relying solely on inherent properties of the Internet latency space. To prove this thesis, I take two complementary approaches. First, I perform an extensive measurement study in which I analyze, using real latency data sets, properties of the Internet latency space: the existence of triangle inequality violations (TIVs) (which expose detour paths: ''indirect'' one-hop paths that have lower round-trip latency than the ''direct'' default paths), the interaction between TIVs and network coordinate systems (which leads to scalable detour discovery), and the presence of mutual advantage (which makes fairness possible). Then, using the results of the measurement study, I design and build PeerWise, the first routing overlay network that reduces end-to-end latency between its participants and is both scalable and fair. I evaluate PeerWise using simulation and through a wide-area deployment on the PlanetLab testbed.Item Terrorism's Communicative Dynamic: Leveraging the Terrorist-Audience Relationship to Assess Evolutionary Trajectories(2009) Gressang, Daniel Seidel; Lalman, David; Government and Politics; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Terrorist groups do not operate in isolation. To survive in the face of counter-pressures from their opponents, the group must establish a beneficial relationship with a targeted audience, a presumed constituency, in order to generate the sympathy and support necessary for maintaining operational viability. Existing studies of terrorism, however, offer few insights into how this might be done. The most common approach revolves around assessments of terrorist messages, yet typically treats those messages as self-serving propaganda or media manipulation. This study takes a different approach, suggesting that terrorists use statements and communiqués in an effort to gain and maintain a supportive audience. Further, the intended audience for the messages infer meaning in terrorist violence, thus augmenting or reducing the impact of persuasive messaging by the terrorist. Understanding this process, in turn, may yield new insights into the dynamic processes of terrorism, offering new opportunities to assess a terrorist group's potential for positive evolutionary growth or greater relative fitness. Using Grunig's situational theory of publics, this study creates and evaluates a new metric, called expected affinity, for examining the terrorist group's effort to establish and strengthen bonds between itself and its targeted and presumptively supportive audience. Expected affinity combines sub-measures addressing problem recognition, expected and desired levels of involvement, and constraint recognition, coupled with an inferred meaning in the symbolism of violent acts in order to evaluate terrorist messages and attacks. The results suggest utility in the expected affinity metric and point to opportunities for making the measure more directly applicable to specific cases through incorporation of detailed case study data.Item Jointly Optimal Placement and Power Allocation of Wireless Networks(2008-04-28) firouzabadi, sina; Martins, Nuno C; Electrical Engineering; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)In this thesis, we investigate the optimal design of wireless networks. We consider wireless networks that have fixed and movable nodes, and we assume that all nodes feature adjustable transmission power. Hence, we aim at maximizing network centric objectives, by optimizing over admissible choices of the positions of the movable nodes as well as the transmission power at all the nodes. We adopt exponential path loss, which is a realistic assumption in urban and sub sea environments, and we propose ways of using this assumption to obtain a tractable optimization problem. Our formulation allows for the optimization of typical network centric objectives, such as power and throughput. It also allows signal-to-interference based constraints, such as rate-regions and outage probabilities, under the high signal to interference regime. We show that our optimization paradigm is convex and that it can be solved up to an arbitrary degree of accuracy via geometric programming techniques. By using a primal-dual decomposition, we also provide a case-study that illustrates how certain instances of our optimization paradigm can be solved via distributed iterative algorithms. We show that such a solution method also leads to a convenient layering in the primal step, whereby the power allocation and the node placement become two independent sub-problems.