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Title: Non-coding RNA identification in large-scale genomic data
Creator: Yuan, Cheng
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Noncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes....
Show moreNoncoding RNAs (ncRNAs), which function directly as RNAs without translating into proteins, play diverse and important biological functions. ncRNAs function not only through their primary structures, but also secondary structures, which are defined by interactions between Watson-Crick and wobble base pairs. Common types of ncRNA include microRNA, rRNA, snoRNA, tRNA. Functions of ncRNAs vary among different types. Recent studies suggest the existence of large number of ncRNA genes. Identification of novel and known ncRNAs becomes increasingly important in order to understand their functionalities and the underlying communities.Next-generation sequencing (NGS) technology sheds lights on more comprehensive and sensitive ncRNA annotation. Lowly transcribed ncRNAs or ncRNAs from rare species with low abundance may be identified via deep sequencing. However, there exist several challenges in ncRNA identification in large-scale genomic data. First, the massive volume of datasets could lead to very long computation time, making existing algorithms infeasible. Second, NGS has relatively high error rate, which could further complicate the problem. Third, high sequence similarity among related ncRNAs could make them difficult to identify, resulting in incorrect output. Fourth, while secondary structures should be adopted for accurate ncRNA identification, they usually incur high computational complexity. In particular, some ncRNAs contain pseudoknot structures, which cannot be effectively modeled by the state-of-the-art approach. As a result, ncRNAs containing pseudoknots are hard to annotate.In my PhD work, I aimed to tackle the above challenges in ncRNA identification. First, I designed a progressive search pipeline to identify ncRNAs containing pseudoknot structures. The algorithms are more efficient than the state-of-the-art approaches and can be used for large-scale data. Second, I designed a ncRNA classification tool for short reads in NGS data lacking quality reference genomes. The initial homology search phase significantly reduces size of the original input, making the tool feasible for large-scale data. Last, I focused on identifying 16S ribosomal RNAs from NGS data. 16S ribosomal RNAs are very important type of ncRNAs, which can be used for phylogenic study. A set of graph based assembly algorithms were applied to form longer or full-length 16S rRNA contigs. I utilized paired-end information in NGS data, so lowly abundant 16S genes can also be identified. To reduce the complexity of problem and make the tool practical for large-scale data, I designed a list of error correction and graph reduction techniques for graph simplification.
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Title: Cybersickness Prioritzation and Modeling
Creator: Rebenitsch, Lisa Renee
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Motion sickness-like symptoms due to visual stimuli, or cybersickness, are a topic of significant public concern. From three-dimensional movies to new head mounted displays such as the Oculus Rift, the general public is being offered ever-increasing opportunities for immersive experiences. However, virtual environment opportunities are often accompanied with increased reports of "movie theatre sickness" and other cybersickness symptoms. Research in the field has posed over forty potential...
Show moreMotion sickness-like symptoms due to visual stimuli, or cybersickness, are a topic of significant public concern. From three-dimensional movies to new head mounted displays such as the Oculus Rift, the general public is being offered ever-increasing opportunities for immersive experiences. However, virtual environment opportunities are often accompanied with increased reports of "movie theatre sickness" and other cybersickness symptoms. Research in the field has posed over forty potential factors that affect cybersickness. The objective of this thesis is to prioritize cybersickness human factors and develop models to predict their effects. A thorough search of the literature is given, which summarizes the support, or lack thereof, for each factor. New experimentation is conducted for the factors with insufficient existing evidence. The factors with sufficient support were fitted to single-term models to use for guidelines and normalization. Cumulative models are then developed to predict individual and configuration level cybersickness effects. The literature on certain factors is unclear due to selective reporting of configuration features, various configurations, different measurement methods, and limited studies. The literature regarding individual susceptibility to cybersickness is particularly limited. Therefore, experiments were performed that specifically addressed individual susceptibility with different display modalities. Common display methods such as head-mounted displays and screens were examined. Stereoscopic effects were also examined due to recent renewed interest and unclear effects. All the experiments included surveys to collect data on individual susceptibility factors.Cybersickness effects are not normally distributed, which decreases the reliability of common statistical measures on cybersickness data. A new statistical method for cybersickness research, called the zero-inflated model, is examined as an improved method for comparing the impact of the features involved in cybersickness. The zero-inflated model has the benefit of having a "non-susceptible" or "well" group built into the statistical test, which can be upwards of 50% of the participants in a study. An analysis of the literature permitted several variables to be removed from further consideration and allowed single factor models to be created. Three independent variability models for individual susceptibility were found. The individual model explains 37% of the adjusted variance of a model and the linear models created for the hardware and software explain 55-70% of the adjusted variance. Since the variance as predicted by the models is not purely additive, the total explained variance with these models is likely 70-80%, which is a significant improvement over the models proposed by Kolasinski's that explain 34% of the variability of the model [1]. The models created using experimental results incorporating the individual, hardware and software had an average absolute residual accuracy of less than 1%.
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Title: Finding optimized bounding boxes of polytopes in d-dimensional space and their properties in k-dimensional projections
Creator: Shahid, Salman (Of Michigan State University)
Date: 2014
Collection: Electronic Theses & Dissertations
Description: Using minimal bounding boxes to encapsulate or approximate a set of points in d-dimensional space is a non-trivial problem that has applications in a variety of fields including collision detection, object rendering, high dimensional databases and statistical analysis to name a few. While a significant amount of work has been done on the three dimensional variant of the problem (i.e. finding the minimum volume bounding box of a set of points in three dimensions), it is difficult to find a...
Show moreUsing minimal bounding boxes to encapsulate or approximate a set of points in d-dimensional space is a non-trivial problem that has applications in a variety of fields including collision detection, object rendering, high dimensional databases and statistical analysis to name a few. While a significant amount of work has been done on the three dimensional variant of the problem (i.e. finding the minimum volume bounding box of a set of points in three dimensions), it is difficult to find a simple method to do the same for higher dimensions. Even in three dimensions existing methods suffer from either high time complexity or suboptimal results with a speed up in execution time. In this thesis we present a new approach to find the optimized minimum bounding boxes of a set of points defining convex polytopes in d-dimensional space. The solution also gives the optimal bounding box in three dimensions with a much simpler implementation while significantly speeding up the execution time for a large number of vertices. The basis of the proposed approach is a series of unique properties of the k-dimensional projections that are leveraged into an algorithm. This algorithm works by constructing the convex hulls of a given set of points and optimizing the projections of those hulls in two dimensional space using the new concept of Simultaneous Local Optimal. We show that the proposed algorithm provides significantly better performances than those of the current state of the art approach on the basis of time and accuracy. To illustrate the importance of the result in terms of a real world application, the optimized bounding box algorithm is used to develop a method for carrying out range queries in high dimensional databases. This method uses data transformation techniques in conjunction with a set of heuristics to provide significant performance improvement.
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Title: Multiple kernel and multi-label learning for image categorization
Creator: Bucak, Serhat Selçuk
Date: 2014
Collection: Electronic Theses & Dissertations
Description: "One crucial step towards the goal of converting large image collections to useful information sources is image categorization. The goal of image categorization is to find the relevant labels for a given an image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning...
Show more"One crucial step towards the goal of converting large image collections to useful information sources is image categorization. The goal of image categorization is to find the relevant labels for a given an image from a closed set of labels. Despite the huge interest and significant contributions by the research community, there remains much room for improvement in the image categorization task. In this dissertation, we develop efficient multiple kernel learning and multi-label learning algorithms with high prediction performance for image categorization... " -- Abstract.
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Title: Energy Conservation in Heterogeneous Smartphone Ad Hoc Networks
Creator: Mariani, James
Date: 2018
Collection: Electronic Theses & Dissertations
Description: In recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped...
Show moreIn recent years mobile computing has been rapidly expanding to the point that there are now more devices than there are people. While once it was common for every household to have one PC, it is now common for every person to have a mobile device. With the increased use of smartphone devices, there has also been an increase in the need for mobile ad hoc networks, in which phones connect directly to each other without the need for an intermediate router. Most modern smart phones are equipped with both Bluetooth and Wifi Direct, where Wifi Direct has a better transmission range and rate and Bluetooth is more energy efficient. However only one or the other is used in a smartphone ad hoc network. We propose a Heterogeneous Smartphone Ad Hoc Network, HSNet, a framework to enable the automatic switching between Wifi Direct and Bluetooth to emphasize minimizing energy consumption while still maintaining an efficient network. We develop an application to evaluate the HSNet framework which shows significant energy savings when utilizing our switching algorithm to send messages by a less energy intensive technology in situations where energy conservation is desired. We discuss additional features of HSNet such as load balancing to help increase the lifetime of the network by more evenly distributing slave nodes among connected master nodes. Finally, we show that the throughput of our system is not affected due to technology switching for most scenarios. Future work of this project includes exploring energy efficient routing as well as simulation/scale testing for larger and more diverse smartphone ad hoc networks.
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Title: Occupant Behavior Prediction Model Based on Energy Consumption Using Machine Learning Approaches
Creator: Mo, Yunjeong
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Building sectors use the largest amount of energy among all energy-consuming sectors, and the residential sector constitutes 39 percent of the electricity consumption in the United States, which is the highest consumption among the various electricity-consuming sectors. The goals of this research are to identify a relationship between energy consumption and occupant behavior in a detailed level while also considering building technology, and to build a behavior prediction model using machine...
Show moreBuilding sectors use the largest amount of energy among all energy-consuming sectors, and the residential sector constitutes 39 percent of the electricity consumption in the United States, which is the highest consumption among the various electricity-consuming sectors. The goals of this research are to identify a relationship between energy consumption and occupant behavior in a detailed level while also considering building technology, and to build a behavior prediction model using machine learning approaches based on energy consumption data. This research consists of four main parts: (1) Part I provides a theoretical foundation for the rest of the research, and develops the Occupant Behavior Prediction Model and apply the model to the American Time Use Survey (ATUS) data, (2) Part II focuses on analyzing energy usage-related behaviors and activities with the ATUS data, (3) Part III analyzes building technologies, including appliances, and energy usage with the Residential Energy Consumption Survey (RECS) data, and (4) Part IV combines the findings from the previous parts and applies the Occupant Behavior Prediction Model to the sensor-measured dataset. This research will have an impact on residential occupant behavior by helping occupants better understand their own behaviors’ effects on energy usage, and detect what changes would improve energy efficiency in their homes. The findings will be beneficial to energy-related industries, and energy research area. In addition, the Occupant Behavior Prediction Model has the potential to be further integrated with research in other fields.
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Title: Representation Learning and Image Synthesis for Deep Face Recognition
Creator: Yin, Xi
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Face recognition has been advanced a lot in recent years thanks to the development of deep neural networks. The large intra-class variations in pose, illumination, and expression are the long-standing challenges. Learning a discriminative representation that is robust to these variations is the key. In the scenarios of profile pose or long-tail training data, image or feature-level data augmentation is needed. This dissertation presents three different methods to solve these problems. First,...
Show moreFace recognition has been advanced a lot in recent years thanks to the development of deep neural networks. The large intra-class variations in pose, illumination, and expression are the long-standing challenges. Learning a discriminative representation that is robust to these variations is the key. In the scenarios of profile pose or long-tail training data, image or feature-level data augmentation is needed. This dissertation presents three different methods to solve these problems. First, we explore a multi-task Convolutional Neural Network (CNN) that aims to leverage side tasks to improve representation learning. A pose-directed multi-task CNN is introduced to better handle pose variation. The proposed framework is effective in pose-invariant face recognition. Second, we propose a Face Frontalization-Generative Adversarial Network (FF-GAN) that can generate a frontal face even from an input image with extreme profile pose. FF-GAN handles pose variation from the perspective of image-level data augmentation. Multiple loss functions are proposed to achieve large-pose face frontalization. The proposed approach is evaluated on various tasks including face reconstruction, landmark detection, face frontalization, and face recognition. Third, a feature transfer learning method is presented to solve the problem of insufficient intra-class variation via feature-level data augmentation. A Gaussian prior is assumed across all the regular classes and the variance are transferred from regular classes to long-tail classes. Further, an alternating training regimen is proposed to simultaneously achieve less biased decision boundaries and more discriminative representations. Extensive experiments have demonstrated the effectiveness of the proposed feature transfer framework.
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Title: M-Kulinda : Exploring Domestic Security in Rural Kenya Using Home Alert System
Creator: Chidziwisano, George Hope
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The use of sensor-based technologies is pervasive in today’s world, especially in the developed world where its applications are well realized. From smart homes to tracking locations to monitoring health, sensor-based technologies are becoming ubiquitous. However, the role of sensor-based technologies in a rural Kenyan household is underexplored. In this thesis, I explore the potential of a sensor-based technology probe’s approach to enhancing domestic security in rural Kenyan households,...
Show moreThe use of sensor-based technologies is pervasive in today’s world, especially in the developed world where its applications are well realized. From smart homes to tracking locations to monitoring health, sensor-based technologies are becoming ubiquitous. However, the role of sensor-based technologies in a rural Kenyan household is underexplored. In this thesis, I explore the potential of a sensor-based technology probe’s approach to enhancing domestic security in rural Kenyan households, where property theft is a persistent problem. A technology probe is a research product that collects information, tests a technology and inspires users and designers about other ways technology can be used to support their needs. The 'technology probes' approach was used to allow participates to reflect on a new technology in their homes and provides data to researchers based on their reflections. I designed “M-Kulinda”, and deployed it in 20 households for four weeks. Over a four week period, participants recorded their experiences with M-Kulinda in diaries and through data logging (M-Kulinda recorded time stamps when the system was used). The study also used semi-structured interviews and observations to understand participants’ experiences with the sensor-based system. These methods of data collection were complemented by pictures I took during home visits and field notes I wrote each and every day. Findings suggest that a technology probe’s approach is useful: I found that participants used the system in ways that addressed their specific needs (e.g., monitoring poultry, electronic possessions, and their family members). These uses are novel in the sense that participants used M-Kulinda in different ways to achieve the same purpose. I also observed changes in participants’ understanding of sensors: M-Kulinda prompted participants to reflect on other areas where sensors could be used in their households. I present design implications based on these findings, and offer new perspectives on the role of technology in deterring crime. Additionally, findings suggested that crime detection systems in rural Kenya prevent burglary. M-Kulinda’s success also suggested that there is a potential in crime detection systems to foster neighborhood cohesion for residents who are living close to each other thereby reducing burglary.The study has shown that residents in rural areas have interest in using sensor-based technologies to solve their problems however there are constraints which prevent them from fully realizing the potential of sensors in their lives. These constraints mainly include electrical power and network problems. The implications of this research encourages researchers in Human Computer Interaction (HCI) in the design space to incorporate solutions to constraints that hinder people in the developing world from using technology. For example, knowing that power failure in developing countries is a major problem, designers cannot include alternative sources of power in their designs thereby solving power problems. The study also contributes to HCI by showing the positive impact of using technology probes of different research products in different settings to understand how people in that particular setting would use that particular product. Sensors should also be used to complement already existing measures of security in the home.
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Title: A FRAMEWORK FOR COMBINING ANCILLARY INFORMATION WITH PRIMARY BIOMETRIC TRAITS
Creator: Ding, Yaohui
Date: 2018
Collection: Electronic Theses & Dissertations
Description: Biometric systems recognize individuals based on their biological attributes such as faces, fingerprints and iris. However, in several scenarios, additional ancillary information such as the biographic and demographic information of a user (e.g., name, gender, age, ethnicity), or the image quality of the biometric sample, anti-spoofing measurements, etc. may be available. While previous literature has studied the impact of such ancillary information on biometric system performance, there is...
Show moreBiometric systems recognize individuals based on their biological attributes such as faces, fingerprints and iris. However, in several scenarios, additional ancillary information such as the biographic and demographic information of a user (e.g., name, gender, age, ethnicity), or the image quality of the biometric sample, anti-spoofing measurements, etc. may be available. While previous literature has studied the impact of such ancillary information on biometric system performance, there is limited work on systematically incorporatingthem into the biometric matching framework. In this dissertation, we develop a principled framework to combine ancillary information with biometric match scores. The incorporation of ancillary information raises several challenges. Firstly, ancillary information such as gender, ethnicity and other demographic attributes lack distinctiveness and can be used to distinguish population groups rather than individuals. Secondly, ancillaryinformation such as image quality and anti-spoof measurements may have different numerical ranges and interpretations. Further, most of the ancillary information cannot be automatically extracted without errors. Even the direct collection of ancillary informationfrom subjects may be susceptible to transcription errors (e.g., errors in entering the data). Thirdly, the relationships between ancillary attributes and biometric traits may not be evident. In this regard, this dissertation makes three contributions. The first contribution entails the design of a Bayesian Belief Network (BBN) to model the relationship between biometric scores and ancillary factors, and exploiting the ensuing structure in a fusion framework.The ancillary information considered by the network includes image quality and anti-spoof measures. Experiments convey the importance of explicitly incorporating such information in a biometric system. The second contribution is the design of a Generalized AdditiveModel (GAM) that uses spline functions to model the correlation between match scores and ancillary attributes, and then learns a transformation function to normalize the match scores prior to fusion. The resulting framework can also be used to predict in advance iffusing match scores with certain demographic attributes is beneficial in the context of a specific biometric matcher. Experiments indicate that the proposed method can be used to significantly improve the recognition accuracy of state-of-the-art face matchers. The thirdcontribution is the design of an ensemble of One Class Support Vector Machines (OC-SVMs) to combine multiple anti-spoofing measurements in order to mitigate the concerns associated with the issue of “imbalanced training sets” and “insufficient spoof samples” encountered by conventional anti-spoofing algorithms. In the proposed method, the spoof detection problem is formulated as a one-class problem, where the focus is on modeling a real fingerprint using multiple feature sets. The one-class classifiers corresponding to these multiple feature sets are then combined to generate a single classifier for spoof detection. Experimental results convey the importance of this technique in detecting spoofs made of materials that were not included in the training data. In summary, this dissertation seeks to advance our understanding of systematically exploiting ancillary information in designing effective biometric recognition systems by developing and evaluating multiple statistical models
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Title: Performance Analysis and Privacy Protection of Network Data
Creator: Ahmed, Faraz
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The goal of this thesis is to address network management research challenges faced by operational networks - with specific focus on cellular networks, content delivery networks, and online social networks. Next, I give an overview of my research on network management of these networks. Cellular networks utilize existing service quality management systems for detecting performance degradation issues inside the network, however, under certain conditions degradation in End-to-End (E2E)...
Show moreThe goal of this thesis is to address network management research challenges faced by operational networks - with specific focus on cellular networks, content delivery networks, and online social networks. Next, I give an overview of my research on network management of these networks. Cellular networks utilize existing service quality management systems for detecting performance degradation issues inside the network, however, under certain conditions degradation in End-to-End (E2E) performance may go undetected. These conditions may arise due to problems in the mobile device hardware, smartphone applications, and content providers. In this thesis, I present a system for detecting and localizing E2E performance degradation at cellular service providers across four administrative domains: cellular network, content providers, device manufacturers, and smartphone applications. Cellular networks also need systems that can prioritize performance degradation issues according to the number of customers impacted. Cell tower outages are performance degradation issues that directly impact connectivity of cellular network users. In this thesis, we design and evaluate a cell tower outage monitoring system that analyzes and estimates device level impact during cell tower outages.Content delivery networks (CDNs) maintain multiple transit routes from content distribution servers to eyeball ISP networks which provide Internet connectivity to end users. Two major considerations for CDNs are transit prices and performance dynamics of delivering content to end users. The dynamic nature of transit pricing and performance makes it challenging to optimize the cost and performance tradeoff. There are thousands of eyeball ISPs which are reachable via different transit routes and different geographical locations. Each choice of transit route for a particular eyeball ISP and geographical location has distinct cost and performance characteristics, which makes the problem of developing a transit routing strategy challenging. In this thesis, I present a measurement approach to actively collect client perceived network performance and then use these measurements towards optimal transit route selection for CDNs.Online Social Networks (OSNs) often refuse to publish their social network graphs due to privacy concerns. Differential privacy has been the widely accepted criteria for privacy preserving data publishing. In this thesis, I present a random matrix approach to OSN graph publishing, which achieves storage and computational efficiency by reducing dimensions of adjacency matrices and achieves differential privacy by adding a small amount of noise.
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Title: HYBRID METHODS FOR RADIATION TRANSPORT USING INTEGRAL DEFERRED CORRECTION
Creator: Crockatt, Michael M.
Date: 2018
Collection: Electronic Theses & Dissertations
Description: This thesis provides roughly three contributions to the study of defect correction methods and hybrid methods for radiation transport. First, a modification of traditional integral deferred correction (IDC) time-integration schemes is proposed that significantly reduces the storage requirements of the methods. These methods, which we call low-storage IDC or LS-IDC methods, require storing only one copy of each stage vector throughout the iteration process, whereas traditional IDC methods...
Show moreThis thesis provides roughly three contributions to the study of defect correction methods and hybrid methods for radiation transport. First, a modification of traditional integral deferred correction (IDC) time-integration schemes is proposed that significantly reduces the storage requirements of the methods. These methods, which we call low-storage IDC or LS-IDC methods, require storing only one copy of each stage vector throughout the iteration process, whereas traditional IDC methods require two copies of each vector. The convergence and stability properties of the methods are examined in a variety of settings, and both analytical and experimental results are provided. A nonlinear ODE and a linear transport equation are used to compare the accuracy and storage requirements of LS-IDC integrators with other fully implicit schemes including diagonally implicit Runge-Kutta (DIRK) and space-time discontinuous Galerkin (STDG) methods. The results demonstrate that LS-IDC methods have similar accuracy but significantly reduced memory requirements compared to other fully implicit methods.Second, extensions of a collision-based hybrid method for time-dependent radiation transport simulations are discussed. The hybrid methods are constructed by splitting the radiation flux into collided and uncollided components to which low- and high-resolution discrete ordinates approximations are applied, respectively. The use of arbitrarily high-order numerical approximations is emphasized, with particular attention paid to high-order time-integration schemes. A range of time-integrators are considered including DIRK, STDG, IDC, and LS-IDC methods of up to fifth-order accuracy. Convergence results in one spatial dimension are provided, and it is found that while the hybrid methods exhibit convergence stagnation and order reduction in certain scenarios, the overall accuracy of the hybrid approximations is comparable to standard discrete ordinates approximations in many cases. A test problem in two-dimensional xy-geometry consisting of a mock-up of a standard hohlraum configuration is used to compare the computational efficiency of the hybrid methods with standard discrete ordinates methods. It is observed that replacing a standard discrete ordinates method using an angular quadrature of order N with a hybrid discrete ordinates method using angular quadratures of order 2N and N/2 for the uncollided and collided fluxes, respectively, usually reduces solve time by a factor of 2 or more and in many cases also yields a reduction in solution error by a factor of up to 2. However, it is noted that the specified hybrid methods require approximately 4.25 times the storage of the corresponding standard discrete ordinates methods.Finally, two mechanisms for increasing the effectiveness of the hybrid methods are presented. The first is a reconstruction procedure for mapping between arbitrary discrete ordinates quadratures within the context of these hybrid methods. This approach, called Nyström reconstruction, is shown to be significantly more accurate than previous reconstruction methods. When applied to the two-dimensional hohlraum problem, it is observed that replacing a standard discrete ordinates method using an angular quadrature of order N with a hybrid discrete ordinates method using a Nyström reconstruction procedure and angular quadratures of order N and N/4 for the uncollided and collided fluxes, respectively, consistently reduces solve time by a factor of between 4 and 8 while increasing memory requirements by only 6% and producing little to no increase in solution error. Lastly, variations of hybrid methods using IDC integrators are described in which the hybrid approach is written as a two-grid iterative method in angle that is combined with an IDC time-integration scheme. It is demonstrated that the resulting methods are able to iteratively reduce the error due to the application of discrete ordinates quadratures of different resolutions to the collided and uncollided components.
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Title: Inference of viral strains using metagenomics data
Creator: Chen, Jiao
Date: 2018
Collection: Electronic Theses & Dissertations
Description: RNA Viruses, such as human immunodeficiency virus (HIV), influenza, and hepatitis C virus (HCV), have a great impact on human health. The high mutation rate of RNA viruses can produce a population of different but closely related virus sequences called viral quasispecies. To develop prevention and treatment strategies for viral pathogens, an essential and fundamental step is to characterize their sequences and abundances at the strain-level for a viral quasispecies.Advances in next-generation...
Show moreRNA Viruses, such as human immunodeficiency virus (HIV), influenza, and hepatitis C virus (HCV), have a great impact on human health. The high mutation rate of RNA viruses can produce a population of different but closely related virus sequences called viral quasispecies. To develop prevention and treatment strategies for viral pathogens, an essential and fundamental step is to characterize their sequences and abundances at the strain-level for a viral quasispecies.Advances in next-generation sequencing (NGS) technologies have opened up new opportunities to study viruses. Viral metagenomic data, which contains the genetic information for a bunch of viruses in the same habitat, have become the major resource for characterizing RNA viruses. Although there are many pipelines for analyzing viruses in metagenomic data, they usually lack three functions. First, novel viruses or viruses that lack closely related reference genomes cannot be detected with high sensitivity. Second, strain-level analysis is usually missing. Although there are several assembly tools specifically designed for viral haplotypes, de novo assembly of virus genomes is still a computationally challenge task due to the error-prone short reads, high similarities between related strains, and unknown number or abundance of virus haplotypes. Third, it is hard to estimate the number of haplotypes and their abundances in a quasispecies.In this dissertation, we have developed a pipeline with three tools, TAR-VIR PEHaplo and VirBin to address the challenges existing for viral metagenomic assembly and analysis. TAR-VIR is a tool for classifying and enriching viral reads from metagenomic data without relying on complete or high-quality reference genomes. It is optimized for identifying RNA viruses from metagenomic data with an efficient Burrows-Wheeler Transform (BWT) based overlapping method. TAR-VIR was tested on both simulated and real viral metagenomic datasets. The results demonstrated TAR-VIR competes favorably with benchmarked tools. PEHaplo is a de novo haplotype reconstruction tool, which employs paired-end reads to distinguish highly similar strains for viral quasispecies data. It was applied on both simulated and real quasispecies data, and the results were benchmarked against several recently published de novo haplotype reconstruction tools. The comparison shows that PEHaplo outperforms the benchmarked tools in a comprehensive set of metrics. With assembled viral contigs, VirBin focuses on estimating the number of haplotypes and clustering the contigs to different haplotypes. VirBin firstly identifies windows from contigs alignment profile to estimate haplotype number and better calculate the contig abundances. Then, it applies an Expectation-Maximization method to cluster the contigs based on contig abundance levels. The experimental results of VirBin on both simulated and real data sets show that the window-based method can precisely estimate the haplotype number, and generate more accurate abundance estimation for contigs that will eventually lead to superior clustering results. In addition, this dissertation also contains one chapter for the other work we have done for identifying the primary transcription start sites for miRNA genes in Caenorhabditis elegans and mouse with Cap-seq data.
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Title: AN EVOLUTIONARY MULTI-OBJECTIVE APPROACH TO SUSTAINABLE AGRICULTURAL WATER AND NUTRIENT OPTIMIZATION
Creator: Kropp, Ian Meyer
Date: 2018
Collection: Electronic Theses & Dissertations
Description: One of the main problems that society is facing in the 21st century is that agricultural production must keep pace with a rapidly increasing global population in an environmentally sustainable manner. One of the solutions to this global problem is a system approach through the application of optimization techniques to manage farm operations. However, unlike existing agricultural optimization research, this work seeks to optimize multiple agricultural objectives at once via multi-objective...
Show moreOne of the main problems that society is facing in the 21st century is that agricultural production must keep pace with a rapidly increasing global population in an environmentally sustainable manner. One of the solutions to this global problem is a system approach through the application of optimization techniques to manage farm operations. However, unlike existing agricultural optimization research, this work seeks to optimize multiple agricultural objectives at once via multi-objective optimization techniques. Specifically, the algorithm Unified Non-dominated Sorting Genetic Algorithm-III (U-NSGA-III) searched for irrigation and nutrient management practices that minimized combinations of environmental objectives (e.g., total irrigation applied, total nitrogen leached) while maximizing crop yield for maize. During optimization, the crop model named the Decision Support System for Agrotechnology Transfer (DSSAT) calculated the yield and nitrogen leaching for each given management practices. This study also developed a novel bi-level optimization framework to improve the performance of the optimization algorithm, employing U-NSGA-III on the upper level and Monte Carlo optimization on the lower level. The multi-objective optimization framework resulted in groups of equally optimal solutions that each offered a unique trade-off among the objectives. As a result, producers can choose the one that best addresses their needs among these groups of solutions, known as Pareto fronts. In addition, the bi-level optimization framework further improved the number, performance, and diversity of solutions within the Pareto fronts.
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Title: Learning Algorithms for Detecting Disinformation on Social Media
Creator: VanDam, Courtland
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Social media has become a widely accessible medium for users to share their opinions and details of their personal lives, including first hand accounts of emerging/disaster events, to a wide audience. However malicious entities may abuse users' trust to disseminate disinformation, i.e. false and misleading information. The disinformation disseminated on social media can have a significant impact offline. For example, fake news is suspected to have influenced the 2016 U.S. political election....
Show moreSocial media has become a widely accessible medium for users to share their opinions and details of their personal lives, including first hand accounts of emerging/disaster events, to a wide audience. However malicious entities may abuse users' trust to disseminate disinformation, i.e. false and misleading information. The disinformation disseminated on social media can have a significant impact offline. For example, fake news is suspected to have influenced the 2016 U.S. political election. Rumors on social media can mislead criminal investigations, e.g. the investigation of the 2013 Boston Bombing. To mitigate such impacts, automated detection of social media disinformation is thus an important research problem.This dissertation proposes algorithms to detect two approaches hackers use to disseminate disinformation --- hashtag hijacking and compromising accounts. Hashtags are terms added to social media posts that are used to provide context to the posts, so those seeking to learn more about a given topic or event can search for posts containing related hashtags. However critics and attention-seeking trolls can mislead the public via hashtag hijacking. Hashtag hijacking occurs when one group of users takes control of a hashtag by using it in a different context than what was intended upon creation. Anyone can participate in hashtag hijacking, but to be successful, a coordinated effort among several accounts posting that hashtag is needed. This dissertation proposes HASHTECT, an unsupervised learning framework that uses a multi-modal nonnegative matrix factorization method for detecting hijacked hashtags. Experimental results on a large-scale Twitter data showed that HASHTECT is capable of detecting more hijacked hashtags than previously proposed algorithms. Another approach for disseminating disinformation is by compromising users' accounts. A social media account is compromised when it is accessed by a third party, i.e. hacker, without the genuine user's knowledge. Compromised accounts are damaging to the account holder as well as the accounts audience, e.g. followers. Hackers can damage the user's reputation, e.g. by posting hateful rhetoric. They also disseminate misleading information including rumors and malicious websites, e.g. phishing or malware. In this dissertation, I propose two compromised account detection algorithms, CADET and CAUTE. CADET is an unsupervised multi-view learning framework that employs nonlinear autoencoders to learn the feature embedding from multiple views. The rationale behind this approach is that an anomalous behavior observed in one view, e.g. abnormal time of day, may not indicate a compromised account. By aggregating the data from multiple views, CADET projects the features from all the views into a common lower-rank feature representation and detects compromised accounts in the shared subspace. On the other hand, CAUTE focuses on detecting compromised accounts early, by detecting the compromised posts. Given a user-post pair, CAUTE is a deep learning framework which simultaneously learns the encodings for the user as well as the post to detect whether the post was compromised, i.e. was written by a different user. By training a neural network on the residuals from the post and user encodings, CAUTE can classify whether a post is compromised with higher accuracy than several existing compromised account detection algorithms.
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Title: CONSISTENCY FOR DISTRIBUTED DATA STORES
Creator: Roohitavaf, Mohammad
Date: 2019
Collection: Electronic Theses & Dissertations
Description: Geo-replicated data stores are one of the integral parts of today's Internet services. Service providers usually replicate their data on different data centers worldwide to achieve higher performance and data durability. However, when we use this approach, the consistency between replicas becomes a concern. At the highest level of consistency, we want strong consistency that provides the illusion of having only a single copy of the data. However, strong consistency comes with high performance...
Show moreGeo-replicated data stores are one of the integral parts of today's Internet services. Service providers usually replicate their data on different data centers worldwide to achieve higher performance and data durability. However, when we use this approach, the consistency between replicas becomes a concern. At the highest level of consistency, we want strong consistency that provides the illusion of having only a single copy of the data. However, strong consistency comes with high performance and availability costs. In this work, we focus on weaker consistency models that allow us to provide high performance and availability while preventing certain inconsistencies. Session guarantees (aka. client-centric consistency models) are one of such weaker consistency models that prevent some of the inconsistencies from occurring in a client session. We provide modified versions of session guarantees that, unlike traditional session guarantees, do not cause the problem of slowdown cascade for partitioned systems. We present a protocol to provide session guarantees for eBay NuKV that is a key-value store designed for eBay's internal services with high performance and availability requirements. We utilize Hybrid Logical Clocks (HLCs) to provide wait-free write operations while providing session guarantees. Our experiments, done on eBay cloud platform, show our protocol does not cause significant overhead compared with eventual consistency. In addition to session guarantees, a large portion of this dissertation is dedicated to causal consistency. Causal consistency is especially interesting as it is has been proved to be the strongest consistency model that allows the system to be available even during network partitions. We provide CausalSpartanX protocol that, using HLCs, improves current time-based protocols by eliminating the effect of clock anomalies such as clock skew between servers. CausalSpartanX also supports non-blocking causally consistent read-only transactions that allow applications to read a set of values that are causally consistent with each other. Read-only transactions provide a powerful abstraction that is impossible to be replaced by a set of basic read operations. CausalSpartanX, like other causal consistency protocols, assumes sticky clients (i.e. clients that never change the replica that they access). We prove if one wants immediate visibility for local updates in a data center, clients have to be sticky. Based on the structure of CausalSpartanX, we provide our Adaptive Causal Consistency Framework (ACCF) that is a configurable framework that generalizes current consistency protocols. ACCF provides a basis for designing adaptive protocols that can constantly monitor the system and clients' usage pattern and change themselves to provide better performance and availability. Finally, we present our Distributed Key-Value Framework (DKVF), a framework for rapid prototyping and benchmarking consistency protocols. DKVF lets protocol designers only focus on their high-level protocols, delegating all lower level communication and storage tasks to the framework.
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Title: A Differential Evolution Approach to Feature Selection in Genomic Prediction
Creator: Whalen, Ian
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often...
Show moreThe use of genetic markers has become widespread for prediction of genetic merit in agricultural applications and is a beginning to show promise for estimating propensity to disease in human medicine. This process is known as genomic prediction and attempts to model the mapping between an organism's genotype and phenotype. In practice, this process presents a challenging problem. Sequencing and recording phenotypic traits are often expensive and time consuming. This leads to datasets often having many more features than samples. Common models for genomic prediction often fall victim to overfitting due to the curse of dimensionality. In this domain, only a fraction of the markers that are present significantly affect a particular trait. Models that fit to non-informative markers are in effect fitting to statistical noise, leading to a decrease in predictive performance. Therefore, feature selection is desirable to remove markers that do not appear to have a significant effect on the trait being predicted. The method presented here uses differential evolution based search for feature selection. This study will characterize differential evolution's efficacy in feature selection for genomic prediction and present several extensions to the base search algorithm in an attempt to apply domain knowledge to guide the search toward better solutions.
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Title: E-CONSTITUTIONS : CONCEPTUALIZATION, THEORY, DESIGN MODEL AND EXPERIMENTAL EVALUATIONS
Creator: Khaledi, Hamed
Date: 2018
Collection: Electronic Theses & Dissertations
Description: This project addresses the problem of collective design in cyberspace using computerized governance rules. Despite many applications of computerized rules, there is no systematic method or model to design them. I conceptualized an e-constitution as a set of rules in computer code that allocate decision rights and incentives to govern decision making process. This dissertation develops a design model consisting of a structured representation that breaks down a constitution into 14 components...
Show moreThis project addresses the problem of collective design in cyberspace using computerized governance rules. Despite many applications of computerized rules, there is no systematic method or model to design them. I conceptualized an e-constitution as a set of rules in computer code that allocate decision rights and incentives to govern decision making process. This dissertation develops a design model consisting of a structured representation that breaks down a constitution into 14 components including a state transition function and a weighting function. As a meta-artifact, this model provides a unified architecture for governance structures in a wide range of situations including crowdsourcing, blockchains and corporate governance. This model enables use of quantifiable performance measures to evaluate constitutions objectively, liberated from the fairness criteria used in impossibility theorems. A systematic methodology is also presented to improve the performance of constitutions efficiently. As a proof of concept, I implemented a generic e-constitution in a web application and measured the effects of different factors on the constitutional performance metrics through online experiments. One finding is that approval voting is significantly superior to the plurality voting, even under a prediction voting incentive scheme.
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Title: An Analysis of Fitness in Long-Term Asexual Evolution Experiments
Creator: Wiser, Michael J.
Date: 2015
Collection: Electronic Theses & Dissertations
Description: Evolution is the central unifying concept of modern biology. Yet it can be hard to study in natural system, as it unfolds across generations. Experimental evolution allows us to ask questions about the process of evolution itself: How repeatable is the evolutionary process? How predictable is it? How general are the results? To address these questions, my collaborators and I carried out experiments both within the Long-Term Evolution Experiment (LTEE) in the bacteria Escherichia coli, and the...
Show moreEvolution is the central unifying concept of modern biology. Yet it can be hard to study in natural system, as it unfolds across generations. Experimental evolution allows us to ask questions about the process of evolution itself: How repeatable is the evolutionary process? How predictable is it? How general are the results? To address these questions, my collaborators and I carried out experiments both within the Long-Term Evolution Experiment (LTEE) in the bacteria Escherichia coli, and the digital evolution software platform Avida. In Chapter 1, I focused on methods. Previous research in the LTEE has relied on one particular way of measuring fitness, which we know becomes less precise as fitness differentials increase. I therefore decided to test whether two alternate ways of measuring fitness would improve precision, using one focal population. I found that all three methods yielded similar results in both fitness and coefficient of variation, and thus we should retain the traditional method.In Chapter 2, I turned to measuring fitness in each of the populations. Previous work had considered fitness to change as a hyperbola. A hyperbolic function is bounded, and predicts that fitness will asymptotically approach a defined upper bound; however, we knew that fitness in these populations routinely exceeded the asymptotic limit calculated from a hyperbola fit to the earlier data. I instead used to a power law, a mathematical function that does not have an upper bound. I found that this function substantially better describes fitness in this system, both among the whole set of populations, and in most of the individual populations. I also found that the power law models fit on just early subsets of the data accurately predict fitness far into the future. This implies that populations, even after 50,000 generations of evolution in consistent environment, are so far from the tops of fitness peaks that we cannot detect evidence of those peaks.In Chapter 3, I examined to how variance in fitness changes over long time scales. The among-population variance over time provides us information about the adaptive landscape on which the populations have been evolving. I found that among-population variance remains significant. Further, competitions between evolved pairs of populations reveal additional details about fitness trajectories than can be seen from competitions against the ancestor. These results demonstrate that our populations have been evolving on a complex adaptive landscape.In Chapter 4, I examined whether the patterns found in Chapter 2 apply to a very different evolutionary system, Avida. This system incorporates many similar evolutionary pressures as the LTEE, but without the details of cellular biology that underlie nearly all organic life. I find that in both the most complex and simplest environments in Avida, fitness also follows the same power law dynamics as seen in the LTEE. This implies that power law dynamics may be a general feature of evolving systems, and not dependent on the specific details of the system being studied.
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Title: Some contributions to semi-supervised learning
Creator: Mallapragada, Paven Kumar
Date: 2010
Collection: Electronic Theses & Dissertations

Title: Statistical Approaches for Analysis, Measurement and Modeling of RFID Systems
Creator: Wang, Liyan
Date: 2018
Collection: Electronic Theses & Dissertations
Description: The goal of this thesis is to develop statistical and learning algorithms for the analysis, measurement, and modeling of wireless networking( Radio frequency identification systems).Radio frequency identification (RFID) systems are widely used in logistic, supply chain industry and inventory management. RFID is already in use in multiple industries and for various purposes. The device in your car that lets you zoom by in the fast lane at a tollbooth, while deducting a dollar amount from your...
Show moreThe goal of this thesis is to develop statistical and learning algorithms for the analysis, measurement, and modeling of wireless networking( Radio frequency identification systems).Radio frequency identification (RFID) systems are widely used in logistic, supply chain industry and inventory management. RFID is already in use in multiple industries and for various purposes. The device in your car that lets you zoom by in the fast lane at a tollbooth, while deducting a dollar amount from your account, is an example of RFID technology in everyday use. Mostly, existing RFID systems are primarily used to identify the RFID tags present in a tag population(e.g., tracking a specific tag from a tag population) while identifying some specific tags is a critical operation, it is usually very time consuming and is not desired or necessary in some situations. For instance, if the objective is to determine whether any of the tags are missing(e.g., to detect some items according to a consignment), the first thing to do is to identify all tags’ ID and then compare with the original record to determine if there is any tags are missing. Definitely, the whole process will be very slow if we have a very large tag population. In this thesis, I present novel statistical algorithms to enable fast and new applications in RFID systems. For example, detecting the missing tags in a large tag population with high accuracy while using the existing infrastructure of RFID systems which is already deployed in industry. More pacifically, I present my work on designing statistical algorithms for estimation the number of missing tags in a population of RFID tags, for detecting and identifying the missing tags from a population of RFID tags.The key distinction of my work compared to prior art is that my methods are compliant with EPCGlobal Class 1 Generation 2 (C1G2) RFID standard. It is critical for RFID methods to be compliant with the C1G2 standard since the commercially available of-the- shelf RFID equipment follows the C1G2 standard. A method which does not comply with the C1G2 standard cannot be deployed on the existing installations of RFID systems because it requires custom hardware, which will cost a lot. In an RFID-enabled warehouse, there may be thousands of tagged items that belong to different categories, e.g., different places of origin or different brands [72]. Each tag attached to an item has a unique ID that consists of two fields: a category ID that specifies the category of the attached object, and a member ID that identifies this object within its category. As a manager of the warehouse, one may desire to timely monitor the product stock of each category. If the stock of a category is too high, it may indicate that this product category is not popular, and the seller needs to adjust the marketing strategy (e.g., lowering prices to increase sales). On the contrary, if the stock of a category is too low, the seller should perform stock replenishment as soon as possible. Manual checking is laborious and of low time-efficiency. You cannot imagine how difficult it is for a manager to manually count the number of items in each category that may be stacked together or placed on high shelves. Hence, it is desirable to exploit the RFID technique to quickly obtain the number of tagged items in each category. A multi-category RFID estimation protocol should satisfy three additional requirements. First, it should be standard compliant; otherwise, it will be difficult to be deployed. Second, it should preserve the privacy of tags by not reading their member IDs. Third, it should work with both a single-reader and multiple-reader environments. As the communication range between a tag and a reader is limited, a large population of tags is often covered by multiple readers whose regions often overlap.
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