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Title: Iris Recognition : Enhancing Security and Improving Performance
Creator: Sharma, Renu
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Biometric systems recognize individuals based on their physical or behavioral traits, viz., face, iris, and voice. Iris (the colored annular region around the pupil) is one of the most popular biometric traits due to its uniqueness, accuracy, and stability. However, its widespread usage raises security concerns against various adversarial attacks. Another challenge is to match iris images with other compatible biometric modalities (i.e., face) to increase the scope of human identification....
Show moreBiometric systems recognize individuals based on their physical or behavioral traits, viz., face, iris, and voice. Iris (the colored annular region around the pupil) is one of the most popular biometric traits due to its uniqueness, accuracy, and stability. However, its widespread usage raises security concerns against various adversarial attacks. Another challenge is to match iris images with other compatible biometric modalities (i.e., face) to increase the scope of human identification. Therefore, the focus of this thesis is two-fold: firstly, enhance the security of the iris recognition system by detecting adversarial attacks, and secondly, accentuate its performance in iris-face matching.To enhance the security of the iris biometric system, we work over two types of adversarial attacks - presentation and morph attacks. A presentation attack (PA) occurs when an adversary presents a fake or altered biometric sample (plastic eye, cosmetic contact lens, etc.) to a biometric system to obfuscate their own identity or impersonate another identity. We propose three deep learning-based iris PA detection frameworks corresponding to three different imaging modalities, namely NIR spectrum, visible spectrum, and Optical Coherence Tomography (OCT) imaging inputting a NIR image, visible-spectrum video, and cross-sectional OCT image, respectively. The techniques perform effectively to detect known iris PAs as well as generalize well across unseen attacks, unseen sensors, and multiple datasets. We also presented the explainability and interpretability of the results from the techniques. Our other focuses are robustness analysis and continuous update (retraining) of the trained iris PA detection models. Another burgeoning security threat to biometric systems is morph attacks. A morph attack entails the generation of an image (morphed image) that embodies multiple different identities. Typically, a biometric image is associated with a single identity. In this work, we first demonstrate the vulnerability of iris recognition techniques to morph attacks and then develop techniques to detect the morphed iris images.The second focus of the thesis is to improve the performance of a cross-modal system where iris images are matched against face images. Cross-modality matching involves various challenges, such as cross-spectral, cross-resolution, cross-pose, and cross-temporal. To address these challenges, we extract common features present in both images using a multi-channel convolutional network and also generate synthetic data to augment insufficient training data using a dual-variational autoencoder framework. The two focus areas of this thesis improve the acceptance and widespread usage of the iris biometric system.
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Title: LIDAR AND CAMERA CALIBRATION USING A MOUNTED SPHERE
Creator: Li, Jiajia
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Extrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically...
Show moreExtrinsic calibration between lidar and camera sensors is needed for multi-modal sensor data fusion. However, obtaining precise extrinsic calibration can be tedious, computationally expensive, or involve elaborate apparatus. This thesis proposes a simple, fast, and robust method performing extrinsic calibration between a camera and lidar. The only required calibration target is a hand-held colored sphere mounted on a whiteboard. The convolutional neural networks are developed to automatically localize the sphere relative to the camera and the lidar. Then using the localization covariance models, the relative pose between the camera and lidar is derived. To evaluate the accuracy of our method, we record image and lidar data of a sphere at a set of known grid positions by using two rails mounted on a wall. The accurate calibration results are demonstrated by projecting the grid centers into the camera image plane and finding the error between these points and the hand-labeled sphere centers.
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Title: Learning to Detect Language Markers
Creator: Tang, Fengyi
Date: 2021
Collection: Electronic Theses & Dissertations
Description: In the world of medical informatics, biomarkers play a pivotal role in determining the physical state of human beings, distinguishing the pathologic from the clinically normal. In recent years, behavioral markers, due to their availability and low cost, have attracted a lot of attention as a potential supplement to biomarkers. “Language markers” such as spoken words and lexical preference have been shown to be both cost-effective as well as predictive of complex diseases such as mild...
Show moreIn the world of medical informatics, biomarkers play a pivotal role in determining the physical state of human beings, distinguishing the pathologic from the clinically normal. In recent years, behavioral markers, due to their availability and low cost, have attracted a lot of attention as a potential supplement to biomarkers. “Language markers” such as spoken words and lexical preference have been shown to be both cost-effective as well as predictive of complex diseases such as mild cognitive impairment (MCI).However, language markers, although universal, do not possess many of the favorable properties that characterize traditional biomakers. For example, different people may exhibit similar use of language under certain conversational contexts (non-unique), and a person's lexical preferences may change over time (non-stationary). As a result, it is unclear whether any set of language markers can be measured in a consistent manner. My thesis projects provide solutions to some of the limitations of language markers: (1) We formalize the problem of learning a dialog policy to measure language markers as an optimization problem which we call persona authentication. We provide a learning algorithm for finding such a dialog policy that can generalize to unseen personalities. (2) We apply our dialog policy framework on real-world data for MCI prediction and show that the proposed pipeline improves prediction against supervised learning baselines. (3) To address non-stationarity, we introduce an effective way to do temporally-dependent and non-i.i.d. feature selection through an adversarial learning framework which we call precision sensing. (4) Finally, on the prediction side, we propose a method for improving the sample efficiency of classifiers by retaining privileged information (auxiliary features available only at training time).
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Title: MICROBLOG GUIDED CRYPTOCURRENCY TRADING AND FRAMING ANALYSIS
Creator: Pawlicka Maule, Anna Paula
Date: 2020
Collection: Electronic Theses & Dissertations
Description: With 56 million people actively trading and investing in cryptocurrency online and globally, there is an increasing need for an automatic social media analysis tool to help understand trading discourse and behavior. Previous works have shown the usefulness of modeling microblog discourse for the prediction of trading stocks and their price fluctuations, as well as content framing. In this work, I present a natural language modeling pipeline that leverages language and social network behaviors...
Show moreWith 56 million people actively trading and investing in cryptocurrency online and globally, there is an increasing need for an automatic social media analysis tool to help understand trading discourse and behavior. Previous works have shown the usefulness of modeling microblog discourse for the prediction of trading stocks and their price fluctuations, as well as content framing. In this work, I present a natural language modeling pipeline that leverages language and social network behaviors for the prediction of cryptocurrency day trading actions and their associated framing patterns. Specifically, I present two modeling approaches. The first determines if the tweets of a 24-hour period can be used to guide day trading behavior, specifically if a cryptocurrency investor should buy, sell, or hold their cryptocurrencies in order to make a trading profit. The second is an unsupervised deep clustering approach to automatically detect framing patterns. My contributions include the modeling pipeline for this novel task, a new dataset of cryptocurrency-related tweets from influential accounts, and a transaction volume dataset. The experiments executed show that this weakly-supervised trading pipeline achieves an 88.78% accuracy for day trading behavior predictions and reveals framing fluctuations prior to and during the COVID-19 pandemic that could be used to guide investment actions.
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Title: Memory-efficient emulation of physical tabular data using quadtree decomposition
Creator: Carlson, Jared
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Computationally expensive functions are sometimes replaced in simulations with an emulator that approximates the true function (e.g., equations of state, wavelength-dependent opacity, or composition-dependent materials properties). For functions that have a constrained domain of interest, this can be done by discretizing the domain and performing a local interpolation on the tabulated function values of each local domain. For these so-called tabular data methods, the method of discretizing...
Show moreComputationally expensive functions are sometimes replaced in simulations with an emulator that approximates the true function (e.g., equations of state, wavelength-dependent opacity, or composition-dependent materials properties). For functions that have a constrained domain of interest, this can be done by discretizing the domain and performing a local interpolation on the tabulated function values of each local domain. For these so-called tabular data methods, the method of discretizing the domain and mapping the input space to each subdomain can drastically influence the memory and computational costs of the emulator. This is especially true for functions that vary drastically in different regions. We present a method for domain discretization and mapping that utilizes quadtrees, which results in significant reductions in the size of the emulator with minimal increases to computational costs or loss of global accuracy. We apply our method to the electron-positron Helmholtz free energy equation of state and show over an order of magnitude reduction in memory costs for reasonable levels of numerical accuracy.
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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: Network analysis with negative links
Creator: Derr, Tyler Scott
Date: 2020
Collection: Electronic Theses & Dissertations
Description: As we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively extract insightful patterns. We can then seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis...
Show moreAs we rapidly continue into the information age, the rate at which data is produced has created an unprecedented demand for novel methods to effectively extract insightful patterns. We can then seek to understand the past, make predictions about the future, and ultimately take actionable steps towards improving our society. Thus, due to the fact that much of today's big data can be represented as graphs, emphasis is being taken to harness the natural structure of data through network analysis. Traditionally, network analysis has focused on networks having only positive links, or unsigned networks. However, in many real-world systems, relations between nodes in a graph can be both positive and negative, or signed networks. For example, in online social media, users not only have positive links such as friends, followers, and those they trust, but also can establish negative links to those they distrust, towards their foes, or block and unfriend users.Thus, although signed networks are ubiquitous due to their ability to represent negative links in addition to positive links, they have been significantly under explored. In addition, due to the rise in popularity of today's social media and increased polarization online, this has led to both an increased attention and demand for advanced methods to perform the typical network analysis tasks when also taking into consideration negative links. More specifically, there is a need for methods that can measure, model, mine, and apply signed networks that harness both these positive and negative relations. However, this raises novel challenges, as the properties and principles of negative links are not necessarily the same as positive links, and furthermore the social theories that have been used in unsigned networks might not apply with the inclusion of negative links.The chief objective of this dissertation is to first analyze the distinct properties negative links have as compared to positive links and towards improving network analysis with negative links by researching the utility and how to harness social theories that have been established in a holistic view of networks containing both positive and negative links. We discover that simply extending unsigned network analysis is typically not sufficient and that although the existence of negative links introduces numerous challenges, they also provide unprecedented opportunities for advancing the frontier of the network analysis domain. In particular, we develop advanced methods in signed networks for measuring node relevance and centrality (i.e., signed network measuring), present the first generative signed network model and extend/analyze balance theory to signed bipartite networks (i.e., signed network modeling), construct the first signed graph convolutional network which learns node representations that can achieve state-of-the-art prediction performance and then furthermore introduce the novel idea of transformation-based network embedding (i.e., signed network mining), and apply signed networks by creating a framework that can infer both link and interaction polarity levels in online social media and constructing an advanced comprehensive congressional vote prediction framework built around harnessing signed networks.
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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: Novel Depth Representations for Depth Completion with Application in 3D Object Detection
Creator: Imran, Saif Muhammad
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Depth completion refers to interpolating a dense, regular depth grid from sparse and irregularly sampled depth values, often guided by high-resolution color imagery. The primary goal of depth completion is to estimate depth. In practice methods are trained by minimizing an error between predicted dense depth and ground-truth depth, and are evaluated by how well they minimize this error. Here we identify a second goal which is to avoid smearing depth across depth discontinuities. This second...
Show moreDepth completion refers to interpolating a dense, regular depth grid from sparse and irregularly sampled depth values, often guided by high-resolution color imagery. The primary goal of depth completion is to estimate depth. In practice methods are trained by minimizing an error between predicted dense depth and ground-truth depth, and are evaluated by how well they minimize this error. Here we identify a second goal which is to avoid smearing depth across depth discontinuities. This second goal is important because it can improve downstream applications of depth completion such as object detection and pose estimation. However, we also show that the goal of minimizing error can conflict with the goal of eliminating depth smearing.In this thesis, we propose two novel representations of depths that can encode depth discontinuity across object surfaces by allowing multiple depth estimation in the spatial domain. In order to learn these new representations, we propose carefully designed loss functions and show their effectiveness in deep neural network learning. We show how our representations can avoid inter-object depth mixing and also beat state of the art metrics for depth completion. The quality of ground-truth depth in real-world depth completion problems is another key challenge for learning and accurate evaluation of methods. Ground truth depth created from semi-automatic methods suffers from sparse sampling and errors at object boundaries. We show that the combination of these errors and the commonly used evaluation measure has promoted solutions that mix depths across boundaries in current methods. The thesis proposes alternate depth completion performance measures that reduce preference for mixed depths and promote sharp boundaries.The thesis also investigates whether additional points from depth completion methods can help in a challenging and high-level perception problem; 3D object detection. It shows the effect of different depth noises originated from depth estimates on detection performances and proposes some effective ways to reduce noise in the estimate and overcome architecture limitations. The method is demonstrated on both real-world and synthetic datasets.
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Title: OPTIMIZATION OF LARGE SCALE ITERATIVE EIGENSOLVERS
Creator: Afibuzzaman, Md
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Sparse matrix computations, in the form of solvers for systems of linear equations, eigenvalue problem or matrix factorizations constitute the main kernel in problems from fields as diverse as computational fluid dynamics, quantum many body problems, machine learning and graph analytics. Iterative eigensolvers have been preferred over the regular method because the regular method not being feasible with industrial sized matrices. Although dense linear algebra libraries like BLAS, LAPACK,...
Show moreSparse matrix computations, in the form of solvers for systems of linear equations, eigenvalue problem or matrix factorizations constitute the main kernel in problems from fields as diverse as computational fluid dynamics, quantum many body problems, machine learning and graph analytics. Iterative eigensolvers have been preferred over the regular method because the regular method not being feasible with industrial sized matrices. Although dense linear algebra libraries like BLAS, LAPACK, SCALAPACK are well established and some vendor optimized implementation like mkl from Intel or Cray Libsci exist, it is not the same case for sparse linear algebra which is lagging far behind. The main reason behind slow progress in the standardization of sparse linear algebra or library development is the different forms and properties depending on the application area. It is worsened for deep memory hierarchies of modern architectures due to low arithmetic intensities and memory bound computations. Minimization of data movement and fast access to the matrix are critical in this case. Since the current technology is driven by deep memory architectures where we get the increased capacity at the expense of increased latency and decreased bandwidth when we go further from the processors. The key to achieve high performance in sparse matrix computations in deep memory hierarchy is to minimize data movement across layers of the memory and overlap data movement with computations. My thesis work contributes towards addressing the algorithmic challenges and developing a computational infrastructure to achieve high performance in scientific applications for both shared memory and distributed memory architectures. For this purpose, I started working on optimizing a blocked eigensolver and optimized specific computational kernels which uses a new storage format. Using this optimization as a building block, we introduce a shared memory task parallel framework focusing on optimizing the entire solvers rather than a specific kernel. Before extending this shared memory implementation to a distributed memory architecture, I simulated the communication pattern and overheads of a large scale distributed memory application and then I introduce the communication tasks in the framework to overlap communication and computation. Additionally, I also tried to find a custom scheduler for the tasks using a graph partitioner. To get acquainted with high performance computing and parallel libraries, I started my PhD journey with optimizing a DFT code named Sky3D where I used dense matrix libraries. Despite there might not be any single solution for this problem, I tried to find an optimized solution. Though the large distributed memory application MFDn is kind of the driver project of the thesis, but the framework we developed is not confined to MFDn only, rather it can be used for other scientific applications too. The output of this thesis is the task parallel HPC infrastructure that we envisioned for both shared and distributed memory architectures.
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Title: Object Detection from 2D to 3D
Creator: Brazil, Garrick
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Monocular camera-based object detection plays a critical role in widespread applications including robotics, security, self-driving cars, augmented reality and many more. Increased relevancy is often given to the detection and tracking of safety-critical objects like pedestrians, cyclists, and cars which are often in motion and in close association to people. Compared to other generic objects such as animals, tools, food — safety-critical objects in urban scenes tend to have unique challenges...
Show moreMonocular camera-based object detection plays a critical role in widespread applications including robotics, security, self-driving cars, augmented reality and many more. Increased relevancy is often given to the detection and tracking of safety-critical objects like pedestrians, cyclists, and cars which are often in motion and in close association to people. Compared to other generic objects such as animals, tools, food — safety-critical objects in urban scenes tend to have unique challenges. Firstly, such objects usually have a wide range of detection scales such that they may appear anywhere from 5-50+ meters from the camera. Safety-critical objects also tend to have a high variety of textures and shapes, exemplified by the clothing of people and variability of vehicle models. Moreover, the high-density of objects in urban scenes leads to increased levels of self-occlusion compared to general objects in the wild. Off-the-shelf object detectors do not always work effectively due to these traits, and hence special attention is needed for accurate detection. Moreover, even successful detection of safety-critical is not inherently practical for applications designed to function in the real 3D world, without integration of expensive depth sensors. To remedy this, in this thesis we aim to improve the performance of 2D object detection and extend boxes into 3D, while using only monocular camera-based sensors. We first explore how pedestrian detection can be augmented using an efficient simultaneous detection and segmentation technique, while notably requiring no additional data or annotations. We then propose a multi-phased autoregressive network which progressively improves pedestrian detection precision for difficult samples, while critically maintaining an efficient runtime. We additionally propose a single-stage region proposal networks for 3D object detection in urban scenes, which is both more efficient and up to 3x more accurate than comparable state-of-the-art methods. We stabilize our 3D object detector using a highly tailored 3D Kalman filter, which both improves localization accuracy and provides useful byproducts such as ego-motion and per-object velocity. Lastly, we utilize differentiable rendering to discover the underlying 3D structure of objects beyond the cuboids used in detection, and without relying on expensive sensors or 3D supervision. For each method, we provide comprehensive experiments to demonstrate effectiveness, impact and runtime efficiency.
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Title: Online Learning Algorithms for Mining Trajectory data and their Applications
Creator: Wang, Ding
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Trajectories are spatio-temporal data that represent traces of moving objects, such as humans, migrating animals, vehicles, and tropical cyclones. In addition to the geo-location information, a trajectory data often contain other (non-spatial) features describing the states of the moving objects. The time-varying geo-location and state information would collectively characterize a trajectory dataset, which can be harnessed to understand the dynamics of the moving objects. This thesis focuses...
Show moreTrajectories are spatio-temporal data that represent traces of moving objects, such as humans, migrating animals, vehicles, and tropical cyclones. In addition to the geo-location information, a trajectory data often contain other (non-spatial) features describing the states of the moving objects. The time-varying geo-location and state information would collectively characterize a trajectory dataset, which can be harnessed to understand the dynamics of the moving objects. This thesis focuses on the development of efficient and accurate machine learning algorithms for forecasting the future trajectory path and state of a moving object. Although many methods have been developed in recent years, there are still numerous challenges that have not been sufficiently addressed by existing methods, which hamper their effectiveness when applied to critical applications such as hurricane prediction. These challenges include their difficulties in terms of handling concept drifts, error propagation in long-term forecasts, missing values, and nonlinearities in the data. In this thesis, I present a family of online learning algorithms to address these challenges. Online learning is an effective approach as it can efficiently fit new observations while adapting to concept drifts present in the data. First, I proposed an online learning framework called OMuLeT for long-term forecasting of the trajectory paths of moving objects. OMuLeT employs an online learning with restart strategy to incrementally update the weights of its predictive model as new observation data become available. It can also handle missing values in the data using a novel weight renormalization strategy.Second, I introduced the OOR framework to predict the future state of the moving object. Since the state can be represented by ordinal values, OOR employs a novel ordinal loss function to train its model. In addition, the framework was extended to OOQR to accommodate a quantile loss function to improve its prediction accuracy for larger values on the ordinal scale. Furthermore, I also developed the OOR-ε and OOQR-ε frameworks to generate real-valued state predictions using the ε insensitivity loss function.Third, I developed an online learning framework called JOHAN, that simultaneously predicts the location and state of the moving object. JOHAN generates its predictions by leveraging the relationship between the state and location information. JOHAN utilizes a quantile loss function to bias the algorithm towards predicting more accurately large categorical values in terms of the state of the moving object, say, for a high intensity hurricane.Finally, I present a deep learning framework to capture non-linear relationships in trajectory data. The proposed DTP framework employs a TDM approach for imputing missing values, coupled with an LSTM architecture for dynamic path prediction. In addition, the framework was extended to ODTP, which applied an online learning setting to address concept drifts present in the trajectory data.As proof of concept, the proposed algorithms were applied to the hurricane prediction task. Both OMuLeT and ODTP were used to predict the future trajectory path of a hurricane up to 48 hours lead time. Experimental results showed that OMuLeT and ODTP outperformed various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center. OOR was applied to predict the intensity of a hurricane up to 48 hours in advance. Experimental results showed that OOR outperformed various state-of-the-art online learning methods and can generate predictions close to the NHC official forecasts. Since hurricane intensity prediction is a notoriously hard problem, JOHAN was applied to improve its prediction accuracy by leveraging the trajectory information, particularly for high intensity hurricanes that are near landfall.
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Title: Optimal Learning of Deployment and Search Strategies for Robotic Teams
Creator: Wei, Lai
Date: 2021
Collection: Electronic Theses & Dissertations
Description: In the problem of optimal learning, the dilemma of exploration and exploitation stems from the fact that gathering information and exploiting it are, in many cases, two mutually exclusive activities. The key to optimal learning is to strike a balance between exploration and exploitation. The Multi-Armed Bandit (MAB) problem is a prototypical example of such an explore-exploit tradeoff, in which a decision-maker sequentially allocates a single resource by repeatedly choosing one among a set of...
Show moreIn the problem of optimal learning, the dilemma of exploration and exploitation stems from the fact that gathering information and exploiting it are, in many cases, two mutually exclusive activities. The key to optimal learning is to strike a balance between exploration and exploitation. The Multi-Armed Bandit (MAB) problem is a prototypical example of such an explore-exploit tradeoff, in which a decision-maker sequentially allocates a single resource by repeatedly choosing one among a set of options that provide stochastic rewards. The MAB setup has been applied in many robotics problems such as foraging, surveillance, and target search, wherein the task of robots can be modeled as collecting stochastic rewards. The theoretical work of this dissertation is based on the MAB setup and three problem variations, namely heavy-tailed bandits, nonstationary bandits, and multi-player bandits, are studied. The first two variations capture two key features of stochastic feedback in complex and uncertain environments: heavy-tailed distributions and nonstationarity; while the last one addresses the problem of achieving coordination in uncertain environments. We design several algorithms that are robust to heavy-tailed distributions and nonstationary environments. Besides, two distributed policies that require no communication among agents are designed for the multi-player stochastic bandits in a piece-wise stationary environment.The MAB problems provide a natural framework to study robotic search problems. The above variations of the MAB problems directly map to robotic search tasks in which a robot team searches for a target from a fixed set of view-points (arms). We further focus on the class of search problems involving the search of an unknown number of targets in a large or continuous space. We view the multi-target search problem as a hot-spots identification problem in which, instead of the global maximum of the field, all locations with a value greater than a threshold need to be identified. We consider a robot moving in 3D space with a downward-facing camera sensor. We model the robot's sensing output using a multi-fidelity Gaussian Process (GP) that systematically describes the sensing information available at different altitudes from the floor. Based on the sensing model, we design a novel algorithm that (i) addresses the coverage-accuracy tradeoff: sampling at a location farther from the floor provides a wider field of view but less accurate measurements, (ii) computes an occupancy map of the floor within a prescribed accuracy and quickly eliminates unoccupied regions from the search space, and (iii) travels efficiently to collect the required samples for target detection. We rigorously analyze the algorithm and establish formal guarantees on the target detection accuracy and the detection time.An approach to extend the single robot search policy to multiple robots is to partition the environment into multiple regions such that workload is equitably distributed among all regions and then assign a robot to each region. The coverage control focuses on such equitable partitioning and the workload is equivalent to the so-called service demands in the coverage control literature. In particular, we study the adaptive coverage control problem, in which the demands of robotic service within the environment are modeled as a GP. To optimize the coverage of service demands in the environment, the team of robots aims to partition the environment and achieve a configuration that minimizes the coverage cost, which is a measure of the average distance of a service demand from the nearest robot. The robots need to address the explore-exploit tradeoff: to minimize coverage cost, they need to gather information about demands within the environment, whereas information gathering deviates them from maintaining a good coverage configuration. We propose an algorithm that schedules learning and coverage epochs such that its emphasis gradually shifts from exploration to exploitation while never fully ceasing to learn. Using a novel definition of coverage regret, we analyze the algorithm and characterizes its coverage performance over a finite time horizon.
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Title: Optimizing and Improving the Fidelity of Reactive, Polarizable Molecular Dynamics Simulations on Modern High Performance Computing Architectures
Creator: O'Hearn, Kurt A.
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Reactive, polarizable molecular dynamics simulations are a crucial tool for the high-fidelity study of large systems with chemical reactions. In support of this, several approaches have been employed with varying degrees of computational cost and physical accuracy. One of the more successful approaches in recent years, the reactive force field (ReaxFF) model, wasdesigned to fill the gap between traditional classical models and quantum mechanical models by incorporating a dynamic bond order...
Show moreReactive, polarizable molecular dynamics simulations are a crucial tool for the high-fidelity study of large systems with chemical reactions. In support of this, several approaches have been employed with varying degrees of computational cost and physical accuracy. One of the more successful approaches in recent years, the reactive force field (ReaxFF) model, wasdesigned to fill the gap between traditional classical models and quantum mechanical models by incorporating a dynamic bond order potential term. When coupling ReaxFF with dynamic global charges models for electrostatics, special considerations are necessary for obtaining highly performant implementations, especially on modern high-performance computing architectures.In this work, we detail the performance optimization of the PuReMD (PuReMD Reactive Molecular Dynamics) software package, an open-source, GPLv3-licensed implementation of ReaxFF coupled with dynamic charge models. We begin byexploring the tuning of the iterative Krylov linear solvers underpinning the global charge models in a shared-memory parallel context using OpenMP, with the explicit goal of minimizing the mean combined preconditioner and solver time. We found that with appropriate solver tuning, significant speedups and scalability improvements were observed. Following these successes, we extend these approaches to the solvers in the distributed-memory MPI implementation of PuReMD, as well as broaden the scope of optimization to other portions of the ReaxFF potential such as the bond order computations. Here again we find that sizable performance gains were achieved for large simulations numbering in the hundreds of thousands of atoms.With these performance improvements in hand, we next change focus to another important use of PuReMD -- the development of ReaxFF force fields for new materials. The high fidelity inherent in ReaxFF simulations for different chemistries oftentimes comes at the expense of a steep learning curve for parameter optimization, due in part to complexities in the high dimensional parameter space and due in part to the necessity of deep domain knowledge of how to adequately control the ReaxFF functional forms. To diagnose and combat these issues, a study was undertaken to optimize parameters for Li-O systems using the OGOLEM genetic algorithms framework coupled with a modified shared-memory version of PuReMD. We found that with careful training set design, sufficient optimization control with tuned genetic algorithms, and improved polarizability through enhanced charge model use, higher accuracy was achieved in simulations involving ductile fracture behavior, a difficult phenomena to hereto model correctly.Finally, we return to performance optimization for the GPU-accelerated distributed-memory PuReMD codebase. Modern supercomputers have recently achieved exascale levels of peak arithmetic rates due in large part to the design decision to incorporate massive numbers of GPUs. In order to take advantage of such computing systems, the MPI+CUDA version of PuReMD was re-designed and benchmarked on modern NVIDIA Tesla GPUs. Performance on-par with or exceeding the LAMMPS Kokkos, a ReaxFF implementation developed at Scandia National Laboratories, with PuReMD typically out-performing LAMMPS Kokkos at larger scales.
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Title: PALETTEVIZ : A METHOD FOR VISUALIZATION OF HIGH-DIMENSIONAL PARETO-OPTIMAL FRONT AND ITS APPLICATIONS TO MULTI-CRITERIA DECISION MAKING AND ANALYSIS
Creator: Talukder, AKM Khaled Ahsan
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Visual representation of a many-objective Pareto-optimal front in four or more dimensional objective space requires a large number of data points. Moreover, choosing a single point from a large set even with certain preference information is problematic, as it imposes a large cognitive burden on the decision-makers. Therefore, many-objective optimization and decision-making practitioners have been interested in effective visualization methods to en- able them to filter down a large set to a...
Show moreVisual representation of a many-objective Pareto-optimal front in four or more dimensional objective space requires a large number of data points. Moreover, choosing a single point from a large set even with certain preference information is problematic, as it imposes a large cognitive burden on the decision-makers. Therefore, many-objective optimization and decision-making practitioners have been interested in effective visualization methods to en- able them to filter down a large set to a few critical points for further analysis. Most existing visualization methods are borrowed from other data analytics domains and they are too generic to be effective for many-criterion decision making. In this dissertation, we propose a visualization method, using star-coordinate and radial visualization plots, for effectively visualizing many-objective trade-off solutions. The proposed method respects some basic topological, geometric and functional decision-making properties of high-dimensional trade- off points mapped to a three-dimensional space. We call this method Palette Visualization (PaletteViz). We demonstrate the use of PaletteViz on a number of large-dimensional multi- objective optimization test problems and three real-world multi-objective problems, where one of them has 10 objective and 16 constraint functions. We also show the uses of NIMBUS and Pareto-Race concepts from canonical multi-criterion decision making and analysis literature and introduce them into PaletteViz to demonstrate the ease and advantage of the proposed method.
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Title: PRECISION DIAGNOSTICS AND INNOVATIONS FOR PLANT BREEDING RESEARCH
Creator: Hugghis, Eli
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Major technological advances are necessary to reach the goal of feeding our world’s growing population. To do this, there is an increasing demand within the agricultural field for rapid diagnostic tools to improve the efficiency of current methods in plant disease and DNA identification. The use of gold nanoparticles has emerged as a promising technology for a range of applications from smart agrochemical delivery systems to pathogen detection. In addition to this, advances in image...
Show moreMajor technological advances are necessary to reach the goal of feeding our world’s growing population. To do this, there is an increasing demand within the agricultural field for rapid diagnostic tools to improve the efficiency of current methods in plant disease and DNA identification. The use of gold nanoparticles has emerged as a promising technology for a range of applications from smart agrochemical delivery systems to pathogen detection. In addition to this, advances in image classification analyses have allowed machine learning approaches to become more accessible to the agricultural field. Here we present the use of gold nanoparticles (AuNPs) for the detection of transgenic gene sequences in maize and the use of machine learning algorithms for the identification and classification of Fusarium spp. infected wheat seed. AuNPs show promise in their ability to diagnose the presence of transgenic insertions in DNA samples within 10 minutes through colorimetric response. Image-based analysis with the utilization of logistic regression, support vector machines, and k-nearest neighbors were able to accurately identify and differentiate healthy and diseased wheat kernels within the testing set at an accuracy of 95-98.8%. These technologies act as rapid tools to be used by plant breeders and pathologists to improve their ability to make selection decisions efficiently and objectively.
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Title: Predicting the Properties of Ligands Using Molecular Dynamics and Machine Learning
Creator: Donyapour, Nazanin
Date: 2022
Collection: Electronic Theses & Dissertations
Description: The discovery and design of new drugs requires extensive experimental assays that are usually very expensive and time-consuming. To cut down the cost and time of the drug development process and help design effective drugs more efficiently, various computational methods have been developed that are referred to collectively as in silico drug design. These in silico methods can be used to not only determine compounds that can bind to a target receptor but to determine whether compounds show...
Show moreThe discovery and design of new drugs requires extensive experimental assays that are usually very expensive and time-consuming. To cut down the cost and time of the drug development process and help design effective drugs more efficiently, various computational methods have been developed that are referred to collectively as in silico drug design. These in silico methods can be used to not only determine compounds that can bind to a target receptor but to determine whether compounds show ideal drug-like properties. I have provided solutions to these problems by developing novel methods for molecular simulation and molecular property prediction. Firstly, we have developed a new enhanced sampling MD algorithm called Resampling of Ensembles by Variation Optimization or “REVO” that can generate binding and unbinding pathways of ligand-target interactions. These pathways are useful for calculating transition rates and Residence Times (RT) of protein-ligand complexes. This can be particularly useful for drug design as studies for some systems show that the drug efficacy correlates more with RT than the binding affinity. This method is generally useful for generating long-timescale transitions in complex systems, including alternate ligand binding poses and protein conformational changes. Secondly, we have developed a technique we refer to as “ClassicalGSG” to predict the partition coefficient (log P) of small molecules. log P is one of the main factors in determining the drug likeness of a compound, as it helps determine bioavailability, solubility, and membrane permeability. This method has been very successful compared to other methods in literature. Finally, we have developed a method called ``Flexible Topology'' that we hope can eventually be used to screen a database of potential ligands while considering ligand-induced conformational changes. After discovering molecules with drug-like properties in the drug design pipeline, Virtual Screening (VS) methods are employed to perform an extensive search on drug databases with hundreds of millions of compounds to find candidates that bind tightly to a molecular target. However, in order for this to be computationally tractable, typically, only static snapshots of the target are used, which cannot respond to the presence of the drug compound. To efficiently capture drug-target interactions during screening, we have developed a machine-learning algorithm that employs Molecular Dynamics (MD) simulations with a protein of interest and a set of atoms called “Ghost Particles”. During the simulation, the Flexible Topology method induces forces that constantly modify the ghost particles and optimizes them toward drug-like molecules that are compatible with the molecular target.
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Title: Quantitative methods for calibrated spatial measurements of laryngeal phonatory mechanisms
Creator: Ghasemzadeh, Hamzeh
Date: 2020
Collection: Electronic Theses & Dissertations
Description: The ability to perform measurements is an important cornerstone and the prerequisite of any quantitative research. Measurements allow us to quantify inputs and outputs of a system, and then to express their relationships using concise mathematical expressions and models. Those models would then enable us to understand how a target system works and to predict its output for changes in the system parameters. Conversely, models would enable us to determine the proper parameters of a system for...
Show moreThe ability to perform measurements is an important cornerstone and the prerequisite of any quantitative research. Measurements allow us to quantify inputs and outputs of a system, and then to express their relationships using concise mathematical expressions and models. Those models would then enable us to understand how a target system works and to predict its output for changes in the system parameters. Conversely, models would enable us to determine the proper parameters of a system for achieving a certain output. Putting these in the context of voice science research, variations in the parameters of the phonatory system could be attributed to individual differences. Thus, accurate models would enable us to account for individual differences during the diagnosis and to make reliable predictions about the likely outcome of different treatment options. Analysis of vibration of the vocal folds using high-speed videoendoscopy (HSV) could be an ideal candidate for constructing computational models. However, conventional images are not spatially calibrated and cannot be used for absolute spatial measurements. This dissertation is focused on developing the required methodologies for calibrated spatial measurements from in-vivo HSV recordings. Specifically, two different approaches for calibrated horizontal measurements of HSV images are presented. The first approach is called the indirect approach, and it is based on the registration of a specific attribute of a common object (e.g. size of a lesion) from a calibrated intraoperative still image to its corresponding non-calibrated in-vivo HSV recording. This approach does not require specialized instruments and can be implemented in many clinical settings. However, its validity depends on a couple of assumptions. Violation of those assumptions could lead to significant measurement errors. The second approach is called the direct approach, and it is based on a laser-projection flexible fiberoptic endoscope. This approach would enable us to make accurate calibrated spatial measurements. This dissertation evaluates the accuracy of the first approach indirectly, and by studying its underlying fundamental assumptions. However, the accuracy of the second approach is evaluated directly, and using benchtop experiments with different surfaces, different working distances, and different imaging angles. The main significances and contributions of this dissertation are the following: (1) a formal treatment of indirect horizontal calibration is presented, and the assumptions governing its validity and reliability are discussed. A battery of tests is presented that can indirectly assess the validity of those assumptions in laryngeal imaging applications; (2) recordings from pre- and post-surgery from patients with vocal fold mass lesions are used as a testbench for the developed indirect calibration approach. In that regard, a full solution is developed for measuring the calibrated velocity of the vocal folds. The developed solution is then used to investigate post-surgery changes in the closing velocity of the vocal folds from patients with vocal fold mass lesions; (3) the method for calibrated vertical measurement from a laser-projection fiberoptic flexible endoscope is developed. The developed method is evaluated at different working distances, different imaging angles, and on a 3D surface; (4) a detailed analysis and investigation of non-linear image distortion of a fiberoptic flexible endoscope is presented. The effect of imaging angle and spatial location of an object on the magnitude of that distortion is studied and quantified; (5) the method for calibrated horizontal measurement from a laser-projection fiberoptic flexible endoscope is developed. The developed method is evaluated at different working distances, different imaging angles, and on a 3D surface.
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Title: Replaying Life's Virtual Tape : Examining the Role of History in Experiments with Digital Organisms
Creator: Bundy, Jason Nyerere
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Evolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not...
Show moreEvolution is a complex process with a simple recipe. Evolutionary change involves three essential “ingredients” interacting over many generations: adaptation (selection), chance (random variation), and history (inheritance). In 1989’s Wonderful Life, the late paleontologist Stephen Jay Gould advocated for the importance of historical contingency—the way unique events throughout history influence future possibilities—using a clever thought experiment of “replaying life’s tape”. But not everyone was convinced. Some believed that chance was the primary driver of evolutionary change, while others insisted that natural selection was the most powerful influence. Since then, “replaying life’s tape” has become a core method in experimental evolution for measuring the relative contributions of adaptation, chance, and history. In this dissertation, I focus on the effects associated with history in evolving populations of digital organisms—computer programs that self-replicate, mutate, compete, and evolve in virtual environments. In Chapter 1, I discuss the philosophical significance of Gould’s thought experiment and its influence on experimental methods. I argue that his thought experiment was a challenge to anthropocentric reasoning about natural history that is still popular, particularly outside of the scientific community. In this regard, it was his way of advocating for a “radical” view of evolution. In Chapter 2—Richard Lenski, Charles Ofria, and I describe a two-phase, virtual, “long-term” evolution experiment with digital organisms using the Avida software. In Phase I, we evolved 10 replicate populations, in parallel, from a single genotype for around 65,000 generations. This part of the experiment is similar to the design of Lenski’s E. coli Long-term Evolution Experiment (LTEE). We isolated the dominant genotype from each population around 3,000 generations (shallow history) into Phase I and then again at the end of Phase I (deep history). In Phase II, we evolved 10 populations from each of the genotypes we isolated from Phase I in two new environments, one similar and one dissimilar to the old environment used for Phase I. Following Phase II, we estimated the contributions of adaptation, chance, and history to the evolution of fitness and genome length in each new environment. This unique experimental design allowed us to see how the contributions of adaptation, chance, and history changed as we extended the depth of history from Phase I. We were also able to determine whether the results depended on the extent of environmental change (similar or dissimilar new environment). In Chapter 3, we report an extended analysis of the experiment from the previous chapter to further examine how extensive adaptation to the Phase I environment shaped the evolution of replicates during Phase II. We show how the form of pleiotropy (antagonistic or synergistic) between the old (Phase I) and new (Phase II) habitats was influenced by the depth of history from Phase I (shallow or deep) and the extent of environmental change (similar or dissimilar new environment). In the final chapter Zachary Blount, Richard Lenski, and I describe an exercise we developed using the educational version of Avida (Avida-ED). The exercise features a two-phase, “replaying life’s tape” activity. Students are able to explore how the unique history of founders that we pre-evolved during Phase I influences the acquisition of new functions by descendent populations during Phase II, which the students perform during the activity.
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Title: Robust Learning of Deep Neural Networks under Data Corruption
Creator: Liu, Boyang
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Training deep neural networks in the presence of corrupted data is challenging as the corrupted data points may significantly impact generalization performance of the models. Unfortunately, the data corruption issue widely exists in many application domains, including but not limited to, healthcare, environmental sciences, autonomous driving, and social media analytics. Although there have been some previous studies that aim to enhance the robustness of machine learning models against data...
Show moreTraining deep neural networks in the presence of corrupted data is challenging as the corrupted data points may significantly impact generalization performance of the models. Unfortunately, the data corruption issue widely exists in many application domains, including but not limited to, healthcare, environmental sciences, autonomous driving, and social media analytics. Although there have been some previous studies that aim to enhance the robustness of machine learning models against data corruption, most of them either lack theoretical robustness guarantees or unable to scale to the millions of model parameters governing deep neural networks. The goal of this thesis is to design robust machine learning algorithms that 1) effectively deal with different types of data corruption, 2) have sound theoretical guarantees on robustness, and 3) scalable to large number of parameters in deep neural networks.There are two general approaches to enhance model robustness against data corruption. The first approach is to detect and remove the corrupted data while the second approach is to design robust learning algorithms that can tolerate some fraction of corrupted data. In this thesis, I had developed two robust unsupervised anomaly detection algorithms and two robust supervised learning algorithm for corrupted supervision and backdoor attack. Specifically, in Chapter 2, I proposed the Robust Collaborative Autoencoder (RCA) approach to enhance the robustness of vanilla autoencoder methods against natural corruption. In Chapter 3, I developed Robust RealNVP, a robust density estimation technique for unsupervised anomaly detection tasks given concentrated anomalies. Chapter 4 presents the Provable Robust Learning (PRL) approach, which is a robust algorithm against agnostic corrupted supervision. In Chapter 5, a meta-algorithm to defend against backdoor attacks is proposed by exploring the connection between label corruption and backdoor data poisoning attack. Extensive experiments on multiple benchmark datasets have demonstrated the robustness of the proposed algorithms under different types of corruption.
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