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Title: IMPROVED DETECTION AND MANAGEMENT OF PHYTOPHTHORA SOJAE
Creator: McCoy, Austin Glenn
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Phytophthora spp. cause root and stem rots, leaf blights and fruit rots on agricultural and economically important plant species. Symptoms of Phytophthora infected plants, particularly root rots, can be difficult to distinguish from other oomycete and fungal pathogens and often result in devastating losses. Phytophthora spp. can lie dormant for many years in the oospore stage, making long-term management of these diseases difficult. Phytophthora sojae is an important and prevalent pathogen of...
Show morePhytophthora spp. cause root and stem rots, leaf blights and fruit rots on agricultural and economically important plant species. Symptoms of Phytophthora infected plants, particularly root rots, can be difficult to distinguish from other oomycete and fungal pathogens and often result in devastating losses. Phytophthora spp. can lie dormant for many years in the oospore stage, making long-term management of these diseases difficult. Phytophthora sojae is an important and prevalent pathogen of soybean (Glycine max L.) worldwide, causing Phytophthora stem and root rot (PRR). PRR disease management during the growing season relies on an integrated pest management approach using a combination of host resistance, chemical compounds (fungicides; oomicides) and cultural practices for successful management. Therefore, this dissertation research focuses on improving the detection and management recommendations for Phytophthora sojae. In Chapter 1 I provide background and a review of the current literature on Phytophthora sojae management, including genetic resistance, chemical control compounds (fungicides; oomicides) and cultural practices used to mitigate losses to PRR. In my second chapter I validate the sensitivity and specificity of a preformulated Recombinase Polymerase Amplification assay for Phytophthora spp. This assay needs no refrigeration, does not require extensive DNA isolation, can be used in the field, and different qPCR platforms could reliably detect down to 3.3-330.0 pg of Phytophthora spp. DNA within plant tissue in under 30 minutes. Based on the limited reagents needed, ease of use, and reliability, this assay would be of benefit to diagnostic labs and inspectors monitoring regulated and non-regulated Phytophthora spp. Next, I transitioned the Habgood-Gilmour Spreadsheet (‘HaGiS’) from Microsoft Excel format to the subsequent R package ‘hagis’ and improved upon the analyses readily available to compare pathotypes from different populations of P. sojae (Chapter 3; ‘hagis’ beta-diversity). I then implemented the R package ‘hagis’ in my own P. sojae pathotype and fungicide sensitivity survey in the state of Michigan, identifying effective resistance genes and seed treatment compounds for the management of PRR. This study identified a loss of Rps1c and Rps1k, the two most widely plant Phytophthora sojae resistance genes, as viable management tools in Michigan and an increase in pathotype complexity, as compared to a survey conducted twenty years ago in Michigan (Chapter 4). In Chapter 5 I led a multi-state integrated pest management field trial that was performed in Michigan, Indiana, and Minnesota to study the effects of partial resistance and seed treatments with or without ethaboxam and metalaxyl on soybean stand, plant dry weights, and final yields under P. sojae pressure. This study found that oomicide treated seed protects stand across three locations in the Midwest, but the response of soybean varieties based on seed treatment, was variety and year specific. Significant yield benefits from using oomicide treated seed were only observed in one location and year. The effects of partial resistance were inconclusive and highlighted the need for a more informative and reliable rating system for soybean varieties partial resistance to P. sojae. Finally, in Chapter 6 I present conclusions and impacts on the studies presented in this dissertation. Overall, the studies presented provide an improvement to the detection, virulence data analysis, and integrated pest management recommendations for Phytophthora sojae.
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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: Detecting and Mitigating Bias in Natural Languages
Creator: Liu, Haochen
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Natural language processing (NLP) is an increasingly prominent subfield of artificial intelligence (AI). NLP techniques enable intelligent machines to understand and analyze natural languages and make it possible for humans and machines to communicate through natural languages. However, more and more evidence indicates that NLP applications show human-like discriminatory bias or make unfair decisions. As NLP algorithms play an increasingly irreplaceable role in promoting the automation of...
Show moreNatural language processing (NLP) is an increasingly prominent subfield of artificial intelligence (AI). NLP techniques enable intelligent machines to understand and analyze natural languages and make it possible for humans and machines to communicate through natural languages. However, more and more evidence indicates that NLP applications show human-like discriminatory bias or make unfair decisions. As NLP algorithms play an increasingly irreplaceable role in promoting the automation of people's lives, bias in NLP is closely related to users' vital interests and demands considerable attention.While there are a growing number of studies related to bias in natural languages, the research on this topic is far from complete. In this thesis, we propose several studies to fill up the gaps in the area of bias in NLP in terms of three perspectives. First, existing studies are mainly confined to traditional and relatively mature NLP tasks, but for certain newly emerging tasks such as dialogue generation, the research on how to define, detect, and mitigate the bias in them is still absent. We conduct pioneering studies on bias in dialogue models to answer these questions. Second, previous studies basically focus on explicit bias in NLP algorithms but overlook implicit bias. We investigate the implicit bias in text classification tasks in our studies, where we propose novel methods to detect, explain, and mitigate the implicit bias. Third, existing research on bias in NLP focuses more on in-processing and post-processing bias mitigation strategies, but rarely considers how to avoid bias being produced in the generation process of the training data, especially in the data annotation phase. To this end, we investigate annotator bias in crowdsourced data for NLP tasks and its group effect. We verify the existence of annotator group bias, develop a novel probabilistic graphical framework to capture it, and propose an algorithm to eliminate its negative impact on NLP model learning.
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Title: Computational methods to investigate connectivity in evolvable systems
Creator: Ackles, Acacia Lee
Date: 2022
Collection: Electronic Theses & Dissertations
Description: Evolution sheds light on all of biology, and evolutionary dynamics underlie some of the most pressing issues we face today. If we can deepen our understanding of evolution, we can better respond to these various challenges. However, studying such processes directly can be difficult; biological data is naturally messy, easily confounded, and often limited. Fortunately, we can use computational modeling to help simplify and systematically untangle complex evolutionary processes. The aim of this...
Show moreEvolution sheds light on all of biology, and evolutionary dynamics underlie some of the most pressing issues we face today. If we can deepen our understanding of evolution, we can better respond to these various challenges. However, studying such processes directly can be difficult; biological data is naturally messy, easily confounded, and often limited. Fortunately, we can use computational modeling to help simplify and systematically untangle complex evolutionary processes. The aim of this dissertation is therefore to develop innovative computational frameworks to describe, quantify, and build intuition about evolutionary phenomena, with a focus on connectivity within evolvable systems. Here I introduce three such computational frameworks which address the importance of connectivity in systems across scales.First, I introduce rank epistasis, a model of epistasis that does not rely on baseline assumptions of genetic interactions. Rank epistasis borrows rank-based comparison testing from parametric statistics to quantify mutational landscapes around a target locus and identify how much that landscape is perturbed by mutation at that locus. This model is able to correctly identify lack of epistasis where existing models fail, thereby providing better insight into connectivity at the genome level.Next, I describe the comparative hybrid method, an approach to piecewise study of complex phenotypes. This model creates hybridized structures of well-known cognitive substrates in order to address what facilitates the evolution of learning. The comparative hybrid model allowed us to identify both connectivity and discretization as important components to the evolution of cognition, as well as demonstrate how both these components interact in different cognitive structures. This approach highlights the importance of recognizing connected components at the level of the phenotype.Finally, I provide an engineering point of view for Tessevolve, a virtual reality enabled system for viewing fitness landscapes in multiple dimensions. While traditional methods have only allowed for 2D visualization, Tessevolve allows the user to view fitness landscapes scaled across 2D, 3D, and 4D. Visualizing these landscapes in multiple dimensions in an intuitive VR-based system allowed us to identify how landscape traversal changes as dimensions increase, demonstrating the way that connections between points across fitness landscapes are affected by dimensionality. As a whole, this dissertation looks at connectivity in computational structures across a broad range of biological scales. These methods and metrics therefore expand our computational toolkit for studying evolution in multiple systems of interest: genotypic, phenotypic, and at the whole landscape level.
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Title: Dissertation : novel parallel algorithms and performance optimization techniques for the multi-level fast multipole algorithm
Creator: Lingg, Michael
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Since Sir Issac Newton determined that characterizing orbits of celestial objects required considering the gravitational interactions among all bodies in the system, the N-Body problem has been a very important tool in physics simulations. Expanding on the early use of the classical N-Body problem for gravitational simulations, the method has proven invaluable in fluid dynamics, molecular simulations and data analytics. The extension of the classical N-Body problem to solve the Helmholtz...
Show moreSince Sir Issac Newton determined that characterizing orbits of celestial objects required considering the gravitational interactions among all bodies in the system, the N-Body problem has been a very important tool in physics simulations. Expanding on the early use of the classical N-Body problem for gravitational simulations, the method has proven invaluable in fluid dynamics, molecular simulations and data analytics. The extension of the classical N-Body problem to solve the Helmholtz equation for groups of particles with oscillatory interactions has allowed for simulations that assist in antenna design, radar cross section prediction, reduction of engine noise, and medical devices that utilize sound waves, to name a sample of possible applications. While N-Body simulations are extremely valuable, the computational cost of directly evaluating interactions among all pairs grows quadratically with the number of particles, rendering large scale simulations infeasible even on the most powerful supercomputers. The Fast Multipole Method (FMM) and the broader class of tree algorithms that it belongs to have significantly reduced the computational complexity of N-body simulations, while providing controllable accuracy guarantees. While FMM provided a significant boost, N-body problems tackled by scientists and engineers continue to grow larger in size, necessitating the development of efficient parallel algorithms and implementations to run on supercomputers. The Laplace variant of FMM, which is used to treat the classical N-body problem, has been extensively researched and optimized to the extent that Laplace FMM codes can scale to tens of thousands of processors for simulations involving over trillion particles. In contrast, the Multi-Level Fast Multipole Algorithm (MLFMA), which is aimed for the Helmholtz kernel variant of FMM, lags significantly behind in efficiency and scaling. The added complexity of an oscillatory potential results in much more intricate data dependency patterns and load balancing requirements among parallel processes, making algorithms and optimizations developed for Laplace FMM mostly ineffective for MLFMA. In this thesis, we propose novel parallel algorithms and performance optimization techniques to improve the performance of MLFMA on modern computer architectures. Proposed algorithms and performance optimizations range from efficient leveraging of the memory hierarchy on multi-core processors to an investigation of the benefits of the emerging concept of task parallelism for MLFMA, and to significant reductions of communication overheads and load imbalances in large scale computations. Parallel algorithms for distributed memory parallel MLFMA are also accompanied by detailed complexity analyses and performance models. We describe efficient implementations of all proposed algorithms and optimization techniques, and analyze their impact in detail. In particular, we show that our work yields significant speedups and much improved scalability compared to existing methods for MLFMA in large geometries designed to test the range of the problem space, as well as in real world problems.
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Title: Achieving reliable distributed systems : through efficient run-time monitoring and predicate detection
Creator: Tekken Valapil, Vidhya
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Runtime monitoring of distributed systems to perform predicate detection is critical as well as a challenging task. It is critical because it ensures the reliability of the system by detecting all possible violations of system requirements. It is challenging because to guarantee lack of violations one has to analyze every possible ordering of system events and this is an expensive task. In this report, wefocus on ordering events in a system run using HLC (Hybrid Logical Clocks) timestamps,...
Show moreRuntime monitoring of distributed systems to perform predicate detection is critical as well as a challenging task. It is critical because it ensures the reliability of the system by detecting all possible violations of system requirements. It is challenging because to guarantee lack of violations one has to analyze every possible ordering of system events and this is an expensive task. In this report, wefocus on ordering events in a system run using HLC (Hybrid Logical Clocks) timestamps, which are O(1) sized timestamps, and present some efficient algorithms to perform predicate detection using HLC. Since, with HLC, the runtime monitor cannot find all possible orderings of systems events, we present a new type of clock called Biased Hybrid Logical Clocks (BHLC), that are capable of finding more possible orderings than HLC. Thus we show that BHLC based predicate detection can find more violations than HLC based predicate detection. Since predicate detection based on both HLC and BHLC do not guarantee detection of all possible violations in a system run, we present an SMT (Satisfiability Modulo Theories) solver based predicate detection approach, that guarantees the detection of all possible violations in a system run. While a runtime monitor that performs predicate detection using SMT solvers is accurate, the time taken by the solver to detect the presence or absence of a violation can be high. To reduce the time taken by the runtime monitor, we propose the use of an efficient two-layered monitoring approach, where the first layer of the monitor is efficient but less accurate and the second layer is accurate but less efficient. Together they reduce the overall time taken to perform predicate detection drastically and also guarantee detection of all possible violations.
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Title: IMPROVING THE PREDICTABILITY OF HYDROLOGIC INDICES IN ECOHYDROLOGICAL APPLICATIONS
Creator: Hernandez Suarez, Juan Sebastian
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Monitoring freshwater ecosystems allow us to better understand their overall ecohydrological condition within large and diverse watersheds. Due to the significant costs associated with biological monitoring, hydrological modeling is widely used to calculate ecologically relevant hydrologic indices (ERHIs) for stream health characterization in locations with lacking data. However, the reliability and applicability of these models within ecohydrological frameworks are major concerns....
Show moreMonitoring freshwater ecosystems allow us to better understand their overall ecohydrological condition within large and diverse watersheds. Due to the significant costs associated with biological monitoring, hydrological modeling is widely used to calculate ecologically relevant hydrologic indices (ERHIs) for stream health characterization in locations with lacking data. However, the reliability and applicability of these models within ecohydrological frameworks are major concerns. Particularly, hydrologic modeling’s ability to predict ERHIs is limited, especially when calibrating models by optimizing a single objective function or selecting a single optimal solution. The goal of this research was to develop model calibration strategies based on multi-objective optimization and Bayesian parameter estimation to improve the predictability of ERHIs and the overall representation of the streamflow regime. The research objectives were to (1) evaluate the predictions of ERHIs using different calibration techniques based on widely used performance metrics, (2) develop performance and signature-based calibration strategies explicitly constraining or targeting ERHIs, and (3) quantify the modeling uncertainty of ERHIs using the results from multi-objective model calibration and Bayesian inference. The developed strategies were tested in an agriculture-dominated watershed in Michigan, US, using the Unified Non-dominated Sorting Algorithm III (U-NSGA-III) for multi-objective calibration and the Soil and Water Assessment Tool (SWAT) for hydrological modeling. Performance-based calibration used objective functions based on metrics calculated on streamflow time series, whereas signature-based calibration used ERHIs values for objective functions’ formulation. For uncertainty quantification purposes, a lumped error model accounting for heteroscedasticity and autocorrelation was considered and the multiple-try Differential Evolution Adaptive Metropolis (ZS) (MT-DREAM(ZS)) algorithm was implemented for Markov Chain Monte Carlo (MCMC) sampling. In relation to the first objective, the results showed that using different sets of solutions instead of a single optimal introduces more flexibility in the predictability of various ERHIs. Regarding the second objective, both performance-based and signature-based model calibration strategies were successful in representing most of the selected ERHIs within a +/-30% relative error acceptability threshold while yielding consistent runoff predictions. The performance-based strategy was preferred since it showed a lower dispersion of near-optimal Pareto solutions when representing the selected indices and other hydrologic signatures based on water balance and Flow Duration Curve characteristics. Finally, regarding the third objective, using near-optimal Pareto parameter distributions as prior knowledge in Bayesian calibration generally reduced both the bias and variability ranges in ERHIs prediction. In addition, there was no significant loss in the reliability of streamflow predictions when targeting ERHIs, while improving precision and reducing the bias. Moreover, parametric uncertainty drastically shrank when linking multi-objective calibration and Bayesian parameter estimation. Still, the representation of low flow magnitude and timing, rate of change, and duration and frequency of extreme flows were limited. These limitations, expressed in terms of bias and interannual variability, were mainly attributed to the hydrological model’s structural inadequacies. Therefore, future research should involve revising hydrological models to better describe the ecohydrological characteristics of riverine systems.
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Title: Face Anti-Spoofing : Detection, Generalization, and Visualization
Creator: Liu, Yaojie
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Face anti-spoofing is the process of distinguishing genuine faces and face presentation attacks: attackers presenting spoofing faces (e.g. photograph, digital screen, and mask) to the face recognition system and attempting to be authenticated as the genuine user. In recent years, face anti-spoofing has brought increasing attention to the vision community as it is a crucial step to prevent face recognition systems from a security breach. Previous approaches formulate face anti-spoofing as a...
Show moreFace anti-spoofing is the process of distinguishing genuine faces and face presentation attacks: attackers presenting spoofing faces (e.g. photograph, digital screen, and mask) to the face recognition system and attempting to be authenticated as the genuine user. In recent years, face anti-spoofing has brought increasing attention to the vision community as it is a crucial step to prevent face recognition systems from a security breach. Previous approaches formulate face anti-spoofing as a binary classification problem, and many of them struggle to generalize to different conditions(such as pose, lighting, expressions, camera sensors, and unknown spoof types). Moreover, those methods work as a black box and cannot provide interpretation or visualization to their decision. To address those challenges, we investigate face anti-spoofing in 3 stages: detection, generalization and visualization. In the detection stage, we learn a CNN-RNN model to estimate auxiliary tasks of face depth and rPPG signals estimation, which can bring additional knowledge for the spoof detection. In the generalization stage, we investigate the detection of unknown spoof attacks and propose a novel Deep Tree Network (DTN) to well represent the unknown spoof attacks. In the visualization stage, we find “spoof trace, the subtle image pattern in spoof faces (e.g., color distortion, 3D mask edge, and Moire pattern), is effective to explain why a spoof is a spoof. We provide a proper physical modeling of the spoof traces and design a generative model to disentangle the spoof traces from input faces. In addition, we also show that a proper physical modeling can benefit other face problems, such as face shadow detection and removal. A proper shadow modeling can not only detect the shadow region effectively, but also remove the shadow in a visually plausible manner.
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Title: Towards a Robust Unconstrained Face Recognition Pipeline with Deep Neural Networks
Creator: Shi, Yichun
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Face recognition is a classic problem in the field of computer vision and pattern recognition due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has lead to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance...
Show moreFace recognition is a classic problem in the field of computer vision and pattern recognition due to its wide applications in real-world problems such as access control, identity verification, physical security, surveillance, etc. Recent progress in deep learning techniques and the access to large-scale face databases has lead to a significant improvement of face recognition accuracy under constrained and semi-constrained scenarios. Deep neural networks are shown to surpass human performance on Labeled Face in the Wild (LFW), which consists of celebrity photos captured in the wild. However, in many applications, e.g. surveillance videos, where we cannot assume that the presented face is under controlled variations, the performance of current DNN-based methods drop significantly. The main challenges in such an unconstrained face recognition problem include, but are not limited to: lack of labeled data, robust face normalization, discriminative representation learning and the ambiguity of facial features caused by information loss.In this thesis, we propose a set of methods that attempt to address the above challenges in unconstrained face recognition systems. Starting from a classic deep face recognition pipeline, we review how each step in this pipeline could fail on low-quality uncontrolled input faces, what kind of solutions have been studied before, and then introduce our proposed methods. The various methods proposed in this thesis are independent but compatible with each other. Experiment on several challenging benchmarks, e.g. IJB-C and IJB-S show that the proposed methods are able to improve the robustness and reliability of deep unconstrained face recognition systems. Our solution achieves state-of-the-art performance, i.e. 95.0\% TAR@FAR=0.001\% on IJB-C dataset and 61.98\% Rank1 retrieval rate on the surveillance-to-booking protocol of IJB-S dataset.
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Title: The Evolution of Fundamental Neural Circuits for Cognition in Silico
Creator: Tehrani-Saleh, Ali
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Despite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In...
Show moreDespite decades of research on intelligence and fundamental components of cognition, we still know very little about the structure and functionality of nervous systems. Questions in cognition and intelligent behavior are addressed by scientists in the fields of behavioral biology, neuroscience, psychology, and computer science. Yet it is difficult to reverse engineer observed sophisticated intelligent behaviors in animals and even more difficult to understand their underlying mechanisms.In this dissertation, I use a recently-developed neuroevolution platform -called Markov brain networks- in which Darwinian selection is used to evolve both structure and functionality of digital brains. I use this platform to study some of the most fundamental cognitive neural circuits: 1) visual motion detection, 2) collision-avoidance based on visual motion cues, 3) sound localization, and 4) time perception. In particular, I investigate both the selective pressures and environmental conditions in the evolution of these cognitive components, as well as the circuitry and computations behind them. This dissertation lays the groundwork for an evolutionary agent-based method to study the neural circuits for cognition in silico.
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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: Evolving Phenotypically Plastic Digital Organisms
Creator: Lalejini, Alexander
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The ability to dynamically respond to cues from the environment is a fundamental feature of most adaptive systems. In biological systems, changes to an organism based on environmental cues is called phenotypic plasticity. Indeed, phenotypic plasticity underlies many of the adaptive traits and developmental patterns found in nature and serves as a key mechanism for responding to spatially or temporally variable environments. Most computer programs require phenotypic plasticity, as they must...
Show moreThe ability to dynamically respond to cues from the environment is a fundamental feature of most adaptive systems. In biological systems, changes to an organism based on environmental cues is called phenotypic plasticity. Indeed, phenotypic plasticity underlies many of the adaptive traits and developmental patterns found in nature and serves as a key mechanism for responding to spatially or temporally variable environments. Most computer programs require phenotypic plasticity, as they must respond dynamically to stimuli such as user input, sensor data, et cetera. As such, phenotypic plasticity also has practical applications in genetic programming, wherein we apply the natural principles of evolution to automatically synthesize computer programs rather than writing them by hand. In this dissertation, I achieve two synergistic aims: (1) I use populations of self-replicating computer programs (digital organisms) to empirically study the conditions under which adaptive phenotypic plasticity evolves and how its evolution shapes subsequent evolutionary outcomes; and (2) I transfer insights from biology to develop novel genetic programming techniques in order to evolve more responsive (i.e., phenotypically plastic) computer programs. First, I illustrate the importance of mutation rate, environmental change, and partially-plastic building blocks for the evolution of adaptive plasticity. Next, I show that adaptive phenotypic plasticity stabilizes populations against environmental change, allowing them to more easily retain novel adaptive traits. Finally, I improve our ability to evolve phenotypically plastic computer programs with three novel genetic programming techniques: (1) SignalGP, which provides mechanisms to control code expression based on environmental cues, (2) tag-based genetic regulation to adjust code expression based on current context, and (3) tag-accessed memory to provide more dynamic mechanisms for storing data.
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Title: Face Recognition : Representation, Intrinsic Dimensionality, Capacity, and Demographic Bias
Creator: Gong, Sixue
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Face recognition is a widely adopted technology with numerous applications, such as mobile phone unlock, mobile payment, surveillance, social media and law enforcement. There has been tremendous progress in enhancing the accuracy of face recognition systems over the past few decades, much of which can be attributed to deep learning. Despite this progress, several fundamental problems in face recognition still remain unsolved. These problems include finding a salient representation, estimating...
Show moreFace recognition is a widely adopted technology with numerous applications, such as mobile phone unlock, mobile payment, surveillance, social media and law enforcement. There has been tremendous progress in enhancing the accuracy of face recognition systems over the past few decades, much of which can be attributed to deep learning. Despite this progress, several fundamental problems in face recognition still remain unsolved. These problems include finding a salient representation, estimating intrinsic dimensionality, representation capacity, and demographic bias. With growing applications of face recognition, the need for an accurate, robust, compact and fair representation is evident.In this thesis, we first develop algorithms to obtain practical estimates of intrinsic dimensionality of face representations, and propose a new dimensionality reduction method to project feature vectors from ambient space to intrinsic space. Based on the study in intrinsic dimensionality, we then estimate capacity of face representation, casting the face capacity estimation problem under the information theoretic framework of capacity of a Gaussian noise channel. Numerical experiments on unconstrained faces (IJB-C) provide a capacity upper bound of 27,000 for FaceNet and 84,000 for SphereFace representation at 1% FAR. In the second part of the thesis, we address the demographic bias problem in face recognition systems where errors are lower on certain cohorts belonging to specific demographic groups. We propose two de-biasing frameworks that extract feature representations to improve fairness in face recognition. Experiments on benchmark face datasets (RFW, LFW, IJB-A, and IJB-C) show that our approaches are able to mitigate face recognition bias on various demographic groups (biasness drops from 6.83 to 5.07) as well as maintain the competitive performance (i.e., 99.75% on LFW, and 93.70% TAR @ 0.1% FAR on IJB-C). Lastly, we explore the global distribution of deep face representations derived from correlations between image samples of within-class and cross-class to enhance the discriminativeness of face representation of each identity in the embedding space. Our new approach to face representation achieves state-of-the-art performance for both verification and identification tasks on benchmark datasets (99.78% on LFW, 93.40% on CPLFW, 98.41% on CFP-FP, 96.2% TAR @ 0.01% FAR and 95.3% Rank-1 accuracy on IJB-C). Since, the primary techniques we employ in this dissertation are not specific to faces only, we believe our research can be extended to other problems in computer vision, for example, general image classification and representation learning.
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Title: CS1 AND GENDER : UNDERSTANDING EFFECTS OF BACKGROUND AND SELF- EFFICACY ON ACHIEVEMENT AND INTEREST
Creator: Sands, Philip
Date: 2021
Collection: Electronic Theses & Dissertations
Description: Over the past 20 years, the field of computer science has experienced a growth in studentinterest. Despite this increase in participation rates, longstanding gender gaps persist in computer science. Recent research has examined a wide variety of individual factors (e.g., self-efficacy, sense of belonging, etc.) that impact student interest and achievement in computer science; however, these factors are rarely considered in the context of existing learning theories. In this correlational study...
Show moreOver the past 20 years, the field of computer science has experienced a growth in studentinterest. Despite this increase in participation rates, longstanding gender gaps persist in computer science. Recent research has examined a wide variety of individual factors (e.g., self-efficacy, sense of belonging, etc.) that impact student interest and achievement in computer science; however, these factors are rarely considered in the context of existing learning theories. In this correlational study, I explored the relationship between prior knowledge of computer programming, self-efficacy, and the sources of self-efficacy as they differed by gender in a theoretical model of achievement and interest for students in first-year computer science (CS1) courses. This model was based on prior work from Bandura (1997) and others exploring self- efficacy and social cognitive theory in the context of mathematics and science fields. Using cross-sectional data from N=182 CS1 students at two universities, structural regressions were conducted between factors impacting CS1 students across the entire population and for men (N=108) and women (N=70) individually. This data was then used to address the following research questions. (1A) How do prior knowledge of computer programming, the sources of self- efficacy, and self-efficacy for computing predict CS1 achievement and student intentions to continue study in CS? (1B) How does self-efficacy mediate the relationship between student prior knowledge of computer programming and achievement in CS1? (1C) How are thoserelationships moderated by gender? (2) How does feedback in the form of student grades impact intention to continue in CS when considering gender as a moderating factor? For all students, student self-efficacy for CS positively impacted CS1 achievement and post-CS1 interest. Aligning with past research, self-efficacy was derived largely from mastery experiences, with vicarious experiences and social persuasions also contributing to a moderate degree. Social persuasions had a negative effect on self-efficacy, which diverged from research in other fields. The relationship between prior knowledge of computer programming and CS1 achievement was not mediated by self-efficacy and had a small positive effect. For women, vicarious experiences played a stronger role in defining student self-efficacy in CS. Additionally, while the importance of self-efficacy on achievement was similar to that for men, self-efficacy and achievement both played a much stronger role in determining student interest in CS for women. All these findings are in need of further exploration as the analysis was underpowered due to a small, COVID-19 impacted sample size. Future work should focus on the role of feedback on student self-efficacy, the potential misalignment of CS1 feedback and social network feedback, and interventions that address student beliefs about CS abilities to increase opportunities for authentic mastery and vicarious experiences.
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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: Contributions to Fingerprint Recognition
Creator: Engelsma, Joshua James
Date: 2021
Collection: Electronic Theses & Dissertations
Description: From the early days of the mid to late nineteenth century when scientific research first began to focus on fingerprints, to the present day fingerprint recognition systems we find deployed on our day to day devices, the science of fingerprint recognition has come a long way. In spite of this progress, there remains challenging problems to be solved. This thesis highlights a few of these problems, and proposes solutions to address them. One area of further research that must be conducted on...
Show moreFrom the early days of the mid to late nineteenth century when scientific research first began to focus on fingerprints, to the present day fingerprint recognition systems we find deployed on our day to day devices, the science of fingerprint recognition has come a long way. In spite of this progress, there remains challenging problems to be solved. This thesis highlights a few of these problems, and proposes solutions to address them. One area of further research that must be conducted on fingerprint recognition systems is that of robust, operational evaluations. In chapter two of this thesis, we show how the current practices of using calibration patterns to evaluate fingerprint readers are limited. We then propose a realistic fake finger called the Universal Target. The Universal Target is a realistic, 3D, fake finger (or phantom) which can be imaged by all major types of fingerprint sensing technologies. We show the entire manufacturing (molding and casting) process for fabricating the Universal Targets. Then, we show a series of evaluations which demonstrate how the Universal Targets can be used to operationally evaluate current commercial fingerprint readers. Our Universal Target is a significant step forward in enabling more realistic, standardized evaluations of fingerprint readers. In our third chapter, we shift gears from improving the evaluation standards of fingerprint readers to instead focus on the security of fingerprint readers. In particular, we turn our attention towards detecting fake fingerprint (spoof) attacks. To do so, we open source a fingerprint reader (built from low-cost ubiquitous components), called RaspiReader. RaspiReader is a high-resolution fingerprint reader customized with both direct-view imaging and FTIR imaging in order to better detect fingerprint spoofs. We show through a number of experiments that RaspiReader enables state-of-the-art fingerprint spoof detection accuracy. We also demonstrate that RaspiReader enables better generalization to what are known as "unseen attacks" (those attacks which were not seen during training of the spoof detector). Finally, we show that fingerprints captured by RaspiReader are completely compatible with images captured by legacy fingerprint readers for matching.In chapter four, we move on to propose a major improvement to the fingerprint feature extraction and matching sub-modules of fingerprint recognition systems. In particular, we propose a deep network, called DeepPrint, to extract a 200 byte fixed-length fingerprint representation. While prevailing fingerprint matchers primarily utilize minutiae points and expensive graph matching algorithms for comparison, two DeepPrint representations can be compared with only 192 multiplications and 191 additions. This is extremely useful for large scale search where potentially billions of pairwise fingerprint comparisons must be made. The DeepPrint representation also enables practical encrypted matching using a fully homomorphic encryption scheme. This enables better protection of the fingerprint templates which are stored in the database. While discriminative fixed-length representations are available for both face and iris recognition, such a representation has eluded fingerprint recognition. This chapter aims to fill that void.Finally, we conclude our thesis by working to extend fingerprint recognition to all ages. While current fingerprint recognition systems are being used by billions of teenagers and adults around the world, the youngest people among us remain disenfranchised. In particular, modern day fingerprint recognition systems do not work well on infants and young children. In this penultimate chapter, we aim to rectify this major shortcoming. To that end, we prototype a high-resolution (1900 ppi) infant fingerprint reader. Then, we track and fingerprint 315 infants (under the age of 3 months at enrollment) at the Dayalbagh Children's Hospital in Agra India over the course of 1 year (4 different sessions). To match the infant fingerprints, we develop our own high-resolution infant fingerprint matcher. Our experimental results demonstrate significant promise for the extension of fingerprint recognition to all ages. This work has the potential for major global good as all young infants and children could be given a verifiable digital identity for better vaccination tracking as a child and for government benefits and assistance as an adult. In summary, this thesis makes major contributions to the entire end-to-end fingerprint recognition system and extends its use case to all ages.
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Title: Towards Robust and Secure Face Recognition : Defense Against Physical and Digital Attacks
Creator: Deb, Debayan
Date: 2021
Collection: Electronic Theses & Dissertations
Description: The accuracy, usability, and touchless acquisition of state-of-the-art automated face recognition systems (AFR) have led to their ubiquitous adoption in a plethora of domains, including mobile phone unlock, access control systems, and payment services. Despite impressive recognition performance, prevailing AFR systems remain vulnerable to the growing threat of face attacks which can be launched in both physical and digital domains. Face attacks can be broadly classified into three attack...
Show moreThe accuracy, usability, and touchless acquisition of state-of-the-art automated face recognition systems (AFR) have led to their ubiquitous adoption in a plethora of domains, including mobile phone unlock, access control systems, and payment services. Despite impressive recognition performance, prevailing AFR systems remain vulnerable to the growing threat of face attacks which can be launched in both physical and digital domains. Face attacks can be broadly classified into three attack categories: (i) Spoof attacks: artifacts in the physical domain (e.g., 3D masks, eye glasses, replaying videos), (ii) Adversarial attacks: imperceptible noises added to probes for evading AFR systems, and (iii) Digital manipulation attacks: entirely or partially modified photo-realistic faces using generative models. Each of these categories is composed of different attack types. For example, each spoof medium, e.g., 3D mask and makeup, constitutes one attack type. Likewise, in adversarial and digital manipulation attacks, each attack model, designed by unique objectives and losses, may be considered as one attack type. Thus, the attack categories and types form a 2-layer tree structure encompassing the diverse attacks. Such a tree will inevitably grow in the future. Given the growing dissemination of ``fake news” and "deepfakes", the research community and social media platforms alike are pushing towards generalizable defense against continuously evolving and sophisticated face attacks. In this dissertation, we first propose a set of defense methods that achieve state-of-the-art performance in detecting attack types within individual attack categories, both physical (e.g., face spoofs) and digital (e.g., adversarial faces and digital manipulation), then introduce a method for simultaneously safeguarding against each attack.First, in an effort to impart generalizability and interpretability to face spoof detection systems, we propose a new face anti-spoofing framework specifically designed to detect unknown spoof types, namely, Self-Supervised Regional Fully Convolutional Network (SSR-FCN), that is trained to learn local discriminative cues from a face image in a self-supervised manner. The proposed framework improves generalizability while maintaining the computational efficiency of holistic face anti-spoofing approaches (< 4 ms on a Nvidia GTX 1080Ti GPU). The proposed method is also interpretable since it localizes which parts of the face are labeled as spoofs. Experimental results show that SSR-FCN can achieve True Detection Rate (TDR) = 65% @ 2.0% False Detection Rate (FDR) when evaluated on a dataset comprising of 13 different spoof types under unknown attacks while achieving competitive performances under standard benchmark face anti-spoofing datasets (Oulu-NPU, CASIA-MFSD, and Replay-Attack).Next, we address the problem of defending against adversarial attacks. We first propose, AdvFaces, an automated adversarial face synthesis method that learns to generate minimal perturbations in the salient facial regions. Once AdvFaces is trained, it can automatically evade state-of-the-art face matchers with attack success rates as high as 97.22% and 24.30% at 0.1% FAR for obfuscation and impersonation attacks, respectively. We then propose a new self-supervised adversarial defense framework, namely FaceGuard, that can automatically detect, localize, and purify a wide variety of adversarial faces without utilizing pre-computed adversarial training samples. FaceGuard automatically synthesizes diverse adversarial faces, enabling a classifier to learn to distinguish them from bona fide faces. Concurrently, a purifier attempts to remove the adversarial perturbations in the image space. FaceGuard can achieve 99.81%, 98.73%, and 99.35% detection accuracies on LFW, CelebA, and FFHQ, respectively, on six unseen adversarial attack types.Finally, we take the first steps towards safeguarding AFR systems against face attacks in both physical and digital domains. We propose a new unified face attack detection framework, namely UniFAD, which automatically clusters similar attacks and employs a multi-task learning framework to learn salient features to distinguish between bona fides and coherent attack types. The proposed UniFAD can detect face attacks from 25 attack types across all 3 attack categories with TDR = 94.73% @ 0.2% FDR on a large fake face dataset, namely GrandFake. Further, UniFAD can identify whether attacks are adversarial, digitally manipulated, or contain spoof artifacts, with 97.37% classification accuracy.
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Title: Semi-Adversarial Networks for Imparting Demographic Privacy to Face Images
Creator: Mirjalili, Vahid
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Face recognition systems are being widely used in a number of applications ranging from user authentication in hand-held devices to identifying people of interest from surveillance videos. In several such applications, face images are stored in a central database. In such cases, it is necessary to ensure that the stored face images are used for the stated purpose and not for any other purposes. For example, advanced machine learning methods can be used to automatically extract age, gender,...
Show moreFace recognition systems are being widely used in a number of applications ranging from user authentication in hand-held devices to identifying people of interest from surveillance videos. In several such applications, face images are stored in a central database. In such cases, it is necessary to ensure that the stored face images are used for the stated purpose and not for any other purposes. For example, advanced machine learning methods can be used to automatically extract age, gender, race and so on from the stored face images. These cues are often referred to as demographic attributes. When such attributes are extracted without the consent of individuals, it can lead to potential violation of privacy. Indeed, the European Union's General Data Protection and Regulation (GDPR) requires the primary purpose of data collection to be declared to individuals prior to data collection. GDPR strictly prohibits the use of this data for any purpose beyond what was stated. In this thesis, we consider this type of regulation and develop methods for enhancing the privacy accorded to face images with respect to the automatic extraction of demogrpahic attributes. In particular, we design algorithms that modify input face images such that certain specified demogrpahic attributes cannot be reliably extracted from them. At the same time, the biometric utility of the images is retained, i.e., the modified face images can still be used for matching purposes. The primary objective of this research is not necessarily to fool human observers, but rather to prevent machine learning methods from automatically extracting such information. The following are the contributions of this thesis. First, we design a convolutional autoencoder known as a semi-adversarial neural network, or SAN, that perturbs input face images such that they are adversarial with respect to an attribute classifier (e.g., gender classifier) while still retaining their utility with respect to a face matcher. Second, we develop techniques to ensure that the adversarial outputs produced by the SAN are generalizable across multiple attribute classifiers, including those that may not have been used during the training phase. Third, we extend the SAN architecture and develop a neural network known as PrivacyNet, that can be used for imparting multi-attribute privacy to face images. Fourth, we conduct extensive experimental analysis using several face image datasets to evaluate the performance of the proposed methods as well as visualize the perturbations induced by the methods. Results suggest the benefits of using semi-adversarial networks to impart privacy to face images while still retaining the biometric utility of the ensuing face images.
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Title: Sequence learning with side information : modeling and applications
Creator: Wang, Zhiwei
Date: 2020
Collection: Electronic Theses & Dissertations
Description: Sequential data is ubiquitous and modeling sequential data has been one of the most long-standing computer science problems. The goal of sequence modeling is to represent a sequence with a low-dimensional dense vector that incorporates as much information as possible. A fundamental type of information contained in sequences is the sequential dependency and a large body of research has been devoted to designing effective ways to capture it. Recently, sequence learning models such as recurrent...
Show moreSequential data is ubiquitous and modeling sequential data has been one of the most long-standing computer science problems. The goal of sequence modeling is to represent a sequence with a low-dimensional dense vector that incorporates as much information as possible. A fundamental type of information contained in sequences is the sequential dependency and a large body of research has been devoted to designing effective ways to capture it. Recently, sequence learning models such as recurrent neural networks (RNNs), temporal convolutional networks, and Transformer have gained tremendous popularity in modeling sequential data. Equipped with effective structures such as gating mechanisms, large receptive fields, and attention mechanisms, these models have achieved great success in many applications of a wide range of fields.However, besides the sequential dependency, sequences also exhibit side information that remains under-explored. Thus, in the thesis, we study the problem of sequence learning with side information. Specifically, we present our efforts devoted to building sequence learning models to effectively and efficiently capture side information that is commonly seen in sequential data. In addition, we show that side information can play an important role in sequence learning tasks as it can provide rich information that is complementary to the sequential dependency. More importantly, we apply our proposed models in various real-world applications and have achieved promising results.
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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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