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Title: Towards Utility-Driven Data Analytics with Differential Privacy
Creator: Wang, Han
Date: 2023
Description: The widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information...
Show moreThe widespread use of personal devices and dedicated recording facilities has led to the generation of massive amounts of personal information or data. Some of them are high-dimensional and unstructured data, such as video and location data. Analyzing these data can provide significant benefits in real-world scenarios, such as videos for monitoring and location data for traffic analysis. However, while providing benefits, these complicated data always raise serious privacy concerns since all of them involve personal information. To address privacy issues, existing privacy protection methods often fail to provide adequate utility in practical applications due to the complexity of high-dimensional and unstructured data. For example, most video sanitization techniques merely obscure the video by detecting and blurring sensitive regions, such as faces, vehicle plates, locations, and timestamps. Unfortunately, privacy breaches in blurred videos cannot be effectively contained, especially against unknown background knowledge. In this thesis, we propose three different differentially private frameworks to preserve the utility of video and location data (both are high-dimensional and unstructured data) while meeting the privacy requirements, under different well-known privacy settings. Specifically, to our best knowledge, wepropose the first differentially private video analytics platform (VideoDP) which flexibly supports different video queries or query-based analyze with a rigorous privacy guarantee. Given the input video, VideoDP randomly generates a utility-driven private video in which adding or removing any sensitive visual element (e.g., human, and object) does not significantly affect the output video. Then, different video analyses requested by untrusted video analysts can be flexibly performed over the sanitized video with differential privacy. Secondly, we define a novel privacy notion ϵ-Object Indistinguishability for all the predefined sensitive objects (e.g., humans, vehicles) in the video, and then propose a video sanitization technique VERRO that randomly generates utility-driven synthetic videos with indistinguishable objects. Therefore, all the objects can be well protected in the generated utility-driven synthetic videos which can be disclosed to any untrusted video recipient. Third, we propose the first strict local differential privacy (LDP) framework for location-based service (LBS) (“L-SRR”) to privately collect and analyze user locations or trajectories with ε-LDP guarantees. Specifically, we design a novel LDP mechanism “staircase randomized response” (SRR) and extend the empirical estimation to further boost the utility for a diverse set of LBS Apps (e.g., traffic density estimation, k nearest neighbors search, origin-destination analysis, and traffic-aware GPS navigation). Finally, we conduct experiments on real videos and location dataset, and the experimental results demonstrate all frameworks can have good performance.
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Title: Latent Price Model for Market Microstructure: Estimation and Simulation
Creator: Yin, Yuan
Date: 2023
Description: This thesis focuses on exploring and solving several problems based on partiallyobserved diffusion models. The thesis has two parts....
Show moreThis thesis focuses on exploring and solving several problems based on partiallyobserved diffusion models. The thesis has two parts. In the first part we present a tractable sufficient condition for the consistency of maximum likelihood estimators (MLEs) in partially observed diffusion models, stated in terms of stationary distributions of the associated test processes, under the assumption that the set of unknown parameter values is finite. We illustrate the tractability of this sufficient condition by verifying it in the context of a latent price model of market microstructure. Finally, we describe an algorithm for computing MLEs in partially observed diffusion models and test it on historical data to estimate the parameters of the latent price model. In the second part we provide a thorough analysis of the particle filtering algorithm for estimating the conditional distribution in partially observed diffusion models. Specifically, we focus on estimating the distribution of unobserved processes using observed data. The algorithm involves several steps and assumptions, which are described in detail. We also examine the convergence of the algorithm and identify the sufficient conditions under which it converges. Finally, we derive an explicit upper bound of the convergence rate of the algorithm, which depends on the set of parameters and the choice of time frequency. This bound provides a measure of the algorithm’s performance and can be used to optimize its parameters to achieve faster convergence.
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Title: Electric Machine Windings with Reduced Space Harmonic Content
Creator: Tang, Nanjun
Date: 2023
Description: The reduction of magnetomotive force (MMF) space harmonic content in electric machine windings can significantly improve the machine's...
Show moreThe reduction of magnetomotive force (MMF) space harmonic content in electric machine windings can significantly improve the machine's electromagnetic performance. Potential benefits include a reduction of torque ripple, a more sinusoidal back EMF, and reduced power losses. With the proposal of a uniform mathematical representation that applies to both distributed windings and fractional-slot concentrated windings (FSCWs), closed-form expressions can be derived for harmonic magnitudes, winding factors, etc. These expressions can then be used to formulate the MMF space harmonic suppression problem for windings, which looks for improved windings with certain harmonic orders reduced or even eliminated, by varying the slot distribution and coil turns. Different solution techniques are explored to gain additional insights about the solution space. The underlying mathematical relations between different harmonic orders are mathematically proved to establish the family phenomenon, which presents clear pictures of the higher order part of the harmonic spectrum and is the foundation for exact calculation of the total harmonic distortion (THD) of windings. The exact THD calculation further indicates how the minimal THD can be achieved for a winding. Windings can also be analyzed and designed from the view of subsets to incorporate distribution and excitation phase shift effects. With reduced or the minimal space harmonic content, new winding designs can help significantly improve the Pareto front when combined with motor geometry optimization. Design examples including a 12-slot 2-pole mixed-layer distributed winding, a 18-slot 2-pole mixed-layer distributed winding, and a four-layer 24-slot 22-pole FSCW with excitation phase shift are presented with finite element analysis (FEA) results to verify the performance improvements.
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Title: Polarization Induced by A Terahertz Electric Field in A Semiconductor Nanodimer in the Overlapping Regime
Creator: Wang, Zi
Date: 2023
Description: Boltzmann transport equation is a theoretical framework for the description of thermodynamics or charge reactions in a system not in...
Show moreBoltzmann transport equation is a theoretical framework for the description of thermodynamics or charge reactions in a system not in equilibrium, which can be applied to the analysis of the interactions of mobile charges with an electromagnetic wave. When the dimensions of the object are small compared to the wavelength, the induced dipole moment provides a means to characterize the collective response while providing insight to the nature of the charge-field interactions. Semiconductor nanoparticles exhibit surface plasmon resonance in the terahertz frequency range and are of current interest for the development of components and circuits in that part of the electromagnetic spectrum. By changing the plasmon frequencies of doped semiconductors through the change of carrier concentration, new opportunities arise for plasmonic manipulation in terahertz region leading to various promising applications. Despite the Drude model's long-term success and convenience in describing the electrical conductivity of metals in terms of dielectric functions, some aspects of polarization are not accounted for by bulk properties. By incorporating the transport equations of the charge carriers with Maxwell's equations, screening effects of charge carriers can be accounted for, enabling the internal field, space charge and induced dipole moment of a semiconductor nanoparticle to be studied.The computations performed for elementary dimer structures in overlapping cases revealed the internal field screening, while the complex dipole moments show dispersion and absorption effects. The numerical algorithms are implemented using the finite element method to investigate the surface plasmon resonance (SPR) induced on the semiconductor particles. Unique SPR modes evolution is observed as the thickness of the overlap region is varied. The characteristics can be interpreted by the migration of local space charge as the level of overlap is varied. This degree of freedom provided by a semiconductor nanodimer could be employed to control the local field near a simple cluster of nanoparticles, with potential for application in sensing and circuit components in the terahertz frequency range.
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Title: High Frequency Trading and the Impact of Volume-Duration on Market Quality in the U.S. Futures Markets
Creator: Xu, Xiaoruo
Date: 2023
Description: This paper examines the impact of High Frequency Trading (HFT) on market quality in the U.S. futures market through the lens of Adjusted...
Show moreThis paper examines the impact of High Frequency Trading (HFT) on market quality in the U.S. futures market through the lens of Adjusted Volume-Durations (AVD). By using the unique nanosecond level TAQ CME datasets of commodities futures in 2018, which include Crude Oil, E-mini S&P 500, Eurodollar, Gold, Corn and Soybean, I create the AVDs of each dataset, then conduct the regression analysis on market quality variables with the independent variables including AVDs and other key variables, and the results show that as AVD decreases, the market quality deteriorates, thus HFT positively affects market quality in the U.S. futures market. In order to explore the main driver of AVD on market quality in the futures market, I use the Autoregressive Conditional Duration Model to decompose AVDs into expected AVDs (AEVD), which is the component of AVD that is influenced by past AVDs and unexpected AVDs (AUVD), which is the component of AVD that is not captured by past AVDs but by unanticipated events, and then conduct the regression analysis on market quality variables with the independent variables including AEVD, AUVD and other key variables. The result shows that AEVD has a higher impact on liquidity than AUVD, but the impact of AEVD and AUVD on volatility is mixed in the U.S. futures market. However, except for the conclusions get from the based multivariate regression results, I also explain why there are some outliers for the Eurodollar, soybean and gold, and why HFT has more explicit impact on agricultural futures market.
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Title: Phase field modeling and computation of vesicle growth or shrinkage
Creator: Tang, Xiaoxia
Date: 2023
Description: Lipid bilayers are the basic structural component of all biological cell membranes. It is a semipermeable barrier to most solutes, including...
Show moreLipid bilayers are the basic structural component of all biological cell membranes. It is a semipermeable barrier to most solutes, including ions, glucoses, proteins and other molecules. Vesicles formed by a bilayer lipid membrane are often used as a model system for studying fundamental physics underlying complicated biological systems such as cells and microcapsules. Mathematical modeling of membrane deformation has become an important topic in biological and industrial system for a long time. In this thesis, we develop a phase field model for vesicle growth or shrinkage based on osmotic pressure that arises due to a chemical potential gradient. This thesis consists of three main parts.In the first part, we establish a phase field model for vesicle growth or shrinkage without flow. It consists of an Allen-Cahn equation, which describes the evolution of the phase field parameter (the shape of the vesicle), and a Cahn-Hilliard-type equation, which simulates the evolution of the ionic fluid. The model is mass conserved and surface area constrained during the membrane deformation. Conditions for vesicle growth or shrinkage are analyzed via the common tangent construction. We develop the numerical computing in two-dimensional space using a nonlinear multigrid method which is a combination of nonlinear Gauss-Seidel relaxation operator and V-cycles multigrid solver, and perform convergence tests that suggest an $\mathcal{O}(t+h^2)$ accuracy. Numerical results demonstrate the growth and shrinkage effects graphically and numerically, which agree with the conditions analyzed via the common tangent construction.In the second part, we present a model for vesicle growth or shrinkage with flow. The dynamical equations considered are an Allen-Cahn equation, which describes the phase field evolution, a Cahn-Hilliard-type equation, which simulates the fluid concentration, and a Stokes-type equation, which models the flow. The numerical scheme in two-dimensional space includes a nonlinear multigrid method comprised of a standard FAS method for the Allen-Cahn and Cahn-Hilliard part, and the Vanka smoothing strategy for the Stokes part. Convergence tests imply an $\mathcal{O}(t+h^2)$ accuracy. Numerical results are demonstrated under zero velocity boundary condition and with boundary-driven shear flows, respectively.In the last part, we give an unconditionally energy stable and uniquely solvable finite difference scheme for the model established in the first part. The finite difference scheme is based on a convex splitting of the discrete energy and is semi-implicit. One key difficulty associated with the energy stability is due to the fact that some nonlinear energy functional terms in the expansion is neither convex nor concave. To overcome this subtle difficulty, we add auxiliary terms to make the combined term convex, which in turn yields a convex–concave decomposition of the physical energy. As a result, both the unique solvability and energy stability of the proposed numerical scheme are assured. In addition, we show the scheme is stable in the defined discrete norm.
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Title: Approximation Algorithms for Selected Network and Graph Problems
Creator: Wang, Xiaolang
Date: 2023
Description: This dissertation proposes new polynomial-time approximation algorithms for selected optimization problems, including network and classic...
Show moreThis dissertation proposes new polynomial-time approximation algorithms for selected optimization problems, including network and classic graph problems. We employed distinct strategies and techniques to solve these problems. In Chapter 1, we consider a problem we term FCSA, which aims to find an optimum way how clients are assigned to servers such that the largest latency on an interactivity path between two clients (client 1 to server 1, server 1 to server 2, then server 2 to client 2) is minimized. We present a (3/2)-approximation algorithm for FCSA and a (3/2)-approximation algorithm when server capacity constraints are considered. In Chapter 2, we focus on two variants of the Steiner Tree Problem and present better approximation ratios using known algorithms. For the Steiner Tree with minimum number of Steiner points and bounded edge length problem, we provide a polynomial time algorithm with ratio 2.277. For the Steiner Tree in quasi-bipartite graphs, we improve the best-known approximation ratio to 298/245 . In Chapter 3, we address the problem of searching for a maximum weighted series-parallel subgraph in a given graph, and present a (1/2 + 1/60)-approximation for this problem. Although there is currently no known real-life application of this problem, it remains an important and challenging open question in the field.
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Title: Research through Provocation: A Structured Process to Design New Information Technologies
Creator: Rivera Gomez, Jaime Alejandro
Date: 2023
Description: This doctoral research presents a structured way to generate provocative prototypes, called provotypes, to design new information technologies...
Show moreThis doctoral research presents a structured way to generate provocative prototypes, called provotypes, to design new information technologies. Emphasizing the exploration of alternative interaction models beyond the current archetypes, this study considers emerging complexities about our relationship with technology in the long term to incorporate knowledge from science models in the early stages of the project when cross-disciplinary consensus is required. Thus, avoiding personal biases that are not aligned with how people use technology.The methodology analyzes six case studies using provotypes in multiple contexts, including academic research explorations, corporate innovation projects, and students applying the approach in educational settings. The research also involved a controlled experiment studying how different interactive configurations influence one's motivation to engage in positive behavior change. The results can be summarized in three main contributions: A provocation model to influence the shared meaning inside cross-functional teams, a tool to create provocations exploring alternative interaction models, and finally, the heuristics of provotyping to guide researchers and designers to generate early low fidelity prototyping.
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Title: Two Essays on Corporate Finance and Fixed-income Securities
Creator: Shen, Hao
Date: 2023
Description: In this study, we empirically investigate the relation between corporate finance and fixed income securities. Specifically, we employ...
Show moreIn this study, we empirically investigate the relation between corporate finance and fixed income securities. Specifically, we employ staggered changes in state corporate income tax rates as exogenous shocks and estimate how these state tax changes affect bond at-issue yield spreads. We find a significant increase in bond yield spreads after state tax increases but not after state tax decrease. Tax increases result in a 36 basis points increase in the yield spreads, which translates into a $12 million increase in interest expenses for firms experiencing tax increases. Besides, we employ the staggered adoption of universal demand (UD) laws by different states in the United States as a quasi-experimental setting and investigate the effect of UD laws on bond yield spreads at issuance. The adoption of UD laws raises the hurdle for shareholders to bring derivate lawsuits against firms and weakens shareholder litigation rights. Using a sample of bond issuances from 1985 to 2009, we find that the adoption of UD laws is positively associated with yield spreads of bonds issued by U.S. firms.
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Title: Melt Growth of Indium-Iodide on Earth and in Microgravity
Creator: Riabov, Vladimir
Date: 2023
Description: Indium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray...
Show moreIndium Iodide is a heavy metal halide and a wide band-gap semiconductor which has a potential for application in room temperature γ- and X-ray detectors. Its physical properties are similar to those of other materials used as room temperature radiation detectors. Over the years the technology of purification and crystal growth of InI was developed. Significant advances were made to improve purity, crystal structure and resulting electronic properties of the material. Nevertheless, the desired detector performance has not been achieved yet. Stress-induced crystal lattice defects resulting from solidification in contact with crucible are suspected to be responsible for the limited performance. Microgravity environment was previously used to study its effects on the process of crystal growth from the melt applied to semiconductors. It was observed that unlike on Earth materials can solidify without contact with the wall, when the sample is confined by the crucible. It was also shown that such detached solidification can drastically reduce stress-induced defects of the crystal lattice and improve electronic properties of the material. In this study crystal growth of InI was studied in microgravity, attempting to achieve detached solidification, and observe it in a transparent zone of a furnace. Partially detached solidification (a large free surface) has occurred in one of the samples. The resulting crystals were characterized by measuring their electronic properties and estimating the radiation detector performance of the devices manufactured using the crystals.
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Title: Modeling and Optimization of Power Plant Cooling Tower Systems Using Physics-Based and Neural-Network-Based Models
Creator: Salomon, Basile Clément Paul
Date: 2023
Description: Condensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed...
Show moreCondensers and cooling towers are commonly used in steam power plants to condense the steam exiting the turbine and to recycle the condensed-water into the boiler in a closed-loop system. These condensers typically use cooling water drawn from a water body (lake, river etc) to condense the steam. Cooling towers are used to lower the temperature of the warm water exiting the condenser. Since the steam condensation temperature plays an important role in the power plant efficiency, cool- ing tower performance which is limited by the wet-bulb temperature of the ambient air has been extensively studied. This work investigates the modeling of an enhanced cooling tower technology using a new pre-cooling and dehumidifying system (PDHS). This new system, based on a reversed Brayton cycle, is made out of a compressor, an air-cooled heat exchanger (HX), a heat and mass exchanger (HMX) and an expander. The goal of this PDHS concept is to pre-cool the air entering the cooling tower in order to improve its performance. In this work, a systems model has been developed. Thermodynamic models have been used for the compressor, the air-cooled heat exchanger and the expander. For the remaining components, i.e. the heat and mass exchanger, the cooling tower and the condenser, physics-based models have been developed and tested. Once tested and validated, each model can be integrated into the integrated PDHS-cooling tower-condenser system. Two different configurations of the PDHS have been considered in this thesis. In the open water loop configuration, the water in the HMX is obtained from the municipal water supply (or an alternate water source) and is released back to the source after exiting the HMX. In the closed water loop configuration, the water used to cool down the air in the HMX is being recirculated and cooled in the power plant cooling tower. The physics-based model of the PDHS developed in this work has been validated using results from an empirical model of the PDHS by GTI Energy. This first case study also shows how the PDHS can be used to save water in the cooling tower (CT). Indeed, when using the PDHS, a 37% reduction in the cooling tower evaporation rate can be observed when comparing to the baseline. This decrease in the CT evaporation rate is the main source of make-up water savings. Moreover, the water harvested by condensation in the PDHS can be redirected towards the CT, bringing another source of water savings. These two combined lead to an overall 46% decrease of the make-up water usage in the cooling tower. Another case study has been conducted on a 500 MW condenser unit. It shows that, under summer ambient conditions i.e. Ta,db = 35°C and φ = 47%, the PDHS can help the condenser restore its designed cooling load of 453 MW. Finally, using the physics-based model to create a dataset, an artificial neural network model of the PDHS has been developed to constitute a black box for the PDHS that would be able to predict with sufficient accuracy the condenser and HMX loads, the air conditions at the inlet of the CT and water temperature at both ends of the condenser and CT given the ambient air condition, the compressor pressure ratio and the water split between the condenser and the heat and mass exchanger.
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Title: Functionalized 2D Materials as Enablers of High Energy and High Power Energy Storage Devices
Creator: Radhakrishnan, Sivaviswa
Date: 2023
Description: The present Thesis concerns with the synthesis of novel functionalized 2D materials for applications as cathodes in lithium-ion batteries. It...
Show moreThe present Thesis concerns with the synthesis of novel functionalized 2D materials for applications as cathodes in lithium-ion batteries. It further concerns with the role of porosity in these novel cathode materials to achieve simultaneously high energy and power density. Examples of the novel cathode materials synthesized here include several functionalized hexagonal boron nitride (hBN) and graphene (G) species. hBN was functionalized with Li₂C₂O₄, LiBF₄, -OBF₂ groups, NOBF₄, etc. The color of the functionalized hBN species ranges from white through brown to black indicating drastic changes in the band structure of hBN due to functionalization. Functionalized G species include Li₂C₂O₄ and -OBF₃ functionalized ones. Preliminary electrochemical tests were carried out for an initial assessment of the properties of these materials. Additionally, the role of the DOL solvent was also investigated in high power CFx batteries
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Title: Design and Fabrication of Battery-Operated Radiator Control (BORC) Utilizing 3D Printing Strategies
Creator: Riley, Christopher W.
Date: 2023
Description: The scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam...
Show moreThe scope of this work aims to serve as a continuation of prior research focused on the “development and evaluation of an automatic steam radiator control system or retrofitting legacy heating systems in existing buildings” (Syed Ali et al., 2020) by describing and testing the mechanical components of the developed controller in full detail. Other aspects of radiator efficiency are also explored. Primarily, this work aims to elaborate on the importance of material selection and mechanical properties of the design process. It also proposes initiative-taking solutions for the building’s energy recovery by monitoring the initial set up and focusing on certain details such as cardinal direction, thermal breaks, etc. These legacy systems are generally problematic when attempting to calculate energy efficiency, as a majority of radiator controls tend to be manual. Though there are comparable products within the European market, they cater to hot water systems and not steam, and in some instances require an internet bridge for operation (Tahersima et al., 2010). Since this is an extension of our earlier project, I will refer to it as Battery Operated Radiator Control (BORC) and the previous version as BERG’s Automated Radiator Control (ARC).
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Title: The Effect of Time Step on HSPF Model Performance
Creator: Rubinstein, Benjamin J.
Date: 2023
Description: Hydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time...
Show moreHydrological modeling is a mature and well researched field; however, because most climate data are collected on hour or greater time intervals there is very little research on the effect of using high resolution data as inputs for the models. A Python tool for downloading high resolution five minute interval data from the Oklahoma Mesonet was created and the PyHSPF Python package was used to generate, calibrate, and validate HSPF models using five minute, one hour, and daily time steps. Flow errors, R², and Nash-Sutcliffe efficiency for simulated outflows, and resource usage were compared for each model. The hourly and five minute models performed similarly well, and the daily model performed significantly worse. The results of this work could prove useful for policy makers and researchers looking to update or create new climate data collection protocols, and the tools used can be applied to many different kinds of future research.
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Title: Understanding Location Bias in Fake News Datasets of Twitter
Creator: Patil, Kayenat Kailas
Date: 2023
Description: Fake news tends to spread faster and wider than real news. It has a greater impact and can lead to negative and dangerous outcomes. With the...
Show moreFake news tends to spread faster and wider than real news. It has a greater impact and can lead to negative and dangerous outcomes. With the world spending an increasing amount of time on their mobile devices, people tend to get more of their news from their desired social media platform. It has become part of our daily lives, whether it is to keep in touch with friends and family, to getting gossip on celebrities or even shopping. In 2022, the average time a person spends per day on the internet on a social media platform has been accounted to be about 147 minutes,[1] indicating an increase in time spent scrolling through information online.It has become a widespread phenomenon in recent years, thanks in part to the rapid spread of information through social media and other online channels. It is increasingly important to explore and understand fake news and its impact on society, as well as to develop effective tools and methods for detecting and combating it. There are several factors that can tamper with the successful detection of fake news. Machine learning models often fall to such biases that result in inaccurate predictions. There are several biases that have been identified like age, gender, sex and many more. In this thesis, we are exploring location as a form of a bias and if it hinders prediction. We have looked at location from two perspectives. One, taking location as co-ordinates in the form of latitude and longitude and analyzing the likelihood of a tweet coming from a location to be fake or not. The second method we have used is that we have considered location as an entity and used natural language processing model to see if its able to predict if the given tweet is fake or not, along with masking the location mentioned in the tweet and analyzing how the performance of the model changes. Machine learning models can play an important role in fake news detection models, by analyzing large amounts of data and identifying patterns and indicators that suggest a piece of information may be false or misleading, but they are often susceptible to some form of biases. By studying biases on machine learning models on fake news datasets, we can develop more effective tools for identifying fake news and taking steps towards mitigating it, ultimately helping to protect the integrity of information and promote informed decision-making in society.
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Title: Effect of Pre-Processing Data on Fairness and Fairness Debugging using GOPHER
Creator: Sarkar, Mousam
Date: 2023
Description: At present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed...
Show moreAt present, Artificial intelligence has been contributing to the decision-making process heavily. Bias in machine learning models has existed throughout and present studies’ direct usage of eXplainable Artificial Intelligence (XAI) approaches to identify and study bias. To solve the problem of locating bias and then mitigating it has been achieved by Gopher [1]. It generates interpretable top-k explanations for the unfairness of the model and it also identifies subsets of training data that are the root cause of this unfair behavior. We utilize this system to study the effect of pre-processing on bias through provenance. The concept of data lineage through tagging of data points during and after the pre-processing stage is implemented. Our methodology and results provide a useful point of reference for studying the relation of pre-processing data with the unfairness of the machine learning model.
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Title: Establishing Bisphenol A Degradation and Enhancing Microbial Fuel Cell Performance by Biofilm Optimization of Shewanella Oneidensis MR1
Creator: Zhou, Jiacheng
Date: 2023
Description: Bisphenol A (BPA) has been widely used as a plasticizer in the production of synthetic polymers, such as those used in food storage containers...
Show moreBisphenol A (BPA) has been widely used as a plasticizer in the production of synthetic polymers, such as those used in food storage containers and bottles. However, BPA interferes with endocrine systems, causing carcinogenicity, immunotoxicity, and embryotoxicity. Biological water treatment processes scarcely remove BPA, owing to the poor BPA degradability and efficiency of the applied microorganisms. Shewanella oneidensis has been studied and used for the biodegradation process in wastewater treatment because of its excellent extracellular electron transfer properties. In this work, we engineered S. oneidensis MR1 to enable BPA degradation by producing ferredoxin (Fdbisd) and cytochrome P450 (P450bisd) originating from Sphingomonas bisphenolicum AO1. The engineered S. oneidensis exhibited a higher BPA degradation efficiency than that of Escherichia coli producing the same enzymes. The endogenous ferredoxin and ferredoxin reductase of S. oneidensis participated in BPA degradation, and overexpression of mtrC, omcA, and So0521, which encode S. oneidensis cytochromes, decreased BPA. We developed BPA-degrading S. oneidensis biofilms. We measured these optimized BPA-degrading S. oneidensis biofilm in a single chamber microbial fuel cell formed on different carbon electrodes by morphology. Cyclic voltammetry and electrochemical impedance spectroscopy were measured to analyze the biofilm-electrode performance. The biofilm colonization was also measured by confocal laser scanning microscope and scanning electron microscope. And the developed microbial fuel cell was used to degrade BPA and the biofilm developed on different type of carbon anodes was identified. This study provides insights into biocatalyst utilization for the biological degradation of toxic organic compounds.
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Title: Machine Learning for NDE Imaging Applications
Creator: Zhang, Xin
Date: 2023
Description: Infrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges...
Show moreInfrared Thermography and Ultrasonic Imaging of materials are promising non-destructive evaluation (NDE) methods but signals face challenges to be analyzed and characterized due to the nature of complex signal patterns and poor signal-to-noise ratios (SNR). Industries such as nuclear energy, are constructed with components produced using high-strength superalloys. These metallic components face challenges for wide deployment because material defects and mechanical conditions need to be non-destructively evaluated to identify potential danger before they enter service. Low NDE performance and lack of automation, particularly considering the complex environment in the in-situation NDE and nuclear power plant, present a major challenge to implement conventional NDE. This study solves the problems of using the advantages of machine learning as signal processing methods for Infrared Thermography and Ultrasonic NDE imaging applications. In Pulsed Infrared Thermography (PIT), for quality control of metal additive manufacturing, we proposed an intelligent PIT NDE system and developed innovative unsupervised learning models and thermal tomography 3D imaging algorithms to detect calibrated internal defects (pores) of various sizes and depths for different nuclear-grade metallic structures. Unsupervised learning aims to learn the latent principal patterns (dictionaries) in PIT data to detect defects with minimal human supervision. Difficulties to detect defects by using PIT are thermal imaging noise patterns; uneven heating of the specimen; defects of micron-level size with overly weak temperature signals and so on. The unsupervised learning methods overcome these barriers and achieve the high defect detection accuracies (F-score) of 0.96 to detect large defects and 0.89 to detect microscopic defects, and can successfully detect defects with diameter of only 0.101-mm. In addition, we researched and developed innovative unsupervised learning models to compress high-resolution PIT imaging data and achieve the average high compression ratio >30 and a highest compression of 46 with reconstruction accuracy peak signal-to-noise ratio (PSNR) >73dB while preserving weak thermal features corresponding to microscopic defects. In ultrasonic NDE imaging, for structural health monitoring of materials, we built a high-performance ultrasonic computational system to inspect the integrity of high-strength metallic materials which are used in high-temperature corrosive environments of nuclear reactors. For system automation, we have been developing neural networks with various architectures for grain size estimation by characterizing the ultrasonic backscattered signals with high accuracy and data-efficiency. In addition, we introduce a response-based teacher-student knowledge distillation training framework to train neural networks and achieve 99.27% characterization accuracy with a high image processed throughput of 192 images/second on testing. Furthermore, we introduce a reinforcement learning based neural architecture search framework to automatically model the optimal neural networks design for ultrasonic flaws detection. At last, we comprehensively researched the performance of using unsupervised learning methods to compress 3D ultrasonic data and achieve high compression performance using only 4.25% of the acquired experimental data.
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Title: Image Synthesis with Generative Adversarial Networks
Creator: Ouyang, Xu
Date: 2023
Description: Image synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely...
Show moreImage synthesis refers to the process of generating new images from an existing dataset, with the objective of creating images that closely resemble the target images, learned from the source data distribution. This technique has a wide range of applications, including transforming captions into images, deblurring blurred images, and enhancing low-resolution images. In recent years, deep learning techniques, particularly Generative Adversarial Network (GAN), has achieved significant success in this field. GAN consists of a generator (G) and a discriminator (D) and employ adversarial learning to synthesize images. Researchers have developed various strategies to improve GAN performance, such as controlling learning rates for different models and modifying the loss functions. This thesis focuses on image synthesis from captions using GANs and aims to improve the quality of generated images. The study is divided into four main parts:In the first part, we investigate the LSTM conditional GAN which is to generate images from captions. We use the word2vec as the caption features and combine these features’ information by LSTM and generate images via conditional GAN. In the second part, to improve the quality of generated images, we address the issue of convergence speed and enhance GAN performance using an adaptive WGAN update strategy. We demonstrate that this update strategy is applicable to Wasserstein GAN(WGAN) and other GANs that utilize WGAN-related loss functions. The proposed update strategy is based on a loss change ratio comparison between G and D. In the third part, to further enhance the quality of synthesized images, we investigate a transformer-based Uformer GAN for image restoration and propose a two-step refinement strategy. Initially, we train a Uformer model until convergence, followed by training a Uformer GAN using the restoration results obtained from the first step.In the fourth part, to generate fine-grained image from captions, we delve into the Recurrent Affine Transformation (RAT) GAN for fine-grained text-to-image synthesis. By incorporating an auxiliary classifier in the discriminator and employing a contrastive learning method, we improve the accuracy and fine-grained details of the synthesized images.Throughout this thesis, we strive to enhance the capabilities of GANs in various image synthesis applications and contribute valuable insights to the field of deep learning and image processing.
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Title: Online Satire in Iran: Social Critique, Counter-Publicity and Public Opinion
Creator: Mirghaderi, Leilasadat
Date: 2023
Description: This dissertation examines and investigates the recontextualization of theories of satire, public sphere, and communicative action in the...
Show moreThis dissertation examines and investigates the recontextualization of theories of satire, public sphere, and communicative action in the context of Iranian Instagram satirists. The importance of this investigation is two-fold. First, since the advent of social media sites, several theoretical concepts have gone through paradigm shifts. In this regard, theories of satire, public sphere, and communicative action are no exceptions. Second, these theoretical concepts were primarily developed and analyzed in Western democratic nations and need to be revisited in the context of Iran. To this end, by looking through the lenses of these theories and taking into account the context of Iran, this dissertation identifies the major topics that Iranian Instagram satirists address and criticize. Therefore, the first goal of this dissertation is to provide insights with regards to how Iranian Instagram satirists are using humor to criticize different aspects of social, cultural, and political matters that are more salient in the context of Iran and to investigate how they are trying to transfer their knowledge and raise awareness about topics that are less publicly discussed.Furthermore, by analyzing the topics that are the main focus of Iranian Instagram satirists, accompanied by observations from my netnography study, and considering the current atmosphere and historical background of Iran, this dissertation argues the extent to which Iranian satirical content on Instagram facilitates the democratic exchange of ideas as a requirement for transformation from a public space to public sphere(s). This dissertation also examines the communicative action strategies that Iranian Instagram satirists employ to reach an understanding with the state as well as their followers. Finally, the dimensions and attributes of satire as well as the features of Instagram that facilitate the formation of public discourse and public opinion formation in Iran are identified. Methodologically, this dissertation utilized a combination of qualitative research methods including but not limited to netnography, thematic and content analysis, transcription of publicly available trade publication interviews, and critical discourse analysis. While themes and topics were extracted from the collected media from the Instagram pages of the Iranian Instagram satirists, the transcription of the publicly available trade publication interviews as well as critical discourse analysis were used to describe why some topics were more salient and to provide answers to the developed research questions. From the analysis of the posts and stories, ten themes that fall into two broad categories of inward and outward criticism were extracted. In the context of this research, the inward criticism category refers to the topics that unravel the social and cultural dimensions of relationships and interactions, while the outward criticism category refers to the topics that criticize the state and its established institutions. Taking a closer look at the results, we can see that in the context of Iran, it can be concluded that the online world accompanied by satire facilitates the democratic exchange of ideas as a requirement for transformation from a public space to public sphere(s). However, as Iranian Instagram influencers are expressing their opposing thoughts against the established hegemony and are also challenging what matters belong to the private or public domains, it can be said that they are forming subaltern counterpublics in the online world.
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