Recent Submissions

  • Learning from Scholarly Attributed Graphs for Scientific Discovery

    Akujuobi, Uchenna Thankgod (2020-10-18) [Dissertation]
    Advisor: Zhang, Xiangliang
    Committee members: Moshkov, Mikhail; Hoehndorf, Robert; Zhang, Min
    Research and experimentation in various scientific fields are based on the knowledge and ideas from scholarly literature. The advancement of research and development has, thus, strengthened the importance of literary analysis and understanding. However, in recent years, researchers have been facing massive scholarly documents published at an exponentially increasing rate. Analyzing this vast number of publications is far beyond the capability of individual researchers. This dissertation is motivated by the need for large scale analyses of the exploding number of scholarly literature for scientific knowledge discovery. In the first part of this dissertation, the interdependencies between scholarly literature are studied. First, I develop Delve – a data-driven search engine supported by our designed semi-supervised edge classification method. This system enables users to search and analyze the relationship between datasets and scholarly literature. Based on the Delve system, I propose to study information extraction as a node classification problem in attributed networks. Specifically, if we can learn the research topics of documents (nodes in a network), we can aggregate documents by topics and retrieve information specific to each topic (e.g., top-k popular datasets). Node classification in attributed networks has several challenges: a limited number of labeled nodes, effective fusion of topological structure and node/edge attributes, and the co-existence of multiple labels for one node. Existing node classification approaches can only address or partially address a few of these challenges. This dissertation addresses these challenges by proposing semi-supervised multi-class/multi-label node classification models to integrate node/edge attributes and topological relationships. The second part of this dissertation examines the problem of analyzing the interdependencies between terms in scholarly literature. I present two algorithms for the automatic hypothesis generation (HG) problem, which refers to the discovery of meaningful implicit connections between scientific terms, including but not limited to diseases, drugs, and genes extracted from databases of biomedical publications. The automatic hypothesis generation problem is modeled as a future connectivity prediction in a dynamic attributed graph. The key is to capture the temporal evolution of node-pair (term-pair) relations. Experiment results and case study analyses highlight the effectiveness of the proposed algorithms compared to the baselines’ extension.
  • Dynamic Programming Multi-Objective Combinatorial Optimization

    Mankowski, Michal (2020-10-18) [Dissertation]
    Advisor: Moshkov, Mikhail
    Committee members: Keyes, David E.; Shihada, Basem; Boros, Endre
    In this dissertation, we consider extensions of dynamic programming for combinatorial optimization. We introduce two exact multi-objective optimization algorithms: the multi-stage optimization algorithm that optimizes the problem relative to the ordered sequence of objectives (lexicographic optimization) and the bi-criteria optimization algorithm that simultaneously optimizes the problem relative to two objectives (Pareto optimization). We also introduce a counting algorithm to count optimal solution before and after every optimization stage of multi-stage optimization. We propose a fairly universal approach based on so-called circuits without repetitions in which each element is generated exactly one time. Such circuits represent the sets of elements under consideration (the sets of feasible solutions) and are used by counting, multi-stage, and bi-criteria optimization algorithms. For a given optimization problem, we should describe an appropriate circuit and cost functions. Then, we can use the designed algorithms for which we already have proofs of their correctness and ways to evaluate the required number of operations and the time. We construct conventional (which work directly with elements) circuits without repetitions for matrix chain multiplication, global sequence alignment, optimal paths in directed graphs, binary search trees, convex polygon triangulation, line breaking (text justi cation), one-dimensional clustering, optimal bitonic tour, and segmented least squares. For these problems, we evaluate the number of operations and the time required by the optimization and counting algorithms, and consider the results of computational experiments. If we cannot nd a conventional circuit without repetitions for a problem, we can either create custom algorithms for optimization and counting from scratch or can transform a circuit with repetitions into a so-called syntactical circuit, which is a circuit without repetitions that works not with elements but with formulas representing these elements. We apply both approaches to the optimization of matchings in trees and apply the second approach to the 0/1 knapsack problem. We also brie y introduce our work in operation research with applications to health care. This work extends our interest in the optimization eld from developing new methods included in this dissertation towards the practical application.
  • A Real-Time Monitoring of Fluids Properties in Tubular Architectures

    Nour, Maha A. (2020-10) [Dissertation]
    Advisor: Hussain, Muhammad Mustafa
    Committee members: Alouini, Mohamed-Slim; Schwingenschlögl, Udo; Kurinec, Santosh
    Real-time monitoring of fluid properties in tubular systems, such as viscosity, flow rate, and pressure, is essential for industries utilizing the liquid medium. Today such fluid characteristics are studied off-line using laboratory facilities that can provide accurate results. Nonetheless, it is inadequate to match the pace demanded by the industries. Therefore, off-line measurements are slow and ineffective. On the other hand, commercially available real-time monitoring sensors for fluid properties are generally large and bulky, generating considerable pressure reduction and energy loss in tubular systems. Furthermore, they produce significant and persistent damage to the tubular systems during the installation process because of their bulkiness. To address these challenges, industries have realigned their attention on non-destructive testing and noninvasive methodologies installed on the outer tubular surface to avoid flow disturbance and shutting systems for installations. Although, such monitoring sensors showed greater performance in monitoring and inspecting pipe health conditions, they are not effective for monitoring the properties of the fluids. It is limited to flowmeter applications and does not include fluid characteristics such as viscometers. Therefore, developing a convenient real-time integrated sensory system for monitoring different fluid properties in a tubular system is critical. In this dissertation, a fully compliant compact sensory system is designed, developed, examined and optimized for monitoring fluid properties in tubular architectures. The proposed sensor system consists of a physically flexible platform connected to the inner surface of tubes to adopt the different diameters and curvature shapes with unnoticeable flow disruption. Also, it utilizes the microchannel bridge to serve in the macro application inside pipe systems. It has an array of pressure sensors located bellow the microchannel as the primary measurement unit for the device. The dissertation is supported by simulation and modeling for a deeper understanding of the system behavior. In the last stage, the sensory module is integrated with electronics for a fully compliant stand-alone system.
  • Maximizing I/O Bandwidth for Out-of-Core HPC Applications on Homogeneous and Heterogeneous Large-Scale Systems

    Alturkestani, Tariq (2020-09-30) [Dissertation]
    Advisor: Keyes, David E.
    Committee members: Shihada, Basem; Moshkov, Mikhail; Sun, Xian-He
    Out-of-Core simulation systems often produce a massive amount of data that cannot t on the aggregate fast memory of the compute nodes, and they also require to read back these data for computation. As a result, I/O data movement can be a bottleneck in large-scale simulations. Advances in memory architecture have made it feasible and a ordable to integrate hierarchical storage media on large-scale systems, starting from the traditional Parallel File Systems (PFSs) to intermediate fast disk technologies (e.g., node-local and remote-shared NVMe and SSD-based Burst Bu ers) and up to CPU main memory and GPU High Bandwidth Memory (HBM). However, while adding additional and faster storage media increases I/O bandwidth, it pressures the CPU, as it becomes responsible for managing and moving data between these layers of storage. Simulation systems are thus vulnerable to being blocked by I/O operations. The Multilayer Bu er System (MLBS) proposed in this research demonstrates a general and versatile method for overlapping I/O with computation that helps to ameliorate the strain on the processors through asynchronous access. The main idea consists in decoupling I/O operations from computational phases using dedicated hardware resources to perform expensive context switches. MLBS monitors I/O tra c in each storage layer allowing fair utilization of shared resources. By continually prefetching up and down across all hardware layers of the memory and storage subsystems, MLBS transforms the original I/O-bound behavior of evaluated applications and shifts it closer to a memory-bound or compute-bound regime. The evaluation on the Cray XC40 Shaheen-2 supercomputer for a representative I/Obound application, seismic inversion, shows that MLBS outperforms state-of-the-art PFSs, i.e., Lustre, Data Elevator and DataWarp by 6.06X, 2.23X, and 1.90X, respectively. On the IBM-built Summit supercomputer, using 2048 compute nodes equipped with a total of 12288 GPUs, MLBS achieves up to 1.4X performance speedup compared to the reference PFS-based implementation. MLBS is also demonstrated on applications from cosmology, combustion, and a classic out-of-core computational physics and linear algebra routines.
  • Photophysical Processes in Lead Halide Perovskite Solar Cells Revealed by Ultrafast Spectroscopy

    Ugur, Esma (2020-09-16) [Dissertation]
    Advisor: Laquai, Frédéric
    Committee members: De Wolf, Stefaan; Ooi, Boon S.; Albrecht, Steve
    Metal halide perovskites have emerged as photoactive materials in solution-processed devices thanks to their unique properties such as high absorption coefficient, sharp absorption edge, long carrier diffusion lengths, and tunable bandgap, together with ease of fabrication. The single-junction perovskite solar cells have reached power conversion efficiencies of more than 25%. Although the efficiency of perovskite devices has increased tremendously in a very short time, the efficiency is still limited by carrier recombination at defects and interfaces. Thus, understanding these losses and how to reduce them is the way forward towards the Shockley-Queisser limit. This thesis aims to apply ultrafast optical spectroscopy techniques to investigate the recombination pathways in halide perovskites, and understand the charge extraction from perovskite to transport layers and nonradiative losses at the interface. The first part focuses on perovskite solar cells with planar n–i–p device architecture which offers significant advantages in terms of large scale processing, the potential use of flexible substrates, and applicability to tandems. In addition to the optimization of MAPbI3 solar cell fabrication using a modified sequential interdiffusion protocol, the photophysics of perovskites exposed to humid air and illumination are discussed. The MAPbI3 film processed with the addition of glycol ethers to the methylammonium iodide solution results in the control of PbI2 to perovskite conversion dynamics, thus enhanced morphology and crystallinity. For samples exposed to humid air and illumination, the formation of sub-bandgap states and increased trap-assisted recombination are observed, using highly-sensitive absorption and time-resolved photoluminescence measurements, respectively. It appears that such exposure primarily affects the perovskite surface. The second part discusses the hole extraction from Cs0.07Rb0.03FA0.765MA0.135PbI2.55Br0.45 to the polymeric hole transport layer and interfacial recombination using ultrafast transient absorption spectroscopy technique. To illustrate this, PDPP-3T was used as HTL, since its ground state absorption is red-shifted compared to the perovskite’s photobleach, thereby allowing direct probing of the interfacial hole extraction and recombination. Moreover, carrier diffusion is investigated by varying the perovskite film thickness, and carrier mobility is found to be 39 cm2V-1s-1. Finally, hole extraction is found to be one order of magnitude faster than the recombination at the interface.
  • Machine Learning Models for Biomedical Ontology Integration and Analysis

    Smaili, Fatima Z. (2020-09-13) [Dissertation]
    Advisor: Gao, Xin
    Committee members: Rzhetsky, Andrey; Hoehndorf, Robert; Arold, Stefan T.
    Biological knowledge is widely represented in the form of ontologies and ontologybased annotations. Biomedical ontologies describe known phenomena in biology using formal axioms, and the annotations associate an entity (e.g. genes, diseases, chemicals, etc.) with a set of biological concepts. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation properties expressed mostly in natural language which provide valuable pieces of information that characterize ontology concepts. The structure and information contained in ontologies and their annotations make them valuable for use in machine learning, data analysis and knowledge extraction tasks. I develop the rst approaches that can exploit all of the information encoded in ontologies, both formal and informal, to learn feature embeddings of biological concepts and biological entities based on their annotations to ontologies. Notably, I develop the rst approach to use all the formal content of ontologies in the form of logical axioms and entity annotations to generate feature vectors of biological entities using neural language models. I extend the proposed algorithm by enriching the obtained feature vectors through representing the natural language annotation properties within the ontology meta-data as axioms. Transfer learning is then applied to learn from the biomedical literature and apply on the formal knowledge of ontologies. To optimize learning that combines the formal content of biomedical ontologies and natural language data such as the literature, I also propose a new approach that uses self-normalization with a deep Siamese neural network that improves learning from both the formal knowledge within ontologies and textual data. I validate the proposed algorithms by applying them to the Gene Ontology to generate feature vectors of proteins based on their functions, and to the PhenomeNet ontology to generate features of genes and diseases based on the phenotypes they are associated with. The generated features are then used to train a variety of machinelearning based classi ers to perform di erent prediction tasks including the prediction of protein interactions, gene{disease associations and the toxicological e ects of chemicals. I also use the proposed methods to conduct the rst quantitative evaluation of the quality of the axioms and meta-data included in ontologies to prove that including axioms as background improves ontology-based prediction. The proposed approaches can be applied to a wide range of other bioinformatics research problems including similarity-based prediction and classi cation of interaction types using supervised learning, or clustering.
  • Induction of Salt Tolerance by Enterobacter sp. SA187 in the Model Organism Arabidopsis thaliana

    Alzubaidy, Hanin S. (2020-09) [Dissertation]
    Advisor: Hirt, Heribert
    Committee members: Blilou, Ikram; Aranda, Manuel; deZelicourt, Axel; Krasensky-Wrzaczek, Julia
    Arid and semi-arid regions, mostly found in developing countries with exponentially increasing populations, are in chronic lack of water thereby severely limiting agricultural production. Irrigation with saline water, which is available in large quantities, could be an obvious solution, but current crops are all salt sensitive. Although major efforts are underway to breed salt tolerant crops, no breakthrough results have yet been obtained. One alternative could rely on plant-interacting microbiota communities. Indeed, rhizophere and endosphere microbial communities are distinct from those of the surrounding soils, and these specific communities contribute to plant growth and health by increasing nutrient availability or plant resistance towards abiotic and biotic stresses. Here we show that plant microbe interactions induce plant tolerance to multiple stresses. From a collection of strains isolated from the desert plant Indigofera argentea, we could identify at least four different strategies to induce salt stress tolerance in Arabidopsis thaliana. A deep analysis of Enterobacter sp. SA187 showed that it induces Arabidopsis tolerance to salinity through activation of the ethylene signaling pathway. Interestingly, although SA187 does not produce ethylene as such, the association of SA187 with plants induces the expression of the methionine salvage pathway in SA187 resulting in the conversion of bacterially produced 2-keto-4-methylthiobutyric acid (KMBA) to ethylene. In addition, a metabolic network characterization of both SA187 and Arabidopsis in their free-living and endophytic state revealed that the sulfur metabolic pathways are strongly upregulated in both organisms. Furthermore, plant genetic experiments verified the essential role of the sulfur metabolism and ethylene signaling in plant salt stress tolerance. Our findings demonstrate how successful plant microbes of a given community can help other plants to enhance tolerance to abiotic stress, and reveal a part of the complex molecular communication process during beneficial plant-microbe interaction.
  • Functional Analysis of the Caenorhabditis elegans HP1 Homolog HPL-2 in a Chromatin Context

    Miller, Elizabeth Victoria (2020-09) [Dissertation]
    Advisor: Fischle, Wolfgang
    Committee members: Orlando, Valerio; Mahfouz, Magdy M.; Jensen, Christian Froekjaer; Becker, Peter
    The heterochromatin 1 (HP1) family of non-histone chromosomal proteins is evolutionarily conserved and is involved in numerous biological processes, including the stabilization of heterochromatin, a state of compacted DNA along a protein scaffold. HP1 proteins and trimethylated histone H3 on lysine 9 (H3K9me3) are major constituents of heterochromatin and have been characterized extensively in vitro. The binding of HP1 proteins to H3K9 methylation marks plays an essential role in mammalian development and chromatin organization. However, due to their critical function, dissecting the molecular mechanism by which HP1 proteins exert their function in vivo is difficult. C. elegans is a unique model because not only are deletion mutants of the two HP1 homologs, HPL-1 and HPL-2, viable, but also H3K9 methylation is not essential to worm development. Interestingly, HPL-2 is alternatively spliced to generate two HP1 proteins, but in vivo experimentation has vastly ignored the potential contributions of the alternative transcripts to hpl-2 function, thus obfuscating which phenotypes associated with hpl-2 knockdown are due to the loss of one or more of the splicing variants. In this dissertation, I characterized the HPL-2 splicing variants (A and B) on a biochemical level in relation to the canonical human HP1b protein and on a physiological level in splicing variant-specific knockout worms. I show that both recombinant HPL-2A and HPL-2B bind H3K9me3 through their chromodomain (CD). But while HPL-2A acts as a canonical HP1 protein, namely it dimerizes and phase-separates like hHP1b, HPL-2B does not. In contrast to recombinant protein, in extracts both proteins rely on other factors, such as the MBT domain-containing protein LIN-61, for their recruitment to H3K9me3. Although HPL-2A and HPL-2B display distinct characteristics in vitro, both hpl-2a and hpl-2b worms are phenotypically wildtype. In agreement, knockout of either splicing variant leads to upregulated expression of the other one, suggesting a certain level of functional redundancy. Nevertheless, I show that the C-terminal extension of HPL-2B, which is absent in HPL-2A, resembles that of the CEC-4 heterochromatin anchor. I therefore hypothesize that the main functions of HPL-2 are distinct: HPL-2A mediates chromatin compaction and HPL-2B facilitates heterochromatin anchoring to the nuclear periphery.
  • Insights into the Physical and Chemical Effects Governing Auto-ignition and Heat Release in Internal Combustion Engines

    AlRamadan, Abdullah (2020-09) [Dissertation]
    Advisor: Johansson, Bengt
    Committee members: Sarathy, Mani; Farooq, Aamir; Ng, Kim Choon; Kalghatgi, Gautam T.
    Extensive analysis of the physical and chemical effects controlling the operation of combustion modes driven by auto-ignition is presented in this thesis. Specifically, the study integrates knowledge attained by analyzing the effects of fuel molecular structure on auto-ignition, quantity or quality of charge dilution, and in-cylinder temperature and pressure on burning characteristics in single and multiple injection strategies employed in compression ignition (CI), partially premixed combustion (PPC) and homogenous charge compression ignition (HCCI) engines. In the first section of the thesis, a multiple injection strategy aimed to produce heat at a constant pressure, commonly known as isobaric combustion, has been studied. Then, to eliminate the complexity of spray-to-spray interactions observed with isobaric combustion, the second section of the thesis is focused on compression ignition (CI) through single injection. In the final section, the presentation will move towards moderate conditions with high dilution, in which combustion becomes dominated by chemical kinetics. At these conditions, there is emerging evidence that certain fuels exhibit unusual heat release characteristics where fuel releases heat in three distinctive stages. Overall, the thesis discusses factors controlling the auto-ignition for CI, PPC and HCCI engines that can provide valuable insights to improve their operation. Isobaric combustion in CI engine involves large interactions between physical and chemical effects. Injection of spray jets into oxygen-deprived regions catalyzes the mechanism for soot production – urging to employ either multiple injectors, low reactivity fuel or an additional expansion stage. Fuels – regardless of their auto-ignition tendency – share the same combustion characteristics in the high load CI, where auto-ignition is controlled by only the injector’s physical specifications. Such observation is a showcase of the fuel flexible engines that has the potential of using sustainable fuels – without being restrained by the auto-ignition properties of the fuel. The thesis provides evidence from experiment and simulation that three-stage auto-ignition is indeed a phenomenon driven by chemical kinetics. Three-stage auto-ignition opens the perspective to overcome the limitation of the high-pressure rise rates associated with HCCI engine.
  • Indoor 3D Scene Understanding Using Depth Sensors

    Lahoud, Jean (2020-09) [Dissertation]
    Advisor: Ghanem, Bernard
    Committee members: Heidrich, Wolfgang; Wonka, Peter; Cremers, Daniel
    One of the main goals in computer vision is to achieve a human-like understanding of images. Nevertheless, image understanding has been mainly studied in the 2D image frame, so more information is needed to relate them to the 3D world. With the emergence of 3D sensors (e.g. the Microsoft Kinect), which provide depth along with color information, the task of propagating 2D knowledge into 3D becomes more attainable and enables interaction between a machine (e.g. robot) and its environment. This dissertation focuses on three aspects of indoor 3D scene understanding: (1) 2D-driven 3D object detection for single frame scenes with inherent 2D information, (2) 3D object instance segmentation for 3D reconstructed scenes, and (3) using room and floor orientation for automatic labeling of indoor scenes that could be used for self-supervised object segmentation. These methods allow capturing of physical extents of 3D objects, such as their sizes and actual locations within a scene.
  • Fate of Plastic Pollution in the Arabian Seas

    Martin, Cecilia (2020-09) [Dissertation]
    Advisor: Duarte, Carlos M.
    Committee members: Agusti, Susana; Zhang, Xiangliang; Reisser, Julia
    Plastic pollution has become of public concern recently and only in the last decades the need of quantifying loads of plastic in the marine environment and identifying their ultimate destination has been urged as a mean to point at where interventions should concentrate. The Arabian seas (Red Sea and Arabian Gulf) have oceanographic features that candidate them as accumulation zones for marine plastics, but, especially the Red Sea, are largely unexplored. The dissertation here presented provides significant advances in the understanding of the marine plastic distribution in the two basins. Despite the initial hypothesis, the Red Sea was found to hold a remarkably low abundance of plastic particles in its surface waters. Similarly, previous assessments have reported the same in the Arabian Gulf. In line with the global estimates, only a small portion of the plastic that is discarded yearly in the marine environment is found in its surface waters, implying the presence of removal processes. However, the unexpectedly low loads of floating plastics in the Arabian seas indicate that sinks are likely more significant here than elsewhere. In the Red Sea, an extensive survey of macroplastic stranded on shores, globally considered a major sink of marine plastic, has indicated that Avicennia marina mangrove forests, through the mesh created by their pneumatophores, contribute significantly more than unvegetated shores in retaining plastics. Loads of plastic in the Arabian Gulf mangrove stands, more impacted by coastal development than stands in the Red Sea, are even larger. The role of mangroves as significant sinks of plastics is further corroborated by the finding that the burial rates of plastic in their sediments follow an exponential increase in line with the global plastic production increase, ultimately demonstrating that plastic is likely sequestered there permanently. Mangrove forests alone are, however, not enough to justify the mismatch between plastic inputs and loads in surface waters. The experimental finding showed here that coral structures can passively trap substantial loads of microplastics and the large extension of reefs, especially in the Red Sea, suggest that reefs might constitute a missing sink of marine plastic in the basin worth exploring.
  • Trajectory Planning for Autonomous Underwater Vehicles: A Stochastic Optimization Approach

    Albarakati, Sultan (2020-08-30) [Dissertation]
    Advisors: Knio, Omar; Shamma, Jeff S.
    Committee members: Hoteit, Ibrahim; Lermusiaux, Pierre F.J.
    In this dissertation, we develop a new framework for 3D trajectory planning of Autonomous Underwater Vehicles (AUVs) in realistic ocean scenarios. The work is divided into three parts. In the rst part, we provide a new approach for deterministic trajectory planning in steady current, described using Ocean General Circulation Model (OGCM) data. We apply a Non-Linear Programming (NLP) to the optimal time trajectory planning problem. To demonstrate the effectivity of the resulting model, we consider the optimal time trajectory planning of an AUV operating in the Red Sea and the Gulf of Aden. In the second part, we generalize our 3D trajectory planning framework to time-dependent ocean currents. We also extend the framework to accommodate multi-objective criteria, focusing speci cally on the Pareto front curve between time and energy. To assess the effectiveness of the extended framework, we initially test the methodology in idealized settings. The scheme is then demonstrated for time-energy trajectory planning problems in the Gulf of Aden. In the last part, we account for uncertainty in the ocean current eld, is described by an ensemble of flow realizations. The proposed approach is based on a non-linear stochastic programming methodology that uses a risk-aware objective function, accounting for the full variability of the flow ensemble. We formulate stochastic problems that aim to minimize a risk measure of the travel time or energy consumption, using a fexible methodology that enables the user to explore various objectives, ranging seamlessly from risk-neutral to risk-averse. The capabilities of the approach are demonstrated using steady and transient currents. Advanced visualization tools have been further designed to simulate results.
  • Optimization for Supervised Machine Learning: Randomized Algorithms for Data and Parameters

    Hanzely, Filip (2020-08-20) [Dissertation]
    Advisor: Richtarik, Peter
    Committee members: Tempone, Raul; Ghanem, Bernard; Wright, Stephen; Zhang, Tong
    Many key problems in machine learning and data science are routinely modeled as optimization problems and solved via optimization algorithms. With the increase of the volume of data and the size and complexity of the statistical models used to formulate these often ill-conditioned optimization tasks, there is a need for new e cient algorithms able to cope with these challenges. In this thesis, we deal with each of these sources of di culty in a di erent way. To e ciently address the big data issue, we develop new methods which in each iteration examine a small random subset of the training data only. To handle the big model issue, we develop methods which in each iteration update a random subset of the model parameters only. Finally, to deal with ill-conditioned problems, we devise methods that incorporate either higher-order information or Nesterov's acceleration/momentum. In all cases, randomness is viewed as a powerful algorithmic tool that we tune, both in theory and in experiments, to achieve the best results. Our algorithms have their primary application in training supervised machine learning models via regularized empirical risk minimization, which is the dominant paradigm for training such models. However, due to their generality, our methods can be applied in many other elds, including but not limited to data science, engineering, scienti c computing, and statistics.
  • Halide Perovskites: Materials Properties and Emerging Applications

    Haque, Mohammed (2020-08-11) [Dissertation]
    Advisor: Baran, Derya
    Committee members: Alshareef, Husam N.; Mohammed, Omar F.; Saliba, Michael
    Semiconducting materials have emerged as the cornerstone of modern electronics owing to their extensive device applications. There is a continuous quest to find cost-effective and low-temperature compatible materials for future electronics. The recent reemergence of solution processable halide perovskites have taken the optoelectronics research to new paradigms. Apart from photovoltaics, the versatile characteristics of halide perovskites have resulted in a multitude of applications. This dissertation focuses on various properties and emerging applications particularly, photodetection and thermoelectrics of both hybrid and all-inorganic halide perovskites. It is important to understand the underlying properties of perovskites to further develop this class of materials. One of the major hurdles restricting the practical devices of perovskites is their sensitivity to moisture. A systematic investigation on the effect of humidity on hybrid perovskites revealed different degree of moisture uptake behaviour for micropatterns, films, and single crystals. Degradation pathways and processing limitations of hybrid perovskites are discussed which will aid in designing strategies to overcome these impediments for future large scale device integration. There is a recent surge of reports on doping hybrid perovskites to control its optoelectronic properties but in-depth understanding of these dopants and their ramifications remain unexplored. The effect of doping on the optoelectronic properties of hybrid perovskites is studied and a model is proposed for the observed behavior. Leveraging on the rapid growth of microcrystalline perovskite films, for the first time tunable bifacial perovskite photodetectors were fabricated, operating in both broadband and narrowband regimes. Furthermore, self-biased single crystalline photodetectors based on all-inorganic perovskite were developed with high on-off ratio and low dark current. Halide perovskites are emerging as a new class of materials for thermoelectric applications owing to their ultralow thermal conductivity and decent Seebeck coefficient. Here, halide perovskites are evaluated in terms of composition, stability, and performance tunability to understand their thermoelectric efficacy. Finally, as an alternative to Pb and Sn-based perovskites, a new hybrid was discovered with ultralow thermal conductivity and a general synthetic route to design such hybrids is proposed.
  • Sustainable Approaches to Reduce Biofouling and Biocorrosion in Seawater and Wastewater Environment

    Scarascia, Giantommaso (2020-08) [Dissertation]
    Advisor: Hong, Pei-Ying
    Committee members: Pain, Arnab; Saikaly, Pascal; Suarez, Laura Machuca
    Biofouling and biocorrosion are due to unwanted deposition of microorganisms on surfaces that are exposed to different types of water. This dissertation focuses on the application of innovative strategies to inhibit biofouling and biocorrosion. Specifically, the strategies examined in this dissertation, namely the use of bacteriophages and quorum quenchers, aim to minimize reliance on the conventional chemical cleaning agents and to reduce chemical-induced hazards on health, safety and environment. First, we analyzed the use of bacteriophages to remove biofoulants on ultrafiltration membrane used in seawater reverse osmosis pretreatment. Our findings revealed that bacteriophages were able to remain active against membrane-associated Pseudomonas aeruginosa at a broad range of temperature, pH and salinity. Bacteriophages were also shown to inhibit biofilm and to reduce transmembrane pressure increment, when applied alone or in combination with chemical agents. Second, this dissertation explores the use of quorum quenchers to inhibit biocorrosion in seawater environment. To do so, we first examined for the presence of quorum sensing system in sulfate reducing bacteria (SRB). Through transcriptomic analysis, we further demonstrate a strong correlation between quorum sensing, biofilm formation and biocorrosion. Therefore, the use of quorum sensing inhibitors was suggested to prevent biofilm formation and biocorrosion caused by SRB in seawater. Through findings from Chapter 2 and 3, we introduced the use of alternative biocidal agents to tackle biofouling and biocorrosion. Compared to quorum quenchers, bacteriophages showed better antibiofilm potential and easier applicability at larger scale. However, bacteriophages alone were insufficient to reduce biofilm formation as phage resistance was observed over long-term experiments. Hence in the last chapter, we further explored the use of bacteriophages to alleviate biofouling that occurred during wastewater treatment process, by combining their infection with UV irradiation. UV was used both for its biocidal effect and to trigger phage infection against bacteria. Our findings indicate that the combined treatment was able to remove mature biofoulants from the membrane. Overall, this dissertation demonstrates the use of bacteriophages and quorum quenchers against biofilm. These two approaches can serve to reduce the amount of chemicals used during cleaning, thus providing a more sustainable way of minimizing biofilm-associated problems.
  • Additively Manufactured Vanadium Dioxide (VO2) based Radio Frequency Switches and Reconfigurable Components

    Yang, Shuai (2020-08) [Dissertation]
    Advisor: Shamim, Atif
    Committee members: Fariborzi, Hossein; Anthopoulos, Thomas D.; Tentzeris, Manos M.
    In a wireless system, the frequency-reconfigurable RF components are highly desired because one such component can replace multiple RF components to reduce the size, cost, and weight. Typically, the reconfigurable RF components are realized using capacitive varactors, PIN diodes, or MEMS switches. Most of these RF switches are expensive, rigid, and need tedious soldering steps, which are not suitable for futuristic flexible and wearable applications. Therefore, there is a need to have a solution for low cost, flexible, and easy to integrate RF switches. All the above-mentioned issues can be alleviated if these switches can be simply printed at the place of interest. In this work, we have demonstrated vanadium dioxide (VO2) based RF switches that have been realized through additive manufacturing technologies (inkjet printing and screen printing), which dramatically brings the cost down to a few cents. Also, no soldering or additional attachment step is required as the switch can be simply printed on the RF component. The printed VO2 switches are configured in two types (shunt configuration and series configuration) where both types have been characterized with two activation mechanisms (thermal activation and electrical activation) up to 40 GHz. The measured insertion loss of 1-3 dB, isolation of 20-30 dB, and switching speed of 400 ns are comparable to other non-printed and expensive RF switches. As an application for the printed VO2 switches, a fully printed frequency reconfigurable filter has also been designed in this work. An open-ended dual-mode resonator with meandered loadings has been co-designed with the VO2 switches, resulting in a compact filter with decent insertion loss of 2.6 dB at both switchable frequency bands (4 GHz and 3.75 GHz). Moreover, the filter is flexible and highly immune to the bending effect, which is essential for wearable applications. Finally, a multi-parameter (switch thickness, width, length, temperature) model has been established using a customized artificial neural network (ANN) to achieve a faster simulation speed. The optimized model’s average error and correlation coefficient are only 0.0003 and 0.9905, respectively, which both indicate the model’s high accuracy.
  • Halide Perovskite Single Crystals: Design, Growth, and Characterization

    Zhumekenov, Ayan A. (2020-08) [Dissertation]
    Advisor: Bakr, Osman
    Committee members: Mohammed, Omar F.; Alshareef, Husam N.; Stranks, Samuel D.
    Halide perovskites have recently emerged as the state-of-the-art semiconductors with the unique combination of outstanding optoelectronic properties and facile solution synthesis. Within only a decade of research, they have witnessed a remarkable success in photovoltaics and shown great potential for applications in light-emitting devices, photodetectors, and high-energy sensors. Yet, the majority of current perovskite-based devices still rely on polycrystalline thin films which, as will be discussed in Chapter 2, exhibit inferior charge transport characteristics and increased tendency to chemical degradation compared to their single-crystalline analogues. In this regard, unburdened from the effects of grain boundaries, single crystals demonstrate the upper limits of semiconductor performance. Their study is, thus, important from both fundamental and practical aspects, which present the major objectives of this dissertation. In Chapter 3, we study the intrinsic charge transport and recombination characteristics of single crystals of formamidinium lead halide perovskites. While, in Chapter 4, we investigate the mechanistic origins of rapid synthesis of halide perovskite single crystals by inverse temperature crystallization. Understanding the nucleation and growth mechanisms of halide perovskites enables us to design strategies toward integrating their single crystals into device applications. Namely, in Chapters 5 and 6, we demonstrate crystal engineering approaches for tailoring the thicknesses and facets of halide perovskite single crystals to make them suitable for, respectively, vertical and planar architecture optoelectronic devices. The findings of this dissertation are expected to benefit future studies on fundamental characterization of halide perovskites, as well as motivate researchers to develop perovskite-based optoelectronic devices with better crystallinity, performance and stability.
  • NonlinearWave Motion in Viscoelasticity and Free Surface Flows

    Ussembayev, Nail (2020-07-24) [Dissertation]
    Advisor: Markowich, Peter A.
    Committee members: Thoroddsen, Sigurdur T; Tzavaras, Athanasios; Bona, Jerry L.
    This dissertation revolves around various mathematical aspects of nonlinear wave motion in viscoelasticity and free surface flows. The introduction is devoted to the physical derivation of the stress-strain constitutive relations from the first principles of Newtonian mechanics and is accessible to a broad audience. This derivation is not necessary for the analysis carried out in the rest of the thesis, however, is very useful to connect the different-looking partial differential equations (PDEs) investigated in each subsequent chapter. In the second chapter we investigate a multi-dimensional scalar wave equation with memory for the motion of a viscoelastic material described by the most general linear constitutive law between the stress, strain and their rates of change. The model equation is rewritten as a system of first-order linear PDEs with relaxation and the well-posedness of the Cauchy problem is established. In the third chapter we consider the Euler equations describing the evolution of a perfect, incompressible, irrotational fluid with a free surface. We focus on the Hamiltonian description of surface waves and obtain a recursion relation which allows to expand the Hamiltonian in powers of wave steepness valid to arbitrary order and in any dimension. In the case of pure gravity waves in a two-dimensional flow there exists a symplectic coordinate transformation that eliminates all cubic terms and puts the Hamiltonian in a Birkhoff normal form up to order four due to the unexpected cancellation of the coefficients of all fourth order non-generic resonant terms. We explain how to obtain higher-order vanishing coefficients. Finally, using the properties of the expansion kernels we derive a set of nonlinear evolution equations for unidirectional gravity waves propagating on the surface of an ideal fluid of infinite depth and show that they admit an exact traveling wave solution expressed in terms of Lambert’s W-function. The only other known deep fluid surface waves are the Gerstner and Stokes waves, with the former being exact but rotational whereas the latter being approximate and irrotational. Our results yield a wave that is both exact and irrotational, however, unlike Gerstner and Stokes waves, it is complex-valued.
  • Hierarchical Approximation Methods for Option Pricing and Stochastic Reaction Networks

    Ben Hammouda, Chiheb (2020-07-22) [Dissertation]
    Advisor: Tempone, Raul
    Committee members: Gomes, Diogo A.; Jasra, Ajay; Gobet, Emmanuel; Kebaier, Ahmed
    In biochemically reactive systems with small copy numbers of one or more reactant molecules, stochastic e ects dominate the dynamics. In the rst part of this thesis, we design novel e cient simulation techniques for a reliable and fast estimation of various statistical quantities for stochastic biological and chemical systems under the framework of Stochastic Reaction Networks. In the rst work, we propose a novel hybrid multilevel Monte Carlo (MLMC) estimator, for systems characterized by having simultaneously fast and slow timescales. Our hybrid multilevel estimator uses a novel split-step implicit tau-leap scheme at the coarse levels, where the explicit tau-leap method is not applicable due to numerical instability issues. In a second work, we address another challenge present in this context called the high kurtosis phenomenon, observed at the deep levels of the MLMC estimator. We propose a novel approach that combines the MLMC method with a pathwise-dependent importance sampling technique for simulating the coupled paths. Our theoretical estimates and numerical analysis show that our method improves the robustness and complexity of the multilevel estimator, with a negligible additional cost. In the second part of this thesis, we design novel methods for pricing nancial derivatives. Option pricing is usually challenging due to: 1) The high dimensionality of the input space, and 2) The low regularity of the integrand on the input parameters. We address these challenges by developing di erent techniques for smoothing the integrand to uncover the available regularity. Then, we approximate the resulting integrals using hierarchical quadrature methods combined with Brownian bridge construction and Richardson extrapolation. In the rst work, we apply our approach to e ciently price options under the rough Bergomi model. This model exhibits several numerical and theoretical challenges, implying classical numerical methods for pricing being either inapplicable or computationally expensive. In a second work, we design a numerical smoothing technique for cases where analytic smoothing is impossible. Our analysis shows that adaptive sparse grids' quadrature combined with numerical smoothing outperforms the Monte Carlo approach. Furthermore, our numerical smoothing improves the robustness and the complexity of the MLMC estimator, particularly when estimating density functions.
  • Two-scale Homogenization and Numerical Methods for Stationary Mean-field Games

    Yang, Xianjin (2020-07) [Dissertation]
    Advisor: Gomes, Diogo A.
    Committee members: Shamma, Jeff S.; Parsani, Matteo; Achdou, Yves
    Mean-field games (MFGs) study the behavior of rational and indistinguishable agents in a large population. Agents seek to minimize their cost based upon statis- tical information on the population’s distribution. In this dissertation, we study the homogenization of a stationary first-order MFG and seek to find a numerical method to solve the homogenized problem. More precisely, we characterize the asymptotic behavior of a first-order stationary MFG with a periodically oscillating potential. Our main tool is the two-scale convergence. Using this convergence, we rigorously derive the two-scale homogenized and the homogenized MFG problems. Moreover, we prove existence and uniqueness of the solution to these limit problems. Next, we notice that the homogenized problem resembles the problem involving effective Hamiltoni- ans and Mather measures, which arise in several problems, including homogenization of Hamilton–Jacobi equations, nonlinear control systems, and Aubry–Mather theory. Thus, we develop algorithms to solve the homogenized problem, the effective Hamil- tonians, and Mather measures. To do that, we construct the Hessian Riemannian flow. We prove the convergence of the Hessian Riemannian flow in the continuous setting. For the discrete case, we give both the existence and the convergence of the Hessian Riemannian flow. In addition, we explore a variant of Newton’s method that greatly improves the performance of the Hessian Riemannian flow. In our numerical experiments, we see that our algorithms preserve the non-negativity of Mather mea- sures and are more stable than related methods in problems that are close to singular. Furthermore, our method also provides a way to approximate stationary MFGs.

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