Now showing items 1-20 of 1561

    • Assembly of Two CCDD Rice Genomes, Oryza grandiglumis and Oryza latifolia, and the Study of Their Evolutionary Changes

      Alsantely, Aseel O. (2021-01) [Thesis]
      Advisor: Wing, Rod Anthony
      Committee members: Gojobori, Takashi; Zuccolo, Andrea
      Every day more than half of the world consumes rice as a primary dietary resource. Thus, rice is one of the most important food crops in the world. Rice and its wild relatives are part of the genus Oryza. Studying the genome structure, function, and evolution of Oryza species in a comparative genomics framework is a useful approach to provide a wealth of knowledge that can significantly improve valuable agronomic traits. The Oryza genus includes 27 species, with 11 different genome types as identified by genetic and cytogenetic analyses. Six genome types, including that of domesticated rice - O. sativa and O. glaberrima, are diploid, and the remaining 5 are tetraploids. Three of the tetraploid species contain the CCDD genome types (O. grandiglumis, O. latifolia, and O. alta), which arose less than 2 million years ago. Polyploidization is one of the major contributors to evolutionary divergence and can thereby lead to adaptation to new environmental niches. An important first step in the characterization of the polyploid Oryza species is the generation of a high-quality reference genome sequence. Unfortunately, up until recently, the generation of such an important and fundamental resource from polyploid species has been challenging, primarily due to their genome complexity and repetitive sequence content. In this project, I assembled two high-quality genomes assemblies for O. grandiglumis and O. latifolia using PacBio long-read sequencing technology and an assembly pipeline that employed 3 genome assemblers (i.e., Canu/2.0, Mecat2, and Flye/2.5) and multiple rounds of sequence polishing with 5 both Arrow and Pilon/1.23. After the primary assembly, sequence contigs were arranged into pseudomolecules, and homeologous chromosomes were assigned to their respective genome types (i.e., CC or DD). Finally, the assemblies were extensively edited manually to close as many gaps as possible. Both assemblies were then analyzed for transposable element and structural variant content between species and homoeologous chromosomes. This enabled us to study the evolutionary divergence of those two genomes, and to explore the possibility of neo-domesticating either species in future research for my PhD dissertation.
    • Sustainability Evaluation of Hybrid Desalination Systems: Multi Effect Distillation – Adsorption (MED-AD) and Forward Osmosis – Membrane Distillation (FO-MD)

      Son, Hyuk Soo (2020-12) [Dissertation]
      Advisor: Ghaffour, NorEddine
      Committee members: Vrouwenvelder, Johannes S.; Pinnau, Ingo; Orfi, Jamel
      Water is life for all living organisms on earth, and all human beings need water for every socio-economic activity in their daily lives. However, constant challenges are faced in securing quality water resources due to environmental pollution, a growing demand, and climate changes. To overcome imminent worldwide challenges on water resources, desalination of seawater and saline wastewater became inevitable, and significant efforts have been deployed by the desalination research community to advance the technology. However, there is still a gap to take it to a higher sustainability and compatibility compared to conventional water treatment technologies. Among all efforts, the hybridization of two or more processes stands among the promising solutions for sustainable desalination, which synergizes benefits of multiple technologies. To evaluate the sustainability of hybrid desalination technologies, two different systems, namely; (i) multi-effect distillation – adsorption (MED-AD) and (ii) forward osmosis – membrane distillation (FO-MD), are investigated in this study. The method developed for the analysis of primary energy consumption in complex desalination systems is used to evaluate the performance of the MED-AD pilot facility at King Abdullah University of Science and Technology (KAUST). Results of the MED-AD pilot operation showed an improvement in water production with a higher energy efficiency under the same operating conditions (near the ambient temperature with the solar thermal system). For the FO-MD hybrid system, an investigation is carried out on a novel in-house integrated module and a comparative analysis with the conventional module is provided. An isolation barrier carefully placed in the novel design enhanced the hybrid performance by reducing both concentration and temperature polarization. In addition, the FO-MD hybrid process is evaluated for brine reclamation application in a SWRO-MD-FO system. The sustainability of the proposed system and the potential of a flexible sustainable operation are presented with the experimental study with real seawater and brine from the full-scale desalination plant.
    • Mesoscale Eddy Dynamics and Scale in the Red Sea

      Campbell, Michael F (2020-12) [Dissertation]
      Advisor: Jones, Burton
      Committee members: Ellis, Joanne; Berumen, Michael L.; Hoteit, Ibrahim; Rainville, Luc
      Recent efforts in understanding the variability inherent in coastal and offshore waters have highlighted the need for higher resolution sampling at finer spatial and temporal resolutions. Gliders are increasingly used in these transitional waters due to their ability to provide these finer resolution data sets in areas where satellite coverage may be poor, ship-based surveys may be impractical, and important processes may occur below the surface. Since no single instrument platform provides coverage across all needed spatial and temporal scales, Ocean Observation systems are using multiple types of instrument platforms for data collection. However, this results in increasingly large volumes of data that need to be processed and analyzed and there is no current “best practice” methodology for combining these instrument platforms. In this study, high resolution glider data, High Frequency Radar (HFR), and satellite-derived data products (MERRA_2 and ARMOR3D NRT Eddy Tracking) were used to quantify: 1) dominant scales of variability of the central Red Sea, 2) determine the minimum sampling frequency required to adequately characterize the central Red Sea, 3) discriminate whether the fine scale persistency of oceanographic variables determined from the glider data are comparable to those identified using HFR and satellite-derived data products, and 4) determine additional descriptive information regarding eddy occurrence and strength in the Red Sea from 2018-2019. Both Integral Time Scale and Characteristic Length Scale analysis show that the persistence time frame from glider data for temperature, salinity, chlorophyll-α, and dissolved oxygen is 2-4 weeks and that these temporal scales match for HFR and MERRA_2 data, matching a similar description of a ”weather-band” level of temporal variability. Additionally, the description of eddy activity in the Red Sea also supports this 2-4-week time frame, with the average duration of cyclonic and anticyclonic eddies from 2018-2019 being 22 and 27 days, respectively. Adoption of scale-based methods across multiple ocean observation areas can help define “best practice” methodologies for combining glider, HFR, and satellite-derived data to better understand the naturally occurring variability and improve resource allocation.
    • On Using D2D Collaboration and a DF-CF Relaying Scheme to Mitigate Channel Interference

      Hassan, Osama (2020-12) [Thesis]
      Advisor: Alouini, Mohamed-Slim
      Committee members: Shihada, Basem; Park, Kihong
      Given the exponentially increasing number of connected devices to the network which will lead to a larger number of installed celluar towers and base stations that are in closer proximity to one another when compared to the current cellular network setup, and the increasing demand of higher data rates by end users, it becomes essential to investigate new methods that will more effectively mitigate the larger interference introduced by the more packed celluar grid and that result in higher data rates. This paper investigates using Device-to-Device communication where neighboring users can cooperate to mitigate the correlated interference they both receive, where one user acts as a relay and the other as the intended destination of a broadcast message sent by the source base station. The setup studied utalizes a non-orthogonal multiple access (NOMA) scheme and a combined decode-forward and compress-forward relaying scheme. We show that this combined scheme outperforms the individual schemes for some channels and network setups, or reduces to either scheme when the combination does not offer any achievable rate gains. The performance of each scheme is measured with respect to the locations of the base station and the two devices, and to the capacity of the digital link between the users.
    • Modeling Human Learning in Games

      Alghamdi, Norah K. (2020-12) [Thesis]
      Advisor: Shamma, Jeff S.
      Committee members: Feron, Eric; Laleg-Kirati, Taous-Meriem
      Human-robot interaction is an important and broad area of study. To achieve success- ful interaction, we have to study human decision making rules. This work investigates human learning rules in games with the presence of intelligent decision makers. Par- ticularly, we analyze human behavior in a congestion game. The game models traffic in a simple scenario where multiple vehicles share two roads. Ten vehicles are con- trolled by the human player, where they decide on how to distribute their vehicles on the two roads. There are hundred simulated players each controlling one vehicle. The game is repeated for many rounds, allowing the players to adapt and formulate a strategy, and after each round, the cost of the roads and visual assistance is shown to the human player. The goal of all players is to minimize the total congestion experienced by the vehicles they control. In order to demonstrate our results, we first built a human player simulator using Fictitious play and Regret Matching algorithms. Then, we showed the passivity property of these algorithms after adjusting the passivity condition to suit discrete time formulation. Next, we conducted the experiment online to allow players to participate. A similar analysis was done on the data collected, to study the passivity of the human decision making rule. We observe different performances with different types of virtual players. However, in all cases, the human decision rule satisfied the passivity condition. This result implies that human behavior can be modeled as passive, and systems can be designed to use these results to influence human behavior and reach desirable outcomes.
    • Shales: Comprehensive Laboratory Characterization

      Gramajo, Eduardo (2020-12) [Dissertation]
      Advisor: Santamarina, Carlos
      Committee members: Vahrenkamp, Volker C.; Mai, Paul Martin; Frost, David; Finkbeiner, Thomas
      Unconventional formations have become an increasingly important source of energy resources. Proper rock mechanic characterization is needed not only to identify the most promising areas for stimulation, but to increase our understanding of the sealing capabilities of cap-rock formations for carbon geological storage. However, shale assessment is challenging with current standard techniques. This research explores the index and rock mechanic properties of different shale specimens considered as source rocks for oil and gas (Eagle Ford, Wolfcamp, Jordanian, Mancos, Bakken, and Kimmeridge), and presents an in-depth analysis of tools and protocols to identify inherent biases. New test protocols proposed in this thesis provide robust and cost-effective measurement techniques to characterize shale formations in general; these include: 1) new energy methods computed from the area under the stress-strain curve or proposed boundary asymptotes (strength and stiffness) to assess brittle/ductile conditions in the field, 2) tensile strength analyses to determine anisotropy in shale formations, 3) Coda wave analysis to monitor pre-failure damage evolution during compression, and 4) a combination of index tests to anticipate the complicated geology or layered characteristics, which include high-resolution imaging, hardness, and scratch tests. Experimental results combined with extensive databases provide unprecedented information related to the mechanical behavior of shale formations needed for the enhanced design and analysis of geo-engineering applications. Calcareous shales display strong interlayer bonding and lower compressive strength anisotropy than siliceous shales. Tensile strength anisotropy is more pronounced than in compressive strength and reflects bedding orientation and loading conditions that affect fracture propagation and ensuing failure surface topography.
    • Exploring Entity Relationship in Pairwise Ranking: Adaptive Sampler and Beyond

      Yu, Lu (2020-12) [Dissertation]
      Advisor: Zhang, Xiangliang
      Committee members: Moshkov, Mikhail; Hoehndorf, Robert; Karypis, George
      Living in the booming age of information, we have to rely on powerful information retrieval tools to seek the unique piece of desired knowledge from such a big data world, like using personalized search engine and recommendation systems. As one of the core components, ranking model can appear in almost everywhere as long as we need a relative order of desired/relevant entities. Based on the most general and intuitive assumption that entities without user actions (e.g., clicks, purchase, comments) are of less interest than those with user actions, the objective function of pairwise ranking models is formulated by measuring the contrast between positive (with actions) and negative (without actions) entities. This contrastive relationship is the core of pairwise ranking models. The construction of these positive-negative pairs has great influence on the model inference accuracy. Especially, it is challenging to explore the entity relationships in heterogeneous information network. In this thesis, we aim at advancing the development of the methodologies and principles of mining heterogeneous information network through learning entity relations from a pairwise learning to rank optimization perspective. More specifically we first show the connections of different relation learning objectives modified from different ranking metrics including both pairwise and list-wise objectives. We prove that most of popular ranking metrics can be optimized in the same lower bound. Secondly, we propose the class-imbalance problem imposed by entity relation comparison in ranking objectives, and prove that class-imbalance problem can lead to frequency 5 clustering and gradient vanishment problems. As a response, we indicate out that developing a fast adaptive sampling method is very essential to boost the pairwise ranking model. To model the entity dynamic dependency, we propose to unify the individual-level interaction and union-level interactions, and result in a multi-order attentive ranking model to improve the preference inference from multiple views.
    • Spots and Sequences: Multi-method population assessment of whale sharks in the Red Sea

      Hardenstine, Royale (2020-12) [Dissertation]
      Advisor: Berumen, Michael L.
      Committee members: Gojobori, Takashi; Jones, Burton; Hsu, Hua Hsun
      In 1938 Dr. Eugene Gudger concluded of the Red Sea that "whale sharks must surely abound in this region." Seventy years later, multi-method research began on a whale shark (Rhincodon typus) aggregation at Shib Habil, a reef near Al Lith, Saudi Arabia. However, in 2017 and 2018, a dramatic decline in encounters at this site drew questions about the aggregation's future and overall whale shark population trends in the region. In this dissertation, I describe and discuss the two-year decline in encounters and show that neither remotely sensed sea surface temperature nor chlorophyll-a concentrations were significantly different in seasons with or without sharks. Citizen science-based photo identification was used to characterize the northern Red Sea population, the Red Sea population as a whole, show limited crossover within the basin, and connections with another aggregation in Djibouti. Scarring rates within the Red Sea are compared to recent global studies, and the Red Sea uniquely had no predator bites observed, suggesting boat collisions are likely the leading cause of major scars. Finally, building upon previous genetic work comparing Red Sea and Tanzanian sharks using microsatellites, the mitochondrial control region was sequenced, and two global haplotype networks were produced and compared to each other and previous work. The stability of genetic diversity within the Shib Habil aggregation is compared to declines previously measured in Australia. As tourism develops along the northern Saudi Arabian coast and citizen science increases in the Red Sea, population dynamics within the region could be better understood. The genetic connectivity of Red Sea whale sharks to the Indo-Pacific population exemplifies the need for continued collaborative research beyond local aggregations and multinational conservation measures.
    • BICNet: A Bayesian Approach for Estimating Task Effects on Intrinsic Connectivity Networks in fMRI Data

      Tang, Meini (2020-11-25) [Thesis]
      Advisor: Ombao, Hernando
      Committee members: Sun, Ying; Laleg-Kirati, Taous-Meriem; Ting, Chee-Ming
      Intrinsic connectivity networks (ICNs) refer to brain functional networks that are consistently found under various conditions, during tasks or at rest. Some studies demonstrated that while some stimuli do not impact intrinsic connectivity, other stimuli actually activate intrinsic connectivity through suppression, excitation, moderation or modi cation. Most analyses of functional magnetic resonance imaging (fMRI) data use ad-hoc methods to estimate the latent structure of ICNs. Modeling the effects on ICNs has also not been fully investigated. Bayesian Intrinsic Connectivity Network (BICNet) captures the ICN structure with We propose a BICNet model, an extended Bayesian dynamic sparse latent factor model, to identify the ICNs and quantify task-related effects on the ICNs. BICNet has the following advantages: (1) It simultaneously identifies the individual and group-level ICNs; (2) It robustly identifies ICNs by jointly modeling resting-state fMRI (rfMRI) and task-related fMRI (tfMRI); (3) Compared to independent component analysis (ICA)-based methods, it can quantify the difference of ICNs amplitudes across different states; (4) The sparsity of ICNs automatically performs feature selection, instead of ad-hoc thresholding. We apply BICNet to the rfMRI and language tfMRI data from the Human Connectome Project (HCP) and identify several ICNs related to distinct language processing functions.
    • Deep GCNs with Random Partition and Generalized Aggregator

      Xiong, Chenxin (2020-11-25) [Thesis]
      Advisor: Ghanem, Bernard
      Committee members: Thabet, Ali Kassem; Zhang, Xiangliang
      Graph Convolutional Networks (GCNs) draws significant attention due to its power of representation learning on graphs. Recent works developed frameworks to train deep GCNs. Such works show impressive results in tasks like point cloud classification and segmentation, and protein interaction prediction. While for large-scale graphs, doing full-batch training by GCNs is still challenging especially when GCNs go deeper. By fully analyzing a clustering-based mini-batch training algorithm ClusterGCN, we propose random partition which is a more efficient and effective method to implement mini-batch training. Besides, selecting different permutation invariance function (such as max, mean or add) for neighbors’ information aggregation will result in every different results. Therefore, we propose to alleviate it by introducing a novel Generalized Aggregation Function. In this thesis, I analyze the drawbacks caused by ClusterGCN and discuss about its limits. I further compare the performance of ClusterGCN with random partition and the final experimental results show that simple random partition outperforms ClusterGCN with very obvious advantageous for node property prediction task. For the techniques which are commonly used to make GCNs go deeper, I demonstrate a better way of applying residual connections (pre-activation) to stack more layers for GCNs. Last, I show the complete work of training deeper GCNs with generalized aggregators and display the promising results over several datasets from the Open Graph Benchmark (OGB).
    • The equations of polyconvex thermoelasticity

      Galanopoulou, Myrto Maria (2020-11-25) [Dissertation]
      Advisor: Tzavaras, Athanasios
      Committee members: Hoteit, Ibrahim; Markowich, Peter A.; Christoforou, Cleopatra; Dafermos Constantine, M.
      In my Dissertation, I consider the system of thermoelasticity endowed with poly- convex energy. I will present the equations in their mathematical and physical con- text, and I will explain the relevant research in the area and the contributions of my work. First, I embed the equations of polyconvex thermoviscoelasticity into an aug- mented, symmetrizable, hyperbolic system which possesses a convex entropy. Using the relative entropy method in the extended variables, I show convergence from ther- moviscoelasticity with Newtonian viscosity and Fourier heat conduction to smooth solutions of the system of adiabatic thermoelasticity as both parameters tend to zero and convergence from thermoviscoelasticity to smooth solutions of thermoelasticity in the zero-viscosity limit. In addition, I establish a weak-strong uniqueness result for the equations of adiabatic thermoelasticity in the class of entropy weak solutions. Then, I prove a measure-valued versus strong uniqueness result for adiabatic poly- convex thermoelasticity in a suitable class of measure-valued solutions, de ned by means of generalized Young measures that describe both oscillatory and concentra- tion e ects. Instead of working directly with the extended variables, I will look at the parent system in the original variables utilizing the weak stability properties of certain transport-stretching identities, which allow to carry out the calculations by placing minimal regularity assumptions in the energy framework. Next, I construct a variational scheme for isentropic processes of adiabatic polyconvex thermoelasticity. I establish existence of minimizers which converge to a measure-valued solution that dissipates the total energy. Also, I prove that the scheme converges when the limit- ing solution is smooth. Finally, for completeness and for the reader's convenience, I present the well-established theory for local existence of classical solutions and how it applies to the equations at hand.
    • Quantile Function Modeling and Analysis for Multivariate Functional Data

      Agarwal, Gaurav (2020-11-25) [Dissertation]
      Advisor: Sun, Ying
      Committee members: Ombao, Hernando; Tester, Mark A.; He, Xuming
      Quantile function modeling is a more robust, comprehensive, and flexible method of statistical analysis than the commonly used mean-based methods. More and more data are collected in the form of multivariate, functional, and multivariate functional data, for which many aspects of quantile analysis remain unexplored and challenging. This thesis presents a set of quantile analysis methods for multivariate data and multivariate functional data, with an emphasis on environmental applications, and consists of four significant contributions. Firstly, it proposes bivariate quantile analysis methods that can predict the joint distribution of bivariate response and improve on conventional univariate quantile regression. The proposed robust statistical techniques are applied to examine barley plants grown in saltwater and freshwater conditions providing interesting insights into barley’s responses, informing future crop decisions. Secondly, it proposes modeling and visualization of bivariate functional data to characterize the distribution and detect outliers. The proposed methods provide an informative visualization tool for bivariate functional data and can characterize non-Gaussian, skewed, and heavy-tailed distributions using directional quantile envelopes. The radiosonde wind data application illustrates our proposed quantile analysis methods for visualization, outlier detection, and prediction. However, the directional quantile envelopes are convex by definition. This feature is shared by most existing methods, which is not desirable in nonconvex and multimodal distributions. Thirdly, this challenge is addressed by modeling multivariate functional data for flexible quantile contour estimation and prediction. The estimated contours are flexible in the sense that they can characterize non-Gaussian and nonconvex marginal distributions. The proposed multivariate quantile function enjoys the theoretical properties of monotonicity, uniqueness, and the consistency of its contours. The proposed methods are applied to air pollution data. Finally, we perform quantile spatial prediction for non-Gaussian spatial data, which often emerges in environmental applications. We introduce a copula-based multiple indicator kriging model, which makes no distributional assumptions on the marginal distribution, thus offers more flexibility. The method performs better than the commonly used variogram approach and Gaussian kriging for spatial prediction in simulations and application to precipitation data.
    • Imitation Learning based on Generative Adversarial Networks for Robot Path Planning

      Yi, Xianyong (2020-11-24) [Thesis]
      Advisor: Michels, Dominik L.
      Committee members: Wonka, Peter; Moshkov, Mikhail
      Robot path planning and dynamic obstacle avoidance are defined as a problem that robots plan a feasible path from a given starting point to a destination point in a nonlinear dynamic environment, and safely bypass dynamic obstacles to the destination with minimal deviation from the trajectory. Path planning is a typical sequential decision-making problem. Dynamic local observable environment requires real-time and adaptive decision-making systems. It is an innovation for the robot to learn the policy directly from demonstration trajectories to adapt to similar state spaces that may appear in the future. We aim to develop a method for directly learning navigation behavior from demonstration trajectories without defining the environment and attention models, by using the concepts of Generative Adversarial Imitation Learning (GAIL) and Sequence Generative Adversarial Network (SeqGAN). The proposed SeqGAIL model in this thesis allows the robot to reproduce the desired behavior in different situations. In which, an adversarial net is established, and the Feature Counts Errors reduction is utilized as the forcing objective for the Generator. The refinement measure is taken to solve the instability problem. In addition, we proposed to use the Rapidly-exploring Random Tree* (RRT*) with pre-trained weights to generate adequate demonstration trajectories in dynamic environment as the training data, and this idea can effectively overcome the difficulty of acquiring huge training data.
    • Efficient Ensemble Data Assimilation and Forecasting of the Red Sea Circulation

      Toye, Habib (2020-11-23) [Dissertation]
      Advisor: Hoteit, Ibrahim
      Committee members: Knio, Omar; Al-Naffouri, Tareq Y.; Iskandarani, Mohamad
      This thesis presents our efforts to build an operational ensemble forecasting system for the Red Sea, based on the Data Research Testbed (DART) package for ensemble data assimilation and the Massachusetts Institute of Technology general circulation ocean model (MITgcm) for forecasting. The Red Sea DART-MITgcm system efficiently integrates all the ensemble members in parallel, while accommodating different ensemble assimilation schemes. The promising ensemble adjustment Kalman filter (EAKF), designed to avoid manipulating the gigantic covariance matrices involved in the ensemble assimilation process, possesses relevant features required for an operational setting. The need for more efficient filtering schemes to implement a high resolution assimilation system for the Red Sea and to handle large ensembles for proper description of the assimilation statistics prompted the design and implementation of new filtering approaches. Making the most of our world-class supercomputer, Shaheen, we first pushed the system limits by designing a fault-tolerant scheduler extension that allowed us to test for the first time a fully realistic and high resolution 1000 ensemble members ocean ensemble assimilation system. In an operational setting, however, timely forecasts are of essence, and running large ensembles, albeit preferable and desirable, is not sustainable. New schemes aiming at lowering the computational burden while preserving reliable assimilation results, were developed. The ensemble Optimal Interpolation (EnOI) algorithm requires only a single model integration in the forecast step, using a static ensemble of preselected members for assimilation, and is therefore computationally significantly cheaper than the EAKF. To account for the strong seasonal variability of the Red Sea circulation, an EnOI with seasonally-varying ensembles (SEnOI) was first implemented. To better handle intra-seasonal variabilities and enhance the developed seasonal EnOI system, an automatic procedure to adaptively select the ensemble members through the assimilation cycles was then introduced. Finally, an efficient Hybrid scheme combining the dynamical flow-dependent covariance of the EAKF and a static covariance of the EnOI was proposed and successfully tested in the Red Sea. The developed Hybrid ensemble data assimilation system will form the basis of the first operational Red Sea forecasting system that is currently being implemented to support Saudi Aramco operations in this basin.
    • Active Control of Surface Plasmons in MXenes for Advanced Optoelectronics

      El Demellawi, Jehad K. (2020-11-18) [Dissertation]
      Advisor: Alshareef, Husam N.
      Committee members: Mohammed, Omar F.; Schwingenschlögl, Udo; Ooi, Boon S.; Gogotsi, Yury
      MXenes, a new class of two-dimensional (2D) materials, have recently demonstrated impressive optoelectronic properties associated with its ultrathin layered structure. Particularly, Ti3C2Tx, the most studied MXene by far, was shown to exhibit intense surface plasmons (SPs), i.e. collective oscillations of free charge carriers, when excited by electromagnetic waves. However, due to the lack of information about the spatial and energy variation of those SPs over individual MXene flakes, the potential use of MXenes in photonics and plasmonics is still marginally explored. Hence, the main objective of this dissertation is to shed the light upon the plasmonic behavior of MXenes at the nanoscale and extend their use beyond their typical electrochemical applications. To fulfill our objective, we first elucidated the underlying characteristics governing the plasmonic behavior of MXenes. Then, we revealed the existence of various tunable SP modes supported by different MXenes, i.e. Ti3C2Tx and Mo2CTx, and investigated their energy and spatial distribution over individual flakes. Further, we fabricated an array of MXene-based flexible photodetectors that only operate at the resonant frequency of the SPs supported by MXenes. We also unveiled the existence of tunable SPs supported by another 2D nanomaterial (i.e. MoO2) and juxtaposed its plasmonic behavior with that of MXenes, to underline the uniqueness of the latter. Noteworthy, as in the case of MXenes, this was the first progress made on studying specific SP modes supported by MoO2 nanostructures. In this part of the dissertation, we were able to identify and tailor multipolar SPs supported by MoO2 and illustrate their dependence on their bulk band structure. In the end, we show that, on the contrary, SPs in MXenes are mainly controlled by the surface band structure. To confirm this, we selectively altered the subsurface band structure of Ti3C2Tx and modulated its work function (from 4.37 to 4.81 eV) via charge transfer doping. Interestingly, thanks to the unchanged surface stoichiometry of Ti3C2Tx, the plasmonic behavior of Ti3C2Tx was not affected by its largely tuned electronic structure. Notably, the ability to attain MXenes with tunable work functions, yet without disrupting their plasmonic behavior, is appealing to many application fields.
    • Leveraging Graph Convolutional Networks for Point Cloud Upsampling

      Qian, Guocheng (2020-11-16) [Thesis]
      Advisor: Ghanem, Bernard
      Committee members: Wonka, Peter; Pottmann, Helmut
      Due to hardware limitations, 3D sensors like LiDAR often produce sparse and noisy point clouds. Point cloud upsampling is the task of converting such point clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling using deep neural networks. The effectiveness of a point cloud upsampling neural network heavily relies on the upsampling module and the feature extractor used therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle. NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves the performance of previous upsampling methods. I also propose a new GCN-based multi-scale feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, Inception DenseGCN learns a hierarchical feature representation and enables further performance gains. I combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling network called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.
    • Development and application of novel fusion approaches for elemental analysis of carbon-based materials

      Simoes, Filipa R. F. (2020-11-16) [Dissertation]
      Advisor: Da Costa, Pedro M. F. J.
      Committee members: Nunes, Suzana Pereira; Cavallo, Luigi; Pumera, Martin
      Graphite and graphitic materials underpin a number of modern technologies such as electrodes for energy storage and conversion systems. Due to their aromatic honeycomb-type lattice and layered structure, these carbons host a rich variety of foreign elements in their interstices. Whether possessing a tubular morphology - that enables the encapsulation of inorganic compounds, or a planar texture - where anions and molecules can intercalate, the chemical analysis of graphite and graphitic materials is often confronted with the need to disintegrate the carbon matrix to quantify target elements, most often metals. However, the resilience of the sp2-hybridized carbon lattice to chemical attacks is an obstacle to its facile solubilization, a necessary step to perform some of the most common elemental analysis measurements. Over the years, a range of alternative approaches have sprung out to address this issue such as the combustion of the carbon matrix followed by the acid dissolution of its ash product. Unfortunately, none of these represents a viable method that can be applied to batteries, in great part because of the different components that make up the carbon-based electrodes. In this dissertation, a new protocol has been developed to digest graphitic materials aiming to access their elemental composition in bulk scale. The approach is based on the use of molten alkaline salts to promote the oxidation of the carbon lattice and leach out metals into a dilute acid solution. As a model sample, given the existence of standards with a matching matrix, single-walled carbon nanotubes were examined. After being subjected to the alkaline oxidation (a.k.a. fusion), they were solubilized and analyzed with Inductively Coupled Plasma-Optical Emission Spectroscopy, a widely popular tool for elemental analysis of metals. Structural analysis ensued to understand the interaction of the molten salts with the nanotubes. After evaluating the applicability of the protocol to other carbons, a more complex system was investigated, namely the carbon-based anode of an intercalation-type potassium ion battery. In this process, a direct way to quantify the mass of the alkali metal was discovered, one which makes use of complementary chemical and structural analytical tools.
    • III-Oxide Epitaxy, Heterostructure, Material Characterizations, and Applications

      Li, Kuang-Hui (2020-11-15) [Dissertation]
      Advisor: Ooi, Boon S.
      Committee members: Schwingenschlögl, Udo; Tung, Vincent; Zhao, Chao
      B-Ga2O3 is one of the emerging semiconductor materials with high breakdown field strength (~ 8 MV/cm) and ultrawide-bandgap (UWBG) 4.9 eV. Therefore, B-Ga2O3 and related compound semiconductors are ideal for power electronics and deep/vacuum ultraviolet-wavelength photodetector applications. High-crystal-quality B-Ga2O3 semiconductor materials epitaxially deposited on the various substrate are prerequisites for realizing any practical application. However, it is still challenging to grow high-crystal-quality V-Ga2O3 layer and to integrate B-Ga2O3 with other semiconductor materials by direct epitaxy. Understanding the epitaxial relationship of the integrated oxide heterostructure and the substrate used helps to shed light on the feasibility of heterojunctions formation for photonic applications, such as the ultraviolet-wavelength photodetectors developed in this thesis. By optimizing pulsed laser deposition (PLD) conditions, such as laser energy, ambient gas, pressure, etc., a single-crystalline oxide heterostructure were successfully integrated into a photonic platform. This included p-NiO/n-B-Ga2O3/a-Al2O3, B-Ga2O3/y-In2O3/a-Al2O3, and B-Ga2O3/TiN/MgO structures. The epitaxial thin film crystallographic and chemical properties were investigated by different characterization techniques. The high-resolution X-ray diffraction (HRXRD) was applied to study the heterostructures’ epitaxial orientation relationship by out-of-plane XRD w-2θ-scan and asymmetric skew ɸ-scan. The lattice-mismatch at the heterostructure interfaces were examined and the crystal quality of the epitaxial thin films were measured by means of full-width at half-maximum (FWHM) fitting. Scanning-TEM energy-dispersive X-ray spectroscopy (STEM-EDX) was applied to qualitatively study the chemical elements’ spatial distribution. Rutherford backscattering spectroscopy (RBS) was applied to study the epitaxial thin film chemical composition, material stoichiometry, and inter-diffusion. The X-ray photoelectron spectroscopy (XPS) was applied to study the conduction and valence band offsets which is essential to determine the types of heterostructures formed. Finally, the p-NiO/n-B-Ga2O3/a-Al2O3 B-Ga2O3/y-In2O3/a-Al2O3, and B-Ga2O3/TiN/MgO epitaxial thin-film were fabricated into ultraviolet-wavelength photodetectors. The wavelength-dependent and power-dependent characterizations were applied to measure the cut-off wavelength and the peak responsivity. The time response characterization was applied to measure the photodetectors’ responses to pulse signals, and the rise and decay times were fitted by a double exponential function.
    • Prediction of Novel Virus–Host Protein Protein Interactions From Sequences and Infectious Disease Phenotypes

      Wang, Liu-Wei (2020-11-11) [Thesis]
      Advisor: Tegner, Jesper
      Committee members: Hoehndorf, Robert; Ombao, Hernando
      Infectious diseases from novel viruses have become a major public health concern. Rapid identification of virus–host interactions can reveal mechanistic insights into infectious diseases and shed light on potential treatments. Current computational prediction methods for novel viruses are based mainly on protein sequences. However, it is not clear to what extent other important features, such as the symptoms caused by the viruses, could contribute to a predictor. Disease phenotypes (i.e., signs and symptoms) are readily accessible from clinical diagnosis and we hypothesize that they may act as a potential proxy and an additional source of information for the underlying molecular interactions between the pathogens and hosts. We developed DeepViral, a deep learning based method that predicts protein– protein interactions (PPI) between humans and viruses. Motivated by the potential utility of infectious disease phenotypes, we first embedded human proteins and viruses in a shared space using their associated phenotypes and functions, supported by formalized background knowledge from biomedical ontologies. By jointly learning from protein sequences and phenotype features, DeepViral significantly improves over existing sequence-based methods for intra- and inter-species PPI prediction. Lastly, we propose a novel experimental setup to realistically evaluate prediction methods for novel viruses.
    • Spatio-Temporal Statistical Modeling with Application to Wind Energy Assessment in Saudi Arabia

      Chen, Wanfang (2020-11-08) [Dissertation]
      Advisor: Genton, Marc G.
      Committee members: Huser, Raphaël G.; Stenchikov, Georgiy L.; Zhang, Hao
      Saudi Arabia has been trying to change its long tradition of relying on fossil fuels and seek renewable energy sources such as wind power. In this thesis, I firstly provide a comprehensive assessment of wind energy resources and associated spatio-temporal patterns over Saudi Arabia in both current and future climate conditions, based on a Regional Climate Model output. A high wind energy potential exists and is likely to persist at least until 2050 over a vast area ofWestern Saudi Arabia, particularly in the region between Medina and the Red Sea coast and during Summer months. Since an accurate assessment of wind extremes is crucial for risk management purposes, I then present the first high-resolution risk assessment of wind extremes over Saudi Arabia. Under the Bayesian framework, I measure the uncertainty of return levels and produce risk maps of wind extremes, which show that locations in the South of Saudi Arabia and near the Red Sea and the Persian Gulf are at very high risk of disruption of wind turbine operations. In order to perform spatial predictions of the bivariate wind random field for efficient turbine control, I propose parametric variogram matrix (function) models for cokriging, which have the advantage of allowing for a smooth transition between a joint second-order and intrinsically stationary vector random field. Under Gaussianity, the covariance function is central to spatio-temporal modeling, which is useful to understand the dynamics of winds in space and time. I review the various space-time covariance structures and models, some of which are visualized with animations, and associated tests. I also discuss inference issues and a case study based on a high-resolution wind-speed dataset. The Gaussian assumption commonly made in statistics needs to be validated, and I show that tests for independently and identically distributed data cannot be used directly for spatial data. I then propose a new multivariate test for spatial data by accounting for the spatial dependence. The new test is easy to compute, has a chi-square null distribution, and has a good control of the type I error and a high empirical power.