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  • Paving the Way for Efficient Content Delivery in Mobile Networks

    Lau, Chun Pong (2018-07-10)
    The flexibility of future mobile networks exploiting modern technologies such as cloud-optimized radio access and software-defined networks opens a gateway to deploying dynamic strategies for live and on-demand content delivery. Traditional live broadcasting systems are spectral inefficient. It takes up a lot more radio spectrum than that of mobile networks, to cover the same size of an area. Furthermore, content caching at base stations reduces network traffic in core networks. However, numerous duplicated copies of contents are still transmitted in the unicast fashion in radio access networks. It consumes valuable radio spectrum and unnecessary energy. Finally, due to the present of numerous mobile receivers with a wide diversity of wireless channels in a base station coverage area, it is a challenge to select a proper modulation scheme for video broadcasting to optimize the quality of services for users. In this thesis, the challenges and the problems in the current strategies for content delivery are addressed. A holistic novel solution is proposed that considers user preferences, user mobility, device-to-device communication, physical-layer resource allocation, and video quality prediction. First, a system-level scheduling framework is introduced to increase the spectral efficiency on broadcasting live contents onto mobile networks. It considers the audience preferences for allocating radio resources spatially and temporally. Second, to reduce the redundant transmissions in radio access networks, a content distribution system that exploits user mobility is proposed that utilizes the urban-scale user mobility and broadcasting nature of wireless communication for delay-tolerant large size content. Third, to further reduce the energy consumption in network infrastructure, a content distribution system that relies on both user mobility, and device-to-device communication is proposed. It leverages the mobile users as content carriers to offload the heavy mobile traffic from network-level onto device-level. Fourth, to mitigate the multi-user channel diversity problem, a cross-layer approach is deployed to increase the video quality for users especially for those who have a low signal-to-noise ratio signal. Finally, data mining techniques are employed to predict video qualities of wireless transmissions over mobile networks. The holistic solution has been empirically developed and evaluated. It achieves high spectral and energy efficiency and mitigates the video quality degradation in mobile networks.
  • Experimental Investigation on The Influence of Liquid Fuels Composition on The Operational Characteristics of The Liquid Fueled Resonant Pulse Combustor

    Qatomah, Mohammad (2018-07)
    In this study, the response of a liquid-fueled resonant pulse combustor to changes in liquid fuel composition was investigated. Experiments were performed with gasoline- ethanol, gasoline-diesel, and gasoline-heptane mixtures selected to produce meaningful variations in the ignition delay time. A review of ignition quality tester (IQT) data provided an expected increase in the overall delay for gasoline-ethanol mixtures with increasing ethanol concentrations, and a decrease for gasoline-diesel mixtures with increasing diesel concentrations in the mixture. By taking the phase of the ion signal as an indicator of heat release timing, the experimental results showed an agreement of gasoline-ethanol cases with the IQT data with a near linear increase with increasing ethanol concentrations. However, for gasoline-diesel, there exit no linear relation with the IQT data. For the case of gasoline-heptane mixtures, the results showed a linear decrease in delay with increasing heptane concentrations. Furthermore, it was shown that small changes in the physical properties of the fuel can significantly in sequence the cold-start operation of the combustor and alter the coupling between the unsteady heat release and resonant acoustic pressure wave during resonant operation. Dynamic combustion chamber pressure, stagnation temperature and pressure are recorded after a fixed warm-up time to characterize the performance and operation of the device. Results are interpreted in the context of fuel sensitivity and performance optimization of a resonant pulse combustor for pressure gain turbine applications.
  • Ecology of the Mangrove Microbiome

    Booth, Jenny (2018-07)
    Plants and animals have evolved unique morpho-physiological adaptions to cope with the harsh and steep environmental gradients that characterise the mangrove ecosystem. However, the capacity of these two main components of the system to thrive, and the extraordinary productivity of mangrove forests in extreme conditions, has been overlooked in terms of the role of the microbiome. By combining approaches that included molecular microbial ecology, biogeochemical analyses, microscopy, raman spectroscopy and microsensor measurements, this thesis aimed to investigate the potential role of bacterial symbiosis in the adaptation of mangrove crabs to their environment and subsequently how these different animals modify their environment. Finally, with a field-based approach monitoring microbial communities, sediment metabolism and plant performance, the thesis aimed to investigate the plant/animal/bacterial dynamics in relation to seasonal environmental changes to contribute to understand the mangrove plant productivity paradox of high productivity under conditions of limited nutrents. Crab species were associated with distinct gill-bacteria communities, that produced carotenoids, according with their level of terrestrial adaptation. These carotenoids may be involved in protecting the gills from oxidative stress during air exposure. The main groups of ecosystem engineering crabs in mangroves had significant but diverse effects on the sediment environment and microbiome predominantly related to their ecology (i.e. filter feeder vs herbivore). Burrows increase aerobic microbial activity in the immediate burrow wall with a cascade effect on sediment microbial communities and nutrient distribution observed consistently across mangroves in different locations and with diverse environmental conditions. Microorganisms play an important role in adapting crabs on their evolutionary path to land and could contribute to the success of their colonization. At high population densities, of more than 50 individuals per square meter in some mangroves, these crabs deeply impact the functioning of the mangrove ecosystem, affecting microbial networks and nutrient recycling in the sediment, which may ameliorate conditions for plant growth. The microbiome is an understudied component of mangroves that lies at the basis of the functioning of these systems, influencing the success of the animal inhabitants (ecosystem engineers) that deeply modify the sediment microbiome, therefore influencing ecosystem functioning and resilience and, potentially, the success of the plants themselves (ecosystem architects).
  • Scalable Discovery and Analytics on Web Linked Data

    Abdelaziz, Ibrahim (2018-07)
    Resource Description Framework (RDF) provides a simple way for expressing facts across the web, leading to Web linked data. Several distributed and federated RDF systems have emerged to handle the massive amounts of RDF data available nowadays. Distributed systems are optimized to query massive datasets that appear as a single graph, while federated systems are designed to query hundreds of decentralized and interlinked graphs. This thesis starts with a comprehensive experimental study of the state-of-the-art RDF systems. It identifies a set of research problems for improving the state-of-the-art, including: supporting the emerging RDF analytics required by many modern applications, querying linked data at scale, and enabling discovery on linked data. Addressing these problems is the focus of this thesis. First, we propose Spartex; a versatile framework for complex RDF analytics. Spartex extends SPARQL to seamlessly combine generic graph algorithms with SPARQL queries. Spartex implements a generic SPARQL operator as a vertex-centric program that interprets SPARQL queries and executes them efficiently using a built-in optimizer. We demonstrate that Spartex scales to datasets with billions of edges, and is at least as fast as the state-of-the-art specialized RDF engines. For analytical tasks, Spartex is an order of magnitude faster than existing alternatives. To address the scalability limitation of federated RDF engines, we propose Lusail; a scalable system for querying geo-distributed RDF graphs. Lusail follows a two-tier strategy: (i) locality-aware decomposition of the query into subqueries to maximize the computations at the endpoints and minimize intermediary results, and (ii) selectivity-aware execution to reduce network latency and increase parallelism. Our experiments on billions of triples show that Lusail outperforms existing systems by orders of magnitude in scalability and response time. Finally, enabling discovery on linked data is challenging due to the prior knowledge required to formulate SPARQL queries. To address these challenges; we develop novel techniques to (i) predict semantically equivalent SPARQL queries from a set of keywords by leveraging word embeddings, and (ii) generate fine-grained and non-blocking query plans to get fast and early results.
  • Indoor Localization Using Three dimensional Multi-PDs Receiver Based on RSS

    Liu, Yinghao (2018-07)
    In modern life, there are many applications where positioning plays an important role. People have developed the global positioning system (GPS) to locate world wide position with error in decameter scales, which brings people much convenience. However, the accuracy of GPS is too low for indoor localization. The signals will drop down due to the signal attenuation caused by construction materials. With the well-developed GPS being indispensable for outdoor activities, many researchers have been also devoted to seeking an indoor positioning system to realize indoor localization with acceptable error. Indoor localization can be very useful in different situations, like locating, tracking, navigation and identification. For example, in the mall, locating the exact goods for customers can provide much convenience and benefits. Locating and tracking in the airport can greatly help passengers save their time and energy in reaching the destination. In another general scenario of identification, the population of observed targets is usually larger than just one. Hence, only with small error, indoor localization system (ILS) can be able to identify the targets despite the neighbors. Due to the emerging and urging demands of increasing the accuracy of indoor localization, we propose a novel design of three dimensional (3-D). optical receiver for visible light communication (VLC) indoor positioning system. First, we model the optical wireless channel. Then we utilize modified triangulation method to obtain more robust receiver position by using at least two light-emitting diodes (LEDs) and one receiver consisting of nine photodetectors (PDs). Finally, the improved algorithm is implemented and the results are shown under our three dimensional multiple photodetectors (multi-PDs) structure receiver. In the simulation, we take the parameters of Lambertian radiation pattern, LEDs and PDs as those shown in [1] . To be noticed, our design of multi-PDs receiver is fully expanded into three dimensions compared with the pyramid receiver (PR), which allows indoor positioning with our receiver structure to be more robust to the higher or corner positions. The details will be explained in the following sections. Based on Multiple-Photodiodebased Indoor Positioning algorithm [1], the indoor positioning algorithm is improved by redefining the optimization problem of obtaining the direction from receiver to LED and using weighted triangulation method to locate receiver position. We admit the solution under the redefined problem is not optimal to the actual problem. Yet, our given solution is better to that in [1] due to the existence of noise, which is reasonable and has been verified.
  • Diversity, ecology, and biotechnological potential of microorganisms naturally associated with plants in arid lands

    Mosqueira Santillán, María José (2018-07)
    Plants naturally host complex microbial communities in which the plant and the symbiotic partners act as an integrated metaorganism. These communities include beneficial (i.e. plant growth promoting, PGP) microorganisms which provide fundamental ecological services able to enhance host plant fitness and stress tolerance. PGP microorganisms represent a potential bioresource for agricultural applications, especially for desert farming under the harsh environmental conditions occurring in hot/arid regions (i.e. drought and salinity). In this context, understanding the ecological aspects of the associated microorganisms is crucial to take advantage of their ecological services. Here, hot/desert ecosystems were selected and two contrasting plant categories were used as models: (i) wild plants (i.e. speargrasses) growing in hot-desert sand dunes and (ii) the main crop cultivated in desert ecosystems, the date palm. By using highthroughput DNA sequencing and microscopy, the ecology and functionality of the microbial communities associated with these plants were characterized. Additionally, the PGP services of bacteria isolated from date palm were explored. I found that the harsh conditions of the desert strongly affect the selection and assembly of microbial communities associated with three different speargrass species, determining a plant species-independent core microbiome always present among the three plant species and carrying important PGP traits. On the contrary, in agroecosystems where desert farming practices are used, the plant species, i.e. date palm exerts a stronger selective pressure than the environmental and edaphic factors favoring the recruitment of conserved microbial assemblages, independent of the differences in the soil and environmental conditions among the studied oases. Such selective pressure also favors the recruitment of conserved PGP microorganisms (i.e. Pseudomonas sp. bacterial strains) able to protect their host from salinity stress through the induction of root architectural changes regulated by the modification of the root system auxin homeostasis. Overall, we found that deserts are unique ecosystems that challenge the paradigm of microbial community assembly, as it was defined from studies in non-arid ecosystems. The understanding of the ecological features regulating the ecological properties of such unique microbial community assembly will be a key-step to improve the chances of successful application of ‘PGP microorganisms’ in arid agroecosystems.
  • Single molecule analysis of the diffusion and conformational dynamics

    Abadi, Maram (2018-07)
    Spatial and temporal dynamics of polymer chains play critical roles in their rheological properties, which have a significant influence on polymer processing and fabrication of polymer-based (nano) materials. Many theoretical and experimental studies have aimed at understanding polymer dynamics at the molecular level that give rise to its bulk phase properties. While much progress has been made in the field over the past ~60 years, many aspects of polymers are still not understood, especially in complicated systems such as entangled fluids and polymers of different topologies. In addition, the physical properties of biological macromolecules, i.e. DNA, are expected to affect the spatial organization of chromosome in a cell, which has the potential impact on a broad epigenetics research. Here, we propose new methods for simultaneous visualization of diffusive motion and conformational dynamics of individual polymer chains, two most important factors that characterize polymer dynamics, based on a new single-molecule tracking technique, cumulative-area (CA) tracking method. We demonstrate the applicability of the CA tracking to the quantitative characterization of the motion and relaxation of individual topological polymer molecules under entangled conditions, which is possible only by using the newly-developed CA tracking, using fluorescently-labeled linear and cyclic dsDNA as model systems. We further extend the technique to multi-color CA tracking that allows for the direct visualization and characterization of motion and conformation of interacting molecules. We also develop a new imaging method based on recently developed 3D super-resolution fluorescence microscopy technique, which allows direct visualization of nanoscale motion and conformation of the single molecules that is not possible by any other methods. Using these techniques, we investigate spatial and temporal dynamics of polymers at the single-molecule level, with special emphasis on the effect of topological forms of the molecules and the confined geometry on their spatiotemporal dynamics. Our results demonstrate that the new methods developed in this thesis provide an experimental platform to address key questions in the entangled topological polymer dynamics. The research will provide a platform for developing new polymer-based materials and open the possibility of studying spatial organization of DNA in a confined geometry from physics point of view.
  • Fabrication and Characterization of Geometrically Confined Fe3Sn2 Skyrmion-based Devices

    GONG, CHEN (2018-06-27)
    Skyrmion is a topologically protected nanometer-sized spin configuration, which makes it a promising candidate for future memory devices. All skyrmion applications are based on the formation and manipulation of spin textures in nanostructured elements. Therefore, fabrication of geometrically confined skyrmion-based nanodevices is an essential step in the investigation of skyrmion properties. In this study, my research mainly focuses on the fabrication of high-quality Fe3Sn2 nanostripes with different geometric parameters for Lorentz transmission electron microscopy (LTEM) by a focused ion beam (FIB) system. The observation of the skyrmions using LTEM was mainly performed by Dr. Qiang Zhang, although I have deeply involved the discussion on new samples to be fabricated based on the results obtained from LTEM and also performed some LTEM experiments. To investigate the formation process and thermal stability of skyrmions in a geometrically confined environment, I have fabricated more than fifty high-quality nanostripes with a width of 265-4,000 nm. Studying with LTEM, a distinct evolutionary path of stripe-skyrmion transformation is observed after gradually increasing the magnetic field (out-of-plane direction) and the critical magnetic field of skyrmion is found to decrease with an increasing strength of confinements. Moreover, a series of racetrack devices with controlled thicknesses (125-404 nm) is fabricated to study the effect of thickness in skyrmion formation. Overall, in order to obtain less damaged, flat skyrmion-based devices by FIB system, experimental parameters are optimized and fabrication skills are improved. This method develops the possible application of centrosymmetric frustrated magnet Fe3Sn2 in skyrmion-based racetrack devices.
  • Decision and Inhibitory Trees for Decision Tables with Many-Valued Decisions

    Azad, Mohammad (2018-06-06)
    Decision trees are one of the most commonly used tools in decision analysis, knowledge representation, machine learning, etc., for its simplicity and interpretability. We consider an extension of dynamic programming approach to process the whole set of decision trees for the given decision table which was previously only attainable by brute-force algorithms. We study decision tables with many-valued decisions (each row may contain multiple decisions) because they are more reasonable models of data in many cases. To address this problem in a broad sense, we consider not only decision trees but also inhibitory trees where terminal nodes are labeled with “̸= decision”. Inhibitory trees can sometimes describe more knowledge from datasets than decision trees. As for cost functions, we consider depth or average depth to minimize time complexity of trees, and the number of nodes or the number of the terminal, or nonterminal nodes to minimize the space complexity of trees. We investigate the multi-stage optimization of trees relative to some cost functions, and also the possibility to describe the whole set of strictly optimal trees. Furthermore, we study the bi-criteria optimization cost vs. cost and cost vs. uncertainty for decision trees, and cost vs. cost and cost vs. completeness for inhibitory trees. The most interesting application of the developed technique is the creation of multi-pruning and restricted multi-pruning approaches which are useful for knowledge representation and prediction. The experimental results show that decision trees constructed by these approaches can often outperform the decision trees constructed by the CART algorithm. Another application includes the comparison of 12 greedy heuristics for single- and bi-criteria optimization (cost vs. cost) of trees. We also study the three approaches (decision tables with many-valued decisions, decision tables with most common decisions, and decision tables with generalized decisions) to handle inconsistency of decision tables. We also analyze the time complexity of decision and inhibitory trees over arbitrary sets of attributes represented by information systems in the frameworks of local (when we can use in trees only attributes from problem description) and global (when we can use in trees arbitrary attributes from the information system) approaches.
  • Numerical Computation of Detonation Stability

    Kabanov, Dmitry (2018-06-03)
    Detonation is a supersonic mode of combustion that is modeled by a system of conservation laws of compressible fluid mechanics coupled with the equations describing thermodynamic and chemical properties of the fluid. Mathematically, these governing equations admit steady-state travelling-wave solutions consisting of a leading shock wave followed by a reaction zone. However, such solutions are often unstable to perturbations and rarely observed in laboratory experiments. The goal of this work is to study the stability of travelling-wave solutions of detonation models by the following novel approach. We linearize the governing equations about a base travelling-wave solution and solve the resultant linearized problem using high-order numerical methods. The results of these computations are postprocessed using dynamic mode decomposition to extract growth rates and frequencies of the perturbations and predict stability of travelling-wave solutions to infinitesimal perturbations. We apply this approach to two models based on the reactive Euler equations for perfect gases. For the first model with a one-step reaction mechanism, we find agreement of our results with the results of normal-mode analysis. For the second model with a two-step mechanism, we find that both types of admissible travelling-wave solutions exhibit the same stability spectra. Then we investigate the Fickett’s detonation analogue coupled with a particular reaction-rate expression. In addition to the linear stability analysis of this model, we demonstrate that it exhibits rich nonlinear dynamics with multiple bifurcations and chaotic behavior.
  • Numerically investigating the effects of gasoline surrogate physical and chemical properties in a gasoline compression ignition (GCI) engine

    Atef, Nour (2018-06)
    Gasoline compression ignition (GCI) engines show promise in meeting stringent new environmental regulations, as they are characterized by high efficiency and low emissions. Simulations using chemical kinetic models provide an important platform for investigating the behaviors of the fuels inside these engines. However, because real fuels are complex, simulations require surrogate mixtures of small numbers of species that can replicate the properties of real fuels. Accordingly, the development of high fidelity, well-validated kinetic models for surrogates is critical in order to accurately replicate the combustion chemistry of different fuels under engine-related conditions. This work focuses on the development of combustion kinetic models to better understand gasoline fuel combustion in GCI engines. An updated iso-octane detailed kinetic model was developed based on new thermodynamic group values and recently evaluated rate coefficients from literature. The model was validated against a wide range of experimental data and conditions. The iso-octane model was further used in 0D simulations for a homogeneous charge compression ignition (HCCI) engine. The results showed that the low-temperature heat release in engines increases with engine boosting when the addition of alky radicals to molecular oxygen is more favored. Ethanol addition was also found to act as a radical sink which inhibits the radical pool formation and results in lower reactivity. Although detailed models provide clarification of the combustion chemistry, their high computational cost impedes their utilization in 3-D engine simulations. Hence, a reduced model for toluene primary reference fuels was developed and validated against ignition delay time and flame speed experiments from literature. The model was then used in numerically investigating the effects of the fuel’s physical properties using hollow-cone and multi-hole injectors in a partially premixed compression ignition (PPCI) engine. It was concluded that the effects of physical properties are evident in multi-hole injection cases, which is attributable to the differences in mixture stratification. Finally, reduced models for multi-components surrogates for three full-blend fuels (light naphtha-Haltermann straight-run naphtha and GCI fuels) were developed. The models were validated against ignition delay time experiments from the literature and tested in 3D engine simulations.
  • Monitoring the effects of offshore aquaculture on water quality in the Red Sea

    Dunne, Aislinn (2018-06)
    The Saudi Arabian government has announced an economic development plan (Vision 2030) to invest in a range of industries across the Kingdom, one of which is the development of aquaculture. In the face of a likely increase in Red Sea fish farming, we investigated the impacts of offshore fish farms on the coastal water quality of the Red Sea by a) measuring the environmental impacts of an operational Red Sea fish farm, and b) testing whether an existing aquaculture modeling software can be used as a meaningful planning tool in the development of Red Sea aquaculture. Water quality parameters such as dissolved oxygen, nutrients, particulate matter, chlorophyll, ammonium, and bacterial abundance were measured seasonally over the course of a year around an offshore fish farm along the south-central coast of Saudi Arabia to determine the impacts of fish farm effluent on the surrounding waters. Bacteria, phosphate, inorganic nitrogen, and suspended particulate matter showed patterns of enrichment close to the fish farm. Additionally, dissolved oxygen has slightly lower concentrations close to and down current from the fish farms. Benthic sediments from a nearby coral reef were also assessed for organic enrichment, but concentrations of total organic carbon and total nitrogen were not significantly different from those at an offshore reef. The data from these sampling efforts were then used as input parameters for an aquaculture modeling software (, however many of the input parameters required to run the model were unavailable and meaningful conclusions could not be drawn from the results. Through field studies and modeling, we assessed the current impact of a Red Sea fish farm on water quality with the goal of predicting the potential impacts of future offshore aquaculture development in Saudi Arabia.
  • Underwater Wireless Optical Communications Systems: from System-Level Demonstrations to Channel Modeling

    Oubei, Hassan M. (2018-06)
    Approximately, two-thirds of earth's surface is covered by water. There is a growing interest from the military and commercial communities in having, an efficient, secure and high bandwidth underwater wireless communication (UWC) system for tactical underwater applications such as oceanography studies and offshore oil exploration. The existing acoustic and radio frequency (RF) technologies are severely limited in bandwidth because of the strong frequency dependent attenuation of sound in seawater and the high conductivity of seawater at radio frequencies, respectively. Recently, underwater wireless optical communication (UWOC) has been proposed as the best alternative or complementary solution to meet this challenge. Taking advantage of the low absorption window of seawater in blue-green (400-550 nm) regime of the electromagnetic spectrum, UWOC is expected to establish secure, efficient and high data rate communication links over short and moderate distances (< 100 m) for versatile applications such as underwater oil pipe inspection, remotely operated vehicle (ROV) and sensor networks. UWOC uses the latest gallium nitrite (GaN) visible light-emitting diode (LED) and laser diode (LD) transmitters. Although some research on LED lased UWOC is being conducted, both the military and academic 5 research communities are favoring the use of laser beams, which potentially could enhance the available bandwidth by up to three orders of magnitude. However, the underwater wireless channel is optically very challenging and difficult to predict. The propagation of laser beams in seawater is significantly affected by the harsh marine environments and suffers from severe attenuation which is a combined effect of absorption and scattering, optical turbulence, and multipath effects at high transmission rates. These limitations distort the intensity and phase structure of the optical beam leading to a decrease in signal-to-noise ratio (SNR) which ultimately degrades the performance of UWOC links by increasing the probability of error. In this dissertation, we seek to experimentally demonstrate the feasibility of short range (≤ 20 m) UWOC systems over various underwater channel water types using different modulation schemes as well as to model and describe the statistical properties of turbulence-induced fading in underwater wireless optical channels using laser beam intensity fluctuations measurements.
  • Exploiting Data Sparsity In Covariance Matrix Computations on Heterogeneous Systems

    Charara, Ali (2018-05-24)
    Covariance matrices are ubiquitous in computational sciences, typically describing the correlation of elements of large multivariate spatial data sets. For example, covari- ance matrices are employed in climate/weather modeling for the maximum likelihood estimation to improve prediction, as well as in computational ground-based astronomy to enhance the observed image quality by filtering out noise produced by the adap- tive optics instruments and atmospheric turbulence. The structure of these covariance matrices is dense, symmetric, positive-definite, and often data-sparse, therefore, hier- archically of low-rank. This thesis investigates the performance limit of dense matrix computations (e.g., Cholesky factorization) on covariance matrix problems as the number of unknowns grows, and in the context of the aforementioned applications. We employ recursive formulations of some of the basic linear algebra subroutines (BLAS) to accelerate the covariance matrix computation further, while reducing data traffic across the memory subsystems layers. However, dealing with large data sets (i.e., covariance matrices of billions in size) can rapidly become prohibitive in memory footprint and algorithmic complexity. Most importantly, this thesis investigates the tile low-rank data format (TLR), a new compressed data structure and layout, which is valuable in exploiting data sparsity by approximating the operator. The TLR com- pressed data structure allows approximating the original problem up to user-defined numerical accuracy. This comes at the expense of dealing with tasks with much lower arithmetic intensities than traditional dense computations. In fact, this thesis con- solidates the two trends of dense and data-sparse linear algebra for HPC. Not only does the thesis leverage recursive formulations for dense Cholesky-based matrix al- gorithms, but it also implements a novel TLR-Cholesky factorization using batched linear algebra operations to increase hardware occupancy and reduce the overhead of the API. Performance reported of the dense and TLR-Cholesky shows many-fold speedups against state-of-the-art implementations on various systems equipped with GPUs. Additionally, the TLR implementation gives the user flexibility to select the desired accuracy. This trade-off between performance and accuracy is, currently, a well-established leading trend in the convergence of the third and fourth paradigm, i.e., HPC and Big Data, when moving forward with exascale software roadmap.
  • A Study of Recurrent and Convolutional Neural Networks in the Native Language Identification Task

    Werfelmann, Robert (2018-05-24)
    Native Language Identification (NLI) is the task of predicting the native language of an author from their text written in a second language. The idea is to find writing habits that transfer from an author’s native language to their second language. Many approaches to this task have been studied, from simple word frequency analysis, to analyzing grammatical and spelling mistakes to find patterns and traits that are common between different authors of the same native language. This can be a very complex task, depending on the native language and the proficiency of the author’s second language. The most common approach that has seen very good results is based on the usage of n-gram features of words and characters. In this thesis, we attempt to extract lexical, grammatical, and semantic features from the sentences of non-native English essays using neural networks. The training and testing data was obtained from a large corpus of publicly available essays written by authors of several countries around the world. The neural network models consisted of Long Short-Term Memory and Convolutional networks using the sentences of each document as the input. Additional statistical features were generated from the text to complement the predictions of the neural networks, which were then used as feature inputs to a Support Vector Machine, making the final prediction. Results show that Long Short-Term Memory neural network can improve performance over a naive bag of words approach, but with a much smaller feature set. With more fine-tuning of neural network hyperparameters, these results will likely improve significantly.
  • Optical and Temporal Carrier Dynamics Investigations of III-Nitrides for Semiconductor Lighting

    Ajia, Idris A. (2018-05-22)
    III-nitride semiconductors suffer significant efficiency limitations; ‘efficiency’ being an umbrella term that covers an extensive list of challenges that must be overcome if they are to fulfil their vast potential. To this end, it is imperative to understand the underlying phenomena behind such limitations. In this dissertation, I combine powerful optical and structural characterization techniques to investigate the effect of different defects on the carrier dynamics in III-nitride materials for light emitting devices. The results presented herein will enhance the current understanding of the carrier mechanisms in such devices, which will lead to device efficiency improvements. In the first part of this dissertation, the effects of some important types of crystal defects present in III-nitride structures are investigated. Here, two types of defects are studied in two different III-nitride-based light emitting structures. The first defects of interest are V-pit defects in InGaN/GaN multiple quantum well (MQW) blue LEDs, where their contribution to the high-efficiency of such LEDs is discussed. In addition, the effect of these defects on the efficiency droop phenomenon in these LEDs is elucidated. Secondly, the optical effects of grain boundary defects in AlN-rich AlGaN/AlGaN MQWs is studied. In this study, it is shown that grain boundary defects may result in abnormal carrier localization behavior in these deep ultraviolet (UV) structures. While both defects are treated individually, it is evident from these studies that threading dislocation (TD) defects are an underlying contributor to the more undesirable outcomes of the said defects. In the second part, the dissertation reports on the carrier dynamics of III-nitride LED structures grown on emerging substrates—as possible efficiency enhancing techniques—aimed at mitigating the effects of TD defects. Thus, the carrier dynamics of GaN/AlGaN UV MQWs grown, for the first time, on (2̅01) – oriented β-Ga2O3 is studied. It is shown to be a candidate substrate for highly efficient vertical UV devices. Finally, results from the carrier dynamics investigation of an AlGaN/AlGaN MQW LED structure homoepitaxially grown on AlN substrate are discussed, where it is shown that its high-efficiency is sustained at high temperatures through the thermal redistribution of carriers to highly efficient recombination sites.
  • Neural Inductive Matrix Completion for Predicting Disease-Gene Associations

    Hou, Siqing (2018-05-21)
    In silico prioritization of undiscovered associations can help find causal genes of newly discovered diseases. Some existing methods are based on known associations, and side information of diseases and genes. We exploit the possibility of using a neural network model, Neural inductive matrix completion (NIMC), in disease-gene prediction. Comparing to the state-of-the-art inductive matrix completion method, using neural networks allows us to learn latent features from non-linear functions of input features. Previous methods use disease features only from mining text. Comparing to text mining, disease ontology is a more informative way of discovering correlation of dis- eases, from which we can calculate the similarities between diseases and help increase the performance of predicting disease-gene associations. We compare the proposed method with other state-of-the-art methods for pre- dicting associated genes for diseases from the Online Mendelian Inheritance in Man (OMIM) database. Results show that both new features and the proposed NIMC model can improve the chance of recovering an unknown associated gene in the top 100 predicted genes. Best results are obtained by using both the new features and the new model. Results also show the proposed method does better in predicting associated genes for newly discovered diseases.
  • Large-scale Comparative Study of Hi-C-based Chromatin 3D Structure Modeling Methods

    Wang, Cheng (2018-05-17)
    Chromatin is a complex polymer molecule in eukaryotic cells, primarily consisting of DNA and histones. Many works have shown that the 3D folding of chromatin structure plays an important role in DNA expression. The recently proposed Chro- mosome Conformation Capture technologies, especially the Hi-C assays, provide us an opportunity to study how the 3D structures of the chromatin are organized. Based on the data from Hi-C experiments, many chromatin 3D structure modeling methods have been proposed. However, there is limited ground truth to validate these methods and no robust chromatin structure alignment algorithms to evaluate the performance of these methods. In our work, we first made a thorough literature review of 25 publicly available population Hi-C-based chromatin 3D structure modeling methods. Furthermore, to evaluate and to compare the performance of these methods, we proposed a novel data simulation method, which combined the population Hi-C data and single-cell Hi-C data without ad hoc parameters. Also, we designed a global and a local alignment algorithms to measure the similarity between the templates and the chromatin struc- tures predicted by different modeling methods. Finally, the results from large-scale comparative tests indicated that our alignment algorithms significantly outperform the algorithms in literature.
  • A Study of Schrödinger–Type Equations Appearing in Bohmian Mechanics and in the Theory of Bose–Einstein Condensates

    Sierra Nunez, Jesus Alfredo (2018-05-16)
    The Schrödinger equations have had a profound impact on a wide range of fields of modern science, including quantum mechanics, superfluidity, geometrical optics, Bose-Einstein condensates, and the analysis of dispersive phenomena in the theory of PDE. The main purpose of this thesis is to explore two Schrödinger-type equations appearing in the so-called Bohmian formulation of quantum mechanics and in the study of exciton-polariton condensates. For the first topic, the linear Schrödinger equation is the starting point in the formulation of a phase-space model proposed in [1] for the Bohmian interpretation of quantum mechanics. We analyze this model, a nonlinear Vlasov-type equation, as a Hamiltonian system defined on an appropriate Poisson manifold built on Wasserstein spaces, the aim being to establish its existence theory. For this purpose, we employ results from the theory of PDE, optimal transportation, differential geometry and algebraic topology. The second topic of the thesis is the study of a nonlinear Schrödinger equation, called the complex Gross-Pitaevskii equation, appearing in the context of Bose-Einstein condensation of exciton-polaritons. This model can be roughly described as a driven-damped Gross-Pitaevskii equation which shares some similarities with the complex Ginzburg-Landau equation. The difficulties in the analysis of this equation stem from the fact that, unlike the complex Ginzburg-Landau equation, the complex Gross-Pitaevskii equation does not include a viscous dissipation term. Our approach to this equation will be in the framework of numerical computations, using two main tools: collocation methods and numerical continuation for the stationary solutions and a time-splitting spectral method for the dynamics. After performing a linear stability analysis on the computed stationary solutions, we are led to postulate the existence of radially symmetric stationary ground state solutions only for certain values of the parameters in the equation; these parameters represent the “strength” of the driving and damping terms. Moreover, numerical continuation allows us to show, for fixed parameters, the ground and some of the excited state solutions of this equation. Finally, for the values of the parameters that do not produce a stable radially symmetric solution, our dynamical computations show the emergence of rotating vortex lattices.
  • Efficiency-limiting processes in OPV bulk heterojunctions of GeNIDTBT and IDT-based acceptors

    Al-Saggaf, Sarah M. (2018-05-16)
    The successful realization of highly efficient bulk heterojunction OPV devices requires the development of organic donor and acceptor materials with tailored properties. Recently, non-fullerene acceptors (NFAs) have emerged as an alternative to the ubiquitously used fullerene derivatives. NFAs showed a rapid increase in efficiencies, now exceeding a PCE of 13%. In my thesis research, I used two small molecule IDT-based acceptors, namely O-IDTBR and O-IDTBCN, in combination with a wide bandgap donor polymer, GeNIDT-BT, as active material in BHJ solar cells and investigated their photophysical characteristics. The polymer combined with O-IDTBR as acceptor achieved a power conversion efficiency of only 2%, which is significantly lower than that obtained for the system of GeNIDT-BT: O-IDTBCN (5.3%). Using nano- to microsecond transient absorption spectroscopy, I investigated both systems and demonstrated that GeNIDT-BT:O-IDTBR exhibits more geminate recombination of interfacial charge-transfer states, leading to lower short circuit currents. Using time-delayed collection field experiments, I studied the field dependence of charge generation and its impact on the device fill factor. Overall, my results provide a qualitative understanding of the efficiency-limiting processes in both systems and their impact on device performance.

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