6,906 research outputs found

    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Deep generative models for network data synthesis and monitoring

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    Measurement and monitoring are fundamental tasks in all networks, enabling the down-stream management and optimization of the network. Although networks inherently have abundant amounts of monitoring data, its access and effective measurement is another story. The challenges exist in many aspects. First, the inaccessibility of network monitoring data for external users, and it is hard to provide a high-fidelity dataset without leaking commercial sensitive information. Second, it could be very expensive to carry out effective data collection to cover a large-scale network system, considering the size of network growing, i.e., cell number of radio network and the number of flows in the Internet Service Provider (ISP) network. Third, it is difficult to ensure fidelity and efficiency simultaneously in network monitoring, as the available resources in the network element that can be applied to support the measurement function are too limited to implement sophisticated mechanisms. Finally, understanding and explaining the behavior of the network becomes challenging due to its size and complex structure. Various emerging optimization-based solutions (e.g., compressive sensing) or data-driven solutions (e.g. deep learning) have been proposed for the aforementioned challenges. However, the fidelity and efficiency of existing methods cannot yet meet the current network requirements. The contributions made in this thesis significantly advance the state of the art in the domain of network measurement and monitoring techniques. Overall, we leverage cutting-edge machine learning technology, deep generative modeling, throughout the entire thesis. First, we design and realize APPSHOT , an efficient city-scale network traffic sharing with a conditional generative model, which only requires open-source contextual data during inference (e.g., land use information and population distribution). Second, we develop an efficient drive testing system — GENDT, based on generative model, which combines graph neural networks, conditional generation, and quantified model uncertainty to enhance the efficiency of mobile drive testing. Third, we design and implement DISTILGAN, a high-fidelity, efficient, versatile, and real-time network telemetry system with latent GANs and spectral-temporal networks. Finally, we propose SPOTLIGHT , an accurate, explainable, and efficient anomaly detection system of the Open RAN (Radio Access Network) system. The lessons learned through this research are summarized, and interesting topics are discussed for future work in this domain. All proposed solutions have been evaluated with real-world datasets and applied to support different applications in real systems

    Graduate Catalog of Studies, 2023-2024

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    The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

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    The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-Îł CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

    Subgrid scale modeling for large eddy simulation of supercritical mixing and combustion

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    Large eddy simulation (LES) is a widely used modeling and simulation technique in turbulent flow research. While the LES methodology and accompanying subgrid scale (SGS) modeling have been developed and applied over decades, primarily in the context of ideal gas conditions, their extension to complex multi-physics flows encountered in aerospace propulsion requires further refinement. In particular, the application of LES to turbulent flows at supercritical conditions presents several new modeling challenges and uncertainties. The scope of this dissertation is to investigate the theoretical LES formalism and SGS modeling framework for multi-species turbulent mixing and combustion at supercritical pressures. The goal is to identify the deficiencies with the current methodology and to establish a refined and consistent framework that accurately accounts for all the necessary physics. In this dissertation, a consistent theoretical formulation of the filtered governing equations for LES is derived. Direct numerical simulations (DNS) are performed for spatially evolving non-reacting and reacting mixing layers at supercritical pressures. The complete set of terms in the filtered equations are quantified and analyzed using the DNS datasets. Based on the analyses, two new groups of subgrid terms are identified as important quantities to account in the LES framework. Parametric analyses are performed as a function of the filter resolution to derive resolution considerations for practical LES applications. The performance and accuracies of two state-of-the-art subgrid modeling approaches for the traditional subgrid fluxes are assessed. The study demonstrates the better performance of scale-similarity based models over the eddy-viscosity based approaches. The study also reveals the deficiencies of conventional subgrid modeling approaches for LES of supercritical combustion. To address the additional modeling requirement for the filtered equation of state, novel subgrid modeling approaches are proposed. The performance of these models are tested and good improvements are demonstrated.Ph.D

    LIPIcs, Volume 251, ITCS 2023, Complete Volume

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    LIPIcs, Volume 251, ITCS 2023, Complete Volum

    Investigation of the metabolism of rare nucleotides in plants

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    Nucleotides are metabolites involved in primary metabolism, and specialized metabolism and have a regulatory role in various biochemical reactions in all forms of life. While in other organisms, the nucleotide metabolome was characterized extensively, comparatively little is known about the cellular concentrations of nucleotides in plants. The aim of this dissertation was to investigate the nucleotide metabolome and enzymes influencing the composition and quantities of nucleotides in plants. For this purpose, a method for the analysis of nucleotides and nucleosides in plants and algae was developed (Chapter 2.1), which comprises efficient quenching of enzymatic activity, liquid-liquid extraction and solid phase extraction employing a weak-anionexchange resin. This method allowed the analysis of the nucleotide metabolome of plants in great depth including the quantification of low abundant deoxyribonucleotides and deoxyribonucleosides. The details of the method were summarized in an article, serving as a laboratory protocol (Chapter 2.2). Furthermore, we contributed a review article (Chapter 2.3) that summarizes the literature about nucleotide analysis and recent technological advances with a focus on plants and factors influencing and hindering the analysis of nucleotides in plants, i.e., a complex metabolic matrix, highly stable phosphatases and physicochemical properties of nucleotides. To analyze the sub-cellular concentrations of metabolites, a protocol for the rapid isolation of highly pure mitochondria utilizing affinity chromatography was developed (Chapter 2.4). The method for the purification of nucleotides furthermore contributed to the comprehensive analysis of the nucleotide metabolome in germinating seeds and in establishing seedlings of A. thaliana, with a focus on genes involved in the synthesis of thymidilates (Chapter 2.5) and the characterization of a novel enzyme of purine nucleotide degradation, the XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Chapter 2.6). Protein homology analysis comparing A. thaliana, S. cerevisiae, and H. sapiens led to the identification and characterization of an enzyme involved in the metabolite damage repair system of plants, the INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Chapter 2.7). It was shown that this enzyme dephosphorylates deaminated purine nucleotide triphosphates and thus prevents their incorporation into nucleic acids. Lossof-function mutants senesce early and have a constitutively increased content of salicylic acid. Also, the source of deaminated purine nucleotides in plants was investigated and it was shown that abiotic factors contribute to nucleotide damage.Nukleotide sind Metaboliten, die am PrimĂ€rstoffwechsel und an spezialisierten StoffwechselvorgĂ€ngen beteiligt sind und eine regulierende Rolle bei verschiedenen biochemischen Reaktionen in allen Lebensformen spielen. WĂ€hrend bei anderen Organismen das Nukleotidmetabolom umfassend charakterisiert wurde, ist in Pflanzen vergleichsweise wenig ĂŒber die zellulĂ€ren Konzentrationen von Nukleotiden bekannt. Ziel dieser Dissertation war es, das Nukleotidmetabolom und die Enzyme zu untersuchen, die die Zusammensetzung und Menge der Nukleotide in Pflanzen beeinflussen. Zu diesem Zweck wurde eine Methode zur Analyse von Nukleotiden und Nukleosiden in Pflanzen und Algen entwickelt (Kapitel 2.1), die ein effizientes Stoppen enzymatischer AktivitĂ€t, eine FlĂŒssig-FlĂŒssig-Extraktion und eine Festphasenextraktion unter Verwendung eines schwachen Ionenaustauschers umfasst. Mit dieser Methode konnte das Nukleotidmetabolom von Pflanzen eingehend analysiert werden, einschließlich der Quantifizierung von Desoxyribonukleotiden und Desoxyribonukleosiden mit geringer Abundanz. Die Einzelheiten der Methode wurden in einem Artikel zusammengefasst, der als Laborprotokoll dient (Kapitel 2.2). DarĂŒber hinaus wurde ein Übersichtsartikel (Kapitel 2.3) verfasst, der die Literatur ĂŒber die Analyse von Nukleotiden und die jĂŒngsten technologischen Fortschritte zusammenfasst. Der Schwerpunkt lag hierbei auf Pflanzen und Faktoren, die die Analyse von Nukleotiden in Pflanzen beeinflussen oder behindern, d. h. eine komplexe Matrix, hochstabile Phosphatasen und physikalisch-chemische Eigenschaften von Nukleotiden. Um die subzellulĂ€ren Konzentrationen von Metaboliten zu analysieren, wurde ein Protokoll fĂŒr die schnelle Isolierung hochreiner Mitochondrien unter Verwendung einer AffinitĂ€tschromatographie entwickelt (Kapitel 2.4). Die Methode zur Analyse von Nukleotiden trug außerdem zu einer umfassenden Analyse des Nukleotidmetaboloms in keimenden Samen und in sich etablierenden Keimlingen von A. thaliana bei, wobei der Schwerpunkt auf Genen lag, die an der Synthese von Thymidilaten beteiligt sind (Kapitel 2.5), sowie zu der Charakterisierung eines neuen Enzyms des Purinnukleotidabbaus, der XANTHOSINE MONOPHOSPHATE PHOSPHATASE (Kapitel 2.6). Eine Proteinhomologieanalyse, die A. thaliana, S. cerevisiae und H. sapiens miteinander verglich fĂŒhrte zur Identifizierung und Charakterisierung eines Enzyms, das an der Reparatur von geschĂ€digten Metaboliten in Pflanzen beteiligt ist, der INOSINE TRIPHOSPHATE PYROPHOSPHATASE (Kapitel 2.7). Es konnte gezeigt werden, dass dieses Enzym desaminierte Purinnukleotidtriphosphate dephosphoryliert und so deren Einbau in NukleinsĂ€uren verhindert. Funktionsverlustmutanten altern frĂŒh und weisen einen konstitutiv erhöhten Gehalt an SalicylsĂ€ure auf. Außerdem wurde die Quelle der desaminierten Purinnukleotide in Pflanzen untersucht, und es wurde gezeigt, dass abiotische Faktoren zur NukleotidschĂ€digung beitragen

    Posthuman Creative Styling can a creative writer’s style of writing be described as procedural?

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    This thesis is about creative styling — the styling a creative writer might use to make their writing unique. It addresses the question as to whether such styling can be described as procedural. Creative styling is part of the technique a creative writer uses when writing. It is how they make the text more ‘lively’ by use of tips and tricks they have either learned or discovered. In essence these are rules, ones the writer accrues over time by their practice. The thesis argues that the use and invention of these rules can be set as procedures. and so describe creative styling as procedural. The thesis follows from questioning why it is that machines or algorithms have, so far, been incapable of producing creative writing which has value. Machine-written novels do not abound on the bookshelves and writing styled by computers is, on the whole, dull in comparison to human-crafted literature. It came about by thinking how it would be possible to reach a point where writing by people and procedural writing are considered to have equal value. For this reason the thesis is set in a posthuman context, where the differences between machines and people are erased. The thesis uses practice to inform an original conceptual space model, based on quality dimensions and dynamic-inter operation of spaces. This model gives an example of the procedures which a posthuman creative writer uses when engaged in creative styling. It suggests an original formulation for the conceptual blending of conceptual spaces, based on the casting of qualities from one space to another. In support of and informing its arguments are ninety-nine examples of creative writing practice which show the procedures by which style has been applied, created and assessed. It provides a route forward for further joint research into both computational and human-coded creative writing
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