6,659 research outputs found

    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

    Southern Adventist University Undergraduate Catalog 2023-2024

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    Southern Adventist University\u27s undergraduate catalog for the academic year 2023-2024.https://knowledge.e.southern.edu/undergrad_catalog/1123/thumbnail.jp

    UMSL Bulletin 2023-2024

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    The 2023-2024 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1088/thumbnail.jp

    Graduate Catalog of Studies, 2023-2024

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    Infrastructuring educational genomics:Associations, architectures and apparatuses

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    Technoscientific transformations in molecular genomics have begun to influence knowledge production in education. Interdisciplinary scientific consortia are seeking to identify ‘genetic influences’ on ‘educationally relevant’ traits, behaviors, and outcomes. This article examines the emerging ‘knowledge infrastructure’ of educational genomics, attending to the assembly and choreography of organizational associations, epistemic architecture, and technoscientific apparatuses implicated in the generation of genomic understandings from masses of bioinformation. As an infrastructure of datafied knowledge production, educational genomics is embedded in data-centered epistemologies and practices which recast educational problems in terms of molecular genetic associations—insights about which are deemed discoverable from digital bioinformation and potentially open to genetically informed interventions in policy and practice. While scientists claim to be ‘opening the black box of the genome’ and its association with educational outcomes, we open the black box of educational genomics itself as a source of emerging scientific authority. Data-intensive educational genomics does not straightforwardly ‘discover’ the biological bases of educationally relevant behaviors and outcomes. Rather, this knowledge infrastructure is also an experimental ‘ontological infrastructure’ supporting particular ways of knowing, understanding, explaining, and intervening in education, and recasting the human subjects of education as being surveyable and predictable through the algorithmic processing of bioinformation

    INTEGRATED COMPUTER-AIDED DESIGN, EXPERIMENTATION, AND OPTIMIZATION APPROACH FOR PEROVSKITES AND PETROLEUM PACKAGING PROCESSES

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    According to the World Economic Forum report, the U.S. currently has an energy efficiency of just 30%, thus illustrating the potential scope and need for efficiency enhancement and waste minimization. In the U.S. energy sector, petroleum and solar energy are the two key pillars that have the potential to create research opportunities for transition to a cleaner, greener, and sustainable future. In this research endeavor, the focus is on two pivotal areas: (i) Computer-aided perovskite solar cell synthesis; and (ii) Optimization of flow processes through multiproduct petroleum pipelines. In the area of perovskite synthesis, the emphasis is on the enhancement of structural stability, lower costs, and sustainability. Utilizing modeling and optimization methods for computer-aided molecular design (CAMD), efficient, sustainable, less toxic, and economically viable alternatives to conventional lead-based perovskites are obtained. In the second area of optimization of flow processes through multiproduct petroleum pipelines, an actual industrial-scale operation for packaging multiple lube-oil blends is studied. Through an integrated approach of experimental characterization, process design, procedural improvements, testing protocols, control mechanisms, mathematical modeling, and optimization, the limitations of traditional packaging operations are identified, and innovative operational paradigms and strategies are developed by incorporating methods from process systems engineering and data-driven approaches

    Combined Nutrition and Exercise Interventions in Community Groups

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    Diet and physical activity are two key modifiable lifestyle factors that influence health across the lifespan (prevention and management of chronic diseases and reduction of the risk of premature death through several biological mechanisms). Community-based interventions contribute to public health, as they have the potential to reach high population-level impact, through the focus on groups that share a common culture or identity in their natural living environment. While the health benefits of a balanced diet and regular physical activity are commonly studied separately, interventions that combine these two lifestyle factors have the potential to induce greater benefits in community groups rather than strategies focusing only on one or the other. Thus, this Special Issue entitled “Combined Nutrition and Exercise Interventions in Community Groups” is comprised of manuscripts that highlight this combined approach (balanced diet and regular physical activity) in community settings. The contributors to this Special Issue are well-recognized professionals in complementary fields such as education, public health, nutrition, and exercise. This Special Issue highlights the latest research regarding combined nutrition and exercise interventions among different community groups and includes research articles developed through five continents (Africa, Asia, America, Europe and Oceania), as well as reviews and systematic reviews

    UMSL Bulletin 2022-2023

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    The 2022-2023 Bulletin and Course Catalog for the University of Missouri St. Louis.https://irl.umsl.edu/bulletin/1087/thumbnail.jp

    From Model-Based to Data-Driven Simulation: Challenges and Trends in Autonomous Driving

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    Simulation is an integral part in the process of developing autonomous vehicles and advantageous for training, validation, and verification of driving functions. Even though simulations come with a series of benefits compared to real-world experiments, various challenges still prevent virtual testing from entirely replacing physical test-drives. Our work provides an overview of these challenges with regard to different aspects and types of simulation and subsumes current trends to overcome them. We cover aspects around perception-, behavior- and content-realism as well as general hurdles in the domain of simulation. Among others, we observe a trend of data-driven, generative approaches and high-fidelity data synthesis to increasingly replace model-based simulation.Comment: Ferdinand M\"utsch, Helen Gremmelmaier, and Nicolas Becker contributed equally. Accepted for publication at CVPR 2023 VCAD worksho
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