5,548 research outputs found

    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

    How do patients and providers navigate the “corruption complex” in mixed health systems? The case of Abuja, Nigeria.

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    INTRODUCTION: Over the last decades, scholars have sought to investigate the causes, manifestations, and impacts of corruption in healthcare. Most of this scholarship has focused on corruption as it occurs in public health facilities. However, in Nigeria, in which most residents attend private health facilities for at least some of their care needs, this focus is incomplete. In such contexts, it is important to understand corruption as it occurs across both public and private settings, and in the interactions between them. This study seeks to address this gap. It aims to examine how corruption is experienced by, and impacts upon, patients and providers as they navigate the “corruption complex” in the mixed health system of Abuja, Nigeria. OBJECTIVES: This over-arching aim is addressed via three interrelated objectives, as follows: 1.To investigate the experiences of patients and providers concerning the causes, manifestations, and impacts of corruption in public health facilities, in Abuja, Nigeria. 2.To investigate patients / provider experiences of corruption as they relate to private health facilities in Abuja, Nigeria. 3.To investigate how, and the extent to which, corruption is enabled by the co-existence of and interactions between public and private health facilities in the context of the mixed health system of Nigeria – and of Abuja in particular. METHODS: All three objectives are addressed via a qualitative exploratory study. Data was collected in Abuja, Nigeria’s Federal Capital Territory (between October 2021 to May 2022) through: (i) in-depth interviews with 53 key informants, representing a range of patient and provider types, and policymakers; and (ii) participant observation over eight months of fieldwork. The research took place in three secondary-level public health facilities (Gwarinpa, Kubwa, and Wuse General hospital) and three equivalent-sized private health facilities (Nissa, Garki, and King's Care Hospital) in Abuja. The empirical data was analysed using Braun and Clarke's (2006) reflexive thematic analysis approach and presented in a narrative form. Abuja was selected as the research setting, as the city is representative of the mixed health system structures that exist in Nigeria, especially in the country’s larger urban areas. RESULTS: Objective 1: Corruption in public health facilities is driven by a shortage of resources, low salaries, commercialisation of health and relationships between patients and providers, and weak accountability structures. Corruption takes various forms which include: bribery, informal payments, theft, influence- activities associated with nepotism, and pressure from informal rules. Impacts include erosion of the right to health care and patient dignity, alongside increased barriers to access, including financial barriers, especially for poorer patients. Objective 2: Corruption in private health facilities is driven by incentives aimed at profit maximisation, poor regulation, and lack of oversight. Corruption takes various forms which include: inappropriate or unnecessary prescriptions (often driven by the potential for kickbacks), forging of medical reports, over-invoicing, and other related types of fraud, and under/over-treatment of patients. Impacts include reductions to the quality of care provided and exacerbation of financial risks to patients. Objective 3: The nature of public-private sector interactions creates scope for several forms of corruption. For example, these interactions contribute to the causes of corruption in the public sector - especially the problem of scarcity of resources. Related manifestations include dual practice, absenteeism, and theft (e.g., diversion of patients, medical supplies, and equipment from public to private facilities). The impacts of such practices include inequities of access, for example, due to delays in and denials of needed services and additional financial barriers encountered in public facilities, alongside reductions to quality of care, pricing transparency and financial protection in private facilities. CONCLUSION: Patients experience corruption in both public and private health facilities in Abuja, Nigeria. The causes, manifestations and impacts of corruption differ across these settings. In the public sector, corruption creates financial and non-financial barriers to care – aggravating inequities of access. In the private health sector, corruption undermines quality of care and exacerbates financial risks. The public-private mix is itself implicated in the problem – giving rise to new opportunities for corruption, to the detriment of patients’ health and welfare. For policymakers in Nigeria to address the problem of corruption, a cross-sectoral approach - inclusive of the full range of providers within the mixed health system – will be required

    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

    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

    Machine learning and mixed reality for smart aviation: applications and challenges

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    The aviation industry is a dynamic and ever-evolving sector. As technology advances and becomes more sophisticated, the aviation industry must keep up with the changing trends. While some airlines have made investments in machine learning and mixed reality technologies, the vast majority of regional airlines continue to rely on inefficient strategies and lack digital applications. This paper investigates the state-of-the-art applications that integrate machine learning and mixed reality into the aviation industry. Smart aerospace engineering design, manufacturing, testing, and services are being explored to increase operator productivity. Autonomous systems, self-service systems, and data visualization systems are being researched to enhance passenger experience. This paper investigate safety, environmental, technological, cost, security, capacity, and regulatory challenges of smart aviation, as well as potential solutions to ensure future quality, reliability, and efficiency

    Cost-aware Defense for Parallel Server Systems against Reliability and Security Failures

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    Parallel server systems in transportation, manufacturing, and computing heavily rely on dynamic routing using connected cyber components for computation and communication. Yet, these components remain vulnerable to random malfunctions and malicious attacks, motivating the need for fault-tolerant dynamic routing that are both traffic-stabilizing and cost-efficient. In this paper, we consider a parallel server system with dynamic routing subject to reliability and stability failures. For the reliability setting, we consider an infinite-horizon Markov decision process where the system operator strategically activates protection mechanism upon each job arrival based on traffic state observations. We prove an optimal deterministic threshold protecting policy exists based on dynamic programming recursion of the HJB equation. For the security setting, we extend the model to an infinite-horizon stochastic game where the attacker strategically manipulates routing assignment. We show that both players follow a threshold strategy at every Markov perfect equilibrium. For both failure settings, we also analyze the stability of the traffic queues under control. Finally, we develop approximate dynamic programming algorithms to compute the optimal/equilibrium policies, supplemented with numerical examples and experiments for validation and illustration.Comment: Major Revision in Automatic

    Sensing Collectives: Aesthetic and Political Practices Intertwined

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    Are aesthetics and politics really two different things? The book takes a new look at how they intertwine, by turning from theory to practice. Case studies trace how sensory experiences are created and how collective interests are shaped. They investigate how aesthetics and politics are entangled, both in building and disrupting collective orders, in governance and innovation. This ranges from populist rallies and artistic activism over alternative lifestyles and consumer culture to corporate PR and governmental policies. Authors are academics and artists. The result is a new mapping of the intermingling and co-constitution of aesthetics and politics in engagements with collective orders

    Swift: A modern highly-parallel gravity and smoothed particle hydrodynamics solver for astrophysical and cosmological applications

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    Numerical simulations have become one of the key tools used by theorists in all the fields of astrophysics and cosmology. The development of modern tools that target the largest existing computing systems and exploit state-of-the-art numerical methods and algorithms is thus crucial. In this paper, we introduce the fully open-source highly-parallel, versatile, and modular coupled hydrodynamics, gravity, cosmology, and galaxy-formation code Swift. The software package exploits hybrid task-based parallelism, asynchronous communications, and domain-decomposition algorithms based on balancing the workload, rather than the data, to efficiently exploit modern high-performance computing cluster architectures. Gravity is solved for using a fast-multipole-method, optionally coupled to a particle mesh solver in Fourier space to handle periodic volumes. For gas evolution, multiple modern flavours of Smoothed Particle Hydrodynamics are implemented. Swift also evolves neutrinos using a state-of-the-art particle-based method. Two complementary networks of sub-grid models for galaxy formation as well as extensions to simulate planetary physics are also released as part of the code. An extensive set of output options, including snapshots, light-cones, power spectra, and a coupling to structure finders are also included. We describe the overall code architecture, summarize the consistency and accuracy tests that were performed, and demonstrate the excellent weak-scaling performance of the code using a representative cosmological hydrodynamical problem with \approx300300 billion particles. The code is released to the community alongside extensive documentation for both users and developers, a large selection of example test problems, and a suite of tools to aid in the analysis of large simulations run with Swift.Comment: 39 pages, 18 figures, submitted to MNRAS. Code, documentation, and examples available at www.swiftsim.co
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