4,750 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

    Multidisciplinary perspectives on Artificial Intelligence and the law

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    This open access book presents an interdisciplinary, multi-authored, edited collection of chapters on Artificial Intelligence (‘AI’) and the Law. AI technology has come to play a central role in the modern data economy. Through a combination of increased computing power, the growing availability of data and the advancement of algorithms, AI has now become an umbrella term for some of the most transformational technological breakthroughs of this age. The importance of AI stems from both the opportunities that it offers and the challenges that it entails. While AI applications hold the promise of economic growth and efficiency gains, they also create significant risks and uncertainty. The potential and perils of AI have thus come to dominate modern discussions of technology and ethics – and although AI was initially allowed to largely develop without guidelines or rules, few would deny that the law is set to play a fundamental role in shaping the future of AI. As the debate over AI is far from over, the need for rigorous analysis has never been greater. This book thus brings together contributors from different fields and backgrounds to explore how the law might provide answers to some of the most pressing questions raised by AI. An outcome of the Católica Research Centre for the Future of Law and its interdisciplinary working group on Law and Artificial Intelligence, it includes contributions by leading scholars in the fields of technology, ethics and the law.info:eu-repo/semantics/publishedVersio

    An investigation into interdental arch relationship outcomes of 5-Year-Olds born with a Unilateral Cleft Lip and Palate using the Modified Huddart Bodenham Index following the centralisation of cleft services within the United Kingdom

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    Aim: To investigate the interdental arch relationship outcomes of 5-year-old children withunilateral cleft lip and palate (UCLP) before and after centralisation of cleft services in theUnited Kingdom (UK) using the Modified Huddart-Bodenham Index (MHBI).Design: Retrospective cross-sectional study.Setting: Evaluation of 3D orthodontic study models of children with a complete UCLP.Participants: All available 5-year-old orthodontic study models of participants with UCLP fromthe pre-centralisation (Clinical Standard Advisory Group CSAG n=107) and post-centralisation(Cleft Care UK CCUK n=195) studies.Outcome measure: Differences between the interdental arch relationship outcomes for theCSAG and CCUK cohorts were assessed using the Modified Huddart-Bodenham Index (MHBI).This index scored the buccal/palatal or labial/palatal relationships of 8 maxillary deciduousteeth with the opposing mandibular dentition. The anterior segment (deciduous centralincisors), buccal (deciduous canine, first and second deciduous molar) cleft segment and noncleft segment scores were calculated along with the sum of the three segments combined tocalculate the total arch MHBI scores.Results: The inter- and intra-examiner reliability had a high level of agreement. Statisticallysignificant differences in the anterior segment, buccal non cleft segment, and total arch MHBI3scores were found between CCUK and CSAG cohorts, with CCUK performing better. There wasno difference in the buccal cleft segment scores.Conclusions: There were improved transverse and anterior interdental arch relationshipspost centralisation (CCUK) of cleft services in the UK, suggestive of better primary surgicaloutcomes post CSAG

    Natural and Technological Hazards in Urban Areas

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    Natural hazard events and technological accidents are separate causes of environmental impacts. Natural hazards are physical phenomena active in geological times, whereas technological hazards result from actions or facilities created by humans. In our time, combined natural and man-made hazards have been induced. Overpopulation and urban development in areas prone to natural hazards increase the impact of natural disasters worldwide. Additionally, urban areas are frequently characterized by intense industrial activity and rapid, poorly planned growth that threatens the environment and degrades the quality of life. Therefore, proper urban planning is crucial to minimize fatalities and reduce the environmental and economic impacts that accompany both natural and technological hazardous events

    Software Design Change Artifacts Generation through Software Architectural Change Detection and Categorisation

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    Software is solely designed, implemented, tested, and inspected by expert people, unlike other engineering projects where they are mostly implemented by workers (non-experts) after designing by engineers. Researchers and practitioners have linked software bugs, security holes, problematic integration of changes, complex-to-understand codebase, unwarranted mental pressure, and so on in software development and maintenance to inconsistent and complex design and a lack of ways to easily understand what is going on and what to plan in a software system. The unavailability of proper information and insights needed by the development teams to make good decisions makes these challenges worse. Therefore, software design documents and other insightful information extraction are essential to reduce the above mentioned anomalies. Moreover, architectural design artifacts extraction is required to create the developer’s profile to be available to the market for many crucial scenarios. To that end, architectural change detection, categorization, and change description generation are crucial because they are the primary artifacts to trace other software artifacts. However, it is not feasible for humans to analyze all the changes for a single release for detecting change and impact because it is time-consuming, laborious, costly, and inconsistent. In this thesis, we conduct six studies considering the mentioned challenges to automate the architectural change information extraction and document generation that could potentially assist the development and maintenance teams. In particular, (1) we detect architectural changes using lightweight techniques leveraging textual and codebase properties, (2) categorize them considering intelligent perspectives, and (3) generate design change documents by exploiting precise contexts of components’ relations and change purposes which were previously unexplored. Our experiment using 4000+ architectural change samples and 200+ design change documents suggests that our proposed approaches are promising in accuracy and scalability to deploy frequently. Our proposed change detection approach can detect up to 100% of the architectural change instances (and is very scalable). On the other hand, our proposed change classifier’s F1 score is 70%, which is promising given the challenges. Finally, our proposed system can produce descriptive design change artifacts with 75% significance. Since most of our studies are foundational, our approaches and prepared datasets can be used as baselines for advancing research in design change information extraction and documentation

    Insights into temperature controls on rockfall occurrence and cliff erosion

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    A variety of environmental triggers have been associated with the occurrence of rockfalls however their role and relative significance remains poorly constrained. This is in part due to the lack of concurrent data on rockfall occurrence and cliff face conditions at temporal resolutions that mirror the variability of environmental conditions, and over durations for large enough numbers of rockfall events to be captured. The aim of this thesis is to fill this data gap, and then to specifically focus on the role of temperature in triggering rockfall that this data illuminates. To achieve this, a long-term multiannual 3D rockfall dataset and contemporaneous Infrared Thermography (IRT) monitoring of cliff surface temperatures has been generated. The approaches used in this thesis are undertaken at East Cliff, Whitby, which is a coastal cliff located in North Yorkshire, UK. The monitored section is ~ 200 m wide and ~65 m high, with a total cliff face area of ~9,592 m². A method for the automated quantification of rockfall volumes is used to explore data collected between 2017–2019 and 2021, with the resulting inventory including > 8,300 rockfalls from 2017–2019 and > 4,100 rockfalls in 2021, totalling > 12,400 number of rockfalls. The analysis of the inventory demonstrates that during dry conditions, increases in rockfall frequency are coincident with diurnal surface temperature fluctuations, notably at sunrise, noon and sunset in all seasons, leading to a marked diurnal pattern of rockfall. Statistically significant relationships are observed to link cliff temperature and rockfall, highlighting the response of rock slopes to absolute temperatures and changes in temperature. This research also shows that inclement weather constitutes the dominant control over the annual production of rockfalls but also quantifies the period when temperature controls are dominant. Temperature-controlled rockfall activity is shown to have an important erosional role, particularly in periods of iterative erosion dominated by small size rockfalls. As such, this thesis provides for the first high-resolution evidence of temperature controls on rockfall activity, cliff erosion and landform development

    Study for the scientific development of the Sardinia Radio Telescope/SDSA configured for solar observations and radio-science aimed at Space Weather and Fundamental Physics applications

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    The Sun produces radiation across virtually the entire electromagnetic spectrum, each frequency range helps to better understand a different aspect of our star. In the radio domain, it is an interesting celestial object to study for the richness of physical phenomena that involve not only the astrophysical area of interest, but also plasma, nuclear and fundamental physics. However, even after decades of studies, our star still presents lots of mysteries. My PhD aims to investigate the Sun environment and its emission mechanism in the radio domain to better understand some of the complex solar phenomena, their connections and find applications in the Space Weather and Fundamental Physics fields. This work is possible thanks to new challenging development of the radio telescopes managed by the Italian National Institute of Astrophysics (INAF) and the Italian Space Agency (ASI) in a joint collaboration. SRT is an ideal instrument for this Thesis project thanks to its double configuration: Sardinia Deep Space Antenna (SDSA)/radio astronomy for radio science experiments and solar imaging. The SDSA is in the implementation phase. We are inquiring the most stringent observation scientific requirements that would be necessary to prepare the antenna to perform interplanetary spacecraft tracking in radio-science configuration. The radio-astronomy configuration is already operative and has permitted us to monitor the Sun for the last few years in K-band (18-26 GHz). Moreover, the Medicina radio telescope is fully equipped to perform solar observation and has contributed considerably to the solar imaging studies. Starting 2018, we obtained more than 300 maps of the entire solar disk in the K-band, filling the observational gap in the field of solar imaging at these frequencies. I performed a new calibration procedure adopting the Supernova Remnant Cas A as a flux reference, which provided typical errors <3% for the estimation of the quiet-Sun level components. My work includes a study on the active regions brightness and spectral characterization. The interpretation of the observed emission as thermal bremsstrahlung components combined with gyro-magnetic variable emission paves the way for the use of our system for long-term monitoring of the Sun. We are also starting to explore possible interesting connections between macro-features in our data and explosive Space Weather Phenomena

    Pulmonary arterial hypertension in repaired congenital heart disease: a multicentre study

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    This doctoral thesis aims to investigate the demographics, treatment patterns and prognosis of paediatric pulmonary hypertension (PH) and the emerging group of children and adults with PH in the setting of repaired congenital heart disease (CHD). I have conducted three studies, each with a distinct focus. The first is a retrospective, longitudinal study of ten CHD centres across the UK that assesses the clinical characteristics and treatment patterns of adults with a Fontan-type circulation receiving pulmonary vasodilators. I have compared treated patients with a matched cohort of Fontan patients who are not receiving pulmonary vasodilator therapy, complemented by an expert survey to determine current practice and the goals of therapy. In the second study, I created a 20-year national UK registry of paediatric PH and derived estimates of incidence and prevalence for all groups of paediatric PH in different age categories. I determined the natural history of paediatric PH and performed survival analysis. I then focused on patients with CHD and described the changes in demographics, with a substantial increase in patients with previously repaired CHD, who now form the largest PAH-CHD subgroup. The third study focuses on this latter group of repaired PAH-CHD. I highlighted the heterogeneity in terms of severity and onset of PAH. I identified prognostic markers and variables associated with PH resolution and developed a novel risk score for predicting adverse clinical outcomes in this group. This score will form the basis for the risk stratification of this high-risk population, to inform prognostication and guide treatment.Open Acces
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