4,753 research outputs found

    Inter-individual variation of the human epigenome & applications

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    Undergraduate Catalog of Studies, 2023-2024

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    Graduate Catalog of Studies, 2023-2024

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    Robust and Flexible Persistent Scatterer Interferometry for Long-Term and Large-Scale Displacement Monitoring

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    Die Persistent Scatterer Interferometrie (PSI) ist eine Methode zur Überwachung von Verschiebungen der ErdoberflĂ€che aus dem Weltraum. Sie basiert auf der Identifizierung und Analyse von stabilen Punktstreuern (sog. Persistent Scatterer, PS) durch die Anwendung von AnsĂ€tzen der Zeitreihenanalyse auf Stapel von SAR-Interferogrammen. PS Punkte dominieren die RĂŒckstreuung der Auflösungszellen, in denen sie sich befinden, und werden durch geringfĂŒgige Dekorrelation charakterisiert. Verschiebungen solcher PS Punkte können mit einer potenziellen Submillimetergenauigkeit ĂŒberwacht werden, wenn Störquellen effektiv minimiert werden. Im Laufe der Zeit hat sich die PSI in bestimmten Anwendungen zu einer operationellen Technologie entwickelt. Es gibt jedoch immer noch herausfordernde Anwendungen fĂŒr die Methode. Physische VerĂ€nderungen der LandoberflĂ€che und Änderungen in der Aufnahmegeometrie können dazu fĂŒhren, dass PS Punkte im Laufe der Zeit erscheinen oder verschwinden. Die Anzahl der kontinuierlich kohĂ€renten PS Punkte nimmt mit zunehmender LĂ€nge der Zeitreihen ab, wĂ€hrend die Anzahl der TPS Punkte zunimmt, die nur wĂ€hrend eines oder mehrerer getrennter Segmente der analysierten Zeitreihe kohĂ€rent sind. Daher ist es wĂŒnschenswert, die Analyse solcher TPS Punkte in die PSI zu integrieren, um ein flexibles PSI-System zu entwickeln, das in der Lage ist mit dynamischen VerĂ€nderungen der LandoberflĂ€che umzugehen und somit ein kontinuierliches Verschiebungsmonitoring ermöglicht. Eine weitere Herausforderung der PSI besteht darin, großflĂ€chiges Monitoring in Regionen mit komplexen atmosphĂ€rischen Bedingungen durchzufĂŒhren. Letztere fĂŒhren zu hoher Unsicherheit in den Verschiebungszeitreihen bei großen AbstĂ€nden zur rĂ€umlichen Referenz. Diese Arbeit befasst sich mit Modifikationen und Erweiterungen, die auf der Grund lage eines bestehenden PSI-Algorithmus realisiert wurden, um einen robusten und flexiblen PSI-Ansatz zu entwickeln, der mit den oben genannten Herausforderungen umgehen kann. Als erster Hauptbeitrag wird eine Methode prĂ€sentiert, die TPS Punkte vollstĂ€ndig in die PSI integriert. In Evaluierungsstudien mit echten SAR Daten wird gezeigt, dass die Integration von TPS Punkten tatsĂ€chlich die BewĂ€ltigung dynamischer VerĂ€nderungen der LandoberflĂ€che ermöglicht und mit zunehmender ZeitreihenlĂ€nge zunehmende Relevanz fĂŒr PSI-basierte Beobachtungsnetzwerke hat. Der zweite Hauptbeitrag ist die Vorstellung einer Methode zur kovarianzbasierten Referenzintegration in großflĂ€chige PSI-Anwendungen zur SchĂ€tzung von rĂ€umlich korreliertem Rauschen. Die Methode basiert auf der Abtastung des Rauschens an Referenzpixeln mit bekannten Verschiebungszeitreihen und anschließender Interpolation auf die restlichen PS Pixel unter BerĂŒcksichtigung der rĂ€umlichen Statistik des Rauschens. Es wird in einer Simulationsstudie sowie einer Studie mit realen Daten gezeigt, dass die Methode ĂŒberlegene Leistung im Vergleich zu alternativen Methoden zur Reduktion von rĂ€umlich korreliertem Rauschen in Interferogrammen mittels Referenzintegration zeigt. Die entwickelte PSI-Methode wird schließlich zur Untersuchung von Landsenkung im Vietnamesischen Teil des Mekong Deltas eingesetzt, das seit einigen Jahrzehnten von Landsenkung und verschiedenen anderen Umweltproblemen betroffen ist. Die geschĂ€tzten Landsenkungsraten zeigen eine hohe VariabilitĂ€t auf kurzen sowie großen rĂ€umlichen Skalen. Die höchsten Senkungsraten von bis zu 6 cm pro Jahr treten hauptsĂ€chlich in stĂ€dtischen Gebieten auf. Es kann gezeigt werden, dass der grĂ¶ĂŸte Teil der Landsenkung ihren Ursprung im oberflĂ€chennahen Untergrund hat. Die prĂ€sentierte Methode zur Reduzierung von rĂ€umlich korreliertem Rauschen verbessert die Ergebnisse signifikant, wenn eine angemessene rĂ€umliche Verteilung von Referenzgebieten verfĂŒgbar ist. In diesem Fall wird das Rauschen effektiv reduziert und unabhĂ€ngige Ergebnisse von zwei Interferogrammstapeln, die aus unterschiedlichen Orbits aufgenommen wurden, zeigen große Übereinstimmung. Die Integration von TPS Punkten fĂŒhrt fĂŒr die analysierte Zeitreihe von sechs Jahren zu einer deutlich grĂ¶ĂŸeren Anzahl an identifizierten TPS als PS Punkten im gesamten Untersuchungsgebiet und verbessert damit das Beobachtungsnetzwerk erheblich. Ein spezieller Anwendungsfall der TPS Integration wird vorgestellt, der auf der Clusterung von TPS Punkten basiert, die innerhalb der analysierten Zeitreihe erschienen, um neue Konstruktionen systematisch zu identifizieren und ihre anfĂ€ngliche Bewegungszeitreihen zu analysieren

    Graduate Catalog of Studies, 2023-2024

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    A Survey on Few-Shot Class-Incremental Learning

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    Large deep learning models are impressive, but they struggle when real-time data is not available. Few-shot class-incremental learning (FSCIL) poses a significant challenge for deep neural networks to learn new tasks from just a few labeled samples without forgetting the previously learned ones. This setup can easily leads to catastrophic forgetting and overfitting problems, severely affecting model performance. Studying FSCIL helps overcome deep learning model limitations on data volume and acquisition time, while improving practicality and adaptability of machine learning models. This paper provides a comprehensive survey on FSCIL. Unlike previous surveys, we aim to synthesize few-shot learning and incremental learning, focusing on introducing FSCIL from two perspectives, while reviewing over 30 theoretical research studies and more than 20 applied research studies. From the theoretical perspective, we provide a novel categorization approach that divides the field into five subcategories, including traditional machine learning methods, meta learning-based methods, feature and feature space-based methods, replay-based methods, and dynamic network structure-based methods. We also evaluate the performance of recent theoretical research on benchmark datasets of FSCIL. From the application perspective, FSCIL has achieved impressive achievements in various fields of computer vision such as image classification, object detection, and image segmentation, as well as in natural language processing and graph. We summarize the important applications. Finally, we point out potential future research directions, including applications, problem setups, and theory development. Overall, this paper offers a comprehensive analysis of the latest advances in FSCIL from a methodological, performance, and application perspective

    Pragmatic randomised controlled trial of guided self-help versus individual cognitive behavioural therapy with a trauma focus for post-traumatic stress disorder (RAPID)

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    This is the final version. Available on open access from the NIHR Journals Library via the DOI in this recordData availability: All available data can be obtained from the corresponding author.BACKGROUND: Guided self-help has been shown to be effective for other mental conditions and, if effective for post-traumatic stress disorder, would offer a time-efficient and accessible treatment option, with the potential to reduce waiting times and costs. OBJECTIVE: To determine if trauma-focused guided self-help is non-inferior to individual, face-to-face cognitive-behavioural therapy with a trauma focus for mild to moderate post-traumatic stress disorder to a single traumatic event. DESIGN: Multicentre pragmatic randomised controlled non-inferiority trial with economic evaluation to determine cost-effectiveness and nested process evaluation to assess fidelity and adherence, dose and factors that influence outcome (including context, acceptability, facilitators and barriers, measured qualitatively). Participants were randomised in a 1 : 1 ratio. The primary analysis was intention to treat using multilevel analysis of covariance. SETTING: Primary and secondary mental health settings across the United Kingdom's National Health Service. PARTICIPANTS: One hundred and ninety-six adults with a primary diagnosis of mild to moderate post-traumatic stress disorder were randomised with 82% retention at 16 weeks and 71% at 52 weeks. Nineteen participants and ten therapists were interviewed for the process evaluation. INTERVENTIONS: Up to 12 face-to-face, manualised, individual cognitive-behavioural therapy with a trauma focus sessions, each lasting 60-90 minutes, or to guided self-help using Spring, an eight-step online guided self-help programme based on cognitive-behavioural therapy with a trauma focus, with up to five face-to-face meetings of up to 3 hours in total and four brief telephone calls or e-mail contacts between sessions. MAIN OUTCOME MEASURES: Primary outcome: the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, at 16 weeks post-randomisation. Secondary outcomes: included severity of post-traumatic stress disorder symptoms at 52 weeks, and functioning, symptoms of depression, symptoms of anxiety, alcohol use and perceived social support at both 16 and 52 weeks post-randomisation. Those assessing outcomes were blinded to group assignment. RESULTS: Non-inferiority was demonstrated at the primary end point of 16 weeks on the Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [mean difference 1.01 (one-sided 95% CI -∞ to 3.90, non-inferiority p = 0.012)]. Clinician-Administered PTSD Scale for Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, score improvements of over 60% in both groups were maintained at 52 weeks but the non-inferiority results were inconclusive in favour of cognitive-behavioural therapy with a trauma focus at this timepoint [mean difference 3.20 (one-sided 95% confidence interval -∞ to 6.00, non-inferiority p = 0.15)]. Guided self-help using Spring was not shown to be more cost-effective than face-to-face cognitive-behavioural therapy with a trauma focus although there was no significant difference in accruing quality-adjusted life-years, incremental quality-adjusted life-years -0.04 (95% confidence interval -0.10 to 0.01) and guided self-help using Spring was significantly cheaper to deliver [£277 (95% confidence interval £253 to £301) vs. £729 (95% CI £671 to £788)]. Guided self-help using Spring appeared to be acceptable and well tolerated by participants. No important adverse events or side effects were identified. LIMITATIONS: The results are not generalisable to people with post-traumatic stress disorder to more than one traumatic event. CONCLUSIONS: Guided self-help using Spring for mild to moderate post-traumatic stress disorder to a single traumatic event appears to be non-inferior to individual face-to-face cognitive-behavioural therapy with a trauma focus and the results suggest it should be considered a first-line treatment for people with this condition. FUTURE WORK: Work is now needed to determine how best to effectively disseminate and implement guided self-help using Spring at scale. TRIAL REGISTRATION: This trial is registered as ISRCTN13697710. FUNDING: This award was funded by the National Institute for Health and Care Research (NIHR) Health Technology Assessment programme (NIHR award ref: 14/192/97) and is published in full in Health Technology Assessment; Vol. 27, No. 26. See the NIHR Funding and Awards website for further award information.National Institute for Health and Care Research (NIHR

    3D electrical resistivity of Gran Canaria island using magnetotelluric data

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    Gran Canaria, one of the two main islands of the Canary Archipelago off NW Africa, has been volcanically active for at least 15 million years. The island went through several volcanic cycles that varied greatly in composition and extrusive and intrusive activity. The complex orography of the island has excluded extensive land geophysical surveys on the island. A review of the available geophysical information on the island shows that it has been obtained mainly through marine and airborne geophysical surveys. A new dataset comprising 100 magnetotelluric soundings acquired on land has been used to obtain the first 3D electrical resistivity model of the island at crustal scale. The model shows high resistivity values close to the surface in the exposed Tejeda Caldera that extends at depth to the SE cutting the islands in half. Outside the inferred limits of the Tejeda Caldera the 3D model shows low resistivity values that could be explained by hydrothermal alteration at deeper levels and the presence of marine saltwater intrusion at shallower levels near the coast. The presence of unconnected vertical-like structures, with very low resistivity (<10 ohm m) could be associated to small convective cells is confirmed by the sensitivity analysis carried out in the present study. Those structures are the most likely candidates for a detailed analysis in order to determine their geothermal economic potential. A comprehensive review of existing geophysical data and models of Gran Canaria island and their comparison with the new 3D electrical resistivity model is presented.</p

    iBUST: An intelligent behavioural trust model for securing industrial cyber-physical systems

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    To meet the demand of the world's largest population, smart manufacturing has accelerated the adoption of smart factories—where autonomous and cooperative instruments across all levels of production and logistics networks are integrated through a Cyber-Physical Production System (CPPS). However, these networks are comprised of various heterogeneous devices with varying computational power and memory capabilities. As a result, many secure communication protocols – that demand considerably high computational power and memory – can not be verbatim employed on these networks, and thereby, leaving them more vulnerable to security threats and attacks over conventional networks. These threats can largely be tackled by employing a Trust Management Model (TMM) by exploiting the behavioural patterns of nodes to identify their trust class. In this context, ML-based models are best suited due to their ability to capture hidden patterns in data, learning and improving the pattern detection accuracy over time to counteract and tackle threats of a dynamic nature, which is absent in most of the conventional models. However, among the existing ML-based solutions in detecting attack patterns, many of them are computationally expensive, require a long training time, and a considerably large amount of training data—which are seldom available. An aid to this is the association rule learning (ARL) paradigm, whose models are computationally inexpensive and do not require a long training time. Therefore, this paper proposes an ARL-based intelligent Behavioural Trust Model (iBUST) for securing the CPPS. For this intelligent TMM, a variant of Frequency Pattern Growth (FP-Growth), called enhanced FP-Growth (EFP-Growth) algorithm is developed by altering the internal data structures for faster execution and by developing a modified exponential decay function (MEDF) to automatically calculate minimum supports for adapting trust evolution characteristics. In addition, a new optimisation model for finding optimum parameter values in the MEDF and an algorithm for transmuting a 1D quantitative feature into a respective categorical feature are developed to facilitate the model. Afterwards, the trust class of an object is identified employing the Naïve Bayes classifier. This proposed model is evaluated on a trust evolution-supported experimental environment along with other compared models taking a benchmark dataset into consideration, where it outperforms its counterparts
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