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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
1

The Manaqib al-Arifin as a source for political history /

Trepanier, Nicolas. January 2001 (has links)
The Manaqib al-`Arifin is a series of hagiographic biographies of the first Mevlevi masters. It was written in Persian by the dervish Eflaki in the first half of the fourteenth century in Konya. Because of the limited number of narrative sources from that period, Eflaki appears as an outstanding witness of the late Seldjuk, Ilkhanid, and early Beylik period in Anatolia. / This thesis intends to evaluate the Manaqib al-`Arifin as a source for political history. While previous historical scholarship has made frequent use of this source for isolated episodes, barely any systematic study of the Manaqib has been published yet. / The evaluation presented in this thesis results from a comparison between every element of information that can be considered "political" in the Manaqib al-`Arifin and current scholarship on the respective topic. These elements of information fall into four broad categories: The Seldjuk of Anatolia, the akhi organization, the Ilkhan state, and the beylik states. / The most part of this thesis consists in an exhaustive enumeration of the elements of political history that could be found in the Manaqib. In turn, these elements set the tone of the global conclusion of the inquiry, that is to say that the Manaqib al-`Arifin, without being a revolutionary source, offers information which is often original and which, under certain conditions, can prove highly reliable.
2

The Manaqib al-Arifin as a source for political history /

Trepanier, Nicolas. January 2001 (has links)
No description available.
3

Computer-based diagnostic and prognostic approaches in medical research using brain MRI

Weygandt, Martin 03 August 2016 (has links)
Die vorliegende Habilitationsschrift zu „Computer-based diagnostic and prognostic approaches in medical research using brain MRI“ ist in zwei Abschnitte gegliedert. Konkret wird im ersten Abschnitt eine Übersicht über verschiedene Aspekte des Computer- und MRT-basierten Vorhersageansatzes gegeben. Im zweiten Abschnitt werden die Artikel aus diesem Feld beschrieben, die ich für die Habilitation eingereicht habe. Konkret beginnt der erste Abschnitt der Habilitationsschrift damit, das grundlegende methodische Konzept des Vorhersageansatzes zu beschreiben. Danach werden die drei prozeduralen Stadien beschrieben, die seine Anwendung charakterisieren, d.h. die Phase der Feature-Bestimmung, des Trainings von Regressionsalgorithmen und schließlich des Tests dieser Algorithmen mit Daten unbekannter Genese. Daran schließt sich eine Beschreibung der Entwicklung des Ansatzes in Form von drei Epochen an, die charakterisiert sind durch die Entdeckung diagnostischer Information in Signalen der Magnetresonanz, die erste Nutzung statistischer Regressionsverfahren zu deren Analyse, und die massenhaften Anwendung des Ansatzes. Schließlich werden zum Ende des ersten Abschnittes die Forschungsfragen skizziert, die mit dem Ansatz adressiert werden, d.h. die automatisierte Diagnostik, die Verfeinerung bestehender diagnostischer Richtlinien und die Identifikation neuer Biomarker. Im zweiten Abschnitt beschreibe ich im Detail die Forschungsartikel, die ich im Rahmen der Habilitation eingereicht habe. Über diese Artikel oder Studien hinweg wurden alle oben genannten Forschungsfragen adressiert, die mit dem Verfahren in der Literatur untersucht werden. Darüber hinaus wurden vielfältige technische Herausforderungen des Ansatzes in unterschiedlicher Weise bearbeitet. Zusammenfassend lässt sich daher sagen, dass die vorliegende Habilitationsschrift und die darin beschriebenen Fachartikel einen umfassenden Überblick über die konzeptionelle und methodische Vielfalt des Ansatzes geben. / This habilitation thesis on ‘Computer-based diagnostic and prognostic approaches in medical research using brain MRI’ is divided in two parts – an introductory first part that gives an overview on various aspects of the computer- and MRI-based disease prediction approach and a second part describing the research articles from this field that I submitted for habilitation. In particular, in the first part the habilitation synopsis starts by outlining the basic methodological concept of the disease prediction approach and by describing the three fundamental procedural stages characterizing it, i.e. the feature determination, training and test stages. Then, it continues by delineating the development of the approach in terms of three epochs that are characterized by the discovery of diagnostic information in MR signals, the first use of statistical regression techniques to analyze this information, and the mass use of the approach. Finally, it outlines the research aims pursued with the approach, i.e. automated diagnosis, refinement of diagnostic guidelines, and identification of novel diagnostic biomarkers. In the second part, I describe the peer-reviewed research articles that I submitted for habilitation. Across these articles or studies respectively, all of the three research aims pursued with the approach were addressed. Furthermore, technical challenges connected to the approach were addressed in various different fashions. Thus together, these studies and this habilitation thesis provide a substantial overview on the methodological and conceptual diversity of the field.
4

Education and episcopacy : the universities of Scotland in the fifteenth century

Woodman, Isla January 2011 (has links)
Educational provision in Scotland was revolutionised in the fifteenth century through the foundation of three universities, or studia generale, at St Andrews, Glasgow and Aberdeen. These institutions can be viewed as part of the general expansion in higher education across Europe from the late-fourteenth century, which saw the establishment of many new centres of learning, often intended to serve local needs. Their impact on Scotland ought to have been profound; in theory, they removed the need for its scholars to continue to seek higher education at the universities of England or the continent. Scotland’s fifteenth-century universities were essentially episcopal foundations, formally instituted by bishops within the cathedral cities of their dioceses, designed to meet the educational needs and career aspirations of the clergy. They are not entirely neglected subjects; the previous generation of university historians – including A. Dunlop, J. Durkan and L. J. Macfarlane – did much to recover the institutional, organisational and curricular developments that shaped their character. Less well explored, are the over-arching political themes that influenced the evolution of university provision in fifteenth-century Scotland as a whole. Similarly under-researched, is the impact of these foundations on the scholarly community, and society more generally. This thesis explores these comparatively neglected themes in two parts. Part I presents a short narrative, offering a more politically sensitive interpretation of the introduction and expansion of higher educational provision in Scotland. Part II explores the impact of these foundations on Scottish scholars. The nature of extant sources inhibits reconstruction of the full extent of their influence on student numbers and patterns of university attendance. Instead, Part II presents a thorough quantitative and qualitative prosopographical study of the Scottish episcopate within the context of this embryonic era of university provision in Scotland. In so doing, this thesis offers new insights into a neglected aspect of contemporary clerical culture as well as the politics of fifteenth-century academic learning.
5

Explainable deep learning classifiers for disease detection based on structural brain MRI data

Eitel, Fabian 14 November 2022 (has links)
In dieser Doktorarbeit wird die Frage untersucht, wie erfolgreich deep learning bei der Diagnostik von neurodegenerativen Erkrankungen unterstützen kann. In 5 experimentellen Studien wird die Anwendung von Convolutional Neural Networks (CNNs) auf Daten der Magnetresonanztomographie (MRT) untersucht. Ein Schwerpunkt wird dabei auf die Erklärbarkeit der eigentlich intransparenten Modelle gelegt. Mit Hilfe von Methoden der erklärbaren künstlichen Intelligenz (KI) werden Heatmaps erstellt, die die Relevanz einzelner Bildbereiche für das Modell darstellen. Die 5 Studien dieser Dissertation zeigen das Potenzial von CNNs zur Krankheitserkennung auf neurologischen MRT, insbesondere bei der Kombination mit Methoden der erklärbaren KI. Mehrere Herausforderungen wurden in den Studien aufgezeigt und Lösungsansätze in den Experimenten evaluiert. Über alle Studien hinweg haben CNNs gute Klassifikationsgenauigkeiten erzielt und konnten durch den Vergleich von Heatmaps zur klinischen Literatur validiert werden. Weiterhin wurde eine neue CNN Architektur entwickelt, spezialisiert auf die räumlichen Eigenschaften von Gehirn MRT Bildern. / Deep learning and especially convolutional neural networks (CNNs) have a high potential of being implemented into clinical decision support software for tasks such as diagnosis and prediction of disease courses. This thesis has studied the application of CNNs on structural MRI data for diagnosing neurological diseases. Specifically, multiple sclerosis and Alzheimer’s disease were used as classification targets due to their high prevalence, data availability and apparent biomarkers in structural MRI data. The classification task is challenging since pathology can be highly individual and difficult for human experts to detect and due to small sample sizes, which are caused by the high acquisition cost and sensitivity of medical imaging data. A roadblock in adopting CNNs to clinical practice is their lack of interpretability. Therefore, after optimizing the machine learning models for predictive performance (e.g. balanced accuracy), we have employed explainability methods to study the reliability and validity of the trained models. The deep learning models achieved good predictive performance of over 87% balanced accuracy on all tasks and the explainability heatmaps showed coherence with known clinical biomarkers for both disorders. Explainability methods were compared quantitatively using brain atlases and shortcomings regarding their robustness were revealed. Further investigations showed clear benefits of transfer-learning and image registration on the model performance. Lastly, a new CNN layer type was introduced, which incorporates a prior on the spatial homogeneity of neuro-MRI data. CNNs excel when used on natural images which possess spatial heterogeneity, and even though MRI data and natural images share computational similarities, the composition and orientation of neuro-MRI is very distinct. The introduced patch-individual filter (PIF) layer breaks the assumption of spatial invariance of CNNs and reduces convergence time on different data sets without reducing predictive performance. The presented work highlights many challenges that CNNs for disease diagnosis face on MRI data and defines as well as tests strategies to overcome those.

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