@phdthesis{Abbas2011, author = {Abbas, Raya}, title = {Die Verm{\"o}gensbeziehungen der Ehegatten und nichtehelichen Lebenspartner im serbischen Recht}, series = {Studien zum ausl{\"a}ndischen und internationalen Privatrecht}, volume = {260}, journal = {Studien zum ausl{\"a}ndischen und internationalen Privatrecht}, publisher = {Mohr Siebeck}, address = {T{\"u}bingen}, isbn = {978-3-16-150847-9}, issn = {0720-1141}, pages = {297 S.}, year = {2011}, language = {de} } @phdthesis{AbdAllahSalem2018, author = {Abd Allah Salem, Mohamed}, title = {Comparative and systemic metabolomic analysis of the model plant Arabidopsis thaliana after perturbing the essential Target of Rapamycin (TOR) pathway}, school = {Universit{\"a}t Potsdam}, pages = {113}, year = {2018}, language = {en} } @phdthesis{AbdelHaliem2003, author = {Abdel-Haliem, Mahmoud E. F.}, title = {Molecular-physiological analysis of two novel isoforms of phosphoinositide kinases from Arabidopisis thaliana (L.) Heynh.}, pages = {122 S.}, year = {2003}, language = {en} } @phdthesis{AbdelHamid1998, author = {Abdel-Hamid, Hamed Ahmed}, title = {Stellar populations, dust and gas in NGC 3077}, publisher = {Wiss. Verl. Berlin}, address = {Berlin}, isbn = {3-932089-08-1}, pages = {117 S.}, year = {1998}, language = {en} } @phdthesis{Abdelfadil2013, author = {Abdelfadil, Khaled Mohamed}, title = {Geochemistry of Variscan lamprophyre magmatism in the Saxo-Thuringian Zone}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-68854}, school = {Universit{\"a}t Potsdam}, year = {2013}, abstract = {Lamprophyres are mantle-derived magmatic rocks, commonly occurring as dikes. They are readily identified from their field setting, petrography, chemical and mineralogical composition. These rocks not only provide important information on melting processes in the mantle, but also on geodynamic processes modifying the mantle. There are numerous occurrences of lamprophyres in the Saxo-Thuringian Zone of Variscan Central Europe, which are useful to track the variable effects of the Variscan orogeny on local mantle evolution. This work presents and evaluates the mineralogical, geochemical, and Sr-Nd-Pb isotopic data of late-Variscan calc-alkaline lamprophyres, post-Variscan ultramafic lamprophyres, of alkaline basalt from Lusatia, and, for comparison, of pre-Variscan gabbros. In addition, lithium isotopic signatures combined with Sr-Nd-Pb isotopic data of late-Variscan calc-alkaline lamprophyres from three different Variscan Domains (i.e., Erzgebirge, Lusatia, and Sudetes) are used to assess compositional changes of the mantle during Variscan orogeny.}, language = {de} } @phdthesis{AbdelwahabHusseinAbdelwahabElsayed2019, author = {Abdelwahab Hussein Abdelwahab Elsayed, Ahmed}, title = {Probabilistic, deep, and metric learning for biometric identification from eye movements}, doi = {10.25932/publishup-46798}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-467980}, school = {Universit{\"a}t Potsdam}, pages = {vi, 65}, year = {2019}, abstract = {A central insight from psychological studies on human eye movements is that eye movement patterns are highly individually characteristic. They can, therefore, be used as a biometric feature, that is, subjects can be identified based on their eye movements. This thesis introduces new machine learning methods to identify subjects based on their eye movements while viewing arbitrary content. The thesis focuses on probabilistic modeling of the problem, which has yielded the best results in the most recent literature. The thesis studies the problem in three phases by proposing a purely probabilistic, probabilistic deep learning, and probabilistic deep metric learning approach. In the first phase, the thesis studies models that rely on psychological concepts about eye movements. Recent literature illustrates that individual-specific distributions of gaze patterns can be used to accurately identify individuals. In these studies, models were based on a simple parametric family of distributions. Such simple parametric models can be robustly estimated from sparse data, but have limited flexibility to capture the differences between individuals. Therefore, this thesis proposes a semiparametric model of gaze patterns that is flexible yet robust for individual identification. These patterns can be understood as domain knowledge derived from psychological literature. Fixations and saccades are examples of simple gaze patterns. The proposed semiparametric densities are drawn under a Gaussian process prior centered at a simple parametric distribution. Thus, the model will stay close to the parametric class of densities if little data is available, but it can also deviate from this class if enough data is available, increasing the flexibility of the model. The proposed method is evaluated on a large-scale dataset, showing significant improvements over the state-of-the-art. Later, the thesis replaces the model based on gaze patterns derived from psychological concepts with a deep neural network that can learn more informative and complex patterns from raw eye movement data. As previous work has shown that the distribution of these patterns across a sequence is informative, a novel statistical aggregation layer called the quantile layer is introduced. It explicitly fits the distribution of deep patterns learned directly from the raw eye movement data. The proposed deep learning approach is end-to-end learnable, such that the deep model learns to extract informative, short local patterns while the quantile layer learns to approximate the distributions of these patterns. Quantile layers are a generic approach that can converge to standard pooling layers or have a more detailed description of the features being pooled, depending on the problem. The proposed model is evaluated in a large-scale study using the eye movements of subjects viewing arbitrary visual input. The model improves upon the standard pooling layers and other statistical aggregation layers proposed in the literature. It also improves upon the state-of-the-art eye movement biometrics by a wide margin. Finally, for the model to identify any subject — not just the set of subjects it is trained on — a metric learning approach is developed. Metric learning learns a distance function over instances. The metric learning model maps the instances into a metric space, where sequences of the same individual are close, and sequences of different individuals are further apart. This thesis introduces a deep metric learning approach with distributional embeddings. The approach represents sequences as a set of continuous distributions in a metric space; to achieve this, a new loss function based on Wasserstein distances is introduced. The proposed method is evaluated on multiple domains besides eye movement biometrics. This approach outperforms the state of the art in deep metric learning in several domains while also outperforming the state of the art in eye movement biometrics.}, language = {en} } @phdthesis{Abed2010, author = {Abed, Jamil}, title = {An iterative approach to operators on manifolds with singularities}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-44757}, school = {Universit{\"a}t Potsdam}, year = {2010}, abstract = {We establish elements of a new approach to ellipticity and parametrices within operator algebras on manifolds with higher singularities, only based on some general axiomatic requirements on parameter-dependent operators in suitable scales of spaes. The idea is to model an iterative process with new generations of parameter-dependent operator theories, together with new scales of spaces that satisfy analogous requirements as the original ones, now on a corresponding higher level. The "full" calculus involves two separate theories, one near the tip of the corner and another one at the conical exit to infinity. However, concerning the conical exit to infinity, we establish here a new concrete calculus of edge-degenerate operators which can be iterated to higher singularities.}, language = {en} } @phdthesis{Abedjan2014, author = {Abedjan, Ziawasch}, title = {Improving RDF data with data mining}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus-71334}, school = {Universit{\"a}t Potsdam}, year = {2014}, abstract = {Linked Open Data (LOD) comprises very many and often large public data sets and knowledge bases. Those datasets are mostly presented in the RDF triple structure of subject, predicate, and object, where each triple represents a statement or fact. Unfortunately, the heterogeneity of available open data requires significant integration steps before it can be used in applications. Meta information, such as ontological definitions and exact range definitions of predicates, are desirable and ideally provided by an ontology. However in the context of LOD, ontologies are often incomplete or simply not available. Thus, it is useful to automatically generate meta information, such as ontological dependencies, range definitions, and topical classifications. Association rule mining, which was originally applied for sales analysis on transactional databases, is a promising and novel technique to explore such data. We designed an adaptation of this technique for min-ing Rdf data and introduce the concept of "mining configurations", which allows us to mine RDF data sets in various ways. Different configurations enable us to identify schema and value dependencies that in combination result in interesting use cases. To this end, we present rule-based approaches for auto-completion, data enrichment, ontology improvement, and query relaxation. Auto-completion remedies the problem of inconsistent ontology usage, providing an editing user with a sorted list of commonly used predicates. A combination of different configurations step extends this approach to create completely new facts for a knowledge base. We present two approaches for fact generation, a user-based approach where a user selects the entity to be amended with new facts and a data-driven approach where an algorithm discovers entities that have to be amended with missing facts. As knowledge bases constantly grow and evolve, another approach to improve the usage of RDF data is to improve existing ontologies. Here, we present an association rule based approach to reconcile ontology and data. Interlacing different mining configurations, we infer an algorithm to discover synonymously used predicates. Those predicates can be used to expand query results and to support users during query formulation. We provide a wide range of experiments on real world datasets for each use case. The experiments and evaluations show the added value of association rule mining for the integration and usability of RDF data and confirm the appropriateness of our mining configuration methodology.}, language = {en} } @phdthesis{Abegg1998, author = {Abegg, Christoph}, title = {Parameterisierung atmosph{\"a}rischer Grenzschichtprozesse in einem regionalen Klimamodell der Arktis}, address = {Potsdam}, pages = {116 S.}, year = {1998}, language = {de} } @phdthesis{Abel2015, author = {Abel, Johanna}, title = {Transatlantisches K{\"o}rperDenken}, series = {Potsdamer inter- und transaktuelle Texte (POINTE)}, journal = {Potsdamer inter- und transaktuelle Texte (POINTE)}, number = {12}, publisher = {Frey}, address = {Berlin}, isbn = {978-3-938944-89-9}, pages = {334}, year = {2015}, language = {de} }