@phdthesis{Rabe2024,
  author    = {Rabe, Maximilian Michael},
  title     = {Modeling the interaction of sentence processing and eye-movement control in reading},
  doi       = {10.25932/publishup-62279},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-622792},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xiii, 171},
  year      = {2024},
  abstract  = {The evaluation of process-oriented cognitive theories through time-ordered observations is crucial for the advancement of cognitive science. The findings presented herein integrate insights from research on eye-movement control and sentence comprehension during reading, addressing challenges in modeling time-ordered data, statistical inference, and interindividual variability. Using kernel density estimation and a pseudo-marginal likelihood for fixation durations and locations, a likelihood implementation of the SWIFT model of eye-movement control during reading (Engbert et al., Psychological Review, 112, 2005, pp. 777-813) is proposed. Within the broader framework of data assimilation, Bayesian parameter inference with adaptive Markov Chain Monte Carlo techniques is facilitated for reliable model fitting. Across the different studies, this framework has shown to enable reliable parameter recovery from simulated data and prediction of experimental summary statistics. Despite its complexity, SWIFT can be fitted within a principled Bayesian workflow, capturing interindividual differences and modeling experimental effects on reading across different geometrical alterations of text. Based on these advancements, the integrated dynamical model SEAM is proposed, which combines eye-movement control, a traditionally psychological research area, and post-lexical language processing in the form of cue-based memory retrieval (Lewis \& Vasishth, Cognitive Science, 29, 2005, pp. 375-419), typically the purview of psycholinguistics. This proof-of-concept integration marks a significant step forward in natural language comprehension during reading and suggests that the presented methodology can be useful to develop complex cognitive dynamical models that integrate processes at levels of perception, higher cognition, and (oculo-)motor control. These findings collectively advance process-oriented cognitive modeling and highlight the importance of Bayesian inference, individual differences, and interdisciplinary integration for a holistic understanding of reading processes. Implications for theory and methodology, including proposals for model comparison and hierarchical parameter inference, are briefly discussed.},
  language  = {en}
}
@phdthesis{Sareeto2024,
  author    = {Sareeto, Apatsara},
  title     = {Algebraic properties of a subsemigroup of the symmetric inverse semigroup},
  school      = {Universit{\"a}t Potsdam},
  pages     = {92},
  year      = {2024},
  language  = {en}
}
@misc{Reimann2024,
  type      = {Master Thesis},
  author    = {Reimann, Hans},
  title     = {Towards robust inference for Bayesian filtering of linear Gaussian dynamical systems subject to additive change},
  doi       = {10.25932/publishup-64946},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-649469},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ix, 156},
  year      = {2024},
  abstract  = {State space models enjoy wide popularity in mathematical and statistical modelling across disciplines and research fields. Frequent solutions to problems of estimation and forecasting of a latent signal such as the celebrated Kalman filter hereby rely on a set of strong assumptions such as linearity of system dynamics and Gaussianity of noise terms. We investigate fallacy in mis-specification of the noise terms, that is signal noise and observation noise, regarding heavy tailedness in that the true dynamic frequently produces observation outliers or abrupt jumps of the signal state due to realizations of these heavy tails not considered by the model. We propose a formalisation of observation noise mis-specification in terms of Huber's ε-contamination as well as a computationally cheap solution via generalised Bayesian posteriors with a diffusion Stein divergence loss resulting in the diffusion score matching Kalman filter - a modified algorithm akin in complexity to the regular Kalman filter. For this new filter interpretations of novel terms, stability and an ensemble variant are discussed. Regarding signal noise mis-specification, we propose a formalisation in the frame work of change point detection and join ideas from the popular CUSUM algo- rithm with ideas from Bayesian online change point detection to combine frequent reliability constraints and online inference resulting in a Gaussian mixture model variant of multiple Kalman filters. We hereby exploit open-end sequential probability ratio tests on the evidence of Kalman filters on observation sub-sequences for aggregated inference under notions of plausibility. Both proposed methods are combined to investigate the double mis-specification problem and discussed regarding their capabilities in reliable and well-tuned uncertainty quantification. Each section provides an introduction to required terminology and tools as well as simulation experiments on the popular target tracking task and the non-linear, chaotic Lorenz-63 system to showcase practical performance of theoretical considerations.},
  language  = {en}
}
@phdthesis{Fischer2024,
  author    = {Fischer, Florian},
  title     = {Hardy inequalities on graphs},
  doi       = {10.25932/publishup-64773},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-647730},
  school      = {Universit{\"a}t Potsdam},
  pages     = {vi, 160},
  year      = {2024},
  abstract  = {Die Dissertation befasst sich mit einer zentralen Ungleichung der nicht-linearen Potentialtheorie, der Hardy-Ungleichung. Sie besagt, dass das nicht-lineare Energiefunktional von unten durch eine p-te Potenz einer gewichteten p-Norm abgesch{\"a}tzt werden kann, p>1. Das Energiefunktional besteht dabei aus einem Divergenz- und einem beliebigen Potentialteil. Als zugrundeliegender Raum wurden hier lokal summierbare unendliche Graphen gew{\"a}hlt. Bisherige Ver{\"o}ffentlichungen zu Hardy-Ungleichungen auf Graphen haben vor allem den Spezialfall p=2 betrachtet, oder lokal endliche Graphen ohne Potentialteil. Zwei grundlegende Fragestellungen ergeben sich nun ganz nat{\"u}rlich: F{\"u}r welche Graphen gibt {\"u}berhaupt es eine Hardy-Ungleichung? Und, wenn es sie gibt, gibt es einen Weg um ein optimales Gewicht zu erhalten? Antworten auf diese Fragen werden in Theorem 10.1 und Theorem 12.1 gegeben. Theorem 10.1 gibt eine Reihe an Charakterisierungen an; unter anderem gibt es eine Hardy-Ungleichung auf einem Graphen genau dann, wenn es eine Greensche Funktion gibt. Theorem 12.1 gibt eine explizite Formel an, um optimale Hardy-Gewichte f{\"u}r lokal endliche Graphen unter einigen technischen Zusatzannahmen zu berechnen. In Beispielen wird gezeigt, dass Greensche Funktionen gute Kandidaten sind um in die Formel eingesetzt zu werden. Um diese beiden Theoreme beweisen zu k{\"o}nnen, m{\"u}ssen eine Vielzahl an Techniken erarbeitet werden, welche in den ersten Kapiteln behandelt werden. Dabei sind eine Verallgemeinerung der Grundzustandstransformation (Theorem 4.1), ein Agmon-Allegretto-Piepenbrink-artiges Resultat (Theorem 6.1) und das Vergleichsprinzip (Proposition 7.3) besonders hervorzuheben, da diese Resultate sehr h{\"a}ufig angewendet werden und somit das Fundament der Dissertation bilden. Es wird zudem darauf Wert gelegt die Theorie durch Beispiele zu veranschaulichen. Hierbei wird der Fokus auf die nat{\"u}rlichen Zahlen, Euklidische Gitter, B{\"a}ume und Sterne gelegt. Als Abschluss werden noch eine nicht-lineare Version der Heisenbergschen Unsch{\"a}rferelation und eine Rellich-Ungleichung aus der Hardy-Ungleichung geschlussfolgert.},
  language  = {en}
}