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Dissertation
Explaining and Visualizing Structural Knowledge in Bipartite Graphs
(2023)
Bipartite graphs are an important model for the representation and analysis of relationships between two different types of entities. Datasets in this form are commonly found in many fields, such as social networks, biology, and economics. Formal concept analysis is a research approach that allows for the analysis of such bipartite graphs by clustering the data into so-called concepts and ordering those in a lattice structure.
In this thesis we propose multiple approaches for the extraction and visualization of ...
Dissertation
Learning planning communication in cooperative multi-agent settings
(2023)
This work is a holistic investigation of the question of how and why reinforcement learning (RL) agents fail to develop planning communication in a cooperative setting. It is also a guide to anyone trying to build a system that learns to use planning communication patterns. We examine the state of the art in multi-agent systems and emergent communication for the existence of communication patterns that share information about future actions and plans. To do this, we strategically formulate hypotheses that question ...
Dissertation
Attribute Exploration with Multiple Experts
(2023)
Attribute exploration is a knowledge acquisition method from the realm of formal concept analysis that allows a domain expert to efficiently uncover the dependencies in a domain. It is based on a question-answering scheme where the exploration algorithm generates questions about dependencies in the domain that are then answered by a domain expert. Even though many variants and extension to this were developed, only few attempts to incorporate multiple experts were made. The overarching goal of this thesis is to extend ...
Habilitation
Computing Ground States for Fermi-Bose Mixtures through Efficient Numerical Methods
(2023-05)
In this work, we will first review the Quantum Mechanics theory to derive the main equations. Next, we will analyze these equations by Functional Analysis methods to find conditions for existence, uniqueness, multiplicity, and other properties as positivity. Next, we will review and develop some numerical methods for solving the nonlinear Schrödinger equation, its time version, generalizations with rotational terms, and systems of NLSE (NLSS). We notice that the main problem to run numerical methods is the memory ...
Dissertation
Situative Teams in Cooperative Autonomous Systems
(2023)
Distributed systems have been established in many areas of IT and will play an even more significant role in the future. Such systems are no longer limited to specific fields of application but interconnect many different domains. They encompass, e. g. Cloud Computing, Internet of Things, service robotics, and autonomous vehicles. The integrated sub-systems communicate in order to exchange information and, if necessary, perform tasks together. Moreover, the number of interconnected sub-systems is constantly growing. ...
Aufsatz
Continuous Feature Networks: A Novel Method to Process Irregularly and Inconsistently Sampled Data With Position-Dependent Features
(2023-12-30)
Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable if the position of detected features is relevant for the prediction task. However, the capabilities of continuous kernels to ...
Konferenzveröffentlichung
Utilizing Continuous Kernels for Processing Irregularly and Inconsistently Sampled Data With Position-Dependent Features
(2023-03-13)
Continuous Kernels have been a recent development in convolutional neural networks. Such kernels are used to process data sampled at different resolutions as well as irregularly and inconsistently sampled data. Convolutional neural networks have the property of translational invariance (e.g., features are detected regardless of their position in the measurement domain), which is unsuitable for certain types of data, where the position of detected features is relevant. However, the capabilities of continuous kernels ...
Dissertation
Effektive Integration von heterogenen Produktkatalogen im schnelllebigen Umfeld des E-Commerce
(2023)
Online-Marktplätze generieren von Jahr zu Jahr einen größeren Anteil des Einzelhandelsumsatzes. Ein wichtiger Faktor für den Erfolg von Online-Marktplätzen ist die korrekte Darstellung der Produktdaten für ihre Kunden. Diese Daten werden häufig von Zulieferern in Form von Produktkatalogen zur Verfügung gestellt, die in den Online-Marktplatz integriert werden müssen. Um dies zu erreichen, sind insbesondere kleine und mittelständische Unternehmen häufig auf aufwändige manuelle Arbeitsschritte bei der Datenintegration ...
Zeitschrift
Jahresbericht 2022
(Universität Kassel, Fachbereich Wirtschaftswissenschaften, 2023)
Dissertation
Development of Selectively Actuatable Micromirror Arrays and Scalable Lithography Processes for Large-Area Applications as Smart Window
(2023)
This thesis provides an extensive overview on the state-of-the-art daylighting systems for applications in buildings and subsequently addresses the implementation of subfield addressing in micromirror arrays and investigation of a potential scalable lithography process for the large-area fabrication, both have been identified as the research gap in achieving the envisioned application as smart window. The work is conducted in continuation of the optimized fabrication process and the successful fabrication of a lab ...