@phdthesis{Lindinger2023,
  author    = {Lindinger, Jakob},
  title     = {Variational inference for composite Gaussian process models},
  doi       = {10.25932/publishup-60444},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-604441},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 122},
  year      = {2023},
  abstract  = {Most machine learning methods provide only point estimates when being queried to predict on new data. This is problematic when the data is corrupted by noise, e.g. from imperfect measurements, or when the queried data point is very different to the data that the machine learning model has been trained with. Probabilistic modelling in machine learning naturally equips predictions with corresponding uncertainty estimates which allows a practitioner to incorporate information about measurement noise into the modelling process and to know when not to trust the predictions. A well-understood, flexible probabilistic framework is provided by Gaussian processes that are ideal as building blocks of probabilistic models. They lend themself naturally to the problem of regression, i.e., being given a set of inputs and corresponding observations and then predicting likely observations for new unseen inputs, and can also be adapted to many more machine learning tasks. However, exactly inferring the optimal parameters of such a Gaussian process model (in a computationally tractable manner) is only possible for regression tasks in small data regimes. Otherwise, approximate inference methods are needed, the most prominent of which is variational inference. In this dissertation we study models that are composed of Gaussian processes embedded in other models in order to make those more flexible and/or probabilistic. The first example are deep Gaussian processes which can be thought of as a small network of Gaussian processes and which can be employed for flexible regression. The second model class that we study are Gaussian process state-space models. These can be used for time-series modelling, i.e., the task of being given a stream of data ordered by time and then predicting future observations. For both model classes the state-of-the-art approaches offer a trade-off between expressive models and computational properties (e.g. speed or convergence properties) and mostly employ variational inference. Our goal is to improve inference in both models by first getting a deep understanding of the existing methods and then, based on this, to design better inference methods. We achieve this by either exploring the existing trade-offs or by providing general improvements applicable to multiple methods. We first provide an extensive background, introducing Gaussian processes and their sparse (approximate and efficient) variants. We continue with a description of the models under consideration in this thesis, deep Gaussian processes and Gaussian process state-space models, including detailed derivations and a theoretical comparison of existing methods. Then we start analysing deep Gaussian processes more closely: Trading off the properties (good optimisation versus expressivity) of state-of-the-art methods in this field, we propose a new variational inference based approach. We then demonstrate experimentally that our new algorithm leads to better calibrated uncertainty estimates than existing methods. Next, we turn our attention to Gaussian process state-space models, where we closely analyse the theoretical properties of existing methods.The understanding gained in this process leads us to propose a new inference scheme for general Gaussian process state-space models that incorporates effects on multiple time scales. This method is more efficient than previous approaches for long timeseries and outperforms its comparison partners on data sets in which effects on multiple time scales (fast and slowly varying dynamics) are present. Finally, we propose a new inference approach for Gaussian process state-space models that trades off the properties of state-of-the-art methods in this field. By combining variational inference with another approximate inference method, the Laplace approximation, we design an efficient algorithm that outperforms its comparison partners since it achieves better calibrated uncertainties.},
  language  = {en}
}
@phdthesis{Discher2023,
  author    = {Discher, S{\"o}ren},
  title     = {Real-Time Rendering Techniques for Massive 3D Point Clouds},
  doi       = {10.25932/publishup-60164},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-601641},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ix, 123},
  year      = {2023},
  abstract  = {Today, point clouds are among the most important categories of spatial data, as they constitute digital 3D models of the as-is reality that can be created at unprecedented speed and precision. However, their unique properties, i.e., lack of structure, order, or connectivity information, necessitate specialized data structures and algorithms to leverage their full precision. In particular, this holds true for the interactive visualization of point clouds, which requires to balance hardware limitations regarding GPU memory and bandwidth against a naturally high susceptibility to visual artifacts. This thesis focuses on concepts, techniques, and implementations of robust, scalable, and portable 3D visualization systems for massive point clouds. To that end, a number of rendering, visualization, and interaction techniques are introduced, that extend several basic strategies to decouple rendering efforts and data management: First, a novel visualization technique that facilitates context-aware filtering, highlighting, and interaction within point cloud depictions. Second, hardware-specific optimization techniques that improve rendering performance and image quality in an increasingly diversified hardware landscape. Third, natural and artificial locomotion techniques for nausea-free exploration in the context of state-of-the-art virtual reality devices. Fourth, a framework for web-based rendering that enables collaborative exploration of point clouds across device ecosystems and facilitates the integration into established workflows and software systems. In cooperation with partners from industry and academia, the practicability and robustness of the presented techniques are showcased via several case studies using representative application scenarios and point cloud data sets. In summary, the work shows that the interactive visualization of point clouds can be implemented by a multi-tier software architecture with a number of domain-independent, generic system components that rely on optimization strategies specific to large point clouds. It demonstrates the feasibility of interactive, scalable point cloud visualization as a key component for distributed IT solutions that operate with spatial digital twins, providing arguments in favor of using point clouds as a universal type of spatial base data usable directly for visualization purposes.},
  language  = {en}
}
@phdthesis{Kossmann2023,
  author    = {Koßmann, Jan},
  title     = {Unsupervised database optimization},
  doi       = {10.25932/publishup-58949},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-589490},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 203},
  year      = {2023},
  abstract  = {The amount of data stored in databases and the complexity of database workloads are ever- increasing. Database management systems (DBMSs) offer many configuration options, such as index creation or unique constraints, which must be adapted to the specific instance to efficiently process large volumes of data. Currently, such database optimization is complicated, manual work performed by highly skilled database administrators (DBAs). In cloud scenarios, manual database optimization even becomes infeasible: it exceeds the abilities of the best DBAs due to the enormous number of deployed DBMS instances (some providers maintain millions of instances), missing domain knowledge resulting from data privacy requirements, and the complexity of the configuration tasks. Therefore, we investigate how to automate the configuration of DBMSs efficiently with the help of unsupervised database optimization. While there are numerous configuration options, in this thesis, we focus on automatic index selection and the use of data dependencies, such as functional dependencies, for query optimization. Both aspects have an extensive performance impact and complement each other by approaching unsupervised database optimization from different perspectives. Our contributions are as follows: (1) we survey automated state-of-the-art index selection algorithms regarding various criteria, e.g., their support for index interaction. We contribute an extensible platform for evaluating the performance of such algorithms with industry-standard datasets and workloads. The platform is well-received by the community and has led to follow-up research. With our platform, we derive the strengths and weaknesses of the investigated algorithms. We conclude that existing solutions often have scalability issues and cannot quickly determine (near-)optimal solutions for large problem instances. (2) To overcome these limitations, we present two new algorithms. Extend determines (near-)optimal solutions with an iterative heuristic. It identifies the best index configurations for the evaluated benchmarks. Its selection runtimes are up to 10 times lower compared with other near-optimal approaches. SWIRL is based on reinforcement learning and delivers solutions instantly. These solutions perform within 3 \% of the optimal ones. Extend and SWIRL are available as open-source implementations. (3) Our index selection efforts are complemented by a mechanism that analyzes workloads to determine data dependencies for query optimization in an unsupervised fashion. We describe and classify 58 query optimization techniques based on functional, order, and inclusion dependencies as well as on unique column combinations. The unsupervised mechanism and three optimization techniques are implemented in our open-source research DBMS Hyrise. Our approach reduces the Join Order Benchmark's runtime by 26 \% and accelerates some TPC-DS queries by up to 58 times. Additionally, we have developed a cockpit for unsupervised database optimization that allows interactive experiments to build confidence in such automated techniques. In summary, our contributions improve the performance of DBMSs, support DBAs in their work, and enable them to contribute their time to other, less arduous tasks.},
  language  = {en}
}
@masterthesis{Repp2023,
  type      = {Bachelor Thesis},
  author    = {Repp, Leo},
  title     = {Extending the automatic theorem prover nanoCoP with arithmetic procedures},
  doi       = {10.25932/publishup-57619},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-576195},
  school      = {Universit{\"a}t Potsdam},
  pages     = {52},
  year      = {2023},
  abstract  = {In dieser Bachelorarbeit implementiere ich den automatischen Theorembeweiser nanoCoP-Ω. Es handelt sich bei diesem neuen System um das Ergebnis einer Portierung von Arithmetik-behandelnden Prozeduren aus dem automatischen Theorembeweiser mit Arithmetik leanCoP-Ω in das System nanoCoP 2.0. Dazu wird zuerst der mathematische Hintergrund zu automatischen Theorembeweisern und Arithmetik gegeben. Ich stelle die Vorg{\"a}ngerprojekte leanCoP, nanoCoP und leanCoP-Ω vor, auf dessen Vorlage nanoCoP-Ω entwickelt wurde. Es folgt eine ausf{\"u}hrliche Erkl{\"a}rung der Konzepte, um welche der nicht-klausale Konnektionskalk{\"u}l erweitert werden muss, um eine Behandlung von arithmetischen Ausdr{\"u}cken und Gleichheiten in den Kalk{\"u}l zu integrieren, sowie eine Beschreibung der Implementierung dieser Konzepte in nanoCoP-Ω. Als letztes folgt eine experimentelle Evaluation von nanoCoP-Ω. Es wurde ein ausf{\"u}hrlicher Vergleich von Laufzeit und Anzahl gel{\"o}ster Probleme im Vergleich zum {\"a}hnlich aufgebauten Theorembeweiser leanCoP-Ω auf Basis der TPTP-Benchmark durchgef{\"u}hrt. Ich komme zu dem Ergebnis, dass nanoCoP-Ω deutlich schneller ist als leanCoP-Ω ist, jedoch weniger gut geeignet f{\"u}r gr{\"o}ßere Probleme. Zudem konnte ich feststellen, dass nanoCoP-Ω falsche Beweise liefern kann. Ich bespreche, wie dieses Problem gel{\"o}st werden kann, sowie einige m{\"o}gliche Optimierungen und Erweiterungen des Beweissystems.},
  language  = {en}
}
@phdthesis{Molitor2023,
  author    = {Molitor, Louise},
  title     = {Strategic Residential Segregation},
  doi       = {10.25932/publishup-60135},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-601359},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xi, 210},
  year      = {2023},
  abstract  = {Residential segregation is a widespread phenomenon that can be observed in almost every major city. In these urban areas, residents with different ethnical or socioeconomic backgrounds tend to form homogeneous clusters. In Schelling's classical segregation model two types of agents are placed on a grid. An agent is content with its location if the fraction of its neighbors, which have the same type as the agent, is at least 𝜏, for some 0 < 𝜏 ≤ 1. Discontent agents simply swap their location with a randomly chosen other discontent agent or jump to a random empty location. The model gives a coherent explanation of how clusters can form even if all agents are tolerant, i.e., if they agree to live in mixed neighborhoods. For segregation to occur, all it needs is a slight bias towards agents preferring similar neighbors. Although the model is well studied, previous research focused on a random process point of view. However, it is more realistic to assume instead that the agents strategically choose where to live. We close this gap by introducing and analyzing game-theoretic models of Schelling segregation, where rational agents strategically choose their locations. As the first step, we introduce and analyze a generalized game-theoretic model that allows more than two agent types and more general underlying graphs modeling the residential area. We introduce different versions of Swap and Jump Schelling Games. Swap Schelling Games assume that every vertex of the underlying graph serving as a residential area is occupied by an agent and pairs of discontent agents can swap their locations, i.e., their occupied vertices, to increase their utility. In contrast, for the Jump Schelling Game, we assume that there exist empty vertices in the graph and agents can jump to these vacant vertices if this increases their utility. We show that the number of agent types as well as the structure of underlying graph heavily influence the dynamic properties and the tractability of finding an optimal strategy profile. As a second step, we significantly deepen these investigations for the swap version with 𝜏 = 1 by studying the influence of the underlying topology modeling the residential area on the existence of equilibria, the Price of Anarchy, and the dynamic properties. Moreover, we restrict the movement of agents locally. As a main takeaway, we find that both aspects influence the existence and the quality of stable states. Furthermore, also for the swap model, we follow sociological surveys and study, asking the same core game-theoretic questions, non-monotone singlepeaked utility functions instead of monotone ones, i.e., utility functions that are not monotone in the fraction of same-type neighbors. Our results clearly show that moving from monotone to non-monotone utilities yields novel structural properties and different results in terms of existence and quality of stable states. In the last part, we introduce an agent-based saturated open-city variant, the Flip Schelling Process, in which agents, based on the predominant type in their neighborhood, decide whether to change their types. We provide a general framework for analyzing the influence of the underlying topology on residential segregation and investigate the probability that an edge is monochrome, i.e., that both incident vertices have the same type, on random geometric and Erdős-R{\´e}nyi graphs. For random geometric graphs, we prove the existence of a constant c > 0 such that the expected fraction of monochrome edges after the Flip Schelling Process is at least 1/2 + c. For Erdős-R{\´e}nyi graphs, we show the expected fraction of monochrome edges after the Flip Schelling Process is at most 1/2 + o(1).},
  language  = {en}
}
@phdthesis{Doskoč2023,
  author    = {Doskoč, Vanja},
  title     = {Mapping restrictions in behaviourally correct learning},
  doi       = {10.25932/publishup-59311},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-593110},
  school      = {Universit{\"a}t Potsdam},
  pages     = {ix, 74},
  year      = {2023},
  abstract  = {In this thesis, we investigate language learning in the formalisation of Gold [Gol67]. Here, a learner, being successively presented all information of a target language, conjectures which language it believes to be shown. Once these hypotheses converge syntactically to a correct explanation of the target language, the learning is considered successful. Fittingly, this is termed explanatory learning. To model learning strategies, we impose restrictions on the hypotheses made, for example requiring the conjectures to follow a monotonic behaviour. This way, we can study the impact a certain restriction has on learning. Recently, the literature shifted towards map charting. Here, various seemingly unrelated restrictions are contrasted, unveiling interesting relations between them. The results are then depicted in maps. For explanatory learning, the literature already provides maps of common restrictions for various forms of data presentation. In the case of behaviourally correct learning, where the learners are required to converge semantically instead of syntactically, the same restrictions as in explanatory learning have been investigated. However, a similarly complete picture regarding their interaction has not been presented yet. In this thesis, we transfer the map charting approach to behaviourally correct learning. In particular, we complete the partial results from the literature for many well-studied restrictions and provide full maps for behaviourally correct learning with different types of data presentation. We also study properties of learners assessed important in the literature. We are interested whether learners are consistent, that is, whether their conjectures include the data they are built on. While learners cannot be assumed consistent in explanatory learning, the opposite is the case in behaviourally correct learning. Even further, it is known that learners following different restrictions may be assumed consistent. We contribute to the literature by showing that this is the case for all studied restrictions. We also investigate mathematically interesting properties of learners. In particular, we are interested in whether learning under a given restriction may be done with strongly Bc-locking learners. Such learners are of particular value as they allow to apply simulation arguments when, for example, comparing two learning paradigms to each other. The literature gives a rich ground on when learners may be assumed strongly Bc-locking, which we complete for all studied restrictions.},
  language  = {en}
}
@phdthesis{Hagedorn2023,
  author    = {Hagedorn, Christopher},
  title     = {Parallel execution of causal structure learning on graphics processing units},
  doi       = {10.25932/publishup-59758},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-597582},
  school      = {Universit{\"a}t Potsdam},
  pages     = {8, 192},
  year      = {2023},
  abstract  = {Learning the causal structures from observational data is an omnipresent challenge in data science. The amount of observational data available to Causal Structure Learning (CSL) algorithms is increasing as data is collected at high frequency from many data sources nowadays. While processing more data generally yields higher accuracy in CSL, the concomitant increase in the runtime of CSL algorithms hinders their widespread adoption in practice. CSL is a parallelizable problem. Existing parallel CSL algorithms address execution on multi-core Central Processing Units (CPUs) with dozens of compute cores. However, modern computing systems are often heterogeneous and equipped with Graphics Processing Units (GPUs) to accelerate computations. Typically, these GPUs provide several thousand compute cores for massively parallel data processing. To shorten the runtime of CSL algorithms, we design efficient execution strategies that leverage the parallel processing power of GPUs. Particularly, we derive GPU-accelerated variants of a well-known constraint-based CSL method, the PC algorithm, as it allows choosing a statistical Conditional Independence test (CI test) appropriate to the observational data characteristics. Our two main contributions are: (1) to reflect differences in the CI tests, we design three GPU-based variants of the PC algorithm tailored to CI tests that handle data with the following characteristics. We develop one variant for data assuming the Gaussian distribution model, one for discrete data, and another for mixed discrete-continuous data and data with non-linear relationships. Each variant is optimized for the appropriate CI test leveraging GPU hardware properties, such as shared or thread-local memory. Our GPU-accelerated variants outperform state-of-the-art parallel CPU-based algorithms by factors of up to 93.4× for data assuming the Gaussian distribution model, up to 54.3× for discrete data, up to 240× for continuous data with non-linear relationships and up to 655× for mixed discrete-continuous data. However, the proposed GPU-based variants are limited to datasets that fit into a single GPU's memory. (2) To overcome this shortcoming, we develop approaches to scale our GPU-based variants beyond a single GPU's memory capacity. For example, we design an out-of-core GPU variant that employs explicit memory management to process arbitrary-sized datasets. Runtime measurements on a large gene expression dataset reveal that our out-of-core GPU variant is 364 times faster than a parallel CPU-based CSL algorithm. Overall, our proposed GPU-accelerated variants speed up CSL in numerous settings to foster CSL's adoption in practice and research.},
  language  = {en}
}
@book{BarkowskyGiese2023,
  author    = {Barkowsky, Matthias and Giese, Holger},
  title     = {Modular and incremental global model management with extended generalized discrimination networks},
  number    = {154},
  isbn      = {978-3-86956-555-2},
  issn      = {1613-5652},
  doi       = {10.25932/publishup-57396},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-573965},
  publisher      = {Universit{\"a}t Potsdam},
  pages     = {63 -- 63},
  year      = {2023},
  abstract  = {Complex projects developed under the model-driven engineering paradigm nowadays often involve several interrelated models, which are automatically processed via a multitude of model operations. Modular and incremental construction and execution of such networks of models and model operations are required to accommodate efficient development with potentially large-scale models. The underlying problem is also called Global Model Management. In this report, we propose an approach to modular and incremental Global Model Management via an extension to the existing technique of Generalized Discrimination Networks (GDNs). In addition to further generalizing the notion of query operations employed in GDNs, we adapt the previously query-only mechanism to operations with side effects to integrate model transformation and model synchronization. We provide incremental algorithms for the execution of the resulting extended Generalized Discrimination Networks (eGDNs), as well as a prototypical implementation for a number of example eGDN operations. Based on this prototypical implementation, we experiment with an application scenario from the software development domain to empirically evaluate our approach with respect to scalability and conceptually demonstrate its applicability in a typical scenario. Initial results confirm that the presented approach can indeed be employed to realize efficient Global Model Management in the considered scenario.},
  language  = {en}
}
@phdthesis{Shekhar2023,
  author    = {Shekhar, Sumit},
  title     = {Image and video processing based on intrinsic attributes},
  doi       = {10.25932/publishup-62004},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-620049},
  school      = {Universit{\"a}t Potsdam},
  pages     = {xii, 143},
  year      = {2023},
  abstract  = {Advancements in computer vision techniques driven by machine learning have facilitated robust and efficient estimation of attributes such as depth, optical flow, albedo, and shading. To encapsulate all such underlying properties associated with images and videos, we evolve the concept of intrinsic images towards intrinsic attributes. Further, rapid hardware growth in the form of high-quality smartphone cameras, readily available depth sensors, mobile GPUs, or dedicated neural processing units have made image and video processing pervasive. In this thesis, we explore the synergies between the above two advancements and propose novel image and video processing techniques and systems based on them. To begin with, we investigate intrinsic image decomposition approaches and analyze how they can be implemented on mobile devices. We propose an approach that considers not only diffuse reflection but also specular reflection; it allows us to decompose an image into specularity, albedo, and shading on a resource constrained system (e.g., smartphones or tablets) using the depth data provided by the built-in depth sensors. In addition, we explore how on-device depth data can further be used to add an immersive dimension to 2D photos, e.g., showcasing parallax effects via 3D photography. In this regard, we develop a novel system for interactive 3D photo generation and stylization on mobile devices. Further, we investigate how adaptive manipulation of baseline-albedo (i.e., chromaticity) can be used for efficient visual enhancement under low-lighting conditions. The proposed technique allows for interactive editing of enhancement settings while achieving improved quality and performance. We analyze the inherent optical flow and temporal noise as intrinsic properties of a video. We further propose two new techniques for applying the above intrinsic attributes for the purpose of consistent video filtering. To this end, we investigate how to remove temporal inconsistencies perceived as flickering artifacts. One of the techniques does not require costly optical flow estimation, while both provide interactive consistency control. Using intrinsic attributes for image and video processing enables new solutions for mobile devices - a pervasive visual computing device - and will facilitate novel applications for Augmented Reality (AR), 3D photography, and video stylization. The proposed low-light enhancement techniques can also improve the accuracy of high-level computer vision tasks (e.g., face detection) under low-light conditions. Finally, our approach for consistent video filtering can extend a wide range of image-based processing for videos.},
  language  = {en}
}
@inproceedings{DeselOpelSiegerisetal.2023,
  author    = {Desel, J{\"o}rg and Opel, Simone and Siegeris, Juliane and Draude, Claude and Weber, Gerhard and Schell, Timon and Schwill, Andreas and Thorbr{\"u}gge, Carsten and Sch{\"a}fer, Len Ole and Netzer, Cajus Marian and Gerstenberger, Dietrich and Winkelnkemper, Felix and Schulte, Carsten and B{\"o}ttcher, Axel and Thurner, Veronika and H{\"a}fner, Tanja and Ottinger, Sarah and Große-B{\"o}lting, Gregor and Scheppach, Lukas and M{\"u}hling, Andreas and Baberowski, David and Leonhardt, Thiemo and Rentsch, Susanne and Bergner, Nadine and Bonorden, Leif and Stemme, Jonas and Hoppe, Uwe and Weicker, Karsten and Bender, Esther and Barbas, Helena and Hamann, Fabian and Soll, Marcus and Sitzmann, Daniel},
  title     = {Hochschuldidaktik Informatik HDI 2021},
  series = {Commentarii informaticae didacticae},
  booktitle = {Commentarii informaticae didacticae},
  number    = {13},
  editor    = {Desel, J{\"o}rg and Opel, Simone and Siegeris, Juliane},
  publisher = {Universit{\"a}tsverlag Potsdam},
  address   = {Potsdam},
  isbn      = {978-3-86956-548-4},
  issn      = {1868-0844},
  doi       = {10.25932/publishup-56507},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-565070},
  pages     = {299},
  year      = {2023},
  abstract  = {Die Fachtagungen HDI (Hochschuldidaktik Informatik) besch{\"a}ftigen sich mit den unterschiedlichen Aspekten informatischer Bildung im Hochschulbereich. Neben den allgemeinen Themen wie verschiedenen Lehr- und Lernformen, dem Einsatz von Informatiksystemen in der Hochschullehre oder Fragen der Gewinnung von geeigneten Studierenden, deren Kompetenzerwerb oder auch der Betreuung der Studierenden widmet sich die HDI immer auch einem Schwerpunktthema. Im Jahr 2021 war dies die Ber{\"u}cksichtigung von Diversit{\"a}t in der Lehre. Diskutiert wurden beispielsweise die Einbeziehung von besonderen fachlichen und {\"u}berfachlichen Kompetenzen Studierender, der Unterst{\"u}tzung von Durchl{\"a}ssigkeit aus nichtakademischen Berufen, aber auch die Gestaltung inklusiver Lehr- und Lernszenarios, Aspekte des Lebenslangen Lernens oder sich an die Diversit{\"a}t von Studierenden adaptierte oder adaptierende Lehrsysteme. Dieser Band enth{\"a}lt ausgew{\"a}hlte Beitr{\"a}ge der 9. Fachtagung 2021, die in besonderer Weise die Konferenz und die dort diskutierten Themen repr{\"a}sentieren.},
  language  = {de}
}