@inbook{65078, author = {{Schroeter-Wittke, Harald}}, booktitle = {{Auf der Suche nach Frieden. Evangelische Kirchentage in Ost und West seit 1949}}, editor = {{Kuhn, Thomas K. and David, Philipp}}, pages = {{253--271}}, publisher = {{Evangelische Verlagsanstalt}}, title = {{{"Mitten unter euch"?! Frieden und Bibelarbeit auf Kirchentagen}}}, year = {{2026}}, } @article{65077, author = {{Schroeter-Wittke, Harald}}, journal = {{Praktische Theologie}}, pages = {{82--84}}, title = {{{Amor mio, perche piangi? Meine Liebe, warum weinst du? Zum 450. Geburtstag von Vittoria/Raffaella Aleotti (1575 - um 1646)}}}, volume = {{61}}, year = {{2026}}, } @inbook{65090, abstract = {{Abstract If XAI are to become social XAI, XAI methods must have capabilities enabling them to ‘extract’ information about the underlying AI model and to generate explanatory content based on that information. In a dialog between explainer and explainee, the explanans presented in every explanation move have to relate to each other understandably and coherently in order to remain trustworthy. This signifies that the generated explanantia have to be consistent—independently of what question is answered by each explanans, in what modality, in what vocabulary, and at what level of abstraction. Moreover, it is advantageous to be able to provide a rich palette of different kinds of explanantia in order to be able to have a fluent dialog in which the explanantia can be generated and adapted to the context, the explainee, feedback, reactions during the interaction with the explainee, and so forth. This chapter attempts to identify relevant questions that an explainee might ask during an explanatory dialog, and it assesses to what extent different XAI methods are capable of addressing these questions in a coherent way. The Contextual Importance and Utility (CIU) method is used to illustrate how an XAI method can generate explanantia for most of the identified questions. CIU also provides a flexibility in how explanatory content is generated that makes it possible to create a meaningful dialog with the explainee.}}, author = {{Främling, Kary and Thommes, Kirsten and Wrede, Britta}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Generation of Explanatory Content and Requirements for Social XAI}}}, doi = {{10.1007/978-981-96-5290-7_15}}, year = {{2026}}, } @inbook{65088, abstract = {{Abstract Quantitatively evaluating the benefits of eXplainable Artificial Intelligence (XAI) and social XAI for humans is not a trivial pursuit. Therefore, we categorize the potential measures in terms of subjective and objective outcomes and short- and long-term outcomes of interactive social XAI. When reviewing the current state of the art, we observed some measurement problems in the literature: (a) Researchers do not clearly state whether they want to measure the inner state of users, users’ behavioral response, or the overall AI-human collaborative performance. (b) Moreover, most measures implicitly assume that all humans either do not react or improve in attitudes or performance. Psychological reactance (feeling or doing the opposite) is usually not captured. (c) Many researchers invent their own scale when measuring psychological constructs, thereby jeopardizing the validity of their measures and slowing down progress in the field, because general evidence and subsequent learning can be achieved only by collecting many compatible pieces of evidence. (d) Most studies look into short-term outcomes and neglect that experiences in social interactions with XAI may evolve and have long-term outcomes not only for the individual but also for groups or society at large.}}, author = {{Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Measuring the Outcome of sXAI}}}, doi = {{10.1007/978-981-96-5290-7_28}}, year = {{2026}}, } @inbook{65086, abstract = {{Abstract Explainable AI (XAI) aims to make the decisions and behavior of an AI understandable to the people interacting with it and to those affected by its outcomes. To make XAI social, real-world XAI systems need to simulate not only the ways in which human explainers behave within explanatory dialogs but also the ways in which such dialogs can successfully achieve the intended understanding on the explainee’s side. This, in turn, requires an operationalization of the three core aspects of social XAI: multimodality, incrementality, and patterns. This chapter lays the ground for this goal by defining a basic operational model of social interactions that can be refined and extended to account for the specificities of any explanatory real-world setting. This serves as a basis for summarizing and discussing existing ideas from explainability research and related areas in order to operationalize each core aspect. Selected examples and case studies illustrate how to concretely realize such an operationalization, thereby serving as a starting point for future research on social interaction with XAI.}}, author = {{Wachsmuth, Henning and Thommes, Kirsten and Alshomary, Milad}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Operationalizing Social Interaction}}}, doi = {{10.1007/978-981-96-5290-7_27}}, year = {{2026}}, } @inbook{65091, abstract = {{Abstract This chapter examines key challenges and potential improvements in the areas of user interaction and dynamic explanations. It highlights the need for XAI systems to address context factors beyond their predefined scope, it points to the potential need to cocreate new concepts that are adapted to particular explainees, and it provides a clear overview of the XAI system’s underlying knowledge structure and interaction steps. Emphasis is placed on mixed-initiative interaction in which the system can lead or respond based on the context and the explainee’s reactions while asserting the importance of maintaining coherence across consecutive explanations. These advances aim to make XAI systems more flexible, interactive, and user-centric. An operationalization section outlines how such social XAI systems could be implemented based on the XAI capabilities provided by the Contextual Importance and Utility XAI method described in the previous chapter.}}, author = {{Främling, Kary and Wrede, Britta and Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Exploration of Explaining Content}}}, doi = {{10.1007/978-981-96-5290-7_16}}, year = {{2026}}, } @inbook{65087, abstract = {{Abstract Much research in XAI focuses on single, one-shot interactions, implicitly assuming that interactions have no past, no future, and no surroundings. Although this assumption may be necessary for many empirical research settings, it is overly simplifying and unrealistic. Whereas empirical research focuses on a world in which no social context exists, real applications are embedded in a temporal (past and future) and social context. Social science research shows that repeated interactions and secondhand knowledge in the social space massively affect human attitudes and behaviors. This chapter explains how not only repeated interactions between XAI and humans but also the social space and secondhand information may affect social XAI research.}}, author = {{Thommes, Kirsten and Främling, Kary and Wrede, Britta and Kubler, Sylvain}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Interaction History in Social XAI}}}, doi = {{10.1007/978-981-96-5290-7_17}}, year = {{2026}}, } @inbook{65089, abstract = {{Abstract In the past, there has been much research aiming to evaluate XAI practices—that is, explanations that can add to a user’s understanding of “why” or “why not.” However, because there is such a huge amount of diversity in social contexts, optimizing for the mean neglects the social dimensions of to whom, what, why, when, and where explanations are provided. Nonetheless, these dimensions matter. We give some brief examples on the accuracy of the mental model (as an example for who?), on measuring explanation practices (as an example of what?), on human motivation (as an example of why?), on repeated interactions (as an example of when), and on bystander effects (as an example of where?). Importantly, controlling for these factors (or randomizing them) is as important as attempting to perform external validations.}}, author = {{Thommes, Kirsten}}, booktitle = {{Social Explainable AI}}, isbn = {{9789819652891}}, publisher = {{Springer Nature Singapore}}, title = {{{Evaluation Principles}}}, doi = {{10.1007/978-981-96-5290-7_26}}, year = {{2026}}, } @inproceedings{63469, author = {{Knickenberg, Margarita and Löper, Marwin Felix and Grosche, Michael and Grüßing, Meike and Hellmich, Frank}}, publisher = {{Technische Universität München}}, title = {{{Förderung sozial-emotionaler Kompetenzen von Kindern für das kooperative Lernen im diversitätssensiblen Mathematikunterricht der Grundschule (soko-M). Posterpräsentation auf der 13. Tagung der Gesellschaft für Empirische Bildungsforschung (GEBF). Thema: „Bildungsforschung für technologiebedingte gesellschaftliche Entwicklungen“.}}}, year = {{2026}}, } @article{65093, author = {{Marten, Thorsten and Ostermann, Moritz and Behm, Jonathan and Leitenmaier, Samuel}}, issn = {{21991944}}, journal = {{Berufsbildung - Zeitschrift für Theorie-Praxis-Dialog}}, number = {{1}}, pages = {{23--27}}, publisher = {{wbv Publikation}}, title = {{{NeMo.bil - Individualisierter öffentlicher Personennahverkehr - iÖV}}}, doi = {{10.3278/BB2601}}, volume = {{209}}, year = {{2026}}, } @inproceedings{61542, author = {{Hellmich, Frank and Löper, Marwin Felix and Görel, Gamze}}, publisher = {{Universität Paderborn}}, title = {{{Möglichkeiten der Förderung der sozialen Partizipation in der inklusiven Grundschule. Workshop auf dem 10. Paderborner Grundschultag. Thema: „Zu­kunft ge­mein­sam ge­stal­ten – Bildung für nach­hal­ti­ge Ent­wick­lung von An­fang an“. }}}, year = {{2026}}, } @inproceedings{65101, abstract = {{Various methods to measure the dynamic behavior of particles require the calculation of autocorrelation functions. For this purpose, fast multi-tau correlators have been developed in dedicated hardware, in software, and on FPGAs. However, for methods such as X-ray Photon Correlation Spectroscopy (XPCS), which requires to calculate the autocorrelation function independently for hundreds of thousands to millions of pixels from high-resolution detectors, current approaches rely on offline processing after data acquisition. Moreover, the internal pipeline state of so many independent correlators is far too large to keep it on-chip. In this work, we propose a design approach on FPGAs, where pipeline contexts are stored in off-chip HBM memory. Each compute unit iteratively loads the state for a single pixel, processes a short time series for this pixel, and afterwards writes back the context in a dataflow pipeline. We have implemented the required compute kernels with Vitis HLS and analyze resulting designs on an Alveo U280 card. The design achieves the expected performance and for the first time provides sufficient throughput for current high-end detectors used in XPCS.}}, author = {{Tareen, Abdul Rehman and Plessl, Christian and Kenter, Tobias}}, booktitle = {{2025 International Conference on Field Programmable Technology (ICFPT)}}, publisher = {{IEEE}}, title = {{{Fast Multi-Tau Correlators on FPGA with Context Switching From and to High- Bandwidth Memory}}}, doi = {{10.1109/icfpt67023.2025.00027}}, year = {{2026}}, } @inbook{61325, author = {{Vollmer, Anna-Lisa and Buhl, Heike M. and Alami, Rachid and Främling, Kary and Grimminger, Angela and Booshehri, Meisam and Ngonga Ngomo, Axel-Cyrille}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kirsten}}, pages = {{39--53}}, publisher = {{Springer}}, title = {{{Components of an explanation for co-constructive sXAI}}}, doi = {{10.1007/978-981-96-5290-7_3}}, year = {{2026}}, } @inbook{65084, author = {{Buhl, Heike M. and Vollmer, Anna-Lisa and Alami, Rachid and Booshehri, Meisam and Främling, Kary}}, booktitle = {{Social explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kisten}}, pages = {{269--295}}, publisher = {{Springer}}, title = {{{Models of the situation, the explanandum, and the interaction partner}}}, doi = {{https://doi.org/10.1007/978-981-96-5290-7_14}}, year = {{2026}}, } @inbook{65083, author = {{Buhl, Heike M. and Wrede, Britta and Fisher, Josephine Beryl and Matarese, Marco}}, booktitle = {{Social Explainable AI}}, editor = {{Rohlfing, Katharina J. and Främling, Kary and Lim, Brian and Alpsancar, Suzana and Thommes, Kirsten}}, pages = {{247--267}}, publisher = {{Springer}}, title = {{{Adaptation}}}, doi = {{https://doi.org/10.1007/978-981-96-5290-7_13}}, year = {{2026}}, } @article{65099, author = {{Weber, Daniel and Schmies, Dominik and Lange, Jarren H. and Schenke, Maximilian and Wallscheid, Oliver}}, issn = {{2169-3536}}, journal = {{IEEE Access}}, pages = {{38517--38535}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Optimal Control of Voltage-Forming Grid Inverters by Model Predictive Control and Reinforcement Learning}}}, doi = {{10.1109/access.2026.3670948}}, volume = {{14}}, year = {{2026}}, } @article{65098, author = {{Weber, Daniel and Lange, Jarren and Wallscheid, Oliver}}, issn = {{2687-9735}}, journal = {{IEEE Journal of Emerging and Selected Topics in Industrial Electronics}}, pages = {{1--12}}, publisher = {{Institute of Electrical and Electronics Engineers (IEEE)}}, title = {{{Reinforcement Learning-Based Control of Voltage-Forming Grid Inverters With Arbitrary Loads}}}, doi = {{10.1109/jestie.2026.3654784}}, year = {{2026}}, } @article{65104, author = {{Hermelingmeier, Lucas and Beule, Felix and Teutenberg, Dominik and Meschut, Gerson}}, issn = {{0143-7496}}, journal = {{International Journal of Adhesion and Adhesives}}, publisher = {{Elsevier BV}}, title = {{{Comparison of fixture-based and manual fiber integration in adhesive joints: Effects on strain signal quality}}}, doi = {{10.1016/j.ijadhadh.2026.104319}}, volume = {{149}}, year = {{2026}}, } @article{65094, abstract = {{ The development of practical sensors for optical coherence tomography (OCT) with undetected photons requires miniaturization via integration. To be practical, these sensors must exhibit a large spectral bandwidth and a high brightness, which are linked to a high axial resolution and a sufficient signal-to-noise ratio, respectively. Here, we combine these requirements in a scheme for OCT measurements with undetected photons based on nonlinear Ti : Li Nb O 3 waveguides. We investigate the performance benchmarks of the commonly used SU(1,1) scheme in comparison to an induced-coherence scheme and find that the latter is actually better suited when implementing measurements with undetected photons in integrated systems. In both schemes, we perform pump-gain optimization and OCT measurements with undetected photons with an axial resolution as low as 28 μ m . }}, author = {{Roeder, Franz and Pollmann, René and Quiring, Viktor and Eigner, Christof and Brecht, Benjamin and Silberhorn, Christine}}, issn = {{2331-7019}}, journal = {{Physical Review Applied}}, number = {{3}}, publisher = {{American Physical Society (APS)}}, title = {{{Toward integrated sensors for optimized optical coherence tomography with undetected photons}}}, doi = {{10.1103/cwsx-42c4}}, volume = {{25}}, year = {{2026}}, } @article{65096, abstract = {{ Precise measurements of both the arrival time and carrier frequency of light pulses are essential for time–frequency-encoded quantum technologies. Quantum mechanics, however, imposes fundamental limits on the simultaneous determination of these quantities. In this work, we derive and experimentally verify the quantum uncertainty bounds governing joint time–frequency measurements. We show that when detection is restricted to finite time windows, the problem is naturally described by a quantum rotor, rendering the commonly used Heisenberg uncertainty relation inapplicable. We further propose an optimal detection scheme that saturates these fundamental limits. By sampling the Q -function, we demonstrate the reconstruction of the Wigner function beyond the harmonic oscillator. Using an experimental implementation based on a quantum pulse gate, we confirm that the proposed scheme approaches the ultimate quantum limit for simultaneous time–frequency measurements. These results provide a framework for joint time–frequency detection with direct implications for precision measurements and quantum information processing. }}, author = {{Folge, Patrick Fabian and Serino, Laura Maria and Mišta, Ladislav and Brecht, Benjamin and Silberhorn, Christine and Řeháček, Jaroslav and Hradil, Zdeněk}}, issn = {{2334-2536}}, journal = {{Optica}}, number = {{3}}, publisher = {{Optica Publishing Group}}, title = {{{Quantum-limited detection of the arrival time and the carrier frequency of time-dependent signals}}}, doi = {{10.1364/optica.579459}}, volume = {{13}}, year = {{2026}}, }