TY - JOUR A1 - Haug, Sonja A1 - Currle, Edda A1 - Weber, Karsten T1 - Acceptance of Medical History-Taking Supported by Artificial Intelligence and Chatbots: A Population-Based Survey in Germany JF - Healthcare N2 - Background/Objectives: Digital anamnesis tools, including chatbots, are increasingly being developed and evaluated, yet their implementation in German medical practices remains limited. This study examines the acceptance of medical history-taking assisted by artificial intelligence (AI) among the German population. The objective is to derive implications for integrating such systems into digitalization strategies of medical practices. Methods: This study is based on an online survey of the German population, aged between 18 and 74 years, conducted in two independent cross-sectional waves (trend design) in 2024 and 2025 with n = 1000 respondents in each year. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), three hypotheses regarding the use of AI in medical history-taking were developed and tested using linear regression models. Results: Both waves reveal a high acceptance level of AI-supported anamnesis systems for people aged between 18 and 74, regardless of whether a chatbot is used in medical practice (Scenario 1) or at home (Scenario 2). The latter received slightly less approval for the intention to use (mean intention scores: 3.50 and 3.45, range from 1.0 to 5.0) than Scenario 1 (3.59, 3.56). The indices of Performance Expectancy (PE), Effort Expectancy (EE), and perceived Social Influence (SI) determine the intention to use a chatbot with the strongest correlation of the PE index (Scenario 1: ß =0.466, Scenario 2: ß = 0.475). Most respondents (73% and 75%) expressed a favorable opinion for digitally storing medical history data within their electronic health record (EHR). Conclusions: The findings suggest that gender- and age- specific differentiation—aside from considering the needs of older adults—may be less relevant for designing digitalization strategies than previously assumed. Instead, the focus of medical practices should lie on the practicability of the tool used. Despite currently low EHR utilization rates in Germany, medical practices may expect broad patient approval regarding the digital storage of medical history data. Y1 - 2026 U6 - https://doi.org/10.3390/healthcare14070905 N1 - Corresponding author der OTH Regensburg: Sonja Haug VL - 14 PB - MDPI CY - Basel ER - TY - JOUR A1 - Schmiedt, Anja B. A1 - Balakrishnan, Narayanaswamy A1 - Cramer, Erhard T1 - Generalized chi-squared based goodness-of-fit tests under progressive Type-II censoring for exponential and Weibull distributions JF - Communications in Statistics - Simulation and Computation N2 - We propose new goodness-of-fit tests for exponentiality based on progressively Type-II censored data. These tests utilize scale-invariant statistics obtained from the Mahalanobis norm of normalized order statistics, leading to three test statistics, corresponding to 𝐿2-, 𝐿1-, and 𝐿∞-norms of centered uniform spacings. Exact and asymptotic distributions of these statistics are presented. A power study evaluates the proposed tests against existing benchmarks across various alternative distributions and censoring plans, demonstrating superior performance in cases with small and moderate sample sizes. Furthermore, we extend the methodology to approximate goodness-of-fit tests for Weibull distributions via power transformation, ensuring robustness w.r.t. the approximated significance level under unknown shape parameters. An illustrative data example confirms the practical applicability of our tests. Our findings highlight the potential for further extending goodness-of-fit tests under progressive Type-II censoring to other null distributions. Y1 - 2026 U6 - https://doi.org/10.1080/03610918.2026.2625230 PB - Taylor & Francis ER - TY - JOUR A1 - Apel, Thomas A1 - Fellner, Klemens A1 - Kempf, Volker A1 - Salcedo-Lagunero, Reymart A1 - Zilk, Philipp T1 - Lipolysis on Lipid Droplets: Mathematical Modelling and Numerical Discretisations JF - Results in Mathematics N2 - Lipolysis is a life-essential metabolic process, which supplies fatty acids stored in lipid droplets to the body in order to match the demands of building new cells and providing cellular energy. In this paper, we present a first mathematical modelling approach for lipolysis, which takes into account that the involved enzymes act on the surface of lipid droplets. We postulate an active region near the surface where the substrates are within reach of the surface-bound enzymes and formulate a system of reaction-diffusion PDEs, which connect the active region to the inner core of lipid droplets via interface conditions. We establish two numerical discretisations based on finite element method and isogeometric analysis, and validate them to perform reliably. Since numerical tests are best performed on non-zero explicit stationary state solutions, we introduce and analyse a model, which describes besides lipolysis also a reverse process (yet in a physiologically much oversimplified way). The system is not coercive such that establishing well-posedness is a non-standard task. We prove the unique existence of global and equilibrium solutions. We establish exponential convergence to the equilibrium solutions using the entropy method. We then study the stationary state model and compute explicitly for radially symmetric solutions. Concerning the finite element methods, we show numerically the linear and quadratic convergence of the errors with respect to the - and -norms, respectively. Finally, we present numerical simulations of a prototypical PDE model of lipolysis and illustrate that enzyme clustering on lipid droplets can significantly slow down lipolysis. Y1 - 2026 U6 - https://doi.org/10.1007/s00025-026-02639-y VL - 81 IS - 3 PB - Springer Nature ER - TY - CHAP A1 - Hoffmann, Tim A1 - Dietrich, Florian A1 - Melzer, Matthias A1 - Dünnweber, Jan ED - Yurish, Sergey Y. T1 - A Code Generation Framework for Indoor Robot Applications Based on Building Information Modeling (BIM) T2 - Automation, Robotics & Communications for Industry 4.0/5.0: Proceedings of the 6th Winter IFSA Conference on Automation, Robotics & Communications for Industry 4.0/5.0/6.0 (ARCI' 2026) 25-27 February 2026 Salzburg, Austria N2 - We present BIM2Robot, a framework connecting the digital planning world (Building Information Modeling) with real-world robotics. The aim of the tools we developed is to convert building data from IFC building plans (Industry Foundation Classes) such that autonomous robots can use them for navigation and task planning. Conventional robotics systems rely on hard-coded maps or proprietary data. In the BIM2Robot framework, we generate a robot-compatible building model automatically from existing BIM data. This model describes rooms, walls, doors, and connections allowing robots to understand their environment, move around in it, and carry out useful missions. Besides the data transformation tools, our software framework comprises a graphical user interface which allows for interactive planning, path visualization and remote process control. The generated building model can be used directly by ROS-compliant robots or in robot simulation environments, forming a basis for navigation, perception, and interaction. The manual post-processing of the generated robot motion plans is supported at GUI- and also at code-level. Y1 - 2026 UR - https://www.researchgate.net/profile/Sergey-Yurish/publication/401427390_Automation_Robotics_Communications_for_Industry_405060/links/69a567ae16faea00ba9b9b02/Automation-Robotics-Communications-for-Industry-40-50-60.pdf#page=41 SN - 978-84-09-82030-6 SP - 40 EP - 44 PB - IFSA ER - TY - JOUR A1 - Brunner, Philipp A1 - Vogl, Stefanie T1 - Extracting Product Improvement Insights from Social Media Comments Using Machine Learning: a Case Study in the Automotive Industry JF - Machine Learning and Knowledge Extraction N2 - This paper presents a scalable machine learning pipeline for extracting actionable, product-related insights from user-generated social media comments. Leveraging sentence embeddings from SBERT and unsupervised clustering (k-Means and agglomerative), the approach structures informal and noisy comments from Instagram and YouTube into topic groups intended to support thematic analysis. A case study on feedback regarding BMW vehicles, comprising more than 26,000 comments, illustrates how the pipeline can reveal recurring user concerns, such as design critiques, usability issues, and technology-related expectations, even in short and unstructured social media comments. The proposed pipeline operates without labeled data or manual annotation, enabling scalable application and transferability across product categories and industries. By transforming large-scale, unstructured consumer feedback into interpretable themes, the pipeline provides product teams with an efficient and structured basis for data-driven product development and improvement. KW - social media mining; sentence embeddings; unsupervised clustering; product feedback analysis; SBERT; natural language processing Y1 - 2026 U6 - https://doi.org/10.3390/make8020042 VL - 8 IS - 2 PB - MDPI ER - TY - JOUR A1 - Wallner, M. A1 - Gutbrod, Max A1 - Rauber, David A1 - Ebigbo, Alanna A1 - Probst, Andreas A1 - Palm, Christoph A1 - Messmann, Helmut A1 - Roser, David T1 - KI-gestützte Detektion und Segmentierung von Magenkarzinomen in westlichen endoskopischen Bilddaten anhand eines fine-tuned Deep-Learning Ansatzes JF - Zeitschrift für Gastroenterologie N2 - Diese vorläufige monozentrische Studie zeigt, dass ein aus einem Barrett-Ösophagus-KI-System feinjustiertes Deep-Learning-Modell Magenkarzinome in westlichen multimodalen endoskopischen Bilddaten zuverlässig detektieren und präzise segmentieren kann. Die hohe Segmentierungsgenauigkeit und Detektionssensitivität über verschiedene Bildmodalitäten hinweg unterstreichen die Machbarkeit eines pathologiegestützten KI-Ansatzes auch in einer westlichen Niedriginzidenzpopulation. Aufgrund der ausschließlichen Verwendung von Bildern mit sichtbaren Tumoren lassen sich keine Aussagen zur Spezifität treffen; eine Übertragbarkeit auf Screening- oder Mischkollektive ist daher limitiert. Weitere Studien mit a) größerem Datensatz inklusive Videodaten, b) externer Validierung an einer multizentrischen westlichen Kohorte, sowie c) Anwendung und Prüfung an nicht-neoplastischen Vergleichsbildern oder anderen Pathologien sind erforderlich. Nach unserem Kenntnisstand zählt dieses System zu den ersten in einer westlichen Population entwickelten endoskopischen KI-Ansätzen zur Magenkarzinomdetektion, und zu wenigen, die vollständige ESD-präparatbasierte Referenzdaten für Training und Validierung nutzen. Y1 - 2026 U6 - https://doi.org/10.1055/s-0046-1817751 VL - 64 IS - 03 SP - e64 EP - e65 PB - Thieme ER - TY - INPR A1 - Höß, Nicole A1 - Paradis, Carlos A1 - Kazman, Rick A1 - Mauerer, Wolfgang T1 - Oops!... I did it again. Conclusion (In-)Stability in Quantitative Empirical Software Engineering: A Large-Scale Analysis N2 - Context: Mining software repositories is a popular means to gain insights into a software project's evolution, monitor project health, support decisions and derive best practices. Tools supporting the mining process are commonly applied by researchers and practitioners, but their limitations and agreement are often not well understood. Objective: This study investigates some threats to validity in complex tool pipelines for evolutionary software analyses and evaluates the tools' agreement in terms of data, study outcomes and conclusions for the same research questions. Method: We conduct a lightweight literature review to select three studies on collaboration and coordination, software maintenance and software quality from high-ranked venues, which we formally replicate with four independent, systematically selected mining tools to quantitatively and qualitatively compare the extracted data, analysis results and conclusions. Results: We find that numerous technical details in tool design and implementation accumulate along the complex mining pipelines and can cause substantial differences in the extracted baseline data, its derivatives, subsequent results of statistical analyses and, under specific circumstances, conclusions. Conclusions: Users must carefully choose tools and evaluate their limitations to assess the scope of validity in an adequate way. Reusing tools is recommended. Researchers and tool authors can promote reusability and help reducing uncertainties by reproduction packages and comparative studies following our approach. Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2510.06844 ER - TY - JOUR A1 - Schmidbauer, Lukas A1 - Lobe, Elisabeth A1 - Schaefer, Ina A1 - Mauerer, Wolfgang T1 - It’s Quick to be Square: Fast Quadratisation for Quantum Toolchains JF - ACM Transactions on Quantum Computing N2 - Many of the envisioned use-cases for quantum computers involve optimisation processes. While there are many algorithmic primitives to perform the required calculations, all eventually lead to quantum gates operating on quantum bits, with an order as determined by the structure of the objective function and the properties of target hardware. When the structure of the problem representation is not aligned with structure and boundary conditions of the executing hardware, various overheads degrading the computation may arise, possibly negating any possible quantum advantage. Therefore, automatic transformations of problem representations play an important role in quantum computing when descriptions (semi-)targeted at humans must be cast into forms that can be “executed” on quantum computers. Mathematically equivalent formulations are known to result in substantially different non-functional properties depending on hardware, algorithm and detail properties of the problem. Given the current state of noisy intermediate-scale quantum (NISQ) hardware, these effects are considerably more pronounced than in classical computing. Likewise, efficiency of the transformation itself is relevant because possible quantum advantage may easily be eradicated by the overhead of transforming between representations. In this paper, we consider a specific class of higher-level representations, that is, PUBOs, and devise novel automatic transformation mechanisms into widely used QUBOs that substantially improve efficiency and versatility over the state of the art. In addition, we conduct a comprehensive investigation of industry-relevant problem formulations and their conversion into a quantum-specific representation, identifying significant obstacles in scaling behaviour and demonstrating how these can be circumvented. KW - Algorithmic optimisation KW - Graphs KW - Performance KW - Pseudo Boolean Function Y1 - 2026 U6 - https://doi.org/10.1145/3800943 N1 - Preprint ebenfalls auf OPUS veröffentlicht: https://opus4.kobv.de/opus4-oth-regensburg/frontdoor/index/index/docId/7937 VL - 7 IS - 2 PB - ACM ER - TY - CHAP A1 - Schönberger, Manuel A1 - Trummer, Immanuel A1 - Mauerer, Wolfgang T1 - Hybrid Mixed Integer Linear Programming for Large-Scale Join Order Optimisation T2 - Proceedings of the VLDB Endowment N2 - Finding optimal join orders is among the most crucial steps to be performed by query optimisers. Though extensively studied in data management research, the problem remains far from solved: While query optimisers rely on exhaustive search methods to determine ideal solutions for small problems, such methods reach their limits once queries grow in size. Yet, large queries become increasingly common in real-world scenarios, and require suitable methods to generate efficient execution plans. While a variety of heuristics have been proposed for large-scale query optimisation, they suffer from degrading solution quality as queries grow in size, or feature highly sub-optimal worst-case behavior, as we will show. We propose a novel method based on the paradigm of mixed integer linear programming (MILP): By deriving a novel MILP model capable of optimising arbitrary bushy tree structures, we address the limitations of existing MILP methods for join ordering, and can rely on highly optimised MILP solvers to derive efficient tree structures that elude competing methods. To ensure optimisation efficiency, we embed our MILP method into a hybrid framework, which applies MILP solvers precisely where they provide the greatest advantage over competitors, while relying on more efficient methods for less complex optimisation steps. Thereby, our approach gracefully scales to extremely large query sizes joining up to 100 relations, and consistently achieves the most robust plan quality among a large variety of competing join ordering methods. Y1 - 2026 U6 - https://doi.org/10.14778/3778092.3778097 VL - 19 IS - 3 SP - 348 EP - 360 PB - VLDB Endowment ER - TY - INPR A1 - Eichenseher, Vincent A1 - Franz, Maja A1 - Wolff, Christian A1 - Mauerer, Wolfgang T1 - Pattern or Not? QAOA Parameter Heuristics and Potentials of Parsimony N2 - Structured variational quantum algorithms such as the Quantum Approximate Optimisation Algorithm (QAOA) have emerged as leading candidates for exploiting advantages of near-term quantum hardware. They interlace classical computation, in particular optimisation of variational parameters, with quantum-specific routines, and combine problem-specific advantages -- sometimes even provable -- with adaptability to the constraints of noisy, intermediate-scale quantum (NISQ) devices. While circuit depth can be parametrically increased and is known to improve performance in an ideal (noiseless) setting, on realistic hardware greater depth exacerbates noise: The overall quality of results depends critically on both, variational parameters and circuit depth. Although identifying optimal parameters is NP-hard, prior work has suggested that they may exhibit regular, predictable patterns for increasingly deep circuits and depending on the studied class of problems. In this work, we systematically investigate the role of classical parameters in QAOA performance through extensive numerical simulations and suggest a simple, yet effective heuristic scheme to find good parameters for low-depth circuits. Our results demonstrate that: (i) optimal parameters often deviate substantially from expected patterns; (ii) QAOA performance becomes progressively less sensitive to specific parameter choices as depth increases; and (iii) iterative component-wise fixing performs on par with, and at shallow depth may even outperform, several established parameter-selection strategies. We identify conditions under which structured parameter patterns emerge, and when deviations from the patterns warrant further consideration. These insights for low-depth circuits may inform more robust pathways to harnessing QAOA in realistic quantum compute scenarios. Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2510.08153 ER - TY - INPR A1 - Gierisch, Vincent A1 - Mauerer, Wolfgang T1 - QEF: Reproducible and Exploratory Quantum Software Experiments N2 - Commercially available Noisy Intermediate-Scale Quantum (NISQ) devices now make small hybrid quantum-classical experiments practical, but many tools hide configuration or demand ad-hoc scripting. We introduce the Quantum Experiment Framework (QEF): A lightweight framework designed to support the systematic, hypothesis-driven study of quantum algorithms. Unlike many existing approaches, QEF emphasises iterative, exploratory analysis of evolving experimental strategies rather than exhaustive empirical evaluation of fixed algorithms using predefined quality metrics. The framework's design is informed by a comprehensive review of the literature, identifying principal parameters and measurement practices currently reported in the field. QEF captures all key aspects of quantum software and algorithm experiments through a concise specification that expands into a Cartesian product of variants for controlled large-scale parameter sweeps. This design enables rigorous and systematic evaluation, as well as precise reproducibility. Large sweeps are automatically partitioned into asynchronous jobs across simulators or cloud hardware, and ascertain full hyper-parameter traceability. QEF supports parameter reuse to improve overall experiment runtimes, and collects all metrics and metadata into a form that can be conveniently explored with standard statistical and visualisation software. By combining reproducibility and scalability while avoiding the complexities of full workflow engines, QEF seeks to lower the practical barriers to empirical research on quantum algorithms, whether these are designed for current NISQ devices or future error-corrected quantum systems. Y1 - 2026 U6 - https://doi.org/10.48550/arXiv.2511.04563 ER - TY - JOUR A1 - Weigert, Alexander A1 - Galka, Stefan A1 - Dünnweber, Jan A1 - Meißner, Sebastian A1 - Riege, Jacob A1 - Böck, Hendrik A1 - Wagner, Martin T1 - Simulationsframework mit Anordnungsalgorithmus von Petri-Netzen JF - Zeitschrift für wirtschaftlichen Fabrikbetrieb N2 - Die serienmäßige Produktion von Elektrolysezellen befindet sich im Anfangsstadium, sodass belastbare Erfahrungen für standardisierte Produktionssysteme fehlen. Die Planung ist daher durch hohe Unsicherheit geprägt. Unterschiedliche Gestaltungsmöglichkeiten der Produktionssysteme müssen frühzeitig bewertet und ihre Leistungsfähigkeit mit dynamischen Methoden, wie der Simulation, abgesichert werden. Anhand eines von den Autoren entwickelten Simulationsframeworks mit automatisierter Modellgenerierung auf Basis der Petri-Netz-Logik zeigt dieser Beitrag die Möglichkeit der Simulation von Produktionssystemen in der Planung. Ein besonderes Leistungsmerkmal des Frameworks ist, dass zunächst unübersichtlich generierte Modelle mit einem Graphenalgorithmus so angeordnet werden, dass ihre Funktionsweise aus der Darstellung nachvollziehbar wird. KW - Continuous Simulation; Framework; Automated Simulation Model Generation; Graph-Layout-algorithm; Petri-Net Y1 - 2026 U6 - https://doi.org/10.1515/zwf-2026-1035 VL - 121 IS - 3 SP - 125 EP - 130 PB - DeGruyter ER - TY - GEN ED - Handels, Heinz ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Bildverarbeitung für die Medizin 2026 : Proceedings, German Conference on Medical Image Computing, Lübeck March 15–17, 2026 N2 - Die Konferenz "BVM – Bildverarbeitung für die Medizin" ist seit vielen Jahren als die nationale Plattform für den Austausch von Ideen und die Diskussion der neuesten Forschungsergebnisse im Bereich der Medizinischen Bildverarbeitung und der Künstlichen Intelligenz (KI) etabliert. Auch 2026 haben (junge) Wissenschaftler*innen, Industrie und Anwender*innen diesen Austausch vertieft. Die Beiträge dieses Bandes – die meisten davon in englischer Sprache – umfassen alle Bereiche der medizinischen Bildverarbeitung, insbesondere die Bildgebung und -akquisition, Segmentierung und Analyse, Registrierung, Visualisierung und Animation, computerunterstützte Diagnose sowie bildgestützte Therapieplanung und Therapie. Hierbei kommen Methoden des maschinellen Lernens, der biomechanischen Modellierung sowie der Validierung und Qualitätssicherung zum Einsatz. KW - Bildverarbeitung KW - Computerunterstützte Medizin KW - Bildgebendes Verfahren KW - Bildanalyse KW - Deep Learning Y1 - 2026 SN - 978-3-658-51099-2 U6 - https://doi.org/10.1007/978-3-658-51100-5 SN - 1431-472X PB - Springer Vieweg CY - Wiesbaden ER - TY - CHAP A1 - Gutbrod, Max A1 - Rauber, David A1 - Palm, Christoph ED - Handels, Heinz ED - Breininger, Katharina ED - Deserno, Thomas M. ED - Maier, Andreas ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Improving Generalization in Mitotic Cell Detection via Domain Transformations T2 - Bildverarbeitung für die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Lübeck March 15-17, 2026 N2 - We address domain generalization (DG) in mitotic-cell (MC) detection by combining a β-variational autoencoder (VAE) for domain transformations with feature-space alignment together with an object detector. The β-VAE synthesizes domain-transformed images, and the detector is trained to map originals and their transformed counterparts to equal representations. On the MIDOG++ dataset, this approach improves out-of-domain detection F1 scores by 7 and 3 percentage points compared to the color-variation augmentation and stain-normalization baselines. Results further suggest that morphology shifts hinder generalization more than stain shifts. KW - Künstliche Intelligenz KW - Bildverarbeitung Y1 - 2026 U6 - https://doi.org/10.1007/978-3-658-51100-5_71 SP - 362 EP - 367 PB - Springer Vieweg CY - Wiesbaden ER - TY - JOUR A1 - Bürkner, Leonhard A1 - Westner, Markus T1 - KI erfolgreich einführen: Status quo und Erfolgsfaktoren in deutschen Großunternehmen JF - Wirtschaftsinformatik & Management N2 - Viele deutsche Großunternehmen experimentieren derzeit intensiv mit künstlicher Intelligenz (KI), stehen aber vor der Frage, wie sich erste Pilotprojekte in einen nachhaltigen, wirtschaftlich wirksamen Einsatz überführen lassen. Eine empirische Studie mit 34 Chief Information Officers (CIO) und IT-Entscheidern in deutschen Großunternehmen zeigt: 112 identifizierte KI-Use-Cases, ein klar erkennbarer Reifezuwachs – aber auch deutliche Hürden bei Daten, Kompetenzen und Akzeptanz. Der Beitrag fasst den Status quo zusammen, validiert zentrale Erfolgsfaktoren aus der Forschung und leitet konkrete Empfehlungen für die Praxis von IT- und Fachbereichsverantwortlichen ab. KW - Künstliche Intelligenz KW - Generative KI KW - Wirtschaftsinformatik KW - Digitalisierung KW - Management KW - KI-Adoption KW - KI-Einführung KW - KI-Use-Cases KW - KI-Reifegrad KW - Großunternehmen Y1 - 2026 U6 - https://doi.org/10.1365/s35764-026-00593-6 N1 - Corresponding author der OTH Regensburg: Markus Westner PB - Springer ER - TY - INPR A1 - Feeß, Alexander A1 - Weiß, Martin T1 - Analysis of a Cuspidal 6R Robot N2 - We present a theoretical and numerical analysis of the kinematics for the Transpressor, a cuspidal 6R robot. It admits up to 16 inverse kinematics solutions which are described geometrically. For special target poses, we provide the solutions analytically and present a simple numerical solver for the general case. Moreover, an analytical estimate of the Jacobian determinant on a path between two solutions proves cuspidality for a class of robots similar to the transpressor. KW - Kinematik KW - Industrierobotik KW - inverse kinematics KW - kinematic singularities Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-88626 CY - Regensburg ER - TY - INPR A1 - Feeß, Alexander A1 - Weiß, Martin T1 - Uniqueness Domains of Offset Wrist Robots via a 4D Workspace Representation N2 - We present a new method for studying the workspace of 6R robots using a four-dimensional representation. This is applied to numerically determine connected workspace components with a constant number of solutions of the inverse kinematics, and to describe critical values that form the boundaries these regions. To define and maximize uniqueness domains, nonsingular transition of these boundaries are investigated. This provides a method to determine workspace regions in which the robot can move avoiding configuration changes and singularities. KW - kinematic workspace analysis KW - kinematic singularities KW - cuspidal robot Y1 - 2026 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:898-opus4-88635 CY - Regensburg ER - TY - CHAP A1 - Deutel, Mark A1 - Mallah, Maen A1 - Wissing, Julio A1 - Scheele, Stephan ED - Vermesan, Ovidiu ED - Pagani, Alain ED - Meloni, Paolo T1 - Recent Trends in Edge AI: Efficient Design, Training and Deployment of Machine Learning Models T2 - Charting the Intelligence Frontiers – Edge AI Systems Nexus N2 - With a rising demand for ubiquitous smart systems, processing and interpreting large quantities of data generated on the edge at a high velocity is becoming an increasingly important challenge. Machine learning (ML) models such as Deep Neural Networks (DNNs) are an essential tool of today’s artificial intelligence due to their ability to make accurate predictions given complex tasks and environments. However, Deep Learning is computationally complex and energy intensive. This seems to contradict the characteristics of many edge devices, which have only limited memory, computational resources, and energy budget available. To overcome this challenge, an efficient ML model design is crucial that incorporates available optimization techniques from hardware, software, and methodological perspective to enable energy-efficient deployment and operation on the edge. This work comprehensively summarizes recent techniques for training, optimizing, and deploying ML models targeting edge devices. We discuss different strategies for finding deployable ML models, scalable DNN architectures, neural architecture search, and multi-objective optimization approaches, to enable feasible trade-offs considering available resources and latency. Furthermore, we give insight into DNN compression methods such 182as quantization and pruning. We conclude by investigating different forms of cascaded processing, from simple multi-level approaches to highly branched compute graphs and early-exit DNNs. Y1 - 2026 SN - 9788743808862 U6 - https://doi.org/10.1201/9788743808862-9 SP - 181 EP - 220 PB - River Publishers CY - New York ER - TY - JOUR A1 - Rueckert, Tobias A1 - Rauber, David A1 - Maerkl, Raphaela A1 - Klausmann, Leonard A1 - Yildiran, Suemeyye R. A1 - Gutbrod, Max A1 - Nunes, Danilo Weber A1 - Moreno, Alvaro Fernandez A1 - Luengo, Imanol A1 - Stoyanov, Danail A1 - Toussaint, Nicolas A1 - Cho, Enki A1 - Kim, Hyeon Bae A1 - Choo, Oh Sung A1 - Kim, Ka Young A1 - Kim, Seong Tae A1 - Arantes, Gonçalo A1 - Song, Kehan A1 - Zhu, Jianjun A1 - Xiong, Junchen A1 - Lin, Tingyi A1 - Kikuchi, Shunsuke A1 - Matsuzaki, Hiroki A1 - Kouno, Atsushi A1 - Manesco, João Renato Ribeiro A1 - Papa, João Paulo A1 - Choi, Tae-Min A1 - Jeong, Tae Kyeong A1 - Park, Juyoun A1 - Alabi, Oluwatosin A1 - Wei, Meng A1 - Vercauteren, Tom A1 - Wu, Runzhi A1 - Xu, Mengya A1 - Wang, An A1 - Bai, Long A1 - Ren, Hongliang A1 - Yamlahi, Amine A1 - Hennighausen, Jakob A1 - Maier-Hein, Lena A1 - Kondo, Satoshi A1 - Kasai, Satoshi A1 - Hirasawa, Kousuke A1 - Yang, Shu A1 - Wang, Yihui A1 - Chen, Hao A1 - Rodríguez, Santiago A1 - Aparicio, Nicolás A1 - Manrique, Leonardo A1 - Palm, Christoph A1 - Wilhelm, Dirk A1 - Feussner, Hubertus A1 - Rueckert, Daniel A1 - Speidel, Stefanie A1 - Nasirihaghighi, Sahar A1 - Al Khalil, Yasmina A1 - Li, Yiping A1 - Arbeláez, Pablo A1 - Ayobi, Nicolás A1 - Hosie, Olivia A1 - Lyons, Juan Camilo T1 - Comparative validation of surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation in endoscopy: Results of the PhaKIR 2024 challenge JF - Medical Image Analysis N2 - Reliable recognition and localization of surgical instruments in endoscopic video recordings are foundational for a wide range of applications in computer- and robot-assisted minimally invasive surgery (RAMIS), including surgical training, skill assessment, and autonomous assistance. However, robust performance under real-world conditions remains a significant challenge. Incorporating surgical context – such as the current procedural phase – has emerged as a promising strategy to improve robustness and interpretability. To address these challenges, we organized the Surgical Procedure Phase, Keypoint, and Instrument Recognition (PhaKIR) sub-challenge as part of the Endoscopic Vision (EndoVis) challenge at MICCAI 2024. We introduced a novel, multi-center dataset comprising thirteen full-length laparoscopic cholecystectomy videos collected from three distinct medical institutions, with unified annotations for three interrelated tasks: surgical phase recognition, instrument keypoint estimation, and instrument instance segmentation. Unlike existing datasets, ours enables joint investigation of instrument localization and procedural context within the same data while supporting the integration of temporal information across entire procedures. We report results and findings in accordance with the BIAS guidelines for biomedical image analysis challenges. The PhaKIR sub-challenge advances the field by providing a unique benchmark for developing temporally aware, context-driven methods in RAMIS and offers a high-quality resource to support future research in surgical scene understanding. KW - Surgical phase recognition KW - Instrument keypoint estimation KW - Instrument instance segmentation KW - Robot-assisted surgery Y1 - 2026 U6 - https://doi.org/10.1016/j.media.2026.103945 SN - 1361-8415 N1 - Corresponding author der OTH Regensburg: Tobias Rueckert Die Preprint-Version ist ebenfalls in diesem Repositorium verzeichnet unter: https://opus4.kobv.de/opus4-oth-regensburg/solrsearch/index/search/start/0/rows/10/sortfield/score/sortorder/desc/searchtype/simple/query/2507.16559 VL - 109 PB - Elsevier ER - TY - JOUR A1 - Winter, Lina A1 - Artinger, Annalena A1 - Böck, Hendrik A1 - Ramakrishnan, Vignesh A1 - Reible, Bruno A1 - Albin, Jan A1 - Schüffler, Peter J. A1 - Raptis, Georgios A1 - Brochhausen, Christoph T1 - Developing a smart and scalable tool for histopathological education—PATe 2.0 JF - Journal of Pathology Informatics N2 - Digital microscopy plays a crucial role in pathology education, providing scalable and standardized access to learning resources. In response, we present PATe 2.0, a scalable redeveloped web-application of the former PATe system from 2015. PATe 2.0 was developed using an agile, iterative process and built on a microservices architecture to ensure modularity, scalability, and reliability. It integrates a modern web-based user interface optimized for desktop and tablet use and automates key workflows such as whole-slide image uploads and processing. Performance tests demonstrated that PATe 2.0 significantly reduces tile request times compared to PATe, despite handling larger tiles. The platform supports open formats like DICOM and OpenSlide, enhancing its interoperability and adaptability across institutions. PATe 2.0 represents a robust digital microscopy solution in pathology education enhancing usability, performance, and flexibility. Its design enables future integration of research algorithms and highlights it as a pivotal tool for advancing pathology education and research. Y1 - 2026 U6 - https://doi.org/10.1016/j.jpi.2025.100535 SN - 2153-3539 VL - 20 PB - Elsevier ER - TY - JOUR A1 - Bedbur, Stefan A1 - Kamps, Udo A1 - Schmiedt, Anja Bettina T1 - A flexible model of ordered random variables for non-metallic inclusions in steels and related statistical inference JF - Applied Mathematical Modelling N2 - In a data set of non-metallic inclusion sizes in samples from engineering steel, common order statistics fail to serve as a suitable model for ascendingly ordered measurements within single samples. Therefore, a flexible model of ordered random variables is proposed, which allows for changes of distributions described by model parameters. Joint maximum likelihood estimation of these parameters and the shape parameter of an underlying left-truncated Weibull distribution is considered, and a model test is developed for the null-hypothesis of common order statistics being an adequate model. To overcome small data situations, a link-function approach is examined in order to reduce the number of involved model parameters as well as to propose to use a link-function parameter as a material indicator. An asymptotic test is provided to check for the presence of a linear link function, and tests for hypotheses about two link-function parameters are studied. Moreover, the construction of simultaneous confidence regions for the link-function parameters as well as of confidence bands for the entire graph of the link function are presented. Throughout, the findings are applied to the real metallurgical data set. Similar problems and data structures arise in other fields of material science and applications such as geology. Y1 - 2026 U6 - https://doi.org/10.1016/j.apm.2025.116284 SN - 0307-904X N1 - Corresponding author der OTH Regensburg: Anja Bettina Schmiedt VL - 149 PB - Elsevier ER -