@unpublished{AchhammerFioritiPatoniaetal., author = {Achhammer, Anton and Fioriti, Davide and Patonia, Aliaksei and Sterner, Michael}, title = {The impact of hydrogen underground storage on fair partnerships: a GIS-based integration of salt caverns into PyPSA-Earth}, publisher = {SSRN}, doi = {10.2139/ssrn.6307406}, pages = {22}, abstract = {The increasing demand for hydrogen in Europe and the development of cross-border infrastructure, such as the SoutH2 Corridorconnecting Tunisia, Italy, Austria, and Germany, underscore the importance for hydrogen storage solutions to ensure supplysecurity and competitive pricing. Without storage, producers face increased market dependency, as electrolyzers require con-tinuous operation to remain economically viable. At the same time, storage offers opportunities to strengthen domestic valuechains by securing hydrogen supply for local industries. To assess the system-level impact of underground hydrogen storageand its implications for hydrogen partnerships, we integrate GIS-based salt cavern potentials into PyPSA-Earth and apply theframework to Tunisia. Salt caverns are currently largely considered the most economical option for large-scale hydrogen storage,offering operational flexibility. Underground storage is represented as an endogenously optimised, regionally constrained option,enabling a direct comparison between scenarios with and without geological storage under identical demand, technology, andpolicy assumptions.Our results show that underground hydrogen storage enables seasonal balancing at multi-terawatt-hour scale, reshaping hydro-gen system design. Storage availability substitutes most aboveground hydrogen tank capacity, improves electrolyser utilisation,and reduces levelised hydrogen production costs by approximately 0.10 € kg-1. Moreover, it decouples hydrogen production fromshort-term electricity variability and export demand, enhancing supply stability and export competitiveness.Beyond the Tunisian case, the findings underscore the strategic role of geological storage in international hydrogen trade. Byincreasing resilience and reducing cost volatility, underground hydrogen storage strengthens the position of exporting regionsand supports more balanced and sustainable hydrogen partnerships.}, language = {en} } @unpublished{Falter, author = {Falter, Thomas}, title = {Werkstattbericht: Erfahrungen aus der Zusammenarbeit mit KI-Agenten f{\"u}r individualisiertes Lernen}, doi = {10.13140/RG.2.2.20204.63360}, pages = {15}, abstract = {DDer Artikel reflektiert praxisbasiert und philosophisch die Zusammenarbeit von Menschen mit KI-Agenten - insbesondere im Kontext des Lernens von Skills auf Experten-Level im Hochschulbereich. Am Beispiel der Entwicklung des KI-unterst{\"u}tzten Multiagentensystem LASSI, das Lernen unterst{\"u}tzt, wird eine Lernreise in drei Phasen erz{\"a}hlt - vom produktiven Staunen {\"u}ber die Verschiebung von Autorenschaft bis hin zur {\"U}bernahme von Verantwortung. Die Perspektiven von Entwickler, Professor und Studierenden zeigen, wie sich Mensch-Agenten-Systeme durch Zusammenarbeit ver{\"a}ndern. Erg{\"a}nzend werden drei philosophische Dimensionen - Embodied Knowledge, Enhanced Technologies und Embedded Ethics - herangezogen, um Potenziale und Risiken dieser Systeme zu beleuchten: Was passiert, wenn Intelligenz entk{\"o}rperlicht, menschliche F{\"a}higkeiten an Technologien ausgelagert und Werte kodiert werden? Der Text pl{\"a}diert daf{\"u}r, Agenten nicht als neutrale Werkzeuge, sondern als Mitgestalter im Soziotechnischen System Bildung zu begreifen. In der Schlussbetrachtung wird die Hochschule als Denkwerkstatt skizziert, die durch KI ihre Rolle neu definieren muss: weg vom Pr{\"u}fungsbetrieb hin zu einem Raum f{\"u}r Urteilskraft und Selbstreflexion. Lernen mit und von Agenten heißt: Lernen neu denken - gemeinsam gestalten.}, language = {de} } @article{BrunnerVogl, author = {Brunner, Philipp and Vogl, Stefanie}, title = {Extracting Product Improvement Insights from Social Media Comments Using Machine Learning: a Case Study in the Automotive Industry}, series = {Machine Learning and Knowledge Extraction}, volume = {8}, journal = {Machine Learning and Knowledge Extraction}, number = {2}, publisher = {MDPI}, doi = {10.3390/make8020042}, pages = {14}, abstract = {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.}, language = {en} } @article{WallnerGutbrodRauberetal., author = {Wallner, M. and Gutbrod, Max and Rauber, David and Ebigbo, Alanna and Probst, Andreas and Palm, Christoph and Messmann, Helmut and Roser, David}, title = {KI-gest{\"u}tzte Detektion und Segmentierung von Magenkarzinomen in westlichen endoskopischen Bilddaten anhand eines fine-tuned Deep-Learning Ansatzes}, series = {Zeitschrift f{\"u}r Gastroenterologie}, volume = {64}, journal = {Zeitschrift f{\"u}r Gastroenterologie}, number = {03}, publisher = {Thieme}, doi = {10.1055/s-0046-1817751}, pages = {e64 -- e65}, abstract = {Diese vorl{\"a}ufige monozentrische Studie zeigt, dass ein aus einem Barrett-{\"O}sophagus-KI-System feinjustiertes Deep-Learning-Modell Magenkarzinome in westlichen multimodalen endoskopischen Bilddaten zuverl{\"a}ssig detektieren und pr{\"a}zise segmentieren kann. Die hohe Segmentierungsgenauigkeit und Detektionssensitivit{\"a}t {\"u}ber verschiedene Bildmodalit{\"a}ten hinweg unterstreichen die Machbarkeit eines pathologiegest{\"u}tzten KI-Ansatzes auch in einer westlichen Niedriginzidenzpopulation. Aufgrund der ausschließlichen Verwendung von Bildern mit sichtbaren Tumoren lassen sich keine Aussagen zur Spezifit{\"a}t treffen; eine {\"U}bertragbarkeit auf Screening- oder Mischkollektive ist daher limitiert. Weitere Studien mit a) gr{\"o}ßerem Datensatz inklusive Videodaten, b) externer Validierung an einer multizentrischen westlichen Kohorte, sowie c) Anwendung und Pr{\"u}fung an nicht-neoplastischen Vergleichsbildern oder anderen Pathologien sind erforderlich. Nach unserem Kenntnisstand z{\"a}hlt dieses System zu den ersten in einer westlichen Population entwickelten endoskopischen KI-Ans{\"a}tzen zur Magenkarzinomdetektion, und zu wenigen, die vollst{\"a}ndige ESD-pr{\"a}paratbasierte Referenzdaten f{\"u}r Training und Validierung nutzen.}, language = {de} } @article{TanSchreinerHausladenetal., author = {Tan, Jing Jie and Schreiner, Rupert and Hausladen, Matthias and Asgharzade, Ali and Edler, Simon and Bartsch, Julian and Bachmann, Michael and Schels, Andreas and Kwan, Ban-Hoe and Ng, Danny Wee-Kiat and Hum, Yan-Chai}, title = {SiMiC: Context-aware silicon microstructure characterization using attention-based convolutional neural networks for field-emission tip analysis}, series = {Journal of Vacuum Science \& Technology B}, volume = {43}, journal = {Journal of Vacuum Science \& Technology B}, number = {6}, publisher = {AVS}, doi = {10.1116/6.0005068}, abstract = {Accurate characterization of silicon microstructures is essential for advancing microscale fabrication, quality control, and device performance. Traditional analysis using scanning electron microscopy (SEM) often requires labor-intensive, manual evaluation of feature geometry, limiting throughput and reproducibility. In this study, we propose SiMiC: Context-aware Silicon Microstructure Characterization Using Attention-based Convolutional Neural Networks for Field-Emission Tip Analysis. By leveraging deep learning, our approach efficiently extracts morphological features—such as size, shape, and apex curvature—from SEM images, significantly reducing human intervention while improving measurement consistency. A specialized dataset of silicon-based field-emitter tips was developed, and a customized convolutional neural network architecture incorporating attention mechanisms was trained for multiclass microstructure classification and dimensional prediction. Comparative analysis with classical image processing techniques demonstrates that SiMiC achieves high accuracy while maintaining interpretability. The proposed framework establishes a foundation for data-driven microstructure analysis directly linked to field-emission performance, opening avenues for correlating emitter geometry with emission behavior and guiding the design of optimized cold-cathode and SEM electron sources. The related dataset and algorithm repository that could serve as a baseline in this area can be found at https://research.jingjietan.com/?q=SIMIC.}, language = {en} } @article{WeiherervonRiedheimBrebantetal., author = {Weiherer, Maximilian and von Riedheim, Antonia and Br{\´e}bant, Vanessa and Egger, Bernhard and Palm, Christoph}, title = {Learning Neural Parametric 3D Breast Shape Models for Metrical Surface Reconstruction From Monocular RGB Videos}, series = {Machine Learning for Biomedical Imaging (MELBA)}, journal = {Machine Learning for Biomedical Imaging (MELBA)}, number = {MELBA-BVM 2025 Special Issue}, publisher = {Melba}, doi = {10.59275/j.melba.2026-8b23}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-89791}, pages = {95 -- 114}, abstract = {We present a neural parametric 3D breast shape model and, based on this model, introduce a low-cost and accessible 3D surface reconstruction pipeline capable of recovering accurate breast geometry from a monocular RGB video. In contrast to widely used, commercially available yet expensive 3D breast scanning solutions and existing low-cost alternatives, our method requires neither specialized hardware nor proprietary software and can be used with any device that is able to record RGB videos. The key building blocks of our pipeline are a state-of-the-art, off-the-shelf Structure-from-Motion pipeline, paired with a parametric breast model for robust surface reconstruction. Our model, similarly to the recently proposed implicit Regensburg Breast Shape Model (iRBSM), leverages implicit neural representations to model breast shapes. However, unlike the iRBSM, which employs a single global neural Signed Distance Function (SDF), our approach—inspired by recent state-of-the-art face models—decomposes the implicit breast domain into multiple smaller regions, each represented by a local neural SDF anchored at anatomical landmark positions. When incorporated into our surface reconstruction pipeline, the proposed model, dubbed liRBSM (short for localized iRBSM), significantly outperforms the iRBSM in terms of reconstruction quality, yielding more detailed surface reconstruction than its global counterpart. Overall, we find that the introduced pipeline is able to recover high-quality and metrically correct 3D breast geometry within an error margin of less than 2 mm. Our method is fast (requires less than six minutes), fully transparent and open-source, and together with the model publicly available at https://rbsm.re-mic.de/local-implicit.}, language = {en} } @inproceedings{BielmeierRamsauerYoshidaetal., author = {Bielmeier, Benno and Ramsauer, Ralf and Yoshida, Takahiro and Mauerer, Wolfgang}, title = {From Tracepoints to Timeliness: a Semi-Markov Framework for Predictive Runtime Analysis}, series = {IEEE 31th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 20-22 August 2025, Singapore}, booktitle = {IEEE 31th International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA), 20-22 August 2025, Singapore}, publisher = {IEEE}, doi = {10.1109/RTCSA66114.2025.00021}, pages = {114 -- 125}, abstract = {Detecting and resolving violations of temporal constraints in real-time systems is both, time-consuming and resource-intensive, particularly in complex software environments. Measurement-based approaches are widely used during development, but often are unable to deliver reliable predictions with limited data. This paper presents a hybrid method for worst-case execution time estimation, combining lightweight runtime tracing with probabilistic modelling. Timestamped system events are used to construct a semi-Markov chain, where transitions represent empirically observed timing between events. Execution duration is interpreted as time-to-absorption in the semi-Markov chain, enabling worst-case execution time estimation with fewer assumptions and reduced overhead. Empirical results from real-time Linux systems indicate that the method captures both regular and extreme timing behaviours accurately, even from short observation periods. The model supports holistic, low-intrusion analysis across system layers and remains interpretable and adaptable for practical use.}, language = {en} } @article{FeilnerRamsauer, author = {Feilner, Markus and Ramsauer, Ralf}, title = {Open-Source-Wissenschaftler bereiten Software f{\"u}r das Quantencomputing vor}, series = {Linux-Magazin}, volume = {25}, journal = {Linux-Magazin}, number = {08}, publisher = {Deloitte}, pages = {7}, abstract = {Um Software und Architekturen f{\"u}r das Quantencomputing zu entwickeln, zu definieren und zu testen, braucht man keinen Quantencomputer. Weil die teuer und rar sind, entschieden sich Wissenschaftler der OTH Regensburg daf{\"u}r, sie einfach zu virtualisieren - mit messbarem Erfolg.}, language = {en} } @inproceedings{SchmidbauerRiofrioHeinrichetal., author = {Schmidbauer, Lukas and Riofr{\´i}o, Carlos A. and Heinrich, Florian and Junk, Vanessa and Schwenk, Ulrich and Husslein, Thomas and Mauerer, Wolfgang}, title = {Path Matters: Industrial Data Meet Quantum Optimization}, series = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, booktitle = {2025 IEEE International Conference on Quantum Computing and Engineering (QCE), 30 August - 05 September 2025, Albuquerque}, publisher = {IEEE}, doi = {10.1109/QCE65121.2025.00230}, pages = {2101 -- 2111}, abstract = {Real-world optimization problems must undergo a series of transformations before becoming solvable on current quantum hardware. Even for a fixed problem, the number of possible transformation paths-from industry-relevant formulations through binary constrained linear programs (BILPs), to quadratic unconstrained binary optimization (QUBO), and finally to a hardware-executable representation-is remarkably large. Each step introduces free parameters, such as Lagrange multipliers, encoding strategies, slack variables, rounding schemes or algorithmic choices-making brute-force exploration of all paths intractable. In this work, we benchmark a representative subset of these transformation paths using a realworld industrial production planning problem with industry data: the optimization of work allocation in a press shop producing vehicle parts. We focus on QUBO reformulations and algorithmic parameters for both quantum annealing (QA) and the Linear Ramp Quantum Approximate Optimization Algorithm (LR-QAOA). Our goal is to identify a reduced set of effective configurations applicable to similar industrial settings. Our results show that QA on D-Wave hardware consistently produces near-optimal solutions, whereas LR-QAOA on IBM quantum devices struggles to reach comparable performance. Hence, the choice of hardware and solver strategy significantly impacts performance. The problem formulation and especially the penalization strategy determine the solution quality. Most importantly, mathematically-defined penalization strategies are equally successful as hand-picked penalty factors, paving the way for automated QUBO formulation. Moreover, we observe a strong correlation between simulated and quantum annealing performance metrics, offering a scalable proxy for predicting QA behavior on larger problem instances.}, language = {en} } @article{FranzSchoenbergerStrobletal., author = {Franz, Maja and Sch{\"o}nberger, Manuel and Strobl, Melvin and K{\"u}hn, Eileen and Streit, Achim and Zurita, P{\´i}a and Diefenthaler, Markus and Mauerer, Wolfgang}, title = {From Hope to Heuristic: Realistic Runtime Estimates for Quantum Optimisation in NHEP}, series = {EPJ Web of Conferences; 27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)}, volume = {337}, journal = {EPJ Web of Conferences; 27th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2024)}, publisher = {EDP Sciences}, doi = {10.1051/epjconf/202533701282}, pages = {8}, abstract = {Noisy Intermediate-Scale Quantum (NISQ) computers, despite their limitations, present opportunities for near-term quantum advantages in Nuclear and High-Energy Physics (NHEP) when paired with specially designed quantum algorithms and processing units. This study focuses on core algorithms that solve optimization problems through the quadratic Ising or Quadratic Unconstrained Binary Optimisation model, specifically Quantum Annealing and the Quantum Approximate Optimisation Algorithm (QAOA). In particular, we estimate runtimes and scalability for the task of particle Track Reconstruction (TR), a key computing challenge in NHEP, and investigate how the classical parameter space in QAOA, along with techniques like a Fourieranalysis based heuristic, can facilitate future quantum advantages. The findings indicate that lower frequency components in the parameter space are crucial for effective annealing schedules, suggesting that heuristics can improve resource efficiency while achieving near-optimal results. Overall, the study highlights the potential of NISQ computers in NHEP and the significance of co-design approaches and heuristic techniques in overcoming challenges in quantum algorithms.}, language = {en} } @unpublished{FranzStroblChaichenetsetal., author = {Franz, Maja and Strobl, Melvin and Chaichenets, Leonid and K{\"u}hn, Eileen and Streit, Achim and Mauerer, Wolfgang}, title = {Out of Tune: Demystifying Noise-Effects on Quantum Fourier Models}, doi = {10.48550/arXiv.2506.09527}, pages = {32}, abstract = {Variational quantum algorithms have received substantial theoretical and empirical attention. As the underlying variational quantum circuit (VQC) can be represented by Fourier series that contain an exponentially large spectrum in the number of input features, hope for quantum advantage remains. Nevertheless, it remains an open problem if and how quantum Fourier models (QFMs) can concretely outperform classical alternatives, as the eventual sources of non-classical computational power (for instance, the role of entanglement) are far from being fully understood. Likewise, hardware noise continues to pose a challenge that will persist also along the path towards fault tolerant quantum computers. In this work, we study VQCs with Fourier lenses, which provides possibilities to improve their understanding, while also illuminating and quantifying constraints and challenges. We seek to elucidate critical characteristics of QFMs under the influence of noise. Specifically, we undertake a systematic investigation into the impact of noise on the Fourier spectrum, expressibility, and entangling capability of QFMs through extensive numerical simulations and link these properties to training performance. The insights may inform more efficient utilisation of quantum hardware and support the design of tailored error mitigation and correction strategies. Decoherence imparts an expected and broad detrimental influence across all Ans{\"a}tze. Nonetheless, we observe that the severity of these deleterious effects varies among different model architectures, suggesting that certain configurations may exhibit enhanced robustness to noise and show computational utility.}, language = {en} }