@inproceedings{BanerjeeBertramAmmelingetal.2025, author = {Banerjee, Sweta and Bertram, Christof and Ammeling, Jonas and Weiss, Viktoria and Conrad, Thomas and Klopfleisch, Robert and Kaltenecker, Christopher and Breininger, Katharina and Aubreville, Marc}, title = {Comprehensive Dataset of Coarse Tumor Annotations for The Cancer Genome Atlas Breast Invasive Carcinoma}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas Martin and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-47422-5}, doi = {https://doi.org/10.1007/978-3-658-47422-5_56}, pages = {260 -- 265}, year = {2025}, language = {en} } @inbook{SchneiderEberlHirsch2025, author = {Schneider, Yvonne and Eberl, Sabine and Hirsch, Bernhard}, title = {Nachhaltigkeit in der Unternehmensberichterstattung: Eine Analyse der Nachhaltigkeitsberichte ausgew{\"a}hlter Chemieunternehmen}, booktitle = {Controlling \& Innovation 2025/2026: Nachhaltigkeit}, editor = {K{\"u}mpel, Thomas and Heupel, Thomas and Schlenkrich, Kay}, publisher = {Springer Gabler}, address = {Wiesbaden}, isbn = {978-3-658-48987-8}, doi = {https://doi.org/10.1007/978-3-658-48987-8_11}, pages = {203 -- 225}, year = {2025}, language = {de} } @inbook{Ketterl2025, author = {Ketterl, Simone}, title = {Achtung, Europa! (1936)}, booktitle = {Thomas Mann-Handbuch: Leben - Werk - Wirkung}, editor = {Bl{\"o}dorn, Andreas and Marx, Friedhelm}, edition = {2., erweiterte Auflage}, publisher = {J.B. Metzler}, address = {Berlin}, isbn = {978-3-662-69437-4}, doi = {https://doi.org/10.1007/978-3-662-69437-4_51}, pages = {315 -- 316}, year = {2025}, language = {de} } @inproceedings{Krueger2025, author = {Kr{\"u}ger, Max}, title = {Cross-Validation of Random-Forests' Classification Performance in Maritime Scenarios with Aggregated AIS Messages}, booktitle = {Operations Research Proceedings 2023: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Germany, August 29-September 1, 2023}, editor = {Voigt, Guido and Fliedner, Malte and Haase, Knut and Br{\"u}ggemann, Wolfgang and Hoberg, Kai and Meissner, Joern}, publisher = {Springer}, address = {Cham}, isbn = {978-3-031-58405-3}, doi = {https://doi.org/10.1007/978-3-031-58405-3_78}, pages = {609 -- 616}, year = {2025}, language = {en} } @inbook{Stummeyer2025, author = {Stummeyer, Christian}, title = {Der Einsatz von K{\"u}nstlicher Intelligenz (KI) im Handel der Zukunft}, booktitle = {Modernes Handelsmanagement: Wissenschaftliche Erkenntnisse und Ans{\"a}tze aus der Unternehmenspraxis f{\"u}r die Zukunft des Handels}, editor = {Berentzen, Johannes and Hoog, Philipp}, publisher = {Springer Gabler}, address = {Wiesbaden}, isbn = {978-3-658-46881-1}, doi = {https://doi.org/10.1007/978-3-658-46881-1_17}, pages = {193 -- 200}, year = {2025}, language = {de} } @inproceedings{BertramWeissDonovanetal.2025, author = {Bertram, Christof and Weiss, Viktoria and Donovan, Taryn and Banerjee, Sweta and Conrad, Thomas and Ammeling, Jonas and Klopfleisch, Robert and Kaltenecker, Christopher and Aubreville, Marc}, title = {Histologic Dataset of Normal and Atypical Mitotic Figures on Human Breast Cancer (AMi-Br)}, booktitle = {Bildverarbeitung f{\"u}r die Medizin 2025: Proceedings, German Conference on Medical Image Computing, Regensburg March 09-11, 2025}, editor = {Palm, Christoph and Breininger, Katharina and Deserno, Thomas Martin and Handels, Heinz and Maier, Andreas and Maier-Hein, Klaus H. and Tolxdorff, Thomas}, publisher = {Springer Vieweg}, address = {Wiesbaden}, isbn = {978-3-658-47422-5}, doi = {https://doi.org/10.1007/978-3-658-47422-5_25}, pages = {113 -- 118}, year = {2025}, language = {en} } @unpublished{DoenmezTejerodelaPiedraKloseetal.2026, author = {D{\"o}nmez, {\"O}mer and Tejero de la Piedra, Ricardo and Klose, Simona and Riolet, Matthieu and Rozek, Lukas and Vaculin, Ondrej and Hach, Christian}, title = {Approach for Passive Safety Assessment of Rearward-Sitting Occupants}, publisher = {Zenodo}, address = {Genf}, doi = {https://doi.org/10.5281/zenodo.18338626}, year = {2026}, abstract = {The introduction of highly automated vehicles (HAVs) will allow vehicle occupants to take advantage of new seating configurations, such as sitting rearward in the first row. One critical aspect of assessing occupant safety during high-speed impacts is the lack of a dedicated safety framework for rearward-facing passengers in the first row. This paper introduces a method to develop new assessment criteria for these novel seat configurations. Thus, this research presents some preliminary results of rearward-facing occupant injury biomechanics analyses carried out employing a variety of anthropomorphic test devices (ATDs) and the VIVA+ 50M human body model (HBM), restrained with different belt configurations and considering different seat typologies. It reviews the suitability of 50th percentile male ATDs to capture a biofidelic engagement with the seat structure and belt system and evaluates the reaction loads on the occupant, along with the energy management resulting from seat back rotational stiffness and energy-absorbing foams layered behind the seat cushion. Based on the results, the THOR-AV-50M is a suitable candidate for further biofidelity analysis. Torso occupant loads can be effectively reduced utilizing seat back rotation but pelvis load management requires further studies.}, language = {en} } @article{MohanSchoen2026, author = {Mohan, Adithya and Sch{\"o}n, Torsten}, title = {Toward Robust Agents: A Survey of Adversarial Attacks and Defenses in Deep Reinforcement Learning}, volume = {14}, journal = {IEEE Access}, publisher = {IEEE}, address = {New York}, issn = {2169-3536}, doi = {https://doi.org/10.1109/ACCESS.2026.3657855}, pages = {14481 -- 14497}, year = {2026}, abstract = {Deep Reinforcement Learning (DRL) has demonstrated remarkable success in autonomous decision-making across diverse domains, including robotics, autonomous driving, and game playing. However, recent studies have uncovered a critical vulnerability: DRL agents are highly susceptible to adversarial attacks that can significantly degrade their performance or lead to catastrophic failure. These attacks exploit different components of the learning pipeline observations, actions, rewards, and policies exposing new challenges unique to DRL compared to supervised learning. This survey provides a comprehensive examination of adversarial threats and corresponding defense mechanisms within the DRL paradigm. This also aims to serve as a foundational reference for researchers and practitioners seeking to understand and mitigate adversarial vulnerabilities in DRL.}, language = {en} } @article{AmmelingGanzRosbachetal.2026, author = {Ammeling, Jonas and Ganz, Jonathan and Rosbach, Emely and Lausser, Ludwig and Bertram, Christof and Breininger, Katharina and Aubreville, Marc}, title = {Benchmarking Foundation Models for Mitotic Figure Classification}, volume = {3}, pages = {2026:003}, journal = {Machine Learning for Biomedical Imaging}, number = {MELBA-BVM 2025 Special Issue}, publisher = {Melba editors}, address = {[s. l.]}, issn = {2766-905X}, doi = {https://doi.org/10.59275/j.melba.2026-a3eb}, pages = {38 -- 55}, year = {2026}, abstract = {The performance of deep learning models is known to scale with data quantity and diversity. In pathology, as in many other medical imaging domains, the availability of labeled images for a specific task is often limited. Self-supervised learning techniques have enabled the use of vast amounts of unlabeled data to train large-scale neural networks, i.e., foundation models, that can address the limited data problem by providing semantically rich feature vectors that can generalize well to new tasks with minimal training effort increasing model performance and robustness. In this work, we investigate the use of foundation models for mitotic figure classification. The mitotic count, which can be derived from this classification task, is an independent prognostic marker for specific tumors and part of certain tumor grading systems. In particular, we investigate the data scaling laws on multiple current foundation models and evaluate their robustness to unseen tumor domains. Next to the commonly used linear probing paradigm, we also adapt the models using low-rank adaptation (LoRA) of their attention mechanisms. We compare all models against end-to-end-trained baselines, both CNNs and Vision Transformers. Our results demonstrate that LoRA-adapted foundation models provide superior performance to those adapted with standard linear probing, reaching performance levels close to 100 \% data availability with only 10 \% of training data. Furthermore, LoRA-adaptation of the most recent foundation models almost closes the out-of-domain performance gap when evaluated on unseen tumor domains. However, full fine-tuning of traditional architectures still yields competitive performance.}, language = {en} } @article{BaumFinkFischeretal.2025, author = {Baum, Heinz-Georg and Fink, Christian and Fischer, Thomas M. and G{\"u}nther, Edeltraud and G{\"u}nther, Thomas and Haller, Axel and Kafadar, Kalina and Marten, Kai-Uwe and Schultze, Wolfgang and Wittmann, Robert}, title = {„Konnektivit{\"a}t" als Strukturprinzip akademischen Wirkens von Prof. Dr. Dr. h.c. mult. Adolf G. Coenenberg - Ein Nachruf}, volume = {5}, journal = {Schmalenbach Impulse}, publisher = {Schmalenbach-Gesellschaft fur Betriebswirtschaft}, address = {Frechen}, issn = {2749-9804}, doi = {https://doi.org/10.54585/JAEY3059}, year = {2025}, language = {de} } @unpublished{ThepautBlaschKhadjavietal.2025, author = {Th{\´e}paut, Louison and Blasch, Julia and Khadjavi, Menusch and Spadaro, Giuliana}, title = {Energy Transition at Home: Nudging the Uptake of Energy Audits by Dutch Homeowners}, titleParent = {SSRN}, publisher = {Elsevier}, address = {Amsterdam}, doi = {https://dx.doi.org/10.2139/ssrn.5387432}, year = {2025}, language = {en} }