Wetzel, S. ., & Bertel, S. . (2024). Data-Driven Analysis of Physical and Mental Rotation Strategies. CogSci 2025 conference. Rotterdam: Cognitive Science Society.
Abstract
Studying physical rotation (i.e., rotation tasks during which figures can be physically rotated, such as through gestures) can offer insights also into problem solving processes at work during mental rotation. We present a novel method for behavioral pattern analysis which we applied to data from 2,999 physical rotation tasks gathered in-class from 50 secondary school students. The method uses normalized, resampled, time-dependent data on angular offsets between figures over time and agglomerative, correlation-based clustering. Each cluster represents a distinct behavioral pattern and its respective prototype a problem solving strategy. Results indicate that multiple strategies were employed: The dominant strategy matches the classical model of mental rotation, in which angular offsets between figures are decreased over time. For the secondary strategy, angular offsets were actually increased. A subsequent analysis shows that the secondary strategy was more frequently used for symmetric figures, possibly indicating problems with correctly matching segments across figures.
Baganal-Krishna, N. ., Lübben, R. ., Liotou, E. ., Katsaros, K. V., & Rizk, A. . (2024). A federated learning approach to QoS forecasting in cellular vehicular communications: Approaches and empirical evidence. Computer Networks, 242, 110239. http://doi.org/https://doi.org/10.1016/j.comnet.2024.110239
Abstract
QoS forecasting for cellular vehicular communications allows cooperative, connected and automated mobility applications to tailor their behavior to the expected communication conditions on the road. In a nutshell, vehicles may, for example, execute cooperative maneuvers if the communication quality of service is only above a certain quantitative level whereas if not they revert to the individual autonomous mode. In this paper, we propose and show empirical methods for estimating packet-based QoS metrics obtained from 5G network measurements with a direct application to vehicular applications. As many distributed vehicular applications possess strict QoS requirements, we focus here on bounding packet-based statistical QoS quantiles, specifically for latency and loss. Our approach is based on training regression neural networks in a federated learning fashion and show that it can obtain predictions on par with centralized training without the vehicles needing to transmit raw measurement data. In contrast to QoS prediction using physical layer information, we briefly discuss the embedding of such much simpler application-level service within the 5G architecture. We also validate our approach through recovering classical closed-form delay quantiles that are obtained from analytical models of simple queueing systems. We show that our approach goes beyond these simple models in that it provides quantile estimates for the complex scenario of cellular vehicle communications and under different application traffic patterns including empirical data traffic traces as well as 5G testbed measurements.
Ganz, J. ., Marzahl, C. ., Ammeling, J. ., Rosbach, E. ., Richter, B. ., Puget, C. ., … Aubreville, M. . (2024). Information mismatch in PHH3-assisted mitosis annotation leads to interpretation shifts in H&E slide analysis. Scientific Reports, 14, 26273. http://doi.org/10.1038/s41598-024-77244-6
Abstract
Abstract The count of mitotic figures (MFs) observed in hematoxylin and eosin (H&E)-stained slides is an important prognostic marker, as it is a measure for tumor cell proliferation. However, the identification of MFs has a known low inter-rater agreement. In a computer-aided setting, deep learning algorithms can help to mitigate this, but they require large amounts of annotated data for training and validation. Furthermore, label noise introduced during the annotation process may impede the algorithms’ performance. Unlike H&E, where identification of MFs is based mainly on morphological features, the mitosis-specific antibody phospho-histone H3 (PHH3) specifically highlights MFs. Counting MFs on slides stained against PHH3 leads to higher agreement among raters and has therefore recently been used as a ground truth for the annotation of MFs in H&E. However, as PHH3 facilitates the recognition of cells indistinguishable from H&E staining alone, the use of this ground truth could potentially introduce an interpretation shift and even label noise into the H&E-related dataset, impacting model performance. This study analyzes the impact of PHH3-assisted MF annotation on inter-rater reliability and object level agreement through an extensive multi-rater experiment. Subsequently, MF detectors, including a novel dual-stain detector, were evaluated on the resulting datasets to investigate the influence of PHH3-assisted labeling on the models’ performance. We found that the annotators’ object-level agreement significantly increased when using PHH3-assisted labeling (F1: 0.53 to 0.74). However, this enhancement in label consistency did not translate to improved performance for H&E-based detectors, neither during the training phase nor the evaluation phase. Conversely, the dual-stain detector was able to benefit from the higher consistency. This reveals an information mismatch between the H&E and PHH3-stained images as the cause of this effect, which renders PHH3-assisted annotations not well-aligned for use with H&E-based detectors. Based on our findings, we propose an improved PHH3-assisted labeling procedure.
Gürgan, S. ., Henze, V. ., Gall, C. ., Schoenfisch, B. ., Brucker, S. ., Hahn, M. ., … Teistler, M. . (2024). Training for Ultrasound Imaging: An Evaluation of SonoGame. ACM Symposium on Spatial User Interaction (SUI 2024). Trier, Germany: ACM, New York. http://doi.org/10.1145/3677386.3688880
Afanasieva, N. ., & Schlipf, D. . (2023). Improving the Quality of Wind Field Reconstruction Techniques for Lidar-assisted Control of Wind Turbines. In 19thEAWE PhD Seminar on Wind Energy. http://doi.org/10.5281/zenodo.8335186 (Original work published 2025)
Guo, F. ., Schlipf, D. ., Lemmer, F. ., Raach, S. ., Özinan, U. ., Adam, R. ., & Choisnet, T. . (2023). The performance of two control strategies for floating wind turbines: lidar-assisted feedforward and multi-variable feedback. In Journal of Physics: Conference Series (Bd. 2626, S. 012005). http://doi.org/10.1088/1742-6596/2626/1/012005 (Original work published 2025)
Pitter, M. ., Slinger, C. ., Guo, F. ., Schlipf, D. ., Raach, S. ., & White, S. . (2023). A data-driven approach to the design and implementation of retrofit lidar assisted control systems. http://doi.org/10.5281/zenodo.8037351 (Original work published 2025)
Fu, W. ., Guo, F. ., Schlipf, D. ., & Peña, A. . (2023). Feedforward control for a 15-MW wind turbine using a spinner-mounted single-beam lidar. http://doi.org/10.5281/zenodo.8033374 (Original work published 2025)
Schlipf, D. ., Guo, F. ., Raach, S. ., & Lemmer, F. . (2023). A Tutorial on Lidar-Assisted Control for Floating Offshore Wind Turbines. In American Control Conference. San Diego, CA, USA. http://doi.org/10.23919/ACC55779.2023.10156419 (Original work published 2025)