TY - BOOK A1 - Staat, Manfred A1 - Digel, Ilya A1 - Trzewik, Jürgen A1 - Sielemann, Stefanie A1 - Erni, Daniel A1 - Zylka, Waldemar T1 - Symposium Proceedings; 4th YRA MedTech Symposium 2024 : February 1 / 2024 / FH Aachen Y1 - 2024 SN - 978-3-940402-65-3 U6 - https://doi.org/10.17185/duepublico/81475 PB - Universität Duisburg-Essen CY - Duisburg ER - TY - RPRT A1 - Barnat, Miriam A1 - Arntz, Kristian A1 - Bernecker, Andreas A1 - Fissabre, Anke A1 - Franken, Norbert A1 - Goldbach, Daniel A1 - Hüning, Felix A1 - Jörissen, Jörg A1 - Kirsch, Ansgar A1 - Pettrak, Jürgen A1 - Rexforth, Matthias A1 - Josef, Rosenkranz A1 - Terstegge, Andreas T1 - Strategische Gestaltung von Studiengängen für die Zukunft: Ein kollaborativ entwickeltes Self-Assessment BT - Diskussionspapier Nr. 33 T2 - Hochschulforum Digitalisierung - Diskussionspapier N2 - Das Diskussionspapier beschreibt einen Prozess an der FH Aachen zur Entwicklung und Implementierung eines Self-Assessment-Tools für Studiengänge. Dieser Prozess zielte darauf ab, die Relevanz der Themen Digitalisierung, Internationalisierung und Nachhaltigkeit in Studiengängen zu stärken. Durch Workshops und kollaborative Entwicklung mit Studiendekan:innen entstand ein Fragebogen, der zur Reflexion und strategischen Weiterentwicklung der Studiengänge dient. Y1 - 2024 SN - 2365-7081 PB - Stifterverband für die Deutsche Wissenschaft CY - Berlin ER - TY - JOUR A1 - Kohl, Philipp A1 - Krämer, Yoka A1 - Fohry, Claudia A1 - Kraft, Bodo ED - Fred, Ana ED - Hadjali, Allel ED - Gusikhin, Oleg ED - Sansone, Carlo T1 - Scoping review of active learning strategies and their evaluation environments for entity recognition tasks JF - Deep learning theory and applications N2 - We conducted a scoping review for active learning in the domain of natural language processing (NLP), which we summarize in accordance with the PRISMA-ScR guidelines as follows: Objective: Identify active learning strategies that were proposed for entity recognition and their evaluation environments (datasets, metrics, hardware, execution time). Design: We used Scopus and ACM as our search engines. We compared the results with two literature surveys to assess the search quality. We included peer-reviewed English publications introducing or comparing active learning strategies for entity recognition. Results: We analyzed 62 relevant papers and identified 106 active learning strategies. We grouped them into three categories: exploitation-based (60x), exploration-based (14x), and hybrid strategies (32x). We found that all studies used the F1-score as an evaluation metric. Information about hardware (6x) and execution time (13x) was only occasionally included. The 62 papers used 57 different datasets to evaluate their respective strategies. Most datasets contained newspaper articles or biomedical/medical data. Our analysis revealed that 26 out of 57 datasets are publicly accessible. Conclusion: Numerous active learning strategies have been identified, along with significant open questions that still need to be addressed. Researchers and practitioners face difficulties when making data-driven decisions about which active learning strategy to adopt. Conducting comprehensive empirical comparisons using the evaluation environment proposed in this study could help establish best practices in the domain. Y1 - 2024 SN - 978-3-031-66694-0 (online ISBN) SN - 978-3-031-66693-3 (print ISBN) U6 - https://doi.org/10.1007/978-3-031-66694-0_6 SP - 84 EP - 106 PB - Springer CY - Cham ER - TY - BOOK A1 - Elsaesser, Evelyn A1 - Klebinggat, Michael A1 - Kuhn, Wilfried A1 - Michielsens, Constant A1 - Pauels, Willibert A1 - Popkes, Enno E. A1 - Schneider, Elke A1 - Laack, Walter van A1 - Warven, Rinus van ED - Laack, Walter van T1 - Schnittstelle Tod - Ist die Menschheit zu retten ohne Vertrauen auf ein Danach Y1 - 2024 SN - 978-3-936624-58-8 N1 - 8. Tagungsband zum NTE-Seminar in Aachen am, 11.11.2023 PB - van Laack Buchverlag CY - Aachen ER - TY - INPR A1 - Bremm, Florian A1 - Blaneck, Patrick Gustav A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - Preprint: Detecting sexism in German online newspaper comments with open-source text embeddings BT - (Team GDA, GermEval2024 Shared Task 1: GerMS-Detect, Subtasks 1 and 2, Closed Track) T2 - arXiv N2 - Sexism in online media comments is a pervasive challenge that often manifests subtly, complicating moderation efforts as interpretations of what constitutes sexism can vary among individuals. We study monolingual and multilingual open-source text embeddings to reliably detect sexism and misogyny in Germanlanguage online comments from an Austrian newspaper. We observed classifiers trained on text embeddings to mimic closely the individual judgements of human annotators. Our method showed robust performance in the GermEval 2024 GerMS-Detect Subtask 1 challenge, achieving an average macro F1 score of 0.597 (4th place, as reported on Codabench). It also accurately predicted the distribution of human annotations in GerMS-Detect Subtask 2, with an average Jensen-Shannon distance of 0.301 (2nd place). The computational efficiency of our approach suggests potential for scalable applications across various languages and linguistic contexts. Y1 - 2024 U6 - https://doi.org/10.48550/arXiv.2403.08592 ER - TY - INPR A1 - Grieger, Niklas A1 - Mehrkanoon, Siamak A1 - Bialonski, Stephan T1 - Preprint: Data-efficient sleep staging with synthetic time series pretraining T2 - arXiv N2 - Analyzing electroencephalographic (EEG) time series can be challenging, especially with deep neural networks, due to the large variability among human subjects and often small datasets. To address these challenges, various strategies, such as self-supervised learning, have been suggested, but they typically rely on extensive empirical datasets. Inspired by recent advances in computer vision, we propose a pretraining task termed "frequency pretraining" to pretrain a neural network for sleep staging by predicting the frequency content of randomly generated synthetic time series. Our experiments demonstrate that our method surpasses fully supervised learning in scenarios with limited data and few subjects, and matches its performance in regimes with many subjects. Furthermore, our results underline the relevance of frequency information for sleep stage scoring, while also demonstrating that deep neural networks utilize information beyond frequencies to enhance sleep staging performance, which is consistent with previous research. We anticipate that our approach will be advantageous across a broad spectrum of applications where EEG data is limited or derived from a small number of subjects, including the domain of brain-computer interfaces. Y1 - 2024 ER - TY - JOUR A1 - Pieronek, Lukas A1 - Kleefeld, Andreas T1 - On trajectories of complex-valued interior transmission eigenvalues JF - Inverse problems and imaging : IPI N2 - This paper investigates the interior transmission problem for homogeneous media via eigenvalue trajectories parameterized by the magnitude of the refractive index. In the case that the scatterer is the unit disk, we prove that there is a one-to-one correspondence between complex-valued interior transmission eigenvalue trajectories and Dirichlet eigenvalues of the Laplacian which turn out to be exactly the trajectorial limit points as the refractive index tends to infinity. For general simply-connected scatterers in two or three dimensions, a corresponding relation is still open, but further theoretical results and numerical studies indicate a similar connection. KW - Interior transmission problem KW - Eigenvalue trajectories KW - Complex-valued eigenvalues Y1 - 2024 U6 - https://doi.org/10.3934/ipi.2023041 SN - 1930-8337 (Print) SN - 1930-8345 (Online) VL - 18 IS - 2 SP - 480 EP - 516 PB - AIMS CY - Springfield, Mo ER - TY - JOUR A1 - Schoenrock, Britt A1 - Muckelt, Paul E. A1 - Hastermann, Maria A1 - Albracht, Kirsten A1 - MacGregor, Robert A1 - Martin, David A1 - Gunga, Hans-Christian A1 - Salanova, Michele A1 - Stokes, Maria J. A1 - Warner, Martin B. A1 - Blottner, Dieter T1 - Muscle stiffness indicating mission crew health in space JF - Scientific Reports N2 - Muscle function is compromised by gravitational unloading in space affecting overall musculoskeletal health. Astronauts perform daily exercise programmes to mitigate these effects but knowing which muscles to target would optimise effectiveness. Accurate inflight assessment to inform exercise programmes is critical due to lack of technologies suitable for spaceflight. Changes in mechanical properties indicate muscle health status and can be measured rapidly and non-invasively using novel technology. A hand-held MyotonPRO device enabled monitoring of muscle health for the first time in spaceflight (> 180 days). Greater/maintained stiffness indicated countermeasures were effective. Tissue stiffness was preserved in the majority of muscles (neck, shoulder, back, thigh) but Tibialis Anterior (foot lever muscle) stiffness decreased inflight vs. preflight (p < 0.0001; mean difference 149 N/m) in all 12 crewmembers. The calf muscles showed opposing effects, Gastrocnemius increasing in stiffness Soleus decreasing. Selective stiffness decrements indicate lack of preservation despite daily inflight countermeasures. This calls for more targeted exercises for lower leg muscles with vital roles as ankle joint stabilizers and in gait. Muscle stiffness is a digital biomarker for risk monitoring during future planetary explorations (Moon, Mars), for healthcare management in challenging environments or clinical disorders in people on Earth, to enable effective tailored exercise programmes. KW - Ageing KW - Anatomy KW - Muscle KW - Musculoskeletal system KW - Physiology Y1 - 2024 U6 - https://doi.org/10.1038/s41598-024-54759-6 SN - 2045-2322 N1 - Corresponding author: Dieter Blottner VL - 14 IS - Article number: 4196 PB - Springer Nature CY - London ER - TY - CHAP A1 - Simsek, Beril A1 - Krause, Hans-Joachim A1 - Engelmann, Ulrich M. ED - Digel, Ilya ED - Staat, Manfred ED - Trzewik, Jürgen ED - Sielemann, Stefanie ED - Erni, Daniel ED - Zylka, Waldemar T1 - Magnetic biosensing with magnetic nanoparticles: Simulative approach to predict signal intensity in frequency mixing magnetic detection T2 - YRA MedTech Symposium (2024) N2 - Magnetic nanoparticles (MNP) are investigated with great interest for biomedical applications in diagnostics (e.g. imaging: magnetic particle imaging (MPI)), therapeutics (e.g. hyperthermia: magnetic fluid hyperthermia (MFH)) and multi-purpose biosensing (e.g. magnetic immunoassays (MIA)). What all of these applications have in common is that they are based on the unique magnetic relaxation mechanisms of MNP in an alternating magnetic field (AMF). While MFH and MPI are currently the most prominent examples of biomedical applications, here we present results on the relatively new biosensing application of frequency mixing magnetic detection (FMMD) from a simulation perspective. In general, we ask how the key parameters of MNP (core size and magnetic anisotropy) affect the FMMD signal: by varying the core size, we investigate the effect of the magnetic volume per MNP; and by changing the effective magnetic anisotropy, we study the MNPs’ flexibility to leave its preferred magnetization direction. From this, we predict the most effective combination of MNP core size and magnetic anisotropy for maximum signal generation. Y1 - 2024 SN - 978-3-940402-65-3 U6 - https://doi.org/10.17185/duepublico/81475 N1 - 4th YRA MedTech Symposium, February 1, 2024. FH Aachen, Campus Jülich SP - 27 EP - 28 PB - Universität Duisburg-Essen CY - Duisburg ER - TY - CHAP A1 - Maurer, Florian A1 - Nitsch, Felix A1 - Kochems, Johannes A1 - Schimeczek, Christoph A1 - Sander, Volker A1 - Lehnhoff, Sebastian T1 - Know your tools - a comparison of two open agent-based energy market models T2 - 2024 20th International Conference on the European Energy Market (EEM) N2 - Due to the transition to renewable energies, electricity markets need to be made fit for purpose. To enable the comparison of different energy market designs, modeling tools covering market actors and their heterogeneous behavior are needed. Agent-based models are ideally suited for this task. Such models can be used to simulate and analyze changes to market design or market mechanisms and their impact on market dynamics. In this paper, we conduct an evaluation and comparison of two actively developed open-source energy market simulation models. The two models, namely AMIRIS and ASSUME, are both designed to simulate future energy markets using an agent-based approach. The assessment encompasses modelling features and techniques, model performance, as well as a comparison of model results, which can serve as a blueprint for future comparative studies of simulation models. The main comparison dataset includes data of Germany in 2019 and simulates the Day-Ahead market and participating actors as individual agents. Both models are comparable close to the benchmark dataset with a MAE between 5.6 and 6.4 €/MWh while also modeling the actual dispatch realistically. KW - Comparative simulation KW - Measurement KW - Analytical models KW - Renewable energy sources KW - Simulation KW - Instruments KW - Refining KW - Focusing KW - Agent-based modeling KW - Energy market KW - Open source KW - Energy dispatch Y1 - 2024 U6 - https://doi.org/10.1109/EEM60825.2024.10609021 N1 - 2024 20th International Conference on the European Energy Market (EEM), 10-12 June 2024, Istanbul, Turkiye PB - IEEE CY - New York, NY ER -