TY - JOUR A1 - Poghossian, Arshak A1 - Wagner, Holger A1 - Schöning, Michael Josef T1 - Automatisiertes „wafer level“-Testsystem zur Charakterisierung von siliziumbasierten Chemo- und Biosensoren JF - Tagungsband: Sensoren und Messsysteme 2010 N2 - Es wurde ein automatisiertes, computerunterstütztes Testsystem für die Funktionsprüfung und Charakterisierung von (bio-)chemischen Sensoren auf Waferebene entwickelt und in einen konventionellen Spitzenmessplatz integriert. Das System ermöglicht die Charakterisierung und Identifizierung „funktionstauglicher“ Sensoren bereits auf Waferebene zwischen den einzelnen Herstellungsschritten, wodurch weitere, bisher übliche Verarbeitungsschritte wie das Fixieren, Bonden und Verkapseln für die defekten oder nicht funktionstauglichen Sensorstrukturen entfällt. Außerdem bietet eine speziell entworfene miniaturisierte Durchflussmesszelle die Möglichkeit, bereits auf Waferlevel die Sensitivität, Drift, Hysterese und Ansprechzeit der (bio-)chemischen Sensoren zu charakterisieren. Das System wurde exemplarisch mit kapazitiven, pH-sensitiven EIS- (Elektrolyt-Isolator-Silizium) Strukturen und ISFET- (ionensensitiver Feldeffekttransistor) Strukturen mit verschiedenen Geometrien und Gate-Layouts getestet. Y1 - 2010 SN - 978-3-8007-3260-9 N1 - Sensoren und Messsysteme 2010 - 15. ITG/GMA-Fachtagung, 18.05.2010 - 19.05.2010 in Nürnberg SP - 89 EP - 92 PB - VDE Verlag CY - Berlin ER - TY - CHAP A1 - Blaneck, Patrick Gustav A1 - Bornheim, Tobias A1 - Grieger, Niklas A1 - Bialonski, Stephan T1 - Automatic readability assessment of german sentences with transformer ensembles T2 - Proceedings of the GermEval 2022 Workshop on Text Complexity Assessment of German Text N2 - Reliable methods for automatic readability assessment have the potential to impact a variety of fields, ranging from machine translation to self-informed learning. Recently, large language models for the German language (such as GBERT and GPT-2-Wechsel) have become available, allowing to develop Deep Learning based approaches that promise to further improve automatic readability assessment. In this contribution, we studied the ability of ensembles of fine-tuned GBERT and GPT-2-Wechsel models to reliably predict the readability of German sentences. We combined these models with linguistic features and investigated the dependence of prediction performance on ensemble size and composition. Mixed ensembles of GBERT and GPT-2-Wechsel performed better than ensembles of the same size consisting of only GBERT or GPT-2-Wechsel models. Our models were evaluated in the GermEval 2022 Shared Task on Text Complexity Assessment on data of German sentences. On out-of-sample data, our best ensemble achieved a root mean squared error of 0:435. Y1 - 2022 U6 - https://doi.org/10.48550/arXiv.2209.04299 N1 - Proceedings of the 18th Conference on Natural Language Processing / Konferenz zur Verarbeitung natürlicher Sprache (KONVENS 2022), 12-15 September, 2022, University of Potsdam, Potsdam, Germany SP - 57 EP - 62 PB - Association for Computational Linguistics CY - Potsdam ER - TY - CHAP A1 - Sildatke, Michael A1 - Karwanni, Hendrik A1 - Kraft, Bodo A1 - Schmidts, Oliver A1 - Zündorf, Albert T1 - Automated Software Quality Monitoring in Research Collaboration Projects T2 - ICSEW'20: Proceedings of the IEEE/ACM 42nd International Conference on Software Engineering Workshops N2 - In collaborative research projects, both researchers and practitioners work together solving business-critical challenges. These projects often deal with ETL processes, in which humans extract information from non-machine-readable documents by hand. AI-based machine learning models can help to solve this problem. Since machine learning approaches are not deterministic, their quality of output may decrease over time. This fact leads to an overall quality loss of the application which embeds machine learning models. Hence, the software qualities in development and production may differ. Machine learning models are black boxes. That makes practitioners skeptical and increases the inhibition threshold for early productive use of research prototypes. Continuous monitoring of software quality in production offers an early response capability on quality loss and encourages the use of machine learning approaches. Furthermore, experts have to ensure that they integrate possible new inputs into the model training as quickly as possible. In this paper, we introduce an architecture pattern with a reference implementation that extends the concept of Metrics Driven Research Collaboration with an automated software quality monitoring in productive use and a possibility to auto-generate new test data coming from processed documents in production. Through automated monitoring of the software quality and auto-generated test data, this approach ensures that the software quality meets and keeps requested thresholds in productive use, even during further continuous deployment and changing input data. Y1 - 2020 U6 - https://doi.org/10.1145/3387940.3391478 N1 - ICSE '20: 42nd International Conference on Software Engineering, Seoul, Republic of Korea, 27 June 2020 - 19 July 2020 SP - 603 EP - 610 PB - IEEE CY - New York, NY ER - TY - JOUR A1 - Grieger, Niklas A1 - Schwabedal, Justus T. C. A1 - Wendel, Stefanie A1 - Ritze, Yvonne A1 - Bialonski, Stephan T1 - Automated scoring of pre-REM sleep in mice with deep learning JF - Scientific Reports N2 - Reliable automation of the labor-intensive manual task of scoring animal sleep can facilitate the analysis of long-term sleep studies. In recent years, deep-learning-based systems, which learn optimal features from the data, increased scoring accuracies for the classical sleep stages of Wake, REM, and Non-REM. Meanwhile, it has been recognized that the statistics of transitional stages such as pre-REM, found between Non-REM and REM, may hold additional insight into the physiology of sleep and are now under vivid investigation. We propose a classification system based on a simple neural network architecture that scores the classical stages as well as pre-REM sleep in mice. When restricted to the classical stages, the optimized network showed state-of-the-art classification performance with an out-of-sample F1 score of 0.95 in male C57BL/6J mice. When unrestricted, the network showed lower F1 scores on pre-REM (0.5) compared to the classical stages. The result is comparable to previous attempts to score transitional stages in other species such as transition sleep in rats or N1 sleep in humans. Nevertheless, we observed that the sequence of predictions including pre-REM typically transitioned from Non-REM to REM reflecting sleep dynamics observed by human scorers. Our findings provide further evidence for the difficulty of scoring transitional sleep stages, likely because such stages of sleep are under-represented in typical data sets or show large inter-scorer variability. We further provide our source code and an online platform to run predictions with our trained network. Y1 - 2021 U6 - https://doi.org/10.1038/s41598-021-91286-0 SN - 2045-2322 N1 - Corresponding author: Stephan Bialonski VL - 11 IS - Art. 12245 PB - Springer Nature CY - London ER - TY - JOUR A1 - Schwabedal, Justus T. C. A1 - Sippel, Daniel A1 - Brandt, Moritz D. A1 - Bialonski, Stephan T1 - Automated Classification of Sleep Stages and EEG Artifacts in Mice with Deep Learning N2 - Sleep scoring is a necessary and time-consuming task in sleep studies. In animal models (such as mice) or in humans, automating this tedious process promises to facilitate long-term studies and to promote sleep biology as a data-driven f ield. We introduce a deep neural network model that is able to predict different states of consciousness (Wake, Non-REM, REM) in mice from EEG and EMG recordings with excellent scoring results for out-of-sample data. Predictions are made on epochs of 4 seconds length, and epochs are classified as artifactfree or not. The model architecture draws on recent advances in deep learning and in convolutional neural networks research. In contrast to previous approaches towards automated sleep scoring, our model does not rely on manually defined features of the data but learns predictive features automatically. We expect deep learning models like ours to become widely applied in different fields, automating many repetitive cognitive tasks that were previously difficult to tackle. Y1 - 2018 U6 - https://doi.org/10.48550/arXiv.1809.08443 ER - TY - BOOK A1 - Laack, Walter van T1 - Aufruf zum Nachdenken: Corona und neue Kriege – Wie kann die Menschheit überleben? T1 - Call for Reflection: Corona and New Wars – How can Mankind survive? Y1 - 2022 SN - 978-3-936624-56-4 N1 - Dritter Band der Buchreihe “Vorträge & Einsichten – Lectures & Insights” und hier das zweite “Upside-Down-Buch”: Deutsche und englische Version in einem Buch. PB - van Laack GmbH CY - Aachen ER - TY - JOUR A1 - Röth, A. A1 - Slabu, I. A1 - Kolvenbach, K. A1 - Engelmann, Ulrich M. A1 - Baumann, M. A1 - Schmitz-Rode, T. A1 - Trahms, L. A1 - Neumann, U. T1 - Aufnahmekinetik von magnetischen Nanopartikeln zur Tumortherapie in humanen Pankreaskarzinomzelllinien JF - Zeitschrift für Gastroenterologie Y1 - 2015 U6 - https://doi.org/10.1055/s-0035-1559529 SN - 1439-7803 VL - 53 IS - 8 SP - KC139 PB - Thieme CY - Stuttgart ER - TY - BOOK A1 - Grüters, Hugo T1 - Aufgabensammlung Mechanik / von Hugo Grüters Y1 - 1987 N1 - [circa 1987] PB - Becker-Kuns CY - Aachen ER - TY - JOUR A1 - Kleines, H. A1 - Erki, I. A1 - Ziemons, Karl A1 - Zwoll, K. ED - Lehmann, Thomas Martin T1 - ATM- und Multimedia Pilotsystem im Rahmen des Projektes M-FIBRe Aufbau und Erfahrungen JF - Bildverarbeitung für die Medizin : Algorithmen - Systeme - Anwendungen Y1 - 1997 SN - 3-86073-519-5 N1 - Proceedings des Aachener Workshops am 8. u. 9. November 1996 ; PP_001 Posterpräsentation SP - 241 EP - 248 PB - Verl. der. Augustinus-Buchh. CY - Aachen ER - TY - JOUR A1 - Grotendorst, Johannes A1 - Scott, Tony C. A1 - Aubert-Frécon, Monique A1 - Hadinger, Gisèle T1 - Asymptotically exact calculation of the exchange energies of one-active-electron diatomic ions with the surface integral method / Scott, Tony C. ; Aubert-Frécon, Monique ; Hadinger, Gisèle ; Andrae, Dirk ; Grotendorst, Johannes ; Morgan Ill, John D. JF - Journal of Physics B: Atomic, Molecular and Optival Physics. 37 (2004), H. 22 Y1 - 2004 SN - 0953-4075 SP - 4451 EP - 4469 ER -