Publications

Decision-Focused Retraining of Forecast Models for Optimization Problems in Smart Energy Systems
Beichter, M.; Werling, D.; Heidrich, B.; Phipps, K.; Neumann, O.; Friederich, N.; Mikut, R.; Hagenmeyer, V.
2024. e-Energy ’24 : Proceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems, 170–181, Association for Computing Machinery (ACM). doi:10.1145/3632775.3661952

EMSIG – Event-driven Microscopy for Smart Microfluidic Single-cell Analysis
Friederich, N.; Seiffarth, J.; Yamachui Sitcheu, A. J.; Yildiz, E.; Pesch, M.; Scholtes, L.; Neumann, O.; Scharr, H.; Nöh, K.; Mikut, R.
2024, May 14. 4th Helmholtz Imaging Conference (2024), Heidelberg, Germany, May 14–15, 2024

ObiWan-Microbi: OMERO-based integrated workflow for annotating microbes in the cloud
Seiffarth, J.; Scherr, T.; Wollenhaupt, B.; Neumann, O.; Scharr, H.; Kohlheyer, D.; Mikut, R.; Nöh, K.
2024. SoftwareX, 26, Article no: 101638. doi:10.1016/j.softx.2024.101638

Discovery of Hidden Dynamic Processes through AI-based Automated Active Learning: A Use Case in Light Microscopy
Friederich, N.; Yamachui Sitcheu, A. J.; Neumann, O.; Mikut, R.; Hilbert, L.
2024, March 12. AI and biology (2024), Heidelberg, Germany, March 12–15, 2024

Intrinsic Explainable Artificial Intelligence Using Trainable Spatial Weights on Numerical Weather Predictions
Neumann, O.; Beichter, M.; Heidrich, B.; Friederich, N.; Hagenmeyer, V.; Mikut, R.
2024. 15th ACM International Conference on Future and Sustainable Energy Systems (e-Energy 2024), Singapore, Singapore, June 4–7, 2024

Using conditional Invertible Neural Networks to perform mid‐term peak load forecasting
Heidrich, B.; Hertel, M.; Neumann, O.; Hagenmeyer, V.; Mikut, R.
2024. IET Smart Grid, 7 (4), 460–472. doi:10.1049/stg2.12169

Managing Anomalies in Energy Time Series for Automated Forecasting
Turowski, M.; Neumann, O.; Mannsperger, L.; Kraus, K.; Layer, K.; Mikut, R.; Hagenmeyer, V.
2024. Energy Informatics. Proceedings. Ed.: B. Jørgensen. Pt. 1, 3–29, Springer Nature Switzerland. doi:10.1007/978-3-031-48649-4_1

MLOps for Scarce Image Data: A Use Case in Microscopic Image Analysis
Yamachui Sitcheu, A. J.; Friederich, N.; Baeuerle, S.; Neumann, O.; Reischl, M.; Mikut, R.
2023. Proceedings 33. Workshop Computational Intelligence. Hrsg.: H. Schulte, 169–190, KIT Scientific Publishing

AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy
Friederich, N.; Yamachui Sitcheu, A. J.; Neumann, O.; Eroglu-Kayıkçı, S.; Prizak, R.; Hilbert, L.; Mikut, R.
2023. Proceedings-33. Workshop Computational Intelligence: Berlin, 23.-24. November 2023, 31–51, KIT Scientific Publishing. doi:10.48550/arXiv.2310.04461

Using weather data in energy time series forecasting: the benefit of input data transformations
Neumann, O.; Turowski, M.; Mikut, R.; Hagenmeyer, V.; Ludwig, N.
2023. Energy Informatics, 6 (1), Art.-Nr.: 44. doi:10.1186/s42162-023-00299-8

Transformer training strategies for forecasting multiple load time series
Hertel, M.; Beichter, M.; Heidrich, B.; Neumann, O.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2023. Energy Informatics, 6 (S1), Art.-Nr.: 20. doi:10.1186/s42162-023-00278-z

CoNIC Challenge: Pushing the Frontiers of Nuclear Detection, Segmentation, Classification and Counting
The CoNIC Challenge Consortium; Graham, S.; Vu, Q. D.; Jahanifar, M.; Weigert, M.; Schmidt, U.; Zhang, W.; Zhang, J.; Yang, S.; Xiang, J.; Wang, X.; Rumberger, J. L.; Baumann, E.; Hirsch, P.; Liu, L.; Hong, C.; Aviles-Rivero, A. I.; Jain, A.; Ahn, H.; Hong, Y.; Azzuni, H.; Xu, M.; Yaqub, M.; Blache, M.-C.; Piégu, B.; Vernay, B.; Scherr, T.; Böhland, M.; Löffler, K.; Li, J.; Ying, W.; Wang, C.; Kainmueller, D.; Schönlieb, C.-B.; Liu, S.; Talsania, D.; Meda, Y.; Mishra, P.; Ridzuan, M.; Neumann, O.; Schilling, M. P.; Reischl, M.; Mikut, R.; Huang, B.; Chien, H.-C.; Wang, C.-P.; Lee, C.-Y.; Lin, H.-K.; Liu, Z.; Pan, X.; Han, C.; Cheng, J.; Dawood, M.; Deshpande, S.; Bashir, R. M. S.; Shephard, A.; Costa, P.; Nunes, J. D.; Campilho, A.; Cardoso, J. S.; Hrishikesh, P. S.; Puthussery, D.; Devika, R. G.; Jiji, C. V.; Zhang, Y.; Fang, Z.; Lin, Z.; Zhang, Y.; Lin, C.; Zhang, L.; Mao, L.; Wu, M.; Vo, V. T.-T.; Kim, S.-H.; Lee, T.; Kondo, S.; Kasai, S.; Dumbhare, P.; Phuse, V.; Dubey, Y.; Jamthikar, A.; Vuong, T. T. L.; Kwak, J. T.; Ziaei, D.; Jung, H.; Miao, T.; Snead, D.; Raza, S. E. A.; Minhas, F.; Rajpoot, N. M.
2023. arxiv. doi:10.48550/arXiv.2303.06274

Using Conditional Invertible Neural Networks To Perform Mid-Term Peak Load Forecasting
Heidrich, B.; Neumann, O.; Hertel, M.; Hagenmeyer, V.; Mikut, R.
2023. 43rd International Symposium On Forecasting (2023), Charlottesville, VA, USA, June 25–28, 2023

SATOMI and EMSIG: The Power Couple for Analyzing Microbial Live-Cell Experiments
Yamachui Sitcheu, A. J.; Seiffarth, J.; Friederich, N.; Yildiz, E.; Scherr, T.; Neumann, O.; Wollenhaupt, B.; Scharr, H.; Nöh, K.; Kohlheyer, D.; Mikut, R.
2023. Helmholtz Imaging Conference (2023), Hamburg, Germany, June 14–16, 2023

Winning the Peak Shape and Peak Timing Tasks of the BIGDEAL Load Forecasting Challenge with Conditional Invertible Neural Networks
Neumann, O.; Heidrich, B.; Hertel, M.; Hagenmeyer, V.; Mikut, R.
2023. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2023), Hamburg, Germany, June 12–14, 2023

Electrical Load Forecasting with Transformer Neural Networks
Hertel, M.; Beichter, M.; Heidrich, B.; Neumann, O.; Ott, S. M.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2023. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2023), Hamburg, Germany, June 12–14, 2023

Non-Sequential Machine Learning Pipelines with pyWATTS
Heidrich, B.; Phipps, K.; Meisenbacher, S.; Turowski, M.; Neumann, O.; Mikut, R.; Hagenmeyer, V.
2023. Zenodo. doi:10.5281/zenodo.7740850

EasyMLServe: Easy Development of REST Machine Learning Services
Neumann, O.; Schilling, M.; Reischl, M.; Mikut, R.
2022, December. 32. Workshop Computational Intelligence (2022), Berlin, Germany, December 1–2, 2022

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2022. Proceedings of the 7th bwHPC Symposium, 69–74, Kommunikations- und Informationszentrum (kiz). doi:10.18725/OPARU-46069

Enhancing anomaly detection methods for energy time series using latent space data representations
Turowski, M.; Heidrich, B.; Phipps, K.; Schmieder, K.; Neumann, O.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 208–227, Association for Computing Machinery (ACM). doi:10.1145/3538637.3538851

Automating Time Series Analysis Workflows with pyWATTS
Heidrich, B.; Phipps, K.; Neumann, O.; Meisenbacher, S.; Turowski, M.; Mikut, R.; Hagenmeyer, V.
2022, June. Helmholtz Artificial Intelligence Conference (Helmholtz AI 2022), Dresden, Germany, June 2–3, 2022

ObiWan-Microbi and microbeSEG: Deep Learning Tools for Microbial Image Analysis
Neumann, O.; Seiffarth, J.; Scherr, T.; Wollenhaupt, B.; Scharr, H.; Kohlheyer, D.; Nöh, K.; Mikut, R.
2022, May 31. Helmholtz Imaging Conference (HIP 2022), Berlin, Germany, May 31–June 1, 2022

microbeSEG dataset (1.0) [Data set]
Scherr, T.; Seiffarth, J.; Wollenhaupt, B.; Neumann, O.; Schilling, M.; Kohlheyer, D.; Scharr, H.; Mikut, R.; Nöh, K.
2022, April 28. doi:10.5281/zenodo.6497715

Tuning a Distance-Prediction-Based Cell Segmentation
Scherr, T.; Löffler, K.; Schilling, M.; Neumann, O.; Mikut, R.
2022, March 28. 20th IEEE International Symposium on Biomedical Imaging (ISBI 2022), Kolkata, India, March 28–31, 2022

Transformer Neural Networks for Building Load Forecasting
Hertel, M.; Ott, S.; Neumann, O.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.
2022. 36th Annual Conference on Neural Information Processing Systems (NIPS 2022), Online, November 28–December 9, 2022

microbeSEG: A deep learning software tool with OMERO data management for efficient and accurate cell segmentation
Scherr, T.; Seiffarth, J.; Wollenhaupt, B.; Neumann, O.; Schilling, M. P.; Kohlheyer, D.; Scharr, H.; Nöh, K.; Mikut, R.
2022. (A. Imran, Ed.) PLOS ONE, 17 (11), Art.-Nr.: e0277601. doi:10.1371/journal.pone.0277601

EasyMLServe: Easy Deployment of REST Machine Learning Services
Neumann, O.; Schilling, M.; Reischl, M.; Mikut, R.
2022. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 11–30, KIT Scientific Publishing

Evaluation of Transformer Architectures for Electrical Load Time-Series Forecasting
Hertel, M.; Ott, S.; Schäfer, B.; Mikut, R.; Hagenmeyer, V.; Neumann, O.
2022. Proceedings - 32. Workshop Computational Intelligence: Berlin, 1. - 2. Dezember 2022. Hrsg.: H. Schulte, F. Hoffmann; R. Mikut, 93–110, KIT Scientific Publishing

Ciscnet - a Single-Branch Cell Nucleus Instance Segmentation and Classification Network
Böhland, M.; Neumann, O.; Schilling, M. P.; Reischl, M.; Mikut, R.; Loffler, K.; Scherr, T.
2022. 2022 IEEE International Symposium on Biomedical Imaging Challenges (ISBIC), 1–5, Institute of Electrical and Electronics Engineers (IEEE). doi:10.1109/ISBIC56247.2022.9854734

Modeling and Generating Synthetic Anomalies for Energy and Power Time Series
Turowski, M.; Weber, M.; Neumann, O.; Heidrich, B.; Phipps, K.; Çakmak, H. K.; Mikut, R.; Hagenmeyer, V.
2022. e-Energy ’22: The Thirteenth ACM International Conference on Future Energy Systems, Virtual Event, 28 June 2022- 1 July 2022. Ed.: S. Lehnhoff, 471–484, Association for Computing Machinery (ACM). doi:10.1145/3538637.3539760

Automated Annotator Variability Inspection for Biomedical Image Segmentation
Schilling, M. P.; Scherr, T.; Munke, F. R.; Neumann, O.; Schutera, M.; Mikut, R.; Reischl, M.
2022. IEEE access, 10, 2753–2765. doi:10.1109/ACCESS.2022.3140378

A Computational Workflow for Interdisciplinary Deep Learning Projects utilizing bwHPC Infrastructure
Schilling, M. P.; Neumann, O.; Scherr, T.; Cui, H.; Popova, A. A.; Levkin, P. A.; Götz, M.; Reischl, M.
2021, November 8. 7th bwHPC Symposium (2021), Online, November 8, 2021

Smart Data Representations: Impact on the Accuracy of Deep Neural Networks
Neumann, O.; Turowski, M.; Ludwig, N.; Heidrich, B.; Hagenmeyer, V.; Mikut, R.
2021. Proceedings - 31. Workshop Computational Intelligence : Berlin, 25. - 26. November 2021. Hrsg.: H. Schulte; F. Hoffmann; R. Mikut, 113–130, KIT Scientific Publishing

pyWATTS: Python Workflow Automation Tool for Time Series
Heidrich, B.; Bartschat, A.; Turowski, M.; Neumann, O.; Phipps, K.; Meisenbacher, S.; Schmieder, K.; Ludwig, N.; Mikut, R.; Hagenmeyer, V.
2021. Cornell University