Publications

Bogdan Franczyk, Marcin Hernes, Adrianna Kozierkiewicz, Agata Kozina, Marcin Pietranik, Ingolf Roemer, and Martin Schieck. Deep learning for grape variety recognition. Procedia Comput. Sci., (176):1211--1220, Elsevier BV, 2020.

Heinz Pampel, Lea Maria Ferguson, Reinhard Messerschmidt, and Katja Faensen. Indikatoren für Open Science: Diskussionspapier des Helmholtz Open Science Office. Helmholtz Open Science Office, 2020.

Nico Hoffmann. Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion. 2016.

Jakob Bossek, Pascal Kerschke, Aneta Neumann, Markus Wagner, Frank Neumann, and Heike Trautmann. Evolving diverse TSP instances by means of novel and creative mutation operators. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, ACM, New York, NY, USA, August 2019.

Jewgeni Rose, and Jens Lehmann. CASQAD -- A new dataset for context-aware spatial question answering. Lecture Notes in Computer Science, 3--17, Springer International Publishing, Cham, 2020.

Lars-Peter Meyer, Jan Frenzel, Eric Peukert, René Jäkel, and Stefan Kühne. Big Data Services. Service Engineering, 63--77, Springer Fachmedien Wiesbaden, Wiesbaden, 2018. [PUMA: topic_lifescience]

Julia Koehler Leman, Sergey Lyskov, Steven M Lewis, Jared Adolf-Bryfogle, Rebecca F Alford, Kyle Barlow, Ziv Ben-Aharon, Daniel Farrell, Jason Fell, William A Hansen, Ameya Harmalkar, Jeliazko Jeliazkov, Georg Kuenze, Justyna D Krys, Ajasja Ljubetic, Amanda L Loshbaugh, Jack Maguire, Rocco Moretti, Vikram Khipple Mulligan, Morgan L Nance, Phuong T Nguyen, Shane Ó Conchúir, Shourya S Roy Burman, Rituparna Samanta, Shannon T Smith, Frank Teets, Johanna K S Tiemann, Andrew Watkins, Hope Woods, Brahm J Yachnin, Christopher D Bahl, Chris Bailey-Kellogg, David Baker, Rhiju Das, Frank DiMaio, Sagar D Khare, Tanja Kortemme, Jason W Labonte, Kresten Lindorff-Larsen, Jens Meiler, William Schief, Ora Schueler-Furman, Justin B Siegel, Amelie Stein, Vladimir Yarov-Yarovoy, Brian Kuhlman, Andrew Leaver-Fay, Dominik Gront, Jeffrey J Gray, and Richard Bonneau. Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks. Nat. Commun., (12)1:6947, Springer Science and Business Media LLC, November 2021. [PUMA: topic_lifescience]

Chiara Hergl, Christian Blecha, Vanessa Kretzschmar, Felix Raith, Fabian Günther, Markus Stommel, Jochen Jankowai, Ingrid Hotz, Thomas Nagel, and Gerik Scheuermann. Visualization of tensor fields in mechanics. Comput. Graph. Forum, (40)6:135--161, Wiley, September 2021.

Kuldeep Singh, Mohamad Yaser Jaradeh, Saeedeh Shekarpour, Akash Kulkarni, Arun Sethupat Radhakrishna, Ioanna Lytra, Maria-Esther Vidal, and Jens Lehmann. Towards optimisation of collaborative Question Answering over knowledge graphs. arXiv, 2019.

Aykut Argun, Tobias Thalheim, Stefano Bo, Frank Cichos, and Giovanni Volpe. Enhanced force-field calibration via machine learning. Appl. Phys. Rev., (7)4:041404, AIP Publishing, December 2020.

Isaiah Onando Mulang', Kuldeep Singh, Chaitali Prabhu, Abhishek Nadgeri, Johannes Hoffart, and Jens Lehmann. Evaluating the impact of knowledge graph context on entity disambiguation models. arXiv, 2020.

Vera Steinhoff, Pascal Kerschke, Pelin Aspar, Heike Trautmann, and Christian Grimme. Multiobjectivization of local search: Single-objective optimization benefits from multi-objective gradient descent. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, December 2020.

Marco Wilhelm, Gabriele Kern-Isberner, Andreas Ecke, and Franz Baader. Counting strategies for the probabilistic description logic $ALC^ME$ under the principle of maximum entropy. Logics in Artificial Intelligence, 434--449, Springer International Publishing, Cham, 2019.

Kamil Kaleta, and René L Schilling. Progressive intrinsic ultracontractivity and heat kernel estimates for non-local Schrödinger operators. J. Funct. Anal., (279)6:108606, Elsevier BV, October 2020.

Hajira Jabeen, Rajjat Dadwal, Gezim Sejdiu, and Jens Lehmann. Divided we stand out! Forging cohorts fOr numeric outlier detection in large scale knowledge graphs (CONOD). Lecture Notes in Computer Science, 534--548, Springer International Publishing, Cham, 2018.

Johannes Dorn, Sven Apel, and Norbert Siegmund. Generating attributed variability models for transfer learning. Proceedings of the 14th International Working Conference on Variability Modelling of Software-Intensive Systems, ACM, New York, NY, USA, February 2020.

Jakob Lykke Andersen, Rolf Fagerberg, Christoph Flamm, Rojin Kianian, Daniel Merkle, and Peter F. Stadler. Towards mechanistic prediction of mass spectra using graph transformation. MATCH Communications in Mathematical and in Computer Chemistry, (80)3:705--731, Max-Planck-Institut für Strahlenchemie; Universitet u Kragujevcu, Jan 1, 2018.

Christopher Rost, Philip Fritzsche, Lucas Schons, Maximilian Zimmer, Dieter Gawlick, and Erhard Rahm. Bitemporal property graphs to organize evolving systems. arXiv, 2021.

David Georg Reichelt, and Stefan Kühne. How to detect performance changes in software history. Companion of the 2018 ACM/SPEC International Conference on Performance Engineering, ACM, New York, NY, USA, April 2018.

Mikhail Galkin, Priyansh Trivedi, Gaurav Maheshwari, Ricardo Usbeck, and Jens Lehmann. Message passing for hyper-relational knowledge graphs. arXiv, 2020.