PUBLICATIONS

Publications

  • peer-reviewed articles

  • conference proceedings

  • preprints

2024

2024

  • M. Schubach, T. Maass, L. Nazaretyan, S. Röner, and Martin Kircher (2024). CADD v1.7: Using protein language models, regulatory CNNs and other nucleotide-Level scores to improve genome-wide variant predictions. Nucleic Acids Research, 52, D1, D1143-D1154. https://doi.org/10.1093/nar/gkad989

  • X. Wang, P. Krause, T. Kirschbaum, K. Palczynski, J. Dzubiella and A. Bande (2024). Photo-excited charge transfer from adamantane to electronic bound states in water. Phys. Chem. Chem. Phys. https://doi.org/10.1039/D3CP04602H

2023

2023

  • C. Utama, C. Meske, J. Schneider, R. Schlatmann, and C. Ulbrich (2023). Explainable artificial intelligence for photovoltaic fault detection: A comparison of instrumentsSolar Energy, 249, 139–151. doi:10.1016/j.solener.2022.11.018

  • P. Graniero, M. Khenkin, H. Köbler, N.T. Putri Hartono, R. Schlatmann, A. Abate, E. Unger, T.J. Jacobsson, and C. Ulbrich (2023). The challenge of studying perovskite solar cells’ stability with machine learning. Front. Energy Res., Sec. Solar Energy, 11. doi:10.3389/fenrg.2023.1118654

  • N.H. Chan, M.Langer, B. Juhls, T. Rettelbach, P. Overduin, K. Huppert, and J. Braun (2023). An Arctic Delta Reduced Complexity Model and its Reproduction of Key Geomorphological Structures. Earth Surface Dynamics, 259–285. doi:10.5194/esurf-11-259-2023

  • W.J. Foster, B. J. Allen, N.H. Kitzmann, J. Münchmeyer, T. Rettelbach, J.D. Witts, , R.J. Whittle, E. Larina, M.E. Clapham, and A.M.  Dunhill (2023). How predictable are mass extinction events? Royal Society Open Science, 10 (3), 221507. doi:10.1098/rsos.221507
  • K. Palczynski, T. Kirschbaum, A. Bande, J. Dzubiella (2023). Hydration Structure of Diamondoids from Reactive Force Fields. J. Phys. Chem. C, 127, 6, 3217–3227. doi:10.1021/acs.jpcc.2c07777
  • T. Kirschbaum, B. von Seggern, J. Dzubiella, A. Bande, and F. Noé (2023). Machine Learning Frontier Orbital Energies of Nanodiamonds. J. Chem. Theory Comput. 19, 14, 4461–4473. doi:10.1021/acs.jctc.2c01275
  • M. Yang, E. Robertson, L. Esguerra, K. Busch, and J. Wolters (2023). Optical convolutional neural network with atomic nonlinearity. arXiv. doi:10.48550/arXiv.2301.09994 [Preprint]

  • L. Meßner, E. Robertson, L. Esguerra, K. Lüdge, and J. Wolters (2023). Multiplexed random-access optical memory in warm cesium vapor. arXiv. doi:10.48550/arXiv.2301.04885 [Preprint]

  • B.M. Jones, S. Schaeffer Tessier, ..., G. Grosse, I. Nitze, T. Rettelbach, ..., and K.D. Tape (2023). Integrating local environmental observations and remote sensing to better understand the life cycle of a thermokarst lake in Arctic Alaska. Arctic, Antarctic, and Alpine Research, 55(1). doi:10.1080/15230430.2023.2195518
  • S. Westermann, T. Ingeman-Nielsen, ..., J. Boike, B. Groenke, ..., and M. Langer (2023). The CryoGrid community model (version 1.0) – a multi-physics toolbox for climate-driven simulations in the terrestrial cryosphere. Geoscientific Model Development, 16(9), pp. 2607-2647. doi:10.5194/gmd-16-2607-2023
  • F. Schintke, N.D. Mecquenem, D. Frantz, V.E. Guarino, M. Hilbrich, F. Lehmann, R. Sattler, J.A. Sparka, D. Speckhard, H. Stolte, A.D. Vu, and U. Leser (2023). Validity constraints for data analysis workflows. arXiv. https://arxiv.org/abs/2305.08409 [Preprint]
  • T.M. Pentimalli, S. Schallenberg, D. León-Periñán, ..., F. Klauschen, and N. Rajewsky (2023). High-resolution molecular atlas of a lung tumor in 3D. bioRxiv. doi:10.1101/2023.05.10.539644 [Preprint]
  • G. Pfalz, B. Diekmann, J-C. Freytag, and B.K. Biskaborn (2023). Effect of temperature on carbon accumulation in northern lake systems over the past 21,000 years. Front. Earth Sci., Sec. Quaternary Science, Geomorphology and Paleoenvironment, 11. doi:10.3389/feart.2023.1233713
  • B. Groenke, M. Langer, J. Nitzbon, S. Westermann, G. Gallego, and  J. Boike, (2023). Investigating the thermal state of permafrost with Bayesian inverse modeling of heat transfer. The Cryosphere, 17, pp. 3505–3533. doi:10.5194/tc-17-3505-2023
  • N.T.P. Hartono, H. Köbler, P. Graniero, et al. (2023). Stability follows efficiency based on the analysis of a large perovskite solar cells ageing dataset. Nat Commun, 14, 4869. doi:10.1038/s41467-023-40585-3
  • N. Veigel, H. Kreibich, and A. Cominola (2023). Interpretable machine learning reveals potential to overcome reactive flood adaptation in the continental US. Earth's Future, 11, e2023EF003571. doi:10.1029/2023EF003571
  • M. Schubach, L. Nazaretyan, and M. Kircher (2023). The Regulatory Mendelian Mutation score for GRCh38. GigaScience, 12. doi:10.1093/gigascience/giad024
  • T. Rettelbach, I. Nitze, I. Grünberg, J. Hammar, S. Schäffler, D. Hein, M. Gessner, T. Bucher, J. Brauchle, J. Hartmann, T. Sachs, J. Boike, and G. Grosse (2023). Aerial imagery datasets of permafrost landscapes in Alaska and northwestern Canada acquired by the Modular Aerial Camera SystemPANGAEA. https://doi.pangaea.de/10.1594/PANGAEA.961577
  • T. Rettelbach, I. Nitze, I. Grünberg, J. Hammar, S. Schäffler, D. Hein, M. Gessner, T. Bucher, J. Brauchle, J. Hartmann, T. Sachs, J. Boike, and G. Grosse (2023). Super-high-resolution aerial imagery datasets of permafrost landscapes in Alaska and northwestern CanadaEarth System Science Data Discussions. 1-35. doi:10.5194/essd-2023-193 [Preprint]
  • E. Celikkan, M. Saberioon, M. Herold, and N. Klein (2023). Semantic Segmentation of Crops and Weeds with Probabilistic Modeling and Uncertainty Quantification. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, pp. 582-592.
  • A. Kotobi, K. Singh, D. Höche, S. Bari, R. Meißner, and A. Bande (2023). Integrating Explainability into Graph Neural Network Models for the Prediction X-ray Absorption Spectra. J. Am. Chem. Soc., 145, 22584. doi:10.1021/jacs.3c07513
  • L. Weber, F. Barth, L. Lorenz, F. Konrath, K. Huska, J. Wolf, and U. Leser (2023). PEDL+: Protein-centered relation extraction from PubMed at your fingertip. Bioinformatics, 39, 11. doi:10.1093/bioinformatics/btad603
  • O. Kondrateva, S. Dietzel, A. Lößer, and B. Scheuermann (2023). Parameter Prioritization for Efficient Transmission of Neural Networks in Small Satellite Applications. In Proceedings of the 21st Mediterranean Communication and Computer Networking Conference (MedComNet). doi:10.1109/MedComNet58619.2023.10168858
  • O. Kondrateva, S. Dietzel, M. Schambach, J. Otterbach, and B. Scheuermann (2023). Filling the Gap: Fault-Tolerant Updates of On-Satellite Neural Networks Using Vector Quantization. In Proceedings of the 2023 IFIP Networking Conference. doi:10.23919/IFIPNetworking57963.2023.10186407
  • O. Kondrateva, S. Dietzel, and B. Scheuermann (2023). Joint Source-and-Channel Coding for Small Satellite Applications. In Proceedings of the 2023 IEEE 48th Conference on Local Computer Networks (LCN). doi:10.1109/LCN58197.2023.10223379
  • T. Kirschbaum, X. Wang, and A. Bande (2023). Ground and excited state charge transfer at aqueous nanodiamonds. J. Comput. Chem. https://doi.org/10.1002/jcc.27279

2022

2022

  • T. Kirschbaum, T. Petit, J. Dzubiella, and A. Bande (2022). Effects of oxidative adsorbates and cluster formation on the electronic structure of nanodiamonds.  J. Comput. Chem., 43, 13, 923-929. https://doi.org/10.1002/jcc.26849

  • G. Pfalz, B. Diekmann, J-C. Freytag, L. Sryrkh, D.A. Subetto, and B.K. Biskaborn (2022). Improving age-depth correlations by using the LANDO model ensemble. Geochronology, 4, 269–295. https://doi.org/10.5194/gchron-4-269-2022

  • T. Rettelbach, M. Langer, I. Nitze, B. Jones, V. Helm, J-C. Freytag, and G. Grosse (2022). From images to hydrologic networks - Understanding the Arctic landscape with graphs. In ACM Proceedings of the 34th International Conference on Scientific and Statistical Database Management (SSDBM 2022). https://doi.org/10.1145/3538712.3538740

  • B. Ghosh, S. Garg, and M. Motagh (2022). Automatic flood detections from Sentinel-1 data using deep learning architectures. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 201–208. https://doi.org/10.5194/isprs-annals-V-3-2022-201-2022

  • K. Singh, J. Münchmeyer, L. Weber, U. Leser and A. Bande (2022)Graph neural networks for learning molecular excitation spectra. J. Chem. Theory Comp., 18, 7, 4408-4417. DOI: 10.1021/acs.jctc.2c00255

  • J.* Woollam, J.* Münchmeyer, F. Tilmann, A. Rietbrock, D. Lange, ..., and H. Soto (2022). SeisBench - A Toolbox for machine learning in seismology. Seismological Research Letters, 93(3), 1695–1709. https://doi.org/10.1785/0220210324 *Equal contribution

  • J. Münchmeyer, U. Leser, and F. Tilmann (2022). A probabilistic view on rupture predictability: All earthquakes evolve similarly. Geophysical Research Letters, 49, 13, e2022GL098344https://doi.org/10.1029/2022GL098344

  • H. Lilienkamp, S. von Specht, G. Weatherill, G. Caire, and Fabrice Cotton (2022). Ground‐motion modeling as an image processing task: Introducing a neural network based, fully data-driven, and nonergodic approachBull. Seismol. Soc. Am. 112, 1565–1582. https://doi.org/10.1785/0120220008

  • P. Tillmann, K. Jäger, A. Karsenti, L. Kreinin, and C. Becker (2022). Model-chain validation for estimating the energy yield of bifacial Perovskite/Silicon tandem solar cells. Sol. RRL, 202200079. https://doi.org/10.1002/solr.202200079

  • O. Kondrateva, B. Scheuermann, and S. Dietzel (2022). Scalable Flow Optimization for Small Satellite Networks using Benders Decomposition. In Proceedings of the IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM), 221-230https://doi.org/10.1109/WoWMoM54355.2022.00041

  • R. Shahan, C.W. Hsu, T.M. Nolan, B.J. Cole, I.W. Taylor, A.H.C. Vlot, P.N. Benfey, and U. Ohler  (2022).  A single cell Arabidopsis root atlas reveals developmental trajectories in wild type and cell identity mutants. Developmental Cell, 57(4), 543-560.e9. https://doi.org/10.1016/j.devcel.2022.01.008

  • A.H.C. Vlot, S. Maghsudi, and U. Ohler (2022). Cluster-independent marker feature identification from single-cell omics data using SEMITONES. Nucleic Acids Research, gkac639. https://doi.org/10.1093/nar/gkac639

  • J.A. Fries, N. Seelam, G. Altay, L. Weber, ..., and W. Kusa (2022). Dataset Debt in Biomedical Language Modeling. In Proceedings of the Workshop on Challenges & Perspectives in Creating Large Language Models, 137-145. https://doi.org/10.18653/v1/2022.bigscience-1.10

  • X. Wang, U. Leser and L. Weber (2022). BEEDS: Large-Scale Biomedical Event Extraction using Distant Supervision and Question Answering. In Proceedings of BioNLP, 298-309. 10.18653/v1/2022.bionlp-1.28

  • N. Miranda, J-C. Freytag, J. Nordin, R. Biswas, V. Brinnel, C. Fremling, ... and J. van Santen (2022). SNGuess: A method for the selection of young extragalactic transients. Astronomy & Astrophysics, 665, A99. https://doi.org/10.1051/0004-6361/202243668

  • N. Veigel, H. Kreibich, and A. Cominola (2022). A Gradient Boosting Approach to Identify Behavioral and Policy Determinants of Flood Resilience in the Continental US. IFAC-PapersOnLine,55, 33, 85-91. https://doi.org/10.1016/j.ifacol.2022.11.014

  • L. Weber, M. Sänger, S. Garda, F. Barth, C. Alt, and U. Leser (2022). Chemical-Protein Relation Extraction with Ensembles of Carefully Tuned Pretrained Language Models. Database,  2022, baac098. https://doi.org/10.1093/database/baac098

  • C. Utama, B. Karg, C. Meske, and S. Lucia (2022). Explainable artificial intelligence for deep learning-based model predictive controllers. In Proceedings of the 26th International Conference on System Theory, Control and Computing (ICSTCC), 464-471. https://doi.org/10.1109/ICSTCC55426.2022.9931794

  • F. Buchner, T. Kirschbaum, A. Venerosy, H. Girard, J-C. Arnault, B. Kiendl, A. Krueger, K. Larsson, A. Bande, T. Petit, and C. Merschjann (2022). Early dynamics of the emission of solvated electrons from nanodiamonds in water. Nanoscale, 14,17188-17195. https://doi.org/10.1039/D2NR03919B

  • F. van Geffen, B. Heim, F. Brieger, ..., U. Herzschuh, and S. Kruse. SiDroForest: A comprehensive forest inventory of Siberian boreal forest investigations including drone-based point clouds, individually labelled trees, synthetically generated tree crowns and Sentinel-2 labelled image patches. Earth Syst. Sci. Data, 14, 4967–4994, 2022. https://doi.org/10.5194/essd-14-4967-2022

  • I. Michaelis, K. Styp-Rekowski, J. Rauberg, C. Stolle, and M. Korte (2022). Geomagnetic data from the GOCE satellite mission. Earth, Planets, and Space, 74, 135. https://doi.org/10.1186/s40623-022-01691-6

  • K. Styp-Rekowski, I. Michaelis, C. Stolle, J. Baerenzung, M. Korte, and O. Kao (2022). Machine Learning-based Calibration of the GOCE Satellite Platform Magnetometers. Earth, Planets, and Space, 74, 138. https://doi.org/10.1186/s40623-022-01695-2

  • L. Esguerra, L. Meßner, E. Robertson, N.V. Ewald, M. Gündoğan, and J. Wolters (2022). Optimization and readout-noise analysis of a hot vapor EIT memory on the Cs D1 line. Quantum Physics. https://doi.org/10.48550/arXiv.2203.06151 [Preprint]

  • J.A. Fries, L. Weber, N. Seelam, G. Altay, et al. (2022). BigBIO: A Framework for data-centric biomedical natural language processing. https://arxiv.org/abs/2206.15076 [Preprint]

  • H. Laurençon, L. Saulnier, T. Wang, C. Akik, A.V. del Moral, T. Le Scao, ... L. Weber, et al. (2022). The BigScience corpus: A 1.6 TB composite multilingual dataset. https://openreview.net/forum?id=UoEw6KigkUn [Preprint]

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2021

2021

  • L. Weber, M. Sänger, J. Münchmeyer, M. Habibi, U. Leser, and A. Akbik (2021). HunFlair: An easy-to-use tool for state-of-the-art biomedical named entity recognition.  Bioinformatics, btab042. 
    https://doi.org/10.1093/bioinformatics/btab042

  • S. Redyuk, Z. Kaoudi, V. Markl, and S. Schelter (2021). Automating data quality validation for dynamic data ingestion.  In Proceedings of the International Conference on Extending Database Technology. ISBN 978-3-89318-084-4 on OpenProceedings.org.

  • S. Agarwal, N. Tosi, P. Kessel, S. Padovan, D. Breuer, and G. Montavon (2021). Towards constraining Mars’ thermal evolution using Machine Learning.  Earth and Space Science, 8(4). https://doi.org/10.1029/2020EA001484

  • G. Pfaltz, B. Diekmann, J-C. Freytag, and B.K. Biskaborn (2021). Harmonizing heterogeneous multi-proxy data from lake systems. Computers & Geosciences. https://doi.org/10.1016/j.cageo.2021.104791

  • J. Münchmeyer,  D. Bindi, U. Leser, and F. Tilmann (2021). Earthquake magnitude and location estimation from real time seismic waveforms with a Transformer Network. Geophysical Journal International, 226(2), 1086-1104. https://doi.org/10.1093/gji/ggab139

  • B. Ghosh, M. Haghshenas Haghighi, M. Motagh, and S. Maghsudi (2021). Using generative adversarial networks for extraction of InSAR  signals from large-scale Sentinel-1 interferograms by improving tropospheric noise correction. ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2021, 57–64. https://doi.org/10.5194/isprs-annals-V-3-2021-57-2021

  • T. Rettelbach, M. Langer, I. Nitze, B. Jones. V. Helm, J-C. Freytag, and G. Grosse (2021). A quantitative graph-based approach to monitoring ice-wedge trough dynamics in polygonal permafrost landscapes. Remote Sens. 2021, 13, 3098. https://doi.org/10.3390/rs13163098

  • B. Ghosh, M. Motagh, M. Haghshenas Haghighi, M. Stefanova Vassileva, T. Walter, and S. Maghsudi (2021). Automatic detection of volcanic unrest using blind source separation with a minimum spanning tree based stability analysisIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. doi.org/10.1109/JSTARS.2021.3097895

  • J.L. Rumberger, X. Yu, P. Hirsch, M. Dohmen, V.E. Guarino, A. Mokarian, L. Mais, J. Funke, and D. Kainmueller (2021). How shift equivariance impacts metric learning for instance segmentation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV).

  • B.K. Biskaborn, L. Nazarova, T. Kröger, L.A. Pestryakova, L. Syrykh, G. Pfalz, U. Herzschuh, and B. Diekmann (2021). Late quaternary climate reconstruction and lead-lag relationships of biotic and sediment-geochemical indicators at lake Bolshoe Toko, Siberia. Front. Earth Sci., 9, 703https://doi.org/10.3389/feart.2021.737353

  • P. Tillmann, B. Bläsi, S. Burger, M. Hammerschmidt, O. Höhn, C. Becker, and K. Jäger (2021).  Optimizing metal grating back reflectors for III-V-on-silicon multijunction solar cells. Opt. Express, 29, 22517. https//doi.org/ 10.1364/OE.426761

  • P. Rautenstrauch, A.H.C. Vlot, S. Saran, and U. Ohler (2021). Intricacies of single-cell multi-omics data integrationTrends in Genetics. https://doi.org/10.1016/j.tig.2021.08.012

  • L. Weber, M. Sänger, S. Garda, F. Barth, C. Alt, and U. Leser (2021). Humboldt @ DrugProt: Chemical-protein relation extraction with pretrained transformers and entity descriptions. In Proceedings of the 7th BioCreative Challenge Evaluation Workshop.

  • S. Agarwal, N. Tosi, P. Kessel,  D. Breuer, and G. Montavon (2021). Deep learning for surrogate modeling of two-dimensional mantle convection. Physical Review Fluids, 6, 113801. https://doi.org/10.1103/PhysRevFluids.6.113801

  • W.J. Foster, G. Ayzel, J. Münchmeyer, T. Rettelbach, N. Kitzmann, T.T. Isson, M. Mutti, and M. Aberhan (2021). Machine learning identifies ecological selectivity patterns across the end-Permian mass extinction. Paleobiology, 1-15.  https://doi.org/10.1017/pab.2022.1 

  • L. Weber, S. Garda, J. Münchmeyer, and U. Leser (2021). Extend, don’t rebuild: Phrasing conditional graph modification as autoregressive sequence labelling. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, 1213–1224.

  • S. Baunsgaard, M. Boehm, ..., V. Markl, C. Neubauer, S. Osterburg, O. Ovcharenko, S. Redyuk, ..., and S. Zeuch (2021). ExDRa: Exploratory data science on federated raw data. In Proceedings of the 2021 International Conference on Management of Data, 2450-2463.

  • J.* Münchmeyer, J.* Woollam, F. Tilmann, A. Rietbrock, D. Lange, ..., and H. Soto (2021). Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers.Journal of Geophysical Research: Solid Earth, 127, 1, e2021JB023499. https://doi.org/10.1029/2021JB023499 *Equal contribution

  • K. Styp-Rekowski, C. Stolle, I. Michaelis, and O. Kao (2021). Calibration of the GRACE-FO satellite platform magnetometers and co-estimation of intrinsic time shift in data. IEEE International Conference on Big Data, 5283-5290. https://doi.org/10.1109/BigData52589.2021.9671977

  • C. Stolle, I. Michaelis, C. Xiong, M. Rother, T. Usbeck, Y. Yamazaki, J.  Rauberg, and K. Styp-Rekowski (2021). Observing Earth’s magnetic environment with the GRACE-FO mission. Earth, Planets and Space, 73, 51. https://doi.org/10.1186/s40623-021-01364-w

  • L. Jaurigue, E. Robertson, J. Wolters, and K. Lüdge (2021). Reservoir computing with delayed input for fast and easy optimisation. Entropy, 23, 1560. https://doi.org/10.3390/e23121560

  • S.A. Vyse, U. Herzschuh, G. Pfalz, L.A. Pestryakova, B. Diekmann, N. Nowaczyk, and B.K. Biskaborn (2021). Sediment and carbon accumulation in a glacial lake in Chukotka (Arctic Siberia) during the late Pleistocene and Holocene: Combining hydroacoustic profiling and down-core analyses. Biogeosciences. https://doi.org/10.5194/bg-18-4791-2021

  • L. Hughes-Allen, F. Bouchard, C. Hatté, H. Meyer, LA. Pestryakova, G. Pfalz, B. Diekmann, D.A. Subetto, and B.K. Biskaborn (2021). 14 000-year Carbon Accumulation Dynamics in a Siberian Lake Reveal Catchment and Lake Productivity Changes. Front. Earth Sci., 9, 1–19. https://doi.org/10.3389/feart.2021.710257

  • Y. Yao, S.R. Kulkarni, K.B. Burdge, I. Caiazzo, K. De, D. Dong, C. Fremling, …, N. Miranda, ..., and M.T.  Soumagnac (2021). Multi-wavelength Observations of AT2019wey: A New Candidate Black Hole Low-mass X-ray Binary. The Astrophysical Journal, 920(2), 120. https://doi.org/10.3847/1538-4357/ac15f9

2020

2020

  • P. Tillmann, K. Jäger, and C. Becker (2020). Minimising the levelised cost of electricity for bifacial solar panel arrays using Bayesian optimization. Sustainable Energy Fuels4, 254-264.  10.1039/C9SE00750D

  • S. Agarwal, N. Tosi, D. Breuer, S. Padovan, P. Kessel, and G. Montavon (2020). A machine-learning-based surrogate model of Mars’ thermal evolutionGeophysical Journal International, 222(3), 1656-1670. https://doi.org/10.1093/gji/ggaa234

  • L. Weber, K. Thobe, O.A.M. Lozano, J. Wolf, and U. Leser (2020). PEDL: Extracting protein-protein associations using deep language models and distant supervision.  Bioinformatics, 36, Suppl. 1, 490–498. https://doi.org/10.1093/bioinformatics/btaa430.

  • W. D. Xing, L. Weber, and U. Leser (2020). Biomedical event extraction as multi-turn question answering. In Proceedings of the 11th Int. Workshop on Health Text Mining and Information Analysis, 88-96. 10.18653/v1/2020.louhi-1.1

  • J. Ren, L. Lin, K. Lieutenant, C. Schulz, D. Wong, T. Gimm, A. Bande, X. Wang, and T. Petit (2020). Role of dopants on the local electronic structure of polymeric carbon nitride photocatalysts. Small Methods, 2000707. https://doi.org/10.1002/smtd.202000707

  • K. Jäger, P. Tillmann, E.A. Katz, and C. Becker (2020). Perovskite/silicon tandem solar cells: Effect of luminescent coupling and bifaciality. Sol. RRL.https://doi.org/10.1002/solr.202000628

  • J. Münchmeyer, D. Bindi, U. Leser and F. Tilmann (2020). The transformer earthquake alerting model: A new versatile approach to earthquake early warningGeophysical Journal International, ggaa609. doi.org/10.1093/gji/ggaa609

  • K. Jäger, P. Tillmann, and C. Becker (2020). Detailed illumination model for bifacial solar cells. Opt. Express, 28, 4, 4751-4762. https://doi.org/10.1364/OE.383570

  • H. Grotheer, V. Meyer, T. Riedel, G. Pfalz, L. Mathieu, J. Hefter et al. (2020). Burial and origin of permafrost‐derived carbon in the nearshore zone of the southern Canadian Beaufort Sea. Geophysical Research Letters, 47, e2019GL085897. https://doi.org/10.1029/2019GL085897

2019

2019

  • J. Münchmeyer, D. Bindi, C. Sippl, U. Leser, and F. Tilmann (2019). Low uncertainty multi-feature magnitude estimation with 3D corrections and boosting tree regression: application to North ChileGeophysical Journal International, 220(1), 142-159. doi.org/10.1093/gji/ggz416

  • L. Weber, J. Münchmeyer, T. Rocktäschel, M. Habibi, and U. Leser (2019). HUNER: Improving biomedical NER with pretraining. Bioinformatics, 36(1), 295-302. 10.1093/bioinformatics/btz528

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