Selected publications

We publish in the field of basic research in the areas of computational linguistics, information systems, computer vision or machine learning.

 

    1. Hirst W, Fachet D, Kuropka B, Weise C, Saliba K and Reber S. (2021) Purification of functional Plasmodium falciparum tubulin allows for the identification of parasite-specific microtubule inhibitors. bioRxiv/2021/445550; doi:https://doi.org/10.1101/2021.05.25.445550
    2. Janneck,  Merceron, A., Sauer, P.: DiSEA: Analysing Success and Dropout in Online-Degrees, Companion Proceedings of the 11th Learning Analytics and Knowledge Conference (LAK’21), p. 261-269. Workshop on Addressing Dropout Rates in Higher Education, Online - Everywhere, 2021.
    3. Wagner, K., Hilliger, I., Merceron, A., Sauer, P.: Eliciting Students Needs and Concerns about a Novel Course Enrollment Support System, Companion Proceedings of the 11th Learning Analytics and Knowledge Conference (LAK’21), p. 294-304. Workshop on Addressing Dropout Rates in Higher Education, Online - Everywhere, 2021.
    4. Novoseltseva, D., Wagner, K., Merceron, A., Sauer, P., Jessel, N., Sedes, F.:Wagner, K., Hilliger, I., Merceron, A., Sauer, P.: Investigating the Impact of Outliers on Dropout Prediction in Higher Education, Proceedings of the Delfi Workshops 2021 at the 19th e-Learning Conference of the German Society for Computer Science, Dortmund-Online, Germany, September 13, 2021,  p. 120-129.
    5. Betty van Aken, Jens-Michalis Papaioannou, Manuel Mayrdorfer, Klemens Budde, Felix Gers, Alexander Löser: Self-Supervised Knowledge Integration for Clinical Outcome Prediction from Admission Notes, EACL 2021
    6. Kletter T, Reusch S, Dempenwolf N, Tischer C and Reber S (2021). Volumetric morphometry reveals mitotic spindle width as the best predictor of spindle scaling. bioRxiv 04.08.438956; doi: https://doi.org/10.1101/2021.04.08.438956.
    7. Biswas A, Kim K, Cojoc G, Guck J and Reber S (2021). The Xenopus spindle is as dense as the surrounding cytoplasm. Developmental Cell 56, 1-5.
      *Preview in Developmental Cell by Masahito Tanaka and Yuta Shimamoto “Local body weight measurement of the spindle” Dev Cell. 2021 Apr 5;56(7): 871-872. doi: 10.1016/j.devcel.2021.03.019.
    8. Jens-Michalis Papaioannou, Manuel Mayrdorfer, Sebastian Arnold, Felix A. Gers, Klemens Budde and Alexander Löser: Aspect-based Passage Retrieval with Contextualized Discourse Vectors, Proceedings of the 43rd European Conference on Information Retrieval (ECIR 2021)
    9. Tom Oberhauser, Tim Bischoff, Karl Brendel, Maluna Menke, Tobias Klatt, Amy Siu, Felix Alexander Gers and Alexander Löser: TrainX – Named Entity Linking with Active Sampling and Bi-Encoders. COLING 2020 Demo Track
    10. Betty van Aken Benjamin Winter Winter Felix Gers Alexander Löser, VisBERT: Hidden-State Visualizations for Transformers. The Web Conference 2020
    11. Rudolf Schneider Tom Oberhauser Paul Grundmann Felix Alexander Gers Alexander Löser, Steffen Staab: Is Language Modeling Enough? Evaluating Effective Embedding Combinations. LREC 2020
    12. Konstantina Lazaridou Maria Rosario Mestre Alexander Löser Felix Naumann: Discovering Biased News Articles Leveraging Multiple Human Annotations" is on news media bias detection with crowd-sourcing and curriculum learning. LREC 2020
    13. Reusch S, Biswas A, and Reber S (2020). Affinity-Purification of Label-free Tubulins from Xenopus Egg Extracts. STAR Protocols 1, 100151.
    14. Sebastian Arnold, Betty van Aken, Paul Grundmann, Felix A. Gers, Alexander Löser: Learning Contexualized Document Representations for Heathcare Answer Retrieval, ACM WWW 2020
    15. Rachel Bawden, Kevin Bretonnel Cohen, Cristian Grozea, Antonio Jimeno Yepes, Madeleine Kittner, Martin Krallinger, Nancy Mah, Aurélie Névéol Mariana Neves, Felipe Soares, Amy Siu, Karin Verspoor, Maika Vicente Navarro. Automatic Translation of Biomedical Texts: The Biomedical Task at the Workshop for Machine Translation. AMIA (American Medical Informatics Association) NLP (Natural Language Processing) Working Group Pre-Symposium: Community Challenges/Workshops, 2019.
    16. Hirst WG, Kiefer C, Schaeffer E, and Reber S (2020). In vitro Reconstitution and Imaging of Microtubule Dynamics by TIRF and IR Microscopy. STAR Protocols 1, 100177.
    17. Felix Biessmann, Tammo Rukat, Philipp Schmidt, Prathik Naidu, Sebastian Schelter, Andrey Taptunov, Dustin Lange, David Salinas: DataWig - Missing Value Imputation for Tables 2019, Journal of Machine Learning Research (JMLR).
    18. Hirst WG, Biswas A, Mahalingan KK & Reber S (2020). Differences in intrinsic tubulin dynamic properties contribute to spindle length control in Xenopus species. Current Biology, 30-11, 2184-2190.
      *Dispatch in Current Biology by Daniel L. Levy "Tubulin Contributes to Spindle Size Scaling” Current Biology, 30(11), pp.R637-R639. doi: 10.1016/j.cub.2020.04.017
    19. Granada AE, Jiménez A, Stewart-Ornstein J, Blüthgen N., Reber S, Jambhekar A & Lahav G (2020). The effects of proliferation status and cell cycle phase on the responses of single cells to chemotherapy. Mol Biol Cell, 31(8), 845-857.
    20. Sebastian Schelter, Tammo Rukat, Felix Biessmann: Learning to Validate the Predictions of Black Box Classifiers on Unseen Data ACM SIGMOD 2020
    21. Livne, Michelle and Rieger, Jana and Aydin, Orhun and Taha, Abdel and Akay, Ela and Kossen, Tabea and Sobesky, Jan and Kelleher, John and Hildebrand, Kristian and Frey, Dietmar and Madai, Vince:U-net Deep Learning Framework for High Performance Vessel Segmentation in Patients with Cerebrovascular Disease, Frontiers in Neuroscience 2019 https://www.frontiersin.org/articles/10.3389/fnins.2019.00097/full
    22. Rachel Bawden, Kevin Bretonnel Cohen, Cristian Grozea, Antonio Jimeno Yepes, Madeleine Kittner, Martin Krallinger, Nancy Mah, Aurélie Névéol Mariana Neves, Felipe Soares, Amy Siu, Karin Verspoor, Maika Vicente Navarro. Findings of the WMT 2019 Biomedical Translation Shared Task: Evaluation for MEDLINE Abstracts and Biomedical Terminologies. Conference on Machine Translation (WMT) at  ACL, 2019.
    23. Krauss C., Merceron, A., Arbanowski, S.:  The Timeliness Deviation: A novel Approach to Evaluate Educational Recommender Systems for Closed-Courses.
      Proceedings of the 9th International Conference on Learning Analytics & Knowledge
      Tempe, AZ, USA — March 04 - 08, 2019 , ACM, pp. 195-204, doi>10.1145/3303772.3303774
    24. Agathe merceron, Chair International Educational Data Mining Conference 2019, Proceedings of the 12th International Conference on Educational Data Mining EDM2019. Desmarais, M., Lynch, C. Merceron, A. and Nkambou, R (Eds), Montréal, Canada, July 2-5, 2019, ISBN: 978-1-7336736-0-0
    25. , , , , , , , : Differential Data Quality Verification on Partitioned Data. ICDE : 1940-1945
    26. , , , , , , , :Unit Testing Data with Deequ. SIGMOD Conference : 1993-1996
    27. , , , : How Does BERT Answer Questions? A Layer-Wise Analysis of Transformer Representations. ACM CIKM
    28. Kapoor V, Hirst WG, Hentschel C, Preibisch S, Reber S. (2019) Mtrack: Automated detection, tracking, and analysis of dynamic microtubules. Sci Rep, 7;9(1):3794.
    29. Camargo Ortega G, Falk S, Johansson PA, Peyre E, Broix L, Sahu SK, Hirst W, Schlichthaerle T, De Juan Romero C, Draganova K, Vinopal S, Chinnappa K, Gavranovic A, R, Bradke F, Borrell V, Geerlof A, Reber S, Tiwari VK, Huttner WB, Wilsch-Bräuninger M, Nguyen L, Götz M. (2019) A new centrosomal protein regulates neurogenesis by microtubule organisation. Nature; 567(7746):113-117.
    30. , , , , :
      SECTOR: A Neural Model for Coherent Topic Segmentation and Classification. TACL 7: 169-184 ()
    31. , , , , , : Automating Large-Scale Data Quality Verification. PVLDB 11(12): 1781-1794 ()
    32. , , , , :
      "Deep" Learning for Missing Value Imputationin Tables with Non-Numerical Data. ACM CIKM : 2017-2025
    33. , , : HighLife: Higher-arity Fact Harvesting. ACM WWW : 1013-102, Best paper award (see paper)
    34. Smart-MD: Neural Paragraph Retrieval of Medical Topics". In ACM WWW ’18 Companion: The 2018 Web Conference Companion, April 23–27, 2018, Lyon, France. ACM, New York, NY, USA, 4 pages. [video]
    35. Rudolf Schneider, , , , : RelVis: Benchmarking OpenIE Systems. International Semantic Web Conference (Posters, Demos & Industry Tracks)
    36. Rudolf Schneider, Tom Oberhauser, Tobias Klatt, Felix A. Gers, and Alexander Löser, “Analysing Errors of Open Information Extraction Systems,” accepted for BLGNLP 2017 Building Linguistically Generalizable NLP Systems at EMNLP 2017
    37. Troung-Sinh An, Agathe Merceron, Cristopher Krauss,  "Can Typical Behaviors Identified in MOOCs be Discovered in Other Courses?" at EDM 2017
    38.  ACM TOG (Transaction on Graphics) 2017:Optimal discrete slicing on 3D printing optimization. Kristian Hildebrand, Marc Alexa und Sylvain Lefebvre
    39. Interactive Relation Extraction in Main Memory  Database Systems. Rudolf Schneider, Cordula Guder, Torsten Kilias, Alexander Löser, Jens Graupmann and Oleksandr Kozachuk. COLING 2016
    40. TASTY: Interactive Entity Linking As-You-Type. Sebastian Arnold, Robert Dziuba, Alexander Löser. COLING 2016
    41. Sebastian Arnold, Felix A. Gers, Torsten Kilias, Alexander Löser: Robust Named Entity Recognition in Idiosyncratic Domains. arXiv:1608.06757 [cs.CL] 2016
    42. Resolving Common Analytical Tasks in Text Databases. Sebastian Arnold, Alexander Löser, Torsten Kilias.  ACM DOLAP 2015
    43. Proceedings of the 5th International Conference on Learning Analytics and Knowledge, ACM LAK15: Blinkstein, A.; Merceron, A.; Siemens, G.; Baron, J.; Marziaz, N.; Lynch, G. (Eds), Poughkeepsie, NY, USA, March 16-20, 2015, ACM, ISBN: 978-1-4503-3417-4 http://dl.acm.org/citation.cfm?id=2723576
    44. Reber S and Goehring NW (2015). Intracellular Scaling Mechanisms. Cold Spring Harb Perspect Biol., 2015 Aug 7;7(12).
    45. INDREX: In-database relation extraction. Torsten Kilias, Alexander Löser, Perikilis Andritsos Elsevier Information Systems Journal. 2015
    46. Asif, R., Merceron, A. and Pathan, M.K.: Investigating Performance of Students: a Longitudinal Study. ACM LAK 15, Blinkstein, A.; Merceron, A.; Siemens, G.; Baron, J.; Marziaz, N.; Lynch, G. (Eds), Poughkeepsie, NY, USA, March 16-20, 2015, ACM, ISBN: 978-1-4503-3417-4. http://dl.acm.org/citation.cfm?id=2723579
    47. Hyman AA and Reber S (2015). Emergent Properties of the Metaphase Spindle. Cold Spring Harb Perspect Biol, 1;7(7):a015784.
    48. Johannes Kirschnick, Torsten Kilias, Holmer Hemsen, Alexander Löser, Peter Adolphs, Heiko Ehrig, Holger Düwiger: A Marketplace for Web Scale Analytics and Text Annotation Services. COLING (Demos) 2014: 100-104
    49. Reber S, Baumgart J, PO, Pozniakovsky A, Howard J, Hyman AA, Jülicher F (2013). XMAP215 activity sets spindle length by controlling the total mass of spindle microtubules. Nat Cell Biol; 15(9):1116-22.
    50. Torsten Kilias, Alexander Löser, Periklis Andritsos: INDREX: in-database distributional relation extraction. ACM DOLAP 2013: 93-100

    2012 and before

    • Scolz M, Widlund PO, Piazza S, Bublik DR, Reber S, Peche LY, Ciani Y, Hubner N, Isokane M, Monte M, Ellenberg J, Hyman AA, Schneider C, Bird AW (2012). GTSE1 is a microtubule plus-end tracking protein that regulates EB1-dependent cell migration. PLoS One; 7(12):e51259.
    • Widlund PO, Podolski M, Reber S, Alper J, Storch M, Hyman AA, Howard J, Drechsel DN (2012). One step purification of assembly-competent tubulin from diverse eukaryotic sources. Mol Biol Cell, 23(22):4393-401.
    • Reber S & Hyman AA (2011). Samurai Sword Sets Spindle Size. Cell, 147(6), 1224-1225.
    • Tang, S, Renz M, Driscoll M, Reber S, Nguyen A, Daniels B, and Lippincott-Schwartz J (2011). Cytoplasmic self-organization of internal membranes, microtubule-and actincytoskeleton inside microfluidics generated droplets. Mol Biol Cell, 22: 8120.
    • Reber S. Isolation of centrosomes from cultured cells (2011). Methods Mol Biol, 777:107-16.
    • Widlund PO, Stear JH, Pozniakovsky A, Zanic M, Reber S, Brouhard GJ, Hyman AA, Howard J (2011). XMAP215 polymerase activity is built by combining multiple tubulin binding TOG domains and a basic lattice-binding region. Proc Natl Acad Sci USA, 15;108(7):2741-6.
    • A. Krueger, A. Merceron, B. Wolf:A Data Model to Ease Analysis and Mining of Educational Data Proc. 3rd International Conference on Educational Data Mining EDM2010, Pittsburgh, USA, June 11-13, pp. 131-140, ISBN: 978-0-615-37529-8.
    • Berger SM, Pesold B, Reber S, Schönig K., Berger AJ, Weidenfeld I. & Bartsch D. (2010). Quantitative analysis of conditional gene inactivation using rationally designed, tetracycline-controlled miRNAs. Nucleic Acids Res, 38(17), e168-e168.
    • Baker, R.S.J.d., Merceron, A., Pavlik, P.I. Jr. (Eds),
      Pittsburgh, PA, USA, June 11-13, 2010, ISBN: 978-0-615-37529-8. Proceedings of the 3rd International Conference on Educational Data Mining EDM2010.
    • Reber S, Over S, Kronja I, Gruss OJ (2008). CaM kinase II initiates meiotic spindle depolymerization independently of APC/C activation. J Cell Biol, 15;183(6):1007-17.
    • Tegha-Dunghu J, Neumann B, Reber S, Krause R, Erfle H, Walter T, ... & Ellenberg J. (2008). EML3 is a nuclear microtubule-binding protein required for the correct alignment of chromosomes in metaphase. J Cell Sci, 121(10), 1718-1726.
    • A. Merceron, K. Yacef: Interestingness Measures for Association Rules in Educational Data Proceedings of the first International Conference on Educational Data Mining (EDM'08), Montreal, Canada, ISBN - 10: 0615306292. Nominated for best paper award.
    • Taxis C, Maeder C, Reber S, Rathfelder N, Miura K, Greger K & Knop M. (2006). Dynamic organization of the actin cytoskeleton during meiosis and spore formation in budding yeast. Traffic, 7(12), 1628-1642.
    • Janke C, Magiera MM, Rathfelder N, Taxis C, Reber S, Maekawa H & Knop M. (2004). A versatile toolbox for PCR‐based tagging of yeast genes: new fluorescent proteins, more markers and promoter substitution cassettes. Yeast, 21(11), 947-962.
    • Moreno-Borchart AC, Finkbeiner MG, Maier P, Reber S & Knop M. (2003). Function of the yeast spindle pole body during meiotic cell differentiation. Cell Motil Cytoskel, 54, (2)165-165.
    • Felix A. Gers, Nicol N. Schraudolph, Jürgen Schmidhuber: Learning Precise Timing with LSTM Recurrent Networks. Journal of Machine Learning Research 3: 115-143 (2002)

    National conferences


    • Gers F., Bießmann F. Deep Learning im Inverted Classroom Szenario, Berlin Journal of Data Science 2020
    • Florian Stahl, Alexander Löser, Gottfried Vossen: Preismodelle für Datenmarktplätze. Informatik Spektrum 38(2): 133-141 (2015)
    • Herrmann F.; Sauer P.: Fachspezifische 3D-Modelle in Oracle Spatial. DOAG Konferenz, Nürnberg, 2013;
    • Krämer M.; Sauer P.: Integration von Geodaten und Daten des Facility Managements zur Verbesserung der Liegenschaftsverwaltung. In: Angewandte Forschung zur Stadt der Zukunft, Logos Verlag Berlin, S. 49 – 52, 2013
    • Sauer P.; Herrmann F.; Matusewicz J.: Virtuelle Integration von Datenquellen mit einer Graph-Datenbank. Fossgis2014, Berlin, 2014, S. 11 – 14;