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1Preserving Constraints with the Stable Chase
D. Carral, M. Krötzsch, M. Marx, A. Ozaki, and S. Rudolph. 21st International Conference on Database Theory (ICDT 2018), volume 98 of Leibniz International Proceedings in Informatics (LIPIcs), page 12:1--12:19. Dagstuhl, Germany, Schloss Dagstuhl--Leibniz-Zentrum fuer Informatik, (2018)
5 months ago by @scadsfct
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1Automatic improvement of continuous colormaps in euclidean colorspaces
P. Nardini, M. Chen, M. Böttinger, G. Scheuermann, and R. Bujack. Comput. Graph. Forum, 40 (3): 361--373 (June 2021)
5 months ago by @scadsfct
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1DBpedia FlexiFusion the best of Wikipedia > wikidata > your data
J. Frey, M. Hofer, D. Obraczka, J. Lehmann, and S. Hellmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2019)
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1Deep learning for grape variety recognition
B. Franczyk, M. Hernes, A. Kozierkiewicz, A. Kozina, M. Pietranik, I. Roemer, and M. Schieck. Procedia Comput. Sci., (2020)
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1Indikatoren für Open Science: Diskussionspapier des Helmholtz Open Science Office
H. Pampel, L. Ferguson, R. Messerschmidt, and K. Faensen. (2020)
5 months ago by @scadsfct
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1Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion
N. Hoffmann. Dissertation, Dresden, Technische Universität Dresden, 2016, (2016)
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1Evolving diverse TSP instances by means of novel and creative mutation operators
J. Bossek, P. Kerschke, A. Neumann, M. Wagner, F. Neumann, and H. Trautmann. Proceedings of the 15th ACM/SIGEVO Conference on Foundations of Genetic Algorithms, New York, NY, USA, ACM, (August 2019)
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1CASQAD -- A new dataset for context-aware spatial question answering
J. Rose, and J. Lehmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2020)
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1Big Data Services
L. Meyer, J. Frenzel, E. Peukert, R. Jäkel, and S. Kühne. Service Engineering, Springer Fachmedien Wiesbaden, Wiesbaden, (2018)
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1Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks
J. Koehler Leman, S. Lyskov, S. Lewis, J. Adolf-Bryfogle, R. Alford, K. Barlow, Z. Ben-Aharon, D. Farrell, J. Fell, W. Hansen and 40 other author(s). Nat. Commun., 12 (1): 6947 (November 2021)
5 months ago by @scadsfct
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1Visualization of tensor fields in mechanics
C. Hergl, C. Blecha, V. Kretzschmar, F. Raith, F. Günther, M. Stommel, J. Jankowai, I. Hotz, T. Nagel, and G. Scheuermann. Comput. Graph. Forum, 40 (6): 135--161 (September 2021)
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1Towards optimisation of collaborative Question Answering over knowledge graphs
K. Singh, M. Jaradeh, S. Shekarpour, A. Kulkarni, A. Radhakrishna, I. Lytra, M. Vidal, and J. Lehmann. (2019)
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1Enhanced force-field calibration via machine learning
A. Argun, T. Thalheim, S. Bo, F. Cichos, and G. Volpe. Appl. Phys. Rev., 7 (4): 041404 (December 2020)
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1Evaluating the impact of knowledge graph context on entity disambiguation models
I. Mulang', K. Singh, C. Prabhu, A. Nadgeri, J. Hoffart, and J. Lehmann. (2020)
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1Multiobjectivization of local search: Single-objective optimization benefits from multi-objective gradient descent
V. Steinhoff, P. Kerschke, P. Aspar, H. Trautmann, and C. Grimme. 2020 IEEE Symposium Series on Computational Intelligence (SSCI), IEEE, (December 2020)
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1Counting strategies for the probabilistic description logic $ALC^ME$ under the principle of maximum entropy
M. Wilhelm, G. Kern-Isberner, A. Ecke, and F. Baader. Logics in Artificial Intelligence, Springer International Publishing, Cham, (2019)
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1Progressive intrinsic ultracontractivity and heat kernel estimates for non-local Schrödinger operators
K. Kaleta, and R. Schilling. J. Funct. Anal., 279 (6): 108606 (October 2020)
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1Divided we stand out! Forging cohorts fOr numeric outlier detection in large scale knowledge graphs (CONOD)
H. Jabeen, R. Dadwal, G. Sejdiu, and J. Lehmann. Lecture Notes in Computer Science, Springer International Publishing, Cham, (2018)
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1Generating attributed variability models for transfer learning
J. Dorn, S. Apel, and N. Siegmund. Proceedings of the 14th International Working Conference on Variability Modelling of Software-Intensive Systems, New York, NY, USA, ACM, (February 2020)
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1Towards mechanistic prediction of mass spectra using graph transformation
{. Andersen, R. Fagerberg, C. Flamm, R. Kianian, D. Merkle, and {. Stadler. MATCH Communications in Mathematical and in Computer Chemistry, 80 (3): 705--731 (Jan 1, 2018)
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PUMA

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1Preserving Constraints with the Stable Chase

1Automatic improvement of continuous colormaps in euclidean colorspaces

1DBpedia FlexiFusion the best of Wikipedia > wikidata > your data

1Deep learning for grape variety recognition

1Indikatoren für Open Science: Diskussionspapier des Helmholtz Open Science Office

1Dynamic Thermal Imaging for Intraoperative Monitoring of Neuronal Activity and Cortical Perfusion

1Evolving diverse TSP instances by means of novel and creative mutation operators

1CASQAD -- A new dataset for context-aware spatial question answering

1Big Data Services

1Ensuring scientific reproducibility in bio-macromolecular modeling via extensive, automated benchmarks

1Visualization of tensor fields in mechanics

1Towards optimisation of collaborative Question Answering over knowledge graphs

1Enhanced force-field calibration via machine learning

1Evaluating the impact of knowledge graph context on entity disambiguation models

1Multiobjectivization of local search: Single-objective optimization benefits from multi-objective gradient descent

1Counting strategies for the probabilistic description logic $ALC^ME$ under the principle of maximum entropy

1Progressive intrinsic ultracontractivity and heat kernel estimates for non-local Schrödinger operators

1Divided we stand out! Forging cohorts fOr numeric outlier detection in large scale knowledge graphs (CONOD)

1Generating attributed variability models for transfer learning

1Towards mechanistic prediction of mass spectra using graph transformation

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