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1The query translation landscape: A survey
M. Mami, D. Graux, H. Thakkar, S. Scerri, S. Auer, and J. Lehmann. (2019)
5 months ago by @scadsfct
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1Ferric reduction by a CYBDOM protein counteracts increased iron availability in root meristems induced by phosphorus deficiency
R. Maniero, C. Picco, A. Hartmann, F. Engelberger, A. Gradogna, J. Scholz-Starke, M. Melzer, G. Künze, A. Carpaneto, N. von Wirén and 1 other author(s). Nature Communications, (January 2024)
3 months ago by @scadsfct
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imported
topic_lifescience
importedtopic_lifescience
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1Spectra of Cardinality Queries over Description Logic Knowledge Bases (Extended Abstract)
Q. Manière, and M. Przybylko. Proceedings of the 37th International Workshop on Description Logics (DL 2024), Bergen, Norway, June 18-21, 2024, volume 3739 of CEUR Workshop Proceedings, CEUR-WS.org, (2024)
2 months ago by @scadsfct
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imported
xack
importedxack
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1SATPin: Axiom pinpointing for lightweight description logics through incremental SAT
N. Manthey, R. Peñaloza, and S. Rudolph. KI - Künstl. Intell., 34 (3): 389--394 (September 2020)
5 months ago by @scadsfct
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1Scalable distributed genetic algorithm using Apache spark (S-GA)
F. Maqbool, S. Razzaq, J. Lehmann, and H. Jabeen. Intelligent Computing Theories and Application, Springer International Publishing, Cham, (2019)
5 months ago by @scadsfct
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2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks
A. Marcolongo, M. Vladymyrov, S. Lienert, N. Peleg, S. Haug, and J. Zscheischler. Environmental Data Science, (2022)
2 months ago by @scadsfct
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Convolutional
Networks
Predicting
low
gross
production
data
primary
Neural
weather
ConvolutionalNetworksPredictinglowgrossproductiondataprimaryNeuralweather
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1Evaluating GPU Programming Models for the LUMI Supercomputer
G. Markomanolis, A. Alpay, J. Young, M. Klemm, N. Malaya, A. Esposito, J. Heikonen, S. Bastrakov, A. Debus, T. Kluge and 4 other author(s). page 79-101. (January 2022)
a month ago by @scadsfct
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Supercomputer
LUMI
Programming
GPU
Zno
SupercomputerLUMIProgrammingGPUZno
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1Docking cholesterol to integral membrane proteins with Rosetta
B. Marlow, G. Kuenze, J. Meiler, and J. Koehler Leman. PLoS Comput. Biol., 19 (3): e1010947 (March 2023)
5 months ago by @scadsfct
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topic_lifescience
topic_lifescience
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1Model soups improve performance of dermoscopic skin cancer classifiers
R. Maron, A. Hekler, S. Haggenmüller, C. von Kalle, J. Utikal, V. Müller, M. Gaiser, F. Meier, S. Hobelsberger, F. Gellrich and 23 other author(s). Eur. J. Cancer, (September 2022)
5 months ago by @scadsfct
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Artificial
intelligence;
learning;
Deep
Melanoma;
soups;
Dermatology;
Robustness
Calibration;
Ensembles;
topic_lifescience
Model
Generalisation;
Nevus;
Artificialintelligence;learning;DeepMelanoma;soups;Dermatology;RobustnessCalibration;Ensembles;topic_lifescienceModelGeneralisation;Nevus;
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1Atomic population kinetics for Particle in Cell
B. Marré, U. Schramm, M. Bussmann, T. Kluge, T. Cowan, R. Widera, S. Bastrakov, and A. Huebl. (2023)
a month ago by @scadsfct
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imported
Atomic
Zno
population
kinetics
importedAtomicZnopopulationkinetics
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1Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis
B. Martinez-Vega, M. Tkachenko, M. Matkabi, S. Ortega, H. Fabelo, F. Balea-Fernandez, M. Salvia, E. Torti, F. Leporati, G. Callico and 1 other author(s). Sensors, 22 (22): 8917 (2022)
5 months ago by @scadsfct
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topic_lifescience
topic_lifescience
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1Learning extreme vegetation response to climate forcing: A comparison of recurrent neural network architectures
F. Martinuzzi, M. Mahecha, G. Camps-Valls, D. Montero, T. Williams, and K. Mora. EGUsphere, (2023)
3 months ago by @scadsfct
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imported
imported
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2SpectralIndices. jl: Streamlining spectral indices access and computation for Earth system research
F. Martinuzzi, M. Mahecha, D. Montero, L. Alonso, and K. Mora. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, (2024)
3 months ago by @scadsfct
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imported
imported
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2ReservoirComputing.Jl: An efficient and modular library for reservoir computing models
F. Martinuzzi, C. Rackauckas, A. Abdelrehim, M. Mahecha, and K. Mora. (2022)
5 months ago by @scadsfct
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topic_earthenvironment
topic_earthenvironment
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1GradTree: Learning axis-aligned decision trees with gradient descent
S. Marton, S. Lüdtke, C. Bartelt, and H. Stuckenschmidt. (2023)
a month ago by @scadsfct
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descent
axis-aligned
Learning
GradTree
decision
gradient
Zno
trees
descentaxis-alignedLearningGradTreedecisiongradientZnotrees
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1Einstieg in den ML-Workflow durch Entwicklung einer intelligenten Museumsapp
E. Marx, and N. Bergner. INFORMATIK 2023 - Designing Futures: Zukünfte gestalten, Gesellschaft für Informatik e.V., Bonn, (2023)
a month ago by @scadsfct
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Einstieg
Entwicklung
Museumsapp
ML-Workflow
Yaff
EinstiegEntwicklungMuseumsappML-WorkflowYaff
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1Maschinelles Lernen in der Sekundarstufe I erlebbar machen: Workshop-Konzept zur Entwicklung einer intelligenten Museumsapp
E. Marx, and N. Bergner. (2023)20. GI-Fachtagung "Informatik und Schule" : Informatikunterricht zwischen Aktualität und Zeitlosigkeit, INFOS 2023 ; Conference date: 20-09-2023 Through 22-09-2023.
3 months ago by @scadsfct
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FIS_scads
imported
area_responsibleai
FIS_scadsimportedarea_responsibleai
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1Seminarkonzept zur fachlichen und fachdidaktischen Qualifizierung von Informatiklehramtsstudierenden zum Maschinellen Lernen
E. Marx, and N. Bergner. 10. Fachtagung Hochschuldidaktik Informatik (HDI) 2023, 10, page 65--74. Deutschland, Gesellschaft fur Informatik (GI), (2023)
3 months ago by @scadsfct
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FIS_scads
imported
area_responsibleai
FIS_scadsimportedarea_responsibleai
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1EduInf - Education in Informatics
E. Marx, A. Hamann, D. Baberowski, C. Nenner, A. Kazimiers, T. Leonhardt, and N. Bergner. (2021)
5 months ago by @scadsfct
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Bildung,
Informatische
Schüler:innen-Workshops,
Schülerlabor,
Grundkompetenzen,
Lab,
Lehrkr\"{a}ftebildung,
Lehramtsstudium
Teaching
Bildung,InformatischeSchüler:innen-Workshops,Schülerlabor,Grundkompetenzen,Lab,Lehrkr\"{a}ftebildung,LehramtsstudiumTeaching
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1Using Matchboxes to Teach the Basics of Machine Learning: an Analysis of (Possible) Misconceptions
E. Marx, T. Leonhardt, D. Baberowski, and N. Bergner. Proceedings of the Second Teaching Machine Learning and Artificial Intelligence Workshop, page 25--29. PMLR, (Sep 8, 2022)
3 months ago by @scadsfct
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FIS_scads
imported
area_responsibleai
FIS_scadsimportedarea_responsibleai
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PUMA

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1The query translation landscape: A survey

1Ferric reduction by a CYBDOM protein counteracts increased iron availability in root meristems induced by phosphorus deficiency

1Spectra of Cardinality Queries over Description Logic Knowledge Bases (Extended Abstract)

1SATPin: Axiom pinpointing for lightweight description logics through incremental SAT

1Scalable distributed genetic algorithm using Apache spark (S-GA)

2Predicting years with extremely low gross primary production from daily weather data using Convolutional Neural Networks

1Evaluating GPU Programming Models for the LUMI Supercomputer

1Docking cholesterol to integral membrane proteins with Rosetta

1Model soups improve performance of dermoscopic skin cancer classifiers

1Atomic population kinetics for Particle in Cell

1Evaluation of Preprocessing Methods on Independent Medical Hyperspectral Databases to Improve Analysis

1Learning extreme vegetation response to climate forcing: A comparison of recurrent neural network architectures

2SpectralIndices. jl: Streamlining spectral indices access and computation for Earth system research

2ReservoirComputing.Jl: An efficient and modular library for reservoir computing models

1GradTree: Learning axis-aligned decision trees with gradient descent

1Einstieg in den ML-Workflow durch Entwicklung einer intelligenten Museumsapp

1Maschinelles Lernen in der Sekundarstufe I erlebbar machen: Workshop-Konzept zur Entwicklung einer intelligenten Museumsapp

1Seminarkonzept zur fachlichen und fachdidaktischen Qualifizierung von Informatiklehramtsstudierenden zum Maschinellen Lernen

1EduInf - Education in Informatics

1Using Matchboxes to Teach the Basics of Machine Learning: an Analysis of (Possible) Misconceptions

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