Zobrazeno 1 - 10
of 11 365
pro vyhledávání: '"A, Atzberger"'
Autor:
Atzberger, Kaia R., Usman, Sam A., Ji, Alexander P., Cullinane, Lara R., Erkal, Denis, Hansen, Terese T., Lewis, Geraint F., Li, Ting S., Limberg, Guilherme, Luna, Alice, Martell, Sarah L., McKenzie, Madeleine, Pace, Andrew B., Zucker, Daniel B.
Chemical abundances of stellar streams can be used to determine the nature of a stream's progenitor. Here we study the progenitor of the recently discovered Leiptr stellar stream, which was previously suggested to be a tidally disrupted halo globular
Externí odkaz:
http://arxiv.org/abs/2410.17312
Autor:
Jasuja, Dev, Atzberger, Paul J.
We formulate theoretical modeling approaches and develop practical computational simulation methods for investigating the non-equilibrium statistical mechanics of fluid interfaces with passive and active immersed particles. Our approaches capture phe
Externí odkaz:
http://arxiv.org/abs/2410.01165
Autor:
Atzberger, Daniel, Cech, Tim, Scheibel, Willy, Döllner, Jürgen, Behrisch, Michael, Schreck, Tobias
The semantic similarity between documents of a text corpus can be visualized using map-like metaphors based on two-dimensional scatterplot layouts. These layouts result from a dimensionality reduction on the document-term matrix or a representation w
Externí odkaz:
http://arxiv.org/abs/2407.17876
Standard datasets are frequently used to train and evaluate Machine Learning models. However, the assumed standardness of these datasets leads to a lack of in-depth discussion on how their labels match the derived categories for the respective use ca
Externí odkaz:
http://arxiv.org/abs/2406.13552
Scientists often model physical processes to understand the natural world and uncover the causation behind observations. Due to unavoidable simplification, discrepancies often arise between model predictions and actual observations, in the form of sy
Externí odkaz:
http://arxiv.org/abs/2405.18953
Scientific datasets present unique challenges for machine learning-driven compression methods, including more stringent requirements on accuracy and mitigation of potential invalidating artifacts. Drawing on results from compressed sensing and rate-d
Externí odkaz:
http://arxiv.org/abs/2405.14270
We introduce Geometric Neural Operators (GNPs) for accounting for geometric contributions in data-driven deep learning of operators. We show how GNPs can be used (i) to estimate geometric properties, such as the metric and curvatures, (ii) to approxi
Externí odkaz:
http://arxiv.org/abs/2404.10843
Advances in machine learning have boosted the use of Earth observation data for climate change research. Yet, the interpretability of machine-learned representations remains a challenge, particularly in understanding forests' biophysical reactions to
Externí odkaz:
http://arxiv.org/abs/2403.02922
Autor:
Atzberger, Daniel, Cech, Tim, Scheibel, Willy, Trapp, Matthias, Richter, Rico, Döllner, Jürgen, Schreck, Tobias
Topic models are a class of unsupervised learning algorithms for detecting the semantic structure within a text corpus. Together with a subsequent dimensionality reduction algorithm, topic models can be used for deriving spatializations for text corp
Externí odkaz:
http://arxiv.org/abs/2307.11770
Publikováno v:
Remote Sensing, Vol 5, Iss 11, Pp 5572-5573 (2013)
Due to an oversight by the authors, in the upper graph in Figure 4 [1] only the determination coefficients for Morocco are correct. Those for the other three countries are wrong.
Externí odkaz:
https://doaj.org/article/1b966234ce1043a7b1ea2ff255b8961b
