Zobrazeno 1 - 10
of 4 563
pro vyhledávání: '"A. Hense"'
Autor:
M. Chevalier, A. Dallmeyer, N. Weitzel, C. Li, J.-P. Baudouin, U. Herzschuh, X. Cao, A. Hense
Publikováno v:
Climate of the Past, Vol 19, Pp 1043-1060 (2023)
Comparing temporal and spatial vegetation changes between reconstructions or between reconstructions and model simulations requires carefully selecting an appropriate evaluation metric. A common way of comparing reconstructed and simulated vegetation
Externí odkaz:
https://doaj.org/article/d0170f080bf642be9db97680216f8e02
Deep learning has led to remarkable advancements in computational histopathology, e.g., in diagnostics, biomarker prediction, and outcome prognosis. Yet, the lack of annotated data and the impact of batch effects, e.g., systematic technical data diff
Externí odkaz:
http://arxiv.org/abs/2411.05489
Publikováno v:
Advances in Statistical Climatology, Meteorology and Oceanography, Vol 6, Pp 103-113 (2020)
The multiple correlation and/or regression information that two competing forecast systems have on the same observations is decomposed into four components, adapting the method of multivariate information decomposition of Williams and Beer (2010), Wi
Externí odkaz:
https://doaj.org/article/a2b4deb8b37b424eb56e0065b9572944
The question to what extent climate change is responsible for extreme weather events has been at the forefront of public and scholarly discussion for years. Proponents of the "risk-based" approach to attribution attempt to give an unconditional answe
Externí odkaz:
http://arxiv.org/abs/2407.10776
Autor:
Dippel, Jonas, Prenißl, Niklas, Hense, Julius, Liznerski, Philipp, Winterhoff, Tobias, Schallenberg, Simon, Kloft, Marius, Buchstab, Oliver, Horst, David, Alber, Maximilian, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick
While previous studies have demonstrated the potential of AI to diagnose diseases in imaging data, clinical implementation is still lagging behind. This is partly because AI models require training with large numbers of examples only available for co
Externí odkaz:
http://arxiv.org/abs/2406.14866
Autor:
Hense, Julius, Idaji, Mina Jamshidi, Eberle, Oliver, Schnake, Thomas, Dippel, Jonas, Ciernik, Laure, Buchstab, Oliver, Mock, Andreas, Klauschen, Frederick, Müller, Klaus-Robert
Multiple instance learning (MIL) is an effective and widely used approach for weakly supervised machine learning. In histopathology, MIL models have achieved remarkable success in tasks like tumor detection, biomarker prediction, and outcome prognost
Externí odkaz:
http://arxiv.org/abs/2406.04280
Publikováno v:
Climate of the Past, Vol 15, Pp 1275-1301 (2019)
Probabilistic spatial reconstructions of past climate states are valuable to quantitatively study the climate system under different forcing conditions because they combine the information contained in a proxy synthesis into a comprehensible product.
Externí odkaz:
https://doaj.org/article/a2f0126acd37493bb278d4781c15091b
Publikováno v:
Atmospheric Chemistry and Physics, Vol 16, Pp 6863-6881 (2016)
The high density of European surface ozone monitoring sites provides unique opportunities for the investigation of regional ozone representativeness and for the evaluation of chemistry climate models. The regional representativeness of European oz
Externí odkaz:
https://doaj.org/article/653a344a79b74931aac980d3ab4d2be3
Autor:
A. Düsterhus, A. Hense
Publikováno v:
Advances in Science and Research, Vol 8, Pp 99-104 (2012)
A new method for testing time series of environmental data for internal inconsistencies is presented. The method divides the dataset into several disjunct blocks. By means of a comparison of the blocks' estimated probability density distributions, ea
Externí odkaz:
https://doaj.org/article/d22c440a23304340a3b757e50330295b
Autor:
S. Emeis, M. Hagen, A. Hense, W. Kuttler, S. Blankenstein, M.W. Rotach, N. Dotzek, M. Hantel, C. Kessler, R. Glaser
Publikováno v:
Meteorologische Zeitschrift, Vol 12, Iss 1, Pp 51-64 (2003)
Externí odkaz:
https://doaj.org/article/6c5325fa002148deb71690cfc876dc83