Zobrazeno 1 - 10
of 34 170
pro vyhledávání: '"P. Baumann"'
Publikováno v:
SOIL, Vol 10, Pp 349-365 (2024)
Predicting the quantity of soil organic carbon (SOC) requires understanding how different factors control the amount of SOC. Land use has a major influence on the function of the soil as a carbon sink, as shown by substantial organic carbon (OC) loss
Externí odkaz:
https://doaj.org/article/d4279dc8f4cf459aa960e73747c3c4c0
Autor:
L. Summerauer, P. Baumann, L. Ramirez-Lopez, M. Barthel, M. Bauters, B. Bukombe, M. Reichenbach, P. Boeckx, E. Kearsley, K. Van Oost, B. Vanlauwe, D. Chiragaga, A. B. Heri-Kazi, P. Moonen, A. Sila, K. Shepherd, B. Bazirake Mujinya, E. Van Ranst, G. Baert, S. Doetterl, J. Six
Publikováno v:
SOIL, Vol 7, Pp 693-715 (2021)
Information on soil properties is crucial for soil preservation, the improvement of food security, and the provision of ecosystem services. In particular, for the African continent, spatially explicit information on soils and their ability to sustain
Externí odkaz:
https://doaj.org/article/cd9d66c6f0374fd0a44d445200718cc1
Autor:
P. Baumann, J. Lee, E. Frossard, L. P. Schönholzer, L. Diby, V. K. Hgaza, D. I. Kiba, A. Sila, K. Sheperd, J. Six
Publikováno v:
SOIL, Vol 7, Pp 717-731 (2021)
Low soil fertility is challenging the sustainable production of yam and other staple crops in the yam belt of West Africa. Quantitative soil measures are needed to assess soil fertility decline and to improve crop nutrient supply in the region. We de
Externí odkaz:
https://doaj.org/article/9cd2b43568d346fa9dd8baf88c29c164
How can robots learn and adapt to new tasks and situations with little data? Systematic exploration and simulation are crucial tools for efficient robot learning. We present a novel black-box policy search algorithm focused on data-efficient policy i
Externí odkaz:
http://arxiv.org/abs/2411.14246
The numerical method of dynamical low-rank approximation (DLRA) has recently been applied to various kinetic equations showing a significant reduction of the computational effort. In this paper, we apply this concept to the linear Boltzmann-Bhatnagar
Externí odkaz:
http://arxiv.org/abs/2411.06844
Autor:
Rox, Hannes, Ränke, Fabian, Mädler, Jonathan, Marzec, Mateusz M., Sokolowski, Krystian, Baumann, Robert, Hamedi, Homa, Yang, Xuegeng, Mutschke, Gerd, Urbas, Leon, Lasagni, Andrés Fabián, Eckert, Kerstin
Laser-structuring techniques like Direct Laser Interference Patterning show great potential for optimizing electrodes for water electrolysis. Therefore, a systematic experimental study based on statistical design of experiments is performed to analyz
Externí odkaz:
http://arxiv.org/abs/2411.03373
Recently, an interesting pattern was found in the differential equations satisfied by the Feynman integrals describing tree-level correlators of conformally coupled scalars in a power-law FRW cosmology [1,2]. It was proven that simple and universal g
Externí odkaz:
http://arxiv.org/abs/2410.17994
Advances in lightweight neural networks have revolutionized computer vision in a broad range of IoT applications, encompassing remote monitoring and process automation. However, the detection of small objects, which is crucial for many of these appli
Externí odkaz:
http://arxiv.org/abs/2410.16769
There exists a dictionary between hereditary orders and smooth stacky curves, resp. tame orders of global dimension 2 and Azumaya algebras on smooth stacky surfaces. We extend this dictionary by explaining how the restriction of a tame order to a cur
Externí odkaz:
http://arxiv.org/abs/2410.07620
Predictive Spliner: Data-Driven Overtaking in Autonomous Racing Using Opponent Trajectory Prediction
Autor:
Baumann, Nicolas, Ghignone, Edoardo, Hu, Cheng, Hildisch, Benedict, Hämmerle, Tino, Bettoni, Alessandro, Carron, Andrea, Xie, Lei, Magno, Michele
Head-to-head racing against opponents is a challenging and emerging topic in the domain of autonomous racing. We propose Predictive Spliner, a data-driven overtaking planner that learns the behavior of opponents through Gaussian Process (GP) regressi
Externí odkaz:
http://arxiv.org/abs/2410.04868