Zobrazeno 1 - 10
of 29 790
pro vyhledávání: '"Zahn A."'
The teaching of general relativity at the secondary school and lower undergraduate university levels is necessarily based on approaches with a restricted use of mathematics. An important aspect of teaching general relativity at this level are suitabl
Externí odkaz:
http://arxiv.org/abs/2406.02324
Autor:
Jung, H., Dong, S., Zahn, D., Vasileiadis, T., Seiler, H., Schneider, R., de Vasconcellos, S. Michaelis, Taylor, V. C. A., Bratschitsch, R., Ernstorfer, R., Windsor, Y. W.
We study monolayer WSe2 using ultrafast electron diffraction. We introduce an approach to quantitatively extract atomic-site-specific information, providing an element-specific view of incoherent atomic vibrations following femtosecond excitation. Vi
Externí odkaz:
http://arxiv.org/abs/2405.17099
Autor:
Ratzenberger, Julius, Kiseleva, Iuliia, Koppitz, Boris, Beyreuther, Elke, Zahn, Manuel, Gössel, Joshua, Hegarty, Peter A., Amber, Zeeshan H., Rüsing, Michael, Eng, Lukas M.
Ferroelectric domain walls (DWs) are promising structures for assembling future nano-electronic circuit elements on a larger scale, since reporting domain wall currents of up to 1 mA per single DW. One key requirement hereto is their reproducible man
Externí odkaz:
http://arxiv.org/abs/2405.08156
Publikováno v:
Proceedings of the 38th ACM International Conference on Supercomputing (ICS '24), June 4--7, 2024, Kyoto, Japan
Anomaly detection in distributed systems such as High-Performance Computing (HPC) clusters is vital for early fault detection, performance optimisation, security monitoring, reliability in general but also operational insights. Deep Neural Networks h
Externí odkaz:
http://arxiv.org/abs/2405.07749
Autor:
Alt, Benjamin, Zahn, Johannes, Kienle, Claudius, Dvorak, Julia, May, Marvin, Katic, Darko, Jäkel, Rainer, Kopp, Tobias, Beetz, Michael, Lanza, Gisela
While recent advances in deep learning have demonstrated its transformative potential, its adoption for real-world manufacturing applications remains limited. We present an Explanation User Interface (XUI) for a state-of-the-art deep learning-based r
Externí odkaz:
http://arxiv.org/abs/2404.19349
Autor:
Lee, Sang-Eun, Windsor, Yoav William, Zahn, Daniela, Kraiker, Alexej, Kummer, Kurt, Kliemt, Kristin, Krellner, Cornelius, Schüßler-Langeheine, Christian, Pontius, Niko, Staub, Urs, Vyalikh, Denis V., Ernst, Arthur, Rettig, Laurenz
Optical manipulation of magnetism holds promise for future ultrafast spintronics, especially with lanthanides and their huge, localized 4f magnetic moments. These moments interact indirectly via the conduction electrons (RKKY exchange), influenced by
Externí odkaz:
http://arxiv.org/abs/2404.12119
Autor:
Weber, Dominik, Morgenstern, Annika, Beer, Daniel, Zahn, Dietrich R. T., Deibel, Carsten, Salvan, Georgeta, Schondelmaier, Daniel
We present the development of multifunctional blue-emission organic light-emitting diodes (OLEDs) using TADF-exciplex materials. These OLEDs exhibit sensitivity to external stimuli and achieve a maximum external quantum efficiency (EQE) of 11.6 % thr
Externí odkaz:
http://arxiv.org/abs/2403.02987
We characterize the connectivity structure of feed-forward, deep neural networks (DNNs) using network motif theory. To address whether a particular motif distribution is characteristic of the training task, or function of the DNN, we compare the conn
Externí odkaz:
http://arxiv.org/abs/2403.00974
Autor:
Allouche, Mohammad, Sevostianov, Vladislav I., Zahn, Einara, Zondlo, Mark, Dias, Nelson Luís, Katul, Gabriel G., Fuentes, Jose D., Bou-Zeid, Elie
Conventional and recently developed approaches for estimating turbulent scalar fluxes under stable conditions are evaluated. The focus is on methods that do not require fast scalar sensors such as the relaxed eddy accumulation (REA) approach, the dis
Externí odkaz:
http://arxiv.org/abs/2401.11756
Autor:
Doumani, Jacques, Zahn, Keshav, Yu, Shengjie, Barrios, Gustavo Rodriguez, Sasmal, Somesh, Kikuchi, Rikuta, Kritzell, T. Elijah, Xu, Hongjing, Baydin, Andrey, Kono, Junichiro
Because of their natural one-dimensional (1D) structure combined with intricate chiral variations, carbon nanotubes (CNTs) exhibit various exceptional physical properties, such as ultrahigh electrical and thermal conductivity, exceptional mechanical
Externí odkaz:
http://arxiv.org/abs/2312.00984