Zobrazeno 1 - 10
of 6 981
pro vyhledávání: '"Ruff, P."'
Autor:
Sextro, Marvin, Dernbach, Gabriel, Standvoss, Kai, Schallenberg, Simon, Klauschen, Frederick, Müller, Klaus-Robert, Alber, Maximilian, Ruff, Lukas
Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent adv
Externí odkaz:
http://arxiv.org/abs/2411.07643
Hyperbolic random graphs inherit many properties that are present in real-world networks. The hyperbolic geometry imposes a scale-free network with a strong clustering coefficient. Other properties like a giant component, the small world phenomena an
Externí odkaz:
http://arxiv.org/abs/2410.11549
Autor:
Kauffmann, Jacob, Dippel, Jonas, Ruff, Lukas, Samek, Wojciech, Müller, Klaus-Robert, Montavon, Grégoire
Unsupervised learning has become an essential building block of AI systems. The representations it produces, e.g. in foundation models, are critical to a wide variety of downstream applications. It is therefore important to carefully examine unsuperv
Externí odkaz:
http://arxiv.org/abs/2408.08041
Autor:
Alvarado, S. J. Gomez, Pokharel, G., Ortiz, B. R., Paddison, Joseph A. M., Sarker, Suchismita, Ruff, J. P. C., Wilson, Stephen D.
Publikováno v:
Phys Rev. B 110, L140304 (2024)
Here we resolve the real-space nature of the high-temperature, short-range charge correlations in the kagome metal ScV$_6$Sn$_6$. Diffuse scattering appears along a frustrated wave vector $\textbf{q}_H=(\frac{1}{3},\frac{1}{3},\frac{1}{2})$ at temper
Externí odkaz:
http://arxiv.org/abs/2407.12099
Autor:
Wang, Haozhe, de la Torre, Alberto, Race, Joseph T., Wang, Qiaochu, Ruff, Jacob P. C., Woodward, Patrick M., Plumb, Kemp W., Walker, David, Xie, Weiwei
In this study, we report a tetragonal perovskite structure of SrIrO$_3$ (P4/mmm, a = 3.9362(9) \r{A}, c = 7.880(3) \r{A}) synthesized at 6 GPa and 1400 $\deg$C, employing the ambient pressure monoclinic SrIrO$_3$ with distorted 6H structure as a prec
Externí odkaz:
http://arxiv.org/abs/2407.07984
Autor:
de la Torre, A., Wang, Q., Campbell, B., Riffle, J. V., Balasundaram, D., Vora, P. M., Ruff, J. P. C., Hollen, S. M., Plumb, K. W.
Ultrafast light-matter interaction has emerged as a new mechanism to exert control over the macroscopic properties of quantum materials toward novel functionality. To date, technological applications of these non-thermal phases are limited by their u
Externí odkaz:
http://arxiv.org/abs/2407.07953
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
Today we rely on networks that are created and maintained by smart devices. For such networks, there is no governing central authority but instead the network structure is shaped by the decisions of selfish intelligent agents. A key property of such
Externí odkaz:
http://arxiv.org/abs/2403.15307
We report X-ray diffraction and resonant elastic X-ray scattering (REXS) studies on two $\alpha$-RuCl$_{3}$ crystals with distinct magnetic transition temperatures: T$_{N}$=7.3K and 6.5K. We find that the sample with T$_{N}$=6.5K exhibits a high degr
Externí odkaz:
http://arxiv.org/abs/2403.04176
Autor:
Dippel, Jonas, Feulner, Barbara, Winterhoff, Tobias, Milbich, Timo, Tietz, Stephan, Schallenberg, Simon, Dernbach, Gabriel, Kunft, Andreas, Heinke, Simon, Eich, Marie-Lisa, Ribbat-Idel, Julika, Krupar, Rosemarie, Anders, Philipp, Prenißl, Niklas, Jurmeister, Philipp, Horst, David, Ruff, Lukas, Müller, Klaus-Robert, Klauschen, Frederick, Alber, Maximilian
Artificial intelligence has started to transform histopathology impacting clinical diagnostics and biomedical research. However, while many computational pathology approaches have been proposed, most current AI models are limited with respect to gene
Externí odkaz:
http://arxiv.org/abs/2401.04079