Zobrazeno 1 - 10
of 15 890
pro vyhledávání: '"A, Reinert"'
Autor:
Wang, Yan, Grabicki, Niklas, Orio, Hibiki, Li, Juan, Gao, Jie, Zhang, Xiaoxi, Cerqueira, Tiago F. T., Marques, Miguel A. L., Jiang, Zhaotan, Reinert, Friedrich, Dumele, Oliver, Palma, Carlos-Andres
The fabrication of well-defined, low-dimensional diamondoid-based materials is a promising approach for tailoring diamond properties such as superconductivity. On-surface self-assembly of halogenated diamondoids under ultrahigh vacuum conditions repr
Externí odkaz:
http://arxiv.org/abs/2410.19466
Autor:
Aminian, Gholamali, Asadi, Amir R., Li, Tian, Beirami, Ahmad, Reinert, Gesine, Cohen, Samuel N.
The generalization error (risk) of a supervised statistical learning algorithm quantifies its prediction ability on previously unseen data. Inspired by exponential tilting, Li et al. (2021) proposed the tilted empirical risk as a non-linear risk metr
Externí odkaz:
http://arxiv.org/abs/2409.19431
The anticanonical complex is a combinatorial tool that was invented to extend the features of the Fano polytope from toric geometry to wider classes of varieties. In this note we show that the Gorenstein index of Fano varieties with torus action of c
Externí odkaz:
http://arxiv.org/abs/2409.03649
Autor:
Jürgens, Boyung, Seele, Hagen, Schricker, Hendrik, Reinert, Christiane, von der Assen, Niklas
Stochastic programming is widely used for energy system design optimization under uncertainty but can exponentially increase the computational complexity with the number of scenarios. Common scenario reduction techniques, like moments-matching or dis
Externí odkaz:
http://arxiv.org/abs/2407.11457
Conformal prediction is a non-parametric technique for constructing prediction intervals or sets from arbitrary predictive models under the assumption that the data is exchangeable. It is popular as it comes with theoretical guarantees on the margina
Externí odkaz:
http://arxiv.org/abs/2407.07700
Autor:
Min, C. -H., Müller, S., Choi, W. J., Dudy, L., Zabolotny, V., Heber, M., Denlinger, J. D., Kang, C. -J., Kalläne, M., Wind, N., Scholz, M., Lee, T. L., Schlueter, C., Gloskovskii, A., Rienks, E. D. L., Hinkov, V., Bentmann, H., Kwon, Y. S., Reinert, F., Rossnagel, K.
Hybridization between localized 4$f$ and itinerant 5$d$6$s$ states in heavy fermion compounds is a well-studied phenomenon and commonly captured by the paradigmatic Anderson model. However, the investigation of additional electronic interactions, bey
Externí odkaz:
http://arxiv.org/abs/2406.02408
Autor:
Reinert, Gesine, Xu, Wenkai
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small. Here, we tackle the problem of graph generation from only one observed graph. Th
Externí odkaz:
http://arxiv.org/abs/2403.18578
Autor:
Figgemeier, T., Ünzelmann, M., Eck, P., Schusser, J., Crippa, L., Neu, J. N., Geldiyev, B., Kagerer, P., Buck, J., Kalläne, M., Hoesch, M., Rossnagel, K., Siegrist, T., Lim, L. -K., Moessner, R., Sangiovanni, G., Di Sante, D., Reinert, F., Bentmann, H.
We report the experimental discovery of orbital vortex lines in the three-dimensional (3D) band structure of a topological semimetal. Combining linear and circular dichroism in soft x-ray angle-resolved photoemission (SX-ARPES) with first-principles
Externí odkaz:
http://arxiv.org/abs/2402.10031
This work provides a theoretical framework for assessing the generalization error of graph neural networks in the over-parameterized regime, where the number of parameters surpasses the quantity of data points. We explore two widely utilized types of
Externí odkaz:
http://arxiv.org/abs/2402.07025
For analysing real-world networks, graph representation learning is a popular tool. These methods, such as a graph autoencoder (GAE), typically rely on low-dimensional representations, also called embeddings, which are obtained through minimising a l
Externí odkaz:
http://arxiv.org/abs/2402.01614