Zobrazeno 1 - 10
of 92 765
pro vyhledávání: '"GRUBER, A"'
Autor:
Robles, Roberto, Li, Chao, Realista, Sara, Martinho, Paulo Nuno, Gruber, Manuel, Weismann, Alexander, Lorente, Nicolás, Berndt, Richard
Scanning tunneling microscope data from a dinuclear Co(II) complex adsorbed on Au(111) are analysed using density functional theory calculations. We find that the interaction with the substrate substantially changes the geometry of the non-planar mol
Externí odkaz:
http://arxiv.org/abs/2410.17156
Autor:
Bechtle, Philip, Breton, Dominique, Canet, Carlos Orero, Desch, Klaus, Dreiner, Herbi, Freyermuth, Oliver, Gauld, Rhorry, Gruber, Markus, Gutiérrez, César Blanch, Hajjar, Hazem, Hamer, Matthias, Heinrichs, Jan-Eric, Irles, Adrian, Kaminski, Jochen, Klipphahn, Laney, Lupberger, Michael, Maalmi, Jihane, Pöschl, Roman, Richarz, Leonie, Schiffer, Tobias, Schwäbig, Patrick, Schürmann, Martin, Zerwas, Dirk
We present a proposal for a future light dark matter search experiment at the Electron Stretcher Accelerator ELSA in Bonn: Lohengrin. It employs the fixed-target missing momentum based technique for searching for dark-sector particles. The Lohengrin
Externí odkaz:
http://arxiv.org/abs/2410.10956
Autor:
Chen, Jie, Gruber, Susan, Lee, Hana, Chu, Haitao, Lee, Shiowjen, Tian, Haijun, Wang, Yan, He, Weili, Jemielita, Thomas, Song, Yang, Tamura, Roy, Tian, Lu, Zhao, Yihua, Chen, Yong, van der Laan, Mark, Nie, Lei
Real-world data (RWD) and real-world evidence (RWE) have been increasingly used in medical product development and regulatory decision-making, especially for rare diseases. After outlining the challenges and possible strategies to address the challen
Externí odkaz:
http://arxiv.org/abs/2410.06586
Autor:
Chen, Jie, Nie, Lei, Lee, Shiowjen, Chu, Haitao, Tian, Haijun, Wang, Yan, He, Weili, Jemielita, Thomas, Gruber, Susan, Song, Yang, Tamura, Roy, Tian, Lu, Zhao, Yihua, Chen, Yong, van der Laan, Mark, Lee, Hana
Developing drugs for rare diseases presents unique challenges from a statistical perspective. These challenges may include slowly progressive diseases with unmet medical needs, poorly understood natural history, small population size, diversified phe
Externí odkaz:
http://arxiv.org/abs/2410.06585
Autor:
Gruber, Sebastian G., Bach, Francis
In this work, we propose a mean-squared error-based risk that enables the comparison and optimization of estimators of squared calibration errors in practical settings. Improving the calibration of classifiers is crucial for enhancing the trustworthi
Externí odkaz:
http://arxiv.org/abs/2410.07014
Autor:
Kang, Mingu, Lee, Dongseok, Cho, Woojin, Park, Jaehyeon, Lee, Kookjin, Gruber, Anthony, Hong, Youngjoon, Park, Noseong
Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and pre-training techniques. Motivated by this, we explore whether a simila
Externí odkaz:
http://arxiv.org/abs/2410.06442
A machine-learnable variational scheme using Gaussian radial basis functions (GRBFs) is presented and used to approximate linear problems on bounded and unbounded domains. In contrast to standard mesh-free methods, which use GRBFs to discretize stron
Externí odkaz:
http://arxiv.org/abs/2410.06219
Autor:
Gruber, Jonathan, Mancini, Gaëtan
Let $G$ be a simple algebraic group over an algebraically closed field $\Bbbk$ of positive characteristic. We consider the questions of when the tensor product of two simple $G$-modules is multiplicity free or completely reducible. We develop tools f
Externí odkaz:
http://arxiv.org/abs/2409.07888
This paper presents and evaluates an approach for coupling together subdomain-local reduced order models (ROMs) constructed via non-intrusive operator inference (OpInf) with each other and with subdomain-local full order models (FOMs), following a do
Externí odkaz:
http://arxiv.org/abs/2409.01433
The evaluation of image generators remains a challenge due to the limitations of traditional metrics in providing nuanced insights into specific image regions. This is a critical problem as not all regions of an image may be learned with similar ease
Externí odkaz:
http://arxiv.org/abs/2409.01314