Zobrazeno 1 - 10
of 2 395
pro vyhledávání: '"Fábián, J."'
In this paper, we study how generative AI and specifically large language models (LLMs) impact learning in coding classes. We show across three studies that LLM usage can have positive and negative effects on learning outcomes. Using observational da
Externí odkaz:
http://arxiv.org/abs/2409.09047
In this study we employ staggered fermions to calculate the two-pion taste singlet states at rest. Leveraging the Clebsch-Gordan coefficients of the symmetry group associated with staggered fermions, we effectively compute the $\pi\pi$ contributions
Externí odkaz:
http://arxiv.org/abs/2401.00514
Historically, applications of RFT in fMRI have relied on assumptions of smoothness, stationarity and Gaussianity. The first two assumptions have been addressed in Part 1 of this article series. Here we address the severe non-Gaussianity of (real) fMR
Externí odkaz:
http://arxiv.org/abs/2312.10849
Autor:
Consens, Micaela E., Dufault, Cameron, Wainberg, Michael, Forster, Duncan, Karimzadeh, Mehran, Goodarzi, Hani, Theis, Fabian J., Moses, Alan, Wang, Bo
In the rapidly evolving landscape of genomics, deep learning has emerged as a useful tool for tackling complex computational challenges. This review focuses on the transformative role of Large Language Models (LLMs), which are mostly based on the tra
Externí odkaz:
http://arxiv.org/abs/2311.07621
Autor:
Engelmann, Jan P., Palma, Alessandro, Tomczak, Jakub M., Theis, Fabian J., Casale, Francesco Paolo
Predicting patient features from single-cell data can help identify cellular states implicated in health and disease. Linear models and average cell type expressions are typically favored for this task for their efficiency and robustness, but they ov
Externí odkaz:
http://arxiv.org/abs/2311.02455
Autor:
Tejada-Lapuerta, Alejandro, Bertin, Paul, Bauer, Stefan, Aliee, Hananeh, Bengio, Yoshua, Theis, Fabian J.
Advances in single-cell omics allow for unprecedented insights into the transcription profiles of individual cells. When combined with large-scale perturbation screens, through which specific biological mechanisms can be targeted, these technologies
Externí odkaz:
http://arxiv.org/abs/2310.14935
Autor:
de Brito, C. Serati, Junior, P. E. Faria, Ghiasi, T. S., Ingla-Aynés, J., Rabahi, C. R., Cavalini, C., Dirnberger, F., Mañas-Valero, S., Watanabe, K., Taniguchi, T., Zollner, K., Fabian, J., Schüller, C., van der Zant, H. S. J., Gobato, Y. Galvão
Van der Waals (vdW) heterostructures composed of two-dimensional (2D) transition metal dichalcogenides (TMD) and vdW magnetic materials offer an intriguing platform to functionalize valley and excitonic properties in non-magnetic TMDs. Here, we repor
Externí odkaz:
http://arxiv.org/abs/2309.03766
Autor:
Pauw, Fabian J., Palm, Felix A., Schollwöck, Ulrich, Bohrdt, Annabelle, Paeckel, Sebastian, Grusdt, Fabian
Topological phase transitions go beyond Ginzburg and Landau's paradigm of spontaneous symmetry breaking and occur without an associated local order parameter. Instead, such transitions can be characterized by the emergence of non-local order paramete
Externí odkaz:
http://arxiv.org/abs/2309.03666
In the present work, neural networks are applied to formulate parametrised hyperelastic constitutive models. The models fulfill all common mechanical conditions of hyperelasticity by construction. In particular, partially input-convex neural network
Externí odkaz:
http://arxiv.org/abs/2307.03463
This paper presents a novel approach that leverages domain variability to learn representations that are conditionally invariant to unwanted variability or distractors. Our approach identifies both spurious and invariant latent features necessary for
Externí odkaz:
http://arxiv.org/abs/2307.00558