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pro vyhledávání: '"Carmona, Juan P."'
We introduce and study module structures on both the dgla of multiplicative vector fields and the graded algebra of functions on Lie groupoids. We show that there is an associated structure of a graded Lie-Rinehart algebra on the vector fields of a d
Externí odkaz:
http://arxiv.org/abs/2412.07436
Autor:
Botella, Ignacio Garrido, Águeda, Ignacio Arranz, Carmona, Juan Carlos Armenteros, Vorontsov, Oleg, Robledo, Fernando Bayón, Solovykh, Evgeny, Andreevich, Obrubov Aleksandr, Barriuso, Adrián Alonso
Accurate identification, localization, and segregation of teeth from Cone Beam Computed Tomography (CBCT) images are essential for analyzing dental pathologies. Modeling an individual tooth can be challenging and intricate to accomplish, especially w
Externí odkaz:
http://arxiv.org/abs/2407.05892
In this paper we extend the Chern-Weil-Lecomte characteristic map to the setting of $L_{\infty}$-algebras. In this general framework, characteristic classes of $L_{\infty}$-algebra extensions are defined by means of the Chern-Weil-Lecomte map which t
Externí odkaz:
http://arxiv.org/abs/2306.04465
In text mining, topic models are a type of probabilistic generative models for inferring latent semantic topics from text corpus. One of the most popular inference approaches to topic models is perhaps collapsed Gibbs sampling (CGS), which typically
Externí odkaz:
http://arxiv.org/abs/2301.12974
We study isometric actions of Lie $2$-groups on Riemannian groupoids by exhibiting some of their immediate properties and implications. Firstly, we prove an existence result which allows both to obtain 2-equivariant versions of the Slice Theorem and
Externí odkaz:
http://arxiv.org/abs/2209.08643
Autor:
Carmona, Juan Felipe, Onshuus, Alf
We generalize the notions of definable amenability and extreme definable amenability to continuous structures and show that the stable and ultracompact groups are definable amenable. In addition, we characterize both notions in terms of fixed-point p
Externí odkaz:
http://arxiv.org/abs/2201.09971
Autor:
Lee, Benjamin D., Gitter, Anthony, Greene, Casey S., Raschka, Sebastian, Maguire, Finlay, Titus, Alexander J., Kessler, Michael D., Lee, Alexandra J., Chevrette, Marc G., Stewart, Paul Allen, Britto-Borges, Thiago, Cofer, Evan M., Yu, Kun-Hsing, Carmona, Juan Jose, Fertig, Elana J., Kalinin, Alexandr A., Signal, Beth, Lengerich, Benjamin J., Triche Jr, Timothy J., Boca, Simina M.
Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. A
Externí odkaz:
http://arxiv.org/abs/2105.14372
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