Zobrazeno 1 - 10
of 3 093
pro vyhledávání: '"BOBADILLA, P."'
Autor:
Aliberti, Riccardo, Beltrame, Paolo, Budassi, Ettore, Calame, Carlo M. Carloni, Colangelo, Gilberto, Cotrozzi, Lorenzo, Denig, Achim, Driutti, Anna, Engel, Tim, Flower, Lois, Gurgone, Andrea, Hoferichter, Martin, Ignatov, Fedor, Kollatzsch, Sophie, Kubis, Bastian, Kupść, Andrzej, Lange, Fabian, Lusiani, Alberto, Müller, Stefan E., Paltrinieri, Jérémy, Rosàs, Pau Petit, Piccinini, Fulvio, Price, Alan, Punzi, Lorenzo, Rocco, Marco, Shekhovtsova, Olga, Siódmok, Andrzej, Signer, Adrian, Stagnitto, Giovanni, Stoffer, Peter, Teubner, Thomas, Bobadilla, William J. Torres, Ucci, Francesco P., Ulrich, Yannick, Venanzoni, Graziano
We present the results of Phase I of an ongoing review of Monte Carlo tools relevant for low-energy hadronic cross sections. This includes a detailed comparison of Monte Carlo codes for electron-positron scattering into a muon pair, pion pair, and el
Externí odkaz:
http://arxiv.org/abs/2410.22882
We compute analytically the three-loop correlation function of the local operator $\text{tr} \, \phi^3$ inserted into three on-shell states, in maximally supersymmetric Yang-Mills theory. The result is expressed in terms of Chen iterated integrals. W
Externí odkaz:
http://arxiv.org/abs/2410.22465
Autor:
Gehrmann, Thomas, Henn, Johannes, Jakubčík, Petr, Lim, Jungwon, Mella, Cesare Carlo, Syrrakos, Nikolaos, Tancredi, Lorenzo, Bobadilla, William J. Torres
We describe a general method for constructing a minimal basis of transcendental functions tailored to a scattering amplitude. Starting with formal solutions for all master integral topologies, we grade the appearing functions by properties such as th
Externí odkaz:
http://arxiv.org/abs/2410.19088
Autor:
Bobadilla, Jesús, Gutiérrez, Abraham
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, 2023
The published method Generative Adversarial Networks for Recommender Systems (GANRS) allows generating data sets for collaborative filtering recommendation systems. The GANRS source code is available along with a representative set of generated datas
Externí odkaz:
http://arxiv.org/abs/2410.17651
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, Volume 8, number 6, Pages 15-23, 2024
Matrix factorization models are the core of current commercial collaborative filtering Recommender Systems. This paper tested six representative matrix factorization models, using four collaborative filtering datasets. Experiments have tested a varie
Externí odkaz:
http://arxiv.org/abs/2410.17644
Publikováno v:
International Journal of Interactive Multimedia and Artificial Intelligence, Volume 7, number 4, Pages 18-26, 2022
Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This p
Externí odkaz:
http://arxiv.org/abs/2410.16838
Using open-source dependencies is essential in modern software development. However, this practice implies significant trust in third-party code, while there is little support for developers to assess this trust. As a consequence, attacks have been i
Externí odkaz:
http://arxiv.org/abs/2410.16049
From the classical theory of Lie algebras, it is well-known that the bilinear form $B(X,Y)={\rm tr}(XY)$ defines a non-degenerate scalar product on the simple Lie algebra ${\mathfrak{sl}}(n,{\mathbb R})$. Diagonalizing the Gram matrix $Gr$ associated
Externí odkaz:
http://arxiv.org/abs/2410.15478
Robots performing navigation tasks in complex environments face significant challenges due to uncertainty in state estimation. Effectively managing this uncertainty is crucial, but the optimal approach varies depending on the specific details of the
Externí odkaz:
http://arxiv.org/abs/2410.15178
In this paper, we study the mechanical system associated with rolling a Lorentzian manifold $(M,g)$ of dimension $n+1\geq2$ on flat Lorentzian space $\widehat{M}={\mathbb R}^{n,1}$, without slipping or twisting. Using previous results, it is known th
Externí odkaz:
http://arxiv.org/abs/2408.05863