Zobrazeno 1 - 10
of 2 383
pro vyhledávání: '"Lionetti, P."'
Real-time bioaerosol monitoring is improving the quality of life for people affected by allergies, but it often relies on deep-learning models which pose challenges for widespread adoption. These models are typically trained in a supervised fashion a
Externí odkaz:
http://arxiv.org/abs/2406.09984
Conformal back-reaction generates cosmological models where the trace anomaly reflects the breaking of Weyl invariance. Analyzing these actions yields a dynamic approach to dark energy through anomaly-induced actions (AIAs), that are variational solu
Externí odkaz:
http://arxiv.org/abs/2404.09225
We investigate the gravitational anomaly vertex $\langle TTJ_5\rangle$ (graviton - graviton - axial current) under conditions of finite density and temperature. Through a direct analysis of perturbative contributions, we demonstrate that neither fini
Externí odkaz:
http://arxiv.org/abs/2404.06272
We discuss fundamental aspects of chiral anomaly-driven interactions in conformal field theory (CFT) in four spacetime dimensions. They find application in very general contexts, from early universe plasma to topological condensed matter. We outline
Externí odkaz:
http://arxiv.org/abs/2403.15641
Autor:
Gonzalez-Jimenez, Alvaro, Lionetti, Simone, Bazazian, Dena, Gottfrois, Philippe, Gröger, Fabian, Pouly, Marc, Navarini, Alexander
Out-Of-Distribution (OOD) detection is critical to deploy deep learning models in safety-critical applications. However, the inherent hierarchical concept structure of visual data, which is instrumental to OOD detection, is often poorly captured by c
Externí odkaz:
http://arxiv.org/abs/2403.15260
We investigate the general structure of the chiral anomaly $AVV/AAA$ and $(LLL, RRR)$ vertices, in the presence of chemical potentials in perturbation theory. The study finds application in anomalous transport, whenever chirally unbalanced matter is
Externí odkaz:
http://arxiv.org/abs/2402.03151
Autor:
Gröger, Fabian, Lionetti, Simone, Gottfrois, Philippe, Gonzalez-Jimenez, Alvaro, Groh, Matthew, Daneshjou, Roxana, Consortium, Labelling, Navarini, Alexander A., Pouly, Marc
Publikováno v:
Proceedings of the 3rd Machine Learning for Health Symposium, PMLR 225:101-128, 2023
Benchmark datasets for digital dermatology unwittingly contain inaccuracies that reduce trust in model performance estimates. We propose a resource-efficient data-cleaning protocol to identify issues that escaped previous curation. The protocol lever
Externí odkaz:
http://arxiv.org/abs/2309.06961
We illustrate how the Conformal Ward Identities (CWI) and the gravitational chiral anomaly completely determine the structure of the $\langle TTJ_{5}\rangle$ (graviton-graviton-chiral gauge current) correlator in momentum space. This analysis extends
Externí odkaz:
http://arxiv.org/abs/2309.05374
We analyze the parity-odd correlators $\langle JJO\rangle_{odd}$, $\langle JJT\rangle_{odd}$, $\langle TTO\rangle_{odd}$ and $\langle TTT\rangle_{odd}$ in momentum space, constrained by conformal Ward identities, extending our former investigation of
Externí odkaz:
http://arxiv.org/abs/2307.03038
Autor:
Gonzalez-Jimenez, Alvaro, Lionetti, Simone, Gottfrois, Philippe, Gröger, Fabian, Pouly, Marc, Navarini, Alexander
This paper presents a new robust loss function, the T-Loss, for medical image segmentation. The proposed loss is based on the negative log-likelihood of the Student-t distribution and can effectively handle outliers in the data by controlling its sen
Externí odkaz:
http://arxiv.org/abs/2306.00753