Strange and charm mesons at FAIR

We study the properties of strange and charm mesons in hot and dense matter within a self-consistent coupled-channel approach for the experimental conditions of density and temperature expected for the CBM experiment at FAIR/GSI. The in-medium solution at finite temperature accounts for Pauli blocking effects, mean-field binding of all the baryons involved, and meson self-energies. We analyze the behaviour in this hot and dense environment of dynamically-generated baryonic resonances together with the evolution with density and temperature of the strange and open-charm meson spectral functions. We test the spectral functions for strange mesons using energy-weighted sum rules and finally discuss the implications of the properties of charm mesons on the D_{s0}(2317) and the predicted X(3700) scalar resonances.Comment: 12 pages, 9 figures, invited talk at XXXI Mazurian Lakes Conference on Physics: Nuclear Physics and the Road to FAIR, August 30-September 6, 2009, Piaski, Polan

Chiral dynamics of hadrons in nuclei

In this talk I report on selected topics of hadron modification in the nuclear medium using the chiral unitary approach to describe the dynamics of the problems. I shall mention how antikaons, $\eta$, and $\phi$ are modified in the medium and will report upon different experiments done or planned to measure the $\phi$ width in the medium.Comment: 10 pgs, 3 figs. Invited talk in the Workshop on in Medium Hadron Physics, Giessen, Nov 200

Cardiac Segmentation using Transfer Learning under Respiratory Motion Artifacts

Methods that are resilient to artifacts in the cardiac magnetic resonance imaging (MRI) while performing ventricle segmentation, are crucial for ensuring quality in structural and functional analysis of those tissues. While there has been significant efforts on improving the quality of the algorithms, few works have tackled the harm that the artifacts generate in the predictions. In this work, we study fine tuning of pretrained networks to improve the resilience of previous methods to these artifacts. In our proposed method, we adopted the extensive usage of data augmentations that mimic those artifacts. The results significantly improved the baseline segmentations (up to 0.06 Dice score, and 4mm Hausdorff distance improvement).Comment: accepted for the STACOM2022 workshop @ MICCAI202