Isospin effects on sub-threshold kaon production at intermediate energies

We show that in collisions with neutron rich heavy ions at energies around the production threshold K^0 and K^+ yields might probe the isospin dependent part of the nuclearEquation of State (EoS) at high baryon densities. In particular we suggest the K^0/K^+ ratio as a promising observable. Results obtained in a fully covariant relativistic transport approach are presented for central Au+Au collisions in the beam energy range 0.8-1.8~AGeV. The focus is put on the EoS influence which goes beyond the "collision-cascade" picture. The isovector part of the in-medium interaction affects the kaon multiplicities via two mechanisms: i) a "symmetry potential" effect, i.e. a larger neutron repulsion in n-rich systems (isospin fractionation); ii) a "threshold" effect, due to the change in the self-energies of the particles involved in inelastic processes. Genuine relativistic contributions are revealed, that could allow to directly ``measure'' the Lorentz structure of the effective isovector interaction.Comment: 5 pages, 2 figures, revtex

An Adaptive Sampling Scheme to Efficiently Train Fully Convolutional Networks for Semantic Segmentation

Deep convolutional neural networks (CNNs) have shown excellent performance in object recognition tasks and dense classification problems such as semantic segmentation. However, training deep neural networks on large and sparse datasets is still challenging and can require large amounts of computation and memory. In this work, we address the task of performing semantic segmentation on large data sets, such as three-dimensional medical images. We propose an adaptive sampling scheme that uses a-posterior error maps, generated throughout training, to focus sampling on difficult regions, resulting in improved learning. Our contribution is threefold: 1) We give a detailed description of the proposed sampling algorithm to speed up and improve learning performance on large images. We propose a deep dual path CNN that captures information at fine and coarse scales, resulting in a network with a large field of view and high resolution outputs. We show that our method is able to attain new state-of-the-art results on the VISCERAL Anatomy benchmark