6,812 research outputs found
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
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